Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer.

BACKGROUND: Synchronous liver metastasis (SLM) is a significant contributor to morbidity in colorectal cancer (CRC). There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC. AIM: To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix (GLCM) features collected from magnetic resonance imaging (MRI). METHODS: Our study retrospectively enrolled 392 patients with CRC from Yichang Central People's Hospital from January 2015 to May 2023. Patients were randomly divided into a training and validation group (3:7). The clinical parameters and GLCM features extracted from MRI were included as candidate variables. The prediction model was constructed using a generalized linear regression model, random forest model (RFM), and artificial neural network model. Receiver operating characteristic curves and decision curves were used to evaluate the prediction model. RESULTS: Among the 392 patients, 48 had SLM (12.24%). We obtained fourteen GLCM imaging data for variable screening of SLM prediction models. Inverse difference, mean sum, sum entropy, sum variance, sum of squares, energy, and difference variance were listed as candidate variables, and the prediction efficiency (area under the curve) of the subsequent RFM in the training set and internal validation set was 0.917 [95% confidence interval (95%CI): 0.866-0.968] and 0.09 (95%CI: 0.858-0.960), respectively. CONCLUSION: A predictive model combining GLCM image features with machine learning can predict SLM in CRC. This model can assist clinicians in making timely and personalized clinical decisions.

Deep learning model for predicting postoperative survival of patients with gastric cancer.

Background: Prognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients. Methods: Eleven 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). Results: It 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. Conclusion: In 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.

Long-term outcomes of 1-2 cm rectal neuroendocrine tumors after local excision or radical resection: A population-based multicenter study.

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 < 0.050). Eighteen (6.6%) patients had lymph node metastases. Multivariable analysis revealed that tumor location (odds ratio [OR] = 3.19, 95% confidence interval [CI] 1.04-10.07, P = 0.010), neutrophil-to-lymphocyte ratio (NLR) > 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 < 0.001) were independent risk factor for lymph node metastasis. Conclusions: R-NETs measuring 1-2 cm generally have a favorable prognosis, and there is no difference in postoperative survival between LE and RR. For patients without lymph node metastasis, LE should be the preferred choice; however, for patients with a higher tumor location, preoperative NLR >1.8 or T3/T4 tumors, RR should be considered.

The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study.

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 < 0.001). DCA demonstrated the risk score stratification as more clinically beneficial and discriminatory than AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of GIST patients. This study established a high-performance prediction model for projecting GIST patients based on deep learning, which has advantages in predicting each person's prognosis and risk stratification.

Transcription factor RELA promotes hepatocellular carcinoma progression by promoting the transcription of m6A modulator METTL3.

OBJECTIVE: To explore the biological function of RELA proto-oncogene, NF-kB subunit (RELA) in hepatocellular carcinoma (HCC) progression, and its potential regulatory effects on the regulators of m6A modification. METHODS AND MATERIALS: GEPIA, UALCAN and Human Protein Atlas databases were applied to analyze the expression characteristics of RELA in HCC tissues and non-cancer liver tissues, and its relationship with clinicopathologic indicators and prognosis. Quantitative real-time PCR (qRT-PCR) was used to examine the expression level of RELA mRNA in HCC cells. Cell counting kit-8 (CCK-8) assay, EdU assay and flow cytometry were used to examine cell growth and apoptosis. PROMO database was applied to predict the binding sequence between RELA and methyltransferase like protein 3 (METTL3) promoter region, and this prediction was verified by dual luciferase reporter gene experiment and chromatin immunoprecipitation assay. The effect of RELA on METTL3 expression was examined by Western blot and qRT-PCT, and the regulatory effects of RELA on the other m6A regulators were evaluated by qRT-PCR. RESULTS: RELA was highly expressed in HCC tissues and cell lines, and was closely associated with adverse clinicopathologic indicators and poor prognosis of patients. Overexpression of RELA promoted the growth of HCC cells and inhibited apoptosis; Knocking down RELA had the opposite effects. Overexpression of RELA promoted METTL3 transcription. Knockdown or overexpression of METTL3 reversed the effects of overexpression or knockdown of RELA on HCC cell growth and apoptosis, respectively. RELA also promoted the expression of a series of m6A regulators at mRNA expression level in HCC cell lines. CONCLUSION: RELA promotes the transcription of METTL3 by binding to METTL3 promoter region, thus promoting the malignancy of HCC cells. This study suggests NF-κB signaling contributes the dysregulation of m6A modification in HCC tumorigenesis.

