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

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

10.21203/rs.3.rs-39969/v1 ◽

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.

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LRG1 Promotes Metastatic Dissemination of Melanoma through Regulating EGFR/STAT3 Signalling

Cancers ◽

10.3390/cancers13133279 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3279

Author(s):

Yuet Ping Kwan ◽

Melissa Hui Yen Teo ◽

Jonathan Chee Woei Lim ◽

Michelle Siying Tan ◽

Graciella Rosellinny ◽

...

Keyword(s):

Metastatic Melanoma ◽

Melanoma Cell ◽

Molecular Mechanisms ◽

Complex Disease ◽

Treatment Options ◽

Melanoma Metastasis ◽

Dependent Manner ◽

Promoting Effect ◽

Human Patient ◽

Cell Invasiveness

Although less common, melanoma is the deadliest form of skin cancer largely due to its highly metastatic nature. Currently, there are limited treatment options for metastatic melanoma and many of them could cause serious side effects. A better understanding of the molecular mechanisms underlying the complex disease pathophysiology of metastatic melanoma may lead to the identification of novel therapeutic targets and facilitate the development of targeted therapeutics. In this study, we investigated the role of leucine-rich α-2-glycoprotein 1 (LRG1) in melanoma development and progression. We first established the association between LRG1 and melanoma in both human patient biopsies and mouse melanoma cell lines and revealed a significant induction of LRG1 expression in metastatic melanoma cells. We then showed no change in tumour cell growth, proliferation, and angiogenesis in the absence of the host Lrg1. On the other hand, there was reduced melanoma cell metastasis to the lungs in Lrg1-deficient mice. This observation was supported by the promoting effect of LRG1 in melanoma cell migration, invasion, and adhesion. Mechanistically, LRG1 mediates melanoma cell invasiveness in an EGFR/STAT3-dependent manner. Taken together, our studies provided compelling evidence that LRG1 is required for melanoma metastasis but not growth. Targeting LRG1 may offer an alternative strategy to control malignant melanoma.

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Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Journal of Diabetes Research ◽

10.1155/2021/5546199 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Bojun Xu ◽

Lei Wang ◽

Huakui Zhan ◽

Liangbin Zhao ◽

Yuehan Wang ◽

...

Keyword(s):

Gene Expression ◽

Diabetic Nephropathy ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Topological Analysis ◽

Gene Expression Omnibus ◽

Complement Cascade ◽

Hub Genes ◽

Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

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CXC Chemokines as Therapeutic Targets and Prognostic Biomarkers in Skin Cutaneous Melanoma Microenvironment

Frontiers in Oncology ◽

10.3389/fonc.2021.619003 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xuezhi Zhou ◽

Manjuan Peng ◽

Ye He ◽

Jingjie Peng ◽

Xuan Zhang ◽

...

Keyword(s):

