scholarly journals Identification of Significant Genes With Poor Colorectal Cancer Prognosis In Via Bioinformatical Analysis

2021 ◽  
Author(s):  
Shaodong Li ◽  
Ruizhi Dong ◽  
Bin Liang ◽  
Zhenhua Kang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Reduction Process ◽  
Zinc Ion ◽  
Cancer Prognosis ◽  
Venn Diagram ◽  
Normal Tissues ◽  
Bioinformatical Analysis

Abstract Purpose:Identification of significant genes with poor colorectal cancer prognosis in via bioinformatical analysis.Method:Gene expression profiles of GSE74602、 GSE110223、GSE113513 and GSE 141174 were available from GEO database. There are 65 CRC tissues and 65 normal tissues in the four profile datasets. Differentially expressed genes (DEGs) between CRC tissues and normal tissues were picked out by GEO2R tool and Venn diagram software. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery (DAVID) to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO). Then protein-protein interaction (PPI) of these DEGs with Search Tool for the Retrieval of Interacting Genes (STRING).Results:There were total of 171 consistently expressed genes in the four datasets, including 148 up-regulated and 23 down-regulated genes. up-regulated DEGs were particularly enriched in oxidation-reduction process, in extracellular exosome, in zinc ion binding, in Metabolic pathways, Mineral absorption; and down-regulated DEGs in positive regulation of cell proliferation, in cytosol, in One carbon pool by folate. Furthermore, for the analysis of overall survival among those genes, Kaplan–Meier analysis was implemented and 30 of 88 genes had a significantly worse prognosis. For validation in Gene Expression Profiling Interactive Analysis (GEPIA), 13 of 30 genes were discovered highly expressed in CRC tissues compared to normal tissues. Furthermore, MYC 和 FGFR3 markedly enriched in the Bladder cancer pathway.Conclusion: We have identified two significant up-regulated DEGs with poor prognosis in CRC , which could be potential therapeutic targets for CRC patients.

scholarly journals Bioinformatics Analysis of Genes Upregulating Poor Prognosis In Colorectal Cancer And Gastric Cancer

2021 ◽  
Author(s):  
Ruizhi Dong ◽  
Shaodong Li ◽  
Bin Liang ◽  
Zhenhua Kang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Gastric Cancer ◽  
Survival Rate ◽  
Poor Prognosis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venn Diagram ◽  
Expression Levels ◽  
Significant Difference

Abstract Purpose : To analyze the up-regulated genes of poor prognosis in colorectal cancer and gastric cancer by bioinformatics. Methods: We searched the gene expression profiles GSE156355 and GSE64916 in colorectal cancer and gastric cancer tissues in NCBI-GEO. With P value < 0.05 and log2>1 as the standard, Venn diagram software was used to identify the common DEGs in the two data sets. Kaplan Meier plotter was used to analyze the survival rate data of common differentially expressed genes, draw and select survival curves, and analyze their expression levels. Results: A total of 97 genes were detected to be up-regulated in the two gene expression profiles. There were 19 genes in the prognosis of gastric cancer and 15 genes in the prognosis of colorectal cancer that had significant differences in the survival rate. Among them, KCNQ1, TRIM29, GART, MSX1, SNAI1, SUV39H2, LOXL2 and KCTD14 significantly decreased the survival rate of gastric cancer and colorectal cancer. The expression of MSX1 was the highest in gastric cancer. The expression level of KCTD14 was the highest in colorectal cancer, and there was no significant difference in the expression levels of other genes. Conclusion: There are 19 and 15 genes with significantly different prognostic viability in gastric cancer and colorectal cancer, respectively. The survival rates of KCNQ1, TRIM29, GART, Msx1, SNAI1, SUV39H2, LOXL2 and KCTD14 were significantly decreased in gastric cancer and colorectal cancer. The expression of MSX1 was the highest in gastric cancer. The expression of KCTD14 was the highest in colorectal cancer.

