Department of Radiation Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Peripheral Vascular, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Sci Rep. 2022 Feb 4;12(1):1960. doi: 10.1038/s41598-022-05922-4.
Esophageal squamous cell carcinoma (ESCC) is the main subtype of esophageal cancer. Since autophagy-related genes (ARGs) play a key role in the pathogenesis of many tumors, including ESCC, the purpose of this study is to establish an autophagy-related prognostic risk signature based on ARGs expression profile, and to provide a new method for improving prediction of clinical outcomes. We obtained the expression profiles of ESCC from public data (GSE53625) and extracted the portion of ARGs. Differential expression analysis and enrichment analysis were performed to confirm abnormal autophagy-related biological functions. Univariate and multivariate Cox regression analyses were performed on RNA microarray data (GSE53625) to construct a prognostic risk signature associated with autophagy. The performance of the model was evaluated by receiver operating characteristic (ROC) analysis, survival analysis and Brier score. The model was subjected to bootstrap internal validation. The potential molecular mechanism of gene signature was explored by gene set enrichment analysis (GSEA). Spearman correlation coefficient examined the correlation between risk score and immune status and ferroptosis. The expression levels of genes and proteins were validated by qRT-PCR and immunohistochemistry in ESCC cell lines and ESCC tissues. We constructed and validated an autophagy-related prognostic risk signature in 179 patients with ESCC. The long-term survival of patients in high-risk group was lower than that in low-risk group (log-rank, P value < 0.001). ROC analysis and Brier score confirmed the reliability of the signature. GSEA results showed significant enrichment of cancer- and autophagy-related signaling pathways in the high-risk ESCC patients and immunoregulatory signaling pathways in the low-risk ESCC patients. Correlation analysis showed that the risk signature can effectively predict the effect of immunotherapy. About 33.97% (71/209) ferroptosis-related genes were significantly correlated with risk scores. Finally, the results of qRT-PCR and immunohistochemistry experiments were consistent with bioinformatics analysis. In brief, we constructed a novel autophagy-related gene signature (VIM, UFM1, TSC2, SRC, MEFV, CTTN, CFTR and CDKN1A), which could improve the prediction of clinical outcomes in patients with ESCC.
食管鳞状细胞癌（ESCC）是食管癌的主要亚型。由于自噬相关基因（ARGs）在许多肿瘤的发病机制中发挥关键作用，包括 ESCC，因此本研究旨在建立基于 ARGs 表达谱的自噬相关预后风险特征，并为改善临床结局预测提供新方法。我们从公共数据（GSE53625）中获取 ESCC 的表达谱，并提取 ARGs 部分。进行差异表达分析和富集分析以确认异常自噬相关的生物学功能。对 RNA 微阵列数据（GSE53625）进行单变量和多变量 Cox 回归分析，构建与自噬相关的预后风险特征。通过接收者操作特征（ROC）分析、生存分析和 Brier 评分评估模型的性能。对模型进行 bootstrap 内部验证。通过基因集富集分析（GSEA）探索基因特征的潜在分子机制。Spearman 相关系数检验风险评分与免疫状态和铁死亡的相关性。通过 qRT-PCR 和免疫组织化学在 ESCC 细胞系和 ESCC 组织中验证基因和蛋白质的表达水平。我们在 179 例 ESCC 患者中构建和验证了一个与自噬相关的预后风险特征。高风险组患者的长期生存率低于低风险组（log-rank，P 值<0.001）。ROC 分析和 Brier 评分证实了该特征的可靠性。GSEA 结果表明，高风险 ESCC 患者中存在显著富集的癌症和自噬相关信号通路，而低风险 ESCC 患者中存在免疫调节信号通路。相关性分析表明，风险特征可有效预测免疫治疗效果。约 33.97%（71/209）铁死亡相关基因与风险评分显著相关。最后，qRT-PCR 和免疫组织化学实验结果与生物信息学分析一致。简而言之，我们构建了一个新的自噬相关基因特征（VIM、UFM1、TSC2、SRC、MEFV、CTTN、CFTR 和 CDKN1A），可以提高 ESCC 患者临床结局的预测能力。