Immune Cells ◽

Cutaneous Melanoma ◽

Immune Cell ◽

Gene Activation ◽

Skin Tumor ◽

Pathological Stage ◽

Normal Tissues ◽

The World ◽

Cxc Chemokine ◽

The Relationship

BackgroundSkin Cutaneous Melanoma (SKCM) is a tumor of the epidermal melanocytes induced by gene activation or mutation. It is the result of the interaction between genetic, constitutional, and environmental factors. SKCM is highly aggressive and is the most threatening skin tumor. The incidence of the disease is increasing year by year, and it is the main cause of death in skin tumors around the world. CXC chemokines in the tumor microenvironment can regulate the transport of immune cells and the activity of tumor cells, thus playing an anti-tumor immunological role and affecting the prognosis of patients. However, the expression level of CXC chemokine in SKCM and its effect on prognosis are still unclear.MethodOncomine, UALCAN, GEPIA, STRING, GeneMANIA, cBioPortal, TIMER, TRRUST, DAVID 6.8, and Metascape were applied in our research.ResultThe transcription of CXCL1, CXCL5, CXCL8, CXCL9, CXCL10, and CXCL13 in SKCM tissues were significantly higher than those in normal tissues. The pathological stage of SKCM patients is closely related to the expression of CXCL4, CXCL9, CXCL10, CXCL11, CXCL12, and CXCL13. The prognosis of SKCM patients with low transcription levels of CXCL4, CXCL9, CXCL10, CXCL11, and CXCL13 is better. The differential expression of CXC chemokines is mainly associated with inflammatory response, immune response, and cytokine mediated signaling pathways. Our data indicate that the key transcription factors of CXC chemokines are RELA, NF-κB1 and SP1. The targets of CXC chemokines are mainly LCK, LYN, SYK, MAPK2, MAPK12, and ART. The relationship between CXC chemokine expression and immune cell infiltration in SKCM was closed.ConclusionsOur research provides a basis for screening SKCM biomarkers, predicting prognosis, and choosing immunotherapy.

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SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

10.21203/rs.3.rs-595148/v1 ◽

2021 ◽

Author(s):

Hongpeng Fang ◽

Zhansen Huang ◽

Xianzi Zeng ◽

Jiaming Wan ◽

Jieying Wu ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Transcriptional Level ◽

Hub Genes ◽

Kaplan Meier ◽

High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

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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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The Downregulation of PTGS2 Mediated by ncRNAs is Tightly Correlated with Systemic Sclerosis-Interstitial Lung Disease

Frontiers in Genetics ◽

10.3389/fgene.2021.795034 ◽

2022 ◽

Vol 12 ◽

Author(s):

Zhixiao Xu ◽

Chengshui Chen

Keyword(s):

Systemic Sclerosis ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Immune Cell ◽

Gene Expression Omnibus ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Cd20 Expression ◽

Key Genes

Background: Interstitial lung disease in systemic sclerosis (SSc-ILD) is one of the most severe complications of systemic sclerosis (SSc) and is the main cause of mortality. In this study, we aimed to explore the key genes in SSc-ILD and analyze the relationship between key genes and immune cell infiltration as well as the key genes relevant to the hallmarks of cancer.Methods: Weighted gene co-expression network analysis (WGCNA) algorithm was implemented to explore hub genes in SSc-ILD samples from the Gene Expression Omnibus (GEO) database. Logistic regression analysis was performed to screen and verify the key gene related to SSc-ILD. CIBERSORT algorithms were utilized to analyze immune cell infiltration. Moreover, the correlation between the key genes and genes relevant to cancer was also evaluated. Furthermore, non-coding RNAs (ncRNAs) linking to PTGS2 were also explored.Results: In this study, we first performed WGCNA analysis for three GEO databases to find the potential hub genes in SSc-ILD. Subsequently, we determined PTGS2 was the key gene in SSC-ILD. Furthermore, in CIBERSORT analyses, PTGS2 were tightly correlated with immune cells such as regulatory T cells (Tregs) and was negatively correlated with CD20 expression. Moreover, PTGS2 was associated with tumor growth. Then, MALAT1, NEAT1, NORAD, XIST identified might be the most potential upstream lncRNAs, and LIMS1 and RANBP2 might be the two most potential upstream circRNAs.Conclusion: Collectively, our findings elucidated that ncRNAs-mediated downregulation of PTGS2, as a key gene in SSc-ILD, was positively related to the occurrence of SSc-ILD and abnormal immunocyte infiltration. It could be a promising factor for SSc-ILD progression to malignancy.

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Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

BioMed Research International ◽

10.1155/2022/1776082 ◽

2022 ◽

Vol 2022 ◽

pp. 1-17

Author(s):

Md. Rakibul Islam ◽

Lway Faisal Abdulrazak ◽

Mohammad Khursheed Alam ◽

Bikash Kumar Paul ◽

Kawsar Ahmed ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Clinical Information ◽

Gene Expression Omnibus ◽

Hub Genes ◽

New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log fold change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

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