Arecoline Is Associated With Inhibition of Cuproptosis and Proliferation of Cancer-Associated Fibroblasts in Oral Squamous Cell Carcinoma: A Potential Mechanism for Tumor Metastasis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35875097", "kind": "science-record", "revision": 4, "content_hash": "sha256:192525799711f74b906311e78019f877581ee9f443d4b84eb0025b5994d435b5", "created_at": "2026-11-01T18:14:57Z", "updated_at": "2026-11-01T22:07:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Arecoline Is Associated With Inhibition of Cuproptosis and Proliferation of Cancer-Associated Fibroblasts in Oral Squamous Cell Carcinoma: A Potential Mechanism for Tumor Metastasis.", "doi": "10.3389/fonc.2022.925743", "pmid": "35875097", "authors": ["Jinfei Li", "Shuangyi Chen", "Yuxuan Liao", "Hongyi Wang", "Dawei Zhou", "Bo Zhang"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: Metastatic disease remains the primary cause of death in patients with oral squamous cell carcinoma (OSCC), especially those who use betel nut. The different steps of the metastatic cascade rely on reciprocal interactions between cancer cells and the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the TME of OSCC. However, the precise mechanisms regulating CAFs in OSCC are poorly understood.\nMethods: Thirteen genes related to the arecoline were analyzed to explore the significant ones involved in arecoline-related OSCC metastasis. The GSE139869 (n = 10) and The Cancer Genome Atlas (TCGA)-OSCC data (n = 361) were mined for the identification of the differentially expressed genes. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the independent prognostic signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functional enrichment of selected genes, and gene set enrichment analysis of cuproptosis-related genes was completed. Spearman's analysis and Tumor Immune Estimation Resource (TIMER) were used to visualize the correlation between the infiltration of CAFs and the gene expression. The correlation analysis of the cells and different genes, including CAF infiltration and transcripts per million expression, was assessed. The relationship between arecoline and CAFs was confirmed by cell counting kit-8 assay (CCK-8). CancerSEA was searched to identify the single-cell phenotype.\nResult: Arecoline-associated fibrosis-related OSCC differentially expressed genes (AFOC-DEGs), namely, PLAU, IL1A, SPP1, CCL11, TERT, and COL1A2, were screened out and selected from the Gene Expression Omnibus (GEO) database and TCGA database. AFOC-DEGs were highly expressed in OSCC, which led to poor survival of patients. Functional enrichment analysis, protein-protein interaction network construction, and Spearman's correlation analysis all suggested that AFOC-DEGs were closely associated with cuproptosis. Cellular experiments demonstrated that arecoline stimulation could significantly increase the cell viability of CAFs. Single-sample Gene Set Enrichment Analysis (ssGSEA) results showed that GLS and MTF1 were highly expressed when fibroblasts proliferated at high enrichment levels. In addition, analysis of single-cell sequencing results suggested that OSCC cells with high expression of AFOC-DEGs were associated with OSCC metastasis.\nConclusion: We found a close association between arecoline, cuproptosis, and CAFs, which might play an important role in the metastasis of OSCC.", "claims": [{"id": "claim-method", "statement": "To assess the prognostic value of the DEGs from TCGA, the least absolute shrinkage and selection operator (LASSO) regression was conducted using the R package “glmnet” to feature selection, and 10-fold cross-validation was used to narrow down the candidate genes associated with OSCC prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35875097", "locator": "sections[5] \"Evaluation of the Prognostic Value of Differentially Expressed Genes\" paragraph 2", "quote": "To assess the prognostic value of the DEGs from TCGA, the least absolute shrinkage and selection operator (LASSO) regression was conducted using the R package “glmnet” to feature selection, and 10-fold cross-validation was used to narrow down the candidate genes associated with OSCC prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The datasets [GSE139869, GSE160395, GSE59414 and GSE38227] for this study can be found in the [GEO Database] [www.ncbi.nlm.nih.gov/geo]. The datasets [TCGA-OSCC] for this study can be found in the [TCGA Database] [https://tcga-data.nci.nih.gov/tcga]. The datasets [FAG] for this study can be found in the [FibROAD Database][https://www.fibroad.org/download.php].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35875097", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The datasets [GSE139869, GSE160395, GSE59414 and GSE38227] for this study can be found in the [GEO Database] [www.ncbi.nlm.nih.gov/geo]. The datasets [TCGA-OSCC] for this study can be found in the [TCGA Database] [https://tcga-data.nci.nih.gov/tcga]. The datasets [FAG] for this study can be found in the [FibROAD Database][https://www.fibroad.org/download.php]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six-gene LASSO-Cox prognostic model achieved AUCs above 0.6 for 1-, 3-, and 5-year survival, with a maximum AUC of 0.636.