The potential of the application of artificial intelligence and biochemical markers of oxidative stress to predict the prognosis of older patients with gastric cancer (GC) remains unclear.

This retrospective multicenter study included consecutive patients with GC aged ≥ 65 years treated between January 2012 and April 2018. The patients were allocated into three cohorts (training, internal, and external validation). The GC-Integrated Oxidative Stress Score (GIOSS) was developed using Cox regression to correlate biochemical markers with patient prognosis. Predictive models for five-year overall survival (OS) were constructed using random forest (RF), decision tree (DT), and support vector machine (SVM) methods, and validated using area under the curve (AUC) and calibration plots. The SHapley Additive exPlanations (SHAP) method was used for model interpretation.

This study included a total of 1,859 older patients. The results demonstrated that a low GIOSS was a predictor of poor prognosis. RF was the most efficient method, with AUCs of 0.999, 0.869, and 0.796 in the training, internal validation, and external validation sets, respectively. The DT and SVM models showed low AUC values. Calibration and decision curve analyses demonstrated the considerable clinical usefulness of the RF model. The SHAP results identified pN, pT, perineural invasion, tumor size, and GIOSS as key predictive features. An online web calculator was constructed based on the best model.

Incorporating the GIOSS, the RF model effectively predicts postoperative OS in older patients with GC and is a robust prognostic tool. Our findings emphasize the importance of oxidative stress in cancer prognosis and provide a pathway for improved management of GC.

Retrospectively registered at ClinicalTrials.gov (trial registration number: NCT06208046, date of registration: 2024-05-01).

Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer (GC) with a poor prognosis. Furthermore, the current pathological staging system for HAS does not distinguish it from that for common gastric cancer (CGC).

The clinicopathological data of 251 patients with primary HAS who underwent radical surgery at 14 centers in China from April 2004 to December 2019 and 5082 patients with primary CGC who underwent radical surgery at two centers during the same period were retrospectively analyzed. A modified staging system was established based on the differences in survival.

After 1:4 propensity score matching (PSM), 228 patients with HAS and 828 patients with CGC were analyzed. Kaplan-Meier (K-M) analysis showed patients with HAS had a poorer prognosis compared with CGC. Multivariate analysis identified pN stage, CEA level, and perineural invasion (PNI) as independent prognostic factors in patients with HAS. A modified pT (mpT) staging was derived using recursive partitioning analysis (RPA) incorporating PNI and pT staging. The modified pathological staging system (mpTNM) integrated the mpT and the eighth American Joint Committee on Cancer (AJCC) pN definitions. Multivariate analysis showed that the mpTNM stage outperformed other pathological variables as independent predictors of OS and RFS in patients with HAS. The mpTNM staging system exhibited significantly higher predictive accuracy for 3-year OS in patients with HAS (0.707, 95% CI: 0.650-0.763) compared to that of the eighth AJCC staging system (0.667, 95% CI: 0.610-0.723, P <0.05). Analysis using the Akaike information criterion favored the mpTNM staging system over the eighth AJCC staging system (824.69 vs. 835.94) regarding the goodness of fit. The mpTNM stages showed improved homogeneity in survival prediction (likelihood ratio: 41.51 vs. 27.10). Comparatively, the mpTNM staging system outperformed the eighth AJCC staging system in survival prediction, supported by improvements in the net reclassification index (NRI: 47.7%) and integrated discrimination improvement (IDI: 0.083, P <0.05). The time-dependent ROC curve showed that the mpTNM staging system consistently outperformed the eighth AJCC staging system with increasing observation time.

The mpTNM staging system exhibited superior postoperative prognostic accuracy for patients with HAS compared to the eighth AJCC staging system.

The purpose of this study was to construct a nomogram to identify patients at high risk of gastric precancerous lesions (GPLs). This identification will facilitate early diagnosis and treatment and ultimately reduce the incidence and mortality of gastric cancer.

In this single-center retrospective cohort study, 563 participants were divided into a gastric precancerous lesion (GPL) group (n=322) and a non-atrophic gastritis (NAG) group (n=241) based on gastroscopy and pathology results. Laboratory data and demographic data were collected. A derivation cohort (n=395) was used to identify the factors associated with GPLs to develop a predictive model. Then, internal validation was performed (n=168). We used the area under the receiver operating characteristic curve (AUC) to determine the discriminative ability of the predictive model; we constructed a calibration plot to evaluate the accuracy of the predictive model; and we performed decision curve analysis (DCA) to assess the clinical practicability predictive model.

Four -predictors (i.e., age, body mass index, smoking status, and -triglycerides) were included in the predictive model. The AUC values of this predictive model were 0.715 (95% CI: 0.665-0.765) and 0.717 (95% CI: 0.640-0.795) in the derivation and internal validation cohorts, respectively. These values indicated that the predictive model had good discrimination ability. The calibration plots and DCA suggested that the predictive model had good accuracy and clinical net benefit. The Hosmer-Lemeshow test results in the derivation and validation cohorts for this predictive model were 0.774 and 0.468, respectively.

