Search Results (83)

Quantitative proteomics relies on robust statistical methods for differential expression, critically impacting downstream functional enrichment. This meta-analysis systematically investigated how statistical hypothesis testing approaches and criteria for defining biological relevance influence functional enrichment concordance. We reanalyzed five independent label-free quantitative proteomics datasets using diverse frequentist (t-test, Limma, DEqMS, MSstats) and Bayesian (rstanarm) approaches. Concordance of Gene Ontology (GO) and KEGG pathways was assessed using Jaccard indices and correlation metrics, grouping comparisons by statistical test and biological relevance consistency. The results demonstrated highly significant differences in similarity distributions among the comparison groups. Comparisons varying only hypothesis testing methods (with constant relevance criteria, FC or Bayesian) showed the highest consistency. Conversely, comparisons with differing biological relevance criteria (or varied methodological choices) yielded significantly lower consistency, highlighting this definition’s critical impact on GO term overlaps. KEGG pathways displayed more uniform, method-insensitive concordance. Sensitivity analysis confirmed the findings’ robustness, underscoring that methodological choices profoundly influence functional enrichment outcomes. This work emphasizes the critical need for transparency and careful consideration of analytical decisions in proteomics research to ensure reproducible and biologically sound interpretations. Full article

Sarcopenia and type 2 diabetes mellitus (T2DM) are chronic conditions that gradually affect the elderly, often coexisting and interacting in complex ways. Sarcopenia, which is characterized by the progressive loss of muscle mass and function, is frequently observed in individuals with T2DM. Although the clinical association is well known, the molecular mechanisms remain unclear. Gene expression datasets were retrieved from the Gene Expression Omnibus database. DEGs were identified using the limma package in R (R 4.4.0). Shared DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein–protein interaction networks were constructed using the STRING database and were visualized with Cytoscape. Hub genes were identified via six topological algorithms in the CytoHubba plugin. Pearson’s correlation analysis was conducted between hub genes and selected metabolic regulators. GO and KEGG enrichment analyses indicated that mitochondrial function, oxidative phosphorylation, and immune–inflammatory responses were significantly enriched. A PPI network revealed a mitochondrial hub of five key genes involved in energy metabolism, whose downregulation suggests mitochondrial dysfunction as a shared mechanism in sarcopenia and T2DM. Our results provide new insight into the molecular overlap between T2DM and sarcopenia, highlighting potential biomarkers and therapeutic targets for addressing both metabolic disruption and muscle decline. Full article

Background and Aim: The immunological interactions between soil-transmitted helminths (STHs) and herpes simplex virus type 2 (HSV-2), particularly in the context of co-infection, are poorly understood. Next-generation sequencing (NGS) offers a powerful approach to explore these complex immune responses and uncover potential therapeutic targets. This study leveraged NGS and bioinformatic tools to investigate transcriptional changes and immunological pathways in female genital tract (FGT) tissues of BALB/c mice acutely infected with Nippostrongylus brasiliensis (Nb), HSV-2, or co-infected. Methods: Total RNA was harvested from FGT tissues of BALB/c mice infected with Nb, HSV-2, co-infected with both pathogens, and uninfected controls. Differentially expressed genes (DEGs) were identified by comparing uninfected versus infected FGT tissues in R using edgeR and limma packages. Immune-related genes were identified by intersecting DEGs in each group-wise comparison with immune function gene sets derived from the Mouse Genome Informatics (MGI) database. Functional and pathway enrichment analyses were performed with g: Profiler and protein–protein interaction networks were built using the STRING database and visualized with Cytoscape. Key hub genes and significant gene modules were identified using the Cytoscape plugins CytoHubba and MCODE, followed by further functional analysis of these modules. Results: NGS analysis revealed distinct gene expression profiles in response to single infection with Nb or HSV-2, with both showing significant differences when uninfected controls were compared to infected FGT tissues at a 5% false discovery rate. Notably, there were no significant differences in gene expression profiles between uninfected and co-infected FGT tissues. In the comparison of uninfected versus Nb-infected FGT tissues, 368 DEGs were identified, with 356 genes upregulated and 12 downregulated. Several immune-related genes, such as Ptprc, Ccl11, Ccr2, and Cx3cr1, were significantly altered. Pathway analysis of DEGs, hub genes, and significant modules indicated modulation of immune and defense responses. Notably, Nb infection induced a robust Th2-dominant immune response in the FGT, with downregulation of pro-inflammatory genes. This likely reflects helminth-driven modulation that may impair protective Th1 responses and highlights the systemic impact of Nb on the FGT immunity. In the comparison of uninfected versus HSV-2-infected FGT tissues, 140 DEGs were identified, with 121 upregulated and 19 downregulated. Immune-related genes, including Ldlr, Camk1d, Lrp8 and Epg5, were notably altered. HSV-2 infection led to early and predominant downregulation of immune genes, consistent with viral immune evasion strategies. In addition, functional analysis revealed enrichment in cell cycle and sterol biosynthesis pathways, suggesting that HSV-2 modulates host metabolism to support viral replication while influencing immune responses. In co-infection, no significant transcriptional changes were observed, potentially reflecting immune antagonism where Nb-induced Th2 responses may suppress HSV-2-driven Th1 immune responses. Conclusions: This preliminary study offers insights into the gene expression responses in the FGT to acute single and co-infection with Nb and HSV-2. Together, these findings reveal distinct transcriptomic changes in the FGT following Nb and HSV-2 infection, with co-infection potentially leading to immune antagonism and transcriptional equilibrium. This highlights the complex interplay between helminth- and virus-induced immune modulation in shaping FGT immunity. By leveraging NGS, this study highlights important immune-related pathways and serves as a foundation for further investigations into the mechanistic roles of DEGs in immunity to these pathogens, with potential implications for developing novel therapeutic strategies. Full article

