scholarly journals Identification of a Five-Gene Prognostic Model and Its Potential Drug Repurposing in Colorectal Cancer Based on TCGA, GTEx and GEO Databases

2021 ◽  
Vol 11 ◽  
Author(s):  
Feng Yang ◽  
Shaoyi Cai ◽  
Li Ling ◽  
Haiji Zhang ◽  
Liang Tao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regulatory Region ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Pathway Enrichment Analysis ◽  
Potential Drug ◽  
Hub Genes

Colorectal cancer (CRC) is a major cause of cancer deaths worldwide. Unfortunately, many CRC patients are still being diagnosed at an advanced stage of the cancer, and the 5-year survival rate is only ~30%. Effective prognostic markers of CRC are therefore urgently needed. To address this issue, we performed a detailed bioinformatics analysis based on the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases to identify prognostic biomarkers for CRC, which in turn help in exploring potential drug-repurposing. We identified five hub genes (PGM2, PODXL, RHNO1, SCD, and SEPHS1), which had good performance in survival prediction and might be involved in CRC through three key pathways (“Cell cycle,” “Purine metabolism,” and “Spliceosome” KEGG pathways) identified by a KEGG pathway enrichment analysis. What is more, we performed a co-expression analysis between five hub genes and transcription factors to explore the upstream regulatory region. Furthermore, we screened the potential drug-repurposing for the five hub genes in CRC according to the Binding DB and ZINC15 databases. Taking together, we constructed a five-gene signature to predict overall survival of CRC and found the potential drug-repurposing, which may improve the outcome of CRC in the future.

scholarly journals Identification of Hub Genes Related to Carcinogenesis and Prognosis in Colorectal Cancer Based on Integrated Bioinformatics

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Benjiao Gong ◽  
Yanlei Kao ◽  
Chenglin Zhang ◽  
Fudong Sun ◽  
Zhaohua Gong ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Proportional Hazards ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Preneoplastic Lesions ◽  
Hub Genes

The high mortality of colorectal cancer (CRC) patients and the limitations of conventional tumor-node-metastasis (TNM) stage emphasized the necessity of exploring hub genes closely related to carcinogenesis and prognosis in CRC. The study is aimed at identifying hub genes associated with carcinogenesis and prognosis for CRC. We identified and validated 212 differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database. We investigated functional enrichment analysis for DEGs. The protein-protein interaction (PPI) network was constructed, and hub modules and genes in CRC carcinogenesis were extracted. A prognostic signature was developed and validated based on Cox proportional hazards regression analysis. The DEGs mainly regulated biological processes covering response to stimulus, metabolic process, and affected molecular functions containing protein binding and catalytic activity. The DEGs played important roles in CRC-related pathways involving in preneoplastic lesions, carcinogenesis, metastasis, and poor prognosis. Hub genes closely related to CRC carcinogenesis were extracted including six genes in model 1 (CXCL1, CXCL3, CXCL8, CXCL11, NMU, and PPBP) and two genes and Metallothioneins (MTs) in model 2 (SLC26A3 and SLC30A10). Among them, CXCL8 was also related to prognosis. An eight-gene signature was proposed comprising AMH, WBSCR28, SFTA2, MYH2, POU4F1, SIX4, PGPEP1L, and PAX5. The study identified hub genes in CRC carcinogenesis and proposed an eight-gene signature with good reproducibility and robustness at the molecular level for CRC, which might provide directive significance for treatment selection and survival prediction.

scholarly journals Prediction and Validation of Hub Genes Associated with Colorectal Cancer by Integrating PPI Network and Gene Expression Data

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yongfu Xiong ◽  
Wenxian You ◽  
Rong Wang ◽  
Linglong Peng ◽  
Zhongxue Fu
Keyword(s):  
Colorectal Cancer ◽  
Mrna Expression ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Advanced Tumor Stage ◽  
Ppi Network ◽  
Hub Genes ◽  
Clinical Value ◽  
Advanced Tumor

