A Novel Model Based on Necroptosis-Related Genes for Predicting Prognosis of Patients With Prostate Adenocarcinoma

Abstract

Background: Necroptosis is a newly recognized form of cell death. Here, we applied bioinformatics tools to identify necroptosis-related genes using a dataset from The Cancer Genome Atlas (TCGA) database, then constructed a model for prognosis of patients with prostate cancer.Methods: RNA sequence (RNA‐seq) data and clinical information for Prostate adenocarcinoma (PRAD) patients were obtained from the TCGA portal (http://tcga-data.nci.nih.gov/tcga/). We performed comprehensive bioinformatics analyses to identify hub genes as potential prognostic biomarkers in PRAD u followed by establishment and validation of a prognostic model. Next, we assessed the overall prediction performance of the model using receiver operating characteristic (ROC) curves and the area under curve (AUC) of the ROC.Results: A total of 5 necroptosis-related genes, namely ALOX15, BCL2, IFNA1, PYGL and TLR3, were used to construct a survival prognostic model. The model exhibited excellent performance in the TCGA cohort and validation group and had good prediction accuracy in screening out high-risk prostate cancer patients.Conclusion: We successfully identified necroptosis-related genes and constructed a prognostic model that can accurately predict 1- 3-and 5-years overall survival (OS) rates of PRAD patients. Our riskscore model has provided novel strategy for the prediction of PRAD patients’ prognosis.

A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma

“…2 F), which further validated the effectiveness and high accuracy of the prognosis model. Compared with other relevant reported risk models [19] , [20] , [21] , our risk score model had better effect and was robust ( Figure S2 B-S2D). Simultaneously, to avoid the potential influence of down-regulated gene (FAM107A), we also analyzed another 7 up-regulated genes, and similar results could be found ( Figure S2 E-S2F).…”

Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils the Characteristics of the Immune Microenvironment and Prognosis Signature in Prostate Cancer

“…Moreover, a significant difference was observed in the chemotherapy response to the other 83 drugs, although they are not yet approved for the treatment of PRAD. Despite the advances in PRAD treatment, therapeutic options for PRAD remain limited [ 124 , 125 ]. These 83 drugs can be considered a pool when looking for novel therapies.…”

Establishment and validation of a clinicopathological prognosis model of gastroenteropancreatic neuroendocrine carcinomas

“…proved that colorectal mixed adenocarcinoma neuroendocrine carcinoma and neuroendocrine carcinoma are genetically closely related to colorectal adenocarcinoma through gene sequencing ( 30 ). Gene technology is also widely used not only in the research of neuroendocrine tumors but also in other disease fields ( 31 – 33 ). The advantage of prediction models related to genomic information is that they can more accurately combine certain diseases with a single gene to achieve the purpose of precise treatment.…”