Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA
This study aimed to construct an immune-related prognostic model and a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. We applied single-sample gene set enrichment analysis to classify 1,053 breast cancer samples from The Cancer Genome Atlas (TCGA) databas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.957675/full |
| _version_ | 1797956555596890112 |
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| author | Linrong Li Lin Li Mohan Liu Yan Li Qiang Sun |
| author_facet | Linrong Li Lin Li Mohan Liu Yan Li Qiang Sun |
| author_sort | Linrong Li |
| collection | DOAJ |
| description | This study aimed to construct an immune-related prognostic model and a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. We applied single-sample gene set enrichment analysis to classify 1,053 breast cancer samples from The Cancer Genome Atlas (TCGA) database into high and low immune cell infiltration clusters. In cluster construction and validation, the R packages “GSVA,” “hclust,” “ESTIMATE,” and “CIBERSORT” and GSEA software were utilized. ImmPort, univariate Cox regression analysis, and Venn analysis were then used to identify 42 prognostic immune-related genes. Eventually, the genes TAPBPL, RAC2, IL27RA, ULBP2, PSMB8, SOCS3, NFKBIE, IGLV6-57, CXCL1, IGHD, AIMP1, and CXCL13 were chosen for model construction utilizing least absolute shrinkage and selection operator regression analysis. The Kaplan–Meier curves of both the training and validation sets indicated that the overall survival of patients in the low-risk group was superior to that of patients in the high-risk group (p < .05). The areas under curves (AUCs) of the model at 1, 3, and 5 years were, respectively, .697, .710, and .675 for the training set and .930, .688, and .712 for the validation set. Regarding clinicopathologic characteristics, breast cancer-related genes, and tumor mutational burden, effective differentiation was achieved between high-risk and low-risk groups. A nomogram integrating the risk model and clinicopathologic factors was constructed using the “rms” R software package. The nomogram’s 1-, 3-, and 5-year AUCs were .828, .783, and .751, respectively. Overall, our study developed an immune-related model and a nomogram that could reliably predict OS for breast cancer patients, and offered insights into tumor immune and pathological mechanisms. |
| first_indexed | 2024-04-10T23:50:43Z |
| format | Article |
| id | doaj.art-e36b7987ce4c4cc583d2d57a537eac75 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-10T23:50:43Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-e36b7987ce4c4cc583d2d57a537eac752023-01-10T19:01:24ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-01-011310.3389/fgene.2022.957675957675Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEALinrong Li0Lin Li1Mohan Liu2Yan Li3Qiang Sun4Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of Joint and Orthopedics, Zhujiang Hospital, Second Clinical Medical College, Southern Medical University, Guangzhou, ChinaDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaThis study aimed to construct an immune-related prognostic model and a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. We applied single-sample gene set enrichment analysis to classify 1,053 breast cancer samples from The Cancer Genome Atlas (TCGA) database into high and low immune cell infiltration clusters. In cluster construction and validation, the R packages “GSVA,” “hclust,” “ESTIMATE,” and “CIBERSORT” and GSEA software were utilized. ImmPort, univariate Cox regression analysis, and Venn analysis were then used to identify 42 prognostic immune-related genes. Eventually, the genes TAPBPL, RAC2, IL27RA, ULBP2, PSMB8, SOCS3, NFKBIE, IGLV6-57, CXCL1, IGHD, AIMP1, and CXCL13 were chosen for model construction utilizing least absolute shrinkage and selection operator regression analysis. The Kaplan–Meier curves of both the training and validation sets indicated that the overall survival of patients in the low-risk group was superior to that of patients in the high-risk group (p < .05). The areas under curves (AUCs) of the model at 1, 3, and 5 years were, respectively, .697, .710, and .675 for the training set and .930, .688, and .712 for the validation set. Regarding clinicopathologic characteristics, breast cancer-related genes, and tumor mutational burden, effective differentiation was achieved between high-risk and low-risk groups. A nomogram integrating the risk model and clinicopathologic factors was constructed using the “rms” R software package. The nomogram’s 1-, 3-, and 5-year AUCs were .828, .783, and .751, respectively. Overall, our study developed an immune-related model and a nomogram that could reliably predict OS for breast cancer patients, and offered insights into tumor immune and pathological mechanisms.https://www.frontiersin.org/articles/10.3389/fgene.2022.957675/fullimmuneprognosticmodelbreast cancerssGSEAnomogram |
| spellingShingle | Linrong Li Lin Li Mohan Liu Yan Li Qiang Sun Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA Frontiers in Genetics immune prognostic model breast cancer ssGSEA nomogram |
| title | Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA |
| title_full | Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA |
| title_fullStr | Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA |
| title_full_unstemmed | Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA |
| title_short | Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA |
| title_sort | novel immune related prognostic model and nomogram for breast cancer based on ssgsea |
| topic | immune prognostic model breast cancer ssGSEA nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.957675/full |
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