Anti-Helicobacter pylori Treatment in Patients With Gastric Cancer After Radical Gastrectomy.

Importance: Whether anti-Helicobacter pylori treatment can provide survival benefits for patients with gastric cancer who are diagnosed with H pylori infection is an area with limited research. Objective: To explore the potential survival benefits of anti-H pylori treatment after radical gastrectomy in patients with gastric cancer and presurgical confirmation of H pylori infection. Design, Setting, and Participants: This retrospective cohort study was conducted using data from patients with gastric cancer treated between January 1, 2010, and December 31, 2018, and followed up for outcome ascertainment until May 19, 2021. Propensity score matching was performed in patients treated with or without anti-H pylori treatment. This study involved a single institute in a comprehensive cancer treatment and research center located in Guangzhou, Guangdong Province, China. The study included patients with gastric or esophagogastric junction adenocarcinoma who underwent curative gastrectomy with D2 lymphadenectomy and tested positive for H pylori infection. Data were analyzed from March to June 2023. Exposure: Anti-H pylori treatment, which primarily includes triple therapy regimens consisting of amoxicillin, clarithromycin, and omeprazole for 14 days. Main Outcomes and Measures: Clinical outcomes, including overall survival (OS) and disease-free survival (DFS), were analyzed by Kaplan-Meier method, log-rank test, and Cox proportional hazards regression model. Subgroup analysis based on crucial clinical information was also conducted. Results: All 1293 patients (median [IQR] age, 59 [50-65] years; 860 [66.5%] male) were divided into 2 groups, with 125 patients in the anti-H pylori treatment group and 1168 patients in the non-anti-H pylori treatment group based on whether they received anti-H pylori treatment during the perioperative period and the follow-up. Survival analysis showed that the 5-year OS rates were 94.1% (95% CI, 89.3%-99.2%) in the anti-H pylori group and 73.8% (95% CI, 70.7%-77.0%) in the non-anti-H pylori group, and the hazard ratio (HR) of these 2 groups was 0.33 (95% CI, 0.18-0.60; P < .001). The survival benefit remained after propensity score matching (HR, 0.50; 95% CI, 0.26-0.99; P = .048). Multivariable analysis for OS and DFS further showed the survival benefit of anti-H pylori treatment, with HRs of 0.38 (95% CI, 0.17-0.87; P = .02) and 0.48 (95% CI, 0.28-0.83; P = .008), respectively. Among patients with TNM stage II/III disease who received adjuvant chemotherapy, anti-H pylori treatment was associated with survival benefits (OS: HR, 0.49; 95% CI, 0.24-0.99; P = .046), whereas among those who did not receive adjuvant chemotherapy, anti-H pylori treatment was not associated with survival benefits (OS: HR, 0.29; 95% CI, 0.04-2.08; P = .22). Conclusions and Relevance: This cohort study indicates that anti-H pylori treatment may be associated with improved survival in patients with gastric cancer who have H pylori infections. The study reinforces the importance of including H pylori screening and treatment in the surgical treatment of these patients.

The molecular classification of cancer-associated fibroblasts on a pan-cancer single-cell transcriptional atlas.

