In vitro efficacy of next-generation dihydrotriazines and biguanides against babesiosis and malaria parasites.

Babesia and Plasmodium pathogens, the causative agents of babesiosis and malaria, are vector-borne intraerythrocytic protozoan parasites, posing significant threats to both human and animal health. The widespread resistance exhibited by these pathogens to various classes of antiparasitic drugs underscores the need for the development of novel and more effective therapeutic strategies. Antifolates have long been recognized as attractive antiparasitic drugs as they target the folate pathway, which is essential for the biosynthesis of purines and pyrimidines, and thus is vital for the survival and proliferation of protozoan parasites. More efficacious and safer analogs within this class are needed to overcome challenges due to resistance to commonly used antifolates, such as pyrimethamine, and to address liabilities associated with the dihydrotriazines, WR99210 and JPC-2067. Here, we utilized an in vitro culture condition suitable for the continuous propagation of Babesia duncani, Babesia divergens, Babesia MO1, and Plasmodium falciparum in human erythrocytes to screen a library of 50 dihydrotriazines and 29 biguanides for their efficacy in vitro and compared their potency and therapeutic indices across different species and isolates. We identified nine analogs that inhibit the growth of all species, including the P. falciparum pyrimethamine-resistant strain HB3, with IC50 values below 10 nM, and display excellent in vitro therapeutic indices. These compounds hold substantial promise as lead antifolates for further development as broad-spectrum antiparasitic drugs.

Diversity matters: Cross-head mutual mean-teaching for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data, giving rise to disruptive noise during training and susceptibility to erroneous information overfitting. Moreover, applying perturbations to inaccurate predictions further impedes consistent learning. To address these concerns, we propose a novel cross-head mutual mean-teaching network (CMMT-Net) incorporated weak-strong data augmentations, thereby benefiting both co-training and consistency learning. More concretely, our CMMT-Net extends the cross-head co-training paradigm by introducing two auxiliary mean teacher models, which yield more accurate predictions and provide supplementary supervision. The predictions derived from weakly augmented samples generated by one mean teacher are leveraged to guide the training of another student with strongly augmented samples. Furthermore, two distinct yet synergistic data perturbations at the pixel and region levels are introduced. We propose mutual virtual adversarial training (MVAT) to smooth the decision boundary and enhance feature representations, and a cross-set CutMix strategy to generate more diverse training samples for capturing inherent structural data information. Notably, CMMT-Net simultaneously implements data, feature, and network perturbations, amplifying model diversity and generalization performance. Experimental results on three publicly available datasets indicate that our approach yields remarkable improvements over previous SOTA methods across various semi-supervised scenarios. The code is available at https://github.com/Leesoon1984/CMMT-Net.

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking.

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

Background: Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs. Materials and methods: RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored. Results: A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immune-related genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH. Conclusion: This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions.

PROTAC-Mediated HDAC7 Protein Degradation Unveils Its Deacetylase-Independent Proinflammatory Function in Macrophages.

Class IIa histone deacetylases (Class IIa HDACs) play critical roles in regulating essential cellular metabolism and inflammatory pathways. However, dissecting the specific roles of each class IIa HDAC isoform is hindered by the pan-inhibitory effect of current inhibitors and a lack of tools to probe their functions beyond epigenetic regulation. In this study, a novel PROTAC-based compound B4 is developed, which selectively targets and degrades HDAC7, resulting in the effective attenuation of a specific set of proinflammatory cytokines in both lipopolysaccharide (LPS)-stimulated macrophages and a mouse model. By employing B4 as a molecular probe, evidence is found for a previously explored role of HDAC7 that surpasses its deacetylase function, suggesting broader implications in inflammatory processes. Mechanistic investigations reveal the critical involvement of HDAC7 in the Toll-like receptor 4 (TLR4) signaling pathway by directly interacting with the TNF receptor-associated factor 6 and TGFß-activated kinase 1 (TRAF6-TAK1) complex, thereby initiating the activation of the downstream mitogen-activated protein kinase/nuclear factor-κB (MAPK/NF-κB) signaling cascade and subsequent gene transcription. This study expands the insight into HDAC7's role within intricate inflammatory networks and highlights its therapeutic potential as a novel target for anti-inflammatory treatments.

DIAS: A dataset and benchmark for intracranial artery segmentation in DSA sequences.

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://doi.org/10.5281/zenodo.11401368 and https://github.com/lseventeen/DIAS.

Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer.

