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

Abstract

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.

Keywords:

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

Acknowledgments

We thank the Hainan Province Clinical Medical Center and Academician Workstation in Hainan Province for their support and assistance with this study.

Authors’ contribution

PZ and JL proposed and managed the study. SC and SL wrote the manuscript. SC and SL collected the material and performed the bioinformatic analysis. RW and SC provided the explanations of the results. Data analysis consisted of SL, SC and PY. All authors contributed to the article and approved the submitted version.

Disclosure statement

No potential conflict of interest was reported by the author(s). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data availability statement

Public gene expression profiles used in this work can be acquired from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). The datasets used and/or analyzed during the present study are available from the corresponding authors on reasonable request.

Additional information

Funding

This work was supported by National Science Foundation of China (82260474), Hainan Province Science and Technology Special Fund (ZDYF2022SHFZ132).