Rational design of proteasome inhibitors based on the structure of the endogenous inhibitor PI31/Fub1.

Proteasome inhibitors are widely used anticancer drugs. The three clinically approved agents are modified small peptides that preferentially target one of the proteasome's three active sites (ß5) at physiologic concentrations. In addition to these drugs, there is also an endogenous proteasome inhibitor, PI31/Fub1, that enters the proteasome's interior to simultaneously yet specifically inhibit all three active sites. Here, we have used PI31's evolutionarily optimized inhibitory mechanisms to develop a suite of potent and specific ß2 inhibitors. The lead compound strongly inhibited growth of multiple myeloma cells as a standalone agent, indicating the compound's cell permeability and establishing ß2 as a potential therapeutic target in multiple myeloma. The lead compound also showed strong synergy with the existing ß5 inhibitor bortezomib; such combination therapies might help with existing challenges of resistance and severe side effects. These results represent an effective method for rational structure-guided development of proteasome inhibitors.

Systems Genetics Analyses Reveals Key Genes Related to Behavioral Traits in the Striatum of CFW Mice.

The striatum plays a central role in directing many complex behaviors ranging from motor control to action choice and reward learning. In our study, we used 55 male CFW mice with rapid decay linkage disequilibrium to systematically mine the striatum-related behavioral functional genes by analyzing their striatal transcriptomes and 79 measured behavioral phenotypic data. By constructing a gene coexpression network, we clustered the genes into 13 modules, with most of them being positively correlated with motor traits. Based on functional annotations as well as Fisher's exact and hypergeometric distribution tests, brown and magenta modules were identified as core modules. They were significantly enriched for striatal-related functional genes. Subsequent Mendelian randomization analysis verified the causal relationship between the core modules and dyskinesia. Through the intramodular gene connectivity analysis, Adcy5 and Kcnma1 were identified as brown and magenta module hub genes, respectively. Knock outs of both Adcy5 and Kcnma1 lead to motor dysfunction in mice, and KCNMA1 acts as a risk gene for schizophrenia and smoking addiction in humans. We also evaluated the cellular composition of each module and identified oligodendrocytes in the striatum to have a positive role in motor regulation.

The crustacean DNA virus tegument protein VP26 binds to SNAP29 to inhibit SNARE complex assembly and autophagic degradation.

Autophagy generally functions as a cellular surveillance mechanism to combat invading viruses, but viruses have evolved various strategies to block autophagic degradation and even subvert it to promote viral propagation. White spot syndrome virus (WSSV) is the most highly pathogenic crustacean virus, but little is currently known about whether crustacean viruses such as WSSV can subvert autophagic degradation for escape. Here, we show that even though WSSV proliferation triggers the accumulation of autophagosomes, autophagic degradation is blocked in the crustacean species red claw crayfish. Interestingly, the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex including CqSNAP29, CqVAMP7, and the novel autophagosome SNARE protein CqSyx12 is required for autophagic flux to restrict WSSV replication, as revealed by gene silencing experiments. Simultaneously, the expressed WSSV tegument protein VP26, which likely localizes on autophagic membrane mediated by its transmembrane region, binds the Qb-SNARE domain of CqSNAP29 to competitively inhibit the binding of CqSyx12-Qa-SNARE with CqSNAP29-Qb-SNARE; this in turn disrupts the assembly of the CqSyx12-SNAP29-VAMP7 SNARE complex, which is indispensable for the proposed fusion of autophagosomes and lysosomes. Consequently, the autophagic degradation of WSSV is likely suppressed by the expressed VP26 protein in vivo in crayfish, thus probably protecting WSSV components from degradation via the autophagosome-lysosome pathway, resulting in evasion by WSSV. Collectively, these findings highlight how a DNA virus can subvert autophagic degradation by impairing the assembly of the SNARE complex to achieve evasion, paving the way for understanding host-DNA virus interactions from an evolutionary point of view, from crustaceans to mammals.IMPORTANCEWhite spot syndrome virus (WSSV) is one of the largest animal DNA viruses in terms of its genome size and has caused huge economic losses in the farming of crustaceans such as shrimp and crayfish. Detailed knowledge of WSSV-host interactions is still lacking, particularly regarding viral escape from host immune clearance. Intriguingly, we found that the presence of WSSV-VP26 might inhibit the autophagic degradation of WSSV in vivo in the crustacean species red claw crayfish. Importantly, this study is the first to show that viral protein VP26 functions as a core factor to benefit WSSV escape by disrupting the assembly of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex, which is necessary for the proposed fusion of autophagosomes with lysosomes for subsequent degradation. These findings highlight a novel mechanism of DNA virus evasion by blocking SNARE complex assembly and identify viral VP26 as a key candidate for anti-WSSV targeting.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

Drug repositioning based on weighted local information augmented graph neural network.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.

