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).

Proteomic profiling of gastric cancer with peritoneal metastasis identifies a protein signature associated with immune microenvironment and patient outcome.

BACKGROUND: Peritoneal metastasis (PM) frequently occurs in patients with gastric cancer (GC) and is a major cause of mortality. Risk stratification for PM can optimize decision making in GC treatment. METHODS: A total of 25 GC patients (13 with synchronous, 6 with metachronous PM and 6 PM-free) were included in this study. Quantitative proteomics by high-depth tandem mass tags labeling and whole-exome sequencing were conducted in primary GC and PM samples. Proteomic signature and prognostic model were established by machine learning algorithms in PM and PM-free GC, then validated in two external cohorts. Tumor-infiltrating immune cells in GC were analyzed by CIBERSORT. RESULTS: Heterogeneity between paired primary and PM samples was observed at both genomic and proteomic levels. Compared to primary GC, proteome of PM samples was enriched in RNA binding and extracellular exosomes. 641 differently expressed proteins (DEPs) between primary GC of PM group and PM-free group were screened, which were enriched in extracellular exosome and cell adhesion pathways. Subsequently, a ten-protein signature was derived based on DEPs by machine learning. This signature was significantly associated with patient prognosis in internal cohort and two external proteomic datasets of diffuse and mixed type GC. Tumor-infiltrating immune cell analysis showed that the signature was associated with immune microenvironment of GC. CONCLUSIONS: We characterized proteomic features that were informative for PM progression of GC. A protein signature associated with immune microenvironment and patient outcome was derived, and it could guide risk stratification and individualized treatment.

Dual-branch collaborative learning network for crop disease identification.

Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture's efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels via the multi-layer cascade design strategy. Extensive experiments on the Plant Village dataset demonstrated the best classification performance of our DBCLNet method compared to the state-of-the-art methods for the identification of 38 categories of crop diseases. Besides, the Accuracy, Precision, Recall, and F-score of our DBCLNet for the identification of 38 categories of crop diseases are 99.89%, 99.97%, 99.67%, and 99.79%, respectively. 811.

SCGNet: efficient sparsely connected group convolution network for wheat grains classification.

Introduction: Efficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification. Methods: Specifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation. Results: We conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F 1-score of 99.57%. Discussion: Notably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.

Multivessel vs. culprit vessel-only percutaneous coronary intervention in ST-segment elevation myocardial infarction with and without cardiogenic shock.

Background: Early revascularization of the culprit vessel is the most effective treatment for reducing the risk of mortality from acute STEMI with and without cardiogenic shock. However, the most recent trends and impact of multivessel percutaneous coronary intervention (PCI) during the index hospitalization on in-hospital outcomes are unknown. Methods: The National Inpatient Sample was queried from October 2015 to 2019 for hospitalizations with STEMI. The impact of multivessel PCI on in-hospital outcomes of patients with and without cardiogenic shock was evaluated. Results: Of 624,605 STEMI hospitalizations treated with PCI, 12.5% were complicated by cardiogenic shock. Among hospitalizations without cardiogenic shock, 15.7% were treated by multivessel PCI, which declined from 20.8% in 2015 to 13.9% in 2019 (P trend < 0.001). Multivessel and culprit-only PCI had similar rates of In-hospital mortality (2.4 vs. 2.3%, p = 0.027) and major adverse cardiac and cerebrovascular events (MACCE; 7.4 vs. 7.2%, p = 0.072). Among hospitalizations with cardiogenic shock, 22.1% were treated by multivessel PCI, which declined from 29.2% in 2015 to 19.4% in 2019 (P trend < 0.001). Multivessel PCI was associated with higher rates of in-hospital mortality (30.9 vs. 28.4%, p < 0.001) and MACCE (39.9 vs. 36.5%, p < 0.001) than culprit-only PCI. Conclusion: The frequency of multivessel PCI for STEMI with and without cardiogenic shock is declining. Multivessel PCI is associated with worse in-hospital outcomes for STEMI with cardiogenic shock but not for STEMI without cardiogenic shock.

High-level of intratumoral GITR+ CD4 T cells associate with poor prognosis in gastric cancer.

Immunotherapy targeting glucocorticoid-induced TNFR-related protein (GITR) exhibited strong anti-tumor capacity in mouse model but poor efficacy in clinical trials. This may be attributed to the different GITR expression mode between human and mouse. In this study, we analyzed single-cell RNA sequencing (scRNA-seq) data of human gastric cancer (GC) and used flow to explore the GITR expression across T cell subsets and tissue types in GC patients. We revealed that GITR+ CD4 T cells, including regulatory CD4 T (Treg) cells and conventional CD4 T (Tconv) cells, might contribute to the immunosuppressive microenvironment in GC. The enrichment of these cells was associated with a worse prognosis. Moreover, we found the cellular distribution of GITR protein in Treg cells was microenvironment dependent. In conclusion, GITR is still an important immune checkpoint need to be studied.

MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model.

MOTIVATION: Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes. RESULTS: Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein-protein interactions, gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns and gene ontology. Then, we constructed a multi-dimensional gene network consisting of approximately 20 000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves and area under the receiver operating characteristic curves. AVAILABILITY AND IMPLEMENTATION: The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.