Prognosis Of Skin Cutaneous Melanoma Research Articles

Development of a biomarker signature associated with anoikis to predict prognosis and immunotherapy response in melanoma.

Skin cutaneous melanoma (SKCM) is malignant cancer known for its high aggressiveness and unfavorable prognosis, particularly in advanced tumors. Anoikis is a specific pattern of programmed cell death associated with tumor regeneration, migration, and metastasis. Nevertheless, limited research has been conducted to investigate the function of anoikis in SKCM. Anoikis-related genes (ARGs) were extracted from Genecards to identify SKCM subtypes and to explore the immune microenvironment between the different subtypes. Prognostic models of SKCM were developed by LASSO COX regression analysis. Subsequently, the predictive value of risk scores in SKCM and the association with immunotherapy were further explored. Finally, the expression of 6 ARGs involved in the model construction was detected by immunohistochemistry and PCR. This study identified 20 ARGs significantly associated with SKCM prognosis and performed disease subtype analysis of samples based on these genes, different subtypes exhibited significantly different clinical features and tumor immune microenvironment (TIME) landscapes. The risk score prognostic model was generated by further screening and identification of the six ARGs. The model exhibited a high degree of sensitivity and specificity to predict the prognosis of individuals with SKCM. These high- and low-risk populations showed different immune statuses and drug sensitivity. Further immunohistochemical and PCR experiments identified significant differential expression of the six ARGs in tumor and normal samples. Anoikis-based features may serve as novel prognostic biomarkers for SKCM and may provide important new insights for survival prediction and individualized treatment development.

Experimentally validated oxidative stress -associated prognostic signatures describe the immune landscape and predict the drug response and prognosis of SKCM.

Skin Cutaneous Melanoma (SKCM) incidence is continually increasing, with chemotherapy and immunotherapy being among the most common cancer treatment modalities. This study aims to identify novel biomarkers for chemotherapy and immunotherapy response in SKCM and explore their association with oxidative stress. Utilizing TCGA-SKCM RNA-seq data, we employed Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to identify six core genes. Gene co-expression analysis and immune-related analysis were conducted, and specific markers associated with oxidative stress were identified using Gene Set Variation Analysis (GSVA). Single-cell analysis revealed the expression patterns of Oxidative Stress-Associated Genes (OSAG) in the tumor microenvironment. TIDE analysis was employed to explore the association between immune therapy response and OSAG, while CIBERSORT was used to analyze the tumor immune microenvironment. The BEST database demonstrated the impact of the Oxidative Stress signaling pathway on chemotherapy drug resistance. Immunohistochemical staining and ROC curve evaluation were performed to assess the protein expression levels of core genes in SKCM and normal samples, with survival analysis utilized to determine their diagnostic value. We identified six central genes associated with SKCM metastasis, among which the expression of DSC2 and DSC3 involved in the oxidative stress pathway was closely related to immune cell infiltration. DSC2 influenced drug resistance in SKMC patients. Furthermore, downregulation of DSC2 and DSC3 expression enhanced the response of SKCM patients to immunotherapy. This study identified two Oxidative Stress-Associated genes as novel biomarkers for SKCM. Additionally, targeting the oxidative stress pathway may serve as a new strategy in clinical practice to enhance SKCM chemotherapy and sensitivity.

Role of hippo pathway and cuproptosis-related genes in immune infiltration and prognosis of skin cutaneous melanoma.

