scholarly journals Correlation of Hepatocellular Carcinoma Stemness Indices and Clinical Characteristics and Prognosis

2020 ◽  
Author(s):  
Juan Li ◽  
Chunting Zhang ◽  
Xin Yuan ◽  
Zujiang Yu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Clinical Characteristics ◽  
Cancer Genome ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Mrna Gene ◽  
The Relationship ◽  
Mrna Gene Expression

Abstract Background Recent studies have shown that cancer stem cell (CSC) is related to the occurrence and development of hepatocellular carcinoma(HCC), but the mechanism has not yet been elucidated. Therefore, it is necessary to explore the relationship between HCC stem cells and the survival time of HCC patients and its mechanism to guide the treatment. Methods We downloaded the RNA-Seq data and clinical information from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The mRNA gene expression-based stemness index (mRNAsi) and DNA methylation-based stemness index (mDNAsi) were calculated through one-class logistic regression (OCLR). By applying the univariable Cox regression analysis, we found that mRNAsi and mRNAsi were significantly correlated with overall survival (OS). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were used to seek for different genes, and searched for hub genes through protein-protein interaction (PPI) analysis. The spearman’s rank correlation coefficient test was applied to analyze the relationship between these hub genes and the stemness indices. Results The mRNA gene expression-based stemness index (mRNAsi) and DNA methylation-based stemness index (mDNAsi) levels of HCC samples from TCGA and ICGC were significantly negatively related to clinical characteristics and OS. Analysis of differentially expressed genes and PPI revealed that SNAP25, KPT19, GABBR1 and EPCAM were significantly negative correlation with mDNAsi. Conclusion Our new model based on stemness indices-related genes was available for predicting prognosis. The SNAP25, KPT19, GABBR1 and EPCAM were potentially therapeutic targets for HCC patients.

scholarly journals Hub gene identification and prognostic model establishment for patients with HBV-related hepatocellular carcinoma

2021 ◽  
Author(s):  
Lianmei Wang ◽  
Jing Meng ◽  
Shasha Qin ◽  
Aihua Liang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Cyclin B1 ◽  
Mitotic Checkpoint ◽  
Cancer Genome ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Model Based

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~50% of HCC cases. Identification of biomarkers is pivotal for the therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. Cyclin-dependent kinase 1 (CDK1), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Aurora kinase A (AURKA), Cyclin B2 (CCNB2), Enhancer of zeste homolog 2 (EZH2), Cell division cycle 20 (CDC20), DNA topoisomerase II alpha (TOP2A), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and ZW10 interactor (ZWINT), were identified as the top-ten hub genes. The expression of hub-genes was verified in the liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP), the cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. A four-gene prognostic related model based on the expression of ZWINT, EZH2, FOXM1 and CDK1 were established through Cox regression analysis in ICGC-LIRI-JP project, and verified in TCGA-HCC cohort. Furthermore, a nomogram model based on pathology stage, gender and four-genes prognostic model was built to predict the prognosis for HBV–HCC patients. In conclusion, ZWINT, EZH2, FOXM1 and CDK1 play a pivotal role in HBV-HCC, and are potential therapeutic targets of HBV HCC.

scholarly journals The diagnostic and prognostic value of H2AFY in hepatocellular carcinoma

2020 ◽  
Author(s):  
xuyang ma ◽  
Ying Ding ◽  
Li Zeng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Normal Liver ◽  
Diagnostic Value ◽  
The Relationship ◽  
Diagnostic And Prognostic Value