The nomogram constructed herein demonstrated good performance in terms of predicting the risk of GPLs. This nomogram can be beneficial for the early detection of patients at high risk of GPLs, thus facilitating early treatment and ultimately reducing the incidence and mortality of gastric cancer.

Gastric cancer (GC) is a relatively frequent clinical phenomenon, referring to malignant tumors emerging in the gastric mucosal epithelial cells. It has a high morbidity and mortality rate, posing a significant threat to the health of patients. Hence, how to diagnose and treat GC has become a heated topic in this research field.

To discuss the effectiveness and safety of nab-paclitaxel in combination with oxaliplatin and S-1 (P-SOX) for the treatment of GC, and to analyze the factors that may influence its outcomes.

A total of 219 eligible patients with advanced GC, who were treated at Qinghai University Affiliated Hospital Gastrointestinal Oncology between January 2018 and March 2020, were included in the study. Among them, 149 patients received SOX regimen and 70 patients received S-1 regimen. All patients underwent both preoperative and postoperative chemotherapy consisting of 2-4 cycles each, totaling 6-8 cycles, along with parallel D2 radical surgical treatment. The patients were followed up for a period of three years or until reaching the event endpoint.

The short-term and long-term efficacy of the P-SOX group was significantly higher than that of the SOX group, and the safety was manageable. Cox multivariate analysis revealed that progression-free survival was associated with perioperative chemotherapy efficacy, tumor diameter ≤ 2cm, high differentiation, and early cTNM (T stands for invasion depth; N stands for node metastasis; M stands for distant invasion) stage.

In comparison to the SOX regimen, the P-SOX regimen demonstrates improved short-term and long-term efficacy with tolerable adverse reactions. It is anticipated that the P-SOX regimen will emerge as a first-line chemotherapy option for GC. Patients with GC who receive effective perioperative chemotherapy (Response Evaluation Criteria in Solid Tumors 1.1, Tumor Regression Grade), have a tumor diameter ≤ 2cm, exhibit high degree of differentiation, and are at an early cTNM stage show better prognosis.

Gastric cancer (GC) remains a malignant tumor with high morbidity and mortality, accounting for approximately 1,080,000 diagnosed cases and 770,000 deaths worldwide annually. Disulfidptosis, characterized by the stress-induced abnormal accumulation of disulfide, is a recently identified form of programmed cell death. Substantial studies have demonstrated the significant influence of immune clearance on tumor progression. Therefore, we aimed to explore the intrinsic correlations between disulfidptosis and immune-related genes (IRGs) in GC, as well as the potential value of disulfidptosis-related immune genes (DRIGs) as biomarkers.

This study incorporated the single-cell RNA sequencing (scRNA-seq) dataset GSE183904 and transcriptome RNA sequencing of GC from the TCGA database. Disulfidptosis-related genes (DRGs) and IRGs were derived from the representative literature on both cell disulfidptosis and immunity. The expression and distribution of DRGs were investigated at the single-cell level in different GC cell types. Pearson correlation analysis was used to identify the IRGs closely related to disulfidptosis. The prognostic signature of DRIGs was established using Cox and LASSO analyses. We then analyzed and evaluated the differences in long-term prognosis, Gene Set Enrichment Analysis (GSEA), immune infiltration, mutation profile, CD274 expression, and response to chemotherapeutic drugs between the two groups. A tissue array containing 63 paired GC specimens was used to verify the expression of 4 DRIGs and disulfidptosis regulator SLC7A11 through immunohistochemistry staining.

The scRNA-seq analysis found that SLC7A11, SLC3A2, RPN1 and NCKAP1 were enriched in specific cell types and closely related to immune infiltration. Four DIRGs (GLA, HIF-1α, VPS35 and CDC37) were successfully identified to establish a signature to potently predict the survival time of GC patients. Patients with high risk scores generally experienced worse prognoses and exhibited greater resistant to classical chemotherapy drugs. Furthermore, the expression of GLA, HIF-1α, VPS35, CDC37 and SLC7A11 were elevated in GC tissues. A high expression of GLA, HIF-1α, VPS35 or CDC37 was associated with more advanced clinical stage of GC and increased SLC7A11 expression.

Current study first highlights the potential value of DRIGs as biomarkers in GC. We successfully constructed a robust model incorporating four DRIGs to accurately predict the survival time and clinicopathological characteristics of GC patients.

To establish a nomogram to predict the probability of survival of patients with stage II/III gastric cancer (GC) who received incomplete peri-operative adjuvant chemotherapy (PAC).

The medical records of stage II/III GC patients who received curative resection and 1 to 5 cycles of PAC from two tertiary hospitals were retrospectively reviewed. Patients were randomly classified into either a training group or validation group at a ratio of 7:3. The nomogram was constructed based on various prognostic factors using Cox regression analysis in the training cohort, and was validated by the validation group. Concordance index and calibration curves were used to evaluate the discrimination and calibration of the nomogram. Additionally, decision curve analysis (DCA) was used to compare the net clinical benefits of the nomogram and eighth version of TNM staging system.

