Construction and validation of a prognostic and therapeutic cuproptosis- and immune-related gene signature in hepatocellular carcinoma.

Background: Abnormal accumulation of copper could induce cell death and tumor growth, and affect tumor immune escape by regulating programmed cell death ligand 1 (PD-L1) expression. This study aims to establish and verify a risk signature based on cuproptosis- and immune-related genes (CIRGs) for hepatocellular carcinoma (HCC) management. Methods: HCC RNA-seq and clinical data were obtained from open databases. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were utilized to screen CIRGs and develop a risk signature. The signature's value for clinical applications, functional enrichment, tumor mutation burden (TMB), and immune profile analyses were investigated systematically. Results: A risk signature was developed utilizing seven CIRGs, and it performed well in predicting the prognosis of HCC patients in both the training and external validation cohorts. The model's risk score was discovered to be related to important clinical features. Top 15 mutated genes in HCC were significantly different among different risk groups. High-risk patients showed higher TMB, and high TMB was closely identified with a poorer prognosis. Immune profile analyses showed that immune infiltration level was higher in low-risk patients than high-risk patients, and the level of immune checkpoint genes expression varied significantly between patients in two different risk groups. Low-risk patients responded well to immunotherapy treatment, whereas high-risk patients were more sensitive to sorafenib, doxorubicin, gemcitabine and AKT (also known as protein kinase B) inhibitors. Conclusions: The established risk signature based on CIRGs can not only well predict the prognosis of HCC patients but is also promising in evaluating TMB and treatment response to immunotherapy, targeted therapy and chemotherapy, which has the potential to assist in the clinical management of HCC.

Demonstrating the effectiveness of intra-articular prolotherapy combined with peri-articular perineural injection in knee osteoarthritis: a randomized controlled trial.

BACKGROUND: This study aimed to compare the efficacy of intra-articular prolotherapy (IG) combined with peri-articular perineural injection (PG) in the management of knee osteoarthritis (KOA). METHODS: A total of 60 patients with the diagnosis of KOA were included in this double-blinded randomized controlled clinical trials. The inclusion criteria were as follow: (1) 48-80 years old; (2) the diagnose of KOA; (3) the grade 2 and 3 of the Kellgern-Lawrence grading scale; (4) the pain, crepitation, and knee joint stiffness continuing for 3 months at least. The main exclusion criteria were as follow: (1) any infection involving the knee skin; (2) history of any Influencing factors of disease. All patients were divided into three groups and received either IG, PG and I + PG under the ultrasound guidance and the 2, 4 and 8 weeks follow-up data of patients were available. (IG n = 20 or PG n = 20, I + PG n = 20). Visual Analogue Scale (VAS), The Western Ontario McMaster University Osteoarthritis Index (WOMAC) and the pressure pain threshold (PPT) were used as outcome measures at baseline, 2, 4 and 8 weeks. RESULTS: There were no statistically significant differences in terms of age, sex, BMI, duration of current condition and baseline assessments of pain intensity, WOMAC scores and PPT. After treatment, the improvement of VAS activity, WOMAC and PPT values was showed compared with pre-treatment in all groups (p < 0.05). At 4 and 8 weeks after treatment, the VAS and WOMAC scores of the I + PG were significantly lower than those of the PG or IG, and the difference was statistically significant (p < 0.05). The PPT values of PG and I + PG were significantly improved compared to IG at 2, 4, and 8 weeks after treatment. CONCLUSION: The ultrasound guided I + PG of 5% glucose seem to be more effective to alleviate pain and improve knee joint function than single therapy in short term. Clinical rehabilitators could clinically try this combination of I + PG to improve clinical symptoms in patients with KOA.

S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images.

