Prognostic Implications of Metabolism Related Gene Signature in Cutaneous Melanoma

Identification of a metabolism-linked genomic signature for prognosis and immunotherapeutic efficiency in metastatic skin cutaneous melanoma

Metastatic skin cutaneous melanoma (MSCM) is the most rapidly progressing/invasive skin-based malignancy, with median survival rates of about 12 months. It appears that metabolic disorders accelerate disease progression. However, correlations between metabolism-linked genes (MRGs) and prognosis in MSCM are unclear, and potential mechanisms explaining the correlation are unknown. The Cancer Genome Atlas (TCGA) was utilized as a training set to develop a genomic signature based on the differentially expressed MRGs (DE-MRGs) between primary skin cutaneous melanoma (PSCM) and MSCM. The Gene Expression Omnibus (GEO) was utilized as a validation set to verify the effectiveness of genomic signature. In addition, a nomogram was established to predict overall survival based on genomic signature and other clinic-based characteristics. Moreover, this study investigated the correlations between genomic signature and tumor micro-environment (TME). This study established a genomic signature consisting of 3 genes (CD38, DHRS3, and TYRP1) and classified MSCM patients into low and high-risk cohorts based on the median risk scores of MSCM cases. It was discovered that cases in the high-risk cohort had significantly lower survival than cases in the low-risk cohort across all sets. Furthermore, a nomogram containing this genomic signature and clinic-based parameters was developed and demonstrated high efficiency in predicting MSCM case survival times. Interestingly, Gene Set Variation Analysis results indicated that the genomic signature was involved in immune-related physiological processes. In addition, this study discovered that risk scoring was negatively correlated with immune-based cellular infiltrations in the TME and critical immune-based checkpoint expression profiles, indicating that favorable prognosis may be influenced in part by immunologically protective micro-environments. A novel 3-genomic signature was found to be reliable for predicting MSCM outcomes and may facilitate personalized immunotherapy.

Ferroptosis in cancer: From molecular mechanisms to therapeutic strategies

Ferroptosis is a non-apoptotic form of regulated cell death characterized by the lethal accumulation of iron-dependent membrane-localized lipid peroxides. It acts as an innate tumor suppressor mechanism and participates in the biological processes of tumors. Intriguingly, mesenchymal and dedifferentiated cancer cells, which are usually resistant to apoptosis and traditional therapies, are exquisitely vulnerable to ferroptosis, further underscoring its potential as a treatment approach for cancers, especially for refractory cancers. However, the impact of ferroptosis on cancer extends beyond its direct cytotoxic effect on tumor cells. Ferroptosis induction not only inhibits cancer but also promotes cancer development due to its potential negative impact on anticancer immunity. Thus, a comprehensive understanding of the role of ferroptosis in cancer is crucial for the successful translation of ferroptosis therapy from the laboratory to clinical applications. In this review, we provide an overview of the recent advancements in understanding ferroptosis in cancer, covering molecular mechanisms, biological functions, regulatory pathways, and interactions with the tumor microenvironment. We also summarize the potential applications of ferroptosis induction in immunotherapy, radiotherapy, and systemic therapy, as well as ferroptosis inhibition for cancer treatment in various conditions. We finally discuss ferroptosis markers, the current challenges and future directions of ferroptosis in the treatment of cancer.

Microsomal glutathione transferase 1 controls metastasis and therapeutic response in melanoma

While recent targeted and immunotherapies in malignant melanoma are encouraging, most patients acquire resistance, implicating a need to identify additional drug targets to improve outcomes. Recently, attention has been given to pathways that regulate redox homeostasis, especially the lipid peroxidase pathway that protects cells against ferroptosis. Here we identify microsomal glutathione S-transferase 1 (MGST1), a non-selenium-dependent glutathione peroxidase, as highly expressed in malignant and drug resistant melanomas and as a specific determinant of metastatic spread and therapeutic sensitivity. Loss of MGST1 in mouse and human melanoma enhanced cellular oxidative stress, and diminished glycolysis, oxidative phosphorylation, and pentose phosphate pathway. Gp100 activated pmel-1 T cells killed more Mgst1 KD than control melanoma cells and KD cells were more sensitive to cytotoxic anticancer drugs and ferroptotic cell death. When compared to control, mice bearing Mgst1 KD B16 tumors had more CD8+ T cell infiltration with reduced expression of inhibitory receptors and increased cytokine response, large reduction of lung metastases and enhanced survival. Targeting MGST1 alters the redox balance and limits metastases in melanoma, enhancing the therapeutic index for chemo- and immunotherapies.

Machine learning modeling and prognostic value analysis of invasion-related genes in cutaneous melanoma

In this study, we aimed to develop an invasion-related risk signature and prognostic model for personalized treatment and prognosis prediction in skin cutaneous melanoma (SKCM), as invasion plays a crucial role in this disease. We identified 124 differentially expressed invasion-associated genes (DE-IAGs) and selected 20 prognostic genes (TTYH3, NME1, ORC1, PLK1, MYO10, SPINT1, NUPR1, SERPINE2, HLA-DQB2, METTL7B, TIMP1, NOX4, DBI, ARL15, APOBEC3G, ARRB2, DRAM1, RNF213, C14orf28, and CPEB3) using Cox and LASSO regression to establish a risk score. Gene expression was validated through single-cell sequencing, protein expression, and transcriptome analysis. Negative correlations were discovered between risk score, immune score, and stromal score using ESTIMATE and CIBERSORT algorithms. High- and low-risk groups exhibited significant differences in immune cell infiltration and checkpoint molecule expression. The 20 prognostic genes effectively differentiated between SKCM and normal samples (AUCs >0.7). We identified 234 drugs targeting 6 genes from the DGIdb database. Our study provides potential biomarkers and a risk signature for personalized treatment and prognosis prediction in SKCM patients. We developed a nomogram and machine-learning prognostic model to predict 1-, 3-, and 5-year overall survival (OS) using risk signature and clinical factors. The best model, Extra Trees Classifier (AUC = 0.88), was derived from pycaret's comparison of 15 classifiers. The pipeline and app are accessible at https://github.com/EnyuY/IAGs-in-SKCM.

A role for microsomal glutathione transferase 1 in melanin biosynthesis and melanoma progression

Recent advancements in the treatment of melanoma are encouraging, but there remains a need to identify additional therapeutic targets. We identify a role for microsomal glutathione transferase 1 (MGST1) in biosynthetic pathways for melanin and as a determinant of tumor progression. Knockdown (KD) of MGST1 depleted midline-localized, pigmented melanocytes in zebrafish embryos, while in both mouse and human melanoma cells, loss of MGST1 resulted in a catalytically dependent, quantitative, and linear depigmentation, associated with diminished conversion of L-dopa to dopachrome (eumelanin precursor). Melanin, especially eumelanin, has antioxidant properties, and MGST1 KD melanoma cells are under higher oxidative stress, with increased reactive oxygen species, decreased antioxidant capacities, reduced energy metabolism and ATP production, and lower proliferation rates in 3D culture. In mice, when compared to nontarget control, Mgst1 KD B16 cells had less melanin, more active CD8+ T cell infiltration, slower growing tumors, and enhanced animal survival. Thus, MGST1 is an integral enzyme in melanin synthesis and its inhibition adversely influences tumor growth.

Microsomal glutathione transferase 1 in cancer and the regulation of ferroptosis

Microsomal glutathione transferase 1 (MGST1) is a member of the MAPEG family (membrane associated proteins in eicosanoid and glutathione metabolism), defined according to enzymatic activities, sequence motifs, and structural properties. MGST1 is a homotrimer which can bind three molecules of glutathione (GSH), with one modified to a thiolate anion displaying one-third-of-sites-reactivity. MGST1 has both glutathione transferase and peroxidase activities. Each is based on stabilizing the GSH thiolate in the same active site. MGST1 is abundant in the liver and displays a broad subcellular distribution with high levels in endoplasmic reticulum and mitochondrial membranes, consistent with a physiological role in protection from reactive electrophilic intermediates and oxidative stress. In this review paper, we particularly focus on recent advances made in understanding MGST1 activation, induction, broad subcellular distribution, and the role of MGST1 in apoptosis, ferroptosis, cancer progression, and therapeutic responses.

