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 .
ObjectiveTo explore the expression of genes related to hepatocellular carcinoma (HCC) stem cells and their prognostic correlation by using weighted gene co-expression network analysis (WGCNA).MethodsFirstly, the transcriptome sequencing (RNA-seq) and clinical data of HCC were downloaded from the public database the Cancer Genome Atlas (TCGA), and the mRNA expression-based stiffness index (mRNAsi) table of cancer stem cells was downloaded and sorted out to analyze the relationship between mRNAsi and pathological grade and prognosis of HCC. The mRNAsi of HCC was downloaded and the prognostic value of mRNAsi was discussed. Then we used WGCNA to screen the key modules related to liver cancer stem cells (LSCS). Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. The online database STRING was used to construct hub genes coding proteins interaction (PPI) network and screen key genes. Finally, the key genes were analyzed for expression differences and expression correlations. The online database Kaplan-Meier plotter was used for survival analysis and verified.ResultsmRNAsi was significantly upregulated in cancer tissues (P<0.001), and increased with the increase of pathological grade of HCC (P=0.001). The mortality rate of the higher mRNAsi group was higher than that of the lower mRNAsi group (P=0.006). GO analysis found that hub genes were mainly involved in biological processes, such as mitosis and DNA replication, and KEGG showed that hub genes were enriched in cell cycle, DNA mismatch repair, oocyte meiosis, and other signaling pathways. We screened 10 key genes (included CCNB1, CDC20, CDCA8, NDC80, KIF20A, TTK, CDC45, KIF15, MCM2, and NCAPG) related to mRNAsi of HCC based on WGCNA. The key genes were highly expressed in the tumor samples compared to the normal samples. In addition, there was a strong interaction between proteins of these key genes (P<0.05), a strong co-expression relationship at the transcriptional level, and all related to prognosis of HCC.ConclusionsmRNAsi plays an important role in the occurrence and development of HCC. Ten key genes related to LSCS were screened, which may act as therapeutic targets for inhibiting the stem cell characteristics of HCC.
ObjectiveTo find the hub genes related to bone metastasis of breast cancer by weighted geneco-expression network analysis (WGCNA) method, and provide theoretical support for the development of new targeted therapeutic drugs.MethodsThe basic clinical features of 286 breast cancer patients and the gene expression information of tumor specimens were downloaded from the GSE2034 dataset from the Gene Expression Omnibus. R software was used to analyze the gene microarray. The WGCNA package embedded in the R software was used for various analysis in weighted correlation network analysis. Cox proportional hazard regression was performed by using SPSS software.ResultsThe top one quarter genes with the greatest variance variability were selected by WGCNA, and a total of 5 000 genes were used for further enrichment analysis. Finally, 15 gene co-expression modules were constructed, and the magenta module (r=0.94, P<0.001) was significantly positively correlated with bone metastasis of breast cancer. It was further found that six hub genes highly associated with bone metastasis in the magenta module were: Ral GTPase-activating protein subunitalpha-1 (RALGAPA1), B-cell antigen receptor complex-associated protein alpha chain (CD79A), immunoglobulin kappa chain C region (IGKC), arrestin beta 2 (ARRB2), differentially expressed in FDCP 6 homolog (DEF6), and immunoglobulin lambda variable 2 (IGLV2).ConclusionWe found that RALGAPA1, CD79A, IGKC, ARRB2. DEF6, and IGLV2 may play an important role in bone metastasis of breast cancer.
Objective To explore the potential mechanism of the occurrence and development of lupus nephritis (LN) and identify key biomarkers and immune-related pathways associated with the progression of LN. Methods We downloaded a dataset from the Gene Expression Omnibus database. By analyzing the differential expression of genes and performing weighted gene co-expression network analysis (WGCNA), as well as Gene Ontology enrichment, Disease Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, we explored the biological functions of differentially expressed genes in LN. Using three machine learning models, namely LASSO regression, support vector machine, and random forest, we identified the hub genes in LN, and constructed a line diagram diagnosis model based on the hub genes. The diagnostic accuracies of the hub genes were evaluated using the receiver operating characteristic curve, and the relationship between known marker gene sets and hub gene expression was analyzed using single sample gene set enrichment analysis. Results We identified a total of 2297 differentially expressed genes. WGCNA generated 7 co-expression modules, among which the cyan module had the highest correlation with LN. We obtained 347 target genes by combining differential genes. Using the three machine learning methods, LASSO regression, support vector machine, and random forest, we identified three hub genes (CLC, ADGRE4P, and CISD2) that could serve as potential biomarkers for LN. The area under the receiver operating characteristic curve (AUC) analysis showed that these three hub genes had significant diagnostic value (AUCCLC=0.718, AUCADGRE4P=0.813, AUCCISD2=0.718). According to single sample gene set enrichment analysis, the hub genes were mainly associated with apoptosis, glycolysis, metabolism, hypoxia, and tumor necrosis factor-α-nuclear factor-κB-related pathways. Conclusions By combining WGCNA and machine learning techniques, three hub genes (CLC, ADGRE4P, and CISD2) that may be involved in the occurrence and development of LN are identified. These genes have the potential to aid in the early clinical diagnosis of LN and provide insight into the mechanisms underlying LN progression.
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.
ObjectiveTo investigate the pan-cancer expression profile, prognostic value, co-expression networks, immune regulatory roles of BRF1, and its biological functions and molecular mechanisms in esophageal squamous cell carcinoma (ESCC). MethodsIntegrated analysis of TCGA pan-cancer datasets was performed to evaluate BRF1 expression differences between tumor/normal tissues, survival correlations, co-expressed gene-enriched pathways, and immune features (immune checkpoints, cytokines, immune cell infiltration). GEO datasets were used to validate BRF1 expression in ESCC. BRF1 was knocked down using siRNA in ESCC cells, with MTT and Transwell assays assessing proliferation/migration, and Western blot analyzing proliferation- (PCNA) and migration-related proteins (Vimentin, MMP, E-Cadherin). TCGA data were analyzed to explore BRF1-ferroptosis correlations. ResultsBRF1 was significantly upregulated in over 20 cancer types. High BRF1 expression predicted poor prognosis in adrenocortical carcinoma (ACC) and prostate adenocarcinoma (PRAD). BRF1 positively regulated T cell-mediated cell death pathways in ESCA and circadian rhythm pathways in PAAD. BRF1 exhibited cancer-type-specific correlations with immune checkpoints, cytokine networks, and immune cell infiltration. In vitro, BRF1 knockdown suppressed ESCC proliferation (PCNA downregulation) and migration (Vimentin/MMP downregulation, E-Cadherin upregulation). BRF1 expression positively correlated with ferroptosis antagonists (GPX4, HSPA5, SLC7A11). ConclusionBRF1 demonstrates complex pan-cancer expression and functional heterogeneity, modulating tumor progression and immune infiltration. BRF1 promotes ESCC proliferation and migration, potentially via ferroptosis resistance regulation, highlighting its potential as a therapeutic target in ESCC.