VEGF-B prevents chronic hyperglycemia-induced retinal vascular leakage by regulating the CDC42-ZO1/VE-cadherin pathway.

Non-proliferative diabetic retinopathy (NPDR) is the early stage of diabetic retinopathy (DR) and is a chronic oxidative stress-related ocular disease. Few treatments are approved for early DR. This study aimed to investigate the pathogenic mechanisms underlying the retinal micro-vasculopathy induced by diabetes and to explore an early potential for treating early DR in a mouse model. The mouse model of type 1 diabetes was established by intraperitoneal injection of streptozotocin (STZ, 180 mg/kg), which was used as the early DR model. The body weight and blood glucose mice were measured regularly; The retinal vascular leakage in the early DR mice was determined by whole-mount staining; Label-free quantitative proteomic analysis and bioinformatics were used to explore the target proteins and signaling pathways associated with the retinal tissues of early DR mice; To detect the effects of target protein on endothelial cell proliferation, migration, and tube formation, knockdown and overexpression of VEGF-B were performed in human retinal vascular endothelial cells (HRECs); Western blotting was used to detect the expression of target proteins in vitro and in vivo; Meanwhile, the therapeutic effect of VEGF-B on vascular leakage has also been evaluated in vitro and in vivo. The protein expressions of vascular endothelial growth factor (VEGF)-B and the Rho GTPases family member CDC42 were reduced in the retinal tissues of early DR. VEGF-B upregulated the expression of CDC42/ZO1/VE-cadherin and prevented hyperglycemia-induced vascular leakage in HRECs. Standard intravitreal VEGF-B injections improved the retinal vascular leakage and neurovascular response in early DR mice. Our findings demonstrated, for the first time, that in diabetes, the retinal vessels are damaged due to decreased VEGF-B expression through downregulation of CDC42/ZO1/VE-cadherin expression. Therefore, VEGF-B could be used as a novel therapy for early DR.

Multifunctional Gold Nanozyme-Engineered Amphotericin B for Enhanced Antifungal Infection Therapy.

Chronic wounds of significant severity and acute injuries are highly vulnerable to fungal infections, drastically impeding the expected wound healing trajectory. The clinical use of antifungal therapeutic drug is hampered by poor solubility, high toxicity and adverse reactions, thereby necessitating the urgent development of novel antifungal therapy strategy. Herein, this study proposes a new strategy to enhance the bioactivity of small-molecule antifungal drugs based on multifunctional metal nanozyme engineering, using amphotericin B (AmB) as an example. AmB-decorated gold nanoparticles (AmB@AuNPs) are synthesized by a facile one-pot reaction strategy, and the AmB@AuNPs exhibit superior peroxidase (POD)-like enzyme activity, with maximal reaction rates (Vmax) 3.4 times higher than that of AuNPs for the catalytic reaction of H2O2. Importantly, the enzyme-like activity of AuNPs significantly enhanced the antifungal properties of AmB, and the minimum inhibitory concentrations of AmB@AuNPs against Candida albicans (C. albicans) and Saccharomyces cerevisiae (S. cerevisiae) W303 are reduced by 1.6-fold and 50-fold, respectively, as compared with AmB alone. Concurrent in vivo studies conducted on fungal-infected wounds in mice underscored the fundamentally superior antifungal ability and biosafety of AmB@AuNPs. The proposed strategy of engineering antifungal drugs with nanozymes has great potential for enhanced therapy of fungal infections and related diseases.

DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network.

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

TEAD1, MYO7A and NDUFC2 are novel functional genes associated with glucose metabolism in BXD recombinant inbred population.

AIM: The liver is an important metabolic organ that governs glucolipid metabolism, and its dysfunction may cause non-alcoholic fatty liver disease, type 2 diabetes mellitus, dyslipidaemia, etc. We aimed to systematic investigate the key factors related to hepatic glucose metabolism, which may be beneficial for understanding the underlying pathogenic mechanisms for obesity and diabetes mellitus. MATERIALS AND METHODS: Oral glucose tolerance test (OGTT) phenotypes and liver transcriptomes of BXD mice under chow and high-fat diet conditions were collected from GeneNetwork. QTL mapping was conducted to pinpoint genomic regions associated with glucose homeostasis. Candidate genes were further nominated using a multi-criteria approach and validated to confirm their functional relevance in vitro. RESULTS: Our results demonstrated that plasma glucose levels in OGTT were significantly affected by both diet and genetic background, with six genetic regulating loci were mapped on chromosomes 1, 4, and 7. Moreover, TEAD1, MYO7A and NDUFC2 were identified as the candidate genes. Functionally, siRNA-mediated TEAD1, MYO7A and NDUFC2 knockdown significantly decreased the glucose uptake and inhibited the transcription of genes related to insulin and glucose metabolism pathways. CONCLUSIONS: Our study contributes novel insights to the understanding of hepatic glucose metabolism, demonstrating the impact of TEAD1, MYO7A and NDUFC2 on mitochondrial function in the liver and their regulatory role in maintaining in glucose homeostasis.

