Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

A Comparative Study of Clinical and Aortic Morphological Characteristics between Bovine Aortic Arch and Normal Aortic Arch in Patients with Acute Type B Aortic Dissection.

INTRODUCTION: The purpose of this study was to analyze the difference in clinical and aortic morphological features between the bovine aortic arch and normal aortic arch in patients with acute type B aortic dissection (aTBAD). METHODS: A total of 133 patients diagnosed with aTBAD were retrospectively collected. Based on aortic arch morphology, they were divided into the bovine aortic arch group (n = 20) and the normal aortic arch group (n = 113). Aortic morphological features were assessed on computed tomographic angiography. Clinical and aortic morphological features were then compared between the bovine aortic arch and normal aortic arch groups. RESULTS: Patients in the bovine aortic arch group were significantly younger and with higher weight and BMI than the normal aortic arch group (p < 0.001, p = 0.045, and p = 0.016, respectively). The total aortic length in the bovine aortic arch group was significantly shorter than that in the normal aortic arch group (p = 0.039). The tortuosity of descending thoracic aorta, the tortuosity of descending aorta, and the angulation of aortic arch were significantly lower in the bovine aortic arch group (p = 0.004, p = 0.015, and p = 0.023, respectively). The width of descending aorta, the height of aorta arch, and the angle of ascending aorta were significantly smaller in the bovine aortic arch group (p = 0.045, p = 0.044, and p = 0.042, respectively). CONCLUSION: When the aTBAD occurred, patients with bovine aortic arch were prone to be younger and with higher BMI than those with normal aortic arch. The aortic curvature and the total aortic length were lower in patients with bovine aortic arch.

Molecular Mechanism of Integrin αvß6 in Liver Metastasis of Colon Cancer Based on SDF-1/CXCR4.

With the changes in people's dietary life, the incidence and mortality of colon cancer have risen sharply. Invasive metastasis of colon cancer is the main reason affecting the prognosis. Therefore, it is very important to study the molecular mechanism of SDF-1/CXCR4 and integrin αvß6 in liver metastasis of colon cancer. Floating cells were used to detect the appearance of ß6, and the relationship between SDF-1/CXCR4 and the molecular mechanism of colon cancer redirection was analyzed. Use immunohistochemistry to detect the appearance of SDF-1/CXCR4 and αvß6 protein, and combine the data with clinical-pathological data for statistical analysis. Experimental data showed that the positive expression rates of αvß6 protein in well-differentiated and poorly differentiated colon cancer tissues were 21.4% and 30.6%, respectively, and the difference was statistically significant (P<0.01). The results show that the appearance of SDF-1/CXCR4 in colon cancer cells (CCC) has nothing to do with the type of cancer cells, and increases with the decrease of cell differentiation. This has a great relationship with the classification of the clinical TNM disease stage. The later the disease stage, the higher the expression level. The αvß6 has a strong correlation with the invasion and metastasis of colon cancer and can be used as a criterion for judging liver metastasis and prognosis.

FBXO31 is upregulated by METTL3 to promote pancreatic cancer progression via regulating SIRT2 ubiquitination and degradation.

FBXO31, a member of F-box family to comprise of SCF complex, contributes to a pivotal role in cancer progression. However, the possible involvements of FBXO31 in PC are unelucidated. Here, we reported that FBXO31 was overexpressed in PC patients, which was negatively associated with survival in PC patients. Furthermore, FBXO31 significantly enhanced growth, migration and invasion of PC cells in vitro. Consistently, FBXO31 overexpression promoted tumor growth in nude mice. Mechanistically, SIRT2 was a target of FBXO31 and interacted with FBXO31. Protein half-life and ubiquitination analysis demonstrated that FBXO31 promoted proteasome-dependent degradation of SIRT2. In addition, FBXO31 binds to sirtuin-type domain of SIRT2. Moreover, SIRT2 is required for the oncogenic role of FBXO31 in PC progression. Impressively, METTL3 induced m6A modification of FBXO31 and up-regulated FBXO31 expression, subsequently leading to SIRT2 down-regulation in PC cells. The results showed that METTL3 enhanced FBXO31 mRNA translation in YTHDF1-dependent manner. Taken together, we suggest that METTL3-FBXO31-SIRT2 axis was involved in PC tumorigenesis, which could identify new targets for PC treatment.

Noncontrast cardiac computed tomography-derived mitral annular calcification scores in mitral valve disease.

