Identification of hub genes in rheumatoid arthritis through an integrated bioinformatics approach

Abstract Background Rheumatoid arthritis (RA) is a common chronic autoimmune disease characterized by inflammation of the synovial membrane. However, the etiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify the key genes involved in RA. Methods GSE77298 was downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen the differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by using the DAVID online tool. The STRING database was used to analyze the interaction of differentially encoded proteins. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape. Gene set enrichment analysis (GSEA) was performed to identify relevant biological functions. qRT-PCR analysis was also performed to verify the expression of identified hub DEGs. Results A total of 4062 differentially expressed genes were selected, including 1847 upregulated genes and 2215 downregulated genes. In the biological process, DEGs were mainly concentrated in the fields of muscle filament sliding, muscle contraction, intracellular signal transduction, cardiac muscle contraction, signal transduction, and skeletal muscle tissue development. In the cellular components, DEGs were mainly concentrated in the parts of cytosol, Z disk, membrane, extracellular exosome, mitochondrion, and M band. In molecular functions, DEGs were mainly concentrated in protein binding, structural constituent of muscle, actin binding, and actin filament binding. KEGG pathway analysis shows that DEGs mainly focuses on pathways about lysosome, Wnt/β-catenin signaling pathway, and NF-κB signaling pathway. CXCR3, GNB4, and CXCL16 were identified as the core genes that involved in the progression of RA. By qRT-PCR analysis, we found that CXCR3, GNB4, and CXCL16 were significantly upregulated in RA tissue as compared to healthy controls. Conclusion In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the progression of RA, and provide candidate targets for diagnosis and treatment of RA.

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Identification of critical pathways and potential therapeutic targets in poorly differentiated duodenal papilla adenocarcinoma

Cancer Cell International ◽

10.1186/s12935-020-01709-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yuanxiang Lu ◽

Wensen Li ◽

Ge Liu ◽

Yongbo Yang ◽

Erwei Xiao ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Therapeutic Targets ◽

Differentially Expressed ◽

Clinical Samples ◽

Rna Seq ◽

Duodenal Papilla ◽

Ppi Network ◽

Hub Genes ◽

Qrt Pcr

Abstract Background Duodenal papilla carcinoma (DPC) is a rare malignancy of the gastrointestinal tract with high recurrence rate, and the pathogenesis of this highly malignant neoplasm is yet to be fully elucidated. This study aims to identify key genes to further understand the biology and pathogenesis underlying the molecular alterations driving DPC, which could be potential diagnostic or therapeutic targets. Methods Tumor samples of three DPC patients were collected and integrating RNA-seq analysis of tumor tissues and matched normal tissues were performed to discover differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were carried out to understand the potential bio-functions of the DPC differentially expressed genes (DEGs). Protein–protein interaction (PPI) network was constructed for functional modules analysis and identification of hub genes. qRT-PCR of clinical samples was conducted to validate the expression level of the hub genes. Results A total of 110 DEGs were identified from our RNA-seq data, GO and KEGG analyses showed that the DEGs were mainly enriched in multiple cancer-related functions and pathways, such as cell proliferation, IL-17signaling pathway, Jak-STAT signaling pathway, PPAR signaling pathway. The PPI network screened out five hub genes including IL-6, LCN2, FABP4, LEP and MMP1, which were identified as core genes in the network and the expression value were validated by qRT-PCR. The hub genes identified in this work were suggested to be potential therapeutic targets of DPC. Discussion The current study may provide new insight into the exploration of DPC pathogenesis and the screened hub genes may serve as potential diagnostic indicator and novel therapeutic target.

