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.

/pdf/a-novel-necroptosis-related-gene-signature-in-skin-cutaneous-3iflotud.pdf

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

Objective: Accumulated evidence highlights the biological significance of oxidative stress in tumorigenicity and progression of colorectal cancer (CRC). Our study aimed to establish a reliable oxidative stress-related signature to predict patients’ clinical outcomes and therapeutic responses. Methods: Transcriptome profiles and clinical features of CRC patients were retrospectively analyzed from public datasets. LASSO analysis was used to construct an oxidative stress-related signature to predict overall survival, disease-free survival, disease-specific survival, and progression-free survival. Additionally, antitumor immunity, drug sensitivity, signaling pathways, and molecular subtypes were analyzed between different risk subsets through TIP, CIBERSORT, oncoPredict, etc. approaches. The genes in the signature were experimentally verified in the human colorectal mucosal cell line (FHC) along with CRC cell lines (SW-480 and HCT-116) through RT-qPCR or Western blot. Results: An oxidative stress-related signature was established, composed of ACOX1, CPT2, NAT2, NRG1, PPARGC1A, CDKN2A, CRYAB, NGFR, and UCN. The signature displayed an excellent capacity for survival prediction and was linked to worse clinicopathological features. Moreover, the signature correlated with antitumor immunity, drug sensitivity, and CRC-related pathways. Among molecular subtypes, the CSC subtype had the highest risk score. Experiments demonstrated that CDKN2A and UCN were up-regulated and ACOX1, CPT2, NAT2, NRG1, PPARGC1A, CRYAB, and NGFR were down-regulated in CRC than normal cells. In H2O2-induced CRC cells, their expression was notably altered. Conclusion: Altogether, our findings constructed an oxidative stress-related signature that can predict survival outcomes and therapeutic response in CRC patients, thus potentially assisting prognosis prediction and adjuvant therapy decisions.

/pdf/establishment-and-external-verification-of-an-oxidative-h256b2fb.pdf

Establishment and external verification of an oxidative stress-related gene signature to predict clinical outcomes and therapeutic responses of colorectal cancer

Melanoma is the most dangerous form of skin cancer because of its high metastatic potential. Potential-N6-methyladenosine (m6A)-related long noncoding RNAs (pMRlncRNAs) play a vital role in malignancy. The identification of prognostic-related pMRlncRNAs and development of risk signatures could improve the prognosis and promote the precise treatment of melanoma.Gene expression and relevant clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic-related pMRlncRNAs were selected using univariate Cox regression analysis. Patients with melanoma were classified into different subtypes using the "ConsensusClusterPlus" package, and the ESTIMATE algorithm was applied to depict their immune landscape. A pMRlncRNA risk signature was developed using least absolute shrinkage and selection operator regression analysis and verified using survival analysis and receiver operating characteristic curves. Gene set enrichment analysis (GSEA) was used to investigate the underlying biological pathways. The relationships between risk score and clinicopathological characteristics, as well as programmed cell death-ligand 1 (PD-L1) expression level, were investigated. A nomogram with calibration curves was established to comprehensively predict the outcome of melanoma.Fifteen pMRlncRNAs were significantly associated with overall survival (OS). Two cluster subtypes were identified by consensus clustering. Patients in cluster 2 were associated with better OS, higher PD-L1 expression level, lower T stage, and higher ESTIMATEScore, ImmuneScore, and StromalScore than those in cluster 1. There were differences in immune cell infiltration between the 2 clusters. Ten pMRlncRNAs with prognostic value were selected to develop a risk signature, that functioned as an independent prognostic factor for melanoma. Patients with low-risk scores had a better prognosis in general. The area under the curve (AUC) value (0.720), as well as 1-, 3-, and 5-year calibration curves, revealed that the risk signature has suitable predictive power for prognosis. GSEA revealed 10 pathways that might play important roles in melanoma. Moreover, patients with high-risk scores were associated with advanced T stage, cluster 1, lower ImmuneScore, and higher PD-L1 expression level.We developed a novel 10-pMRlncRNA risk signature that could elucidate the crucial role of pMRlncRNAs in the immune landscape of melanoma and predict prognosis. 

