Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer
BackgroundThe most frequent malignant tumor in women is breast cancer (BRCA). It has been discovered that T-cell exhaustion and macrophages play significant roles in BRCA. It was necessary to explore prognostic genes associated with T-cell exhaustion and macrophage polarization in BRCA.MethodsThe fo...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1556496/full |
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| author | Fengqiang Cui Changjiao Yan Jiang Wu Yuqing Yang Jixin Yang Jialing Luo Nanlin Li |
| author_facet | Fengqiang Cui Changjiao Yan Jiang Wu Yuqing Yang Jixin Yang Jialing Luo Nanlin Li |
| author_sort | Fengqiang Cui |
| collection | DOAJ |
| description | BackgroundThe most frequent malignant tumor in women is breast cancer (BRCA). It has been discovered that T-cell exhaustion and macrophages play significant roles in BRCA. It was necessary to explore prognostic genes associated with T-cell exhaustion and macrophage polarization in BRCA.MethodsThe following data were included: 35 macrophage polarization-related genes (MPRGs), 683 T-cell exhaustion-related genes (TEXRGs), GSE20685, as well as TCGA-BRCA. Initially, candidate genes were identified through crossing differentially expressed genes (DEGs) obtained by differential expression analysis, key module genes associated with MPRGs, as well as TEXRGs. Next, 101 combinations of 10 machine learning algorithms and univariate Cox analysis were utilized to screen for prognostic genes. Concurrently, a risk model was built for validation in TCGA-BRCA and GSE20685. Next, we conducted immune infiltration, immunotherapy, mutation analysis, molecular regulatory network, as well as drug sensitivity between the two risk groups. Ultimately, we did the reverse transcription-quantitative polymerase chain reaction (RT-qPCR).ResultsAccording to random survival forest (RSF) algorithm (the best combination with the greatest C-index of 0.799), 7 prognostic genes were selected, which are PGK1, BTG2, TANK, CFB, EIF4E3, TNFRSF18, and BATF. After that, we created a risk model, and in the low-risk samples, there was a relatively high survival rate. Next, between two risk parts, the 7 differential immune cells were found. There was a significant difference in 25 immunological checkpoint (ICI) genes between the two risk parts. Next, a lncRNAs-miRNA-mRNA network with 65 nodes and 70 edges was built. Additionally, 84 medications were shown to differ significantly between the two risk groups. Finally, the expression of BTG2, TANK, and EIF4E3 was verified by RT-PCR, which was consistent with the bioinformatics analysis.ConclusionThe 7 prognostic genes (PGK1, BTG2, TANK, CFB, EIF4E3, TNFRSF18, and BATF) were screened, providing new insights into potential treatments for BRCA. |
| format | Article |
| id | doaj-art-795f61ae3fe74af48eec2585c82490a0 |
| institution | OA Journals |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Endocrinology |
| spelling | doaj-art-795f61ae3fe74af48eec2585c82490a02025-08-20T02:29:19ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-05-011610.3389/fendo.2025.15564961556496Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancerFengqiang CuiChangjiao YanJiang WuYuqing YangJixin YangJialing LuoNanlin LiBackgroundThe most frequent malignant tumor in women is breast cancer (BRCA). It has been discovered that T-cell exhaustion and macrophages play significant roles in BRCA. It was necessary to explore prognostic genes associated with T-cell exhaustion and macrophage polarization in BRCA.MethodsThe following data were included: 35 macrophage polarization-related genes (MPRGs), 683 T-cell exhaustion-related genes (TEXRGs), GSE20685, as well as TCGA-BRCA. Initially, candidate genes were identified through crossing differentially expressed genes (DEGs) obtained by differential expression analysis, key module genes associated with MPRGs, as well as TEXRGs. Next, 101 combinations of 10 machine learning algorithms and univariate Cox analysis were utilized to screen for prognostic genes. Concurrently, a risk model was built for validation in TCGA-BRCA and GSE20685. Next, we conducted immune infiltration, immunotherapy, mutation analysis, molecular regulatory network, as well as drug sensitivity between the two risk groups. Ultimately, we did the reverse transcription-quantitative polymerase chain reaction (RT-qPCR).ResultsAccording to random survival forest (RSF) algorithm (the best combination with the greatest C-index of 0.799), 7 prognostic genes were selected, which are PGK1, BTG2, TANK, CFB, EIF4E3, TNFRSF18, and BATF. After that, we created a risk model, and in the low-risk samples, there was a relatively high survival rate. Next, between two risk parts, the 7 differential immune cells were found. There was a significant difference in 25 immunological checkpoint (ICI) genes between the two risk parts. Next, a lncRNAs-miRNA-mRNA network with 65 nodes and 70 edges was built. Additionally, 84 medications were shown to differ significantly between the two risk groups. Finally, the expression of BTG2, TANK, and EIF4E3 was verified by RT-PCR, which was consistent with the bioinformatics analysis.ConclusionThe 7 prognostic genes (PGK1, BTG2, TANK, CFB, EIF4E3, TNFRSF18, and BATF) were screened, providing new insights into potential treatments for BRCA.https://www.frontiersin.org/articles/10.3389/fendo.2025.1556496/fullbreast cancerT-cell exhaustionmacrophage polarizationprognostic risk modelimmune infiltration analysis |
| spellingShingle | Fengqiang Cui Changjiao Yan Jiang Wu Yuqing Yang Jixin Yang Jialing Luo Nanlin Li Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer Frontiers in Endocrinology breast cancer T-cell exhaustion macrophage polarization prognostic risk model immune infiltration analysis |
| title | Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer |
| title_full | Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer |
| title_fullStr | Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer |
| title_full_unstemmed | Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer |
| title_short | Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer |
| title_sort | identification and validation of prognostic genes associated with t cell exhaustion and macrophage polarization in breast cancer |
| topic | breast cancer T-cell exhaustion macrophage polarization prognostic risk model immune infiltration analysis |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1556496/full |
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