Accurate liver tumor segmentation is crucial for aiding radiologists in hepatocellular carcinoma evaluation and surgical planning. While convolutional neural networks (CNNs) have been successful in medical image segmentation, they face challenges in capturing long-term dependencies among pixels. On the other hand, Transformer-based models demand a high number of parameters and involve significant computational costs. To address these issues, we propose the Spatial and Spectral-learning Double-branched Aggregation Network (S2DA-Net) for liver tumor segmentation. S2DA-Net consists of a double-branched encoder and a decoder with a Group Multi-Head Cross-Attention Aggregation (GMCA) module, Two branches in the encoder consist of a Fourier Spectral-learning Multi-scale Fusion (FSMF) branch and a Multi-axis Aggregation Hadamard Attention (MAHA) branch. The FSMF branch employs a Fourier-based network to learn amplitude and phase information, capturing richer features and detailed information without introducing an excessive number of parameters. The FSMF branch utilizes a Fourier-based network to capture amplitude and phase information, enriching features without introducing excessive parameters. The MAHA branch incorporates spatial information, enhancing discriminative features while minimizing computational costs. In the decoding path, a GMCA module extracts local information and establishes long-term dependencies, improving localization capabilities by amalgamating features from diverse branches. Experimental results on the public LiTS2017 liver tumor datasets show that the proposed segmentation model achieves significant improvements compared to the state-of-the-art methods, obtaining dice per case (DPC) 69.4 % and global dice (DG) 80.0 % for liver tumor segmentation on the LiTS2017 dataset. Meanwhile, the pre-trained model based on the LiTS2017 datasets obtain, DPC 73.4 % and an DG 82.2 % on the 3DIRCADb dataset.

Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans. METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110). RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001). CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.

Using teach-back in patient education to improve patient satisfaction and the clarity of magnetic resonance imaging.

OBJECTIVE: To explore the effects of using the teach-back method prior to contrast-enhanced magnetic resonance imaging (MRI) on patients' knowledge and satisfaction as well as the clarity of the resulting scans. METHODS: A total of 254 patients who underwent contrast-enhanced MRI examination from July 4, 2022 to September 19, 2022 were enrolled and assigned to the intervention and control groups. Patients in the intervention group received education using the teach-back method, while those in the control group were given routine health education. A questionnaire that included patients' knowledge of contrast-enhanced MRI examination was answered before and after patient education. Data on patient satisfaction with nursing services were also collected. The clarity of the MRI images of all patients was assessed. RESULTS: The scores of knowledge related to MRI after receiving education were significantly higher than those before receiving education (P < 0.001), and there were no significant differences between the intervention and control groups (11.27 ± 9.74 vs. 12.07 ± 8.71, P = 0.498). The score of satisfaction with nursing service in the teach-back group was significantly higher than that in the control group (39.82 ± 0.86 vs. 38.59 ± 3.73, P < 0.001), as was the image clarity score (96.4 ± 0.5 vs. 95.0 ± 0.4, P = 0.039). CONCLUSION: Teach-back improves patient satisfaction and contrast-enhanced MRI clarity. PRACTICE IMPLICATIONS: Including teach-back in patient education improves patient satisfaction and contrast-enhanced MRI clarity.

A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma.

PURPOSE: This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC). METHODS: 320 pathologically confirmed HCC patients (58 women and 262 men) from two hospitals were included in this retrospective study. Institution 1 (n = 219) and Institution 2 (n = 101) served as the training and external test cohorts, respectively. Tumors were evaluated three-dimensionally and regions of interest were segmented manually in the arterial, portal venous, and delayed phases (AP, PP, and DP). Three ResNet-34 DL models were developed, consisting of three models based on a single sequence. The fusion model was developed by inputting the prediction probability of the output from the three single-sequence models into logistic regression. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs. Early recurrence (ER) was defined as recurrence within two years of surgery and early recurrence-free survival (ERFS) rate was evaluated by Kaplan-Meier survival analysis. RESULTS: Among the 320 HCC patients, 227 were VETC- and 93 were VETC+ . In the external test cohort, the fusion model showed an AUC of 0.772, a sensitivity of 0.80, and a specificity of 0.61. The fusion model-based prediction of VETC high-risk and low-risk categories exhibits a significant difference in ERFS rates, akin to the outcomes observed in VETC + and VETC- confirmed through pathological analyses (p < 0.05). CONCLUSIONS: A DL framework based on ResNet-34 has demonstrated potential in facilitating non-invasive prediction of VETC as well as patient prognosis.

Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.