Although hundreds of colorectal cancer- (CRC-) related genes have been screened, the significant hub genes still need to be further identified. The aim of this study was to identify the hub genes based on protein-protein interaction network and uncover their clinical value. Firstly, 645 CRC patients’ data from the Tumor Cancer Genome Atlas were downloaded and analyzed to screen the differential expression genes (DEGs). And then, the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed, and PPI network of the DEGs was constructed by Cytoscape software. Finally, four hub genes (CXCL3, ELF5, TIMP1, and PHLPP2) were obtained from four subnets and further validated in our clinical setting and TCGA dataset. The results showed that mRNA expression of CXCL3, ELF5, and TIMP1 was increased in CRC tissues, whereas PHLPP2 mRNA expression was decreased. More importantly, high expression of CXCL3, ELF5, and TIMP1 was significantly associated with lymphatic invasion, distance metastasis, and advanced tumor stage. In addition, a shorter overall survival was observed in patients with increased CXCL3, TIMP1, and ELF5 expression and decreased PHLPP2 expression. In conclusion, the four hub genes screened by our strategy could serve as novel biomarkers for prognosis prediction of CRC patients.

scholarly journals A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Shiqiang Hou ◽  
Jinyi Wang ◽  
Zhengjun Chai ◽  
Xuan Hong ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Gene Expression Signature ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Pathway Enrichment Analysis ◽  
Prognostic Information ◽  
Specificity And Sensitivity

Background. Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD. Method. RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs. Results. Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection. Conclusion. Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients.

scholarly journals P4HA1, a Prognostic Biomarker that Correlates With Immune Infiltrates in Lung Adenocarcinoma and Pan-Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Qi Zhao ◽  
Junfeng Liu
Keyword(s):  
Prognostic Value ◽  
Immune Cell ◽  
Prognostic Biomarker ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Pathway Enrichment Analysis ◽  
Immune Cell Infiltration ◽  
Pan Cancer

Objective: Prolyl 4-hydroxylase, alpha polypeptide I (P4HA1), a key enzyme in collagen synthesis, comprises two identical alpha subunits and two beta subunits. However, the immunomodulatory role of P4HA1 in tumor immune microenvironment (TIME) remains unclear. This study aimed to evaluate the prognostic value of P4HA1 in pan-cancer and explore the relationship between P4HA1 expression and TIME.Methods: P4HA1 expression, clinical features, mutations, DNA methylation, copy number alteration, and prognostic value in pan-cancer were investigated using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data. Pathway enrichment analysis of P4HA1 was performed using R package “clusterProfiler.” The correlation between immune cell infiltration level and P4HA1 expression was analyzed using three sources of immune cell infiltration data, including ImmuCellAI database, TIMER2 database, and a published work.Results: P4HA1 was substantially overexpressed in most cancer types. P4HA1 overexpression was associated with poor survival in patients. Additionally, we discovered that P4HA1 expression was positively associated with infiltration levels of immunosuppressive cells, such as tumor-associated macrophages, cancer-associated fibroblasts, nTregs, and iTregs, and negatively correlated with CD8+ T and NK cells in pan-cancer.Conclusions: Our results highlighted that P4HA1 might serve as a potential prognostic biomarker in pan-cancer. P4HA1 overexpression is indicative of an immunosuppressive microenvironment. P4HA1 may be a potential target of immunotherapy.

scholarly journals Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6555 ◽  
Author(s):  
Wang-Xiao Xia ◽  
Qin Yu ◽  
Gong-Hua Li ◽  
Yao-Wen Liu ◽  
Fu-Hui Xiao ◽  
...  
Keyword(s):  
Adrenocortical Carcinoma ◽  
Cox Model ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Search Tool ◽  
Division Cell ◽  
Blue Module

Background Adrenocortical carcinoma (ACC) is a rare and aggressive malignant cancer in the adrenal cortex with poor prognosis. Though previous research has attempted to elucidate the progression of ACC, its molecular mechanism remains poorly understood. Methods Gene transcripts per million (TPM) data were downloaded from the UCSC Xena database, which included ACC (The Cancer Genome Atlas, n = 77) and normal samples (Genotype Tissue Expression, n = 128). We used weighted gene co-expression network analysis to identify gene connections. Overall survival (OS) was determined using the univariate Cox model. A protein–protein interaction (PPI) network was constructed by the search tool for the retrieval of interacting genes. Results To determine the critical genes involved in ACC progression, we obtained 2,953 significantly differentially expressed genes and nine modules. Among them, the blue module demonstrated significant correlation with the “Stage” of ACC. Enrichment analysis revealed that genes in the blue module were mainly enriched in cell division, cell cycle, and DNA replication. Combined with the PPI and co-expression networks, we identified four hub genes (i.e., TOP2A, TTK, CHEK1, and CENPA) that were highly expressed in ACC and negatively correlated with OS. Thus, these identified genes may play important roles in the progression of ACC and serve as potential biomarkers for future diagnosis.

