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Abstract Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Results Program (SEER) database based on the three different deep learning models. Four thousand five hundred thirty-eight patients were enrolled in this study and divided into training and test cohorts with a 7:3 ratio; the training cohort was used to develop three different models, including Cox regression, RSF, and DeepSurv model. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The net benefits at risk score stratification of GIST patients based on the optimal model was compared with the traditional AJCC staging system using decision curve analysis (DCA). The clinical usefulness of risk score stratification compared to AJCC tumor staging was further assessed using the Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI). The DeepSurv model predicted cancer-specific survival (CSS) in GIST patients showed a higher c-index (0.825), lower Brier scores (0.142), and greater AUC of receiver operating characteristic (ROC) analysis (1-year ROC:0.898; 3-year:0.853, and 5-year ROC: 0.856). The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values ( training cohort: 0.425 for 1-year, 0.329 for 3-year and 0.264 for 5-year CSS prediction; test cohort:0.552 for 1-year,0.309 for 3-year and 0.255 for 5-year CSS prediction) and IDI (training cohort: 0.130 for 1-year,0.141 for 5-year and 0.155 for 10-year CSS prediction; test cohort: 0.154 for 1-year,0.159 for 3-year and 0.159 for 5-year CSS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P

BackgroundPrognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients.MethodsEleven thousand seventy-five patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and 122 patients from the Chinese database were used for external validation. The training cohort was created to create three separate models, including Cox regression, RSF, and DeepSurv, using data from the SEER database split into training and test cohorts with a 7:3 ratio. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The new risk stratification based on the best model will be compared with the AJCC stage on the test and Chinese cohorts using decision curve analysis (DCA), the net reclassification index (NRI), and integrated discrimination improvement (IDI).ResultsIt was discovered that the DeepSurv model predicted postoperative gastric cancer patients’ overall survival (OS) with a c-index of 0.787; the area under the curve reached 0.781, 0.798, 0.868 at 1-, 3- and 5- years, respectively; the Brier score was below 0.25 at different time points; showing an advantage over the Cox and RSF models. The results are also validated in the China cohort. The calibration plots demonstrated good agreement between the DeepSurv model’s forecast and actual results. The NRI values (test cohort: 0.399, 0.288, 0.267 for 1-, 3- and 5-year OS prediction; China cohort:0.399, 0.288 for 1- and 3-year OS prediction) and IDI (test cohort: 0.188, 0.169, 0.157 for 1-, 3- and 5-year OS prediction; China cohort: 0.189, 0.169 for 1- and 3-year OS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.05). DCA showed that the risk score stratification was clinically useful and had better discriminative ability than the AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of patients with postoperative gastric cancer.ConclusionIn this study, a high-performance prediction model for the postoperative prognosis of gastric cancer was developed using DeepSurv, which offers essential benefits for risk stratification and prognosis prediction for each patient.

Objectives: Studies on rectal neuroendocrine tumors (R-NETs) that are 1–2 cm in size are limited, and the optimal treatment for these tumors is not well established. Methods: Data from patients with primary localized R-NETs 1–2 cm in size were retrospectively collected from 17 large-scale referral medical centers in China. Long-term prognosis, quality of life (QOL), and fecal incontinence were evaluated, and the effects of local excision (LE) or radical resection (RR) were elucidated using propensity score matching (PSM). Results: A total of 272 patients were included in this study; 233 underwent LE, and the remaining 39 underwent RR. Patients in the LE group showed lower tumor location, fewer postoperative Clavien–Dindo III–V complications, more G1 tumors, and lower tumor stage. There were no significant differences in the relapse-free survival or overall survival (OS) between the LE and RR groups after PSM. Patients in the LE group reported superior physical, role, emotional, social, and cognitive functions, global QOL, and Wexner fecal incontinence scores compared with those in the RR group (all P 1.80 (OR = 4.50, 1.46–15.89, P = 0.012), and T3–T4 (OR = 36.31, 95% CI 7.85–208.62, P 1.8 or T3/T4 tumors, RR should be considered.