BACKGROUND: Cancer-associated fibroblasts (CAFs), integral to the tumour microenvironment, are pivotal in cancer progression, exhibiting either pro-tumourigenic or anti-tumourigenic functions. Their inherent phenotypic and functional diversity allows for the subdivision of CAFs into various subpopulations. While several classification systems have been suggested for different cancer types, a unified molecular classification of CAFs on a single-cell pan-cancer scale has yet to be established. METHODS: We employed a comprehensive single-cell transcriptomic atlas encompassing 12 solid tumour types. Our objective was to establish a novel molecular classification and to elucidate the evolutionary trajectories of CAFs. We investigated the functional profiles of each CAF subtype using Single-Cell Regulatory Network Inference and Clustering and single-cell gene set enrichment analysis. The clinical relevance of these subtypes was assessed through survival curve analysis. Concurrently, we employed multiplex immunofluorescence staining on tumour tissues to determine the dynamic changes of CAF subtypes across different tumour stages. Additionally, we identified the small molecule procyanidin C1 (PCC1) as a target for matrix-producing CAF (matCAF) using molecular docking techniques and further validated these findings through in vitro and in vivo experiments. RESULTS: In our investigation of solid tumours, we identified four molecular clusters of CAFs: progenitor CAF (proCAF), inflammatory CAF (iCAF), myofibroblastic CAF (myCAF) and matCAF, each characterised by distinct molecular traits. This classification was consistently applicable across all nine studied solid tumour types. These CAF subtypes displayed unique evolutionary pathways, functional roles and clinical relevance in various solid tumours. Notably, the matCAF subtype was associated with poorer prognoses in several cancer types. The targeting of matCAF using the identified small molecule, PCC1, demonstrated promising antitumour activity. CONCLUSIONS: Collectively, the various subtypes of CAFs, particularly matCAF, are crucial in the initiation and progression of cancer. Focusing therapeutic strategies on targeting matCAF in solid tumours holds significant potential for cancer treatment.

The role of computed tomography features in assessing response to neoadjuvant chemotherapy in locally advanced gastric cancer.

OBJECTIVE: To compare the computed tomography (CT) images of patients with locally advanced gastric cancer (GC) before and after neoadjuvant chemotherapy (NAC) in order to identify CT features that could predict pathological response to NAC. METHODS: We included patients with locally advanced GC who underwent gastrectomy after NAC from September 2016 to September 2021. We retrieved and collected the patients' clinicopathological characteristics and CT images before and after NAC. We analyzed CT features that could differentiate responders from non-responders and established a logistic regression equation based on these features. RESULTS: We included 97 patients (69 [71.1%] men; median [range] age, 60 [26-75] years) in this study, including 66 (68.0%) responders and 31 (32.0%) non-responders. No clinicopathological variable prior to treatment was significantly associated with pathological response. Out of 16 features, three features (ratio of tumor thickness reduction, ratio of reduction of primary tumor attenuation in arterial phase, and ratio of reduction of largest lymph node attenuation in venous phase) on logistic regression analysis were used to establish a regression equation that demonstrated good discrimination performance in predicting pathological response (area under receiver operating characteristic curve 0.955; 95% CI, 0.911-0.998). CONCLUSION: Logistic regression equation based on three CT features can help predict the pathological response of patients with locally advanced GC to NAC.

Efficacy and safety of herbal medicines intervention for cachexia associated with cancer: A systematic review and meta-analysis.