scholarly journals Mining of RNA Methylation-Related Genes and Elucidation of Their Molecular Biology in Gallbladder Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Changhong Yang ◽  
Jialei Chen ◽  
Zhe Yu ◽  
Jing Luo ◽  
Xuemei Li ◽  
...  
Keyword(s):  
Gallbladder Carcinoma ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Curative Surgery ◽  
Rna Methylation ◽  
Early Diagnose ◽  
Biliary Tract Malignancy

Gallbladder carcinoma (GBC), which has high invasion and metastasis risks, remains the most common biliary tract malignancy. Surgical resection for GBC is the only effective treatment, but most patients miss the opportunity for curative surgery because of a lack of timely diagnosis. The aim of this study was to identify and verify early candidate diagnostic and prognostic RNA methylation related genes for GBC via integrated transcriptome bioinformatics analysis. Lists of GBC-related genes and methylation-related genes were collected from public databases to screen differentially expressed genes (DEGs) by using the limma package and the RobustRankAggreg (RRA) package. The core genes were collected with batch effects corrected by the RRA algorithm through protein interaction network analysis, signaling pathway enrichment analysis and gene ranking. Four modules obtained from four public microarray datasets were found to be related to GBC, and FGA, F2, HAO1, CFH, PIPOX, ITIH4, GNMT, MAT1A, MTHFD1, HPX, CTH, EPHX2, HSD17B6, AKR1C4, CFHR3, ENNP1, and NAT2 were revealed to be potential hub genes involved in methylation-related pathways and bile metabolism-related pathways. Among these, FGA, CFH, F2, HPX, and PIPOX were predicted to be methylated genes in GBC, but POPIX had no modification sites for RNA methylation. Furthermore, survival analysis of TCGA (the Cancer Genome Atlas) database showed that six genes among the hub genes, FGA, CFH, ENPP1, CFHR3, ITIH4, and NAT2, were highly expressed and significantly correlated with worse prognosis. Gene correlation analysis revealed that the FGA was positively correlated with the ENPP1, NAT2, and CFHR3, while CFH was positively correlated with the NAT2, CFHR3, and FGA. In addition, the results of immunohistochemistry (IHC) showed that the expressions of FGA, F2, CFH, PIPOX, ITIH4, GNMT, MAT1A, MTHFD1, HPX, CFHR3, NAT2, and ENPP1 were higher in GBC tissues than that in control tissues. In conclusion, two genes, FGA and CFH, were identified as RNA methylation-related genes also involved in bile metabolism in GBC, which may be novel biomarkers to early diagnose and evaluate prognosis for GBC.

scholarly journals Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

2020 ◽  
Vol 10 ◽  
Author(s):  
Fang-Ze Wei ◽  
Shi-Wen Mei ◽  
Zhi-Jie Wang ◽  
Jia-Nan Chen ◽  
Hai-Yu Shen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Ncbi Gene Expression Omnibus ◽  
Public Datasets

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

scholarly journals Bioinformatics analysis of KIF20A, a potential therapeutic target for glioblastoma

2021 ◽  
Author(s):  
Lu Han ◽  
Jiayang Wang
Keyword(s):  
Therapeutic Target ◽  
High Mobility ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Normal Brain ◽  
Hub Genes ◽  
R Software ◽  
Potential Therapeutic Target ◽  
Geo Database

Abstract Background: Glioblastoma (GBM) is a malignant brain tumor with high mobility. The median survival time of GBM patients is 15 months. Currently, there is no effective treatment for improving the prognosis of the GBM due to a lack of prognostic markers. Materials and methods: To predict core therapeutic targets for GBM, we analyzed four microarray datasets (GSE49810, GSE50161, GSE65624, and GSE90604) selected from the Gene Expression Omnibus (GEO) database and the other datasets obtained from The Cancer Genome Atlas (TCGA) database. Expression protein array of 227 GBM samples and 18 normal samples were clustered to summarize GBM tissue classification. Differentially expressed genes (DEGs) were analyzed by comparing GBM and normal brain tissues in each profile using the limma package of R software. GO function and KEGG pathway enrichment analysis was performed using the DAVID database. Overlapping DEGs were ranked based on protein expression ratios from the comparison between cancer and normal samples using robustRankaggreg package of R software and scored from high to low. Protein-protein interaction (PPI) network was visualized using CytoHubba and Cluego plugins in Cytoscape software. Core hub genes were analyzed by MCC, MNC, DMNC, and EPC methods. Besides, the GEPIA tool was used to create the survival curves and boxplots to evaluate the prognostic effect of hub genes for improving the diagnostic outcomes and treatment of GBM. Results: A total of 2064 DEGs were analyzed (1400 downregulated DEGs and 1664 upregulated DEGs) in the GEO database. 3292 DEGs were found (1485 upregulated DEGs and 1807 downregulated DEGs) in TCGA. We selected 221 significant DEGs from four microarrays. Combining the GEO results with the results of TCGA, we found only 181 common DEGs by using Venn analysis. Further, expression levels of KIF20A selected from 10 hub genes closely associated with the survival rate. Conclusion: Up-regulation of KIF20A has a pivotal role in controlling the prognosis of GBM in 2 years follow-up period; KIF20A should be considered as a potential therapeutic target for GBM.

scholarly journals Identification of Issue Inhibitor of Metalloproteinase 1 (TIMP1) as a Potential Biomarker for the Diagnosis, Pathogenesis and Prognosis of Colorectal Cancer via Integrated Bioinformatic Analysis

2020 ◽  
Author(s):  
Guona Li ◽  
Mengmeng Kang ◽  
Siyuan Sheng ◽  
Ziyi Chen ◽  
Kunshan Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Molecular Therapy ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Potential Biomarker

Abstract Background: Colorectal cancer (CRC) is a common malignant tumor of the digestive system. It is crucial to screen potential biomarkers for the diagnosis, pathogenesis, and prognosis of CRC because there are limited clinical symptoms associated with this cancer. Therefore, we attempted to identify biomarkers associated with the occurrence and progression of CRC by utilizing bioinformatic analysis and to elucidate a molecular mechanism for the diagnosis and treatment of CRC. Methods: Two independent gene expression profile datasets of colonic neoplasms (GSE44076 and GSE37182) were collected from public GEO datasets, which included 182 tumor tissues and 236 normal tissues. Next, differentially expressed genes (DEGs) between CRC colonic samples and non-CRC colonic samples were obtained via GEO2R online tools. Subsequently, hub genes were selected by several analyses of DEGs, including GO pathway enrichment analysis, KEGG pathway enrichment analysis, and PPI network analysis. Finally, the correlation between the hub genes and the occurrence of CRC was tested by harnessing survival analysis and ROC curve analysis. Results: Sixty-one shared DEGs were screened, including 44 high-expression genes and 17 low-expression genes, in CRC samples. Four genes (MYC, TIMP1, MMP7, and COL1A1) were considered to be hub genes because they exhibited higher connectivity degree scores through PPI network analysis. More importantly, there was a significant correlation between increased expression of TIMP1 and reduced survival time in patients with colorectal cancer. Conclusion: By using bioinformatic analysis, this study suggested that Timp-1 may represent a potential biomarker for the diagnosis and prognosis of targeted molecular therapy for CRC.

scholarly journals Association Between Obesity and Incident Colorectal Cancer: An Analysis Based on Colorectal Cancer Database in the Cancer Genome Atlas

2021 ◽  
Author(s):  
Su Yongxian ◽  
Chen Tonghua
Keyword(s):  
Colorectal Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Obese Group ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Significant Difference ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Background To investigate gene factors of colorectal cancer (CRC) in obesity and potential molecular markers. Methods Clinical data and mRNA expression data from The Cancer Genome Atlas (TCGA) was collected and divided into obese group and non-obese group according to BMI. The differential expressed genes (DEGs) were screened out by “Limma” package of R software based on (|log2(fold change)|>2 and p < 0.05). The functions of DEGs were revealed with Gene Ontology and Kyoto Encyclopedia Genes and Genomes pathway enrichment analysis using the DAVID database. Then STRING database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. Kaplan-Meier analysis was used to assess the potential prognostic genes for CRC patients. Results It has revealed 2055 DEGs in obese group with CRC, 7615 DEGs in non-obese group and 9046 DEGs in total group. MS4A12, TMIGD1, CA2, GBA3 and SLC51B were the top five downregulated genes in obese group. A PPI network consisted of 1042 nodes and 4073 edges, and top ten hub genes SST, PYY, GNG12, CCL13, MCHR2, CCL28, ADCY9, SSTR1, CXCL12 and ADRA2A were identified in obese group. PDCD11 may well predict overall survivals of CRC patients in non-obese group. The survival time of obese group was shorter than that of non-obese group, but there was no significant difference. Conclusions PDCD11 may be a potential molecular marker for non-obese patients with CRC.

Sign in / Sign up

Export Citation Format

Share Document