Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma
Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immu...
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| Format: | Article |
| Language: | English |
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BMC
2020-12-01
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| Series: | Cancer Cell International |
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| Online Access: | https://doi.org/10.1186/s12935-020-01672-3 |
| _version_ | 1830298418830901248 |
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| author | Naiqiang Zhu Jingyi Hou |
| author_facet | Naiqiang Zhu Jingyi Hou |
| author_sort | Naiqiang Zhu |
| collection | DOAJ |
| description | Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis. |
| first_indexed | 2024-12-19T07:51:00Z |
| format | Article |
| id | doaj.art-acc4b57af1a842f6a61a2fef88093b51 |
| institution | Directory Open Access Journal |
| issn | 1475-2867 |
| language | English |
| last_indexed | 2024-12-19T07:51:00Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj.art-acc4b57af1a842f6a61a2fef88093b512022-12-21T20:30:11ZengBMCCancer Cell International1475-28672020-12-0120111110.1186/s12935-020-01672-3Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcomaNaiqiang Zhu0Jingyi Hou1Department of Minimally Invasive Spinal Surgery, Affiliated Hospital of Chengde Medical CollegeDepartment of Minimally Invasive Spinal Surgery, Affiliated Hospital of Chengde Medical CollegeAbstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.https://doi.org/10.1186/s12935-020-01672-3SarcomasImmune infiltrationPrognosisWeighted gene co-expression analysisTumor microenvironment |
| spellingShingle | Naiqiang Zhu Jingyi Hou Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma Cancer Cell International Sarcomas Immune infiltration Prognosis Weighted gene co-expression analysis Tumor microenvironment |
| title | Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma |
| title_full | Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma |
| title_fullStr | Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma |
| title_full_unstemmed | Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma |
| title_short | Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma |
| title_sort | assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma |
| topic | Sarcomas Immune infiltration Prognosis Weighted gene co-expression analysis Tumor microenvironment |
| url | https://doi.org/10.1186/s12935-020-01672-3 |
| work_keys_str_mv | AT naiqiangzhu assessingimmuneinfiltrationandthetumormicroenvironmentforthediagnosisandprognosisofsarcoma AT jingyihou assessingimmuneinfiltrationandthetumormicroenvironmentforthediagnosisandprognosisofsarcoma |