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Cited by in F6Publishing
For: Qin Y, Deng J, Zhang L, Yuan J, Yang H, Li Q. Tumor microenvironment characterization in triple-negative breast cancer identifies prognostic gene signature. Aging (Albany NY) 2021;13:5485-505. [PMID: 33536349 DOI: 10.18632/aging.202478] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Capobianco E. Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research. Ann Transl Med 2022;10:1300. [PMID: 36660729 DOI: 10.21037/atm-22-5477] [Reference Citation Analysis]
2 Gou Q, Liu Z, Xie Y, Deng Y, Ma J, Li J, Zheng H. Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation. Front Pharmacol 2022;13:995555. [DOI: 10.3389/fphar.2022.995555] [Reference Citation Analysis]
3 Araujo JM, De la Cruz-ku G, Cornejo M, Doimi F, Dyer R, Gomez HL, Pinto JA. Prognostic Capability of TNBC 3-Gene Score among Triple-Negative Breast Cancer Subtypes. Cancers 2022;14:4286. [DOI: 10.3390/cancers14174286] [Reference Citation Analysis]
4 Peng W, Lin C, Jing S, Su G, Jin X, Di G, Shao Z. A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer. Front Oncol 2021;11:746763. [PMID: 34604089 DOI: 10.3389/fonc.2021.746763] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
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