A Nomogram Combining a Four-Gene Biomarker and Clinical Factors for Predicting Survival of Melanoma

Chuan Zhang,Xianling Cong,Yuqian Wang,Dan Dang

doi:10.3389/fonc.2021.593587

Chuan Zhang, Xianling Cong + Show 2 more

Open Access

https://doi.org/10.3389/fonc.2021.593587

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Abstract

BackgroundCurrently there is no effective prognostic indicator for melanoma, the deadliest skin cancer. Thus, we aimed to develop and validate a nomogram predictive model for predicting survival of melanoma.MethodsFour hundred forty-nine melanoma cases with RNA sequencing (RNA-seq) data from TCGA were randomly divided into the training set I (n = 224) and validation set I (n = 225), 210 melanoma cases with RNA-seq data from Lund cohort of Lund University (available in GSE65904) were used as an external test set. The prognostic gene biomarker was developed and validated based on the above three sets. The developed gene biomarker combined with clinical characteristics was used as variables to develop and validate a nomogram predictive model based on 379 patients with complete clinical data from TCGA (Among 470 cases, 91 cases with missing clinical data were excluded from the study), which were randomly divided into the training set II (n = 189) and validation set II (n = 190). Area under the curve (AUC), concordance index (C-index), calibration curve, and Kaplan-Meier estimate were used to assess predictive performance of the nomogram model.ResultsFour genes, i.e., CLEC7A, CLEC10A, HAPLN3, and HCP5 comprise an immune-related prognostic biomarker. The predictive performance of the biomarker was validated using tROC and log-rank test in the training set I (n = 224, 5-year AUC of 0.683), validation set I (n = 225, 5-year AUC of 0.644), and test set I (n = 210, 5-year AUC of 0.645). The biomarker was also significantly associated with improved survival in the training set (P < 0.01), validation set (P < 0.05), and test set (P < 0.001), respectively. In addition, a nomogram combing the four-gene biomarker and six clinical factors for predicting survival in melanoma was developed in the training set II (n = 189), and validated in the validation set II (n = 190), with a concordance index of 0.736 ± 0.041 and an AUC of 0.832 ± 0.071.ConclusionWe developed and validated a nomogram predictive model combining a four-gene biomarker and six clinical factors for melanoma patients, which could facilitate risk stratification and treatment planning.

Highlights

Cutaneous melanoma is the deadliest type of skin cancer [1, 2], and its morbidity has been on the rise annually, especially in the Caucasian population [3, 4]
Four hundred seventy-two melanoma cases with RNA sequencing data were download from The Cancer Genome Atlas (TCGA), and 449 of them with complete survival data were randomly divided into the training set I (n = 224) and validation set I (n = 225) (Table S1)
Module Black was significantly associated with stromal and estimate scores in melanoma (Figure 2D)

Summary

Introduction

Cutaneous melanoma is the deadliest type of skin cancer [1, 2], and its morbidity has been on the rise annually, especially in the Caucasian population [3, 4]. The identification of a comparatively reliable and applicable prognostic biomarker for melanoma in order to guide clinical decision-making is essential. A series of bioinformatics tools, including weighted gene coexpression network analysis (WGCNA) [13], cell-type identification by estimating relative subsets of RNA transcripts (CYBERSORT) [14], gene set enrichment analysis (GSEA) [15, 16], and least absolute shrinkage and selection operator (LASSO), have been used to process such big data. There is no effective prognostic indicator for melanoma, the deadliest skin cancer. We aimed to develop and validate a nomogram predictive model for predicting survival of melanoma

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