Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Abstract

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4+ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

Redox high phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma

“…Clarifying the interplay between genetic alterations and metabolic changes, and their collective role in immune evasion, is pivotal for the development of effective therapies to bypass immunotherapy resistance. Previous studies has found a link between high redox signatures, 24–27 mutations in genes associated with redox homeostasis (such as STK11 or KEAP1), 28–30 and adverse outcomes in lung cancer. However, the dynamic between metabolic states, specific genetic alterations, and TME composition in LUAD has not been fully elucidated.…”

Construction and validation of a prognostic model for tongue cancer based on three genes signature

“…Recently, an increasing number of molecular biomarkers have been integrated into models for the prognostic assessment of cancer patients. [4–8] However, the prognostic features and nomographs of multiple mRNA in TSCC patients have not been sufficiently explored.…”

“…Recently, an increasing number of molecular biomarkers have been integrated into models for the prognostic assessment of cancer patients. [4][5][6][7][8] However, the prognostic features and nomographs With the development of second-generation sequencing technology, attention has gradually turned to the analysis and alignment of high-throughput sequencing data, leading to the establishment of The Cancer Genome Atlas (TCGA) database, which provides an avenue for tumor-related research through the mining and analysis of genomic data on cancer genes. Initiated and completed by the National Cancer Institute and the National Human Genome Research Institute in the United States, TCGA is a genomic variation map of human tumors obtained through large-scale sequencing of the human tumor genome, containing information on genomics, transcriptomics, epigenetics, proteomics, as well as clinical information.…”