Interpretable Machine Learning Algorithms Identify Inetetamab‐Mediated Metabolic Signatures and Biomarkers in Treating Breast Cancer DOI Creative Commons
Ning Xie, Dehua Liao, Binliang Liu

et al.

Journal of Clinical Laboratory Analysis, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

ABSTRACT Background HER2‐positive breast cancer (BC), a highly aggressive malignancy, has been treated with the targeted therapy inetetamab for metastatic cases. Inetetamab (Cipterbin) is recently approved BC, significantly prolonging patients' survival. Currently, there no established biomarker to reliably predict or assess therapeutic efficacy of in BC patients. Methods This study harnesses power metabolomics and machine learning uncover biomarkers therapy. A total 23 plasma samples from inetetamab‐treated patients were collected stratified into responders nonresponders. Ultra‐high‐performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry was utilized analyze metabolites blood samples. combination univariate multivariate statistical analyses employed identify these metabolites, their biological functions then ascertained by Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) enrichment analysis. Finally, algorithms screen responsive all differentially expressed metabolites. Results Our finding revealed 6889 unique that detected. Pathways like retinol metabolism, fatty acid biosynthesis, steroid hormone biosynthesis enriched Notably, two key associated response identified: FAPy‐adenine 2‐Pyrocatechuic acid. There some negative correlation between progress‐free survival (PFS) kurtosis content. Conclusions In summary, identification significant differential holds promise as potential evaluating predicting treatment outcomes ultimately contributing diagnosis disease discovery prognostic markers.

Language: Английский

CircSpna2 attenuates cuproptosis by mediating ubiquitin ligase Keap1 to regulate the Nrf2‐Atp7b signalling axis in depression after traumatic brain injury in a mouse model DOI Creative Commons

Mengran Du,

Jiayuanyuan Fu,

Jie Zhang

et al.

Clinical and Translational Medicine, Journal Year: 2024, Volume and Issue: 14(11)

Published: Nov. 1, 2024

Language: Английский

Citations

5
N4‐Acetylcytidine‐Mediated CD2BP2‐DT Drives YBX1 Phase Separation to Stabilize CDK1 and Promote Breast Cancer Progression DOI Creative Commons
Hongyu Wang,

Bohui Zhao,

Jiayu Zhang

et al.

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Abstract Long noncoding RNAs (lncRNAs) play critical roles in the initiation and progression of breast cancer. However, specific mechanisms biological functions lncRNAs cancer remain incompletely understood. Bioinformatics analysis identifies a novel lncRNA, CD2BP2‐DT, that is overexpressed correlates with adverse clinicopathological features poor overall survival. Both vivo vitro experiments demonstrate CD2BP2‐DT promotes proliferation cells. Mechanistically, NAT10 mediates N4‐acetylcytidine (ac4C) modification enhancing its RNA stability expression. More importantly, enhances CDK1 mRNA by mediating YBX1 phase separation, thereby promoting In conclusion, lncRNA identified as crucial driver cell through YBX1/CDK1 axis, highlighting potential promising biomarker therapeutic target for

Language: Английский

Citations

0
Recent advances in the role of circRNA in cisplatin resistance in tumors DOI
Jiawen Zhang,

Qiwen Yu,

Weijin Zhu

et al.

Cancer Gene Therapy, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Language: Английский

Citations

0
Interpretable Machine Learning Algorithms Identify Inetetamab‐Mediated Metabolic Signatures and Biomarkers in Treating Breast Cancer DOI Creative Commons
Ning Xie, Dehua Liao, Binliang Liu

et al.

Journal of Clinical Laboratory Analysis, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

ABSTRACT Background HER2‐positive breast cancer (BC), a highly aggressive malignancy, has been treated with the targeted therapy inetetamab for metastatic cases. Inetetamab (Cipterbin) is recently approved BC, significantly prolonging patients' survival. Currently, there no established biomarker to reliably predict or assess therapeutic efficacy of in BC patients. Methods This study harnesses power metabolomics and machine learning uncover biomarkers therapy. A total 23 plasma samples from inetetamab‐treated patients were collected stratified into responders nonresponders. Ultra‐high‐performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry was utilized analyze metabolites blood samples. combination univariate multivariate statistical analyses employed identify these metabolites, their biological functions then ascertained by Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) enrichment analysis. Finally, algorithms screen responsive all differentially expressed metabolites. Results Our finding revealed 6889 unique that detected. Pathways like retinol metabolism, fatty acid biosynthesis, steroid hormone biosynthesis enriched Notably, two key associated response identified: FAPy‐adenine 2‐Pyrocatechuic acid. There some negative correlation between progress‐free survival (PFS) kurtosis content. Conclusions In summary, identification significant differential holds promise as potential evaluating predicting treatment outcomes ultimately contributing diagnosis disease discovery prognostic markers.

Language: Английский

Citations

0