As a worldwide public health issue, cancer-induced cachexia can result in decreasing physical function and survival rate. However, the therapeutic effects of conventional approaches, including pharmacotherapy, exercise and nutritional intervention, are far from satisfactory. Herbal medicines (HMs), especially Traditional Chinese Medicine (TCM), are reported to effectively treat cachexia for centuries. The inclusion criteria of all participants in this study pointed to the diagnosis of cachexia, the trial group used herbal medicine (HM) in complementary and alternative medicine, etc. Twelve databases, including EMbase, PubMed, Web of science, Cochrane CENTRAL, CINAHL, CINAHLPlus, PsycINFO, AMED, China Biology Medicine disc (CBM), China National Knowledge Infrastructure (CNKI), Wanfang and Chongqing VIP (CQVIP) were retrieved from inception to March 28, 2022. We conducted the meta-analysis utilizing RevMan 5.3. A trial sequential analysis (TSA) was conducted to assess the adequacy of the sample size for the outcomes. We have registered the protocol and the registration number was CRD42022336446. A total of 66 studies were included, containing 3654 patients diagnosed with cancer cachexia, of which 1833 patients were assigned to the trial group and 1821 patients were treated in the control group. Outcomes cover the primary indicator KPS (RR = 1.84, 95%CI = [1.61, 2.09], p < 0.00001), and other outcomes including adverse events rate (RR = 0.37, 95%CI = [0.23, 0.58], p < 0.0001), albumin (MD = 2.14, 95%CI = [1.56, 2.71], p < 0.00001), haemoglobin (MD = 4.88, 95%CI = [3.26, 6.50], p < 0.00001), TCM syndrome effect (MD = 1.47, 95%CI = [1.31, 1.65], p < 0.00001), effect of weight (RR = 1.62, 95%CI = [1.34, 1.95], p < 0.00001), effect of appetite (RR = 1.23, 95%CI = [1.13, 1.34], p < 0.00001), FAACT (RR = 7.81, 95%CI = [6.12, 9.50], p < 0.00001), PG-SGA (MD = -2.16, 95%CI = [-2.65, -1.67], p < 0.00001) and QOL (MD = 5.76, 95%CI = [4.04, 7.48], p < 0.00001), suggesting that HMs or HMs combined with conventional treatment have an ameliorating effect on cachexia in each respect. Subgroup analysis showed that the five HMs with the best effect on improving KPS and their optimal doses were Coicis Semen (Yiyiren) in 10 g group, Citri Reticulatae Pericarpium (Chenpi) in 15 g group, Dioscoreae Rhizoma (Shanyao) in 10 g group, Ophiopogonis Radix (Maidong) in 10 g group and Ginseng Radix Et Rhizoma (Renshen) in 20 g group. In addition, there were HM combinations of levels 2-6. Egger's test showed publication bias for five outcomes. HMs have a significant effect on improving cancer cachexia on FAACT, TCM syndrome, KPS, QOL, appetite, nutritional status (evaluated by PG-SGA scale), weight, levels of albumin and haemoglobin. And the Adverse events rate is less than that of Western Medicine. The herbs with the best curative effect and their optimal dose were Dioscoreae R. (10 g), Citri R.P. (15 g), Coicis S. (10 g), Ophiopogonis R. (10 g) and Ginseng R.E.R. (20 g). Due to the quality of included studies is not high, further high-quality studies are needed to firmly establish the clinical efficacy of HM.

Combined Use of Tumor Markers in Gastric Cancer: A Novel Method with Promising Prognostic Accuracy and Practicality.

BACKGROUND: The effect of a single tumor marker on the prognosis of gastric cancer patients is not ideal. This study explored a novel prognostic assessment method for gastric cancer (GC) patients using a combination of three important tumor markers (CEA, CA72-4, and CA19-9). METHOD: Data from 1966 GC patients who underwent curative gastrectomy at Sun Yat-Sen University Cancer Center (Guangzhou, China) were included. Hazard ratios (HR) for all factors for overall survival (OS) were analyzed by Cox regression. A nomogram and calibration curve were used to establish the survival prediction model. The prediction accuracy was evaluated with the concordance index (C-index). RESULTS: All patients were divided into four groups (C0-C3) according to the number of elevated tumor markers. The 5-year OS rates of the patients in preoperative groups C0-C3 were 83.8% (81.3-86.4%), 72.8% (68.5-77.4%), 58.9% (50.4-68.9%), and 18.5% (4.0-33.0%), respectively, and those in postoperative groups C0-C3 were 82.1% (79.4-84.8%), 76.1% (72.2-80.3%), 57.6% (48.4-68.5%), and 16.8% (5.1-28.5%), respectively, with significant differences between each C0-C3 subgroup in both preoperative and postoperative cohorts. Multivariate analysis showed that preoperative (HR: 6.001, 95% CI: 3.523-10.221) and postoperative (HR: 8.149, 95% CI: 4.962-13.528) elevated tumor markers were independent risk factors for GC patients. The C-index for the combined use of tumor markers was 0.65-0.66, which was higher than that for using a single tumor marker (0.53-0.56). CONCLUSION: The combined use of tumor markers significantly improved the prognostic value compared with using a single tumor marker. The survival prediction model including the combined tumor markers was accurate and effective.

Prognostic Significance of PD-L1 Expression in Gastric Cancer Patients with Peritoneal Metastasis.

