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.
Highlights
Breast cancer (BC) is the most common female cancer and despite important advances in early detection and research development, it continues to be the second leading cause of death in women worldwide [1]
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 or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions
In our recent paper [2], we analysed a total of 505 BC subjects (229 Luminal A, 120 Luminal B, 58 HER2-enriched, and 98 Basal-like) and we identified a total of 108 switch genes (S1 Table)
Summary
Breast cancer (BC) is the most common female cancer and despite important advances in early detection and research development, it continues to be the second leading cause of death in women worldwide [1]. Triple-negative BC (TNBC) accounts for a minority of all diagnosed BCs (15–20%) [2] It is a subtype with a heterogeneous nature, defined by the low or absent expression of estrogen (ER), progesterone (PR) receptors and the lack of expression of the human epidermal growth factor (EGF) receptor-2 (HER2) receptors [3]. The development of new prognostic indicators for basal-like subtype represents an unmet clinical challenge that might be of benefit to the clinical management of this disease. To achieve this goal, we started from data extracted from our recent computational analysis of BC phenotypes [2]. Our findings led to an in silico recognition of a basal-like prognostic gene signature composed of 11 genes to be further investigated
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