Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38882157", "kind": "science-record", "revision": 4, "content_hash": "sha256:c5ce74ede03efb39b7032d5bccc49bd5b3319a9d78de20121b5421b0ea3e765d", "created_at": "2026-11-01T20:28:56Z", "updated_at": "2026-11-01T22:54:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach.", "doi": "10.1021/acsomega.4c00011", "pmid": "38882157", "authors": ["Ambritha Balasundaram", "Tanisha Saurav Mitra", "Iftikhar Aslam Tayubi", "Hatem Zayed", "George Priya C Doss"], "journal": "ACS omega", "year": 2024, "abstract": "Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.", "claims": [{"id": "claim-method", "statement": "The DE-mRNAs were identified with the cutoff values of |log2FC| +1 ≥ (upregulated DE-mRNAs) and |log2FC| ≤ −1 (downregulated DE-mRNAs) with p-values ≤0.05, obtained using the built-in R/Bioconductor limma package from the GEO2R web server tool.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38882157", "locator": "sections[12] \"Identification of Differently Expressed mRNA (DE-mRNAs)\" paragraph 2", "quote": "The DE-mRNAs were identified with the cutoff values of |log2FC| +1 ≥ (upregulated DE-mRNAs) and |log2FC| ≤ −1 (downregulated DE-mRNAs) with p-values ≤0.05, obtained using the built-in R/Bioconductor limma package from the GEO2R web server tool."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Based on these criteria, we obtained the GSE42568 GEO Data Set containing breast cancer and normal tissues from the NCBI database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38882157", "locator": "sections[3] \"Data Collection and Identification of DE-mRNAs\" paragraph 2", "quote": "Based on these criteria, we obtained the GSE42568 GEO Data Set containing breast cancer and normal tissues from the NCBI database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Across GSE42568, GSE185645, and TCGA-BRCA, the analysis identified 159 common differentially expressed mRNAs, comprising 78 upregulated and 81 downregulated mRNAs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38882157", "locator": "sections[12] \"Identification of Differently Expressed mRNA (DE-mRNAs)\" paragraph 4", "quote": "After overlapping the DE-mRNAs of 2 GEO data sets with the TCGA data set, 159 DE-mRNAs (78 upregulated and 81 downregulated) were obtained and visualized in the Venn diagram (Figure 2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Across GSE42568, GSE185645, and TCGA-BRCA, the analysis identified 159 common differentially expressed mRNAs, comprising 78 upregulated and 81 downregulated mRNAs.", "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:38882157"], "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:GSE185645"], "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:GSE185645"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38882157", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1021/acsomega.4c00011", "sha256": 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