We used integrated bioinformatics analysis to identify potential biomarkers in breast carcinoma in the present study. Microarray data, 127breast tumor samples and 23 non-tumor samples, received from the Gene Expression Omnibus (GEO) dataset; 121 differentially expressed genes (DEGs) were selected. Functional analysis using DAVID revealed that these DEGs were highly gathered in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds  were identified using Connectivity Map. We used Cytoscape software and STRING dataset to structure a protein–protein interaction (PPI) network. The expression of CD24, MMP1, SDC1, and SPP1 was much higher in breast carcinoma tissue than in Para cancerous tissues analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and ONCOMINE. Overexpression ofCD24, MMP1, SDC1, and SPP1 indicated the poor prognosis in breast carcinoma patients analyzed by Kaplan–Meier (KM) Plotter. Immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor sections than in Para cancerous tissues. Hub genes expression at the protein level was correlated tothe breast cancer subtype and grade. Furthermore, immunity analysis showed that CD24, MMP1, SDC1, and SPP1 were potentially associated with five immune cell types infiltration  by TIMER. Thus, this study indicates potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, further studies are required for verifying these results  Breast cancer occurs frequently in non-skin carcinoma and leading to death in America females at the second level, with 268600 new patients and 41760 deaths in 2019, accounting for up to 30% of all new cancers and 15% of cancer deaths [Although the survival rate has improved quickly with the development of surgical techniques and exploration of new targeted drugs, the effects of therapies for advanced breast cancer have remained poor, only 27% for a 5-year survival rate [High-throughput microarray technology and bioinformatics analysis can be used to identify differences in gene expression between cancerous and para- cancerous tissues, analyze the DEGs, and identify the pathways leading to tumorigenesis and cancer progression.To understand the molecular mechanisms associated with breast cancer progression, we performed bioinformatics analysis using the GEO and The Cancer Genome Atlas (TCGA) databases to review all DEGs in breast cancer, for identifying prognostic biomarkers and potential molecular targets. Then, tissue microarray analysis (TMA) was used to validate the protein expression of hub genes. Our results indicated that MMP1, CD24, SDC1, and SPP1 are potential novel prognostic biomarkers and candidate immunotherapy targets for breast cancer.Microarray platform GLP570) was used0 was useP<0.05 and |log (fold change)| > 2. An online Venn diagram tool was used to identify up- and down-regulated genes in the two datasets.After downloading the datasets from GEO, the GEO2R online tool was used to find the DEGs between breast cancer tissue and non-tumor tissue. The conditions for screening of the DEGs were CMap was used to identify possible drugs that could be used to inhibit tumor progression in breast cancer patients based on targeting the discovered DEGs . First, Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways correlated to the DEGs were identified by DAVID online tool . GO analSTRING was used for construction a PPI network and molecular function network for the DEGs . After cP<0.05 looked as statistical significance. ONCOMINE dataset, confirmed the focus genes expression between cancer and para cancerous tissues in breast cancer [We checked the 32 selected genes expression in tumor tissues and non-tumor tissues based on TCGA data by GEPIA . The DEGt cancer .We separated breast carcinoma tissues in two groups through the hub genes median expression on TCGA dataset. GSEA was usedTIMER used for systematically analyzing the relation between immune infiltration and hub gene expression . Hub genA total of 657 breast carcinoma samples and paired para-cancerous samples were collected from 2013 to 2018 at Tenth Hospital of Tongji University, China. Pathologic diagnoses and classifications were made according to the UICC Classification of Malignant Tumors.Tissue cores were obtained from the formalin-fixed paraffin-embedded (FFPE) blocks; the diagnosis of breast tumor was based on review by a pathologist after coloration with Hematoxylin and Eosin. TMA analysis  checked by 2-mm tissue cores two areas of the tumor in each patients(invasive margin and tumor bulk). Ethical approval for this study was obtained from The Human Research Ethics Committee of Tenth Hospital of Tongji University. All patients provided informed consent.2O2, after that blocking for 20 min with avidin. Furthermore, rabbit polyclonal antibodies against MMP1  incubated on the slides overnight at 4 °C, CD24 , SDC1 , and SPP1  for 24 h, before being incubated with secondary antibodies at room temperature for 30 min. Tissue sections were incubated with 3,3′-diaminobenzidine for 10 min, then counterstained, dehydrated, and mounted.Antigen retrieval was conducted by microwave pre-treatment in EDTA buffer (pH 9.0) for 20 min; then, endogenous peroxidase was removed using 3% HReading slides by a Nano-Zoomer 2.0-HT slide scanner , analyzing the images by the NDP. Captured images in TMA slides. The tissue immunostaining was read independently by two trained pathologists. The criteria for immunohistochemistry evaluation were as follows. 0, negative; 1, weak; 2, medium; and 3, strong scored as the staining intensity; 0, 0%; 1, 1–25%; 2, 26–50%; 3, 51–75%; and 4, 76–100% scored as the staining extent, which counted by the percentage of positively stained areas in relation to the whole cancer area. Sum of the extent score and the intensity score as the final expressed results, which was graded as follows: −, score 0–2; +, score 3 or 4; ++, score 5 or 6; or +++, score 7. Here, − and 1+ represent low expression; and 2+ and 3+ represent high expression.GSE26910 and GSE42568 datasets were obtained in the GEO database, containing 127 breast carcinoma tissues and 23 non-tumor samples. There were 325 DEGs (130 up-regulated and 195 down-regulated) obtained from GSE26910, and 1170 DEGs (459 up-regulated and 711 down-regulated) from GSE42568 between tumor and non-tumor tissues. These results are shown as volcano plots in DAVID online was used to further analyze the functions of the overlapping two GEO datasets DEGs in breast cancer tissues. The up-regulated DEGs were mainly enriched in endodermal cell differentiation, collagen catabolic process, and cell adhesion ; proteinaceous extracellular matrix ; and lipid binding (molecular function). Down-regulated DEGs were mainly enriched in negative regulation of cell proliferation, extracellular region, and lipid binding. In the KEGG pathway analysis, DEGs were mainly enriched in ECM–receptor interaction and TNF signaling pathway  and 2.CMap was used to identify small molecular drugs according to the up- and down-regulated DEGs. Prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine were the five small molecules most significantly negatively correlated with the DEGs in breast cancer. Thus, these molecules are potentially targeted drugs for breast cancer .We identified protein connections between the overlapping DEGs in STRING online database; connection score was 0.2. Next, Cystoscope was used to establish a PPI network consisting of 32 nodes and 134 edges A. MCODE GEPIA online tool was used for checking the hub genes expression in breast cancer and non-tumor tissues by TCGA dataset. CD24, MMP1, MMP9, SDC1, COMP, GOLM1, POSTN, FN1, SPP1, CXCL10, WISP1, ERBB3, COL10A1, KRT19 and MMP11 were upregulated in breast cancer tissues compared with normal tissues A. FOSB, KM Plotter was used to check the prognostic value of the 33 DEGs. Higher MMP1, SDC1, SPP1, and CD24 expression was correlated with worse overall survival (OS) and relapse-free survival (RFS) in breast cancer patients B,C. OtheIn the luminal A subtype, higher MMP1 and CD24 expression levels were correlated with worse OS. In the luminal B subtype, SDC1 and SPP1 expression was significantly correlated with prognosis of breast cancer patients. MMP1 and SDC1 expression was correlated with prognosis in the triple-negative subtype. In HER-2+ breast cancer, MMP1, SDC1, SPP1, and CD24 expression showed no significant association with prognosis A–D. NoneTo find the potential function of the four genes according to the TCGA breast cancer dataset, we evaluated the KEGG pathways involved in the highly expressed samples by GSEA. The most enriched KEGG pathways were chemokine signaling pathway, cytokine receptor interaction, natural killer cell-mediated cytotoxicity, and T cell receptor signaling pathway .P=1.13e-03), CD8+ T cells , macrophages , neutrophils , and dendritic cells . CD24 expression was positively correlated with infiltration of CD8+ T cells  and neutrophils . SDC1 expression was significantly positive associated with CD8+ T cells , macrophages , neutrophils , and DCs . SPP1 expression higher, the infiltration of CD4+ T cells , CD8+ T cells , macrophages , DC , and neutrophils  was much more , stage 4 (P=0.000), CD24 (P=0.002), and MMP1 (P=0.042) were significantly correlated with survival. Then, we performed subtype analysis; the BRCA-basal group comprised 118 patients with the following associations with survival: stage (P=0.000), purity (P=0.000), CD4+ T cells infiltration (P=0.000), DC infiltration (P=0.001), SDC1 expression (P=0.025), and SPP1 expression (P=0.037). In the BRCA-luminal group, 591 patients were analyzed; stage (P=0.000), DC infiltration (P=0.008), SDC1 expression (P=0.014), CD24 expression (P=0.028), and MMP1 expression (P=0.001) were correlated with survival. However, in the BRAC-HER2 group, no clinically relevant associations with survival were found among the 65 patients .P=0.00). The percentages of high protein expression of CD24, SDC1, and SPP1 were 28.6, 34.2, and 19.2% in breast cancer tissue, compared with 1.5, 1.5, and 1.1% in pan-cancer tissue, respectively  (P=0.03) but not with that of SDC1 or CD24 (P=0.33 and P=0.74); CD24 protein expression was significantly correlated with SDC1 and SPP1 (P=0.00 and P=0.00) but not MMP1 protein expression (P=0.74); SDC1 protein expression was significantly correlated with SDC1 and SPP1  but not MMP1 protein expression (P=0.33); and SPP1 protein expression was significantly correlated with MMP1, CD24, and SDC1 protein expression   B. The pr P=0.00) . In the  P=0.00) .In this study, two microarray datasets GSE26910 and GSE42568 were collected from GEO database, and a total of 121 DEGs between breast cancer and non-cancer tissues were identified, comprising 31 up-regulated and 90 down-regulated genes. Bioinformatics analysis was conducted based on these DEGs.2/M phase and inducing apoptosis of breast cancer cells [in vivo and clinical studies. Tanespimycin, an inhibitor of heat shock protein 90, combination with trastuzumab showed higher anticancer affection in metastatic breast cancer patients for HER2+ molecular subtype, compared with trastuzumab alone in a phase II study [in vitro and in vivo, with low toxicity; thus, it should be the subject of further investigation as a potential targeted therapy [First, several target small molecules that could be used for inhibition of breast cancer development were identified. Prostaglandin J2, the endogenous product of the cyclooxygenase pathway, mediated pro- and anti-inflammatory effects through receptor-dependent or -independent pathways. Moreover, 15-deoxy-δ -prostaglandin J (2), one of the main subtypes of prostaglandin J2, inhibited cancer growth through arresting cell growth in Ger cells . HoweverII study . HoweverII study . HoweverII study . The groII study . A benzyII study ; its effII study . Flunari therapy .Second, upregulated DEGs mainly associated with endodermal cell differentiation and tumor behavior; generally, tumors with immature differentiation are much more aggressive than those with more mature differentiation. In breast cancer, stem cell activation and inhibition of cell differentiation are associated with tumorigenesis. Hypoxia is correlated with tumor differentiation, and increased protein levels of HIF-1 and HIF-2 are linked to worse prognosis of breast