scholarly journals Prognostic signature and immune efficacy of m 1 A‐, m 5 C‐ and m 6 A‐related regulators in cutaneous melanoma

2021 ◽  
Author(s):  
Xian rui Wu ◽  
Zheng Chen ◽  
Yang Liu ◽  
Zi zi Chen ◽  
Fengjie Tang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Signature ◽  
Immune Efficacy

scholarly journals A Novel Signature of 23 Immunity-related Gene Pairs is Prognostic of Cutaneous Melanoma

2020 ◽  
Author(s):  
Yanan Xue ◽  
Yinan Xue ◽  
Zhengcai Wang ◽  
Yongzhen Mo ◽  
Pinyan Wang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Related Gene ◽  
Prognostic Signature ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Technical Bias ◽  
Tcga Dataset

Abstract Background: We aimed to identify immune-related signature for predicting cutaneous melanoma (CM) prognosis. Methods: We used TCGA samples (n=471) to develop the best 23 Immune related gene pairs (23-IRGP) prognostic signature and divided patients into high- and low-immune risk group in TCGA dataset and validation datasets: GSE65904 (n=214), GSE59455 (n=141), and GSE22153 (n=79). Results: 23-IRGP presented precise ability in cutaneous melanoma (CM) which high-risk groups showed poor prognosis and indicated significant predict power in immune micro-environment and biological analysis as well. Conclusions: we established a novel promising prognostic model in CM and built the bridge between immune micro-environment and CM patient results. This approach can be applied to discover the signatures in other diseases without technical bias from different platforms.

scholarly journals Development of a Four-mRNA Expression-Based Prognostic Signature for Cutaneous Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Haiya Bai ◽  
Youliang Wang ◽  
Huimin Liu ◽  
Junyang Lu
Keyword(s):  
Rna Sequencing ◽  
Cutaneous Melanoma ◽  
Test Group ◽  
Training Group ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Learning Approaches ◽  
Prognostic Signature ◽  
Sequencing Data

We aim to find a biomarker that can effectively predict the prognosis of patients with cutaneous melanoma (CM). The RNA sequencing data of CM was downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training group and test group. Survival statistical analysis and machine-learning approaches were performed on the RNA sequencing data of CM to develop a prognostic signature. Using univariable Cox proportional hazards regression, random survival forest algorithm, and receiver operating characteristic (ROC) in the training group, the four-mRNA signature including CD276, UQCRFS1, HAPLN3, and PIP4P1 was screened out. The four-mRNA signature could divide patients into low-risk and high-risk groups with different survival outcomes (log-rank p < 0.001). The predictive efficacy of the four-mRNA signature was confirmed in the test group, the whole TCGA group, and the independent GSE65904 (log-rank p < 0.05). The independence of the four-mRNA signature in prognostic prediction was demonstrated by multivariate Cox analysis. ROC and timeROC analyses showed that the efficiency of the signature in survival prediction was better than other clinical variables such as melanoma Clark level and tumor stage. This study highlights that the four-mRNA model could be used as a prognostic signature for CM patients with potential clinical application value.

scholarly journals Analysis of immune subtypes based on immunogenomic profiling identifies prognostic signature for cutaneous melanoma

2020 ◽  
Vol 89 ◽  
pp. 107162
Author(s):  
Baohui Hu ◽  
Qian Wei ◽  
Chenyi Zhou ◽  
Mingyi Ju ◽  
Lin Wang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Signature

scholarly journals Identification of Prognostic Signature and Immune Infiltration in the Skin Cutaneous Melanoma Microenvironment

2021 ◽  
Author(s):  
Xiaoyong Li ◽  
Xueyi Jian ◽  
Xiaofeng Wei ◽  
Yan Lin ◽  
Zena Huang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Lymphocyte Activation ◽  
Risk Scores ◽  
Training Dataset ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Tumor Immune Microenvironment

Abstract Skin cutaneous melanoma (SKCM), characterized by high immunogenicity, has an increasing incidence in recent years. The development of immunotherapy recently offered a promising treatment for patients with SKCM. Unfortunately, not all patients derive benefit from such treatment, so we still face considerable challenges. Hence, it is imperative to develop novel prognostic signature and identify immunotherapeutic targets. In the present study, patients in high immune scores group presented a higher survival rate, while no statistical difference was observed in groups with different stromal scores. 493 DEGs were identified for functional analysis, which were enriched in function related to immune regulation such as lymphocyte activation and cytokine-cytokine receptor interaction. Subsequently, 84 DEGs intersected from Univariate Cox regression analysis and top 100 hub genes in PPI network were identified for the construction of prognostic model. Finally, a prognostic signature including 3 genes (HLA-DQB2, CD80 and GBP4) was established in TCGA training dataset, which was effectively validated in test dataset. Moreover, the model was considered as an independent prognostic factor via univariate and multivariate analysis. Besides, CIBERSORT and correlation analysis demonstrated that the expression level of risk scores was significantly correlated to infiltration levels of immune cells in SKCM. Above all, our study developed a novel prognostic signature, serving as potential prognostic biomarker for SKCM patients. A closely correlation between the prognostic model and tumor immune microenvironment was confirmed, offering a novel insight for the pathogenesis and potential immunotherapy for SKCM.

scholarly journals Prognostic Value and Immunological Characteristics of a Novel RNA Binding Protein Signature in Cutaneous Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Tian ◽  
Chongzhi Ma ◽  
Li Yang ◽  
Yang Sun ◽  
Yuan Zhang
Keyword(s):  
High Risk ◽  
Cutaneous Melanoma ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Potential Biomarker ◽  
Melanoma Patients

BackgroundThe existing studies indicate that RNA binding proteins (RBPs) are closely correlated with the genesis and development of cancers. However, the role of RBPs in cutaneous melanoma remains largely unknown. Therefore, the present study aims to establish a reliable prognostic signature based on RBPs to distinguish cutaneous melanoma patients with different prognoses and investigate the immune infiltration of patients.MethodsAfter screening RBPs from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were then used to establish a prediction model. The relationship between the signature and the abundance of immune cell types, the tumor microenvironment (TME), immune-related pathways, and immune checkpoints were also analyzed.ResultsIn total, 7 RBPs were selected to establish the prognostic signature. Patients categorized as a high-risk group demonstrated worse overall survival (OS) rates compared to those of patients categorized as a low-risk group. The signature was validated in an independent external cohort and indicated a promising prognostic ability. Further analysis indicated that the signature wasan independent prognostic indicator in cutaneous melanoma. A nomogram combining risk score and clinicopathological features was then established to evaluate the 3- and 5-year OS in cutaneous melanoma patients. Analyses of immune infiltrating, the TME, immune checkpoint, and drug susceptibility revealed significant differences between the two groups. GSEA analysis revealed that basal cell carcinoma, notch signaling pathway, melanogenesis pathways were enriched in the high-risk group, resulting in poor OS.ConclusionWe established and validated a robust 7-RBP signature that could be a potential biomarker to predict the prognosis and immunotherapy response of cutaneous melanoma patients, which provides new insights into cutaneous melanoma immunotherapeutic strategies.

scholarly journals Construction of autophagy prognostic signature and analysis of prospective molecular mechanisms in skin cutaneous melanoma patients

Medicine ◽  
2021 ◽  
Vol 100 (22) ◽  
pp. e26219
Author(s):  
Shian Liao ◽  
Juliang He ◽  
Chong Liu ◽  
Zide Zhang ◽  
Hongyu Liao ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Molecular Mechanisms ◽  
Prognostic Signature ◽  
Melanoma Patients

scholarly journals Development and validation of an immune gene set-based prognostic signature in cutaneous melanoma

2021 ◽  
Author(s):  
Qi Tian ◽  
Huan Gao ◽  
Wen Zhao ◽  
Yan Zhou ◽  
Jin Yang
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

We aimed to fully understand the landscape of the skin cutaneous melanoma (SKCM) microenvironment and develop an immune prognostic signature that can predict the prognosis for SKCM patients. RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases. The immune-prognostic signature was constructed by LASSO Cox regression analysis. We calculated the relative abundance of 29 immune-related gene sets based on the mRNA expression profiles of 314 SKCM patients in the Cancer Genome Atlas training set. Hierarchical clustering was performed to classify SKCM patients into three clusters: immunity-high, -medium and -low. The values of our prognostic model in predicting disease progression, metastasis and immunotherapeutic responses were also validated. In conclusion, the prognostic model demonstrated a powerful ability to distinguish and predict SKCM patients’ prognosis.

scholarly journals Development and Validation of the B Cell-Associated Fc Receptor-like Molecule-Based Prognostic Signature in Skin Cutaneous Melanoma

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yu Liu ◽  
Yiding Chen ◽  
Xianyu Hu ◽  
Jialin Meng ◽  
Xiaojing Li
Keyword(s):  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Absolute Number ◽  
Fc Receptor ◽  
Prognostic Signature ◽  
Kaplan Meier ◽  
Skin Cancers ◽  
Development And Validation ◽  
Lower Expression

Backgroud. Cutaneous melanoma (CM) causes the highest absolute number of deaths among all types of skin cancers; however, the association between Fc receptor- (FcR-) like (FCRL) molecules and CM remains unclear. Methods. 461 patients with CM from The Cancer Genome Atlast- (TCGA-) CM cohort and 290 pateints from the GSE65904 cohort were enrolled. Student’s t-test was used to compare the differences, and Pearson’s correlation coefficient was employed to evaluate associations. The Kaplan-Meier (K-M) survival analysis was used to evaluate overall survival (OS). The multivariate Cox regression was conducted to generate the FCRL prognostic signature. GSEA analysis and TIMER were employed to study the potential mechanisms. Result. Patients with Breslow’s depth high or equal to 3 cm had the lower expression of FCRL1-6 (all, P<0.05), which indicates poor OS, as well as age, stage, and Breslow’s depth subgroups (all, P<0.001). The overall FCRL1-6 prognostic signature was generated in the TCGA cohort (K-M, P<0.001; area under the curve (AUC), 0.649 for 3-year OS) and validated in the GSE65904 cohort (K-M, P<0.001; AUC, 0.659 for 3-year OS). The GSEA results revealed that high expression of FCRLs indicated activated immune-associated pathways, and FCRLs are positively associated with the infiltration of B cells. Conclusion. Highly expressed FCRLs were observed associated with a favourable OS of CM. FCRL1-6-based prediction signature could act as a biomarker to predict the prognosis of patients with CM.

Cutaneous melanoma in a patient with neurofibromatosis

1979 ◽  
Vol 115 (7) ◽  
pp. 864-865 ◽  
Author(s):  
M. J. Mastrangelo
Keyword(s):  
Cutaneous Melanoma
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