Introduction: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer. Methods: ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs. Results: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes. Conclusion: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Microarray data enables biologists to extract differentially expressed genes (DEGs) across multiple phenotypes. While several pipelines and tools exist to perform microarray data analysis, they are targeted to users with moderate to advanced computational understanding and lack an easy-to-use, interactive and dynamic methodology to perform analysis assisted with comprehensive learning resources. In this study, we developed an interactive application “sMAP” (Standard Microarray Analysis Pipeline) to make transcriptome microarray data analysis more accessible in learning environments and to enable the identification of significant pathological biomarkers. In a case study of colorectal cancer, we showed that sMAP enabled us to reproduce previous findings and discover relevant pathways. sMAP provides a comprehensive set of tutorials and learning documentation to help early-stage researchers. The latest URLs of sMAP’s hosting, tutorial, and frequently updated documentation can be found at https://github.com/BI-STEM-Away/sMAP .

Background: Gastric cancer (GC) remains a leading cause of global cancer mortality, with late-stage diagnosis contributing significantly to poor patient outcomes. Circulating microRNAs (miRNAs) offer promise due to their stability in biofluids and established roles in carcinogenesis. However, existing miRNA biomarker candidates for GC suffer from inconsistent validation across studies, limited specificity, and insufficient mechanistic links to gastric tumor biology. We addressed this by integrating tissue and blood transcriptomics to identify GC-specific miRNAs, which were then validated using machine learning. Methods: Dysregulated genes (DEGs) and miRNAs (DEMs) were identified from tissue mRNA (GSE54129, GSE113255) and blood miRNA/mRNA datasets (GSE106817, GSE174302). Pathway enrichment (Reactome) revealed GC-specific pathways shared between tissue DEGs and blood DEM targets. Targets of 59 DEMs were enriched in these pathways in the blood miRNA dataset. From these, a 5-miRNA panel was selected using 10 machine learning feature selection methods (e.g., Gini Index, Information Gain) and validated using Random Forest and Naïve Bayes classifiers on discovery (GSE106817) and external (GSE164174) datasets. Results: Integration identified 59 GC-specific extracellular miRNAs linked to 39 enriched pathways (e.g., signaling, metabolism). The 5-miRNA panel (hsa-miR-124-3p, hsa-miR-23a-3p, hsa-miR-22-3p, hsa-miR-29b-3p, hsa-miR-92a-3p) achieved near-perfect discovery performance (RF: AUC=98.50%, ACC=98.36%) and high external validation (AUC=95.30%, ACC=89.24%). Conclusion: Our pipeline bridges tissue pathology and circulating miRNA profiles, yielding a highly specific 5-miRNA Blood Signature with clinical diagnostic potential for GC.