BACKGROUND: Vessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging. PURPOSE: To develop and validate a deep learning radiomic (DLR) model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of VETC and prognosis of HCC. STUDY TYPE: Retrospective. POPULATION: A total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time-independent validation set (n = 67). FIELD STRENGTH/SEQUENCE: A 1.5 T and 3.0 T; DCE imaging with T1-weighted three-dimensional fast spoiled gradient echo. ASSESSMENT: Histological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC-. The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical-radiological (CR) models based on AP, PP, and DP of DCE-MRI for evaluating VETC status and association with recurrence. STATISTICAL TESTS: The Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan-Meier survival analysis. P value <0.05 was considered as statistical significance. RESULTS: Pathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri-PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri-PP DLR model-predicted VETC+ and VETC- status were found. DATA CONCLUSIONS: The DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Determining rib fracture age from CT scans with a radiomics-based combined model: a multicenter retrospective study.

OBJECTIVES: We aimed to develop a combined model based on clinical and radiomic features to classify fracture age. METHODS: We included 1219 rib fractures from 239 patients from our center between March 2016 and September 2022. We created an external dataset using 120 rib fractures from 32 patients from another center between October 2019 and August 2023. According to tasks (fracture age between < 3 and ≥ 3 weeks, 3-12, and > 12 weeks), the internal dataset was randomly divided into training and internal test sets. A radiomic model was built using radiomic features. A combined model was constructed using clinical features and radiomic signatures by multivariate logistic regression, visualized as a nomogram. Internal and external test sets were used to validate model performance. RESULTS: For classifying fracture age between < 3 and ≥ 3 weeks, the combined model had higher areas under the curve (AUCs) than the radiomic model in the training set (0.915 vs 0.900, p = 0.009), internal test (0.897 vs 0.854, p < 0.001), and external test sets (0.881 vs 0.811, p = 0.003). For classifying fracture age between 3-12 and > 12 weeks, the combined model had higher AUCs than the radiomic model in the training model (0.848 vs 0.837, p = 0.12) and internal test sets (0.818 vs 0.793, p < 0.003). In the external test set, the AUC of the nomogram-assisted radiologist was 0.966. CONCLUSION: The combined radiomic and clinical model showed good performance and has the potential to assist in the classification of rib fracture age. This will be beneficial for clinical practice and forensic decision-making. CRITICAL RELEVANCE STATEMENT: This study describes the development of a combined radiomic and clinical model with good performance in the classification of the age of rib fractures, with potential clinical and forensic applications. KEY POINTS: • Complex factors make it difficult to determine the age of a fracture. • Our model based on radiomic features performed well in classifying fracture age. • Associating the radiomic features with clinical features improved the model's performance.

Construction of a prediction model for postoperative prognosis in patients with resectable cholangiocarcinoma based on silence information regulator 2 expression. / æ²‰é»˜ä¿¡æ¯è°ƒèŠ‚å› å­2åœ¨èƒ†ç®¡ç™Œä¸­çš„è¡¨è¾¾åŠå ¶å¯¹å¯åˆ‡é™¤èƒ†ç®¡ç™Œæ‚£è€ é¢„åŽçš„é¢„æµ‹ä»·å€¼.

