Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages  are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools , Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI’s Gene Expression Omnibus following the FAIR  Data Principles. IRIS-EDA is freely available at  PLOS Computational Biology Software paper.This is a Advanced computational tools with appropriate experimental designs and interactive interface are needed to build integrated models of biological systems and devise deliverable strategies to prevent or treat disease –3. RNA-SWhile a wide variety of computational methods can be applied to expression data to determine particular qualities of the data on a sample or cell level –13, diffInteractive RNA-Seq Interpretation System for Expression Data Analysis. It provides a user-friendly interactive platform to analyze gene expression data comprehensively and to generate interactive summary visualizations readily. In contrast to other analysis platforms, IRIS-EDA provides the user with a more comprehensive and multi-level analysis platform. IRIS-EDA outperforms other tools in several critical areas related to efficiency and versatility offering: 1) Single-cell and bulk RNA-Seq analysis capabilities, 2) GEO submission compatibility, 3) seven useful discovery-driven and DGE analyses, 4) seven experimental design approaches through three integrated tools for DGE analysis, and 5) seven interactive visualizations  data analysis is a growing area of study within RNA-Seq analyses and can provide unique insights into gene expression patterns considering cell variations , 32. TheSingle-cell RNA-Seq section of S1 Text.IRIS-EDA requires two or three user-provided input files, depending on the type of data used: (1) a gene expression estimation matrix , (2) a condition matrix with factor levels corresponding to the provided samples in the EEM, and (3) a gene length matrix indicating the base-pair length of each gene to be used for filtering of scRNA-Seq data only. When uploading data, users will select gene expression data type: either bulk or single-cell RNA-Seq data. If using scRNA-Seq data, the additional requirement for gene length matrix will be shown on the web server. Also, default parameterizations for optimized analysis for single-cell data will be populated throughout the server. Methods to obtain gene lengths from GFF/GTF/GFF3 annotation files can be found in the After submitting the required inputs, one of the three normalization approaches can be selected, or users can choose not to normalize the data. The three normalization methods available in IRIS-EDA are the normal log transformation, regularized log transformation, and variance stabilizing transformation. The normal log transformation uses a base-2 log function to normalize the expression for each gene. Doing so improved the expression distribution visualizations, particularly for sparse expression matrices where the large number of zeros can lead to little information collected from non-transformed plots. The regularized log transformation provides a method to minimize the differences between samples with small gene counts and regularizes based on library size . The regDiscovery-driven analyses include tools and algorithms designed to provide an investigative approach of expression data, especially for the situation where users do not have a strong direction or hypothesis for their data analysis procedures. These algorithms assist users in analyzing and visualizing their EEM input information and discovering trends in their data that may provide additional hypotheses for downstream analyses. In particular, discovery-driven analyses can help users define a specific hypothesis within their RNA-Seq study, which can assist in development of experimental design methods for DGE analysis. Discovery-driven analyses processes available in IRIS-EDA include: sample correlation analysis and pairwise expression scatterplots, expression heatmaps, clustering, biclustering, principal component analysis, multidimensional scaling, t-distributed Stochastic Neighbor Embedding, and sample distance matrix. The figures generated through the discovery-driven analysis feature of IRIS-EDA are provided in an interactive manner, allowing users to select specific samples or pairwise comparisons to further evaluate. One such example is with the sample correlation analysis and pairwise scatterplots. Users can choose one cell of the sample correlation matrix corresponding to a comparison between two samples. This will display the pairwise scatterplot for that specific comparison. The user can then scroll over the scatterplot and display the gene ID for an indicated data point. A detailed example with more tutorial information will be shown in the Results section.DESeq2 [edgeR [limma [DESeq2, based on independent evidence supporting its performance [Sleuth, [Cuffdiff [DGE analysis in IRIS-EDA is performed using one of three tools provided: DESeq2 , edgeR [2 [edgeR , and limR [limma . These tR [limma . The defformance  and our [Sleuth,  or are nCuffdiff , are notIn addition to the DGE tools, experimental design can also be specified by the user. The designs provided in IRIS-EDA include two-group (pairwise) comparisons, multiple factorial comparisons, classic interaction design, additive models for pairing or blocking of data, main effect testing (testing time-series data) and blocked main effect testing. IRIS-EDA provides additional flexibility for the instances when the user needs a design not already included in IRIS-EDA. Each of these methods has unique parameters to be specified by the user, typically including which factors are intended for analysis and which specific comparisons are required. After analyzing the data, IRIS-EDA provides an overview displaying the number of up- and down-regulated IDs for each indicated comparison, along with a histogram displaying this information. The results table is also available through IRIS-EDA, along with interactive MA and Volcano plots. Both of these plots allow users to compare DGE results metrics, such as log fold-change, mean expression, and adjusted p-value.Similar to the figures generated in the Discovery-Driven Analysis section of IRIS-EDA, the plots in the DGE section are also highly interactive. Discovery-Driven Analysis features allow users to gain more specific information from their plots, including highlighting individual or regions of data points on the plot. These features highlight the corresponding row of the DGE results table, showing users gene information identifying them as outliers or falling within a certain region. Conversely, users can select specific gene IDs from the results table, resulting in the highlighting of that gene ID’s or set of gene IDs’ data points on the corresponding plot. This feature can be used to easily determine the relative location of specific genes or gene sets in the plot.Results obtained from the DGE analysis section of IRIS-EDA are often not the end of the analysis procedures. Based on the information collected, users may choose to further investigate their expression data using additional analyses provided in the Discovery-Driven Analyses section, such as the clustering or biclustering. This feedback loop between DGE and Discovery-Driven analyses allows for supporting and complementing analyses to function in tandem, providing more comprehensive data interpretation.IRIS-EDA provides users with methods for extracting content based on discovery-driven and DGE analyses. All figures in the Quality Control, Discovery-Driven Analysis, and DGE Analysis sections have the option for users to download as a static image in PDF or PNG format. Additionally, all tables in the DGE Analysis section are downloadable as CSV files, with the final results table being downloaded in its entirety or filtered based on user-provided or default-adjusted p-value and log fold-change cutoffs. The DGE Analysis results can also be used for functional enrichment analysis, with detailed instructions included in this tab and in GEO Usage section of S1 Text.Many users are also interested in submitting their RNA-Seq data to a public repository for accessibility, but this process can be tedious and troublesome. NCBI’s GEO database has specific requirements related to the data, results, and accompanying metadata file. To assist users in their preparation of documents for GEO submission, IRIS-EDA offers an optional GEO page. In following with the standard set forth by the FAIR Data Principles , this paTo demonstrate the effectiveness of IRIS-EDA, we analyzed a scRNA-Seq dataset consisting of human tissue cells from various cell types. The expression data was taken from Yan, et al. . The rawAfter data upload, the three input files are first analyzed by IRIS-EDA quality control. Input data quality is evaluated using boxplots and histograms of the read count distributions. The purpose of the quality control process is to enable exploration of the submitted data and to verify that there are no unexpected or unexplainable abnormalities in the data, such as low total read counts or individual samples displaying strange distribution behavior. Based on the scRNA-Seq filtration method of TPM > 1, 78 out of the original 3,679 genes were filtered, leaving a total of 3,601 genes. This process is conducted to reduce the false-positive rate experienced in analyses related to scRNA-Seq data. Following data upload and initial quality check, users can continue on to the Discovery-Driven Analyses section of IRIS-EDA, which is broken down further into five subsections.The analyses under the “Correlation” tab of the Discovery-Driven Analyses provide a pairwise sample Pearson correlation value through an interactive heatmap. Selecting a cell in this heatmap generates the indicated pairwise scatterplot of gene expression values. In this example, two two-cell embryo samples are chosen, indicating a correlation value of 0.931, which is relatively high compared to the correlation observed in the dark blue cells, such as the morulae cells compared with the late blastocyst cells . The scaPCA, MDS, and t-SNE provide linear, non-linear, and non-parametric transformations, respectively, of the gene expression vectors represented by each sample for dimension reduction. The transformations are then commonly plotted as scatterplots by the first two principal components representing the most variance between samples. Where the most variance is observed in the first and second components, particular clusters of cells appear to group together, indicating high similarity about that transformation. In the scRNA-Seq example, clusters Five and Six and clusters One, Two, Three, and Four group together closely in both the PCA  and MDS Clustering of samples based on gene expression can provide helpful insight to group samples and conditions with similar expression level across all genes. For scRNA-Seq data, clustering can provide information related to cell types. While clustering methods alone cannot lead to full cell-type prediction, they can help support other cell-type prediction methods. State-of-the-art cell-type prediction methods involve at least two steps, one of which is a clustering approach . In IRISBiclustering can group together subsets of the expression profile, indicating genes that have high expression similarity in only a subset of cells. Heatmaps for the first three biclusters are shown in For the purpose of analyzing the example scRNA-Seq data, we will be using the basic two group comparison design, which looks for differences between selected clusters. Based on the information in the Discovery-Driven Analyses section, we know the samples in cluster Seven appear different than the other samples. Because of this, the factor levels chosen for comparison are all comparisons involving cluster Seven.The Overview tab of the DGE Analysis section in IRIS-EDA provides basic information related to the number of DEGs in the selected comparisons, specifically the number of up- and down-regulated genes. This information is provided as a table and as a bar plot . The sinTwo interactive plots are provided following DGE analysis on IRIS-EDA. Both are accompanied by a linked table, which highlights the results information for a selected gene in the figure or highlights the gene in the figure corresponding to the selected gene from the table. Since the Seven vs. Four cluster comparison shows the highest number of DEGs from the DGE Overview table, this comparison seems like an interesting choice to explore further using the DGE plots. Both the MA plot and Volcano plot show features of potential genes of interest. The MA plot gives a visual representation of mean expression compared with log fold-change for a selected comparison, while the Volcano plot compares log fold-change with adjusted p-values. BANK1, which is associated with calcium binding in the central nervous system, is highly differentially expressed between the late blastocyst and 4-cell embryo samples. This high absolute log fold-change is shown in both the MA and Volcano plots  by selechttps://github.com/btmonier/iris). The tutorial found on the server and in S1 Text provides a comprehensive explanation of all features within the IRIS-EDA tool, including descriptions of how to optimally use each feature. Descriptions of the interpretations for each analysis can also be found in this document.The Shiny open-source tool can be accessed through the direct URL bmbl.sdstate.edu/IRIS/ or can be loaded locally using basic R code loaded through GitHub Click here for additional data file.


