Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37580667", "kind": "science-record", "revision": 3, "content_hash": "sha256:66fabc87682a336885a72790ecd92d7ef02bc491cd7b8f3f50a1afe91f88376f", "created_at": "2026-11-01T19:02:22Z", "updated_at": "2026-11-01T22:10:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma.", "doi": "10.1186/s12935-023-03007-4", "pmid": "37580667", "authors": ["Zhengyu Yu", "Bingquan Qiu", "Hui Zhou", "Linfeng Li", "Ting Niu"], "journal": "Cancer cell international", "year": 2023, "abstract": "BACKGROUND: About 10% of hematologic malignancies are multiple myeloma (MM), an untreatable cancer. Although lactate and branched-chain amino acids (BCAA) are involved in supporting various tumor growth, it is unknown whether they have any bearing on MM prognosis.\nMETHODS: MM-related datasets (GSE4581, GSE136337, and TCGA-MM) were acquired from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Lactate and BCAA metabolism-related subtypes were acquired separately via the R package \"ConsensusClusterPlus\" in the GSE4281 dataset. The R package \"limma\" and Venn diagram were both employed to identify lactate-BCAA metabolism-related genes. Subsequently, a lactate-BCAA metabolism-related prognostic risk model for MM patients was constructed by univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The gene set enrichment analysis (GSEA) and R package \"clusterProfiler\"were applied to explore the biological variations between two groups. Moreover, single-sample gene set enrichment analysis (ssGSEA), Microenvironment Cell Populations-counter (MCPcounte), and xCell techniques were applied to assess tumor microenvironment (TME) scores in MM. Finally, the drug's IC50 for treating MM was calculated using the \"oncoPredict\" package, and further drug identification was performed by molecular docking.\nRESULTS: Cluster 1 demonstrated a worse prognosis than cluster 2 in both lactate metabolism-related subtypes and BCAA metabolism-related subtypes. 244 genes were determined to be involved in lactate-BCAA metabolism in MM. The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets. For the high-risk group, a total of 13 entries were enriched. 16 entries were enriched to the low-risk group. Immune scores, stromal scores, immune infiltrating cells (except Type 17 T helper cells in ssGSEA algorithm), and 168 drugs'IC50 were statistically different between two groups. Alkylating potentially serves as a new agent for MM treatment.\nCONCLUSIONS: CKS2 and LYZ were identified as lactate-BCAA metabolism-related genes in MM, then a novel prognostic risk model was built by using them. In summary, this research may uncover novel characteristic genes signature for the treatment and prognostic of MM.", "claims": [{"id": "claim-method", "statement": "By using univariate Cox analysis of intersecting genes (lactate-BCAA metabolism-related gene) in the training set, the prognosis-related genes were acquired (P < 0.005) Subsequently, the most predictive prognostic genes were identified by LASSO [20] analysis and multivariate Cox analysis then sequentially.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37580667", "locator": "sections[6] \"Construction and validation of the prognostic risk model of MM\" paragraph 2", "quote": "By using univariate Cox analysis of intersecting genes (lactate-BCAA metabolism-related gene) in the training set, the prognosis-related genes were acquired (P < 0.005) Subsequently, the most predictive prognostic genes were identified by LASSO [20] analysis and multivariate Cox analysis then sequentially."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The public data used in this study can be found from the Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database. All other data supporting the findings of this study are available within the paper. The raw data that support the findings of this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37580667", "locator": "sections[36] \"notes\" paragraph 1", "quote": "The public data used in this study can be found from the Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database. All other data supporting the findings of this study are available within the paper. The raw data that support the findings of this study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37580667", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets.", "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:37580667"], "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:GSE136337"], "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:GSE136337"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37580667", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12935-023-03007-4", "sha256": "sha256:7c474af8fb501182a3c2ab1bf954c4304663d62f020cefd653a08de1a4b00f9f", "bytes": 46323, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12935-023-03007-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10426219", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE136337", "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=GSE136337", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE136337", "version": null, "note": "The public data used in this study can be found from the Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database. All other data supporting the findings of this study are available within the paper. The raw data that support the findings of this study are available from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The public data used in this study can be found from the Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database. All other data supporting the findings of this study are available within the paper. 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