BACKGROUND: Recently, many studies have explored the relationship between the expression of programmed death ligand 1 (PD-L1) and prognosis in gastric cancer, but there is still controversy. Additionally, few studies have specifically investigated the expression of PD-L1 in patients with peritoneal metastasis. METHODS: Immunohistochemistry was used to analyze the expression of PD-L1 in gastric cancer patients with peritoneal metastasis. The combined positive score (CPS) was calculated to evaluate the expression of PD-L1, and the clinicopathological data were analyzed to explore prognostic significance. RESULTS: In total, 147 gastric cancer patients with peritoneal metastasis were enrolled. The negative PD-L1 expression was defined as a CPS < 1, and high PD-L1 expression was defined as a CPS ≥ 10. PD-L1 expression with CPS ≥ 1 and CPS-negative was detected in 67 (45.58%) and 80 (54.42%) patients, respectively. High PD-L1 expression at PD-L1 CPS ≥ 10 was detected in 21(14.29%) patients. The median overall survival (OS) was 18.53 months in the CPS < 10 group and 27.00 months in the CPS ≥ 10 group; the OS difference between the two groups was significant (p = 0.015). Multivariate analysis demonstrated that a poor Eastern Cooperative Oncology Group performance score (ECOG PS) (p = 0.002) and severe peritoneal metastasis (p = 0.033) were significantly associated with poor survival, while palliative chemotherapy (p = 0.002) and high PD-L1 expression (p = 0.008) were independent and significantly favorable prognostic factors. CONCLUSIONS: Our study demonstrated that PD-L1 expression was widely presented in gastric cancer patients with peritoneal metastasis, while a CPS no less than 10 predicted better prognosis.

Probabilistic real-time natural gas jet fire consequence modeling of offshore platforms by hybrid deep learning approach.

Natural gas jet fire induced by igniting blowouts has the potential to cause critical structure damage and great casualties of offshore platforms. Real-time natural gas jet fire plume prediction is essential to support the emergency planning to mitigate subsequent damage consequence and ocean pollution. Deep learning based on a large amount of Computational fluid dynamics (CFD) simulations has recently been applied to real-time fire modeling. However, existing approaches based on point-estimation theory are 'over-confident' when prediction deficiency exists, which reduce robustness and accuracy for emergency planning support. This study proposes probabilistic deep learning approach for real-time natural gas jet fire consequence modeling by integrating variational Bayesian inference with deep learning. Numerical model of natural gas jet fire from offshore platform is built and the natural gas jet fire scenarios are simulated to construct the benchmark dataset. Sensitivity analysis of pre-defined parameters such as MC (Monte Carlo) sampling number m and dropout probability p is conducted to determine the trade-off between model's accuracy and efficiency. The results demonstrated our model exhibits competitive accuracy with R2 = 0.965 and real-time capacity with an inference time of 12 ms. In addition, the predicted spatial uncertainty corresponding to spatial jet fire flame plume provides more comprehensive and reliable support for the following mitigation decision-makings compared to the state-of-the-art point-estimation based deep learning model. This study provides a robust alternative for constructing a digital twin of fire and explosion associated emergency management on offshore platforms.

Evaluating relapse-free survival as an endpoint for overall survival in adjuvant immunotherapy trials.

BACKGROUND: Relapse-free survival (RFS) has been considered a primary endpoint to assess the effects of immunotherapy in the adjuvant setting among patients with early-stage disease. However, it is not clear whether RFS is a valid surrogate endpoint for overall survival (OS) in this clinical context. METHODS: Phase II or III clinical trials of adjuvant immunotherapy that reported hazard ratios on OS and RFS were identified. We used a weighted regression analysis at the arm and trial levels to assess the efficacy of RFS as a surrogate for OS, quantified by the weighted coefficient of determination (R2). Strong correlations (R2 ≥ 0.7) at the arm and trial levels indicated valid surrogacy. The surrogate threshold effect was also evaluated. RESULTS: Fifteen high-quality randomized clinical trials involving 13 715 patients were included. At the arm level, moderate and strong associations were observed between RFS2-year and OS3-year (R2 = 0.58, 95% confidence interval [CI] = 0.25 to 0.92) and RFS3-year and OS5-year (R2 = 0.72, 95% CI = 0.38 to 1.00), respectively. At the trial level, a moderate association was observed between effect of treatment on RFS and OS (R2 = 0.63, 95% CI = 0.33 to 0.94). The surrogate threshold effect for RFS was 0.86. Consistent results were confirmed in several sensitivity analyses based on different trial phases, experimental arms, cancer types, and treatment strategies. CONCLUSIONS: Our meta-analysis failed to find a clinically strong association between RFS and OS in randomized clinical trials of adjuvant immunotherapy. Our findings challenge the use of RFS as the primary efficacy endpoint and suggest the use of OS in this clinical context.