Dose-dense chemotherapy is a widely accepted regimen for high-risk breast cancer patients. However, conflicting survival benefits of pure dose-dense chemotherapy have been reported in different randomized controlled trials (RCTs). This meta-analysis aimed to further assess the efficacy and safety of pure dose-dense chemotherapy in breast cancer.A literature search of electronic databases and websites was performed to identify phase III RCTs reporting the efficacy and toxicity of pure dose-dense chemotherapy. The endpoints of interest were overall survival (OS), disease-free survival (DFS), and toxicities. The hazard ratios (HRs) of death and recurrence and the odds ratios (ORs) of adverse events were estimated and pooled.p = 0.0001) than those treated with the conventional schedule, while OS benefit of dose-dense chemotherapy was less impressive . However, significant OS benefit was observed in node-positive patients . The incidence of anemia, pain, and transaminase elevation was higher in the dose-dense chemotherapy arm.Seven studies  were eligible, encompassing a total of 9851 patients. Patients treated with dose-dense chemotherapy obtained better DFS (HR = 0.83; 95% CI 0.75–0.91; Dose-dense chemotherapy leads to better prognosis; these findings suggest that it may be a potentially preferred treatment for breast cancer patients, particularly for women with lymph node involvement. However, more RCTs are warranted to better define the best candidates for dose-dense chemotherapy. Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer death among women in the USA . AlthougAccording to the Norton-Simon hypothesis  and the This meta-analysis was performed in accordance with the recommendation outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement . A literThis meta-analysis was based on phase III RCTs in which the dose-dense regimen of the experimental arm was narrowly defined as delivering drugs over a shorter interval with the same cycle and dosage of the conventional schedule in the control arm. Full papers and conference abstracts providing sufficient data were eligible. Studies that included metastatic breast cancer patients and studies based on impure dose-dense regimens (with different type or dosage of drugs) were ineligible. In addition, studies without outcomes of interest were excluded.The methodological quality of the eligible studies was independently assessed by two reviewers using the Cochrane risk-of-bias tool, which consists of the following domains of bias: selection bias, performance bias, detection bias, attrition bias, and reporting bias , 16. DisA standardized Excel form was used to extract data from eligible studies, including first author, year of publication, sample size, inclusion criteria, chemotherapy regimen, and median follow-up. The primary endpoint was OS (measured from randomization until death from any cause); other outcomes of interest were DFS  and incidence of grade 3 to 5 toxicities. The hazard ratios (HRs) and variances of time-to-event data were extracted from the original studies or were estimated as described by Parmar et al.  and TierHazard ratios (HRs) and odds ratios (ORs) were calculated to compare time to event outcomes and dichotomous data, respectively. An HR or OR less than one favored the dose-dense chemotherapy arm. The meta-analyses of outcomes were based on a fixed-effect model, except the outcomes with significant heterogeneity, for which a random-effect model was used.I2) and the p value of the χ2 test. Significant heterogeneity was considered to exist for p values less than 0.1 or I2 greater than 50%. Subgroup analyses were conducted according to hormone receptor status of the tumors and the inclusion criteria of the studies to assess potential contributions to outcomes. Publication bias was evaluated by funnel plots and Egger’s test [Heterogeneity was quantified using the inconsistency index . A random effect model was used due to the high heterogeneity among studies (I2 = 59%) , but not in hormone receptor-positive patients . However, there was no sign of interaction between survival benefit of the dose-dense regimen and hormone receptor status . Figure A total of 9731 patients were included in the OS meta-analysis. Updated abstracts , 22 of tp = 0.0001), with no heterogeneity (I2 = 0%) , while patients with hormone receptor-positive tumor obtained no significant DFS benefit (p = 0.53) .The meta-analysis of DFS covered 5340 patients. According to the result, DFS was significantly improved in the dose-dense arm , leukopenia , and neuropathy  were significantly lower in the dose-dense arm than those in the conventional arm. However, pooled analyses demonstrated that dose-dense chemotherapy significantly increased the incidences of grade 3 to 5 anemia , pain , and transaminase elevation  compared with the conventional regimen. There was no difference between dose-dense and conventional chemotherapy in terms of thrombocytopenia, asthenia, diarrhea, stomatitis, nausea/vomiting, and infection. The details are shown in Table The incidences of grade 3 to 5 neutropenia . Neither the funnel plot  indicated significant publication bias. To explore the between-study heterogeneity, a subgroup analysis was performed based on the characteristics of the included patients. The pooled analysis of three studies in which eligible patients all had nodal involvement demonstrated significantly better OS in the dose-dense arm . While the pooled results from the other two studies, which included both node-positive and high-risk node-negative patients, failed to show significant benefit . In addition, there was a significant interaction between OS benefit of dose-dense chemotherapy and patient characteristics . Therefore, the heterogeneity may largely be driven by the different inclusion criteria, especially the nodal status of patients. This was supported by the AGO trial [p = 0.0012). A similar effect was also observed in the MIG-1 trial [As a newly updated meta-analysis based on phase III RCTs regarding the efficacy and safety of pure dose-dense chemotherapy, the pooled results demonstrated a 17% reduction in risk of recurrence and a 14% reduction in risk of death, though the OS benefit was less obvious. A possible explanation for the lack of significant OS benefit may be the insufficient follow-up duration, which prevents the real impact of the dose-dense regimen to be verified. Additionally, the specific agents and total dose in eligible studies may vary from state-of-the-art regimens, especially those conducted in early years. According to the subgroup analysis by inclusion criterion, the pooled analysis of studies based on patients with lymph node involvement showed a significant OS benefit in the dose-dense arm, while studies including patients without lymph node involvement did not. Furthermore, the interaction test showed significant evidence of interaction between dose-dense benefit and patient selection  may be due to higher incidence of neutropenia resulting from FEC-P  in the GIM2 trial [With the support of pegfilgrastim, the incidence of neutropenia was significantly reduced in the dose-dense arm. The high heterogeneity I2 = 7% may beM2 trial  demonstrM2 trial . Hence, Similar meta-analyses performed by Bonilla et al.  and Petrp = 0.36). Thus, the subgroup analysis should be interpreted cautiously, and the greater efficacy of dose-dense chemotherapy in hormone receptor-negative patients elucidated in previous studies may need further investigation. To our knowledge, an EBCTCG meta-analysis reported at the 40th San Antonio Breast Cancer Symposium at an oral session revealed significant reductions in DFS and 10-year breast cancer mortality with dose-dense chemotherapy, which, together with our results, provides further evidence of the efficacy and safety of dose-dense chemotherapy [Therefore, this newest updated meta-analysis further confirms that pure dose-dense chemotherapy leads to prolonged DFS and highlights a significant OS benefit in node-positive patients. Despite the significant improvement in OS among hormone receptor-negative patients treated with dose-dense chemotherapy, there was no sign of interaction between dose-dense benefit and hormone receptor status (interaction test, otherapy .There are some limitations of this meta-analysis that need to be addressed. First, the limited number of eligible studies may contribute to unstable results, which may be influenced by unpublished data and further studies, even though there was no sign of significant publication bias according to the funnel plot and Egger’s test. Second, this is a meta-analysis based on published literature instead of individual patients, inevitable bias resulting from different study designs may lead to a less reliable result. Third, the chemotherapy regimens of the studies were different, and therefore, it is unclear whether the benefit of dose-dense chemotherapy was derived from taxane or anthracycline. Therefore, to gain further understanding of the survival benefit of dose-dense chemotherapy and to identify subgroups of patients who could gain significant benefit from a dose-dense schedule, more RCTs with pure dose-dense designs and longer follow-ups are warranted.This study demonstrates that dose-dense chemotherapy leads to improved DFS and highlights a significant OS benefit in node-positive patients. Although limitations exist, this meta-analysis provides further evidence of the dominance and manageable toxicities of dose-dense chemotherapy. It may be a potential preferred treatment for breast cancer patients, particularly for women with lymph node involvement. However, further investigations are needed to better define specific groups of patients who may derive greater benefit from dose-dense chemotherapy.


Drosophila and Caenorhabditis. Analytical strategies for the glycans and glycoconjugates are also summarized.Glycosylation refers to the covalent attachment of sugar residues to a protein or lipid, and the biological importance of this modification has been widely recognized. While glycosylation in mammals is being extensively investigated, lower level animals such as invertebrates have not been adequately interrogated for their glycosylation. The rich diversity of invertebrate species, the increased database of sequenced invertebrate genomes and the time and cost efficiency of raising and experimenting on these species have enabled a handful of the species to become excellent model organisms, which have been successfully used as tools for probing various biologically interesting problems. Investigation on invertebrate glycosylation, especially on model organisms, not only expands the structural and functional knowledgebase, but also can facilitate deeper understanding on the biological functions of glycosylation in higher organisms. Here, we reviewed the research advances in invertebrate glycosylation, including N- and O-glycosylation, glycosphingolipids and glycosaminoglycans. The aspects of glycan biosynthesis, structures and functions are discussed, with a focus on the model organisms  Compared to higher organisms such as mammals whose glycobiology is being extensively studied, invertebrate glycobiology is frequently neglected and the investigation is limited, fragmentary and unsystematic, partly due to perceived lack of importance compared to that of vertebrates. It is estimated that over 97% of earth's animal species are invertebrates; however, only a limited number of species have been studied with respect to their molecular biology. So far, many invertebrate glycomic studies have focused on recombinant glycoproteins, for example expressed using the baculovirus system. Recent years, increasing numbers of studies have been focusing on the glycomes originally derived from invertebrate species.Drosophila and Caenorhabditis are by far the most well-studied invertebrates in glycobiology. As multicellular organisms, they serve as better models compared to lower eukaryotes such as yeast for the investigation of glycosylation and glycoengineering. Some invertebrates can have similar biological activities to those of higher organisms, and yet do not pose safety and ethical issues typically associated with vertebrate models for research purposes. Additionally, the time and cost input for establishing an invertebrate model can be greatly reduced. As a result, the knowledge base for invertebrate glycobiology is continually expanding. Studying invertebrate glycosylation, especially on a model organism, often sheds light on biological functions of the glycoconjugates and assists understanding of glycobiology and targeted glycoengineering in both invertebrates and vertebrates.Drosophila and the nematode worm Caenorhabditis. The review aims to focus on the biosynthesis, structures and functions of the glycans and glycoconjugates found in invertebrates, including protein N- and O-glycans, glycosphingolipids (GSLs) and glycosaminoglycans (GAGs). Meanwhile, analytical strategies for glycan and glycoconjugate analysis will be discussed.This review intends to discuss and summarize the knowledge and research advances related to invertebrate glycosylation, with a focus on the Arthropoda model insect 2.N-glycans to a protein in the endoplasmic reticulum (ER) and Golgi compartments and ensures that the glycan decoration follows a core machinery. Cells have complex and exquisite machinery for protein glycosylation. Inside animal cells, protein N-glycosylation initiates in the ER, where the carbohydrate moiety Glc3Man9GlcNAc2 is synthesized through the action of a series of glycosyltransferases anchored in the ER and then attached to a newly translated protein [N-glycosylation refers to the attachment of a glycan to the asparagine side chain of a protein. This modification occurs almost exclusively when the asparagine residue is followed by the XT/S sequon regardless of species type, where X refers to any amino acid residue except proline. The highly conserved sequon directs the biosynthesis of  protein . The ter protein .Bombyx mori, possess a similar glycan synthesis route to that of the mammals [N-acetylglucosaminidase in the insect cells interrupts the biosynthesis of the complex and hybrid-type N-glycans, resulting in almost exclusively high mannose and paucimannose type N-glycans [Drosophila, one of the best studied model insect in glycobiology, possesses similar N-glycosylation machinery to that of vertebrates and produces hybrid- and complex-type N-glycans that were once thought absent in insects [N-glycans tends to be low, usually below 10% of the entire N-glycan pool. Another well-studied invertebrate, the nematode Caenorhabditis, also contains a nearly contiguous series of N-glycans , N-acetylgalactosamine , xylose, fucose, N-acetylneuraminic acid (NeuAc), N-glycoylneuraminic acid (NeuGc), glucuronic acid (GlcA) and iduronic acid. Further modifications of the monosaccharides such as methylation, sulfation and zwitterionic modification including phosphorylcholine, phosphoethanolamine and aminoethyl phosphate have also been identified in invertebrates.The structural diversity and heterogeneity of glycoconjugates is attributed mainly to the attached glycans. Unlike proteins and nucleotides, glycan structures can be highly branched, and the monosaccharide units composing the glycans are often isomeric, leading to increased structural complexity. Additional structural complexity arises from the different glycosidic linkages between the monosaccharides as well as further decoration of the monosaccharides by sulfation and phosphoethanolamine. Bacteria and archaea have the broadest diversity of monosaccharides, with a total of approximately a hundred different types of monosaccharides . Plants Because of the high structural complexity, structural analysis of glycans and glycoconjugates remains very challenging. The main workhorse in today's glycomic and glycoproteomic analysis is the mass spectrometry (MS)-based approach due to its high-sensitivity and high-throughput capabilities. Auxiliary methods, such as glycosidase digestion, and less frequently nuclear magnetic resonance spectroscopy, could also be used as complementary tools to confirm carbohydrate structures. In MS-based approaches, improving glycan detection sensitivity and assigning MS spectra are among the most challenging steps. Here, in this part, we will briefly discuss large-scale mapping of glycans and glycoconjugates in invertebrates by MS-based techniques.et al. [18O water, a fixed mass shift of 2.989 occurs, which can be readily detected by mass spectrometry and analysed automatically using software platforms such as MaxQuant [Arabidopsis thaliana, Schizosaccharomyces pombe, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Danio rerio [N-Glycoproteins are found to be almost exclusively located outside the cell including the cell membrane, or in anticipated intracellular organelles such as ER and Golgi [Currently, large-scale identification of N-glycosylation sites have been quite routine. More than a thousand insect proteins have been found to be N-glycosylated. Zielinska et al.  developeMaxQuant . Using tio rerio . N-Glycond Golgi . Analysend Golgi . TherefoultiGlycan [imGlycan [lycoWorkBench [Drosophila [Caenorhabditis [Although the filter-aided deamidation method can perform large-scale identification of protein glycosylation sites, it cannot provide information on the specific glycan structures attached to the glycosylation site. Typically, there are two means to obtain glycan structural information. The first way is to isolate the glycans from proteins and analyse the glycome separately. Currently, automated and high-throughput glycomic profiling is under rapid development and has achieved great progress. In typical high-throughput glycomic analysis, glycans are cleaved from proteins either by PNGase or by other chemical methods, enriched and then labelled before LC–MS analysis . Glycan tiGlycan , SimGlycimGlycan  and GlycorkBench , which hosophila ,32 and Chabditis ,34 have habditis .lycoMaster DB [yonic [rMone [The other way to obtain glycan structural information is to study the glycopeptides with the glycans attached, which is even more challenging compared to glycomic study alone, because the former involves peptides and glycan identification simultaneously. Developing robust and reliable pipelines for large-scale profiling of glycopeptides is still under way. The most challenging steps during the pipeline development, other than developing high-sensitivity MS detection methods for glycopeptides, perhaps is automated assignment of MS and MS/MS spectra to specific glycopeptides. Currently, software such as Gaster DB , Byonic B [yonic  and ArMoc [rMone  has been4.O-glycan biosynthesis is carried out by the addition of monosaccharide one after another, which is different from the case of N-glycosylation. O-GalNAcylation is one of the most common forms of O-glycosylation, and was first identified in mucin, a heavily glycosylated protein 40% of whose molecular weight is occupied by glycans. Therefore, O-GalNAcylated glycans are also called mucin-type glycans. Approximately 90% of all O-glycans in Drosophila belong to the mucin type [O-glycans, other types of O-glycans in invertebrates have not been investigated in detail. There are eight common O-glycosylation cores found in mammals [O-glycans for the model organisms Caenorhabditis and Drosophila are shown in Depending on the first monosaccharides linked to the protein, O-glycosylation can be further divided into O-GalNAcylation, O-mannosylation, O-fucosylation and so on. Current knowledge on invertebrate O-glycosylation is still very rudimentary. In contrast with N-glycosylation, there is no consensus sequence for O-glycosylation. Serine and threonine are the most common accepting amino acids for these modifications. Tyrosine, hydroxylysine and hydroxyproline are also possible residues for O-glycosylation. In addition, cin type . In muci mammals ; howeverCaenorhabditis and Drosophila -like domains and thrombospondin type 1 (TSP1) domains. The O-fucosylation site of the EGF domain is usually flanked by cysteine residues that form disulfide bridges, which can be recognized by O-fucosyltransferase (OFUT)1, whereas in TSP1 domains another O-fucosyltransferase, OFUT2, recognizes and directs the transfer of fucose to the protein. In ethality ,45. The elopment  and adulelopment . In O-glelopment , and theelopment . In Drososylated . O-glucoosophila .O-Mannose glycans constitute less than 1% of the total Drosophila glycan pool [O-mannosyltransferases. In Drosophila, O-mannosylation occurs in the protein known as dystroglycan. The Drosophila homologues of two O-mannosyltransferase genes, POMT1 and POMT2, function in association with each other to maintain normal muscle development [Caenorhabditis and Drosophila is yet to be proven. In Drosophila, POMT mutation results in rotated abdomen, defective synaptic transmission and muscle dystrophy [can pool . O-mannocan pool . The traelopment . Anotherelopment ; howeverystrophy ,55. In hystrophy . A summaN-glycan analysis, O-glycan poses more analytical challenges, partly because there are currently no universal enzymes available to cleave off O-glycans from the proteins. General strategies to removed O-glycans from proteins are through chemical methods such as alkaline β-elimination or