Profiles Of Immune Infiltration Research Articles

Unveiling vital biomarkers and immune infiltration profiles in endoplasmic reticulum stress following spinal cord injury

Spinal cord injury (SCI) is a profound affliction of the central nervous system that often remains inadequately addressed. Prior research has indicated that endoplasmic reticulum stress (ERS), associated with apoptotic signaling, plays a part in subsequent injuries post-SCI. However, the exact mechanisms are still unclear. ERS-related genes and SCI-associated datasets were sourced from the Genecard and GEO databases. We identified 68 ERSDEGs and pinpointed 6 marker genes vital for SCI diagnosis (CYBB, PRDX6, PTGS1, GCH1, TLR2 and PIK3CG) which were all upregulated in SCI based on bioinformatics and qRT-PCR. The nomogram exhibited that these genes could effectively predict the occurrence of SCI. Functional analysis revealed the potential roles of these genes was closely related to neuron cells and immune response. Immune infiltration research underscored the substantial roles of macrophage and CD56 dim NK cells in SCI. The ceRNA network analysis further revealed the complex interplay among marker genes, lncRNAs and miRNAs in SCI. We screened six marker genes with great diagnostic value, and found that these genes may affect the occurrence of SCI by affecting the immune response and recovery of neurons.

Exploring the role of ADAMTSL2 across multiple cancer types: A pan-cancer analysis and validated in colorectal cancer

BackgroundRecent studies have established a correlation between ADAMTSL2 (ADAMTS-like 2) and the development of various cancers. This study aims to conduct a comprehensive pan-cancer analysis in 37 cancer types and investigate its potential role in colon and rectal adenocarcinoma (COADREAD).MethodPan-cancer and mutation data were sourced from The Cancer Genome Atlas (TCGA) database and analyzed using Sangerbox analysis platform. We explored the expression patterns and prognostic implications of ADAMTSL2, and investigated its relationships with tumor heterogeneity, stemness, immune checkpoint genes, immune cell infiltration, RNA modifications, and mutational profiles across different cancers. Additionally, with Ethics Committee approval, we conducted immunohistochemical (IHC) analysis on 120 COADEAD samples to evaluate ADAMTSL2 expression and its association with clinicopathological parameters.ResultsADAMTSL2 expression was positively correlated with the hazard ratio of OS, DSS, DFI and PFI for ESCA and COADREAD. A negative correlation was observed between ADAMTSL2 expression and NEO levels in COAD. Gene alterations in ADAMTSL2 were observed, with a mutation frequency of 5.0% in COAD. There is a significant correlation between ADAMTSL2 expression and immune cell infiltration in a variety of cancers. The expression level of ADAMTSL2 protein was associated with T stage, N stage, M stage (p < 0.05). Kaplan‒Meier survival curves demonstrated that the high ADAMTSL2 group had a shorter OS time (p = 0.047) and progression free survival time (p = 0.026) than the low ADAMTSL2 group.ConclusionIn summary, we conducted a comprehensive pan-cancer analysis of ADAMTSL2 and we demonstrated that ADAMTSL2 may serve as a novel prognostic biomarker and immunotherapy target in COADREAD.

Prognostic Significance and Immune Landscape of an Efferocytosis-Related Gene Signature in Bladder Cancer.

