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A twelve-gene signature for survival prediction in malignant melanoma patients.

ABSTRACT: Background:Melanoma is defined as a highly mutational heterogeneous disease containing numerous alternations of the molecule. However, due to the phenotypically and genetically heterogeneity of malignant melanoma, conventional clinical characteristics remain restricted or limited in the ability to accurately predict individual outcomes and survival. This study aimed to establish an accurate gene expression signature to predict melanoma prognosis. Methods:In this study, we established an RNA sequencing-based 12-gene signature data of melanoma patients obtained from 2 independent databases: the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We evaluated the quality of each gene to predict survival conditions in each database by employing univariate and multivariate regression models. A prognostic risk score based on a prognostic signature was determined. This prognostic gene signature further classified patients into low-risk and high-risk groups. Results:Based on a prognostic signature, a prognostic risk score was determined. This prognostic gene signature further divided the patients into low-risk and high-risk groups. In the chemotherapy and radiotherapy groups of the TCGA cohort and V-raf murine sarcoma viral oncogene homolog B1 (BRAF) expression group in the GEO cohort, patients in the low-risk group had a longer survival duration compared to patients in the high-risk group. Nevertheless, the immunotherapy group in the TCGA database and neuroblastoma RAS viral oncogene homolog (NRAS) expression group in the GEO database had no significant differences in statistics. Moreover, this gene signature was associated with patient prognosis regardless of whether the Breslow depth was greater than or less than 3.75 mm. Stratified gene set enrichment analysis (GSEA) revealed that certain immune-related pathways, such as the T-cell signaling pathway, chemokine signaling pathway, and primary immunodeficiency, were significantly enriched in the low-risk group of both TCGA and GEO cohorts. This information implied the immune-related properties of the 12-gene signature. Conclusions:Our study emphasizes the significance of the gene expression signature in that it may be an indispensable prognostic predictor in melanoma and has great potential for application in personalized treatment.

SUBMITTER: Song LB

PROVIDER: S-EPMC7186619 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A twelve-gene signature for survival prediction in malignant melanoma patients.

Song Le-Bin LB Zhang Qi-Jie QJ Hou Xiao-Yuan XY Xiu Yan-Yan YY Chen Lin L Song Ning-Hong NH Lu Yan Y

Annals of translational medicine 20200301 6

<h4>Background</h4>Melanoma is defined as a highly mutational heterogeneous disease containing numerous alternations of the molecule. However, due to the phenotypically and genetically heterogeneity of malignant melanoma, conventional clinical characteristics remain restricted or limited in the ability to accurately predict individual outcomes and survival. This study aimed to establish an accurate gene expression signature to predict melanoma prognosis.<h4>Methods</h4>In this study, we establis ...[more]

PMID: 32355756

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Project description:Background:The prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes. Methods:Six gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed. Results:A twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P?<?0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes. Conclusions:We identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

Project description:Background We aimed to construct a novel epithelial-mesenchymal transition (EMT)-related gene pairs (ERGPs) signature to predict overall survival (OS) in skin cutaneous melanoma (CM) patients. Methods Expression data of the relevant genes, corresponding clinicopathological parameters, and follow-up data were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis was utilized to identify ERGPs significantly associated with OS, and LASSO analysis was used to identify the genes used for the construction of the ERGPs signature. The optimal cutoff value determined by the receiver operating characteristic curve was used to classify patients into high-risk and low-risk groups. Survival curves were generated using the Kaplan–Meier method, and differences between the two groups were estimated using the log-rank test. The independent external datasets GSE65904 and GSE19234 were used to verify the performance of the ERGPs signature using the area under the curve (AUC) values. In addition, we also integrated clinicopathological parameters and risk scores to develop a nomogram that can individually predict the prognosis of patients with CM. Results A total of 104 ERGPs related to OS were obtained, of which 21 ERGPs were selected for the construction of the signature. All CM patients were stratified into high-and low-risk groups based on an optimal risk score cutoff value of 0.281. According to the Kaplan–Meier analysis, the mortality rate in the low-risk group was lower than that in the high-risk group in the TCGA cohort (P < 0.001), GSE65904 cohort (P = 0.006), and GSE19234 cohort (P = 0.002). Multivariate Cox regression analysis indicated that our ERGP signature was an independent risk factor for OS in CM patients in the three cohorts (for TCGA: HR, 2.560; 95% CI [1.907–3.436]; P < 0.001; for GSE65904: HR = 2.235, 95% CI [1.492–3.347], P < 0.001; for GSE19234: HR = 2.458, 95% CI [1.065–5.669], P = 0.035). The AUC value for predicting the 5-year survival rate of patients with CM of our developed model was higher than that of two previously established prognostic signatures. Both the calibration curve and the C-index (0.752, 95% CI [0.678–0.826]) indicated that the developed nomogram was highly accurate. Most importantly, the decision curve analysis results showed that the nomogram had a higher net benefit than that of the American Joint Committee on Cancer stage system. Conclusion Our study established an ERGPs signature that could be potentially used in a clinical setting as a genetic biomarker for risk stratification of CM patients. In addition, the ERGPs signature could also predict which CM patients will benefit from PD-1 and PD-L1 inhibitors.

Dataset Information

A twelve-gene signature for survival prediction in malignant melanoma patients.

Publications

A twelve-gene signature for survival prediction in malignant melanoma patients.

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