Integration of bulk/scRNA-seq and multiple machine learning algorithms identifies PIM1 as a biomarker associated with cuproptosis and ferroptosis in abdominal aortic aneurysm DOI Creative Commons

Zonglin Han,

LU Xiu-lian,

Yuxiang He

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 11, 2024

Background Abdominal aortic aneurysm (AAA) is a serious life-threatening vascular disease, and its ferroptosis/cuproptosis markers have not yet been characterized. This study was aiming to identify associated with in AAA by bioinformatics analysis combined machine learning models perform experimental validation. Methods used three scRNA-seq datasets from different mouse human PBMC bulk RNA-seq dataset. Candidate genes were identified integrated of scRNA-seq, cell communication analysis, monocle pseudo-time hdWGCNA analysis. Four algorithms, LASSO, REF, RF SVM, construct prediction model for the dataset, above results comprehensively analyzed, targets confirmed RT-qPCR. Results showed Mo/MF as most sensitive type AAA, 34 cuproptosis ferroptosis obtained. Pseudo-time series construction performed on these genes. Subsequent comparison that only PIM1 appeared all algorithms. RT-qPCR western blot consistent sequencing results, showing significantly upregulated AAA. Conclusion In conclusion, novel biomarker cuproptosis/ferroptosis highlighted.

Language: Английский

Advances in Cuproptosis of Hepatocellular Carcinoma DOI Open Access
Yuxia Gao, Ligong Lu

Journal of Biosciences and Medicines, Journal Year: 2024, Volume and Issue: 12(08), P. 167 - 181

Published: Jan. 1, 2024

Language: Английский

Citations

0
Integration of bulk/scRNA-seq and multiple machine learning algorithms identifies PIM1 as a biomarker associated with cuproptosis and ferroptosis in abdominal aortic aneurysm DOI Creative Commons

Zonglin Han,

LU Xiu-lian,

Yuxiang He

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 11, 2024

Background Abdominal aortic aneurysm (AAA) is a serious life-threatening vascular disease, and its ferroptosis/cuproptosis markers have not yet been characterized. This study was aiming to identify associated with in AAA by bioinformatics analysis combined machine learning models perform experimental validation. Methods used three scRNA-seq datasets from different mouse human PBMC bulk RNA-seq dataset. Candidate genes were identified integrated of scRNA-seq, cell communication analysis, monocle pseudo-time hdWGCNA analysis. Four algorithms, LASSO, REF, RF SVM, construct prediction model for the dataset, above results comprehensively analyzed, targets confirmed RT-qPCR. Results showed Mo/MF as most sensitive type AAA, 34 cuproptosis ferroptosis obtained. Pseudo-time series construction performed on these genes. Subsequent comparison that only PIM1 appeared all algorithms. RT-qPCR western blot consistent sequencing results, showing significantly upregulated AAA. Conclusion In conclusion, novel biomarker cuproptosis/ferroptosis highlighted.

Language: Английский

Citations

0