Melanoma is the most lethal type of skin cancer with an increasing incidence. Cuproptosis is the most recently identified copper-dependent form of cell death that relies on mitochondrial respiration. The hippocampal (Hippo) pathway functions as a tumor suppressor by regulating Yes-associated protein/transcriptional coactivator with PDZ-binding motif (YAP/TAZ) activity. However, its role in cuproptosis remains unknown. In addition, the correlation of cuproptosis-related genes and Hippo pathway-related genes with tumor prognosis warrants further investigation. In the present study, we explored the correlation of cuproptosis-related genes and Hippo pathway-related genes with the prognosis of melanoma through analysis of data from a public database and experimental verification. We found eight Hippo pathway-related genes that were downregulated in melanoma and exhibited predictive value for prognosis. There was a significant positive correlation between cuproptosis-related genes and Hippo pathway-related genes in skin cutaneous melanoma. YAP1 expression was positively correlated with ferredoxin 1 (FDX1) expression in the GSE68599 dataset and A2058 cells. Moreover, YAP1 was positively and negatively correlated with M2 macrophages and regulatory T cell infiltration, respectively. In conclusion, the present study demonstrated the prognostic value of Hippo pathway-related genes (particularly YAP1) in melanoma, revealing the correlation between the expression of Hippo pathway-related genes and immune infiltration. Thus, the present findings may provide new clues on the prognostic assessment of patients with melanoma and a new target for the immunotherapy of this disease.

Establishment of skin cutaneous melanoma prognosis model based on vascular mimicry risk score.

Studies have indicated that Vascular mimicry (VM) could contribute to the unfavorable prognosis of skin cutaneous melanoma (SKCM). Thus, the objective of this study was to identify therapeutic targets associated with VM in SKCM and develop a novel prognostic model. Gene expression data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were utilized to identify differentially expressed genes (DEGs). By intersecting these DEGs with VM genes, we acquired VM-related DEGs specific to SKCM, and then identified prognostic-related VM genes. A VM risk score system was established based on these prognosis-associated VM genes, and patients were then categorized into high- and low-score groups using the median score. Subsequently, differences in clinical characteristics, gene set enrichment analysis (GSEA), and other analyses were further presented between the 2 groups of patients. Finally, a novel prognostic model for SKCM was established using the VM score and clinical characteristics. 26 VM-related DEGs were identified in SKCM, among the identified DEGs associated with VM in SKCM, 5 genes were found to be prognostic-related. The VM risk score system, comprised of these genes, is an independent prognostic risk factor. There were significant differences between the 2 patient groups in terms of age, pathological stage, and T stage. VM risk scores are associated with epithelial biological processes, angiogenesis, regulation of the SKCM immune microenvironment, and sensitivity to targeted drugs. The novel prognostic model demonstrates excellent predictive ability. Our study identified VM-related prognostic markers and therapeutic targets for SKCM, providing novel insights for clinical diagnosis and treatment.

Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma.

We explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood. We integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments. Analysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness. SRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients.

Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma

BackgroundImmunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed.Methods10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model.ResultsBased on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy.ConclusionsOur study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.

Comprehensive Analysis to Identify LINC00511-hsa-miR-625-5p-SEMA6A Pathway Fuels Progression of Skin Cutaneous Melanoma.

Skin cutaneous melanoma (SKCM) is a highly lethal malignancy that poses a significant threat to human health. Recent research has shown that competing endogenous RNA (ceRNA) regulatory networks play a critical role in the development and progression of various types of cancer, including SKCM. The objective of this study is to investigate the ceRNA regulatory network associated with the transmembrane protein semaphorin 6A (SEMA6A) and identify the underlying molecular mechanisms involved in SKCM. Expression profiles of four RNAs, including pseudogenes, long non-coding RNAs, microRNAs, and mRNAs were obtained from The Cancer Genome Atlas database. The analysis was completed by bioinformatics methods, and the expression levels of the selected genes were verified by cell experiments. Bioinformatics analysis revealed that the LINC00511-hsa-miR-625-5p-SEMA6A ceRNA network was associated with SKCM prognosis. Furthermore, immune infiltration analysis indicated that the LINC00511-hsa-miR-625-5p-SEMA6A axis may have an impact on changes in the tumor immune microenvironment of SKCM. The LINC00511-hsa-miR-625-5p-SEMA6A axis could be a promising therapeutic target and a prognostic biomarker for SKCM.