cancer patients . Wnt/β-cin vitro study, knockdown of MMP1 inhibited cell proliferation in triple-negative breast cancer MBA-231 cells [in vivo and clinical studies are required.Third, four hub genes that could have important key functions in tumor growth in breast cancer, MMP1, SDC1, CD24, and SPP1, were identified using GEO and TCGA public datasets as potential prognostic biomarkers. In breast cancer tissue samples, MMP1, SDC1, CD24, and SPP1 showed higher protein expression compared with that in tumor-adjacent tissues, and their expression was positively correlation with tumor subtype and grade. MMP1, a member of the matrix metalloproteinase family, functions as an interstitial collagenase and fibroblast collagenase. Higher MMP1 expression could predict worse disease-free survival (DFS) and OS in patients with invasive breast cancer, but the mechanism underlying this association is not clear . In the 31 cells . In MCF-31 cells ; in immuin vitro and in vivo studies are needed to clarify its potential.CD24 is a small and heavily glycosylated mucin-like glycosylphosphatidyl-inositol-linked cell surface protein, was detected in several types of carcinomas but is rarely expressed in normal tissues . In breaSDC1, a heparin cell surface proteoglycan that functions as a growth factors and chemokines co-receptor, which strongly correlated to the tumor aggressiveness and clinical results . In breaSPP1 is a protein overexpressed in breast tumors. Higher plasma levels of OPN with shorter OS in patients through inducing the tumor burden. In our in vitro and in vivo studies are needed to clarify this. SDC1 expression was significantly positively correlated with infiltration of CD8+ T cells, macrophages, DC, and neutrophils. In pancreatic cancer, SDC1 regulates micropinocytosis to enhance tumor growth [Fourth, the higher expression of MMP1, CD24, SDC1 and SPP1 enriched in NK cell-mediated cytotoxicity and T cell receptor signaling pathway according to GSEA analysis indicated that immune cell infiltration might be correlated with expression of hub genes. High expression of MMP1 was positively correlated with infiltration of CD4+ T cells, CD8+ T cells, DC, neutrophils, and macrophages. In dermatofibrosarcoma protuberance tumor tissues, MMP1 was found to be prominent in tumor-associated macrophages . CD24 exr growth . More str growth . SPP1 coin vitro and in vivo studies to verify the results, some other datasets were not used for verification the hub genes and immunity, like ArrayExpress database, InSilicoDB and METABRIC [In our results, high expression of MMP1, CD24, SDC1, and SPP1 correlated to the development of breast cancer, worse OS and immune cell infiltration, indicating that MMP1, CD24, SDC1, and SPP1 might be as the potential prognostic biomarkers and immunotherapy targets for breast tumor. We verified the results in several different datasets, including our own clinical datasets. However, there were some limitations of our study. First, there was a lack of METABRIC ,51. SecoClick here for additional data file.


Intravenous (IV) golimumab, a TNFi, is approved for treating rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS). We analyzed pooled safety results from three phase 3 IV golimumab trials in these rheumatologic diseases and hypothesized that the safety profile of IV golimumab would be similar to that established for other TNFi, including subcutaneous golimumab.Data from three double-blind, randomized trials of IV golimumab in patients with RA, PsA, and AS, each with a placebo-controlled period and an extension of active treatment, were included. Golimumab 2 mg/kg was administered at weeks 0 and 4, then every 8 weeks through week 100 (RA) or week 52 . Concomitant low-dose, oral corticosteroids were permitted. Concomitant methotrexate was required in the RA trial and permitted in the PsA and AS trials; placebo patients crossed over to golimumab at weeks 24  and 16 (AS), respectively. Adverse events (AEs), including infections, serious infections, malignancies, and major adverse cardiovascular events (MACE), were assessed through week 112 (RA) or week 60 .In total, 539 patients were randomized to placebo, and 740 patients were randomized to golimumab; 1248 patients received ≥ 1 golimumab administration. Among the placebo and golimumab patients, respectively, during the placebo-controlled periods, 40.6% and 50.3% had an AE, 2.4% and 3.8% had a serious AE, and 0.4% and 0.8% had a serious infection. Among all golimumab-treated patients, the numbers of events/100 patient-years (95% CI) were as follows: AEs, 175.2 ; serious AEs, 12.7 ; serious infections, 3.4 ; active tuberculosis, 0.4 ; opportunistic infection, 0.2 ; malignancies, 0.4 , and MACE, 0.5 . There were no cases of lymphoma. Three (0.6%) placebo-treated patients and 6 (0.5%) golimumab-treated patients died during the studies. Concomitant methotrexate was associated with increased occurrence of elevated alanine transaminase levels and lower incidence of antibodies to golimumab. During the placebo-controlled periods, serious infections in the placebo and golimumab groups were more common in patients receiving concomitant low-dose oral corticosteroids vs. those not receiving corticosteroids.IV golimumab demonstrated a safety profile that was broadly consistent across these rheumatologic indications and with other TNFi, including subcutaneous golimumab. Concomitant methotrexate or corticosteroids were associated with an increase in specific AEs.ClinicalTrials.gov, NCT00973479. Registered on September 9, 2009. ClinicalTrials.gov, NCT02181673. Registered on July 4, 2014. ClinicalTrials.gov, NCT02186873. Registered on July 10, 2014. Golimumab is a human monoclonal antibody that binds and inhibits the pro-inflammatory cytokine tumor necrosis factor alpha (TNFα). Intravenous (IV) golimumab is approved for the treatment of rheumatoid arthritis (RA) (in combination with methotrexate [MTX]), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) . EfficacIntravenous golimumab has demonstrated significantly greater improvements, compared with placebo, in signs and symptoms in patients with RA , PsA 5,,5, 15, aHere, we report pooled safety results from the three double-blind, randomized, placebo-controlled, phase 3 trials of IV golimumab in patients with RA –4, PsA , 6, and These analyses utilized data from three double-blind, randomized, placebo-controlled, phase 3 trials of IV golimumab: GO-FURTHER (NCT00973479), GO-VIBRANT NCT02181673), and GO-ALIVE (NCT02186873)  were eligible ) with 95% confidence intervals are also provided across indications by treatment groups. Adverse events of interest included serious infections, malignancies, major adverse cardiovascular events , and infusion reactions.n = 539) or IV golimumab (n = 740). A total of 1248 patients received ≥ 1 golimumab administration, including those who were randomized to placebo and initiated IV golimumab at pre-specified time points for crossover or early escape  , septic shock secondary to a pyogenic lung abscess, dehydration due to RA trial –4 and onRA trial . No deatRA trial , 8.Malignancies occurred in 2 (0.4%) patients receiving placebo , urinary tract infection (n = 5), sepsis (n = 4), appendicitis (n = 2), empyema (n = 2), and erysipelas (n = 2); other serious infections were singular events and included infected dermal cyst, acute pyelonephritis, periodontitis, and acute hepatitis of mixed etiology [n = 3; PsA trial, n = 2; AS trial, n = 1) (Table n = 1) and localized vertebral candidiasis (n = 1) (both classified as serious and led to discontinuation of golimumab) [In total, 49 3.9%) of all golimumab-treated patients had a serious infection; the incidence/100 PY (95% CI) was 3.4  across the three trials  occurred in a patient receiving golimumab in the PsA trial . Two eveTwo patients experienced heart failure : one patient in the placebo group developed acute heart failure that had a fatal outcome, and one patient in the golimumab group had a non-serious AE of chronic heart failure . Two patients experienced deep vein thrombosis: one golimumab-treated patient in the RA trial and one placebo-treated patient in the PsA trial. The incidence 95% CI) of MACE among all golimumab-treated patients was 0.5/100 PY 0.2, 1.0), all occurring in the RA trial Table : two dea, 1.0, al% CI of MDuring the placebo-controlled periods, infusion reactions were seen in 2.8% of all IV golimumab-treated patients Table . No infun = 391; golimumab, n = 574) were receiving concomitant MTX at baseline  patients were receiving concomitant low-dose oral corticosteroids at baseline  patients were positive for antibodies to golimumab , including 71 (6%) who were positive for neutralizing antibodies. Infusion reactions occurred in 20 (2.1%) patients who tested negative for antibodies to golimumab and in 11 4.1%) who tested positive for antibodies to golimumab; the proportions of infusions with an infusion reaction were 0.4% and 0.8%, respectively. In the PsA and AS trials, in which not all patients received MTX, the occurrence of antibodies to golimumab was lower in patients who were receiving MTX at baseline compared with patients who were not  acquired active TB in countries where TB is endemic , no case of latent TB converted to active TB.During the placebo-controlled periods, SAEs were uncommon in both the placebo group (2.4%) and the combined golimumab group (3.8%). Infections were the most common type of AE in the pooled results, which was consistent with the results reported individually from each of these studies. The incidence of serious infections per 100 PY was highest in the RA trial, followed by the PsA and AS trials; the pooled incidence of 3.4 serious infections per 100 PY in all golimumab-treated patients was comparable to that reported for other TNFi agents –25. OtheAmong the three studies, AEs tended to be more common in patients with RA, with infections being the most common class of AE reported. Previous analyses of patients receiving TNFi have shown a higher incidence of serious infections in RA patients than in patients with AS or PsA , 27. In Fewer than 3% of all golimumab-treated patients had an infusion reaction; none was serious or severe. Approximately 22% of patients treated with golimumab developed antibodies to golimumab, although only 6% of patients tested positive for neutralizing antibodies. The proportion of patients with antibodies to golimumab and neutralizing antibodies was numerically lower in patients who received concomitant MTX than in those who received golimumab monotherapy; however, interpretation of these results is limited by the lack of a golimumab monotherapy population in the RA study and the relatively small number of patients receiving golimumab+MTX in the AS study. Infusion reactions were more common in patients with antibodies to golimumab than those without antibodies to golimumab.Oral corticosteroid use has been associated with an increased risk of serious infections in patients with RA, even with doses ≤ 5 mg/day  . In the With data from over 700 golimumab-treated patients and approximately 71 patient-years of follow-up, these results represent the largest pooled analysis, to date, from phase 3 studies of IV golimumab in patients with RA, PsA, and AS. The studies included in the current analysis were limited to 1 or 2 years in duration, and the studies were not powered to detect rare events. Differences in study design also limited some of the analyses on concomitant medications. However, the totality of the pooled data across three rheumatologic indications demonstrated consistency with each of the individual trials.These pooled analyses from three double-blind, randomized, placebo-controlled, phase 3 trials of patients with RA, PsA, and AS included safety data from 1280 patients for a total of 1697 PY of follow-up. Infusion reactions were uncommon across the three studies. Concomitant use of MTX and of low-dose oral corticosteroids was associated with a higher occurrence of ALT elevations and serious infections, respectively. Overall, most elevations in liver transaminases were mild and transient, and few patients experienced a serious infection. The safety profile of IV golimumab was generally consistent across the RA, PsA, and AS trials, as well as with other TNFi.