BACKGROUND: 5-Fluorouracil (5-FU) has been used as a standard first-line treatment for colorectal cancer (CRC) patients. Although 5-FU-based chemotherapy and immune checkpoint blockade (ICB) have achieved success in treating CRC, drug resistance and low response rates remain substantial limitations. Thus, it is necessary to construct a 5-FU resistance-related signature (5-FRSig) to predict patient prognosis and identify ideal patients for chemotherapy and immunotherapy. METHODS: Using bulk and single-cell RNA sequencing data, we established and validated a novel 5-FRSig model using stepwise regression and multiple CRC cohorts and evaluated its associations with the prognosis, clinical features, immune status, immunotherapy, neoadjuvant therapy, and drug sensitivity of CRC patients through various bioinformatics algorithms. Unsupervised consensus clustering was performed to categorize the 5-FU resistance-related molecular subtypes of CRC. The expression levels of 5-FRSig, immune checkpoints, and immunoregulators were determined using quantitative real-time polymerase chain reaction (RT‒qPCR). Potential small-molecule agents were identified via Connectivity Map (CMap) and molecular docking. RESULTS: The 5-FRSig and cluster were confirmed as independent prognostic factors in CRC, as patients in the low-risk group and Cluster 1 had a better prognosis. Notably, 5-FRSig was significantly associated with 5-FU sensitivity, chemotherapy response, immune cell infiltration, immunoreactivity phenotype, immunotherapy efficiency, and drug selection. We predicted 10 potential compounds that bind to the core targets of 5-FRSig with the highest affinity. CONCLUSION: We developed a valid 5-FRSig to predict the prognosis, chemotherapeutic response, and immune status of CRC patients, thus optimizing the therapeutic benefits of chemotherapy combined with immunotherapy, which can facilitate the development of personalized treatments and novel molecular targeted therapies for patients with CRC.

Efficient residual network using hyperspectral images for corn variety identification.

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

CD177 drives the transendothelial migration of Treg cells enriched in human colorectal cancer.

Objectives: Regulatory T (Treg) cells regulate immunity in autoimmune diseases and cancers. However, immunotherapies that target tumor-infiltrating Treg cells often induce unwanted immune responses and tissue inflammation. Our research focussed on exploring the expression pattern of CD177 in tumor-infiltrating Treg cells with the aim of identifying a potential target that can enhance immunotherapy effectiveness. Methods: Single-cell RNA sequencing (scRNA-seq) data and survival data were obtained from public databases. Twenty-one colorectal cancer patient samples, including fresh tumor tissues, peritumoral tissues and peripheral blood mononuclear cells (PBMCs), were analysed using flow cytometry. The transendothelial activity of CD177+ Treg cells was substantiated using in vitro experiments. Results: ScRNA-seq and flow cytometry results indicated that CD177 was exclusively expressed in intratumoral Treg cells. CD177+ Treg cells exhibited greater activation status and expressed elevated Treg cell canonical markers and immune checkpoint molecules than CD177- Treg cells. We further discovered that both intratumoral CD177+ Treg cells and CD177-overexpressing induced Treg (iTreg) cells had lower levels of PD-1 than their CD177- counterparts. Moreover, CD177 overexpression significantly enhanced the transendothelial migration of Treg cells in vitro. Conclusions: These results demonstrated that Treg cells with higher CD177 levels exhibited an enhanced activation status and transendothelial migration capacity. Our findings suggest that CD177 may serve as an immunotherapeutic target and that overexpression of CD177 may improve the efficacy of chimeric antigen receptor T (CAR-T) cell therapy.

FOXP3+ regulatory T cell perturbation mediated by the IFNγ-STAT1-IFITM3 feedback loop is essential for anti-tumor immunity.

Targeting tumor-infiltrating regulatory T cells (Tregs) is an efficient way to evoke an anti-tumor immune response. However, how Tregs maintain their fragility and stability remains largely unknown. IFITM3 and STAT1 are interferon-induced genes that play a positive role in the progression of tumors. Here, we showed that IFITM3-deficient Tregs blunted tumor growth by strengthening the tumor-killing response and displayed the Th1-like Treg phenotype with higher secretion of IFNγ. Mechanistically, depletion of IFITM3 enhances the translation and phosphorylation of STAT1. On the contrary, the decreased IFITM3 expression in STAT1-deficient Tregs indicates that STAT1 conversely regulates the expression of IFITM3 to form a feedback loop. Blocking the inflammatory cytokine IFNγ or directly depleting STAT1-IFITM3 axis phenocopies the restored suppressive function of tumor-infiltrating Tregs in the tumor model. Overall, our study demonstrates that the perturbation of tumor-infiltrating Tregs through the IFNγ-IFITM3-STAT1 feedback loop is essential for anti-tumor immunity and constitutes a targetable vulnerability of cancer immunotherapy.

CVANet: Cascaded visual attention network for single image super-resolution.

Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction and detail reconstruction capabilities for single image super-resolution (SISR). Nevertheless, most previous DCNN-based methods do not fully utilize the complementary strengths between feature maps, channels, and pixels. Therefore, it hinders the ability of DCNNs to represent abundant features. To tackle the aforementioned issues, we present a Cascaded Visual Attention Network for SISR called CVANet, which simulates the visual attention mechanism of the human eyes to focus on the reconstruction process of details. Specifically, we first designed a trainable feature attention module (FAM) for feature-level attention learning. Afterward, we introduce a channel attention module (CAM) to reinforce feature maps under channel-level attention learning. Meanwhile, we propose a pixel attention module (PAM) that adaptively selects representative features from the previous layers, which are utilized to generate a high-resolution image. Satisfactory, our CVANet can effectively improve the resolution of images by exploring the feature representation capabilities of different modules and the visual perception properties of the human eyes. Extensive experiments with different methods on four benchmarks demonstrate that our CVANet outperforms the state-of-the-art (SOTA) methods in subjective visual perception, PSNR, and SSIM.The code will be made available https://github.com/WilyZhao8/CVANet.

Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients.

Background: Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism. Methods: CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR. Results: We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility. Conclusion: We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.

Blockade of TNF-α/TNFR2 signalling suppresses colorectal cancer and enhances the efficacy of anti-PD1 immunotherapy by decreasing CCR8+ T regulatory cells.

Enrichment of regulatory T cells (Tregs) in the tumour microenvironment (TME) has been recognized as one of the major factors in the initiation and development of resistance to immune checkpoint inhibitors. C-C motif chemokine receptor 8 (CCR8), a marker of activated suppressive Tregs, has a significant impact on the functions of Tregs in the TME. However, the regulatory mechanism of CCR8 in Tregs remains unclear. Here, we reveal that a high level of TNF-α in the colorectal cancer (CRC) microenvironment upregulates CCR8 expression in Tregs via the TNFR2/NF-κB signalling pathway and the FOXP3 transcription factor. Furthermore, in both anti-PD1-responsive and anti-PD1-unresponsive tumour models, PD1 blockade induced CCR8+ Treg infiltration. In both models, Tnfr2 depletion or TNFR2 blockade suppressed tumour progression by reducing CCR8+ Treg infiltration and thus augmented the efficacy of anti-PD1 therapy. Finally, we identified that TNFR2+CCR8+ Tregs but not total Tregs are positively correlated with adverse prognosis in CRC and gastric cancer. Our work reveals the regulatory mechanisms of CCR8 in Tregs and identifies TNFR2 as a promising target for immunotherapy.

Anticoagulation strategies in patients with extracorporeal membrane oxygenation: A network meta-analysis and systematic review.

OBJECTIVES: Extracorporeal membrane oxygenation (ECMO) plays an important role in providing temporary life support for patients with severe cardiac or pulmonary failure, but requires strict anticoagulation and monitoring. This network meta-analysis systematically explored the most effective anticoagulation and monitoring strategies for patients receiving ECMO. METHODS: MEDLINE, Embase, Web of Science, and the Cochrane Central Register of Controlled Trials were searched up to January 31, 2023, for studies comparing unfractionated heparin (UFH), argatroban (Arg), bivalirudin (Biv), and/or nafamostat mesylate (NM) in patients receiving ECMO. The primary outcomes included device-related thrombosis, patient-related thrombosis, and major bleeding events. The secondary outcomes included ECMO survival, ECMO duration, and in-hospital mortality. RESULTS: A total of 2522 patients from 23 trials were included in the study. Biv was associated with a decreased risk of device-related thrombosis (odd ratio [OR] 0.51, 95% confidence interval [CI]: 0.33-0.84) compared with UFH, whereas NM (OR 2.2, 95% CI: 0.24-65.0) and Arg (OR 0.92, 95% CI: 0.43-2.0) did not reduce the risk of device-related thrombosis compared with UFH. Biv was superior to Arg in decreasing the risk of device-related thrombosis (OR 0.14, 95% CI: 0.03-0.51). Biv reduced the risk of patient-related thrombosis compared with UFH (OR 0.44, 95% CI: 0.18-0.85); NM (OR 0.65, 95% CI: 0.14-3.3) and Arg (OR 3.1, 95% CI: 0.94-12.0) did not decrease risk of patient-related thrombosis compared with UFH. No significant difference was observed in the risk of major bleeding between three alternatives and UFH: Biv (OR 0.54, 95% CI: 0.23-1.3), Arg (OR 1.3, 95% CI: 0.34-5.8), and NM (OR 0.60, 95% CI: 0.13-2.6). NM showed a reduced risk of in-hospital mortality compared with UFH (OR 0.27, 95% CI: 0.091-0.77), whereas Arg (OR 0.43, 95% CI: 0.15-1.2) and Biv (OR 0.75, 95% CI: 0.52-1.1) did not decrease risk of in-hospital mortality. CONCLUSIONS: Compared with UFH and Arg, Biv reduces the risk of thrombosis and appears to be a better choice for patients requiring ECMO. NM was associated with a reduced risk of in-hospital mortality.