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Background: Gastric cancer (GC) represents a significant global health burden with considerable heterogeneity in clinical and molecular behavior. The anatomical site of tumor origin—proximal versus distal—has emerged as a determinant of prognosis and response to therapy. The aim of this paper is to elucidate the transcriptional and regulatory differences between proximal gastric cancer (PGC) and distal gastric cancer (DGC) through master regulator (MR) analysis. Methods: We analyzed RNA-seq data from TCGA-STAD and microarray data from GEO (GSE62254, GSE15459). Differential gene expression and MR analyses were performed using DESeq2, limma, corto, and RegEnrich pipelines. A harmonized matrix of 4785 genes was used for MR inference following normalization and batch correction. Functional enrichment and survival analyses were conducted to explore prognostic associations. Results: Among 364 TCGA and 492 GEO patients, PGC was associated with more aggressive clinicopathological features and poorer outcomes. We identified 998 DEGs distinguishing PGC and DGC. PGC showed increased FOXM1 (a key regulator of cell proliferation), STAT3, and NF-κB1 activity, while DGC displayed enriched GATA6, CDX2 (a marker of intestinal differentiation), and HNF4A signaling. Functional enrichment highlighted proliferative and inflammatory programs in PGC, and differentiation and metabolic pathways in DGC. MR activity stratified survival outcomes, reinforcing prognostic relevance. Conclusions: PGC and DGC are governed by distinct transcriptional regulators and signaling networks. Our findings provide a biological rationale for location-based stratification and inform targeted therapy development. Full article

Background/Objectives: This study investigated variations in DNA methylation patterns associated with chronic pain and propensity for recurrent pressure injuries (PrI) in persons with spinal cord injury (SCI). Methods: Whole blood was collected from 81 individuals with SCI. DNA methylation was quantified using Illumina genome-wide arrays (EPIC and EPICv2). Comprehensive clinical profiles collected included secondary health complications, in particular current PrI and chronic pain. Relationships between recurrent PrI and chronic pain and whether the co-occurrence of both traits was mediated by changes in DNA methylation were investigated using R packages limma, DMRcate and mCSEA. Results: Three differentially methylated positions (DMPs) (cg09867095, cg26559694, cg24890286) and one region in the micro-imprinted locus for BLCAP/NNAT are associated with chronic pain in persons with SCI. The study cohort was stratified by PrI status to identify any sites associated with chronic pain and while the same three sites and region were replicated in the group with no recurrent PrI, two novel, hypermethylated (cg21756558, cg26217441) sites and one region in the protein-coding gene FDFT1 were identified in the group with recurrent PrI. Gene enrichment and genes associated with specific promoters using MetaScape identified several shared disorders and ontology terms between independent phenotypes of pain and recurrent PrI and interactive sub-groups. Conclusions: DMR analysis using mCSEA identified several shared genes, promoter-associated regions and CGI associated with overall pain and PrI history, as well as sub-groups based on recurrent PrI history. These findings suggest that a much larger gene regulatory network is associated with each phenotype. These findings require further validation. Full article

Background/Objectives: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. Methods: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, n = 108) as the discovery dataset and GSE248612 (RNA-seq, n = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted p < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. Results: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A. The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT17, ATP4A, CHIA, KRT16, and CRABP2. Enrichment analyses revealed involvement in ECM–receptor interaction, PI3K-Akt signaling, EMT, and cell cycle. Conclusions: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, CST1, TIMP1, KRT16, and ATP4A emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer. Full article

