Dissecting the tumour immune microenvironment in merkel cell carcinoma based on a machine learning framework

Abstract

AbstractMerkel 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.

Keywords

• Tumor microenvironment
• machine learning
• cellular states
• merkel cell carcinoma
• transcriptional regulation