Gene Instability-Related lncRNA Prognostic Model of Melanoma Patients via Machine Learning Strategy

Kexin Yan; Yutao Wang; Yining Shao; Ting Xiao

doi:10.1155/2021/5582920

Gene Instability-Related lncRNA Prognostic Model of Melanoma Patients via Machine Learning Strategy

J Oncol. 2021 May 25:2021:5582920. doi: 10.1155/2021/5582920. eCollection 2021.

Authors

Kexin Yan¹, Yutao Wang², Yining Shao³, Ting Xiao¹

Affiliations

¹ Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, Liaoning, China.
² Department of Urology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China.
³ Department of Plastic Surgery, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China.

Abstract

Background: Melanoma is a common tumor characterized by a high mortality rate in its late stage. After metastasis, current treatment methods are relatively ineffective. Many studies have shown that long noncoding RNA (lncRNA) may participate in gene mutation and genomic instability in cancer.

Methods: We downloaded transcriptome data, mutation data, and clinical follow-up data of melanoma patients from The Cancer Genome Atlas. We divided samples into groups according to the number of somatic cell mutations and then performed a differential analysis to screen out the differentially expressed genes. We then divided samples into genomic unstable and genomic stable groups. We compared lncRNA expression profiles in these groups and constructed a protein-coding genes network coexpressed with selected lncRNA to analyze the pathways enriched by these genes. Two machine learning methods, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to conduct the lncRNA-related prognostic model. Afterward, we performed survival analysis, risk correlation analysis, independent prognostic analysis, and clinical subgroup model validation. Finally, through wound healing assay and transwell assay, the function of AATBC was verified by A375 cell lines.

Results: We screened 61 prognostic-related lncRNAs and constructed an lncRNA-mRNA coexpression network based on these lncRNAs. Seven lncRNAs were selected as common characteristic factors based on the two machine learning methods. The model formula was as follows: risk score = 0.085∗AATBC + 0.190∗ AC026689.1-0.117∗AC083799.1 + 0.036∗ AC091544.6-0.039∗ LINC01287-0.291∗ SPRY4.AS1 + 0.056∗ ZNF667.AS1. The seven lncRNAs in this formula are key candidates. Cell experiments have verified that knocking down AATBC in A375 cell lines can reduce the proliferation and invasion ability of melanoma cells.

Conclusion: The lncRNA we identified provides a new way to study lncRNA's role in the genomic instability of melanoma. Our findings may provide essential candidate biomarkers for the diagnosis and treatment of melanoma.