Identification Of Both Shared And Specific Potential Molecular Mechanisms Of Arvc And Dcm Based On A Genome-Wide Microarray Dataset

Abstract Background: The study aimed to detect the shared differentially expressed genes (DEGs) and specific DEGs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and dilated cardiomyopathy (DCM) as well as their pathways.Methods: The GSE29819 dataset was examined for the DEGs of ARVC vs. non-failing transplant donor hearts (NF), DCM vs. NF, and ARVC vs. DCM based on 6 patients with ARVC, 7 patients with DCM, and 6 non-failing transplant donor hearts that were never actually transplanted. The shared DEGs and specific DEGs were screened out using a Venn diagram. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) of the DEGs were determined using online analytical tools. Then, the modules and hub genes were identified using Cytoscape software.Results: A total of 684 shared DEGs of ARVC vs. NF and DCM vs. NF, 1371 specific DEGs of ARVC vs. NF, and 1075 specific DEGs of DCM vs. NF were identified. The shared DEGs were enriched in 63 biological processes (BP), 11 molecular functions (MF), 10 cellular components (CC), and 25 KEGG pathways. The DEGs of ARVC vs. DCM were enriched in 71 BPs, 19 MFs, 14 CCs, and 26 KEGG pathways. A PPI network with 187 nodes, 700 edges, and 2 modules, and another PPI network with 575 nodes, 2834 edges, and 7 modules were constructed based on the shared and specific DEGs, respectively. The top ten hub genes CCR3, CCR5, CXCL2, CXCL10, CXCR4, FPR1, APLNR, PENK, BDKRB2, GRM8, and RPS8, RPS3A, RPS12, RPS14, RPS21, RPL14, RPL18A, RPL21, RPL31 were identified for the shared and specific PPI networks, respectively.Conclusions: Our findings may help further the understanding of both shared and specific potential molecular mechanisms of ARVC and DCM.

Download Full-text

Identification of Both Shared and Specific Potential Molecular Mechanisms of ARVC and DCM Based on A Genome-Wide Microarray Dataset

10.21203/rs.3.rs-47567/v1 ◽

2020 ◽

Author(s):

Tang Zhang ◽

Yao-Zong Guan ◽

Hao Liu

Keyword(s):

Molecular Mechanisms ◽

Arrhythmogenic Right Ventricular Cardiomyopathy ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Go Annotation ◽

Kegg Pathways ◽

Ppi Networks ◽

A Genome ◽

Specific Potential

Abstract Background：The study aimed to detect the shared differentially expressed genes (DEGs) and specific DEGs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and dilated cardiomyopathy (DCM) as well as their pathways. Methods: The GSE29819 dataset was examined for the DEGs of ARVC vs. non-failing transplant donor hearts (NF), DCM vs. NF, and ARVC vs. DCM based on 8 patients with ARVC, 7 patients with DCM, and 4 non-failing transplant donor hearts that were never actually transplanted. The shared DEGs and specific DEGs were screened out using a Venn diagram. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) of the DEGs were determined using online analytical tools. Then, the modules and hub genes were identified using Cytoscape software. Results: A total of 684 shared DEGs of ARVC vs. NF and DCM vs. NF, 1371 specific DEGs of ARVC vs. NF, and 1075 specific DEGs of DCM vs. NF were identified. The shared DEGs were enriched in 63 biological processes (BP), 11 molecular functions (MF), 10 cellular components (CC), and 25 KEGG pathways. The DEGs of ARVC vs. DCM were enriched in 71 BPs, 19 MFs, 14 CCs, and 26 KEGG pathways. A PPI network with 187 nodes, 700 edges, and 2 modules, and another PPI network with 575 nodes, 2834 edges, and 7 modules were constructed based on the shared and specific DEGs, respectively. The top ten hub genes CCR3, CCR5, CXCL2, CXCL10, CXCR4, FPR1, APLNR, PENK, BDKRB2, GRM8, and RPS8, PRS3A, PRS12, RPS14, RPS21, RPL14, RPL18A, RPL21, RPL31 were identified for the shared and specific PPI networks, respectively. Conclusions: Our findings may help further the understanding of both shared and specific potential molecular mechanisms of ARVC and DCM.