Identification of anoikis-related subtypes and development of risk stratification system in skin cutaneous melanoma

BackgroundAnoikis, a form of apoptosis induced by cell detachment, plays a key role in cancer metastasis. However, the potential roles of anoikis-related genes (ARGs) in assessing the prognosis of skin cutaneous melanoma (SKCM) and the tumor microenvironment (TME) remain unclear. MethodsThe data from TCGA corresponding to transcriptomic expression patterns for patients with SKCM were downloaded and utilized to screen distinct molecular subtypes by a non-negative matrix factorization algorithm. The prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and was validated in SKCM patients from the GEO cohort. Moreover, the relationship of the ARG_score with prognosis, tumor-infiltrating immune cells, gene mutation, microsatellite instability (MSI), and immunotherapy efficacy. ResultsWe screened 100 anoikis-related differentially expressed genes between SKCM tissues and normal skin tissues, which could divide all patients into three different subtypes with significantly distinct prognosis and immune cell infiltration. Then, an anoikis-related signature was developed based on subtype-related DEGs, which could classify all SKCM patients into low and high ARG_score groups with differing overall survival (OS) rates. ARG_score was confirmed to be a strong independent prognostic indicator for SKCM patients. By combining ARG_score with clinicopathological features, a nomogram was constructed, which could accurately predict the individual OS of patients with SKCM. Moreover, low ARG_score patients presented with higher levels of immune cell infiltration, TME score, higher tumor mutation burden, and better immunotherapy responses. ConclusionsOur comprehensive analysis of ARGs in SKCM provides important insights into the immunological microenvironment within the tumor of SKCM patients and helps to forecast prognosis and the response to immunotherapy in SKCM patients, thereby making it easier to tailor more effective treatment strategies to individual patients.

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma.

Background: Ferroptosis is an iron-dependent cell death mode and closely linked to various cancers, including skin cutaneous melanoma (SKCM). Although attempts have been made to construct ferroptosis-related gene (FRG) signatures for predicting the prognosis of SKCM, the prognostic impact of ferroptosis-related genetic mutations in SKCM remains lacking. This study aims to develop a prediction model to explain the relationship between ferroptosis-related genetic mutations and clinical outcomes of SKCM patients and to explore the potential value of ferroptosis in SKCM treatment. Methods: FRGs which significantly correlated with the prognosis of SKCM were firstly screened based on their single-nucleotide variant (SNV) status by univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) and Cox regressions were performed to construct a new ferroptosis-related genetic mutation risk (FerrGR) model for predicting the prognosis of SKCM. We then illustrate the survival and receiver operating characteristic (ROC) curves to evaluate the predictive power of the FerrGR model. Moreover, independent prognostic factors, genomic and clinical characteristics, immunotherapy, immune infiltration, and sensitive drugs were compared between high-and low-FerrGR groups. Results: The FerrGR model was developed with a good performance on survival and ROC analysis. It was a robust independent prognostic indicator and followed a nomogram constructed to predict prognostic outcomes for SKCM patients. Besides, FerrGR combined with tumor mutational burden (TMB) or MSI (microsatellite instability) was considered as a combined biomarker for immunotherapy response. The high FerrGR group patients were associated with an inhibitory immune microenvironment. Furthermore, potential drugs target to high FerrGR samples were predicted. Conclusion: The FerrGR model is valuable to predict prognosis and immunotherapy in SKCM patients. It offers a novel therapeutic option for SKCM.

Discovery and Validation of a SIT1-Related Prognostic Signature Associated with Immune Infiltration in Cutaneous Melanoma.