Abstract Background: The potential correlation between H2AFY (also known as MacroH2A1) and the clinical characteristics of hepatocellular carcinoma (HCC) patients was analysed through gene expression profiles and clinical data in The Cancer Genome Atlas (TCGA) database, and the diagnostic and prognostic value of H2AFY in HCC was discussed. Methods: The gene expression data of HCC and the corresponding clinical characteristics of HCC patients were downloaded from the TCGA database. The differences in H2AFY in normal liver tissues and HCC were analysed. The relationship between H2AFY and clinical characteristics was analysed by Wilcoxon signed-rank test, logistic regression and Kruskal-Wallis test. The Kaplan-Meier method and the Cox regression method were used to analyse the relationship between overall survival and clinical characteristics of the patients. An ROC curve was used to predict the diagnostic value of H2AFY in HCC. Gene set enrichment analysis (GSEA) was used to analyse the pathway enrichment of H2AFY. Result: Compared with normal liver tissues, H2AFY was significantly highly expressed in HCC. H2AFY was positively correlated with the age, clinical stage, G stage (grade) and T stage (tumor stage) of liver cancer patients. Higher H2AFY expression predicted a poor prognosis in HCC patients. Cox regression analysis suggested that H2AFY was an independent risk factor for the prognosis of HCC patients. The ROC curve suggested that H2AFY had certain diagnostic value in HCC. GSEA suggested that H2AFY was correlated with lipid metabolism and a variety of tumour pathways. Conclusion: Our study showed that H2AFY was significantly overexpressed in HCC. H2AFY may be a potential diagnostic and prognostic marker for HCC, and high expression of H2AFY predicts a poor prognosis in patients with HCC.

scholarly journals A New Risk Score Based on Eight Hepatocellular Carcinoma- Immune Gene Expression Can Predict the Prognosis of the Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Dingde Ye ◽  
Yaping Liu ◽  
Guoqiang Li ◽  
Beicheng Sun ◽  
Jin Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Clinical Outcomes ◽  
Gene Expression Data ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Expression Data ◽  
Database Analysis ◽  
Cox Regression Analysis

BackgroundHepatocellular carcinoma (HCC) is one of the malignant tumors with high morbidity and mortality worldwide. Immunotherapy has emerged as an increasingly important cancer treatment modality. However, the potential relationship between immune genes and HCC still needs to be explored. The purpose of this study is to construct a new prognostic risk signature to predict the prognosis of HCC patients based on the expression of immune-related genes (IRGs) and explore its potential mechanism.MethodsWe analyzed the gene expression data of 332 HCC patient samples and 46 adjacent normal tissues samples (Solid Tissue Normal including cirrhotic tissue) in The Cancer Genome Atlas (TCGA) database and clinical characteristics. We analyzed the gene expression data, identified differentially expressed IRGs in HCC tissues, filtered IRGs with prognostic value to construct an IRG signature, and classified patients into high and low gene expression groups based on the expression of IRGs in their tumor tissues. We also investigated the potential molecular mechanisms of IRGs through a bioinformatics approach using Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis and Gene Ontology (GO) database analysis. Differentially expressed IRGs associated with significant clinical outcomes (SIRGs) were identified by univariate Cox regression analysis. An immune-related risk score model (IRRSM) was established based on Lasso Cox regression analysis and multivariate Cox regression analysis. Based on the IRRSM, the immune score of the patients was calculated, and the patients were divided into high-risk and low-risk patients according to the median score, and the differences in survival between the two groups were compared. Then, the correlation analysis between the IRRSM and clinical characteristics was performed, and the IRRSM was validated using the International Cancer Genome Consortium (ICGC) database.ResultsThe IRRSM was eventually constructed and confirmed to be an independent prognostic model for HCC patients. The IRRSM was shown to be positively correlated with the infiltration of four types of immune cells.ConclusionOur results showed that some SIRGs have potential value for predicting the prognosis and clinical outcomes of HCC patients. IRGs affect the prognosis of HCC patients by regulating the tumor immune microenvironment (TIME). This study provides a new insight for immune research and treatment strategies in HCC patients.

scholarly journals Identification of Potential Biomarkers From Hepatocellular Carcinoma With MT1 Deletion

2021 ◽  
Vol 27 ◽  
Author(s):  
Ruohao Zhang ◽  
Miao Huang ◽  
Hong Wang ◽  
Shengming Wu ◽  
Jiali Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Independent Prognostic Factor ◽  
Comparative Genomic ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Potential Biomarkers

