Identification of novel alternative splicing isoform biomarkers and their association with overall survival in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32503434", "kind": "science-record", "revision": 7, "content_hash": "sha256:7c3c3ef08a52a71ed48a543c8c74e55cd5bb51f998edd346333a09f1c1bb7d51", "created_at": "2026-11-01T15:41:03Z", "updated_at": "2026-11-01T22:01:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of novel alternative splicing isoform biomarkers and their association with overall survival in colorectal cancer.", "doi": "10.1186/s12876-020-01288-x", "pmid": "32503434", "authors": ["Haifeng Lian", "Aili Wang", "Yuanyuan Shen", "Qian Wang", "Zhenru Zhou", "Ranran Zhang", "Kun Li", "Chengxia Liu", "Hongtao Jia"], "journal": "BMC gastroenterology", "year": 2020, "abstract": "BACKGROUND: Alternative splicing (AS) is an important mechanism of regulating eukaryotic gene expression. Understanding the most common AS events in colorectal cancer (CRC) will help developing diagnostic, prognostic or therapeutic tools in CRC.\nMETHODS: Publicly available RNA-seq data of 28 pairs of CRC and normal tissues and 18 pairs of metastatic and normal tissues were used to identify AS events using PSI and DEXSeq methods.\nRESULT: The highly significant splicing events were used to search a database of The Cancer Genome Atlas (TCGA). We identified AS events in 9 genes in CRC (more inclusion of CLK1-E4, COL6A3-E6, CD44v8-10, alternative first exon regulation of ARHGEF9, CHEK1, HKDC1 and HNF4A) or metastasis (decrease of SERPINA1-E1a, CALD-E5b, E6). Except for CHEK1, all other 8 splicing events were confirmed by TCGA data with 382 CRC tumors and 51 normal controls. The combination of three splicing events was used to build a logistic regression model that can predict sample type (CRC or normal) with near perfect performance (AUC = 1). Two splicing events (COL6A3 and HKDC1) were found to be significantly associated with patient overall survival. The AS features of the 9 genes are highly consistent with previous reports and/or relevant to cancer biology.\nCONCLUSIONS: The significant association of higher expression of the COL6A3 E5-E6 junction and HKDC1 E1-E2 with better overall survival was firstly reported. This study might be of significant value in the future biomarker, prognosis marker and therapeutics development of CRC.", "claims": [{"id": "claim-method", "statement": "A logistic regression model was built based on randomly selected 50% of the samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32503434", "locator": "sections[7] \"Logistic regression analysis of TCGA data\" paragraph 2", "quote": "A logistic regression model was built based on randomly selected 50% of the samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Availability of data and materialsThe scripts (Perl and R) used to calculate read counts mapped to Refseq-defined exons and introns are available at https://github.com/tianjiagene/GneneBlockReadCount. The two public RNA-seq data were from GEO database (accession number: GSE50760 and GSE95132).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32503434", "locator": "sections[25] \"notes\" paragraph 1", "quote": "Availability of data and materialsThe scripts (Perl and R) used to calculate read counts mapped to Refseq-defined exons and introns are available at https://github.com/tianjiagene/GneneBlockReadCount. The two public RNA-seq data were from GEO database (accession number: GSE50760 and GSE95132)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "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:32503434"], "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:GSE50760"], "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:GSE50760"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32503434", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12876-020-01288-x", "sha256": "sha256:7611e51fa84f8cebad98d856f87c9409ed4691a5ce726590fc38958df22f3eb7", "bytes": 38873, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12876-020-01288-x", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7275609", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE50760", "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=GSE50760", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE50760", "version": null, "note": "Availability of data and materialsThe scripts (Perl and R) used to calculate read counts mapped to Refseq-defined exons and introns are available at https://github.com/tianjiagene/GneneBlockReadCount. 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METHODS: Publicly available RNA-seq data of 28 pairs of CRC and normal tissues a", "ulated alternative exon splicing events from RNA-seq data using multiple CRC sample cohorts We co", "e CRC sample cohorts We collected two public RNA-seq data related to CRC from GEO database (Suppl"], "order_basis": "paper_text_order:359", "id": "assay:1"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["A logistic regression model was built based on randomly selected 50% of the samples."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}