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

Cheng, Shaowen; Li, Si; Yang, Ping; Wang, Rong; Zhou, Ping; Li, Jingquan

doi:10.1080/21691401.2023.2244998

Cited by 2 publications

(1 citation statement)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Transcriptional regulation analysis revealed the critical transcription factors (i.e. E2F1, E2F3 and E2F7), which play important roles in regulating the TME of MCC, providing an initial knowledge to understand the intrinsic subtypes of MCCs and the pathways involved in distinct subtype oncogenesis [90]. Moreover, liquid-biopsies-based detection of miRNA biomarkers in AIbased models for early-stage cancer subtyping and prognosis is emerging, which could be useful in machine-based predictive modeling of cancer staging and progression [91].…”

Section: Application Of Artificial Intelligence In MCCmentioning

confidence: 99%

MicroRNAs in Merkel Cell Carcinoma: A State of Art from Biogenesis to Clinical Future Perspectives and Artificial Intelligence Possible Applications

Li Pomi,

Incorvaia,

Badalamenti

et al. 2024

Preprint

View full text Add to dashboard Cite

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

show abstract

Section: Application Of Artificial Intelligence In MCCmentioning

confidence: 99%

MicroRNAs in Merkel Cell Carcinoma: A State of Art from Biogenesis to Clinical Future Perspectives and Artificial Intelligence Possible Applications

Li Pomi,

Incorvaia,

Badalamenti

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Exploring the effects of Merkel cell polyomavirus T antigens expression in REH and MCC13 cells by methylome and transcriptome profiling

Macamo,

Liu,

Färber

et al. 2024

Journal of Medical Virology

View full text Add to dashboard Cite

Merkel cell carcinoma (MCC) is a rare, aggressive skin cancer with a tripled incidence in the US and Europe over the past decade. Around 80% of MCC is linked to Merkel cell polyomavirus, but the cell of origin remains unknown. We stably introduced Merkel cell polyomavirus (MCPyV)‐sT) and LT antigens to MCC13 and REH cell lines, analyzing DNA methylation and gene transcriptional regulation. Gene ontology analysis assessed MCPyV effects, and integrative analysis correlated gene expression and methylation. Expression patterns were compared with 15 previously sequenced primary MCCs. We found that MCPyV‐LT induces DNA methylation changes in both cell lines, while MCPyV‐sT only affected REH cells. Greater gene expression changes are observed in MCC13 cells, with upregulated genes associated with cellular components and downregulated genes related to biological processes. Integrative analysis of differentially expressed genes (DEG) and differentially methylated regions (DMR) of REH cell lines revealed that no genes were commonly methylated and differentially expressed. The study compared DEGs and DMG in MCC13 and REH cells to overlapping genes in MCPyV‐positive cell lines (MKL1, MKL2, and WaGa), identifying hypomethylated genes in the gene body and hypermethylated genes at TSS1500. GO analysis of the two cell lines showed that MCPyV‐TAs can downregulate genes in MHC‐I pathways; this downregulation offers a target that can be used to create novel and efficient MCC immunotherapy approaches. Finally, it was confirmed that MCPyV‐LT controls gene expression in MCC tissues using an integrative investigation of DNA methylation and gene expression.

show abstract

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

Cited by 2 publications

References 64 publications

MicroRNAs in Merkel Cell Carcinoma: A State of Art from Biogenesis to Clinical Future Perspectives and Artificial Intelligence Possible Applications

MicroRNAs in Merkel Cell Carcinoma: A State of Art from Biogenesis to Clinical Future Perspectives and Artificial Intelligence Possible Applications

Exploring the effects of Merkel cell polyomavirus T antigens expression in REH and MCC13 cells by methylome and transcriptome profiling

Contact Info

Product

Resources

About