Dataset Information

A Novel Six Metastasis-Related Prognostic Gene Signature for Patients With Osteosarcoma.

ABSTRACT: Osteosarcoma is the most common malignant bone tumor, and although there has been significant progress in its management, metastases often herald incurable disease. Here we defined genes differentially expressed between primary and metastatic osteosarcoma as metastasis-related genes (MRGs) and used them to construct a novel six-MRG prognostic signature for overall survival of patients with osteosarcoma. Validation in internal and external datasets confirmed satisfactory accuracy and generalizability of the prognostic model, and a nomogram based on the signature and clinical variables was constructed to aid clinical decision-making. Of the six MRGs, FHIT is a well-documented tumor suppressor gene that is poorly defined in osteosarcoma. Consistent with tumor suppressor function, FHIT was downregulated in osteosarcoma cells and human osteosarcoma samples. FHIT overexpression inhibited osteosarcoma proliferation, migration, and invasion both in vitro and in vivo. Mechanistically, FHIT overexpression upregulate the epithelial marker E-cadherin while repressing the mesenchymal markers N-cadherin and vimentin. Our six-MRG signature represents a novel and clinically useful prognostic biomarker for patients with osteosarcoma, and FHIT might represent a therapeutic target by reversing epithelial to mesenchymal transition.

SUBMITTER: Zheng D

PROVIDER: S-EPMC8343004 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A Novel Six Metastasis-Related Prognostic Gene Signature for Patients With Osteosarcoma.

Zheng Di D Xia Kezhou K Yu Ling L Gong Changtian C Shi Yubo Y Li Wei W Qiu Yonglong Y Yang Jian J Guo Weichun W

Frontiers in cell and developmental biology 20210723

Osteosarcoma is the most common malignant bone tumor, and although there has been significant progress in its management, metastases often herald incurable disease. Here we defined genes differentially expressed between primary and metastatic osteosarcoma as metastasis-related genes (MRGs) and used them to construct a novel six-MRG prognostic signature for overall survival of patients with osteosarcoma. Validation in internal and external datasets confirmed satisfactory accuracy and generalizabi ...[more]

PMID: 34368151

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Project description:BackgroundOsteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.MethodWe retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.ResultIn our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.ConclusionThe five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

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

A Novel Six Metastasis-Related Prognostic Gene Signature for Patients With Osteosarcoma.

Publications

A Novel Six Metastasis-Related Prognostic Gene Signature for Patients With Osteosarcoma.

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