SIGLEC10+ macrophages drive gastric cancer progression by suppressing CD8+ T cell function.

Existing immune checkpoint inhibitors focus on activating T cells and show limited effectiveness in gastric cancer (GC). SIGLEC10 is identified as a novel tumor-associated macrophage-related immune checkpoint in other cancer types. However, its immunosuppressive role and clinical significance in GC remain unclear. In this study, we find a dominant expression of SIGLEC10 on CD68+ macrophages in GC. SIGLEC10 can suppress the proliferation and function of tumor-infiltrating CD8+ T cells in vitro via the Akt/P38/Erk signaling pathway. Furthermore, in ex vivo and in vivo models, SIGLEC10 blockade promotes CD8+ T cell effector function. Finally, SIGLEC10+ macrophages are positively correlated with the adverse prognosis of GC. Our study highlights that SIGLEC10 directly suppresses T cell function and serves as a promising target for immunotherapy and suggests SIGLEC10+ macrophages as a novel potential predictor of the clinical prognosis of GC.

MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis.

A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

Improving the tumor selectivity of T cell engagers by logic-gated dual tumor-targeting.

Targeting single tumor antigens makes it difficult to provide sufficient tumor selectivity for T cell engagers (TCEs), leading to undesirable toxicity and even treatment failure, which is particularly serious in solid tumors. Here, we designed novel trispecific TCEs (TriTCEs) to improve the tumor selectivity of TCEs by logic-gated dual tumor-targeting. TriTCE can effectively redirect and activate T cells to kill tumor cells (∼18 pM EC50) by inducing the aggregation of dual tumor antigens, which was ∼70- or 750- fold more effective than the single tumor-targeted isotype controls, respectively. Further in vivo experiments indicated that TriTCE has the ability to accumulate in tumor tissue and can induce circulating T cells to infiltrate into tumor sites. Hence, TriTCE showed a stronger tumor growth inhibition ability and significantly prolonged the survival time of the mice. Finally, we revealed that this concept of logic-gated dual tumor-targeted TriTCE can be applied to target different tumor antigens. Cumulatively, we reported novel dual tumor-targeted TriTCEs that can mediate a robust T cell response by simultaneous recognition of dual tumor antigens at the same cell surface. TriTCEs allow better selective T cell activity on tumor cells, resulting in safer TCE treatment.

Long noncoding RNA SPRY4-IT1 acts as a miR-101-5p sponge to promote gastrointestinal stromal tumor progression by inhibiting ZEB1.

OBJECTIVES: Research on long noncoding RNAs (lncRNAs) has been conducted in different areas of oncology. Currently, the biological significance of lncRNAs and their regulatory features in gastrointestinal stromal tumors (GIST) remain largely unknown. We have previously identified SPRY4-IT1 overexpression in GIST through lncRNA sequencing of GIST tissues. Coincidentally, SPRY4-IT1 is an intron of the SPRY4 gene, and SPRY4 is specifically highly expressed in GIST. Thus the aim of the present study was to investigate the role of lncRNA SPRY4-IT1 in GIST pathogenesis. METHODS: Herein, we screened for SPRY4-IT1 and analyzed its possible phenotypes using Gene set enrichment analysis (GSEA). The phenotypes of GIST were verified using CCK-8, colony formation, and wound-healing assays. The ceRNA mechanism was determined by the location of lncRNA SPRY4-IT1, and its relationship to the Ago2 protein. The SPRY4-IT1/miR-101-5p/ZEB1 axis was predicted using online software and sequencing. Luciferase and pull-down assays were performed for verification. Pathway-associated and phenotype-associated proteins were detected by western blotting. RESULTS: Sequencing analysis revealed 117 differentially expressed lncRNAs in GIST and normal gastric tissue samples. Accordingly, SPRY4-IT1 was screened out and its phenotype was predicted by GSEA. Mechanistically, SPRY4-IT1 was identified as a competing endogenous RNA (ceRNA) that downregulated miR-101-5p and upregulated ZEB1, which activated extracellular signal-regulated kinase (ERK) signaling to stimulate GIST proliferation, invasion, and epithelial-mesenchymal transition. Although this effect was regulated by a negative feedback loop through SPRY4, it was still controlled by SPRY4-IT1. CONCLUSIONS: In GIST, we revealed a ceRNA mechanism by which SPRY4-IT1 modulates ZEB1 by sponging miR-101-5p, eventually driving tumor cell proliferation, migration, and epithelial-mesenchymal transition (EMT).