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Updated: Feb 12, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Expression of MIR155HG, LOC283856, KIAA0125, and LOC100190986 as potential prognostic and predictive biomarkers for
A C Pavanelli1,2, F R R Mangone1,2, G P de Jesus1
1Laboratório de Genética Molecular, Centro de Investigação Translacional em Oncologia, Instituto do Câncer do Estado de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brasil.
Abstract:
Accumulating evidence has pointed out that the altered expression of long non-coding RNAs (lncRNAs) is involved in the physiopathology of breast cancer (BC). However, the role of lncRNAs in BC progression remains poorly understood. Here, we evaluated cDNA microarray data from a previous study from our group to investigate the effects of SPARC expression on the transcriptome of MCF7 cells before and after treatment with docetaxel. We analyzed our gene expression data to identify differentially expressed long non-coding RNAs (DELncRNAs). In combination with in silico analysis, we selected a group of DELncRNAs with potential prognostic and predictive value for BC patients with tumors of different intrinsic subtypes. Overall, we identified 260 DELncRNAs comparing MCF7 cells with different expressions of SPARC after docetaxel treatment. Nine DELncRNAs (LOC646762, FLJ13224, CASC2, LOC100130691, MGC12916, LOC100190986, LOC283856, KIAA0125, and MIR155HG) showed significant associations with BC survival on the KM Plotter platform. Of these 9 DELncRNAs, MIR155HG, LOC283856, LOC100190986, and KIAA0125 were significantly correlated with recurrence-free survival and overall survival rates of BC patients, suggesting they could help predict the outcome of BC patients as prognostic factors. Moreover, in silico analysis showed that these DELncRNAs were able to predict BC patients' responses to different treatment protocols.
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