Abstract Real-time and accurate estimation of surgical hemoglobin (Hb) loss is essential for fluid resuscitation management and evaluation of surgical techniques. In this study, we aimed to explore a novel surgical Hb loss estimation method using deep learning-based medical sponges image analysis. Whole blood samples of pre-measured Hb concentration were collected, and normal saline was added to simulate varying levels of Hb concentration. These blood samples were distributed across blank medical sponges to generate blood-soaked sponges. Eight hundred fifty-one blood-soaked sponges representing a wide range of blood dilutions were randomly divided 7:3 into a training group (n = 595) and a testing group (n = 256). A deep learning model based on the YOLOv5 network was used as the target region extraction and detection, and the three models (Feature extraction technology, ResNet-50, and SE-ResNet50) were trained to predict surgical Hb loss. Mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient (R 2) value, and the Bland–Altman analysis were calculated to evaluate the predictive performance in the testing group. The deep learning model based on SE-ResNet50 could predict surgical Hb loss with the best performance (R 2 = 0.99, MAE = 11.09 mg, MAPE = 8.6%) compared with other predictive models, and Bland–Altman analysis also showed a bias of 1.343 mg with narrow limits of agreement (− 29.81 to 32.5 mg) between predictive and actual Hb loss. The interactive interface was also designed to display the real-time prediction of surgical Hb loss more intuitively. Thus, it is feasible for real-time estimation of surgical Hb loss using deep learning-based medical sponges image analysis, which was helpful for clinical decisions and technical evaluation.

Deep learning-based template matching in remote sensing has received increasing research attention. Existing anchor box-based and anchor-free methods often suffer from low template localization accuracy in the presence of multimodal, nonrigid deformation and occlusion. To address this problem, we transform the template matching task into a center-point localization task for the first time and propose an end-to-end template matching method based on a novel fully convolutional Siamese network. Furthermore, we propose an adaptive shrinkage cross-correlation scheme, which improves the precision of template localization and alleviates the impact of background clutter without adding any parameters. We also design a scheme that leverages keypoint information to assist in locating the template center, thereby enhancing the precision of template localization. We construct a multimodal template matching dataset to verify the performance of the method in dealing with differences in view, scale, rotation and occlusion in practical application scenarios. Extensive experiments on a public dataset, OTB, the proposed dataset, as well as a remote sensing dataset, SEN1-2, demonstrate that our method achieves state-of-the-art performance.

Abstract Disulfidptosis is a newly-identified non-programmed cell death mode with tight associations with glucose metabolism. Elevated glycolysis is an important metabolic feature of tumor cells, which fulfills the energy requirement for their rapid growth and progression. Our present study determined to develop a disulfidptosis and glycolysis related gene (DGRG) risk score signature to predict the prognosis and ICI therapeutic responsiveness for CRC patients. First, the gene expression and clinical profiles for CRC patients were obtained from TCGA and GEO database. Using weighted gene co-expression network analysis, we identified hub genes showing the strongest correlations with both disulfidptosis and glycolysis activities. Next, a DGRG risk score signature was successfully developed through univariate and least absolute shrinkage and selection operator method Cox regression method. A DGRG risk score-based nomogram could further enhance the predictive performance. In addition, an array of systemic analysis was performed to unravel the correlation of DGRG risk score with tumor microenvironment. The results showed that CRC patients with low DGRG risk level had up-regulated immune cell infiltrations, enhanced metabolic activities and heightened gene mutation frequencies, while high risk patients was the opposite. Moreover, our present study identified low risk CRC patients as potential beneficiaries from immune checkpoint inhibitor (ICI) therapies. Our present work highlighted the potential utility of DGRG risk score signature in prognosis prediction and ICI responsiveness determination for CRC patients, which demonstrated promising clinical application value.

Macrophages and T cells in the tumor microenvironment (TME) play an important role in tumorigenesis and progression. However, TME is also characterized by metabolic reprogramming, which may affect macrophage and metabolic activity of T cells and promote tumor escape. Immunotherapy is an approach to fight tumors by stimulating the immune system in the host, but requires support and modulation of cellular metabolism. In this process, the metabolic roles of macrophages and T cells become increasingly important, and their metabolic status and interactions play a critical role in the success of immunotherapy. Therefore, understanding the metabolic state of T cells and macrophages in the TME and the impact of metabolic reprogramming on tumor therapy will help optimize subsequent immunotherapy strategies.

