Shi Xiaobo, Liu Xiaoxiao, Pan Shupei, Ke Yue, Li Yuxing, Guo Wei, Wang Yuchen, Ruan Qinli, Zhang Xiaozhi, Ma Hongbing
Department of Radiation Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Int J Gen Med. 2021 Nov 16;14:8325-8339. doi: 10.2147/IJGM.S333697. eCollection 2021.
Considering the significance of autophagy and long non-coding RNAs (lncRNAs) in the biology of esophageal squamous cell carcinoma (ESCC), the present study aimed to identify a new autophagy-related lncRNA signature to forecast the clinical outcomes of ESCC patients and to guide individualized treatment.
The expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. We extracted autophagy-related genes from the Human Autophagy Database and identified autophagy-related lncRNAs through Spearman correlation analysis. Univariate, least absolute shrinkage and selection operator and multivariate Cox regression analyses were performed on GSE53625 to construct an autophagy-related lncRNAs prognostic signature. The model was subjected to bootstrap internal validation, and the expression levels of lncRNAs were verified by TCGA database. The potential molecular mechanism of the model was explored by gene set enrichment analysis (GSEA). Spearman correlation coefficient examined the correlation between risk score and ferroptosis-associated genes as well as the response to immunotherapy and chemotherapy.
We identified and validated an autophagy-related lncRNAs prognostic signature in 179 patients with ESCC. The prognosis of patients in the low-risk group was significantly better than that in the high-risk group (-value <0.001). The reliability of the model was verified by Brier score and ROC. GSEA results showed significant enrichment of cancer- and autophagy-related signaling pathways in the high-risk group and metabolism-related pathways in the low-risk group. Correlation analysis indicated that the model can effectively forecast the effect of immunotherapy and chemotherapy. About 35.41% (74/209) ferroptosis-related genes were significantly correlated with risk scores.
In brief, we constructed a novel autophagy-related lncRNAs signature (LINC02024, LINC01711, LINC01419, LCAL1, FENDRR, ADAMTS9-AS1, AC025244.1, AC015908.6 and AC011997.1), which could improve the prediction of clinical outcomes and guide individualized treatment of ESCC patients.
鉴于自噬和长链非编码RNA（lncRNAs）在食管鳞状细胞癌（ESCC）生物学中的重要性，本研究旨在确定一种新的自噬相关lncRNA特征，以预测ESCC患者的临床结局并指导个体化治疗。
从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库中获取表达谱。我们从人类自噬数据库中提取自噬相关基因，并通过Spearman相关性分析鉴定自噬相关lncRNAs。对GSE53625进行单变量、最小绝对收缩和选择算子以及多变量Cox回归分析，以构建自噬相关lncRNAs预后特征。对该模型进行自助法内部验证，并通过TCGA数据库验证lncRNAs的表达水平。通过基因集富集分析（GSEA）探索该模型的潜在分子机制。Spearman相关系数检测风险评分与铁死亡相关基因之间的相关性以及对免疫治疗和化疗的反应。
我们在179例ESCC患者中鉴定并验证了一种自噬相关lncRNAs预后特征。低风险组患者的预后明显优于高风险组（P值<0.001）。通过Brier评分和ROC验证了模型的可靠性。GSEA结果显示，高风险组中癌症和自噬相关信号通路显著富集，低风险组中代谢相关通路显著富集。相关性分析表明，该模型可以有效预测免疫治疗和化疗的效果。约35.41%（74/209）的铁死亡相关基因与风险评分显著相关。
简而言之，我们构建了一种新的自噬相关lncRNAs特征（LINC02024、LINC01711、LINC01419、LCAL1、FENDRR、ADAMTS9-AS1、AC025244.1、AC015908.6和AC011997.1），可改善ESCC患者临床结局的预测并指导个体化治疗。
