Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

IF 5.7 2区 医学 Q1 Medicine
Cancer Science Pub Date : 2024-11-03 DOI:10.1111/cas.16384
Yanan Wu, Yishuang Cui, Xuan Zheng, Xuemin Yao, Guogui Sun
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引用次数: 0

Abstract

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD), with a preliminary investigation into their clinical significance and underlying molecular mechanisms. Transcriptome data for SARS-CoV-2 infection and LUAD were sourced from public databases. Differentially expressed genes (DEGs) associated with SARS-CoV-2 infection were identified and subsequently overlapped with TCGA-LUAD DEGs to discern the crosstalk genes (CGs). In addition, CGs pertaining to both diseases were further refined using LUAD TCGA and GEO datasets. Univariate Cox regression was conducted to identify genes associated with LUAD prognosis, and these genes were subsequently incorporated into the construction of a prognosis signature using 10 different machine learning algorithms. Additional investigations, including tumor mutation burden assessment, TME landscape, immunotherapy response assessment, as well as analysis of sensitivity to antitumor drugs, were also undertaken. We discovered the risk stratification based on the prognostic signature revealed that the low-risk group demonstrated superior clinical outcomes (p < 0.001). Gene set enrichment analysis results predominantly exhibited enrichment in pathways related to cell cycle. Our analyses also indicated that the low-risk group displayed elevated levels of infiltration by immunocytes (p < 0.001) and superior immunotherapy response (p < 0.001). In our study, we reveal a close association between CGs and the immune microenvironment of LUAD. This provides preliminary insight for further exploring the mechanism and interaction between the two diseases.

基于SARS-COV-2和肺腺癌串联基因的综合机器学习预测肺腺癌患者的预后。
人们普遍认为,病毒与人类实体瘤和血液恶性肿瘤之间存在着错综复杂的联系。本研究的主要目的是阐明SARS-CoV-2感染与肺腺癌(LUAD)之间基因的相互作用,并对其临床意义和潜在的分子机制进行初步研究。SARS-CoV-2 感染和肺腺癌的转录组数据来自公共数据库。确定了与SARS-CoV-2感染相关的差异表达基因(DEGs),随后将其与TCGA-LUAD的DEGs重叠,以发现串联基因(CGs)。此外,还利用 LUAD TCGA 和 GEO 数据集进一步完善了与这两种疾病相关的 CGs。通过单变量考克斯回归确定了与LUAD预后相关的基因,随后使用10种不同的机器学习算法将这些基因纳入到预后特征的构建中。此外还进行了其他调查,包括肿瘤突变负荷评估、TME情况、免疫疗法反应评估以及抗肿瘤药物敏感性分析。我们发现,根据预后特征进行的风险分层显示,低风险组的临床疗效更好(p
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
Cancer Science
Cancer Science ONCOLOGY-
CiteScore
9.90
自引率
3.50%
发文量
406
审稿时长
17 weeks
期刊介绍: Cancer Science (formerly Japanese Journal of Cancer Research) is a monthly publication of the Japanese Cancer Association. First published in 1907, the Journal continues to publish original articles, editorials, and letters to the editor, describing original research in the fields of basic, translational and clinical cancer research. The Journal also accepts reports and case reports. Cancer Science aims to present highly significant and timely findings that have a significant clinical impact on oncologists or that may alter the disease concept of a tumor. The Journal will not publish case reports that describe a rare tumor or condition without new findings to be added to previous reports; combination of different tumors without new suggestive findings for oncological research; remarkable effect of already known treatments without suggestive data to explain the exceptional result. Review articles may also be published.
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