Evidence map›Paper›PMID 39323079›Full record

ArticleThe clinical respiratory journal2024

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

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

Abstract read
In one paragraph

Article in The clinical respiratory journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jingwei ShiDepartment of Thoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China.
Rusong YangDepartment of Thoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China.
Xinyi JiangDepartment of Cardiovascular and Thoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Nanjing University, Nanjing, Jiangsu Province, China.
Kangle ZhuDepartment of Thoracic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu Province, China.ORCID https://orcid.org/0000-0002-8829-0545
Zhengcheng LiuDepartment of Thoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China.ORCID https://orcid.org/0009-0001-4611-5503

Funding

Key Project of Nanjing Medical Science and Technology Program ZKX21015
6 · The paper itself

Abstract

backgroundLung 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.

methodsThrough 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.

resultsIn 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.

conclusionsACSL3 and ACAT1 were effective prognostic biomarkers and potential immunotherapeutic targets in LUAD.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorFatty AcidsLung NeoplasmsCoenzyme A LigasesFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansImmunotherapyLong-Chain-Fatty-Acid-CoA LigaseMaleMiddle AgedPrognosisBiomarkers, TumorCoenzyme A LigasesFatty AcidsLong-Chain-Fatty-Acid-CoA LigaseACAT1ACSL3biomarkerfatty acid metabolismlung adenocarcinomaqRT‐PCR

Identifiers

PMID39323079
PMCPMC11424681

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.