Genome-wide screening and immune landscape suggest a potential-m6A-related lncRNA risk signature for predicting prognosis of melanoma

Background Melanoma is among the most malignant immunologic tumor types and is associated with high mortality. However, a considerable number of melanoma patients cannot benefit from immunotherapy owing to individual differences. This study attempts to build a novel prediction model of melanoma that fully considers individual differences in the tumor microenvironment. Methods An immune-related risk score (IRRS) was constructed based on cutaneous melanoma data from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to calculate immune enrichment scores of 28 immune cell signatures. We performed pairwise comparisons to obtain scores for cell pairs based on the difference in the abundance of immune cells within each sample. The resulting cell pair scores, in the form of a matrix of relative values of immune cells, formed the core of the IRRS. Results The area under the curve (AUC) for the IRRS was over 0.700, and when the IRRS was combined with clinical information, the AUC reached 0.785, 0.817, and 0.801 for the 1-, 3-, and 5-year survival, respectively. Differentially expressed genes between the two groups were enriched in staphylococcal infection and estrogen metabolism pathway. The low IRRS group showed a better immunotherapeutic response and exhibited more neoantigens, richer T-cell receptor and B-cell receptor diversity, and higher tumor mutation burden. Conclusion The IRRS enables a good prediction of prognosis and immunotherapy effect, based on the difference in the relative abundance of different types of infiltrating immune cells, and could provide support for further research in melanoma.

/pdf/immune-related-risk-score-an-immune-cell-pair-based-2d7wyhq1.pdf

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma

http://www.cell.com/article/S2405844023031468/pdf

A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma

Background Oxidative stress (OS) is key to various diseases and is implicated in cancer progression and oncogenesis. However, the potential diagnostic value of OS-related genes in skin cutaneous melanoma (SKCM) remains unclear. Methods We used data of RNA sequencing from 471 tumor tissues and one healthy tissue acquired from The Cancer Genome Atlas (TCGA)-SKCM cohort. The Genome Tissue Expression database was used to acquire transcriptome data from 812 healthy samples. OS-related genes that were differentially expressed between SKCM and healthy samples were investigated and 16 prognosis-associated OS genes were identified. The prognostic risk model was built using univariate and Cox multivariate regressions. The prognostic value of the hub genes was validated in the GSE65904 cohort, which included 214 SKCM patients. Results The overall survival rate of SKCM patients in the high-risk group was decreased compared to the low-risk group. In both TCGA and GSE65904 cohorts, the ROC curves suggested that our prognostic risk model was more accurate than other clinicopathological characteristics to diagnose SKCM. Moreover, risk score and nomograms associated with the expression of hub genes were developed. These presented reiterated our prognostic risk model. Altogether, this study provides novel insights with regards to the pathogenesis of SKCM. The 16 hub genes identified may help in SKCM prognosis and individualized clinical treatment.

Development and validation of an oxidative stress-associated prognostic risk model for melanoma.

Cutaneous melanoma (CM) is a malignant and aggressive skin cancer that is the leading cause of skin cancer-related deaths. Increasing evidence shows that immunity plays a vital role in the prognosis of CM. In this study, we developed an immune-related gene pair (IRGP) signature to predict the clinical prognosis of patients with CM. Immune-related genes from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected to construct the IRGPs, and patients with CM in these two cohorts were assigned to low- and high-risk subgroups. Moreover, we investigated the IRGPs and their individualized prognostic signatures using Kaplan-Meier survival analysis, univariate and multivariate Cox analyses, and analysis of immune cell infiltration in CM. A 41-IRGP signature was constructed from 2498 immune genes that could significantly predict the overall survival of patients with CM in both the TCGA and GEO cohorts. Immune infiltration analysis indicated that several immune cells, especially M1 macrophages and activated CD4 T cells, were significantly associated with the prognostic effect of the IRGP signature in patients with CM. Overall, the IRGP signature constructed in this study was useful for determining the prognosis of patients with CM and for providing further understanding of CM immunotherapy.

https://www.tandfonline.com/doi/pdf/10.1080/21655979.2021.1924556

Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature.

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Development and validation of an oxidative stress-associated prognostic risk model for melanoma.

Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature.