ArticleBiochemical genetics2026
Integrated Analysis of Bulk and Single-Cell Transcriptomics Identifies Prognostic Biomarkers in Breast Cancer Metastasis.
Article in Biochemical genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Computational identification of putative mRNA-miRNA signatures in chemoresistant and metastatic triple-negative breast cancer.Biochemistry and biophysics reports · 2026Article
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Authors and funding
15 authors.
Funding
Abstract
Breast cancer (BC) remains one of the leading causes of cancer-related mortality among women worldwide, with distant metastasis being the primary contributor to poor prognosis. However, the molecular mechanisms driving BC metastasis are not yet fully understood. We integrated three public microarray datasets (GSE14776, GSE103357, and GSE32489) to identify the differentially expressed genes (DEGs) associated with breast cancer metastasis. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene identification were performed using bioinformatics tools including DAVID, STRING, Cytoscape, and R. The prognostic significance of hub genes was assessed using Kaplan-Meier plotter and GEPIA. Expression validation was conducted through UALCAN, immunohistochemistry (IHC), and single-cell RNA sequencing (scRNA-seq) analysis from the GSE180286 dataset. A total of 295 co-DEGs were identified across the three datasets, enriched in pathways such as MAPK signaling, Rap1 signaling, and cell adhesion molecules. Twenty hub genes were identified from the PPI network, with eight showing strong prognostic value. Among them, PRC1 and POLR3H emerged as potential novel biomarkers. IHC confirmed the differential protein expression of PRC1, CDCA8, KIF14, and POLR3H. scRNA-seq analysis revealed that these hub genes were predominantly expressed in malignant epithelial and EMT (epithelial-mesenchymal transition) cells, particularly those from metastatic lymph node sites. This integrative analysis combining bulk and single-cell transcriptomic data identified key metastasis-associated genes in breast cancer. PRC1 and POLR3H, in particular, may serve as novel prognostic biomarkers and potential therapeutic targets.
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Registered trials
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