RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37818590", "kind": "science-record", "revision": 4, "content_hash": "sha256:d6fe2034d338506fcf81de2b412afc3d9429e54ff6545cd1669405809a6c6227", "created_at": "2026-11-01T19:02:40Z", "updated_at": "2026-11-01T22:10:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery.", "doi": "10.1111/nan.12943", "pmid": "37818590", "authors": ["Natalie Grima", "Sidong Liu", "Dean Southwood", "Lyndal Henden", "Andrew Smith", "Albert Lee", "Dominic B Rowe", "Susan D'Silva", "Ian P Blair", "Kelly L Williams"], "journal": "Neuropathology and applied neurobiology", "year": 2023, "abstract": "AIM: Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease with limited therapeutic options. A key factor limiting the development of effective therapeutics is the lack of disease biomarkers. We sought to assess whether biomarkers for diagnosis, prognosis or cohort stratification could be identified by RNA sequencing (RNA-seq) of ALS patient peripheral blood.\nMETHODS: Whole blood RNA-seq data were generated for 96 Australian sporadic ALS (sALS) cases and 48 healthy controls (NCBI GEO accession GSE234297). Differences in sALS-control gene expression, transcript usage and predicted leukocyte proportions were assessed, with pathway analysis used to predict the activity state of biological processes. Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms were applied to search for diagnostic and prognostic gene expression patterns. Unsupervised clustering analysis was employed to determine whether sALS patient subgroups could be detected.\nRESULTS: Two hundred and forty-five differentially expressed genes were identified in sALS patients relative to controls, with enrichment of immune, metabolic and stress-related pathways. sALS patients also demonstrated switches in transcript usage across a small set of genes. We established a classification model that distinguished sALS patients from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes. Clustering analysis identified four patient subgroups with gene expression signatures and immune cell proportions reflective of distinct peripheral effects.\nCONCLUSIONS: Our findings suggest that peripheral blood RNA-seq can identify diagnostic biomarkers and distinguish molecular subtypes of sALS patients however, its prognostic value requires further investigation.", "claims": [{"id": "claim-method", "statement": "To identify genes that can differentiate sALS cases from controls, we followed a previous study [36] and used the selected features to train a Random Forest model [37] for sALS and control classification.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37818590", "locator": "sections[16] \"Classification model\" paragraph 2", "quote": "To identify genes that can differentiate sALS cases from controls, we followed a previous study [36] and used the selected features to train a Random Forest model [37] for sALS and control classification."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Raw FASTQ files, raw gene counts and corresponding offset matrix are available under NCBI Gene Expression Omnibus (GEO) accession GSE234297.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37818590", "locator": "sections[46] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "Raw FASTQ files, raw gene counts and corresponding offset matrix are available under NCBI Gene Expression Omnibus (GEO) accession GSE234297."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A 20-gene classification model distinguished sporadic ALS patients from controls with 78% accuracy, sensitivity of 79%, and specificity of 75%.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37818590", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We established a classification model that distinguished sALS patients from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A 20-gene classification model distinguished sporadic ALS patients from controls with 78% accuracy, sensitivity of 79%, and specificity of 75%.", "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:37818590"], "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. 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METHODS: W", "tient peripheral blood. METHODS: Whole blood RNA-seq data were generated for 96 Australian sporad", ": Our findings suggest that peripheral blood RNA-seq can identify diagnostic biomarkers and disti"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["1.9% of detected genes. Previous RNA‐seq and microarray studies that examined whole blood gene expre", "est whole blood sALS–control gene expression microarray study to date (397 ALS patients, 645 control"], "order_basis": "paper_text_order:14573", "id": "assay:2"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["ranscription regulation, immune response and apoptosis [3, 4, 11]. The ferroptosis signalling pathw"], "order_basis": "paper_text_order:15677", "id": "assay:3"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To identify genes that can differentiate sALS cases from controls, we followed a previous study [36] and used the selected features to train a Random Forest model [37] for sALS and control classification."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}