In this study, a total of thirteen mcr-carrying Escherichia coli (MCREC) strains were recovered from a tertiary hospital in Zhejiang, China, between 2016 and 2019. The minimum inhibitory concentration (MIC) of antimicrobial agents, epidemiological characteristics, and transmission dynamics of mcr-carrying isolates were analyzed using antimicrobial susceptibility testing, whole-genome sequencing, S1 nuclease pulsed-field gel electrophoresis (S1-PFGE), and southern blotting analysis. All strains were discovered to be resistant to colistin, and the majority displayed MDR phenotype. However, none of the 13 MCREC strains were resistant to carbapenems. The 13 MCREC isolates were divided into 10 different STs, including ST744, ST156, ST453, ST410, ST57, ST131, ST7034, ST2599, ST457, and ST13239, in which ST13239 was discovered for the first time. Based on core genome single nucleotide polymorphism (cgSNP) analysis, no clear epidemiological link was discovered in these strains with the exception of EC2118 and EC3807, which differ by just one SNP. A total of 35 antimicrobial resistance genes which can be divided into 14 classes were identified from the 13 MCREC isolates. According to S1-PFGE and southern blotting analyses, all 13 MCREC strains had plasmid-mediated mcr-1, and nine of them carried conjugative plasmids. In conclusion, our study revealed the emergence and dissemination of colistin-resistant E. coli isolates carrying mcr-1 in a Chinese hospital, which poses a potential risk to anti-infective therapy. More efforts should be taken to monitor the prevalence of mcr-1-carrying bacteria in China.Colistin is an important antimicrobial agent in the treatment of infections caused by multidrug resistant (MDR) Gram-negative bacteria. The horizontal transfer of mobile colistin resistance gene ( Escherichia coli is a widespread pathogen that can cause a variety of infections in both humans and animals. The spread of antibiotic resistance genes (ARGs) by transposons and transmissible plasmids in clinical strains makes it more challenging to treat [Bacillus polymyxa. There are five chemically distinct polymyxins—A, B, C, D, and E—of which only polymyxin E (colistin) and polymyxin B are used in clinical settings [to treat . Polymyxsettings . Colistisettings . Severalsettings . The emesettings .mcr). The mcr-1 gene was the first one to be described in an E. coli strain in China [mcr genes are frequently carried by a variety of plasmids, including IncI2, IncX4, IncP, IncX, and IncFII types [Apl1), class 1–3 integrons, and transposons like Tn6330, TnAs2, and Tn3-family, were thought to be the primary drivers of horizontal gene transfer of the mcr genes [The primary mechanism for colistin resistance, which is well known, is horizontal gene transfer of the plasmid-mediated mobile colistin resistance gene  have so far been identified, and mcr-1 continues to be the main character and is widely distributed [E. coli have been shown to increase by 4 to 8-fold when the mcr-1 gene is present, indicating that mcr-1 alone can provide adequate resistance against colistin in E. coli and other Enterobacteriaceae [mcr-carrying E. coli (MCREC) in clinical settings. To actively combat the threat posed by MDR Gram-negative Enterobacteriaceae, we must monitor MCREC strains and research their prevalence in order to stop further spread of these pathogens.Ten es mcr-1– have so tributed ,12. The eriaceae ,14. It imcr-carrying isolates.In this study, 13 MCREC isolates from a tertiary hospital in China were collected from 2016 to 2019. Whole-genome sequencing (WGS), S1 nuclease pulsed-field gel electrophoresis (S1-PFGE), and southern blotting analysis were performed to explore the antimicrobial resistant genes, epidemiological characteristics, and transmission dynamics of E. coli isolates were collected between 2016 and 2019. Urine is the most common (57.9%) of the clinical specimens, which also include blood (22.1%), sputum (4.9%), pus (3.6%), bile (3.4%), ascitic fluid (1.8%), and others. We collected 13 MCREC isolates from 12 patients. These MCREC clinical isolates were cultured from sputum (2/13), urine (8/13), pus (2/13), and ascitic fluid (1/13). The percentage of MCREC was 2.9% in 2016 (2/70), 3.4% in 2017 (6/175), 0.9% in 2018 (3/339), and 0.7% in 2019 (2/298). From 2018, there was a discernible decline in the occurrence of MCREC. Clinical data of the 12 patients with MCREC infection are presented in A total of 882 clinical A total of 21 antimicrobial agents—ampicillin (AMP), piperacillin (PRL), aztreonam (ATM), cefotaxime (CTX), cefepime (FEP), meropenem (MEM), gentamicin (GEN), trimethoprim-sulfamethoxazole (SXT), levofloxacin (LVX), moxifloxacin(MOX), amoxicillin-clavulanic acid (AMC), ampicillin/sulbactam (A/S), piperacillin-tazobactam (PRL/TZP), cefazolin (CFZ), ceftazidime (CAZ), imipenem (IPM), amikacin (AMK), chloramphenicol (CAP), tetracycline (TET), ciprofloxacin (CIP), and colistin (COL)—were used. Antimicrobial susceptibility profiles of the 13 MCREC strains are presented in mcr-1), six β-lactams-resistance genes , ten aminoglycosides-resistance genes -IIa, aac(3)-IId, aac(3)-Iva, aac(6’)-Ib-cr, aadA, aph(3’’)-Ib, aph(3’)-IIa, aph(3’)-Ia, aph(4)-Ia, and aph(6)-Id), three sulfonamides-resistance genes , two tetracyclines-resistance genes  and tet(B)), one trimethoprim-resistance gene , one fosfomycin-resistance gene , three macrolides-resistance genes , mdf(A) and mph(A)), three quinolones-resistance genes , one rifamycin-resistance gene , one florfenicol-resistance gene , one chloramphenicol-resistance gene , one phenicol-resistance gene , and one lincosamide-resistance gene ). Colistin-resistance gene mcr-1 was found in all 13 MCREC isolates. Except for EC1420, all of the other 12 MCREC isolates had at least one BL and one AG antimicrobial resistance gene. EC1420 was found to have only two resistance genes, mcr-1 and mdf(A), which is consistent with its antibiotic susceptibility profile (susceptible to most antibiotics). SUL were found in 11 MCREC isolates, while TET were found in 10 MCREC isolates. On the other hand, only EC5171 possessed LCM (lnu(F)), EC4361 and EC854 possessed PHE (catB3), and EC4361, EC854, and EC1412 possessed CPL (cmlA1). The presence of up to 24 genes encoding resistance to 13 classes of antibiotics (except LCM) in EC4361 is consistent with its antimicrobial resistance profile (resistant to most antibiotics).Antimicrobial resistance genes were identified and shown in Based on the phylogenetic tree , the 13 mcr-1-carrying plasmids from the 13 MCREC isolates were determined by conjugation experiment. S1-PFGE and southern blotting analysis were performed to estimate the size of the mcr-1-carrying plasmid. Nine out of the thirteen MCRECs, with the exception of EC5963, EC5171, EC891, and EC1412, have conjugative plasmids carrying the mcr-1 gene. The conjugation efficiency is calculated with the high conjugation efficiency of 8.6 × 10−2 for EC1335, 7.3 × 10−2 for EC2118, and 1.1 × 10−2 for EC3807, the middle conjugation efficiency of 2.1 × 10−3 for EC1526, 2.1 × 10−3 for EC854, 1.8 × 10−3 for EC1114, 8.0 × 10−4 for EC4361, and the low conjugation efficiency of 6.3 × 10−5 for EC1420, 3.5 × 10−6 for EC567. mcr-1-carrying plasmids was heterogeneous, ranging from 30 kb to 250 kb. The size of mcr-1-carrying plasmids in the nine conjugative MCREC isolates can be divided into two types: the smaller one is between 33.3kb and 54.7 kb , while the larger one is between 54.7 kb and 78.2 kb . All of the mcr-1-carrying plasmids in the four nonconjugative MCREC isolates were between 80 kb and 250 kb in size. Furthermore, EC567, EC1526, and EC1420 harbored two types of plasmids of varying sizes that can be conjugative. According to WGS, the 13 MCREC plasmids were divided into three plasmid incompatibility groups: seven IncI2 , five IncX4 , and one IncHI2 (EC1412).The transferability of E. coli clinical isolates were collected from patients at a tertiary hospital in Zhejiang province, China. Using the previously published primers, a standard PCR was used to detect the mcr-1 gene [mcr-1 positive E. coli isolates from 12 patients were identified. Ascitic fluid, pus, urine, and sputum were among the specimens used to cultivate the isolates. All isolates were generated as part of routine clinical laboratory procedures. The patient administration system was used to extract demographic information, such as gender, age, the hospital department where the patient was admitted, the clinical diagnosis, and the outcome, without obtaining any personally identifiable information about the patient. This study was previously approved by the Research Ethics Committee of Zhejiang Provincial People’s Hospital.Between January 2016 and October 2019, a total of 882 E. coli ATCC 25922 was used as a quality control. The European Committee on Antibiotic Susceptibility Testing  12.0 guidelines were used to interpret the susceptibility breakpoint for colistin and isolates with MIC > 2 mg/L were interpreted as resistance. CLSI 2021 guidelines were used to interpret the susceptibility breakpoint for other antimicrobial agents [All isolates were identified using the MALDI-TOF-MS system . Antimicrobial susceptibility testing was conducted using standard broth microdilution tests following the guidelines of the Clinical and Laboratory Standards Institute (CLSI). l agents . MDR isomcr gene were sent for whole-genome sequencing using the Illumina NovaSeq 6000 platform . In brief, genomic DNA was isolated using a QIAamp DNA MiniKit  and sent for sequencing according to the paired-end 2 × 150-bp protocol. SPAdes 3.13.0 was used to assemble the draft genome sequences, and the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) server was used to automatically annotate the genome sequences [Isolates confirmed to carrying the equences .mcr-carrying isolates were analyzed using the BacWGSTdb 2.0 server [mcr-carrying isolates was analyzed using the neighbor joining (NJ)/unweighted pair group method with arithmetic mean (UPGMA) phylogeny method (MAFFT version 7) based on a core genome single nucleotide polymorphism strategy [The MLST, acquired ARGs, and plasmid replicons of the 0 server ,18,19. Tstrategy . The resstrategy . The Intstrategy .mcr-carrying plasmids from isolates was assessed using filter mating with E. coli J53 as the recipient strain, and the transconjugants were chosen on BHI agar plates supplemented with 2 mg/L colistin. The mcr-1 gene was then detected in transconjugants by screening and sequencing, as previously described [https://openwetware.org/wiki/conjugation, accessed on 16 July 2021. A Salmonella serotype Braenderup strain (H9812) was chosen as the common size standard [mcr gene-specific probes was used to pinpoint the genetic location of mcr-1, as previously described [The transferability of escribed . The constandard . S1-nuclescribed .mcr-1 positive E. coli strains in NCBI GenBank under the BioProject accession number PRJNA515159 (JANYUL000000000-JANYUX000000000).We deposited the genome sequences of the E. coli is the most common species carrying plasmid-mediated colistin resistance in the Enterobacteriaceae [mcr-1 in 882 E. coli strains that