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Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

10.21203/rs.3.rs-1023446/v1 ◽

2021 ◽

Author(s):

Li Guoquan ◽

Du Junwei ◽

He Qi ◽

Fu Xinghao ◽

Ji Feihong ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Inflammatory Responses ◽

Expression Profiles ◽

Treatment Options ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Chronic Lymphocytic Thyroiditis ◽

Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

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Screening the Hub Genes and Analyzing the Mechanisms in Discharged COVID-19 Patients Retesting Positive through Bioinformatics Analysis

10.21203/rs.3.rs-991635/v1 ◽

2021 ◽

Author(s):

Ke-Ying Fang ◽

Gui-Ning Liang ◽

Zhuo-Qing Zhuang ◽

Yong-Xin Fang ◽

Yu-Qian Dong ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Social Order ◽

Expression System ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Virus Reactivation ◽

Hub Genes ◽

Messenger Ribonucleic Acid ◽

Ppi Networks ◽

Significant Enrichment

Abstract Background: With the worldwide spread of COVID-19, people’s health and social order have been exposed to enormous risks. After encountering patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation.Methods: A separate microarray was acquired from the Integrated Gene Expression System (GEO), and its samples were divided into two groups: a “convalescent-RTP” group consisting of recovery and “retesting-positive” (RTP) patients (group CR) and a “health-RTP” group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the gene ontology (GO) and Kyoto pluripotent stem cells (KEGG) of the genes and genomes. Subsequently, the protein–protein interaction (PPI) networks of each group were established and the hub genes were discovered using the cytoHubba plug-in.Results: In this study, 20 differentially expressed genes were identified, and 6622 genes were identified in the group CR, consisting of 5003 up-regulated and 1619 down-regulated genes. Meanwhile, 7335 genes were screened in the group HR, including 4323 up-regulated and 3012 down-regulated ones. The GO and KEGG analysis of the two groups revealed significant enrichment of these differentially expressed genes in pathways associated with immune response and apoptosis. In the PPI network constructed, 10 hub genes in group CR were identified, including TP53BP1, SNRPD1, SNRPD2, SF3B1, SNRNP200, MRPS16, MRPS9, CALM1, PPP2R1A, YWHAZ. Similarly, TP53BP1, RPS15, EFTUD2, MRPL16, MRPL17, MRPS14, RPL35A, MRPL32, MRPS6, POLR2G were selected as hub genes.Conclusions: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we explore the pathogenesis of retesting positive in COVID-19 from the immune mechanism and molecular level. We found TP53BP1, SNRPD1 and SNRPD2 as hub genes in RTP patients. Hence, their regulatory pathway is vital to the management and prognostic prediction of RTP patients, rendering the further study of these hub genes necessary.

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Comprehensive Analysis of Differentially Expressed Genes and Key Pathways in Neuropathic Pain by Spinal Nerve Ligation

10.21203/rs.3.rs-74125/v1 ◽

2020 ◽

Author(s):

Chao Xu ◽

HuiFang Li ◽

YunPeng Zhang ◽

TianYu Liu ◽

Yi Feng

Keyword(s):

Functional Analysis ◽

Neuropathic Pain ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Spinal Nerve ◽

Spinal Nerve Ligation ◽

Differentially Expressed ◽

Hub Genes ◽

Long Term Treatment

Abstract Background: Neuropathic pain can cause significant physical and economic burden to people, and there are no effective long-term treatment methods for this condition. We conducted a bioinformatics analysis of microarray data to identify related mechanisms to determine strategies for more effective treatments of neuropathic pain.Methods: GSE24982 and GSE63442 microarray datasets were extracted from the Gene Expression Omnibus (GEO) database to analyze transcriptome differences of neuropathic pain in the dorsal root ganglions caused by spinal nerve ligation. We filtered the differentially expressed genes (DEGs) in the two datasets and Webgestalt was applied to conduct GeneOntology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the shared DEGs. String Database and Cytoscape software were used to construct the Protein-Protein Interaction (PPI) network to determine the hub genes, which were subsequently verified in the GSE30691 dataset. Finally, miRDB and miRWalk Databases were used to predict potential miRNA of the selected DEGs.Results: A total of 182 overlapped DEGs were found between GSE24982 and GSE63442 datasets. The GO functional analysis and KEGG enrichment analysis showed that the selected DEGs were mainly enriched in infection, transmembrane transport of ion channels, and synaptic transmission. Combining the results of PPI analysis and the verification of the GSE30691 dataset, we identified seven hub genes related to neuropathic pain (Atf3, Aif1, Ctss, Gfap, Scg2, Jun, and Vgf). Predicted miRNA targeting each selected hub genes were identified.Conclusion: Seven hub genes related to the pathogenesis of neuropathic pain and potential targeting miRNA were identified, expanding understanding of the mechanism of neuropathic pain and facilitating treatment development.

