Shandong University of Traditional Chinese Medicine, Jinan, 250014, Shandong, China.
Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, Shandong, China.
Sci Rep. 2022 Sep 29;12(1):16285. doi: 10.1038/s41598-022-20858-5.
Necroptosis, a programmed form of necrotic cell death, plays critical regulatory roles in the progression and metastatic spread of cancers such as cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). However, there are few articles systematically analyzing the necroptosis-related long non-coding RNAs (NRlncRNAs) correlated with CESC patients. Both RNA-sequencing and clinical data of CESC patients are downloaded from TCGA database in this study. Pearson correlation analysis, least absolute shrinkage, operator algorithm selection and Cox regression model are employed to screen and create a risk score model of eleven-NRlncRNAs (MIR100HG, LINC00996, SNHG30, LINC02688, HCG15, TUBA3FP, MIAT, DBH-AS1, ERICH6-AS1SCAT1, LINC01702) prognostic. Thereafter, a series of tests are carried out in sequence to evaluate the model for independent prognostic value. Gene set enrichment analytic paper, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analytic paper make it clear that immune-related signaling pathways are very rich in the high-risk subgroup. Additionally, the prognostic risk score model is correlated to immune cell infiltration, potential immune checkpoint, immune function, immune micro-environmental and m6A-related gene. Mutation frequency in mutated genes and survival probability trend are higher in the low-risk subgroup in most of test cases when compared to the high-risk subgroup. This study constructs a renewed prognostic model of eleven-NRlncRNAs, which may make some contribution to accurately predicting the prognosis and the immune response from CESC patients, and improve the recognition of CESC patients and optimize customized treatment regimens to some extent.
细胞程序性坏死(Necroptosis)作为一种细胞坏死形式,在宫颈癌和子宫内膜腺癌(Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma,CESC)等癌症的进展和转移扩散中发挥着关键的调控作用。然而,目前很少有文章系统地分析与 CESC 患者相关的细胞程序性坏死相关长非编码 RNA(long non-coding RNAs,lncRNAs)。本研究从 TCGA 数据库中下载了 CESC 患者的 RNA-seq 数据和临床数据。采用 Pearson 相关分析、最小绝对收缩和选择算子算法、Cox 回归模型筛选并构建了 11 个 lncRNAs(MIR100HG、LINC00996、SNHG30、LINC02688、HCG15、TUBA3FP、MIAT、DBH-AS1、ERICH6-AS1、SCAT1、LINC01702)的风险评分模型。随后,对模型进行了一系列独立预后价值的检验。基因集富集分析、基因本体论分析、京都基因与基因组百科全书通路富集分析表明,高风险亚组中富含免疫相关信号通路。此外,预后风险评分模型与免疫细胞浸润、潜在免疫检查点、免疫功能、免疫微环境和 m6A 相关基因相关。在大多数测试中,与高风险亚组相比,低风险亚组中突变基因的突变频率和生存概率趋势更高。本研究构建了一个新的 11 个 lncRNAs 预后模型,该模型可能有助于准确预测 CESC 患者的预后和免疫反应,并在一定程度上提高对 CESC 患者的认识,优化个体化治疗方案。