Melanomas are malignant tumors that can occur in different body parts or tissues such as the skin, mucous membrane, uvea, and pia mater. Long non-coding RNAs (lncRNAs) are key factors in the occurrence and development of many malignant tumors, and are involved in the prognosis of some patients.

To identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis, treatment, and prognosis of melanoma patients.

We retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas (TCGA) database. Then, we identified autophagy-related genes in the Human Autophagy Database. Using R, coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels. We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma. Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established. The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores. Based on the risk scores, melanoma patients were randomly divided into high- and low-risk groups. Receiver operating characteristic curve analysis, dependent on time, was performed to assess the accuracy of the prognostic model. At the same time, we also downloaded the melanoma data sets GSE65904, GSE19234, and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification. Finally, we performed Gene Set Enrichment Analysis functional annotation, which showed that the low and the high-risk groups had different enriched pathways.

The co-expression network for autophagy-related genes was constructed using R, and 936 lncRNAs related to autophagy were identified. Then, 52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients’ survival by univariate Cox proportional risk analysis (P < 0.01). Further, the 52 autophagy-related lncRNAs mentioned above were analyzed by multivariate Cox analysis with R. Fifteen lncRNAs were selected: LINC01943, AC090948.3, USP30-AS1, AC068282.1, AC004687.1, AL133371.2, AC242842.1, PCED1B-AS1, HLA-DQB1-AS1, AC011374.2, LINC00324, AC018553.1, LINC00520, DBH-AS1, and ITGB2-AS1. The P values in all survival analyses using these 15 lncRNAs were < 0.05. These lncRNAs were used to build a risk model based on the risk score. Negative correlations were observed between risk scores and overall survival rate in melanoma patients over time. Additionally, the melanoma risk curve and scatter plot analyses showed that the death number increased along with the increase in the risk score. Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs. The risk model constructed with these lncRNAs can help and guide melanoma patient prognosis predictions and individualized treatments in the future.

Overall, the risk model developed based on the 15 autophagy-related lncRNAs can have important prognostic value and may provide autophagy-related clinical targets for melanoma treatment.

Melanomas are malignant tumors that can occur in different body parts or tissues such as the skin, mucous membrane, uvea, and pia mater. While the incidence of many tumors is declining, the incidence of melanoma continues to increase[1]. The global prevalence of melanoma is increasing by about 3% to 7% per year[2]. Although many melanoma patients have localized tumors at diagnosis that can be completely removed by surgery, many patients present signs of metastasis[3]. Early melanoma has a good prognosis, but advanced melanoma patients have a very poor prognosis, with a 5%-10% survival rate, even with treatment[4]. Autophagy, originally considered a process of lysosomal-dependent degradation of cytoplasmic components in response to starvation, has been shown to affect multiple homeostasis aspects and to constitute a prevention barrier to malignant transformation[5]. Therefore, the combination of autophagy-related factors and pathological classification for risk stratification in melanoma patients can be crucial to prognosis and treatment response predictions.

Long non-coding RNAs (lncRNAs) are transcribed RNAs with a length of more than 200 nucleotides that are not translated into proteins[6]. Many studies have shown that noncoding RNAs can be associated with tumor pathogenesis by epigenetic regulation, as well as transcriptional and/or post-transcriptional processes. Moreover, lncRNAs can be used as sensitive and specific cancer biomarkers[7]. Previous studies have shown that melanoma pathogenesis was associated with different lncRNAs, in vitro and in vivo, and that some of them can be potential melanoma therapy targets, such as UCA1, DSCAM-AS1, and miR155HG[8,9]. However, the lncRNAs involved in autophagy and their prognostic value were not previously investigated, and many mechanisms remain unclear.

Therefore, we retrieved human clinical melanoma datasets from The Cancer Genome Atlas (TCGA) database and screened and analyzed genes associated with melanoma prognosis. Finally, 15 autophagy-related lncRNAs were identified as melanoma prognosis biomarkers. Compared to other clinical indicators, these lncRNAs had higher accuracy to predict melanoma patients’ survival.

As an aggressive tumor, melanoma has a poor prognosis and increasing incidence[11]. The main reason for the high mortality rate is late diagnosis[12]. Studies have found that the survival rate of melanoma patients with distant metastases was only 5%-10%[13]. Therefore, early detection and diagnosis of melanoma are crucial to improving the survival rate. At present, TNM staging is commonly used to evaluate the melanoma prognosis. However, some studies have found that TNM staging methods might predict different overall survival rates[14]. Increasing studies have shown that tumor TNM staging also has clinical limitations[15,16]. Therefore, new melanoma diagnosis and prognosis methods are required to improve melanoma patients’ prognosis and survival rate. The promotive and suppressive effects of autophagy on the development of tumors were observed by many previous studies[17,18]. In other words, autophagy is a dual process regarding tumors. For example, Mgrditchian et al[19] reported that autophagy can recruit natural killer cell infiltration into tumor tissues, and subsequently reduce the melanoma growth in vivo[19]. Luan et al[20] showed that POL can downregulate miR1290, leading to BECN1 upregulation and enhanced autophagy, thereby reducing the survival of melanoma cells. However, Xiao et al[21] showed that beclin-1 expression and LC3-II/I ratio were significantly increased by miR-24-1-5p. Meanwhile, they also observed the promotive effect of miR-24-1-5p on autophagy and subsequently inhibitory function in melanoma cell proliferation[21]. The studies listed above showed both positive and negative effects of autophagy on tumor development, and that autophagy plays a crucial role in this process.

Additionally, the roles of lncRNAs in the pathogenesis of different tumors have been identified by other researchers, including breast cancer, lung cancer, and hepatocellular carcinoma[22,23]. In this study, we analyzed and identified 15 lncRNAs that can be related to melanoma survival and prognosis, and established a risk model for melanoma prognosis. We consulted many previous studies and found that only LINC00520 and DBH-AS1 have been linked to melanoma. USP30-AS1, PCED1B-AS1, LINC00324, ITGB2-AS1, LINC00520, and DBH-AS1 have also been reported in other tumors. The remaining lncRNAs (LINC01943, AC090948.3, AC068282.1, AC004687.1, AL133371.2, AC242842.1, HLA-DQB1-AS1, AC011374.2, and AC018553.1) have not yet been reported. LICN00520 can promote melanoma’s proliferative and metastatic capabilities by competitively targeting miR-125b-5p and acting on the miR-125b-5p/eIF5A2 axis in vivo[24]. Also, LINC00520 can be involved in the progressive processes of many tumors. For example, Luan et al[24] pointed out that through MiR-3175 suppression, LINC00520 was involved in lung cancer progression[25]. Jin et al[26] found that LINC00520 can act as a miR-577 competitive suppressor to enhance HSP27 expression, leading to colorectal cancer progression[26]. Besides, LINC00520 can promote thyroid papillary carcinoma, nasopharyngeal carcinoma, breast cancer, and non-small cell lung cancer progression, being a poor prognostic factor for these tumors[27-31]. Chen et al[32] found that DBH-AS1 can enhance the glycolytic activity of melanoma cells, thereby blocking the miR-223-3p/EGFR/AKT axis[32]. Crucial roles of the lncRNA DBH-AS1 were also observed in osteosarcoma, diffuse large b-cell lymphoma, liver cancer, and non-small cell lung cancer[33-36]. In our analyses, both DBH-AS1 and LINC00520 were high-risk autophagy-related lncRNAs. Their elevated expression in melanoma patients indicated a poor prognosis. Through sequestering miR-229-3p and subsequently enhancing the expression of PTP4A1[37], USP30-AS1 expression can promote cervical cancer malignant progression. The lncRNA pCED1B-AS1 can activate glioma cell proliferation and limit apoptosis, working with the miR-194-5p/ pCED1B axis[38]. pCED1B-AS1 can also induce hepatocellular carcinoma immunosuppression through PD-1 and PD-L1 regulation via sponging has-miR-1945p[39]. Other studies have shown that the lncRNA pcED1B-AS1 can also promote clear cell renal carcinoma and pancreatic ductal adenocarcinoma progression[40,41]. Important roles of the lncRNAs LINC00324 and A ITGB2-AS1 have been observed during different tumors progressions. For example, the proliferative and invasive capabilities of osteosarcoma were promoted by LINC00324 through WDR66 regulation. Also, the promotive effects of LNC00324 on papillary thyroid carcinoma progression were found, due to its regulatory effect on Notch-related pathways[42,43]. The lncRNA ITGB2-AS1 can upregulate ITGB2 and promote breast cancer cell migration and invasion. Besides, ITGB2-AS1 can also promote pancreatic ductal adenocarcinoma growth and metastasis, acting on the miR-4319/RAF1 axis[44,45].