GL-V9 ameliorates liver fibrosis by inhibiting TGF-ß/smad pathway.

Liver fibrosis is a wound-healing response that arises from various aetiologies. Flavonoid compounds have been proved of their anti-liver fibrosis effects. This study aimed to elucidate the protective effect and mechanism of flavonoid compound GL-V9 on CCl4-induced and DDC-induced liver fibrosis. Treatment with GL-V9 alleviated hepatic injury and exhibited a dramatic protection effect of liver fibrosis. Further experiments found that GL-V9 treatment inhibited extracellular matrix (ECM) expression. Activation of hepatic stellate cells (HSCs) is a central driver of fibrosis. GL-V9 could inhibit the activation of HSCs through directly binding to TGFßRI, subsequently inhibit TGF-ß/Smad pathway. In conclusion, this study proved that GL-V9 executed a protective effect on liver fibrosis by inhibiting TGF-ß/Smad pathway.

Successful pregnancy in a patient with IgA nephropathy treated with telitacicept: a case report.

BACKGROUND: IgA nephropathy (IgAN) is the most common cause of primary glomerulonephritis, with complex pathogenic mechanisms involving abnormal B-cell activation. As a novel biologic agent, telitacicept inhibits both B-lymphocyte stimulating factor and a proliferation-inducing ligand. It also inhibits both B cells and plasma cells and the production of galactose-deficient IgA1 (Gd-IgA1) and its autoantibodies, thus exerting an immunosuppressive effect. Women with IgAN are at a higher risk of adverse pregnancy outcomes such as preeclampsia and miscarriage, especially those with uncontrolled massive proteinuria and advanced chronic kidney disease. Therefore, IgAN disease control before and during pregnancy is essential. Here, we report the case of a woman with IgAN who had a successful pregnancy with significant improvement and long-term remission after treatment with telitacicept. This is the first report of a pregnancy following exposure to telitacicept. CONCLUSION: This report describes the efficacy of telitacicept in patients with IgAN and explores its value in women of childbearing age, suggesting effective and safe treatment options for women who wish to conceive.

NIR-IIb fluorescence antiangiogenesis copper nano-reaper for enhanced synergistic cancer therapy.

The formation of blood vessel system under a relatively higher Cu2+ ion level is an indispensable precondition for tumor proliferation and migration, which was assisted in forming the tumor immune microenvironment. Herein, a copper ions nano-reaper (LMDFP) is rationally designed not only for chelating copper ions in tumors, but also for combination with photothermal therapy (PTT) to improve antitumor efficiency. Under 808 nm laser irradiation, the fabricated nano-reaper converts light energy into thermal energy to kill tumor cells and promotes the release of D-penicillamine (DPA) in LMDFP. Photothermal properties of LMDFP can cause tumor ablation in situ, which further induces immunogenic cell death (ICD) to promote systematic antitumor immunity. The released DPA exerts an anti-angiogenesis effect on the tumor through chelating copper ions, and inhibits the expression of programmed death ligand 1 (PD-L1), which synergizes with PTT to enhance antitumor immunity and inhibit tumor metastasis. Meanwhile, the nanoplatform can emit near-infrared-IIb (NIR-IIb) fluorescence under 980 nm excitation, which can be used to track the nano-reaper and determine the optimal time point for PTT. Thus, the fabricated nano-reaper shows powerful potential in inhibiting tumor growth and metastasis, and holds great promise for the application of copper nanochelator in precise tumor treatment.

Rationally designed catalytic nanoplatform for enhanced chemoimmunotherapy via deploying endogenous plus exogenous copper and remodeling tumor microenvironment.

