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一种基于肿瘤微环境基因集的非小细胞肺癌预后特征

A tumor microenvironment gene set-Based prognostic signature for non-small-cell lung cancer.

作者信息

Wen Yingsheng, Guo Guangran, Yang Longjun, Chen Lianjuan, Zhao Dechang, He Xiaotian, Zhang Rusi, Huang Zirui, Wang Gongming, Zhang Lanjun

机构信息

State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.

出版信息

Front Mol Biosci. 2022 Aug 10;9:849108. doi: 10.3389/fmolb.2022.849108. eCollection 2022.

DOI:10.3389/fmolb.2022.849108
PMID:36032673
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9400803/
Abstract

The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components of lung cancer and develop a prognostic signature to predict overall survival (OS). Expression data was retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed TME-related genes were calculated between tumor and normal tissues. Then nonnegative matrix factorization (NMF) clustering was used to identify two distinct subtypes. Our analysis yielded a gene panel consisting of seven TME-related genes as candidate signature set. With this panel, our model showed that the high-risk group experienced a shorter survival time. This model was further validated by an independent cohort with data from Gene Expression Omnibus (GEO) database (GSE50081 and GSE13213). Additionally, we integrated the clinical factors and risk score to construct a nomogram for predicting prognosis. Our data suggested less immune cells infiltration but more fibroblasts were found in tumor tissues derived from patients at high-risk and those patients exhibited a worse immunotherapy response. The signature set proposed in this work could be an effective model for estimating OS in lung cancer patients. Hopefully analysis of the TME could have the potential to provide novel diagnostic, prognostic and therapeutic opportunities.

摘要

肿瘤微环境（TME）参与肺癌的发生和发展。对TME格局的更深入了解将有助于深入研究预后生物标志物和潜在治疗靶点。为此，我们旨在确定肺癌的TME组成部分，并开发一种预后特征来预测总生存期（OS）。从癌症基因组图谱（TCGA）数据库中检索表达数据，并计算肿瘤组织与正常组织之间差异表达的TME相关基因。然后使用非负矩阵分解（NMF）聚类来识别两种不同的亚型。我们的分析产生了一个由七个TME相关基因组成的基因panel作为候选特征集。基于这个panel，我们的模型显示高危组的生存时间较短。该模型通过来自基因表达综合数据库（GEO）（GSE50081和GSE13213）的独立队列数据进一步验证。此外，我们整合临床因素和风险评分以构建预测预后的列线图。我们的数据表明，高危患者的肿瘤组织中免疫细胞浸润较少，但成纤维细胞较多，且这些患者的免疫治疗反应较差。本研究中提出的特征集可能是评估肺癌患者OS的有效模型。有望对TME的分析能够提供新的诊断、预后和治疗机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/9400803/fb481fcd1a2a/fmolb-09-849108-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/9400803/fb481fcd1a2a/fmolb-09-849108-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/9400803/385c8c4f1e21/fmolb-09-849108-g002.jpg
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