A total of 1,070 consecutive patients were included and 749 patients were enrolled into the training group. Lower body mass index (< 18.5 kg/m2), total gastrectomy, stage III disease and fewer cycles of PAC were identified to be independent predictors for poorer survival. The area under the curve (AUC) values of receiver operating characteristics (ROC) curve predicting 5-year survival probabilities and C-index were 0.768 and 0.742, 0.700 (95%CI: 0.674-0.726) and 0.689 (95%CI: 0.646-0.732) in the training and validation groups, respectively. The calibration curves in the validation cohort showed good agreement between the prediction and observation of 1-, 3- and 5-year survival probabilities. Furthermore, DCA showed that our model has a better net benefit than that of TNM staging system.

The findings emphasize the value of completing PAC. The nomogram which was established to predict survival probability in patients with stage II/III GC receiving radical gastrectomy and incomplete PAC had good accuracy and was verified through both internal and external validation.

Mounting studies indicate that oxidative stress (OS) significantly contributes to tumor progression. Our study focused on bladder urothelial cancer (BLCA), an escalating malignancy worldwide that is growing rapidly. Our objective was to verify the predictive precision of genes associated with overall survival (OS) by constructing a model that forecasts outcomes for bladder cancer and evaluates the prognostic importance of these genetic markers.

Transcriptomic data were obtained from TCGA-BLCA and GSE31684, which are components of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. To delineate distinct molecular subtypes, we employed the non-negative matrix factorization (NMF)method. The significance of OS-associated genes in predicting outcomes was assessed using lasso regression, multivariate Cox analysis, and univariate Cox regression analysis. For external validation, we employed the GSE31684 dataset. CIBERSORT was utilized to examine the tumor immune microenvironment (TIME). A nomogram was created and verified using calibration and receiver operating characteristic (ROC) curves, which are based on risk signatures. We examined variations in clinical characteristics and tumor mutational burden (TMB) among groups classified as high-risk and low-risk. To evaluate the potential of immunotherapy, the immune phenomenon score (IPS) was computed based on the risk score. In the end, the pRRophetic algorithm was employed to forecast the IC50 values of chemotherapy medications.

In our research, we examined the expression of 275 genes associated with OS in 19 healthy and 414 cancerous tissues of the bladder obtained from the TCGA database. As a result, a new risk signature was created that includes 4 genes associated with OS (RBPMS, CRYAB, P4HB, and PDGFRA). We found two separate groups, C1 and C2, that showed notable variations in immune cells and stromal score. According to the Kaplan-Meier analysis, patients classified as high-risk experienced a considerably reduced overall survival in comparison to those categorized as low-risk (P<0.001). The predictive capability of the model was indicated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve surpassing 0.6. Our model showed consistent distribution of samples from both the GEO database and TCGA data. Both the univariate and multivariate Cox regression analyses validated the importance of the risk score in relation to overall survival (P < 0.001). According to our research, patients with a lower risk profile may experience greater advantages from using a CTLA4 inhibitor, whereas patients with a higher risk profile demonstrated a higher level of responsiveness to Paclitaxel and Cisplatin. In addition, methotrexate exhibited a more positive outcome in patients with low risk compared to those with high risk.

Our research introduces a novel model associated with OS gene signature in bladder cancer, which uncovers unique survival results. This model can assist in tailoring personalized treatment approaches and enhancing patient therapeutic effect in the management of bladder cancer.

We developed a nomogram to predict 3-year, 5-year and 7-year cancer survival rates of cancer patients.

This prospective cohort study included 20,491 surviving patients first diagnosed with cancer in Guangzhou from 2010 to 2019. They were divided into a training and a validation group. Lifestyle, clinical and histological parameters (LCH) were included in multivariable Cox regression. Akaike information criterion was used to select prediction factors for the nomogram. The discrimination and calibration of models were assessed by concordance index (C-index), area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration plots. We used net reclassification index (NRI) and integrated discrimination improvement (IDI) to compare the clinical utility of LCH prediction model with the prediction model based on lifestyle factors (LF).

13 prediction factors including age, sex, BMI, smoking status, physical activity, sleep duration, regular diet, tumor grading, TNM stage, multiple primary cancer and anatomical site were included in the LCH model. The LCH model showed satisfactory discrimination and calibration (C-index = 0.81 (95% CI 0.80-0.82) for training group and 0.80 (0.79-0.81) for validation group, both time-dependent AUC > 0.70). The LF model including smoking status, physical activity, sleep duration, regular diet, and BMI showed less satisfactory discrimination (C-index = 0.60 (95% CI 0.59-0.61) for training and 0.60 (0.58-0.62) for validation group). The LCH model had better accuracy and discriminative ability than the LF model, as indicated by positive NRI and IDI values.

The LCH model shows good accuracy, clinical utility and precise prognosis prediction, and may serve as a tool to predict cancer survival of cancer patients.