Abstract Background The greater omentum is derived from the foregut, and the right hemicolon is derived from the midgut based on developmental anatomy. This study aimed to investigate whether the greater omentum should be resected in laparoscopic complete mesocolic excision based on developmental anatomy for right-sided colon cancer. Methods A total of 183 consecutive patients with right-sided colon cancer were recruited in this study between February 2020 and July 2022. Ninety-eight patients underwent standard laparoscopic complete mesocolic excision surgery (CME group). The presence of isolated tumor cells and micrometastases was detected in resected greater omentum by the HE staining and immunohistochemistry analysis. Based on developmental anatomy, laparoscopic CME surgery with greater omentum preservation (DACME group) was proposed and performed on 85 right-sided colon cancer patients. To overcome selection bias, we performed a 1:1 match between two groups using four variables: age, sex, BMI, and ASA scores. Results No isolated tumor cells and micrometastases were found in the resected greater omentum specimen in the CME group. After the propensity score, 81 pairs were balanced and analyzed. Patients in the DACME group showed shorter operative time (194.9 ± 16.4 min vs.201.5 ± 11.5 min, p = 0.002), less blood loss (23.5 ± 24.7 ml vs.33.6 ± 26.3 ml, p = 0.013), and the shorter hospital stays (9.6 ± 1.7 days vs.10.3 ± 2.0 days, p = 0.010) compared with patients in the CME group. In addition, patients in the DACME group had a lower incidence of postoperative complications (4.9% vs.14.8%, p = 0.035) than patients in the CME group. Conclusion The greater omentum should be preserved during right-sided colon cancer surgery, laparoscopic CME surgery based on developmental anatomy is technically safe and feasible for right-sided colon cancer.

Abstract Treatment of infected wounds remains a challenge owing to antibiotic resistance; thus, developing smart biomaterials for the healing of infected wounds is urgently needed. In this study, a microneedle (MN) patch system with antimicrobial and immunomodulatory properties is developed to promote and accelerate infected wound healing. In the MN patch (termed PFG/M MNs), a nanoparticle with polydopamine (PDA)‐loaded iron oxide is grafted with glucose oxidase (GOx) and hyaluronic acid (HA) and then integrated into the tips, and amine‐modified mesoporous silica nanoparticles (AP‐MSNs) are incorporated into the bases. Results show that PFG/M MNs eradicate bacterial infections and modulate the immune microenvironment, combining the advantages of chemodynamic therapy, photothermal therapy, and M2 macrophage polarization from Fe/PDA@GOx@HA in the tips as well as anti‐inflammatory effect of AP‐MSNs from the MN bases. Thus, the PFG/M MN system is a promising clinical candidate for promoting infected wound healing.

BackgroundThe currently available prediction models, such as the Cox model, were too simplistic to correctly predict the outcome of gastric adenocarcinoma patients. This study aimed to develop and validate survival prediction models for gastric adenocarcinoma patients using the deep learning survival neural network.MethodsA total of 14,177 patients with gastric adenocarcinoma from the Surveillance, Epidemiology, and End Results (SEER) database were included in the study and randomly divided into the training and testing group with a 7:3 ratio. Two algorithms were chosen to build the prediction models, and both algorithms include random survival forest (RSF) and a deep learning based-survival prediction algorithm (DeepSurv). Also, a traditional Cox proportional hazard (CoxPH) model was constructed for comparison. The consistency index (C-index), Brier score, and integrated Brier score (IBS) were used to evaluate the model’s predictive performance. The accuracy of predicting survival at 1, 3, 5, and 10 years was also assessed using receiver operating characteristic curves (ROC), calibration curves, and area under the ROC curve (AUC).ResultsGastric adenocarcinoma patients were randomized into a training group (n = 9923) and a testing group (n = 4254). DeepSurv showed the best performance among the three models (c-index: 0.772, IBS: 0.1421), which was superior to that of the traditional CoxPH model (c-index: 0.755, IBS: 0.1506) and the RSF with 3-year survival prediction model (c-index: 0.766, IBS: 0.1502). The DeepSurv model produced superior accuracy and calibrated survival estimates predicting 1-, 3- 5- and 10-year survival (AUC: 0.825-0.871).ConclusionsA deep learning algorithm was developed to predict more accurate prognostic information for gastric cancer patients. The DeepSurv model has advantages over the CoxPH and RSF models and performs well in discriminative performance and calibration.

