Identification of the Signature Genes and Network of Reactive Oxygen Species Related Genes and DNA Repair Genes in Lung Adenocarcinoma

Abstract

Reactive Oxygen Species (ROS) are present in excess amounts in patients with tumors, and these ROS can kill and destroy tumor cells. Therefore, tumor cells upregulate ROS-related genes to protect them and reduce their destructing effects. Cancer cells already damaged by ROS can be repaired by expressing DNA repair genes consequently promoting their proliferation. The present study aimed to identify the signature genes of and regulating network of ROS-related genes and DNA repair genes in lung adenocarcinoma (LUAD) using transcriptomic data of public databases and our own developed clinical data. The LUAD transcriptome data in the TCGA database and gene expressions from Gene Expression Omnibus (GEO) were analyzed and samples were clustered into 5 ROS-related categories and 6 DNA repair categories. Survival analysis revealed a significant difference in patient survival between the two classification methods. In addition, the samples corresponding to the two categories overlap, thus, the gene expression profile of the same sample with different categories and survival prognosis was further explored, and the connection between ROS-related and DNA repair genes was investigated. The interactive sample recombination classification was used, revealing that the patient's prognosis was worse when the ROS-related and DNA repair genes were expressed at the same time. The further research on the potential regulatory network of the two categories of genes and the correlation analysis revealed that ROS-related genes and DNA repair genes have a mutual regulatory relationship. The ROS-related genes namely NQO1, TXNRD1, and PRDX4 could establish links with other DNA repair genes through the DNA repair gene NEIL3, thereby increasing the growth of tumor cells and balancing the level of ROS, leading to tumor cell death and constant damage to the tumor cell repair system, and consequently prolonging patient survival. Therefore, targeting ROS-related genes and DNA repair genes might be a promising strategy in the treatment of LUAD. Finally, a survival prognostic model of ROS-related genes and DNA repair genes was established (TERT, PRKDC, PTTG1, SMUG1, TXNRD1, CAT, H2AFX, and PFKP). The risk score obtained from our survival prognostic model could be used as an independent prognostic factor in LUAD patients.

Construction of a Prognostic Model for Lung Adenocarcinoma Based on Nucleotide Metabolism-Related Genes and Bioinformatics Analysis

“…Our experiments also con rm that RRM2 expression is signi cantly higher in LUAD tissues compared to adjacent normal tissues. Cytoplasmic selenoprotein thioredoxin reductase 1 (TXNRD1) plays multiple roles associated with malignant tumors, as it can protect normal cells from malignant transformation [32]. However, in liver cancer, upregulated TXNRD1 expression promotes hepatocellular carcinoma progression through the activation of the Akt/mTOR signaling pathway and is associated with lower patient survival rates [33].…”

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

“…Except for NQO1, the remaining nine candidate mRNAs had a substantial predictive ability. Next, we conducted a literature-based validation of NQO1's prognostic outcome in LUAD (59). We found that NQO1 is a potential therapeutic Glutathione and arachidonic acid metabolic pathways: Red tags showed enriched mRNAs.…”

Reactive oxygen species profiles reveal the tumor immune micro-environment and clinical outcomes of skin cutaneous melanoma

“…However, due to the absence of survival time or survival status data, some samples were excluded from the study and 746 samples were included in the following analysis 23 . To identify the potential role of ROSRGs in SKCM, a comprehensive list of 260 ROSRGs was compiled using previous research and the Molecular Signatures Database (MSigDB) (Supplementary Table S1) [24][25][26] . The obtained RNA-seq data and clinical information from 8739 samples from 32 types of tumors (excluding acute myeloid leukemia) were acquired from USCS Xena and analyzed to provide a broad perspective on the association between ROSRGs and different forms of cancer 27 .…”