Background: Recent studies highlighted the functional role of protein arginine methyltransferases (PRMTs) catalyzing the methylation of protein arginine in malignant progression of various tumors. Stratification the subtypes of hepatocellular carcinoma (HCC) is fundamental for exploring effective treatment strategies. Here, we aim to conduct a comprehensive analysis of PRMTs with bioinformatic tools to identify novel biomarkers for HCC subtypes classification and prognosis prediction, which may be potential ideal targets for therapeutic intervention.Methods: The expression profiling of PRMTs in HCC tissues was evaluated based on the data of TCGA-LIHC cohort, and further validated in HCC TMA cohort and HCC cell lines. HCC was systematically classified based on PRMT family related genes. Subsequently, the differentially expressed genes (DEGs) between molecular subtypes were identified, and prognostic risk model were constructed using least absolute shrinkage and selection operator (LASSO) and Cox regression analysis to evaluate the prognosis, gene mutation, clinical features, immunophenotype, immunotherapeutic effect and antineoplastic drug sensitivity of HCC.Results: PRMTs expression was markedly altered both in HCC tissues and HCC cell lines. Three molecular subtypes with distinct immunophenotype were generated. 11 PRMT-related genes were enrolled to establish prognostic model, which presented with high accuracy in predicting the prognosis of two risk groups in the training, validation, and immunotherapy cohort, respectively. Additionally, the two risk groups showed significant difference in immunotherapeutic efficacy. Further, the sensitivity of 72 anticancer drugs was identified using prognostic risk model.Conclusion: In summary, our findings stratified HCC into three subtypes based on the PRMT-related genes. The prognostic model established in this work provide novel insights into the exploration of related therapeutic approaches in treating HCC.
Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.