Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies.

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.

Bioinformatics combined with clinical data to analyze clinical characteristics and prognosis in patients with HER2 low expression breast cancer.

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.