Chemodynamic therapy represents a novel tumor therapeutic modality via triggering catalytic reactions in tumors to yield highly toxic reactive oxygen species (ROS). Nevertheless, low efficiency catalytic ability, potential systemic toxicity and inefficient tumor targeting, have hindered the efficacy of chemodynamic therapy. Herein, a rationally designed catalytic nanoplatform, composed of folate acid conjugated liposomes loaded with copper peroxide (CP) and chloroquine (CQ; a clinical drug) (denoted as CC@LPF), could power maximal tumor cytotoxicity, mechanistically via maneuvering endogenous and exogenous copper for a highly efficient catalytic reaction. Despite a massive autophagosome accumulation elicited by CP-powered autophagic initiation and CQ-induced autolysosomal blockage, the robust ROS, but not aberrant autophagy, underlies the synergistic tumor inhibition. Otherwise, this combined mode also elicits an early onset, above all, long-term high-level existence of immunogenic cell death markers, associated with ROS and aberrant autophagy -triggered endoplasmic reticulum stress. Besides, CC@LPF, with tumor targeting capability and selective tumor cytotoxicity, could elicit intratumor dendritic cells (mainly attributed to CQ) and tumor infiltrating CD8+ T cells, upon combining with PD-L1 therapeutic antibody, further induce significant anti-tumor effect. Collectively, the rationally designed nanoplatform, CC@LPF, could enhance tumor chemoimmunotherapy via deploying endogenous plus exogenous copper and remodeling tumor microenvironment.

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Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder with onset in infancy or early childhood. Mendelian randomization (MR) is a statistical method used to infer causal relationships between exposures and outcomes. This article summarizes MR studies related to ASD. Existing research supports a causal relationship between maternal inflammatory bowel disease in children with ASD, parental education levels, screen time exposure, obesity, insomnia, serum transferrin, decreased blood selenium, abnormal signals in brain functional MRI, interleukin-6, phosphodiesterase 2A, mitogen-activated protein kinase kinase 3, mitochondrial ribosomal protein L33, serotonin, and ASD. However, it does not support a causal relationship between parental rheumatoid arthritis, systemic lupus erythematosus, neonatal jaundice in children with ASD, cytomegalovirus infection, asthma, oral ulcers, vitamin D levels, and ASD. This article reviews the etiological factors related to ASD and MR studies, aiming to explore and deepen the understanding of the pathophysiology of ASD. It provides strong statistical support for the prevention, diagnosis, and treatment of ASD, and offers new methods and strategies for the etiological analysis of complex traits.

The Combination of Quantitative Proteomics and Systems Genetics Analysis Reveals that PTN Is Associated with Sleep-Loss-Induced Cognitive Impairment.

Sleep loss is associated with cognitive dysfunction. However, the detailed mechanisms remain unclear. In this study, we established a para-chlorophenylalanine (PCPA)-induced insomniac mouse model with impaired cognitive function. Mass-spectrometry-based proteomics showed that the expression of 164 proteins was significantly altered in the hippocampus of the PCPA mice. To identify critical regulators among the potential markers, a transcriptome-wide association screening was performed in the BXD mice panel. Among the candidates, the expression of pleiotrophin (Ptn) was significantly associated with cognitive functions, indicating that Ptn-mediates sleep-loss-induced cognitive impairment. Gene co-expression analysis further revealed the potential mechanism by which Ptn mediates insomnia-induced cognitive impairment via the MAPK signaling pathway; that is, the decreased secretion of Ptn induced by insomnia leads to reduced binding to Ptprz1 on the postsynaptic membrane with the activation of the MAPK pathway via Fos and Nr4a1, further leading to the apoptosis of neurons. In addition, Ptn is genetically trans-regulated in the mouse hippocampus and implicated in neurodegenerative diseases in human genome-wide association studies. Our study provides a novel biomarker for insomnia-induced cognitive impairment and a new strategy for seeking neurological biomarkers by the integration of proteomics and systems genetics.

HOXD8 suppresses renal cell carcinoma growth by upregulating SHMT1 expression.

Amplification of amino acids synthesis is reported to promote tumorigenesis. The serine/glycine biosynthesis pathway is a reversible conversion of serine and glycine catalyzed by cytoplasmic serine hydroxymethyltransferase (SHMT)1 and mitochondrial SHMT2; however, the role of SHTM1 in renal cell carcinoma (RCC) is still unclear. We found that low SHMT1 expression is correlated with poor survival of RCC patients. The in vitro study showed that overexpression of SHMT1 suppressed RCC proliferation and migration. In the mouse tumor model, SHMT1 significantly retarded RCC tumor growth. Furthermore, by gene network analysis, we found several SHMT1-related genes, among which homeobox D8 (HOXD8) was identified as the SHMT1 regulator. Knockdown of HOXD8 decreased SHMT1 expression, resulting in faster RCC growth, and rescued the SHMT1 overexpression-induced cell migration defects. Additionally, ChIP assay found the binding site of HOXD8 to SHMT1 promoter was at the -456~-254 bp region. Taken together, SHMT1 functions as a tumor suppressor in RCC. The transcription factor HOXD8 can promote SHMT1 expression and suppress RCC cell proliferation and migration, which provides new mechanisms of SHMT1 in RCC tumor growth and might be used as a potential therapeutic target candidate for clinical treatment.