were isolated from human clinical samples in Zhejiang Province, China, between 2016 and 2019. These samples included sputum, urine, pus, and ascitic fluid. MCR-1 positivity and colistin resistance were found in 13 of the E. coli strains. Notably, the mcr-1 carriage rate dropped from 2.9% and 3.4% in 2016 and 2017 to 0.9% and 0.7% in 2018 and 2019. Colistin use in animal farming is thought to be a major driver in the dissemination of colistin resistance [mcr-1 carriage rate may be related to the policy that the Ministry of Agriculture of China (Article number 2428) withdrew colistin as a feed additive and growth promoter in November 2016 and formally implemented in April 2017 [In China, eriaceae . In thisril 2017 . Severalril 2017 ,28,29,30E. coli has been reported to be a significant reservoir of ARGs [E. coli, primarily through horizontal gene transfer, plays critical roles in the spread of the mcr genes [mcr-1 gene on plasmids. However, MICs of these MCREC strains varied greatly, ranging from 4 to >64 mg/L. This has led us to speculate on the existence of other mechanisms, such as different expression activities of different mcr-carrying plasmid types or different genomic background of the MCREC strains which might possess active efflux pump or particular genetic mutations, and so on. Deeper sequencing and further analysis will give more information about the different mechanisms of colistin resistance.Due to its high capacity to accumulate ARGs,  of ARGs . Additiocr genes . In thismcr-1 in EC1420 was the smaller one, ranging from 33.3 to 54.7 kb, and it demonstrated the lowest transferability frequency, whereas the plasmid carrying mcr-1 in EC1526 was the larger one, ranging from 54.7 to 78.2 kb, and it demonstrated a higher transferability frequency. It is possible that EC1526, not EC1420, was responsible for Pa1420’s final death from severe septicopyemia. However, further investigation is still required to identify the mechanisms underlying the extreme colistin resistance of EC1526. It was fortunate that none of the MCREC isolates were carbapenem-resistant isolates and that none of the 13 MCREC isolates contained any carbapenem-resistant genes.In particular, the isolates EC1420 and EC1526 were taken from the same patient Pa1420, who passed away during this study, but the two strains were dispersed sporadic ones. The later isolate EC1526 showed super resistance to colistin with the MIC exceeding 64 mg/L and showed resistance to 16 antibiotics, whereas the earlier isolate EC1420 carried the fewest resistance genes and only demonstrated colistin resistance with a MIC of 8 mg/L. In addition, the plasmid carrying mcr-1 gene. The plasmids were found to be between 30 and 250 kb in size. The plasmid carrying the transferable mcr-1 gene was present in nine of the thirteen MCREC isolates. It should be noted that the genome sequences of EC2118 and EC3807 differ only by one SNP, despite coming from different patients and being collected over nine months apart. Their plasmid profiles vary greatly from each other, which may be the cause of their various antimicrobial resistance profiles. It was hypothesized that EC2118 and EC3807 shared a common ancestor but had different plasmids when they underwent horizontal transfer.WGS identified three groups of plasmids from the 13 MCREC, including IncI2, IncX4, and IncHI2. Despite the fact that we did not examine the complete plasmid sequences, we used S1-PFGE and southern blotting analysis to determine the size of the plasmids and the transferability of plasmids containing the E. coli isolates carrying mcr-1 in China, provided some genomic characteristics of mcr-harboring plasmids, and emphasized their role in dissemination of antimicrobial resistance in clinical settings. More efforts should be taken to monitor the prevalence of mcr-1 in a One Health approach.In summary, our study revealed the emergence and dissemination of colistin-resistant


To better characterize the heterogeneity of multiple myeloma (MM), we profiled plasma cells (PCs) and their B cell lymphopoiesis in the BM samples from patients with monoclonal gammopathy of undetermined significance, smoldering MM, and active MM by mass cytometry (CyTOF) analysis. Characterization of intra- and interneoplastic heterogeneity of malignant plasmablasts and PCs revealed overexpression of the MM SET domain (MMSET), Notch-1, and CD47. Variations in upregulation of B cell signaling regulators  and aberrant markers  were associated with different clinical outcomes in clonal PC subsets. In addition, prognosis was related to heterogeneity in subclonal expression of stemness markers, including neuroepithelial stem cell protein (Nestin), SRY-box transcription factor 2 (Sox2), Krüppel-like factor 4 (KLF-4), and Nanog. Furthermore, we have defined significantly elevated levels of MMSET, MYD88, c-Myc, CD243, Notch-1, and CD47 from hematopoietic stem cells to PCs in myeloma B cell lymphopoiesis, noted even in premalignant conditions, with variably modulated expression of B cell development regulators, including IRF-4, Bcl-2, Bcl-6, and sXBP-1; aberrant PC markers ; and stemness-controlling regulators, including Nanog, KLF-4, octamer-binding transcription factor 3/4 (Oct3/4), Sox2, and retinoic acid receptor α2 (RARα2). This study provides the rationale for precise molecular profiling of patients with MM by CyTOF technology to define disease heterogeneity and prognosis.  Multiple myeloma (MM), the second most common hematologic malignancy worldwide, represents a prototypical disease model for the study of tumor heterogeneity due to the high frequency of intraclonal diversity within malignant clones of plasma cells (PCs) in the BM. The clinical manifestations of the disease are associated with high levels of monoclonal Ig protein in serum and/or urine, BM plasmacytosis, and CRAB features, namely hypercalcemia, renal insufficiency, anemia, and/or bone disease . Even wiAb-secreting PCs develop from hematopoietic stem cells (hscs) in several rounds of differentiation stages of B cell development, from B cell precursors to naive mature B lymphocytes in the BM and followed by maturation to the memory/effector B cells in the secondary lymphoid organs. Different stages of B cell development are accompanied by multiple changes of the cell immunophenotype and regulators of differentiation . MoreoveIn this study, we report a comprehensive analysis of PC heterogeneity within the B cell development of the BM microenvironment in 16 patients with MGUS, 25 patients with SMM, 43 newly diagnosed patients with MM (NDMM), and 104 patients with relapsed or relapsed/refractory MM (RRMM), as well as 10 healthy donors (HDs), using data-driven high-dimensional mass cytometry (CyTOF) analysis. Our pipeline has been designed for profiling PCs and maturation stages of B cell lymphopoiesis/B lineage differentiation during MM evolution and progression. Characterization of malignant plasmablast (PB) and PC heterogeneity revealed overexpression of MMSET, Notch-1, and CD47, with variations in upregulation of B cell signaling regulators , PC aberrant markers , and stemness markers, including neuroepithelial stem cell protein (Nestin), SRY-box transcription factor 2 (Sox2), Krüppel-like factor 4 (KLF-4), and Nanog, that were associated with different clinical outcomes in clonal PC subsets. In addition, various immunophenotypic profiles and modulation of signaling in B cell lymphopoiesis were defined, even in premalignant myeloma stages, confirming clonal hematopoiesis. Our in-depth molecular characterization of PCs in the B cell ecosystem at various stages of MM demonstrates the utility of CyTOF technology for defining disease heterogeneity and prognosis in patients with MM.https://doi.org/10.1172/jci.insight.159924DS1 We designed a pipeline for deep characterization of malignant PC within the B cell lymphopoiesis in patients with MGUS (n = 16), SMM (n = 25), NDMM (n = 43), and RRMM (n = 104)  . We focun = 104) . Brieflyn = 104)  followedn = 104) .+ cells) did not impact analysis using BI and BII panels, we compared 5 sorted versus unsorted HD BM samples. Correlation analyses between the same sorted and unsorted samples showed a high degree of reproducibility and accuracy for CyTOF analysis with R2 = 0.98 (het) expression of CD19, CD20, CD27, and CD138 with surface membrane Igs (mIgs), such as IgA, IgG, and IgD  in BI and BII panels to profile B lymphoid maturation stages by spanning-tree progression analysis of density-normalized events (SPADE) clustering analysis. A representative NDMM BM showing B lymphoid subsets is seen in CD38 expression  and othe2 = 0.98 . In addi2 = 0.98 . As MM i and IgD . Moreove and IgD . Taken t+, CD38lo, and CD45lo hscs to pre-pro-B , pro-B , pre-BI , and pre-BII  cells. Comparing MM disease stages and clustering differences with HDs on early B cell progenies , we observed a significant increase in expression of CD34 and CD38 in MGUS, SMM, NDMM, and RRMM , transitional , and naive  B cells. In-depth analysis of immature clusters showed decreased expression of IgM and increased expression of IgD in MGUS, SMM, NDMM, and RRMM compared with HDs. Although no significant changes were revealed in clusters from transitional to naive B cells in MGUS, SMM, and NDMM, substantial upregulation in CD20, CD22, IgD, IgM, and CD38 expression was observed in RRMM  or switched . Significant differences were identified in CD19, CD22, and CD27 expression profile on unswitched memory B cells (UM1–4) in SMM, NDMM, and RRMM versus HDs, whereas MGUS differed only in increases IgD and IgM expression. Similarly, significant modulation  of CD27, IgM, IgG, and IgA on switched memory B cells (SM1–3) of MGUS, SMM, NDMM, and RRMM was observed  as precursor of PCs and mature PCs . Interestingly, the highest number of clusters PC1–7 was observed in PCs. Comparing MM to HDs, significant upregulation of CD27, IgG, and IgM was observed on PB (PB/PC1–2) clusters  and PB/PC2 . Compared to PB/PC clusters, PC1–5 clusters overexpressed MMSET, CD47, Notch-1, CXCR4, and CD200 and decreased CD81 activation marker associated with upregulation of CD243 (not detected in PC3), CD28 (not in PC5), and downregulation of CD44 and RARα2 (not detected only in PC2) expression compared with HDs. A potentially unique phenotype of each PC cluster was identified: PC1 ; PC2 ; PC3 ; PC4  and PC5 . Immunophenotypic analysis based on the cell surface markers identified PC6 (very high CD138) and PC7 (IgD+) clusters, which differ from other PC clusters but share a common signaling signature with upregulation of c-Myc, CD28, Bcl-6 (higher in PC6), Sox2 (higher in PC7), KLF-4, and CD243 compared with HDs. In addition, upregulation of IRF-4, CD289, Nestin, Blimp-1, CD24, MYD88, sXBP-1, FGFR3, and Nanog associated with downregulation of CD44, CD362, and CD329 was unique in PC6 cluster compared with PC7 cluster (n = 19) even from the MGUS stage is consistent with clonal heterogeneity already present in premalignant stages.Examination of intra- and interneoplastic heterogeneity of PC signaling in patients versus HDs revealed overexpressed MMSET, also known as Wolf-Hirschhorn syndrome candidate 1 (WHSC1), CD47, and