Preoperative systemic immune-inflammation index-based nomogram for lung carcinoma following microwave ablation -a real world single center study.

Background: The preoperative inflammatory condition significantly influences the prognosis of malignancies. We aimed to investigate the potential significance of preoperative inflammatory biomarkers in forecasting the long-term results of lung carcinoma after microwave ablation (MWA). Method: This study included patients who received MWA treatment for lung carcinoma from Jan. 2012 to Dec. 2020. We collected demographic, clinical, laboratory, and outcome information. To assess the predictive capacity of inflammatory biomarkers, we utilized the area under the receiver operating characteristic curve (AUC-ROC) and assessed the predictive potential of inflammatory biomarkers in forecasting outcomes through both univariate and multivariate Cox proportional hazard analyses. Results: A total of 354 individuals underwent MWA treatment, of which 265 cases were included in this study, whose average age was 69.1 ± 9.7 years. The AUC values for the Systemic Inflammatory Response Index (SIRI) to overall survival (OS) and disease-free survival (DFS) were 0.796 and 0.716, respectively. The Cox proportional hazards model demonstrated a significant independent association between a high SIRI and a decreased overall survival (hazard ratio [HR]=2.583, P<0.001). Furthermore, a high SIRI independently correlated with a lower DFS (HR=2.391, P<0.001). We developed nomograms utilizing various independent factors to forecast the extended prognosis of patients. These nomograms exhibited AUC of 0.900, 0.849, and 0.862 for predicting 1-year, 3-year, and 5-year OS, respectively. Additionally, the AUC values for predicting 1-year, 3-year, and 5-year DFS were 0.851, 0.873, and 0.883, respectively. Conclusion: SIRI has shown promise as a valuable long-term prognostic indicator for forecasting the outcomes of lung carcinoma patients following MWA.

Anti-PD-1 therapy reverses TIGIT+CD226+NK depletion in immunotherapy resistance of hepatocellular carcinoma through PVR/TIGIT pathway.

Immunotherapy resistance conducts the main reason for failure of PD-1-based immune checkpoint inhibitors (ICIs) in patients with hepatocellular carcinoma (HCC). This study aims to clarify the mechanism of nature kill cells (NK) depletion in immunotherapy resistance of HCC. Cancerous /paracancerous tissues and peripheral blood (PB) of 55 HCC patients were collected and grouped according to differentiation degree, FCM, IHC and lymphocyte culture drug intervention experiments were used to determine NK cell depletion degree. Furthermore, a mouse model of HCC in situ was constructed and divided into different groups according to intervention measures of ICIs. Immunofluorescence thermography was used to observe changes in tumor burden. NK cells in cancerous tissues significantly up-regulated TIGIT expression (P < 0.001). Intervention experiments revealed that TIGIT and PD-1 expression decreased gradually with increased PD-1 inhibitor dose in moderately-highly differentiated patients (P < 0.05). Animal experiment showed that tumors proliferation in experimental group was inhibited after PD-1 blockage, WB indicated that ICIs decreased TIGIT and PVRL1 protein expression while increased CD226 and PVRL3 protein expression. We concluded that TIGIT+NK cells competitively bind to PVR with CD226 and promote NK cell depletion. Anti-PD-1 decreases PVRL1 expression through PD-1/PD-L1 pathway, reducing the PVR/TIGIT inhibitory signal pathway, and enhancing function of PVR/CD226 activation signal.

Corrigendum: Application of cholecystic duct plasty in the prevention of biliary complications following orthotopic liver transplantation.

[This corrects the article DOI: 10.3389/fsurg.2023.1087327.].

The coagulation-related genes for prognosis and tumor microenvironment in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC. METHODS: We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data. RESULTS: Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC. CONCLUSIONS: Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.

Application of cholecystic duct plasty in the prevention of biliary complications following orthotopic liver transplantation.

