In silico recognition of a prognostic signature in basal-like breast cancer patients

Abstract

Background Triple-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. Results Here, 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. Conclusion The 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.

Preferentially Expressed Antigen in Melanoma Is a Multifaceted Cancer Testis Antigen with Diverse Roles as a Biomarker and Therapeutic Target

“…PRAME was classified as a switch gene that significantly correlates with poor overall survival in patients with basal-type breast cancer. These data suggest that PRAME could serve as a prognostic biomarker and/or therapeutic target in TNBC [78].…”

“…This showed that PRAME expression is a prognostic marker for clinical outcomes of breast cancer, independent of traditional clinicopathological markers [77]. Recently, an in silico study on the Cancer Genome Atlas (TCGA) and Breast Invasive Carcinoma (BRCA) dataset led to recognition of a basal-like specific gene signature composed of 11 potential unfavorable prognostic biomarkers, including PRAME [78]. PRAME was classified as a switch gene that significantly correlates with poor overall survival in patients with basal-type breast cancer.…”

Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer

“…Nowadays, use of gene expression profile to define as prognostic biomarkers of TNBC patients have been still under-investigation. Although other studies found the genes related to poor prognosis, those finding have not applied in clinical practice (10)(11)(12)(13). To develop the new prognostic indicators, more detailed research is required owing to the complex tumor heterogeneity and complicated molecular regulatory mechanism of TNBC.…”