2022
DOI: 10.3390/ijms23179936
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Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq

Abstract: Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the … Show more

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Cited by 10 publications
(8 citation statements)
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“…Subsequently, we binarised the risk model predictions into high and low-risk groups through using median prediction to split patients then constructed Kaplan-Meier (KM) survival curves. Following the analysis of Xu et al ( 29 ), we were able to capture the same distinct KM curves for the RNA-Seq models in each BC subset of patients ( p ≤ 5.1 × 10 −4 ) using the risk predictions of the patients the models were trained on ( Figure 4f-h ). However, we also showed that under cross-validation the distinction between high and low-risk groups were no longer significant except for Hist2ST ( p = 0.05) and ST-Net ( p = 6.9 × 10 −2 ) in the HER2+ subset ( Figure 4i ).…”
Section: Resultsmentioning
confidence: 95%
See 2 more Smart Citations
“…Subsequently, we binarised the risk model predictions into high and low-risk groups through using median prediction to split patients then constructed Kaplan-Meier (KM) survival curves. Following the analysis of Xu et al ( 29 ), we were able to capture the same distinct KM curves for the RNA-Seq models in each BC subset of patients ( p ≤ 5.1 × 10 −4 ) using the risk predictions of the patients the models were trained on ( Figure 4f-h ). However, we also showed that under cross-validation the distinction between high and low-risk groups were no longer significant except for Hist2ST ( p = 0.05) and ST-Net ( p = 6.9 × 10 −2 ) in the HER2+ subset ( Figure 4i ).…”
Section: Resultsmentioning
confidence: 95%
“…Out of the curated TCGA images, we split samples according to breast cancer (BC) subtypes as described in Xu et al 29 . The clinical information such as ER-, PR-, HER2- were defined according to the estrogen, progesterone and HER2 receptor status variables and these were used to define BC subsets.…”
Section: Methodsmentioning
confidence: 99%
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“…To exclude the incorporation of normal epithelial cells into tumor samples due to sampling, we used a previously published method [ 16 ] to infer the exact malignant cells using copy number variation (CNV). According to previously published articles, epithelial cells with high CNV were considered as malignant cells [ [22] , [23] , [24] ]. In our result, almost all tumor tissues, most epithelial cells presented higher CNV compared with artificially inserted epithelial cells derived from normal lung tissues.…”
Section: Resultsmentioning
confidence: 99%
“…Among all BC subtypes, luminal A and B account for 70%, HER2+ accounts for 15–20%, and TNBC accounts for 15–20%. Each BC subtype has specific molecular characteristics, clinical behavior, prognosis, and treatment modalities [ 3 ].…”
Section: Introductionmentioning
confidence: 99%