Zhou Chengsheng, Gan Xiaoshuang, Sun Shandong, Wang Lei, Zhang Yong, Zhang Jicheng
Suzhou Traditional Chinese Medicine Hospital of Anhui Province, Suzhou, 234000, China.
Biochem Biophys Rep. 2023 Sep 1;35:101540. doi: 10.1016/j.bbrep.2023.101540. eCollection 2023 Sep.
Efferocytosis suppresses antitumour immune responses by inducing the release and secretion of cytokines. Long non-coding ribonucleic acids (lncRNAs) have various functions in different forms of programmed cell death and in immune regulation. This study aims to explore the potential role of efferocytosis-related lncRNAs as biomarkers in pancreatic adenocarcinoma (PAAD).
Transcriptome profiles, simple nucleotide variations and clinical data of patients with PAAD were extracted from The Cancer Genome Atlas (TCGA) database. Co-expression algorithms identified efferocytosis-related lncRNAs. The efferocytosis-related lncRNA scoring system (ERLncSys) was established using Cox regression and the Least Absolute Shrinkage and Selection Operator algorithm. Additionally, Kaplan-Meier (K-M) curves, Cox regression, receiver operating characteristic (ROC) curves and clinical parameter stratification analyses were used to evaluate ERlncSys. Moreover, ERlncSys was explored through Gene Set Variation Analysis, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Furthermore, the TIMER platform, ESTIMATE algorithm, single sample Gene Set Enrichment Analysis and immune checkpoint analysis were utilised to explore the predictive power of ERlncSys for the tumour immune microenvironment (TIME). Finally, a consensus clustering algorithm identified distinct molecular profiles among patients with PAAD, aiding in the identification of potential beneficiaries for immunotherapy.
K-M, Cox regression and ROC analyses confirmed the robust prognostic efficacy of ERlncSys. Clinical stratification analysis indicated the broad applicability of ERlncSys in PAAD. Additionally, mmunological analyses indicated that ERlncSys can determine immune cell infiltration status in the TIME. Furthermore, consensus clustering analysis based on ERlncSys divided the TCGA-PAAD cohort into two clusters. Cluster 1 exhibited characteristics consistent with an immune 'hot tumour' compared to cluster 2, suggesting cluster 1 is a more suitable population for immune checkpoint inhibitor therapy.
The established ErlncSys aids in predicting the prognosis and understanding the TIME landscape of patients with PAAD. In turn, it facilitates the identification of optimal candidates for immunotherapy. This study introduces novel insights into the potential value of efferocytosis-related lncRNAs as biomarkers in PAAD.
吞噬作用通过诱导细胞因子的释放和分泌来抑制抗肿瘤免疫反应。长链非编码核糖核酸（lncRNAs）在不同形式的程序性细胞死亡和免疫调节中具有多种功能。本研究旨在探讨与吞噬作用相关的lncRNAs作为胰腺腺癌（PAAD）生物标志物的潜在作用。
从癌症基因组图谱（TCGA）数据库中提取PAAD患者的转录组图谱、单核苷酸变异和临床数据。共表达算法识别与吞噬作用相关的lncRNAs。使用Cox回归和最小绝对收缩和选择算子算法建立与吞噬作用相关的lncRNA评分系统（ERLncSys）。此外，采用Kaplan-Meier（K-M）曲线、Cox回归、受试者工作特征（ROC）曲线和临床参数分层分析来评估ERlncSys。此外，通过基因集变异分析、基因本体论和京都基因与基因组百科全书分析来探索ERlncSys。此外，利用TIMER平台、ESTIMATE算法、单样本基因集富集分析和免疫检查点分析来探索ERlncSys对肿瘤免疫微环境（TIME）的预测能力。最后，一种共识聚类算法确定了PAAD患者之间不同的分子特征，有助于识别免疫治疗的潜在受益者。
K-M、Cox回归和ROC分析证实了ERlncSys具有强大的预后疗效。临床分层分析表明ERlncSys在PAAD中具有广泛的适用性。此外，免疫学分析表明ERlncSys可以确定TIME中的免疫细胞浸润状态。此外，基于ERlncSys的共识聚类分析将TCGA-PAAD队列分为两个聚类。与聚类2相比，聚类1表现出与免疫“热肿瘤”一致的特征，表明聚类1是免疫检查点抑制剂治疗更合适的人群。
建立的ErlncSys有助于预测PAAD患者的预后并了解其TIME格局。反过来，它有助于识别免疫治疗的最佳候选者。本研究为与吞噬作用相关的lncRNAs作为PAAD生物标志物的潜在价值引入了新的见解。