Signaling threshold regulating transmembrane adaptor 1 (SIT1) encodes a disulfide-linked homodimeric lymphocyte-specific glycoprotein involved in immune cell activation. However, the relationship between SIT1 and the prognosis of skin cutaneous melanoma (SKCM) and tumor-infiltrating lymphocytes remains elusive. Here, we first compared the differences in SIT1 expression levels between SKCM tissues and adjacent normal tissues. Next, we found that the immune cell infiltration levels and signature pattern of immune infiltration were positively associated with the SIT1 gene mRNA levels. TCGA_SKCM RNA-seq data unveiled that the SIT1 upregulated several immune-associated signaling pathways in GSEA analysis. The high expression of SIT1 was closely related to improved survival in patients with SKCM. A pathway enrichment analysis of SIT1-associated immunomodulators indicated the involvement of the NF-κB signaling pathways. Based on SIT1-associated immunomodulators, we built a 13-gene signature by LASSO Cox regression which served as an independent prognostic factor for the survival of melanoma patients. By using the signature risk score, we achieved a good prediction result for the immunotherapy response and survival of SKCM patients. Our findings provided evidence for SIT1's implication in tumor immunity and survival of SKCM patients. The nominated immune signature is a promising predictive model for prognosis and immunotherapy sensitivity in SKCM patients.

Integrated analysis from multi-center studies identities m7G-derived modification pattern and risk stratification system in skin cutaneous melanoma.

The m7G modification has been proven to play an important role in RNA post-transcriptional modification and protein translation. However, the potential role of m7G modification patterns in assessing the prognosis of Skin cutaneous melanoma (SKCM) and tumor microenvironment (TME) has not been well studied. In this study, we investigated and finally identified 21 available m7G-related genes. We used hierarchical clustering (K-means) to classify 743 SKCM patients into three m7G-modified subtypes named m7G/gene cluster-A, B, C. We found that both m7G cluster B and gene cluster B exhibited higher prognosis and higher immune cell infiltration in TME compared to other subtypes. EIF4E3 and IFIT5, two m7G related genes, were both markedly elevated in Cluster B. Then, we constructed an m7G score system utilizing principal component analysis (PCA) in order to evaluate the patients' prognosis. High m7G score subtype was associated with better survival prognosis and active immune response. Overall, this article revealed that m7G modification patterns were involved in the development of the tumor microenvironment. Evaluating patients' m7G modification patterns will enhance our understanding of TME characteristics and help to guide personal treatment in clinics in the future.

A Necroptosis-Related Gene Signature to Predict the Prognosis of Skin Cutaneous Melanoma.

The prognosis of skin cutaneous melanoma (SKCM) remains poor, and patients with SKCM show a poor response to immunotherapy. Thus, we aimed to identify necroptosis-related biomarkers, which can help predict the prognosis of SKCM and improve the effectiveness of precision medicine. Data of SKCM were obtained from The Cancer Genome Atlas (TCGA) and GEO databases. TCGA samples were classified into two clusters by consensus clustering of necroptosis-related genes. Univariate Cox and least absolute shrinkage and selection operator regression analyses led to the identification of 11 genes, which were used to construct a prognostic model. GSE65904 was used as the test set. Principal component, t-distributed stochastic neighbor embedding, and Kaplan-Meier survival analyses indicated that samples in the train and test sets could be divided into two groups, with the high-risk group showing a worse prognosis. Univariate and multivariate Cox regression analyses were performed, and a nomogram, calibration curve, and time-dependent receiver operating characteristic curve were constructed to verify the efficacy of our model. The 1-, 3-, and 5-year areas under the receiver operating characteristic curves for the train set were 0.702, 0.663, and 0.701 and for the test set were 0.613, 0.627, and 0.637, respectively. Moreover, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses between the high- and low-risk groups. Single sample gene set enrichment analysis, immune cell infiltration analysis, tumor microenvironment scores, immune checkpoint analysis, and half-maximal inhibitory concentration prediction indicated that the high-risk group showed weaker antitumor immunity; further, the response to immune checkpoint inhibitors was worse, and the high-risk group was sensitive to fewer antitumor drugs. Tumor mutational burden analysis, Kaplan-Meier survival analysis, and correlation analysis between risk score and RNA stemness score revealed that the high-risk group with low tumor mutational burden and high RNA stemness score was potentially associated with poor prognosis. To conclude, our model, which was based on 11 necroptosis-related genes, could predict the prognosis of SKCM; in addition, it has guiding significance for the selection of clinical treatment and provides new research directions to enhance necroptosis against SKCM.

