A Novel Six Metastasis-Related Prognostic Gene Signature for Patients With Osteosarcoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Zheng D
PROVIDER: S-EPMC8343004 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:PurposeMetabolic reprogramming, as one of the hallmarks of cancer, shows promising translational potential for cancer diagnosis, treatment and prognostic prediction. This study aims to construct and validate a prognostic prediction model for osteosarcoma based on glutamine metabolism-related genes.Materials and methodsA group of glutamine metabolism-related genes was identified from a public database and intersected with a list of osteosarcoma survival-related genes, and a risk score model based on sixteen glutamine metabolism-related genes was developed by using LASSO penalized Cox regression analysis.ResultsThe prognosis of patients in the high-risk group was significantly worse than that of patients in the low-risk group in the training dataset (high- vs low-risk, 5-year overall survival: 11% vs 88%, p < 0.0001) and in two other external validation cohorts (high- vs low-risk, 5-year overall survival: 39% vs 81%, p = 0.015; 50% vs 94%, p = 0.011).In addition, a novel nomogram was constructed by integrating the risk score and clinical characteristics, including age, sex, metastasis status and chemotherapy response. This nomogram had superior predictive power compared with a nomogram composed of only conventional factors. Gene set enrichment analysis indicated that several well-known malignancy-associated gene sets, including MYC targets V1, DNA repair, and unfolded protein response, were enriched in the high-risk subgroup.ConclusionA novel glutamine metabolism-related prognostic prediction model and nomogram for osteosarcoma was developed and validated in the present study, which could predict the survival of patients with osteosarcoma and may facilitate individualized clinical decision-making for patients.
| S-EPMC8817953 | biostudies-literature
Project description:BackgroundColon adenocarcinoma (COAD) is one of the most common gastrointestinal tumors worldwide, and immunotherapy is one of the most promising treatments for it. Identifying immune genes involved in the development and maintenance of cancer is key to the use of tumor immunotherapy. This study aimed to determine the prognostic value of immune genes in patients with COAD and to establish an immune-related gene signature. Differentially expressed genes, immune-related genes (DEIGs), and transcription factors (DETFs) were screened using the following databases: Cistrome, The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and InnateDB. We constructed a network showing the regulation of DEIGs by DETFs. Using weighted gene co-expression network analysis, we prepared 5 co-expressed gene modules; 6 hub genes (CD1A, CD1B, FGF9, GRP, SERPINE1, and F2RL2) obtained using univariate and multivariate regression analysis were used to construct a risk model. Patients from TCGA database were divided into high- and low-risk groups based on whether their risk score was greater or less than the mean; the public dataset GSE40967, which contains gene expression profiles of 566 colon cancer patients, was used for validation.ResultsSurvival analysis, somatic gene mutations, and tumor-infiltrating immune cells differed significantly between the high- and low-risk groups.ConclusionsThis immune-related gene signature could play an important role in guiding treatment, making prognoses, and potentially developing future clinical applications.
| S-EPMC9552517 | biostudies-literature
Project description:BackgroundApoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma.MethodsThe differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan-Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry.ResultsHSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients' samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas.ConclusionsThe present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.
| S-EPMC9126984 | biostudies-literature
Project description:Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p?<?0.05, p?<?0.01), and clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.
| S-EPMC7591524 | biostudies-literature
Project description:BackgroundThe correlation between epithelial-mesenchymal transition (EMT) and osteosarcoma (OS) has been widely reported. Integration of the EMT-related genes to predict the prognosis is significant for investigating the mechanism of EMT in OS. Here, we aimed to construct a prognostic EMT-related gene signature for OS.MethodsTranscriptomic and survival data of OS patients were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO). We performed univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and stepwise multivariate Cox regression analysis to construct EMT-related gene signatures. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) were applied to evaluate its predictive performance. GSVA, ssGSEA, ESTIMATE, and scRNA-seq were performed to investigate the tumor microenvironment, and the correlation between IC50 of drugs and ERG score was investigated. Furthermore, Edu and transwell experiments were conducted to assess the malignancy of OS cells.ResultsWe constructed a novel EMT-related gene signature (including CDK3, MYC, UHRF2, STC2, COL5A2, MMD, and EHMT2) for outcome prediction of OS. According to the signature, patients stratified into high- and low-ERG-score groups exhibited significantly different prognoses. ROC curves and Kaplan-Meier analysis revealed a promising performance of the signature with external validation. GSVA, ssGSEA, ESTIMATE algorithm, and scRNA-seq excavated EMT-related pathways and suggested the correlation between ERG score and immune activation. Notably, the pivotal gene CDK3 was upregulated in OS tissue and positively related to OS cell proliferation and migration.ConclusionOur EMT-related gene signature might reference OS risk stratification and guide clinical strategies as an independent prognostic factor in OS.
| S-EPMC10278480 | biostudies-literature
Project description:Objectives: Ferroptosis 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. Methods: Initially, 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. Results: A 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. Conclusions: In 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.