Sesamum indicum L.). The sesame genetic resources present in the country should be explored for local, regional, and international genetic improvement programs to design high-performing and market-preferred varieties. This study’s objective was to determine the extent of genetic variation among 100 diverse cultivated sesame germplasm collections of Ethiopia using phenotypic traits and simple sequence repeat (SSR) markers to select distinct and complementary genotypes for breeding. One hundred sesame entries were field evaluated at two locations in Ethiopia for agro-morphological traits and seed oil content using a 10 × 10 lattice design with two replications. Test genotypes were profiled using 27 polymorphic SSR markers at the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences. Analysis of variance revealed significant (p ≤ 0.05) entry by environment interaction for plant height, internode length, number of secondary branches, and grain yield. Genotypes such as Hirhir Kebabo Hairless-9, Setit-3, Orofalc ACC-2, Hirhir Humera Sel-6, ABX = 2-01-2, and Setit-1 recorded grain yield of >0.73 ton ha−1 with excellent performance in yield component such as oil yield per hectare. Grain yield had positive and significant (p < 0.01) associations with oil yield (r = 0.99), useful for simultaneous selection for yield improvement in sesame. The SSR markers revealed gene diversity and polymorphic information content values of 0.30 and 0.25, respectively, showing that the tested sesame accessions were genetically diverse. Cluster analysis resolved the accessions into two groups, while population structure analysis revealed four major heterotic groups, thus enabling selection and subsequent crossing to develop breeding populations for cultivar development. Based on phenotypic and genomic divergence, the following superior and complementary genotypes: Hirhir Humera Sel-6, Setit-3, Hirhir Kebabo Hairless Sel-4, Hirhir Nigara 1st Sel-1, Humera-1 and Hirhir Kebabo Early Sel-1 (from cluster II-a), Hirhir kebabo hairless-9, NN-0029(2), NN0068-2 and Bawnji Fiyel Kolet, (from cluster II-b). The selected genotypes will serve as parents in the local breeding program in Ethiopia.Ethiopia is one of the centers of genetic diversity of sesame ( Sesamum indicum L.) is a multi-purpose high-value oilseed crop. It is a global commodity serving the food, feed, and cosmetic industries. The seed oil content of sesame is about 60%, the highest when compared with other oilseed crops such as sunflower (~45%), rapeseed (~40%), and soybean (~20%) [Sesame (n ~20%) ,2,3,4. S0% ,2,3,4flower ~4%, rapesen (~20%) . Sesame n (~20%) ,8,9. TheCoffea arabica L.) and a major contributor to Ethiopia’s gross domestic product [Sesame is the second most valuable export crop after coffee  maintains one of the most extensive core collections of sesame genetic resources in Africa. About 5000 genetically diverse sesame germplasm resources are conserved by the EBI . The gerin Egypt ,12. The in Egypt ,14.Sesame genetic resources maintained at the EBI can be explored to search for new sources of useful genetic variation for economic traits. This includes grain yield and yield- components, resistance to diseases and insect pests, tolerance to abiotic stresses, capsule shattering tolerance, and nutritional quality. This will identify desirable and complementary parents and for gene discovery. Hence, rigorous phenotyping and genotyping can establish genetic polymorphism in the germplasm pool and classify the heterotic groups for ideotype breeding.Previous studies have reported considerable phenotypic variation for agronomic and quality traits in Ethiopia’s sesame genetic resources ,16,17,18Several molecular markers such as amplified fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), microsatellites or simple sequence repeat (SSR), and single nucleotide polymorphisms (SNPs) markers are widely used in genetic diversity analysis of various crop genetic resources. SSR or microsatellites have been commonly used in genetic variation studies on sesame ,20,21,22The study used a mini-core collection of 100 sesame entries originally collected from the Amhara, Tigray, Afar, Oromia, and Gambela regions in Ethiopia. The test genotypes were obtained from the sesame and groundnut breeding program of Werer Agricultural Research Centre of the Ethiopian Institute of Agricultural Research (EIAR). The collection comprised of 95 accessions, one landrace (farmer variety), and four released varieties. The landrace variety ‘‘Hirhir” is widely cultivated by farmers in the study areas. The four released varieties  were developed by the Humera Agricultural Research Center (HuARC) through mass selection amongst the local germplasm collections. The details of the germplasm collections used in the study are summarized in The study was conducted in northwestern Ethiopia in two selected locations, namely, Humera  and Kebabo . Humera and Kebabo are agricultural research stations of the Humera Agricultural Research Centre of EIAR and the Tigray Agricultural Research Institute (TARI). The two sites represent the major sesame production environments in Ethiopia. Humera and Kebabo are situated at an altitude of 609 and 696 m above sea level and receive a total rainfall of 576.4 and 888.4 mm, respectively. The mean minimum and maximum temperatures at Humera site range from 20.3 to 36.5 °C. Kebabo has an average minimum and maximum temperatures of 16.9 and 31.7 °C. The two sites have predominantly clay soil .The experiment was conducted under field conditions and laid out using a 10 × 10 simple lattice design, with two replications, at each site. Each entry was planted in four rows plots measuring four meters in length, with an inter-row and intra-row spacings of 0.4 m and 0.1 m, respectively. The trials were maintained following the standard agronomic practices of sesame production .Data were collected on quantitative and qualitative traits. Plant height, internode length, number of primary branches per plant, number of secondary branches per plant, number of capsules per plant, number of seeds per capsule, stem height to first branch, and distance from lowest branch to 1st capsule were recorded from 10 randomly selected and tagged plants during plant growth and at harvest. Plant height (PH) was measured from the base to the tip of the plant. Stem height from the base to the 1st branch (SHB) was measured from the base of the plant to first emerged primary branch. Internode length (INL) was measured between two consecutive nodes situated in the middle of the plant. The number of primary branches per plant (NPB) was counted from the plant’s main stem, while the number of secondary branches per plant (NSB) was counted from the plant’s main branch. Distance from the base of the lowest branch to the first capsule (DFLBC) was measured as the distance between the lowest situated primary branch to the 1st emerged capsule on the main stem and expressed in cm.−1).The number of days to flowering (DF) was recorded by counting the number of days from planting to the date when 50% of the plants showed flowers, while days to maturing (DM) was recorded as the number of days from planting to the date when 75% of the plants reached physiological maturity. The number of capsules per plant (NCPP) and number of seeds per capsule (NSPP) were counted from a composite of three capsules per plant at harvest. Thousand seed weight (TSW) was measured from a random sample of 1000 seeds of each entry. Grain yield (GYH) was measured in grams per plot and converted into ton (t) per hectare  . Oil yield per hectare was calculated and expressed in tons per hectare as the product of grain yield and percent oil content.The phenotypic data were subjected to analysis of variance (ANOVA) using the alpha-lattice and general linear model (GLM) procedures of the SAS software version 9.4 . A combi2O. DNA concentrations were measured using the Quantus TM Fluorometer . Microsatellites from 13 linkage groups were designed and used for the following experiments. The 27 primers were selected because of their suitability in discriminating sesame genotypes. The presently used primers were initially selected amongst 160 candidate primers based on their higher polymorphic information content and provided clear and informative amplicon profiles in sesame genetic analysis [The above 100 sesame entries  were plaThe polymerase chain reaction (PCR) conditions were maintained as follows; each PCR reaction was carried out in a 20 μL solution containing 25 ng of DNA, 4 μmol of forward primers, 4 μmol of reverse primers, 1 × buffer, 0.25 mmol of dNTPs, and 0.80 U Taq polymerase. The temperature profile used for PCR amplification comprised a denaturation step at 94 °C for 1 min, followed by primer annealing temperature at 45.2–53 °C for 1 min, and elongation at 72 °C for 1 min. After 34 cycles, the reaction was terminated with a 10 min final extension time at 72 °C.The PCR reaction conditions were the same for all the primers, except for the annealing temperatures. The PCR products were electrophoresed on 6% Acrylamide gel  at a voltage value of 2000, current 300 A, power 80 W for 1:30 h. After silver staining, the bands on the gels were recorded, and a total of 27 markers  with higThe fragment sizes were determined using the ABI 3730 automatic sequencer. Data were analysed using the software GeneMarker V 2.2.0 to determine peak detection threshold levels that ranged from the minimum intensity of 500 and max intensity of 30,000. The 27 primers were used to detect the band sizes based on the peak detection thresholds, which were then scored using 1 to denote presence and 0 for absence. Genetic parameters, such as major allele frequency (M.A.F.), observed heterozygosity (Ho), expected heterozygosity (He), and the polymorphic information content (PIC) were calculated using Power Marker v3.2. Cluster analysis was carried out using a neighbor-joining (NJ) algorithm using the unweighted pair group method (UWPGM) in R software version 4.0 .The population structure of the 100 sesame accessions was investigated using the Bayesian clustering method in STRUCTURE version 2.3.4 . The lenp ≤ 0.05) entry x environment interaction for plant height, internode length, number of primary branches, number of secondary branches, distance from the base of the lowest branch to 1st capsule, and grain yield per hectare (p ≤ 0.05) differences for days-to-50% flowering, days-to-75% maturity, plant height, internode length, number of secondary branches, number of seeds per capsule, distance from the base of the lowest branch to 1st capsule, and grain yield per hectare.Combined ANOVA revealed significant , Setit-3 (0.84 ton ha−1), Orofalc ACC-2 (0.80 ton ha−1), Hirhir Humera Sel-6 (0.78 ton ha−1), ABX = 2-01-2 (0.74 ton ha−1), and Setit-1 (0.73 ton ha−1). These genotypes expressed high oil yields of 0.40, 0.40, 0.40, 0.39, 0.36, and 0.39 ton ha−1 than other test genotypes. The accessions Bawnji Fiyel Kolet, NN0056, Hirhir Humera