Smad2/3/4 complex could undergo liquid liquid phase separation and induce apoptosis through TAT in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) represents one of the most significant causes of mortality due to cancer-related deaths. It has been previously reported that the TGF-ß signaling pathway may be associated with tumor progression. However, the relationship between TGF-ß signaling pathway and HCC remains to be further elucidated. The objective of our research was to investigate the impact of TGF-ß signaling pathway on HCC progression as well as the potential regulatory mechanism involved. METHODS: We conducted a series of bioinformatics analyses to screen and filter the most relevant hub genes associated with HCC. E. coli was utilized to express recombinant protein, and the Ni-NTA column was employed for purification of the target protein. Liquid liquid phase separation (LLPS) of protein in vitro, and fluorescent recovery after photobleaching (FRAP) were utilized to verify whether the target proteins had the ability to drive force LLPS. Western blot and quantitative real-time polymerase chain reaction (qPCR) were utilized to assess gene expression levels. Transcription factor binding sites of DNA were identified by chromatin immunoprecipitation (CHIP) qPCR. Flow cytometry was employed to examine cell apoptosis. Knockdown of target genes was achieved through shRNA. Cell Counting Kit-8 (CCK-8), colony formation assays, and nude mice tumor transplantation were utilized to test cell proliferation ability in vitro and in vivo. RESULTS: We found that Smad2/3/4 complex could regulate tyrosine aminotransferase (TAT) expression, and this regulation could relate to LLPS. CHIP qPCR results showed that the key targeted DNA binding site of Smad2/3/4 complex in TAT promoter region is -1032 to -1182. In addition. CCK-8, colony formation, and nude mice tumor transplantation assays showed that Smad2/3/4 complex could repress cell proliferation through TAT. Flow cytometry assay results showed that Smad2/3/4 complex could increase the apoptosis of hepatoma cells. Western blot results showed that Smad2/3/4 complex would active caspase-9 through TAT, which uncovered the mechanism of Smad2/3/4 complex inducing hepatoma cell apoptosis. CONCLUSION: This study proved that Smad2/3/4 complex could undergo LLPS to active TAT transcription, then active caspase-9 to induce hepatoma cell apoptosis in inhibiting HCC progress. The research further elucidate the relationship between TGF-ß signaling pathway and HCC, which contributes to discover the mechanism of HCC development.

Identification of cuproptosis hub genes contributing to the immune microenvironment in ulcerative colitis using bioinformatic analysis and experimental verification.

Instruction: Ulcerative colitis (UC) can cause a variety of immune-mediated intestinal dysfunctions and is a significant model of inflammatory bowel disease (IBD). Colorectal cancer (CRC) mostly occurs in patients with ulcerative colitis. Cuproptosis is a type of procedural death that is associated with different types of diseases to various degrees. Methods: We used a combination of bioinformatic prediction and experimental verification to study the correlation between copper poisoning and UC. We used the Gene Expression Omnibus database to obtain disease gene expression data and then identified relevant genes involved in various expression levels in normal and UC samples. The Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to cluster the genes that are highly responsible and find the central interaction in gene crosstalk. Notably, DLD, DLAT, and PDHA1 were present in high-scoring PPI networks. In addition, hub gene expression information in UC tissues was integrated to estimate the relationship between UC copper poisoning and the immune environment. Results: In our study, the expression of DLD, DLAT, and PDHA1 in UC tissues was lower than that in normal tissues. The key genes associated with cuproptosis have therapeutic effects on immune infiltration. We verified the expression of DLD, DLAT, and PDHA1 using real-time quantitative polymerase chain reaction in mouse models of UC induced by DSS. Discussion: Notably, this study clearly indicates that bioinformatic analysis performed to verify the experimental methods provides evidence that cuproptosis is associated with UC. This finding suggests that immune cell infiltration in UC patients is associated with cuproptosis. The key genes associated with cuproptosis can be helpful for discovering the molecular mechanism of UC, thus facilitating the improvement of UC treatment and preventing the associated CRC.

Influence of different region of interest sizes on CT-based radiomics model for microvascular invasion prediction in hepatocellular carcinoma. / æ„Ÿå ´è¶£åŒºèŒƒå›´å¯¹CT影像组学模型预测肝细胞癌微血管侵犯的影响.

OBJECTIVES: Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC. METHODS: Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve. RESULTS: A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05). CONCLUSIONS: The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

Clinical Factors Associated with Progression and Prolonged Viral Shedding in COVID-19 Patients: A Multicenter Study.

Coronavirus disease 2019 (COVID-19) is a global pandemic associated with a high mortality. Our study aimed to determine the clinical risk factors associated with disease progression and prolonged viral shedding in patients with COVID-19. Consecutive 564 hospitalized patients with confirmed COVID-19 between January 17, 2020 and February 28, 2020 were included in this multicenter, retrospective study. The effects of clinical factors on disease progression and prolonged viral shedding were analyzed using logistic regression and Cox regression analyses. 69 patients (12.2%) developed severe or critical pneumonia, with a higher incidence in the elderly and in individuals with underlying comorbidities, fever, dyspnea, and laboratory and imaging abnormalities at admission. Multivariate logistic regression analysis indicated that older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.06), hypertension without receiving angiotensinogen converting enzyme inhibitors or angiotensin receptor blockers (ACEI/ARB) therapy (OR, 2.29; 95% CI, 1.14-4.59), and chronic obstructive pulmonary disease (OR, 7.55; 95% CI, 2.44-23.39) were independent risk factors for progression to severe or critical pneumonia. Hypertensive patients without receiving ACEI/ARB therapy showed higher lactate dehydrogenase levels and computed tomography (CT) lung scores at about 3 days after admission than those on ACEI/ARB therapy. Multivariate Cox regression analysis revealed that male gender (hazard ratio [HR], 1.22; 95% CI, 1.02-1.46), receiving lopinavir/ritonavir treatment within 7 days from illness onset (HR, 0.75; 95% CI, 0.63-0.90), and receiving systemic glucocorticoid therapy (HR, 1.79; 95% CI, 1.46-2.21) were independent factors associated with prolonged viral shedding. Our findings presented several potential clinical factors associated with developing severe or critical pneumonia and prolonged viral shedding, which may provide a rationale for clinicians in medical resource allocation and early intervention.

Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics.

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

[Visualization analysis for radiomics research based on knowledge mapping].

OBJECTIVE: To illustrate the literature distribution, research power distribution, and research hotspots in the radiomics research by using knowledge mapping analysis, and to provide reference for relevant researchers.
 Methods: Bibliographies from literature regarding radiomics in Web of Science database were downloaded. BICOM 2.0.1 and SATI 3.2 were used to clean and caculate the frequency of publication year, journal, author, key word, and research institution. CiteSpace V4.4.R1 was used to build the knowledge map of scientific research collaboration network between countries/regions.Ucinet 6 was used to build the knowledge map of scientific research collaboration network between core authors and institutions. gCLUTO 1.0 was applied to construct high-frequency keywords bi-clustering map.
 Results: A total of 700 literature was screened. Since 2012 the number of publications has been growing rapidly year by year. The United States, China, and Netherlands were leaders in this field. There were 5 major scientific research institution cooperative groups and 10 major author cooperative groups. Eight research hotspots were clustered by using high-frequency key word bi-clustering analysis.
 Conclusion: Radiomics is a new field and develops very fast. More and more countries, research institutions, and researchers with multidisciplinary background are going to participate in this filed. New terminology and new methods are going to appear in the field.