Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation / 药物分析学报

The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma(ESCC) that combines plasma metabolomics with machine learning algorithms.Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls.The dataset was split into a training set and a test set.After identification of differential me-tabolites in training set,single-metabolite-based receiver operating characteristic (ROC) curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls.Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites.Finally,twelve differential plasma metabolites (six up-regulated and six down-regulated) were annotated.The pre-dictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows:arachidonic acid (accuracy:0.887),sebacic acid (accuracy:0.867),indoxyl sulfate (accuracy:0.850),phosphatidylcholine (PC) (14:0/0:0) (accuracy:0.825),deoxycholic acid(accuracy:0.773),and trimethylamine N-oxide (accuracy:0.653).The prediction accuracies of the ma-chine learning models in the test set were partial least-square (accuracy:0.947),random forest (accu-racy:0.947),gradient boosting machine (accuracy:0.960),and support vector machine (accuracy:0.980).Additionally,survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor(hazard ratio (HR):1.752),while PC (14:0/0:0) (HR:0.577) was a favorable prognostic factor for ESCC.This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC.

Sp7/osterix positively regulates dlx2b and bglap to affect tooth development and bone mineralization in zebrafish larvae

Osterix (or Sp7) is an important transcription factor that promotes osteoblast differentiation by modulating the expression ofa range of target genes. Although many studies have focused on Osterix/Sp7 regulatory mechanisms, the detailed functionshave not been fully elucidated. Toward this end, in this study, we used CRISPR/Cas9 technology to knock out the zebrafishsp7 gene, and then analyzed its phenotype and biological function. Two knockout sp7 mutant lines were successfullyobtained. The bone mineralization level was significantly reduced in the zebrafish sp7-/- homozygote, resulting inabnormal tooth development in the larvae. Quantitative real-time polymerase chain reaction showed that loss of sp7 led todown-regulated expression of the dlx2b and bglap genes related to tooth development and bone mineralization, respectively. Moreover, cell transfection experiments demonstrated that Sp7 directly regulates the expression of dlx2b and bglapthrough Sp7-binding sites on the promoter regions of these two genes. Overall, this study provides new insight into the roleof Sp7 in bone mineralization and tooth development.