Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction
Abstract Background This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients. Methods Firstly, pathway analyses using Gene Ontol...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-02-01
|
| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-023-10660-5 |
| _version_ | 1827983832223055872 |
|---|---|
| author | Long Xie Jiaxing Zeng Maolin He |
| author_facet | Long Xie Jiaxing Zeng Maolin He |
| author_sort | Long Xie |
| collection | DOAJ |
| description | Abstract Background This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients. Methods Firstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model’s accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes’ mRNA and protein expressions were verified. Results A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB). Conclusion The new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future. |
| first_indexed | 2024-04-09T22:51:53Z |
| format | Article |
| id | doaj.art-a35b50f33ecf4dc3b255138bdbbb1326 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-09T22:51:53Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-a35b50f33ecf4dc3b255138bdbbb13262023-03-22T11:35:35ZengBMCBMC Cancer1471-24072023-02-0123111410.1186/s12885-023-10660-5Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis predictionLong Xie0Jiaxing Zeng1Maolin He2Division of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical UniversityTrauma Department of Orthopedics, The Fourth Affiliated Hospital of Guangxi Medical UniversityDivision of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical UniversityAbstract Background This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients. Methods Firstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model’s accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes’ mRNA and protein expressions were verified. Results A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB). Conclusion The new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.https://doi.org/10.1186/s12885-023-10660-5OsteosarcomaBMPsBioinformationNomogramRisk score modelImmune infiltration |
| spellingShingle | Long Xie Jiaxing Zeng Maolin He Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction BMC Cancer Osteosarcoma BMPs Bioinformation Nomogram Risk score model Immune infiltration |
| title | Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction |
| title_full | Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction |
| title_fullStr | Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction |
| title_full_unstemmed | Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction |
| title_short | Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction |
| title_sort | identification and verification of a bmps related gene signature for osteosarcoma prognosis prediction |
| topic | Osteosarcoma BMPs Bioinformation Nomogram Risk score model Immune infiltration |
| url | https://doi.org/10.1186/s12885-023-10660-5 |
| work_keys_str_mv | AT longxie identificationandverificationofabmpsrelatedgenesignatureforosteosarcomaprognosisprediction AT jiaxingzeng identificationandverificationofabmpsrelatedgenesignatureforosteosarcomaprognosisprediction AT maolinhe identificationandverificationofabmpsrelatedgenesignatureforosteosarcomaprognosisprediction |