CILP, a Putative Gene Associated With Immune Infiltration in Breast Cancer Brain Metastases.

Breast cancer (BC) is the second leading cause of brain metastases (BM), with high morbidity and mortality. The aim of our study was to explore the effect of the cartilage intermediate layer protein (CILP) on breast cancer brain metastases (BCBM). Using a weighted gene coexpression network analysis (WGCNA) in GSE100534 and GSE125989 datasets, we found that the yellow module was closely related to the occurrence of BCBM, and CILP was a hub gene in the yellow module. Low CILP expression was associated with a poor prognosis, and it was an independent prognostic factor for stage III-IV BC determined using Cox regression analysis. A nomogram model including CILP expression was established to predict the 5-, 7-, and 10-year overall survival (OS) probabilities of stage III-IV BC patients. We found that CILP mRNA expression was downregulated in BCBM through GSE100534, GSE125989, and GSE43837 datasets. In addition, we found that CILP mRNA expression was negatively correlated with vascular endothelial growth factor A (VEGFA), which is involved in regulating the development of BM. UALCAN analysis showed that CILP expression was downregulated in HER2-positive (HER2+) and triple-negative breast cancer (TNBC), which are more prone to BM. The vitro experiments demonstrated that CILP significantly inhibited BC cell proliferation and metastasis. Western blot (WB) results further showed that the mesenchymal protein marker vimentin was significantly downregulated following CILP overexpression, suggesting that CILP could participate in migration through epithelial-mesenchymal transition (EMT). A comparison of CILP expression using immunohistochemistry in BC and BCBM showed that CILP was significantly downregulated in BCBM. In addition, gene set variation analysis (GSVA) revealed that CILP was associated with the T-cell receptor signaling pathway in BCBM and BC, indicating that CILP may be involved in BCBM through immune effects. BCBM showed lower immune infiltration than BC. Moreover, CILP expression was positively correlated with HLA-II, T helper cells (CD4+ T cells), and Type II IFN Response in BCBM. Collectively, our study indicates that CILP is associated with immune infiltration and may be a putative gene involved in BCBM. CILP offers new insights into the pathogenesis of BCBM, which will facilitate the development of novel targets for BCBM patients.

Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering.

Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners' enthusiasm and give full play to different learners' learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.