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Identifying hub genes associated with clinical characteristicsin IgA nephropathy by Weighted Gene Co-expression Network Analysis

10.21203/rs.2.15823/v1 ◽

2019 ◽

Author(s):

Chengyu Yang ◽

Chenyu Li ◽

Long Zhao ◽

Bin Zhou ◽

Xiaofei Man ◽

...

Keyword(s):

Iga Nephropathy ◽

Signaling Pathway ◽

Clinical Characteristics ◽

Enrichment Analysis ◽

Akt Signaling ◽

Differentially Expressed ◽

Akt Signaling Pathway ◽

Hub Genes ◽

Renal Interstitium ◽

Healthy Control

Abstract Background: Clinically, IgA nephropathy has a variety of symptoms including paroxysmal gross hematuria, nephritic and nephrotic syndrome. This study aimed at investigating hub geneand genes modular related to IgA nephropathy clinical characteristics by using weighted gene co-expression network analysis combining clinical, microarray and network database parameters. Methods: We collected 32 human samples from the European Renal cDNA Bank and used RMA method to preprocess the data and utilize the limma package to obtain differentially expressed gene in renal interstitium and glomeruli. We used the WGCNA package to construct the gene co-expression of differential expression genes and identify hub genes associated with clinical characteristics in renal interstitium and glomeruli, respectively. Gene ontology enrichment analysis and KEGG analysis for hub genes which associated with clinical characteristics were performed by DAVID. PPI information was acquired from STRING. Results: For glomeruli, 1470 genes differentially expressed between IgA nephropathy patients and healthy control, containing 10 hub genes associated with age, 8 hub genes associated with sex, 48 hub genes associated with Bp enrichd in ERK1 and ERK2 cascade and Rap1 signaling pathway, 223 hub genes associated with BMI enrich in organic acid catabolic process and fatty acid degradation pathway, 136 hub genes associated GFR enriched in immune response and PI3K-Akt signaling pathway, 82 hub genes associated with proteinuria enriched in extracellular matrix organization and PI3K-Akt signaling pathway. In tubulointerstitium, there were 480 genes differentially expressed between IgA nephropathy patients and healthy control. Among 480 DEGs, 6 hub genes associated with age, 15 hub genes associated with sex, 35 hub genes associated with Bp enrichd in positive regulation of apoptotic process, 87 hub genes associated with GFR enriched in negative regulation of macromolecule metabolic process and RNA transport, 33 hub genes associated with proteinuria enriched in regulation of apoptotic process and FoxO signaling pathway. PPI enrichment analysis shown that all hub genes sets are biologically connected cluster. Conclusions: We made a preliminary investigation on molecular mechanisms of relationship between IgA nephropathy and clinical characteristics and identified hub genes and pathways closely related with BMI, GFR and Proteinuria in IgA nephropathy by a series of bioinformatics analysis.

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A competing endogenous RNA mechanism in glioblastoma is investigated by bioinformatics analysis

10.21203/rs.2.21101/v1 ◽

2020 ◽

Author(s):

Jinsheng Wang ◽

Yutao Wang ◽

Lei Gao ◽

Yuhua Zhao ◽

Junhua Liu ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Lupus Erythematosus ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Dendritic Cell Maturation ◽