Notch-1 across both PB/PC1–2 and PC1–7 clusters . Profili cluster . Compari cluster , we foun cluster . The varTo delineate potential clinical relevance of profiling studies, we next determined whether signaling in each cluster of PBs and PCs of individual patients correlated with progression-free survival (PFS) and overall survival (OS). Specifically, high levels of Bcl-6 (PB/PC2), CD200 (PC1), sXBP-1 (PB/PC2), IRF-4 , c-Myc (PC2), KLF-4 (PC2), Nanog (PB/PC2 and PC2), FGFR3 (PC3), Blimp-1, CD243 (PC4), CD81 (PC5), and CD329 (PC7), as well as lower levels of Sox2 (PB/PC1), FGFR3 (PC2), and CXCR4 , were associated with shorter PFS. In contrast, higher levels of CD52 (PB/PC1), CXCR4 (PB/PC2), and Bcl-2 (PC7), as well as low levels of CD44 (PC1), CD319 (PC2), CD81 (PC3), and KLF-4 (PC4), were associated with longer PFS . High le–CD19+CD38+CD45+CD138+/–, whereas normal PCs lack pan B cell markers (CD20 and CD22) and mIg and show heterogeneous expression of CD19, CD27, CD45, and polyclonal cytoplasmatic light chains . In contrast, malignant PC typically show underexpression of CD19, CD27, CD45, and CD81; overexpression of CD28, CD33, CD56, CD117, and CD200; and asynchronous expression of CD20 and mIg (–CD27hetCD38+CD45+CD138het) exhibited upregulation of B cell memory CD27 and mIg expression. Similarly, tumor PCs (CD19hetCD20hetCD27hetCD38+CD45loCD138+) preferentially showed an increase in expression of B cell marker CD20 and mIg  in premalignant stages, with downregulation of several markers including CD19, CD27, CD38, and CD45 in both NDMM and RRMM. The highest number (n = 7) of subsets defined by immunophenotypic differences was determined in PCs compared with 2 subsets of PBs. The highest incidence of PC2 subset, which co-associated with PC1, PC3, PC4, and PC6 clusters, dominated in SMM, NDMM, and RRMM stages.Ab-producing PCs are critical effectors of the adaptive immune system and early-stage PCs (PBs) migrate from the tissues of origin to the BM where they either rapidly undergo apoptosis after a few days of intensive Ab secretion (short-lived PCs) or reside in specialized niches, where they evolve to long-lived PCs that survive for many years . ImmunopIn-depth profiling of intra- and interneoplastic heterogeneity of malignant PCs of patients versus HDs revealed constitutively overexpressed MMSET, CD47, and Notch-1 across all PB and PC subsets. MMSET (WHSC1) is a member of the nuclear receptor-binding SET domain histone methyltransferase family and as an oncogene regulates many cellular  and molecular  processes , 15. MorSeveral transcription factors are thought to control PB and PC development . PlasmacCXCR4 (CD184) is highly expressed on several B cell subsets, with the highest amounts of CXCR4 on germinal center B cells and decreased expression on normal PBs and PCs . Due to Several markers with downregulated expression on normal PBs and PCs have aberrant expression on MM cells. Among them are immune checkpoint molecule CD200 (known as the OX-2 tumor antigen) and T cell costimulatory receptor CD28, which correlates with an inferior outcome in patients with MM –40. In clo stem-like cells; anti-Sox2 T cell immunity in MGUS patients efficiently inhibits the clonogenic growth of MGUS cells , or potentially IgM (sIgM+) only, or switched IgG, IgA, or even IgE memory B cells  and of CD19 on pre-B cells (both pre-BI and pre-BII) in both premalignant and active MM stages, whereas decreased CD10  expression was observed only on pre-BII cells of premalignant MM stages, indicating immunophenotypic aberrations already at the first step of B cell precursor differentiation. In B cell maturation, immature B cells first express a complete IgM molecule that undergoes VDJ rearrangement to generate functional B cell receptor precursors . In our  B cells , 59. We Identification of the critical network of B cell regulatory signaling, stemness-controlling markers, and other aberrant activators of B cell lymphopoiesis that surround malignant transformation and progression may provide important insight into myeloma disease. In addition, defining cellular and molecular subclonal heterogeneity of PCs, in both premalignant and active stages, represents a major goal to enhance understanding of the pathogenesis of MM. This study provides a framework for delineation of network signaling characteristics and their associated mechanistic or clinical outcomes.n = 16] and SMM [n = 25]; and active symptomatic stages NDMM [n = 43] and RRMM [n = 104]) and HDs (n = 10) were enrolled in this study. The clinical characteristics of patients with MM are listed in n = 188) were collected from patients at the Dana-Farber Cancer Institute and Biomedical Research Center who were undergoing routine diagnostic BM aspiration. Healthy BM control samples from age-matched donors (n = 10) were obtained from AllCells using the same collection protocol.BM aspirates of a total of 188 patients with MM  and the Dana-Farber Cancer Institute in Boston, Massachusetts, USA (protocol number 10-106 for MM cohort) in accordance with the Declaration of Helsinki protocol. All patient and healthy donor samples were taken after receipt of written informed consent to donate a portion of the sample for this study in accordance with the Declaration of Helsinki protocol.JJ conceived and designed the study, performed experiments, analyzed data, and wrote the manuscript. DC performed experiments, analyzed data, and contributed to the writing of the manuscript. GB and LK performed bioinformatics analyses and contributed to the writing of the manuscript. TH contributed to the study design. ML, KJ, PGR, EK, and DMD contributed with clinical specimens and collected clinical data. KCA wrote and edited the manuscript.


Furthermore, the impact of dual therapies in ICI-induced cardiac irAEs and the potential risk factors are reviewed. We propose that self-antigens released from cardiac tissues or cancer cells and the severity/advancement of cancer disease have an important role in ICI cardiotoxicity.Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that block CTLA-4, PD-1, or PD-L1 and induce the activation of the immune system against cancer. Despite the efficacy of ICIs, which has improved the oncotherapy for patients with a variety of malignancies, several immune-related adverse events (irAEs) have been described, including those affecting the heart. Cardiac irAEs after ICI therapies, including myocarditis, can become life-threatening, and their pathogenic mechanisms remain unclear. Here, a systematic analysis was performed regarding the potential immune mechanisms underlying cardiac irAEs based on the immune adverse events induced by the ICIs: 1) recruitment of CD4 T-cell-dependent immunity is mediated by T-cell receptors (TCR), co-stimulatory , and co-inhibitory molecules. The co-inhibitory molecules cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death-ligand 1 (PD-L1), also known as immune checkpoints (IC), act on peripheral immune tolerance regulating the response to self-proteins, inhibiting the destruction of healthy tissues, and preventing autoimmune diseases .+, improving tumor-specific immune responses (Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that block IC, impeding the interaction with their respective ligands. They have been tested in preclinical models and clinical trials. Some ICIs are now approved by drug agencies, such as the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) , for useesponses . The FDAesponses .Despite the efficacy of ICIs against cancer, these therapies are accompanied by immune-related adverse events (irAEs) due to the off-target hyperactivation and dysregulation of the immune system leading to the damage of various organ systems with clinical consequences, including colitis, hepatitis, dermatitis, thyroiditis, myocarditis, or hypophysitis, which closely resemble autoimmune diseases but lack the chronicity . In addiAlthough cardiac irAEs reported in clinical trials after ICI treatments have a relatively low incidence, 3.1% in monotherapies and 5.8% in dual/combination (two different ICIs) therapies , they ca+, CD8+ T cells, and CD68+). The resulting inflammation leads to myocyte death and impaired heart function.There are three main types of heart inflammation: endocarditis , myocarditis (heart muscle), and pericarditis (sac around the heart) that eventually can lead to deteriorating heart function ending in arrhythmias or heart failure . Patholop < 0.01) , nor between dual ICI and single ICI groups   . In a re < 0.01) . DespiteI groups . The rea2.3–2.7) .However, the mechanism of cardiac irAEs after ICIs treatment is still unknown. Knockout CTLA-4 and PD-L1 mice studies have shown different cardiomyopathies, and cardiac inflammatory infiltrates . However+ and CD8+ T cells, 2) the autoantibody-mediated cardiotoxicity, and 3) the overproduction of inflammatory cytokines.Here, we performed a systematic literature review of the main ICI-induced irAEs and employed them to propose the potential mechanisms underlying heart damage after ICIs therapies. These mechanisms include 1) the recruitment of CD4+ and CD8+ T cells recruitment in ICIs therapy, we performed a PubMed search (until December 2020) with the next set of keywords: “*X* and CD4 and CD8, not review, not clinical trial,” where *X* represents anti-CTLA-4, or ipilimumab, or tremelimumab, or anti-PD-1, or nivolumab, or pembrolizumab, or cemiplimab, or libtayo, or anti-PD-L1, or atezolizumab, or durvalumab, or avelumab.To perform the systematic review of the immune mechanisms associated with ICIs-induced irAEs, for CD4Developing the following complete keywords: “anti-CTLA-4 and CD4 and CD8, not review, not clinical trial,” “ipilimumab and CD4 and CD8, not review, not clinical trial,” “Tremelimumab and CD4 and CD8, not review, not clinical trial,” “anti-PD-1 and CD4 and CD8, not review, not clinical trial,” “Nivolumab and CD4 and CD8 not review, not clinical trial”, “Pembrolizumab and CD4 and CD8, not review, not clinical trial,” “Cemiplimab and CD4 and CD8, not review, not clinical trial,” “Libtayo and CD4 and CD8, not review, not clinical trial,” “anti-PD-L1 and CD4 and CD8, not review, not clinical trial,” “Atezolimumab and CD4 and CD8 not review, not clinical trial,” “Durvalumab and CD4 and CD8 not review, not clinical trial,” “Avelumab and CD4 and CD8, not review, not clinical trial”. Additionally, the keywords to complete the search were added: ((*X* and CD4 and cancer [TIAB] OR *X* and CD8 and cancer [TIAB]) OR *X* and CD4 and CD8 [TIAB] and cancer NOT review [Publication type]), *X* are the same terms as described before.To achieve the systematic review of mechanisms including the development of autoantibodies in ICI-mediated toxicity, the following set of the keywords were used: “*X* and autoantibody, not review, not clinical trial” or “((*X* and autoantibody and cancer [TIAB] NOT review [Publication type]),” where *X* represents the same terms as described before. To perform the systematic review of cytokine-mediated ICIs toxicity, the following set of the keywords were used: “*X* AND cytokines NOT review NOT clinical trial” or “((*X* and Cytokine and cancer [TIAB] NOT review [Publication type]),” where *X* represents the same terms as described before.in vitro or in silico studies, or without information of CD4+, CD8+, cytokines, or antibodies in the abstract.Titles and abstracts were screened, if the study complied with the inclusion criteria, then the full text, study details, and results were screened. The exclusion of studies from the systematic search was carried out if any of the following terms contained concomitant immunotherapies (such as dendritic cells (DC) cells, cytokines, oncolytic viruses, immunogenic cell death (ICD) inducers, chemotherapies, radiotherapies, resection, adjuvants (as pattern recognition receptors PRR agonist), vaccines, additional infections, additional antibody-based therapies , natural compounds, peptides, and articles not written in English. Additional exclusions included clinical trials, review articles, as well as After duplicate elimination, PubMed articles were analyzed, identifying 340 scientific studies containing T-cell recruitment, 135 studies containing autoantibodies development, and 662 studies containing cytokine production after ICI therapies. The systematic procedure is schematized in + and CD8+ T-cell recruitment after ICIs therapy. After the systematic review, we concluded that the anti-tumor effect of ICIs is related to the infiltration of both CD4+ and CD8+ T cells in tumors (+: CD4+ T cells (+ T cells Foxp3+ (Tregs)  .