Dataset Information

Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer.

ABSTRACT:

Background

Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics.

Methods

We integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation.

Results

We comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value.

Conclusions

These findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.

SUBMITTER: Liu N

PROVIDER: S-EPMC8073914 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Multi-omics data integration has emerged as a promising approach to identify patient subgroups. However, in terms of grouping genes (or gene products) into co-expression modules, data integration methods suffer from two main drawbacks. First, most existing methods only consider genes or samples measured in all different datasets. Second, known molecular interactions (e.g., transcriptional regulatory interactions, protein-protein interactions and biological pathways) cannot be utilized to assist in module detection. Herein, we present a novel data integration framework, Correlation-based Local Approximation of Membership (CLAM), which provides two methodological innovations to address these limitations: 1) constructing a trans-omics neighborhood matrix by integrating multi-omics datasets and known molecular interactions, and 2) using a local approximation procedure to define gene modules from the matrix. Applying Correlation-based Local Approximation of Membership to human colorectal cancer (CRC) and mouse B-cell differentiation multi-omics data obtained from The Cancer Genome Atlas (TCGA), Clinical Proteomics Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO) and ProteomeXchange database, we demonstrated its superior ability to recover biologically relevant modules and gene ontology (GO) terms. Further investigation of the colorectal cancer modules revealed numerous transcription factors and KEGG pathways that played crucial roles in colorectal cancer progression. Module-based survival analysis constructed four survival-related networks in which pairwise gene correlations were significantly correlated with colorectal cancer patient survival. Overall, the series of evaluations demonstrated the great potential of Correlation-based Local Approximation of Membership for identifying modular biomarkers for complex diseases. We implemented Correlation-based Local Approximation of Membership as a user-friendly application available at https://github.com/free1234hm/CLAM.

Project description:The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools to quantify cancer stemness in clinical tumors, and perhaps lead considerable benefits that could predict tumor prognosis, identify new stemness-related targets and targeted therapies, or improve targeted therapy sensitivity. The five genes identified in this study are expected to be the targets of GBM stem cell therapy.