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. 2021 Jul 15:12:680617.
doi: 10.3389/fgene.2021.680617. eCollection 2021.

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

Haiya Bai  1 , Youliang Wang  2 , Huimin Liu  1 , Junyang Lu  1
Affiliations

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

Haiya Bai et al. Front Genet. .

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.

Keywords: MRNA expression data; cutaneous melanoma; machine learning; prognostic signature; random survival forest.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Development of the prognostic messenger RNA (mRNA) signature. (A) The survival-associated mRNAs obtained from Cox analysis are displayed on the volcano plot. (B) After random forest classification algorithm, the prognosis-associated mRNAs were decreased to 12. (C,D) The prognostic four-mRNA signature was selected because its area under the curve (AUC) was the largest (AUC = 0.708) among the 212–1 = 4,095 signatures.
FIGURE 2
FIGURE 2
Cutaneous melanoma patients were divided by the four-messenger RNA (four-mRNA) signature into two risk groups with significantly different survival outcomes in the (A) training, (B) test, (C) entire The Cancer Genome Atlas (TCGA), and (D) GSE65904 datasets.
FIGURE 3
FIGURE 3
The risk score distribution, survival status, and messenger RNA (mRNA) expression patterns of cutaneous melanoma patients in the (A) training, (B) test, (C) entire The Cancer Genome Atlas (TCGA), and (D) GSE65904 datasets.
FIGURE 4
FIGURE 4
The comparison of the performance in survival prediction between the four-messenger RNA (four-mRNA) signature with tumor stage and Clark level. (A) ROC analysis was performed to compare the performance of the four-mRNA signature with that of tumor stage and Clark level. (B) TimeROC analysis was conduct to compare the performance of the four-mRNA signature with that of tumor stage and Clark level.
FIGURE 5
FIGURE 5
Functional enrichment analysis of the four messenger RNAs (mRNAs) in the signature by Gene Ontology (A) and Kyoto Encyclopedia of Genes and Genomes (B).
See this image and copyright information in PMC

References

    1. Bindea G., Mlecnik B., Hackl H., Charoentong P., Tosolini M., Kirilovsky A., et al. (2009). ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25 1091–1093. 10.1093/bioinformatics/btp101 - DOI - PMC - PubMed
    1. Burska A. N., Roget K., Blits M., Soto Gomez L., van de Loo F., Hazelwood L. D., et al. (2014). Gene expression analysis in RA: towards personalized medicine. Pharmacogenomics J. 14 93–106. 10.1038/tpj.2013.48 - DOI - PMC - PubMed
    1. Castellanos J. R., Purvis I. J., Labak C. M., Guda M. R., Tsung A. J., Velpula K. K., et al. (2017). B7-H3 role in the immune landscape of cancer. Am. J. Clin. Exp. Immunol. 6 66–75. - PMC - PubMed
    1. Coussens L. M., Werb Z. (2002). Inflammation and cancer. Nature 420 860–867. 10.1038/nature01322 - DOI - PMC - PubMed
    1. Dimitriou F., Krattinger R., Ramelyte E., Barysch M. J., Micaletto S., Dummer R., et al. (2018). The world of Melanoma: epidemiologic, genetic, and anatomic differences of Melanoma across the Globe. Curr. Oncol. Rep. 20:87. 10.1007/s11912-018-0732-8 - DOI - PubMed