+ and CD8+ T cells are important in the anti-tumor effect of ICIs   through Supporting preclinical data have shown that co-inhibitory molecules are essential to prevent cardiac diseases  in whichAccording to this systematic review, there are few reports regarding the inflammatory cytokine production in ICI toxicity . IFN-ƴ wDuring heart damage, proinflammatory cytokines and chemokines are released by cardiomyocytes, epithelial cells, and fibroblasts , inducinet al. recently reported a case of complete heart block and cardiac arrest following initial doses of nivolumab and ipilimumab  and impaired left ventricular (LV) function. The analogous results of impaired LV function were found in patients with metastatic melanoma receiving anti-PD1 therapy. Remarkably, they found that the blockade of TNFα preserved the LV function without attenuating the anti-cancer efficacy of anti-PD1 therapy (+ and CD8+ cells) in cardiac biopsies from patients with ICI-associated myocarditis and the upregulation of IFN-ƴ pathway, which may indicate a possible link to the inflammasome activation and M1 macrophages activity(+ T cells in a genetic murine model (Pdcd1−/−Ctla4+/−) that recapitulates clinicopathologic characteristics of ICI-myocarditis. They identified the alpha-myosin as a cardiac-specific antigen; peptides from this protein induce the expansion of peripheral blood T cells from two patients with ICI-myocarditis indicating its clinical importance   and nivolumab (anti-PD-1) , the use. 3.1%) . In a re. 3.1%) . The car. 3.1%) , resulti. 3.1%) . Dual th. 3.1%) . T-cell . 3.1%) .One of the biggest challenges for cardio oncologists is to find relevant immune/cardiac biomarkers as troponin T, autoantibodies, or symptoms as myositis, which could be helpful in specific guidelines for exclusion criteria in the ICI treatments. To date, the diagnostic criteria include an electrocardiogram, biomarkers data and BNP and increased troponin (which is the basis for the diagnosis), and imaging criteria  . There i+ T-cells, and the immunoproteasome is a key player in this autoimmunity. They also showed 2 cases of ICI-related myocarditis associated with TnI-directed autoantibodies, demonstrating ICI-induced autoimmunity (Cardiac irAE mechanisms are still unclear. We have less information about possible biomarkers in ICI-induced irAEs. It has been reported that elevated cardiac troponin levels in serum are a common biomarker for myocardial injury in ICI cardiotoxicity . Here, wimmunity .The mechanisms involved in cardiac irAEs after ICI treatment remain unclear. However, we propose three possible mechanisms involving T-cell recruitment, autoantibodies production, and inflammatory cytokines. A cardiac antigen or a shared self-antigen in the myocardium, skeletal muscle, or tumors, may be the initial point . The rapHere, we found different possible cardiac irAE mechanisms based on the literature review, which could be most likely related to T-cell activation and T cells’ recruitment to the heart, inducing myocarditis. However, the origin, specificity, autoreactivity, and activation of T cells are important challenges for oncotherapy. We found some autoantibodies which could be associated with myositis and myocarditis since they are induced by ICI therapy as anti-striated muscle antibodies and anti-acetylcholine receptors. In addition, we do not have enough evidence regarding autoantigen release in patients treated with ICIs. However, these auto-antigens could be derived from cancer cells or a previous injury, stress, or lysis of the heart or muscle tissues; nevertheless, experimental studies are needed. This review intends to be used by cardiologists and oncologists in predicting the possible appearance of cardiac irAEs and their severity.


In exposed models, PM2.5 increases oxidative stress and impairs mitochondria functionality and biogenesis in WAT and BAT. Chronic exposure also upregulates the main apoptotic/pro-inflammatory pathways and promotes the infiltration of monocytes and the accumulation of activated macrophages. Oxidative stress and inflammation are responsible for the inhibition of insulin signal transduction and glucose uptake in both the adipose tissues. The increased inflammatory status also suppresses the metabolic activity of brown adipocytes, promoting the whitening. Altogether, this evidence suggests the shift of WAT and BAT toward an inflammatory and metabolic dysfunctional phenotype. Although the underlying mechanisms remain to be clarified, the development of inflammation in lungs, gut, and hypothalamus seems to have a pivotal role in the alteration of adipose tissue homeostasis. The potential consequences on systemic cardio-metabolic health render the relationship PM-adipose tissue a key issue to investigate.Chronic exposure to high levels of particulate matter (PM) is correlated to a higher prevalence of cardio-metabolic disturbances. Adipose tissue represents a pivotal regulator of metabolic homeostasis, and its dysfunction is associated with health issues in PM-exposed models. This review discusses the adaptive changes of white (WAT) and brown (BAT) adipose tissue in response to fine particulate matter (PM Variability in size, shape, and chemical composition is responsible for differing toxicity of inhaled PM . Chronicsistance , metabolsistance –8. The fnditions –11.2.5 inhalation negatively influences both white and brown adipose tissue  [Growing evidence suggests that PMctively) , 13. In ctively) , 14–28.2.5 can represent a crucial step for understanding the health consequences of prolonged PM2.5 exposure. This review discusses the adaptive and pathological responses of BAT and WAT of rodents undergoing PM2.5 exposure.Considering these findings, the investigation of adipose tissue response to PMAdipose tissue is an ensemble of different cell types comprising adipocytes, immune, vascular, and stromal cells. Adipocytes express a highly adaptive biological profile  as they WAT is primarily intended to store the energy surplus in the form of triglycerides (TGs) . The tigbrowning [whitening [whitening of brown adipocytes is characterized by decreased mitochondrial fitness and marks dysfunction as white-shifted adipocytes are prone to develop a pro-inflammatory/apoptotic phenotype [BAT promotes the conversion of stored fats into energy through the sympathetic-mediated activation of uncoupling protein (UCP)-1 , 38. Brobrowning , 40; in hitening . The whihenotype , 42Airborne PM is a collection of microscopic particles of different sizes, consisting of carbonaceous particles with adsorbed chemicals . TransitIn the respiratory compartment, PM dissolves in the aqueous lining, coming into direct contact with alveolar cells . AlveolaThe finest fraction of PM (~0.2 μm) enters systemic circulation by crossing the alveolar  barrier and could be internalized in tissues via endocytosis-mediated mechanisms , 49–53. In isolated cells, PM elicits its cytotoxic activity by altering membrane stability and stimulating the production of reactive oxygen species (ROS) . Both th2.5-exposed animals [The upregulation of superoxide anions and markers such as superoxide dismutase (SOD), nuclear factor erythroid 2-related factor (Nrf)-2, and heat shock 70 kDa protein-1 (HSP72) provides supporting evidence for increased ROS in WAT and BAT of PM animals , 55. Chi animals , 57. ROS animals .Mitochondria are essential for energy production. In adipose tissue, they regulate lipid metabolism, insulin sensitivity, and the secretion of key adipokines , 60. The2.5 and its chemical constituents can impair mitochondria [2.5 exposure [2.5, the expression of mitochondrial biogenetic markers–peroxisome proliferator activated receptor gamma coactivator 1-(PGC-1)α and UCP-1–was significantly suppressed [2.5-exposed rodents [PMchondria . Also, pchondria , 61–63. exposure , 64  [2.5 concentrations was found to activate the UPR pathway by upregulating the expression of regulated IRE1-dependent mRNA decay and the constitutive elements of ER-associated degradation pathways [In mice exposed to chronic PMin (BiP) . In addipathways .2.5-exposed animals, mitochondrial and ER impairment is associated with insulin resistance and inflammation [2.5-exposed animals [In WAT of PMammation , 65, 71.ammation , 68 and  animals  2.5 exposure was demonstrated to augment JNK expression in organs critical for metabolic regulation such as liver and adipose tissue [ob/ob mice undergoing PM2.5 inhalation, compared to non-exposed [JNK is a cell regulator, enabled by a variety of stressors ; its acte tissue , 25, 76.whitening of brown adipocytes [. In human observational studies, the expression of p38 and ERK in vWAT was correlated with the concentration of PM [db/db mice PM2.5 inhalation increased p38 and ERK expression [2.5-exposed rodents, the inhibition of monocyte recruitment efficiently prevented the activation of p38 in WAT, suggesting that the immune-mediated inflammation is involved in the activation of the p38 pathway [The activation of p38 mitogen-activated protein kinase (p38) and extracellular signal-regulated kinase (ERK) inhibits insulin signaling , 78 and ipocytes , 80. In on of PM . In leanpression . Interes pathway .2.5 intratracheal instillation significantly reduced phosphorylated AMPK (pAMPK) in the subcutaneous WAT (sWAT) of lean mice [2.5-induced whitening in BAT.The 5′-AMP-activated protein kinase (AMPK) enables the translocation of the glucose transporter (GLUT)-4 to plasma membrane and promotes glucose and FFAs oxidation . In WAT,ean mice . In cont473 (pAKT) is associated with inflammation and insulin resistance in WAT [2.5, the expression of pAKT and GLUT-4 significantly decreased in vWAT and BAT of lean [2.5-exposed animals [The protein kinase B (AKT) mediates the transduction of insulin signal . Reducede in WAT . In resp of lean , 28, 87  of lean . In cont of lean , 28. Thi animals , 88 γ – and – cAMP response element-binding protein (CREB/P)α – [2.5-exposed mice [whitening, can also account for the augmented TGs storage in white adipocytes [Although the underlying mechanisms are unclear, early studies suggest that chronic exposure to PMs in WAT , 71. In EB/P)α – . In addiipocytes , 71, 95.2.5 exposed rodents, for instance, the impairment in mitochondrial biogenesis was associated with WAT mass expansion and adipocyte hypertrophy [On the other hand, adipocyte hypertrophy, in the absence of an energy surplus, suggests the existence of maladaptive mechanisms enhancing TGs accumulation . ER and ertrophy , 28. Likertrophy .2.5 intratracheal instillation [One final aspect is that oxidative stress/inflammation can directly inhibit the oxidation of substrates in adipocytes, stimulating TGs storage and hypertrophy  Fig. 2)2). Suppoillation ; conversillation .whitening of brown adipocytes occur in animals chronically exposed to PM2.5 [2.5-exposed rodents display a higher number of lipid droplets than filtered-air-exposed controls [whitening. PM2.5 exposure reduced mitochondrial size and number in BAT of PM2.5-exposed [2.5-exposed KKay [2.5 suppressed PGC-1α and glucose uptake in BAT [whitening  [Growing evidence reveals that BAT loss and the to PM2.5 . Brown acontrols , which i-exposed , 65, 66.sed KKay , ApoE-/-sed KKay , 23, 28.e in BAT , 21, 23,2.5 inhalation induces the upregulation of p38, TNFα, and IL-6 [browning of adipocyte precursors [browning of white adipocytes [browning is probably mediated by the activation of the protein apoptosis signal-regulating kinase 1 (ASK1), a JNK inducer  downstream cascade [Other organic byproducts derived from the interaction airways-PM cascade , 88, 11414 particles per day can be ingested by consuming a typical western diet, with an overall GI absorption of about 1% [2.5 toward the GI tract [2.5 is less readily absorbed [The lower GI tract can be directly exposed to PM, following the consumption of foods carrying high levels of particles . Evidencabout 1% , 115. BeGI tract . Since Pabsorbed , it migrabsorbed , interfeThe loss of integrity of colonocytes-microbiota lining represents a potential trigger for WAT dysfunction , 119. In2.5 exposure was shown to negatively influence gut-microbiota homeostasis [2.5 induced a switch in the gut microbiota toward a pro-inflammatory phenotype and augmented the proportion of oxidized FFAs within the gut lumen [2.5 solution, gut epithelial cells exhibited molecular and morphologic features of dysfunction and death [2.5 exposure worsened gut permeability by altering the expression of thigh-junctions and by increasing inflammation of the mucosal lining [2.5 intratracheal-instilled mice, the decrease in gut-microbiota diversity was correlated with white adipocytes hypertrophy and the suppression of UCP-1 in BAT [In experimental models, PMeostasis , 129. PMut lumen –132. In nd death . Also, Pl lining , 132–1341 in BAT .The hypothalamus is a brain structure critical for the integration of multiple peripheral signals to ensure metabolic flexibility and systemic homeostasis. In particular, the mediobasal hypothalamus directly modulates lipogenesis, substrate oxidation and insulin sensitivity of WAT , 136.2.5 could affect brain functionality by (i) upregulating circulating pro-inflammatory mediators [gut-brain crosstalk [PMediators ; (ii) inediators ; and , TNFα, and IL-6 in the hypothalamus [2.5 was also shown to upregulate NF-κB and the expression of pro-inflammatory genes in the paraventricular nucleus [2.5, could be driven by hypothalamic inflammation  characterize the activation of lipolysis/lipogenesis as well as the release of adipose tissue-specific mediators ; and (iii) estimate the effect of adipose tissue dysfunction on gluco-metabolic balance in exposed models.Further investigations should be aimed to (i) investigate the accumulation of PM2.5.The research has been carried out on Medline, Scopus and Embase by restricting the language to English. The search strategy was assessed by alternatively combining the keywords “adipose tissue,” “adipocyte,” “metabolism,” “obesity” with “PM,” “fine particulate matter,” and “air pollution.” Data strictly pertaining to experimental evidence investigating the effect of fine particulate matter  on WAT and BAT have been retrieved. In order to describe the pathophysiological mechanisms underlying BAT and WAT dysfunction, we have restricted our research to controlled studies on animals undergoing direct exposure in a confined chamber or intratracheal instillation   of PM2.5


Th1 and Th17 cells can be detrimental to dopamine neurons, whereas Th2 and Tregs are neuroprotective. The results of studies on the serum levels of cytokines such as IFN-γ and TNF-α secreted by Th1 T cells, IL-8 and IL-10 secreted by Th2 T cells, and IL-17 secreted by Th17 cells in PD patients are not uniform. In addition, the relationships between serum cytokine levels and motor and non-motor symptoms of PD are controversial. Surgical stress and anesthesia induce inflammatory responses by disturbing the balance between pro- and anti-inflammatory cytokines, which may exacerbate the neuroinflammatory response in PD patients. Here we review studies on blood inflammatory biomarkers in PD patients and discuss the roles of surgery and anesthesia in PD progression.Parkinson’s disease (PD) is the second most common inflammatory neurodegenerative disorder after dementia. Preclinical and epidemiological data strongly suggest that chronic neuroinflammation slowly induces neuronal dysfunction. Activated microglia secrete several neurotoxic substances, such as chemokines and proinflammatory cytokines, which may promote blood–brain barrier (BBB) permeabilization. CD4 Parkinson’s disease (PD) is the second most common inflammatory neurodegenerative disorder after dementia, affecting 7–10 million people worldwide . People PD is characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, the appearance of Lewy bodies (intracellular inclusions of aggregated a-synuclein), and the presence of neuroinflammation ,5,6,7. AThere is currently no cure for PD to prevent PD or delay its progression, mainly due to the still limited comprehension of the events ultimately leading to neurodegeneration. Available treatments for PD are only symptomatic, aimed at relieving the loss of brain dopaminergic neurons by using levodopa (the dopamine precursor), some dopaminergic agonists, and other indirect dopaminergic agents. Surgery, including deep brain stimulation, may be considered in advanced PD patients who fail to respond to levodopa .+ T lymphocytes play a pivotal role in orchestrating immune responses implicated not only in the pathogenesis of inflammatory diseases, but also in host defense. CD4+ T cells include proinflammatory cells such as T helper (Th) 1 and Th17, and anti-inflammatory cells such as Th2 and T regulatory cells (Tregs) [+ T cells is reduced in patients with PD [+ T cells seen in the peripheral blood of patients with PD is mainly due to decreases in Th2, Th17, and Tregs [CD4 (Tregs) ,16. Inte (Tregs) . Indeed, with PD , but thend Tregs ,21. As and Tregs ,25,26,27nd Tregs ,23,26,27Drosophila model. There are few studies on the effect of the immune response on PD symptoms and prognosis after surgery and anesthesia [Surgical stress and anesthesia induce inflammatory responses by disturbing the balance between pro- and anti-inflammatory cytokines , which mesthesia ,35,36,37Here we review studies on blood inflammatory biomarkers in PD patients and discuss the role of surgery and anesthesia in PD progression. We searched the PubMed, PubMed Central, Medline, Google Scholar, and Google databases for clinical studies using the key words “Parkinson’s disease”, “inflammation”, “immune” “blood”, “cytokine”, and “chemokine.”Only seven markers  were reported in more than 5 of 51 studies. The most frequently studied inflammatory biomarkers are CRP and IL-1β ,56,57,58+ T lymphocytes orchestrate an effective immune response during host defense, as well as in the pathogenesis of inflammatory diseases. CD4+ T cells can select for proinflammatory phenotypes such as Th 1 and Th17 cells, as well as anti-inflammatory phenotypes such as Th2 and Tregs [Although many studies have focused on proinflammatory profiles, inflammation is a balance between pro- and anti-inflammatory processes. CD4nd Tregs ,16. Resund Tregs .The most investigated anti-inflammatory blood biomarker in the context of PD is IL-10. However, while some studies report higher levels in PD patients, others report lower levels or no difference in PD patients compared to healthy controls ,60,67,70High blood levels of CRP, IL-6 ,62,77,87CRP, FABP, IL-6, IL-10, IL-17A, TNF-α, and TNFR are related to worse cognitive function and dementia ,77,80,83Systemic inflammation causes physiological and behavioral changes in humans and animals, characterized by reduced cognitive function, fever, reduced food intake, drowsiness, and general fatigue . AsepticEndothelial cells, pericytes, and astrocyte end-feet are key components of the neurovascular unit . TogetheCytokines and the migration of peripheral immunocompetent cells across the BBB are associated with perioperative neurocognitive impairment in animal models . Other pTissue damage during major surgery induces inflammatory responses by disturbing the balance between pro- and anti-inflammatory cytokines . Other iNeutrophils are the primary effector cells of the innate immune system and represent the first line of defense against invading exogenous pathogens . NeutropMost general anesthetic agents directly or indirectly suppress the immune response ,117,118,There has been diverse research reporting that dysbiosis, obesity, diabetes, and vascular disorders are closely related to the occurrence of neurodegenerative diseases including Parkinson’s disease ,135,136.Drosophila PD model. Tuon et al. [Shan et al.  reportedn et al.  reportedn et al. . Therefon et al. . HoweverInsight into the inflammatory processes induced by surgical and therapeutic interventions during the perioperative period has been provided by clinical and experimental studies. Because inflammation affects disease progression and patient outcomes at multiple levels, knowledge of the molecular mechanisms and pathways involved in these processes would facilitate the development of new therapeutic approaches. CRP, IL-1β, IL-2, IL-6, IL-8, IFN-γ, TNF-α are 7 the most intensely evaluated blood biomarkers in PD patients. Although they are correlated with motor and nonmotor symptoms, the results are controversial and vary by study design. Surgery and anesthesia induce inflammatory responses by disturbing the balance between pro- and anti-inflammatory cytokines. Studies on the effects of surgery and anesthesia on neuroinflammation and the consequent worsening of PD symptoms are scarce. Therefore, research on the effects of surgery and anesthesia on neuroinflammation, and methods for its amelioration, are needed to prevent worsening of perioperative symptoms in patients with PD.