Background: Hepatocellular carcinoma (HCC) is one of the deadliest cancers worldwide. Metallothioneins (MTs) are metal-binding proteins involved in multiple biological processes such as metal homeostasis and detoxification, as well as in oncogenesis. Copy number variation (CNV) plays a vital role in pathogenesis and carcinogenesis. Nevertheless, there is no study on the role of MT1 CNV in HCC.Methods: Array-based Comparative Genomic Hybridization (aCGH) analysis was performed to obtain the CNV data of 79 Guangxi HCC patients. The prognostic effect of MT1-deletion was analyzed by univariate and multivariate Cox regression analysis. The differentially expressed genes (DEGs) were screened based on The Gene Expression Omnibus database (GEO) and the Liver Hepatocellular Carcinoma of The Cancer Genome Atlas (TCGA-LIHC). Then function and pathway enrichment analysis, protein-protein interaction (PPI) and hub gene selection were applied on the DEGs. Lastly, the hub genes were validated by immunohistochemistry, tissue expression and prognostic analysis.Results: The MT1-deletion was demonstrated to affect the prognosis of HCC and can act as an independent prognostic factor. 147 common DEGs were screened. The most significant cluster of DEGs identified by Molecular Complex Detection (MCODE) indicated that the expression of four MT1s were down-regulated. MT1X and other five hub genes (TTK, BUB1, CYP3A4, NR1I2, CYP8B1) were associated with the prognosis of HCC. TTK, could affect the prognosis of HCC with MT1-deletion and non-deletion. NR1I2, CYP8B1, and BUB1 were associated with the prognosis of HCC with MT1-deletion.Conclusions: In the current study, we demonstrated that MT1-deletion can be an independent prognostic factor in HCC. We identified TTK, BUB1, NR1I2, CYP8B1 by processing microarray data, for the first time revealed the underlying function of MT1 deletion in HCC, MT1-deletion may influence the gene expression in HCC, which may be the potential biomarkers for HCC with MT1 deletion.

scholarly journals Hub Gene Identification and Prognostic Model Establishment for Patients with HBV-related Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Lianmei Wang ◽  
Jing Liu ◽  
Zhong Xian ◽  
Jingzhuo Tian ◽  
Chunying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cyclin B1 ◽  
Mitotic Checkpoint ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Hub Genes

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~ 50% of HCC cases. Establishment of a prognostic model is pivotal for clinical therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. DNA topoisomerase II alpha (TOP2A), Disks large-associated protein 5 (DLGAP5), RAD51 associated protein 1 (RAD51AP1), ZW10 interactor (ZWINT), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Cyclin B2 (CCNB2), Aurora kinase A (AURKA), and Cyclin-dependent kinase 1 (CDK1) were identified as the top-ten hub genes. These hub-genes were verified by the Liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP) project, The Cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. FOXM1 and CDK1 were found to be prognostic-related molecules for HBV-HCC patients. The expression patterns of FOXM1 and CDK1were consistently in human and mouse. Furthermore, a nomogram model based on histology grade, pathology stage, sex and, expression of FOXM1 and CDK1 was built to predict the prognosis for HBV–HCC patients. The nomogram model could be used to predict the prognosis of HBV-HCC cases.

scholarly journals The relationship among Girdin DNA methylation, its high expression, and immune infiltration in hepatocellular carcinoma: Clues from in silico analysis

2021 ◽  
Vol 41 (3) ◽  
Author(s):  
Cheng Zhang ◽  
Yang Ke ◽  
Xuefen Lei ◽  
Xin Liu ◽  
Hai Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Methylation Status ◽  
Cancer Genome ◽  
High Expression ◽  
Gene Body ◽  
Immune Infiltration ◽  
Normal Tissues ◽  
The Relationship

Abstract Objective: The aim of the present study was to explore the relationship among Girdin DNA methylation, its high expression, and immune infiltration in human hepatocellular carcinoma (HCC). Materials and methods: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC) databases were used to compare Girdin mRNA expression between HCC tissues and normal tissues, and determine the relationship between Girdin expression and HCC prognosis. TCGA database was also used to analyze the expression of Girdin and its methylation status, as well as the relationship between Girdin DNA methylation and HCC prognosis. The Tumor IMmune Estimation Resource (TIMER) database was used to explore the correlation between Girdin expression and HCC immune infiltration. Results: Girdin expression was elevated in HCC tissues compared with that in normal tissues. The degree of methylation at cg03188526, a CpG site in the Girdin gene body, was positively correlated with Girdin mRNA expression, while high Girdin expression and cg03188526 hypermethylation were both correlated with poor HCC prognosis. Additionally, HCC tissue with high Girdin expression exhibited abundant immune infiltration, and the high Girdin expression was associated with a worse prognosis in macrophage-enriched HCC specimens. Conclusion: Our findings indicated that Girdin likely functions as an oncogene in HCC and that hypermethylation at cg03188526 in the Girdin gene body may explain the high Girdin expression levels in HCC tissue. Furthermore, we report for the first time that the adverse effects of high Girdin expression in HCC patients may be partially mediated by tumor macrophage infiltration.

