Abstract
BackgroundLipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression.ResultsThrough systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets.ConclusionsOur study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors.
Highlights
Reprogramming of cellular metabolism is a well-established hallmark of tumor that has attracted increasing attention in the recent years [1]
Key altered lipid metabolism pathways in pan-cancer Sixteen solid tumor types were selected in the present study with a criterion of the number of para-tumor samples in RNA level over ten samples, namely Bladder cancer (BLCA), Breast cancer (BRCA), Colon adenocarcinoma (COAD), Esophageal carcinoma (ESCA), Squamous cell carcinoma of head and neck (HNSC), Kidney chromophobe cell carcinoma (KICH), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Liver cancer (LIHC), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), PARD, READ, Stomach adenocarcinoma (STAD), Thyroid cancer (THCA) and Uterine corpus endometrial carcinoma (UCEC)
Unsupervised clustering of the pan-cancer by the pathway enrichment false discover rate (FDR), shows that similar lipid metabolism features are shared among the similar tissue origin tumors
Summary
Reprogramming of cellular metabolism is a well-established hallmark of tumor that has attracted increasing attention in the recent years [1]. The perturbations of lipid metabolism in cancer cells may alter cellular function dramatically. Metabolic reprograming provides critical information for clinical oncology, and the recent study from the pan-cancer study showed a clinical relevance of metabolic expression subtypes in human cancers [4]. Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression
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