Unraveling the Molecular Complexity of Adenoid Cystic Carcinoma (ACC): A Comprehensive Exploration of Hub Genes, Protein-Protein Interaction (PPI) Networks, microRNA (miRNA) Involvement, and Drug-Gene Interactions (DGIs).

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38524085", "kind": "science-record", "revision": 5, "content_hash": "sha256:cb210b3bc52f09abe653e63a584f11c2d93858548691ceafd05278f144e94e41", "created_at": "2026-11-01T19:20:28Z", "updated_at": "2026-11-01T22:53:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unraveling the Molecular Complexity of Adenoid Cystic Carcinoma (ACC): A Comprehensive Exploration of Hub Genes, Protein-Protein Interaction (PPI) Networks, microRNA (miRNA) Involvement, and Drug-Gene Interactions (DGIs).", "doi": "10.7759/cureus.54730", "pmid": "38524085", "authors": ["Roja L Karri", "Manasa Bojji", "Amrutha Rudraraju", "Abdul Sadik Mohammad", "Vamseedhar Kosuru", "Sandeep Kalisipudi"], "journal": "Cureus", "year": 2024, "abstract": "Background Adenoid cystic carcinoma (ACC) poses clinical challenges with its unique histology and potential for perineural invasion, recurrence, and distant metastases. Recent genomic advancements have unveiled key genetic alterations in ACC, offering insights into its pathogenesis. Aim This study aims to unravel the intricate molecular landscape of ACC through a comprehensive analysis of gene expression patterns. By integrating data from multiple microarray datasets, the study explores differentially expressed genes (DEGs), their functional enrichment, protein-protein interactions (PPI), hub genes, microRNA (miRNA) involvement, transcription factors, and potential drug-gene interactions. Methods Three microarray datasets (GSE88804, GSE153002, and GSE36820) related to ACC were selected from the Gene Expression Omnibus (GEO) repository. DEGs were identified using GEO2R and further analyzed for commonalities and differences. Functional enrichment analysis, including Gene Set Enrichment Analysis (GSEA), provided insights into biological processes, cellular components, molecular functions, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with ACC. PPI networks and hub genes were identified using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (STRING Consortium, Lausanne, Switzerland) database and Cytoscape (Cytoscape Consortium, California, United States). The study also explored miRNAs, transcription factors, and potential drug-gene interactions. Results The integrated analysis revealed 339 common upregulated and 643 downregulated DEGs in ACC. Functional and pathway enrichment analyses unveiled the involvement of these genes in critical cellular processes, signaling cascades, and pathways. The PPI network, comprising 904 nodes and 4139 edges, highlighted the complexity of interactions. Hub genes, including KIF11, BUB1, and DLGAP5, were identified, shedding light on their pivotal roles in cell cycle regulation. The study also identified miRNAs (e.g., hsa-mir-7-5p and hsa-mir-138-5p) and transcription factors (e.g., E2F1 and TP53) associated with ACC. Drug-gene interactions have identified potential therapeutic options, including amsacrine and rucaparib. Conclusions The ACC gene expression highlights a nuanced molecular landscape, identifying pivotal hub genes such as KIF11 and CDK1 as potential therapeutic targets for ACC, given their roles in cell cycle progression. The dysregulation of microRNAs and transcription factors adds complexity to ACC's molecular profile. Exploration of drug-gene interactions reveals promising therapeutic strategies, involving FDA-approved drugs such as amsacrine and rucaparib, providing avenues for personalized interventions.", "claims": [{"id": "claim-method", "statement": "GEO2R was utilised in conjunction with the R packages GEOquery and limma to identify DEGs that met stringent criteria, such as p-value ≤ 0.05 and logFC > 0.5.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38524085", "locator": "sections[2] \"Materials and methods\" paragraph 5", "quote": "GEO2R was utilised in conjunction with the R packages GEOquery and limma to identify DEGs that met stringent criteria, such as p-value ≤ 0.05 and logFC > 0.5."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE88804 comprised nine ACC tissues and seven normal tissues, while GSE153002 included 30 ACC and seven normal samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38524085", "locator": "sections[2] \"Materials and methods\" paragraph 3", "quote": "GSE88804 comprised nine ACC tissues and seven normal tissues, while GSE153002 included 30 ACC and seven normal samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The integrated analysis identified 339 common upregulated and 643 downregulated genes across GSE88804, GSE153002, and GSE36820.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38524085", "locator": "sections[3] \"Results\" paragraph 3", "quote": "In the integrated analysis of gene expression datasets GSE88804, GSE153002, and GSE36820, a comprehensive exploration of ACC revealed a set of 339 common upregulated and 643 downregulated genes (Figure 1 and Figure 2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The integrated analysis identified 339 common upregulated and 643 downregulated genes across GSE88804, GSE153002, and GSE36820.", "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:38524085"], "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. 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By integrating data from multiple microarray datasets, the study explores differentially", "ential drug-gene interactions. Methods Three microarray datasets (GSE88804, GSE153002, and GSE36820)", "e Expression Omnibus (GEO) repository, three microarray datasets (GSE88804, GSE153002, and GSE36820", "atasets were based on the GPL6244 platforms (Affymetrix Human Gene 1.0 ST Array). Additionally, the"], "order_basis": "paper_text_order:630", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["GEO2R was utilised in conjunction with the R packages GEOquery and limma to identify DEGs that met stringent criteria, such as p-value ≤ 0.05 and logFC > 0.5."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}