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生物信息学鉴定肺腺癌发生发展及预后的关键基因。

Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma.

机构信息

36657The First Affiliated Hospital of Kunming Medical University, Kunming, China.

Department of Dental Research, The Affiliated Stomatological Hospital of Kunming Medical University, Kunming, China.

出版信息

Inquiry. 2022 Jan-Dec;59:469580221096259. doi: 10.1177/00469580221096259.

Abstract

OBJECTIVE

Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. The present study aimed to screen the key genes involved in LUAD development and prognosis.

METHODS

The transcriptome data for 515 LUAD and 347 normal samples were downloaded from The Cancer Genome Atlas and Genotype Tissue Expression databases. The weighted gene co-expression network and differentially expressed genes were used to identify the central regulatory genes for the development of LUAD. Univariate Cox, LASSO, and multivariate Cox regression analyses were utilized to identify prognosis-related genes.

RESULTS

The top 10 central regulatory genes of LUAD included IL6, PECAM1, CDH5, VWF, THBS1, CAV1, , HGF, SPP1, and . Genes that have an impact on survival included PECAM1, HGF, SPP1, and . The favorable prognosis genes included KDF1, ZNF691, DNASE2B, and ELAPOR1, while unfavorable prognosis genes included RPL22, ENO1, PCSK9, SNX7, and LCE5A. The areas under the receiver operating characteristic curves of the risk score model in the training and testing datasets were .78 and .758, respectively.

CONCLUSION

Bioinformatics methods were used to identify genes involved in the development and prognosis of LUAD, which will provide a basis for further research on the treatment and prognosis of LUAD.

摘要

目的

肺腺癌(LUAD)是一种预后较差的常见恶性肿瘤。本研究旨在筛选与 LUAD 发生和预后相关的关键基因。

方法

从癌症基因组图谱和基因组织表达数据库中下载了 515 个 LUAD 和 347 个正常样本的转录组数据。采用加权基因共表达网络和差异表达基因分析方法,鉴定 LUAD 发生的核心调控基因。采用单因素 Cox、LASSO 和多因素 Cox 回归分析鉴定与预后相关的基因。

结果

LUAD 的前 10 个核心调控基因包括 IL6、PECAM1、CDH5、VWF、THBS1、CAV1、CXCL12、MMP9、SERPINE1 和. 对生存有影响的基因包括 PECAM1、HGF、SPP1 和. 有利预后的基因包括 KDF1、ZNF691、DNASE2B 和 ELAPOR1,而不利预后的基因包括 RPL22、ENO1、PCSK9、SNX7 和 LCE5A。风险评分模型在训练和测试数据集的受试者工作特征曲线下面积分别为.78 和.758。

结论

采用生物信息学方法鉴定了与 LUAD 发生和预后相关的基因,为进一步研究 LUAD 的治疗和预后提供了依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8e2/9158403/3398513f7ff8/10.1177_00469580221096259-fig1.jpg

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