BACKGROUND AND AIMS: Mitral annular calcification (MAC) by computed tomography (CT) is reported as an independent predictor of poor outcomes. However, it currently remains unclear if quantitative MAC parameters provide more value for mitral valve disease (MVD) management, therefore, we examined the prognostic value of MAC scores using noncontrast cardiac-CT in MVD patients. METHODS: Between January 2020 and December 2021, we prospectively enrolled 300 consecutive patients with MVD (MAC-present = 80 and MAC-absent = 220) undergoing preoperative cardiac-CT and mitral valve (MV) surgery. Noncontrast cardiac-CT images were used to qualitatively detect MAC (present or absent) and evaluate MAC scores. For analyses, we also collected baseline clinical data, intraoperative conversion (from MV repair to MV replacement), and follow-up arrhythmia data. RESULTS: Compared with the MAC-absent group, MAC-present patients were older (62 ± 7 vs. 58 ± 9 years, p < .001), mostly women (55% vs. 39.5%, p = .017), and also had aortic valve calcification (57.5% vs. 23.2%, p < .001), mitral stenosis (82.5% vs. 61.8%, p < .001), atrial fibrillation (30% vs. 11.8%, p < .001), and larger left atrial end-diastolic dimension (LADD, 49 [44-56] versus 46 [41-50], p = .001]. Furthermore, MAC-present patients underwent more MV replacements (61.8% vs. 82.5%, p = .001) and experienced a higher intraoperative conversion prevalence (11.8% vs. 61.3%, p < .001). Multiple logistic regression analyses showed that the female gender (odds ratio [OR]/95% confidence interval [CI]/p = 2.001/1.042-3.841/0.037) and MAC scores (OR/95% CI/p = 10.153/4.434-23.253/p < .001) were independent predictors of intraoperative conversion. During a follow-up of 263 ± 134 days, MAC-present patients had more arrhythmias (42.5% vs. 9.5%, p < .001). Also, MAC-scores (hazard ratio [HR]/95% CI/p = 6.841/3.322-14.089/p < .001) and LADD (HR/95% CI/p = 1.039/1.018-1.060/p < .001) were independently associated with arrhythmias by Cox regression analyses. CONCLUSIONS: Noncontrast cardiac CT-derived MAC-scores showed a high risk for intraoperative conversion and follow-up arrhythmias in MVD-patients.

The role of epicardial and pericoronary adipose tissue radiomics in identifying patients with non-ST-segment elevation myocardial infarction from unstable angina.

Objectives: This study aimed to ascertain if the radiomics features of epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could identify non-ST-segment elevation myocardial infarction (NSTEMI) from unstable angina (UA). Materials and methods: This retrospective case-control study included 108 patients with NSTEMI and 108 controls with UA. All patients were separated into training cohort (n = 116), internal validation cohort 1 (n = 50), and internal validation cohort 2 (n = 50) based on the time order of admission. The internal validation cohort 1 used the same scanner and scan parameters as the training cohort, while the internal validation cohort 2 used different canners and scan parameters than the training cohort. The EAT and PCAT radiomics features selected by maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted to build logistic regression models. Finally, we developed an EAT radiomics model, three vessel-based (right coronary artery [RCA], left anterior descending artery [LAD], and left circumflex artery [LCX]) PCAT radiomics models, and a combined model by combining the three PCAT radiomics models. Discrimination, calibration, and clinical application were employed to assess the performance of all models. Results: Eight radiomics features of EAT, sixteen of RCA-PCAT, fifteen of LAD-PCAT, and eighteen of LCX-PCAT were selected and used to construct radiomics models. The area under the curves (AUCs) of the EAT, RCA-PCAT, LAD-PCAT, LCX-PCAT and the combined models were 0.708 (95% CI: 0.614-0.802), 0.833 (95% CI:0.759-0.906), 0.720 (95% CI:0.628-0.813), 0.713 (95% CI:0.619-0.807), 0.889 (95% CI:0.832-0.946) in the training cohort, 0.693 (95% CI:0.546-0.840), 0.837 (95% CI: 0.729-0.945), 0.766 (95% CI: 0.625-0.907), 0.675 (95% CI: 0.521-0.829), 0.898 (95% CI: 0.802-0.993) in the internal validation cohort 1, and 0.691 (0.535-0.847), 0.822 (0.701-0.944), 0.760 (0.621-0.899), 0.674 (0.517-0.830), 0.866 (0.769-0.963) in the internal validation cohort 2, respectively. Conclusion: Compared with the RCA-PCAT radiomics model, the EAT radiomics model had a limited ability to discriminate between NSTEMI and UA. The combination of the three vessel-based PCAT radiomics may have the potential to distinguish between NSTEMI and UA.

Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques.

PURPOSE: To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability. METHODS: We retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses. RESULTS: Patients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001-1.116), plaque ulceration (OR 6.106; 95% CI 1.933-19.287), and carotid rim sign (OR 3.285; 95% CI 1.203-8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful. CONCLUSION: Radiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes.

Identification of genes and cellular response factors related to immunotherapy response in mismatch repair-proficient colorectal cancer: a bioinformatics analysis.

Background: Mismatch repair-proficient (pMMR) colorectal cancers (CRCs) are thought to be primarily resistant to immune checkpoint inhibitor (ICI) monotherapy. However, recent clinical trials have reported that early-to-mid stage (non-metastatic) CRC responds well to ICI monotherapy. We hypothesized that the efficacy of immunotherapy is linked to a series of gene expression profiles that can characterize the pMMR CRC disease stage. Methods: Using The Cancer Genome Atlas (TCGA) CRC data sets, we first investigated transcriptomic features that continuously changed (were continuously upregulated or downregulated) with pMMR CRC disease-stage progression. We defined these gene sets as stage-associated genes. The deconvolution algorithm then enriched these genes with the dynamic changes in the cell type populations of the CRC tumor microenvironment (TME). Finally, the stage-associated genes were cross-referenced to the current transcriptome profile data on ICI treatment of pMMR CRC, which revealed the gene set specifying an effective pMMR tumor response. Results: In total, 774 genes were found to increase in expression and 845 genes to decrease in expression as the stage increased. Using deconvolution methods, we discovered 2 major disease stage-associated alterations in the cellular composition of pMMR CRCs, including changes in cell types involved in host immune responses and tumor cell metastasis. The central memory CD8+ T cell population decreased as the pMMR CRC disease stage increased, but the endothelial cell populations associated with proliferation and metastasis increased. Using a different cell type annotation set (LM22), we discovered that as the disease progressed, M1 macrophages and CD8+ T cells decreased in the TME. In mismatch repair-deficient patients with CRC, however, such a decrease was not observed. Finally, we identified 27 signature genes that can be used to assess ICI efficacy in treatment-naïve patients with pMMR CRC. Conclusions: The current study sought to identify the underlying molecular mechanisms, pathways, and cell landscapes that explain why early-to-mid stage pMMR CRC responds well to ICI treatment. This analysis might be valuable for the selection of patients who might benefit from immunotherapeutic strategies.

AML1-ETO triggers epigenetic activation of early growth response gene l, inducing apoptosis in t(8;21) acute myeloid leukemia.

The t(8;21)(q22;q22) translocation is the most common chromosomal translocation in acute myeloid leukemia (AML), and it gives rise to acute myeloid gene 1 (AML1)-myeloid transforming gene 8 (ETO)-positive AML, which has a relatively favorable prognosis. However, the molecular mechanism related to a favorable prognosis in AML1-ETO-positive AML is still not fully understood. Our results show that the AML1-ETO fusion protein triggered activation of early growth response gene l (EGR1) by binding at AML1-binding sites on the EGR1 promoter and, subsequently, recruiting acetyltransferase P300, which is known to acetylate histones. However, AML1-ETO could not recruit DNA methyltransferases and histone deacetylases; therefore, EGR1 expression was affected by histone acetylation but not by DNA methylation. Both transcription and translation of EGR1 were higher in AML1-ETO-positive AML cell lines than in AML1-ETO-negative AML cell lines, owing to acetylation. Furthermore, when AML1-ETO-positive AML cell lines were treated with C646 (P300 inhibitor) and trichostatin A (histone deacetylase inhibitor), EGR1 expression was significantly decreased and increased, respectively. In addition, treatment with 5-azacytidine (methyltransferase inhibitor) did not cause any significant change in EGR1 expression. Overexpression of EGR1 inhibited cell proliferation and promoted apoptosis, and EGR1 knockout promoted cell proliferation. Thus, EGR1 could be a novel prognostic factor for a favorable outcome in AML1-ETO-positive AML. The results of our study may explain the molecular mechanisms underlying the favorable prognosis in AML1-ETO-positive AML.