Bladder cancer poses a significant global health challenge, underscoring the imperative for precise prognostic instruments to advance patient care. Against the backdrop of efferocytosis's increasingly recognized role in cancer, this research endeavors to develop and authenticate a prognostic signature intricately linked to efferocytosis in bladder cancer. LASSO-COX regression analysis crafted an efferocytosis-related genes risk prognostic model, followed by the construction of a column chart. External validation sets confirmed the predictive accuracy of both the model and chart. Clinical, tumor microenvironment, drug sensitivity, and immunotherapy analyses were employed to comprehensively assess efferocytosis-related scores. The expression of TGFB3 key genes was validated via RT-PCR and western blotting. Further validation included Transwell, Wound healing, Colony formation, and EDU assays. We formulated and validated an efferocytosis-related genes risk model in bladder cancer, comprising 13 core genes. The risk model demonstrated autonomous prognostic significance in both univariate and multivariate Cox analyses. Following the multivariate analysis, we devised a nomogram. Moreover, by utilizing individual risk scores derived from this risk model, we successfully stratified patients into two discernible risk cohorts, unveiling noteworthy variances in immune infiltration profiles and responsiveness to immunotherapy. Notably, the model's key gene TGFB3 was validated through comprehensive experimental investigations, including Transwell assays for migration and invasion and Wound healing assays for motility on the T24 and BIU cell lines. This study has furnished innovative perspectives on an efferocytosis-related prognostic signature, elucidating the prognosis and immune milieu intricacies in patients with bladder cancer.

In silico approach uncovers the shared genetic landscape of type 2 diabetes mellitus and asthenozoospermia

Asthenozoospermia (AZS) is one of the most common types of male infertility. Current evidence revealed that type 2 diabetes mellitus (T2DM) is closely associated with declining semen quality, especially for poor sperm motility. This study aimed to uncover the genetic interrelationships and important biomarkers between AZS and T2DM. Transcriptome data regarding AZS and T2DM were downloaded from the Gene Expression Omnibus (GEO) database. We performed GO and pathway analysis, and protein-protein interaction (PPI) network construction for T2DM-related differentially expressed genes (DMRGs). Moreover, we calculated receiver operator characteristic (ROC) curve and conducted external independent validation. Expression of hub DMRGs was assessed for patients using the qPCR method. MiRNA interaction and immune infiltration were subsequently characterized. A total of 554 overlapping DMRGs were identified between the AZS/T2DM and healthy groups. These overlapping DMRG participated in the DNA damage-, energy metabolism-, and immune-related biological pathways. Module function analysis discovered that the top three PPI modules were tightly correlated with DNA damage-related processes. After external validation in other independent datasets, two hub DMRGs (TBC1D12 and SCG5) were obtained. ROC analysis revealed that TBC1D12 and SCG5 had good diagnostic performance (area under the curve > 0.75). Immune infiltration profile showed that the level of T cell co-stimulation and CD8+_T_cells were negatively related to the hub DMRGs expression. Mirna interaction analysis showed 15 significant hub DMRGs-miRNA interactions. The qPCR results showed that expression of TBC1D12 and SCG5 were significantly different between sperm samples from diabetic patients with AZS and controls. The present study revealed molecular signatures and critical pathways between the AZS and T2DM, and identified two hub DMRGs of TBC1D12 and SCG5. The data would provide novel understandings of shared pathogenic mechanisms in T2DM-associated AZS.

Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer.

The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell-cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand-receptor interactions within the TME unveiled significant variations in cell-cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.

Key Disease-Related Genes and Immune Cell Infiltration Landscape in Inflammatory Bowel Disease: A Bioinformatics Investigation.

Inflammatory Bowel Diseases (IBD), which encompass ulcerative colitis (UC) and Crohn's disease (CD), are characterized by chronic inflammation and tissue damage of the gastrointestinal tract. This study aimed to uncover novel disease-gene signatures, dysregulated pathways, and the immune cell infiltration landscape of inflamed tissues. Eight publicly available transcriptomic datasets, including inflamed and non-inflamed tissues from CD and UC patients were analyzed. Common differentially expressed genes (DEGs) were identified through meta-analysis, revealing 180 DEGs. DEGs were implicated in leukocyte transendothelial migration, PI3K-Akt, chemokine, NOD-like receptors, TNF signaling pathways, and pathways in cancer. Protein-protein interaction network and cluster analysis identified 14 central IBD players, which were validated using eight external datasets. Disease module construction using the NeDRex platform identified nine out of 14 disease-associated genes (CYBB, RAC2, GNAI2, ITGA4, CYBA, NCF4, CPT1A, NCF2, and PCK1). Immune infiltration profile assessment revealed a significantly higher degree of infiltration of neutrophils, activated dendritic cells, plasma cells, mast cells (resting/activated), B cells (memory/naïve), regulatory T cells, and M0 and M1 macrophages in inflamed IBD tissue. Collectively, this study identified the immune infiltration profile and nine disease-associated genes as potential modulators of IBD pathogenesis, offering insights into disease molecular mechanisms, and highlighting potential disease modulators and immune cell dynamics.

Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning.

This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to identify novel gene biomarkers and explore immune cell infiltration profiles associated with chronic obstructive pulmonary disease (COPD). Utilizing an integrated analysis of metadata encompassing six gene expression omnibus (GEO) microarray datasets, 987 differentially expressed genes were identified. Further gene ontology and pathway enrichment analyses revealed the enrichment of these genes across various biological processes and pathways. Moreover, a systematic integration of two machine learning algorithms along with pathway-gene correlations identified six candidate biomarkers, which were validated in a separate cohort comprising six additional microarray datasets, ultimately identifying ADD3 and GNAS as diagnostic biomarkers for COPD. Subsequently, the diagnostic efficacy of ADD3 and GNAS was assessed, and the impact of their expression levels on overall survival was further evaluated and quantified in the validation cohort. Examination of immune cell subtype infiltration found increased proportions of cytotoxic CD8+ T cells, resting and activated NK cells, along with decreased M0 and M2 macrophages, in COPD versus control samples. Correlation analyses also uncovered significant associations between ADD3 and GNAS expression and infiltration of various immune cell types. In conclusion, this study elucidates crucial COPD diagnostic biomarkers and immune cell profiles which may illuminate the immunopathological drivers of COPD progression, representing personalized therapeutic targets warranting further investigation.

Expression and functional analyses of TERF2 in esophageal carcinoma

BackgroundEsophageal cancer (ESCA) is a prevalent malignancy with a high incidence of morbidity and mortality, particularly in Asia. Telomeric Repeat-binding Factor 2 (TERF2) is a crucial component of the telomere-binding protein complex that maintains telomere stability. Aberrant TERF2 expression has been implicated in tumorigenesis, however, its specific role in ESCA remains largely unexplored. MethodsThe expression levels of TERF2 were assessed in esophageal squamous cell carcinoma (ESCC) samples using RT-PCR, IHC, and Western blotting (WB). Serum tumor marker concentrations were determined via electrochemiluminescence immunoassay (ECLIA) and chemiluminescent microparticle immunoassay (CMIA). Bioinformatics analyses were employed to elucidate TERF2's function in EC. The impact of TERF2 on ESCC cell proliferation was evaluated through cell counting kit-8 (CCK8) assays and flow cytometry. ResultsTERF2 protein and mRNA expression were elevated in ESCC tissues and correlated with age, sex, cancer stage, tumor grade, lymph node metastasis (LNM), and tumor histology. Univariate Cox regression analysis indicated TERF2 was an independent prognostic factor for overall survival (OS). TERF2 mRNA levels were associated with serum levels of carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), and tissue polypeptide antigen (TPA) in patients with ESCC. Immune infiltration and chemokine profiles were linked to TERF2 expression in ESCA. TERF2 is involved in regulating ESCC cell proliferation may through the DDR/P53 signaling way. ConclusionsTERF2 is overexpressed in ESCA and contributes to ESCC cell proliferation may via DDR/TP53 signaling pathway. These results suggest that TERF2 may serve as a potential target for developing treatments and diagnostic biomarker for ESCA.