Sel-8, NN-0068-1, and ACC-NS-010 had the highest oil content of 55.6, 55.2, 54.7, 54.6, and 54.10% than other genotypes, respectively. The five bottom performing accessions in terms of grain yield were NN-0183-3 (0.17 ton ha−1), NN-0020 (0.24 ton ha−1), NN-0108-2 (0.26 ton ha−1), NN00136-1 (0.26 ton ha−1), and NN-0143 (0.28 ton ha−1) with low oil yield of 0.07, 0.12, 0.04, 0.13, and 0.15 ton ha−1, in that order. These accessions yielded below-average grain and oil yields.Based on grain yield response, the top 10 best performing and the five bottom performing accessions are summarized in r = 0.99; p < 0.01). Significant and positive correlations were also observed between grain yield and internode length , number of secondary branches , number of capsules per plant , number of seeds per capsule , stem height from base to 1st branch , and thousand-seed weight .Phenotypic correlation coefficients for the studied traits are presented in Principal component analysis (PCA) was computed to show each trait’s contribution to the overall observed variation. A scree plot was generated to visualize the number of principal components. Overall, four principal components were identified with >1 Eigen values of which principal components 1 (PC1) and PC2 explained the highest proportion to the total variance . PrincipThe summary statistics describing the SSR markers are presented in Structure analysis revealed four populations amongst the 100 sesame entries . Sixty-tEntries allocated in population I had good branching ability ), many seeds per capsule (Gojam Azene (Aleka), and ABXT-85-Sel-2-1), and significant oil yield (NN-0026), and seed oil content (NN0064-1 and NN0071). Population II accessions were early maturing with tall plants. Some population II accessions had a significant number of seeds per capsule (NN0025 and ABX = 2-01-2), and grain yield. In addition, population II comprises accessions such as NN0056, Hirhir Baeker-Sel-3, and Orofalc ACC-2 with good oil content. Genotypes allocated in population III were early maturing with taller plants. These accessions were outstanding in thousand-seed weight , grain yield (Hirhir Kebabo Hairless-9), oil yield (Hirhir Kebabo Hairless-9), and seed oil content ( Hirhir Kebabo Hairless-9).−1), and oil content (54.7%). To develop new breeding populations possessing desirable economic traits new crosses could be developed between the selected parents. Hence accessions Orofalc ACC-2 (from population II), Hirhir Filwha Large Seeded (population III), and Setit-3, Hirhir Humera Sel-6 (population IV) are ideal candidates with complementary traits for production and further breeding. However, the principal coordinate analysis assigned the 100 genotypes into admixture groups with an inconclusive structure , better thousand-seed weight (3.4 g), higher seed and oil yields  variation for grain yield and yield components  were grouped in sub-population 4. Wei et al.  and AsekCluster analysis identified two major clusters and four sub-clusters, revealing genetic variation among the assessed sesame entries . AsekovaTo develop new breeding populations possessing desirable agronomic traits, especially high grain and oil yields, crosses could be made between distantly related and complementary genotypes selected from different clusters. For instance, for improved grain and oil yields, the following entries were selected such as Setit-3, Orofalc ACC-2, Hirhir Humera Sel-6, ACC-NS-007(2), Hirhir Kebabo Hairless-9, and ACC 205-180. These genotypes are localized in sub-cluster II-a and sub-cluster II-b. The two clusters contained candidates with excellent grain and oil yields.In conclusion, the current study determined the extent of genetic variation among 100 diverse sesame germplasm collections of Ethiopia using phenotypic traits and simple sequence repeat (SSR) markers to select distinct and complementary parents for breeding. The test genotypes exhibited significant phenotypic variation for key agronomic traits including grain yield, oil content, and oil yield, which were underpinned by their genetic diversity. The sesame genotypes were differentiated into four major populations based on the model-based population structure analysis. The moderate heterozygosity and fixation index among the accessions suggests that the accessions have distinct heterotic groups desirable for breeding. Based on wide genetic divergence, the following genotypes were selected for use in future sesame breeding programs: Hirhir Humera Sel-6, Setit-3, Hirhir Kebabo Hairless Sel-4, Hirhir Nigara 1st Sel-1, Humera-1, Orofalc ACC-2, and Hirhir Kebabo Early Sel-1 (selected from subgroup II-a), Hirhir kebabo hairless-9, NN-0029(2), NN0068-2, Hirhir Filwha Large Seeded, and Bawnji Fiyel Kolet, (from subgroup II-b). Progeny development and field evaluation by combining ability analysis are recommended among the selected parents to establish heterotic groups for sesame pre-breeding.


Auxin, primarily indole-3-acetic acid (IAA), is a versatile signal molecule that regulates many aspects of plant growth, development, and stress response. Recently, microRNAs (miRNAs), a type of short non-coding RNA, have emerged as master regulators of the auxin response pathways by affecting auxin homeostasis and perception in plants. The combination of these miRNAs and the autoregulation of the auxin signaling pathways, as well as the interaction with other hormones, creates a regulatory network that controls the level of auxin perception and signal transduction to maintain signaling homeostasis. In this review, we will detail the miRNAs involved in auxin signaling to illustrate its in planta complex regulation. The plant growth regulator, auxin, participates in nearly all aspects of plant growth and development, including cell expansion, vascular differentiation, lateral root (LR) formation, hypocotyl elongation, senescence, abscission, hormone crosstalk and stress responses ,2,3,4. TTIR1/AFB-Aux/IAA complex, the transcription of downstream genes is regulated directly by a third protein family, the Auxin Response Factor (ARF) family . T. TAtMIR3sitivity C 63]. I. IAtMIR3AR genes . SurprisAR genes . In additMIR393a . Overexpf miR393 C. This fIR1/AFB2 ,68.OsMIR393a and OsMIR393b [OsMIR393a and OsMIR393b caused flag leaf inclination, primary/crown root growth and seed germination defects in rice [Similarly, in rice, miR393 is encoded by two loci, sMIR393b ,69, and sMIR393b  and 5′-RsMIR393b . SpatialsMIR393b ,73. Over in rice ,73. ThesMIR393, the exact roles of miR393 in plant development and stress responses still need more investigation.Additionally, the roles of miR393 in regulating both normal plant developmental processes, such as nodulation, adventitious root formation, leaf morphogenesis and fruit development, and the response to different environmental stresses, including aluminum stress, drought stress, cold stress and aphid infection, have been widely studied in different species . HoweverAtNAC1 promotes LR development through transduction of the auxin signal downstream of AtTIR1 [AtNAC1 can restore LR formation in Attir1, and antisense of AtNAC1 can block AtTIR1-induced LR development [auxin resistant 1/2 (axr1/2) and tir1 [AtmiR164 and promoting that of AtNAC2, while AtmiR164 guides the cleavage of AtNAC2 mRNA [OsNAC/MTN2/3/4/6, are reported to negatively regulate the drought stress response via miR164-mediated cleavage (SlNAC1 [The miR164 family is comprised of three members (miR164a/b/c) in Arabidopsis but six members (miR164a/b/c/d/e/f) in rice ,113,114.f AtTIR1 ,116. Oveelopment . Exogenoand tir1  and funcand tir1 . The miRcleavage D. In tom  [AtMIR169 [AtNF-YA2, directly binds to the promoters of flowering time (FT) and the auxin biosynthesis gene AtYUC2 in response to ambient temperature [The fact that miR169 can respond to cold stress was found through a genome-wide expression analysis in wild-type and x/IAA14) . FurtherAtMIR169 . Moreoveperature  , while OsSPL7 can directly bind to the promoter of OsGH3.8, which catalyzes the ATP-dependent formation of IAA-amino acid conjugates [OsGH3.8 in miR156-overexpression (miR156f-OE) lines can partially complement the developmental defects observed in miR156OE plants, e.g., fewer tillers and increased plant height, indicating that miR156f/SPL7 can directly control OsGH3.8 to regulate plant growth and development  and LaARF1/2 through LaHDZIPIII during somatic embyro germination , a novel miRNA-ARF (miRN43-ARF9) regulatory pathway has been discovered, implying that species-specific miRNA may function in the auxin signaling pathway [As mentioned above, the conserved miRNAs, including miR160, miR167, miR390 and miR393, have been extensively investigated in Arabidopsis, rice, tomato, maize, potato, and barley ; Table 1ignaling ,110. In  pathway . Another pathway . Taken tAuxin controls most developmental stages and stress responses in plants, and the communication between auxin and the other phytohormones  is critical to these processes. Although it is clear that there is phytohormone crosstalk, only a few mediators have been investigated thus far, including miRNAs .cis-elements of phytohormones and stress responses have been identified in many MIR gene promoters [trans-regulation capabilities, miRNAs are excellent candidate mediators for the communication between auxin and other hormones.The miRNA is involved in multiple developmental processes, including organs morphogenesis, root growth, leaf development, and stress responses, which is a characteristic common to auxin. Additionally, the romoters . Interesromoters ,125. DueATP/ADP ISOPENTENYLTRANSFERASE 5 (AtIPT5) to promote shoot regeneration [Arabidopsis Response Regulator 5/15 (AtARR5/15) repression is important for maintenance of stem cell niches in shoot apical meristems [Interaction between auxin signaling and cytokinin (CK) in plant development has been investigated in recent years. For example, AtARF3 directly activates neration , and AtAeristems . Moreoveeristems   homeostasis, and modulate adventitious root initiation [AtIAR3, which can hydrolyze IAA conjugates into active IAA and is induced by exogenous JA  is involved in regulating auxin/miRNAs signaling via inhibiting the transcription of miR156, miR164 and miR390. The miR156-SPL module functions as a hinge to integrate multiple phytohormones, including auxin, gibberellin (GA), and ET, in floral transition and lateral organ development . In riceOsGH3.8 . InteresOsGH3.8 , indicatOsGH3.8 . During OsGH3.8 . The incOsGH3.8 ,130. MeaOsGH3.8 ,132. TogAtNAC2 during leaf aging [ein2 and coi1 (a JA receptor mutant) mutants, suggesting the indirect regulation of miR164 by JA [Medicago root, suggesting that miR390 might participate in the crosstalk of different hormones via the downregulation of its target genes, ARF2/3/4 [The expression of miR164 is gradually repressed by AtEIN2, which subsequently de-represses af aging . Moreove64 by JA ,134. UnlARF2/3/4 .With the growing number of studies on the interaction of miRNA and hormone