CEBPB is associated with active tumor immune environment and favorable prognosis of metastatic skin cutaneous melanoma.

Metastatic skin cutaneous melanoma (SKCM) is a common malignancy that accounts for low morbidity but high mortality of skin cancer. SKCM is characterized by high lymphocytic infiltration, whereas the states of infiltrated cells are variable in patients leading to a heterogeneous prognosis and hindering appropriate clinical decisions. It is therefore urgent to identify markers associated with lymphocytic infiltration, cellular conditions, and the prognosis of SKCM. In this study, we report that CEBPB, a transcriptional factor, is mainly expressed in macrophages in metastatic SKCM and associated with an active tumor immune environment and a favorable prognosis through integrated analysis of single-cell and bulk RNA-seq datasets. High CEBPB expression is significantly associated with active inflammation and immune response pathways in both macrophages and bulk SKCM tumor tissues. A signature based on CEBPB-associated genes that are specifically expressed in macrophages could robustly and prognostically separate different metastatic SKCM patients. In addition, the associations between the metastatic SKCM tumor signature and microenvironment with respect to T-cell recruitment and state, inflammation response, angiogenesis, and so on were also determined. In conclusion, we present here the first report on CEBPB in tumor immune environment and prognosis regulation in metastatic SKCM and construct a reliable signature, which should provide a useful biomarker for stratification of the patient's prognosis and therapeutic selection.

A novel pyroptosis-related LncRNA signature predicts prognosis and indicates tumor immune microenvironment in skin cutaneous melanoma

AimsTo explore the correlation between the pyroptosis-related lncRNAs (PRlncRNAs) and the prognosis of skin cutaneous melanoma (SKCM), and clarify the effects of the PRlncRNAs on the tumor immune infiltration. Main methodsIn this study, we utilized RNA-seq and clinical characteristics data obtained from TCGA and GEO database to perform co-expression analysis and LASSO Cox regression analysis to construct a 12-PRlncRNA prognostic prediction model. We also performed functional analysis, immune infiltration analysis and drug sensitivity analysis, as well as correlation analysis with autophagy/ferroptosis genes, tumor mutational burden, and PD-1/PD-L1 genes. Key findingThe model based on the 12-PRlncRNA signature could effectively predict the prognosis of SKCM. Low risk group had a higher anti-tumor immune level generally compared with high-risk group. The signature was correlated with the expression of autophagy/ferroptosis-related genes and PD1/PD-L1 genes and tumor mutational burden. Additionally, drug sensitivity analysis indicated potential therapeutic targets. SignificanceOur study demonstrated the impact of PRlncRNAs on SKCM. The model established based on the 12-PRlncRNA showed significant prognostic value for SKCM and may be instructive in pyroptosis-related targeted therapy in the clinic.

A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment.

Background: Necroptosis has been identified recently as a newly recognized programmed cell death that has an impact on tumor progression and prognosis, although the necroptosis-related gene (NRGs) potential prognostic value in skin cutaneous melanoma (SKCM) has not been identified. The aim of this study was to construct a prognostic model of SKCM through NRGs in order to help SKCM patients obtain precise clinical treatment strategies. Methods: RNA sequencing data collected from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed and prognostic NRGs in SKCM. Depending on 10 NRGs via the univariate Cox regression analysis usage and LASSO algorithm, the prognostic risk model had been built. It was further validated by the Gene Expression Omnibus (GEO) database. The prognostic model performance had been assessed using receiver operating characteristic (ROC) curves. We evaluated the predictive power of the prognostic model for tumor microenvironment (TME) and immunotherapy response. Results: We constructed a prognostic model based on 10 NRGs (FASLG, TLR3, ZBP1, TNFRSF1B, USP22, PLK1, GATA3, EGFR, TARDBP, and TNFRSF21) and classified patients into two high- and low-risk groups based on risk scores. The risk score was considered a predictive factor in the two risk groups regarding the Cox regression analysis. A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group. Conclusions: This risk score signature could be applied to assess the prognosis and classify low- and high-risk SKCM patients and help make the immunotherapeutic strategy decision.

