Machine learning identifies SLC6A14 as a novel biomarker promoting the proliferation and metastasis of pancreatic cancer via Wnt/β-catenin signaling.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38267509", "kind": "science-record", "revision": 4, "content_hash": "sha256:06418a29db1016e346d79580a5182c126bd49b71c5bd298178b56b180fa97678", "created_at": "2026-11-01T19:20:11Z", "updated_at": "2026-11-01T22:53:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning identifies SLC6A14 as a novel biomarker promoting the proliferation and metastasis of pancreatic cancer via Wnt/β-catenin signaling.", "doi": "10.1038/s41598-024-52646-8", "pmid": "38267509", "authors": ["Cunshu Dang", "Quan Bian", "Fengbiao Wang", "Han Wang", "Zhipeng Liang"], "journal": "Scientific reports", "year": 2024, "abstract": "Pancreatic cancer (PC) has the poorest prognosis compared to other common cancers because of its aggressive nature, late detection, and resistance to systemic treatment. In this study, we aimed to identify novel biomarkers for PC patients and further explored their function in PC progression. We analyzed GSE62452 and GSE28735 datasets, identifying 35 differentially expressed genes (DEGs) between PC specimens and non-tumors. Based on 35 DEGs, we performed machine learning and identified eight diagnostic genes involved in PC progression. Then, we further screened three critical genes (CTSE, LAMC2 and SLC6A14) using three GEO datasets. A new diagnostic model was developed based on them and showed a strong predictive ability in screen PC specimens from non-tumor specimens in GEO, TCGA datasets and our cohorts. Then, clinical assays based on TCGA datasets indicated that the expression of LAMC2 and SLC6A14 was associated with advanced clinical stage and poor prognosis. The expressions of LAMC2 and SLC6A14, as well as the abundances of a variety of immune cells, exhibited a significant positive association with one another. Functionally, we confirmed that SLC6A14 was highly expressed in PC and its knockdown suppressed the proliferation, migration, invasion and EMT signal via regulating Wnt/β-catenin signaling pathway. Overall, our findings developed a novel diagnostic model for PC patients. SLC6A14 may promote PC progression via modulating Wnt/β-catenin signaling. This work offered a novel and encouraging new perspective that holds potential for further illuminating the clinicopathological relevance of PC as well as its molecular etiology.", "claims": [{"id": "claim-method", "statement": "The DEGs were used to perform the LASSO regression analysis in the “glmnet” package in R. This analysis was performed in order to screen the key genes. SVM-RFE has been applied in pattern recognition and classification problems.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38267509", "locator": "sections[6] \"Screening of the diagnostic genes\" paragraph 2", "quote": "The DEGs were used to perform the LASSO regression analysis in the “glmnet” package in R. This analysis was performed in order to screen the key genes. SVM-RFE has been applied in pattern recognition and classification problems."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in The Cancer Genome Atlas database (TCGA, https://tcga-data.nci.nih.gov/tcga/) and the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) [GSE15471, GSE62452, GSE16515, GSE32676 and GSE28735]. The other data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38267509", "locator": "sections[35] \"notes\" paragraph 1", "quote": "The datasets analyzed during the current study are available in The Cancer Genome Atlas database (TCGA, https://tcga-data.nci.nih.gov/tcga/) and the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) [GSE15471, GSE62452, GSE16515, GSE32676 and GSE28735]. The other data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:38267509"], "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:GSE15471"], "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:GSE15471"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38267509", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-024-52646-8", "sha256": "sha256:feec619e27f5507df25fb6b6c2926293c5082d895f82e18ba0b6148ed7a0a972", "bytes": 48539, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-024-52646-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10808089", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15471", "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=GSE15471", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15471", "version": null, "note": "The datasets analyzed during the current study are available in The Cancer Genome Atlas database (TCGA, https://tcga-data.nci.nih.gov/tcga/) and the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) [GSE15471, GSE62452, GSE16515, GSE32676 and GSE28735]. 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