Escherichia coli (ExPEC) lineages, which also present a zoonotic character. One of these lineages belongs to ST38, a high-risk globally disseminated ExPEC. To get insights on the aspects of the global ST38 epidemiology and evolution as a multidrug-resistant and pathogenic lineage concerning the three axes of the One Health concept , this study performed a global phylogenomic analysis on ST38 genomes. Methods: A phylogenetic reconstruction based on 376 ST38 genomes recovered from environments, humans, livestock, and wild and domestic animals in all continents throughout three decades was performed. The global information concerning the ST38 resistome and virulome was also approached by in silico analyses. Results: In general, the phylogenomic analyses corroborated the zoonotic character of the ExPEC ST38, since clonal strains were recovered from both animal and human sources distributed worldwide. Moreover, our findings revealed that, independent of host sources and geographic origin, the genomes were distributed in two major clades (Clades 1 and 2). However, the ST38 accessory genome was not strictly associated with clades and sub-clades, as found for the type 2 T3SS ETT2 that was evenly distributed throughout Clades 1 and 2. Of note was the presence of the Yersinia pestis-like high-pathogenicity island (HPI) exclusively in the major Clade 2, in which prevails most of the genomes from human origin recovered worldwide (2000 to 2020). Conclusions: This evidence corroborates the HPI association with successful E. coli ST38 establishment in human infections. Background: Most of the extraintestinal human infections worldwide are caused by specific extraintestinal pathogenic  Escherichia coli is a commensal bacterium and a versatile pathogen capable of causing intestinal and extraintestinal infections. Extraintestinal pathogenic E. coli (ExPEC) presents a complex phylogeny and comprises many lineages with enhanced metabolic repertoires that affect not only humans’ but also animals’ health, besides occurring in the environment .,27.CTX-Mhe world . A stronlsewhere . In compNDM-1, among these strains, is of great global concern due to its clinical outcomes. Here, in spite of the clinical origin of most of the ST38 genomes analyzed, we observed a low prevalence of carbapenem resistance genes   and also in animals (birds/Switzerland). On the other hand, our large-scale resistome analysis revealed that blaOXA-48 distribution in ST38 was not only due to clonal expansion since this gene was found spread among unrelated genomes recovered from distinct countries in Asia  distributed through several sub-clades  . The blaxpansion ,31. In fb-clades . ConcernNDM-1 , blaIMP-4 , and blaVIM-4  ; blaCTX-M-14 + blaIMP-4, in an isolate from China (GCA_002223705); and blaCTX-M-15 + blaNDM-1 in the Brazilian EC35 genome obtained in this study (GCA_019492145)  . So far, in Asia ,33,34,35 members . Interes9492145) . AlthougCTX-M-15 and blaNDM-1, it harbored genes conferring resistance to cephalosporins (blaCMY-2), trimethoprim (dfrA14), sulfonamides (sul2), tetracycline (tet(A)), streptomycin (strBA) and quinolones . Interestingly, the co-occurrence of these two qnr-type genes might synergistically contribute to the accumulation of activity, increasing quinolone resistance. Indeed, the observed MIC of ciprofloxacin for EC35 (0.5 mg/L) was slightly higher than that related to qnrB1 and qnrS1 alone (MICs 0.1–0.25 mg/L) [TEM-181, dfrA5 and tet(A)  and sul2 , while the environmental one (GCA_002231735) had no antibiotic resistance gene. Considering that these genomes are clonal, this finding strongly suggests that the huge resistome presented in the clinical EC35 genome is associated with an acquired mobilome. In fact, the in silico plasmid searches revealed the presence of different plasmid backbones from several incompatibility groups, such as IncHI2 and IncFIb, and most of the ARGs were associated with these plasmid segments. However, due to the technical limitations imposed by the sequencing methodology applied, it was not possible to completely characterized these elements. In contrast, the in vitro resistome analysis of the EC35 strain, recovered in the Amazon region and sequenced in the present study, had revealed an MDR phenotype, being resistant to most antibiotics tested, including the new drug combinations ceftolozane/tazobactam and ceftazidime/avibactam . Its res25 mg/L) . This geCMY-59, blaOXA-10, qnrVC4, blaTEM-181, cmlA6, dfrA14, floR, qacL, sul2, tet(A)), recovered in China from poultry, 2016; GCA_011742905 (aph(3″)-Ib, aph(6″)-Id, aadA5, the carbapenemase blaOXA-48, catI, blaTEM-181, dfrA17, qac∆E1, sul1, sul2, tet(D)), recovered in Switzerland from the natural environment, 2019; GCA_002901285 ), recovered in Brazil from cabbage, 2016; and GCA_008642395 (aac(3″)-IId, aph(3″)-Ib, aph(6)-Id, blaCTX-M-14, blaTEM-181, aadA5, dfrA17, qac∆E1, sul1, sul2, tet(D)), recovered in Norway from mollusks, 2014.It is worth mentioning the occurrence of an expressive arsenal of ARGs among several non-human genomes involved with resistance emergence to a broad variety of antibiotics. These genomes were recovered from distinct spatio-temporal contexts and were distributed throughout Clade 1. It was the case of the genomes GCA_014712515 (aac(6″)-Ib9, ant(3″)-IIa, aph(3″)-Ib, aph(3’)-Ia, aph(6)-Id, blaThe ST38 virulome revealed a robust arsenal of virulence-associated genes related to the successful establishment of both intestinal and extraintestinal infections. The ST38 virulome was characterized by genes involved with crucial roles in host–pathogen interactions and adaptability, competitiveness, and colonization capabilities  . A heterE. coli that shares a significant relationship with the T3SS coded by the Salmonella pathogenicity island 1 (SPI-1). Although present in many E. coli strains, in most cases the ETT2 had undergone extensive mutational attrition, resulting in a functionless system [In addition to the aforementioned virulence factors, the virulome mining revealed interesting and particular features regarding the T3SS type 2 identified in most of ST38 genomes . The ETTs system ,41. The s system ,43. HereSo far, only a small set of complete and canonical ETT2 has been reported ,47,48,49In the virulome of ST38 we also identified the high-pathogenicity island (HPI) which was first described in pathogenic Yersinia species that encodes the siderophore yersiniabactin , and hasE. coli ST38 establishment in human infections. Interestingly, we observed a strong association and high prevalence of HPI in genomes recovered from humans/clinical setting over space and time distributed throughout Clade 2. Conversely, this island was absent in Clade 1, which is composed mainly of animal and environmental ST38 genomes . In factHere, we presented the large-scale genomic analyses of the high-risk ExPEC ST38, focusing on its resistome and virulome, providing novel insights into its global epidemiology in terms of the three axes of the One Health concept. It was demonstrated that ST38 is a host-broad pathogen since clones were found efficiently persisting in humans, animals, and natural environments, underlining its global dissemination. Due to the high prevalence of HPI in human/clinical genomes, we could conclude that HPI plays a major role in the establishment of ST38 as a successful pathogen in humans.