Comprehensive characterisation of immunogenic cell death in melanoma revealing the association with prognosis and tumor immune microenvironment

Increasing evidence has highlighted the critical functions of immunogenic cell death (ICD) within many tumors. However, the therapeutic possibilities and mechanism of utilizing ICD in melanoma are still not well investigated. Melanoma samples involved in our study were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. First, pan-cancer analysis of ICD systematically revealed its expression characteristics, prognostic values, mutation information, methylation level, pathway regulation relationship in multiple human cancers. The non-negative matrix factorization clustering was utilized to separate the TCGA-melanoma samples into two subtypes (i.e. C1 and C2) with different prognosis and immune microenvironment based on the expression traits of ICD. Then, LASSO-Cox regression analysis was utilized to determine an ICD-dependent risk signature (ICDRS) based on the differentially expressed genes (DEGs) between the two subtypes. Principal component analysis and t-distributed stochastic neighbor embedding analysis of ICDRS showed that high- and low-risk subpopulations could be clearly distinguished. Survival analysis and ROC curves in the training, internal validation, and external validation cohorts highlighted the accurate prognosis evaluation of ICDRS. The obvious discrepancies of immune microenvironment between the different risk populations might be responsible for the different prognoses of patients with melanoma. These findings revealed the close association of ICD with prognosis and tumor immune microenvironment. More importantly, ICDRS-based immunotherapy response and targeted drug prediction might be beneficial to different risk subpopulations of patients with melanoma. The innotative ICDRS could function as a marker to determine the prognosis and tumor immune microenvironment in melanoma. This will aid in patient classification for individualized melanoma treatment.

Identification of Age-Associated Transcriptomic Changes Linked to Immunotherapy Response in Primary Melanoma

Melanoma is a lethal form of skin cancer. Immunotherapeutic agents such as anti-PD-1 (pembrolizumab and nivolumab) and anti-CTLA-4 (ipilimumab) have revolutionized melanoma treatment; however, drug resistance is rapidly acquired. Several studies have reported an increase in melanoma rates in older patients. Thus, the impact of ageing on transcriptional profiles of melanoma and response to immunotherapy is essential to understand. In this study, the bioinformatic analysis of RNA seq data of old and young melanoma patients receiving immunotherapy identifies the significant upregulation of extra-cellular matrix and cellular adhesion genes in young cohorts, while genes involved in cell proliferation, inflammation, non-canonical Wnt signaling and tyrosine kinase receptor ROR2 are significantly upregulated in the old cohort. Several Treg signature genes as well as transcription factors that are associated with dysfunctional T cell tumor infiltration are differentially expressed. The differential expression of several genes involved in oxidative phosphorylation, glycolysis and glutamine metabolism is also observed. Taken together, this study provides novel findings on the impact of ageing on transcriptional changes in melanoma, and novel therapeutic targets for future studies.

Construction of immunotherapy-related prognostic gene signature and small molecule drug prediction for cutaneous melanoma

Background

Cutaneous melanoma (CM), a kind of skin cancer with a high rate of advanced mortality, exhibits a wide variety of driver and transmitter gene alterations in the immunological tumor microenvironment (TME) associated with tumor cell survival and proliferation.

Methods

We analyzed the immunological infiltration of TME cells in normal and malignant tissues using 469 CM and 556 normal skin samples. We used a single sample gene set enrichment assay (ssGSEA) to quantify the relative abundance of 28 cells, then used the LASSO COX regression model to develop a riskScore prognostic model, followed by a small molecule drug screening and molecular docking validation, which was then validated using qRT-PCR and IHC.

Results

We developed a prognosis model around seven essential protective genes for the first time, dramatically elevated in tumor tissues, as did immune cell infiltration. Multivariate Cox regression results indicated that riskScore is an independent and robust prognostic indicator, and its predictive value in immunotherapy was verified. Additionally, we identified Gabapentin as a possible small molecule therapeutic for CM.

Conclusions

A riskScore model was developed in this work to analyze patient prognosis, TME cell infiltration features, and treatment responsiveness. The development of this model not only aids in predicting patient response to immunotherapy but also has significant implications for the development of novel immunotherapeutic agents and the promotion of tailored treatment regimens.

