scholarly journals Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Tian-ming Huo ◽  
Zhi-wei Wang
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Lupus Erythematosus ◽  
Inflammatory Responses ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Key Genes

Background. The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA). Methods. We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes. Results. A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways. Conclusion. Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.

Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Xi Pan ◽  
Jian-Hao Liu
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Therapeutic Goals ◽  
Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

scholarly journals Potential Novel Therapeutic Targets In Epilepsy From The View of Intestinal Comorbidities - Inflammatory Bowel Disease

2021 ◽  
Author(s):  
Mi Jiang ◽  
Jia Li ◽  
Zhi Song
Keyword(s):  
Gene Expression ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Refractory Epilepsy ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Inflammatory Bowel ◽  
Novel Therapeutic

Abstract Background: Epilepsy is a complicated neurological disorder with almost 30% refractory. Recent years, several studies showed that epilepsy is associated with its comorbidities by shared molecular mechanisms. However, the association of epilepsy and digestive comorbidities are still unclear. In this study, we aim to explore the association between inflammatory bowel disease (IBD) and epilepsy, and to find promising therapeutic targets for refractory epilepsy. Methods: Two gene expression profiles (GSE134697 and GSE59071) were selected from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R and the DESeq2 package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of intersection DEGs and Gene Set Enrichment Analysis (GSEA) were conducted by clusterProfiler package. The protein-protein interactions (PPI) network was established by using STRING and visualized by Cytoscape. Genes in the most significant module identified by MCODE plug-in were considered as candidate hub genes. Validation of hub genes were performed by using the GSE143272 dataset. Results: Cytokine-cytokine receptor interaction pathway is identified as a communal pathway between IBD and epilepsy. CXCL8, CXCR4 and ITGAX were identified as the hub genes. Conclusions: The identification of the communal pathway and hub genes in this study contributes to a potential novel therapeutic target in refractory epilepsy.

scholarly journals Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Junhong Li ◽  
Yang Zhai ◽  
Peng Wu ◽  
Yueqiang Hu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Common Disease ◽  
Hub Genes ◽  
Neuron Death ◽  
Roc Curve Analysis

Abstract BACKGROUD: Microarray-based gene expression profiling is widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) links microarray data directly to clinical traits and identifies rules for predicting pathological stage and prognosis of disease.WGCNA is useful in understandingmany biological processes. Stroke is a common disease worldwide, however, molecular mechanisms of its pathogenesis are largely unknown. The aim of this study was to construct gene co-expression networks for identification of key modules and hub genes associated with stroke pathogenesis.METHODS: Gene microarray expression profiles of stroke samples were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by the limma package in R software. WGCNA was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment analyses were performed. Further, receiver operating characteristic (ROC) curve analysis was carried out to validate expression of hub genes and literature validation was performed as well.RESULTS: A total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were significantly correlated to stroke pathogenesis. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and repair of DNA damage.On the other hand, yellow module was mainly enriched in ion transport system dysfunction which was correlated with neuron death. A total of eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels and through existing literature.CONCLUSION: The eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) identified in the study are potentialbiomarkers and therapeutic targets for effective diagnosis and treatment of stroke.

scholarly journals Co-expression of key gene modules and pathways of human breast cancer cell lines

2019 ◽  
Vol 39 (7) ◽  
Author(s):  
Yadong Wu ◽  
Feng liu ◽  
Siyang Luo ◽  
Xinhai Yin ◽  
Dengqi He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Network Analysis ◽  
Cell Lines ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Breast Cancer Cell Lines ◽  
Hub Genes ◽  
Weighted Correlation

Abstract Breast cancer (BC) is the most common leading cause of cancer-related death in women worldwide. Gene expression profiling analysis for human BCs has been studied previously. However, co-expression analysis for BC cell lines is still devoid to date. The aim of the study was to identify key pathways and hub genes that may serve as a biomarker for BC and uncover potential molecular mechanism using weighted correlation network analysis. We analyzed microarray data of BC cell lines (GSE 48213) listed in the Gene Expression Omnibus database. Gene co-expression networks were used to construct and explore the biological function in hub modules using the weighted correlation network analysis algorithm method. Meanwhile, Gene ontology and KEGG pathway analysis were performed using Cytoscape plug-in ClueGo. The network of the key module was also constructed using Cytoscape. A total of 5000 genes were selected, 28 modules of co-expressed genes were identified from the gene co–expression network, one of which was found to be significantly associated with a subtype of BC lines. Functional enrichment analysis revealed that the brown module was mainly involved in the pathway of the autophagy, spliceosome, and mitophagy, the black module was mainly enriched in the pathway of colorectal cancer and pancreatic cancer, and genes in midnightblue module played critical roles in ribosome and regulation of lipolysis in adipocytes pathway. Three hub genes CBR3, SF3B6, and RHPN1 may play an important role in the development and malignancy of the disease. The findings of the present study could improve our understanding of the molecular pathogenesis of breast cancer.