We aimed to evaluate the prognostic value of E2F4 expression in oral squamous cell carcinoma (OSCC) and clarify its influence on immune cell infiltration and biological functions.  The Cancer Genome Atlas (TCGA) database, the STRING database, and related online tools as well as single-sample gene set enrichment analysis (ssGSEA) were used for the analyses in our study.  The E2F4 expression was elevated in OSCC tumor tissue compared with paracancerous tissues. The high expression of E2F4 was closely related to the poorer overall survival, disease-free survival, and progression-free interval of OSCC. In addition, pathway enrichment analyses revealed that the top 49 genes most closely related to E2F4 were strongly associated with the cell cycle. E2F4-related proteins were closely related to the following KEGG pathways: cell cycle, cellular senescence, PI3K-Akt signaling pathway, Wnt signaling pathway, and notch signaling pathway. It was also demonstrated that the E2F4 expression was negatively correlated with immune purity and strongly related to immune cell infiltration in OSCC.  E2F4 can be used as a novel biomarker for the diagnosis and prognosis of OSCC. Oral cancer is overall the eighth most common malignancy . Oral sqThe E2F family was first identified in 1987 as transcription factors required to activate the E2 adenovirus promoter . Nine meImmune dysfunction has been shown to be closely related to tumorigenesis and progression. Tumor-infiltrating lymphocytes (TILs) are considered to be the host immune response to tumor cells . DiffereThe purpose of this study was to evaluate the prognostic value of E2F4 in OSCC based on data obtained from The Cancer Genome Atlas (TCGA) database. To gain further insight into the potential functions, the biological pathways involved in OSCC pathogenesis-related E2F4 regulatory network and gene set enrichment analysis were performed based on data obtained from the STRING database. Moreover, the relationship of E2F4 with TILs was analysed using single-sample gene set enrichment analysis (ssGSEA).https://portal.gdc.cancer.gov/), retaining samples belonging to oral cancer sites  and excluding nonoral cancer sites . A total of 329 OSCC tissues and 32 paracancerous tissues were included.RNA seq data in HT Seq-FPKM (fragments per kilobase per million) format from the Head and Neck Squamous Cell Carcinoma (HNSC) project were downloaded from TCGA database  for pan-cancer expression differential analysis [P < 0.05 was considered statistically significant.The expression of E2Fs was normalized to transcripts per million reads (TPM) value before further analysis. The Shapiro-Wilk normality test is used to test the normality distribution of samples. The difference in mRNA levels between two groups were analyzed with Wilcoxon rank sum test. The Kruskal-Wallis test was performed to evaluate the difference among the three groups, and the Bonferroni method was chosen for the correction of analysis , includihttps://string-db.org/, version: 11.5) is an online database search for known protein interactions [P < 0.05.The STRING  as the cut-off value. The Wilcoxon rank sum tests were used to test both groups. Next, univariate and multivariate Cox proportional risk regression analyses were performed using the “survival” package of the R software, including variables such as E2Fs expression, gender, age, clinical stage, and histologic grade. P < 0.05.The tumor microenvironment (TME) is an environment composed of tumor cells, extracellular matrix (ECM), tumor-associated stromal cells, tumor-associated immune cells, and signaling molecules . Stromal+ T cells, CD8+ T cells, B cells, dendritic cells, macrophages, and neutrophils) in OSCC samples was obtained, and the correlation between the expression of E2Fs and these immune cells was explored by Spearman. Moreover, the correlation between E2F expression and different immune cells (aDC (activated DC), B cells, CD8+ T cells, cytotoxic cells, DCs, eosinophils, iDC (immature DC), macrophages, mast cells, neutrophils, NK CD56bright cells, NK CD56dim cells, NK cells, pDC (plasmacytoid DC), T cells, T helper cells, Tcm , Tem (T effector memory), Tfh (T follicular helper), Tgd (T gamma delta), Th1 cells, Th17 cells, Th2 cells, and Treg) was further explored based on the ssGSEA algorithm. P < 0.05 was statistically significant.TIMER is a public resource platform that provides a comprehensive analysis of immune cell infiltration in various cancers . The abuP = 0.001), DSS (P = 0.002), and PFI (P = 0.02) of OSCC  (Figures + T cells, DC, T cells, B cells, Treg, mast cells, pDC, iDC, TFH, cytotoxic cells, and Th17 cells (Immune cell infiltration in tumors is an integral part of TME, and the estimate and ssGSEA algorithms were used to assess the proportion of each component of the TME and the correlation between tumor immune cell infiltration and E2F4. The results showed that patients in the high-expression group of E2F4 had a low StromalScore and ImmuneScore . In term Figures . Based o17 cells .In this study, we observed that the E2F4 expression was upregulated in OSCC tissues compared with paracancerous tissues, and the Kaplan-Meier analyses suggested that E2F4 might be a potential prognostic factor for OSCC. We further provided a detailed picture of E2F4 expression in OSCC in relation to clinical features, pathway crosstalk, and immune cell infiltration.E2F4 is a critical molecule in the RB/E2F pathway. Under physiological effects, E2F4 is the major repressor of cell cycle progression, which can prevent uncontrolled proliferation . However+ T cells, macrophages, neutrophils, and dendritic cells [+ T cells, and dendritic cells. Therefore, we conclude that E2F4 may promote OSCC progression by mediating immune-related pathways. Further experiments, such as the correlation between E2F4 expression and degree of immune infiltrating cells in OSCC by immunohistochemistry, can better verify the role of E2F4 in immune cell infiltration.TME plays an important role in the carcinogenesis of OSCC. Infiltration of immune cells has predicted improved prognosis in many different tumor types . E2F4 exic cells . HoweverTaken together, these findings demonstrate that the upregulation of E2F4 may be a molecular marker that predicts the prognosis of OSCC. Reasonably, the potential mechanisms of E2F4 in immune environment regulation still require further validation.


SPARC (secreted protein acidic and rich in cysteine) and VIM (vimentin) for glioblastoma. The genes CACNA1E , SH3GL2 , and DDN (dendrin) were identified as under-expressed genes as compared to the normal and pan-cancer tissues along with prominent putative prognostic biomarker potentials. The genes DDN and SH3GL2 were found to be upregulated in the proneural subtype, while CACNA1E in the mesenchymal subtype of glioblastoma exhibits good prognostic potential. The mutational analysis also revealed the benign, possibly, and probably damaging substitution mutations. The correlation between the DEG and survival in glioblastoma was evaluated using the Kaplan–Meier plots, and VIM had a greater life expectancy of 60.25 months. Overall, this study identified key candidate genes that might serve as predictive biomarkers for glioblastoma.Glioblastoma is an aggressive malignant tumor of the brain and spinal cord. Due to the blood–brain barrier, the accessibility of its treatments still remains significantly challenging. Unfortunately, the recurrence rates of glioblastoma upon surgery are very high too. Hence, understanding the molecular drivers of disease progression is valuable. In this study, we aimed to investigate the molecular drivers responsible for glioblastoma progression and identify valid biomarkers. Three microarray expression profiles GSE90604, GSE50601, and GSE134470 containing healthy and glioblastoma-affected samples revealed overlapping differentially expressed genes (DEGs). The interrelational pathway enrichment analysis elucidated the halt of cell cycle checkpoints and activation of signaling pathways and led to the identification of 6 predominant hub genes. Validation of hub genes in comparison with The Cancer Genome Atlas datasets identified the potential biomarkers of glioblastoma. The study evaluated two significantly upregulated genes,  Glioma is the primary malignant brain tumor affecting the glial cells of the central nervous system . There aEGFR receptors, and the development of monoclonal antibody bevacizumab as the means to halt the progression of GBM  approved standard-of-care temozolomide  to treatn of GBM . Unfortun of GBM . Thus, tIn this direction, discerning the key aberrant molecular targets and pathways in the initiation and progression of GBM can be a significant strategy for developing potential therapeutics . In prevp-value <0.05 were identified. It has been advocated that the integration and reanalysis of the genomics profiles from different studies offer better solutions to the poor reproducibility of single cohort studies and help ensure consistency in the analysis , and the differentially expressed genes (DEGs) with log2FC > 1 and analysis . The idep-value <0.05 thresholds. The bioinfokit (https://pypi.org/project/bioinfokit/) package implemented in Python was used to create the volcano plot of the DEGs. The protocol and the data files used in the study are made available for access at https://github.com/aishwarya-sekar/Glioma-gene-expression-analysis.git.The Gene Expression Omnibus (GEO) database contained gene expression profiles of GBM data from which three publicly available datasets, GSE90604 , GSE5016https://david-d.ncifcrf.gov/) (http://www.reactome.org) (www.genome.jp/kegg)  tool (rf.gov/) . Pathwayome.org)  and KEGGjp/kegg) . The Clujp/kegg) .http://stringdb.org/) was used to evaluate the evidence-based protein–protein interactions (PPI) (https://www.ebi.ac.uk/gxa/home)   with the TCGA GBM datasets (http://genetics.bwh.harvard.edu/pph2/bgi.shtml) webserver.The STRING database (ns (PPI) . Active xa/home) . The anaxa/home)  in compacgi-bin) . The mutdatasets . The sighttp://kmplot.com/analysis/). It was used to predict the correlation between DEGs and the survival rates of the patients using the log-rank p tests. The relationships below a p-value of 0.05 were considered significant (https://gbm-biodp.nci.nih.gov) to identify their prognostic ability based on the GBM subtypes , 5,375 , and 2,263  DEGs were identified for GSE90604, GSE50161, and GSE134470, respectively. Out of these, 61 genes (23 upregulated and 38 downregulated) were found to be overlapped, as shown in In this work, we analyzed the microarray gene expression profiles of three GBM datasets, namely, GSE90604, GSE50161, and GSE134470, obtained from the NCBI-GEO database. The datasets were analyzed using the limma package by setting the cutoff criterion as log2FC > 1 and a p-values were considered for biological process (BP), molecular functions (MF), and cellular component (CC) sub-ontologies. As shown in From the GO analysis of the overlapping DEGs, the top ten annotations based on their The major metabolic pathway databases, Kyoto Encyclopedia of Genes and Genomes (KEGG) and REACTOME, were used to study the enriched pathways for the annotated genes. The significant pathways involved in GBM were identified for the DEGs. As shown in CTSK, HSPG2, SPARC, VIM, SOCS3, HELLS, CKAP2, ASPM, MCM3, DDX58, and CLEC7A, were identified. A total of 16 genes, namely, MAPK8IP2, CACNA1E, BZRAP1, RIMS3, GABRD, DDN, BRSK1, NAP1L2, PPIP5K1, RASL10B, PPP2R2C, SYNJ2, SH3GL2, LICAM, MCTP1, and SV2B, were identified. Among them, seven upregulated DEGs—VIM, HELLS, SPARC, HSPG2, MCM3, ASPM, and SOCS3 and three downregulated DEGs—SH3GL2, LICAM, and SYNJ2 exhibited interactions with a high confidence score of 0.7.The PPI networks analysis has helped in the identification of the hub genes playing a critical role in the GBM. In the predicted PPI networks for up- and