signaling, the number of known target genes of miRNAs involved in different hormone signaling continues to increase. In addition to the hormones described above, new discoveries of miRNA-mediated interactions between auxin and other hormones, including brassinosteroid (BR), CK, salicylic acid (SA), and strigolactones, will deepen our understanding of the crosstalk between auxin and other hormones and make possible the application of miRNAs for crop improvement.In plants, auxin plays a central role in growth and development as well as responses to abiotic and biotic stresses ,136,137.AtARF3 during leaf development [The control of auxin responses by miRNAs occurs mostly downstream of the TIR1/AFBs by regulating the expression of ARFs and AUX/IAAs, therefore affecting only part of the auxin-regulated processes. Interestingly, auxin and miRNAs independently regulate the target ARF levels when controlling the development of aerial organs and LRs. In addition, some miRNA families  have been found to act upstream by regulating genes involved in auxin biosynthesis, metabolism and perception. Therefore such miRNAs play a more crucial role in controlling the auxin response. Hence, miRNA-auxin signaling regulatory modules provide additional layers of modulation of auxin signaling. Moreover, miR390-derived tasiARFs move intercellularly to create a small RNA gradient, which can pattern the abaxial determinant elopment . Therefo(i)How do plants integrate all these developmental signals or stress responses to control the levels of the individual miRNAs-auxin signaling module?Although there have been great advances in the study of miRNA-mediated auxin signaling, some questions still need to be further investigated:As mentioned above, we have known that plants employ different miRNAs and then their targets in response to different developmental timing and various stress signals; however, it is still unknown how plants precisely control this process. A deeper understanding of the regulatory mechanism(s) will allow us to choose appropriate targets for modulating agriculturally important traits.(ii)Which lineage-, species- and tissue-specific miRNA molecules are also involved in regulating auxin responses?As different environments shape different plants, the plants will evolve the specific miRNAs in response; however, previous studies have mainly focused on the functions of conserved miRNAs. Therefore, it would be valuable to evaluate the role of lineage-, species- and tissue-specific miRNAs to complement our view and to determine how broadly miRNA-dependent genetic networks could influence the auxin signaling in different species.(iii)How can we use miRNA-auxin modules to create tolerance to stresses and/or high-yield crops?Because of the extensive regulation of auxin signaling by miRNAs during plant growth and in response to stresses, miRNAs have been considered as good targets for the genetic improvement of crops . General


In this study, multiple analyses based on 3593 patients with breast cancer and their mRNA expression values were utilized to investigate the clinical relevance of LSM family genes, including cancer aggressiveness, immune cell infiltration, prognostic outcomes, and related signaling pathways. We revealed that LSM4 had higher expression levels in breast tumor and breast cancer sub-types than in normal samples, and was associated with poor survival outcomes. Interestingly, infiltration levels of most immune cell types, including cluster of differentiation for positive CD4−7). MetaCore and GlueGo analyses were also conducted to examine transcript expression signatures of LSM family members and their coexpressed genes, together with their associated signaling pathways, such as “Cell cycle role of APC in cell cycle regulation” and “Immune response IL-15 signaling via MAPK and PI3K cascade” in BRCA. Results showed that LSM family members, specifically LSM4, were significantly correlated with oncogenesis in BRCA patients. In summary, our results suggested that LSM4 could be a prospective prognosticator of BRCA.In recent decades, breast cancer (BRCA) has become one of the most common diseases worldwide. Understanding crucial genes and their signaling pathways remain an enormous challenge in evaluating the prognosis and possible therapeutics. The “Like-Smith” (LSM) family is known as protein-coding genes, and its member play pivotal roles in the progression of several malignancies, although their roles in BRCA are less clear. To discover biological processes associated with LSM family genes in BRCA development, high-throughput techniques were applied to clarify expression levels of LSMs in The Cancer Genome Atlas (TCGA)-BRCA dataset, which was integrated with the cBioPortal database. Furthermore, we investigated prognostic values of LSM family genes in BCRA patients using the Kaplan–Meier database. Among genes of this family, LSM4 expression levels were highly associated with poor prognostic outcomes with a hazard ratio of 1.35 (95% confidence interval 1.21–1.51, p for trend = 3.4 × 10 According to Global Cancer statistics, the estimated 2.3 million confirmed cases of breast cancer (BRCA) in 2020 elevated it to become the most prominent malignant cancer worldwide . BRCA acSmith-like (LSM) proteins are known as a family of RNA-binding proteins that appear in essentially all cellular organisms . First dThe LSM family has 13 members (LSM1~LSM14B) which were strongly associated with tumorigenesis and metastasis of several tumor types. In particular, the LSM1 protein plays roles in the cellular conversion and progression of BRCA, mesotheliomas, and lung cancer ,23,24, aHowever, the biological functions of each member of the LSM family genes are not fully understood, especially with regard to the tumor microenvironment (TME) . MultiplComprehensive methods can help reveal the roles of LSM family genes in BRCA growth, improve prognoses, and personalize effective treatments. We hypothesized that the LSM family, especially LSM4, might possess a novel role in tumor growth, and infiltration of immune cells may provide better predictions of survival rates in BRCA patients.http://ualcan.path.uab.edu, accessed on 1 May 2021) is an inclusive puplic database using The Cancer Genome Atlas (TCGA) “Level 3” RNA-sequencing (RNA seq) and clinical data from more than 30 cancer types. This database includes expression values computed by the RSEM algorithm for 20,502 genes . To obtain expression levels in normal and cancer tissues, LSM genes under the DiffExp module with default settings were used. The “DiffExp” module allows for the investigation of differences in gene expression between normal and malignant tissues for any gene across all TCGA tumors. The “correlation” module presents expression scatterplots between two user-defined genes in a certain cancer type, along with Spearman correlation coefficients and statistical significance, and can be adjusted by tumor purity , with |logFC| > 1.5 set as the threshold [We attempted to validate the results using the Genomic Data Commons (GDC) database , to obtain patient data and query the effects of the expressions of different LSM family members on overall survival (OS). For the survival analysis, a KM plotter was applied, with all default settings, and recurrence-free survival (RFS) was preferred, with the auto-best cutoff values and J best probe set. All possible cutoff values between the lower and upper quartiles were determined, and the best presenting threshold was subsequently used as the cutoff. A log-rank p-value of <0.05 was considered statistically significant.We utilized TCGA Pan-Cancer Atlas, a dataset from cBioPortal (p < 0.001) elevated expressions of LSM1, LSM2, LSM3, LSM4, and LSM5; while LSM7, LSM10, LSM11, LSM12, and LSM14A were significantly overexpressed in stomach adenocarcinoma and uterine corpus endometrial carcinomas compared to healthy samples  and primary tumor samples (n = 793) of each LSMs family gene in TCGA cohort using UALCAN database  in the cBioPortal platform, and found surprisingly high rates of alterations in LSM1 (25%), LSM2 (11%) LSM4 (8%), and LSM14B (23%). We chose the top 25% (5000 genes) from a list of co-expressed genes retrieved from the METABRIC dataset, and then intersected these lists with each gene in the LSM family. Gene ontology (GO) consists of CCs, BPs, and MFs. Results were then input into Cytoscape to build a network of GOs and Kyoto Encyclopedia of Genes and Genomes (KEGG), which are described in detail in We investigated mutations of LSM genes in BRCA patients from TCGA Pan-Cancer Atlas (p for trend = 2.1 × 10−12), LSM2 , LSM3 , LSM4 , LSM6 , LSM7 , LSM14A , and LSM14B . In contrast, low expression levels of LSM5, LSM8, and LSM11 indicated longer recurrence metastasis-free survival . In addition, LSM10 and LSM12 showed non-significant prognostic values. The results are tabulated We evaluated relationships between LSM mRNA expression levels and survival rates of BRCA patients by performing a KM analysis to uncover the prognostic value of each LSM. The results indicated that 8 of 13 members of the LSM family were significantly associated with poor prognostic outcomes of BRCA patients with respect to RFS, such as LSM1 . LSM2 was significantly associated with B cells  and macrophages . Similarly, LSM3 expression was correlated with tumor purity  and CD4+ T-cell markers . LSM4 expression exhibited positive correlations with tumor purity , CD8+ T cells , macrophages , neutrophils , and DCs . LSM7 was associated with CD8+ T-cell markers , macrophages , and neutrophil markers . LSM14B was correlated with purity  and CD4+ T-cell markers   and XCELL , while CD4+ Th1 T cells were highly correlated with basal , human epidermal growth factor receptor 2 (HER2) , luminal A , and luminal B subtypes . On the other hand, LSM4 was positively correlated with CD8+ T cells in CIBERSORT methods in most BRCA subtypes ; as well as T cell follicular helpers in CIBERSORT . In contrast, we found that it had negative correlations with M2 macrophages in CIBERSORT-ABS  and neutrophils in MCP-COUNTER . Results are described in An evolving TME is a convoluted and continually growing entity. The configuration of the TME varies among tumor types. A large body of research has revealed the important role of the TME in cancer progression ,58,59,60 × 10−3)  EventualFor comprehensive exploration, we extracted data from METABRIC and TCGA Pan-Cancer Data to retrieve GO enrichment results, including CCs, BPs, MFs, and KEGG. For BPs, we observed that LSM4 was correlated with non-coding (nc)RNA metabolic processes and ribonucleoprotein complex biogenesis. In contrast, the CC analysis showed localization in mitochondria, such as the mitochondrial inner membrane, mitochondrial matrix, and mitochondrial complex. Finally, from MF results, “catalytic activity, acting on RNA” and “ATPase activity” were strongly associated with high LSM4 expression in BR tumors, while KEGG ontology indicated the role of pathways of