A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer - Zhu, Linhua; Lin, Zhuoqun; Wang, Kai; Gu, Jiaxin; Chen, Xiaojing; Chen, Ruizhe; Wang, Lingfang; Cheng, Xiaodong (Frontiers in Endocrinology, 2024)
Ovarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis.Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response. Ovarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor. This study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.The TCGA OV RNAseq dataset, along with the phenotype and survival data of patients, was utilized in this study [14] (https://portal.gdc.cancer.gov/). Additionally, the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140082) GSE140082 queue provided OV RNAseq gene expression and patient survival information. Furthermore, the gencode annotation was obtained from the European Bioinformatics Institute (EBI) website(ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_38/gencode.v38.chr_p atch_hapl_scaff.annotation.gtf.gz). 63 immune checkpoint genes were sourced from the literature [15]. 58 chemokines and 12 lactate metabolism-related pathways were acquired from the Molecular Signatures Database (MsigDB) database (http://www.gsea-msgdb.org/gsea/msgdb/).