Development of a staging system and survival prediction model for advanced gastric cancer patients without adjuvant treatment after curative gastrectomy: A retrospective multicenter cohort study

Integrated analysis of disulfidptosis-related immune genes signature to boost the efficacy of prognostic prediction in gastric cancer

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Nomogram to predict overall survival of patients receiving radical gastrectomy and incomplete peri-operative adjuvant chemotherapy for stage II/III gastric cancer: a retrospective bi-center cohort study

BACKGROUND

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Prognostic model development and molecular subtypes identification in bladder urothelial cancer by oxidative stress signatures

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Lifestyle, clinical and histological indices-based prediction models for survival in cancer patients: a city-wide prospective cohort study in China

PURPOSE

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

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.