Transcriptomics-based validation of the relatedness of heterogeneous nuclear ribonucleoproteins to chronic lymphocytic leukemia as potential biomarkers of the disease aggressiveness.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30957125", "kind": "science-record", "revision": 7, "content_hash": "sha256:6bd6224a539e96d343ab6f9d29b97d26106efe1ac100f30760f258119a532dbf", "created_at": "2026-11-01T15:40:44Z", "updated_at": "2026-11-01T21:22:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptomics-based validation of the relatedness of heterogeneous nuclear ribonucleoproteins to chronic lymphocytic leukemia as potential biomarkers of the disease aggressiveness.", "doi": "10.15537/smj.2019.4.23380", "pmid": "30957125", "authors": ["Suliman A Alsagaby"], "journal": "Saudi medical journal", "year": 2019, "abstract": "OBJECTIVES: To use independent transcriptomics data sets of cancer patients with prognostic information from public repositories to validate the relevance of our previously described chronic lymphocytic leukemia (CLL)-related proteins at the level of transcription (mRNA) to the prognosis of CLL. Methods: This is a validation study that was conducted at Majmaah University, Kingdom of Saudi Arabia between January-2017 and July-2018. Two independent data sets of CLL transcriptomics from Gene Expression Omnibus (GEO) with time-to-first treatment (TTFT) data (GSE39671; 130 patients) and information about overall survival (OS) (GSE22762; 107 patients) were used for the validation analyses. To further investigate the relatedness of a transcript of interest to other neoplasms, 6 independent data sets of cancer transcriptomics with prognostic information (1865 patients) from the cancer genomics atlas (TCGA) were used. Pathway-enrichment analyses were conducted using Reactome; and correlation analyses of gene expression were performed using Pearson score. Results: Nine of the CLL-related proteins exhibited transcript expression that predicted TTFT and 7 of the CLL-related proteins showed mRNA levels that predicted OS in CLL patients (p≤0.05). Of these transcripts, 8 were different types of heterogeneous nuclear ribonucleoproteins (HNRNPs); and 2 (HNRNPUL2 and HIST1C1H) retained prognostic significance in the 2 independent data sets. Furthermore, genes that enriched CLL-related pathways (p≤0.05; false discovery rate [FDR] ≤0.05) were found to correlate with the expression of HNRNPUL2 (Pearson score: ≥0.50; p lessthan 0.00001). Finally, increased expression of HNRNPUL2 was indicative of poor prognosis of various types of cancer other than CLL (p less than 0.05). Conclusion: The cognate transcripts of 14 of our CLL-related proteins significantly predicted CLL prognosis.", "claims": [{"id": "claim-method", "statement": "Prism Graphpad software was used to create Kaplan-Meier curves of TTFT, RFS, and OS; the Log-rank test was used to calculate p-values and hazard ratios (HRs).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30957125", "locator": "sections[9] \"Statistical analyses\" paragraph 2", "quote": "Prism Graphpad software was used to create Kaplan-Meier curves of TTFT, RFS, and OS; the Log-rank test was used to calculate p-values and hazard ratios (HRs)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data set GSE39671 contained information of TTFT and the data set GSE22762 included details of OS for the individual patients. Both data sets were reported by independent authors and were based on Affymetrix Human Genome U133 Plus 2.0 Array (USA). 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The data set GSE39671 was generated from 130 CLL patients and the data set GSE22762 was reported from 107 CLL patients."}], "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:30957125"], "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:GSE22762"], "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:GSE22762"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30957125", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.15537/smj.2019.4.23380", "sha256": "sha256:874965cc99d7518006f1a0c96210f5049a9a1633de220a7720cb1938296ea678", "bytes": 26870, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.15537/smj.2019.4.23380", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6506648", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE22762", "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=GSE22762", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE22762", "version": null, "note": "The data set GSE39671 contained information of TTFT and the data set GSE22762 included details of OS for the individual patients. 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