Background

Long non-coding RNAs (LncRNAs) regulating the immune microenvironment of cancer is a hot spot. But little is known about the influence of the immune-related lncRNA (IRlncRs) on the chemotherapeutic responses and prognosis of cervical cancer (CC) patients. The purpose of the study was to identify an immune-related lncRNAs (IRlncRs)-based model for the prospective prediction of clinical outcomes in CC patients.

Methods

CC patients’ relevant data was acquired from The Cancer Genome Atlas (TCGA). Correlation analysis and Cox regression analyses were applied. A risk score formula was formulated. Prognostic factors were combined into a nomogram, while sensitivity for chemotherapy drugs was analyzed using the OncoPredict algorithm.

Results

Eight optimal IRlncRs(ATP2A1-AS1, LINC01943, AL158166.1, LINC00963, AC009065.8, LIPE-AS1, AC105277.1, AC098613.1.) were incorporated in the IRlncRs model. The overall survival (OS) of the high-risk group of the model was inferior to those in the low-risk group. Further analysis demonstrated this eight-IRlncRs model as a useful prognostic marker. The Nomogram had a concordance index of survival prediction of 0.763(95% CI 0.746–0.780) and more robust predictive accuracy. Furthermore, patients in the low-risk group were found to be more sensitive to chemotherapy, including Paclitaxel, Rapamycin, Epirubicin, Vincristine, Docetaxel and Vinorelbine.

Conclusions

An eight-IRlncRs-based prediction model was identified that has the potential to be an important tool to predict chemotherapeutic responses and prognosis for CC patients.

中文翻译：

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免疫相关 LncRNA 评分可预测宫颈癌患者的化疗反应和预后

背景

长非编码RNA（LncRNA）调节癌症免疫微环境是一个热点。但免疫相关lncRNA（IRlncRs）对宫颈癌（CC）患者化疗反应和预后的影响知之甚少。该研究的目的是确定一种基于免疫相关 lncRNA (IRlncRs) 的模型，用于前瞻性预测 CC 患者的临床结果。

方法

CC患者的相关数据来自癌症基因组图谱（TCGA）。应用相关分析和Cox回归分析。制定了风险评分公式。将预后因素组合成列线图，同时使用 OncoPredict 算法分析化疗药物的敏感性。

结果

IRlncRs 模型中纳入了八个最佳 IRlncR（ATP2A1-AS1、LINC01943、AL158166.1、LINC00963、AC009065.8、LIPE-AS1、AC105277.1、AC098613.1.）。模型高危组的总生存期（OS）低于低危组。进一步的分析证明这种八个 IRlncRs 模型是一个有用的预后标志物。列线图的生存预测一致性指数为0.763（95% CI 0.746–0.780），预测准确性更强。此外，低风险组患者对化疗更敏感，包括紫杉醇、雷帕霉素、表阿霉素、长春新碱、多西紫杉醇和长春瑞滨。

结论

确定了基于 8-IRlncRs 的预测模型，该模型有可能成为预测 CC 患者化疗反应和预后的重要工具。