Construction and prognostic value of enhanced CT image omics model for noninvasive prediction of HRG in bladder cancer based on logistic regression and support vector machine algorithm

BackgroundUrothelial Carcinoma of the bladder (BLCA) is the most prevalent cancer of the urinary system. In cancer patients, HRG fusion is linked to a poor prognosis. The prediction of HRG expression by imaging omics in BLCA has not yet been fully investigated.MethodsHRG expression in BLCA and healthy adjoining tissues was primarily identified utilizing data sourced from The Cancer Genome Atlas (TCGA). Using Kaplan–Meier survival curves and Landmark analysis, the relationship between HRG expression, clinicopathological parameters, and overall survival (OS) was investigated. Additionally, gene set variation analysis (GSVA) was conducted and CIBERSORTx was used to investigate the relationship between HRG expression and immune cell infiltration. The Cancer Imaging Archive (TCIA) provided CT images that were used for prognostic analysis, radiomic feature extraction, and construction of the model, respectively. The HRG expression levels were predicted using the constructed and evaluated LR and SMV models.ResultsHRG expression was shown to be substantially lower in BLCA tumors as opposed to that observed in normal tissues (p < 0.05). HRG expression had a close positive relationship with Eosinophils and a close negative relationship with B cells naive. The findings of the Landmark analysis illustrated that higher HRG was associated with improved patient survival at an early stage (P=0.048). The predictive performance of the two models, based on logistic regression analysis and support vector machine, was outstanding in the training and validation sets, yielding AUCs of 0.722 and 0.708, respectively, in the SVM model, and 0.727 and 0.662, respectively, in the LR.The models have good predictive efficiency.ConclusionHRG expression levels can have a significant impact on BLCA patients’ prognoses. The radiomic characteristics can successfully predict the pre-surgical HRG expression levels, based on CT- Image omics

Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p <0.049; HR = 1.84; 95% CI = 1.02–3.32) and the training set (p < 0.001; HR = 3.12, 95% CI = 2.23–4.37). The numbers of regulatory T cells (Tregs) were significantly positively correlated with the six immunity-related genes identified, with correlation coefficients were 0.385, 0.415, 0.399, 0.451, 0.485, and 0.333, respectively (p <0.001).Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP

SHQ1 regulation of RNA splicing is required for T-lymphoblastic leukemia cell survival

T-acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with complicated heterogeneity. Although expression profiling reveals common elevated genes in distinct T-ALL subtypes, little is known about their functional role(s) and regulatory mechanism(s). We here show that SHQ1, an H/ACA snoRNP assembly factor involved in snRNA pseudouridylation, is highly expressed in T-ALL. Mechanistically, oncogenic NOTCH1 directly binds to the SHQ1 promoter and activates its transcription. SHQ1 depletion induces T-ALL cell death in vitro and prolongs animal survival in murine T-ALL models. RNA-Seq reveals that SHQ1 depletion impairs widespread RNA splicing, and MYC is one of the most prominently downregulated genes due to inefficient splicing. MYC overexpression significantly rescues T-ALL cell death resulted from SHQ1 inactivation. We herein report a mechanism of NOTCH1-SHQ1-MYC axis in T-cell leukemogenesis. These findings not only shed light on the role of SHQ1 in RNA splicing and tumorigenesis, but also provide additional insight into MYC regulation

Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data

Accurately assessing the prognostic outcomes of patients with acute ischemic stroke and adjusting treatment plans in a timely manner for those with poor prognosis is crucial for intervening in modifiable risk factors. However, there is still controversy regarding the correlation between imaging-based predictions of complications in acute ischemic stroke. To address this, we developed a cross-modal attention module for integrating multidimensional data, including clinical information, imaging features, treatment plans, prognosis, and complications, to achieve complementary advantages. The fused features preserve magnetic resonance imaging (MRI) characteristics while supplementing clinical relevant information, providing a more comprehensive and informative basis for clinical diagnosis and treatment. The proposed framework based on multidimensional data for activity of daily living (ADL) scoring in patients with acute ischemic stroke demonstrates higher accuracy compared to other state-of-the-art network models, and ablation experiments confirm the effectiveness of each module in the framework

Monocarboxylate transporter upregulation in induced regulatory T cells promotes resistance to anti-PD-1 therapy in hepatocellular carcinoma patients

BackgroundProgrammed cell death-1 (PD-1) immune checkpoint inhibitors are not effective in treating all patients with hepatocellular carcinoma (HCC), and regulatory T cells (Tregs) may determine the resistance to anti-PD-1 therapy.MethodsPatients were divided into two groups based on the clinical efficacy of anti-PD-1 therapy. Flow cytometry was used to determine the phenotype of CD4+, CD8+, and Tregs in peripheral blood mononuclear cells (PBMCs). CD4+CD45RA+T cells were sorted to analyze Treg differentiation and function.ResultsNo significant differences were found between resistant and sensitive patients in the percentage of CD4+ T cells and Tregs in PBMCs or the differentiation and function of induced Tregs (iTregs). However, iTregs from resistant patients presented higher monocarboxylate transporter (MCT) expression. Lactate induced more iTregs and improved OXPHOS levels in the resistant group. MCT1 and MCT2 were highly expressed in tumor-infiltrating Tregs, and patients with higher MCT1 expression had worse clinical outcomes. Combinatorial therapy with MCT antibody and anti-PD-1 therapy effectively inhibited tumor growth.ConclusionMCT and its downstream lactate signal in Tregs can confer anti-PD-1 resistance and may be a marker of poor prognosis in HCC