hydrazine hydrolysis. In addition, O-linked glycans do not have a common core structure, and instead can have more than eight types of core structures. Furthermore, unlike N-glycans which appear to be synthesized following predefined structural antennae, O-glycans seem to branch more irregularly, making it difficult to define the specific structure. Often times, glycosidases are used in addition to mass spectrometry to determine the definite structures.Compared to 5.O-GlcNAc profiles of several proteins in the pancreatic β-cell has been reported to associate with the upregulation of insulin secretion from the pancreas [O-linked GlcNAc modification refers to the addition of a single GlcNAc residue to the serine or threonine residue of a protein. This modification is reversible and highly dynamic in that GlcNAc is added and removed regularly depending on the cellular environment by two unique enzymes, the O-linked GlcNAc transferase (OGT) and the O-linked GlcNAcase (OGA). In contrast with other types of O-glycosylation, O-GlcNAcylation can actually occur in the nucleus and cytosol rather than in the ER and Golgi apparatus; therefore, O-GlcNAcylation is in nature more akin to protein phosphorylation than typical O-glycosylation. This modification is particularly heavily present on proteins involved in signalling, stress response and energy metabolism such as nuclear pore proteins, phosphatases, metabolic enzymes, etc. . O-GlcNApancreas . There ipancreas .C. elegans results in up- and down-regulation of hundreds of transcripts, which is likely due to the misguided O-GlcNAc modification of RNA polymerase II as well as the basal transcription complex, indicating the role of O-GlcNAcylation in regulating transcription [C. elegans has been shown to induce metabolic disorder and reduced lifespan [Drosophila, the extent of protein O-GlcNAcylation was found to increase with the developmental stage [Drosophila, which survived through the larval stages but died in the pupal stages [Drosophila.O-GlcNAc modification is ubiquitous and essential in multicellular organisms. Mutations of genes relating to the GlcNAc modification will cause severe growth phenotypes or even death. The deletion of OGT or OGA from cription . The dislifespan . A similal stage . Shortenl stages . BecauseO-GlcNAc modified proteins has been reported in mammals but seldom in invertebrates. As a structurally simple modification, O-GlcNAc modification is even more difficult to detect and determine. First, as mentioned above, O-GlcNAc modification is dynamically cycling on and off the modified peptide depending on the cell environment, which is easily deglycosylated by the glycosidases that exist in the cell during cell lysis. To prevent autodeglycosylation, an O-GlcNAc glycosidase inhibitor, such as PUGNAc, is added during cell lysis and sample preparation. Another issue is associated with O-GlcNAcylation detection, such as by mass spectrometry, the most popular technique currently used for O-GlcNAc analysis. The attached O-GlcNAc is sensitive to the electrospray ionization process and readily falls off from the peptide backbone. Last but not least is the lack of efficient and widely applicable enrichment methods for O-GlcNAcylated peptides prior to MS detection. The traditional glycan enrichment method lectin affinity is not quite applicable in the case of O-GlcNAcylation, because when only a single GlcNAc residue is attached to the peptide, the interaction between O-GlcNAcylated peptide and the lectin is very weak. Nevertheless, by taking advantage of this weak interaction, Vosseller et al. [O-GlcNAc, such as RL2 [O-GlcNAc residues are either enzymatically grafted with an azide-tagged GalNAz or metabolically incorporated with an azide-tagged GlcNAc residue, and labelled with biotin and subsequently enriched by avidin or streptavidin immobilized solid matrix [Most current studies on O-GlcNAcylation have been focused on the functional aspects; profiling of r et al.  developer et al. . O-GlcNAh as RL2 , CTD110.h as RL2 , 18B10.Ch as RL2 , etc. Clh as RL2 . In thisd matrix .6.Aplysia kurodai, whose GSL core is the same as in vertebrates [GSL is a type of glycolipid commonly found in animals. In GSLs, glycans are covalently linked to a ceramide lipid moiety that is composed of a long-chain alcohol known as sphingosine in amide linkage to a fatty acid. GSLs are structurally diverse mainly due to variable sugar modifications to the ceramide, and the glycan moiety of invertebrate GSLs differs notably from those of vertebrates. Vertebrates have a GSL core disaccharide Gal(β1–4)Glcβ linked to the ceramide, whereas for invertebrates the most common core disaccharide is Man(β1–4)Glc –83, excetebrates . In additebrates .Drosophila, the core mactosyl Man(β1–4)Glc structure can be further modified with a GalNAc(β1–4)GlcNAc(β1–3) residue, and the terminal Gal can be further capped with GlcA Man(β1–4)Glc(β1)Cer, similar to that in arthropods . The most abundant cell surface GAG structural subtypes include heparan sulfate characterized by disaccharide unit GlcA(β1–4)GlcNAc(α1–4) and chondroitin sulfate characterized by GlcA(β1–3)GalNAc(β1–4). Variable degrees of sulfation and GlcA epimerization (to iduronic acid) may occur on the GAG backbone. While vertebrates tend to have additional GAG types such as dermatan sulfate and hyaluronic acid, most invertebrates were reported to contain only heparan sulfate and chondroitin sulfate with or without sulfate.Caenorhabditis, indicating previously underestimated GAG structural and functional heterogeneity in invertebrates [Caenorhabditis were once thought not sulfated due to the lack of relative sulfotransferases and epimerase for modifying the sugar residues [Caenorhabditis proteoglycan core proteins were found to be different from those found in vertebrates or the invertebrate Drosophila [sqv mutants [Oesophagostomum dentatum [More than 20 chondroitin sulfate proteoglycans have been found in the model organism tebrates . Chondroresidues ,94; howeresidues . In addiosophila ,96. The osophila , whereby mutants . Recentldentatum .Drosophila chondroitin sulfate is composed of 71% HexA-GalNAc and 29% HexA-GalNAc (4-O-sulfate) [Trachypleus tridentatus is composed of 46% HexA-GalNAc (4-O-sulfate) and 54% GlcA(3-O-sulfate)-GalNAc(4-O-sulfate) [Caenorhabditis, the GAGs in Drosophila are based on the same canonical GlcA-Gal-Gal-Xyl core for attachment to proteins; some of the relevant enzymes have been characterized, such as oxt [Degrees of sulfation on the GAG disaccharide backbone are highly conserved within a class but significantly different between classes of invertebrates , indicatsulfate) , whereassulfate) . GAGs arh as oxt  and GalTh as oxt .Drosophila showed that heparan sulfate GAGs act as core receptors for many growth factors, and participate in the generation and long-range maintenance of gradients for morphogens during embryogenesis and regenerative processes [Drosophila reduces the binding of α C protein, a virulence determinant of group B streptococcus, resulting in longer host survival [Drosophila leads to cell cycle arrest of neuroblasts in the larval brain [Drosophilattv gene, which encodes an enzyme responsible for adding monosaccharide to the GAG backbone, is a homologue of the mammalian Ext class of tumour suppressor genes that cause human bone dysplasia [GAGs are ubiquitously found on the surface and extracellular matrix of mammalian cells, interacting with various ligands and playing crucial roles in many pathophysiological processes. The chondroitin sulfate GAGs are structural constituents of complex matrices such as cartilage, brain, intervertebral discs, tendons and corneas. Genetic studies on the model organism rocesses . Knockdosurvival . The resal brain ,67. The ysplasia ,105.Typical GAG and proteoglycan analyses use bottom-up approaches. The proteoglycans are extracted by strong denaturing agents and purified by ion exchange or size exclusion chromatography. The GAG moiety can be isolated from the protein via β-elimination or hydrazinolysis, and its disaccharide unit can be degraded by bacterial lyases such as heparinase or chondroitinase, and further derivatized before analysis by gas chromatography, liquid chromatography and/or mass spectrometry –108. How8.Glycosylation confers heterogeneity on glycoconjugates and finely tunes their structures and functions ,112. In Caenorhabditis that is resistant to bacterial infection is observed to be deficient in many N- and O-glycans compared with its wild-type [Caenorhabditis bre-1 mutant, which encodes an enzyme that catalyses biosynthesis of GDP-mannose to GDP fucose, was found deficient in fucosylated glycoconjugates and resistant to the toxin Bacillus thuringiensis [B. mori alter after BmNPV viral infection . In addition, the monosaccharide profiles of B. mori nervous system change with different development stages [It is also found that invertebrate glycomic profiles can change upon alteration of physiological, pathological or developmental stages. A mutant ild-type . Similarngiensis . Our labt stages . Therefo


The burden of chronic obstructive lung disease (COPD) is increasing in women, with recent evidence suggesting gender differences in disease characteristics and potentially in treatment outcomes.FLAME was a 52-week randomized controlled trial in patients with severe-to-very-severe COPD and a history of exacerbations. In this post-hoc analysis, gender-based baseline differences and treatment outcomes between indacaterol/glycopyrronium 110/50 μg once daily (IND/GLY) and salmeterol/fluticasone 50/500 twice daily (SFC) were assessed in terms of rate of exacerbations, time-to-first exacerbation, lung function, health status, and rescue medication use.This post-hoc analysis included 2557 men and 805 women. Baseline characteristics differed between genders, with women being younger, having better lung function and more often experiencing ≥2 exacerbations in the previous year. Compared with SFC, IND/GLY treatment was associated with reductions in the annualized rates of moderate/severe exacerbations . Similarly, time-to-first moderate/severe exacerbation was also delayed . Results were similar for all (mild/moderate/severe) exacerbations. Improvements in lung function, health status and rescue medication use with IND/GLY vs SFC were comparable between men and women. The smaller sample size for women may account for some observed discrepancies in treatment responses.Although there were gender differences in baseline characteristics, IND/GLY demonstrated similar trends for exacerbation prevention and lung function improvement in men and women with moderate-to-very-severe COPD and a history of exacerbations compared with SFC. Small differences in the effects seen between genders may be attributed to the different sizes of the two groups and need to be further evaluated in randomized trials that are appropriately powered for gender analysis.ClinicalTrials.gov number: NCT01782326. Registered 1 February 2013.Post hoc analysis of the FLAME study.  