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Time series expression pattern of key genes reveals the molecular process of esophageal cancer.

ABSTRACT: BACKGROUND:Esophageal cancer is one of the most poorly diagnosed and fatal cancers in the world. Although a series of studies on esophageal cancer have been reported, the molecular pathogenesis of the disease is still elusive. AIM:To investigate the molecular process of esophageal cancer comprehensively and deeply. METHODS:Differential expression analysis was performed to identify differentially expressed genes (DEGs) in different stages of esophageal cancer. Then exacting gene interaction modules and hub genes were identified in module interaction network. Further, though survival analysis, methylation analysis, pivot analysis, and enrichment analysis, some important molecules and related function or pathway were identified to elucidate potential mechanism in esophageal cancer. RESULTS:A total of 7457 DEGs and 14 gene interaction modules were identified. These module genes were significantly involved in the positive regulation of protein transport, gastric acid secretion, insulin-like growth factor receptor binding and other biological processes (BPs), as well as p53 signaling pathway, ERBB signaling pathway and epidermal growth factor receptor (EGFR) signaling pathway. Then, transcription factors (TFs) (including HIF1A) and ncRNAs (including CRNDE and hsa-mir-330-3p) significantly regulate dysfunction modules were identified. Further, survival analysis showed that GNGT2 was closely related to survival of esophageal cancer. And DEGs with strong methylation regulation ability were identified, including SST and SH3GL2. CONCLUSION:These works not only help us to reveal the potential regulatory factors in the development of disease, but also deepen our understanding of its deterioration mechanism.

SUBMITTER: Wang J

PROVIDER: S-EPMC7048673 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Time series expression pattern of key genes reveals the molecular process of esophageal cancer.

Wang Jiafu J Xie Xiang X Sun Yurong Y

Bioscience reports 20200201 2

<h4>Background</h4>Esophageal cancer is one of the most poorly diagnosed and fatal cancers in the world. Although a series of studies on esophageal cancer have been reported, the molecular pathogenesis of the disease is still elusive.<h4>Aim</h4>To investigate the molecular process of esophageal cancer comprehensively and deeply.<h4>Methods</h4>Differential expression analysis was performed to identify differentially expressed genes (DEGs) in different stages of esophageal cancer. Then exacting ...[more]

PMID: 32068233

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Project description:BACKGROUND:Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits. RESULTS:In this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm ?-TRIMAX. Its aim is to make optimal use of ?-TRIMAX in extracting groups of co-expressed genes from time series gene expression data, or from any 3D gene expression dataset, by adding the powerful capabilities of an evolutionary algorithm to retrieve overlapping triclusters. We have compared the performance of our newly developed algorithm, EMOA- ?-TRIMAX, with that of other existing triclustering approaches using four artificial dataset and three real-life datasets. Moreover, we have analyzed the results of our algorithm on one of these real-life datasets monitoring the differentiation of human induced pluripotent stem cells (hiPSC) into mature cardiomyocytes. For each group of co-expressed genes belonging to one tricluster, we identified key genes by computing their membership values within the tricluster. It turned out that to a very high percentage, these key genes were significantly enriched in Gene Ontology categories or KEGG pathways that fitted very well to the biological context of cardiomyocytes differentiation. CONCLUSIONS:EMOA- ?-TRIMAX has proven instrumental in identifying groups of genes in transcriptomic data sets that represent the functional categories constituting the biological process under study. The executable file can be found at http://www.bioinf.med.uni-goettingen.de/fileadmin/download/EMOA-delta-TRIMAX.tar.gz .

Project description:Background:Cancer metastasis is well known as the most adverse outcome and the major cause of mortality in cancer patients, including prostate cancer (PCa). There are no credible predictors, to this day, that can reflect the metastatic ability of localized PCa. In the present study, we firstly identified the differentially expressed genes (DEGs) and molecular pathways involved in the metastaic process of PCa by comparing gene expressions of metastaic PCa with localized PCa directly, with the purpose of identifying potential markers or therapeutic targets. Methods:The gene expression profiles (GSE6919 and GSE32269) were downloaded from the Gene Expression Omnibus database, which contained 141 tissue samples, including 87 primary localized PCa samples and 54 metastaic PCa samples. After data processing, DEGs were identified by R language using the Student's t-test adjusted via the Beniamini-Hochberg method. Subsequently, the gene ontology functional and pathway enrichment analyses of DEGs were performed and the protein-protein interaction network was constructed. Hub genes were identified using the plug-in cytoHubba in Cytoscape software by MCC and degree. Furthermore, validation and prognostic significance analysis of the hub genes were performed by UALCAN and gene expression profiling interactive analysis (GEPIA). Results:A total of 90 DEGs were identified between localized and metastaic PCa, which consisted of 47 upregulated and 43 downregulated genes. The enriched functions and pathways of the DEGs include catabolic process, cell cycle, response to steroid hormone, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. A total of 10 genes were identified as hub genes and biological process analysis of hub genes showed that cell cycle phase, cell division, and mitotic cell cycle process were mainly enriched. The expression of hub genes were confirmed in metastaic PCa when compared with localized PCa tissues by The Cancer Genome Atlas database. Moreover, the disease-free survival analysis of hub genes revealed that these genes may play an important role in invasion, progression or recurrence. Therefore, these hub genes might be the key genes contributed to tumor progression or metastasis in PCa and provide candidate therapeutic targets for PCa. Conclusions:The present study identified some DEGs between localized and metastaic PCa tissue samples. These key genes might be potential therapeutic targets and biomarkers for the metastaic process of PCa.

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Time series expression pattern of key genes reveals the molecular process of esophageal cancer.

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Time series expression pattern of key genes reveals the molecular process of esophageal cancer.

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