共0篇 平均IF=NaN (-)更多分析

    加载中

    logo
    Translating bioinformatics in oncology: guilt-by-profiling analysis and identification of KIF18B and CDCA3 as novel driver genes in carcinogenesis. Itzel Timo,Scholz Peter,Maass Thorsten,Krupp Markus,Marquardt Jens U,Strand Susanne,Becker Diana,Staib Frank,Binder Harald,Roessler Stephanie,Wang Xin Wei,Thorgeirsson Snorri,Müller Martina,Galle Peter R,Teufel Andreas Bioinformatics (Oxford, England) MOTIVATION:Co-regulated genes are not identified in traditional microarray analyses, but may theoretically be closely functionally linked [guilt-by-association (GBA), guilt-by-profiling]. Thus, bioinformatics procedures for guilt-by-profiling/association analysis have yet to be applied to large-scale cancer biology. We analyzed 2158 full cancer transcriptomes from 163 diverse cancer entities in regard of their similarity of gene expression, using Pearson's correlation coefficient (CC). Subsequently, 428 highly co-regulated genes (|CC| ≥ 0.8) were clustered unsupervised to obtain small co-regulated networks. A major subnetwork containing 61 closely co-regulated genes showed highly significant enrichment of cancer bio-functions. All genes except kinesin family member 18B (KIF18B) and cell division cycle associated 3 (CDCA3) were of confirmed relevance for tumor biology. Therefore, we independently analyzed their differential regulation in multiple tumors and found severe deregulation in liver, breast, lung, ovarian and kidney cancers, thus proving our GBA hypothesis. Overexpression of KIF18B and CDCA3 in hepatoma cells and subsequent microarray analysis revealed significant deregulation of central cell cycle regulatory genes. Consistently, RT-PCR and proliferation assay confirmed the role of both genes in cell cycle progression. Finally, the prognostic significance of the identified KIF18B- and CDCA3-dependent predictors (P = 0.01, P = 0.04) was demonstrated in three independent HCC cohorts and several other tumors. In summary, we proved the efficacy of large-scale guilt-by-profiling/association strategies in oncology. We identified two novel oncogenes and functionally characterized them. The strong prognostic importance of downstream predictors for HCC and many other tumors indicates the clinical relevance of our findings. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btu586
    Collagen Family and Other Matrix Remodeling Proteins Identified by Bioinformatics Analysis as Hub Genes Involved in Gastric Cancer Progression and Prognosis. International journal of molecular sciences Gastric cancer has remained in the top five cancers for over ten years, both in terms of incidence and mortality due to the shortage of biomarkers for disease follow-up and effective therapies. Aiming to fill this gap, we performed a bioinformatics assessment on our data and two additional GEO microarray profiles, followed by a deep analysis of the 40 differentially expressed genes identified. PPI network analysis and MCODE plug-in pointed out nine upregulated hub genes coding for proteins from the collagen family (COL12A1, COL5A2, and COL10A1) or involved in the assembly (BGN) or degradation of collagens (CTHRC1), and also associated with cell adhesion (THBS2 and SPP1) and extracellular matrix degradation (FAP, SULF1). Those genes were highly upregulated at the mRNA and protein level, the increase being correlated with pathological T stages. The high expression of BGN ( = 8 × 10), THBS2 ( = 1.2 × 10), CTHRC1 ( = 1.1 × 10), SULF1 ( = 3.8 × 10), COL5A1 ( = 1.3 × 10), COL10A1 ( = 5.7 × 10), COL12A1 ( = 2 × 10) correlated with poor overall survival and an immune infiltrate based especially on immunosuppressive M2 macrophages (-value range 4.82 × 10-1.63 × 10). Our results emphasize that these genes could be candidate biomarkers for GC progression and prognosis and new therapeutic targets. 10.3390/ijms23063214
    Identification of differentially expressed genes regulated by methylation in colon cancer based on bioinformatics analysis. Liang Yu,Zhang Cheng,Dai Dong-Qiu World journal of gastroenterology BACKGROUND:DNA methylation, acknowledged as a key modification in the field of epigenetics, regulates gene expression at the transcriptional level. Aberrant methylation in DNA regulatory regions could upregulate oncogenes and downregulate tumor suppressor genes without changing the sequences. However, studies of methylation in the control of gene expression are still inadequate. In the present research, we performed bioinformatics analysis to clarify the function of methylation and supply candidate methylation-related biomarkers and drivers for colon cancer. AIM:To identify and analyze methylation-regulated differentially expressed genes (MeDEGs) in colon cancer by bioinformatics analysis. METHODS:We downloaded RNA expression profiles, Illumina Human Methylation 450K BeadChip data, and clinical data of colon cancer from The Cancer Genome Atlas project. MeDEGs were identified by analyzing the gene expression and methylation levels using the edgeR and limma package in R software. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database and KEGG Orthology-Based Annotation System 3.0, respectively. We then conducted Kaplan-Meier survival analysis to explore the relationship between methylation and expression and prognosis. Gene set enrichment analysis (GSEA) and investigation of protein-protein interactions (PPI) were performed to clarify the function of prognosis-related genes. RESULTS:A total of 5 up-regulated and 81 down-regulated genes were identified as MeDEGs. GO and KEGG pathway analyses indicated that MeDEGs were enriched in multiple cancer-related terms. Furthermore, Kaplan-Meier survival analysis showed that the prognosis was negatively associated with the methylation status of glial cell-derived neurotrophic factor (GDNF) and reelin (RELN). In PPI networks, GDNF and RELN interact with neural cell adhesion molecule 1. Besides, GDNF can interact with GDNF family receptor alpha (GFRA1), GFRA2, GFRA3, and RET. RELN can interact with RAFAH1B1, disabled homolog 1, very low-density lipoprotein receptor, lipoprotein receptor-related protein 8, and NMDA 2B. Based on GSEA, hypermethylation of GDNF and RELN were both significantly associated with pathways including "RNA degradation," "ribosome," "mismatch repair," "cell cycle" and "base excision repair." CONCLUSION:Aberrant DNA methylation plays an important role in colon cancer progression. MeDEGs that are associated with the overall survival of patients may be potential targets in tumor diagnosis and treatment. 10.3748/wjg.v25.i26.3392