Download Full-text

Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

BioMed Research International ◽

10.1155/2020/4505720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Weishuang Xue ◽

Jinwei Li ◽

Kailei Fu ◽

Weiyu Teng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Peripheral Blood ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

Download Full-text

Identification of Key Genes and Pathways for Childhood Obesity Using System Biology Approach Based on Comprehensive Gene Information

Journal of the Endocrine Society ◽

10.1210/jendso/bvab048.098 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. A49-A50

Author(s):

Daisy Crispim ◽

Felipe Mateus Pellenz ◽

Tais Silveira Assmann

Keyword(s):

Childhood Obesity ◽

System Biology ◽

Molecular Mechanisms ◽

Functional Enrichment ◽

Biological Processes ◽

System Biology Approach ◽

Ppi Network ◽

Metabolic Processes ◽

Hub Genes ◽

Kegg Pathways

Abstract Introduction: Childhood obesity is one of the most important public health issues of the 21st century. Epidemiological studies have suggested that obesity during childhood increases the risk of developing comorbidities, such as type 2 diabetes, later in life. Childhood obesity is a complex disease whose molecular mechanisms are not completely elucidated. In this context, a system biology approach could contribute to the scientific knowledge regarding genetic factors related to childhood obesity onset. Aim: To identify molecular mechanisms involved in childhood obesity by implementing a system biology approach. Methods: Experimentally validated and computationally predicted genes related to Pediatric Obesity (C2362324) were downloaded from the DisGeNET v7.0 database. The protein-protein interaction (PPI) network was constructed using the STRING v11.0 database and analyzed using NetworkAnalyst v3.0 and Cytoscape v3.8.1. The relevance of each node for the network structure and functionality was assessed using the degree method to define hub genes. Functional and pathway enrichment analyses were performed based on Gene Ontology (GO) terms and KEGG Pathways. Results: The search on the DisGeNET database retrieved 191 childhood obesity-related genes. The PPI network of these genes showed 19 hub genes (STAT3, SIRT1, BCL2, IRS1, PPARG, SOCS3, TGFB1, HDAC4, DNMT1, ADCY3, PPARA, NEDD4L, ACACB, NR0B2, VEGFA, APOA1, GHR, CALR, and MKKS). These hub genes were involved in biological processes of lipid storage / kinase activity, regulation of fatty-acid metabolic processes, regulation of pri-miRNA transcription by RNA polymerase II, and negative regulation of small molecules and carbohydrate metabolic processes. In terms of molecular functions, repressing of transcription factors biding was found enriched. Regarding KEGG Pathways, the hub genes are involved with adipocytokine signaling, insulin resistance, longevity regulation, and cytokine signaling pathways. Conclusion: Our approach identified 19 hub genes, which are highly connected and probably have a key role in childhood obesity. Moreover, functional enrichment analyses demonstrated they are enriched in several biological processes and pathways related to the underlying molecular mechanisms of obesity. These findings provide a more comprehensive information regarding genetic and molecular factors behind childhood obesity pathogenesis. Further experimental investigation of our findings may shed light on the pathophysiology of this disease and contribute to the identification of new therapeutic targets.

Download Full-text

In silico Identification of 10 Hub Genes and an miRNA–mRNA Regulatory Network in Acute Kawasaki Disease

Frontiers in Genetics ◽

10.3389/fgene.2021.585058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jin Ma ◽

Huan Gui ◽

Yunjia Tang ◽

Yueyue Ding ◽

Guanghui Qian ◽

...

Keyword(s):

Kawasaki Disease ◽

Signaling Pathway ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Unknown Etiology ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

Kawasaki disease (KD) causes acute systemic vasculitis and has unknown etiology. Since the acute stage of KD is the most relevant, the aim of the present study was to identify hub genes in acute KD by bioinformatics analysis. We also aimed at constructing microRNA (miRNA)–messenger RNA (mRNA) regulatory networks associated with acute KD based on previously identified differentially expressed miRNAs (DE-miRNAs). DE-mRNAs in acute KD patients were screened using the mRNA expression profile data of GSE18606 from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DE-mRNAs were performed with the DAVID database. Target genes of DE-miRNAs were predicted using the miRWalk database and their intersection with DE-mRNAs was obtained. From a protein–protein interaction (PPI) network established by the STRING database, Cytoscape software identified hub genes with the two topological analysis methods maximal clique centrality and Degree algorithm to construct a miRNA-hub gene network. A total of 1,063 DE-mRNAs were identified between acute KD and healthy individuals, 472 upregulated and 591 downregulated. The constructed PPI network with these DE-mRNAs identified 38 hub genes mostly enriched in pathways related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, osteoclast differentiation, adipocytokine signaling pathway and tumor necrosis factor signaling pathway. Target genes were predicted for the up-regulated and down-regulated DE-miRNAs, 10,203, and 5,310, respectively. Subsequently, 355, and 130 overlapping target DE-mRNAs were obtained for upregulated and downregulated DE-miRNAs, respectively. PPI networks with these target DE-mRNAs produced 15 hub genes, six down-regulated and nine upregulated hub genes. Among these, ten genes (ATM, MDC1, CD59, CD177, TRPM2, FCAR, TSPAN14, LILRB2, SIRPA, and STAT3) were identified as hub genes in the PPI network of DE-mRNAs. Finally, we constructed the regulatory network of DE-miRNAs and hub genes, which suggested potential modulation of most hub genes by hsa-miR-4443 and hsa-miR-6510-5p. SP1 was predicted to potentially regulate most of DE-miRNAs. In conclusion, several hub genes are associated with acute KD. An miRNA–mRNA regulatory network potentially relevant for acute KD pathogenesis provides new insights into the underlying molecular mechanisms of acute KD. The latter may contribute to the diagnosis and treatment of acute KD.

