Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.
Journal:
Scientific reports
PMID:
40253524
Abstract
As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of breast cancer patients. Lactylation-associated subtypes were obtained by unsupervised consensus clustering analysis. Lactylation-related gene signature (LRS) was constructed by 15 machine learning algorithms, and the relationship between LRS and tumor microenvironment (TME) as well as drug sensitivity was analyzed. In addition, the expression of genes in the LRS in different cells was explored by single-cell analysis and spatial transcriptome. The expression levels of genes in LRS in clinical tissues were verified by RT-PCR. Finally, the potential small-molecule compounds were analyzed by CMap, and the molecular docking model of proteins and small-molecule compounds was constructed. LRS was composed of 6 key genes (SHCBP1, SIM2, VGF, GABRQ, SUSD3, and CLIC6). BC patients in the high LRS group had a poorer prognosis and had a TME that promoted tumor progression. Single-cell analysis and spatial transcriptome revealed differential expression of the key genes in different cells. The results of PCR showed that SHCBP1, SIM2, VGF, GABRQ, and SUSD3 were up-regulated in the cancer tissues, whereas CLIC6 was down-regulated in the cancer tissues. Arachidonyltrifluoromethane, AH-6809, W-13, and clofibrate can be used as potential target drugs for SHCBP1, VGF, GABRQ, and SUSD3, respectively. The gene signature we constructed can well predict the prognosis as well as the treatment response of BC patients. In addition, our predicted small-molecule complexes provide an important reference for personalized treatment of breast cancer patients.