Abstract
Abnormal long non-coding RNAs (lncRNAs) expression has been documented to have oncogene or tumor suppressor functions in the development and progression of cancer, emerging as promising independent biomarkers for molecular cancer stratification and patients’ prognosis. Examining the relationship between lncRNAs and the survival rates in malignancies creates new scenarios for precision medicine and targeted therapy. Breast cancer (BRCA) is a heterogeneous malignancy. Despite advances in its molecular classification, there are still gaps to explain in its multifaceted presentations and a substantial lack of biomarkers that can better predict patients’ prognosis in response to different therapeutic strategies. Here, we performed a re-analysis of gene expression data generated using cDNA microarrays in a previous study of our group, aiming to identify differentially expressed lncRNAs (DELncRNAs) with a potential predictive value for response to treatment with taxanes in breast cancer patients. Results revealed 157 DELncRNAs (90 up- and 67 down-regulated). We validated these new biomarkers as having prognostic and predictive value for breast cancer using in silico analysis in public databases. Data from TCGA showed that compared to normal tissue, MIAT was up-regulated, while KCNQ1OT1, LOC100270804, and FLJ10038 were down-regulated in breast tumor tissues. KCNQ1OT1, LOC100270804, and FLJ10038 median levels were found to be significantly higher in the luminal subtype. The ROC plotter platform results showed that reduced expression of these three DElncRNAs was associated with breast cancer patients who did not respond to taxane treatment. Kaplan–Meier survival analysis revealed that a lower expression of the selected lncRNAs was significantly associated with worse relapse-free survival (RFS) in breast cancer patients. Further validation of the expression of these DELncRNAs might be helpful to better tailor breast cancer prognosis and treatment.
Highlights
Using cDNA microarrays, we evaluated the expression profiles of MCF7 breast cancer cells expressing different levels of PAR4 before and after docetaxel exposure to identify differentially expressed genes potentially involved in PAR4mediated chemosensitivity to docetaxel [21]
Using in silico data mining in publicly available databases such as the Cancer Genome Atlas (TCGA), ROC Plotter, and KM Plotter platform, we have identified four long non-coding RNAs (lncRNAs) as potential new biomarkers candidates for prognosis and predictive value for Breast cancer (BRCA) patients
Expression of MIAT lncRNA in breast cancer compared to normal breast tissue (a) and among different breast intrinsic subtypes using the UALCAN database containing TCGA data (b). (c) MIAT lncRNA expression in groups of responder subtypes using the UALCAN database containing TCGA data (b). (c) MIAT lncRNA expression in groups of responder and non-responder breast cancer patients treated with taxane using the online platform ROCplot. (d–h) Kaplan–Meier curves for relapse-free survival of breast cancer patients for all subtypes (d) or for each intrinsic subtype as luminal A (e), luminal
Summary
Abnormal lncRNA expression patterns have been reported in many malignancies, supporting their potential as novel biomarkers for molecular cancer stratification, prognosis and therapy response [8,9]. Molecular profiling is becoming a vital tool for identifying predictive and prognostic markers for translational studies and personalized treatments. These treatments cause adverse effects, generate resistance, and often limit therapeutic success in patients with breast cancer [18]. We performed a reanalysis of that gene expression profile, aiming to identify differentially expressed lncRNAs with a potential predictive value of response to taxanes treatment for breast cancer patients. Using in silico data mining in publicly available databases such as TCGA, ROC Plotter, and KM Plotter platform, we have identified four lncRNAs as potential new biomarkers candidates for prognosis and predictive value for BRCA patients
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