Advances in macrophage and T cell metabolic reprogramming and immunotherapy in the tumor microenvironment.

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.

Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis.

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 (R2) 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 (R2 = 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.

A novel disulfidptosis and glycolysis related risk score signature for prediction of prognosis and ICI therapeutic responsiveness in colorectal cancer.

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.

Guanylate binding protein 5 accelerates gastric cancer progression via the JAK1-STAT1/GBP5/CXCL8 positive feedback loop.

Guanylate binding protein 5 (GBP5) is a member of the interferon (IFN)-inducible large guanosine triphosphate hydrolases (GTPase) family that regulates cell-autonomous immunity and malignant tumor transformation. However, its specific roles and underlying mechanisms GBP5 in gastric cancer (GC) remain unknown. In this study, we aimed to determine the role GBP5 and underlying mechanism of GBP5 in GC cell progression. Potential oncogenic roles of GBP5 in GC as well as its relationship with the tumor immune microenvironment (TIME) were comprehensively evaluated using bioinformatics analysis. Protein expression levels of GBP5 and their correlation with clinicopathological features of patients were assessed using immunohistochemistry. In addition, diverse in vitro functional experiments were performed to identify the functions of GBP5 in GC. Downstream targets of GBP5 were identified using RNA-sequencing analysis and verified using western blotting or quantitative polymerase chain reaction analysis in different cell lines. GBP5 expression is commonly upregulated and promotes the proliferation and migration of GC cells. Mechanistically, GBP5 was regulated by the IFNγ-Janus kinase (JAK1)-signal transducer and activator of transcription 1 (STAT1) axis and induced CXCL8 expression. Interestingly, GBP5-induced CXCL8 regulated the JAK1-STAT1 signaling pathway to form a positive feedback loop. Moreover, GBP5 is closely related to the TIME and may be used as a biomarker for predicting the efficacy of immunotherapy. Our findings revealed a new JAK1-STAT1/GBP5/CXCL8 pathway and highlighted the value of GBP5 as a predictive biomarker and novel target for GC intervention.

Microneedle Patches with Antimicrobial and Immunomodulating Properties for Infected Wound Healing.

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.

The concept of developmental anatomy: the greater omentum should be resected in right-sided colon cancer?

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.

USP33 enhances cell survival and stemness by deubiquitinating CTNNB1 in BXPC-3 and SW1990 cells.

Ubiquitin-specific protease 33 (USP33) has been implicated in various cancers, but its biological function and mechanism of action remain unknown in pancreatic cancer (PCa) as a deubiquitinating enzyme. Herein, we report that USP33 silencing inhibits PCa cell survival and self-renewal. USPs highly expressed in spherical PCa cells were screened by comparing the levels of ubiquitin-specific proteases in spherical PCa cells and adherent PCa cells. After silencing USP, the effect of USP on the proliferation of PCa cells was detected by CCK-8 and colony formation assay, and the effect of USP on cell stemness was detected by tumor sphere formation assay, flow analysis, and western blot analysis. The interaction of USP with CTNNB1 and the effect of USP on the ubiquitination of CTNNB1 were verified by coimmunoprecipitation assay. After replenishing CTNNB1, cell proliferation and cell stemness were examined. USP33 is upregulated in spheric BXPC-3, PCNA-1, and SW1990, compared with adherent BXPC-3, PCNA-1, and SW1990. USP33 interacts with CTNNB1, and stabilizes CTNNB1 by suppressing its degradation. Furthermore, cell proliferation, colony-forming, and self-renewal abilities of PCa cells in vitro, and the expression of stem cell markers EpCAM and CD44, C-myc, Nanog, and SOX2, were suppressed when USP33 was knocked down, which was reversed when CTNNB1 was ectopically expressed in PCa cells. Thus, USP33 promotes PCa cell proliferation and self-renewal by inhibiting the degradation of CTNNB1. USP33 inhibition may be a new treatment option for PCa patients.

Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study.

Background: The 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. Methods: A 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). Results: Gastric 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). Conclusions: A 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.

Protective effect of laparoscopic functional total mesorectal excision on urinary and sexual functions in male patients with mid-low rectal cancer.