While long considered primarily a disease of older men, recent data have shown an increase in the prevalence and burden of chronic obstructive pulmonary disease (COPD), along with a considerable rise in mortality, in women , 2. In fHistorically, many more men than women participate in randomized clinical trials in COPD, and the increasing prevalence of COPD in women is only slowly being reflected in trial populations, in contrast to real life . Hence, The FLAME study demonstrated superiority of IND/GLY over salmeterol/fluticasone (SFC) in preventing COPD exacerbations in moderate-to-very-severe COPD patients with a prior history of exacerbations . In thisClinicalTrials.gov number: NCT01782326) : 0.76 0.63–0.91]) compared with men and women receiving SFC Fig. . Similar–0.91) co1 was higher in IND/GLY-treated patients compared with those receiving SFC . Improvement in health status  was higher in men and numerically higher in women treated with IND/GLY  compared with those receiving SFC  and women  treated with IND/GLY compared with those treated with SFC achieved ≥4-unit improvement in SGRQ-C total score. The reduction in rescue medication use from baseline was higher in IND/GLY-treated men and women  compared with those receiving SFC  at baseline than men. Similar differences in baseline characteristics between genders, with women being more symptomatic and having a greater history of exacerbations, were reported in the IGNITE program [At baseline, women with COPD differed significantly from men in their clinical characteristics, with more women being current smokers. Data from the FLAME study also supports other recent evidence that women with COPD are generally younger than men with a greater proportion being current smokers despite the lower number of pack-years reported . The ana program , ARCTIC  program , DACCORD program  and TORC program  studies. program , 23.Assessment of health status is a key component in the management of COPD as it reflects the impact of the disease on quality of life. Over the past decade, evaluation of symptom burden using CAT in patients with COPD has gained precedence in clinical settings due to its ease of use and its relationship with severity of exacerbation –27. The Studies suggest that women presenting with COPD symptoms are often misdiagnosed with asthma, potentially due to lack of spirometry testing at diagnosis , 32, 33,During the 4-week run-in period in the FLAME study when all patients received tiotropium alone, only 3.6% of patients discontinued treatment because of exacerbations or lung function deterioration at randomization . This faIt is important to note that effect size in women and men was not always similar in this analysis, which may be due to the smaller sample size of women in the study. Moreover, this is a post-hoc analysis and all results should be interpreted with caution compared to the pre-specified analyses in the original study . The facThis post-hoc gender-based analysis of the FLAME study demonstrated that despite some significant differences in baseline characteristics between the genders, IND/GLY demonstrated similar trends for exacerbation prevention and improvement in lung function, health status and use of rescue medication in men and women with moderate-to-very-severe COPD and a history of exacerbations compared with SFC. Small differences in the effects seen between genders may be due to the different sample sizes of the two groups and need to be evaluated in randomized trials that are appropriately powered for gender analysis.


The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination.A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA).P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871  in the training cohort, and 0.843  in the validation cohort. The DCA confirmed the clinical utility of the radiomics model.The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans. Osteosarcoma is the most common primary malignant bone tumor in children and adolescents with an incidence rate of 2–3 per million , and neaAccurate identification of histologic responses to chemotherapy in patients with HOS is crucial for prognoses and treatment strategy decisions . The che18F-FDG PET/CT has a good performance in predicting the pathologic response, whereas its cost is high [Previously, a patient’s pathologic response was usually estimated by the change of the tumor volume, edema, metabolic indices, etc. through a radiological examination preoperatively –16. Ther is high –16. MRI  is high . Accordi is high . DecreasRadiomics, which involves extracting quantitative features from medical images, is capable of generating imaging biomarkers as decision support tools for clinical practice –26. The Thus, in our retrospective study, we aim to develop and validate a delta-radiomics nomogram in evaluating pathologic responses after NCT in patients with HOS. Consistent with clinical practice, our work combined pre- and posttreatment CT data to noninvasively evaluate the outcomes of patients and identify the non-good response HOS patients.n = 137) and validation (n = 54) datasets according to patients’ admission times. The data of patients admitted after November 2016 were used for validating the developed model.This retrospective study reviewed the medical images and clinical records of all patients with osteosarcoma registered at our hospital between November 2013 and November 2017. This study was approved by the Institutional Research Ethics Board and the informed consent requirement was waived. This study was conducted according to the Declaration of Helsinki. All patients included in the study met the following criteria: they had undergone NCT and subsequent surgical resections; they had diagnostic CTs before and after chemotherapy, and we had access to their complete histologic information. All patients were diagnosed with HOS according to World Health Organization (WHO) Classification of Tumors of Soft Tissue and Bone, they have many subtypes such as osteoblastic, chondroblastic, fibroblastic, telangiectatic, small cell and high-grade surface  . All patAll patients received neoadjuvant chemotherapy followed by surgical resection. The treatment protocol and schedule followed the National Comprehensive Cancer Network guidelines. The conventional three-drug regimen, Regimen-1) consisting of methotrexate, cisplatin and doxorubicin, was followed by a subsequent surgical resection. The patients who suffered severe liver dysfunction or other adverse reactions after the administration of methotrexate during the first cycle of NCT received Regimen-2 treatment consisting of methotrexate, ifosfamide, cisplatin and doxorubicin preoperatively. Regimen-3, consisting of methotrexate, ifosfamide, cisplatin and doxorubicin, was used in cases of tumor progression or new lung metastasis during the first chemotherapy cycle. The total duration of NCT was at least 8–10 weeks. The complete schedules for these regimens are shown in Additional file  consistiWe analyzed the histologic response to preoperative chemotherapy using the method of Bacci et al. by two experienced pathologists . Tumor nFig. We used the pretreatment and posttreatment CT scans to quantify tumor heterogeneity in this study. The detailed imaging parameters are listed above. The 3-dimensional tumor regions were contoured from both the pretreatment and posttreatment CT scans as the region of interest (ROI) for this study. Two experienced orthopedists performed the tumor segmentation using the open-source software ITK-SNAP as reported . The conFeature extraction was performed using open-source Radiomics packages by Vallières M. et al., , 31 whicn = 22), the gray-level size-zone matrix , the gray-level run-length matrix  and the neighborhood gray-tone-difference matrix . In summary, 7 intensity features and 53 texture features were extracted from each ROI.The intensity features measured the gray level distribution in the tumor region and were quantified as mean, energy, entropy, variance, skewness, kurtosis and uniformity. The texture features characterized the tumor’s texture properties based on the gray-level co-occurrence matrix  or low-frequency components (L) at the three directions. Eight categories of wavelet features were acquired and labeled as HHH, HHL, HLH, LHH, LLL, LLH, LHL, HLL based on their different decomposition order. For example, the HLH category features are the texture features derived from the tumor region after a high pass filter on the x-direction, a low pass filter decomposition on the y-direction and a high-frequency wavelet decomposition on the z-direction. For each category, the intensity and texture features were calculated, resulting in 480 wavelet-based radiomic features for each ROI.The radiomic features were extracted from the tumor regions on pre-chemotherapy CTs  and post-chemotherapy CTs , respectively. The delta-CT features (Delta-RFs) were defined as the change of radiomic feature after chemotherapy and calculated by subtracting PRE_RFs from PST_RFs, as shown in Eq. The training datasets were used for feature selection and radiomics signature building. The radiomic features which were robust in both the inter-observer and intra-observer reproducibility tests were used for further analysis. The interclass correlation coefficient (ICC) was used to evaluate the reproducibility of radiomic features across different segmentations and robust radiomic features were defined as those with ICCs of more than 0.75 . To exclWe used the Mann-Whitney U test to assess the ability of the delta-radiomic features in differentiating pGR patients from non-pGR patients. The radiomic features with statistical significance between the pGR group and the non-pGR group were left for further analysis.Radiomics Signature I).The least absolute shrinkage and selection operator (LASSO) regression was used to perform the radiomic features selection in the training dataset. The LASSO method was usually implanted in the feature selection of high-dimensional data by minimizing classification errors, tuning the sum of absolute values of the feature coefficients to be no more than a parameter λ . The coeRadiomics Signature II), PST-RFs (Radiomics Signature III) respectively and combining PRE-RFs and PST-RFs (Radiomics Signature IV). The radiomics signature II, III, IV were constructed using the same analysis workflow with Delta-RFs.To show the unique predictive value of Delta-RFs, we also compare the prediction performance of delta-radiomics signature with the radiomics signatures constructed using only PRE-RFs  was conducted to evaluate whether the nomogram was sufficiently robust for clinical practice [p < 0.05 was considered statistically significant. All p values were two-sided in this study. All statistical analysis was performed with R software . The LASSO logistic regression analysis was performed using the “glmnet” package. The nomogram was plotted based on the “rms” package. The ROC curve was plotted using MedCalc 15.2.2 .Chi-square and Mann-Whitney U tests were used for categorical and continuous clinical factors between the two groups, respectively. The practice . A valuep = 0.6691).Patient characteristics in the training and validation sets are detailed in Table p-value < 0.05 and are shown in Additional file In total, 540 radiomic features were extracted from tumor lesions on the pre-treatment and post-treatment CT scans, respectively, resulting in 540 Delta-RFs. A total of 382 Delta-RFs were robust in both the intra-observer analysis and inter-observer analysis. Then, 198 Delta-RFs with a correlation coefficient < 0.95 were selected for further analysis. By applying the Mann-Whitney test on the pre-selected features, 45 instructive Delta-RFs showed significant differences between the pGR group and the non-pGR group with a p < 0.0001). The ROC analysis exhibited good prediction value of the developed delta-radiomics signature in this study with an AUC of 0.868 in the training dataset and AUC of 0.823 in the validation dataset . The delta-radiomics signature values of patients are shown in Fig. The delta-radiomics signature was significantly different between pGR and