Combined analysis and validation for DNA methylation and gene expression profiles associated with prostate cancer

Abstract

Background Prostate cancer (PCa) is a malignancy cause of cancer deaths and frequently diagnosed in male. This study aimed to identify tumor suppressor genes, hub genes and their pathways by combined bioinformatics analysis. Methods A combined analysis method was used for two types of microarray datasets (DNA methylation and gene expression profiles) from the Gene Expression Omnibus (GEO). Differentially methylated genes (DMGs) were identified by the R package minfi and differentially expressed genes (DEGs) were screened out via the R package limma. A total of 4451 DMGs and 1509 DEGs, identified with nine overlaps between DMGs, DEGs and tumor suppressor genes, were screened for candidate tumor suppressor genes. All these nine candidate tumor suppressor genes were validated by TCGA (The Cancer Genome Atlas) database and Oncomine database. And then, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed by DAVID (Database for Annotation, Visualization and Integrated Discovery) database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. At last, Kaplan–Meier analysis was performed to validate these genes. Results The candidate tumor suppressor genes were IKZF1, PPM1A, FBP1, SMCHD1, ALPL, CASP5, PYHIN1, DAPK1 and CASP8. By validation in TCGA database, PPM1A, DAPK1, FBP1, PYHIN1, ALPL and SMCHD1 were significant. The hub genes were FGFR1, FGF13 and CCND1. These hub genes were identified from the PPI network, and sub-networks revealed by these genes were involved in significant pathways. Conclusion In summary, the study indicated that the combined analysis for identifying target genes with PCa by bioinformatics tools promote our understanding of the molecular mechanisms and underlying the development of PCa. And the hub genes might serve as molecular targets and diagnostic biomarkers for precise diagnosis and treatment of PCa. Electronic supplementary material The online version of this article (10.1186/s12935-019-0753-x) contains supplementary material, which is available to authorized users.

PYCR1 knockdown inhibits the proliferation, migration, and invasion by affecting JAK/STAT signaling pathway in lung adenocarcinoma

“…Recently, a host of cancer studies have been underpinned by bioinformatics analyses which could help to explore the molecular mechanisms of carcinogenesis. [8][9][10][11] The Oncomine database (https://www.oncomine.org) is a publicly accessible online cancer microarray database for the purpose of gene search function in cancers. 12 Indeed, a great range of lung cancer research was involved in this database, such as the prognostic roles of KIF23 in NSCLC 13 and the clinical significance of HOXA13 in LUAD.…”

A Three Protein-Coding Gene Prognostic Model Predicts Overall Survival in Bladder Cancer Patients

“…In response to DNA damage, SMCHD1 is recruited to sites of DNA double-strand breakage, where it promotes repair of the breakage by nonhomologous end joining (NHEJ), while inhibiting repair by homologous recombination [ 20 , 21 ]. One study suggested that SMCHD1 may be a candidate tumor suppressor gene in prostate cancer [ 22 ]. However, there has been comparatively little research into the role of SMCHD1 in different cancer types, so whether it functions as a tumor suppressor or an oncogene is not yet clear.…”

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

“…As shown in Table 6, the literature search in Pubmed/GEO regarding gene expression profiles of PCa versus NP tissues yielded 10 relevant studies, 5 (Chen et al 2012;Endo et al 2009;Fan et al 2018;Fang et al 2017;He et al 2018) of which were based on a single GEO dataset whereas the other 5 studies (Lu 2019;Song et al 2019b;Tan et al 2019;Tong et al 2019;Zhao et al 2017) were integrated bioinformatic analyses based on multiple GEO datasets. The hub genes reported by the 10 eligible studies were extracted and compared with those identified in the present study.…”

“…With the development of high-throughput sequencing technology and bioinformatic analysis methods, the Gene Expression Omnibus (GEO) online public database has been widely utilized to screen out differentially expressed genes (DEGs), to study molecular signals and their relations, and to aid in constructing gene regulatory networks (Clough & Barrett 2016). Up to now, by either analysis of a single dataset or integrated analysis of multiple datasets in GEO, several studies have dug out genes that exert important influence on the occurrence and progression of PCa, such as CDH1 (Fang et al 2017), CDCA8 (Zhao et al 2017), RPS21 (Fan et al 2018), PIK3R1 (He et al 2018), EPCAM (Lu 2019), LMNB1 (Song et al 2019b), IGF2 (Tan et al 2019), and IKZF1 (Tong et al 2019). However, the key genes detected by the above studies are largely different from each other and had little in common, and such discrepancy could be attributed to the fact that PCa is PeerJ reviewing PDF | (2019:06:38928:2:0:NEW 4 Sep 2019)…”