OBJECTIVES: To develop a prediction model for postoperative prognosis in patients with cholangiocarcinoma (CCA) based on the expression of silence information regulator 2 (SIRT2). METHODS: The differential expression of SIRT2 between CCA and normal tissues was analyzed using TCGA and GEO databases. Gene set enrichment analysis (GSEA) was used to explore potential mechanisms of SIRT2 in CCA. The expression of SIRT2 protein in CCA tissues and normal tissues (including 44 pairs of specimens) was also detected by immunohistochemistry (IHC) in 89 resectable CCA patients who underwent surgical treatment in the First Affiliated Hospital of Bengbu Medical College between January 2016 and December 2021. The relationship between SIRT2 expression and clinicopathological characteristics and prognosis of CCA patients was analyzed. A survival prediction model for patients with resectable CCA was constructed with COX regression results, the calibration curve and the time-dependent receiver operating characteristic curve (ROC) were used to evaluate the performance of the constructed model, and the predictive power between this model and the American Joint Committee on Cancer (AJCC)/TNM staging system (8th edition) was compared. RESULTS: SIRT2 mRNA was overexpressed in CCA tissues as shown in TCGA and GEO databases. IHC staining showed that SIRT2 protein expression in CCA tissues was significantly higher than that in adjacent non-tumor tissues. GSEA results showed that elevated SIRT2 expression may be involved in multiple metabolism-related signaling pathway, such as fatty acid metabolism, oxidative phosphorylation and amino acid metabolism. SIRT2 expression was related to serum triglycerides level, tumor size and lymph node metastasis (all P<0.05). The survival analysis results showed that patients with higher SIRT2 expression had a significantly lower overall survival (OS) than patients with lower SIRT2 expression (P<0.05). Univariate COX regression analysis suggested that pathological differentiation, clinical stage, postoperative treatment and SIRT2 expression level were associated with the prognosis of CCA patients (all P<0.05). Multivariate regression analysis confirmed that clinical stage and SIRT2 expression level were independent predictors of OS in postoperative CCA patients (both P<0.05). A nomogram based on SIRT2 for prediction of survival in postoperative CCA patients was constructed. The C-index of the model was 0.675, and the area under the time-dependent ROC curve (AUC) for predicting survival in the first, second, and third years was 0.879, 0.778, and 0.953, respectively, which were superior to those of AJCC/TNM staging system (8th Edition). CONCLUSIONS: SIRT2 is highly expressed in CCA tissues, which is associated with poor prognosis in patients with resectable CCA. The nomogram developed based on SIRT2 may have better predictive power than the AJCC/TNM staging system (8th edition) in prediction of survival of postoperative CCA patients.

sFgl2 gene-modified MSCs regulate the differentiation of CD4+ T cells in the treatment of autoimmune hepatitis.

BACKGROUND: Autoimmune hepatitis (AIH) is a T-cell-mediated autoimmune liver disease that can lead to liver injury and has a poor long-term prognosis. Mesenchymal stromal cells (MSCs) have immunosuppressive effects and can treat AIH. CD4+ T cells express the unique inhibitory Fcγ receptor (FcγRIIB), which is the only receptor for the immunosuppressive factor soluble fibrinogen-like protein 2 (sFgl2). This study aimed to examine the therapeutic effect of sFgl2 gene-modified MSCs (sFgl2-MSCs) on AIH. METHODS: MSCs were obtained from the inguinal fat of mice and cocultured with CD4+ T cells sorted from mouse spleens. FcγRIIB expression on CD4+ T cells was determined by flow cytometry. sFgl2 expression in MSCs transfected with lentiviral vectors carrying the Fgl2 gene and a green fluorescent protein-encoding sequence was determined by enzyme-linked immunosorbent assay. The percentages of Th1 cells Th17 cells and regulatory T cells (Tregs) were determined by flow cytometry And the levels of p-SHP2 and p-SMAD2/3 were detected by Western blotting after the cells were cocultured with MSCs for 72 h. After locating MSCs by in vivo imaging Con A-induced experimental AIH mice were randomly divided into 4 groups and administered different treatments. After 24 h histopathological scores liver function and cytokine levels were examined and the proportions of CD4+ T cells CD8+ T cells Tregs Th17 cells and Th1 cells in the spleen and liver were determined by flow cytometry. In addition immunohistochemical staining was used to detect the liver infiltration of T-bet-, Foxp3- and RORγ-positive cells. RESULTS: FcγRIIB expression on CD4+ T cells was upregulated after coculture with MSCs. After coculture with sFgl2-MSCs, the proportion of Tregs among CD4+ T cells increased, the proportion of Th17 and Th1 cells decreased, and the levels of p-SHP2 and p-SMAD2/3 increased. In vivo, sFgl2-MSCs significantly improved liver function, decreased liver necrosis area, decreased tumor necrosis factor-α, interleukin (IL)-1ß and IL-6 expression, increased IL-10 expression, reduced liver infiltration of CD4+ T and CD8+ T cells, increased the proportion of Tregs and reduced the proportions of Th17 and Th1 cells in mice. CONCLUSION: By promoting Tregs differentiation and inhibiting Th17 and Th1 cell differentiation, sFgl2 gene-modified MSCs have a more powerful therapeutic effect on Con A-induced experimental AIH and may represent a strategy for the clinical treatment of AIH.

A Comprehensive Model Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging Can Better Predict the Preoperative Histological Grade of Breast Cancer Than a Radiomics Model.

Background: Histological grade is an important prognostic factor for patients with breast cancer and can affect clinical decision-making. From a clinical perspective, developing an efficient and non-invasive method for evaluating histological grading is desirable, facilitating improved clinical decision-making by physicians. This study aimed to develop an integrated model based on radiomics and clinical imaging features for preoperative prediction of histological grade invasive breast cancer. Methods: In this retrospective study, we recruited 211 patients with invasive breast cancer and randomly assigned them to either a training group (n=147) or a validation group (n=64) with a 7:3 ratio. Patients were classified as having low-grade tumors, which included grade I and II tumors, or high-grade tumors, which included grade III tumors. Three models were constructed based on basic clinical features, radiomics features, and the sum of the two. To assess diagnostic performance of the radiomics models, we employed measures such as receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity, and the predictive performance of the three models was compared using the DeLong test and net reclassification improvement (NRI). Results: The area under the curve (AUC) of the clinical model, radiomics model, and comprehensive model was 0.682, 0.833, and 0.882 in the training set and 0.741, 0.751, and 0.836 in the validation set, respectively. NRI analysis confirmed that the combined model was better than the other two models in predicting the histological grade of breast cancer (NRI=21.4% in the testing cohort). Conclusion: Compared with the other models, the comprehensive model based on the combination of basic clinical features and radiomics features exhibits more significant potential for predicting histological grade and can better assist clinicians in optimal decision-making.

Effects of pre-anthesis low-temperature stress on the mineral components in wheat grains.

Introduction: The nutritional value of wheat is important to human health. Despite minerals being essential nutrients for the human body, they are often neglected in consideration of the nutritional quality of cereal grains. Extreme low-temperature events have become more frequent due to the current environmental unpredictability, and it is yet unknown how the mineral components in grains are affected by low temperature. Methods: To provide valuable information for enhancing the nutritional quality of wheat under potential climatic conditions, we treated different cold-sensitive wheat cultivars at four low-temperature levels during the individual and combined stages of jointing and booting in controlled-environment phytotrons. Results and Discussion: In general, the contents of P, K, Ca, and Zn in the cold-sensitive cultivar (Yangmai16) and K in the cold-tolerant cultivar (Xumai30) were enhanced by low temperature. However, the accumulation of minerals in mature grains was reduced under low-temperature treatment, except for P, Ca, and Zn in Yangmai16. In addition, the mineral content and accumulation in Yangmai16 (except for Fe) were more susceptible to low temperature during the combined stages, while the mineral content and accumulation of K, Fe, and Zn in Xumai30 were more susceptible to low temperature during the booting stage. Moreover, Yangmai16 under extremely low temperatures (T3 and T4) during booting and Xumai30 under all low-temperature treatments during the combined stages had lower comprehensive evaluation values. These findings offer a crucial reference for enhancing the nutritional quality of wheat grains under climate change.

Elevated MPP6 expression correlates with an unfavorable prognosis, angiogenesis and immune evasion in hepatocellular carcinoma.

