Dataset Information

Network and pathway-based analysis of candidate genes associated with esophageal adenocarcinoma.

ABSTRACT:

Background

Previous studies have made some headway in analyzing esophageal adenocarcinoma (EA) with respect to pathogenic factors, treatment methods, and prognosis. However, far less is known about the molecular mechanisms. Thus, a comprehensive analysis focusing on the biological function and interaction of EA genes would provide valuable information for understanding the pathogenesis of EA, which may provide new insights into gene function as well as potential therapy targets.

Methods

We selected 109 genes related to EA by reviewing 458 publications from the PubMed database. In addition, performing gene enrichment assays, pathway enrichment assays, pathway crosstalk analysis, and extraction of EA-specific subnetwork were used to describe the relevant biochemical processes.

Results

Function analysis revealed that biological processes and biochemical pathways associated with apoptotic and metabolic processes, a variety of cancers, and drug reaction pathways. Further, 12 novel genes (PTHLH, SUMO2, TYMS, APP, PTGIR, SP1, UBC, COL1A1, GSTO1, TRAF6, BMP7, and RAB40B) were identified in the EA-specific network, which might provide helpful information for clinical application.

Conclusions

Overall, by integrating pathways and networks to explore the pathogenetic mechanisms underlying EA, our results could significantly improve our understanding of the molecular mechanisms of EA and form a basis for selection of potential molecular targets for further exploration.

SUBMITTER: Li J

PROVIDER: S-EPMC10007923 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Network and pathway-based analysis of candidate genes associated with esophageal adenocarcinoma.

Li Junfeng J Peng Ling L Li Hanbing H Cai Yan Y Yao Peng P Chen Qin Q Li Xiaolei X Zhou Qiuxi Q

Journal of gastrointestinal oncology 20230215 1

<h4>Background</h4>Previous studies have made some headway in analyzing esophageal adenocarcinoma (EA) with respect to pathogenic factors, treatment methods, and prognosis. However, far less is known about the molecular mechanisms. Thus, a comprehensive analysis focusing on the biological function and interaction of EA genes would provide valuable information for understanding the pathogenesis of EA, which may provide new insights into gene function as well as potential therapy targets.<h4>Metho ...[more]

PMID: 36915458

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Project description:BackgroundEsophageal adenocarcinoma (EAC) is an aggressive malignancy and accounts for the majority of cancer-related death worldwide. It is often diagnosed at an advanced stage and entails a poor prognosis for those afflicted. The mechanisms of its pathogenesis and progress remain unclear and require urgent elucidation. This study aimed to identify specific genes and potential pathways associated with the progression and prognosis of EAC using bioinformatics analyses.MethodsEAC microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs) using bioinformatics analysis. The DEGs in TCGA were then analyzed to construct a co-expression network by weighted correlation network analysis (WGCNA), and module-clinical trait relationships were analyzed to explore the genes that associated with clinicopathological parameters of EAC. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were performed for the cancer-related genes, and a DEG-based protein-protein interaction (PPI) network was used to extract hub genes through Cytoscape plugins. The consensus survival analysis for EAC (OSeac) was performed to identify the prognosis-related genes. The immune infiltration was evaluated by tumor immune estimation resource (TIMER) algorithms, and a risk score prognostic model was established using univariate, multivariate Cox proportional hazards regression, and lasso regression analysis.ResultsUltimately, 190 cancer-related DEGs were identified, 6 of which were found to play vital roles in the progression of EAC, including ACTA2, BGN, CALD1, COL1A1, COL4A1, and DCN. The risk score prognostic model consisted of 6 other genes that had an important impact on the prognosis of EAC, including CLDN3, EPB41L4A, ESM1, MT1X, PAQR5, and PLAU. The area under the curve of the prognostic model for predicting the survival of patients at 1, 2, and 3 years was 0.707, 0.702, and 0.726, respectively.ConclusionsThis study identified several genes with the potential to become useful targets for the diagnosis and treatment of EAC. The 6-gene-related risk score prognostic model and nomogram based on these genes may be a reliable tool for predicting the prognosis of patients with EAC.

Project description:BACKGROUND: Numerous genetic and genomic datasets related to complex diseases have been made available during the last decade. It is now a great challenge to assess such heterogeneous datasets to prioritize disease genes and perform follow up functional analysis and validation. Among complex disease studies, psychiatric disorders such as major depressive disorder (MDD) are especially in need of robust integrative analysis because these diseases are more complex than others, with weak genetic factors at various levels, including genetic markers, transcription (gene expression), epigenetics (methylation), protein, pathways and networks. RESULTS: In this study, we proposed a comprehensive analysis framework at the systems level and demonstrated it in MDD using a set of candidate genes that have recently been prioritized based on multiple lines of evidence including association, linkage, gene expression (both human and animal studies), regulatory pathway, and literature search. In the network analysis, we explored the topological characteristics of these genes in the context of the human interactome and compared them with two other complex diseases. The network topological features indicated that MDD is similar to schizophrenia compared to cancer. In the functional analysis, we performed the gene set enrichment analysis for both Gene Ontology categories and canonical pathways. Moreover, we proposed a unique pathway crosstalk approach to examine the dynamic interactions among biological pathways. Our pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. These two modules are neuro-transmission and immune system related, supporting the neuropathology hypothesis of MDD. Finally, we constructed a MDD-specific subnetwork, which recruited novel candidate genes with association signals from a major MDD GWAS dataset. CONCLUSIONS: This study is the first systematic network and pathway analysis of candidate genes in MDD, providing abundant important information about gene interaction and regulation in a major psychiatric disease. The results suggest potential functional components underlying the molecular mechanisms of MDD and, thus, facilitate generation of novel hypotheses in this disease. The systems biology based strategy in this study can be applied to many other complex diseases.

Dataset Information

Network and pathway-based analysis of candidate genes associated with esophageal adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Network and pathway-based analysis of candidate genes associated with esophageal adenocarcinoma.

Similar Datasets

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