Hematological Prognostic Scoring System Can Predict Overall Survival and Can Indicate Response to Immunotherapy in Patients With Osteosarcoma

Prognostic and predictive value of super-enhancer-derived signatures for survival and lung metastasis in osteosarcoma

BACKGROUND

Risk stratification and personalized care are crucial in managing osteosarcoma due to its complexity and heterogeneity. However, current prognostic prediction using clinical variables has limited accuracy. Thus, this study aimed to explore potential molecular biomarkers to improve prognostic assessment.

METHODS

High-throughput inhibitor screening of 150 compounds with broad targeting properties was performed and indicated a direction towards super-enhancers (SEs). Bulk RNA-seq, scRNA-seq, and immunohistochemistry (IHC) were used to investigate SE-associated gene expression profiles in osteosarcoma cells and patient tissue specimens. Data of 212 osteosarcoma patients who received standard treatment were collected and randomized into training and validation groups for retrospective analysis. Prognostic signatures and nomograms for overall survival (OS) and lung metastasis-free survival (LMFS) were developed using Cox regression analyses. The discriminatory power, calibration, and clinical value of nomograms were evaluated.

RESULTS

High-throughput inhibitor screening showed that SEs significantly contribute to the oncogenic transcriptional output in osteosarcoma. Based on this finding, focus was given to 10 SE-associated genes with distinct characteristics and potential oncogenic function. With multi-omics approaches, the hyperexpression of these genes was observed in tumor cell subclusters of patient specimens, which were consistently correlated with poor outcomes and rapid metastasis, and the majority of these identified SE-associated genes were confirmed as independent risk factors for poor outcomes. Two molecular signatures were then developed to predict survival and occurrence of lung metastasis: the SE-derived OS-signature (comprising LACTB, CEP55, SRSF3, TCF7L2, and FOXP1) and the SE-derived LMFS-signature (comprising SRSF3, TCF7L2, FOXP1, and APOLD1). Both signatures significantly improved prognostic accuracy beyond conventional clinical factors.

CONCLUSIONS

Oncogenic transcription driven by SEs exhibit strong associations with osteosarcoma outcomes. The SE-derived signatures developed in this study hold promise as prognostic biomarkers for predicting OS and LMFS in patients undergoing standard treatments. Integrative prognostic models that combine conventional clinical factors with these SE-derived signatures demonstrate substantially improved accuracy, and have the potential to facilitate patient counseling and individualized management.

The combination of baseline neutrophil to lymphocyte ratio and dynamic changes during treatment can better predict the survival of osteosarcoma patients

Background

Osteosarcoma is a primary malignant bone tumor with a high metastatic potential that accounts for a significant proportion of all bone tumors. The prognosis for patients with metastatic or recurrence disease remains poor. The neutrophil-to-lymphocyte ratio (NLR) has become a potential prognostic biomarker for cancer. Recent evidence suggests that the dynamic changes in neutrophil-to-lymphocyte ratio (NLR) during treatment may be more informative in predicting patient prognosis, but the value of dynamic NLR in osteosarcoma has not yet been determined.

Methods

This retrospective study retrospectively analyzed the clinical information of 251 osteosarcoma patients diagnosed and treated in West China Hospital of Sichuan University, explored the impact of baseline NLR and changes in NLR during treatment on the prognosis of osteosarcoma patients, and further combined baseline NLR with Delta NLR to build an NLR staging system.

Results

The results showed that both baseline NLR and delta NLR had some predictive ability for the prognosis of osteosarcoma patients (P = 6.90e-4, P = 0.022). Patients with high baseline NLR were more likely to have a decrease in delta NLR (P = 1.24e-10). The NLR stage had a better predictive ability than baseline NLR and delta NLR, and was an independent prognostic factor for overall survival in osteosarcoma patients HR: 2.456 (1.625-3.710) (P = 1.97e-05).

Conclusion

NLR has value in continuous monitoring, and continuous monitoring of NLR can better predict the survival of osteosarcoma patients compared to baseline NLR.

A novel nomogram based on the hematological prognosis risk scoring system can predict the overall survival of patients with hepatocellular carcinoma

BACKGROUND

This study aimed to establish and validate a nomogram based on a hematological prognostic risk scoring system to predict the overall survival in patients with unresectable hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Patients diagnosed with unresectable HCC undergoing transcatheter arterial chemoembolization (TACE) in 2012-2016 and 2017-2018 were included in the development set and validation set, respectively. The clinical outcome was overall survival (OS). The LASSO regression analysis was used to construct a hematological prognostic risk scoring system (HPR) by using the 18 hematological markers of patients in the development set. Combining the features of oncology on the basis of HPR to construct a nomogram for OS. In the development set and validation sets, the C-index, calibration curve, and decision curve analysis (DCA) were used to evaluate the prediction performance of the nomogram.

RESULTS

Multiple markers of immunity, coagulation, liver function, and nutrition, including red blood cell distribution width-coefficient of variation (RDW-CV), platelet (PLT), aspartate transferase (AST), alkaline phosphatase (ALP), prognostic nutritional index (PNI), and fibrinogen (Fib), construct the HPR. HPR was an independent risk factor for OS in patients with HCC. The C-index of the nomogram was 0.731 (95% confidence interval (CI) 0.712-0.749) and 0.696 (95% CI 0.668-0.725) in the development set and the validation set, respectively.

CONCLUSIONS

HPR was a complement to the clinical features of patients with unresectable HCC. The nomogram based on HPR proved to be a practical and effective method for prognosticating HCC patients who undergo TACE.