Identification of a Novel Pyroptosis-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Skin Cutaneous Melanoma.

PurposePyroptosis plays an important role in the occurrence and progression of many tumors; however, the specific mechanisms involved remain unknown. Here, we construct a pyroptosis-related gene signature that can be used to predict survival prognosis of skin cutaneous melanoma (SKCM) and provide guidance for clinical treatment.MethodsBy integrating data from the two databases from the GTEx and TCGA, differentially expressed genes (DEGs) from normal tissues and skin cutaneous tumor tissues were identified. The main signaling pathways and function enrichment of these differential genes were determined. Univariate and multivariate COX regression analysis, and risk score analysis were used to construct a signature to assess its predictive value for overall survival. The mRNA expression of these five genes in melanoma cells was determined by quantitative polymerase chain reaction (qPCR). The pRRophetic algorithm was used to estimate the half-maximal inhibitory concentration (IC50) of chemotherapy drugs in SKCM patients. The expression of multiple immune checkpoint genes also was evaluated.ResultsSixteen DEGs associated with pyroptosis in SKCM and normal skin tissues were identified. Of these, 12 pyroptosis-related DEGs were associated with the prognosis of SKCM. A five-gene signature (GSDMA, GSDMC, IL-18, NLRP6, and AIM2) model was constructed. Patients were divided into high-risk and low-risk groups using the risk scores. Of these, the high-risk group had a worse survival prognosis. There are significant differences in the predicted sensitivity of the high-risk and low-risk groups to chemotherapeutic drugs. In addition, compared with the high-risk group, the low-risk group showed higher expression of PD-1, PDL-1, CTLA-4, LAG-3, and VSIR.ConclusionIn this study, we constructed a novel prognostic pyroptosis-related gene-signature for SKCM. These genes showed good predictive value for patient prognosis and could provide guidance for better treatment of SKCM patients.

The role of SGOL1 in the prognosis of skin cutaneous melanoma (SKCM)

Objective: To analyze the expression of SGOL1 in skin cutaneous melanoma(SKCM) and its relationship with prognosis. Methods: The expression of SGOL1 in SKCM and normal tissues was analyzed by TCGA database. Kaplan-Meier curve was used to analyze the relationship between SGOL1 expression and prognosis of SKCM. Mann-Whitney U test and Kruskal-Wallis test were used to analyze the relationship between SGOL1 and clinicopathological features. Results: SGOL1 was over expressed in SKCM (P <0.05). The overall survival of SGOL1 high expression group was lower than low expression group (P < 0.05). There was no difference in the expression of SGOL1 between gender or age groups, but there were different in the expression of SGOL1 between Clark grades, tumo stages and survival status. Conclusion: High expression of SGOL1 in SKCM may be related with poor prognosis.

A new pyroptosis model can predict the immunotherapy response and immune microenvironment characteristics and prognosis of patients with cutaneous melanoma based on TCGA and GEO databases.

