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

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.

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

“…LncRNAs play a critical role in maintaining genomic instability ( Nair et al, 2020 ; Jianfeng et al, 2021 ). Increasing evidence has revealed the prognostic significance of GIRlncRNAs for cancers ( Fang et al, 2021 ; Liang et al, 2021 ; Maimaiti et al, 2021 ; Yan et al, 2021 ). Since NSCLC possesses a poor survival prognosis due to limited diagnosis and treatment ( Zappa and Mousa, 2016 ; Li C. et al, 2019 ), we developed a prognostic GIRlncSig to support the clinical stratification and treatment decision for NSCLC patients.…”

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

“…Some lncRNAs are known to be closely associated with the prognosis of melanoma [ 30 , 64 ]. Machine learning methods have already been utilized to investigate how lncRNAs are involved in the prognosis of melanoma [ 33 ]. Not all lncRNAs contributed to metastatic melanoma and the experimental design supported this through feature selection algorithms.…”

“…Multiple previous investigations have demonstrated that lncRNAs have a close relationship with the prognosis of melanoma [ 30 , 31 , 32 ]. Yan et al screened 61 lncRNAs associated with melanoma prognosis and built a weighted risk score model based on seven key candidate lncRNAs [ 33 ]. The lncRNA U731166 was also observed to be upregulated during the migration and invasion of melanoma and even played a role in developing vemurafenib resistance [ 34 ].…”

Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma

“…Elsewhere, Yang et al (29) created a prognostic signature of renal clear cell carcinoma composed of 7-LncRNA (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606). Furthermore, the studies of Wu et al (30), Yin et al (31), Maimaiti et al (32), and Yan et al (33) also developed a prognostic signature for patients with bladder cancer, colon cancer, low-grade glioma, and melanoma, respectively.…”