Screening of Important Markers in Peripheral Blood Mononuclear Cells to Predict Female Osteoporosis Risk Using LASSO Regression Algorithm and SVM Method

Tang, Hongwei; Han, Qingtian; Yin, Yanru

doi:10.1177/11769343221075014

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Furthermore, IPRN can also alleviate oxidative stress-induced osteoblast injury through the Wnt/β-catenin signaling pathway [ 28 ]. The latest literature pointed out that the eight best differential genes, including AKT1, have a high diagnostic effect on the risk of OP [ 29 ]. In addition, tumor necrosis factor-α (TNF-α) can promote osteoclast differentiation to enhance bone resorption and inhibit osteoblast differentiation to impair bone formation [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

A study on the anti-osteoporosis mechanism of isopsoralen based on network pharmacology and molecular experiments

Wang

Chen

et al. 2023

J Orthop Surg Res

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Objective Osteoporosis (OP) is a disease caused by multiple factors. Studies have pointed out that isopsoralen (IPRN) is one of the most effective drugs for the treatment of OP. Based on network pharmacological and molecular experimental analysis, the molecular mechanism of IPRN in osteoporosis is clarified. Methods IPRN target genes and OP-related genes were predicted from the databases. Intersections were obtained and visualized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on target genes, which was confirmed by experiments internal and external experiments. Molecular docking was used to verify the binding between IPRN and target proteins. Molecular dynamics (MD) simulates the binding affinity of protein targets and active compounds. Results 87 IPRN target genes and 242 disease-related targets were predicted. The protein–protein interaction (PPI) network identified 18 IPRN target proteins for the treatment of OP. GO analysis indicated that target genes were involved in biological processes. KEGG analysis showed that pathways such as PI3K/AKT/mTOR were associated with OP. Cell experiments (qPCR and WB) found that the expressions of PI3K, AKT, and mTOR in MC3T3-E1 cells at 10 μM, 20 μM, and 50 μM IPRN concentrations, especially at 20 μM IPRN treatment, were higher than those in the control group at 48 h. Animal experiments also showed that compared with the control group, 40 mg/kg/time IPRN could promote the expression of the PI3K gene in chondrocytes of SD rats. Conclusions This study predicted the target genes of IPRN in the treatment of OP and preliminarily verified that IPRN plays an anti-OP role through the PI3K/AKT/mTOR pathway, which provides a new drug for the treatment of OP.

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Section: Discussionmentioning

confidence: 99%

A study on the anti-osteoporosis mechanism of isopsoralen based on network pharmacology and molecular experiments

Wang

Chen

et al. 2023

J Orthop Surg Res

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NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes

Liu,

Zhang,

Qiu

2024

Cell Biol Toxicol

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Background Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We aim to utilize non-negative matrix factorization (NMF) clustering and machine learning algorithms to pinpoint disease-specific genes related to the process of ferroptosis in PE and investigate likely underlying biochemistry mechanisms. Methods The acquisition of four microarray datasets from the Gene Expression Omnibus (GEO) repository, the integration of these datasets, and the elimination of batch effects formed the core procedure. Genes related to ferroptosis in PE (DE-FRG) were identified. NMF clustering was performed on DE-FRG for unsupervised analysis, generating a heatmap for clustering validation via principal component analysis. Immunocyte infiltration differences between different subtypes were compared to elucidate the impact of ferroptosis on immune infiltration in the placental tissue of PE patients. The application of weighted gene co-expression network analysis (WGCNA) revealed important module genes linked to sample subtypes and disease status. The screening of PE feature genes involved employing SVM, RF, GLM, and XGB machine learning algorithms, and their predictive performance was validated using various analyses and an external dataset. The iRegulon tool was utilized to predict upstream transcription factors associated with ferroptosis feature genes, from which differentially expressed transcription factors were screened to construct a "Transcription Factor—FRG—ferroptosis" regulatory network. Finally, in vitro (cultured cells) and in vivo (rat) models were utilized to evaluate the regulatory mechanisms of ferroptosis in normal and PE placental tissues. Results Differential analysis of the four merged GEO datasets identified 41 DE-FRGs. NMF clustering based on DE-FRGs revealed two PE subtypes. Immunocyte infiltration analysis indicated significant differences in immune levels between these subtypes. Further WGCNA analysis identified module genes associated with PE and these two subtypes. Subsequently, we developed an integrated machine learning model incorporating five FRGs and validated its predictive efficacy using various analyses and an external validation dataset. Finally, based on the transcription factor ARID3A and ferroptosis feature genes EPHB3 and PAPPA2, we constructed a "Transcription Factor—FRG—ferroptosis" regulatory network, with in vitro and in vivo experiments confirming that ARID3A promotes the progression of PE and ferroptosis by activating the expression of EPHB3 and PAPPA2. Conclusion This analytical journey illuminated a critical regulatory nexus in PE, underscoring the central influence of ARID3A on PE through ferroptosis-mediated pathways. Graphical Abstract Ferroptosis in Preeclampsia: ARID3A reg...

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An Intelligent Auxiliary Diagnostic System for Early Osteoporosis Screening Using Stacking Ensemble Learning

wang,

shang,

et al. 2024

Preprint

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Screening of Important Markers in Peripheral Blood Mononuclear Cells to Predict Female Osteoporosis Risk Using LASSO Regression Algorithm and SVM Method

Cited by 3 publications

References 47 publications

A study on the anti-osteoporosis mechanism of isopsoralen based on network pharmacology and molecular experiments

A study on the anti-osteoporosis mechanism of isopsoralen based on network pharmacology and molecular experiments

NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes

An Intelligent Auxiliary Diagnostic System for Early Osteoporosis Screening Using Stacking Ensemble Learning

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