neurodegeneration-multiple diseases, as well as other disease-related pathways, such as amyotrophic lateral sclerosis, Alzheimer’s disease, Huntington disease, and Parkinson’s disease .It was interesting to discover underlying biological processes of gene sets which were co-expressed with LSM4. A GSEA was utilized to investigate the enrichment of MSigDB Hallmark gene sets in BRCA samples with high expression levels of LSM4. We found that the EMT, coagulation, tumor necrosis factor (TNF)-α-signaling via nuclear factor (NF)-κβ, interleukin (IL)-2/signal transduction and activator of transcription 5 (STAT5) signaling, apical junction, and androgen response, which are known as inflammation- and immune-related gene sets, were enriched with high LSM4 expression in breast tumors. Moreover, LSM4 was also significantly expressed in a procancerous gene set, such as KRAS signaling upregulation, angiogenesis, and transforming growth factor (TGF)-β signaling . The dett-test p-value of <2.2 × 10−16. We observed a congruous outcome with our results above, revealing the significantly high expression of LSM4 in BRCA patients -γ and tumor necrosis factor (TNF)-α, which so far are known to play vital roles in antitumor immunity [When conducting a survival analysis, we observed that high LSM3 expression levels were significantly correlated with shorter survival times in patients with BRCA. Of interest, via a TIMER analysis, LSM3 showed a strong relationship with CD4immunity . In addiimmunity . For the+ T-cell markers, macrophages, and neutrophil markers in a TME analysis. In cancer, neutrophils play major roles in inflammatory functions and in innate and adaptive immunity, and are dynamically involved in progression and metastasis, hence serving as emerging targets for multiple cancer types [Similarly, LSM7 was also found to be upregulated in BRCA patients at different stages. Moreover, the KM plot results showed that BRCA patients with high LSM7 expression had a significantly poorer survival rate compared to a control group. A previous study revealed that triple-negative breast cancer cells react to T cells at the splicing layer, where LSM7 is located . This fier types ,105. Furer types . In vivoer types .There have been no studies determining the precise role of LSM14B in any disease. In this report, we observed evidence of LSM14B overexpression that affected outcomes of the survival analysis and increasing expression levels in more progressive tumor stages. LSM14B showed a relationship with the BP of “Inhibition of oligodendrocyte precursor cell differentiation by Wnt signaling in multiple sclerosis”, a pathway involved in chronic demyelinating diseases. Therefore, additional work is required to determine the precise role of LSM14B in BRCA.+ T cells, macrophages, neutrophils, and DCs were strongly associated with LSM4 in targeting cancer cells, leading to a potential role of LSM4 in immunotherapy. Consistently, previous studies demonstrated that macrophages are widely present in solid tumors [+) and consist of CD4+, CD8+. and T-regulatory (Treg) cells [+, and CD8+ T cells, and a minor component of TILs were represented by B cells and plasma cells. Our results in + T cells, including type 1 T-helper (Th1) cells in most BRCA subtypes. Consistent with previous studies, it increases the antitumor activity of NK cells and macrophages [Our method provides an observation of correlations between LSM4 expression and BRCA tumor, and revealed that LSM4 overexpression was associated with progressive stages of BRCA. Furthermore, the HPA showed that LSM4 had a moderate to strong IHC intensity in BRCA samples compared to normal breast tissues. From the results of a TME analysis, CD8d tumors ,109 and d tumors . Howeverd tumors . While Bd tumors . Tumor-ig) cells . DCs, CDrophages . In addirophages , were alrophages , consistrophages . Interesrophages ,119 in brophages . Among srophages . LSM4 waIn summary, while many observations have demonstrated that LSM2, LSM3, LSM7, and LSM14B play decisive roles in BRCA development, further assessments to verify these findings in BRCA tumors are warranted. Using meta-analysis and combbine with bioinformatics approach, our study suggested that among LSM family genes, LSM4 has prospective value and may serve as a new prognosticator and therapeutic target for BRCA treatment. The main drawback of the present study is the retrospective nature of transcriptomic analyis, which requires further confirmation in a larger prospective study to confirm whether LSM4 expression can be recognized as a useful biomarker in clinical practice.


Response to vaccines generally varies according to individual factors of the vaccinated subjects such as demographics and immune status. While there are various reports of factors associated with immunogenicity of mRNA COVID-19 vaccines, little is known about those of adenovirus vector vaccines. We conducted a prospective observational study to assess the relationships of antibody level with age, sex, body mass index (BMI), and adverse reactions (ARs) to an adenovirus vector vaccine, ChAdOx1 nCoV-19. Healthcare workers who planned to receive both the first and second injections of the ChAdOx1 nCoV-19 vaccine at Hanyang University Hospital, Seoul, Korea, were enrolled in the study. Seven days after each injection, participants were asked to complete an online adverse reaction survey. In addition, anti-SARS-CoV-2 spike (S) protein receptor binding domain (RBD) antibody concentration was measured 4 weeks after the second injection. All participants  showed serologic positivity (≥ 0.8 U/mL) 4 weeks after the second injection of ChAdOx1 nCoV-19 vaccine. Furthermore, the anti-SARS-CoV-2 S protein RBD concentration was similar among groups when stratified by age, sex, BMI, or presence and severity of AR; multivariable linear regression found no associations between antibody response to the ChAdOx1 nCoV-19 vaccine and age, BMI, sex, and vaccine-induced ARs. In conclusion, age, sex, obesity, and ARs were not associated with antibody responses after two doses of ChAdOx1 nCoV-19 vaccination. Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has spread worldwide to become the most serious health problem. To protect people from COVID-19 and provide protective immunity, various types of vaccines have been developed and administered to the public , there iWhile it is difficult to assess the immunogenicity of vaccines, measuring antibody level to SARS-CoV-2 in vaccinated subjects is accepted as a diagnostic test to determine vaccine efficacy despite caveats in interpretation of the results . RegardiEligible participants were healthcare workers (HCW) who received both the first (prime) and the second (booster) injections of ChAdOx1 nCoV-19 vaccine at Hanyang University Hospital, Seoul, Korea. The first and second injections were administered between March 8, 2021, and May 28, 2021, approximately 12 weeks apart. In a previous clinical study of ChAdOx1 nCoV-19 vaccine, the severity and intensity of local and systemic reactions was highest one day after vaccination . Therefo2) according to the Asian-Pacific definition of obesity . For each participant, a serum sample was collected 4 weeks after the second dose of ChAdOx1 nCoV-19 vaccine using tubes containing separating gel and was stored at approximately 4°C. The cutoff value for a positive result was defined as ≥ 0.80 U/mL  (Quantification of anti-SARS-CoV-2 S protein RBD antibodies (including IgG) was performed using the Elecsysresults) . The low® 9.4 , and a two-sided p-value < 0.05 was considered statistically significant.Demographic characteristics were presented as number of subjects (%). We used Beeswarm Boxplot to show the distribution of the anti-SARS-CoV2 S protein RBD antibody concentration according to age, sex, BMI, and severity of ARs. The antibody titers were natural log (ln)-transformed as an outcome measure to ensure normality. Univariable and multivariable linear regression analyses were performed to verify the associations between immunogenicity and age, sex, BMI, and severity of ARs. To assess the independent relationship between immunogenicity and severity of ARs, we determined standardized beta coefficients (β) before and after adjusting for age, sex, and BMI. All statistical analyses were performed using SAS2, and 19.7% (n = 88) of subjects were obese (BMI ≥ 25.0 kg/m2). The majority of participants was nurses or nursing assistants , followed by laboratory staff or physical therapists  and physicians . Most of the participants did not have comorbidities, though some subjects had hypertension (3.1%), allergy (1.8%), diabetes mellitus (1.8%), asthma (0.7%), chronic liver disease (0.7%), or chronic heart disease (0.5%). In addition, one participant (0.2%) reported previous infection of COVID-19.A total of 447 HCWs who received both the first and second injections of ChAdOx1 nCoV-19 vaccine was enrolled in the study, and 388 (86.8%) of them were female Table 1.Most participants  experienced at least one AR  during days 0-7 after either injection (first or second). Overall, the most frequently reported local AR was pain , followed by tenderness  and induration , while the most frequently reported systemic AR was fatigue , followed by muscle pain , chills , and headache . In general, both systemic  and local  ARs were less frequent after the second injection. In terms of severity of ARs, nearly half of the participants  showed at least one grade 3 or 4 AR. At 0-7 days following either (first or second) injection, the most frequently reported grade 3 to grade 4 local AR was tenderness (26.8%), followed by pain (5.8%) and itching (4.4%), while the most frequently reported grade 3 to grade 4 local AR was chills (18.6%), followed by fatigue (18.2%), muscle pain (17%), and fever (6.1%)   showed serologic positivity (≥ 0.8 U/mL) 4 weeks after the second injection of ChAdOx1 nCoV-19 vaccine. Among them, the median (IQR) anti-SARS-CoV-2 S protein RBD concentration was 747.0 (455.0-1324.0) U/mL. The anti-SARS-CoV-2 S protein RBD concentrations were similar among groups when stratified by age , sex , or BMI (median for <18.5 kg/m.5 U/mL) Figure 1The efficacy and immunogenicity of COVID-19 vaccines vary among recipients. In this prospective observational study, we determined antibody levels 4 weeks after the second dose of the ChAdOx1 nCoV-19 vaccine (AZD1222) in HCWs and explored the relationships with host factors including age, sex, obesity, and vaccine-induced adverse reactions. We found a lack of association between antibody response to the ChAdOx1 nCoV-19 vaccine and age, sex, obesity, and vaccine-induced ARs.Protective immunity of the COVID-19 vaccines has been observed in T cell and B cell responses , 29. FolOlder patients with COVID-19 showed decreased adaptive immune responses , which mIn addition to age, associations of sex with vaccine responses have been acknowledged , 40. WitObesity, or high BMI, is a risk factor for morbidity and mortality of COVID-19 . Since oThe reasons are uncertain for why age and sex did not show association with antibody response after ChAdOx1 nCoV-19 vaccine in this study while old age and male sex are reported to be associated with low antibody response after mRNA COVID-19 vaccines. However, neutralizing antibody levels were shown to be relatively higher in mRNA COVID-19 vaccines, while ChAdOx1 nCoV-19 vaccine was shown to induce polyfunctional antibodies, which are capable of mediating neutralization and other antibody-dependent effector mechanisms . TherefoAdverse reactions to vaccines can indicate a strong immune response . There aThe major strength of this study is that we demonstrated that subjects who were vaccinated with two doses of the ChAdOx1 nCoV-19 vaccine had acceptable antibody responses, and host factors of age, sex, BMI, and ARs were not associated with antibody reactions in a prospective observational real-world study. However, this study has limitations that should be acknowledged. First, while we measured anti-SARS-CoV-2 antibody, not the neutralizing antibody or cellular immune response, it cannot project overall immune response and neutralizing activity. Given the strong correlation between anti-SARS-CoV-2 antibody and neutralizing antibody concentrations , we assuIn conclusion, subjects showed fair antibody responses after two doses of the ChAdOx1 nCoV-19 vaccine. Age, sex, and obesity were not associated with antibody responses after vaccinations. In addition, ARs to the vaccines did not predict the antibody responses to the ChAdOx1 nCoV-19 vaccine. These findings help assure the public and vaccine recipients of vaccine efficacy and immunogenicity.