Abstract
Prostate cancer (PCa) is the fifth leading cause of death from cancer in men worldwide. Its treatment remains challenging due to the heterogeneity of the tumor, mainly because of the lack of effective and targeted prognostic markers at the system biology level. First, the data were retrieved from TCGA dataset, and valid samples were obtained by consistent clustering and principal component analysis; next, key genes were analyzed for prognosis of PCa using WGCNA, MEGENA, and LASSO Cox regression model analysis, while key genes were screened based on disease-free survival significance. Finally, TIMER data were selected to explore the relationship between genes and tumor immune infiltration, and GSCAlite was used to explore the small-molecule targeted drugs that act with them. Here, we used tumor subtype analysis and an energetic co-expression network algorithm of WGCNA and MEGENA to identify a signal dominated by the ROMO1 to predict PCa prognosis. Cox regression analysis of ROMO1 was an independent influence, and the prognostic value of this biomarker was validated in the training set, the validated data itself, and external data, respectively. This biomarker correlates with tumor immune infiltration and has a high degree of infiltration, poor prognosis, and strong correlation with CD8+T cells. Gene function annotation and other analyses also implied a potential molecular mechanism for ROMO1. In conclusion, we putative ROMO1 as a portal key prognostic gene for the diagnosis and prognosis of PCa, which provides new insights into the diagnosis and treatment of PCa.
Highlights
Prostate cancer (PCa) is the fifth leading cause of death from cancer in men worldwide
The R software package Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) (v. 1.3.7) is used to perform MEGENA, which consists of the following steps: (1) constructing a planar filtered network (PFN); firstly, calculating correlation coefficients based on gene expression profiles, and filtering and clustering gene pairs using a parallel filtering method to obtain a fast planar filter network; (2) multi-scale clustering analysis; from the initial PFN of the connected components, multi-scale clustering of each parent cluster can obtain more sub-modules, followed by hierarchical clustering results; (3) downstream analysis, using multiscale hub analysis (MHA) to identify important hubs based on the network topology; (4) the correlation between clustering results and clinical information was analyzed by cluster-trait association analysis (CTA)
20 GO_BP GO:0060485 Mesenchyme development of ROMO1 correlated with immune infiltration of CD8 + T cells, macrophages, and neutrophils; different mutant forms of KLF4A correlated with immune levels of CD8 + T cells, macrophages, neutrophils, and dendritic cells; and we found that ROMO1, Polo-like kinase 1 (PLK1), and KLF4A were found to have higher levels of immune infiltration in dendritic cells (Fig. 10 D–F)
Summary
1.3.7) is used to perform MEGENA, which consists of the following steps: (1) constructing a planar filtered network (PFN); firstly, calculating correlation coefficients based on gene expression profiles, and filtering and clustering gene pairs using a parallel filtering method to obtain a fast planar filter network; (2) multi-scale clustering analysis; from the initial PFN of the connected components, multi-scale clustering of each parent cluster can obtain more sub-modules, followed by hierarchical clustering results; (3) downstream analysis, using multiscale hub analysis (MHA) to identify important hubs based on the network topology; (4) the correlation between clustering results and clinical information was analyzed by cluster-trait association analysis (CTA). To assess the prognostic value of hub genes, Cox regression analysis was used to evaluate the correlation between genes and survival status in a cohort of 494 PCas. we chose the glmnet R language package for LASSO Cox regression modeling to narrow down the candidate genes and build prognostic models[60].
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