Immune checkpoint gene signature assesses immune infiltration profiles in bladder cancer and identifies KRT23 as an immunotherapeutic target

BackgroundIn the past few decades, researchers have made promising progress, including the development of immune checkpoint inhibitors (ICIs) in the therapy of bladder cancer (BLCA). Existing studies mainly focus on single immune checkpoint inhibitors but lack relevant studies on the gene expression profiles of multiple immune checkpoints.MethodsRNA-sequencing profiling data and clinical information of BLCA patients and normal human bladder samples were acquired from the Cancer Genome Atlas and Gene Expression Omnibus databases and analyzed to identify different expression profiles of immune checkpoint genes (ICGs) after consensus clustering analysis. Based on the 526 intersecting differentially expressed genes, the LASSO Cox regression analysis was utilized to construct the ICG signature.ResultsAccording to the expression of ICGs, BLCA patients were divided into three subtypes with different phenotypic and mechanistic characteristics. Furthermore, the developed ICG signature were independent predictors of outcome in BLCA patients, and was correlated with the immune infiltration, the expression of ICGs and chemotherapeutic effect.ConclusionsThis study systematically and comprehensively analyzed the expression profile of immune checkpoint genes, and established the ICG signature to investigate the differences in ICGs expression and tumor immune microenvironment, which will help risk stratification and accelerate precision medicine. Finally, we identified KRT23 as the most critical model gene, and highlighted KRT23 as a potential target to enhance immunotherapy against BLCA.

Predicting CTLA4 expression and prognosis in clear cell renal cell carcinoma using a pathomics signature of histopathological images and machine learning

BackgroundCTLA4, an immune checkpoint, plays an important role in tumor immunotherapy. The purpose of this study was to develop a pathomics signature to evaluate CTLA4 expression and predict clinical outcomes in clear cell renal cell carcinoma (ccRCC) patients. MethodsA total of 354 patients from the TCGA-KIRC dataset were enrolled in this study. The patients were stratified into two groups based on the level of CTLA4 expression, and overall survival rates were analyzed between groups. Pathological features were identified using machine learning algorithms, and a gradient boosting machine (GBM) was employed to construct the pathomics signatures for predicting prognosis and CTLA4 expression. The predictive performance of the model was subsequently assessed. Enrichment analysis was performed on diferentially expressed genes related to the pathomics score (PS). Additionally, correlations between PS and TMB, as well as immune infiltration profiles associated with different PS values, were explored. In vitro experiments, CTLA4 knockdown was performed to investigate its impact on cell proliferation, migration, invasion, TGF-β signaling pathway, and macrophage polarization. ResultsHigh expression of CTLA4 was associated with an unfavorable prognosis in ccRCC patients. The pathomics signature displayed good performance in the validation set (AUC = 0.737; P < 0.001 in the log-rank test). The PS was positively correlated with CTLA4 expression. We next explored the underlying mechanism and found the associations between the pathomics signature and TGF-β signaling pathways, TMB, and Tregs. Further in vitro experiments demonstrated that CTLA4 knockdown inhibited cell proliferation, migration, invasion, TGF-β expression, and macrophage M2 polarization. ConclusionHigh expression of CTLA4 was found to correlate with poor prognosis in ccRCC patients. The pathomics signature established by our group using machine learning effectively predicted both patient prognosis and CTLA4 expression levels in ccRCC cases.

Polygenic TB control and the sequence of innate/adaptive immune responses to infection: MHC-II alleles determine the size of the S100A8/9-producing neutrophil population.

Among several quantitative trait loci involved in tuberculosis (TB) control in mice, one was mapped within the chromosome 17 segment occupied by the H2 complex and another within the chromosome 3 segment comprising the S100A8/9 genes, which encode neutrophil inflammatory factor S100A8/9. Previously, we developed a panel of H2-congenic mouse strains differing by small segments of the major histocompatibility complex Class II (MHC-II) region from TB-susceptible H2j mice transferred onto the genetic background of the TB-resistant C57BL/6 (H2b) strain. Susceptible B6.I-9.3 mice differ from B6 progenitors by the alleles of their only classical MHC-II H2-Aβ gene. The goals of the present study were to: (i) comprehensively characterise the differences in TB-related phenotypes between mice of the two strains and (ii) decipher interactions between the H2-Aβ and S100A8/9 genes. Here, we describe the dynamics of TB-related phenotypes differentiating B6.I-9.3 and B6 mice (colony forming unitscounts, histopathology, lung immune cell infiltration and cytokine profiles). We show that disproportionally diminished CD4+ T-cell population, an enlarged S100A8/9-positive neutrophil population and higher S100A8/9 serum levels in B6.I-9.3 mice collectively form the 'susceptible' phenotype before infection. An increase in IL-17 and a decrease in intrferon-gamma production by CD4+ T-cells in these mice provide a mechanistic explanation of this phenotype. Using F2 segregation analysis, we show that the number of S100A8/9-producing neutrophils in lungs and spleens and the proportion of Th17 CD4+ T-cells in lungs are significantly lower in the presence of the MHC-II dominant 'resistant' b allele compared to the recessive 'susceptible' j/j genotype. This provides direct genetic evidence that MHC-II-regulated CD4+ T-cell landscapes determine neutrophil abundance before infection, an important pathogenic factor in TB immunity.

FGFR2 fusion/rearrangement is associated with favorable prognosis and immunoactivation in patients with intrahepatic cholangiocarcinoma.

Increasing evidence highlights that fibroblast growth factor receptor 2 (FGFR2) fusion/rearrangement shows important therapeutic value for patients with intrahepatic cholangiocarcinoma (ICC). This study aims to explore the association of FGFR2 status with the prognosis and immune cell infiltration profiles of patients with ICC. A total of 226 ICC tissue samples from patients who received surgery at the Department of Liver Surgery at Zhongshan Hospital, Fudan University, were collected retrospectively and assigned to a primary cohort (n = 152) and validation cohort (n = 74) group. Fluorescence in situ hybridization was performed to determine FGFR2 status. Multiplex immunofluorescence (mIF) staining and immunohistochemistry were performed to identify immune cells. Thirty-two (14.2%) ICC tissues presented with FGFR2 fusion/rearrangement. FGFR2 fusion/rearrangement was associated with low levels of carcinoembryonic antigen (CEA, P = .026) and gamma glutamyl transferase (γ-GGT, P = .003), low TNM (P = .012), CNLC (P = .008) staging as well as low tumor cell differentiation (P = .016). Multivariate COX regression analyses revealed that FGFR2 fusion/rearrangement was an independent protective factor for both overall survival (OS) and relapse-free survival in patients with ICC. Furthermore, correlation analysis revealed that an FGFR2 fusion/rearrangement was associated with low levels of Tregs and N2 neutrophils and high levels of N1 neutrophils infiltrating into tumors but not with CD8+ T-cell or macrophage tumor infiltration. FGFR2 fusion/rearrangement may exert a profound impact on the prognosis of ICC patients and reprogram the tumor microenvironment to be an immune-activated state. FGFR2 status may be used for ICC prognostic stratification and as an immunotherapeutic target in patients with ICC.

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma

BackgroundThe current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited.MethodsWe conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared.ResultsThe BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group.ConclusionsThis study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis.

Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis. Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes. By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p=2e-19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes. This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches

Background Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited. Methods In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers’ involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA. Results We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity. Conclusions Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes

BackgroundMajor depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. MethodsTranscriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the “Enrichr” platform was utilized to identify potential drugs for MDD. ResultsTotally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. ConclusionOur work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

Identification of driving genes of familial adenomatous polyposis by differential gene expression analysis and weighted gene co-expression network analysis.

Despite the advancement of new screening strategies and the advances in pharmacological therapies, the cancerization rates of familial adenomatous polyposis (FAP) are stable and even increased in the last years. Therefore, it necessitates additional research to characterize and understand the underlying mechanisms of FAP. To determine the genes that drive the pathogenesis of familial adenomatous polyposis (FAP). We performed on a cohort (GSE111156) gene profile, which consist of four group of gene expressions (the gene expressions of cancer, adenoma and normal tissue of duodenal cancer from patients with FAP were defined as Case N, Case A and Case C respectively, while that of adenoma tissue from patients with FAP who did not have duodenal cancer was Ctrl A). Tracking Tumor Immunophenotype (TIP) website was applied to reveal immune infiltration profile and signature genes of FAP. We merged the genes of key module (pink and midnight module) with signature genes to obtained the biomarkers related with FAP pathogenesis. The expression of these five biomarkers in FAP intratumoral region (IT) and tumor rim (TR) was detected with Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). In total, 220, 23 and 63 DEGs were determined in Cases C, A and N, in comparison to Ctrl A. In total, 196 and 10 DEGs were determined in Cases C and A, separately, as compared to Case N. A total of four biomarkers including CCL5, CD3G, CD2 and TLR3 were finally identified associated with pink module, while only one biomarker (KLF2) associated with midnight module was identified. All biomarkers were evidently raised in FAP IT tissues utilizing qRT-PCR. We identified five potential biomarkers for pathogenesis of FAP to understand the fundamental mechanisms of FAP progression and revealed some probable targets for the diagnosis or treatment of FAP.

