Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis
Background: The present study aims to explore the metastasis-related signatures in connection with tumor microenvironment (TME), revealing new molecular targets promising in improving osteosarcoma (OS) patients’ outcomes. Methods: The high-throughput sequencing data was downloaded from the TARGET da...
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Elsevier
2025-01-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024173896 |
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| author | Shiyao Liao Xing Gao Kai Zhou Yao Kang Lichen Ji Xugang Zhong Jun Lv |
| author_facet | Shiyao Liao Xing Gao Kai Zhou Yao Kang Lichen Ji Xugang Zhong Jun Lv |
| author_sort | Shiyao Liao |
| collection | DOAJ |
| description | Background: The present study aims to explore the metastasis-related signatures in connection with tumor microenvironment (TME), revealing new molecular targets promising in improving osteosarcoma (OS) patients’ outcomes. Methods: The high-throughput sequencing data was downloaded from the TARGET database and performed the ESTIMATE algorithm. Metastasis-related information was obtained from the GSE21257 dataset. Differentially expressed genes (DEGs) associated with the stromal and immune cell infiltration patterns were identified. DEGs with similar biological functions were grouped into the same module by Gene Ontology (GO) analysis and MCODE analysis. Prognostic DEGs were selected in two datasets through survival analysis. Weighted gene co-expression network analysis (WGCNA) was performed to find metastasis-related modules and genes. RT-PCR was utilized to evaluate the expression of the key prognostic DEGs associated with metastasis in OS patients. Results: The median scores of the stromal and immune groups of OS samples were 58 and -416, and a total of 200 overlapping DEGs were identified. These DEGs basically played fundamental roles in immune response relevant GO terms and were clustered into 9 different modules. Among them, 24 metastasis-related DEGs were selected from the GSE21257 dataset which contains the stromal and immune cell infiltration patterns. Finally, IRF8, HLA-DMA, and HLA-DMB were proved to exhibit significant higher expression levels in cancerous tissues than in para-cancerous tissues for OS patients. Conclusion: We identified three principal genes as promising signatures for predicting the survival the prognosis of OS patients. Exploration of metastasis-related signatures in TME may be valuable for enhancing treatment strategies for OS. |
| format | Article |
| id | doaj-art-defa0ab8ab04473d8274cc046c2091ba |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-defa0ab8ab04473d8274cc046c2091ba2025-01-17T04:51:02ZengElsevierHeliyon2405-84402025-01-01111e41358Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysisShiyao Liao0Xing Gao1Kai Zhou2Yao Kang3Lichen Ji4Xugang Zhong5Jun Lv6Center for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, ChinaDepartment of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215000, ChinaThe First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325006, ChinaCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, ChinaCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, ChinaCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China; Qingdao University, Qingdao, Shandong, 266000, ChinaCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China; Corresponding author.Background: The present study aims to explore the metastasis-related signatures in connection with tumor microenvironment (TME), revealing new molecular targets promising in improving osteosarcoma (OS) patients’ outcomes. Methods: The high-throughput sequencing data was downloaded from the TARGET database and performed the ESTIMATE algorithm. Metastasis-related information was obtained from the GSE21257 dataset. Differentially expressed genes (DEGs) associated with the stromal and immune cell infiltration patterns were identified. DEGs with similar biological functions were grouped into the same module by Gene Ontology (GO) analysis and MCODE analysis. Prognostic DEGs were selected in two datasets through survival analysis. Weighted gene co-expression network analysis (WGCNA) was performed to find metastasis-related modules and genes. RT-PCR was utilized to evaluate the expression of the key prognostic DEGs associated with metastasis in OS patients. Results: The median scores of the stromal and immune groups of OS samples were 58 and -416, and a total of 200 overlapping DEGs were identified. These DEGs basically played fundamental roles in immune response relevant GO terms and were clustered into 9 different modules. Among them, 24 metastasis-related DEGs were selected from the GSE21257 dataset which contains the stromal and immune cell infiltration patterns. Finally, IRF8, HLA-DMA, and HLA-DMB were proved to exhibit significant higher expression levels in cancerous tissues than in para-cancerous tissues for OS patients. Conclusion: We identified three principal genes as promising signatures for predicting the survival the prognosis of OS patients. Exploration of metastasis-related signatures in TME may be valuable for enhancing treatment strategies for OS.http://www.sciencedirect.com/science/article/pii/S2405844024173896OsteosarcomaMetastasisTumor microenvironmentOverall survivalBioinformatics analysis |
| spellingShingle | Shiyao Liao Xing Gao Kai Zhou Yao Kang Lichen Ji Xugang Zhong Jun Lv Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis Heliyon Osteosarcoma Metastasis Tumor microenvironment Overall survival Bioinformatics analysis |
| title | Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis |
| title_full | Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis |
| title_fullStr | Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis |
| title_full_unstemmed | Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis |
| title_short | Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis |
| title_sort | exploration of metastasis related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis |
| topic | Osteosarcoma Metastasis Tumor microenvironment Overall survival Bioinformatics analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024173896 |
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