Abstract
BackgroundAs one of the many breast cancer subtypes, human epidermal growth factor receptor 2 (Her2)-positive breast cancer has higher invasiveness and poor prognosis, although the advent of anti-Her2 drugs has brought good news to patients. However, the emergence of drug resistance still limits its clinical efficacy, so there is an urgent need to explore new targets and develop a risk scoring system to improve treatments and evaluate patient prognosis.MethodsDifferentially expressed mRNAs associated with Her2-positive breast cancer were screened from a TCGA cohort. The prognostic risk scoring system was constructed according to univariate and Lasso Cox regression model analyses and combined with clinical factors (such as age and TNM) for univariate and multivariate analyses to verify the specificity and sensitivity of the risk scoring system. Finally, based on correlation and CNV mutation analyses, we explored the research value of the mRNAs involved in the system as key genes of the model.ResultsIn this study, six mRNAs were screened and identified to construct a prognostic risk scoring system, including four up-regulated mRNA (RDH16, SPC25, SPC24, and SCUBE3) and two down-regulated mRNA (DGAT2 and CCDC69). The risk scoring system can divide Her2-positive breast cancer samples into high-risk and low-risk groups to evaluate patient prognosis. In addition, whether through the time-dependent receiver operating characteristics curve or compared with clinical factors, the risk scoring system showed high predictive sensitivity and specificity. Moreover, some CNV mutations in mRNA increase patient risk by influencing expression levels.ConclusionThe risk scoring system constructed in this study is helpful to improve the screening of high-risk patients with Her2-positive breast cancer and is beneficial for implementing early diagnosis and personalized treatment. It is suggested that these mRNAs may play an important role in the progression of Her2-positive breast cancer.
Highlights
As one of the many breast cancer subtypes, human epidermal growth factor receptor 2 (Her2)-positive breast cancer has higher invasiveness and poor prognosis, the advent of anti-Her2 drugs has brought good news to patients
Molecular functional (MF) groups are mainly enriched in various combinations, Table 1 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis about the differential mRNAs in HER2-positive breast cancer hsa04151:PI3K-Akt signaling pathway hsa04060:Cytokine-cytokine receptor interaction hsa05203:Viral carcinogenesis hsa04510:Focal adhesion hsa04110:Cell cycle hsa05322:Systemic lupus erythematosus hsa04114:Oocyte meiosis hsa03320:PPAR signaling pathway hsa04512:ECM-receptor interaction hsa04152:AMPK signaling pathway hsa04914:Progesterone-mediated oocyte maturation hsa04974:Protein digestion and absorption hsa04610:Complement and coagulation cascades hsa04923:Regulation of lipolysis in adipocytes
The results showed that five of the six mRNAs defined by the Lasso Cox regression model (RDH16, spindle component 25 (SPC25), spindle component 24 (SPC24), Signal peptide-CUB-EGF domain-containing protein 3 (SCUBE3), and Diglyceride acyltransferase-2 (DGAT2)) showed positive coefficients, indicating that these mRNAs were closely related to the prognostic risk of Her2-positive breast cancer patients, and higher expression corresponded to shorter overall survival (OS)
Summary
As one of the many breast cancer subtypes, human epidermal growth factor receptor 2 (Her2)-positive breast cancer has higher invasiveness and poor prognosis, the advent of anti-Her drugs has brought good news to patients. The emergence of drug resistance still limits its clinical efficacy, so there is an urgent need to explore new targets and develop a risk scoring system to improve treatments and evaluate patient prognosis. Her2-positive breast cancer patients have been greatly improved. Evaluating and improving the prognosis of patients with Her2-positive breast cancer remains a challenging and daunting task. There is an urgent need to further develop new specific biomarkers and build stable independent risk prediction models to improve treatment and evaluate patient prognosis, which has an important impact on clinical decision-making and patient counseling [4]. Tumor-specific CNVs can be used as a new detection tool for early diagnosis and treatment intervention of tumors
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