An integrated approach to identify bimodal genes associated with prognosis in câncer

Justino, Josivan Ribeiro; Reis, Clovis Ferreira dos; Fonseca, André; Souza, Sandro J. de; Stransky, Beatriz

doi:10.1590/1678-4685-gmb-2021-0109

Cited by 4 publications

(2 citation statements)

References 29 publications

(37 reference statements)

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“…However, when the sensitivity β changes sign then the probability mass is concentrated to the extreme values of the internal state value which can be mediated by a potential bistability mechanism. The latter can be relevant in the context of cancer where a bimodal gene expression can be a prognostic biomarker [42,43]. However, such bimodality is not associated only with pathologies, since it can be occurred in healthy immune cells [44].…”

Section: Discussionmentioning

confidence: 99%

Cell decision-making through the lens of Bayesian learning

Barua¹,

Haralampos²

2023

Preprint

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Cell decision-making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker-Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision-making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters.

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Section: Discussionmentioning

confidence: 99%

Cell decision-making through the lens of Bayesian learning

Barua¹,

Haralampos²

2023

Preprint

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“…This can be potentially mediated by a bistability regulation mechanism, e.g., for the receptor production. Such bimodality is relevant in the context of cancer, where it is considered a malignancy prognostic biomarker [ 41 , 42 ]. However, it can also occur in physiological cases such as in healthy immune cells [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Cell Decision Making through the Lens of Bayesian Learning

Barua

Hatzikirou

2023

Entropy

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Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker–Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate the cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters.

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Neurocan expression associates with better survival and viral positivity in Merkel cell carcinoma

Salmikangas

Laaksonen²,

Edgren³

et al. 2023

PLoS ONE

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Merkel cell carcinoma (MCC) is a rare cutaneous neuroendocrine carcinoma that is frequently divided into Merkel cell polyomavirus negative and positive tumors due their distinct genomic and transcriptomic profiles, and disease outcomes. Although some prognostic factors in MCC are known, tumorigenic pathways, which that explain outcome differences in MCC are not fully understood. We investigated transcriptomes of 110 tissue samples of a formalin-fixed, paraffin-embedded MCC series by RNA sequencing to identify genes showing a bimodal expression pattern and predicting outcome in cancer and that potentially could play a role in tumorigenesis. We discovered 19 genes among which IGHM, IGKC, NCAN, OTOF, and USH2A were associated also with overall survival (all p-values < 0.05). From these genes, NCAN (neurocan) expression was detected in all 144 MCC samples by immunohistochemistry. Increased NCAN expression was associated with presence of Merkel cell polyomavirus DNA (p = 0.001) and viral large T antigen expression in tumor tissue (p = 0.004) and with improved MCC-specific survival (p = 0.027) and overall survival (p = 0.034). We conclude that NCAN expression is common in MCC, and further studies are warranted to investigate its role in MCC tumorigenesis.

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An integrated approach to identify bimodal genes associated with prognosis in câncer

Cited by 4 publications

References 29 publications

Cell decision-making through the lens of Bayesian learning

Cell decision-making through the lens of Bayesian learning

Cell Decision Making through the Lens of Bayesian Learning

Neurocan expression associates with better survival and viral positivity in Merkel cell carcinoma

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