Liu Xin, Zhang Weijie, Wan Jun, Xiao Diming, Wei Ming
Department of Blood Transfusion Department, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Department of Pharmacy Department, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Reprod Sci. 2023 Mar;30(3):903-913. doi: 10.1007/s43032-022-01074-y. Epub 2022 Sep 8.
Cervical cancer is a crucial clinical problem with high mortality. Despite much research in therapy, the prognosis of patients with cervical cancer is still not ideal. The data on cervical cancer were downloaded from The Cancer Genome Atlas (TCGA) portal. R language was used to screen out the N6-methyladenosine (m6A)-related lncRNAs (long non-coding RNA). A consensus clustering algorithm was performed to identify m6A-related lncRNAs in cervical cancer; 10 m6A-related lncRNAs showing a significant association with survival were filtrated through a gradually univariate Cox regression model, least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression preliminarily. Furthermore, we conducted Kaplan-Meier curves, receiver operating curve (ROC) analyses, and proportional hazards model to quantify the underlying character of the m6A-related model in the prevision of cervical cancer patients. Gene set enrichment analysis (GSEA) was used to explore several pathways significantly. Finally, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was applied to estimate the immune cell infiltration in the profiling. In the present study, 10 m6A-related lncRNAs make up our prediction model. This prediction model can do duty for an independent predictive biomolecular element. Subsequently, we then found that the model was still valid in further validation of the training group and the test group. Our signature was correlated with immune cell infiltration and partial signaling pathway. These lncRNAs played a no negligible biomolecular role in contributing to the prognosis of cervical cancer.
宫颈癌是一个死亡率很高的关键临床问题。尽管在治疗方面进行了大量研究，但宫颈癌患者的预后仍然不理想。从癌症基因组图谱（TCGA）门户网站下载了宫颈癌数据。使用R语言筛选出N6-甲基腺苷（m6A）相关的长链非编码RNA（lncRNA）。采用一致性聚类算法鉴定宫颈癌中与m6A相关的lncRNA；通过逐步单变量Cox回归模型、最小绝对收缩和选择算子（LASSO）算法以及多变量Cox回归初步筛选出10个与生存显著相关的m6A相关lncRNA。此外，我们进行了Kaplan-Meier曲线分析、受试者工作特征曲线（ROC）分析和比例风险模型，以量化m6A相关模型在预测宫颈癌患者方面的潜在特征。基因集富集分析（GSEA）用于显著探索几种途径。最后，应用通过估计RNA转录本相对子集进行细胞类型鉴定（CIBERSORT）来估计免疫细胞浸润情况。在本研究中，10个与m6A相关的lncRNA构成了我们的预测模型。该预测模型可作为一个独立的预测生物分子元件。随后，我们发现该模型在训练组和测试组的进一步验证中仍然有效。我们的特征与免疫细胞浸润和部分信号通路相关。这些lncRNA在影响宫颈癌预后方面发挥了不可忽视的生物分子作用。