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35875097", "locator": "sections[14] \"Construction of the Prognostic Gene Signature\" paragraph 5", "quote": "Additionally, the ROC curves of the prognosis model for the survival probability at 1, 3, and 5 years are shown in Figure 3F . All the area under the curve (AUC) values were higher than 0.6 with the maximum AUC value reaching 0.636, certifying the satisfying specificity and sensitivity of the prognosis model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-gene LASSO-Cox prognostic model achieved AUCs above 0.6 for 1-, 3-, and 5-year survival, with a maximum AUC of 0.636.", "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:35875097"], "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:GSE139869"], "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:GSE139869"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35875097", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2022.925743", "sha256": "sha256:21514dcfd629b411c82b220cd49b7aa30158f8ca6eaa8891d92746cb37b98eda", "bytes": 62922, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2022.925743", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9303015", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE139869", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139869", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE139869", "version": null, "note": "Publicly available datasets were analyzed in this study. The datasets [GSE139869, GSE160395, GSE59414 and GSE38227] for this study can be found in the [GEO Database] [www.ncbi.nlm.nih.gov/geo]. The datasets [TCGA-OSCC] for this study can be found in the [TCGA Database] [https://tcga-data.nci.nih.gov/tcga]. The datasets [FAG] for this study can be found in the [FibROAD Database][https://www.fibroad.org/download.php].", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. The datasets [GSE139869, GSE160395, GSE59414 and GSE38227] for this study can be found in the [GEO Database] [www.ncbi.nlm.nih.gov/geo]. The datasets [TCGA-OSCC] for this study can be found in the [TCGA Database] [https://tcga-data.nci.nih.gov/tcga]. The datasets [FAG] for this study can be found in the [FibROAD Database][https://www.fibroad.org/download.php].", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4147965", "GSM4147966", "GSM4147967", "GSM4147968", "GSM4147969", "GSM4147970", "GSM4147971", "GSM4147972", "GSM4147973", "GSM4147974"], "sample_count": 10, "group_sizes": null, "group_sizes_basis": null, "platform": "19612", "taxon": "Homo sapiens", "title": "LncRNA expression profiles in areca nut chewing induced Tongue Squamous Cell Carcinoma (TSCC) and matched paracancerous tissues", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE160395", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160395", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE160395", "version": null, "note": "Publicly available datasets were analyzed in this study. The datasets [GSE139869, GSE160395, GSE59414 and GSE38227] for this study can be found in the [GEO Database] [www.ncbi.nlm.nih.gov/geo]. The datasets [TCGA-OSCC] for this study can be found in the [TCGA Database] [https://tcga-data.nci.nih.gov/tcga]. The datasets [FAG] for this study can be found in the [FibROAD Database][https://www.fibroad.org/download.php].", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. The datasets [GSE139869, GSE160395, GSE59414 and GSE38227] for this study can be found in the [GEO Database] [www.ncbi.nlm.nih.gov/geo]. The datasets [TCGA-OSCC] for this study can be found in the [TCGA Database] [https://tcga-data.nci.nih.gov/tcga]. The datasets [FAG] for this study can be found in the [FibROAD Database][https://www.fibroad.org/download.php].", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4872672", "GSM4872678", "GSM4872681", "GSM4872685", "GSM4872686", "GSM4872687", "GSM4872688", "GSM4872689"], "sample_count": 8, "group_sizes": null, "group_sizes_basis": null, "platform": "21185", "taxon": "Homo sapiens", "title": "Transcriptomic profiling between HSC-3 cells and its highly metastatic subline, HSC-3-M3 cells", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE139869 not downloaded", "asset:data:geo:GSE139869 licence not read", "asset:data:geo:GSE160395 not downloaded", "asset:data:geo:GSE160395 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "welch_t_test", "label": "Group comparison", "dsl_operations": ["welch_t"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Welch"}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {"hazard_ratio": "e of the p-values ​​for hazard ratio (HR) in the model"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Log-rank"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.7, "band": "medium", "quote": "Spearman's correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-6", "family": null, "specific": null, "label": "LASSO regression with 10-fold cross-validation", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To assess the prognostic value of the DEGs from TCGA, the least absolute shrinkage and selection operator (LASSO) regression was conducted using the R package “glmnet” to feature selection, and 10-fold cross-validation was used to narrow down the candidate genes associated with OSCC prognosis."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["welch_t"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: welch_t_test."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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