No abstract

No abstract

Objective We used bioinformatics analysis to identify potential biomarker genes and their relationship with breast cancer (BC). Materials and Methods We used a weighted gene co-expression network analysis (WGCNA) to create a co-expression network based on the top 25% genes in the GSE24124, GSE33926, and GSE86166 datasets obtained from the Gene Expression Omnibus. We used the DAVID online platform to perform GO and KEGG pathway enrichment analyses and the Cytoscape CytoHubba plug-in to screen the potential genes. Then, we related the genes to prognostic values in BC using the Oncomine, GEPIA, and Kaplan–Meier Plotter databases. Findings were validated by immunohistochemical (IHC) staining in the Human Protein Atlas and the TCGA-BRCA cohort. LinkedOmics identified the interactive expressions of hub genes. We used UALCAN to evaluate the methylation levels of these hub genes. MethSurv and SurvivalMeth were used to assess the multilevel prognostic value. Finally, we assessed hub gene association with immune cell infiltration using TIMER. Results The mRNA levels of MKI67, UBE2C, GTSE1, CCNA2, and MND1 were significantly upregulated in BC, whereas ESR1, THSD4, TFF1, AGR2, and FOXA1 were significantly downregulated. The DNA methylation signature analysis showed a better prognosis in the low-risk group. Further subgroup analyses revealed that MND1 might serve as an independent risk factor for unfavorable BC prognosis. Additionally, MND1 expression levels positively correlate with the immune infiltration statuses of CD4+ T cells, CD8+ T cells, B cells, neutrophils, dendritic cells, and macrophages. Conclusion Our results indicate that the ten hub genes may be involved in BC’s carcinogenesis, development, or metastasis, and MND1 may be a potential biomarker and therapeutic target for BC.

There is stunning rapid development of human brains in the first year of life. Some studies have revealed the tight connection between cognition skills and cortical morphology in this period. Nonetheless, it is still a great challenge to predict cognitive scores using brain morphological features, given issues like small sample size and missing data in longitudinal studies. In this work, for the first time, we introduce the path signature method to explore hidden analytical and geometric properties of longitudinal cortical morphology features. A novel BrainPSNet is proposed with a differentiable temporal path signature layer to produce informative representations of different time points and various temporal granules. Further, a two-stream neural network is included to combine groups of raw features and path signature features for predicting the cognitive score. More importantly, considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed method achieves the state-of-the-art performance. Additionally, the relationship between morphological features and cognitive abilities is also presented.

No abstract