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Abstract：
BACKGROUND: As the most important modifications on the RNA level, N6-methyladenosine (m6A-) and 5-methylcytosine (m5C-) modification could have a direct influence on the RNAs. Long non-coding RNAs (lncRNAs) could also be modified by methylcytosine modification. Compared with mRNAs, the function of lncRNAs could be more potent to some extent in biological processes like tumorigenesis. Until now, rare reports have been done associated with cutaneous melanoma. Herein, we wonder if the m6A- and m5C- modified lncRNAs could influence the immune landscape and prognosis in melanoma, and we also want to find some lncRNAs which could directly affect the malignant behaviors of melanoma.
METHODS: Systematically, we explored the expression pattern of m6A- and m5C- modified lncRNAs in melanoma from datasets including UCSC Xena and NCBI GEO, and the prognostic lncRNAs were selected. Then, according to the expression pattern of lncRNAs, melanoma samples from these datasets were divided into several subtypes. Prognostic model, nomogram survival model, drug sensitivity, GO, and KEGG pathway analysis were performed. Furthermore, among several selected lncRNAs, we identified one lncRNA named LINC00893 and investigated its expression pattern and its biological function in melanoma cell lines.
RESULTS: We identified 27 m6A- and m5C- related lncRNAs which were significantly associated with survival, and we made a subtype analysis of melanoma samples based on these 27 lncRNAs. Among the two subtypes, we found differences of immune cells infiltration between these two subtypes. Then, LASSO algorithm was used to screen the optimized lncRNAs combination including ZNF252P-AS1, MIAT, FAM13A-AS1, LINC-PINT, LINC00893, AGAP2-AS1, OIP5-AS1, and SEMA6A-AS1. We also found that there was a significant correlation between the different risk groups predicted based on RS model and the actual prognosis. The nomogram survival model based on independent survival prognostic factors was also constructed. Besides, sensitivity to chemotherapeutic agents, GO and KEGG analysis were performed. In different risk groups, a total of 14 drug molecules with different distributions were obtained, which included AZD6482, AZD7762, AZD8055, camptothecin, dasatinib, erlotinib, gefitinib, gemcitabine, GSK269962A, nilotinib, rapamycin, and sorafenib. A total of 55 significantly related biological processes and 17 KEGG signaling pathways were screened. At last, we noticed that LINC00893 had a relatively lower expression in melanoma tissue and cell lines compared with adjacent tissues and epidermal melanocyte, and down-regulation of LINC00893 could promote the malignant behavior of melanoma cells in A875 and MV3. In these two melanoma cell lines, down-regulation of m6A-related molecules like YTHDF3 and METTL3 could promote the expression of LINC00893.
CONCLUSIONS: We made an analysis of m6A- and m5C- related lncRNAs in melanoma samples and a prediction of these lncRNAs\' role in prognosis, tumor microenvironment, immune infiltration, and clinicopathological features. We also found that LINC00893, which is potentially regulated by m6A modification, could serve as a tumor-suppressor in melanoma and play an inhibitory role in melanoma metastasis.
METHODS: Systematically, we explored the expression pattern of m6A- and m5C- modified lncRNAs in melanoma from datasets including UCSC Xena and NCBI GEO, and the prognostic lncRNAs were selected. Then, according to the expression pattern of lncRNAs, melanoma samples from these datasets were divided into several subtypes. Prognostic model, nomogram survival model, drug sensitivity, GO, and KEGG pathway analysis were performed. Furthermore, among several selected lncRNAs, we identified one lncRNA named LINC00893 and investigated its expression pattern and its biological function in melanoma cell lines.
RESULTS: We identified 27 m6A- and m5C- related lncRNAs which were significantly associated with survival, and we made a subtype analysis of melanoma samples based on these 27 lncRNAs. Among the two subtypes, we found differences of immune cells infiltration between these two subtypes. Then, LASSO algorithm was used to screen the optimized lncRNAs combination including ZNF252P-AS1, MIAT, FAM13A-AS1, LINC-PINT, LINC00893, AGAP2-AS1, OIP5-AS1, and SEMA6A-AS1. We also found that there was a significant correlation between the different risk groups predicted based on RS model and the actual prognosis. The nomogram survival model based on independent survival prognostic factors was also constructed. Besides, sensitivity to chemotherapeutic agents, GO and KEGG analysis were performed. In different risk groups, a total of 14 drug molecules with different distributions were obtained, which included AZD6482, AZD7762, AZD8055, camptothecin, dasatinib, erlotinib, gefitinib, gemcitabine, GSK269962A, nilotinib, rapamycin, and sorafenib. A total of 55 significantly related biological processes and 17 KEGG signaling pathways were screened. At last, we noticed that LINC00893 had a relatively lower expression in melanoma tissue and cell lines compared with adjacent tissues and epidermal melanocyte, and down-regulation of LINC00893 could promote the malignant behavior of melanoma cells in A875 and MV3. In these two melanoma cell lines, down-regulation of m6A-related molecules like YTHDF3 and METTL3 could promote the expression of LINC00893.
CONCLUSIONS: We made an analysis of m6A- and m5C- related lncRNAs in melanoma samples and a prediction of these lncRNAs\' role in prognosis, tumor microenvironment, immune infiltration, and clinicopathological features. We also found that LINC00893, which is potentially regulated by m6A modification, could serve as a tumor-suppressor in melanoma and play an inhibitory role in melanoma metastasis.
