Prognostic nomograms based on immune scores for head-neck squamous cell carcinoma patients

Analysis of Related Risk Factors and Prognostic Factors of Gastric Cancer with Liver Metastasis: A SEER and External Validation Based Study

Background

Gastric cancer (GC) has a poor prognosis, particularly in patients with liver metastasis (LM). This study aims to identify relevant factors associated with the occurrence of LM in GC patients and factors influencing the prognosis of gastric cancer with liver metastasis (GCLM) patients, in addition to developing diagnostic and prognostic nomograms specifically.

Patients and Methods

Overall, 6184 training data were from the Surveillance, Epidemiology, and End Results (SEER) database from 2011 to 2015. 1527 validation data were from our hospital between January 2018 and December 2022. Logistic regression was used to identify the risk factors associated with the occurrence of LM in GC patients, Cox regression was used to confirm the prognostic factors of GCLM patients. Two nomogram models were established to predict the risk and overall survival (OS) of patients with GCLM. The performance of the two models was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curves.

Results

A nomogram included five independent factors from multivariate logistic regression: sex, lymph node removal, chemotherapy, T stage and N stage were constructed to calculate the possibility of LM. Internal and external verifications of AUC were 0.786 and 0.885, respectively. The other nomogram included four independent factors from multivariate Cox regression: surgery at primary site, surgery at other site, chemotherapy, and N stage were constructed to predict OS. C-index for internal and external validations were 0.714 and 0.702, respectively, and the calibration curves demonstrated the robust discriminative ability of the models.

Conclusion

Based on the SEER database and validation data, we defined effective nomogram models to predict risk and OS in patients with GCLM. They have important value in clinical decision-making and personalized treatment.

A Random Forest Model for Post-Treatment Survival Prediction in Patients with Non-Squamous Cell Carcinoma of the Head and Neck

BACKGROUND

Compared to squamous cell carcinoma, head and neck non-squamous cell carcinoma (HNnSCC) is rarer. Integrated survival prediction tools are lacking.

METHODS

4458 patients of HNnSCC were collected from the SEER database. The endpoints were overall survivals (OSs) and disease-specific survivals (DSSs) of 3 and 5 years. Cases were stratified-randomly divided into the train & validation (70%) and test cohorts (30%). Tenfold cross validation was used in establishment of the model. The performance was evaluated with the test cohort by the receiver operating characteristic, calibration, and decision curves.

RESULTS

The prognostic factors found with multivariate analyses were used to establish the prediction model. The area under the curve (AUC) is 0.866 (95%CI: 0.844-0.888) for 3-year OS, 0.862 (95%CI: 0.842-0.882) for 5-year OS, 0.902 (95%CI: 0.888-0.916) for 3-year DSS, and 0.903 (95%CI: 0.881-0.925) for 5-year DSS. The net benefit of this model is greater than that of the traditional prediction methods. Among predictors, pathology, involved cervical nodes level, and tumor size are found contributing the most variance to the prediction. The model was then deployed online for easy use.

CONCLUSIONS

The present study incorporated the clinical, pathological, and therapeutic features comprehensively and established a clinically effective survival prediction model for post-treatment HNnSCC patients.

Nomogram Model for Dynamic and Individual Prediction of Cardiac Response and Survival for Light Chain Amyloidosis in 737 Patients With Cardiac Involvement

Objective

Light chain amyloidosis (AL) with cardiac involvement is associated with poor prognosis. The existing prognostic assessment system does not consider treatment-related factors, and there is currently no effective system for predicting the response. The purpose of this study was to build an individualized, dynamic assessment model for cardiac response and overall survival (OS) for AL patients with cardiac involvement.

Methods

The records of 737 AL patients with cardiac involvement were collected through cooperation with 18 hospitals in the Chinese Registration Network for Light-chain Amyloidosis (CRENLA). We used univariate and multivariate analyses to evaluate the prognostic factors for OS and cardiac response. Then, two nomogram models were developed to predict OS and cardiac response in AL patients with cardiac involvement.

Results

A nomogram including four independent factors from the multivariate Cox proportional hazards analysis—Mayo staging, courses of treatment, hematologic response, and cardiac response—was constructed to calculate the possibility of achieving survival by adding all the points associated with four variables. The higher the score, the more likely death would occur. The other nomogram model included the courses of treatment, hematological response, and different treatment regimens, and was correlated with cardiac response. The higher the score, the more likely a cardiac response would occur.

Conclusion

In conclusion, based on the large Chinese cohort of patients with AL and cardiac involvement, we identified nomogram models to predict cardiac response and OS. These models are more individualized and dynamic, and therefore, they have important clinical application value.

Ferroptosis-related genes for predicting prognosis of patients with laryngeal squamous cell carcinoma

BACKGROUND

Previous studies reported that ferroptosis-related genes can regulate the process of tumor cell changes by regulating iron metabolism. However, the prognostic value of ferroptosis-related genes in LC remains to be further elucidated.

METHODS

Ferroptosis-related gene expression profiles of coexisting ferroptosis-related genes were extracted from both cohorts (TCGA and GSE27020) for eligible analysis. LASSO Cox regression was utilized to build an optimum ferroptosis-related prognostic model. Kaplan-Meier curve was performed by log-rank test, and time-dependent ROC curve was constructed to evaluate the predictive power of this signature in both cohorts. GO and KEGG enrichment analysis was used to investigate the potential mechanism of differential enrichment signal pathways.

RESULTS

112 LC patients from the TCGA cohort and 108 LC patients with clinical information from the GEO cohorts were eventually included in the study. Three ferroptosis-related genes were identified as an independent risk factor to establish the prognostic risk score. Kaplan-Meier curve represented that patients with high-risk group favors with worse OS than their low-risk group (P = 0.04). The good performance of the gene signature for predicting OS was evaluated by area under the curve (AUC) of time-dependent ROC curves achieved 0.74 at 3 years, and 0.70 at 5 years. Similar performance has been proved in the external validation cohort. GO and KEGG enrichment analysis have been performed to explore the signaling pathways and underlying mechanisms were significantly active in LC patients.

CONCLUSION

In summary, our study developed a ferroptosis-related model that could be an effective biomarker to predict the prognosis of laryngeal cancer.