Table1_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.DOCX

Introduction: 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. In this study, we aimed to examine the relationship between IRL and CC in detail.Methods: First, the RNAseq data and clinical data of CC patients were collected from The Cancer Genome Atlas (TCGA) database, along with the immune genes from the Import database. We used univariate cox and least absolute shrinkage and selection operator (lasso) to obtain IRLs for prediction after screening the variables. According to the expression levels and risk coefficients of IRLs, the riskscore were calculated. We analyzed the relationship between the model and oxidative stress. We stratified the risk model into two as the high and low-risk groups. We also evaluated the survival differences, immune cell differences, immunotherapeutic response differences, and drug sensitivity differences between the risk groups. Finally, the genes in the model were experimentally validated.Results: Based on the above analyses, we further selected four IRLs (TFAP2A.AS1, AP000911.1, AL133215.2, and LINC02078) to construct the risk model. The model was associated with oxidative-stress-related genes, especially SOD2 and OGG1. Patients in the high-risk group had a lower overall survival than those in the low-risk group. Riskscore was positively correlated with resting mast cells, neutrophils, 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.Discussion: The results suggested that risk modeling might be useful for prognosticating patients with CC and opening up new routes for immunotherapy.

引言：免疫相关长非编码RNA（IRLs）对于宫颈癌（CC）的发展至关重要，然而，IRLs究竟如何具体影响CC尚不明确。本研究旨在对IRL与CC之间的关系进行深入研究。方法：首先，从癌症基因组图谱（TCGA）数据库中收集了宫颈癌患者的RNAseq数据和临床数据，以及来自Import数据库的免疫基因。我们通过单变量Cox回归和最小绝对收缩和选择算子（lasso）筛选变量后，获得了用于预测的IRLs。根据IRLs的表达水平和风险系数，计算了风险评分。我们分析了模型与氧化应激之间的关系。将风险模型分为高低风险两组。我们还评估了两组之间的生存差异、免疫细胞差异、免疫治疗反应差异和药物敏感性差异。最后，对模型中的基因进行了实验验证。结果：基于上述分析，我们进一步选择了四个IRLs（TFAP2A.AS1、AP000911.1、AL133215.2和LINC02078）构建风险模型。该模型与氧化应激相关基因相关，尤其是SOD2和OGG1。高风险组的患者总体生存率低于低风险组。风险评分与静息肥大细胞、中性粒细胞和CD8+ T细胞呈正相关。低风险组的患者对免疫抑制治疗表现出更高的敏感性。此外，我们发现具有PIK3CA突变的患者对达沙替尼、阿法替尼、地那西布和培替尼等化疗药物更为敏感。AL133215.2的功能得到了验证，与先前的研究结果一致，AL133215.2表现出促肿瘤生长效应。我们还发现AL133215.2与氧化应激相关通路密切相关。讨论：结果表明，风险建模可能有助于预测宫颈癌患者的预后，并为免疫治疗开辟新的途径。