ArticlePloS one2022
In silico recognition of a prognostic signature in basal-like breast cancer patients.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- The role of miR-335-5p in the redifferentiation of BRAF p.V600E thyroid cancers.Molecular oncology · 2026Article
- BioLM-NET: an interpretable deep learning model combining prior biological knowledge and contextual LLM gene embeddings on multi-omics data to predict disease.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Integrating multi-omics data: Methods and applications in human complex diseases.Biotechnology reports (Amsterdam, Netherlands) · 2025Review
- CTPS1 is a novel therapeutic target in multiple myeloma which synergizes with inhibition of CHEK1, ATR or WEE1.Leukemia · 2024Article
- Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer.Translational cancer research · 2022Article
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Authors and funding
6 authors.
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Abstract
backgroundTriple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge.
resultsHere, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients' clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas.
conclusionThe application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.
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