Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Zhu, Naiqiang; Hou, Jingyi; Ma, Guiyun; Guo, Shuai; Zhao, Chengliang; Chen, Bin

doi:10.1186/s12935-020-01352-2

Cited by 26 publications

(27 citation statements)

References 61 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bioinformatic analysis based on sequencing RNA data was a feasible approach for risk stratification and targeted-gene identification. Although researchers have constructed risk model based on tumor microenvironment, immune cell infiltrating and energy metabolism Wen et al, 2020;Zhang et al, 2020;Zhu et al, 2020) in osteosarcoma, our study exhibited unique merits compared with previous studies. Firstly, our work focused on the lipid metabolism of osteosarcoma patients, and identified two molecular subgroups with significantly different prognosis and immune status via consensus clustering.…”

Section: Discussionmentioning

confidence: 99%

“…Construction of prognostic risk model was an applicable strategy to evaluate the prognostic performance. Up to now, several risk models have been constructed to explore the prognostic value of genes associated with tumor microenvironment, immune cell infiltrating and energy metabolism Wen et al, 2020;Zhang et al, 2020;Zhu et al, 2020) in osteosarcoma, whereas the role of LMRGs in osteosarcoma has remained poorly understood.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Lei

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

ObjectivesOsteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.MethodsClinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.ResultsTwo molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.ConclusionExpression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Lei

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

show abstract

“…The majority of these genes relate to occurrence or development of cancers and some could serve as the prognostic signature ( Liu et al, 2020 ; Wu et al, 2020 ). In osteosarcoma, several gene signatures related to energy metabolism, tumor microenvironment and immune system have been investigated ( Hong et al, 2020 ; Hu et al, 2020 ; Zhu et al, 2020 ). However, no hypoxia-associated prognostic signature has been established.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Hypoxia-Associated Prognostic Signature Related to Osteosarcoma Metastasis and Immune Infiltration

Bao

Liu

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

BackgroundIncreasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients’ prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis.MethodsThe differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan–Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature.ResultsTwo genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group.ConclusionThe current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.

show abstract

“…In the previous studies, there are several gene expression-based signatures for the prognosis of osteosarcoma [34][35][36][37][38][39][40]. Moreover, gene signatures based on cell death have been reported in diverse cancers, such as lung cancer, hepatocellular carcinoma and gastric cancer [32,33,41].…”

Section: Discussionmentioning

confidence: 99%

Identification of a Programmed Cell Death-related Signature for Prediction of Osteosarcoma

Lin

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: This study aims to perform bioinformatics analysis of programmed cell death-related genes (PCDGs) in osteosarcoma, and to construct a multi-gene signature for predicting the prognosis.Methods: The functional enrichment analysis was applied for prognostic PCDGs, and PPI network as well as drug-gene interactions were constructed. In order to set up the prognosis evaluation system, we established a prognosis model by integrating PCDGs. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis as well as the multivariate Cox proportional hazard regression analysis were used to construct the five-genes signature (MUC1, TCF7L2, TGFB2, TRIAP1, CBS) for prediction in Gene Expression Omnibus (GEO) cohort. According to the median risk score, survival analysis was conducted to evaluate the prognostic value of the risk score in GEO cohort. Next, by combining other clinic-pathological independent prognostic factor, stage at diagnosis, a nomogram was established to predict individual survival probability. Result: GO analysis showed that the 15 prognostic PCDGs were mainly enriched in apoptotic signaling pathway, regulation of secretion and p53 signaling pathway. KEGG analysis showed that aforesaid genes were mainly related to PI3K-Akt signaling pathway, diverse neoplasms signaling pathway and hepatitis B. Drug-gene interactions displayed available drugs for influencing osteosarcoma via programmed cell death, such as adalimumab, tacrolimus and sirolimus. The risk score was constructed based on 5 genes and patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 2.526, 95% CI = 1.597−3.994, P < 0.001). Cumulative curve showed that low-risk score patients were significantly had better prognosis than that of patients with high-risk score (P < 0.001). The external independent TARGET cohort proved the validity of risk score model and the nomograph.Conclusion: From the facet of programmed cell death, we provided a multi-gene signatureand the nomograph for the prognostic predictors of osteosarcoma patients, and available drugs displayed may provide promising treatment strategies.

show abstract

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Cited by 26 publications

References 61 publications

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Development and Validation of a Hypoxia-Associated Prognostic Signature Related to Osteosarcoma Metastasis and Immune Infiltration

Identification of a Programmed Cell Death-related Signature for Prediction of Osteosarcoma

Contact Info

Product

Resources

About