摘要：
背景：作为RNA水平上最重要的修饰，N6-甲基腺苷(m6A-)和5-甲基胞嘧啶(m5C-)修饰可能对RNA具有直接影响。长链非编码RNA(lncRNA)也可以通过甲基胞嘧啶修饰来修饰。与mRNA相比，lncRNAs的功能在一定程度上可能在肿瘤发生等生物过程中更有效。直到现在,罕见的报道与皮肤黑色素瘤有关。在这里，我们想知道m6A-和m5C-修饰的lncRNAs是否可以影响黑色素瘤的免疫景观和预后，我们还希望找到一些可以直接影响黑色素瘤恶性行为的lncRNAs。
方法：系统，我们从包括UCSCXena和NCBIGEO在内的数据集中探索了黑色素瘤中m6A-和m5C-修饰的lncRNAs的表达模式，并选择预后lncRNAs。然后，根据lncRNAs的表达模式，将来自这些数据集的黑素瘤样品分成几个亚型。预后模型，列线图生存模型，药物敏感性,GO,和KEGG通路分析。此外，在几个选定的lncRNAs中，我们鉴定了一个名为LINC00893的lncRNA，并研究了其在黑色素瘤细胞系中的表达模式和生物学功能。
结果：我们鉴定了27个m6A-和m5C-相关的lncRNAs，它们与生存显著相关，我们基于这27个lncRNAs对黑色素瘤样本进行了亚型分析。在这两种亚型中，我们发现这两种亚型之间的免疫细胞浸润存在差异。然后，LASSO算法用于筛选优化的lncRNAs组合，包括ZNF252P-AS1，MIAT，FAM13A-AS1,LINC-PINT,LINC00893、AGAP2-AS1、OIP5-AS1和SEMA6A-AS1。我们还发现，基于RS模型预测的不同风险组与实际预后之间存在显着相关性。还构建了基于独立生存预后因素的列线图生存模型。此外,对化疗药物的敏感性，进行GO和KEGG分析。在不同的风险群体中，共获得14个不同分布的药物分子，其中包括AZD6482，AZD7762，AZD8055，喜树碱，达沙替尼,厄洛替尼,吉非替尼,吉西他滨,GSK269962A,尼洛替尼,雷帕霉素,还有索拉非尼.共筛选出55条显著相关的生物过程和17条KEGG信号通路。最后,我们注意到LINC00893在黑色素瘤组织和细胞系中的表达低于邻近组织和表皮黑素细胞,下调LINC00893可以促进A875和MV3黑色素瘤细胞的恶性行为。在这两种黑色素瘤细胞系中，m6A相关分子如YTHDF3和METTL3的下调可以促进LINC00893的表达。
结论：我们分析了黑色素瘤样本中m6A和m5C相关的lncRNAs，并预测了这些lncRNAs在预后中的作用，肿瘤微环境,免疫浸润，和临床病理特征。我们还发现，LINC00893可能受到m6A修饰的调控，可以作为黑色素瘤的肿瘤抑制因子，并在黑色素瘤转移中起抑制作用。
方法：系统，我们从包括UCSCXena和NCBIGEO在内的数据集中探索了黑色素瘤中m6A-和m5C-修饰的lncRNAs的表达模式，并选择预后lncRNAs。然后，根据lncRNAs的表达模式，将来自这些数据集的黑素瘤样品分成几个亚型。预后模型，列线图生存模型，药物敏感性,GO,和KEGG通路分析。此外，在几个选定的lncRNAs中，我们鉴定了一个名为LINC00893的lncRNA，并研究了其在黑色素瘤细胞系中的表达模式和生物学功能。
结果：我们鉴定了27个m6A-和m5C-相关的lncRNAs，它们与生存显著相关，我们基于这27个lncRNAs对黑色素瘤样本进行了亚型分析。在这两种亚型中，我们发现这两种亚型之间的免疫细胞浸润存在差异。然后，LASSO算法用于筛选优化的lncRNAs组合，包括ZNF252P-AS1，MIAT，FAM13A-AS1,LINC-PINT,LINC00893、AGAP2-AS1、OIP5-AS1和SEMA6A-AS1。我们还发现，基于RS模型预测的不同风险组与实际预后之间存在显着相关性。还构建了基于独立生存预后因素的列线图生存模型。此外,对化疗药物的敏感性，进行GO和KEGG分析。在不同的风险群体中，共获得14个不同分布的药物分子，其中包括AZD6482，AZD7762，AZD8055，喜树碱，达沙替尼,厄洛替尼,吉非替尼,吉西他滨,GSK269962A,尼洛替尼,雷帕霉素,还有索拉非尼.共筛选出55条显著相关的生物过程和17条KEGG信号通路。最后,我们注意到LINC00893在黑色素瘤组织和细胞系中的表达低于邻近组织和表皮黑素细胞,下调LINC00893可以促进A875和MV3黑色素瘤细胞的恶性行为。在这两种黑色素瘤细胞系中，m6A相关分子如YTHDF3和METTL3的下调可以促进LINC00893的表达。
结论：我们分析了黑色素瘤样本中m6A和m5C相关的lncRNAs，并预测了这些lncRNAs在预后中的作用，肿瘤微环境,免疫浸润，和临床病理特征。我们还发现，LINC00893可能受到m6A修饰的调控，可以作为黑色素瘤的肿瘤抑制因子，并在黑色素瘤转移中起抑制作用。