Increasing evidence highlights the role of aberrant circadian rhythm gene expression in glioblastoma (GBM) progression, but the impact of the circadian rhythm gene network on GBM molecular profiles and prognosis remains unclear. A total of 1042 GBM samples from six public datasets, TCGA and CGGA, were analyzed, with GBM samples stratified into three circadian core-gene patterns using unsupervised clustering based on the expression profiles of 17 circadian rhythm genes. The Limma R package identified differentially expressed genes (DEGs) among the three patterns, and a secondary clustering system, termed circadian-related gene pattern, was established based on DEGs. A circadian risk score was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, and the efficiency of these patterns and the circadian risk score in distinguishing molecular profiles and predicting prognosis was systematically analyzed. The relationship between the circadian risk score and response to immune or targeted therapy was examined using the GSE78200 and IMvigor210 datasets. The results showed that GBM patients were clustered into three circadian core-gene patterns based on the expression profiles of 17 core circadian genes, with distinct molecular profiles, malignant characteristics, and patient prognoses among the patterns. Thirty-two DEGs among these patterns were identified and termed circadian-related genes, and secondary clustering based on these 32 DEGs classified GBM samples into two circadian-related gene patterns, which also predicted molecular profiles and prognosis. A circadian risk scoring system was established, allowing the calculation of individual risk scores based on the expression of 10 genes, where GBM patients with lower circadian risk scores had prolonged overall survival and less aggressive molecular subtypes, while higher circadian risk scores correlated with better responses to MAPK-targeted therapy. In conclusion, this study established two clustering patterns based on 17 circadian rhythm genes or 32 circadian-related genes, enabling the rapid classification of GBM patients with distinct molecular profiles and prognoses, while the circadian risk scoring system effectively predicted survival, molecular profiles, and therapeutic responses for individual GBM patients, demonstrating that the circadian rhythm gene network can distinguish molecular profiles and prognosis in GBM. Full article

Background: The skin, a complex organ vital for protecting the body against environmental challenges, undergoes a multifaceted wound healing process involving hemostasis, inflammation, proliferation, and remodeling. The transcription factor FOSL1 has been implicated in various cellular processes crucial for wound healing, including cell cycle regulation, differentiation, and apoptosis. We hypothesize that FOSL1 is a key regulator of wound healing processes. Objective: The objective of this study was to investigate the role of FOSL1 in cutaneous wound healing, identify the core signaling pathways involved, and assess FOSL1′s potential as a therapeutic target. Method: We utilized datasets from the Gene Expression Omnibus (GEO) and applied the ‘limma’ package to discern differentially expressed genes (DEGs). We intersected these DEGs with transcription factor-associated genes from the TRRUST database. Subsequently, we constructed Protein–Protein Interaction (PPI) networks via the STRING database. Machine learning algorithms were instrumental in identifying pivotal genes, a finding corroborated through animal modeling and Western blot analysis of tissue samples. To elucidate biological pathway activities from gene expression data, we deployed the ‘PROGENy’ package, complemented by machine learning for precise pathway identification. Furthermore, Gene Set Variation Analysis (GSVA) was executed across Hallmark, biological process (BP), molecular function (MF), and cellular component (CC) categories to deepen our understanding of the wound healing process. Results: Our analysis revealed that FOSL1 is significantly upregulated in wounded skin. The Mitogen-Activated Protein Kinase (MAPK) and Epidermal Growth Factor Receptor (EGFR) pathways were identified as significantly associated with FOSL1. GSVA identifies critical changes in wound healing processes like ‘apical junction’ and ‘epithelial–mesenchymal transition.’ The upregulation of ‘cytoplasm organization’ and ‘response to gravity’ suggests roles in cellular adaptation. Molecular function analysis indicates alterations in ‘cytokeratin filaments’ and ‘growth factor binding,’ which are key for tissue repair. Cellular component shifts in ‘postsynaptic cytosol’ and ‘endoplasmic reticulum’ suggest changes in communication and protein processing. Conclusions: Our study identifies FOSL1 as a potential regulator of cutaneous wound healing through its modulation of cellular signaling pathways, offering novel insights into the molecular control of tissue repair. These findings highlight FOSL1 as a promising therapeutic target to accelerate healing in chronic or impaired wounds. Full article