BackgroundRecent studies have shown that pyroptosis is related to cancer development. Our previous study also found that gasdermins (GSDMs) was associated with the tumor immune microenvironment. Therefore, we wanted to observe the relationship between pyroptosis and the immune microenvironment and prognosis of skin cutaneous melanoma (SKCM).MethodsPyroptosis-related genes were used for pan-cancer prognostic analysis using the GEPIA2 online analysis website. Prognosis-related genes were clustered using R software and related R packages, and the best clustering results were screened for prognosis analysis. The prognosis-related genes were also used to establish a prognosis-related model. Assess the predictive power of a model by comparing area under the curve (AUC). The t-test was used to analyze the differences of immune-related indicators between the two clusters and between high and low risk groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed on the differential genes.ResultsBy clustering the prognosis-related genes, SKCM could be divided into 2 clusters with significant differences in prognosis P<0.05. A prognostic model can be established using prognosis-related genes. The AUC value of 1 year, 2 years and 3 years was 0.696, 0.702 and 0.664, respectively. The risk score was significantly associated with prognosis in both univariate and multivariate Cox analyses P<0.001. The low-risk group or C2 cluster with better prognosis had higher expression of pyroptosis-related genes, and tended to have a lower exclusion score, greater chemokine expression, more immune cells and higher immune score. However, the C2 cluster or low-risk group was also associated with a higher dysfunction score. At the same time, the C2 or low-risk group was more suitable for immunotherapy because of the higher immunophenoscore (IPS) score P<0.001. Correlation analysis also demonstrated that the risk score was positively correlated with the gene expression of most immunoinhibitors, MHC molecules, immunostimulators, and chemokines and their receptors.ConclusionsPyroptosis is associated with melanoma immune microenvironment, immunotherapy response, and prognoses. The constructed risk scores could effectively predict the characteristics of the immune microenvironment, the sensitivity to immunotherapy, and the prognosis of melanoma patients.

Ferroptosis-Related Gene Signature Predicts the Prognosis of Skin Cutaneous Melanoma and Response to Immunotherapy.

Ferroptosis is a non-apoptotic regulated cell death process, and much research has indicated that ferroptosis can induce the non-apoptotic death of tumor cells. Ferroptosis-related genes are expected to become a biological target for cancer treatment. However, the regulation of ferroptosis-related genes in skin cutaneous melanoma (SKCM) has not been well studied. In the present study, we conducted a systematic analysis of SKCM based on RNA sequencing data and clinical data obtained from The Cancer Genome Atlas (TCGA) database and the FerrD database. SKCM patients from the GSE78220 and MSKCC cohorts were used for external validation. Applying consensus clustering on RNA sequencing data from TCGA the generated ferroptosis subclasses of SKCM, which were analyzed based on the set of differentially expressed ferroptosis-related genes. Then, a least absolute shrinkage and selection operator (LASSO)-Cox regression was used to construct an eight gene survival-related linear signature. The median cut-off risk score was used to divide patients into high- and low-risk groups. The time-dependent receiver operating characteristic curve was used to examine the predictive power of the model. The areas under the curve of the signature at 1, 3, and 5 years were 0.673, 0.716, and 0.746, respectively. Kaplan-Meier survival analysis showed that the prognosis of high-risk patients was worse than that of low-risk patients. Univariate and multivariate Cox regression analyses showed that the risk signature was a robust independent prognostic indicator. By incorporating risk scores with tumor staging, a nomogram was constructed to predict prognostic outcomes for SKCM patients. In addition, the immunological analysis showed different immune cell infiltration patterns. Programmed-death-1 (PD-1) immunotherapy showed more significant benefits in the low-risk group than in the high-risk group. In summary, a model based on ferroptosis-related genes can predict the prognosis of SKCM and could have a potential role in guiding targeted therapy of SKCM.

A Novel Pyroptosis-Related lncRNA Signature for Predicting the Prognosis of Skin Cutaneous Melanoma.

PurposeTo construct a prognosis model of melanoma based on pyroptosis related genes.MethodsMelanoma RNA-sequencing data was downloaded from TCGA. First, the lnRNAs related to pyroptosis were obtained through Pearson correlation analysis. Then, the prognosis model of pyroptosis related genes was constructed by Cox regression and Lasso regression. Melanoma patients were divided into high-risk and low-risk groups by risk score, and the differences in prognosis and immune microenvironment between the two groups were explored.ResultsWe found that the high-risk group had a significantly poorer prognosis, and different groups differed in immune infiltration, m6A methylation, and immune checkpoint.ConclusionOur prognostic model can provide a reference for the study of pyroptosis in melanoma cells and provide a new idea for melanoma treatment.