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional Review Board of Hanyang University Seoul Hospital. The patients/participants provided their written informed consent to participate in this study.SL and J-YM contributed equally to data analysis, data interpretation, and writing of the manuscript. S-KL, HL, and SL contributed to statistical analyses. S-HK and SL designed the study. YY, TP, DP, and T-HK contributed to data acquisition and clearing. JS, HY, and S-HK oversaw data analysis, data interpretation, and writing of the manuscript. All authors contributed to the article and approved the submitted version.This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0218), and by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. 2019M3E5D1A01069363).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


NTRK1, NTRK2, and NTRK3 are rare drivers of cancer that can be targeted with histology-agnostic inhibitors. This study aimed to determine the nationwide landscape of NTRK/TRK testing in the Netherlands and the usage of pan-TRK immunohistochemistry (IHC) as a preselection tool to detect NTRK fusions. All pathology reports in 2017–2020 containing the search term ‘TRK’ were retrieved from the Dutch Pathology Registry (PALGA). Patient characteristics, tumor histology, NTRK/TRK testing methods, and reported results were extracted. NTRK/TRK testing was reported for 7457 tumors. Absolute testing rates increased from 815 (2017) to 3380 (2020). Tumors were tested with DNA/RNA-based molecular assay(s) (48%), IHC (47%), or in combination (5%). A total of 69 fusions involving NTRK1 (n = 22), NTRK2 (n = 6) and NTRK3 (n = 41) were identified in tumors from adult (n = 51) and pediatric (n = 18) patients. In patients tested with both IHC and a molecular assay , pan-TRK IHC had a sensitivity of 77% , 56–91) and a specificity of 84% . These results showed that pan-TRK IHC has a low sensitivity in current routine practice and warrants the introduction of quality guidelines regarding the implementation and interpretation of pan-TRK IHC.Gene fusions involving  NTRK1, NTRK2, and NTRK3, which encode TRKA, TRKB, and TRKC proteins, respectively) result in constitutively active fusion proteins with an active TRK kinase domain that drives the development of cancer [NTRK fusions are pathognomonic in rare types of cancer, such as the ETV6-NTRK3 fusions in secretory carcinoma of the breast or salivary gland [NTRK fusions drive subsets of numerous other tumor types, including Spitzoid neoplasms (10–25%) [Gene fusions that involve the neurotrophin tyrosine/tropomyosin receptor kinases 1, 2, and 3 genes  ,8, and rNTRK1, NTRK2, and NTRK3 [In recent years, multiple small molecule inhibitors targeting the kinase domain of TRKA, TRKB, and TRKC have been developed and tested in clinical trials. TRK-specific inhibitor larotrectinib and the multi-kinase inhibitor entrectinib  have both yielded remarkable responses in these patients ,10. Betwnd NTRK3 ,12,13,14NTRK fusion in tumor tissue. Currently available methods include pan-TRK immunohistochemistry (IHC) [NTRK fusion, and pan-TRK IHC may be used as a screening tool [NTRK3 (55–79%) [The availability of effective inhibitors has sparked interest in testing methods that can screen for or confirm the presence of an ry (IHC) , and molry (IHC) , fusion ry (IHC) , targetery (IHC) , and molry (IHC) . Internaing tool ,21,22,23ing tool ,24,25. H(55–79%) ,27.NTRK fusions reported in routine diagnostics in the Netherlands and to determine the concordance between IHC and molecular assays.The objective of this study was to determine the testing landscape and incidence of NTRK fusions. Patients were excluded if the test failed or if the result was not reported  maintain a database with excerpts of all pathology reports from pathology departments in the Netherlands . For thireported . The datNTRK fusions were, the type of test(s) used to test for TRK/NTRK, and the reported TRK expression and/or NTRK fusion status. Data processing was performed in accordance with the General Data Protection Regulation (EU) 2016/679.Variables were extracted from the pathology reports by a dedicated researcher (BK) and included patient age and sex, tumor histology, mutations that were detected in other (driver) genes, whether TRK expression or NTRK testing results were interpreted in accordance with (inter) national consensus guidelines [NTRK fusion.” A positive molecular result was interpreted as “NTRK fusion positive.” In case of a positive or inconclusive IHC staining, the result was interpreted as “unconfirmed” when no subsequent confirmatory molecular assay was performed and “NTRK fusion positive” if a subsequent molecular assay did demonstrate presence of an NTRK fusion. If a molecular test yielded an inconclusive result, or if there were discrepancies between two or more molecular tests, the result was interpreted as “inconclusive”.The reported TRK/idelines ,21,22,23NTRK fusions was determined by calculating sensitivity and specificity for positive IHC results with the molecular result as a reference. This calculation was performed for cases with only positive IHC and repeated for cases with either positive or inconclusive results. A nominal p-value of 0.05 or less was considered statistically significant.Statistical analysis was performed with SPSS version 23 . Descriptive statistics were used, proportional differences were assessed using Fisher’s exact tests. The performance of IHC as a screening tool in the routine setting for the detection of NTRK fusion testing results were reported in 7457 tumors from 7434 patients . The number of tested tumors gradually increased from 2017 (n = 815) to 2020 (n = 3380)  A.NTRK . Less frequent types of tumors included soft tissue/bone tumors , melanocytic tumors , cancers of unknown primary , CRC , thyroid tumors  and salivary gland tumors , among others , differential diagnosis  or resistance analysis after therapy progression  (p < 0.001) and resistance analysis at progression on targeted therapy . The latter category included 248 lung cancer patients progressing on ALK, BRAF or EGFR inhibitors and two patients with a gastro-intestinal stromal tumor progressing on imatinib.Lung cancer was the most frequent type of tumor tested for g others B. Tumor 0; 3.4%) C. Indican = 3587; 48.1%), IHC only , IHC and a molecular analysis , or with an unspecified test  (n = 655) and A7H6R  D. In totn = 3715) were multiplex RNA analyses, and the vast majority of tumor biopsies were tested with Archer FusionPlex kits   F. The pr = 3273) G.NTRK fusions [Of note, the FDA-approved companion diagnostic FoundationOne CDx was not used by any laboratory. This is in part because this assay was approved only at the end of the inclusion period of the current study (October 2020) . In addi fusions ,22.NTRK fusions, and an NTRK1-3 fusion was detected with a molecular assay in 69 (0.93%) tumors (n = 3939). These percentages do not reflect the true prevalence of NTRK fusions in the population as there is a selection bias in routine diagnostics . For this reason, frequencies could not be calculated for individual tumor types. In addition, there were nine patients with inconclusive molecular results or with discrepancies between two or more molecular tests.Between 2017 and 2020, a total of 7457 tumors (7434 patients) were tested for TRK expression and/or ) tumors . This prNTRK fusions involved NTRK1 (n = 22), NTRK2 (n = 6) and NTRK3 (n = 41). Frequently identified NTRK fusion genes included TMP3-NTRK1 (n = 7), LMNA-NTRK1 (n = 4), ETV6-NTRK3 (n = 28), and MYO5A-NTRK3 (n = 4). In addition to these four fusions, 18 other unique fusion genes were reported  or inconclusive IHC result (n = 63) for whom molecular assay was not performed to confirm (or exclude) the presence of an NTRK fusion. Reasons for not confirming the IHC result varied but were not specified in the vast majority of cases .In addition to the 69 molecular-positive patients, there were 130 patients (1.7%) with a positive (n = 15), with inconclusive molecular results (n = 4) or insufficient information (n = 6), 327 were eligible for a sensitivity/specificity analysis, of which 29 patients had a molecular-confirmed NTRK fusion. There were 114 patients with either a positive (n = 61) or inconclusive (n = 53) IHC result. An NTRK fusion was confirmed in 20% (23 of 114); the positivity rate was 33% (20 of 61) for IHC-positive patients and 6% (3 of 53) for IHC-inconclusive patients. On the other hand, there were six patients who tested negative with IHC, but who nevertheless harbored an NTRK fusion using a molecular test . This included multiple well-described NTRK3 fusions, including ETV6-NTRK3, MYO5A-NTRK3, and TPM3-NTRK1. These six patients with IHC-negative NTRK fusion-positive tumors were tested in five different laboratories, and the antibody clone was only reported in one . Pan-TRK IHC was found to have a sensitivity of 77%  and a specificity of 83%  when excluding patients with an inconclusive IHC result. When patients with an inconclusive IHC result were also included, sensitivity raised slightly to 79% , but specificity declined to 70%   .NTRK fusions in tumors was investigated in the Dutch population between 2017 and 2020 reported in the nationwide PALGA database of pathology reports. Over time, tumor biopsies were increasingly tested for TRK expression or NTRK