Identification of potential significant genes in HBV related hepatocellular carcinoma via bioinformatical analysis

2020 ◽  
Author(s):  
Yuan Zhuang ◽  
Yuanyue Guan ◽  
Bin Sun ◽  
Yabo Ouyang ◽  
Xiaoni Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cytochrome P450 ◽  
Protein Localization ◽  
Expression Profiles ◽  
Reduction Process ◽  
Metabolic Process ◽  
Venn Diagram ◽  
Ppi Network ◽  
Normal Tissues

Abstract Background: The mortality rate of hepatocellular carcinoma（HCC）is the third highest worldwide. Infection with hepatitis B virus (HBV）is an important risk factor for the development of HCC. The fact that there is no available target drug for the HCC highlights the necessity to further explore its underlying mechanism.Methods: Gene expression profiles of GSE121248, GSE55092 and GSE62232 were accessible from GEO database. From 129 HCC tissues and 138 normal tissues in the three profile datasets, we picked out differentially expressed genes (DEGs) using GEO2R and Venn diagram software，analyzed Gene and Genome (KEGG) pathway and gene ontology (GO) in DEGs through DAVID software, and simulated the interactions between DEGs using the plotting function of STRING database, as well as constructed a protein-protein interaction (PPI) network by Cytoscape software Consequently significant genes with potential poor prognosis were selected using UALCAN and validated in Gene Expression Profiling Interactive Analysis.Results: In total of 103 DEGs in the three datasets, there were 26 up-regulated genes rich in regulation of attachment of spindle microtubules to kinetochore, protein localization to kinetochore, mitotic cytokinesis, cytokinesis, positive regulation of cytokinesis, Cell cycle and p53 signaling pathway while 77 down-regulated genes enriched in Retinol metabolism, Caffeine metabolism, Drug metabolism - cytochrome P450, Metabolism of xenobiotics by cytochrome P450, Chemical carcinogenesis, oxidation-reduction process, exogenous drug catabolic process, xenobiotic metabolic process, monocarboxylic acid metabolic process, epoxygenase P450 pathway and drug metabolic process. PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, we found 14 hub genes including TOP2A, CCNB1, RACGAP1, DTL, PBK, NEK2, PRC1, CDK1, RRM2, BUB1B, ECT2, ANLN, HMMR, ASPM, among which demonstrated 13 genes (except PRC1) had a significantly worse prognosis based on UALCAN analysis. All of the 13 genes were highly expressed in HBV related HCC tissues compared to normal tissues through GEPIA analysis. Conclusion: The significant up-regulated DEGs found by using integrated bioinformatical methods could be potential therapeutic targets for HBV related HCC patients.

Gene Expression Profiles as Predictors of Poor Outcomes in Stage II Colorectal Cancer: A Systematic Review and Meta-analysis

2009 ◽  
Vol 8 (4) ◽  
pp. 207-214 ◽  
Author(s):  
An-Ting T. Lu ◽  
Shelley R. Salpeter ◽  
Anthony E. Reeve ◽  
Steven Eschrich ◽  
Patrick G. Johnston ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Stage Ii ◽  
Stage Ii Colorectal Cancer ◽  
Poor Outcomes

Analysis of Gene Expression Profiles Reveals Novel Correlations With the Clinical Course of Colorectal Cancer

2007 ◽  
Vol 16 (11) ◽  
pp. 535-548 ◽  
Author(s):  
Duccio Cavalieri ◽  
Piero Dolara ◽  
Enrico Mini ◽  
Cristina Luceri ◽  
Cinzia Castagnini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Clinical Course ◽  
Expression Profiles ◽  
Gene Expression Profiles

Abstract 5711: Gene expression profiles inBRAF V600Emutant colorectal cancer and association withSFRP2methylation status

2017 ◽  
Author(s):  
Kazuya Yasui ◽  
Takeshi Nagasaka ◽  
Toshiaki Toshima ◽  
Takashi Kawai ◽  
Kunitoshi Shigeyasu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles

Cancer classification of single-cell gene expression data by neural network

2019 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Kijin Yu ◽  
Peter C W Lee
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Supplementary Information ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Normal Tissues

Abstract Motivation Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). Results We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. Availability and implementation Cancer classification by neural network. Supplementary information Supplementary data are available at Bioinformatics online.

