Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma.
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ABSTRACT:
SUBMITTER: Li Y
PROVIDER: S-EPMC8337057 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Skin cutaneous melanoma (SKCM) is a multifactorial disease that presents a poor prognosis due to its rapid progression towards metastasis. This study focused on the identification of prognostic differentially expressed genes (DEGs) between primary and metastatic SKCM. DEGs were obtained using three chip data sets from the Gene Expression Omnibus database. The protein-protein interaction network was described by STRING and Cytoscape. Kaplan-Meier curves were implemented to evaluate survival benefits within distinct groups. A total of 258 DEGs were distinguished as possible candidate biomarkers. Besides, survival curves indicated that DSG3, DSC3, PKP1, EVPL, IVL, FLG, SPRR1A and SPRR1B were of significant value to predict the metastatic transformation of melanoma. To further validate our hypotheses, functional enrichment and significant pathways of the hub genes were performed to indicate that the most involved considerable path. In summary, this study identified substantial DEGs participating in melanoma metastasis. DGS3, DSC3, PKP1, EVPL, IVL, FLG, SPRR1A and SPRR1B may be considered as new biomarkers in the therapeutics of metastatic melanoma, which might help us predict the potential metastatic capability of SKCM patients, thus provide earlier precautionary treatments. However, further experiments are still required to support the specific mechanisms of these hub genes.
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Project description:Skin cutaneous melanoma (SKCM) is one of the most aggressive and life-threatening cancers with high incidence rate, metastasis rate and mortality. Early detection and stratification of risk assessment are essential to treat SKCM and to improve survival rate. The aim of this study is to construct an immune-related lncRNAs (immlncRNAs) prognosis risk model to identify immune biomarkers for early diagnosis, prognosis assessment and target immunotherapy of SKCM. For this purpose, we identified 46 immlncRNAs significantly correlated with SKCM prognosis to construct the prognostic risk model and patients were stratified into the high- and low-risk subgroups according to the developed model. The predictive efficiency of this model has been proved by K-M survival analysis and receiver operating characteristic curve. Moreover, CIBERSORT algorithms confirmed that there were differences in immune cell infiltration between the high- and low-risk groups. Functional enrichment analysis further indicated that immlncRNAs were related to a variety of immune response process signaling pathways, suggesting that relevant immlncRNAs could play an important role in the immune regulation of SKCM. Finally, subgroup analysis and multiple Cox regression analysis further proved the stability of the model. In summary, we successfully constructed a 46 immlncRNA-related prognostic risk score model with excellent predictive efficacy and provided more possibilities to investigate the immune regulation mechanisms and to develop immunotherapy of SKCM.
| S-EPMC9037265 | biostudies-literature
Project description:Skin cutaneous melanoma (SKCM) is a malignant tumor with high mortality rate in human, and its occurrence and development are jointly regulated by genes and the environment. However, the specific pathogenesis of SKCM is not completely understood. In recent years, an increasing number of studies have reported the important role of competing endogenous RNA (ceRNA) regulatory networks in various tumors; however, the complexity and specific biological effects of the ceRNA regulatory network of SKCM remain unclear. In the present study, we obtained a ceRNA regulatory network of long non-coding RNAs, microRNAs, and mRNAs related to the phosphatase and tensin homolog (PTEN) in SKCM and identified the potential diagnostic and prognostic markers related to SKCM. We extracted the above three types of RNA involved in SKCM from The Cancer Genome Atlas database. Through bioinformatics analysis, the OIP5-AS1-hsa-miR-186-5p/hsa-miR-616-3p/hsa-miR-135a-5p/hsa-miR-23b-3p/hsa-miR-374b-5p-PTPRC/IL7R/CD69 and MALAT1-hsa-miR-135a-5p/hsa-miR-23b-3p/hsa-miR-374b-5p-IL7R/CD69 ceRNA networks were found to be related to the prognosis of SKCM. Finally, we determined the OIP5-AS1-PTPRC/IL7R/CD69 and MALAT1-IL7R/CD69 axes in ceRNA as a clinical prognostic model using correlation and Cox regression analyses. Additionally, we explored the possible role of these two axes in affecting gene expression and immune microenvironment changes and the occurrence and development of SKCM through methylation and immune infiltration analyses. In summary, the ceRNA-based OIP5-AS1-PTPRC/IL7R/CD69 and MALAT1-IL7R/CD69 axes may be a novel and important approach for the diagnosis and prognosis of SKCM.