Background: Chronic kidney disease (CKD) is a global health problem marked by a persistent deterioration in the function of the nephrons and kidneys. To identify novel therapies for CKD, we investigated the molecular targets associated with the initiation and progression of the disease. Methods: The transcriptional profile dataset of GSE42303 was downloaded from GEO (The Gene Expression Omnibus). Utilizing the R package limma, the differentially expressed genes (DEGs) were identified between control (Con) and unilateral ureteral obstruction (UUO) mice. Then, functional enrichment, protein–protein interactions (PPI) and subsequent hub genes were identified by multiple bioinformatics approaches. Further validations of these hub genes were confirmed through the GSE118339 dataset and in vivo experiments. Results: We found 381 DEGs between Con and UUO mice (308 up-regulated genes and 73 down-regulated genes). GO functions and pathway analysis indicated that DEGs were mainly enriched in activities associated with inflammation and fibrosis. The mRNA expressions of nine hub genes were identified and confirmed by dataset GSE118339 and in vivo experiments. Conclusions: The hub genes Fgg, Penk, Ckap4, and Gpc3 may be new prospective targets for the treatment of the initiation and progression of CKD. Full article

Background: The increasing global incidence of breast cancer calls for the identification of new therapeutic targets and the assessment of possible neem-derived inhibitors by means of computational modeling and integrated genomic research. Methods: Originally looking at 59,424 genes throughout 42 samples, we investigated gene expression data from The Cancer Genome Atlas—Breast Cancer (TCGA-BRCA) dataset. We chose 286 genes for thorough investigation following strict screening for consistent expression. R’s limma package was used in differential expression analysis. The leading candidate’s protein modeling was done with Swiss-ADME and Discovery Studio. Molecular docking studies, including 132 neem compounds, were conducted utilizing AutoDock Vina. Results: Among the 286 examined, mitochondrially encoded cytochrome C oxidase III (MT—CO3) turned out to be the most greatly overexpressed gene, showing consistent elevation across all breast cancer samples. Protein modeling revealed a substantial hydrophobic pocket (volume: 627.3 ų) inside the structure of MT—CO3. Docking investigations showed five interesting neem-derived inhibitors: 7-benzoylnimbocinol, nimolicinol, melianodiol, isonimocinolide, and stigmasterol. Strong binding affinities ranging from −9.2 to −11.5 kcal/mol and diverse interactions with MT—CO3, mostly involving the residues Phe214, Arg221, and Trp58, these molecules displayed. With hydrophobic interactions dominant across all chemicals, fragment contribution analysis revealed that scaffold percentage greatly influences binding effectiveness. Stigmasterol revealed greater drug-likeness (QED = 0.79) despite minimal interaction variety, while 7-benzoylnimbocinol presented the best-balanced physicochemical profile. Conclusion: Connecting traditional medicine with current genomics and computational biology, this work proposes a methodology for structure-guided drug design and development using neem-derived chemicals and finds MT—CO3 as a potential therapeutic target for breast cancer. Full article

Unstable atherosclerotic plaques are a major cause of acute cardiovascular events and ischemic stroke. Clinical studies have suggested a link between periodontitis and atherosclerotic plaque progression, but the underlying mechanisms remain unclear. To investigate this, transcriptomic datasets related to periodontitis and atherosclerosis were downloaded from Gene Expression Omnibus. A weighted gene co-expression network analysis was used to identify gene modules associated with periodontitis, and the Limma R package identified differentially expressed genes (DEGs) between unstable and stable plaques. Overlapping genes were defined as periodontitis-related DEGs, followed by functional enrichment analysis and protein–protein interaction network construction. Machine learning methods were used to identify biomarkers for unstable plaques related to periodontitis, which were validated using external datasets. Immune infiltration and single-cell analyses were performed to explore the relationship between biomarkers and immune cells. A total of 161 periodontitis-related DEGs were identified, with the pathway analysis showing associations with immune regulation and collagen matrix degradation. HCK, NCKAP1L, and WAS were identified as biomarkers for unstable plaques, demonstrating a high diagnostic value (AUC: 0.9884, 95% CI: 0.9641–1). Immune infiltration analysis revealed an increase in macrophages within unstable plaques. Single-cell analysis showed HCK expression in macrophages and dendritic cells, while NCKAP1L and WAS were expressed in macrophages, dendritic cells, NK cells, and T cells. Consensus clustering identified three expression patterns within unstable plaques. Our findings were validated in atherosclerotic mouse models with periodontitis. This study provides insights into how periodontitis contributes to plaque instability, supporting diagnosis and intervention in patients with periodontitis. Full article

