Identification of Core Predication-Related Candidate Genes in Ovarian Cancer Based on Integrated Bioinformatics and Experienment

Abstract Background: Ovarian cancer is one of the deadliest and most common gynecological malignancies. This study aims to use comprehensive bioinformatics analysis to try to identify the core candidate genes related to the prediction of ovarian cancer for the early diagnosis and prognosis of ovarian cancer. Methods: Obtain expression profiles from Gene Expression Omnibus database, identify differentially expressed genes (DEG) with p<0.05 and (logFC)>1.5, perform functional enrichment, protein-protein interaction (PPI) network construction, functional module analysis, and survival analysis And correlation analysis to obtain the target gene, through immunohistochemical staining, clinicopathological feature analysis to verify the expression and clinical significance of TTK.Results: 1. Identified 135 genes with the same expression. 33 up-regulated DEG were mainly enriched in mitotic spindle assembly checkpoints, chromosome segregation regulation, etc.; 102 down-regulated DEG was mainly enriched in neurotransmitter level regulation, protein serine/threonine Regulation of acid kinase activity, etc. Then the PPI network was constructed to screen 20 hub genes and perform survival analysis and expression correlation analysis. At the same time, the modules that met the requirements were screened and the genes were analyzed by pathway enrichment. It was found that TTK was highly expressed in ovarian cancer and led to a poor prognosis.2. Distant metastasis, lymph node metastasis, clinical staging (stage III-IV), and poor differentiation are independent risk factors for high TTK expression (P<0.05).3. TTK, CA125, HE4 three biological indicators show excellent diagnostic value in joint monitoring of ovarian cancer.Conclusions: TTK plays a vital role in the tumorigenesis, aggressiveness and malignant biological behavior of EOC, and can be used as a potential biomarker and potential therapeutic target for early diagnosis and predictive evaluation of EOC.

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Identification of PKP 2/3 as potential biomarkers of ovarian cancer based on bioinformatics and experiments

Cancer Cell International ◽

10.1186/s12935-020-01602-3 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Lingling Gao ◽

Xiao Li ◽

Qian Guo ◽

Xin Nie ◽

Yingying Hao ◽

...

Keyword(s):

Ovarian Cancer ◽

Signaling Pathway ◽

Poor Prognosis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Biological Behavior ◽

Cell Junction ◽

Immune Infiltration ◽

Potential Biomarker

Abstract Background Plakophilins (PKPs) are widely involved in gene transcription, translation, and signal transduction, playing a crucial role in tumorigenesis and progression. However, the function and potential mechanism of PKP1/2/3 in ovarian cancer (OC) remains unclear. It’s of great value to explore the expression and prognostic values of PKP1/2/3 and their potential mechanisms, immune infiltration in OC. Methods The expression levels, prognostic values and genetic variations of PKP1/2/3 in OC were explored by various bioinformatics tools and databases, and PKP2/3 were selected for further analyzing their regulation network and immune infiltration. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) enrichment were also conducted. Finally, the expression and prognosis of PKP2 were validated by immunohistochemistry. Results The expression level and prognosis of PKP1 showed little significance in ovarian cancer, and the expression of PKP2/3 mRNA and protein were upregulated in OC, showing significant correlations with poor prognosis of OC. Functional enrichment analysis showed that PKP2/3 and their correlated genes were significantly enriched in adaptive immune response, cytokine receptor activity, organization of cell–cell junction and extracellular matrix; KEGG analysis showed that PKP2/3 and their significantly correlated genes were involved in signaling pathways including cytokine-mediated signaling pathway, receptor signaling pathway and pathways in cancer. Moreover, PKP2/3 were correlated with lymphocytes and immunomodulators. We confirmed that high expression of PKP2 was significantly associated with advanced stage, poor differentiation and poor prognosis of OC patients. Conclusion Members of plakophilins family showed various degrees of abnormal expressions and prognostic values in ovarian cancer. PKP2/3 played crucial roles in tumorigenesis, aggressiveness, malignant biological behavior and immune infiltration of OC, and can be regarded as potential biomarker for early diagnosis and prognosis evaluation in OC.

