Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35052318", "kind": "science-record", "revision": 4, "content_hash": "sha256:ee81d5306062eb209edd0e9d0bdaf8bbcf4500997b78ac89267a0a37bdb51aa1", "created_at": "2026-11-01T17:36:47Z", "updated_at": "2026-11-01T22:05:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series.", "doi": "10.3390/healthcare10010155", "pmid": "35052318", "authors": ["Joaquim Carreras", "Naoya Nakamura", "Rifat Hamoudi"], "journal": "Healthcare (Basel, Switzerland)", "year": 2022, "abstract": "Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients' overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.", "claims": [{"id": "claim-method", "statement": "An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35052318", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression data (GEO data sets) were obtained from the publicly available database of the NCBI resources webpage, located at https://www.ncbi.nlm.nih.gov/gds (accessed on 15 August 2021).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35052318", "locator": "sections[32] \"Data Availability Statement\" paragraph 2", "quote": "The gene expression data (GEO data sets) were obtained from the publicly available database of the NCBI resources webpage, located at https://www.ncbi.nlm.nih.gov/gds (accessed on 15 August 2021)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35052318", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9).", "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:35052318"], "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:GSE10846"], "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:GSE10846"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35052318", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/healthcare10010155", "sha256": 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Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes,", "ng (Figure 11, Figure 12 and Figure 13), and immunohistochemistry for RGS1 (Figure 14). Overall survival in a", "(arrows). Overall survival according to the immunohistochemical expression of RGS1. 2.5.2. Algorithm Based o", "thods 1 and 2 with the MCL35 signature. 3.7. Immunohistochemical Analysis of RGS1 RGS1 was identified as an M"], "order_basis": "paper_text_order:890", "id": "assay:1"}, {"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": ["ese genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival"], "order_basis": "paper_text_order:973", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Cox regression"], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}