Preoperative diagnosis of metastatic lymph nodes by CT-histopathologic matching analysis in gastric adenocarcinoma using dual-layer spectral detector CT.

OBJECTIVES: There still remain challenges to accurate diagnosis of lymph node (LN) involvement in gastric cancer (GC) on conventional CT. This study evaluated the quantitative data derived from dual-layer spectral detector CT (DLCT) for preoperative diagnosis of metastatic LNs compared to conventional CT images. METHODS: Patients with adenocarcinoma scheduled for gastrectomy were enrolled in this prospective study from July, 2021, to February, 2022. Regional LNs were labeled on preoperative DLCT. The LNs were located and matched using carbon nanoparticle solution during surgery according to their locations and anatomic landmarks on preoperative images. The matched LNs were randomly split into training and validation cohorts in a ratio of 2:1. The DLCT quantitative parameters in the training cohort were investigated using logistic regression models to identify independent predictors of metastatic LNs, and these predictors were subsequently applied to the validation cohort. Receiver operating characteristic curves were compared between the DLCT parameters and conventional CT images. RESULTS: Fifty-five patients were included in the study, with 267 successfully matched LNs (90 metastatic, 177 nonmetastatic). Independent predictors included arterial phase CT attenuation on 70-keV images, venous phase electron density, and clustered feature. These combination predictors had areas under the curve (AUC) of 0.855 and 0.907 in the training and validation cohorts, respectively. Compared to conventional CT criteria alone, the model had higher AUC and accuracy (0.741 vs. 0.907, 75.28% vs. 87.64%; p < 0.01) for LN diagnosis. CONCLUSION: Incorporating DLCT parameters improved preoperative diagnosis of LN metastasis in GC, increasing the accuracy of clinical N stage. CLINICAL RELEVANCE STATEMENT: Compared to conventional CT criteria, quantitative parameters from dual-layer spectral detector CT showed higher diagnostic efficacy for the preoperative diagnosis of lymph node metastases in gastric cancer, increasing the accuracy of clinical N stage. KEY POINTS: • Quantitative parameters from dual-layer spectral detector CT are useful for the preoperative diagnosis of lymph node metastases in gastric adenocarcinoma, increasing the accuracy of clinical N stage. • The values for metastatic lymph nodes are higher than those of nonmetastatic ones. The arterial phase of CT attenuation on 70-keV images, venous phase of electron density, and clustered feature independently predicted lymph node metastases. • Prediction model had area under the curve of 0.907, sensitivity of 81.82%, specificity of 91.07%, and accuracy of 87.64% for the preoperative diagnosis of lymph node metastasis.

Characterization of cuproptosis in gastric cancer and relationship with clinical and drug reactions.

