WGCNA-based identification of novel T-cell exhaustion-related gene signatures to predict the prognosis and response to immunotherapy of osteosarcoma patients
10.3781/j.issn.1000-7431.2023.2307-0348
- VernacularTitle:基于WGCNA发现的T细胞耗竭相关基因签名并构建骨肉瘤预后及免疫治疗反应的预测模型
- Author:
Huidong CHEN
1
,
2
;
Tianqi XIA
;
Kun HAN
;
Xingxing SUN
;
Meixiang ZHOU
;
Cong TIAN
;
Mengyi JIANG
;
Daliu MIN
Author Information
1. 上海海洋大学食品科学与技术学院,上海 201306
2. 上海交通大学医学院附属第六人民医院肿瘤内科,上海 200080
- Keywords:
Osteosarcoma;
T-cell exhaustion;
Bioinformatics;
Immunotherapy
- From:
Tumor
2023;43(10):763-780
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To screen T-cell exhaustion-related signature genes as the prognostic marker for osteosarcoma and establish a prognostic model for osteosarcoma patients based on weighted gene co-expression network analysis(WGCNA)and Least absolute shrinkage and selection operator(LASSO)-COX regression analysis. Methods:GSE21257 dataset was downloaded from Gene Expression Omnibus(GEO)database for the establishment of the prognostic model for osteosarcoma.4 T-cell exhaustion-related gene sets were downloaded from The Molecular Signatures Database(MisgDB)and their enrichment scores in GSE21257 samples were calculated by single sample gene set enrichment analysis(ssGSEA).WGCNA was carried out to screen the gene module that is highly associated with T-cell exhaustion based on ssGSEA results followed by GO(Gene Ontology)and KEGG(Kyoto Encyclopedia of Genes and Genomes)analysis of the biological processes and signaling transduction pathways that those genes are involved in.The signature genes that are highly associated with the prognosis of osteosarcoma patients were obtained through LASSO-COX regression and a prognostic model was established based on these signature genes.Osteosarcoma-related expression profile data from the GSE21257 and TAEGET datasets on XENA were downloaded from the Gene Expression Omnibus.Clinical information for the training and validation sets was obtained.T-cell exhaustion-related genes were screened using a weighted correlation network analysis.Realtime fluorescence quantitative PCR,COX regression analysis,external dataset and nomogram were used to evaluate the reliability and accuracy of the prognostic model.A immunotherapy-related dataset was used to assess the efficacy of this prognostic model for the prediction of patients'responses to immunotherapy. Results:Analysis results based on the ssGSEA scores showed that T-cell exhaustion-related genes were related to the metastasis and age of osteosarcoma patients.Many T-cell exhaustion-related genes were found to be differentially expressed in metastatic and non-metastatic osteosarcoma patients.1 256 T-cell exhaustion-related genes were identified through WGCNA and these candidate markers were mainly distributed in structures like secretory granule membranes and endocytic vesicles and were involved in T-cell activation.COX regression analysis screened 68 significant prognostic markers out of the 1 256 genes,and 12 signature genes were further confirmed with LASSO-COX regression analysis.A prognostic model was established based on the 12 signature genes.Results of real-time fluorescence quantitative PCR showed a similar trend in the expression of most of the signature genes in different osteosarcoma cell lines.COX regression analysis of the internal and external datasets verified that the risk score calculated with the prognostic model was an independent prognostic factor for osteosarcoma patients,and high-risk score was associated with poor prognosis of the patients.Receiver operating characteristic(ROC)curves demonstrated excellent prognostic efficacy of the model.Nomogram analysis verified the prognostic model is highly accurate and reliable in predicting the prognosis of osteosarcoma patients.Analysis using the immunotherapy-related dataset indicated that this prognostic model could also be used to predict patients'responses to immunotherapy. Conclusion:The 12 signature gene(CD300LB,TRO,SNX3,VENTX,PPM1M,DOT1L,CDC37,NAT9,TRMT1,PPP1R3C,CHTF18 and NSUN5)-based prognostic model can effectively predict the prognosis and responses to immune check-point inhibitors for osteosarcoma patients,which may provide evidence for the prediction of prognosis as well as the selection of immunotherapy plans in clinical practice.