non-pGR patients in both the training and the validation datasets  during chemotherapy. We built a radiomics nomogram which was based on the multivariable logistical regression model using the delta-radiomics signature and NPM as shown in Fig. In this present study, we developed and validated a diagnostic, delta-radiomics signature-based nomogram for the noninvasive, preoperative individualized evaluation of chemotherapeutic response in patients with HOS. The radiomics signature successfully differentiated patients according to their chemotherapeutic response. The easy-to-use nomogram facilitates the noninvasive individualized evaluation of a patient’s chemotherapeutic response and therefore provides an effective tool for clinical decision-making.18F-FDG PET/CT or 18F-FDG PET/CT combining MRI may have a good performance. Maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) that derived from 18F-FDG PET/CT or 18F-FDG PET/CT combining MRI were associated with histologic response and may have a good performance in differentiating histologic response [The accurate identification of non-pGR patients using visual judgment  remains challenging in clinical practice. Methods using response , 14, 16.To use our proposed radiomics model, radiologists must first delineate the regions of interest (ROI) on pre- and post-chemotherapeutic CT scans, after which the model allows for the calculation of the probability of non-pGR for each individual patient. Oncologists can then consider various factors, including the calculated probability of non-pGR and other retrievable clinical information, as well as their own clinical experience, to make a comprehensive judgment on whether to modify the treatment strategy.18F-FDG PET/CT and MRI in assessing the chemotherapy outcome for HOS [Previously, there have been a few studies evaluating the prognostic value of  for HOS , 15, 16. for HOS . Radiomi for HOS , 40. Alt for HOS , 41. To We evaluated the ability of texture features in differentiating non-pGR patients with HOS. The texture analysis was previously used for tissue classification in medical images , showingThe radiomics model we proposed achieved a relatively high negative predictive value and positive predictive value in both the training and validation cohorts. The high negative predictive value in this study indicated that the non-pGR evaluation of the proposed model was reliable. Thus, oncologists may potentially adjust the chemotherapy regimen or intensify the chemotherapy. In some cases, surgeons may even choose aggressive surgery. Conversely, the high positive predictive value suggests that our model can accurately enable oncologists to screen out pGR patients.Recently, many studies have used MRI to predict a pathological response, and the tumors they evaluated were mainly soft tissues. Diffusion-weighted imaging is considered to have strong potential in predicting the responses to chemoradiotherapy in patients with locally advanced rectal cancer , 43. To Changes in tumor volume have previously been suggested as a prediction factor to the pathologic response by several authors, who reported that the sequestration and disappearance of a tumor may be correlated with a good pathologic response. Conversely, the increase or no change in tumor volume suggests a poor response to chemotherapy. However, the situation might be quite different in osteosarcoma, a tumor that does not shrink to a great extent after neoadjuvant chemotherapy . NeverthIn the present study, we use the delta-radiomics method. A clinician could request the radiomic analysis of a patient based on their diagnostic CT images, potentially enabling an improved early chemotherapeutic response evaluation, improved clinical decision-making and, consequently, a better prognosis .limitations. First, we retrospectively analyzed only the patients who met the inclusion criteria, which may have been prone to selection bias. Second, the sample size of the cohort was relatively small. Third, all the patients were from a single institution. The performance of the model may differ when used with multi-centric datasets with different parameters. Further, better-controlled prospective studies in multi-centric settings with a larger sample of patients would be required to validate the reliability and reproducibility of our proposed radiomics model.The present study has some In conclusion, using pre- and posttreatment CT data, we developed a delta-radiomics nomogram with excellent performance for an individualized, noninvasive pathologic response evaluation after NCT. This model may help tailor appropriate treatment decisions for HOS patients.Additional file 1: Table S1 Patient characteristics’ distribution in the training and validation datasets. Table S2: Interclass correlation coefficient (ICC) values of selected delta-radiomic features in the intra-observer and inter-observer reproducibility test. Fig. S1. Recruitment pathway for patients. Fig. S2. Regimens of preoperative treatment protocols of neoadjuvant chemotherapy. MTX: methotrexate; DDP: cisplatin; ADM: doxorubicin; IFO: ifosfamide. Fig. S3. Heatmap for instructive radiomic features in the training set. The x-axis indicates different patients. The y-axis indicates different radiomics features. The color in the box shows the expression level of radiomic features. Fig. S4. The predictive performance of the radiomic signature from four kinds of data for each patient in training (A) and validation (B) sets


Background and Objective: The default mode network (DMN) may be an important component involved in the broad-scale cognitive problems seen in patients with first-episode treatment-naive depression. Nevertheless, information is scarce regarding the changes in network homogeneity (NH) found in the DMN of these patients. Therefore, in this study, we explored the NH of the DMN in patients with first-episode treatment-naive depression.Methods: The study included 66 patients and 74 control participants matched by age, gender, educational level and health status who underwent resting-state functional magnetic resonance imaging (rs-fMRI) and the attentional network test (ANT). To assess data, the study utilizes NH and independent component analysis (ICA). Additionally, Spearman's rank correlation analysis is performed among significantly abnormal NH in depression patients and clinical measurements and executive control reaction time (ECRT).Results: In comparison with the control group, patients with first-episode treatment-naive depression showed lower NH in the bilateral angular gyrus (AG), as well as increased NH in the bilateral precuneus (PCu) and posterior cingulate cortex (PCC). Likewise, patients with first-episode treatment-naive depression had longer ECRT. No significant relation was found between abnormal NH values and the measured clinical variables.Conclusions: Our results suggest patients with first-episode treatment-naive depression have abnormal NH values in the DMN. This highlights the significance of DMN in the pathophysiology of cognitive problems in depression. Our study also found alterations in executive functions in patients with first-episode treatment-naive depression. All subjects signed the written informed consent before participating in this investigation. The study was approved by the ethics committee of the First Affiliated Hospital, Guangxi Medical University and in accordance with the Declaration of Helsinki.This study included 66 patients with first-episode treatment-naive depression and 74 control participants, all of them recruited from the Department of Neurology, Psychology and Radiology of the First Affiliated Hospital of Guangxi Medical University. Patients were diagnosed following criteria from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV), by independent assessments from two psychiatrists. We used the 17-item Hamilton Rating Scale for Depression (HRSD-17) to evaluate depression severity. All patients had total scores ≥17 in the HRSD-17 on the day of MRI evaluation. The exclusion criteria were: left-handedness; family history of neurological disorders, severe physical illnesses and substance abuse; pregnancy; findings of abnormal cerebral structures after the initial MRI scanning, and the presence of other psychiatric disorders, such as personality disorders or schizophrenia and related disorders. In total, 74 individuals were included in the control group, matched for age, gender, educational status and overall health, and it is noteworthy that exclusion standards are the same for depression patients.https://www.sacklerinstitute.org/cornell/assays_and_tools/ant/jin.fan/). In the central testing screen, a “+” sign was placed and regarded as the fixation point. A stimulus signal could be generated above or below the central screen in the form of a target → or a foil *. Four situations involved foils: No foil, one foil in the central part, one foil above the central screen and another one below it, and one foil either above or below the central screen. Arrows could appear in the following ways: A single arrow, five arrows in a direction, and five arrows in different directions. Subjects were required to assure target orientation correctly and quickly. ECRT was calculated by subtracting the consistent arrow direction reaction time (RT) from the inconsistent arrow direction RT. Longer ECRT represents lower efficiency of the executive control network.The attentional network test (ANT) was designed by Fan et al. , which wAn Achieva 3TMRI scanner  was utilized for resting-state functional magnetic resonance imaging (rs-fMRI). Patients were asked to lie down and close their eyes but remain awake. A prototype quadrature birdcage head coil filled with foam was used to minimize head movement. Functional imaging had the following parameters: ratio of repetition time to echo time (TR/TE) , slice thickness (5 mm), pitch (1 mm), field of view (240 × 240 mm) and flip angle (90°). On the structural scan (T1-weighted), the following settings were used: spin-echo sequence, repetition time (TR) = 20 ms, echo time (TE) = 3.5 ms, slice thickness = 1 mm, and field of view (FOV) = 24 × 24 cm.Imaging data from the rs-fMRI was preconditioned using the data processing assistant for resting-state fMRI (DPARSF) software  in Matlahttp://mialab.mrn.org/software/#gica)  theory, p < 0.01 represents a significant statistical modification of multiple comparisons. Voxel significance meets requirements at values of p < 0.01, and cluster significance values for p < 0.01. The study created masks for the parts included in the DMN. Finally, after combination, the DMN masks were utilized in the NH analysis.Independent component analysis (ICA) was performed using the Group ICA utility to remove DMN components in templates from the GIFT fMRI toolbox (e/#gica) . Three pThe results of NH analysis were computed through the application of an in-house script in Matlab , 34. Thet-test was applied for the comparison of continuous variables, while the chi-square test was employed to compare categorical data by using the IBM SPSS Statistics 22.0 software. With the purpose of measuring the discrepancies in the NH regional group, the two-sample t-test assisted the individual-level NH map into one group-level voxel wise t-test analysis. Later, in the DMN mask, through voxel-wise cross-subject statistics, the two-sample t-test was employed to analyze the NH maps. GRF theory is applied into the modification of significance level (p < 0.01) for multiple comparisons. .The study computed demographic information such as age, gender, and education degree, as well