Discover oncology · 2026
Identification and external validation of a prognostic signature based on bone morphogenetic protein-related mRNAs for kidney renal clear cell carcinoma.
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Zhou Yajie, Hu Zijian, Xie Lei, Zhang Shuwen, Huang Qi, Zhang Nan, Liu Yijiang, Zhang Wenxiong, Wang Kang
Аннотация
BACKGROUND: The Bone Morphogenetic Protein (BMP) family acts as a critical modulator of cellular plasticity and lineage commitment in oncogenesis. However, the systematic contribution of BMP-related mRNAs (BRMs) to the progression and immunophenotypic landscape of kidney renal clear cell carcinoma (KIRC) remains poorly defined.
METHODS: Integrating transcriptomic and clinical data, we established a BRM-based risk signature to refine KIRC prognosis. The model's robustness was validated using the E-MTAB-1980 cohort and a comprehensive nomogram. We employed advanced computational frameworks-including tumor mutation burden (TMB), tumor microenvironment (TME) deconvolution, and drug sensitivity profiling-to dissect biological heterogeneity between risk subgroups.
RESULTS: A nine-BRM signature (comprising L1CAM, MPP7, CLIC5, TUBB2B, PLG, CA8, ITGAX, PADI3, and PRR15L) was identified as an independent prognostic factor. Kaplan-Meier analysis demonstrated significantly inferior survival in the high-risk group (HRG) compared to the low-risk group (LRG) (P < 0.001). The BRM-integrated nomogram exhibited superior predictive accuracy (AUC = 0.747) and strong calibration. Functional enrichment revealed that while the LRG was associated with physiological renal tubular secretion, the HRG was predominantly characterized by immunoglobulin production and B cell-mediated immune dysregulation. Furthermore, the HRG exhibited a significantly higher TMB and distinct sensitivity to Dactolisib and Podophyllotoxin, whereas the LRG favored Docetaxel. RT-qPCR successfully validated the differential expression of core BRMs in clinical samples.
CONCLUSIONS: This nine-BRM prognostic model serves as a potential prognostic stratification tool that may complement existing clinical parameters in evaluating the outcomes of KIRC patients.
METHODS: Integrating transcriptomic and clinical data, we established a BRM-based risk signature to refine KIRC prognosis. The model's robustness was validated using the E-MTAB-1980 cohort and a comprehensive nomogram. We employed advanced computational frameworks-including tumor mutation burden (TMB), tumor microenvironment (TME) deconvolution, and drug sensitivity profiling-to dissect biological heterogeneity between risk subgroups.
RESULTS: A nine-BRM signature (comprising L1CAM, MPP7, CLIC5, TUBB2B, PLG, CA8, ITGAX, PADI3, and PRR15L) was identified as an independent prognostic factor. Kaplan-Meier analysis demonstrated significantly inferior survival in the high-risk group (HRG) compared to the low-risk group (LRG) (P < 0.001). The BRM-integrated nomogram exhibited superior predictive accuracy (AUC = 0.747) and strong calibration. Functional enrichment revealed that while the LRG was associated with physiological renal tubular secretion, the HRG was predominantly characterized by immunoglobulin production and B cell-mediated immune dysregulation. Furthermore, the HRG exhibited a significantly higher TMB and distinct sensitivity to Dactolisib and Podophyllotoxin, whereas the LRG favored Docetaxel. RT-qPCR successfully validated the differential expression of core BRMs in clinical samples.
CONCLUSIONS: This nine-BRM prognostic model serves as a potential prognostic stratification tool that may complement existing clinical parameters in evaluating the outcomes of KIRC patients.