Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning DOI Creative Commons
Jinfeng Zhao,

Longpeng Li,

Yaxin Wang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 19, 2025

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

Language: Английский

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning DOI Creative Commons
Jinfeng Zhao,

Longpeng Li,

Yaxin Wang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 19, 2025

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

Language: Английский

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