as imaging data from the patient and control groups. The two-sample p < 0.05 in statistics using the IBM SPSS Statistics 22.0 software.NH values are withdrawn from abnormal values in brain regions. After the evaluation of data normality, Pearson correlations can be found among the variables with Demographic information of the study participants is presented in Table By employing ICA, DMN masks were removed from the control group. The parts involved in the DMN included the bilateral PCC/PCu, MPFC, ventral anterior cingulate cortex (ACC), lateral temporal cortex, parietal lobes , and cerebellum Crus 1 and Crus 2 . In the patient group, Pearson linear correlation analysis was implemented to explore the correlations among NH, ECRT, and illness severity. The results showed no significant correlations of NH with those clinical variables.In this paper, NH was studied on the DMN of patients with first-episode treatment-naive depression. The latter showed significantly lower NH in the bilateral AG and significantly higher NH in the bilateral PCC and PCu compared to controls. These patients also had longer ECRT. However, no significant correlations were found among NH and illness severity or ECRT in any of the regions.The cingulate gyrus is an important area where the frontal cortex, insula, amygdala, and hypothalamus interconnect. As a part of the limbic system, it participates in emotion regulation, cognitive function and self-control. PCC represents a core hub for the DMN and plays a key role in integrating self-relative information, retrieval of episodic memory, and autobiographical search. As reported by Maddock , unpleasThe PCu—situated in the posterior DMN—intervenes in memory and processing of self-references, while its deactivation has been related to consciousness . AbnormaThe AG, in the posterior of the inferior parietal lobe, is regarded as a main hub for various subsystems . The AG It is universally acknowledged that the DMN is associated with executive functions, presenting increased activity at rest and decreased activity during the execution of oriented cognitive tasks , 63. TheOur study had a few limitations. The influence of physiological noises such as cardiac and respiratory rhythm cannot be completely removed. The sample size was notably restricted. Finally, our study focused only on alterations in the DMN, possibly neglecting significant changes in other brain regions.Despite these limitations, our study results corroborate the importance of DMN in the pathophysiology of depression by highlighting the presence of abnormal NH values in the DMN of patients with first-episode treatment-naive depression. In addition, we have posited a method for the assessment of NH, which may improve the comprehension of the pathophysiology of depression in future studies.YG designed experiment and wrote this article. MW, RY, YL, YY, and XC collected and analyzed these data. JZ guided the experiment and revised the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Colorectal cancer is the third most common cancer and the second cause of cancer-related deaths. Rectal cancer presents roughly one-third of all colorectal cancer cases and differs from it on both anatomical and molecular levels. While standard treatment of colon cancer patients is radical surgery, rectal cancer is usually treated with pre-operative chemoradiotherapy followed by total mesorectal excision, which requires precise estimation of TNM staging. Unfortunately, stage evaluation is based solely on imaging modalities, and they often do not correlate with postoperative pathological findings. Moreover, approximately half of rectal cancer patients do not respond to such pre-operative therapy, so they are exposed to its toxic effects without any clinical benefit. Thus, biomarkers that could precisely predict pre-operative TNM staging, and especially response to therapy, would significantly advance rectal cancer treatment—but till now, no such biomarker has been identified. In cancer research, microRNAs are emerging biomarkers due to their connection with carcinogenesis and exceptional stability. Circulating miRNAs are promising non-invasive biomarkers that could allow monitoring of a patient throughout the whole therapeutic process. This mini-review aims to summarize the current knowledge on miRNAs and circulating miRNAs involved in the prediction of response to treatment and pre-operative staging in rectal cancer patients. Colorectal cancer (CRC) accounts for about 10% of all solid tumors and is the third most common cancer worldwide and the second leading cause of cancer-related deaths. Tumors of the rectum—here referred to as “rectal cancer” (RC)—are located up to 15 cm from the anal verge. They comprise approximately 40% of all CRC cases, and approximately half of all RC patients are diagnosed in the stage of locally advanced rectal carcinoma (LARC) ,3,4. SinPatients with a pathologic complete response (pCR) to therapy have another alternative: They can undergo the “watch and wait” treatment, which implies a non-operative surveillance strategy . In thisThe watch-and-wait strategy can result in excellent rectal preservation and pelvic tumor control for some of the rectal cancer patients who achieved a pCR . To asseExperience with other types of cancer suggests that microRNAs (miRNAs) might be such biomarkers. MiRNAs are highly conserved, small, non-coding RNAs, 18–25 nucleotides in length. They act as post-transcriptional regulators of gene expression, by either the post-transcriptional suppression of mRNA translation or induction of mRNA degradation. MiRNA expression is frequently either downregulated or upregulated in tumor tissue versus healthy mucosal tissue, supporting miRNA’s relevance to neoplasia . AnotherGiven the potential miRNA seem to have to predict CRT response and preoperative staging of rectal cancer patients, this mini-review aims to summarize the current knowledge of miRNAs and circulating miRNAs considered as biomarkers in rectal cancer treatment and discuss their possible use in clinical practice.The role of miRNAs in cancer still remains unclear. Although little is known about the specific targets and biological functions of miRNA molecules, it is clear that miRNA plays a crucial role in regulating gene expression and controlling diverse cellular and metabolic pathways . MiRNAs MiRNA genes are localized throughout the whole genome, including intronic, exonic and protein-coding regions. In humans, the majority of miRNAs are encoded within introns of non-coding and coding transcripts. MiRNA genes often occur in clusters and are transcribed as polycistronic transcripts . MiRNA gRectal cancer differs from colon cancer at both the anatomical and molecular levels, and several studies have shown that they partially differ in terms of their global miRNA profiles. Tang et al. found that these two types of cancer shared a common global miRNA profile that differed from other gastrointestinal cancers, which share significant similarities in miRNA profiles. This fact is probably caused by midgut/hindgut origins of the colon and the rectum during embryonic development and the reported roles of miRNAs in terminal differentiation . Weber et al. compared global miRNA profiles of 15 colon cancer biopsies, 35 rectal cancer biopsies, and control matched adjacent tissues. Using next-generation sequencing (NGS), the authors discovered that two-third of the detected microRNAs had dysregulation specific for either colon or rectal cancer. Only one-third of them had similar expression patterns in both tumor types.Weber et al. identified miR-133a-3p and miR-375 as miRNAs with the highest potential to distinguish between colon and rectal cancer as well as healthy mucosa. Receiver operative characteristic (ROC) curves of miR-133a-3p showed sensitivity to detect colon cancer with area under curve (AUC) over 0.88 . ROC analysis of miR-375 showed sensitivity to detect rectal cancer with AUC of 0.90 (95% CI 0.83–0.97) . AccordiStudies by Wu et al. and Gaedcke et al. showed experimentally that miR-21 expression was significantly higher in rectal tumor tissue than in healthy mucosa. Both studies also evaluated miR-31’s potential as a biomarker in colon and rectal cancers. Its expression was significantly upregulated in RC tissues versus healthy mucosa. Both studies reported that miR-31 was upregulated in rectal cancer versus colon cancer, confirming higher invasiveness and metastatic potential of the former ,25. Recently, Mu et al. again confirmed significantly higher expression levels of miR-31 in RC tissues than in healthy mucosa. Similarly, RC cell line SW837 showed higher expression of miR-31 than did a human rectal mucosal epithelial cell line. Mir-31 expression was related to distant metastasis, lymph node metastasis, and clinical staging of rectal cancer. Moreover, the authors conducted in vitro functional studies and evaluated miR-31 overexpression and miR-31 inhibition effects on an RC cell line. Cells transfected with miR-31 mimics showed increased invasiveness while cells transfected with miR-31 inhibitor showed decreased invasiveness in comparison to control cells . In the Administration of neoadjuvant CRT before tumor resection has revolutionized the management of LARC. Nonetheless, many patients are resistant to pre-operative therapy and do not benefit from it. Indeed, response to therapy varies between individuals, ranging from pCR through pathologic incomplete response (pIR) to no pathological response at all. Moreover, pre-operative CRT is often associated with significant adverse symptoms and high medical costs, prompting a need for effective biomarkers of response to CRT. Accurate restaging after neoadjuvant CRT is critical for the optimal planning of surgical treatment. The depth of rectal wall invasion, the presence of nodal metastasis, and the involvement of circumferential resection margin are assessed using pre-operative local staging of rectal cancer, while the presence of metastatic disease is assessed using distant staging. Commonly available imaging modalities include transrectal ultrasound (TRUS), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) ,34,35. PEven though the majority of recent experimental studies on rectal cancer focused on predicting response to CRT, no biomarker have been proposed yet. Hotchi et al. identified miR-233 and miR-142-3p to be differentially expressed between patients classified as responders and non-responders to therapy, based on the histopathological examination of their surgical specimens . SvobodaKheirelseid et al. published a study identifying three miRNAs  with the ability to predict pCR versus pIR to CRT and two miRNAs (miR-519c-3p and miR-561) with the ability to predict good versus poor response to CRT with a median accuracy of 100% . Lopes-Ramos et al. reported miR-21 to be differentially expressed between complete and incomplete responders to CRT. Interestingly, miR-21-5p exhibited overexpression in samples of patients with pCR. The considerably low expression levels of miR-21-5p were observed in patients with early local recurrence similarly to patients with pIR . In 2016 Caramés et al. published another experimental article investigating miR-31 expression in rectal cancer. Patients who developed pCR had low expression levels while those who had pIR had higher expression levels of miR-31. Additionally, patients with higher miR-31 levels had lower overall survival . Salvi eEriksen et al. showed that the expression of miR-21 above the median expression level was associated with major response to treatment. Patients with