The multi-UAV task assignment problem in large-scale group-to-group interception scenarios presents challenges in terms of large computational complexity and the lack of accurate evaluation models. This paper proposes an effective evaluation model and hierarchical task assignment framework to address these challenges. The evaluation model incorporates the dynamics constraints specific to fixed-wing UAVs and improves the Apollonius circle model to accurately describe the cooperative interception effectiveness of multiple UAVs. By evaluating the interception effectiveness during the interception process, the assignment scheme of the multiple UAVs could be given based on the model. To optimize the configuration of UAVs and targets, a hierarchical framework based on the network flow algorithm is employed. This framework utilizes a clustering method based on feature similarity and interception advantage to decompose the large-scale task assignment problem into smaller, complete submodels. Following the assignment, Dubins curves are planned to the optimal interception points, ensuring the effectiveness of the interception task. Simulation results demonstrate the feasibility and effectiveness of the proposed scheme. With the increase in the model scale, the proposed scheme has a greater descending rate of runtime. In a large-scale scenario involving 200 UAVs and 100 targets, the runtime is reduced by 84.86%.

Backgrounds: The tissue resident memory CD8 T cell (Trm) constitutes an important component of the local immunity. In the context of malignant tumors, mounting evidence also supports the potential anti-tumor property of this cell subset. Therefore, identification of Trm marker genes and exploration of the causative effect of Trm in shaping tumor microenvironment (TME) heterogeneity might provide novel insights for the comprehensive management of cancer patients.Methods: By dissecting a single T cell transcriptome dataset, we acquired marker genes for Trm, which were latter applied to bulk RNA sequencing profiles of two large colorectal cancer (CRC) patient cohorts downloaded from TCGA and GEO databases. First, colorectal cancer patients were divided into different Trm clusters using consensus clustering algorithm. Then, we established a Trm-related gene (TRMRG) risk score signature and tested its efficacy in predicting prognosis for colorectal cancer patients. Moreover, a sequence of rigorous and robust analyses were also carried out to investigate the potential role of Trm-related gene risk score in tumor microenvironment remodeling and therapeutic utility of it in colorectal cancer treatment.Results: A total of 49 Trm marker genes were identified by analyzing single cell RNA sequencing profiles. First, colorectal cancer patients were successfully classified into two Trm clusters with significant heterogeneity in functional enrichment patterns and tumor microenvironment landscapes. Then, we developed a Trm-related gene risk score signature and divided patients into different risk levels. High risk patients were characterized by attenuated immunogenicity, weakened sensitivity to immunotherapy, as well as adverse clinical outcomes. While low risk patients with advantages in survival exhibited increased immunogenicity, stronger metabolic activity and improved immunotherapeutic responses.Conclusion: Through combinatorial analysis of single cell and bulk RNA sequencing data, the present study identified Trm to play a non-negligible role in regulating the complexity and heterogeneity of tumor microenvironment for colorectal cancer. Moreover, the Trm-related gene risk score signature developed currently was corroborated to be tightly correlated with prognosis and therapeutic responses of colorectal cancer patients, thus exhibiting potential application value for clinical practice.

