Published online Apr 16, 2022. doi: 10.12998/wjcc.v10.i11.3334
Peer-review started: August 10, 2021
First decision: October 20, 2021
Revised: October 29, 2021
Accepted: January 17, 2022
Article in press: January 17, 2022
Published online: April 16, 2022
At present, melanoma is mainly treated by surgical resection, but many patients have tumor metastasis. Patients with advanced melanoma have a very poor prognosis, so a new method is needed to predict and evaluate the prognosis and survival of patients. Autophagy, originally thought to be a process of lysosomal dependent degradation of cytoplasmic components in response to starvation, has been shown to influence multiple dynamic equilibria and to constitute a barrier against malignant transformation. However, long non-coding RNAs (lncRNAs) involved in autophagy and their prognostic value have not been studied before, and many mechanisms remain unclear. Therefore, risk stratification of melanoma patients based on autophagy-associated lncRNAs combined with pathological classification is crucial to predict prognosis and treatment response.
The main purpose of this study was to identify autophagy lncRNAs associated with melanoma prognosis and establish a risk model to predict survival and prognosis. Among the 15 autophagy-related lncRNAs analyzed in this study, the mechanisms of action of some lncRNAs are still unclear and have not been reported in the literature, so further studies are needed to explore the role of these lncRNAs. The solution of this major problem will help us to have a deeper understanding of melanoma and make it possible to completely cure melanoma.
The main objective of this study was to establish a more accurate method for predicting and evaluating the prognosis and survival of melanoma patients.
First, data from The Cancer Genome Atlas and GENE EXPRESSION OMNIBUS (GEO) databases were processed, and then R was used to analyze the correlation between autophagy-related genes and lncRNAs (correlation coefficient > 0.30; P < 0.001), and a co-expression network was constructed. In order to evaluate the relationship between autophagy-related lncRNAs and melanoma prognosis, univariate proportional risk, Kaplan-Meier survival, and multivariate risk analyses were performed. R software was used to calculate the risk score of each patient, and the calculation formula is as follows: Risk score = expR (lncRNA1) × COEF (lncRNA1) + expr (lncRNA2) × COEF (lncRNA2) +... + expr (lncRNAn) × COEF (lncRNAn). In order to assess the stability of risk models, univariate and multivariate regression analyses and receiver operating characteristic analyses were also performed. Finally, Gene Set Enrichment Analysis was used for functional annotation and GEO data was used for further validation to ensure the accuracy of the results.
Our current study proposed a new melanoma risk model composed of 15 lncRNAs involved in autophagy that can be helpful for future melanoma treatment and prognosis evaluation. However, our research also has limitations. Although the data and analyses used have been verified for their accuracy in different studies, our study was not verified experimentally. The molecular mechanisms of phagocytosis-related lncRNAs have not yet been elucidated, and some lncRNAs have never even been reported in the literature. Therefore, further experimental studies are required to verify our results.
This study identified new and unreported lncRNAs through a series of analyses, which are closely related to the prognosis of melanoma patients, providing a new direction for future research on melanoma-related lncRNAs.
In the future, we can conduct further experimental verification on these 15 lncRNAs to understand the specific mechanism of action and specific role of these 15 lncRNAs in melanoma.