The mechanisms of the remaining nine lncRNAs identified in our study (LINC01943, AC090948.3, AC068282.1, AC004687.1, AL133371.2, AC242842.1, HLA-DQB1-AS1, AC011374.2, and AC018553.1) have not been yet reported and remain to be explored. Five of them, including PCED1B-AS1, ITGB2-AS1, AC018553.1, LINC00520, and DBH-AS1, were risk-related lncRNAs, and their increased expression levels suggested a poor prognosis. The remaining ten lncRNAs (LINC01943, AC090948.3, USP30-AS1, AC068282.1, AC004687.1, AL133371.2, AC242842.1, HLA-DQB1-AS1, AC011374.2, and LINC00324) were protective lncRNAs, and their elevated expression levels in melanoma patients predicted a good prognosis. The survival analysis (P = 2.287e - 14) suggested a significant difference between the survival rate of the high- and low-risk score patients. Meanwhile, we also observed negative correlations between the risk scores and overall survival rate in melanoma patients. Finally, we constructed the ROC curve, and the AUC value was 0.712, higher than the AUC of all clinicopathological indicators analyzed. Not only that, we also downloaded 265 melanoma samples from GEO database to verify the accuracy of this model, and the results are consistent with those of the previous analysis. These results demonstrated that the prognostic model established based on autophagy-related lncRNAs in melanoma patients had a higher prognostic accuracy than other clinical indicators. Altogether, these results suggested that the risk model established based on the 15 autophagy-related lncRNAs could better predict melanoma patients’ prognosis and survival.

The GSEA analysis revealed a difference in signaling pathway enrichment between the high- and low-risk melanoma patients. In low-risk populations, the main significantly enriched pathways were immune-related, such as antigen processing and presentation, Toll-like receptor signaling pathway, systemic lupus erythematosus, and autoimmune thyroid disease. The high-risk populations were mainly enriched in metabolism-related pathways, such as glyoxylate and dicarboxylate signaling pathways. These results indicated that the immunity improvement might be related to melanoma patients’ prognosis improvement and that a poor prognosis can be associated with glyoxylate and dicarboxylate metabolic pathways.

Recently, increasing studies have explored the significance of lncRNAs in cell autophagy, and their functional effect in tumors have attracted researchers’ attention. However, the function, mechanisms, and value of these lncRNAs in melanoma prognosis remain unclear. 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 mechanism of autophagy-related lncRNAs has 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.

Overall, we have identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs: LINC01943, AC090948.3, USP30-AS1, AC068282.1, AC004687.1, AL133371.2, AC242842.1, PCED1B-AS1, HLA-DQB1-AS1, AC011374.2, LINC00324, ITGB2-AS1, AC018553.1, LINC00520, and DBH-AS1. The risk model constructed with these lncRNAs can help guide prognosis prediction and individualized treatment in melanoma patients in the future.