RESUMO
Background: Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that greatly affect the overall survival and quality of life of patients. It is therefore important to use bioinformatics to explore the mechanisms of ACP development and progression and to identify new molecules. Methods: Sequencing data of ACP was downloaded from the comprehensive gene expression database for differentially expressed gene identification and visualized by Gene Ontology, Kyoto Gene, and gene set enrichment analyses (GSEAs). Weighted correlation network analysis was used to identify the genes most strongly associated with ACP. GSE94349 was used as the training set and five diagnostic markers were screened using machine learning algorithms to assess diagnostic accuracy using receiver operating characteristic (ROC) curves, while GSE68015 was used as the validation set for verification. Results: Type I cytoskeletal 15 (KRT15), Follicular dendritic cell secreted peptide (FDCSP), Rho-related GTP-binding protein RhoC (RHOC), Modulates negatively TGFB1 signaling in keratinocytes (CD109), and type II cytoskeletal 6A (KRT6A) (area under their receiver operating characteristic curves is 1 for both the training and validation sets), Nomograms constructed using these five markers can predict progression of ACP patients. Whereas ACP tissues with activated T-cell surface glycoprotein CD4, Gamma delta T cells, eosinophils and regulatory T cells were expressed at higher levels than in normal tissues, which may contribute to the pathogenesis of ACP. According to the analysis of the CellMiner database (Tumor cell and drug related database tools), high CD109 levels showed significant drug sensitivity to Dexrazoxane, which has the potential to be a therapeutic agent for ACP. Conclusions: Our findings extend understandings of the molecular immune mechanisms of ACP and suggest possible biomarkers for the targeted and precise treatment of ACP.
RESUMO
Background: Human epidermal growth factor receptor 2 (HER2) is a landmark protein in determining the targeted treatment of breast cancer (BC). However, the latest research shows that different intensity of HER2 protein expression levels in BC leads to different clinical characteristics, treatment, and prognosis, especially in HER2 low expression patients. Therefore, this study intends to analyze and compare the clinicopathologic features and prognosis of BC patients with low and zero HER2 expression from The Cancer Genome Atlas (TCGA) database and the data collected by our center. Methods: First, the BC dataset was downloaded from TCGA database, including 345 eligible and with complete clinical information BC patients, to compare the difference between HER2 low expression groups and HER2 zero expression groups and their correlation with estrogen receptor (ER) and progesterone receptor (PR) expression. Then, the clinicopathological data and follow-up of 405 patients with HER2 low expression and HER2 zero expression diagnosed with BC admitted to the Affiliated Hospital of Youjiang Medical University for Nationalities (YJMU) from January 2017 to December 2021 were collected to verify the consistency of the results of the two data sets. Results: Both the clinical samples and the TCGA data showed that the ER and PR rates were higher in the HER2 low expression group compared with the HER2 zero expression group. There were no significant differences in tumor size, lymph node metastasis, distant metastasis, and disease-free survival (DFS). In addition, the data analysis of 405 clinical samples also showed that the HER2 low expression group had a lower 3-year recurrence or metastasis rate compared with the HER2 zero expression group. Conclusions: Compared with HER2 zero expression, HER2 low patients express more ER and PR, and have less short-term recurrence and metastasis, but there is no obvious difference in DFS between the two groups.
