RESUMEN
Background: The diagnosis of hepatocellular carcinoma (HCC) often experiences latency, ultimately leading to unfavorable patient outcomes due to delayed therapeutic interventions. Our study is designed to develop and validate a model that employs triple-phase computerized tomography (CT)-based deep learning radiomics and clinical variables for early warning of HCC in patients with cirrhosis. Methods: We studied 1858 patients with cirrhosis primarily from the PreCar cohort (NCT03588442) between June 2018 and January 2020 at 11 centres, and collected triple-phase CT images and laboratory results 3-12 months prior to HCC diagnosis or non-HCC final follow-up. Using radiomics and deep learning techniques, early warning model was developed in the discovery cohort (n = 924), and then validated in an internal validation cohort (n = 231), and an external validation cohort from 10 external centres (n = 703). Findings: We developed a hybrid model, named ALARM model, which integrates deep learning radiomics with clinical variables, enabling early warning of the majority of HCC cases. The ALARM model effectively predicted short-term HCC development in cirrhotic patients with area under the curve (AUC) of 0.929 (95% confidence interval 0.918-0.941) in the discovery cohort, 0.902 (0.818-0.987) in the internal validation cohort, and 0.918 (0.898-0.961) in the external validation cohort. By applying optimal thresholds of 0.21 and 0.65, the high-risk (n = 221, 11.9%) and medium-risk (n = 433, 23.3%) groups, which covered 94.4% (84/89) of the patients who developed HCC, had significantly higher rates of HCC occurrence compared to the low-risk group (n = 1204, 64.8%) (24.3% vs 6.4% vs 0.42%, P < 0.001). Furthermore, ALARM also demonstrated consistent performance in subgroup analysis. Interpretation: The novel ALARM model, based on deep learning radiomics with clinical variables, provides reliable estimates of short-term HCC development for cirrhotic patients, and may have the potential to improve the precision in clinical decision-making and early initiation of HCC treatments. Funding: This work was supported by National Key Research and Development Program of China (2022YFC2303600, 2022YFC2304800), and the National Natural Science Foundation of China (82170610), Guangdong Basic and Applied Basic Research Foundation (2023A1515011211).
RESUMEN
5-hydroxytryptamine (5-HT) exerts various physiological effects on the intestine through different signaling pathways and molecular transmission mechanisms, including pro- and anti-inflammatory effects. Adoptive transfer of regulatory B cells (Bregs) into colitis mice has exhibited significant therapeutic benefits. We aimed to elucidate the mechanism through which 5-HT-treated B cells alleviate ulcerative colitis. To this end, we analyzed the proteomic and phosphoproteomic profiles of 5-HT-stimulated B cells from naïve mice. We identified 3124 phosphorylation sites in proteins via tandem mass tagging and found 110 differential peptides after protein phosphorylation. Furthermore, we obtained three differential proteins, RIPK1, ATXN2l, and Q8C5K5 through integration of both proteomic datasets. We discovered and validated that 5-HT binds to 5-HT7R and increases the expression of RIPK1 in B cells. We propose a theoretical and experimental basis for further research on the RIPK1 signaling pathway, kinase prediction, and phosphorylation sites in ulcerative colitis. SIGNIFICANCE: Some researchers demonstrated that 5-HT can effectively suppress colitis through a variety of molecular mechanisms. Our study discovered and consistently validated the 5-HT/5-HT7R/RIPK1 pathway, further clarifying the molecular mechanism through which 5-HT stimulates B cells to alleviate intestinal inflammation.