Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

No abstract

Background: Breast cancer is one of the malignant tumors that threaten women's health, with HER2+ breast cancer being more aggressive. In this study, bioinformatics methods were used to find potential key genes in HER2 + for diagnosis and treatment.Methods: Datasets of HER2+ breast cancer and normal tissue samples retrieved from TCGA databases were subjected to DEGs analysis using R software. Then WGCNA is constructed for DEGs. The key gene co-expression modules were then subjected to GO and KEGG pathway enrichment analyses, as well as construction of PPI networks using the STRING database for identifying key genes. Finally, key genes were further validated by survival analysis, protein expression, and COX regression models.Results: We identified 2063 DEGs and 4 gene co-expression modules. Functional enrichment analysis showed that these key co-expression modules were mainly associated with extracellular matrix organization, extracellular matrix structural constituent and neuroactive ligand−receptor interaction. PPI network visualization identified 100 key genes, 3 of which were not present in the other subtypes of breast cancer. UTS2 DRD4 and GLP1R are key genes specific to the HER2+ subtype. Survival analysis showed that UTS2 are prognosis-related key genes in HER2+ breast cancer. Finally, UTS2 combined with clinical data to construct Cox regression model.Conclusions: Combined with the two screening methods, 3 key genes closely related to HER2 + breast cancer were identified. UTS2 is a new potential key gene and may become a new therapeutic target for HER2 + breast cancer.

The cervical facet has complicated 3D microstructures and inhomogeneities. The cervical facet joint, which also participates in the formation, plays a certain role in regulating and limiting the movement of the spine. Correct identification and evaluation of its microstructure can help in the diagnosis of orthopedic disease and predict early phases of fracture risk. To evaluate the safety of the cervical spine by measuring and analyzing the microstructures and morphometric parameters of bone trabeculae in the normal cervical facet with high-resolution 3D micro-computed tomography. Thirty-one sets of C3 to C7 lower cervical vertebrae (155 vertebrae) were scanned using micro-computed tomography. The morphological characteristics and direction of trabecular bone in the facet of the lower cervical vertebrae were observed by selecting and rebuilding the areas of interest, and the changes in the microstructure of the areas of interest were calculated to reveal the structural characteristics and weak areas. Images indicated an ossified center between the superior and inferior articular processes of the lower cervical spine. The cellular bone trabeculae of the articular process had complex reticular microstructures. The trabecular bone plate near the cortical bone was lamellar and relatively dense, and it extended around and transformed into a network structure, and then into the rod-shaped trabecular bone. The rod-shaped trabeculae converged with the plate-shaped trabeculae with only 1 to 2 layers surrounding the trabeculae cavity. Statistical results of the morphological parameters of the trabecular bone showed that trabecular bone volume fraction values were significantly higher for C7 than for C3 to C6 (P < .05). There were significant differences between C7 and C3 to C5 and between C6 and C4 in bone surface area/bone volume (P < .05). There was a significant difference between C7 and C3 to C6 in trabecular bone thickness values (P < .05). The degree of anisotropy value was significantly smaller for C3 than for C6 and C7 (P < .05). The changes in the C3 to C7 microstructure were summarized in this study. The loading capacity and stress of the C7 articular process tended to be limited, and the risk of injury tended to be higher for the C7 articular process.