Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches

Da, Zheng; Guo, Rui; Sun, Jianjian; Wang, Ai

doi:10.1186/s12920-023-01672-y

Cited by 2 publications

References 57 publications

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Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

Costa,

Angelini,

Franzese

et al. 2024

J Transl Med

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Background Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. Methods We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. Results We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient’s risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). Conclusions The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient’s risk scores can be used in clinical settings.

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Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

Costa,

Angelini,

Franzese

et al. 2024

J Transl Med

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Identification and Validation of Autophagy-Related Genes in Osteoarthritis through Bioinformatics and Machine Learning

Du,

Xie,

Zhou

et al. 2024

Preprint

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Osteoarthritis (OA) is a common degenerative joint disease affecting the elderly worldwide. Although increasing evidence suggests a close relationship between autophagy and OA, its pathogenesis remains unclear. This study aimed to identify autophagy-related genes in OA using bioinformatics and machine learning methods. Three OA datasets (GSE55235, GSE55457 and GSE12021) were retrieved from the GEO database for differential analysis. Subsequently, differentially expressed genes (DEGs) were intersected with autophagy-related genes to identify differentially expressed autophagy-related genes (DEARGs), which were then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Subsequently, potential key genes were selected using three machine learning algorithms (LASSO, SVM and RF) and their diagnostic accuracy was validated using an external dataset (GSE114007) to determine the key genes. Next, potential interactions between the key genes were predicted using the GeneMANIA database. Additionally, immune cell infiltration analysis was performed to explore the correlation between the key genes and immune cells. Finally, the expression levels of the key genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). In this study, a total of 27 DEARGs were identified. GO and KEGG enrichment analyses indicated that these DEARGs might be associated with pathways related to cellular immunity, autophagy, and inflammation. Four potential key genes were selected through the use of three machine learning algorithms. Notably, validation with the external dataset revealed that the expression levels of PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA and exhibited strong diagnostic performance. Immune infiltration analysis showed that PPP1R15A, GABARAPL1 and FOXO3 were positively correlated with activated mast cells and resting memory CD4 + T cells, but negatively correlated with plasma cells and M0 macrophages. Finally, qRT-PCR confirmed these results, which were consistent with the bioinformatics analysis.In conclusion, this study identifies PPP1R15A, GABARAPL1 and FOXO3 as autophagy key genes in OA, providing potential targets for the diagnosis and treatment of OA.

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Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches

Cited by 2 publications

References 57 publications

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

Identification and Validation of Autophagy-Related Genes in Osteoarthritis through Bioinformatics and Machine Learning

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