Detection of the Fatty Acid Metabolism-Linked Genes in Lung Adenocarcinoma as Biomarkers for Clinical Prognosis and Immunotherapeutic Targets

IF 1.9 4区医学 Q3 RESPIRATORY SYSTEM

Clinical Respiratory Journal Pub Date : 2024-09-25 DOI:10.1111/crj.70013

Jingwei Shi, Rusong Yang, Xinyi Jiang, Kangle Zhu, Zhengcheng Liu

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引用次数: 0

Abstract

Background

Lung cancer, on a global scale, leads to the most common cases of cancer mortalities. Novel therapeutic approaches are urgently needed to disrupt this lethal disease. The rapid development of tumor immunology combining breakthroughs involving fatty acid metabolism brings possibilities. Directing fatty acid metabolism is supposed to help discover potential prognostic biomarkers and treatment targets for lung cancer.

Methods

Through searching the GSE140797 dataset, we identified genes related to fatty acid metabolism as well as fatty acid metabolism-related differentially expressed genes (DEGs). We applied various methods to ascertain the independent prognostic value of the DEGs. The methods we utilized entail prognostic analysis, differential expression analysis, as well as univariate and multivariate Cox regression analyses. The lasso Cox regression model was utilized in examining how DEGs correlate with the immune score, immune checkpoint, ferroptosis, methylation, and OCLR score. The expression levels of ACAT1 and ACSL3 in tissues derived from normal lung and lung adenocarcinoma (LUAD) tissues were compared by qRT-PCR.

Results

In this study, ACSL3 and ACAT1 were identified as fatty acid metabolism-related genes utilizing independent prognostic value and as a result, the risk prognostic model was built using these factors. qRT-PCR results implied that ACSL3 and ACAT1 expressions were upregulated and downregulated, correspondingly in tumor tissues. Additional evaluations suggested that ACSL3 and ACAT1 were affirmed to be remarkably correlated with the immune score, methylation, immune checkpoint, OCLR score, and ferroptosis.

Conclusions

ACSL3 and ACAT1 were effective prognostic biomarkers and potential immunotherapeutic targets in LUAD.

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检测肺腺癌中的脂肪酸代谢相关基因，作为临床预后和免疫治疗靶点的生物标记物

背景肺癌在全球范围内导致最常见的癌症死亡病例。目前迫切需要新的治疗方法来阻断这一致命疾病。肿瘤免疫学的快速发展与脂肪酸代谢方面的突破相结合，带来了各种可能性。引导脂肪酸代谢有助于发现肺癌的潜在预后生物标志物和治疗靶点。方法通过搜索 GSE140797 数据集，我们发现了与脂肪酸代谢相关的基因以及与脂肪酸代谢相关的差异表达基因（DEGs）。我们采用了多种方法来确定 DEGs 的独立预后价值。我们采用的方法包括预后分析、差异表达分析以及单变量和多变量 Cox 回归分析。在研究 DEGs 与免疫评分、免疫检查点、铁突变、甲基化和 OCLR 评分的相关性时，我们使用了 lasso Cox 回归模型。通过 qRT-PCR 技术比较了 ACAT1 和 ACSL3 在正常肺组织和肺腺癌（LUAD）组织中的表达水平。结果本研究发现，ACSL3 和 ACAT1 是具有独立预后价值的脂肪酸代谢相关基因，因此利用这些因子建立了风险预后模型。其他评估结果表明，ACSL3 和 ACAT1 与免疫评分、甲基化、免疫检查点、OCLR 评分和铁变态反应显著相关。结论 ACSL3 和 ACAT1 是 LUAD 的有效预后生物标志物和潜在免疫治疗靶点。

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

Clinical Respiratory Journal 医学-呼吸系统

CiteScore

3.70

自引率

0.00%

发文量

104

审稿时长

>12 weeks

期刊介绍： Overview Effective with the 2016 volume, this journal will be published in an online-only format. Aims and Scope The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic. We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including: Asthma Allergy COPD Non-invasive ventilation Sleep related breathing disorders Interstitial lung diseases Lung cancer Clinical genetics Rhinitis Airway and lung infection Epidemiology Pediatrics CRJ provides a fast-track service for selected Phase II and Phase III trial studies. Keywords Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease, Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Embase (Elsevier) Health & Medical Collection (ProQuest) Health Research Premium Collection (ProQuest) HEED: Health Economic Evaluations Database (Wiley-Blackwell) Hospital Premium Collection (ProQuest) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) ProQuest Central (ProQuest) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier)