Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis.

IF 2.9 3区医学 Q2 ONCOLOGY Expert Review of Anticancer Therapy Pub Date : 2024-09-01 Epub Date: 2024-07-17 DOI:10.1080/14737140.2024.2377184

Min Chen, Yijun Qi, Shenghua Zhang, Yubo Du, Haodong Cheng, Shegan Gao

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Abstract

Objectives: To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value.

Methods: Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts.

Results: Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion.

Conclusion: A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

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筛选食管鳞状细胞癌程序性细胞死亡相关基因并基于转录组分析构建预后模型

目的基于转录组数据筛选食管鳞状细胞癌（ESCC）中程序性细胞死亡（PCD）相关基因，并探讨其临床价值：从TCGA数据库中的ESCC转录组和临床数据中筛选出差异表达的PCD基因（DEPCDGs）。对 ESCC 中的 DEPCDGs 进行单变量 COX 和 LASSO COX 分析，以建立预后模型。通过ssGSEA和CIBERSORT确定了不同RiskScore组免疫细胞浸润的差异。通过肿瘤免疫功能紊乱与排斥（TIDE）和IMvigor210队列探讨了RiskScore在免疫治疗反应中的作用：结果：在 ESCC 中发现了 14 个与预后相关的 DEPCDGs。结果：在 ESCC 中挖掘出了 14 个与预后相关的 DEPCDGs，这些 DEPCDGs 组成了一个对预后具有良好预测能力的 RiskScore。RiskScore 在三组数据中表现出了极高的预测准确性。高RiskScore组中M2巨噬细胞和Tregs的丰度较高，而低RiskScore组中M1巨噬细胞的丰度较高。RiskScore 还显示出良好的免疫治疗敏感性。RT-qPCR 分析显示，AUP1、BCAP31、DYRK2、TAF9 和 UBQLN2 在 KYSE-150 细胞中的表达量较高。敲除BCAP31可抑制细胞的迁移和侵袭：预后风险模型可预测 ESCC 的预后，并可能成为风险分层和免疫疗法评估的有用生物标志物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Expert Review of Anticancer Therapy 医学-肿瘤学

CiteScore

5.10

自引率

3.00%

发文量

100

审稿时长

4-8 weeks

期刊介绍： Expert Review of Anticancer Therapy (ISSN 1473-7140) provides expert appraisal and commentary on the major trends in cancer care and highlights the performance of new therapeutic and diagnostic approaches. Coverage includes tumor management, novel medicines, anticancer agents and chemotherapy, biological therapy, cancer vaccines, therapeutic indications, biomarkers and diagnostics, and treatment guidelines. All articles are subject to rigorous peer-review, and the journal makes an essential contribution to decision-making in cancer care. Comprehensive coverage in each review is complemented by the unique Expert Review format and includes the following sections: Expert Opinion - a personal view of the data presented in the article, a discussion on the developments that are likely to be important in the future, and the avenues of research likely to become exciting as further studies yield more detailed results Article Highlights – an executive summary of the author’s most critical points.