Background: The purpose was aimed to evaluate the safety and effectiveness of cholecystic duct plasty (CDP) and biliary reconstruction techniques preventing biliary complications following orthotopic liver transplantation (OLT) first proposed by our center. Methods: 127 enrolled patients who underwent LT in our center from January 2015 to December 2019 were analyzed retrospectively. According to the mode of biliary tract reconstruction, patients were divided into CDP group (Group 1, n = 53) and control group (Group 2, n = 74). The differences of perioperative general data, biliary complications and long-term prognosis between two groups were compared and analyzed. Results: All patients completed the operation successfully, the incidence of perioperative complications was 22.8%. There was no significant difference in perioperative general data and complications between the two groups. Follow-up ended in June 2020, with a median follow-up period of 31 months. During the follow-up period, biliary complications occurred in 26 patients, with an overall incidence of 20.5%. The overall incidence of biliary complications and anastomotic stenosis in Group 1 was lower than that in Group 2 (P < 0.05). There was no significant difference in overall prognosis between the two groups (P = 0.274), however, the cumulative incidence of biliary complications in Group 1 was lower than that in Group 2 (P = 0.035). Conclusion: Reconstruction of common bile duct by CDP represents considerable safety and practicability, particularly for patients with small diameter of common bile duct or wide discrepancy of bile duct size between donor and recipient.

Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma.

Background: Distal cholangiocarcinoma (dCCA), originating from the common bile duct, is greatly associated with a dismal prognosis. A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. In this study, we explored and compared several novel machine learning models that might lead to an improvement in prediction accuracy and treatment options for patients with dCCA. Methods: In this study, 169 patients with dCCA were recruited and randomly divided into the training cohort (n = 118) and the validation cohort (n = 51), and their medical records were reviewed, including survival outcomes, laboratory values, treatment strategies, pathological results, and demographic information. Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). We measured and compared the performance of models using the receiver operating characteristic (ROC) curve, integrated Brier score (IBS), and concordance index (C-index) following cross-validation. The machine learning model with the best performance was screened out and compared with the TNM Classification using ROC, IBS, and C-index. Finally, patients were stratified based on the model with the best performance to assess whether they benefited from postoperative chemotherapy through the log-rank test. Results: Among medical features, five variables, including tumor differentiation, T-stage, lymph node metastasis (LNM), albumin-to-fibrinogen ratio (AFR), and carbohydrate antigen 19-9 (CA19-9), were used to develop machine learning models. In the training cohort and the validation cohort, C-index achieved 0.763 vs. 0.686 (SVM), 0.749 vs. 0.692 (SurvivalTree), 0.747 vs. 0.690 (Coxboost), 0.745 vs. 0.690 (RSF), 0.746 vs. 0.711 (DeepSurv), and 0.724 vs. 0.701 (CoxPH), respectively. The DeepSurv model (0.823 vs. 0.754) had the highest mean area under the ROC curve (AUC) than other models, including SVM (0.819 vs. 0.736), SurvivalTree (0.814 vs. 0.737), Coxboost (0.816 vs. 0.734), RSF (0.813 vs. 0.730), and CoxPH (0.788 vs. 0.753). The IBS of the DeepSurv model (0.132 vs. 0.147) was lower than that of SurvivalTree (0.135 vs. 0.236), Coxboost (0.141 vs. 0.207), RSF (0.140 vs. 0.225), and CoxPH (0.145 vs. 0.196). Results of the calibration chart and decision curve analysis (DCA) also demonstrated that DeepSurv had a satisfactory predictive performance. In addition, the performance of the DeepSurv model was better than that of the TNM Classification in C-index, mean AUC, and IBS (0.746 vs. 0.598, 0.823 vs. 0.613, and 0.132 vs. 0.186, respectively) in the training cohort. Patients were stratified and divided into high- and low-risk groups based on the DeepSurv model. In the training cohort, patients in the high-risk group would not benefit from postoperative chemotherapy (p = 0.519). In the low-risk group, patients receiving postoperative chemotherapy might have a better prognosis (p = 0.035). Conclusions: In this study, the DeepSurv model was good at predicting prognosis and risk stratification to guide treatment options. AFR level might be a potential prognostic factor for dCCA. For the low-risk group in the DeepSurv model, patients might benefit from postoperative chemotherapy.