Immune cell distribution and DNA methylation signatures differ between tumor and stroma enriched compartment in pancreatic ductal adenocarcinoma

The presence of abundant tumor stroma is a prominent characteristic of pancreatic ductal adenocarcinomas (PDAC) that potentially influences disease progression and therapy response. This study aims to investigate immune cell infiltration and epigenetic profiles in tumor cell enriched (“Tumor”) and stroma cell enriched (“Stroma”) regions within human PDAC tissue samples. By comparing those regions, we identified 25,410 differentially methylated positions (DMPs) distributed across 6,963 unique genes. Pathway enrichment analysis using the top 2,000 DMPs that were either hyper- or hypomethylated indicated that immune response pathways and the estrogen receptor pathway are epigenetically dysregulated in Tumor and Stroma regions, respectively. In terms of immune cell infiltration, we observed overall low levels of T cells in both regions. In Tumor regions however, occurrence of tumor-associated macrophages (TAMs) was higher than in Stroma regions (p = 0.02) concomitant with a dualistic distribution that stratifies PDAC patients into those with high and low TAM infiltration. By categorizing TAM levels into quartiles, our analysis revealed that PDAC patients with more than 1,515 TAMs per mm² exhibited significantly shorter overall survival (p = 0.036). Our data suggest that variations in inflammatory characteristics between the Tumor and Stroma defined compartments of PDAC may primarily stem from the presence of macrophages rather than lymphocytes. The abundance of TAMs within regions enriched with tumor cells correlates with patient survival, underscoring the potential significance of exploring therapeutic interventions targeting TAMs. Furthermore, directing attention towards the estrogen receptor pathway may represent a promising strategy to address the stroma cell component within the PDAC tumor microenvironment.

Deciphering the prognostic landscape of osteosarcoma: Integrating the roles of hippo pathway genes, programmed cell death, and the tumor immune microenvironment.

Osteosarcoma is a highly aggressive cancer prevalent among adolescents and young adults, notorious for its tendency to metastasize to the lungs. This research delves into the molecular foundations of osteosarcoma by examining the role of the Hippo signaling pathway and its interaction with the tumor immune microenvironment (TME). Through analysis of transcriptomic data from the TARGET-OS dataset and control samples from GTEx, we identified a set of 131 genes that link high expression profiles in osteosarcoma with the Hippo pathway. A focused examination through univariate Cox regression analysis revealed eight key genes (DLG5, WNT11, TGFB2, DLG4, WNT16, ID2, WNT10B, and WNT10A) with a significant correlation to patient outcomes. Hierarchical clustering of these genes delineated two distinct patient groups with significantly different survival rates, a finding supported by Kaplan-Meier survival analysis. Further investigation into immune cell infiltration and expression profiles of immunoregulatory factors uncovered a notable pattern of immune evasion in the group with poorer prognosis, marked by reduced effector immune cell activity and lower levels of immunostimulatory factors. Single-cell sequencing highlighted the cellular diversity within osteosarcoma samples and identified markers differentiating malignant from nonmalignant cells, correlating these markers with prognostic risk scores. Our results emphasize the critical prognostic value of Hippo pathway genes and the TME in osteosarcoma, shedding light on new avenues for therapeutic intervention and patient-specific treatment strategies.