downregulated DEGs, a haircut operation with a network scoring cutoff of 2 was applied using the MCODE plug-in of Cytoscape software. It resulted in 11 and 16 nodes for 23 up and 38 downregulated overlapping DEGs with scores of 3 and 3.86, respectively. The PPI networks for up- and downregulated DEGs are shown in MAPK8IP2, SH3GL3, and PPIP5K1 were found to be overly expressed in the tissues of the normal cerebellar hemisphere and cerebellum, whereas high expression of PPP2R2C and BRSK1 was seen in the normal cerebral cortex areas of brains. A very significant expression of SPARC at the rates of 2,327.0 TPM and 2,758.0 TPM were observed in GBM and glioma, respectively. It indicates SPARC to be a strong prognostic biomarker of GBM and glioma. The expression levels of the genes were measured at a maximum rate of 3,642 transcripts per million. We have further extended the validation of the hub genes by comparing the expression levels with The Cancer Genome Atlas (TCGA) data. The expression levels of the hub genes in normal brains were compared to the GBM brains. They were also classified based on TP53 mutant and non-mutant tissue samples from the TCGA datasets (TCGA pan-cancer atlas with 592 samples of GBM) and shortlisted the ones that were in line with the previous comparisons, as shown in PPP2R2C, DDN, SH3GL2, MAPK8IP2, CACNA1E, and BRSK1 were found significantly underexpressed, whereas SPARC, VIM, and MCM3 were highly expressed in both the TCGA datasets (including the TP53 mutant and non-mutant types) and GEO datasets. On the contrary, the genes CTSK, CKAP2, DDX58, and HSPG2 of GBM in TCGA were observed to be significantly overexpressed in comparison to their expressions in GEO datasets. The genes SYNJ2 and RIMS3 were observed to be relatively underexpressed in the TCGA datasets in comparison to the GEO datasets and thus excluded from further analysis.The expression atlas platform was used to further elucidate the levels of hub genes expression on the brain tissues. The set of hub genes was validated by comparing their expression patterns against the datasets of the pan-cancer atlas of the whole-genome consortium . GBM is SH3GL2 had the highest altered frequency (2.2%) with six missense  mutations. These mutations were classified as variants of unknown significance (VUS). BRSK1 had six VUS mutations with five missense and one truncating mutation with a frequency of 1.2% alterations. MCM3 had ten VUS mutations with six missense and one truncating mutation. MAPK8IP2 and PPP2R2C had two VUS mutations. The detailed mutation data of the hub genes are presented in HSPG2 with 35 VUS had a higher number of mutations with 1.5% somatic mutations followed by ASPM and CACNE1 with 26 VUS each. The substitution polymorphism was predicted using the PolyPhen-2 tool. It predicted the damaging effects of the hub genes. VIM had two mutations with probably damaging effects on V161G and E95Q with scores of 1.0 and 0.980, respectively. A possibly damaging effect with a score of 0.95 was also found on A301T. DDN, MCM3, and MCTP1 had no mutations, while SPARC had only one mutation, A127V, which was possibly damaged with a score of 0.909. SH3GL2 had only three mutations, out of which two were benign and the third where T was replaced by N at position 320 was probably damaging. BRSK1 had four mutations, out of which G to A substitution that occurred at position 327 was probably damaging. CACNA1E had 22 mutations R to W substitution at 590th residue was found recurrent in many samples. The significant substitutions of the biomarker genes are represented in ASPM and HSPG2 exhibited 27 and 35 mutations, respectively. Some of their recurrent mutations are shown in VIM, SH3GL2, SPARC, and CACNA1E.Mutational analysis of the hub genes was inferred from the six TCGA datasets using cBioportal with a total of 1,893 samples. DDN, SH3GL2, PPP2R2C, MAPK8IP2, SPARC, CACNA1E, VIM, MCM3, and BRSK1, were identified to have an overall survival of more than 5. Log-rank p-value is only the test of significance based on the time of events. The overexpressed gene SPARC had an overall survival rate of 7.26 median months and the disease-free rate of two months which shows the progression of the disease. The underexpressed genes MAPK8IP2, BRSK1, SH3GL2, DDN, and PPP2R2C had overall survival rates of 10.65, 16.93, 6.54, 7.40, and 17.80 median months, respectively. The disease-free progression rates of MAPK8IP2, BRSK1, SH3GL2, DDN, and PPP2R2C were 11.17, 8.40, 13.21, 4.83, and 10.90 median months, respectively. The overall and disease-free progression of upregulated hub gene VIM had shown log-p ranks of 0.28 and 0.04, respectively. The highest overall survival rate was 65.33 median months. The highest disease-free progression rate was 57.72 median months. With five years of life expectancy, these values indicate a good survival rate and can be proposed as a prognostic biomarker for GBM. MAPK8IP2 , head and neck squamous carcinoma (HNSC), esophageal carcinoma (ESCA), colon adenocarcinoma (COAD), and rectal adenocarcinoma (READ). Apart from GBM, SH3GL2 deviation was marked in the kidney chromophobe tumor (KICH), kidney renal cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), and sarcoma (SARC). Similar to DDN and MAPK8IP2, only the normal brain tissues had the highest expression of CACNA1E with a marked reduction of expressions in GBM. Lower expression levels of CACNA1E were found in pheochromocytoma, paraganglioma (PCPG), kidney renal clear cell carcinoma (KIRC), and sarcoma (SARC). Interestingly, due to less common occurrence in other cancer types, larger differences in expression rates in GBM than the normal tissue three genes, namely, CACNA1E, DDN, and SH3GL2, DDN and SH3GL2 were lower, they can still be considered putative diagnostic markers in GBM. This is due to the fact that they demonstrated a significant drop in expression rate as compared to the normal brain tissues. Significant overexpression of VIM with high overall survival correlated with previous findings as a strong biomarker of the mesenchymal and classical types of GBM  dysfunction as seen in many neurodegenerative processes  and GEO datasets. On the contrary, the genes CTSK, CKAP2, DDX58, and HSPG2 of GBM in TCGA were observed to be significantly overexpressed in comparison to their expressions in GEO datasets. The genes SYNJ2 and RIMS3 were observed to be relatively underexpressed in the TCGA datasets to the GEO datasets. We were also interested in predicting the driver or passenger mutations for the hub genes. We found HSPG2 with 35 VUS had the higher number of mutations followed by ASPM and CACNA1E with 26 mutations each. The mutational frequency of the three genes demands more insight and analysis to be identified as marker genes. CACNA1E, the member of voltage-gated calcium channels, was identified as a significantly downregulated gene and was identified to be one of the unique genes of GBM , SH3GL2 , BRSK1 (BR serine/threonine kinase 1), DDN (dendrin), CACNA1E , and MAPK8IP2 (mitogen-activated protein kinase 8 interacting protein 2) were harmoniously underexpressed in GBM than in the normal tissues. These genes were also observed to be significantly downregulated in GBM, as indicated by the pan-cancer analysis. Interestingly, as per GeneCards, the PPP2R2C gene was already reported to be involved in cell cycle regulation, beta-adrenergic receptor signaling, and PI3-Akt signaling, as well as being responsible for inflammatory bowel disease  and SPARC (secreted protein acidic and rich in cysteine), showed significantly higher expression rates in GBM than in normal brains. Vimentin (VIM), the major intermediary filament constituent, was associated with neuritogenesis, cell signaling, attachment, and migration, causing congenital cataracts . SH3GL2rocytoma . Hence, ataracts . There i cancers . The hiser cells . Higher val rate . Cell suval rate . VIM wass of VIM . Upreguls of VIM . Althougsubtypes  due to w crucial ;  is linked to self-renewal of stem cells and resistance to temozolomide (CD44 is found to be involved in tumor cell migration and proliferation (CD15 (a trisaccharide 3-fucosyl-N-acetyllactosamine) as seen in many cancers is attributed to the GBM grades and survival during hypoxic conditions (CD70 (CD27 L—type II transmembrane protein that belongs to the tumor necrosis factor (TNF) receptor family) is attributed to tumor immunosuppression and aggressiveness of GBM  is linked to tumor proliferation in multiple cancers (OCT-4 (octamer-binding protein transcription factor 4) is found to be upregulated in the hypoxic conditions of GBM (SOX-2 (sex-determining region Y) is found to have an increased expression in GBM stem cells linked to the growth of tumors and relapse after chemo and radiotherapy (IDH1 (isocitrate dehydrogenase) were considered significant with prognostic benefits, while deletion of CDKN2A (cyclin-dependent kinase inhibitor 2A) in IDH mutants was a marker for malignancy. TERT (telomerase and reverse transcriptase) promoter mutations, H3F3A (replication-independent histone 3.3 linked to high-grade gliomas) alterations, and methylation of MGMT (O6-methylguanine-DNA methyltransferase) promoters were proposed as potential markers of GBM (EGFR), p53 (tumor suppressor protein), platelet-derived growth factor receptor (PDGFR), phosphoinositide 3-kinase (PI3K), phosphatase and tensin homolog (PTEN), and 1p/19q (codeletion of chromosomes 1p and 19q) have also been identified, but they failed to achieve prognostic effect in the clinical studies , IDH1, PDPN (podoplanin), TGFB (transforming growth factor-beta), IL-8 (interleukin 8), TIMP1 , and ZAP70 (zeta chain-associated protein kinase 70). According to GBM subtype classification, chromosome 7 amplification together with the deletion of chromosome 10 and EGFR amplification, were identified as a classical GBM  aberrations , H3F3A, and HIST1H3B mutations claiming them to be significant biomarkers of GBM. The drawback of the predicted circulating tumor DNAs is that they are very rarely detected. SH3GL2, a tumor suppressor gene widely prevalent in the central nervous system, was identified to be downregulated by the miRNA biomarker—mir330, thereby causing malignancy in GBM (There is enough evidence on the prognostic biomarkers of GBM. Putative biomarkers for the GBM stem cells were identified: upregulation of zolomide , CD44 isferation , CD15 (anditions , CD70 (Cs of GBM , S100A4 s of GBM , ALDH1A3 cancers , nanog ( cancers , OCT-4 (s of GBM , SOX-2 (otherapy , and nesotherapy . Althougotherapy . Mutatios of GBM . The pot studies . Nanoparical GBM . Mesenchical GBM . Proneurrrations . Sequency in GBM . AlthougSH3GL2, DDN, and CACNA1E to be the potential putative prognostic biomarker candidates of GBM. BRSK1, PPP2R2C, and MAPK8IP2 with striking lower expression levels and better survival rates can also play a role in the early diagnosis of GBM. The study evaluated the biomarkers in comparison with GEO datasets, TCGA datasets, and the Pan-Cancer Analysis of Whole Genomes datasets adding strong validation to the predicted biomarkers.Altogether, through this study, we provide sufficient background for the genes DDN and SH3GL2 along with the already reported VIM, CACNA1E, and SPARC genes. It provides a promising preliminary investigation that employs multiple steps of validation right from the comparison of expression levels between normal and GBM brain tissues to predicting the prognostic potential based on the GBM subtypes. Further biological validation will be more valuable. Also, the clinical examination of the hub genes will endorse the prognostic biomarker candidates obtained through this study.This study sheds light on the identification of the key molecular drivers of GBM. The study elucidated putative prognostic biomarkers through a top–down integrated bioinformatics approach. Through this study, we have predicted novel GBM biomarkers