ssmuБюллетень сибирской медициныBulletin of Siberian Medicine1682-03631819-3684Siberian State Medical University, the Ministry of Healthcare of the Russian Federation10.20538/1682-0363-2026-2-91-100ssmu-6537Research ArticleОРИГИНАЛЬНЫЕ СТАТЬИORIGINAL PAPERSВозможность использования метода «случайного леса» для поиска взаимосвязи микроРНК с тканевыми и молекулярно-генетическими маркерами рака молочной железыThe potential application of the random forest method to explore the association between microRNAs and tissue and molecular genetic biomarkers of breast cancerhttps://orcid.org/0000-0003-3936-1316СтуденикинаА. А.StudenikinaA. A.

Студеникина Анастасия Александровна – канд. мед. наук, ст. науч. сотрудник; науч. сотрудник

Россия, 630091, г. Новосибирск, Красный пр., 52

Россия, 630060, г. Новосибирск, ул. Тимакова, 2

52 Krasny Ave., 630091 Novosibirsk, Russian Federation2 Timakova St., 630060 Novosibirsk, Russian Federation 

aastudenikina@frcftm.ruhttps://orcid.org/0009-0009-2272-6295МихайловскийМ. А.MihailovskyM. A.

Михайловский Михаил Артемович – студент

Россия, 630073, г. Новосибирск, пр. Карла Маркса, 20

20 Karl Marx Ave., 630073 Novosibirsk, Russian Federation

maihailovsky18@mail.ruhttps://orcid.org/0000-0002-6352-3218ТимофеевВ. С.TimofeevV. S.

Тимофеев Владимир Семенович – д-р техн. наук, профессор кафедры теоретической и прикладной информатики, декан факультета прикладной математики и информатики

Россия, 630073, г. Новосибирск, пр. Карла Маркса, 20

20 Karl Marx Ave., 630073 Novosibirsk, Russian Federation

v.timofeev@corp.nstu.ruhttps://orcid.org/0000-0002-1390-4426АрхиповС. А.ArkhipovS. A.

Архипов Сергей Алексеевич – д-р биол. наук, вед. науч. сотрудник; ст. науч. сотрудник

Россия, 630091, г. Новосибирск, Красный пр., 52

Россия, 630060, г. Новосибирск, ул. Тимакова, 2

52 Krasny Ave., 630091 Novosibirsk, Russian Federation2 Timakova St., 630060 Novosibirsk, Russian Federation 

arhipowsergei@yandex.ruhttps://orcid.org/0000-0001-7180-010XАутеншлюсА. И.AutenshlyusA. I.

Аутеншлюс Александр Исаевич – д-р биол. наук, профессор, зав. ЦНИЛ; гл. науч. сотрудник

Россия, 630091, г. Новосибирск, Красный пр., 52

Россия, 630060, г. Новосибирск, ул. Тимакова, 2

52 Krasny Ave., 630091 Novosibirsk, Russian Federation2 Timakova St., 630060 Novosibirsk, Russian Federation 

lpciip@211.ruНовосибирский государственный медицинский университет (НГМУ); Научно-исследовательский институт молекулярной биологии и биофизики (НИИМББ), Федеральный исследовательский центр фундаментальной и трансляционной медицины (ФИЦ ФТМ)РоссияNovosibirsk State Medical University; Institute of Molecular Biology and Biophysics, Federal Research Center of Fundamental and Translational MedicineRussian FederationНовосибирский государственный технический университет (НГТУ)РоссияNovosibirsk State Technical UniversityRussian Federation20262307202625291100Copyright © Студеникина А.А., Михайловский М.А., Тимофеев В.С., Архипов С.А., Аутеншлюс А.И., 20262026Студеникина А.А., Михайловский М.А., Тимофеев В.С., Архипов С.А., Аутеншлюс А.И.Studenikina A.A., Mihailovsky M.A., Timofeev V.S., Arkhipov S.A., Autenshlyus A.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://bulletin.ssmu.ru/jour/article/view/6537