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Integrative analysis of key candidate genes and signaling pathways in autoimmune thyroid dysfunction related to anti-CTLA-4 therapy by bioinformatics

Investigational New Drugs ◽

10.1007/s10637-020-00952-z ◽

2020 ◽

Vol 38 (6) ◽

pp. 1717-1729

Author(s):

Ying Zhang ◽

Francesca Garofano ◽

Xiaolong Wu ◽

Matthias Schmid ◽

Peter Krawitz ◽

...

Keyword(s):

Signaling Pathways ◽

Candidate Genes ◽

Thyroid Dysfunction ◽

Functional Enrichment ◽

Ppi Network ◽

Effective Treatment Option ◽

Autoimmune Thyroid ◽

Go Annotation ◽

Kegg Pathways ◽

Go Terms

Summary Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), the first immune checkpoint to be targeted clinically, has provided an effective treatment option for various malignancies. However, the clinical advantages associated with CTLA-4 inhibitors can be offset by the potentially severe immune-related adverse events (IRAEs), including autoimmune thyroid dysfunction. To investigate the candidate genes and signaling pathways involving in autoimmune thyroid dysfunction related to anti-CTLA-4 therapy, integrated differentially expressed genes (DEGs) were extracted from the intersection of genes from Gene Expression Omnibus (GEO) datasets and text mining. The functional enrichment was performed by gene ontology (GO) annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Protein-protein interaction (PPI) network, module enrichment, and hub gene identification were constructed and visualized by the online Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. A total of 22 and 17 integrated human DEGs in hypothyroidism and hyperthyroidism group related to anti-CTLA-4 therapy were identified, respectively. Functional enrichment analysis revealed 24 GO terms and 1 KEGG pathways in the hypothyroid group and 21 GO terms and 2 KEGG pathways in the hyperthyroid group. After PPI network construction, the top five hub genes associated with hypothyroidism were extracted, including ALB, MAPK1, SPP1, PPARG, and MIF, whereas those associated with hyperthyroidism were ALB, FCGR2B, CD44, LCN2, and CD74. The identification of the candidate key genes and enriched signaling pathways provides potential biomarkers for autoimmune thyroid dysfunction related to anti-CTLA-4 therapy and might contribute to the future diagnosis and management of IRAEs for cancer patients.

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Construction of a novel mRNA-signature prediction model for prognosis of bladder cancer based on a statistical analysis

BMC Cancer ◽

10.1186/s12885-021-08611-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jianpeng Li ◽

Jinlong Cao ◽

Pan Li ◽

Zhiqiang Yao ◽

Ran Deng ◽

...

Keyword(s):

Bladder Cancer ◽

Survival Analysis ◽

Candidate Genes ◽

Risk Score ◽

Cox Regression ◽

Curve Analysis ◽

Functional Enrichment ◽

Time Dependent ◽

Stage Model ◽

Decision Curve Analysis

Abstract Background Bladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer. Methods The RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes. Results SORBS2, GPC2, SETBP1, FGF11, APOL1, and H1–2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer. Conclusions In this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.

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Systemic Analysis of the DNA Replication Regulator MCM Complex in Ovarian Cancer and Its Prognostic Value

Frontiers in Oncology ◽

10.3389/fonc.2021.681261 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yukun Li ◽

Juan Zou ◽

Qunfeng Zhang ◽

Feifei Quan ◽

Lu Cao ◽

...

Keyword(s):