scholarly journals Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Li ◽  
Wenye Zhu ◽  
Chu Wang ◽  
Yuanyuan Zheng ◽  
Shibo Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Immune Status ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA. Methods Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein–protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset. Results Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268. Conclusions Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is clearly heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets.Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were enrolled. After merging all microarray data and adjusted batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out for DEGs. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The potential crucial genes coupled with corresponding predicted microRNAs involved in AF were then assessed.Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 and 5 with FC ≥ 2 of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. Sixteen corresponding predicted microRNAs, of which 5 targeting IGFBP3 and 8 FHL2, might be associated with AF. The comparative toxicogenomics database (CTD) database showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF.Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2, may be associated with risk of AF. MicroRNAs targeting IGFBP3 and FHL2 may be potential biomarkers or therapeutic targets for AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

scholarly journals Gene Correlation Network Analysis To Identify Regulatory Factors In Sciatic Nerve Injury

2021 ◽  
Author(s):  
Liuxun Li ◽  
Xiaokang Du ◽  
Haiqian Ling ◽  
Yuhang Li ◽  
Xuemin Wu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Sciatic Nerve ◽  
Nerve Injury ◽  
Enrichment Analysis ◽  
Sciatic Nerve Injury ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Adult Group ◽  
Hub Genes

Abstract Background: Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.Methods: Sciatic nerve injury dataset GSE18803 with 24 samples was randomly divided into adult group and neonate group. We performed weighted gene co-expression network analysis (WGCNA) to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network, protein-protein interaction (PPI) network, enrichment analysis and gene set enrichment analysis (GSEA) were integrated to identify hub genes and associated pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Finally, we employed DGIdb for predicting the possible therapeutic agents leading to the abnormal up-regulation of hub genes.Results: 14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of DEGs, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance was mostly related to the cytokine-cytokine receptor interaction.Conclusions: The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more lights on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

scholarly journals Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9462
Author(s):  
Dao wei Zhang ◽  
Shenghai Zhang ◽  
Jihong Wu
Keyword(s):  
Gene Expression ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Hub Genes ◽  
Gene Modules ◽  
Key Genes

Purpose Glaucoma is the second commonest cause of blindness. We assessed the gene expression profile of astrocytes in the optic nerve head to identify possible prognostic biomarkers for glaucoma. Method A total of 20 patient and nine normal control subject samples were derived from the GSE9944 (six normal samples and 13 patient samples) and GSE2378 (three normal samples and seven patient samples) datasets, screened by microarray-tested optic nerve head tissues, were obtained from the Gene Expression Omnibus (GEO) database. We used a weighted gene coexpression network analysis (WGCNA) to identify coexpressed gene modules. We also performed a functional enrichment analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Genes expression was represented by boxplots, functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all the key genes. Then the key genes were validated by the external dataset. Results A total 8,606 genes and 19 human optic nerve head samples taken from glaucoma patients in the GSE9944 were compared with normal control samples to construct the co-expression gene modules. After selecting the most common clinical traits of glaucoma, their association with gene expression was established, which sorted two modules showing greatest correlations. One with the correlation coefficient is 0.56 (P = 0.01) and the other with the correlation coefficient is −0.56 (P = 0.01). Hub genes of these modules were identified using scatterplots of gene significance versus module membership. A functional enrichment analysis showed that the former module was mainly enriched in genes involved in cellular inflammation and injury, whereas the latter was mainly enriched in genes involved in tissue homeostasis and physiological processes. This suggests that genes in the green–yellow module may play critical roles in the onset and development of glaucoma. A LASSO regression analysis identified three hub genes: Recombinant Bone Morphogenetic Protein 1 gene (BMP1), Duchenne muscular dystrophy gene (DMD) and mitogens induced GTP-binding protein gene (GEM). The expression levels of the three genes in the glaucoma group were significantly lower than those in the normal group. GSEA further illuminated that BMP1, DMD and GEM participated in the occurrence and development of some important metabolic progresses. Using the GSE2378 dataset, we confirmed the high validity of the model, with an area under the receiver operator characteristic curve of 85%. Conclusion We identified several key genes, including BMP1, DMD and GEM, that may be involved in the pathogenesis of glaucoma. Our results may help to determine the prognosis of glaucoma and/or to design gene- or molecule-targeted drugs.

scholarly journals Identification of Key Modules and Hub Genes in Hypertrophic Cardiomyopathy Based on Integrative Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Jun Jiang ◽  
Delong Chen ◽  
Siyuan Xie ◽  
Qichao Dong ◽  
Yi Yu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Hedgehog Signaling ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Remodeling Of Extracellular Matrix

Abstract BackgroundHypertrophic cardiomyopathy (HCM) is a heterogeneously inherited cardiac disorder with unclear biological pathogenesis. This study aims to identify the key modules and genes involved in the development of HCM.MethodsUsing weighted gene co-expression network analysis (WGCNA) algorithm, we constructed integrative co-expression networks for the two large sample HCM datasets separately. After selecting clinically significant modules with the same clinical trait, functional enrichment analysis was performed to detect their common pathways. Based on the intramodular connectivity (IC), the shared hub genes were generated, validated, and further explored in gene set enrichment analysis (GSEA).ResultsThe orange and pink modules in GSE141910, the green and brown modules in GSE36961 were mostly related to HCM. Functional enrichment analysis suggested that HCM might exhibit enhanced processes including remodeling of extracellular matrix, activation of abnormal protein signaling, aggregation of calcium ion, and organization of cytoskeleton. SMOC2, COL16A1, RASL11B, TUBA3D, IL18R1 were defined as real hub genes due to their top IC values, significantly different expression levels, and excellent diagnostic performance in both datasets. Moreover, GSEA analysis demonstrated that pathways of the five hub genes were mainly involved in neuroactive ligand-receptor interaction, ECM-receptor interaction, Hedgehog signaling pathway.ConclusionOur study provides more comprehensive insights into the molecular mechanisms of HCM, identifies five hub genes as candidate biomarkers for HCM, which might be theoretically feasible for targeted therapy against HCM.

scholarly journals Construction of Gene Modules and Analysis of Prognostic Biomarkers for Cervical Cancer by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiamei Liu ◽  
Shengye Liu ◽  
Xianghong Yang
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Network Analysis ◽  
Cancer Progression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Modules

BackgroundDespite advances in the understanding of neoplasm, patients with cervical cancer still have a poor prognosis. Identifying prognostic markers of cervical cancer may enable early detection of recurrence and more effective treatment.MethodsGene expression profiling data were acquired from the Gene Expression Omnibus database. After data normalization, genes with large variation were screened out. Next, we built co-expression modules by using weighted gene co-expression network analysis to investigate the relationship between the modules and clinical traits related to cervical cancer progression. Functional enrichment analysis was also applied on these co-expressed genes. We integrated the genes into a human protein-protein interaction (PPI) network to expand seed genes and build a co-expression network. For further analysis of the dataset, the Cancer Genome Atlas (TCGA) database was used to identify seed genes and their correlation to cervical cancer prognosis. Verification was further conducted by qPCR and the Human Protein Atlas (HPA) database to measure the expression of hub genes.ResultsUsing WGCNA, we identified 25 co-expression modules from 10,016 genes in 128 human cervical cancer samples. After functional enrichment analysis, the magenta, brown, and darkred modules were selected as the three most correlated modules for cancer progression. Additionally, seed genes in the three modules were combined with a PPI network to identify 31 tumor-specific genes. Hierarchical clustering and Gepia results indicated that the expression quantity of hub genes NDC80, TIPIN, MCM3, MCM6, POLA1, and PRC1 may determine the prognosis of cervical cancer. Finally, TIPIN and POLA1 were further filtered by a LASSO model. In addition, their expression was identified by immunohistochemistry in HPA database as well as a biological experiment.ConclusionOur research provides a co-expression network of gene modules and identifies TIPIN and POLA1 as stable potential prognostic biomarkers for cervical cancer.

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