scholarly journals Identification of Core Genes and Pathways in Melanoma Metastasis via Bioinformatics Analysis

2022 ◽  
Vol 23 (2) ◽  
pp. 794
Author(s):  
Renjian Xie ◽  
Bifei Li ◽  
Lee Jia ◽  
Yumei Li
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Primary Melanoma ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Hub Genes ◽  
Receptor Interactions ◽  
Upregulated Genes ◽  
Core Genes ◽  
A375 Cells

Metastasis is the leading cause of melanoma-related mortality. Current therapies are rarely curative for metastatic melanoma, revealing the urgent need to identify more effective preventive and therapeutic targets. This study aimed to screen the core genes and molecular mechanisms related to melanoma metastasis. A gene expression profile, GSE8401, including 31 primary melanoma and 52 metastatic melanoma clinical samples, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between melanoma metastases and primary melanoma were screened using GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of DEGs were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with Molecular Complex Detection (MCODE) plug-in tools were utilized to detect the protein–protein interaction (PPI) network among DEGs. The top 10 genes with the highest degrees of the PPI network were defined as hub genes. In the results, 425 DEGs, including 60 upregulated genes and 365 downregulated genes, were identified. The upregulated genes were enriched in ECM–receptor interactions and the regulation of actin cytoskeleton, while 365 downregulated genes were enriched in amoebiasis, melanogenesis, and ECM–receptor interactions. The defined hub genes included CDK1, COL17A1, EGFR, DSG1, KRT14, FLG, CDH1, DSP, IVL, and KRT5. In addition, the mRNA and protein levels of the hub genes during melanoma metastasis were verified in the TCGA database and paired post- and premetastatic melanoma cells, respectively. Finally, KRT5-specific siRNAs were utilized to reduce the KRT5 expression in melanoma A375 cells. An MTT assay and a colony formation assay showed that KRT5 knockdown significantly promoted the proliferation of A375 cells. A Transwell assay further suggested that KRT5 knockdown significantly increased the cell migration and cell invasion of A375 cells. This bioinformatics study provided a deeper understanding of the molecular mechanisms of melanoma metastasis. The in vitro experiments showed that KRT5 played the inhibitory effects on melanoma metastasis. Therefore, KRT5 may serve important roles in melanoma metastasis.

scholarly journals Identification of core genes and pathways in melanoma metastasis via bioinformatics analysis

2020 ◽  
Author(s):  
Yumei Li ◽  
Bifei Li ◽  
Fan Chen ◽  
Weiyu Shen ◽  
Vladimir L. Katanaev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metastatic Melanoma ◽  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Primary Melanoma ◽  
Melanoma Cells ◽  
Receptor Interaction ◽  
Melanoma Metastasis ◽  
Hub Genes ◽  
Core Genes

Abstract Background Metastasis is the leading cause of melanoma mortality. Current therapies are rarely curative for metastatic melanoma, revealing the urgent need to identify more effective preventive and therapeutic targets. This study aimed to screen for the key core genes and molecular mechanisms related to the metastasis of melanoma. Methods Gene expression profile, GSE8401 including 31 primary melanoma and 52 metastatic melanoma clinical samples, was downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between metastatic melanoma and primary melanoma were screened using GEO2R. Assays of gene ontology (GO), Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and protein-protein interaction (PPI) were performed to visualize these DEGs through Database for Annotation, Visualization and Integrated Discovery (DAVID) software and Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with Molecular Complex Detection (MCODE) plug-in tools. Top 10 genes with high degree were defined as hub genes. Furthermore, paired post-metastatic melanoma cells and pre-metastatic melanoma cells were established by experimental mouse model of melanoma metastasis to verify the expression of these hub genes. Results 424 DEGs between the metastatic melanoma and primary melanoma were screened, including 60 upregulated genes enriched in ECM-receptor interaction and progesterone-mediated oocyte maturation and 364 downregulated genes enriched in amoebiasis, melanogenesis, and ECM-receptor interaction. CDH1, EGFR, KRT5, COL17A1, KRT14, IVL, DSP, DSG1, FLG and CDK1 were defined as the hub genes. . In addition, paired post-metastatic melanoma cells (A375M) and pre-metastatic melanoma cells (A375) were established and qRT-PCR analysis confirmed the expression of the hub genes during melanoma metastasis. Conclusion This bioinformatic study has provided a deeper understanding of the molecular mechanisms of melanoma metastasis. KRT5, IVL and COL17A1 have emerged as possible biomarkers and therapeutic targets in metastasis of melanoma.

Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis

2021 ◽  
pp. 1-13
Author(s):  
Simei Tu ◽  
Hao Zhang ◽  
Xiaocheng Yang ◽  
Wen Wen ◽  
Kangjing Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Molecular Mechanisms ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Hub Genes ◽  
Normal Tissues ◽  
Significant Difference ◽  
Core Genes

BACKGROUND: Since the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer. OBJECTIVE: The study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer. METHODS: The gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database. RESULTS: A total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and high RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples. CONCLUSIONS: RFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

scholarly journals Exploration and Validation of Metastasis-associated Genes for Skin Cutaneous Melanoma

2021 ◽  
Author(s):  
Hong Luan ◽  
Linge Jian ◽  
Ye He ◽  
Tuo Zhang ◽  
Yanna Su ◽  
...  
Keyword(s):  
Metastatic Melanoma ◽  
Cutaneous Melanoma ◽  
Molecular Mechanisms ◽  
Interaction Analysis ◽  
Primary Melanoma ◽  
Gene Interaction ◽  
Skin Tumor ◽  
Melanoma Metastasis ◽  
Hub Genes ◽  
Hub Gene

Abstract Background: Skin cutaneous melanoma is a malignant and highly metastatic skin tumor, and its morbidity and mortality are still rising worldwide. However, the molecular mechanisms that promote melanoma metastasis are unclear. Methods: Two datasets (GSE15605 and GSE46517) were retrieved to identify the differentially expressed genes (DEGs), including 23 normal skin tissues (N), 77 primary melanoma tissues (T) and 85 metastatic melanoma tissues (M). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed to explore the functions of the DEGs. The protein–protein interaction (PPI) network was constructed using the STRING tool and Cytoscape software. We used the cytoHubba plugin of Cytoscape to identify the most significant hub genes by five topological analyses (Degree, Bottleneck, MCC, MNC, and EPC). Hub gene expression was validated using the UALCAN website. Clinical relevance was investigated using The Cancer Genome Atlas (TCGA) resources. Finally, we explored the association between metastasis-associated genes and immune infiltrates through the Tumor Immune Estimation Resource (TIMER) database and performed drug-gene interaction analysis using the Drug-Gene Interaction database.Results: A total of 294 specific genes were related to melanoma metastasis and were mainly involved in the positive regulation of locomotion, mitotic cell cycle process, and epithelial cell differentiation. Four hub genes (CDK1, FOXM1, KIF11, and RFC4) were identified from the cytoHubba plugin of Cytoscape. CDK1 was significantly upregulated in metastatic melanoma compared with primary melanoma, and high expression of CDK1 was positively correlated with poor prognosis. We found that CDK1 expression correlated positively with the infiltration levels of macrophage cells (Rho = -0.164, P = 2.02e-03) and neutrophil cells (Rho = 0.269, P = 2.72e-07) in SKCM metastasis. In addition, we identified that CDK1 had a close interaction with 10 antitumor drugs. Conclusions: CDK1 was identified as a hub gene involved in the progression of melanoma metastasis and may be regarded as a therapeutic target for melanoma patients to improve prognosis and prevent metastasis in the future.

scholarly journals Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

2021 ◽  
Vol 24 (5-6) ◽  
pp. 267-279
Author(s):  
Xianyang Zhu ◽  
Wen Guo
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Wnt Signaling Pathway ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes

<b><i>Background:</i></b> This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. <b><i>Methods:</i></b> Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). <b><i>Results:</i></b> A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including <i>AKT1</i>, <i>CDC42</i>, <i>HLA-DQA2</i>, <i>TUBB</i>, <i>TWISTNB</i>, <i>GSK3B</i>, <i>FZD2</i>, <i>KLC1</i>, <i>GUSB</i>, and <i>RHOG</i>. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including <i>GSK3B, CDC42, AKT1, FZD2</i>, and <i>GUSB</i>) were identified. <b><i>Conclusion:</i></b> In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that <i>GSK3B, CDC42, AKT1, FZD2</i>, <i>and GUSB</i> may be key genes related to the progress of OA and may become promising therapeutic targets.

