scholarly journals Identification of Genes in Patients for Predicting Ulcerative Colitis-Associated Colorectal Cancer

2020 ◽  
Author(s):  
hongyun wei ◽  
qian zhang ◽  
xiaosa chi ◽  
xiaohui yang ◽  
zibin tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Ulcerative Colitis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Public Datasets

Abstract BackgroundUlcerative colitis (UC) has been considered as a risk factor for colorectal cancer (CRC). However, effective biomarkers for predicting UC-associated CRC are lacking. Therefore, it is necessary to screen biomarkers associated with UC-related CRC, which could be used to evaluate UC-associated CRC early, and provide possible mechanisms involved in UC-associated CRC. Efficient bioinformatics analysis could help us to explore potential biomarkers.MethodsTwo public datasets, including 44 UC without CRC samples and 17 UC-associated CRC samples were chosen from Gene Expression Omnibus (GEO) database. Sva package was used to remove batch effects, and then we screened out differentially expressed genes (DEGs) with limma package. STRING and Cytoscape were used to achieve protein-protein interaction (PPI) network analysis. The survival curves between high and low gene expression were performed by log rank test based on the cancer genome atlas (TCGA) program. The expression of three identified hub genes was validated based on Oncomine. To validate the expression of three hub genes, we compared the expression of three hub genes between normal and colorectal cancer based on Oncomine.Results405 DEGs were identified, including 256 down-regulated genes and 149 up-regulated genes in UC-associated CRC tissues. 16 hub genes were identified. And among them, RPL6, RPL7, and RPL35 were related to poor prognosis of patients in survival analysis. Higher expression of RPL6, RPL7, and RPL35 was validated in CRC tissues based on Oncomine.ConclusionsOur study showed that overexpressed RPL6, RPL7, and RPL 35 may be potential tumor oncogenes and could act as a prognostic factor in clinical diagnosis and treatment.

scholarly journals Identification of Hub Genes Associated With Sensitivity of 5-Fluorouracil Based Chemotherapy for Colorectal Cancer by Integrated Bioinformatics Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Ya Wang ◽  
Qunhui Wei ◽  
Yuqiao Chen ◽  
Shichao Long ◽  
Yuanbing Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Colorectal Adenocarcinoma ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
Chemotherapy Response ◽  
Intrinsic Resistance ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Novel Biomarkers

Colorectal cancer (CRC) is one of the most common malignant tumors. 5-fluorouracil (5-FU) has been used for the standard first-line treatment for CRC patients for several decades. Although 5-FU based chemotherapy has increased overall survival (OS) of CRC patients, the resistance of CRC to 5-FU based chemotherapy is the principal cause for treatment failure. Thus, identifying novel biomarkers to predict response to 5-FU based chemotherapy is urgently needed. In the present study, the gene expression profile of GSE3964 from the Gene Expression Omnibus database was used to explore the potential genes related to intrinsic resistance to 5-FU. A gene module containing 81 genes was found to have the highest correlation with chemotherapy response using Weighted Gene Co-expression Network Analysis (WGCNA). Then a protein-protein interaction (PPI) network was constructed and ten hub genes (TGFBI, NID, LEPREL2, COL11A1, CYR61, PCOLCE, IGFBP7, COL4A2, CSPG2, and VTN) were identified using the CytoHubba plugin of Cytoscape. Seven of these hub genes showed significant differences in expression between chemotherapy-sensitive and chemotherapy-resistant samples. The prognostic value of these seven genes was evaluated using TCGA COAD (Colorectal Adenocarcinoma) data. The results showed that TGFBI was highly expressed in chemotherapy-sensitive patients, and patients with high TGFBI expression have better survival.

Screening and Interaction Analysis of Key Genes in miR-542-3p Over- Expressed Osteosarcoma Cells by Bioinformatics

2020 ◽  
Vol 23 (5) ◽  
pp. 411-418
Author(s):  
Zhongqiu Li ◽  
Peng Zhang ◽  
Feifei Feng ◽  
Qiao Zhang
Keyword(s):  
Gene Expression ◽  
Interaction Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Cancer Genome Atlas ◽  
Key Genes ◽  
Gene Expression Profile Data

Background: Osteosarcoma is one of the most serious primary malignant bone tumors that threaten the lives of children and adolescents. However, the mechanism underlying and how to prevent or treat the disease have not been well understood. Aims & Objective: This aim of the present study was to identify the key genes and explore novel insights into the molecular mechanism of miR-542-3p over-expressed Osteosarcoma. Materials & Methods: Gene expression profile data GDS5367 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. And protein-protein interaction (PPI) network was constructed by the STRING database. In addition, the most highly connected module was screened by plugin MCODE and hub genes by plugin CytoHubba. Furthermore, UALCAN and The Cancer Genome Atlas were performed for survival analysis. Result: In total, 1421 DEGs were identified, including 598 genes were up-regulated and 823 genes were down-regulated. GO analysis showed that DEGs were classified into three groups and DEGs mainly enriched in Steroid biosynthesis, Ubiquitin mediated proteolysis and p53 signaling pathway. Six hub genes (UBA52, RNF114, UBE2H, TRIP12, HNRNPC, and PTBP1) may be key genes with the progression of osteosarcoma. Conclusion: The results could better understand the mechanism of osteosarcoma, which may facilitate a novel insight into treatment targets.

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 Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high-throughput data

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Bin Zuo ◽  
JunFeng Zhu ◽  
Fei Xiao ◽  
ChengLong Wang ◽  
Yun Shen ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Small Molecule ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Chemokine Signaling ◽  
Limma Package ◽  
Chemokine Signaling Pathway

Abstract Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA. Methods: The gene expression profile of GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in GSE55235 and GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA- and RA-specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database. Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA- and OA-specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression. Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.

scholarly journals Identification of key genes and long non-coding RNA associated ceRNA networks in hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8021 ◽  
Author(s):  
Jun Liu ◽  
Wenli Li ◽  
Jian Zhang ◽  
Zhanzhong Ma ◽  
Xiaoyan Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Microarray Dataset ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Hepatic Expression ◽  
Limma Package ◽  
Human Protein Atlas

Background Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Although multiple efforts have been made to understand the development of HCC, morbidity, and mortality rates remain high. In this study, we aimed to discover the mRNAs and long non-coding RNAs (lncRNAs) that contribute to the progression of HCC. We constructed a lncRNA-related competitive endogenous RNA (ceRNA) network to elucidate the molecular regulatory mechanism underlying HCC. Methods A microarray dataset (GSE54238) containing information about both mRNAs and lncRNAs was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and lncRNAs (DElncRNAs) in tumor tissues and non-cancerous tissues were identified using the limma package of the R software. The miRNAs that are targeted by DElncRNAs were predicted using miRcode, while the target mRNAs of miRNAs were retrieved from miRDB, miRTarBas, and TargetScan. Functional annotation and pathway enrichment of DEGs were performed using the EnrichNet website. We constructed a protein–protein interaction (PPI) network of DEGs using STRING, and identified the hub genes using Cytoscape. Survival analysis of the hub genes and DElncRNAs was performed using the gene expression profiling interactive analysis database. The expression of molecules with prognostic values was validated on the UALCAN database. The hepatic expression of hub genes was examined using the Human Protein Atlas. The hub genes and DElncRNAs with prognostic values as well as the predictive miRNAs were selected to construct the ceRNA networks. Results We found that 10 hub genes (KPNA2, MCM7, CKS2, KIF23, HMGB2, ZWINT, E2F1, MCM4, H2AFX, and EZH2) and four lncRNAs (FAM182B, SNHG6, SNHG1, and SNHG3) with prognostic values were overexpressed in the hepatic tumor samples. We also constructed a network containing 10 lncRNA–miRNA–mRNA pathways, which might be responsible for regulating the biological mechanisms underlying HCC. Conclusion We found that the 10 significantly overexpressed hub genes and four lncRNAs were negatively correlated with the prognosis of HCC. Further, we suggest that lncRNA SNHG1 and the SNHG3-related ceRNAs can be potential research targets for exploring the molecular mechanisms of HCC.

scholarly journals Identification of Hub Genes Associated with Lung Adenocarcinoma Based on Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuaiqun Wang ◽  
Xiaoling Xu ◽  
Wei Kong
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Differential Genes

Lung adenocarcinoma (LUAD) is one of the malignant lung tumors. However, its pathology has not been fully understood. The purpose of this study is to identify the hub genes associated with LUAD by bioinformatics methods. Three gene expression datasets including GSE116959, GSE74706, and GSE85841 downloaded from the Gene Expression Omnibus (GEO) database were used in this study. The differentially expressed genes (DEGs) related to LUAD were screened by using the limma package. Gene Ontology (GO) and KEGG analysis of DEGs were carried out through the DAVID website. The protein-protein interaction (PPI) of differentially expressed genes was drawn by the STRING website, and the results were imported into Cytoscape for visualization. Then, the PPI network was analyzed by using MCODE, and the modules with a score greater than 5 were found by using cytoHubba. Finally, the GEPIA database and UALCAN database were used to verify and analyze the survival of hub genes. We identified 67 upregulated genes and 277 downregulated genes from three LUAD datasets. The results of GO analysis showed that the downregulated genes were significantly enriched in matrix adhesion and angiogenesis and upregulated differential genes were significantly enriched in cell adhesion and vascular development. KEGG pathway analysis showed that the differential genes of LUAD were significantly enriched in viral carcinogenesis and adhesion spots. The PPI network of differentially expressed genes consists of 269 nodes and 625 interactions. In addition, three modules with scores greater than 5 and seven hub genes, namely, MCM4, BIRC5, CDC20, CDC25C, FOXM1, GTSE1, and RFC4, playing an important role in the PPI network were screened out. In this study, we obtained the hub genes and pathways related to LUAD, revealing the molecular mechanism and pathogenesis of LUAD, which is helpful for the early detection of LUAD and provides a new idea for the treatment of LUAD.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

scholarly journals OSmfs: An Online Interactive Tool to Evaluate Prognostic Markers for Myxofibrosarcoma

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1523
Author(s):  
Huimin Li ◽  
Longxiang Xie ◽  
Qiang Wang ◽  
Yifang Dang ◽  
Xiaoxiao Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Analysis Data ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Biomarkers ◽  
Rank Test ◽  
Web Tool ◽  
Kaplan Meier ◽  
Cox Analysis

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.

scholarly journals Identification of Differential Gene Expression and Related Signaling Pathways In SARS-COV-2 Infected Human Cells

2021 ◽  
Author(s):  
Tian-Ao Xie ◽  
Hou-He Li ◽  
Zu-En Lin ◽  
Xiao-Ye Lin ◽  
Xin Meng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vaccine Development ◽  
Virus Disease ◽  
Expression Profiles ◽  
Biological Significance ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Human Cells ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background: The Corona Virus Disease 2019 (COVID-19) pandemic poses a serious public health threat to the survival and health of people all over the world. We analyzed related mRNA data and gene expression profiles of human cell lines infected with SARS-CoV-2 obtained from GEO (GSE148729), using bioinformatics tools. Differentially expressed genes (DEGs) of human cells infected with SARS-CoV-2 were identified.Method: The GSE148729 datasets were downloaded from the Gene Expression Omnibus (GEO) database. To explore the Biological significance of DEGs, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were constructed by using the STRING database. The hub genes were selected using the Cytoscape Software, and a t-test was performed to validate the hub genes.Result: A total of 1241 DEGs were screened, including 1049 up-regulated genes and 192 down-regulated genes. Besides, 10 hub genes were obtained from the PPI network, among which the expression level of CXCL2, Etv7, and HIST1H2BG was found to be statistically significant.Conclusion: In conclusion, bioinformatics analysis reveals genes and cellular pathways that are significantly altered in SARS-CoV-2 infected cells. This is conducive to further guide the clinical study of SARS-CoV-2 and provides new perspectives for vaccine development.

scholarly journals Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Guangda Yang ◽  
Liumeng Jian ◽  
Xiangan Lin ◽  
Aiyu Zhu ◽  
Guohua Wen
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Prognostic Value ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Trastuzumab Resistance ◽  
Hub Genes ◽  
Her2 Positive ◽  
Protein Protein Interaction ◽  
Stem Cell Pluripotency

Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

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