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Figure S1 from Circulating miRNA Signature Predicts Cancer Incidence in Lynch Syndrome—A Pilot Study

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posted on 2024-06-04, 07:21 authored by Tero Sievänen, Tiina Jokela, Matti Hyvärinen, Tia-Marje Korhonen, Kirsi Pylvänäinen, Jukka-Pekka Mecklin, Juha Karvanen, Elina Sillanpää, Toni T. Seppälä, Eija K. Laakkonen

Figure S1: Schoenfeld residuals of the c-miR risk prediction models. A, Unadjusted c-miR risk sum score prediction model. B, Sex and MMR-variant adjusted c-miR risk sum score prediction model.

Funding

Päivikki ja Sakari Sohlbergin Säätiö (Päivikki and Sakari Sohlberg Foundation)

European Commission Union Marie Sklodowska-Curie Individual Fellowships

Academy of Finland and iCAN Precision Medicine Flagship of Academy of Finland

Jane ja Aatos Erkon Säätiö (J&AE)

Suomen Lääketieteen Säätiö (Finnish Medical Foundation)

Sigrid Juséliuksen Säätiö (Sigrid Jusélius Stiftelse)

Emil Aaltosen Säätiö (Emil Aaltonen Foundation)

Syöpäsäätiö (Cancerstiftelsen)

Relander Foundation (The Relander Foundation)

State Research Funding, Finnish Government

History

ARTICLE ABSTRACT

Lynch syndrome (LS) is the most common autosomal dominant cancer syndrome and is characterized by high genetic cancer risk modified by lifestyle factors. This study explored whether a circulating miRNA (c-miR) signature predicts LS cancer incidence within a 4-year prospective surveillance period. To gain insight how lifestyle behavior could affect LS cancer risk, we investigated whether the cancer-predicting c-miR signature correlates with known risk-reducing factors such as physical activity, body mass index (BMI), dietary fiber, or NSAID usage. The study included 110 c-miR samples from LS carriers, 18 of whom were diagnosed with cancer during a 4-year prospective surveillance period. Lasso regression was utilized to find c-miRs associated with cancer risk. Individual risk sum derived from the chosen c-miRs was used to develop a model to predict LS cancer incidence. This model was validated using 5-fold cross-validation. Correlation and pathway analyses were applied to inspect biological functions of c-miRs. Pearson correlation was used to examine the associations of c-miR risk sum and lifestyle factors. hsa-miR-10b-5p, hsa-miR-125b-5p, hsa-miR-200a-3p, hsa-miR-3613-5p, and hsa-miR-3615 were identified as cancer predictors by Lasso, and their risk sum score associated with higher likelihood of cancer incidence (HR 2.72, 95% confidence interval: 1.64–4.52, C-index = 0.72). In cross-validation, the model indicated good concordance with the average C-index of 0.75 (0.6–1.0). Coregulated hsa-miR-10b-5p, hsa-miR-125b-5p, and hsa-miR-200a-3p targeted genes involved in cancer-associated biological pathways. The c-miR risk sum score correlated with BMI (r = 0.23, P < 0.01). In summary, BMI-associated c-miRs predict LS cancer incidence within 4 years, although further validation is required. The development of cancer risk prediction models is key to improving the survival of patients with LS. This pilot study describes a serum miRNA signature–based risk prediction model that predicts LS cancer incidence within 4 years, although further validation is required.

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