Download Full-text

Identification of Essential Proteins Based on Improved HITS Algorithm

Genes ◽

10.3390/genes10020177 ◽

2019 ◽

Vol 10 (2) ◽

pp. 177 ◽

Cited By ~ 2

Author(s):

Xiujuan Lei ◽

Siguo Wang ◽

Fang-Xiang Wu

Keyword(s):

Molecular Mechanisms ◽

Clustering Coefficient ◽

New Drugs ◽

Biological Information ◽

Ppi Network ◽

Go Annotation ◽

Detection Techniques ◽

Essential Proteins ◽

Ppi Networks ◽

Topological Characteristics

Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein–protein interaction (PPI) data are available, which facilitates the studies of essential proteins at the network level. Up to now, although various computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a novel method by applying Hyperlink-Induced Topic Search (HITS) on weighted PPI networks to detect essential proteins, named HSEP. First, an original undirected PPI network is transformed into a bidirectional PPI network. Then, both biological information and network topological characteristics are taken into account to weighted PPI networks. Pieces of biological information include gene expression data, Gene Ontology (GO) annotation and subcellular localization. The edge clustering coefficient is represented as network topological characteristics to measure the closeness of two connected nodes. We conducted experiments on two species, namely Saccharomyces cerevisiae and Drosophila melanogaster, and the experimental results show that HSEP outperformed some state-of-the-art essential proteins detection techniques.

Download Full-text

Identification of Aberrantly Expressed Genes during Aging in Rat Nucleus Pulposus Cells

Stem Cells International ◽

10.1155/2019/2785207 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Shi Cheng ◽

Xiaochuan Li ◽

Linghan Lin ◽

Zhiwei Jia ◽

Yachao Zhao ◽

...

Keyword(s):

Nucleus Pulposus ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Vital Role ◽

Differentially Expressed ◽

Ppi Network ◽

Nucleus Pulposus Cells ◽

Hub Genes ◽

Software Module ◽

A Genome

Nucleus pulposus cells (NPCs) play a vital role in maintaining the homeostasis of the intervertebral disc (IVD). Previous studies have discovered that NPCs exhibited malfunction due to cellular senescence during disc aging and degeneration; this might be one of the key factors of IVD degeneration. Thus, we conducted this study in order to investigate the altered biofunction and the underlying genes and pathways of senescent NPCs. We isolated and identified NPCs from the tail discs of young (2 months) and old (24 months) SD rats and confirmed the senescent phenotype through SA-β-gal staining. CCK-8 assay, transwell assay, and cell scratch assay were adopted to detect the proliferous and migratory ability of two groups. Then, a rat Gene Chip Clariom™ S array was used to detect differentially expressed genes (DEGs). After rigorous bioinformatics analysis of the raw data, totally, 1038 differentially expressed genes with a fold change>1.5 were identified out of 23189 probes. Among them, 617 were upregulated and 421 were downregulated. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted and revealed numerous number of enriched GO terms and signaling pathways associated with senescence of NPCs. A protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Module analysis was conducted for the PPI network using the MCODE plugin in Cytoscape. Hub genes were identified by the CytoHubba plugin in Cytoscape. Derived 5 hub genes and most significantly up- or downregulated genes were further verified by real-time PCR. The present study investigated underlying mechanisms in the senescence of NPCs on a genome-wide scale. The illumination of molecular mechanisms of NPCs senescence may assist the development of novel biological methods to treat degenerative disc diseases.

