Merkel细胞癌（MCC）是皮肤神经内分泌癌的原发性皮肤肿瘤，其分子异质性特征是具有多样化的肿瘤微环境（TME）。然而，我们对MCC中细胞状态和生态系统的认识还很匮乏。在这里，我们基于机器学习框架系统地鉴定和表征了MCC中细胞状态和生态系统的景观。我们从9种免疫细胞类型中获得了30种不同的细胞状态，并详细研究了B细胞、CD8T细胞、成纤维细胞和单核/巨噬细胞的细胞状态。对细胞状态的功能分析发现，在这些细胞状态中高表达的基因在免疫和癌症标志通路上显著富集。此外，我们进一步鉴定了四种具有不同患者组成的生态类型。转录调控分析揭示了关键转录因子（如E2F1、E2F3和E2F7），它们在调控MCC的TME中发挥重要作用。总之，本研究的结果可能为我们理解MCC的内在亚型和不同亚型致病过程中涉及的通路提供丰富的知识，并进一步推动特定治疗策略的发展。

Merkel cell carcinoma (MCC) is a primary cutaneous neoplasm of neuroendocrine carcinoma of the skin, which is characterized by molecular heterogeneity with diverse tumour microenvironment (TME). However, we are still lack knowledge of the cellular states and ecosystems in MCC. Here, we systematically identified and characterized the landscape of cellular states and ecotypes in MCC based on a machine learning framework. We obtained 30 distinct cellular states from 9 immune cell types and investigated the B cell, CD8 T cell, fibroblast, and monocytes/macrophage cellular states in detail. The functional profiling of cellular states were investigated and found the genes highly expressed in cellular states were significantly enriched in immune- and cancer hallmark-related pathways. In addition, four ecotypes were further identified which were with different patient compositions. Transcriptional regulation analysis revealed the critical transcription factors (i.e. E2F1, E2F3 and E2F7), which play important roles in regulating the TME of MCC. In summary, the findings of this study may provide rich knowledge to understand the intrinsic subtypes of MCCs and the pathways involved in distinct subtype oncogenesis, and will further advance the knowledge in developing a specific therapeutic strategy for these MCC subtypes.