Abstract
Reactive Oxygen Species (ROS) are present in high amount in patients with tumors, and these ROS can kill and destroy tumor cells. Thus, tumor cells upregulate ROS-related genes to protect themselves and reduce their destruction. Cancer cells already damaged by ROS can be repaired by expressing DNA repair genes consequently promoting their proliferation. In this work, lung adenocarcinoma (LUAD) transcriptome data in the TCGA database was 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 genes 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 genes 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 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, thus prolonging patient survival. Thus, 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 might be used as an independent prognostic factor in LUAD patients.
DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data