Pashaei, E. | bioRxiv Science

bioRxiv2025

DiCE: Differential Centrality-Ensemble Analysis Based on Gene Expression Profiles and Protein-Protein Interaction Network

Uncovering key genes that drive diseases and cancers is crucial for advancing understanding and developing targeted therapies. Traditional differential expression analysis often relies on arbitrary cutoffs, missing critical genes with subtle expression changes. Some methods incorporate protein-protein interactions (PPIs) but depend on prior disease knowledge. To address these challenges, we developed DiCE (Differential Centrality-Ensemble), a novel approach that combines differential expression with network centrality analysis, independent of prior disease annotations. DiCE identifies candidate genes, refines them with an information gain filter, and reconstructs a condition-specific weighted PPI network. Using centrality measures, DiCE ranks genes based on expression shifts and network influence. Validated on prostate cancer datasets, DiCE identified genes over-represented in key pathways and cancer fitness genes, significantly correlating with disease-free survival (DFS), despite DFS not being used in selection. DiCE offers a comprehensive, unbiased approach to identifying disease-associated genes, advancing biomarker discovery and therapeutic development. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/638654v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1ce9b81org.highwire.dtl.DTLVardef@14352e1org.highwire.dtl.DTLVardef@f63caeorg.highwire.dtl.DTLVardef@a0086_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2024

CDHu40: a novel marker gene set of neuroendocrine prostate cancer (NEPC)

Prostate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen (PSA) levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor (AR) signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction (PPI) networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named as CDHu40, demonstrated superior performance in distinguishing NE prostate cancer (NEPC) and non-NEPC samples based on gene expression profiles compared to other published marker sets. Notably, some novel marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression. Significanceour study integrates gene expression variances in multiple NEPC studies and protein-protein interaction network to pinpoint a specific set of NEPC maker genes namely CDHu40. These genes and scores based on their gene expression levels effectively distinguish NEPC samples and underscore the clinical prognostic significance and potential mechanism.

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