Vital Role ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

P53 Signaling

Abstract Background Glioblastoma (GBM) is the most aggressive and most lethal primary malignant brain tumor, the 5-year survival rate of which is less than 5%. Novel potential molecular and mechanism of GBM need to investigate.Materials and methods Microarray data of GSE15824 was downloaded from GEO. Differentially expressed genes and lncRNAs were screened by Limma package in R studio, and pathway enrichment analysis was performed by clusterprofiler package in R studio and IPA. The ceRNA mechanism was analyzed and predicted by several kinds of online public databases.ResultsThere were 567 differentially expressed genes and 121 differentially expressed lncRNAs in GBM. And differentially expressed genes were mainly enriched in Tuberculosis, Staphylococcus aureus infection, Systemic lupus erythematosus, Basal cell carcinoma, TGF-beta signaling pathway and p53 signaling pathway. Besides, Neuroinflammation signaling pathway, Role of NFAT in regulation of the immune response, and Dendritic cell maturation were significantly activated in GBM. According to the analysis of target miRNAs of SEM4D and OSER1-AS1, a possible ceRNA mechanism OSER1-AS1/hsa-miR-520h/SEMA4D axis was predicted in GBM.Conclusion Bioinformatics analysis was employed to analyze GSE15824 chip, and predict the potential mechanism. The results revealed that the ceRNA mechanism, OSER1-AS1/hsa-miR-520h/SEMA4D axis, might play a vital role in GBM.

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Identification of critical pathways and potential therapeutic targets in Poorly differentiated duodenal papilla adenocarcinoma

10.21203/rs.3.rs-90868/v1 ◽

2020 ◽

Author(s):

Yuanxiang Lu ◽

Wensen Li ◽

Ge Liu ◽

Erwei Xiao ◽

Senmao Mu ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Therapeutic Targets ◽

Malignant Neoplasm ◽

Differentially Expressed ◽

High Recurrence Rate ◽

Rna Seq ◽

Duodenal Papilla ◽

Ppi Network ◽

Hub Genes

Abstract Background: Duodenal papilla carcinoma (DPC) is a rare malignancy of the gastrointestinal tract with high recurrence rate, and the pathogenesis of this highly malignant neoplasm is yet to be fully elucidated. This study aims to identify key genes to further understand the biology and pathogenesis underlying the molecular alterations driving DPC, which could be potential diagnostic or therapeutic targets.Methods: Tumor samples of three DPC patients were collected and integrating RNA-seq analysis of tumor tissues and matched normal tissues were performed to discover differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to understand the potential bio-functions of the DPC differentially expressed genes (DEGs) and protein–protein interaction (PPI) network was constructed for functional modules analysis and dentification of hub genes. Results: A total of 110 DEGs were identified from our RNA-Seq data, GO and KEGG analyses showed that the DEGs were mainly enriched in multiple cancer-related functions and pathways, such as cell proliferation, IL-17signaling pathway, Jak-STAT signaling pathway, PPAR signaling pathway. The PPI network screened out six hub genes including IL-6, LEP, LCN2, CCND1, FABP4 and MMP1, which were identified as core genes in the network and potential therapeutic targets of DPC. Discussion: The current study provides new insight into the exploration of DPC pathogenesis and the screened hub genes may serve as potential diagnostic indicator and novel therapeutic target.

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Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis

Cardiovascular Therapeutics ◽

10.1155/2019/8490707 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Da-Qiu Chen ◽

Xiang-Sheng Kong ◽

Xue-Bin Shen ◽

Mao-Zhi Huang ◽

Jian-Ping Zheng ◽

...

Keyword(s):

Gene Expression ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Signaling Pathway ◽

Differentially Expressed Genes ◽

Bioinformatics Analysis ◽

Differentially Expressed ◽

High Morbidity ◽

Hub Genes ◽

Module Analysis

Background. Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods. From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results. 57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion. This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

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Identification of Crucial Genes and Diagnostic Value Analysis in Major Depressive Disorder Using Bioinformatics Analysis

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323999201124204413 ◽

2020 ◽

Vol 23 ◽

Author(s):

Yao Gao ◽

Huiliang Zhao ◽

Teng Xu ◽

Junsheng Tian ◽

Xuemei Qin

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Signaling Pathway ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Major Depressive ◽

