The construction of a prognostic model of cervical cancer based on four immune-related LncRNAs and an exploration of the correlations between the model and oxidative stress - Lv, Xuefeng; Jia, Yanyan; Li, Jinpeng; Deng, Shu; Yuan, Enwu (Frontiers in Pharmacology, 2023)
The immune-related lncRNAs (IRLs) are critical for the development of cervical cancer (CC), but it is still unclear how exactly ILRs contribute to CC. Based on the CC cohort in the cancer genome Atlas (TCGA), we used machine learning to construct a risk model consisting of four IRLs (TFAP2A.AS1, AP000911.1, AL133215.2 and LINC02078). We analyzed the relationship between the model and oxidative stress and found that the model was associated with oxidative-stress-related genes, especially SOD2 and OGG1. Compared to patients with CC in the low-risk group, those in the high-risk group had a lower overall survival rate. The risk model was positively correlated with neutrophils, resting mast cells and CD8+ T-cells. Patients in the low-risk group showed a greater sensitivity to immunosuppression therapy. In addition, we found that patients with the PIK3CA mutation were more sensitive to chemotherapeutic agents such as dasatinib, afatinib, dinaciclib and pelitinib. The function of AL133215.2 was verified, which was consistent with previous findings, and AL133215.2 exerted a pro-tumorigenic effect. We also found that AL133215.2 was closely associated with oxidative-stress-related pathways. The results suggested that risk modeling might be useful for prognosticating patients with CC and opening up new routes for immunotherapy.