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

Background: The guanylate-binding proteins (GBPs) are the latest potential targets of immunotherapy. However, the role of GBP5 in pan-cancer, including colorectal cancer (CRC), remains unclear. This study aims to explore the effect of GBP5 on immunity in pan-cancer.Methods: Based on the RNA sequencing data of 33 cancers obtained from The Cancer Genome Atlas, we analyzed the clinical significance of GBPs and focused on the correlation between GBP5 and tumor microenvironment (TME). Immunotherapy cohort IMvigor210 was used to explore the relationship between treatment response and GBPs. Then, we further analyzed the expression of GBP5 in immune cells using single-cell transcriptome cohort GSE146771 and GSE132465 from the Gene Expression Omnibus database. Finally, a prognostic model based on GBP5 expression was established and validated.Results: We found that the expression of GBP3/4/5 is higher in colorectal cancer than in normal tissues, and GBP5 is a better predictor of good treatment response to immune checkpoint blockade than other GBPs. In most other cancers, GBP5 is also elevated in tumors compared with normal tissues and is associated with a better prognosis. As for TME, GBP5 is generally positively correlated with immune score, the level of tumor-infiltrating immune cells and immune-related genes. Single-cell analysis showed that GBP5 was mainly expressed in myeloid cells and T cells. The GBP5-related prognostic model we constructed in CRC can predict the survival of patients and propose some genes for subsequent research.Conclusion: This study revealed a strong correlation between GBP5 and immunity in generalized cancer and provided evidence that CRC may be a suitable cancer type for anti-GBP5 therapy.

BackgroundThis study aims to screen out differentially expressed genes (DEGs) regulated by BRCA1-associated protein 1 (BAP1) in osteosarcoma cells, and to analyze their biological functions.MethodsThe microarray dataset GSE23035 of BAP1-knockdown osteosarcoma cells was obtained from Gene Expression Omnibus (GEO) database, consisting of shControl, shBAP1#1 and shBAP1#2 samples. The DEGs between the BAP1-knockdown osteosarcoma cells and the untreated osteosarcoma cells were screened with limma package, and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene Set Enrichment Analysis (GSEA) was also performed for the three groups of samples. Hub genes in a protein-protein interaction (PPI) network of DEGs was filtered, and then subjected to prognostic analysis and correlation analysis with BAP1 in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Besides, the correlation between BAP1 and biological processes/pathways was analyzed by Gene Set Variation Analysis (GSVA) method and the correlation between BAP1 and immune infiltration by CIBERSORT and ESTIMATE methods. The roles of BAP1 in regulating proliferation and epithelial-mesenchymal transition (EMT) were validated by CCK-8 and western blot.Results58 upregulated DEGs and 81 downregulated DEGs were obtained with |logFC| ≥ 1 and adj.p < 0.05. Cell cycle, DNA repair, and focal adhesion were associated with BAP1 in datasets. Further, BAP1 was negatively correlated with naïve CD4 T cells infiltration. In vitro, BAP1 inhibited proliferation and EMT.ConclusionBAP1 might be a tumor suppressor in osteosarcoma and a promising therapeutic target.

Abstract Image alignment and colour consistency are two challenging tasks for image stitching. Traditional point correspondence methods are difficult to achieve good alignments due to their insufficiency and unreliability. The results are prone to errors and distortions. On the other hand, the problem of colour inconsistency in overlapping area between image pairs is still difficult to solve, especially when the illumination difference between images is large. To solve these problems, the authors integrate point features and line features into a warping model through a designed energy function. Line features will provide geometric constraints for image stitching, and remedy the defect of point correspondences in low‐textured image stitching. A global colour consistency optimization method with colour mapping via a histogram extreme point‐matching algorithm is proposed. The colour characteristic of reference images will be transferred to the others to achieve a global colour consistency. The proposed method is evaluated on a series of images, and compared with other methods. The experiments demonstrate that the proposed method provides convincing stitching results and achieves satisfied colour consistency results.

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the local binary pattern (LBP)-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce LBP into GANs, enabling the network to have stronger texture feature extraction and utilization capabilities, as well as anti-interference capabilities. In the feature extraction stage, we introduce a pseudo-Siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN by fusing RGB and infrared images on the RoadScene dataset and medical images. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain richly detailed information.

