Dataset Information

Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies.

ABSTRACT:

Background

The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC).

Aim

To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect.

Methods

We collected and curated all well-annotated and publicly available high-throughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue. Comprehensive pathway enrichment analysis was performed using pathDIP for each data type (genomic, gene expression, methylation, miRNA and proteomic), and the overlap of pathways was assessed to elucidate pathway dependencies in HCC.

Results

We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples, published until December 2016. The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor (EGFR) and ?1-integrin pathways. Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival. Using CTD database, estradiol would best modulate and revert these genes appropriately.

Conclusion

By analyzing and integrating all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human HCC tissue, we identified EGFR, ?1-integrin and axon guidance as pathway dependencies in HCC. These are master regulators of key pathways in HCC, such as the mTOR, Ras/Raf/MAPK and p53 pathways. The genes implicated in these pathways had prognostic value in HCC, with Netrin and Slit3 being novel proteins of prognostic importance to HCC. Based on this integrative analysis, EGFR, and ?1-integrin are master regulators that could serve as potential therapeutic targets in HCC.

SUBMITTER: Bhat M

PROVIDER: S-EPMC7856865 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies.

Bhat Mamatha M Pasini Elisa E Pastrello Chiara C Rahmati Sara S Angeli Marc M Kotlyar Max M Ghanekar Anand A Jurisica Igor I

World journal of hepatology 20210101 1

<h4>Background</h4>The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC).<h4>Aim</h4>To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect.<h4>Methods</h4>We collected and curated all well-annotated and publicly available ...[more]

PMID: 33584989

Similar Datasets

Project description:BACKGROUND:Hepatocellular carcinoma (HCC) is the major type of primary liver cancer. Intrahepatic metastasis, such as portal vein tumor thrombosis (PVTT), strongly indicates poor prognosis of HCC. But now, there are limited understandings of the molecular features and mechanisms of those metastatic HCCs. METHODS:To characterize the molecular alterations of the metastatic HCCs, we implemented an integrative analysis of the copy number variations (CNVs), DNA methylations and transcriptomes of matched adjacent normal, primary tumor and PVTT samples from 19 HCC patients. RESULTS:CNV analysis identified a frequently amplified focal region chr11q13.3 and a novel deletion peak chr19q13.41 containing three miRNAs. The integrative analysis with RNA-seq data suggests that CNVs and differential promoter methylations regulate distinct oncogenic processes. Then, we used individualized differential analysis to identify the differentially expressed genes between matched primary tumor and PVTT of each patient. Results show that 5 out of 19 studied patients acquire evidential progressive alterations of gene expressions (more than 1000 differentially expressed genes were identified in each patient). While, another subset of eight patients have nearly identical gene expressions between the corresponding matched primary tumor and PVTT. Twenty genes were found to be recurrently and progressively differentially expressed in multiple patients. These genes are mainly associated with focal adhesion, xenobiotics metabolism by cytochrome P450 and amino acid metabolism. For several differentially expressed genes in metabolic pathways, their expressions are significantly associated with overall survivals and vascular invasions of HCC patients. The following transwell assay experiments validate that they can regulate invasive phenotypes of HCC cells. CONCLUSIONS:The metastatic HCCs with PVTTs have significant molecular alterations comparing with adjacent normal tissues. The recurrent alteration patterns are similar to several previously published general HCC cohorts, but usually with higher severity. By an individualized differential analysis strategy, the progressively differentially expressed genes between the primary tumor and PVTT were identified for each patient. A few patients aquire evidential progressive alterations of gene expressions. And, experiments show that several recurrently differentially expressed genes can strongly regulate HCC cell invasions.

Project description:Overexpression of Golgi membrane protein 1 (GOLM1) is closely associated with hepatocellular carcinoma (HCC) vascular invasion. How GOLM1 may be involved in angiogenesis in HCC remains unclear. We explored how GOLM1 promotes angiogenesis in HCC and potential prognostic value. Expression levels of GOLM1 in HCC patients and healthy controls were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between HCC patients and controls were compared. GOLM1 was knocked out in the HCC cell line, and RNA sequencing and DEG expression analysis were performed compared with control cells. Based on TCGA data and cell line RNA sequencing data, DEGs affected by a high expression of GOLM1 were identified. Subsequently, enrichment analysis was performed to explore the functions and pathways of the DEGs affected by a high expression of GOLM1. A relevant network analysis was built. Cox regression, genomic variance analysis scores, minimum absolute shrinkage and selection operator regression, and random forest regression models were applied to determine the best prognostic model and validated using the GSE54236 dataset from the Gene Expression Omnibus (GEO). We determined the effect of GOLM1 expression on immune cell infiltration in liver cancer. GOLM1 was overexpressed in HCC tissues compared with controls, and its level correlated with tumor purity and prognosis. 400 DEGs affected by highly expressed GOLM1 were identified in TCGA and cell line RNA sequencing data. Enrichment analysis revealed that these DEGs may be related to biological processes of oxidative stress and angiogenesis and involved in the VEGF signaling pathway and protein processing in endoplasmic reticulum. We predicted a comprehensive regulatory network in which GOLM1 activated VEGF signaling to promote HCC angiogenesis. GOLM1 may interact with E2F1 and IGF2BP3 to promote angiogenesis. GOLM1 overexpression was associated with greater immune cell infiltration. A random forest regression model was the best prognostic model. Our study reveals a potential molecular mechanism of GOLM1 in promoting HCC. We developed two prognostic models based on DEG associated with GOLM1 overexpression to help stratify HCC prognosis and improve individualized treatment.

Dataset Information

Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies.

Background

Aim

Methods

Results

Conclusion

Publications

Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets