scholarly journals Bioinformatics analysis of the transcriptional expression of minichromosome maintenance proteins as potential indicators of survival in patients with cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Cervical Cancer ◽  
Dna Replication ◽  
Mrna Expression ◽  
Interaction Network ◽  
Hub Genes ◽  
Clinical Value ◽  
Minichromosome Maintenance ◽  
Kaplan Meier ◽  
Coexpressed Genes ◽  
Kaplan Meier Plotter

Abstract Background As major regulators of DNA replication in eukaryotes, minichromosome maintenance (MCM) proteins play an important role in the initiation and extension of DNA replication. MCMs and their related genes may be new markers of cell proliferation activity, which is of great significance for the diagnosis and prognosis of cervical cancer. Methods To explore the role of MCMs and their related genes in cervical cancer, various bioinformatics methods were performed. First, the ONCOMINE and UALCAN databases were used to analyze the mRNA expression of different MCMs. The Human Protein Atlas database was used to analyze the protein expression of MCMs in normal and tumor tissues. The potential clinical value of MCMs was evaluated using the UALCAN, Kaplan-Meier plotter and cBioPortal databases. Then, the related genes and key coexpressed genes of MCMs were screened using GEPIA2 and cBioPortal analysis. For these genes, we used Metascape and the DAVID database to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, construct the related molecular interaction network, and obtain the key subnetworks and related hub genes. The Kaplan-Meier plotter database was used for survival analysis of cervical cancer patients to evaluate and predict the potential clinical value of the hub genes. Moreover, multiple gene comparisons of the expression of MCMs and related genes in different cancer types also showed the clinical significance of these potential targets. Results The mRNA and protein expression of MCMs increased in tumor tissue. Overexpression of MCM2/3/4/5/6/7/8/10 was found to be significantly associated with clinical cancer stage. Higher mRNA expression levels of MCM3/5/6/7/8 were found to be significantly associated with longer overall survival, and higher mRNA expression of MCM2/3/4/5/6/7/8 was associated with favorable OS. In addition, a high mutation rate of MCMs (71%) was observed. MCM2, MCM4, MCM8, MCM3 and MCM7 were the five genes with the most genetic alterations. In addition, the coexpressed genes and related genes of MCMs were successfully screened for enrichment analysis. These genes were significantly enriched in important pathways, such as the DNA replication, cell cycle, mismatch repair, spliceosome, and Fanconi anemia pathways. A protein-protein interaction network was successfully constructed, and a total of 13 hub genes (CDC45, ORC1, RPA1, CDT1, TARDBP, RBMX, SRSF3, SRSF1, RFC5, RFC2, MSH6, DTL, and MSH2) from 4 key subnetworks were obtained. These genes and MCM2/3/4/5/6/7/8 might have potential clinical value for the survival and prognosis of cervical cancer patients. Conclusions These findings promoted the understanding of the MCM protein family and clinically related molecular targets for cervical epithelial neoplasia and cervical cancer. Our results were helpful to evaluate the potential clinical value of MCMs and related genes in patients with cervical cancer.

scholarly journals The Alterations and Potential Roles of MCMs in Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xinyu Liu ◽  
Ying Liu ◽  
Qiangshan Wang ◽  
Siqi Song ◽  
Lingjun Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Network ◽  
Relapse Free Survival ◽  
Minichromosome Maintenance ◽  
Free Survival ◽  
Oncomine Database ◽  
Kaplan Meier ◽  
Eukaryotic Dna ◽  
Coexpressed Genes ◽  
Kaplan Meier Plotter

The minichromosome maintenance (MCM) protein family plays a key role in eukaryotic DNA replication and has been confirmed to be associated with the occurrence and progression of many tumors. However, the expression levels, functions, and prognostic values of MCMs in breast cancer (BC) have not been clearly and systematically explained. In this article, we studied the transcriptional levels of MCMs in BC based on the Oncomine database. Kaplan-Meier plotter was used to analyze prognostic value of MCMs in human BC patients. Furthermore, we constructed a MCM coexpression gene network and performed functional annotation analysis through DAVID to reveal the functions of MCMs and coexpressed genes. The data showed that the expression of MCM2–8 and MCM10 but not MCM1 and MCM9 was upregulated in BC. Kaplan-Meier plotter analysis revealed that high transcriptional levels of MCM2, MCM4–7, and MCM10 were significantly related to low relapse-free survival (RFS) in BC patients. In contrast, high levels of MCM1 and MCM9 predicted high RFS for BC patients. This study suggests that MCM2, MCM4–7, and MCM10 possess great potential to be valuable prognostic biomarkers for BC and that MCM1 and MCM9 may serve as potential treatment targets for BC patients.

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 Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

2020 ◽  
Author(s):  
Xiaolong Chen ◽  
Zhixiong Xia ◽  
Yafeng Wan ◽  
Ping Huang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Disease Free Survival ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Free Survival ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Human Protein Atlas ◽  
Kaplan Meier Plotter

Abstract BackgroundHepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven-genes and potential drugs in HCC. MethodsThree mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, GO terms analysis and KEGG enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on TCGA, GEPIA and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of the hub genes were further conducted by Kaplan-Meier plotter and the GEPIA. DGIdb database was performed to search the candidate drugs for HCC. ResultsFinally, 197 DEGs were identified. The PPI network was constructed using STRING software. Then ten genes were selected and considered as the hub genes. The ten genes were all closely related to the survival of HCC patients. DGIdb database predicted 39 small molecules as the possible drugs for treating HCC. ConclusionsOur study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in future.

scholarly journals Expression and prognostic roles of PRDXs gene family in hepatocellular carcinoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Mingxing Xu ◽  
Jianliang Xu ◽  
Dun Zhu ◽  
Rishun Su ◽  
Baoding Zhuang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Acid Metabolism ◽  
Family Members ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Database Analysis ◽  
Kaplan Meier ◽  
Canonical Pathways ◽  
Human Protein Atlas ◽  
Kaplan Meier Plotter

Abstract Background As the fourth leading cause of cancer-related death in the world, the therapeutic effect and 5-year overall survival of hepatocellular carcinoma (HCC) are not optimistic. Previous researches indicated that the disorder of PRDXs was related to the occurrence and development of cancers. Methods In this study, PRDXs were found in various tumor cell lines by CCLE database analysis. The analysis results of UALCAN, HCCDB and Human Protein Atlas databases showed the expression of PRDXs mRNA and protein in HCC tissues was dysregulated. Besides, UALCAN was used to assess the correlations between PRDXs mRNA as well as methylation levels and clinical characterization. Results High expression of PRDX1 or low expression of PRDX2/3 suggested poor prognosis for HCC patients which was demonstrated by Kaplan–Meier Plotter. The genetic alterations and biological interaction network of PRDXs in HCC samples were obtained from c-Bioportal. In addition, LinkedOmics was employed to analyze PRDXs related differentially expressed genes, and on this basis, enrichment of KEGG pathway and miRNAs targets of PRDXs were conducted. The results indicated that these genes were involved in several canonical pathways and certain amino acid metabolism, some of which may effect on the progression of HCC. Conclusions In conclusion, the disordered expression of some PRDX family members was associated with the prognosis of HCC patients, suggesting that these PRDX family members may become new molecular targets for the treatment and prognosis prediction of HCC.

scholarly journals Nomogram Models Based on Gene Expression in Prediction of Breast Cancer Bone Metastasis

2021 ◽  
Author(s):  
Teng-di Fan ◽  
Di-kai Bei ◽  
Song-wei Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Bone Metastasis ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Training Set ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Patient Prognosis ◽  
Kaplan Meier Plotter

Abstract Objective: To design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, and GSE14020 was taken as a validation set. In the training cohort, limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC non-bone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the prognostic value of hub genes in BC was explored. Results: A total of 1858 DEGs were obtained. WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival. While GJA1, IGFBP6, MDFI, ITGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival. Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

scholarly journals Mining expression and prognosis of FOLR1 in ovarian cancer by using Oncomine and Kaplan-Meier plotter

Pteridines ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 158-164
Author(s):  
Qingyuan Su ◽  
Qingyuan Lv ◽  
Ruijin Wu
Keyword(s):  
Ovarian Cancer ◽  
Mrna Expression ◽  
Ovarian Tumor ◽  
Ovarian Tissue ◽  
Progression Free Survival ◽  
Free Survival ◽  
Normal Ovarian Tissue ◽  
Oncomine Database ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Objective: To further explore folate receptor 1 (FOLR1) gene expression in ovarian cancer and its association with patients’ prognosis by deep mining the Oncomine and Kaplan-Meier plotter databases. Methods: FOLR1 mRNA expression data of ovarian cancer were retrieved from the Oncomine database and further analyzed by comparing tumor to healthy tissue. The prognostic value of FOLR1 in ovarian cancer was analyzed by Kaplan-Meier Plotter, an online survival analysis database. Results A total of 439 studies were included in the Oncomine database in multiple types of cancers. Of the 439 studies, there were 54 with statistical differences for the expression of FOLR1, 19 with increased expression of FOLR1 and 35 with decreased expression comparing ovarian cancer to normal ovary tissue. After searching the Oncomine database, six datasets were discovered comparing the mRNA expression in ovarian tumor to healthy tissue. FOLR1 mRNA expression in ovarian tumor was significantly higher than that of normal ovarian tissue (all p<0.05). The Kaplan-Meier Plotter database analyzed the correlation between FOLR1 expression and ovarian cancer patient’s prognosis. A significant difference of progression-free survival between FOLR1 high and low expressing groups was found in ovarian cancer patients (HR=1.14, 95%CI: 1.00-1.29, p=0.043). However, the overall survival was not statistically different between high and low FOLR1 expressing patients (HR=0.95, 95%CI: 0.84-1.09, p=0.48). Conclusion FOLR1 mRNA was found to be highly expressed in ovarian tumor compared to normal ovarian tissue. Elevated FOLR1 mRNA expression was associated with the poor progression-free survival.

scholarly journals Screening and Identification of Key Biomarkers in Breast Cancer with Brain Metastasis: Evidence from Bioinformatic Analysis

2020 ◽  
Author(s):  
tao ming Shao ◽  
zhi yang Hu ◽  
wen wei Li ◽  
long yun Pan
Keyword(s):  
Breast Cancer ◽  
Protein Interactions ◽  
Poor Prognosis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Clinical Prognosis ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Purpose. Breast cancer (BC) has a poor prognosis when brain metastases (BM) occur, and the treatment effect is limited. In this study, we aim to identify representative candidate biomarkers for clinical prognosis of patients with BM and explore the mechanisms underlying the progression of BC.Methods. Herein, we examined the Microarray datasets (GSE125989) obtained from the Gene Expression Omnibus database to find the target genes in BC patients with BM. We employed the GEO2R tool to filter the differentially expressed genes (DEGs) that participate in primary BC and BC with BM. Subsequently, using the DAVID tool, we conducted an enrichment analysis with the screened DEGs based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional annotation. The STRING database was employed to analyze the protein-protein interactions of the DEGs and visualized using Cytoscape software. Lastly, the Kaplan-Meier plotter database was employed to determine the prognostic potential of hub genes in BC.Results. We screened out 311 upregulated DEGs and 104 downregulated DEGs. The enrichment analyses revealed that all the DEGs were` enriched in the biological process of extracellular matrix organization, cell adhesion, proteolysis, collagen catabolic process and immune response. The significant enrichment pathways were focal adhesion, protein absorption and digestion, ECM-receptor interaction, PI3K-Akt signalling pathway, and Pathways in cancer. The top ten hub nodes screened out included FN1, VEGFA, COL1A1, MMP2, COL3A1, COL1A2, POSTN, DCN, BGN and LOX. The Kaplan-Meier plotter results showed that the three hub genes (FN1, VEGFA and DCN) are candidate biomarkers for clinical prognosis of patients with BM.Conclusion. we identified seven genes related to poor prognosis in BCBM. FN1, VEGFA and DCN can be considered as potential prognostic markers for BCBM. Meantime, COL1A1, POSTN, BGN and LOX may be linked to the distant transformation of BC.

scholarly journals An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sepideh Dashti ◽  
Mohammad Taheri ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Breast Cancer ◽  
Clinical Importance ◽  
Interaction Network ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ncbi Gene Expression Omnibus ◽  
Kaplan Meier ◽  
Microarray Datasets