miR-125b and miR-145 expression levels below the median had better disease-free survival (DFS) than those with expression levels above the median . D’AngelDu et al. attempted to determine potential mechanisms of miRNAs and investigated specific miRNA signatures as potential biomarkers for prediction of pCR to CRT in rectal cancer. They detected 36 upregulated  and five downregulated  miRNAs in the patients with pCR versus pIR group. Additionally, co-regulatory network analysis indicated that miR-548c-5p and miR-548d-5p might function as a complex to co-regulate four genes associated with colorectal neoplasms, namely, IL6ST, CHEK2, MKI67, and MCC . Campayo et al. identified miR-21, miR-99b, and miR-375 as CRT response-related miRNAs, all of them having lower expression levels in samples from patients who developed pCR. Two other miRNAs—miR-328 and let-7e—were identified as potential prognostic markers for DFS and OS. ROC curve analysis showed that the combination of miR-21, miR-99b, and miR-375 was the most effective in distinguishing between patients with the maximum response and others . In Luo Despite so many studies evaluating tissue miRNAs with potential to predict response to CRT, results are highly inconsistent. In fact, not all studies have succeeded to identify miRNAs with such potential, Pettit et al. being one example . The onlCirculating miRNAs have been shown outstandingly stable, which makes them very promising non-invasive biomarkers. Liquid biopsy-based biomarkers of response to CRT would enable one to stratify patients before therapy into responders and non-responders. Thus, circulating biomarkers may be a future tool to avoid the exposure to toxic and inefficient treatment for LARC patients with pIR. Because of low concentrations in blood plasma and serum, however, circulating miRNA biomarkers are difficult to study. This situation has been changing, thanks to recent advances in biotechnology, in particular improved detection technologies and the widespread availability of kits for the preparation of NGS libraries with RNA low input. The use of NGS increases the chances that new circulating miRNAs will be discovered as potential biomarkers. Orosz et al. analyzed the expression of miR-155, miR-21, miR-221, miR-30a, miR-34a, and miR-29a in the blood of patients with colorectal cancer and of control patients, in order to study whether these miRNAs can differentiate between them as well as between colon and rectal cancer. Five out of six of these miRNAs were dysregulated between sera samples from patients with rectal cancer versus those from control patients, two (miR-221 and miR-30a) being upregulated and three  downregulated. Moreover, three miRNAs  showed different expression levels between colon cancer and rectal cancer . D’Angelo et al. identified miR-125b as a potential predictive marker of response to CRT: It showed higher expression levels in both tissue and serum of the non-responder group of patients versus the responder group. This led the authors to hypothesize that miR-125b’s high expression in the tumor tissue influences also its expression in the serum. Furthermore, in the same patients, the Carcinoembryonic Antigen (CEA) serum level before pCRT showed an AUC of 0.5781, too small a value to mark a clear distinction between the two groups . Yu et al. found that miR-345 expression correlated with unfavorable response to CRT. MiR-345 expression was significantly downregulated in responders versus non-responders. The authors observed similar results for the expression levels of miR-345 in the tissue of LARC patients. Moreover, they found that lower expression levels of miR-345 positively correlated with local recurrence-free survival. Altogether, these results led the authors to propose miR-345 as a potential individual biomarker of response to CRT in LARC patients . Hiyoshi et al. evaluated the potential of 18 circulating miRNAs as predictive biomarkers of CRT response. Only one among them—miR-143—showed significantly higher expression levels in non-responders to CRT versus responders. In addition, the serum miR-143 levels in healthy controls were significantly higher than those in CRT non-responders and responders. No other clinicopathological indicator was associated with a serum miR-143 level . Recently, Meltzer et al. reported that poor response to CRT was related to low expression levels of miR-301a-3p and high expression levels of miR-29a-3 .Orosz et al., cited above, also evaluated the potential of several miRNAs to distinguish between individual TNM stages. None of them had different expression levels in the sera of TNM I patients and in the control sera. As for the more advanced stages of rectal cancer, the expression levels of miR-155, miR-34a, and miR-29a were downregulated in the sera of TNM II, III, and IV stage patients versus the sera of controls . Mjelle et al. provided more complex insight into the global miRNA expression profile in relation to a TNM stage. Using NGS, they identified 26 circulating miRNAs whose expression levels correlated with the TNM stages of the patients. Out them, the authors selected miR-10a-5p, miR-1307-5p, miR-200a-3p, miR-29a-3p, and miR-320d for further qPCR validation. All of them were upregulated in the sera of non-metastatic  versus metastatic patients (TNM IV) . Azizian et al. found that the expression levels of circulating miR-18a and miR-20a were connected to the nodal status of patients. In patients with negative postoperative nodal status, the expression levels of miR-18b and miR-20a declined, while in patients with positive postoperative nodal status, the levels practically did not change . Meltzer et al. observed elevated expression levels of exosomal miR-141-3p and miR-375 in patients with synchronous liver metastases . In a reThe invention and development of more sensitive and specific technologies for analyzing circulating biomarkers have advanced research on their use in cancer studies. Circulating biomarkers studied in connection with RC include mainly circulating tumor cells and circulating proteins.Circulating tumor cells (CTCs) are promising diagnostic biomarkers that allow direct analysis of tumors without invasive intervention. Sun et al. counted CTCs in 115 RC patients and found that patients with TRG 3–4 (Dworak) had fewer post-CRT CTCs than did patients with TRG 0–2 (Dworak). After regrouping the patients according to pCR, the results remained unchanged . Flores et al. chose a different approach: in situ hybridization. It enabled them to detect in isolated CTCs two molecules increasing chemotherapy and radiotherapy resistance: thymidylate synthase (TYMS) and excision repair protein RAD23 homolog B (RAD23B). Interestingly, CTCs in all patients with pCR after CRT had undetectable expression of both TYMS and RAD23B . Sclafani et al. used digital droplet PCR to detect KRAS (a Kirsten ras oncogene) and BRAF  mutations in CTCs. They did not observe any connection between either of the mutations and an RC stage . CTCs seem to be promising circulating biomarkers or specimens for further analysis of the response of RC patients to CRT. Research on CTCs will likely give a deeper insight into the mechanisms involved in response to CRT of RC patients, but we should not forget about the one major flaw of CTCs in comparison to miRNA: The isolation of CTCs requires additional processing, which can introduce bias into the analysis. There is no doubt that CTCs cannot compete with miRNAs or other single-molecule biomarkers in terms of stability and simplicity of pre-analytical processing and storing of samples. As is the case with microRNAs, CTCs need to be further studied in larger cohorts of patients since studies performed up to date have shown inconsistent results. MiRNA-based biomarkers have unique advantages: exceptional stability and involvement in the regulation of cancerogenesis. However, to benefit from these advantages, correct pre-analytical processing and storing of samples is critical ,61. In CMiR-31 has been widely studied in the context of anti-EFGR therapy response. Its upregulation has been proven to be associated with either poor response or resistance to anti-EGFR therapy in metastatic CRC patients. MiR-31 is thus likely to be implemented as an additional biomarker in CRC, when only its effectiveness is proven in randomized clinical trials. What is more, dysregulation of its expression was reported to correlate with response to CRT. Likewise, its upregulation correlates with poor response to therapy and lower overall survival in RC patients . NeverthPredictive and prognostic circulating biomarkers in RC make a relatively small field of study, especially against the background of the field of diagnosing CRC. That said, research on this topic is relatively abundant, likely because of the potential such biomarkers show. In the context of RC, naturally, most studies are dedicated to analyzing response to CRT and comparing responders with non-responders. Interestingly, significantly dysregulated miRNAs vary from study to study, including miRNAs that are dysregulated between responders and non-responders. Other findings of tissue or circulating miRNAs related to TNM clinical stage or response to CRT have been rather sporadic, and their results require confirmation in studies of various patient cohorts. Two of the reasons behind the inconsistency in the results of different studies may be different definitions of patient groups and different biological specimen used . Different processing and storing of samples can matter, too: These processes affect the overall quality of a sample, thereby affecting the miRNA profile of the sample. The majority of the studies cited above, however, used fresh frozen tissue. The most significant differences were observed when comparing IV stage alone with I, II, and III stages. Currently, however, no circulating biomarker can distinguish between early RC stages.Many studies did not examine global miRNA profiles but instead evaluated miRNAs selected based on literature search, another possible reason behind the inconsistencies of the results. The studies that examined global miRNA profiles de novo used microarray technology. Unfortunately, microarray technology is obsolete and provides only limited information about the comprehensive miRNA profile of a sample. Hybridization microarray technology allows for the analysis of a limited number of miRNAs. Thus, researchers have to choose miRNAs for analysis, a possible source of bias and a possible reason behind unplanned omission of new miRNAs that are related to cancer. In contrast, NGS technology has no such limitations and can analyze many miRNAs at the same time. It does not mean it comes without limitations: Data processing, which plays a major role in final results from NGS, is challenging. Another problem resulting in the inconsistency in results could be that too few patients were included in the exploratory phases of the studies. Lastly, intratumor heterogeneity probably contributes to the inability to identify tissue microRNAs involved in the pathogenesis of RC and circulating miRNAs; other cell-free biomarkers might have potential to overcome this issue . In summary, studies of miRNAs in RC lack proper validation phases and usually only miRNA profiling results are published. Hopefully, the use of large-scale profiling methods, like NGS, and the inclusion of a sufficient number of patients in the discovery and validation phases of biomarker studies will enable the detection of new circulating biomarkers of CRT response and more accurate TNM staging of RC patients.