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

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.

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

International Journal of Molecular Sciences ◽

10.3390/ijms23020794 ◽

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.

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

10.21203/rs.3.rs-940144/v2 ◽

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.

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

10.21203/rs.3.rs-940144/v1 ◽

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

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

Cancer Biomarkers ◽

10.3233/cbm-203262 ◽

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.

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Identification of Metastasis-Associated MicroRNAs in Metastatic Melanoma by miRNA Expression Profile and Experimental Validation

Frontiers in Genetics ◽

10.3389/fgene.2021.663110 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yunshu Gao ◽

Jiahua Xu ◽

Hongwei Li ◽

Yi Hu ◽

Guanzhen Yu

Keyword(s):

Metastatic Melanoma ◽

Signaling Pathway ◽

Target Genes ◽

Primary Melanoma ◽

Mirna Expression Profile ◽

Melanoma Metastasis ◽

Hippo Signaling ◽

Invasive Ability ◽

Hub Genes ◽

Proliferation And Apoptosis

It is reported that microRNAs (miRNA) have paramount functions in many cellular biological processes, development, metabolism, differentiation, survival, proliferation, and apoptosis included, some of which are involved in metastasis of tumors, such as melanoma. Here, three metastasis-associated miRNAs, miR-18a-5p (upregulated), miR-155-5p (downregulated), and miR-93-5p (upregulated), were identified from a total of 63 different expression miRNAs (DEMs) in metastatic melanoma compared with primary melanoma. We predicted 262 target genes of miR-18a-5p, 904 miR-155-5p target genes, and 1220 miR-93-5p target genes. They participated in pathways concerning melanoma, such as TNF signaling pathway, pathways in cancer, FoxO signaling pathway, cell cycle, Hippo signaling pathway, and TGF-beta signaling pathway. We identified the top 10 hub nodes whose degrees were higher for each survival-associated miRNA as hub genes through constructing the PPI network. Using the selected miRNA and the hub genes, we constructed the miRNA-hub gene network, and PTEN and CCND1 were found to be regulated by all three miRNAs. Of note, miR-155-5p was obviously downregulated in metastatic melanoma tissues, and miR-18a-5p and miR-93-5p were obviously regulated positively in metastatic melanoma tissues. In validating experiments, miR-155-5p's overexpression inhibited miR-18a-5p's and miR-93-5p's expression, which could all significantly reduce SK-MEL-28 cells' invasive ability. Finally, miR-93-5p and its potential target gene UBC were selected for further validation. We found that miR-93-5p's inhibition could reduce SK-MEL-28 cell's invasive ability through upregulated the expression of UBC, and the anti-invasive effect was reserved by downregulation of UBC. The results show that the selected three metastasis-associated miRNAs participate in the process of melanoma metastasis via regulating their target genes, providing a potential molecular mechanism for this disease.

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

Public Health Genomics ◽

10.1159/000517308 ◽

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

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: 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). Results: 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 AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. 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 GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: 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 GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.663787 ◽

2021 ◽

Vol 12 ◽

Author(s):

Maryum Nisar ◽

Rehan Zafar Paracha ◽

Iqra Arshad ◽

Sidra Adil ◽

Sabaoon Zeb ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Early Stage ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Receptor Interaction ◽

Integrated Analysis ◽

Rna Seq ◽

Hub Genes

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

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Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

10.21203/rs.3.rs-95029/v1 ◽

2020 ◽

Author(s):

Harish Joshi ◽

Basavaraj Mallikarjunayya Vastrad ◽

Nidhi Joshi ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Expression Profiles ◽

Therapeutic Targets ◽

Rank Aggregation ◽

Receiver Operating Curve ◽

Progression Rate ◽

Hub Genes

Abstract Background Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. ObjectivesTherefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. MethodsThe gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network.Results We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. The robust DEGs linked with the development of obesity were screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. ConclusionsOur study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

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Identification of Metastasis-Associated Gene And Its Correlation With Immune Infiltrates For Skin Cutaneous Melanoma

10.21203/rs.3.rs-1059356/v1 ◽

2021 ◽

Author(s):

Hong Luan ◽

Ye He ◽

Linge Jian ◽

Tuo Zhang ◽

Liping Zhou

Keyword(s):

Metastatic Melanoma ◽

Cutaneous Melanoma ◽

Molecular Mechanisms ◽

Immune Cell ◽

Gene Expression Omnibus ◽

Skin Tumor ◽

Melanoma Metastasis ◽

Hub Genes ◽

P53 Signaling ◽

Genes Expression

Abstract Background: Skin cutaneous melanoma is a malignant and highly metastatic skin tumor. As the most common cause of death in skin cancer, its morbidity and mortality are still rising worldwide. However, the molecular mechanisms of melanoma metastasis are unclear. Methods: Three Gene Expression Omnibus (GEO) datasets (GSE15605, GSE7553 and GSE8401) were downloaded to identify the differentially expressed genes (DEGs) between primary and metastatic melanoma samples. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed to explore the functional of DEGs by Metascape. The protein-protein interaction (PPI) network was constructed using STRING tool and Cytoscape software. We used the cytoHubba plugin of Cytoscape to identify the most significant hub genes by four topological analyses (Degree, MCC, DMNC, and MNC). Hub genes expression was validated using UALCAN website. Finally, we explored the association between metastasis-associated genes and immune infiltrates through Tumor Immune Estimation Resource (TIMER) database.Results: In total, we obtained 196 DEGs including 12 upregulated and 184 downregulated genes. GO and KEGG enrichment results indicated that DEGs were mainly concentrated in epidermis development, cornified envelope, structural molecule activity, and p53 signaling pathway. Eight hub genes were identified to be closely related to melanoma metastasis, including SPRR1B, DSC1, PKP1, TGM1, DSG1, IVL, SPRR1A and DSC3. On the ULCAN website, all hub genes expression levels are lower in metastatic tissues than in primary cancers. Results from TIMER database revealed that DSC1 and TGM1 were significantly related with most of immune cell infiltration.Conclusions: SPRR1B, DSC1, PKP1, TGM1, DSG1, IVL, SPRR1A and DSC3 may be hub genes involved in the progression of melanoma metastasis and thus may be regarded as therapeutic targets in the future. DSC1 and TGM1 play an important role in the microenvironment of metastatic melanoma by regulating the tumor infiltration of immune cells.

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Promising novel biomarkers and candidate small-molecule drugs for lung adenocarcinoma: Evidence from bioinformatics analysis of high-throughput data

Open Medicine ◽

10.1515/med-2021-0375 ◽

2021 ◽

Vol 17 (1) ◽

pp. 96-112

Author(s):

Chengrui Li ◽

Yufeng Wan ◽

Weijun Deng ◽

Fan Fei ◽

Linlin Wang ◽

...

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Small Molecules ◽

Molecular Mechanisms ◽

Quantitative Polymerase Chain Reaction ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Receptor Interaction ◽

Hub Genes ◽

Gene Expressions

Abstract Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.

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