BACKGROUND: Urinary and sexual dysfunctions are among the most common complications in rectal cancer surgery. This study aimed to investigate the protective effect of laparoscopic functional total mesorectum excision (TME) on urinary and sexual functions in male patients. METHODS: A total of 248 male patients with mid-low rectal cancer were recruited in this study between February 2017 and July 2020. To overcome selection bias, we performed a 1:1 match using six variables, including age, BMI, ASA score, tumor distance, clinical T stage, and tumor size. The urinary function was assessed by the International Prostate Symptom Score (IPSS), sexual function was assessed by a 5-item version of the International Index of Erectile Function (IIEF-5) and ejaculation grading at postoperative 3 and 12 months. RESULTS: 79 patients received functional TME surgery (FTME group), and 169 patients received routine TME surgery (RTME group). After the propensity score, 79 pairs were balanced and analyzed. Patients in the FTME group showed a lower IPSS score and higher IIEF-5 score than patients in the RTME group at postoperative 3 and 12 months. The incidence of ejaculation dysfunction for patients in the FTME group was lower than patients in the RTME group at postoperative 3 and 12 months. CONCLUSION: Laparoscopic functional total mesorectal excision was beneficial to faster recovery of urinary and sexual function for patients with rectal cancer, and it could be used as a superior surgical technique for pelvic autonomic nerve preservation in mid-low rectal cancer.

Critical role of guanylate binding protein 5 in tumor immune microenvironment and predictive value of immunotherapy response.

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.

Integrated analysis of anti-tumor roles of BAP1 in osteosarcoma.

Background: This 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. Methods: The 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. Results: 58 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. Conclusion: BAP1 might be a tumor suppressor in osteosarcoma and a promising therapeutic target.

Significance of Nerve Plane for Inferior Mesenteric Plexus Preservation in Laparoscopic Rectal Cancer Surgery.

Background: Station 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. Methods: A 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. Results: Lymph 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. Conclusion: The 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.

+HOXA10-AS Promotes Malignant Phenotypes of Gastric Cancer via Upregulating HOXA10.

OBJECTIVE: To study the role of long noncoding RNA HOXA10-AS in gastric cancer (GC) and its underlying mechanism which is one of the most common and fetal malignancies. Long noncoding RNA HOXA10-AS is highly expressed and acts in an oncogenic role in cancers. However, its roles in GC are still unknown. METHODS: The expression of HOXA10-AS and HOXA10 in GC tissues from the TCGA database was analyzed. Western blot and qRT-PCR assays were applied to examine the expression of HOXA10-AS and HOXA10. Cell proliferation was evaluated with CCK-8 and EdU incorporation assays. Cell apoptosis was analyzed by flow cytometry. Migratory and invasive capacities were evaluated with wound healing and transwell assays. RESULTS: HOXA10-AS and HOXA10 were upregulated in GC, and their expressions were positively correlated. Knockdown of HOXA10-AS inhibited HOXA10 expression in GC cells. Furthermore, knockdown of HOXA10-AS restrained GC cell proliferation, migration, and invasion but promoted apoptosis. In addition, overexpression of HOXA10-AS promoted malignant phenotypes of GC cells, but all these effects could be reversed by knockdown of HOXA10. CONCLUSION: HOXA10-AS promoted GC cell proliferation, migration and invasion and enhanced apoptosis via upregulating HOXA10. Our study implies a novel regulatory mechanism of malignant phenotypes and provides potential therapeutic targets for GC.

CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection.

The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases. The use of artificial intelligence technology can assist doctors in locating and identifying lesions in medical images. In order to improve the accuracy of disease diagnosis in medical imaging, we propose a lung disease detection neural network that is superior to the current mainstream object detection model in this paper. By combining the advantages of RepVGG block and Resblock in information fusion and information extraction, we design a backbone RRNet with few parameters and strong feature extraction capabilities. After that, we propose a structure called Information Reuse, which can solve the problem of low utilization of the original network output features by connecting the normalized features back to the network. Combining the network of RRNet and the improved RefineDet, we propose the overall network which was called CXR-RefineDet. Through a large number of experiments on the largest public lung chest radiograph detection dataset VinDr-CXR, it is found that the detection accuracy and inference speed of CXR-RefineDet have reached 0.1686 mAP and 6.8 fps, respectively, which is better than the two-stage object detection algorithm using a strong backbone like ResNet-50 and ResNet-101. In addition, the fast reasoning speed of CXR-RefineDet also provides the possibility for the actual implementation of the computer-aided diagnosis system.

Identification of a tissue resident memory CD8 T cell-related risk score signature for colorectal cancer, the association with TME landscapes and therapeutic responses.

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.