Background: Membrane palmitoylated proteins (MPPs) are engaged in various biological processes, such as cell adhesion and cell polarity. Dysregulated MPP members have different effects on hepatocellular carcinoma (HCC) development. However, the role of MPP6 in HCC has been unknown. Method: HCC transcriptome and clinical data from different public databases were downloaded and analyzed, and the results were further validated by qRT-PCR, Western blotting and immunohistochemistry (IHC) using HCC cell lines and tissues. The association between MPP6 and prognosis, potential pathogenic mechanisms, angiogenesis, immune evasion, tumor mutation burden (TMB) and treatment response in HCC patients was analyzed by bioinformatics and IHC staining. Results: MPP6 was significantly overexpressed in HCC, and its expression was related to T stage, pathologic stage, histologic grade and adverse prognosis in HCC patients. Gene set enrichment analysis revealed that differentially expressed genes were mainly enriched in the synthesis of genetic materials and the WNT signaling pathway. GEPIA database analysis and IHC staining suggested that MPP6 expression had a positive correlation with angiogenesis. Single-cell dataset analysis indicated that MPP6 was associated with features of the tumor microenvironment. Additional analyses discovered that MPP6 expression was inversely related to immune cell infiltration and was involved in tumor immune evasion. MPP6 expression was positively associated with TMB, and patients with high TMB had an adverse prognosis. Immunotherapy was more effective in HCC patients with low MPP6 expression, whereas those with high MPP6 expression responded better to sorafenib, gemcitabine, 5-FU, and doxorubicin. Conclusions: Elevated MPP6 expression is associated with an unfavorable prognosis, angiogenesis and immune evasion in HCC. Moreover, MPP6 has the potential to be used to assess TMB and treatment response. Therefore, MPP6 might serve as a novel prognostic biomarker and therapeutic target for HCC.

[Methylselenocysteine Promotes Etoposide Cytotoxicity by Enhancing Homotypic Gap Junctions Composed of Connexin 26].

Objective: To investigate the effect of methylselenocysteine (MSC) on the function of homotypic gap junction (GJ) composed of connexin (Cx) 26 and its regulation of chemotherapeutic drug cytotoxicity. Methods: The Tet-on HeLa cells transfected with and stably expressing Cx26 were used as the tool cells. Effects of MSC on cell growth, GJ function, and Cx26 protein expression were examined by MTT method, parachute assay, and Western blot analysis, respectively. The cytotoxicity of chemotherapeutic drugs was determined by standard colony-forming assay, and the relationship between MSC's effect on cytotoxicity of these chemotherapeutic drugs and its regulation of GJ was further analyzed. Results: In Tet-on HeLa cells, doxycycline (Dox) can induce the expression of Cx26, which could then form functional GJs. Within a concentration range of 50 µmol/L, MSC had no significant effect on HeLa cell growth. Non-toxic concentrations of MSC can enhance GJs in a concentration-dependent manner and exert its effect at the nanomolar level. This effect was associated with an induction of Cx26 protein expression by MSC. Among the three common chemotherapeutic agents with different mechanisms of action, etoposide (Eto) presented cytotoxicity differences between HeLa cells cultured at low density (nonconfluent, no GJ formed) and high density (confluent, GJ formed). What's more, the inhibitory effect of Eto combined with MSC on HeLa cell colony formation was stronger than that of Eto alone, and this effect occurred only in HeLa cells with GJ formation. Conclusion: MSC can potentiate the cytotoxicity of Eto by enhancing the GJs composed of Cx26, indicating that combined strategy of selenide and chemotherapy shows potential value in the treatment of malignant tumors.

The adapter protein FADD provides an alternate pathway for entry into the cell cycle by regulating APC/C-Cdh1 E3 ubiquitin ligase activity.

The E3 ubiquitin ligase APC/C-Cdh1 maintains the G0/G1 state, and its inactivation is required for cell cycle entry. We reveal a novel role for Fas-associated protein with death domain (FADD) in the cell cycle through its function as an inhibitor of APC/C-Cdh1. Using real-time, single-cell imaging of live cells combined with biochemical analysis, we demonstrate that APC/C-Cdh1 hyperactivity in FADD-deficient cells leads to a G1 arrest despite persistent mitogenic signaling through oncogenic EGFR/KRAS. We further show that FADDWT interacts with Cdh1, while a mutant lacking a consensus KEN-box motif (FADDKEN) fails to interact with Cdh1 and results in a G1 arrest due to its inability to inhibit APC/C-Cdh1. Additionally, enhanced expression of FADDWT but not FADDKEN, in cells arrested in G1 upon CDK4/6 inhibition, leads to APC/C-Cdh1 inactivation and entry into the cell cycle in the absence of retinoblastoma protein phosphorylation. FADD's function in the cell cycle requires its phosphorylation by CK1α at Ser-194 which promotes its nuclear translocation. Overall, FADD provides a CDK4/6-Rb-E2F-independent "bypass" mechanism for cell cycle entry and thus a therapeutic opportunity for CDK4/6 inhibitor resistance.

