Identification of the Hub Genes Associated with Sarcoma Through Integrative Analysis of TCGA and GEO Data

다국어 초록 (Multilingual Abstract)

Background: Sarcomas are rare mesenchymal malignancies originating from connective tissues and are generally associated with poor prognosis. Previous bioinformatics studies have often relied on a single database, limiting generalizability. This study aimed to identify novel hub genes associated with sarcoma using integrative analysis of GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets. Methods: Differential gene expression (DEG) analysis was performed using integrated data from TCGA and GEO. Functional enrichment analyses and protein–protein interaction (PPI) network construction were conducted, and hub genes were identified based on node connectivity. Survival analysis was performed using the Kaplan–Meier method.
Results: After identifying 47 overlapping DEGs from the analysis of 261 TCGA samples and 149 GEO samples, the GO (Gene Ontology) enrichment analysis revealed associations with cell adhesion, plasma membrane components, and calcium ion binding.
Moreover, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis showed that target genes were mainly involved in the chemical carcinogenesis-receptor activation. Then, 29 genes were screened through the PPI network. With calculating protein nodes, eight genes (BCL2, EMCN, CLDN5, LYVE1, CD36, CD93, VWF, and CDH5) were screened from TCGA and GEO datasets. Comparing survival analysis outcomes among these eight genes, highly expressed CD36 (HR=0.627, log-rank p value=0.0202) was identified as being associated with improved survival outcomes in sarcoma patients. Conclusions: CD36 may serve as a candidate prognostic biomarker or survival-associated hub gene in patients with sarcoma. However, further validation is required to clarify its clinical relevance