A risk score model based on lipid metabolism-related genes could predict response to immunotherapy and prognosis of lung adenocarcinoma: a multi-dataset study and cytological validation

• Published in: Discover. Oncology 2023-10, Vol.14 (1), p.188-188, Article 188
• Main Authors: Lei, Yangyang, Zhou, Boxuan, Meng, Xiangzhi, Liang, Mei, Song, Weijian, Liang, Yicheng, Gao, Yushun, Wang, Minghui
• Format: Article
• Language: English
• Subjects: Apoptosis; Biomarkers; Cancer Research; Cancer therapies; Cells; Datasets; Genes; Genomics; Immune features; Immunotherapy; Internal Medicine; Lipid metabolism; Lipids; Lung adenocarcinoma; Lung cancer; Medical prognosis; Medicine; Medicine & Public Health; Metabolism; Microscopy; Molecular blueprint; Molecular Medicine; Oncology; Protein expression; Proteins; PTDSS1; Radiotherapy; Surgical Oncology; Survival analysis
• ISSN: 2730-6011; 2730-6011
• DOI: 10.1007/s12672-023-00802-3

Background Lipid metabolism is a key factor in tumorigenesis and drug resistance, and models related to lipid metabolism have shown potential to predict survival and curative effects of adjuvant therapy in various cancers. However, the relationship between lipid metabolism and prognosis and treatment response of lung adenocarcinoma (LUAD) are still unclear. Methods We enrolled seven bulk RNA-sequence datasets (GSE37745, GSE19188, GSE30219, GSE31547, GSE41271, GSE42127, and GSE72094) from the GEO database and one single-cell RNA-sequencing dataset (GSE117570) from the TISCH2 database. Non-negative matrix factorization (NMF) was utilized to construct the risk score model based on lipid score calculated by GSVA algorithm. Phs000452.v3, PMID: 26359337, PMID: 32472114, PRJEB23709 datasets were used to test the response to immunotherapy. Drug sensitivity analysis was assessed according to the GDSC database, and immunotherapy response was evaluated using the Wilcoxon test. Cellular function assays including clone formation, EDU assays and flow cytometry were implemented to explore the phenotype alteration caused by the knockdown of PTDSS1, which is one of key gene in risk score model. Results We analyzed both bulk and single-cell RNA sequencing data to establish and validate a risk score model based on 18 lipid metabolism-related genes with significant impact on prognosis. After divided the patients into two groups according to risk score, we identified differences in lipid-related metabolic processes and a detailed portrait of the immune landscapes of high- and low-risk groups. Moreover, we investigated the potentials of our risk score in predicting response to immunotherapy and drug sensitivity. In addition, we silenced PTDSS1 in LUAD cell lines, and found that the proliferation of the cells was weakened, and the apoptosis of the cells was increased. Conclusion Our study highlights the crucial roles of lipid metabolism in LUAD and provides a reliable risk score model, which can aid in predicting prognosis and response to immunotherapy. Furthermore, we investigated the roles of PTDSS1 in LUAD carcinogenesis, which showed that PTDSS1 regulated proliferation and apoptosis of LUAD cells.