Network centrality-driven TOPSIS approach for prioritizing cancer therapeutic targets

Cancer remains a major global health challenge, underscoring the need to identify novel and effective therapeutic targets. In this study, we constructed a high-confidence cancer protein–protein interaction network and selected the largest connected component, comprising 2564 cancer-associated proteins linked by 20,747 interactions. We then evaluated 11 centrality measures to quantify the node importance. Using the TOPSIS multi-criteria decision-making approach, we ranked 2564 cancer-associated genes and identified the top 1 % (26 genes) as high-priority candidates. Drug–target mapping showed that 21 of these genes were associated with approved, investigational, or experimental drugs, whereas five genes, namely NXF1, CDC5L, MOV10, EP300, and CUL7 had no known therapeutic associations, marking them as unexplored targets. GO and KEGG enrichment analyses indicated roles in transcriptional regulation, RNA processing, ubiquitin-mediated protein degradation, and pathways such as Notch, JAK-STAT, and mRNA surveillance. The perturbations in these themes are increasingly associated with cancer development and progression, highlighting the possible roles of these genes in cancers. Survival analysis across multiple cancer types using TCGA datasets revealed significant prognostic effects: CDC5L was associated with improved survival in acute myeloid leukemia (hazard ratio (HR) = 0.59), EP300 expression correlated with better outcomes in kidney renal clear cell carcinoma (HR = 0.52), and elevated MOV10 expression predicted poor prognosis in kidney renal clear cell carcinoma (HR=2.5), lung adenocarcinoma (HR=1.5), and liver hepatocellular carcinoma (HR=1.5). Overexpression of CUL7 correlated with poor prognosis in colon adenocarcinoma (HR=2), and glioblastoma (HR=1.6). NXF1 showed cancer-type-specific results, associated with better prognosis in cervical cancer (HR=0.53) but poor prognosis in kidney renal clear cell carcinoma (HR=1.4). These findings provide quantitative evidence supporting the biological and clinical relevance of the prioritized genes, and the five untargeted genes emerge as strong candidates for future experimental validation through CRISPR-based perturbation, gene silencing, and functional phenotypic assays. Overall, this integrative TOPSIS-network framework offers a robust and reproducible strategy for uncovering both established and novel therapeutic targets, expanding the landscape for precision oncology.