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肺腺癌中多种程序性细胞死亡风险特征的综合分析

Comprehensive analysis of multiple regulated cell death risk signatures in lung adenocarcinoma.

作者信息

Gao Shan, Huang Jiaqi, Zhao Rui, He Haiqi, Zhang Jia, Wen Xiaopeng

机构信息

Department of Thoracic Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.

出版信息

Heliyon. 2024 Sep 27;10(19):e38641. doi: 10.1016/j.heliyon.2024.e38641. eCollection 2024 Oct 15.

DOI:10.1016/j.heliyon.2024.e38641
PMID:39398028
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11471212/
Abstract

BACKGROUND

Regulated cell death (RCD) has considerable impact on tumor progress and sensitivity of treatment. Lung adenocarcinoma (LUAD) show a high resistance for conventional radiotherapies and chemotherapies. Currently, regulation of cancer cell death has been emerging as a new promising therapeutic avenue for LUAD patients. However, the crosstalk in each pattern RCD is unclear.

METHODS

We integrated collected the hub-genes of 12 RCD subroutines and compressively analyzed these hub-genes synergistic effect in LUAD. The characters of RCD genes expression and prognosis were developed in The Cancer Genome Atlas (TCGA)-LUAD data. We developed and validated an RCD risk model based on TCGA and GSE70294 data set, respectively. Functional annotation and tumor immunotherapy based on the risk model were also investigated.

RESULTS

28 RCD-related genes and two LUAD molecular cluster were identified. Survival analysis revealed that the prognosis in high-risk group was worser than those in low-risk group. Functional enrichment analysis indicated that the RCD risk model correlated with immune responses. Further analysis indicated that the high-risk group in RCD risk model exhibited an immunosuppressive microenvironment and a lowly immunotherapy responder ratio.

CONCLUSIONS

We present an RCD risk model which have a promising ability in predicting LUAD prognosis and immunotherapy response.

摘要

背景

程序性细胞死亡（RCD）对肿瘤进展和治疗敏感性有重大影响。肺腺癌（LUAD）对传统放疗和化疗表现出高度抗性。目前，调控癌细胞死亡已成为LUAD患者一种新的有前景的治疗途径。然而，每种RCD模式中的相互作用尚不清楚。

方法

我们整合收集了12种RCD子程序的核心基因，并全面分析了这些核心基因在LUAD中的协同作用。利用癌症基因组图谱（TCGA）-LUAD数据揭示RCD基因表达特征和预后情况。我们分别基于TCGA和GSE70294数据集开发并验证了一个RCD风险模型。还研究了基于该风险模型的功能注释和肿瘤免疫治疗。

结果

鉴定出28个与RCD相关的基因和两个LUAD分子簇。生存分析显示，高危组的预后比低危组差。功能富集分析表明，RCD风险模型与免疫反应相关。进一步分析表明，RCD风险模型中的高危组表现出免疫抑制微环境和较低的免疫治疗反应率。

结论

我们提出了一个RCD风险模型，该模型在预测LUAD预后和免疫治疗反应方面具有良好的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/b93b16f0f421/mmcfigs5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/1dda7bff6f04/gr1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/0f7bab3246cd/gr3.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/506712cd97f1/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/477a6209a4b0/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/d347b7475390/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/78a0963ee279/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/ffe861cf121e/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/c056f5a0a3ba/mmcfigs1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/d92cb1139eda/mmcfigs2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/7430e13289c5/mmcfigs3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/cb132de4a4e1/mmcfigs4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/b93b16f0f421/mmcfigs5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/1dda7bff6f04/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/d068c7561e6b/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/0f7bab3246cd/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/d0f32e9e8e17/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/506712cd97f1/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/477a6209a4b0/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/d347b7475390/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/78a0963ee279/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/ffe861cf121e/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/c056f5a0a3ba/mmcfigs1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/d92cb1139eda/mmcfigs2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/7430e13289c5/mmcfigs3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/cb132de4a4e1/mmcfigs4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b489/11471212/b93b16f0f421/mmcfigs5.jpg

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