Epigenetically regulated gene expression profiles reveal four molecular subtypes with prognostic and therapeutic implications in colorectal cancer

2020 ◽  
Author(s):  
Xiaokang Wang ◽  
Jinfeng Liu ◽  
Danwen Wang ◽  
Maohui Feng ◽  
Xiongzhi Wu
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Metabolic Activity ◽  
Poor Prognosis ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Epigenetic Mechanisms ◽  
Regulated Gene Expression ◽  
Marked Proliferation

Abstract Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of colorectal cancer (CRC). Herein, we first demonstrated that the frequencies of the aberrancies of DNA methylation-correlated (METcor) and microRNA (miRNA)-correlated (MIRcor) genes were significantly co-regulated. Next, through integrative clustering of the expression profiles of METcor and MIRcor genes, four molecular subtypes were identified in CRC patients from The Cancer Genome Atlas and then validated in four independent datasets. More importantly, the four subtypes were well characterized and showed distinct clinical and molecular features: (i) S-I: high metabolic activity, sensitive to 5-fluorouracil-based chemotherapy and good prognosis; (ii) S-II: moderate metabolic activity, marked proliferation, frequent KRAS mutation and intermediate prognosis; (iii) S-III: moderate metabolic activity, marked proliferation, promoter DNA hypermethylation, high mutation burden, frequent BRAF and EGFR mutations, moderate levels of epithelial-mesenchymal transition (EMT) and transforming growth factor β (TGFβ) signals, immune-inflamed phenotype, sensitive to cetuximab and death protein-1 inhibitor treatment and relatively poor prognosis and (iv) S-IV: miRNA overexpression, stem/serrated/mesenchymal-like properties, hypoxia, high levels of EMT and TGFβ signals, immune-excluded phenotype and poor prognosis. Overall, this study established a molecular classification based on epigenetically regulated gene expression profiles, thereby providing a better understanding of the epigenetic mechanisms underlying CRC heterogeneity.

The relationship between tumor budding, tumor microenvironment, and survival in patients with primary operable colorectal cancer.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 581-581
Author(s):  
Hester Catharina Van Wyk ◽  
Antonia K. Roseweir ◽  
Ditte Anderson ◽  
Elizabeth Sutton ◽  
Paul G. Horgan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Differential Gene Expression ◽  
Evaluation Method ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venous Invasion ◽  
Tumour Microenvironment ◽  
Differential Gene ◽  
Tumour Budding

581 Background: Tumour budding is an independent prognostic factor in colorectal cancer and has recently been defined by the International Consensus Conference on Tumour Budding. The aim was to use the ITBCC budding evaluation method to examine relationships between tumour budding, tumour factors, tumour microenvironment, gene expression profiles and survival in patients with primary operable CRC. Methods: Hematoxylin and Eosin (H&E) stained slides of 953 CRC patients, diagnosed between 1997 and 2007 were evaluated for tumour budding according to the ITBCC-criteria. The tumour microenvironment was evaluated using tumour stroma percentage (TSP) and Klintrup–Makinen (KM) grade to assess the tumour inflammatory cell infiltrate. Differential gene expression was assessed using TempO-Seq gene expression profiling (BioSpyder Technologies Inc., CA, USA) using the Surrogate+Tox targeted panel (2,733 genes selected for biological diversity, maximal information content, and widespread pathway coverage). Results: High budding (n = 269/ 28%) was significantly associated with TNM stage (P < 0.001), venous invasion (P < 0.001), weak KM grade (P < 0.001), high TSP (P < 0.001) and reduced cancer specific survival (CSS) (HR = 5.04; 95% confidence interval [CI], 3.50-9.51; P < 0.001) and was independent of venous invasion, KM grade, and Ki67 proliferation index. RNA expression analysis was employed using TempO-Seq to determine differential gene expression between tumours with (n = 8) and without budding (n = 18). Three genes were identified as significantly differentially expressed: S100A2 (S100 calcium binding protein A2) was upregulated by 2.9 fold (padj < 0.00001); REG1A (regenerating family member 1 alpha) was downregulated by 4.7 fold (padj < 0.01) and LCN2 (lipocalin 2) was downregulated by 2.2 fold (padj < 0.01). Conclusions: Tumour budding stratifies patients’ survival in primary operable colorectal cancer and associates with differing gene expression profiles and factors of the tumour. Therefore, the ITBCC budding evaluation method should be used to assess tumour budding as supplement the TNM staging system and can help to further subdivide colorectal cancer into new prognostic groups.

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