A Novel Pyroptotic and Inflammatory Gene Signature Predicts the Prognosis of Cutaneous Melanoma and the Effect of Anticancer Therapies

Purpose

The purpose of this study was to construct a gene signature comprising genes related to both inflammation and pyroptosis (GRIPs) to predict the prognosis of patients with cutaneous melanoma patients and the efficacy of immunotherapy, chemotherapy, and targeted therapy in these patients.

Methods

Gene expression profiles were collected from The Cancer Genome Atlas. Weighted gene co-expression network analysis was performed to identify GRIPs. Univariable Cox regression and Lasso regression further selected key prognostic genes. Multivariable Cox regression was used to construct a risk score, which stratified patients into high- and low-risk groups. Areas under the ROC curves (AUCs) were calculated, and Kaplan-Meier analyses were performed for the two groups, following validation in an external cohort from Gene Expression Omnibus (GEO). A nomogram including the GRIP signature and clinicopathological characteristics was developed for clinical use. Gene set enrichment analysis illustrated differentially enriched pathways. Differences in the tumor microenvironment (TME) between the two groups were assessed. The efficacies of immune checkpoint inhibitors (ICIs), chemotherapeutic agents, and targeted agents were predicted for both groups. Immunohistochemical analyses of the GRIPs between the normal and CM tissues were performed using the Human Protein Atlas data. The qRT-PCR experiments validated the expression of genes in CM cell lines, Hacat, and PIG1 cell lines.

Results

A total of 185 GRIPs were identified. A novel gene signature comprising eight GRIPs (TLR1, CCL8, EMP3, IFNGR2, CCL25, IL15, RTP4, and NLRP6) was constructed. The signature had AUCs of 0.714 and 0.659 for predicting 3-year overall survival (OS) in the TCGA entire and GEO validation cohorts, respectively. Kaplan-Meier analyses revealed that the high-risk group had a poorer prognosis. Multivariable Cox regression showed that the GRIP signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The nomogram showed good accuracy and reliability in predicting 3-year OS (AUC = 0.810). GSEA and TME analyses showed that the high-risk group had lower levels of pyroptosis, inflammation, and immune response, such as lower levels of CD8+ T-cell infiltration, CD4+ memory-activated T-cell infiltration, and ICI. In addition, low-risk patients whose disease expressed PD-1 or CTLA-4 were likely to respond better to ICIs, and several chemotherapeutic and targeted agents. Immunohistochemical analysis confirmed the distinct expression of five out of the eight GRIPs between normal and CM tissues.

Conclusion

Our novel 8-GRIP signature can accurately predict the prognosis of patients with CM and the efficacies of multiple anticancer therapies. These GRIPs might be potential prognostic biomarkers and therapeutic targets for CM.

Comprehensive analysis of ferroptosis-related genes and prognosis of cutaneous melanoma

BACKGROUND

Cutaneous Melanoma (CM) is a malignant disease with increasing incidence and high mortality. Ferroptosis is a new kind of cell death and related to tumor blood and lymphatic metastasis. This study aims at using bioinformatics technology to construct a prognostic signature and identify ferroptosis-related biomarkers to improve the prognosis and treatment of cutaneous melanoma.

METHODS

We used bioinformatics tools to analyze RNA sequencing expression data with clinical information from multiple databases, utilized varieties of statistical methods to construct a ferroptosis-related prognostic signature of cutaneous melanoma and screened out specific genes with independent prognostic ability.

RESULTS

We obtained 22 ferroptosis-related (P < 0.05) prognostic DEGs in the uniCox regression analysis, among which 10 high-expressed genes (ATG5, CHAC1, FANCD2, FBXL5, HMOX2, HSPB1, NQO1, PEBP1, PRNP, SLC3A2) were screened out by LASSO regression analysis to establish a predictive model. Meanwhile, the ferroptosis-related signature and the nomogram we drew performed an excellent performance based on Kaplan-Meier (K-M), Receiver operating characteristic (ROC) and calibration curves. Univariate and multivariable cox analyses displayed that our model was greater than other prognostic features. GO and KEGG analyses revealed that 10-biomarker signature was mainly related to epidermis differentiation and immunity. ssGSEA analysis indicated that the immune status between the two risk groups was highly different. Besides, we found that two genes (CP, ZEB1) had independent prognostic ability and can be applied for drug research. Both genes were highly related to immunity. GSEA illustrated that ZEB1 may be involved in cellular functions such as proliferation, apoptosis, and migration, while CP was closely connected to immune cell related functions.