| S-EPMC9556449 | biostudies-literature
Project description:Introduction: Increasing evidences have shown that hypoxia and the immune microenvironment play vital roles in the development of osteosarcoma. However, reliable gene signatures based on the combination of hypoxia and the immune status for prognostic prediction of osteosarcoma have so far not been identified. Methods: The individual hypoxia and immune status of osteosarcoma patients were identified with transcriptomic profiles of a training cohort from the TARGET database using ssGSEA and ESTIMATE algorithms, respectively. Lasso regression and stepwise Cox regression were performed to develop a hypoxia-immune-based gene signature. An independent cohort from the GEO database was used for external validation. Finally, a nomogram was constructed based on the gene signature and clinical features to improve the risk stratification and to quantify the risk assessment for individual patients. Results: Hypoxia and the immune status were significantly associated with the prognosis of osteosarcoma patients. Seven hypoxia- and immune-related genes (BNIP3, SLC38A5, SLC5A3, CKMT2, S100A3, CXCL11 and PGM1) were identified to be involved in our prognostic signature. In the training cohort, the prognostic signature discriminated high-risk patients with osteosarcoma. The hypoxia-immune-based gene signature proved to be a stable and predictive method as determined in different datasets and subgroups of patients. Furthermore, a nomogram based on the prognostic signature was generated to optimize the risk stratification and to quantify the risk assessment. Similar results were validated in an independent GEO cohort, confirming the stability and reliability of the prognostic signature. Conclusion: The hypoxia-immune-based prognostic signature might contribute to the optimization of risk stratification for survival and personalized management of osteosarcoma patients.
| S-EPMC9791087 | biostudies-literature
Project description:Osteosarcoma (OS) is a common malignant primary tumor of skeleton, especially in children and adolescents, characterized by high lung metastasis rate. Apoptosis has been studied in various tumors, while the prognostic role of apoptosis-related genes in OS has been seldom studied. Three OS related datasets were downloaded from Gene Expression Omnibus (GEO) database. Univariate Cox and LASSO Cox regression analysis identified optimal genes, which were used for building prognostic Risk score. Subsequent multivariate Cox regression analysis and Kaplan-Meier survival analysis determined the independent prognostic factors for OS. The immune cell infiltration was analyzed in CIBERSORT. Basing on 680 apoptosis-related genes, the OS patients could be divided into 2 clusters with significantly different overall survival. Among which, 6 optimal genes were identified to construct Risk score. In both training set (GSE21257) and validation set (meta-GEO dataset), high risk OS patients had significantly worse overall survival compared with the low risk patients. Besides, high Risk score was an independent poor prognostic factor for OS with various ages or genders. Three immune cells were differentially infiltrated between high and low risk OS patients. In conclusion, a six-gene (TERT, TRAP1, DNM1L, BAG5, PLEKHF1 and PPP3CB) based prognostic Risk score signature is probably conducive to distinguish different prognosis of OS patients.
| S-EPMC9134960 | biostudies-literature
Project description:Sarcoma is a rare and an extremely aggressive form of cancer that originates from mesenchymal cells. Pyroptosis exerts a dual effect on tumours by inhibiting tumour cell proliferation while creating a microenvironment suitable for tumour cell development and proliferation. However, the significance of pyroptosis-related gene (PRG) expression in sarcoma has not yet been evaluated. Here, we conduct a retrospective analysis to examine PRG expression in 256 sarcoma samples from The Cancer Genome Atlas database. We identified the PRGs that had a significant correlation with overall patient survival in sarcoma by performing a univariate Cox regression analysis. Subsequently, we conducted a LASSO regression analysis and created a risk model for a six-PRG signature. As indicated from the Kaplan-Meier analysis, this signature revealed a significant difference between high- and low-risk sarcoma patients. A receiver operating characteristic curve analysis confirmed that this signature could predict overall patient survival in sarcoma patients with high sensitivity and specificity. Gene ontology annotation and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses revealed that five independent PRGs were closely associated with increased immune activity. Moreover, we also deciphered that increased number of immune cells infiltrated the tumour microenvironment in sarcoma. In brief, the PRG signature can effectively act as novel prognostic biomarker for sarcoma patients and is associated with the tumour immune microenvironment.
| S-EPMC8665299 | biostudies-literature
Project description:Pyroptosis is a programmed cell death, which has garnered increasing attention because it relates to the immune and therapy response. However, few studies focus on the application of pyroptosis-related genes (PRGs) in predicting osteosarcoma (OS) patients' prognoses. In this study, the gene expression and clinical information of OS patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Based on these PRGs and unsupervised clustering analysis, all OS samples can be classified into 2 clusters. The 8 key differential expressions for PRGs (LAG3, ITGAM, CCL2, TLR4, IL2RA, PTPRC, FCGR2B, and CD5) were established through the univariate Cox regression and utilized to calculate the risk score of all samples. According to the 8-gene signature, OS samples can be divided into high and low-risk groups and correlation analysis can be performed using immune cell infiltration and immune checkpoints. Finally, we developed a nomogram to improve the PRG-predictive model in clinical application. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low and high-risk groups. A nomogram was developed with clinical indicators and the risk scores were effective in predicting the prognosis of patients with OS. In this study, a prognostic model was constructed based on 8 PRGs were proved to be independent prognostic factors of OS and associated with tumor immune microenvironment. These 8 prognostic genes were involved in OS development and may serve as new targets for developing therapeutic drugs.
| S-EPMC10994441 | biostudies-literature