A novel risk score model based on glycolysis-related genes and a prognostic model for predicting overall survival of osteosarcoma patients

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predicting prognosis and explored the potential of ML models in clinical decision-making. From 2000 to 2018, 1243 patients with osteosarcoma were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Three ML methods were chosen for model development (DeepSurv, neural multi-task logistic regression [NMTLR]) and random survival forest [RSF]) and compared them with the traditional CoxPH model and TNM staging systems. 871 samples were used for model training, and the rest were used for model validation. The models' overall performance and predictive accuracy for 3- and 5-year survival were assessed by several metrics, including the concordance index (C-index), the Integrated Brier Score (IBS), receiver operating characteristic curves (ROC), area under the ROC curves (AUC), calibration curves, and decision curve analysis. The efficacy of personalized recommendations by ML models was evaluated by the survival curves. The performance was highest in the DeepSurv model (C-index, 0.77; IBS, 0.14; 3-year AUC, 0.80; 5-year AUC, 0.78) compared with other methods (C-index, 0.73-0.74; IBS, 0.16-0.17; 3-year AUC, 0.73-0.78; 5-year AUC, 0.72-0.78). There are also significant differences in survival outcomes between patients who align with the treatment option recommended by the DeepSurv model and those who do not (hazard ratio, 1.88; P < .05). The DeepSurv model is available in an approachable web app format at https://survivalofosteosarcoma.streamlit.app/. We developed ML models capable of accurately predicting the survival of osteosarcoma, which can provide useful information for decision-making regarding the appropriate treatment.

Effects of Doxorubicin, Epirubicin, and Liposomal Doxorubicin (Anthracycline) on cardiac function in patients with osteosarcoma and their influencing factors

OBJECTIVE

The objective of this study was to investigate the impact of Doxorubicin, Epirubicin, and Liposomal Doxorubicin (Anthracycline) on cardiac function in osteosarcoma patients and analyze the factors influencing this effect.

METHODS

A retrospective study was conducted on 165 osteosarcoma patients admitted to our hospital from January 2020 to December 2022. Based on the chemotherapy regimen, the patients were divided into two groups: the control group (n = 62) treated with Cisplatin and cyclophosphamide, and the observation group (n = 103) treated with Doxorubicin, Epirubicin, and Liposomal Doxorubicin (Anthracycline). The general records of both groups were analyzed, and left ventricular ejection fraction (LVEF) was evaluated through echocardiography before and after chemotherapy. Blood cTnT and CK-MB levels were measured using immunoluminescence. The incidence of adverse reactions during chemotherapy was also analyzed. Univariate analysis was performed to identify patients with cardiotoxic events, and multiple logistic regression analysis was done to study the effects of Doxorubicin, Epirubicin, Liposomal Doxorubicin, and their dosages on cardiotoxicity in patients.

RESULTS

The general records between the two groups showed no significant differences (P > 0.05). However, at the fourth cycle of chemotherapy, the observation group exhibited a lower LVEF (P < 0.05), and a higher percentage of LVEF decrease compared to the control group (P < 0.05). Moreover, the observation group had higher levels of blood cTnT and CK-MB (P < 0.05). The incidence of cardiotoxicity in the observation group was also higher (P < 0.05), but no significant differences were seen in other adverse reaction rates (P > 0.05). The occurrence of cardiotoxicity was found to be related to the choice and dosage of chemotherapy drugs (P < 0.05), but not significantly correlated with age, sex, and mediastinal irradiation in patients (P > 0.05). Furthermore, the use of Doxorubicin, Epirubicin, and Liposomal Doxorubicin in chemotherapy, as well as an increase in their dosages, was found to elevate the risk of cardiotoxicity in osteosarcoma patients (P < 0.05). However, age, sex, and mediastinal radiation were not significantly associated with cardiotoxicity in osteosarcoma patients (P > 0.05).

CONCLUSION

We demonstrated that Doxorubicin, Epirubicin, Liposomal Doxorubicin (Anthracycline), and other drugs adversely affected cardiac function in osteosarcoma patients, increasing the risk of cardiac toxicity. Therefore, close monitoring of cardiac function during chemotherapy is crucial, and timely adjustments to the chemotherapy regimen are necessary. In addition, rational control of drug selection and dosage is essential to minimize the occurrence of cardiac toxicity.

Single-Cell RNA Sequencing Pro-angiogenic Macrophage Profiles Reveal Novel Prognostic Biomarkers and Therapeutic Targets for Osteosarcoma

Osteosarcoma (OS) is a malignant bone tumor that most commonly occurs in children and adolescents. OS patients have a poor prognosis, and 5-year survival rates have rarely improved significantly over the past few decades. OS prognosis may be related to the infiltration of tumor-associated macrophages (TAMs). However, the role of proangiogenic macrophages, a subtype of TAMs, in OS prognosis has not been reported. In this study, seven subtypes of TAMs were identified from single-cell RNA sequencing (scRNA-seq) data that we propose defining as proangiogenic TAMs (Angio-TAMs), interferon-primed TAMs (IFN-TAMs), inflammatory cytokine-enriched TAMs (Inflam-TAMs), immune regulatory TAMs (Reg-TAMs), lipid-associated TAMs (LA-TAMs), and resident-tissue macrophages like TAMs (RTM-TAMs) (containing two subcellular types). In the survival analysis of each macrophage subtype, it was found that patients with Angio-TAMs had the most significant difference in survival. Eight genes associated with Angio-TAMs were obtained by differential expression analysis, and these genes were built into a prognostic model using the LASSO algorithm. Clinical OS case samples were categorized into high-risk and low-risk subgroups using median risk scores. In comparison to the low-risk subgroup, the survival time of the high-risk subgroup was much shorter. Additional studies on immune cell infiltration and immune checkpoint molecule expression in the two risk subgroups were carried out. In immunotherapy response prediction, the Angio-TAM-associated gene risk signature was found to be negatively correlated with immune checkpoint responses. In addition, the associated enriched GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were mainly involved in the malignant progression of tumors. As suggested by these findings, the Angio-TAM gene risk signature may be an underlying prognostic biomarker and novel therapeutic target for OS patients.Kindly check and confirm whether the ESM file is correctly identifiedWe have checked this file and confirmed that it can be correctly identified.

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.

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways

BACKGROUND

Metabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored.

METHODS

RNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments.

RESULTS

This study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages.

CONCLUSION

The metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

SIX4 upregulates IDH1 and metabolic reprogramming to promote osteosarcoma progression

Metabolism reprogramming plays an important role in tumorigenesis and osteosarcoma metastasis. Sine oculis homeobox 4 (SIX4) is reported to be a key transcription factor that is involved in glycolysis reprogramming of cancer cells. However, the role of SIX4 in osteosarcoma progression remains unknown. The expression profile of SIX4 in OS was evaluated in surgery samples of osteosarcoma patients. Functional studies were performed in vitro and in vivo. We found that SIX4 is significantly overexpressed in osteosarcoma and related to the undesirable prognosis of osteosarcoma patients. SIX4 promotes progression of osteosarcoma via upregulating isocitrate dehydrogenase 1 (IDH1), which provides novel prognostic biomarkers and promising therapeutic targets for osteosarcoma patients.