Improving Analysis and Annotation of Microarray Data with Protein Interactions

Kotlyar, Max; Wong, Serene; Pastrello, Chiara; Jurišica, Igor

doi:10.1007/978-1-0716-1839-4_5

Cited by 1 publication

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“…In recent years, comprehensive bioinformatics analysis has deepened our understanding of disease pathobiology at the genetic level and facilitated the identification of new genes associated with various diseases. These genes can potentially serve as diagnostic and prognostic biomarkers [19] .…”

Section: Introductionmentioning

confidence: 99%

Identification of shared gene signatures for diagnosing Systemic lupus erythematosus with osteonecrosis of the femoral head through bioinformatics analysis and machine learning

Lu,

Zhang,

et al. 2024

Preprint

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Background: Osteonecrosis of the femoral head (ONFH) is a severe complication of systemic lupus erythematosus (SLE). Although there is a characteristic disease spectrum between SLE and ONFH, the exact pathogenesis remains unclear. Furthermore, the lack of early diagnostic criteria complicates the diagnosis of SLE-ONFH. This study aimed to identify key diagnostic candidate genes in patients with SLE and ONFH. Methods: SLE and ONFH datasets were obtained from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein‒protein interaction (PPI) network was constructed, and machine learning algorithms—extreme gradient boosting (XGB), support vector machine (SVM), random forest (RF), and generalized linear models (GLMs)—were applied to identify candidate hub genes for diagnosing SLE combined with ONFH. Receiver operating characteristic (ROC) curves and nomograms were generated. Results: The SLE dataset included 493 DEGs and 1,171 module genes. The ONFH dataset included 384 DEGs and 525 module genes. By intersecting the DEGs and module genes, a total of 74 genes were obtained, which were mainly enriched in the "chemokine signaling pathway" and "cytokine‒cytokine receptor interaction pathway." By constructing the PPI network, 22 node genes were identified. Using machine learning, the five candidate hub genes with the highest scores were selected for nomogram construction and diagnostic value evaluation. The nomogram and all five candidate key genes demonstrated high diagnostic value. Conclusion: The results of this study indicate that ELANE, LTF, ALAS2, MX1, and CA1 are the selected candidate key genes. The construction of a nomogram provides a new direction for the clinical prediction of SLE combined with ONFH.

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Section: Introductionmentioning

confidence: 99%