NCAPG as a novel prognostic biomarker in numerous cancers: a meta-analysis and bioinformatics analysis.

BACKGROUND: Identification of effective biomarkers for cancer prognosis is a primary research challenge. Recently, several studies have reported the relationship between NCAPG and the occurrence of various tumors. However, none have combined meta-analytical and bioinformatics approaches to systematically assess the role of NCAPG in cancer. METHODS: We searched four databases, namely, PubMed, Web of Science, Embase, and the Cochrane Library, for relevant articles published before April 30, 2022. The overall hazard ratio or odds ratio and 95% confidence intervals were calculated to assess the relationship between NCAPG expression and cancer survival prognosis or clinical characteristics. Furthermore, the aforementioned results were validated using the GEPIA2, Kaplan-Meier plotter, and PrognoScan databases. RESULTS: The meta-analysis included eight studies with 1096 samples. The results showed that upregulation of NCAPG was correlated with poorer overall survival (hazard ratio = 2.90, 95% confidence interval = 2.06-4.10, P < 0.001) in the cancers included in the study. Subgroup analysis showed that in some cancers, upregulation of NCAPG was correlated with age, distant metastasis, lymph node metastasis, TNM stage, relapse, differentiation, clinical stage, and vascular invasion. These results were validated using the GEPIA2, UALCAN, and PrognoScan databases. We also explored the processes of NCAPG methylation and phosphorylation. CONCLUSION: Dysregulated NCAPG expression is associated with the clinical prognostic and pathological features of various cancers. Therefore, NCAPG can serve as a human cancer therapeutic target and a new potential prognostic biomarker.

Deep learning enabled inverse design of nanocrystal-based optical diffusers for efficient white LED lighting.

Angular color uniformity and luminous flux are the most important figures of merit for a white-light-emitting diode (WLED), and simultaneous improvement of both figures of merit is desired. The cellulose-nanocrystal (CNC)-based optical diffuser has been applied on the WLED module to enhance angular color uniformity, but it inevitably causes the reduction of luminous flux. Here we demonstrate a deep-learning-based inverse design approach to design CNC-coated WLED modules. The developed forward neural network successfully predicts two figures of merit with high accuracy, and the inverse predicting model can rapidly design the structural parameters of CNC film. Further explorations taking advantage of both forward and inverse neutral networks can effectively construct the coating layer for WLED modules to reach the best performance.