Dai Shengkang, Yao Desheng
Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China.
People's Hospital of Baise, Baise, China.
Transl Cancer Res. 2021 Dec;10(12):5295-5306. doi: 10.21037/tcr-21-2390.
Several immune-associated long non-coding RNA (lncRNA) signatures have been reported as prognostic models in different types of cancers; however, the immune-associated lncRNA signature for predicting overall survival (OS) in cervical cancer is unknown.
The lncRNA expression profiles and clinical data of cervical cancer were acquired from The Cancer Genome Atlas (TCGA) dataset. Immune-associated genes were extracted from the Molecular Signatures Database (MSigDB), and the immune-associated lncRNAs were extracted for Cox regression analysis. Principal component analysis (PCA) was used to distinguish the high and low risk status of cervical cancer patients. Gene Set Enrichment Analysis (GSEA) was used for functional analyses.
Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to construct an immune-associated ten-lncRNA signature (containing AL021807.1, AL109976.1, LINC02446, MIR4458HG, AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) for predicting OS in cervical cancer. The signature segregated the cervical cancer patients into 2 groups (high-risk group and low-risk group). The Kaplan-Meier survival curves of AL021807.1, AL109976.1, LINC02446, and MIR4458HG were statistically significant (P<0.05) and the others (including AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) were not statistically significant (P>0.05). The Kaplan-Meier survival curves of the signature were statistically significant (P=1.134e-10), and the 5-year survival rate was 0.444 in the high-risk group [95% confidence interval (CI): 0.334 to 0.590] and 0.884 in the low-risk group (95% CI: 0.807 to 0.969). The area under curve (AUC) of the receiver operating characteristic (ROC) curve of the signature was 0.833. The concordance index (C-index) of the signature was 0.788 (95% CI: 0.730 to 0.846, P=1.884778e-22). The PCA successfully distinguished the high-risk group and low-risk group based on the signature. The GSEA showed that the signature-related protein coding genes (PCGs) may participate in immunologic biological processes and pathways.
This study revealed that the immune-associated ten-lncRNA signature is an independent factor for cervical cancer prognosis prediction, providing a bright future for immunotherapy of cervical cancer patients.
已有多项免疫相关长链非编码RNA（lncRNA）特征被报道可作为不同类型癌症的预后模型；然而，用于预测宫颈癌总生存期（OS）的免疫相关lncRNA特征尚不清楚。
从癌症基因组图谱（TCGA）数据集中获取宫颈癌的lncRNA表达谱和临床数据。从分子特征数据库（MSigDB）中提取免疫相关基因，并提取免疫相关lncRNAs进行Cox回归分析。主成分分析（PCA）用于区分宫颈癌患者的高风险和低风险状态。基因集富集分析（GSEA）用于功能分析。
采用Cox回归分析和最小绝对收缩和选择算子（LASSO）Cox回归模型构建了一个免疫相关的十lncRNA特征（包含AL021807.1、AL109976.1、LINC02446、MIR4458HG、AC004540.2、AC009065.8、AC083809.1、AC055822.1、AP000904.1和FBXL19-AS1），用于预测宫颈癌的OS。该特征将宫颈癌患者分为两组（高风险组和低风险组）。AL021807.1、AL109976.1、LINC02446和MIR4458HG的Kaplan-Meier生存曲线具有统计学意义（P<0.05），其他（包括AC004540.2、AC009065.8、AC083809.1、AC055822.1、AP000904.1和FBXL19-AS1）无统计学意义（P>0.05）。该特征的Kaplan-Meier生存曲线具有统计学意义（P=1.134e-10），高风险组的5年生存率为0.444[95%置信区间（CI）：0.334至0.590]，低风险组为0.884（95%CI：0.807至0.969）。该特征的受试者操作特征（ROC）曲线下面积（AUC）为0.833。该特征的一致性指数（C-index）为0.788（95%CI：0.730至0.846，P=1.884778e-22）。PCA基于该特征成功区分了高风险组和低风险组。GSEA显示，该特征相关蛋白编码基因（PCG）可能参与免疫生物学过程和途径。
本研究表明，免疫相关的十lncRNA特征是宫颈癌预后预测的独立因素，为宫颈癌患者的免疫治疗提供了光明前景。