Gastric cancer (GC) is the fifth most common cancer worldwide. Cuproptosis is associated with cell growth and death as well as tumorigenesis. Aiming to lucubrate the potential influence of CRGs in gastric cancer, we acquired datasets of gastric cancer patients from TCGA and GEO. The identification of molecular subtypes with CRGs expression was achieved through unsupervised learning-cluster analysis. To evaluate the application value of subtypes, the K-M survival analysis was conducted to evaluate the clinical prognostic characteristics. Subsequently, we performed Gene Set Variation Analysis (GSVA) and utilized ssGSEA to quantify the extent of immune infiltration. Further, the K-M survival analysis was used to identify the prognosis-related CRGs. Next, signature genes of diagnostic predictive value were screened using the least absolute shrinkage and selection operator (LASSO) algorithm from the expression matrix for TCGA, as well as the signature gene-related subtype was clustered by the "ConsensusClusterPlus" package. Finally, the immunological and drug sensitivity assessments of the signature gene-related subtypes were conducted. A total of 173 CRGs were identified, most of the CRGs undergo copy number variation in gastric cancer. Under different patient subtypes, immune cell levels differed significantly, and the subtype exhibiting high expression of the CRGs had a better prognosis. Furthermore, we selected 34 CRGs that were highly correlated with the prognosis of gastric cancer. By constructing a multivariate Cox proportional-hazards model and a hazard scoring system, we were able to categorize patients into high- and low-risk groups based on their hazard score. K-M analysis demonstrated a significant survival disadvantage in the high-risk group. Based on Lasso regression analysis, we screened 16 signature genes, a multivariate logistic regression model [cutoff: 0.149 (0.000, 0.974), AUC:0.987] and a prognosis network diagram was constructed and their prediction efficiency for gastric cancer prognostic diagnosis was well validated. According to the signature genes, the patients were separated to two signature subtypes. We found that patients with higher CRGs expression and better prognosis had lower levels of immune infiltration. Finally, according to the results of drug susceptibility analysis, docetaxel, 5-Fluorouracil, gemcitabin, and paclitaxel were found to be more sensitive to gastric cancer.

Advanced Computer Vision-Based Subsea Gas Leaks Monitoring: A Comparison of Two Approaches.

Recent years have witnessed the increasing risk of subsea gas leaks with the development of offshore gas exploration, which poses a potential threat to human life, corporate assets, and the environment. The optical imaging-based monitoring approach has become widespread in the field of monitoring underwater gas leakage, but the shortcomings of huge labor costs and severe false alarms exist due to related operators' operation and judgment. This study aimed to develop an advanced computer vision-based monitoring approach to achieve automatic and real-time monitoring of underwater gas leaks. A comparison analysis between the Faster Region Convolutional Neural Network (Faster R-CNN) and You Only Look Once version 4 (YOLOv4) was conducted. The results demonstrated that the Faster R-CNN model, developed with an image size of 1280 × 720 and no noise, was optimal for the automatic and real-time monitoring of underwater gas leakage. This optimal model could accurately classify small and large-shape leakage gas plumes from real-world datasets, and locate the area of these underwater gas plumes.

HAMP as a Potential Diagnostic, PD-(L)1 Immunotherapy Sensitivity and Prognostic Biomarker in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) remains a global medical problem. Programmed cell death protein 1 (PD-1) is a powerful weapon against many cancers, but it is not sensitive to some patients with HCC. We obtained datasets from the Gene Expression Omnibus (GEO) database on HCC patients and PD-1 immunotherapy to select seven intersecting DEGs. Through Lasso regression, two intersecting genes were acquired as predictors of HCC and PD-1 treatment prognosis, including HAMP and FOS. Logistic regression was performed to build a prediction model. HAMP had a better ability to diagnose HCC and predict PD1 treatment sensitivity. Further, we adapted the support vector machine (SVM) technique using HAMP to predict triple-classified outcomes after PD1 treatment in HCC patients, which had an excellent classification ability. We also performed external validation using TCGA data, which showed that HAMP was elevated in the early stage of HCC. HAMP was positively correlated with the infiltration of 18 major immune cells and the expression of 2 important immune checkpoints, PDCD1 and CTLA4. We discovered a biomarker that can be used for the early diagnosis, prognosis and PD1 immunotherapy efficacy prediction of HCC for the first time and developed a diagnostic model, prognostic model and prediction model of PD1 treatment sensitivity and treatment outcome for HCC patients accordingly.

Adjuvant Chemotherapy for Patients with Adenocarcinoma of the Esophagogastric Junction: A Retrospective, Multicenter, Observational Study.