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-exome sequencing is considered the gold standard for determining TMB, it is difficult to be applied in clinical practice due to its high cost. There are also a few DNA panel-based methods to estimate TMB; however, their detection cost is also high, and the associated wet-lab experiments usually take days, which emphasize the need for faster and cheaper alternatives. RESULTS: In this study, we propose a multi-modal deep learning model based on a residual network (ResNet) and multi-modal compact bilinear pooling to predict TMB status (i.e. TMB high (TMB_H) or TMB low(TMB_L)) directly from histopathological images and clinical data. We applied the model to CRC data from The Cancer Genome Atlas and compared it with four other popular methods, namely, ResNet18, ResNet50, VGG19 and AlexNet. We tested different TMB thresholds, namely, percentiles of 10%, 14.3%, 15%, 16.3%, 20%, 30% and 50%, to differentiate TMB_H and TMB_L.For the percentile of 14.3% (i.e. TMB value 20) and ResNet18, our model achieved an area under the receiver operating characteristic curve of 0.817 after 5-fold cross-validation, which was better than that of other compared models. In addition, we also found that TMB values were significantly associated with the tumor stage and N and M stages. Our study shows that deep learning models can predict TMB status from histopathological images and clinical information only, which is worth clinical application.

Discovery of potent and effective inhibitors containing sulfoxide structures targeting EML4-ALK rearrangement and EGFR mutant non-small cell lung cancer.

For non-small cell lung cancer patients with dual mutations in EGFR and ALK, there are currently no effective therapies. Consequently, novel EGFR/ALK dual-target inhibitors are urgently needed for the treatment of NSCLC. Here, we designed a series of highly effective small molecule dual inhibitors of ALK and EGFR. The biological evaluation highlighted that most of these new compounds could effectively inhibit both ALK and EGFR in enzymatic and cellular assays. Compound (+)-8l was investigated for its antitumor properties, and it was found that (+)-8l blocked the phosphorylation of EGFR and ALK induced by ligands and inhibited phosphorylation-ERK and phosphorylation-AKT induced by ligands. Furthermore, (+)-8l also induces apoptosis and G0/G1 cell cycle arrest in cancer cells and inhibits proliferation, migration, and invasion. Notably, (+)-8l significantly suppressed tumor growth in the H1975 cell-inoculated xenograft model (20 mg/kg/d, TGI: 96.11%), PC9 cell-inoculated xenograft model (20 mg/kg/d, TGI: 96.61%) and EML4 ALK-Baf3 cell-inoculated xenograft model (30 mg/kg/d, TGI: 80.86%). These results highlight the differentiated potential of (+)-8l to inhibit ALK rearrangement and EGFR mutation in NSCLC.

PDGFB-targeted functional MRI nanoswitch for activatable T1-T2 dual-modal ultra-sensitive diagnosis of cancer.

As one of the most significant imaging modalities currently available, magnetic resonance imaging (MRI) has been extensively utilized for clinically accurate cancer diagnosis. However, low signal-to-noise ratio (SNR) and low specificity for tumors continue to pose significant challenges. Inspired by the distance-dependent magnetic resonance tuning (MRET) phenomenon, the tumor microenvironment (TME)-activated off-on T1-T2 dual-mode MRI nanoswitch is presented in the current study to realize the sensitive early diagnosis of tumors. The tumor-specific nanoswitch is designed and manufactured on the basis of PDGFB-conjugating ferroferric oxide coated by Mn-doped silica (PDGFB-FMS), which can be degraded under the high-concentration GSH and low pH in TME to activate the T1-T2 dual-mode MRI signals. The tumor-specific off-on dual-mode MRI nanoswitch can significantly improve the SNR and is used successfully for the accurate diagnosis of early-stage tumors, particularly for orthotopic prostate cancer. In addition, the systemic delivery of the nanoswitch did not cause blood or tissue damage, and it can be excreted out of the body in a timely manner, demonstrating excellent biosafety. Overall, the strategy is a significant step in the direction of designing off-on dual-mode MRI nanoprobes to improve imaging accuracy, which opens up new avenues for the development of new MRI probes.