Integrative bioinformatics analysis reveals miR-494 and its target genes as predictive biomarkers of trastuzumab-resistant breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32372307", "kind": "science-record", "revision": 6, "content_hash": "sha256:17442b545625bbc5fb2ca8e43b26fb19ba8f74792b33aead768d0b5b94fd6434", "created_at": "2026-11-01T15:41:00Z", "updated_at": "2026-11-01T21:23:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative bioinformatics analysis reveals miR-494 and its target genes as predictive biomarkers of trastuzumab-resistant breast cancer.", "doi": "10.1186/s43046-020-00028-2", "pmid": "32372307", "authors": ["Adam Hermawan", "Herwandhani Putri"], "journal": "Journal of the Egyptian National Cancer Institute", "year": 2020, "abstract": "BACKGROUND: The focus of trastuzumab resistance biomarkers in recent decades has been on epigenetic and non-coding RNA-based mechanisms. In this study, the potential of miR-494 and its target genes as predictive biomarkers for breast cancer (BC) resistance to trastuzumab was identified. The microarray data were obtained from the GEO database, including GSE101841, GSE75669, and GSE66305. Data processing was conducted using GEO2R to obtain differentially expressed genes (DEGs).\nRESULTS: The data analysis using GEO2R revealed that DEGs from GSE101841 and GSE75669 consisted of 3 and 135 upregulated miRNAs, respectively. On the other hand, the same analysis revealed 8 and 226 downregulated miRNAs for DEGs from GSE101841 and GSE75669, respectively. A Venn diagram showed that one miR was detectable in serum and tissue samples, namely miR-494. The miR-494 target was predicted using the miRecords database and resulted in 69 target genes. A Venn diagram between miR-494 target genes from miRecords and the mRNA array from GSE66305 revealed three potential targets of CNR1, RBM39, and ZNF207. A Kaplan-Meier survival plot showed that BC patients with a high miR-494 level and a low ZNF207 mRNA level had significantly worse overall survival. Validation of target genes in BC samples and trastuzumab-resistant and -sensitive BC cells with GEPIA and ONCOMINE highlighted the potential of CNR1, RBM39, and ZNF207 as predictive biomarkers of trastuzumab resistance in BC cells.\nCONCLUSION: Taken together, these results suggest that miR-494 plays a role in the mechanism of BC resistance to trastuzumab by involving its target genes CNR1, RBM39, and ZNF207.", "claims": [{"id": "claim-method", "statement": "The prognostic value of miR-494 and the target genes (CNR1, RBM39, and ZNF207) was evaluated using Kaplan–Meier survival curves (http://kmplot.com) and the log-rank test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32372307", "locator": "sections[5] \"Kaplan–Meier survival analysis\" paragraph 2", "quote": "The prognostic value of miR-494 and the target genes (CNR1, RBM39, and ZNF207) was evaluated using Kaplan–Meier survival curves (http://kmplot.com) and the log-rank test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article [and its supplementary information files].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32372307", "locator": "sections[20] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analyzed during this study are included in this published article [and its supplementary information files]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:32372307"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE101841"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE101841"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32372307", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s43046-020-00028-2", "sha256": 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