scholarly journals Prognostic Power of a Chaperonin Containing TCP-1 Subunit Genes Panel for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenli Li ◽  
Jun Liu ◽  
Hetong Zhao
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
Gene Signature ◽  
Cancer Genome ◽  
Medical Decision ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
T Complex

Chaperonin containing TCP-1 (T-complex protein 1) (CCT) is a large molecular weight complex that contains nine subunits (TCP1, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT6B, CCT7, CCT8). This study aimed to reveal key genes which encode CCT subunits for prognosis and establish prognostic gene signatures based on CCT subunit genes. The data was downloaded from The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus. CCT subunit gene expression levels between tumor and normal tissues were compared. Corresponding Kaplan-Meier analysis displayed a distinct separation in the overall survival of CCT subunit genes. Correlation analysis, protein-protein interaction network, Gene Ontology analysis, immune cells infiltration analysis, and transcription factor network were performed. A nomogram was constructed for the prediction of prognosis. Based on multivariate Cox regression analysis and shrinkage and selection method for linear regression model, a three-gene signature comprising CCT4, CCT6A, and CCT6B was constructed in the training set and significantly associated with prognosis as an independent prognostic factor. The prognostic value of the signature was then validated in the validation and testing set. Nomogram including the signature showed some clinical benefit for overall survival prediction. In all, we built a novel three-gene signature and nomogram from CCT subunit genes to predict the prognosis of hepatocellular carcinoma, which may support the medical decision for HCC therapy.

scholarly journals The diagnostic and prognostic value of H2AFY in hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xuyang Ma ◽  
Ying Ding ◽  
Li Zeng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Normal Liver ◽  
Diagnostic Value ◽  
The Relationship ◽  
Diagnostic And Prognostic Value

Abstract Background The potential correlation between H2AFY (also known as MacroH2A1) and the clinical characteristics of hepatocellular carcinoma (HCC) patients was analysed through gene expression profiles and clinical data in The Cancer Genome Atlas (TCGA) database, and the diagnostic and prognostic value of H2AFY in HCC was discussed. Methods The gene expression data of HCC and the corresponding clinical characteristics of HCC patients were downloaded from the TCGA database. The differences in H2AFY in normal liver tissues and HCC were analysed. The relationship between H2AFY and clinical characteristics was analysed by Wilcoxon signed-rank test, logistic regression and Kruskal-Wallis test. The Kaplan-Meier method and the Cox regression method were used to analyse the relationship between overall survival and clinical characteristics of the patients. An ROC curve was used to predict the diagnostic value of H2AFY in HCC. Gene set enrichment analysis (GSEA) was used to analyse the pathway enrichment of H2AFY. Result Compared with normal liver tissues, H2AFY was significantly highly expressed in HCC. H2AFY was positively correlated with the age, clinical stage, G stage (grade) and T stage (tumor stage) of liver cancer patients. Higher H2AFY expression predicted a poor prognosis in HCC patients. Cox regression analysis suggested that H2AFY was an independent risk factor for the prognosis of HCC patients. The ROC curve suggested that H2AFY had certain diagnostic value in HCC. GSEA suggested that H2AFY was correlated with lipid metabolism and a variety of tumour pathways. Conclusion Our study showed that H2AFY was significantly overexpressed in HCC. H2AFY may be a potential diagnostic and prognostic marker for HCC, and high expression of H2AFY predicts a poor prognosis in patients with HCC.

scholarly journals The diagnostic and prognostic value of H2AFY in hepatocellular carcinoma