Цель. Поиск возможности выявления с помощью метода «случайного леса» (МСЛ) взаимосвязи между уровнем микроРНК miR-181a, miR-25 и экспрессией маркеров: E-кадгерина (CDH1), коллагена II (CII), интегрина бета-1 (ITGB1), циклина D1 (CCND1) и белка-костимулятора Т-лимфоцитов (CD86), а также их ассоциации с клинически значимыми молекулярно-генетическими маркерами (ER, PR, HER2, Ki-67) у пациенток с раком молочной железы (РМЖ).Материалы и методы. У 44 пациенток с инвазивной протоковой карциномой молочной железы (без неоадъювантной терапии) определяли экспрессию микроРНК miR-181a и miR-25 в сыворотке крови, используя цифровую капельную полимеразную цепную реакцию (ПЦР). Экспрессию CDH1, CII, ITGB1, CCND1, CD86, ER, PR, HER2 и Ki-67 анализировали с помощью иммуногистохимии в образцах опухолевой ткани. Для анализа взаимосвязей использовали корреляционный анализ Спирмена и алгоритм МСЛ.Результаты. Корреляционный анализ не выявил статистически значимых линейных взаимосвязей между исследуемыми параметрами (p > 0,05). Метод «случайного леса» показал наличие нелинейных ассоциаций: для miR-181a оптимальная прогностическая модель включала CDH1 и CCND1 (MAE = 4,267), для miR-25 – ITGB1 и CCND1 (MAE = 16,255). Из клинико-патологических характеристик статистически значимые положительные корреляции выявлены для CII с ER (r = 0,498; p = 0,001) и PR (r = 0,354; p = 0,018). Модели МСЛ продемонстрировали наибольшую точность предсказания HER2-статуса (82,6%) при использовании комбинации ITGB1, miR-181a и CDH1.Заключение. Алгоритм МСЛ выявил сложные нелинейные взаимосвязи между miR-181a, miR-25 и тканевыми маркерами опухолевой прогрессии, которые ранее не обнаружены стандартными статистическими методами. Полученные результаты носят поисковый характер и формируют основу для дальнейших исследований по валидации данных биомаркеров и их роли в патогенезе различных подтипов РМЖ.

Aim. Exploring the potential of using a random forest method (RFM) to identify associations between serum levels of microRNAs miR-181a and miR-25 and the expression of the following markers: E-cadherin (CDH1), type II collagen (CII), integrin beta-1 (ITGB1), cyclin D1 (CCND1), and T-lymphocyte costimulator (CD86), as well as their relationship with clinically relevant molecular genetic markers (ER, PR, HER2, Ki-67) in patients with breast cancer (BC).Materials and methods. We recruited 44 BC patients with invasive ductal carcinoma who had not received neoadjuvant treatment. Serum levels of miR-181a and miR-25 were measured using digital droplet PCR, and the expression of CDH1, CII, ITGB1, CCND1, CD86, ER, PR, HER2, and Ki-67 in tumor tissue samples was assessed via immunohistochemistry. Spearman’s correlation analysis and the RFM were applied to assess associations between variables.Results. Spearman’s correlation analysis did not reveal any significant linear relationships between the studied parameters (p > 0.05). However, the RFM identified nonlinear associations: for miR-181a, the optimal predictive model included CDH1 and CCND1 (MAE = 4.267); for miR-25, ITGB1 and CCND1 (MAE = 16.255). Among clinical and pathological characteristics, statistically significant positive correlations were found between CII and ER (r = 0.498; p = 0.001) and PR (r = 0.354; p = 0.018). The RFM models demonstrated the highest accuracy in predicting the HER2 status (82.6%) when using a combination of ITGB1, miR-181a, and CDH1.Conclusion. The RFM revealed complex nonlinear associations between microRNAs and tissue markers of tumor progression that were not detected by conventional statistical methods. These findings are exploratory in nature and provide a foundation for further research aimed at validating these biomarkers and elucidating their role in the pathogenesis of various breast cancer subtypes.

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The authors declare that there are no conflicts of interest present.