Evidence map›Paper›PMID 42714781›Full record

ArticlePharmacological reports : PR2026

Unveiling novel transcriptomic prognostic biomarkers for specific breast cancer subtypes and treatment regimens.

Krittin Pitinanon, Chotiphuk Kusalasaiyanon, Wathunya Hawhan, Yu-Yu Liu, Pasrawin Taechawattananant, Varalee Yodsurang

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Article in Pharmacological reports : PR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Krittin PitinanonDepartment of Pharmacology and Physiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand.ORCID http://orcid.org/0009-0006-6783-8547
Chotiphuk KusalasaiyanonDepartment of Pharmacology and Physiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand.
Wathunya HawhanDepartment of Pharmacology and Physiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand.ORCID http://orcid.org/0009-0006-8219-1489
Yu-Yu LiuDepartment of Epigenomics, Institute for Advanced Life Sciences, Hoshi University, Tokyo, 142-8501, Japan.ORCID http://orcid.org/0000-0002-4814-8616
Pasrawin TaechawattananantDepartment of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand.ORCID http://orcid.org/0000-0003-4299-873X
Varalee YodsurangDepartment of Pharmacology and Physiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand. varalee.y@pharm.chula.ac.th.ORCID https://orcid.org/0000-0002-3622-532X

Funding

Thailand Science Research and Innovation Fund Chulalongkorn University HEA663300009
6 · The paper itself

Abstract

backgroundBreast cancer (BRCA) is the most common cancer in women worldwide, yet current gene expression panels offer limited insight into treatment responses across different subtypes and therapies. This study aimed to identify reliable biomarkers for predicting treatment outcomes in specific BRCA subtypes and treatment regimens.

methodsThis study analyzed transcriptomic data from The Cancer Genome Atlas to identify differentially expressed genes (DEGs) in patient groups treated with different combinations of hormone therapy (H), chemotherapy (C), radiotherapy (R), and targeted therapy (T). Non-negative matrix factorization clustering was performed to stratify patients into clusters representing different BRCA subtypes. Functional enrichment analysis was performed, and survival assessments were conducted using the METABRIC dataset.

resultsA total of 1,148 DEGs were identified across treatment regimens, with 75 common DEGs shared across multiple regimens. Among these, 12 candidate biomarkers were associated with luminal subtypes treated with H, including LRP1B, of which high expression predicted cancer recurrence. In triple-negative breast cancer (TNBC) treated with C, 76 candidate biomarkers were identified, including TTYH1 for recurrence and ANXA8L1 and MPZ for non-recurrence. Functional analyses identified intermediate filament organization and keratinization as pathways associated with specific candidate biomarkers of TNBC following C. Survival analysis using METABRIC strengthened the prognostic ability of LRP1B and TTYH1 to predict worse survival and ANXA8L1 and MPZ to predict prolonged survival, with four additional prognostic biomarkers.

conclusionThis study identified gene expression prognostic biomarkers for luminal and TNBC subtypes, thereby supporting personalized therapies. Further experimental validation is required to confirm these findings for clinical application. CLINICAL TRIAL REGISTRY: No.

Indexed as

Breast cancerDifferentially expressed geneNon-negative matrix factorizationTranscriptomic biomarkersTreatment response

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.