fusions, with most tumors being tested with IHC and/or multiplex RNA analysis. In this four-year period, 69 NTRK fusion-positive tumors were reported in 7434 patients tested. In addition, the data were used to assess the performance of pan-TRK IHC as a pre-screening tool to detect NTRK fusions. The analysis of real-world data indicated a low sensitivity for pan-TRK IHC to preselect patients for molecular NTRK testing in routine practice.In this study, the incidence of NTRK fusions was not standard-of-care for any type of cancer until 2020, when NTRK became part of the required molecular markers that any advanced NSCLC had to be tested for according to the Dutch guideline for NSCLC [NTRK fusions have been of interest since 2017, as diagnostic markers of disease  or as predictive markers of response to TRK inhibitors, which were then available within clinical trials and in named patient programs. The FDA and EMA have since approved both entrectinib and larotrectinib for the treatment of advanced cancers with an NTRK fusion [NTRK fusions diagnosed in 2017 (1.2% of tumors tested) to 31 NTRK fusions diagnosed in 2020 (0.9% of tumors tested). As larotrectinib and entrectinib have been approved for use in the Netherlands recently, the testing rate will likely further increase in the coming years.Testing for or NSCLC . HoweverK fusion ,12,13,14NTRK fusions are highly enriched in secretory carcinomas , congenital mesoblastic nephroma, and IFS (>90%) [NTRK fusions detected in similar proportions, though the absolute numbers were relatively low. For example, there were only two NTRK fusion-positive secretory carcinomas of the breast, whereas the expected number in the Netherlands between 2017–2020 is approximately 10–11  [NTRK fusions but rather are diagnosed clinicopathologically and/or immunophenotypically, or that patients often present with early-stage disease and are therefore not eligible for treatment with TRK inhibitors [S (>90%) ,3,4,5. Iagnoses) ,32,33. TNTRK fusions (2–25%) [NTRK fusion-positive tumors in the Dutch population (n = 10). In Spitzoid tumors, NTRK1 and NTRK3 fusions have frequently been reported, and these fusions are a diagnostic marker for this category of melanocytic tumors [NTRK1 and NTRK3 fusions were indeed more common in the Dutch population , mostly in benign tumors, and all of these patients were tested for the differential diagnosis. Of note, one patient with a benign Spitz nevus harbored a previously reported SQSTM1-NTRK2 fusion, which confirms findings from a recent case report that Spitz/Reed nevi can harbor fusions in any of the three NTRK genes [Spitzoid melanocytic tumors also commonly harbor  (2–25%) ,7,8. Indc tumors ,35. NTRKRK genes .NTRK fusions are also relatively common (2–25%) [NTRK fusion-positive thyroid cancers), PTC was indeed the most frequent subtype of NTRK fusion-positive thyroid cancer (6/13), but NTRK fusions were also detected in follicular variants of PTC, a Hürthle cell carcinoma, and a poorly differentiated thyroid carcinoma. Thus, the histological subtype of thyroid cancer does not exclude the presence of an NTRK fusion. In thyroid tumors,  (2–25%) ,8, thoug (2–25%) . In the NTRK fusions are rare (<1%) drivers of other types of cancer, such as NSCLC, CRC, primary brain tumors, and soft tissue/bone tumors [NTRK genes. In CRC, 89% of NTRK fusion-positive patients have mismatch repair deficiency (MMRd) and lack mutations in BRAF (BRAFwt); when testing only MMRd/BRAFwt CRC patients, the prevalence increases from 0.23% to 5.3% [NTRK fusion-positive CRC was not identified despite a sample size of 328 patients. In addition, e tumors . In NSCLe tumors ,39. In o to 5.3% . In our NTRK fusions were detected in varying types of non-IFS soft tissue/bone tumors (n = 14), of which the majority (n = 8) were classified as NTRK-rearranged spindle cell neoplasm/sarcoma in accordance with the 2020 World Health Organization Classification of Tumors of Soft Tissue and Bone [NTRK fusion-positive soft tissue/bone tumors diagnosed prior to 2020  may have been classified similarly had they been diagnosed according to the 2020 WHO classification. Considering NTRK fusions have been recommended by the WHO guideline as diagnostic markers for sarcomas, the annual incidence of NTRK fusion-positive sarcomas is expected to further increase in the coming years.Furthermore, and Bone . HoweverNTRK1, NTRK2 and NTRK3 fusions were reported in the Dutch population between 2017 and 2020. A total of 22 unique RNA-confirmed fusion genes were identified. ETV6-NTRK3 fusions, which are among the most well-described NTRK fusions in human tumors [ETV6-NTRK3, 10 other fusion genes identified in our study had been previously reported: CD74-NTRK1 [LMNA-NTRK1 [TPM3-NTRK1 [TPR-NTRK1 [NACC2-NTRK2 [STRN-NTRK2 [SQSTM1-NTRK2 [EML4-NTRK3 [MYH9-NTRK3 [MYO5A-NTRK3 [GRIPAP1-NTRK1 (fusion partner previously reported for TFE3) [TRIM24-NTRK2 (BRAF) [BEND5-NTRK2 (ALK) [EFNA1-NTRK1, GP2-NTRK1, PRG4-NTRK1, FMN2-NTRK2, CDH2-NTRK3, CNTN4-NTRK3, SYNJ1-NTRK3, and UBE2F-NTRK3. Although all these fusions harbored the TRK kinase domain , had no TRK expression with IHC. The other novel fusions were not tested with IHC.A range of different n tumors , and may74-NTRK1 , LMNA-NTNA-NTRK1 , TPM3-NTM3-NTRK1 , TPR-NTRPR-NTRK1 , NACC2-NC2-NTRK2 , STRN-NTRN-NTRK2 , SQSTM1-M1-NTRK2 , EML4-NTL4-NTRK3 , MYH9-NTH9-NTRK3 , MYO5A-N5A-NTRK3 . Three oor TFE3) , TRIM24-2 (BRAF) , and BENK2 (ALK) . The reme domain A, their NTRK fusions are typically mutually exclusive with other driver mutations [NTRK fusions with well-known drivers such as oncogenic BRAF, EGFR, or KRAS mutations [NTRK fusion-positive patients were tested for concurrent driver mutations and/or fusions (EGFR p.(L858R)-mutant patient harbored the novel PRG4-NTRK1 fusion (TRK expression was not tested), and one a KRAS p.(A146T)-mutant patient harbored a CD74-NTRK1 fusion, with TRK expression. The third patient, who harbored an EGFR p.(E746_A750del) mutation, acquired the novel EFNA1-NTRK1 fusion as a resistance mechanism against EGFR inhibitor treatment; in this patient, TRK expression was confirmed with IHC. This finding is in line with previous reports that NTRK fusions can induce resistance to EGFR inhibitors [NTRK fusions with other driver mutations would impact TRK inhibitor sensitivity.Previous studies have demonstrated that utations ,26. Howeutations ,54. In o fusions , of whichibitors . It is uNTRK fusions can be most reliably detected using molecular assays [NTRK fusions. In general, IHC is of interest as a screening tool because it is relatively inexpensive, efficient, and easily implemented in pathology laboratories [NTRK fusions, especially for NTRK3 [NTRK3 fusions, including ETV6-NTRK3 and MYO5A-NTRK3 were reported.Guidelines and consensus recommendations indicate that the presence of r assays ,21,22,23ratories , whereasratories , IHC expratories . Howeverratories ,24,25. Hor NTRK3 ,27, whicNTRK fusions.It is uncertain whether these fusions really do not lead to IHC positivity or whether the IHC procedure or the choice of antibodies requires optimization and/or standardization. For the predictive biomarker PD-L1, it was demonstrated previously that preanalytical variability in immunostaining procedures could lead to inter-rater variability in scoring among pathologists . SimilarTPM3-NTRK1 fusion), which indicates that the current immunohistochemical procedure may not be adequate to screen for NTRK fusions. The A7H6R antibody is not available in a CE-IVD-approved kit and has not been thoroughly investigated in the literature. Future studies should elucidate whether this antibody is more sensitive, and/or focus on the development of novel antibodies that may have improved sensitivity compared to the currently available antibodies.A multitude of antibodies are available, but most laboratories use a pan-TRK antibody, which targets the C-terminus of TRKA, TRKB, and TRKC . Two antNTRK fusion. In addition, depending on the fusion present, TRK IHC can stain differently  [There are no standardized scoring criteria to determine the positivity of TRK IHC, including for the CE-IVD approved assay . In the branous) . This unNTRK fusions, and all international guidelines recommend confirmation of positive pan-TRK IHC with a targeted RNA analysis [NTRK fusions in cancer types with a low incidence, such as the ETV6-NTRK3 fusions in secretory carcinomas, using a suboptimal pre-screening tool that requires confirmation is not practical. This is also true for tumors with high endogenous TRK expression, such as gliomas and some types of soft tissue tumors. Pre-screening with pan-TRK IHC will yield a high rate of false-positivity. Up-front RNA testing may also be feasible for patients with a frequently occurring type of cancer and a low prevalence of NTRK fusions. For example, patients with NSCLC are routinely tested for the presence of fusions in ALK, RET, ROS1, and MET exon 14 skipping in a single RNA-based assay that can often easily be expanded with the NTRK1–3 fusions. In spite of a 100% sensitivity of TRK IHC in the five lung cancer cases in the current study, it is more efficient from a tissue management and cost perspective to test NSCLC patients with an up-front, panel-based, targeted RNA assay rather than adding an IHC screening for TRK expression. For some types of cancer, such as CRC and non-Spitz melanoma, it is not cost-effective to test all patients using a costly RNA-based technique as less than 1% actually harbor an NTRK fusion, and they are not routinely tested using an RNA-based analysis for other reasons. For these types of cancer, a pre-screening test such as pan-TRK IHC may still have its value when procedures  are optimized.Targeted RNA analysis is considered the gold standard for detecting analysis ,21,22,23analysis ,27, indiNTRK fusions in routine diagnostics. Quality guidelines regarding the implementation of validated pan-TRK IHC antibodies and standardized staining and scoring criteria are required.Altogether, the results of the current study support findings from other recent studies that pan-TRK IHC with currently used antibodies is not yet adequate to screen tumors for the presence of NTRK fusions detected in a variety of 69 benign and malignant tumors in 2017–2020. A performance analysis demonstrated low sensitivity and specificity of immunohistochemistry as a preselection tool for the detection of NTRK fusions in current routine practice. Recommendations by international guidelines that pan-TRK IHC is a reliable screening tool should, therefore, be reconsidered in light of the current study and recently published real-world evidence. These results warrant the introduction of quality guidelines regarding the implementation of validated pan-TRK IHC antibodies and standardized staining and scoring criteria for interpreting results in a routine diagnostic setting. More sensitive pan-TRK antibodies may be needed. This nationwide, longitudinal analysis shows that routine TRK testing is gradually increasing, with