| S-EPMC9402985 | biostudies-literature
Project description:Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive forms of cancer. Investigating the mechanisms of carcinogenesis further could lead to the discovery of prognostic biomarkers that could be used to guide cancer treatment. In this study, we conducted integrative bioinformatics analyses of TCGA database, STRING, cBioPortal, TRRUST, The Human Protein Atlas, and DGIdb to determine which hub genes contributed to tumor progression and the cancer-associated immunology of SKCM. The results show that immune-related 873 differential genes grouped SKCM samples into subtypes. The initial results showed that the optimal number of clusters was two subgroups. Further analysis showed that there were significant differences in survival rate and immune infiltration level between the two subgroups. Subsequently, obtaining the different genes between groups, construct PPI to screen 6 hub genes (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DRA, HLA-DRB1, HLA-DRB5). In total, 6 MHC class II molecules were significantly related to overall survival. We then analyzed the expression of these genes along with their mutation landscapes, transcription factor regulation, and drug regulatory networks. In summary, our study identified 6 MHC class II molecules (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DRA, HLA-DRB1, HLA-DRB5) as important biomarkers in the occurrence and progression of SKCM tumors. Their expression levels are closely related to prognosis and immune infiltration and can help us better understand the tumorigenesis of SKCM.
| S-EPMC9077068 | biostudies-literature
Project description: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.
| S-EPMC7985846 | biostudies-literature
Project description:Skin cutaneous melanoma (SKCM) is a type of highly invasive cancer originated from melanocytes. It is reported that aberrant alternative splicing (AS) plays an important role in the neoplasia and metastasis of many types of cancer. Therefore, we investigated whether ASEs of pre-RNA have such an influence on the prognosis of SKCM and the related mechanism of ASEs in SKCM. The RNA-seq data and ASEs data for SKCM patients were obtained from the TCGA and TCGASpliceSeq database. The univariate Cox regression revealed 1265 overall survival-related splicing events (OS-SEs). Screened by Lasso regression, 4 OS-SEs were identified and used to construct an effective prediction model (AUC: .904), whose risk score was proved to be an independent prognostic factor. Furthermore, Kruskal-Wallis test and Mann-Whitney-Wilcoxon test showed that an aberrant splicing type of aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) regulated by CDC-like kinase 1 (CLK1) was associated with the metastasis and stage of SKCM. Besides, the overlapped signal pathway for AIMP2 was galactose metabolism identified by the co-expression analysis. External database validation also confirmed that AIMP2, CLK1, and the galactose metabolism were associated with the metastasis and stage of SKCM patients. ChIP-seq and ATAC-seq methods further confirmed the transcription regulation of CLK1, AIMP2, and other key genes, whose cellular expression was detected by Single Cell Sequencing. In conclusion, we proposed that CLK1-regulated AIMP2-78704-ES might play a critical role in the tumorigenesis and metastasis of SKCM via galactose metabolism. Besides, we established an effective model with MTMR14-63114-ES, URI1-48867-ES, BATF2-16724-AP, and MED22-88025-AP to predict the metastasis and prognosis of SKCM patients.
