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

ABSTRACT: 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.

SUBMITTER: Bai H

PROVIDER: S-EPMC8320537 | biostudies-literature |

REPOSITORIES: biostudies-literature

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A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma.

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| S-EPMC9201746 | biostudies-literature

A Novel Signature of 23 Immunity-Related Gene Pairs Is Prognostic of Cutaneous Melanoma.

Project description:In this study, we aimed to identify an immune-related signature for predicting prognosis in cutaneous melanoma (CM). Sample data from The Cancer Genome Atlas (TCGA; n = 460) were used to develop a prognostic signature with 23 immune-related gene pairs (23 IRGPs) for CM. Patients were divided into high- and low-risk groups using the TCGA and validation datasets GSE65904 (n = 214), GSE59455 (n = 141), and GSE22153 (n = 79). The ability of the 23-IRGP signature to predict CM was precise, with the stratified high-risk groups showing a poor prognosis, and it had a significant predictive power when used for immune microenvironment and biological analyses. We subsequently established a novel promising prognostic model in CM to determine the association between the immune microenvironment and CM patient results. This approach may be used to discover signatures in other diseases while avoiding the technical biases associated with other platforms.

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