ObjectiveTo explore the key modules and Hub genes related to the development of breast cancer from the level of gene network, and to verify whether these Hub genes have breast cancer specificity.MethodsThe key modules for the development of breast cancer were screened by weighted gene co-expression network analysis (WGCNA). The gene annotation database Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to enrich the function of the modules, exploring the Hub genes having the highest correlation between the development of breast cancer. Simultaneously, we analyzed the relationship between Hub genes and tumor collection unit.ResultsWGCNA defined 10 co-expression modules, of which the blue module was the key module related to the development of breast cancer and other malignant tumors. The genes included in this module were significantly enriched in pathways such as the Cell Cycle pathway (KEGG ID: cfa04110), the Viral Oncogenic pathway (KEGG ID: hsa05203), Cancer pathway (KEGG ID: hsa05200), and Systemic Lupus Erythematosus (KEGG ID: hsa05322). The top eight Hub genes were finally extracted from the blue module including NUSAP1, FOXM1, KIF20A, BIRC5, TOP2A, RRM2, CEP55, and ASPM. Among them, NUSAP1, KIF20A, TOP2A, CEP55 and ASPM were also closely related to the occurrence and development of tumor collection unit.ConclusionWGCNA can screen for key modules and Hub genes which are biologically relevant to the clinical features of our interest, and the Hub genes have no breast cancer specificity participating in breast cancer development .
Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.