Diagnostic Ability ◽

Value Analysis

Aim and Objective:: Despite the prevalence and burden of major depressive disorder (MDD), our current understanding of the pathophysiology is still incomplete. Therefore, this paper aims to explore genes and evaluate their diagnostic ability in the pathogenesis of MDD. Methods:: Firstly, the expression profiles of mRNA and microRNA were downloaded from the gene expression database and analyzed by the GEO2R online tool to identify differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs). Then, the DAVID tool was used for functional enrichment analysis. Secondly, the comprehensive protein- protein interaction (PPI) network was analyzed using Cytoscape, and the network MCODE was applied to explore hub genes. Thirdly, the receiver operating characteristic (ROC) curve of the core gene was drawn to evaluate clinical diagnostic ability. Finally, mirecords was used to predict the target genes of DEMs. Results:: A total of 154 genes were identified as DEGs, and 14 microRNAs were identified as DEMs. Pathway enrichment analysis showed that DEGs were mainly involved in hematopoietic cell lineage, PI3K-Akt signaling pathway, cytokinecytokine receptor interaction, chemokine signaling pathway, and JAK-STAT signaling pathway. Three important modules are identified and selected by the MCODE clustering algorithm. The top 12 hub genes including CXCL16, CXCL1, GNB5, GNB4, OPRL1, SSTR2, IL7R, MYB, CSF1R, GSTM1, GSTM2, and GSTP1 were identified as important genes for subsequent analysis. Among these important hub genes, GSTM2, GNB4, GSTP1 and CXCL1 have good diagnostic ability. Finally, by combining these four genes, the diagnostic ability of MDD can be improved to 0.905, which is of great significance for the clinical diagnosis of MDD. Conclusion:: Our results indicate that GSTM2, GNB4, GSTP1 and CXCL1 have potential diagnostic markers and are of great significance in clinical research and diagnostic application of MDD. This result needs a large sample study to further confirm the pathogenesis of MDD.

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Identification of aberrantly methylated differentially expressed genes in melanoma

10.21203/rs.2.24149/v1 ◽

2020 ◽

Author(s):

Wei Han ◽

Guo-liang Shen

Keyword(s):

Gene Expression ◽

Cell Proliferation ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Regulation Of Transcription ◽

Biological Processes ◽

Hub Genes ◽

Positive Regulation ◽

Upregulated Genes

Abstract Background: Skin Cutaneous Melanoma (SKCM) is known as an aggressive malignant cancer, which could be directly derived from melanocytic nevi. However, the molecular mechanisms underlying malignant transformation of melanocytes and melanoma tumor progression still remain unclear. Increasing researches showed significant roles of epigenetic modifications, especially DNA methylation, in melanoma. This study focused on identification and analysis of methylation-regulated differentially expressed genes (MeDEGs) between melanocytic nevus and malignant melanoma in genome-wide profiles. Methods: The gene expression profiling datasets (GSE3189 and GSE114445) and gene methylation profiling datasets (GSE86355 and GSE120878) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified via GEO2R. MeDEGs were obtained by integrating the DEGs and DMGs. Then, functional enrichment analysis of MeDEGs were performed. STRING and Cytoscape were used to describe protein-protein interaction(PPI) network. Furthermore, survival analysis was implemented to select the prognostic hub genes. Finally, we conducted gene set enrichment analysis (GSEA) of hub genes. Results: We identified 237 hypomethylated, upregulated genes and 182 hypermethylated, downregulated genes. Hypomethylation-upregulated genes were enriched in biological processes of the oxidation-reduction process, cell proliferation, cell division, phosphorylation, extracellular matrix disassembly and protein sumoylation. Pathway enrichment showed selenocompound metabolism, small cell lung cancer and lysosome. Hypermethylation-downregulated genes were enriched in biological processes of positive regulation of transcription from RNA polymerase II promoter, cell adhesion, cell proliferation, positive regulation of transcription, DNA-templated and angiogenesis. The most significantly enriched pathways involved the transcriptional misregulation in cancer, circadian rhythm, tight junction, protein digestion and absorption and Hippo signaling pathway. After PPI establishment and survival analysis, seven prognostic hub genes were CKS2, DTL, KIF2C, KPNA2, MYBL2, TPX2 and FBL. Moreover, the most involved hallmarks obtained by GSEA were E2F targets, G2M checkpoint and mitotic spindle. Conclusions: Our study identified potential aberrantly methylated-differentially expressed genes participating in the process of malignant transformation from nevus to melanoma tissues based on comprehensive genomic profiles. Transcription profiles of CKS2, DTL, KIF2C, KPNA2, MYBL2, TPX2 and FBL provided clues of aberrantly methylation-based biomarkers, which might improve the development of precise medicine.

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