ELOVLs Predict Distinct Prognosis Value and Immunotherapy Efficacy In Patients With Hepatocellular Carcinoma

Abstract

BackgroundHepatocellular carcinoma (HCC) is a primary malignancy of the liver with high prevalence worldwide and poor prognosis. It has been verified that elongation of very-long-chain fatty acids gene family (ELOVLs), a group of genes that responsible for elongation of saturated and polyunsaturated fatty acids, participate in the pathogenesis and development of multiplex disease including cancers. However, the functions and prognosis of ELOVLs in HCC are still indistinguishable.MethodsFirst, we searched the mRNA expression and survival data of ELOVLs in patients with HCC via the data of The Cancer Genome Atlas (TCGA). The prognosis value of ELOVLs on HCC was assessed by Kaplan–Meier plotter and Cox regression analysis. reverse transcription quantitative- polymerase chain reaction (RT-qPCR), Western blot (WB), and immunohistochemistry were applied to assess the specific mRNA and protein expression of ELOVLs in HCC clinical specimens of our cohort. Then, the functional enrichment of ELOVL1 especially the pathways relating to the immune was conducted utilizing the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analysis. Additionally, TIMER, CIBERSOR, and tumor immune dysfunction and exclusion (TIDE) were employed to evaluate the relationship between ELOVL1 and immune responses. Last, the correlation of ELOVL1 with genome heterogeneity [microsatellite instability (MSI), tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH), homologous recombination deficiency (HRD), purity, ploidy, loss of heterozygosity (LOH), and neoantigens] and mutational landscape were also evaluated basing on the date in TCGA.ResultsSignificant expression alteration was observed in ELOVLs family at the pan-cancer level. In liver cancer, ELOVL1 and ELOVL3 were strongly associated with poor prognosis of HCC by survival analysis and differential expression analysis. Immunohistochemistry microarray, WB, and RT-qPCR confirmed that ELOVL1 but not ELOVL3 played an important role in HCC. Mechanistically, functional network analysis revealed that ELOVL1 might be involved in the immune response. ELOVL1 could affect immune cell infiltration and immune checkpoint markers such as PD-1 and CTLA4 in HCC. Meanwhile, high expression of ELOVL1 would be insensitive to immunotherapy. Correlation analysis of immunotherapy markers showed that ELOVL1 has been associated with MSI, TMB, and oncogene mutations such as TP53.ConclusionELOVLs play distinct prognostic value in HCC. ELOVL1 could predict the poor prognosis and might be a potential indicator of immunotherapy efficacy in HCC patients.

Glioblastoma Multiforme Tumors in Women Have a Lower Expression of Fatty Acid Elongases ELOVL2, ELOVL5, ELOVL6, and ELOVL7 than in Men

“…The significance of ELOVL1 and ELOVL3 in GBM tumorigenesis remains unclear. The elevated expression of these genes is also associated with a worse prognosis for hepatocellular carcinoma [ 67 ]. At least for ELOVL1 , a higher expression in hepatocellular carcinoma results in cancer immune evasion, including an increased expression of programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) [ 67 ].…”

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth

“…Examining genes in this category reveals that many are involved in lipid metabolism and cancer progression. The genes fatty acid synthase (FASN) and fatty acid elongase 1 (ELOVL1), for instance, are both involved in fatty acid biosynthesis and cancer initiation ( 33 , 34 ), and the genes cholestenol delta-isomerase (EBP), squalene epoxidase (SQLE), and 24-dehydrocholesterol reductase (DHCR24) represent a diverse set of cholesterol biosynthesis-related oncogenes ( 35 ).…”

NUSAP1 promotes pancreatic ductal adenocarcinoma progression by drives the epithelial-mesenchymal transition and reduces AMPK phosphorylation

“…To identify the regulatory role of NUSAP1 expression in PDAC, we integrated NUSAP1 gene expression data in the TCGA with data for other variables. Tumor mutation burden (TMB), microsatellite instability (MSI), homologous recombination deficiency (HRD), and neoantigen were used to evaluate the relationship between NUSAP1 expression, tumor mutation load and treatment sensitivity [ 12 ]. Mutant-allele tumor heterogeneity (MATH), purity, ploidy, and loss of heterozygosity (LOH) were used to assess the association between NUSAP1 expression and tumor heterogeneity [ 13 ].…”