scholarly journals Identification of potential crucial genes and pathways associated with vein graft restenosis based on gene expression analysis in experimental rabbits

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4704 ◽  
Author(s):  
Qiang Liu ◽  
Xiujie Yin ◽  
Mingzhu Li ◽  
Li Wan ◽  
Liqiao Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Vein Graft ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Bypass Graft ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Analyses

Occlusive artery disease (CAD) is the leading cause of death worldwide. Bypass graft surgery remains the most prevalently performed treatment for occlusive arterial disease, and veins are the most frequently used conduits for surgical revascularization. However, the clinical efficacy of bypass graft surgery is highly affected by the long-term potency rates of vein grafts, and no optimal treatments are available for the prevention of vein graft restenosis (VGR) at present. Hence, there is an urgent need to improve our understanding of the molecular mechanisms involved in mediating VGR. The past decade has seen the rapid development of genomic technologies, such as genome sequencing and microarray technologies, which will provide novel insights into potential molecular mechanisms involved in the VGR program. Ironically, high throughput data associated with VGR are extremely scarce. The main goal of the current study was to explore potential crucial genes and pathways associated with VGR and to provide valid biological information for further investigation of VGR. A comprehensive bioinformatics analysis was performed using high throughput gene expression data. Differentially expressed genes (DEGs) were identified using the R and Bioconductor packages. After functional enrichment analysis of the DEGs, protein–protein interaction (PPI) network and sub-PPI network analyses were performed. Finally, nine potential hub genes and fourteen pathways were identified. These hub genes may interact with each other and regulate the VGR program by modulating the cell cycle pathway. Future studies focusing on revealing the specific cellular and molecular mechanisms of these key genes and pathways involved in regulating the VGR program may provide novel therapeutic targets for VGR inhibition.

scholarly journals Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9301
Author(s):  
Dandan Jin ◽  
Yujie Jiao ◽  
Jie Ji ◽  
Wei Jiang ◽  
Wenkai Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Functional Module ◽  
Disease Free Survival ◽  
Ductal Adenocarcinoma ◽  
Literature Mining ◽  
Ppi Network ◽  
Hub Genes

Background Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms. Methods To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot. Conclusions Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

scholarly journals Comprehensive analysis and identification of key genes and signaling pathways in the occurrence and metastasis of cutaneous melanoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10265
Author(s):  
Hanying Dai ◽  
Lihuang Guo ◽  
Mingyue Lin ◽  
Zhenbo Cheng ◽  
Jiancheng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cutaneous Melanoma ◽  
Metabolic Pathways ◽  
Kegg Pathway ◽  
Normal Skin ◽  
Primary Melanoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Structural Molecule Activity ◽  
Geo Database

Background Melanoma is a malignant tumor of melanocytes, and the incidence has increased faster than any other cancer over the past half century. Most primary melanoma can be cured by local excision, but metastatic melanoma has a poor prognosis. Cutaneous melanoma (CM) is prone to metastasis, so the research on the mechanism of melanoma occurrence and metastasis will be beneficial to diagnose early, improve treatment, and prolong life survival. In this study, we compared the gene expression of normal skin (N), primary cutaneous melanoma (PM) and metastatic cutaneous melanoma (MM) in the Gene Expression Omnibus (GEO) database. Then we identified the key genes and molecular pathways that may be involved in the development and metastasis of cutaneous melanoma, thus to discover potential markers or therapeutic targets. Methods Three gene expression profiles (GSE7553, GSE15605 and GSE46517) were downloaded from the GEO database, which contained 225 tissue samples. R software identified the differentially expressed genes (DEGs) between pairs of N, PM and MM samples in the three sets of data. Subsequently, we analyzed the gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the DEGs, and constructed a protein-protein interaction (PPI) network. MCODE was used to seek the most important modules in PPI network, and then the GO function and KEGG pathway of them were analyzed. Finally, the hub genes were calculated by the cytoHubba in Cytoscape software. The Cancer Genome Atlas (TCGA) data were analyzed using UALCAN and GEPIA to validate the hub genes and analyze the prognosis of patients. Results A total of 134, 317 and 147 DEGs were identified between N, PM and MM in pair. GO functions and KEGG pathways analysis results showed that the upregulated DEGs mainly concentrated in cell division, spindle microtubule, protein kinase activity and the pathway of transcriptional misregulation in cancer. The downregulated DEGs occurred in epidermis development, extracellular exosome, structural molecule activity, metabolic pathways and p53 signaling pathway. The PPI network obtained the most important module, whose GO function and KEGG pathway were enriched in oxidoreductase activity, cell division, cell exosomes, protein binding, structural molecule activity, and metabolic pathways. 14, 18 and 18 DEGs were identified respectively as the hub genes between N, PM and MM, and TCGA data confirmed the expression differences of hub genes. In addition, the overall survival curve of hub genes showed that the differences in these genes may lead to a significant decrease in overall survival of melanoma patients. Conclusions In this study, several hub genes were found from normal skin, primary melanoma and metastatic melanoma samples. These hub genes may play an important role in the production, invasion, recurrence or death of CM, and may provide new ideas and potential targets for its diagnosis or treatment.