Background: Recent research indicates that lipid metabolism and autophagy play crucial roles in the development of Alzheimer’s disease (AD). Investigating the relationship between AD diagnosis and gene expression related to lipid metabolism, autophagy, and lipophagy may improve early diagnosis and the identification of therapeutic targets. Methods: Transcription datasets from AD patients were obtained from the Gene Expression Omnibus (GEO). Genes associated with lipid metabolism, autophagy, and lipophagy were sourced from the Gene Set Enrichment Analysis (GSEA) database and the Human Autophagy Database (HADb). Lipophagy-related hub genes were identified using a combination of Limma analysis, weighted gene co-expression network analysis (WGCNA), and machine learning techniques. Based on these hub genes, we developed an AD risk prediction nomogram and validated its diagnostic accuracy using three external validation datasets. Additionally, the expression levels of the hub genes were assessed through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Our analysis identified three hub genes—ACBD5, GABARAPL1, and HSPA8—as being associated with AD progression. The nomogram constructed from these hub genes achieved an area under the curve (AUC) value of 0.894 for AD risk prediction, with all validation sets yielding AUC values greater than 0.8, indicating excellent diagnostic efficacy. qRT-PCR results further corroborated the associations between these hub genes and AD development. Conclusions: This study identified and validated three lipophagy-related hub genes and developed a reliable diagnostic model, offering insights into the pathology of AD and facilitating the diagnosis of AD patients. Full article

Background: Osteoarthritis (OA) is a prevalent degenerative joint disease that causes disability and diminishes quality of life. The pathogenesis of OA remains poorly understood, creating an urgent need for biomarkers to aid research, diagnosis, and treatment. Methods: This study integrated transcriptome data from the GEO database with bioinformatics analyses to identify biomarkers associated with OA. The bioinformatics methods utilized include the Limma package, WGCNA, PPI network analysis, and machine learning algorithms. Genetic variants were used as instrumental variables to evaluate the potential causal impact of circulating white blood cell (WBC) counts on OA. Data sources encompassed the largest genome-wide analysis for OA and a comprehensive GWAS summary for circulating WBC counts. Four mendelian randomization (MR) methods were employed to investigate the genetic association, with a primary focus on findings from the inverse variance-weighted (IVW) method. Results: Total of 233 OA-related genes were identified, showing significant enrichment in pathways associated with WBC function. Key biomarkers, including CD4, CSF1R, and TYROBP, were upregulated in OA samples and exhibited strong diagnostic potential. MR analysis findings provided evidence of a genetic association between elevated neutrophil counts and a reduced risk of OA across sites (IVW: OR = 0.97, 95% CI 0.93–1.00, p = 0.047). Additionally, higher circulating WBC counts, particularly neutrophil counts, were associated with a suggestive decrease in hip OA (WBC IVW: OR = 0.94, 95% CI 0.89–0.99, p = 0.015; neutrophil IVW: OR = 0.93, 95% CI 0.88–0.99, p = 0.017). Conversely, reverse MR analysis found no evidence to support a genetic effect of OA on circulating WBC counts. Conclusion: Our findings suggest that elevated neutrophil counts may offer protective effects against OA, underscoring the interplay between the immune functions and OA pathogenesis. CD4, CSF1R, and TYROBP emerge as promising OA biomarkers, meriting further validation in prospective studies. Full article

Background/Objectives: Colorectal cancer (CRC) frequently co-occurs with intestinal system diseases (ISDs), yet their molecular interplay remains poorly understood. We employed a comprehensive bioinformatics approach to elucidate shared genetic signatures and pathways between CRC and ISDs. Methods: We systematically analyzed 12 microarray and RNA-seq datasets encompassing 989 samples across seven ISDs and CRC. Differentially expressed genes (DEGs) were identified using Limma and DESeq2. Functional enrichment analysis was performed using clusterProfiler. Protein–protein interaction networks were constructed via STRING and visualized with Cytoscape to identify hub genes. Clinical significance of shared genes was further assessed through survival analysis and validated by immunohistochemistry staining of 30 paired CRC–normal tissue samples. Results: Integrating bioinformatics and machine learning approaches, we uncovered 160 shared DEGs (87 upregulated, 73 downregulated), which predominantly enriched cell metabolism, immune homeostasis, gut–brain communication, and inflammation pathways. Network analysis revealed nine key hub proteins linking CRC and ISDs, with seven upregulated (CD44, MYC, IL17A, CXCL1, FCGR3A, SPP1, and IL1A) and two downregulated (CXCL12 and CCL5). Survival analysis demonstrated the prognostic potential of these shared genes, while immunohistochemistry confirmed their differential expression in CRC tissues. Conclusions: Our findings unveil potential biomarkers and therapeutic targets, providing insights into ISD-influenced CRC progression and offering a robust foundation for improved diagnostic and treatment strategies in ISD-associated CRC. Full article