BACKGROUND: Although the incidence of adenocarcinoma of the esophagogastric junction (AEG) has been increasing since the past decade, the proportion of AEG cases in two previous clinical trials (ACTS-GC and CLASSIC) that investigated the efficacy of adjuvant chemotherapy was relatively small. Therefore, whether AEG patients can benefit from adjuvant chemotherapy remains unclear. METHODS: Patients who were diagnosed with pathological stage II/III, Siewert II/III AEG, and underwent curative surgery at three high-volume institutions were assessed. Clinical outcomes were analyzed by using Kaplan-Meier curves, log-rank test, and Cox regression model. Propensity score matching (PSM) was used to reduce the selection bias. RESULTS: A total of 927 patients were included (the chemotherapy group: 696 patients; the surgery-only group: 231 patients). The median follow-up was 39.0 months. The 5-year overall survival was 63.1% (95% confidence interval [CI]: 59.0-67.6%) for the chemotherapy group and 50.2% in the surgery-only group (hazard ratio [HR] = 0.69, 95% CI: 0.54-0.88; p = 0.003). The 5-year, disease-free survival was 35.4% for the chemotherapy group and 16.6% for the surgery-only group (HR = 0.66, 95% CI: 0.53-0.83; p < 0.001). After PSM, the survival benefit of adjuvant chemotherapy for AEG was maintained. Multivariate analysis for overall survival and disease-free survival further demonstrated the survival benefit of adjuvant chemotherapy, with HRs of 0.63 (p < 0.001) and 0.52 (p < 0.001), respectively. CONCLUSIONS: Postoperative adjuvant chemotherapy was associated with improved overall survival and disease-free survival in patients with operable stage II or III AEG after D2 gastrectomy.

Cuproptosis-Related Signature Predicts the Prognosis, Tumor Microenvironment, and Drug Sensitivity of Hepatocellular Carcinoma.

Background: Copper (Cu) metabolism is strongly associated with liver disease. Cuproptosis is a novel format of cell death, and cuproptosis-related genes (CRGs) were identified. However, the role of CRGs in Hepatocellular Carcinoma (HCC) remains unknown. Method: The mRNA transcriptome profiling data, somatic mutation data, and copy number gene level data of The Cancer Genome Atlas-Liver Hepatocellular Carcinoma project (TCGA-LIHC) were downloaded for subsequent analysis. Molecular characterization analysis of CRGs, including differential gene expression analysis, mutation analysis, copy number variation (CNV) analysis, Kaplan-Meier analysis, and immune regulator prioritization analysis, was implemented. The nonnegative matrix factorization (NMF) approach was used to identify the CRG-related molecular subtypes. Principal component analysis was adopted to verify the robustness and reliability of the molecular subtype. The least absolute shrinkage and selection operator regression analysis was performed to construct the prognostic signature based on differentially expressed genes between molecular subtypes. The survival characteristics of the molecular subtype and the signature were analyzed. The Gene Set Variation Analysis was performed for functional annotation. The immune landscape analysis, including immune checkpoint gene analysis, single sample gene set enrichment analysis, tumor immune dysfunction and exclusion (TIDE) analysis, immune infiltration cell, and tumor mutation burden analysis (TMB), was conducted. The ability of the signature to predict conventional anti-HCC agent responses was evaluated. The signature was validated in the LIRI-JP cohort and the IMvigor210 cohort. Result: A total of 13 CRGs are differentially expressed between the tumor and normal samples, while the mutation of CRGs in HCC is infrequent. The expression of CRGs is associated with the CNV level. Fourteen CRGs are associated with the prognosis of HCC. Two clusters were identified and HCC patients were divided into 2 groups with a cutoff risk score value of 1.570. HCC patients in the C1 cluster and high-risk have a worse prognosis. The area under the receiver operating characteristic curve for predicting 1-, 2-, and 3-year overall survival is 0.775, 0.768, and 0.757 in the TCGA-LIHC cohort, and 0.811, 0.741, and 0.775 in the LIRI-JP cohort. Multivariate Cox regression analysis indicates that the signature is an independent prognostic factor. Pathways involved in metabolism and gene stability and immune infiltration cells are significantly enriched. Immune checkpoint genes are highly expressed in the C1 cluster. TMB is positively correlated with the risk score. HCC patients in the high-risk group are more likely to benefit from conventional anti-HCC agents and immune checkpoint inhibitor therapies. Conclusion: The molecular characterization of CRGs in HCC is presented in this study, and a successful prognostic signature for HCC based on the cuproptosis-related molecular subtype was constructed.