BackgroundStation 253 node dissection with high ligation of the inferior mesenteric artery (IMA) is difficult to perform without damage to the surrounding autonomic nerve plexuses. This study aimed to investigate the significance of the nerve plane for inferior mesenteric plexus (IMP) preservation in laparoscopic rectal cancer surgery.MethodsA total of 56 consecutive rectal patients underwent laparoscopic en bloc station 253 node dissection with high ligation of the IMA. Station 253 nodes were divided into the extra- and intra-nerve plane station 253 nodes for further H&E staining and immunohistochemical analysis. Based on IMP nerve plane-based evidence and histopathological results, a novel nerve-sparing technique, IMP nerve plane orientation, was proposed and performed on 68 rectal cancer patients. Urinary and sexual functions in all patients were evaluated at 6 months postoperatively.ResultsLymph node metastasis was not found, but abundant nerve bundles containing gangliocytes were observed in extra-nerve plane station 253 nodes. The nerve plane was identified intraoperatively and then confirmed by both postoperative gross specimen evaluation and histopathological analysis. The novel nerve-sparing technique (IMP nerve plane orientation) was successfully performed with no postoperative complications, and the operated patients had improved postoperative urinary and sexual functions.ConclusionThe nerve plane is helpful for IMP preservation and station 253 node dissection. This novel nerve-sparing technique of nerve plane orientation is technically feasible and safe, which could result in faster recovery of urinary and sexual functions.

BackgroundAs a heterogeneous disease, colorectal cancer (CRC) presents a great challenge to individualized treatment due to its lymph node metastasis (LNM). Existing studies have shown that immune and stromal components in extracellular matrix (ECM) act as important part in tumorigenicity and progression, while their roles in LNM have not been fully elucidated. Here, crucial ECM-related genes responsible for LNM in CRC were selected by multi-omics analysis.MethodsFirstly, we characterized the immune infiltration landscape of CRC samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases by using ssGSEA algorithm. The CRC patients were divided into several immune subgroups by hierarchical clustering analyses. Then, differential genes were identified among immune subgroups and CRC vs. normal tissues in TCGA and GEO GSE39582 cohorts, respectively. Next, weighted correlation network analysis (WGCNA) was employed to construct a co-expression network to find LNM-related modules and hub genes. Subsequently, we evaluated the clinical value of hub gene in prognostic prediction and chemotherapy/immunotherapy. Besides, the protein level of key gene was verified in an external cohort from our center. Finally, we explored the underlying mechanism of FSTL3-mediated LNM by Gene function annotation and correlation analysis.ResultsTwo immune subgroups, namely Immunity_High and Immunity_Low, were defined among the two CRC cohorts using ssGSEA algorithm, respectively. Based on the two immune subgroups, 2,635 overlapping differentially expressed genes were obtained from two cohorts, which were sequentially subjected to WGCNA and univariate Cox regression analysis. Ultimately, FSTL3 was selected as the key gene. Here, we first confirmed that overexpression of FSTL3 correlated with LNM and worse prognosis in CRC and was verified at the protein level in the external validation cohort. Moreover, FSTL3 expression showed strongly positive correlation with immune and stromal components in ECM. We furthermore found that FSTL3 may accelerate LNM through the formation of inhibitory immune microenvironment via promoting macrophage and fibroblast polarization and T cell exhaustion. Interestingly, high FSTL3 expression is linked to chemoresistance, but immunotherapy-sensitive.ConclusionFSTL3 is identified as a biomarker for ECM remodeling and worse clinical outcomes for the first time in CRC and is also a potential immunotherapeutic target to block LNM for CRC.

Background and Aim Colorectal cancer (CRC) ranks the second most common cause of cancer-related mortality worldwide. Ferroptosis, a recently discovered form of programmed cell death different from other, raises promising novel opportunities for therapeutic intervention of CRC. This study intended to systematically assess the prognosis value and multiple roles of the ferroptosis-related genes in the tumor immune microenvironment of CRC. Materials and Methods Of 1,192 CRC patients with complete information from the public datasets (TCGA CRC, GEO GSE39582 and GSE17538 cohorts) were selected for analysis. Firstly, K-means consensus clustering was performed to identify ferroptosis-associated subtypes in CRC patients. Subsequently, we constructed a risk signature based on ferroptosis-related genes in TCGA cohort and acquired its validation in two GEO cohorts. Additionally, we established a nomogram integrating the risk signature and clinical factors to improve risk assessment of CRC patients. Results Five molecular subtypes were identified by consensus clustering for ferroptosis-related genes. There were significant differences in the overall survival, immune cells infiltration status and PD1/PD-L1 mRNA among the five clusters. Then, a risk signature based on the ten-gene was constructed which could distinguish effectively high-risk group among CRC patients in both training and validation sets. The high-risk patients were more likely to have an inhibitory immune microenvironment and lower stemness features. A prognostic nomogram integrated risk signature and clinicopathological features could be used as a more accurate prognostic prediction visualization tool than TNM stage alone. Conclusion This ferroptosis risk signature may accurately differentiate between different risk populations and predict the prognosis of CRC. Besides, this study elucidated the crucial role of ferroptosis in tumor immune microenvironment.

Gastric cancer (GC) is a complex and heterogeneous disease, making it difficult to ascertain the optimal therapeutic approach for individual GC patients. Stromal and immune cell infiltration in GC has a strong correlation with clinical outcomes; however, the underlying mechanisms that drive immunosuppression remain vastly undiscovered. Recent studies validated that anthrax toxin receptor 1 (ANTXR1) is aberrantly expressed in several cancers and holds promise as a new therapeutic target for cancer. However, its immunological roles in GC are still unclear. Here, we show that we identify the distinct stromal and immune cell infiltration in GC between the high and low ANTXR1 expression group by analyzing genomic data. Clinically, ANTXR1 is highly expressed in GC and correlates with adverse clinicopathological characteristics. Additionally, high ANTXR1 expression is linked to markedly poor clinical outcomes and resistance to chemotherapy, whereas the low ANTXR1 expression group is correlated with better outcomes and response to chemotherapy in GC patients. We further revealed the differential landscape of somatic tumor mutation burden (TMB) between the two groups and observed that patients with high ANTXR1 expression suffered from a lower TMB, potentially leading to less sensitivity to checkpoint therapy. Molecularly, results displayed that ANTXR1 is an immunosuppressive element, which may perform its function via promoting the secretion of immunosuppressive factors that play a significant role in modulating tumor-associated fibroblast transformation, M2 macrophage polarization, and T cell exhaustion. Gene set enrichment analysis revealed that cancer-related pathways including epithelial-to-mesenchymal transition, focal adhesion, and transforming growth factor-β (TGF-β) signaling pathways were enriched in high ANTXR1 expression tumors. Our work suggests that ANTXR1 could not only serve as a valuable prognostic biomarker in GC but also be deemed as a potential immunotherapeutic target and useful biomarker of sensitivity to chemotherapy.

The detection of arbitrary-oriented and multi-scale objects in satellite optical imagery is an important task in remote sensing and computer vision. Despite significant research efforts, such detection remains largely unsolved due to the diversity of patterns in orientation, scale, aspect ratio, and visual appearance; the dense distribution of objects; and extreme imbalances in categories. In this paper, we propose an adaptive dynamic refined single-stage transformer detector to address the aforementioned challenges, aiming to achieve high recall and speed. Our detector realizes rotated object detection with RetinaNet as the baseline. Firstly, we propose a feature pyramid transformer (FPT) to enhance feature extraction of the rotated object detection framework through a feature interaction mechanism. This is beneficial for the detection of objects with diverse patterns in terms of scale, aspect ratio, visual appearance, and dense distributions. Secondly, we design two special post-processing steps for rotated objects with arbitrary orientations, large aspect ratios and dense distributions. The output features of FPT are fed into post-processing steps. In the first step, it performs the preliminary regression of locations and angle anchors for the refinement step. In the refinement step, it performs adaptive feature refinement first and then gives the final object detection result precisely. The main architecture of the refinement step is dynamic feature refinement (DFR), which is proposed to adaptively adjust the feature map and reconstruct a new feature map for arbitrary-oriented object detection to alleviate the mismatches between rotated bounding boxes and axis-aligned receptive fields. Thirdly, the focus loss is adopted to deal with the category imbalance problem. Experiments on two challenging satellite optical imagery public datasets, DOTA and HRSC2016, demonstrate that the proposed ADT-Det detector achieves a state-of-the-art detection accuracy (79.95% mAP for DOTA and 93.47% mAP for HRSC2016) while running very fast (14.6 fps with a 600 × 600 input image size).