Dataset Information

Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes.

ABSTRACT: Consensus on the stage of liver hepatocellular carcinoma (LIHC) in patients is difficult, which restricts the diagnosis and treatment of liver cancer. Molecular typing based on genes related to the lipid metabolism pathways can reflect deeper characteristics of liver cancer and complement the deficiency of the clinical staging system. In this study, we constructed and verified two cell subtypes: C1 and C2 in LIHC, based on six lipid metabolic pathway-associated genes identified in two independent external validation cohorts comprising single-cell RNA-sequencing technology (scRNA-Seq) data and bulk RNA-seq data downloaded from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The C2 subtype showed poorer prognosis, higher immune scores, and greater correlation with pathways associated with tumor progression as compared to the C1 subtype. Moreover, the sensitivity of many tested targeted drugs in C1 was relative to C2. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed several significantly enriched oncological signatures and metabolic processes, which might help elucidate the underlying molecular mechanisms. At the same time, we identified there were significantly different metabolites in C1 and C2 subtypes using 11 LIHC tissue samples. In conclusion, we constructed two molecular subtypes based on the lipid metabolism-associated genes, which may provide valuable information to further study the pathogenesis and devise clinical management strategies for LIHC.

SUBMITTER: Cong S

PROVIDER: S-EPMC10408469 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes.

Cong Shan S Bai Shanshan S Bi Yanfang Y Wang Yu Y Jin Shi S He Hui H

American journal of cancer research 20230715 7

Consensus on the stage of liver hepatocellular carcinoma (LIHC) in patients is difficult, which restricts the diagnosis and treatment of liver cancer. Molecular typing based on genes related to the lipid metabolism pathways can reflect deeper characteristics of liver cancer and complement the deficiency of the clinical staging system. In this study, we constructed and verified two cell subtypes: C1 and C2 in LIHC, based on six lipid metabolic pathway-associated genes identified in two independen ...[more]

PMID: 37559997

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Project description:BackgroundMetabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival.MethodGenes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model.ResultTwo metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers.ConclusionThis work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

Project description:BackgroundLipid metabolism plays a pivotal role in asthma pathogenesis. However, a comprehensive analysis of the importance of lipid metabolism-related genes (LMRGs) in regulating the immune microenvironment in asthma remains lacking. The transcriptome matrix was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially expressed analysis and weighted gene coexpression network analysis (WGCNA) were conducted on the GSE74986 dataset to select hub LMRGs, and gene set enrichment analysis (GSEA) was conducted to explore their biological functions. The CIBERSORT algorithm was used to determine immune infiltration in the asthma and control groups, and the correlation of diagnostic biomarkers and immune cells was performed via Spearman correlation analysis. Subsequently, a competitive endogenous RNA (ceRNA) network was constructed to investigate the hidden molecular mechanism of asthma. The expression levels of the hub genes were further validated in the GSE143192 dataset, and RT‒qPCR and immunofluorescence were performed to verify the reliability of the results in the OVA asthma model. Lastly, the ceRNA network was confirmed by qRT-PCR and RNAi experiments in the characteristic cytokine (IL-13)-induced asthma cellular model.ResultsASAH1, ACER3 and SGPP1 were identified as hub LMRGs and were mainly involved in protein secretion, mTORC1 signaling, and fatty acid metabolism. We found more infiltration of CD8+ T cells, activated NK cells, and monocytes and less M0 macrophage infiltration in the asthma group than in the healthy control group. In addition, ASAH1, ACER3, and SGPP1 were negatively correlated with CD8+ T cells and activated NK cells, but positively correlated with M0 macrophages. Within the ceRNA network, SNHG9-hsa-miR-615-3p-ACER3, hsa-miR-212-5p and hsa-miR-5682 may play crucial roles in asthma pathogenesis. The low expression of ASAH1 and SGPP1 in asthma was also validated in the GSE74075 dataset. After SNHG9 knockdown, miR-615-3p expression was significantly upregulated, while that of ACER3 was significantly downregulated.ConclusionASAH1, ACER3 and SGPP1 might be diagnostic biomarkers for asthma, and are associated with increased immune system activation. In addition, SNHG9-hsa-miR-615-3p-ACER3 may be viewed as effective therapeutic targets for asthma. Our findings might provide a novel perspective for future research on asthma.

Dataset Information

Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes.

Publications

Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes.

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