Ovarian Cancer ◽

Protein Structure ◽

Dna Replication ◽

Prognostic Value ◽

Functional Enrichment ◽

Ppi Network ◽

Multiple Cancer ◽

Protein Levels ◽

Network Analyses ◽

Mcm Complex

Microliposome maintenance (MCM) 2, MCM3, MCM4, MCM5, MCM6, and MCM7 are DNA replication regulators and are involved in the progression of multiple cancer types, but their role in ovarian cancer is still unclear. The purpose of this study is to clarify the biological function and prognostic value of the MCM complex in ovarian cancer (OS) progression. We analyzed DNA alterations, mRNA and protein levels, protein structure, PPI network, functional enrichment, and prognostic value in OC based on the Oncomine, cBioPortal, TCGA, CPTAC, PDB, GeneMANIA, DAVID, KEGG, and GSCALite databases. The results indicated that the protein levels of these DNA replication regulators were increased significantly. Moreover, survival analysis showed a prognostic signature based on the MCM complex, which performed moderately well in terms of OS prognostic prediction. Additionally, protein structure, functional enrichment, and PPI network analyses indicated that the MCM complex synergistically promoted OC progression by accelerating DNA replication and the cell cycle. In conclusion, our study suggested that the MCM complex might be a potential target and prognostic marker for OC patients.

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Integrated Analysis of Circular RNA-Associated ceRNA Network Reveals Potential circRNA Biomarkers in Human Breast Cancer

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/1732176 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Han Sheng ◽

Huan Pan ◽

Ming Yao ◽

Longsheng Xu ◽

Jianju Lu ◽

...

Keyword(s):

Breast Cancer ◽

Survival Analysis ◽

Correlation Analysis ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Circular Rna ◽

Functional Enrichment ◽

Differentially Expressed ◽

Integrated Analysis ◽

Cerna Network

Circular RNA (circRNA) is closely related to tumorigenesis and cancer progression. Yet, the roles of cancer-specific circRNAs in the circRNA-related ceRNA network of breast cancer (BRCA) remain unclear. The aim of this study was to construct a ceRNA network associated with circRNA and to explore new therapeutic and prognostic targets and biomarkers for breast cancer. We downloaded the circRNA expression profile of BRCA from Gene Expression Omnibus (GEO) microarray datasets and downloaded the miRNA and mRNA expression profiles of BRCA from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs) were identified, and a competitive endogenous RNA (ceRNA) regulatory network was constructed based on circRNA–miRNA pairs and miRNA–mRNA pairs. Gene ontology and pathway enrichment analyses were performed on mRNAs regulated by circRNAs in ceRNA networks. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network were performed. A total of 72 DEcircRNAs, 158 DEmiRNAs, and 2762 DE mRNAs were identified. The constructed ceRNA network contains 60 circRNA–miRNA pairs and 140 miRNA–mRNA pairs, including 40 circRNAs, 30 miRNAs, and 100 mRNAs. Functional enrichment indicated that DEmRNAs regulated by DEcircRNAs in ceRNA networks were significantly enriched in the PI3K-Akt signaling pathway, microRNAs in cancer, and proteoglycans in cancer. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network showed that 13 mRNAs and 6 miRNAs were significantly associated with overall survival, and 48 miRNA–mRNA interaction pairs had a significant negative correlation. A PPI network was established, and 21 hub genes were determined from the network. This study provides an effective bioinformatics basis for further understanding of the molecular mechanisms and predictions of breast cancer. A better understanding of the circRNA-related ceRNA network in BRCA will help identify potential biomarkers for diagnosis and prognosis.

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Screening key genes related to ovarian cancer and exploring their possible molecular mechanisms

10.21203/rs.2.16321/v1 ◽

2019 ◽

Author(s):

Junzui Li ◽

Yuehua Zhang ◽

Zhixiong Huang ◽

Bin Zhao ◽

Ke Huang ◽

...

Keyword(s):

