A prognostic mathematical model based on tumor microenvironment-related genes expression for breast cancer patients

Frontiers in Oncology

Oncology

Tianyang Zhou¹  Mijia Wang¹  Shan Wang¹  Yuting Zhang¹  Ying Zhang¹  Jia Wang¹  Nan Wu¹  Hong Chen¹  Xinyi Wang¹  Dianlong Zhang²  Yufu Guan²  Xue Gao³  Di Cui⁴ 

[1] Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China;Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China;Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China;Information Center, Second Affiliated Hospital of Dalian Medical University, Dalian, China;

关键词: breast cancer;    tumor microenvironment;    prognostic;    resistance;    therapeutic sensitivity;   

DOI  :  10.3389/fonc.2023.1209707

 received in 2023-04-21, accepted in 2023-09-18,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundTumor microenvironment (TME) status is closely related to breast cancer (BC) prognosis and systemic therapeutic effects. However, to date studies have not considered the interactions of immune and stromal cells at the gene expression level in BC as a whole. Herein, we constructed a predictive model, for adjuvant decision-making, by mining TME molecular expression information related to BC patient prognosis and drug treatment sensitivity.MethodsClinical information and gene expression profiles were extracted from The Cancer Genome Atlas (TCGA), with patients divided into high- and low-score groups according to immune/stromal scores. TME-related prognostic genes were identified using Kaplan-Meier analysis, functional enrichment analysis, and protein-protein interaction (PPI) networks, and validated in the Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct and verify a prognostic model based on TME-related genes. In addition, the patients’ response to chemotherapy and immunotherapy was assessed by survival outcome and immunohistochemistry (IPS). Immunohistochemistry (IHC) staining laid a solid foundation for exploring the value of novel therapeutic target genes.ResultsBy dividing patients into low- and high-risk groups, a significant distinction in overall survival was found (p < 0.05). The risk model was independent of multiple clinicopathological parameters and accurately predicted prognosis in BC patients (p < 0.05). The nomogram-integrated risk score had high prediction accuracy and applicability, when compared with simple clinicopathological features. As predicted by the risk model, regardless of the chemotherapy regimen, the survival advantage of the low-risk group was evident in those patients receiving chemotherapy (p < 0.05). However, in patients receiving anthracycline (A) therapy, outcomes were not significantly different when compared with those receiving no-A therapy (p = 0.24), suggesting these patients may omit from A-containing adjuvant chemotherapy. Our risk model also effectively predicted tumor mutation burden (TMB) and immunotherapy efficacy in BC patients (p < 0.05).ConclusionThe prognostic score model based on TME-related genes effectively predicted prognosis and chemotherapy effects in BC patients. The model provides a theoretical basis for novel driver-gene discover in BC and guides the decision-making for the adjuvant treatment of early breast cancer (eBC).

【 授权许可】

Unknown   
Copyright © 2023 Chen, Wang, Zhang, Gao, Guan, Wu, Wang, Zhou, Zhang, Cui, Wang, Zhang and Wang