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Updated: Aug 13, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on
Zhaohui Ruan1, Dongmei Chi2, Qianyu Wang1
1VIP Section Department, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China.
Background:
Breast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNA-seq) is an effective method for studying the changing omics of cells in complex TMEs.
Methods:
The Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.
Results:
Our results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.
Conclusions:
Our findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor.shinyapps.io/GCNBC/).
