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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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The Expression of the LDLR, LDLRAP1, and PCSK9 Genes has Prognostic Significance in Triple-negative Breast Cancer

Current Medicinal Chemistry. 2025. Vol. 32. No. 32. P. 7045–7061.
Ivan Denisovich Antipenko, Darya Mikhailovna Olkhovik, Solopova O. N., Khayretdinova G. A., Kalacheva O. S., Julia Alekseevna Makarova, Maxim Yurievich Shkurnikov

Aims: The purpose of this study was to investigate the prognostic significance of cholesterol uptake genes in predicting the survival of breast cancer patients.

Background: Cholesterol plays a crucial role in the homeostasis of tumor cells. It is known that cholesterol levels can influence important parameters of the disease, such as sensitivity to therapy, progression, and metastasis of cancer. Previous studies suggest that breast cancer subtypes exhibit differences in metabolism.

Objective: The objectives of this study were to determine whether cholesterol uptake genes have prognostic significance for overall survival in breast cancer patients, evaluate if this prognostic significance varies between breast cancer subtypes, and identify differences in the expression of cholesterol uptake genes among these subtypes.

Methods: Data from mRNA sequencing of tumors from the Cancer Genome Atlas (TCGA) portal were analyzed. Tumors were classified into molecular subtypes, and the prognostic significance of cholesterol uptake gene expression levels was evaluated for each subtype. DESeq2 and Fisher's test were used to assess differences in gene expression.

Results: High expression levels of genes involved in de novo cholesterol synthesis were associated with poor prognosis for the Basal-like and Luminal A breast cancer subtypes. The prognostic significance of low-density lipoprotein receptor (LDLR), LDLR adapter protein 1 (LDLRAP1), and proprotein convertase subtilisin/kexin type 9 (PCSK9), which are responsible for exogenous cholesterol uptake, varied across subtypes. Specifically, low expression of LDLR was associated with a favorable prognosis for the luminal A (OR = 2.17; FDR = 0.0048) and luminal B (OR = 2.21; FDR = 0.015) subtypes but indicated poor prognosis in the basal-like subtype (OR = 0.48; FDR = 0.05). No genes were significant for prognosis prediction in the HER2-positive subtype. The HER2+ subtype exhibited higher expression of cholesterol uptake genes compared to the basal-like subtype based on the analysis of tumor mRNA sequencing (OR = 6.45, p-value = 3.07E-05). This finding was also confirmed through the study of publicly available single-cell sequencing data (OR = 40.3, p-value = 2.19e-07), which may contribute to the differences in their prognostic significance.

Conclusion: The prognostic significance of cholesterol uptake gene expression varies among breast cancer subtypes. Precise fitting of biomarkers into breast cancer subtypes may aid in more accurate patient stratification and improve treatment approaches.

Research target: Biology Basic Medicine
Language: English
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Keywords: cholesterolLDLRHER2PCSK9 Breast cancertriple-negativebasal-like
Publication based on the results of:
Microfluidic models (2025)
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