Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

Abstract

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 (scRNAseq) 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/).

Comparative analysis of single-cell transcriptome reveals heterogeneity and commonality in the immune microenvironment of colorectal cancer and inflammatory bowel disease

“…Lv et al 10.3389/fimmu.2024.1356075 accelerated deleterious inflammation and promoted cancer metastasis and progression (36). MIF interacted with the CD74, inducing its phosphorylation and recruitment of CD44, ultimately leading to ERK1/2 phosphorylation (37).…”

“…Single-cell RNA sequencing (scRNA-Seq) technology has the potential to unravel the diversity of cell states and the heterogeneity of cell populations ( 9 ). It serves as a powerful tool for investigating heterogeneous tissues, such as the tumor microenvironment (TME) ( 10 ). Extensive research has been conducted on the heterogeneity of immune cells in intestinal diseases, particularly tumors.…”