Dataset Information

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

ABSTRACT: Osteosarcoma is the most common primary malignant bone tumor with a high metastatic potential. Nowadays, there is a lack of new markers to identify prognosis of osteosarcoma patients with response to medical treatment. Recent studies have shown that hematological markers can reflect to some extent the microenvironment of an individual with the potential to predict patient prognosis. However, most of the previous studies have studied the prognostic value of a single hematological index, and it is difficult to comprehensively reflect the tumor microenvironment of patients. Here, we comprehensively collected 16 hematological markers and constructed a hematological prognostic scoring system (HPSS) using LASSO cox regression analysis. HPSS contains many indicators such as immunity, inflammation, coagulation and nutrition. Our results suggest that HPSS is an independent prognostic factor for overall survival in osteosarcoma patients and is an optimal addition to clinical characteristics and well suited to further identify high-risk patients from clinically low-risk patients. HPSS-based nomograms have good predictive ability. Finally, HPSS also has some hints for immunotherapy response in osteosarcoma patients.

SUBMITTER: Li L

PROVIDER: S-EPMC9120642 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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

Li Longqing L Wang Yang Y He Xuanhong X Li Zhuangzhuang Z Lu Minxun M Gong Taojun T Chang Qing Q Lin Jingqi J Liu Chuang C Luo Yi Y Min Li L Zhou Yong Y Tu Chongqi C

Frontiers in immunology 20220506

Osteosarcoma is the most common primary malignant bone tumor with a high metastatic potential. Nowadays, there is a lack of new markers to identify prognosis of osteosarcoma patients with response to medical treatment. Recent studies have shown that hematological markers can reflect to some extent the microenvironment of an individual with the potential to predict patient prognosis. However, most of the previous studies have studied the prognostic value of a single hematological index, and it is ...[more]

PMID: 35603156

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Project description:BackgroundCurrently, the prognosis of kidney cancer depends mainly on the pathological grade or tumor stage. Clinicians have few effective tools that can personalize and adequately evaluate the prognosis of kidney cancer patients.MethodsA total of 70 481 kidney cancer patients were selected from the Surveillance, Epidemiology, and End Results database, among which patients diagnosed in 2005-2011 (n = 42 890) were used to establish nomograms for overall survival (OS) and cancer-specific survival (CSS), and those diagnosed in 2012-2015 (n = 24 591) were used for external validation. Univariate and multivariate Cox analyses were used to determine independent prognostic factors. Concordance index (C-index), receiver operating characteristic curve, and calibration curve were used to evaluate the predictive capacity of the nomograms. We further reduced subgroup classification and used propensity score matching to balance clinical informations, and analyzed the effect of other variables on survival. We established a new kidney cancer prognostic score system based on the effect of all available variables on survival. Cox proportional hazard model and Kaplan-Meier curves were used for survival comparison.ResultsAge, gender, marital status, surgery, grade, T stage, and M stage were included as independent risk factors in the nomograms. The favorable area under the curve (AUC) value (for OS, AUC = 0.812-0.858; and for CSS, AUC = 0.890-0.921), internal (for OS, C-index = 0.776; and for CSS, C-index = 0.856), and external (for OS, C-index = 0.814-0.841; and for CSS, C-index = 0.894-0.904) validation indicated that the proposed nomograms could accurately predict 1-, 3-, and 5-year OS and CSS of kidney cancer patients. The Aggtrmmns prognostic scoring system based on age, gender, race, marital status, grade, TNM stage, and surgery of kidney cancer patients could stage patients more explicitly than the AJCC staging system.ConclusionThe nomogram and Aggtrmmns scoring system can predict OS and CSS in kidney cancer patients effectively, which may help clinicians personalize prognostic assessments and clinical decisions.

Project description:BackgroundGlioblastoma is the most common primary malignant brain tumor. Recent studies have shown that hematological biomarkers have become a powerful tool for predicting the prognosis of patients with cancer. However, most studies have only investigated the prognostic value of unilateral hematological markers. Therefore, we aimed to establish a comprehensive prognostic scoring system containing hematological markers to improve the prognostic prediction in patients with glioblastoma.Patients and methodsA total of 326 patients with glioblastoma were randomly divided into a training set and external validation set to develop and validate a hematological-related prognostic scoring system (HRPSS). The least absolute shrinkage and selection operator Cox proportional hazards regression analysis was used to determine the optimal covariates that constructed the scoring system. Furthermore, a quantitative survival-predicting nomogram was constructed based on the hematological risk score (HRS) derived from the HRPSS. The results of the nomogram were validated using bootstrap resampling and the external validation set. Finally, we further explored the relationship between the HRS and clinical prognostic factors.ResultsThe optimal cutoff value for the HRS was 0.839. The patients were successfully classified into different prognostic groups based on their HRSs (P < 0.001). The areas under the curve (AUCs) of the HRS were 0.67, 0.73, and 0.78 at 0.5, 1, and 2 years, respectively. Additionally, the 0.5-, 1-y, and 2-y AUCs of the HRS were 0.51, 0.70, and 0.79, respectively, which validated the robust prognostic performance of the HRS in the external validation set. Based on both univariate and multivariate analyses, the HRS possessed a strong ability to predict overall survival in both the training set and validation set. The nomogram based on the HRS displayed good discrimination with a C-index of 0.81 and good calibration. In the validation cohort, a high C-index value of 0.82 could still be achieved. In all the data, the HRS showed specific correlations with age, first presenting symptoms, isocitrate dehydrogenase mutation status and tumor location, and successfully stratified them into different risk subgroups.ConclusionsThe HRPSS is a powerful tool for accurate prognostic prediction in patients with newly diagnosed glioblastoma.

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Project description:ObjectivesFerroptosis plays vital roles in the pathogenesis of various malignant tumors. However, knowledge on roles of ferroptosis in osteosarcoma remains scarce. In the present study, a comprehensive bioinformatics analysis was performed aiming to identify ferroptosis-related genes (FRGs), construct a FRGs-based model predicting overall survival (OS), and assess the impact of these FRGs on the migration and invasion of osteosarcoma cells.MethodsInitially, data regarding differentially expressed FRGs were obtained from the GSE160881 dataset. Prognostic significance and possible biological functions of these differentially expressed FRGs were comprehensively and systematically explored adopting a series of bioinformatics methods. The impact of cystathionine β-synthase (CBS) on migration and invasion of osteosarcoma cells were assessed using transwell assays.ResultsA total of 50 FRGs were differentially expressed. Four FRGs including G6PD, VEGFA, CBS, and HMOX1 were used to construct a model predicting OS in osteosarcoma patients. In the training cohort, patients with high risk had significantly poorer OS than those with low risk, which was also demonstrated in validation cohorts (GSE16091 and GSE39058). Furthermore, we established a clinically useful nomogram predicting OS using the four FRGs mentioned above. Risk scores were significantly associated with the proportion of tumor-infiltrating immune cells. Additionally, we used the Cytoscape software to identify hub FRGs, and found that TP53, HMOX1, SLC7A11, HRAS, VEGFA, and TXNRD1 were hub FRGs. By performing in vitro cell culture experiments, we demonstrated that invasion and migration capability of Saos2 and HOS cells were significantly weakened after CBS knock down.ConclusionsIn conclusion, gene signatures based on four FRGs were reliable in predicting OS in patients with osteosarcoma. Findings from this study will enable a better understanding of the prognostic significance of FRGs and tumor immunity in osteosarcoma.

Dataset Information

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

Publications

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

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