CONCLUSION

The present study suggested a 10-biomarker signature can be clinically used to predict the prognosis of cutaneous melanoma, which was better than conventional factors. CP and ZEB1 were independent prognostic genes and can be applied to guide treatment. In addition, ZEB1 mutation was highly related to overall survival in cutaneous melanoma, while CP may be associated with tumor progression. Our study comprehensively analyzed the relationship between iron metabolism, ferroptosis-related genes, and the prognosis of cutaneous melanoma, provided new insight for molecular mechanisms and treatment of ferroptosis and cutaneous melanoma.

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed.

Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established.

Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan–Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/).

Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.

Single-Cell Transcriptomic Analysis Reveals a Tumor-Reactive T Cell Signature Associated With Clinical Outcome and Immunotherapy Response In Melanoma

The infiltration of tumor-reactive T cells in the tumor site is associated with better survival and immunotherapy response. However, tumor-reactive T cells were often represented by the infiltration of total CD8+ T cells, which was confounded by the presence of bystander T cells. To identify tumor-reactive T cells at the cancer lesion, we performed integration analyses of three scRNA-seq data sets of T cells in melanoma. Extensive heterogeneous functional states of T cells were revealed in the tumor microenvironment. Among these states, we identified a subset of tumor-reactive T cells which specifically expressed tumor-reactive markers and T cell activation signature, and were strongly enriched for larger T cell receptor (TCR) clones. We further identified and validated a tumor-reactive T cell signature (TRS) to evaluate the tumor reactivity of T cells in tumor patients. Patients with high TRS scores have strong immune activity and high mutation burden in the TCGA-SKCM cohort. We also demonstrated a significant association of the TRS with the clinical outcomes of melanoma patients, with higher TRS scores representing better survival, which was validated in four external independent cohorts. Furthermore, the TRS scores exhibited greater performance on prognosis prediction than infiltration levels of CD8+ T cells and previously published prognosis-related signatures. Finally, we observed the capability of TRS to predict immunotherapy response in melanoma. Together, based on integrated analysis of single-cell RNA-sequencing, we developed and validated a tumor-reactive-related signature that demonstrated significant association with clinical outcomes and response to immunotherapy.

New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation

Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.

Major ceRNA regulation and key metabolic signature analysis of intervertebral disc degeneration

Background and objective

Intervertebral disc degeneration (IDD) is a complex multifactorial and irreversible pathological process. In IDD, multiple competing endogenous RNAs (ceRNA, including mRNA, lncRNA, and pseudogenes) can compete to bind with miRNAs. However, the potential metabolic signatures in nucleus pulposus (NP) cells remain poorly understood. This study investigated key metabolic genes and the ceRNA regulatory mechanisms in the pathogenesis of IDD based on microarray datasets.

Methods

We retrieved and downloaded four independent IDD microarray datasets from the Gene Expression Omnibus. Combining the predicted interactions from online databases (miRcode, miRDB, miRTarBase, and TargetScan), differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified. A ceRNA network was constructed and annotated using GO and KEGG pathway enrichment analyses. Moreover, we searched the online metabolic gene set and used support vector machine (SVM) to find the critical metabolic DEmRNA(s) and other DERNAs. Differential gene expression was validated with a merged dataset.

Results

A total of 45 DEmRNAs, 36 DElncRNAs, and only one DEmiRNA (miR-338-3p) were identified in the IDD microarray datasets. GO and KEGG pathway enrichment analyses revealed that the DEmRNAs were predominantly enriched in the PI3K-Akt signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, apoptosis, and cellular response to oxidative stress. Based on SVM screening, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFK/FBPase) 2 is the critical metabolic gene with lower expression in IDD, and AC063977.6 is the key lncRNA with lower expression in IDD. The ceRNA hypothesis suggests that AC063977.6, miR-338-3p (high expression), and PFKFB2 are dysregulated as an axis in IDD.

Conclusions

The results suggest that lncRNA AC063977.6 correlate with PFKFB2, the vital metabolic signature gene, via targeting miR-338-3p during IDD pathogenesis. The current study may shed light on unraveling the pathogenesis of IDD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04109-8.