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Project description:Although the abnormal expression of members of the E2F family has been reported to participate in carcinogenesis in many human types of cancer, the bioinformatics role of the E2F family in melanoma is unknown. This research was designed to detect the expression, methylation, prognostic value and potential effects of the E2F family in melanoma. We investigated E2F family mRNA expression from the Oncomine and GEPIA databases and their methylation status in the MethHC database. Meanwhile, we detected the relative E2F family expression levels by qPCR and immunohistochemistry. Kaplan-Meier Plotter was used to draw survival analysis charts, and gene functional enrichment analyses were applied through cBioPortal database analysis. E2F1/2/3/4/5/6 mRNA and proteins were clearly upregulated in cutaneous melanoma patients, and high expression levels of E2F1/2/3/6 were statistically related to high methylation levels. Increased mRNA expression of E2F1/2/3/6 was related to lower overall survival rates (OS) and disease-free survival (DFS) in cutaneous melanoma cases. Meanwhile, E2F1/2/3/6 carried out these effects through regulating multiple signaling pathways, including the MAPK, PI3K-Akt and p53 signaling pathways. Taking together, our findings suggest that E2F1/2/3/6 could act as potential targets for precision therapy in cutaneous melanoma patients.
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Project description:Skin cutaneous melanoma (SKCM) has substantial malignancy and a poor prognosis. The function of ageing-related genes (ARGs) in SKCM is unknown. In this study, a prognostic risk-scoring model for ARG was constructed based on SKCM RNA-seq, mutation, and clinical data in The Cancer Genome Atlas. Our novel prognostic model, which included four ARGs (IRS2, PDGFRA, TFAP2A, and SOD2), could distinguish between low-risk and high-risk groups. Low-risk patients benefited more from immunotherapy and commonly used targeted and chemotherapy drugs than high-risk patients. There were also considerable differences in immunocyte infiltration and tumour microenvironment between the two groups. Furthermore, multivariate Cox regression analysis revealed that age, pT_stage, pM_stage, body mass index, tumour mutation burden, and risk score were independent factors influencing the prognosis of patients with SKCM; therefore, we devised a prognosis nomogram. Last, a long non-coding (lncRNA) NEAT1/miR-33a-5p/IRS2 regulatory axis of the competing endogenous RNA network was built to investigate the mechanisms of SKCM metastasis progression. Grouping based on the scoring system could predict the prognosis of SKCM and predict the sensitivity of patients to immunotherapy, targeted therapy, and chemotherapy. This could facilitate the formulation of individualised treatment strategies and help drug research and development. These findings highlight the regulatory axis of the lncRNA NEAT1/miR-33a-5p/IRS2, which may play a role in SKCM metastasis.
| S-EPMC9834281 | biostudies-literature
Project description:Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.
| S-EPMC8642691 | biostudies-literature
Project description:Skin cutaneous melanoma (SKCM) is the most lethal form of skin cancers owing to high invasiveness and high metastatic potential. Tumor microenvironment (TME) provides powerful evidences for discerning SKCM, raising the prospect to identify biomarkers of SKCM. Based on the transcriptome profiles of patients with SKCM and the corresponding clinical information from The Cancer Genome Atlas (TCGA), we used ESTIMATE algorithm to calculate ImmuneScore and StromalScore and identified the TME-Related differentially expressed genes (DEGs), than the intersected TME-Related DEGs were used for subsequent functional enrichment analysis. Protein-protein interaction (PPI) analysis was used to identify the functionality-related DEGs and univariate Cox regression analysis was used to identify the survival-related DEGs. Furthermore, SKCM-related DEGs were identified based on two Gene Expression Omnibus (GEO) datasets. Finally, we intersected functionality-related DEGs, survival-related DEGs, and SKCM-related DEGs, ascertaining that six DEGs (CCL4, CXCL10, CCL5, GZMB, C1QA, and C1QB) function as core TME-related genes (CTRGs). Significant differences of GZMB, C1QA, and C1QB expressions were found in gender and clinicopathologic staging of SKCM. High levels of GZMB, C1QA, and C1QB expressions were associated with favorable prognosis. Gene set enrichment analysis (GSEA) showed that cell-cell interaction, cell behavior, and intracellular signaling transduction may be mainly involved in both C1QA, C1QB and GZMB expressions and metabolism of phospholipid and amino acid, transcription, and translation may be implicated in low GZMB expressions. C1QA, C1QB, and GZMB are novel SKCM-relating CTRGs, providing promising immune-related prognostic biomarkers for SKCM.
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