Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma

Abstract

BackgroundIntrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA.MethodsWe performed integrated multiomics analysis in 116 iCCA samples, including whole‐exome sequencing, bulk RNA‐sequencing and proteome analysis. Based on the non‐negative matrix factorization method and the protein abundance of metabolic genes in human genome‐scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5‐ethynyl‐2'‐deoxyuridine (EdU) assay, colony formation assay, RNA‐sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells.ResultsThree metabolic subtypes (S1‐S3) with subtype‐specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune‐suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT‐rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype‐specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen‐activated protein kinase (MAPK) signaling.ConclusionVia multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

“…Therefore, using enrichment-analysis methods to filter and screen data, integrating data with overlapping functions, and identifying significantly overexpressed genes/proteins or differential genes/proteins compared to background sets in biological composition/functions can generate more meaningful functional information. For instance, the authors enriched significantly upregulated genes in each subtype and discovered that Nudix hydrolase 12, Acetyl-CoA synthetase 1, and Nicotinamide nucleotide Adenylyl transferase 3 in the S3 subtype are all related to nucleotide biosynthesis processes [ 58 ]. Currently, there are two most commonly used enrichment-analysis methods.…”

“…Biomolecules 2024, 14, x FOR PEER REVIEW 8 of 24 [58]. Currently, there are two most commonly used enrichment-analysis methods.…”