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| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Rong, Chao |b 0 |
| 245 | _ | _ | |a Integrative bioinformatics analysis and experimental validation identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas. |
| 260 | _ | _ | |a [London] |c 2025 |b Springer Nature |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1754299940_1784 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Soft tissue sarcomas (STS), including rhabdomyosarcoma (RMS), exhibit significant heterogeneity and limited responsiveness to immune checkpoint blockade (ICB). Unsupervised tumor immune phenotype based on multi-omics expression profiling of STS has been less studied. To reveal the tumor immune phenotype of STS and identify promising therapeutic targets, multi-omics expression profiling across various subtypes of STS was investigated. Here, we established a novel molecular classifier based on immune cell subsets related to TGFβ1 and IFNγ to identify distinct immune phenotypes with higher or lower cytotoxic contents. Immune-high clusters demonstrated enriched immune cell infiltration, elevated IFNγ-related signatures, and favorable clinical outcomes. In contrast, immune-low clusters were enriched for immunosuppressive cell types and exhibited poor survival. CHEK1 emerged as a key node associated with immunosuppressive phenotypes and was significantly overexpressed in immune-low tumors. In situ analysis of independent validation cohorts revealed the significant correlation between CHEK1 and tumor-infiltrating immune cells. Collectively, our findings establish a novel risk assessment strategy for RMS and STS patients, and highlight the potential of CHEK1 as a promising therapeutic target in combination with immune checkpoint inhibitor therapy. |
| 536 | _ | _ | |a 315 - Bildgebung und Radioonkologie (POF4-315) |0 G:(DE-HGF)POF4-315 |c POF4-315 |f POF IV |x 0 |
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| 700 | 1 | _ | |a Liu, Yun |b 1 |
| 700 | 1 | _ | |a Xiang, Fang |b 2 |
| 700 | 1 | _ | |a Zhao, Xin |b 3 |
| 700 | 1 | _ | |a Zhang, Jinjin |b 4 |
| 700 | 1 | _ | |a Xiao, Zuorun |b 5 |
| 700 | 1 | _ | |a Wang, Jinsha |b 6 |
| 700 | 1 | _ | |a Chen, Lin |b 7 |
| 700 | 1 | _ | |a Guo, Zhiqi |b 8 |
| 700 | 1 | _ | |a Zhang, Ziyu |b 9 |
| 700 | 1 | _ | |a An, Jingnan |b 10 |
| 700 | 1 | _ | |a Shen, Jing |b 11 |
| 700 | 1 | _ | |a Hess, Jochen |0 P:(DE-He78)2e5f34f1c58eda4787a14c9dc139ca5f |b 12 |u dkfz |
| 700 | 1 | _ | |a Yuan, Xiaodong |b 13 |
| 700 | 1 | _ | |a Zhang, Qiong |b 14 |
| 700 | 1 | _ | |a Wang, Shouli |b 15 |
| 773 | _ | _ | |a 10.1038/s41698-025-01064-8 |g Vol. 9, no. 1, p. 268 |0 PERI:(DE-600)2891458-2 |n 1 |p 268 |t npj precision oncology |v 9 |y 2025 |x 2397-768X |
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