Expression Profiling of Calcium Channels and Calcium-Activated Potassium Channels in Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31010205", "kind": "science-record", "revision": 8, "content_hash": "sha256:a54766234257fa3e41fd3b6533dcc1279560304a2937b597e1a5288267d11a5a", "created_at": "2026-11-01T15:40:44Z", "updated_at": "2026-11-01T22:00:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Expression Profiling of Calcium Channels and Calcium-Activated Potassium Channels in Colorectal Cancer.", "doi": "10.3390/cancers11040561", "pmid": "31010205", "authors": ["Sajida Ibrahim", "Hassan Dakik", "Christophe Vandier", "Romain Chautard", "Gilles Paintaud", "Frédéric Mazurier", "Thierry Lecomte", "Maxime Guéguinou", "William Raoul"], "journal": "Cancers", "year": 2019, "abstract": "Background: Colorectal cancer (CRC) is a highly devastating cancer. Ca2+-dependent channels are now considered key regulators of tumor progression. In this study, we aimed to investigate the association of non-voltage gated Ca2+ channels and Ca2+-dependent potassium channels (KCa) with CRC using the transcriptional profile of their genes. Methods: We selected a total of 35 genes covering KCa channels KCNN1-4, KCNMA1 and their subunits KCNMB1-4, endoplasmic reticulum (ER) calcium sensors STIM1 and STIM2, Ca2+ channels ORAI1-3 and the family of cation channels TRP (TRPC1-7, TRPA1, TRPV1/2,4-6 and TRPM1-8). We analyzed their expression in two public CRC datasets from The Cancer Genome Atlas (TCGA) and GSE39582. Results: KCNN4 and TRPM2 were induced while KCNMA1 and TRPM6 were downregulated in tumor tissues comparing to normal tissues. In proximal tumors, STIM2 and KCNN2 were upregulated while ORAI2 and TRPM6 were downregulated. ORAI1 decreased in lymph node metastatic tumors. TRPC1 and ORAI3 predicted poor prognosis in CRC patients. Moreover, we found that ORAI3/ORAI1 ratio is increased in CRC progression and predicted poor prognosis. Conclusions: KCa and Ca2+ channels could be important contributors to CRC initiation and progression. Our results provide new insights on KCa and Ca2+ channels remodeling in CRC.", "claims": [{"id": "claim-method", "statement": "Cox proportional hazard regression was used to perform dichotomic univariate analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31010205", "locator": "sections[12] \"4.3. Survival Analysis\" paragraph 3", "quote": "Cox proportional hazard regression was used to perform dichotomic univariate analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Two CRC datasets, available from public databases, were used in this paper: an RNAseq dataset (TCGA, n = 656 including 47 normal samples) and an expression microarray dataset (GSE39582, n = 585 including 19 normal samples) [43]. The two datasets were selected because of their large size, diversity and the availability of clinical annotations. For the RNAseq dataset, Fragment Per Kilobase Million (FPKM) values were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository) [44] and were log2 transformed after the addition of one (log2(x + 1)) prior to analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31010205", "locator": "sections[10] \"4.1. Public Datasets\" paragraph 2", "quote": "Two CRC datasets, available from public databases, were used in this paper: an RNAseq dataset (TCGA, n = 656 including 47 normal samples) and an expression microarray dataset (GSE39582, n = 585 including 19 normal samples) [43]. The two datasets were selected because of their large size, diversity and the availability of clinical annotations. For the RNAseq dataset, Fragment Per Kilobase Million (FPKM) values were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository) [44] and were log2 transformed after the addition of one (log2(x + 1)) prior to analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:31010205"], "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:GSE39582"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE39582"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31010205", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/cancers11040561", "sha256": "sha256:0273b4923cc1f998b3ea7ba8c70458942a7ad248cfb542e6177185364f0c9ba1", "bytes": 30247, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/cancers11040561", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6521016", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE39582", "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=GSE39582", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE39582", "version": null, "note": "Two CRC datasets, available from public databases, were used in this paper: an RNAseq dataset (TCGA, n = 656 including 47 normal samples) and an expression microarray dataset (GSE39582, n = 585 including 19 normal samples) [43]. The two datasets were selected because of their large size, diversity and the availability of clinical annotations. For the RNAseq dataset, Fragment Per Kilobase Million (FPKM) values were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository) [44] and were log2 transformed after the addition of one (log2(x + 1)) prior to analysis.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Two CRC datasets, available from public databases, were used in this paper: an RNAseq dataset (TCGA, n = 656 including 47 normal samples) and an expression microarray dataset (GSE39582, n = 585 including 19 normal samples) [43]. The two datasets were selected because of their large size, diversity and the availability of clinical annotations. 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