Synergistic Anticancer Activity of Combined Use of Caffeic Acid with Paclitaxel Enhances Apoptosis of Non-Small-Cell Lung Cancer H1299 Cells in Vivo and in Vitro

Background/Aims: Caffeic acid (CA) is known to possess multiple biological activities including anti-cancer activities. However, the molecular mechanisms underlying these activities in non-small-cell lung cancer (NSCLC) cells are not fully understood. We attempted to clarify whether CA could enhance paclitaxel (PTX)-induced cytotoxicity in H1299 cells. Methods: First, we tested the cytotoxic effects in both H1299 cells and normal human Bease-2b cells by cell proliferation experiments. Next, we use Annexin V/propidium iodide apoptosis analysis and flow cytometric analysis to investigate apoptosis and cell cycle arrest under the treatments mentioned above. To further pinpoint changes in apoptosis, we tested the caspase-associated apoptotic pathway, which involves the activities of caspase-3 and caspase-9. Moreover, apoptosis-related proteins and MAPK pathway proteins were examined by western blot. An H1299 xenograft nude mice model was used to further evaluate the tumor-suppressing effects of CA and PTX in vivo. Results: Combination treatment with low-dose CA and PTX decreased the proliferation of NSCLC H1299 cells but not normal Beas-2b cells. Flow cytometry showed that H1299 cells were arrested in the sub-G1 phase and apoptosis was significantly increased in H1299 cells after CA treatment. Caspase-3 and caspase-9 activities were both increased after CA treatment. Furthermore, CA increased the PTX-induced activation of Bax, Bid, and downstream cleaved PARP, and phosphorylation of extracellular signal regulated kinase1/2 and c-Jun NH2-terminal protein kinase1/2. An in vivo tumor-suppression assay demonstrated that CA and PTX combined treatment exerted a more effective suppressive effect on tumor growth in H1299 xenografts without causing significant adverse effects. Conclusions: Our results indicated that CA inhibited NSCLC H1299 cell growth by inducing apoptosis and CA and PTX combined produced a synergistic anti-cancer effect in H1299 cells

Magic auxeticity angle of graphene

Solids exhibit transverse shrinkage when they are stretched, except auxetics that abnormally demonstrate lateral expansion instead. Graphene possesses the unique normal-auxeticity (NA) transition when it is stretched along the armchair direction but not along the zigzag direction. Here we report on the anisotropic temperature-dependent NA transitions in strained graphene using molecular dynamics simulations. The critical strain where the NA transition occurs increases with respect to an increase in the tilt angle deviating from armchair direction upon uniaxial loading. The magic angle for the NA transition is 10.9°, beyond which the critical strain is close to fracture strain. In addition, the critical strain decreases with an increasing temperature when the tilt angle is smaller than the NA magic angle. Our results shed lights on the unprecedented nonlinear dimensional response of graphene to the large mechanical loading at various temperatures

ADAMTS16 activates latent TGF-β, accentuating fibrosis and dysfunction of the pressure-overloaded heart

AIMS: Cardiac fibrosis is a major cause of heart failure (HF), and mediated by the differentiation of cardiac fibroblasts into myofibroblasts. However, limited tools are available to block cardiac fibrosis. ADAMTS16 is a member of the ADAMTS superfamily of extracellular protease enzymes involved in extracellular matrix (ECM) degradation and remodelling. In this study, we aimed to establish ADAMTS16 as a key regulator of cardiac fibrosis. METHODS AND RESULTS: Western blot and qRT-PCR analyses demonstrated that ADAMTS16 was significantly up-regulated in mice with transverse aortic constriction (TAC) associated with left ventricular hypertrophy and HF, which was correlated with increased expression of Mmp2, Mmp9, Col1a1, and Col3a1. Overexpression of ADAMTS16 accelerated the AngII-induced activation of cardiac fibroblasts into myofibroblasts. Protein structural analysis and co-immunoprecipitation revealed that ADAMTS16 interacted with the latency-associated peptide (LAP)-transforming growth factor (TGF)-β via a RRFR motif. Overexpression of ADAMTS16 induced the activation of TGF-β in cardiac fibroblasts; however, the effects were blocked by a mutation of the RRFR motif to IIFI, knockdown of Adamts16 expression, or a TGF-β-neutralizing antibody (ΝAb). The RRFR tetrapeptide, but not control IIFI peptide, blocked the interaction between ADAMTS16 and LAP-TGF-β, and accelerated the activation of TGF-β in cardiac fibroblasts. In TAC mice, the RRFR tetrapeptide aggravated cardiac fibrosis and hypertrophy by up-regulation of ECM proteins, activation of TGF-β, and increased SMAD2/SMAD3 signalling, however, the effects were blocked by TGF-β-NAb. CONCLUSION: ADAMTS16 promotes cardiac fibrosis, cardiac hypertrophy, and HF by facilitating cardiac fibroblasts activation via interacting with and activating LAP-TGF-β signalling. The RRFR motif of ADAMTS16 disrupts the interaction between ADAMTS16 and LAP-TGF-β, activates TGF-β, and aggravated cardiac fibrosis and hypertrophy. This study identifies a novel regulator of TGF-β signalling and cardiac fibrosis, and provides a new target for the development of therapeutic treatment of cardiac fibrosis and HF