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Gene Ontology Analysis of and Subtyping of Breast Tumors by RNA‐Seq and BiNGO
Author(s) -
Soneral Paula,
Skorseth Claire
Publication year - 2018
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2018.32.1_supplement.lb117
Subject(s) - subtyping , breast cancer , biology , gene expression profiling , gene , gene expression , cancer research , rna seq , computational biology , cancer , transcriptome , genetics , computer science , programming language
Breast cancer, the second deadliest among women, kills about 40,000 individuals worldwide per year. Such lethality is due, in part, to the inherent heterogeneity of breast cancer disease, posing challenges for clinicians to accurately diagnose and subtype tumors. Recently, RNA‐seq technology has made it possible to subtype and classify tumors using gene expression profiling. In this experiment, we validated the efficacy of using RNA‐seq gene expression technology as a diagnostic tool for breast tumor subtyping in the context of an undergraduate course. Publicly available datasets from the Sequence Read Archive (SRA) of MCF‐7 breast cancer cells were subjected to quality control analysis using the Green Line of DNA Subway (Tuxedo Pipeline, FAST‐X, FAST‐QC, Tophat, and CuffDiff algorithms). Log‐transformed gene expression ratios were subjected to the BiNGO gene ontology algorithm. Significant over‐expression of genes for metabolic and cellular processes pathways included cell death and cell division (p<0.01 Binomial). Significantly down‐regulated pathways included those in development (organ development, etc), regulation (stimulus response, inflammatory response, etc), and signaling (cellular communication, etc; p<0.01 Binomial). Furthermore, using previously established biomarker genes, we subtyped the cells as basal‐like ‐ showing little change in expression for Estrogen Receptor (ER), Her2/neu (HER), or Ki‐67 biomarkers, but a significant over‐expression of the progesterone receptor (q = .0031). Using the Mammaprint suite of biomarkers we diagnosed a highly progressed tumor, wherein 60% and 73% of the Mammaprint mid‐hallmark genes and 38% late hallmarks were differentially expressed. This analysis was corroborated by previous datasets from DNA microarrays. Taken together, our findings suggest that RNA‐seq sequence data can be a valid approach for subtyping human breast cancers, and determining progression of disease. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .