Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning
Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-09-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1136783/full |
| _version_ | 1797693855987924992 |
|---|---|
| author | Yuqiao Ji Zhengjun Lin Guoqing Li Xinyu Tian Yanlin Wu Jia Wan Tang Liu Min Xu |
| author_facet | Yuqiao Ji Zhengjun Lin Guoqing Li Xinyu Tian Yanlin Wu Jia Wan Tang Liu Min Xu |
| author_sort | Yuqiao Ji |
| collection | DOAJ |
| description | Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma.Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates.Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils.Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients. |
| first_indexed | 2024-03-12T02:49:38Z |
| format | Article |
| id | doaj.art-d41afe8ded854d509df3ba6b9e204b6a |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-12T02:49:38Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-d41afe8ded854d509df3ba6b9e204b6a2023-09-04T05:54:43ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-09-011410.3389/fgene.2023.11367831136783Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learningYuqiao Ji0Zhengjun Lin1Guoqing Li2Xinyu Tian3Yanlin Wu4Jia Wan5Tang Liu6Min Xu7Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Critical Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaObjectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma.Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates.Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils.Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients.https://www.frontiersin.org/articles/10.3389/fgene.2023.1136783/fullosteosarcomamachine learningimmune cell infiltrationdiagnosishub genes |
| spellingShingle | Yuqiao Ji Zhengjun Lin Guoqing Li Xinyu Tian Yanlin Wu Jia Wan Tang Liu Min Xu Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning Frontiers in Genetics osteosarcoma machine learning immune cell infiltration diagnosis hub genes |
| title | Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning |
| title_full | Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning |
| title_fullStr | Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning |
| title_full_unstemmed | Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning |
| title_short | Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning |
| title_sort | identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning |
| topic | osteosarcoma machine learning immune cell infiltration diagnosis hub genes |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1136783/full |
| work_keys_str_mv | AT yuqiaoji identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning AT zhengjunlin identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning AT guoqingli identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning AT xinyutian identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning AT yanlinwu identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning AT jiawan identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning AT tangliu identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning AT minxu identificationandvalidationofnovelbiomarkersassociatedwithimmuneinfiltrationforthediagnosisofosteosarcomabasedonmachinelearning |