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Abstract Background Upregulation of RNA polymerase (Pol) III products, including tRNAs and 5S rRNA, in tumor cells leads to enhanced protein synthesis and tumor formation, making it a potential target for cancer treatment. In this study, we evaluated the inhibition of Pol III transcription by triptolide and the anti-cancer effect of this drug in colorectal tumorigenesis. Methods The effect of triptolide on colorectal cancer development was assessed in colorectal cancer mouse models, 3D organoids, and cultured cells. Colorectal cancer cells were treated with triptolide. Pol III transcription was measured by real-time quantitative polymerase chain reaction (PCR). The formation of TFIIIB, a multi-subunit transcription factor for Pol III, was determined by chromatin immunoprecipitation (ChIP), co-immunoprecipitation (Co-IP), and fluorescence resonance energy transfer (FRET). Results Triptolide reduced both tumor number and tumor size in adenomatous polyposis coli (Apc) mutated (ApcMin/+) mice as well as AOM/DSS-induced mice. Moreover, triptolide effectively inhibited colorectal cancer cell proliferation, colony formation, and organoid growth in vitro, which was associated with decreased Pol III target genes. Mechanistically, triptolide treatment blocked TBP/Brf1interaction, leading to the reduced formation of TFIIIB at the promoters of tRNAs and 5S rRNA. Conclusions Together, our data suggest that inhibition of Pol III transcription with existing drugs such as triptolide provides a new avenue for developing novel therapies for colorectal cancer.

Abstract Background There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. Methods We recruited patients with respiratory failure at the First People’s Hospital of Yancheng and the People’s Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness. Results The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value. Conclusions A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.