Download Full-text

Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

10.21203/rs.3.rs-56834/v1 ◽

2020 ◽

Author(s):

Denghua Liu ◽

Rui Zhou ◽

Aiguo Zhou

Keyword(s):

Lung Metastasis ◽

Molecular Mechanisms ◽

Interaction Network ◽

Mapk Signaling ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes ◽

Go Annotation ◽

Functional Pathways ◽

Key Genes

Abstract Background: In osteosarcoma, the lung is the most common metastatic organ. Intensive work has been made to illuminate the pathogeny, but the specific metastatic mechanism remains unclear. Thus, we conducted the study to seek to find the key genes and critical functional pathways associated with progression and treatment in osteosarcoma with lung metastasis.Methods: Two independent datasets (GSE14359 and GSE85537) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using DAVID. Meanwhile, the protein-protein interaction (PPI) network constructed by STRING was visualized using Cytoscape. Afterwards, the key module and hub genes were extracted from the PPI network using the MCODE and cytoHubba plugin. Moreover, the raw data obtained from GSE73166 and GSE21257 was applied to verify the expression differences and conduct the survival analyses of hub genes, respectively. Finally, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape.Results: A total of 364 DEGs were identified, comprising 96 downregulated genes and 268 upregulated genes, which were mainly involved in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, MAPK signaling pathway. Subsequently, 10 hub genes were obtained and survival analysis demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma. Finally, hsa-miR-340-5p were found to be most closely associated with these hub genes according to the interaction network of miRNAs and hub genes.Conclusion: The key genes and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of osteosarcoma with lung metastasis, and provide potential diagnostic and therapeutic targets.

Download Full-text

Identification of hub genes and molecular mechanisms in infant acute lymphoblastic leukemia withMLLgene rearrangement

PeerJ ◽

10.7717/peerj.7628 ◽

2019 ◽

Vol 7 ◽

pp. e7628

Author(s):

Hao Zhang ◽

Juan Cheng ◽

Zijian Li ◽

Yaming Xi

Keyword(s):

Acute Lymphoblastic Leukemia ◽

Cellular Response ◽

Molecular Mechanisms ◽

Lymphoblastic Leukemia ◽

Gene Interactions ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways ◽

Go Terms ◽

Infant Acute Lymphoblastic Leukemia

Infant acute lymphoblastic leukemia (ALL) with the mixed lineage leukemia (MLL) gene rearrangement (MLL-R) is considered a distinct leukemia from childhood or non-MLL-R infant ALL. To detect key genes and elucidate the molecular mechanisms ofMLL-R infant ALL, microarray expression data were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) betweenMLL-R and non-MLL-R infant ALL were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out. Then, we constructed a protein-protein interaction (PPI) network and identified the hub genes. Finally, drug-gene interactions were mined. A total of 139 cases ofMLL-R infant ALL including 77 (55.4%) fusions withAF4, 38 (27.3%) withENL, 14 (10.1%) withAF9, and 10 (7.2%) other gene fusions were characterized. A total of 236 up-regulated and 84 down-regulated DEGs were identified. The up-regulated DEGs were mainly involved in homophilic cell adhesion, negative regulation of apoptotic process and cellular response to drug GO terms, while down-regulated DEGs were mainly enriched in extracellular matrix organization, protein kinase C signaling and neuron projection extension GO terms. The up-regulated DEGs were enriched in seven KEGG pathways, mainly involving transcriptional regulation and signaling pathways, and down-regulated DEGs were involved in three main KEGG pathways including Alzheimer’s disease, TGF-beta signaling pathway, and hematopoietic cell lineage. The PPI network included 297 nodes and 410 edges, withMYC,ALB,CD44,PTPRCandTNFidentified as hub genes. Twenty-three drug-gene interactions including four up-regulated hub genes and 24 drugs were constructed by Drug Gene Interaction database (DGIdb). In conclusion,MYC,ALB,CD44,PTPRCandTNFmay be potential bio-markers for the diagnosis and therapy ofMLL-R infant ALL.

Download Full-text

Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

International Journal of Molecular Sciences ◽

10.3390/ijms22126505 ◽

2021 ◽

Vol 22 (12) ◽

pp. 6505

Author(s):

Jishizhan Chen ◽

Jia Hua ◽

Wenhui Song

Keyword(s):

Stem Cells ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Negative Control ◽

Gene Set Enrichment Analysis ◽

Venn Diagram ◽

Hub Genes ◽

Protein Protein Interaction ◽

Two Samples ◽

Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

Download Full-text

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Download Full-text