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

Dai, Hanying; Guo, Lihuang; Lin, Mingyue; Cheng, Zhenbo; Li, Jiancheng; Tang, Jinxia; Huan, Xisha; Huang, Yue; Xu, Ke

doi:10.7717/peerj.10265

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…Another study validated that PKP1 was related to the metastasis of melanoma and could even distinguish low-and high-grade of metastatic melanomas [25]. In addition, through pairwise comparisons of normal skin, primary and metastatic cutaneous melanoma, PKP1 may also serve as novel markers for discriminating whether cutaneous melanoma metastasizes [26].…”

Section: Discussionmentioning

confidence: 85%

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

Luan

Jian

et al. 2021

Preprint

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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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Section: Discussionmentioning

confidence: 85%

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

Luan

Jian

et al. 2021

Preprint

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“…The top 20 nodes with the degree, EPC, MCC and MNC were selected, and we take the intersection of the four algorithm as the hub genes. The Cytoscape plug-in Molecular Complex Detection (MCODE) (degree cutoff = 2, Node Score Cutoff = 0.2, and K −Core = 2) was used to capture the hub network modules ( Dai et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Liu

Liao

et al. 2022

PeerJ

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Background Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis. Methods In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues. Results A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues. Conclusions The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

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“…As for KEGG pathways, focal adhesion, proteoglycans in cancer, pathogenic Escherichia coli infection, cell cycle, apoptosis, small cell lung cancer, p53 signaling pathway and ECM − receptor interaction most often enriched in DEGs (Fig. 3b) [23,24] .…”

Section: Go Enrichment and Kegg Pathway Analysis Of Degsmentioning

confidence: 99%

Mining of Gene Modules and Identification of Key Genes for early diagnosis of gastric cancer

Sun

Zhang

et al. 2022

Preprint

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Background Gastric cancer (GC) is one of the most common malignant tumors with high incidence and mortality rates. Most patients with GC are not diagnosed until the advanced stage of cancer or during tumor screening, resulting in missing the best treatment time. Methods This study identified key modules and hub genes associated with GC by weighted gene co-expression network analysis (WGCNA). RNA sequencing profiles and clinical information data were downloaded from The Cancer Genome Atlas (TCGA). Compared with normal samples, the “limma” package in R was used to identify differentially expressed genes (DEGs) in GC samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to detect the related pathways and functions of DEGs. Gene modules associated with clinical characteristics were identified with WGCNA in tumor and normal samples. Hub genes of key modules were identified using survival and expression analysis. Finally, one-way ANOVA was used to explore the relationship between hub gene expression in normal tissues and different pathological stages of GC. Results A total of 4892 DEGs were screened. These DEGs were primarily associated with extracellular matrix organization, DNA replication, cell cycle, and p53 signaling pathway. Based on WGCNA, six gene modules were obtained, of which two modules were significantly correlated with GC. Through survival and expression analysis, a total of 19 genes with good prognosis and significantly different expression in tumor tissues (compared with normal tissues) were identified. There were significant differences in the expression levels of hub genes in normal tissues and different pathological stages of GC, indicating that these genes have important diagnostic value for early GC. Conclusions In this study, the expression levels of 19 hub genes were significantly different in different GC pathological stages from normal samples, indicating that these hub genes can be used as auxiliary indicators in the diagnosis of early GC.

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

Cited by 7 publications

References 58 publications

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

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

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Mining of Gene Modules and Identification of Key Genes for early diagnosis of gastric cancer

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