A pyroptosis-related gene signature for prognosis prediction in hepatocellular carcinoma.

Introduction: Hepatocellular carcinoma (HCC) is one of the most invasive cancers with a low 5-year survival rate. Pyroptosis, a specialized form of cell death, has shown its association with cancer progression. However, its role in the prognosis of HCC has not been fully understood. Methods: In our study, clinical information and mRNA expression for 1076 patients with HCC were obtained from the five public cohorts. Pyroptotic clusters were generated by unsupervised clustering based on 40 pyroptosis-related genes (PRGs) in the TCGA and ICGC cohort. A pyroptosis-related signature was constructed using least absolute shrinkage and selection operator (LASSO) regression according to differentially expressed genes (DEGs) of pyroptotic clusters. The signature was then tested in the validation cohorts (GES10142 and GSE14520) and subsequently validated in the CPTAC cohort (n=159) at both mRNA and protein levels. Response to sorafenib was explored in GSE109211. Results: Three clusters were identified based on the 40 PRGs in the TCGA cohort. A total of 24 genes were selected based on DEGs of the above three pyroptotic clusters to construct the pyroptotic risk score. Patients with the high-risk score showed shorter overall survival (OS) compared to those with the low-risk score in the training set (P<0.001; HR, 3.06; 95% CI, 2.22-4.24) and the test set (P=0.008; HR, 1.61; 95% CI, 1.13-2.28). The predictive ability of the risk score was further confirmed in the CPTAC cohort at both mRNAs (P<0.001; HR, 2.99; 95% CI, 1.67-5.36) and protein levels (P<0.001; HR, 2.97; 95% CI 1.66-5.31). The expression of the model genes was correlated with immune cell infiltration, angiogenesis-related genes, and sensitivity to antiangiogenic therapy (P<0.05). Discussion: In conclusion, we established a prognostic signature of 24 genes based on pyroptosis clusters for HCC patients, providing insight into the risk stratification of HCC.

18F-FDG PET/CT radiomics signature and clinical parameters predict progression-free survival in breast cancer patients: A preliminary study.

Introduction: This study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters. Methods: Breast cancer patients who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecular subtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of rad-score and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance. Results: Eleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (P< 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (P< 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726-0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit. Conclusion: The ICR model, which combined clinical parameters and preoperative 18F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and rad-score alone.

Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma.

PURPOSE: This study aimed to analyze imaging features based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the identification of vessels encapsulating tumor clusters (VETC)-microvascular invasion (MVI) in hepatocellular carcinoma (HCC), VM-HCC pattern. METHODS: Patients who underwent hepatectomy and preoperative DCE-MRI between January 2015 and March 2021 were retrospectively analyzed. Clinical and imaging features related to VM-HCC (VETC + /MVI-, VETC-/MVI +, VETC + /MVI +) and Non-VM-HCC (VETC-/MVI-) were determined by multivariable logistic regression analyses. Early and overall recurrence were determined using the Kaplan-Meier survival curve. Indicators of early and overall recurrence were identified using the Cox proportional hazard regression model. RESULTS: In total, 221 patients (177 men, 44 women; median age, 60 years; interquartile range, 52-66 years) were evaluated. The multivariable logistic regression analyses revealed fetoprotein > 400 ng/mL (odds ratio [OR] = 2.17, 95% confidence interval [CI] 1.07, 4.41, p = 0.033), intratumor vascularity (OR 2.15, 95% CI 1.07, 4.31, p = 0.031), and enhancement pattern (OR 2.71, 95% CI 1.17, 6.03, p = 0.019) as independent predictors of VM-HCC. In Kaplan-Meier survival analysis, intratumor vascularity was associated with early and overall recurrence (p < 0.05). CONCLUSION: Based on DCE-MRI, intratumor vascularity can be used to characterize VM-HCC and is of prognostic significance for recurrence in patients with HCC.