Abstract Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein–protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan–Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA–lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

scholarly journals N6-Methyladenosine-Regulated mRNAs: Potential Prognostic Biomarkers for Patients With Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Junjun Sun ◽  
Yili Ping ◽  
Jingjuan Huang ◽  
Bingjie Zeng ◽  
Ping Ji ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Mrna Expression ◽  
Cox Regression ◽  
Interaction Network ◽  
Prognostic Biomarkers ◽  
Cox Regression Analysis ◽  
Receiver Operating Characteristic Curves ◽  
Network Analyses ◽  
Kaplan Meier ◽  
Selection Operator

Aberrant regulation of m6A mRNA modification can lead to changes in gene expression, thus contributing to tumorigenesis in several types of solid tumors. In this study, by integrating analyses of m6A methylation and mRNA expression, we identified 84 m6A-regulated mRNAs in lung adenocarcinoma (LUAD). Although the m6A methylation levels of total RNA in LUAD patient tumor tissue were reduced, the majority (75.2%) of m6A-regulated mRNAs were hypermethylated. The m6A-hypermethylated mRNAs were mainly enriched in terms related to transcription factor activity. We established a 10-m6A-regulated-mRNA signature score system through least absolute shrinkage and selection operator Cox regression analysis, with its predictive value validated by Kaplan–Meier curve and time-dependent receiver operating characteristic curves. RFXAP and KHDRBS2 from the signature also exhibited an independent prognostic value. The co-expression and interaction network analyses demonstrated the strong correlation between m6A regulators and the genes in the signature, further supporting the results of the m6A methylation modification patterns. These findings highlight the potential utility of integrating multi-omics data (m6A methylation level and mRNA expression) to accurately obtain potential prognostic biomarkers, which may provide important insights into developing novel and effective therapies for LUAD.

scholarly journals ITGB2 as a Novel Biomarker Correlating With Prognosis and Immune Infiltrates in Ovarian Cancer

2021 ◽  
Author(s):  
chanyuan li ◽  
Ting Wan ◽  
Ting Deng ◽  
Junya Cao ◽  
He Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Protein Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background: Epithelial ovarian cancer is nowadays one of the malignancies in women, this study aimed to identify novel biomarkers to predict prognosis and immunotherapy efficacy.Methods: The differentially expressed genes (DEGs) obtained from online database Gene Expression Omnibus (GEO)were screened via GEO2R and Venn diagram software, gene enrichment was analysed by Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG), then protein protein interaction(PPI)network and Cytoscape software were used to confirm the genes closely related to ovarian cancer. Survival analysis of hub genes were obtained from Kaplan–Meier plotter, with their differential expression in specimen validated by Gene Expression Profiling Interactive Analysis (GEPIA) and an integrated repository portal for tumor-immune system interactions (TISIDB). Finally, we used the Tumor Immune Estimation Resource 2.0 (TIMER2.0) and application Estimate the Proportion of Immune and Cancer cells (EPIC) to search the immune infiltration characteristics of the genes.Results: 355 DEGs between epithelial ovarian cancer and normal ovarian tissue were screened out. These DEGs were associated with extracellular exosome, bicellular tight junction and cell-cell junction, and remarkably enriched in molecules of cell adhesion and leukocyte transendothelial migration activity. Ten hub genes were identified via protein protein interaction (PPI) network: PTAFR, HLA-DRA, OAS2, OAS3, PTPN6, LYN, VAMP8, IRF6, ITGB2, CD47. Furthermore, the Kaplan–Meier plotter was conducted, overexpression of four genes was positively connected to poor prognosis in ovarian cancer:OAS2, OAS3, ITGB2, CD47,which were also correlated with immune infiltrates in ovarian cancer and had the highest degree of correlation with tumor associated macrophages (TAMs) infiltration, among which ITGB2 was highly correlated with TAMs infiltration level.Conclusion: ITGB2, OAS2, OAS3, and CD47 are upregulated with unfavorable prognosis in ovarian cancer, and ITGB2 may act as a novel prognostic biomarker with immune infiltration values.

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