Wang Yi-Xiao, Ding Zhang-Jun, Wang Qian-Ling, Zhao Cai-Chou, Liu Si-Qi, Du Shu-Li, Zhou Shan, Zheng Li-Yun, Gao Min, Shen Cong-Cong, Chen Xiao-Dong
Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, 230032, Anhui Province, China.
Institute of Dermatology, Anhui Medical University, Hefei, 230032, Anhui Province, China.
Arch Dermatol Res. 2024 Dec 7;317(1):86. doi: 10.1007/s00403-024-03633-6.
Our primary objective was to identify genes critical for cutaneous melanoma (CM) and related typing, based on essential genes, to generate novel insights for clinical management and immunotherapy of patients with CM. We analyzed RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and sequencing data of 29 CM cell line from Cancer Cell Line Encyclopedia (CCLE) databases. Combined with DepMap database, 406 CM essential cancer genes were finally obtained. Based on the expression of essential genes in cancer, the patients included in TCGA and Gene Expression Omnibus (GEO) databases were divided into three different molecular subtypes (C1, C2, and C3) by the NMF algorithm. Data analysis from TCGA and GEO datasets revealed that subtype C3 had the poorest prognosis, while subtype C1 exhibited the best prognosis. Combined with the CIBERSORT, ESTIMATE and ssGSEA algorithm, patients with different molecular subtypes can be divided into two immune subtypes (hot and cold). We found that subtype C1 was characterized by hot tumors, in contrast to subtypes C2 and C3, which were characterized by cold tumors. Then, we used univariate Cox regression, LASSO, and multifactor Cox regression analysis to select risk genes and constructed a prognostic model based on eight genes: RABIF, CDCA8, FOXM1, SPRR2E, AIP, CAP1, CTSW, and IFITM3. All patients were divided into two risk subtypes (high and low ) according to the median of risk scores. We found that most hot tumor subtypes were found in the low-risk subtypes and most patients with this subtype survived for longer. Ultimately, we selected RABIF, which exhibits the highest risk coefficient, for histological and cytological verification. The results showed that RABIF was overexpressed in melanoma. Inhibition of RABIF expression could suppress the proliferation and invasion of melanoma cells and promote the apoptosis of melanoma cells. In conclusion, we used CRISPR-Cas9 screening to verify the association between molecular subtypes (C1, C2, and C3), immune subtypes (hot and cold), and risk subtypes (high and low) in patients with CM, particularly in distinguishing survival and prognosis. These findings can be used to guide clinical management and immunotherapy of patients with CM.
我们的主要目标是基于必需基因来鉴定对皮肤黑色素瘤（CM）至关重要的基因及其相关分型，从而为CM患者的临床管理和免疫治疗提供新的见解。我们分析了来自癌症基因组图谱（TCGA）和基因型-组织表达（GTEx）的RNA测序（RNA-seq）数据，以及来自癌细胞系百科全书（CCLE）数据库的29个CM细胞系的测序数据。结合DepMap数据库，最终获得了406个CM必需癌症基因。基于癌症中必需基因的表达，通过非负矩阵分解（NMF）算法将TCGA和基因表达综合数据库（GEO）中纳入的患者分为三种不同的分子亚型（C1、C2和C3）。来自TCGA和GEO数据集的数据分析显示，C3亚型的预后最差，而C1亚型的预后最佳。结合CIBERSORT、ESTIMATE和单样本基因集富集分析（ssGSEA）算法，不同分子亚型的患者可分为两种免疫亚型（热和冷）。我们发现，与以冷肿瘤为特征的C2和C3亚型相反，C1亚型的特征是热肿瘤。然后，我们使用单变量Cox回归、套索回归和多因素Cox回归分析来选择风险基因，并基于八个基因构建了一个预后模型：RABIF、CDCA8、FOXM1、SPRR2E、AIP、CAP1、CTSW和IFITM3。根据风险评分的中位数将所有患者分为两种风险亚型（高和低）。我们发现，大多数热肿瘤亚型存在于低风险亚型中，且该亚型的大多数患者存活时间更长。最终，我们选择了风险系数最高的RABIF进行组织学和细胞学验证。结果显示，RABIF在黑色素瘤中过表达。抑制RABIF的表达可抑制黑色素瘤细胞的增殖和侵袭，并促进黑色素瘤细胞的凋亡。总之，我们使用CRISPR-Cas9筛选来验证CM患者分子亚型（C1、C2和C3）、免疫亚型（热和冷）和风险亚型（高和低）之间的关联，特别是在区分生存和预后方面。这些发现可用于指导CM患者的临床管理和免疫治疗。