Ovarian Cancer ◽

Early Diagnosis ◽

Candidate Genes ◽

Molecular Mechanisms ◽

Clinical Symptoms ◽

Malignant Tumors ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Key Genes

Abstract Background As one of the common malignant tumors in women, ovarian cancer (OC) often exerts the atypically early clinical symptoms. Therefore, it is particularly important for seeking more effectively early diagnosis of OC (biomarkers). Besides, although a lot of sequencing and chip research have been done on the pathogenesis of OC, the pathogenesis, clinical and genetic features of OC is still not very clear.Methods In this study, 4 GEO data (GSE66957, GSE119054, GSE14407 and GSE54388) were selected for differential expression gene analysis (DEGs), and the important template of the 4 DEGS overlapping genes was taken as Hub genes. Then, the GO and pathway enrichment analysis were conducted to confirm the enrichment of these Hub genes, and these Hub genes were identified as key genes. In addition, the transcriptional levels of these Hub genes in OC and their impacts on the overall survival rate of OC were validated via the UCSC and TCGA datasets.. Besides, cBioPortal, TargetScan, UCSC, DiseaseMeth and TIMER software were performed to explore the potential biological functions of these key genes in OC.Results We screened out 10 Hub genes related to OC including VEGFA, ZWINT, CDKN2A, SLC2A1, TOP2A, MKI67, CCND1, KPNA2, FGF2 and SMC4, and further demonstrated that they were most significantly enriched in protein binding, cytoplasm, nucleus, extracellular exosome, membrane, cell division, cell adhesion and pathways in cancer. Meanwhile, CCND1, TOP2A, SMC4 and FGF2 were screened out as key candidate genes associated with OC. Further analysis proved these key candidate genes may regulate the occurrence and development of OC through mediating the gene mutation, miRNAs and genetic epigenetics such as methylation and acetylation.Conclusion These data would improve our understanding of the causes and underlying molecular events of OC, be of clinical significance for the early diagnosis and prevention of OC, and may provide the promising therapeutic targets in OC.

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Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

PeerJ ◽

10.7717/peerj.7313 ◽

2019 ◽

Vol 7 ◽

pp. e7313 ◽

Cited By ~ 6

Author(s):

Tingting Guo ◽

Hongtao Ma ◽

Yubai Zhou

Keyword(s):

Survival Analysis ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Prion Diseases ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes

Background Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood. Methods Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database. Results Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.

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The Interactions of Small Proline-rich Proteins With Late Cornified Envelope Proteins Are Involved in the Pathogenesis of Psoriasis

10.21203/rs.3.rs-850424/v1 ◽

2021 ◽

Author(s):

Siyu Tian ◽

Shuming Chen ◽

Yongyi Feng ◽

Yong Li

Keyword(s):

Gene Expression ◽

Correlation Analysis ◽

Gene Expression Profile ◽

Expression Profile ◽

Protein Interactions ◽

Functional Enrichment ◽

Ppi Network ◽

Protein Protein Interactions ◽

Expression Levels ◽

Cornified Envelope

Abstract Background: Psoriasis is a common cutaneous disease with many characteristics including inflammation and aberrant keratinocyte proliferation. However, the pathogenesis of psoriasis is not completely clear. Methods: We explore the differentially expressed genes (DEGs) in psoriasis by analyzing the gene expression profile obtained from the Gene Expression Omnibus (GEO) database. The DEGs were examined by gene ontology (GO) functional enrichment analysis and protein-protein interactions (PPI) network. Correlation analysis in R studio software analyzed the association of SPRR and LCE genes. The potential direct protein-protein interactions between SPRR proteins and LCE3D was further verified by co-localization observed in 293T cells and co-immunoprecipitation (CO-IP). The expression levels of SPRR and LCE genes were detected in the IMQ-induced psoriasiform dermatitis mice. Results: The small proline-rich (SPRR) and late cornified envelope (LCE) genes were identified as a module in constructed PPI network. The gene expression profile GSE63684 analysis showed that both SPRR family and LCE family genes were significantly upregulated in imiquimod (IMQ) induced psoriasiform dermatitis mice. Correlation analysis in R studio software recognized the association of SPRR and LCE genes, in which the potential direct protein-protein interactions between SPRR proteins and LCE3D was further verified by co-localization observed in 293T cells and co-immunoprecipitation (CO-IP) results that suggest direct interaction between SPRR2 and LCE3D. Notably, we found that the expression levels of SPRR and LCE genes were significantly increased in the IMQ-induced psoriasiform dermatitis mice while tazarotene cream treatment specifically decreased the mRNA expression of these genes, which indicated that the SPRR and LCEs were regulated simultaneously in psoriasis. Conclusion: Our studies found the interactions of SPRR proteins with LCE proteins, which may provide new insights into the pathogenesis of psoriasis.

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