MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

Purpose: The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics. Methods: We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes. Results: Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM. Conclusions: MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.

研究目的：本研究聚焦于皮肤黑色素瘤（Skin Cutaneous Melanoma, SKCM）——一种侵袭性强、异质性显著且病因学背景复杂的黑素细胞癌，上述特征共同为该疾病的预后判断带来了极大挑战。本研究基于多组学技术，构建了一套专为皮肤黑色素瘤定制的新型分类器系统。

研究方法：我们收集了423例携带多组学数据集的皮肤黑色素瘤样本，采用10种机器学习算法开展一致性聚类分析，并在2个独立队列中完成验证。对不同亚型的临床特征、生物学特性、免疫浸润模式、治疗响应及突变图谱进行了系统性比较分析。

研究结果：基于一致性聚类算法，我们在癌症基因组图谱（The Cancer Genome Atlas, TCGA）计划的皮肤黑色素瘤数据中鉴定出2种基于多组学的癌症亚型（Multi-Omics-Based-Cancer-Subtypes, MOCS），并在GSE19234与GSE65904队列中完成验证。其中MOCS2为预后不良亚型，其特征为免疫微环境复杂、抗肿瘤免疫状态失调、癌症干细胞指数偏高及基因组不稳定性显著。MOCS2对厄洛替尼、舒尼替尼等化疗药物表现出耐药性，但对雷帕霉素、NSC87877、MG132及FH355较为敏感。此外，本研究鉴定出ELSPBP1为参与皮肤黑色素瘤糖酵解与M2巨噬细胞浸润的靶标分子。

研究结论：MOCS分类系统可稳定预测皮肤黑色素瘤的预后；癌症干细胞指数偏高且伴随基因组不稳定性的患者，更易出现免疫耗竭状态。