2020 ◽  
Author(s):  
xuyang ma ◽  
Ying Ding ◽  
Li Zeng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Normal Liver ◽  
Diagnostic Value ◽  
The Relationship ◽  
Diagnostic And Prognostic Value

Abstract Background The potential correlation between H2AFY (also known as MacroH2A1) and the clinical characteristics of hepatocellular carcinoma (HCC) patients was analysed through gene expression profiles and clinical data in The Cancer Genome Atlas (TCGA) database, and the diagnostic and prognostic value of H2AFY in HCC was discussed. Methods The gene expression data of HCC and the corresponding clinical characteristics of HCC patients were downloaded from the TCGA database. The differences in H2AFY in normal liver tissues and HCC were analysed. The relationship between H2AFY and clinical characteristics was analysed by Wilcoxon signed-rank test, logistic regression and Kruskal-Wallis test. The Kaplan-Meier method and the Cox regression method were used to analyse the relationship between overall survival and clinical characteristics of the patients. An ROC curve was used to predict the diagnostic value of H2AFY in HCC. Gene set enrichment analysis (GSEA) was used to analyse the pathway enrichment of H2AFY. Result Compared with normal liver tissues, H2AFY was significantly highly expressed in HCC. H2AFY was positively correlated with the age, clinical stage, G stage and T stage of liver cancer patients. Higher H2AFY expression predicted a poor prognosis in HCC patients. Cox regression analysis suggested that H2AFY was an independent risk factor for the prognosis of HCC patients. The ROC curve suggested that H2AFY had certain diagnostic value in HCC. GSEA suggested that H2AFY was correlated with lipid metabolism and a variety of tumour pathways. Conclusion Our study showed that H2AFY was significantly overexpressed in HCC. H2AFY may be a potential diagnostic and prognostic marker for HCC, and high expression of H2AFY predicts a poor prognosis in patients with HCC.

scholarly journals Identification of diagnostic biomarkers in patients with gestational diabetes mellitus based on transcriptome gene expression and methylation correlation analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Enchun Li ◽  
Tengfei Luo ◽  
Yingjun Wang
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Dna Methylation ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Enrichment Analysis ◽  
Data Sets ◽  
Hub Genes ◽  
Promoter Regions ◽  
The Relationship

Abstract Background Gestational diabetes mellitus (GDM) has a high prevalence in the period of pregnancy. However, the lack of gold standards in current screening and diagnostic methods posed the biggest limitation. Regulation of gene expression caused by DNA methylation plays an important role in metabolic diseases. In this study, we aimed to screen GDM diagnostic markers, and establish a diagnostic model for predicting GDM. Methods First, we acquired data of DNA methylation and gene expression in GDM samples (N = 41) and normal samples (N = 41) from the Gene Expression Omnibus (GEO) database. After pre-processing the data, linear models were used to identify differentially expressed genes (DEGs). Then we performed pathway enrichment analysis to extract relationships among genes from pathways, construct pathway networks, and further analyzed the relationship between gene expression and methylation of promoter regions. We screened for genes which are significantly negatively correlated with methylation and established mRNA-mRNA-CpGs network. The network topology was further analyzed to screen hub genes which were recognized as robust GDM biomarkers. Finally, the samples were randomly divided into training set (N = 28) and internal verification set (N = 27), and the support vector machine (SVM) ten-fold cross-validation method was used to establish a diagnostic classifier, which verified on internal and external data sets. Results In this study, we identified 465 significant DEGs. Functional enrichment analysis revealed that these genes were associated with Type I diabetes mellitus and immunization. And we constructed an interactional network including 1091 genes by using the regulatory relationships of all 30 enriched pathways. 184 epigenetics regulated genes were screened by analyzing the relationship between gene expression and promoter regions’ methylation in the network. Moreover, the accuracy rate in the training data set was increased up to 96.3, and 82.1% in the internal validation set, and 97.3% in external validation data sets after establishing diagnostic classifiers which were performed by analyzing the gene expression profiles of obtained 10 hub genes from this network, combined with SVM. Conclusions This study provided new features for the diagnosis of GDM and may contribute to the diagnosis and personalized treatment of GDM.

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