Combined transcriptomic and lipidomic analysis reveals aberrant lipid metabolism in central nervous system hemangioblastomas

Abstract

Peritumoral cysts are commonly detected in the central nervous system tumors, especially hemangioblastomas (HBs). However, the molecular mechanisms driving their formation and propagation are still unknown. We conducted an integrated lipidomics and transcriptomics analysis on solid and cystic HB samples in order to elucidate the changes in the lipid profile and expression of lipid metabolism-related genes during cyst formation. Transcriptomic analysis revealed differential expression of several genes between the solid and cystic HBs, and those associated with lipid metabolism, such as ADCY4, MGLL, ACOT2, DGKG, SHC1 and LPAR2, were markedly dysregulated in the cystic HBs. The lipidomic analysis further showed a significant reduction in the abundance of triacylglycerol, ceramide, lysophosphatidylcholine and lysophosphatidylethanolamine, and an increase in phosphatidylcholine and phosphatidylethanolamine levels in the cystic HBs. Furthermore, bioinformatics analysis revealed altered lipid biosynthesis, glycerophospholipid metabolism and phospholipase activity in the cystic HBs. Taken together, our findings indicate that cyst formation in HBs is related with aberrant lipid metabolism.

DNA methylation profiling of central nervous system hemangioblastomas identifies two distinct subgroups

“…As part of the CNS brain tumor classifier, HBs have previously been described as a rather uniform subgroup using the Illumina 450 K methylation array platform [16], suggesting that the successor EPIC methylation array mainly harboring additional CpG sites in intergenic regions [33] might help to uncover distinct biological subgroups that are overseen with the 450 K microarray. A previous study has demonstrated transcriptomic dysregulation of genes involved in lipid metabolism such as ADCY4, MGLL, ACOT2, DGKG, SHC1 and LPAR2 in cystic compared to solid HBs [15], but supervised methylation analysis in our cohort did not identify different methylation in these genes or any other CpGs suggesting that these transcriptional differences are not due to DNA methylation dysregulation. Similarly, there were no global methylation differences in sporadic HBs compared to VHL-related tumors, but, importantly, it needs to be considered that the EPIC methylation array can only detect up to 870,000 CpGs across the entire epigenome containing 28 million CpGs (representing 3.1% of all CpGs) with improved, but still suboptimal coverage of regulatory elements [33].…”

“…Despite copy‐number alterations in a fraction of cases [ 10 , 14 ], the exome of HBs is remarkably simple and devoid of additional oncogenic driver mutations [ 12 , 13 ]. Transcriptome and lipidomic analysis of cystic and solid HBs revealed dysregulated lipid metabolism‐related genes in cyst‐forming tumors [ 15 ], but the underlying molecular alterations remain elusive. Recently, molecular characterization of the VHL promoter uncovered epigenetic differences in sporadic and VHL‐associated HBs [ 10 ], raising the possibility that changes in DNA methylation might help to identify relevant biological subgroups.…”

WWTR1(TAZ)-CAMTA1 gene fusion is sufficient to dysregulate YAP/TAZ signaling and drive epithelioid hemangioendothelioma tumorigenesis

“…First, we identified a human EHE-specific gene signature by performing a cross endothelial tumor analysis in which we compared human EHE with other malignant vascular sarcomas. We performed whole transcriptome analysis of human EHE (n = 6) and used publicly available transcriptomes from angiosarcoma (AS; n = 24), hemangioblastoma (HAB; n = 5), and Kaposi sarcoma (KS; n = 4) for comparison (Tso et al 2018;Lesluyes et al 2019;Wang et al 2021). Principal component analysis of these tumors demonstrates that each tumor forms their own independent cluster (Fig.…”

“…For cross-tumor analysis, FASTQ files from GEO data sets of RNA sequencing of samples of angiosarcoma (GSE102055), hemangioblastoma (GSE148216), and Kaposi sarcoma (GSE100684) were used (Tso et al 2018;Lesluyes et al 2019;Wang et al 2021). RNA sequencing of human liver transcriptomes from ENCODE were similarly used for comparisons (libraries ENCLB173ZZZ, ENCLB648FPF, and ENCLB490UAX) (Supplemental Table S2; The ENCODE Project Consortium 2012; Lin et al 2014;Zhang et al 2019).…”

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

“…After metabolic pathway analysis, it was found that LPC 19:2 involved in the glycerol phospholipid metabolic pathway. Glycerophospholipid metabolism is one of the important pathways of lipid metabolism in vivo , and changes in glycerophospholipid levels may affect cellular function, cytocytosis, cytospin, cytoskeletal regulation, and membrane fusion ( Wang et al, 2021b ). Previous studies have found that there is dysregulated glycerophospholipid metabolism pathway in some cancers, such as gastric cancer ( Yu et al, 2021 ), hepatocellular carcinoma ( Yu et al, 2022 ), prostate cancer ( Xu et al, 2021 ), non-small cell lung cancer ( Chen et al, 2018 ), ovarian cancer ( Gan et al, 2020 ), colorectal cancer ( Gumpenberger et al, 2021 ), and pancreatic ductal adenocarcinoma ( Martin-Blazquez et al, 2020 ).…”