Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Abstract

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

A Ductal-Cell-Related Risk Model Integrating Single-Cell and Bulk Sequencing Data Predicts the Prognosis of Patients With Pancreatic Adenocarcinoma

“…To further validate the predictive power of the ductal cell risk model, we compared it with three recently published pancreatic cancer risk scoring models, including the Deng model ( Deng et al, 2021 ), the Qiu model ( Qiu et al, 2020 ), and the Wu model ( Wu et al, 2019 ). Notably, these three models were all built on bulk sequencing data.…”

Three Immune-Related Prognostic mRNAs as Therapeutic Targets for Pancreatic Cancer

“…Gene-based markers have been widely explored for pancreatic cancer in recent years ( 20 ). Recently, several prognosis-related gene signatures have been established for pancreatic cancer ( 21 – 23 ). For example, Zhuang et al developed a prognosis-related lncRNA signature for pancreatic cancer ( 24 ).…”

Development of epithelial-mesenchymal transition-related lncRNA signature for predicting survival and immune microenvironment in pancreatic cancerwithexperiment validation

“…Transcriptome data models are the most common models. Differentially expressed genes between normal and tumor tissues [ 17 ] and specific gene set, such as immune-related gene set [ 18 ] and oxidative stress-related gene set [ 19 ], were used to construct risk model to predict poor outcomes. Alizadeh et al .…”