Omics Integration and Network Medicine to Decipher Human Complex Diseases

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Objectives: DFU is a serious chronic disease with high disability and fatality rates, yet there is no completely effective therapy. While ferroptosis is integrated to inflammation and infection, its involvement in DFU is still unclear. The study aimed to identify ferroptosis-related genes in DFU, providing potential therapeutic targets.

Methods: In the GEO database, two DFU microarray datasets (GSE147890 and GSE80178) were collected. WGCNA was conducted to identify the modular genes most involved in DFU. Subsequently, enrichment analysis and PPI analysis were performed. To yield the DFU-associated ferroposis genes, the ferroposis genes were retrieved from the FerrDb database and overlapped with the modular genes. Eventually, an optimal DFU prediction model was created by combining multiple machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to detect ferroposis genes most closely associated with DFU. The accuracy of the model was verified by utilizing external datasets (GSE7014) based on ROC curves.

Results: WGCNA yielded seven modules in all, and 1223 DFU-related modular genes were identified. GO analysis revealed that inflammatory response, decidualization, and protein binding were the most highly enriched terms. These module genes were also enriched in the ErbB signaling, IL-17 signaling, MAPK signaling, growth hormone synthesis, secretion and action, and tight junction KEGG pathways. Twenty-five DFU-associated ferroposis genes were obtained by cross-linking with modular genes, which could distinguish DFU patients from controls. Ultimately, the prediction model based on machine learning algorithms was well established, with high AUC values (0.79 of LASSO, 0.80 of SVM, 0.75 of Boruta, 0.70 of XGBoost). MAFG and MAPK3 were identified by the prediction model as the most highly associated ferroposis-genes in DFU. Furthermore, the external dataset (GSE29221) validation revealed that MAPK3 (AUC = 0.81) had superior AUC values than MAFG (AUC = 0.62).

Conclusion: As the most related ferroptosis-genes with DFU, MAFG and MAPK3 may be employed as potential therapeutic targets for DFU patients. Moreover, MAPK3, with higher accuracy, could be the more potential ferroptosis-related biomarker for further experimental validation.

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Background: Previous studies revealed that the gene signatures are associated with the modulation and pathogenesis of pulmonary arterial hypertension (PAH). However, identifying critical transcriptional signatures in the blood of PAH patients remains lacking.

Methods: The differentially expressed transcriptional signatures in the blood of PAH patients were identified by a meta-analysis from four microarray datasets. Then we investigated the enrichment of gene ontology and KEGG pathways and identified top hub genes. Besides, we investigated the correlation of crucial hub genes with immune infiltrations, hallmark gene sets, and blood vessel remodeling genes. Furthermore, we investigated the diagnostic efficacy of essential hub genes and their expression validation in an independent cohort of PAH, and we validate the expression level of hub genes in monocrotaline (MCT) induced PAH rats’ model. Finally, we have identified the FDA-approved drugs that target the hub genes and their molecular docking.

Results: We found 1,216 differentially expressed genes (DEGs), including 521 up-regulated and 695 down-regulated genes, in the blood of the PAH patients. The up-regulated DEGs are significantly associated with the enrichment of KEGG pathways mainly involved with immune regulation, cellular signaling, and metabolisms. We identified 13 master transcriptional regulators targeting the dysregulated genes in PAH. The STRING-based investigation identified the function of hub genes associated with multiple immune-related pathways in PAH. The expression levels of RPS27A, MAPK1, STAT1, RPS6, FBL, RPS3, RPS2, and GART are positively correlated with ssGSEA scores of various immune cells as positively correlated with the hallmark of oxidative stress. Besides, we found that these hub genes also regulate the vascular remodeling in PAH. Furthermore, the expression levels of identified hub genes showed good diagnostic efficacy in the blood of PAH, and we validated most of the hub genes are consistently dysregulated in an independent PAH cohort. Validation of hub genes expression level in the monocrotaline (MCT)-induced lung tissue of rats with PAH revealed that 5 screened hub genes (MAPK1, STAT1, TLR4, TLR2, GART) are significantly highly expressed in PAH rats, and 4 screened hub genes (RPS6, FBL, RPS3, and RPS2) are substantially lowly expressed in rats with PAH. Finally, we analyzed the interaction of hub proteins and FDA-approved drugs and revealed their molecular docking, and the results showed that MAPK1, TLR4, and GART interact with various drugs with appropriate binding affinity.

Conclusion: The identified blood-derived key transcriptional signatures significantly correlate with immune infiltrations, hypoxia, glycolysis, and blood vessel remodeling genes. These findings may provide new insight into the diagnosis and treatment of PAH patients.

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Frontiers in Genetics

Deciphering Brain Complexity through Multi-Omics Research: Insights into Cancers and Non-Neoplastic Diseases
Edited by Bharati Mehani, Hernando Lopez-Bertoni, Simona D'Aguanno, Yogesh Kumar
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19 February 2025
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