Breast cancer prognosis signature: linking risk stratification to disease subtypes

Yu, Fulong; Quan, Fei; Xu, Jiu Hua; Zhang, Yan; Xie, Youhua; Zhang, Jingyu; Lan, Yujia; Yuan, Huating; Zhang, Hongyi; Cheng, Shujun; Xiao, Yun; Li, Xia

doi:10.1093/bib/bby073

Cited by 73 publications

(60 citation statements)

References 51 publications

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“…Given the heterogeneity of cancer, reliable prognostic biomarkers are needed to identify patients who can benefit from therapy ( Li et al, 2017 ; Zhang et al, 2018 ). There is growing research on several gene signatures to improve decision-making and individualization of BRCA therapy ( Cronin et al, 2007 ; Cardoso et al, 2016 ; Yu et al, 2019 ). However, it is difficult to apply all of them for clinical management.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients

et al. 2020

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Breast cancer (BRCA) is the most common cancer and a major cause of death in women. Long non-coding RNAs (lncRNAs) are emerging as key regulators and have been implicated in carcinogenesis and prognosis. In this study, we aimed to develop a lncRNA signature of BRCA patients to improve risk stratification. In the training cohort (GSE21653, n = 232), 17 lncRNAs were identified by univariate Cox proportional hazards regression, which were significantly associated with patients’ survival. The least absolute shrinkage and selection operator-penalized Cox proportional hazards regression analysis was used to identify a six-lncRNA signature. According to the median of the signature risk score, patients were divided into a high-risk group and a low-risk group with significant disease-free survival differences in the training cohort. A similar phenomenon was observed in validation cohorts (GSE42568, n = 101; GSE20711, n = 87). The six-lncRNA signature remained as independent prognostic factors after adjusting for clinical factors in these two cohorts. Furthermore, this signature significantly predicted the survival of grade III patients and estrogen receptor-positive patients. Furthermore, in another cohort (GSE19615, n = 115), the low-risk patients that were treated with tamoxifen therapy had longer disease-free survival than those who underwent no therapy. Overall, the six-lncRNA signature can be a potential prognostic tool used to predict disease-free survival of patients and to predict the benefits of tamoxifen treatment in BRCA, which will be helpful in guiding individualized treatments for BRCA patients.

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Section: Discussionmentioning

confidence: 99%

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients

et al. 2020

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“…Even in the same cancer, it can be divided into different subtypes (Jiang et al, 2019), but most of the existing methods do not take this into account (Yu et al, 2016; Pawlovsky and Matsuhashi, 2017). Recent literature has confirmed that considering the subtype of cancer and then constructing the cancer prognosis model is conducive to the improvement of the performance of the cancer prognosis model (Yu et al, 2018).…”

Section: Introductionmentioning

confidence: 98%

An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules

2019

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Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on the hypothesis that the gene co-expression network may reveal relationships among genes, some communities in the network could influence the prognosis of cancer patients and all the prognosis-related communities could fully reveal the prognosis of cancer patients. To predict the prognosis for cancer patients in each subtype, we adopted an ensemble classifier based on the gene co-expression network of the corresponding subtype. Using the gene expression data of ovarian cancer patients in TCGA (The Cancer Genome Atlas), three subtypes were identified. Survival analysis showed that patients in different subtypes had different survival risks. Three ensemble classifiers were constructed for each subtype. Leave-one-out and independent validation showed that our method outperformed control and literature methods. Furthermore, the function annotation of the communities in each subtype showed that some communities were cancer-related. Finally, we found that the current drug targets can partially support our method.

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“…Still clinical markers commonly used to classify breast cancer are tumor size, lymph node involvement, histological grade, expression of steroid receptors (estrogen receptors [ER]/progesterone receptors [PR]) and human epidermal growth factor receptor 2 (Her2), but recent studies have focused on more detailed biological characteristics to improve patient risk stratification and the benefit to sideeffect ratio from a specific treatment modality [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Zinc distribution within breast cancer tissue of different intrinsic subtypes

Rüsch

Hirner

Schmitz

et al. 2020

Arch Gynecol Obstet

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Purpose To show feasibility of laser ablation inductively coupled mass spectrometry (LA-ICPMS) for analysis of zinc content and concentration in breast cancer tissue and to correlate this with validated prognostic and predictive markers, i.e. histological grading and expression of steroid receptors (estrogen receptor, ER; progesterone receptor, PR) and human epidermal growth-factor receptor 2 (Her2). Methods 28 samples of human invasive ductal breast cancer tissue were subclassified into groups of four different intrinsic subtypes according to the expression of ER, PR and Her2 by immunohistological staining and then analyzed for zinc content and distribution by LA-ICPMS applying a calibration technique based on spiked polyacrylamide gels. A correlation of zinc concentration with histological grading and molecular subtypes was analyzed. Results Consistent with results of a pilot-study LA-ICPMS was feasible to show zinc accumulation in cancerous tissue, even more adjacent healthy stroma was with proportional increase of zinc. Zinc levels were most elevated in triple-positive (TPBC) and in triple-negative (TNB) breast cancers. Conclusion LA-ICPMS was feasible to confirm a connection between zinc and grade of malignancy; furthermore, focusing on a correlation of zinc and intrinsic breast cancer subtypes, LA-ICPMS depicted an upwards trend of zinc for “high-risk-cancers” with highest levels in Her2-positive and in triple-negative (TNBC) disease. The currently uncommon alliance of clinicians and analytical chemists in basic research is most promising to exploit the full potential of diagnostic accuracy in the efforts to solve the enigma of breast cancer initiation and course of disease.

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Breast cancer prognosis signature: linking risk stratification to disease subtypes

Cited by 73 publications

References 51 publications

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients

An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules

Zinc distribution within breast cancer tissue of different intrinsic subtypes

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