Development and verification of a nomogram for predicting the prognosis of resectable gastric cancer with outlet obstruction.

BACKGROUND: The prognosis of patients with gastric cancer (GC) with gastric outlet obstruction (GOO) after gastrectomy is highly variable. In this study, we aimed to develop a nomogram to predict the prognosis of these patients. PATIENTS AND METHODS: Data from 218 GC patients with GOO who underwent gastrectomy at Sun Yat-sen University Cancer Center were retrospectively collected as a training cohort. The data of 59 patients with the same diagnosis who underwent gastrectomy at the First Affiliated Hospital of Guangxi Medical University were collected as an external verification cohort. A nomogram for the overall survival (OS) was developed using the Cox regression model in the training cohort, which was validated in a verification cohort. RESULTS: Multivariate analysis showed that the surgical procedure (P < 0.001), period of chemotherapy (P < 0.001), T stage (P = 0.006), N stage (P = 0.040), systemic immune-inflammatory index (SII) (P < 0.001), and fibrinogen level (P = 0.026) were independent factors affecting OS. The nomogram constructed on the aforementioned factors for predicting the 1- and 3-year OS achieved a Harrell's concordance index (C-index) of 0.756 and 0.763 for the training and verification cohorts, respectively. Compared with the 8th American Joint Committee on Cancer (AJCC) Tumour-Node-Metastasis (TNM) staging system, the nomogram had higher C-index values and areas under the curve (AUCs) and slightly higher net clinical benefit. CONCLUSION: Compared to the 8th AJCC staging system, the newly developed nomogram showed superior performance in predicting the survival of GC patients with GOO after gastrectomy.

LAG3-PD1 or CTLA4-PD1 Inhibition in Advanced Melanoma: Indirect Cross Comparisons of the CheckMate-067 and RELATIVITY-047 Trials.

Objective: To compare the inhibition of LAG3-PD1 versus the inhibition of CTLA-4-PD1 in patients with previously untreated advanced melanoma. Methods: The individual participant data (IPD) were extracted from the KM plots using a graphical reconstructive algorithm. Log-rank, Cox proportional hazard model, Bayesian hierarchical model with time-varying hazard ratio (HR) effect, and restricted mean survival time (RMST) were performed to estimate survival benefits. Results: The CheckMate-067 (n = 630) and RELATIVITY-047 (n = 714) trials were included for analysis. The graphical reconstructive algorithm showed that IPD had similar HRs and log-rank values as the original plots. The HR of nivolumab plus relatlimab (LAG3 inhibitor) versus nivolumab plus ipilimumab (CTLA4 inhibitor) was 1.19 (95% confidence interval [CI] 0.96 to1.48). The 24-months RMST of nivolumab plus relatlimab versus nivolumab was 2.35 (95% CI 0.77-3.94) months, compared with 1.87 (95% CI, 0.25-3.49) months for nivolumab plus ipilimumab versus nivolumab. The Bayesian hierarchical model showed that patients treated with nivolumab plus relatlimab had earlier PFS benefits than those with nivolumab plus ipilimumab. Grade 3 or 4 treatment-related adverse events occurred in 18.9% of patients using nivolumab plus relatlimab and 55.0% of patients using nivolumab plus ipilimumab. Conclusions: These findings suggest that the PFS of LAG3-PD1 and CTLA4-PD1 inhibition were similar and LAG3-PD1 inhibition exhibited earlier survival benefit and lesser TRAEs.