Back

Comprehensive Pan-Cancer Analysis Reveals the Prognostic, Molecular, and Immunological Significance of EPS8

Juoairia, A.; Akter, A.; Nima, R. J.; Haque, M. A.

2026-09-16 cancer biology
10.64898/2026.09.13.751329 bioRxiv
Show abstract

Epidermal Growth Factor Receptor Pathway Substrate 8 (EPS8) is believed to function as a tumor driver; however, to understand its molecular characteristics across tumors, a comprehensive pan-cancer analysis of EPS8 is lacking. Objective: This study aimed to investigate the prognostic, molecular, and immunological significance of EPS8 across multiple human cancers. Methods: Comprehensive analyses were conducted using GEPIA2, UALCAN, TIMER2.0, cBioPortal, SMART, GSCA, Enrichr, and the TCGAplot R package to evaluate survival prognosis, gene expression, DNA methylation, immune infiltration, proteomic expression, tumor mutational burden (TMB), microsatellite instability (MSI), genetic alterations, drug sensitivity, and enriched pathways. Results: EPS8 overexpression in LGG (p=7.2x10-5) and PAAD (p=9.4x10-6) was significantly associated with poor overall survival and disease-free survival. Amplification was the most common genetic alteration, with alteration frequencies approaching 6% in TGCT and UCEC. In KIRC and PAAD, EPS8 expression correlated with tumor stage. Immune infiltration analysis revealed significant associations between EPS8 expression and immune cells. EPS8 expression also showed significant correlations with both TMB and MSI in STAD and ESCA. Enrichment analysis indicated an association between EPS8 and regulation of the actin cytoskeleton and similar pathways. Conclusion: These findings suggest that EPS8 has prognostic and immunological significance across multiple cancer types and may serve as a potential biomarker. The observed associations with immune-related features and drug response provide a basis for future experimental studies to evaluate its role in cancer biology and its potential relevance to immunotherapy.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.