scholarly journals Exploration and Validation of Metastasis-associated Genes for Skin Cutaneous Melanoma

2021 ◽  
Author(s):  
Hong Luan ◽  
Linge Jian ◽  
Ye He ◽  
Tuo Zhang ◽  
Yanna Su ◽  
...  
Keyword(s):  
Metastatic Melanoma ◽  
Cutaneous Melanoma ◽  
Molecular Mechanisms ◽  
Interaction Analysis ◽  
Primary Melanoma ◽  
Gene Interaction ◽  
Skin Tumor ◽  
Melanoma Metastasis ◽  
Hub Genes ◽  
Hub Gene

Abstract Background: Skin cutaneous melanoma is a malignant and highly metastatic skin tumor, and its morbidity and mortality are still rising worldwide. However, the molecular mechanisms that promote melanoma metastasis are unclear. Methods: Two datasets (GSE15605 and GSE46517) were retrieved to identify the differentially expressed genes (DEGs), including 23 normal skin tissues (N), 77 primary melanoma tissues (T) and 85 metastatic melanoma tissues (M). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed to explore the functions of the DEGs. The protein–protein interaction (PPI) network was constructed using the STRING tool and Cytoscape software. We used the cytoHubba plugin of Cytoscape to identify the most significant hub genes by five topological analyses (Degree, Bottleneck, MCC, MNC, and EPC). Hub gene expression was validated using the UALCAN website. Clinical relevance was investigated using The Cancer Genome Atlas (TCGA) resources. Finally, we explored the association between metastasis-associated genes and immune infiltrates through the Tumor Immune Estimation Resource (TIMER) database and performed drug-gene interaction analysis using the Drug-Gene Interaction database.Results: A total of 294 specific genes were related to melanoma metastasis and were mainly involved in the positive regulation of locomotion, mitotic cell cycle process, and epithelial cell differentiation. Four hub genes (CDK1, FOXM1, KIF11, and RFC4) were identified from the cytoHubba plugin of Cytoscape. CDK1 was significantly upregulated in metastatic melanoma compared with primary melanoma, and high expression of CDK1 was positively correlated with poor prognosis. We found that CDK1 expression correlated positively with the infiltration levels of macrophage cells (Rho = -0.164, P = 2.02e-03) and neutrophil cells (Rho = 0.269, P = 2.72e-07) in SKCM metastasis. In addition, we identified that CDK1 had a close interaction with 10 antitumor drugs. Conclusions: CDK1 was identified as a hub gene involved in the progression of melanoma metastasis and may be regarded as a therapeutic target for melanoma patients to improve prognosis and prevent metastasis in the future.

scholarly journals Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

2020 ◽  
Author(s):  
Wenxing Su ◽  
Yi Guan ◽  
Biao Huang ◽  
Juanjuan Wang ◽  
Yuqian Wei ◽  
...  
Keyword(s):  
Differentially Expressed Gene ◽  
Primary Melanoma ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Pathway Enrichment Analysis ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. Methods: With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. Results: A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression. Conclusions: To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

scholarly journals Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Wenxing Su ◽  
Yi Guan ◽  
Biao Huang ◽  
Juanjuan Wang ◽  
Yuqian Wei ◽  
...  
Keyword(s):  
Differentially Expressed Gene ◽  
Primary Melanoma ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Pathway Enrichment Analysis ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. Methods With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein-protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. Results A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression. Conclusions To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

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