Integrating RNA-seq and scRNA-seq to explore the biological significance of NAD + metabolism-related genes in the initial diagnosis and relapse of childhood B-cell acute lymphoblastic leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36439178", "kind": "science-record", "revision": 4, "content_hash": "sha256:d9253f9cfdbf86a49bee563930f47f2d1716946b19696bca3e5c95bf414f6296", "created_at": "2026-11-01T19:00:55Z", "updated_at": "2026-11-01T22:50:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrating RNA-seq and scRNA-seq to explore the biological significance of NAD + metabolism-related genes in the initial diagnosis and relapse of childhood B-cell acute lymphoblastic leukemia.", "doi": "10.3389/fimmu.2022.1043111", "pmid": "36439178", "authors": ["Chao Lin", "Jia-Qi Xu", "Gui-Chao Zhong", "Hui Chen", "Hong-Man Xue", "Mo Yang", "Chun Chen"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Nicotinamide Adenine Dinucleotide (NAD) depletion is reported to be a potential treatment for B-cell Acute Lymphoblastic Leukemia (B-ALL), but the mechanism of NAD metabolism-related genes (NMRGs) in B-ALL relapse remains unclear.\nMethods: Transcriptome data (GSE3912), and single-cell sequencing data (GSE130116) of B-ALL patients were downloaded from Gene Expression Omnibus (GEO) database. NMRGs were sourced from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Further, the differentially expressed NMRGs (DE-NMRGs) were selected from the analysis between initial diagnosis and relapse B-ALL samples, which further performed functional enrichment analyses. The biomarkers were obtained through random forest (RF) algorithm and repeated cross validation. Additionally, cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the immune cell differences between the initial diagnosis and relapse samples, and the correlations between biomarkers and gene markers of differential immune cells were analyzed. Furthermore, single cell RNA sequencing was conducted in the GSE130116 dataset to find key cell clusters. In addition, according to biomarkers expressions, cell clusters were categorized into high and low biomarker expression groups, and Gene Set Enrichment Analysis (GSEA) analysis was performed on them. Finally, the cell clusters with the highest expression of biomarkers were selected to explore the roles of biomarkers in different cell clusters and identify transcription factors (TFs) influencing biological markers.\nResults: 23 DE-NMRGs were screened out, which were mainly enriched in nucleoside phosphate metabolic process, nucleotide metabolic process, and Nicotinate and nicotinamide metabolism. Moreover, 3 biomarkers (NADSYN1, SIRT3, and PARP6) were identified from the machine learning. CIBERSORT results demonstrated that four types of immune cells (B Cells naive, Monocyte, Neutrophils, and T cells CD4 memory Activated) were significantly different between the initial diagnosis and the relapse B-ALL samples, and there were strong correlations between biomarkers and differential immune cells such as positive correlation between NADSYN1 and B Cells naive. The single cell analyses showed that the biomarkers were highly expressed in common myeloid progenitors (CMP), granulocyte-macrophage progenitor (GMP), and megakaryocyte-erythroid progenitor (MEP) cell clusters. Gene set enrichment analysis (GSEA) results indicated that 55 GO terms and 3 KEGG pathways were enriched by the genes in high and low biomarker expression groups. It was found that TF CREB3L2(+) was significantly reduced in the high expression group, which may be the TF affecting biomarkers in the high expression group.\nConclusion: This study identified NADSYN1, SIRT3, and PARP6 as the biomarkers of B-ALL, explored biological significance of NMRGs in the initial diagnosis and relapse of B-ALL, and revealed mechanism of biomarkers at the level of the single cell.", "claims": [{"id": "claim-method", "statement": "Based on 51 obtained NMRGs, the expression levels of NMRGs were found in the GSE3912 dataset, and the differentially expressed NMRGs (DE-NMRGs) between the initial diagnosis and relapse samples were compared using R-package limma (Version 3.48.3), with |log2FC| > 0.5, p Value < 0.05 as screening criteria (15).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36439178", "locator": "sections[4] \"Screening of differentially expressed NMRGs\" paragraph 2", "quote": "Based on 51 obtained NMRGs, the expression levels of NMRGs were found in the GSE3912 dataset, and the differentially expressed NMRGs (DE-NMRGs) between the initial diagnosis and relapse samples were compared using R-package limma (Version 3.48.3), with |log2FC| > 0.5, p Value < 0.05 as screening criteria (15)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. GSE3912 and GSE130116 datasets were acquired from GEO database (https://www.ncbi.nlm.nih.gov/geo/). NAD+ metabolization-related genes (NMRGs) were selected from Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/) (Pathway: hsa00760) and the Reactome (http://reactome.org/, R-HSA-196807) databases. Moreover, 9 pediatric B-ALL samples were acquired from the TARGET (https://ocg.cancer.gov/programs/target) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36439178", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. GSE3912 and GSE130116 datasets were acquired from GEO database (https://www.ncbi.nlm.nih.gov/geo/). NAD+ metabolization-related genes (NMRGs) were selected from Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/) (Pathway: hsa00760) and the Reactome (http://reactome.org/, R-HSA-196807) databases. Moreover, 9 pediatric B-ALL samples were acquired from the TARGET (https://ocg.cancer.gov/programs/target) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified NADSYN1, SIRT3, and PARP6 as biomarkers associated with B-ALL and showed that their expression was higher in relapse samples.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "The expression levels of all the 3 biomarkers were significantly higher in relapse samples (P < 0.05), which were consistent with the previous bioinformatics differential analysis in GEO ( Table 2 and Figure 8 )."}, "evidence": [{"asset": "source:pmid:36439178", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study identified NADSYN1, SIRT3, and PARP6 as the biomarkers of B-ALL, explored biological significance of NMRGs in the initial diagnosis and relapse of B-ALL, and revealed mechanism of biomarkers at the level of the single cell."}, {"asset": "source:pmid:36439178", "locator": "sections[15] \"Three biomarkers were screened out\" paragraph 2", "quote": "The expressions of 3 biomarkers were significantly higher in relapse group ( Figure 2B )."}, {"asset": "source:pmid:36439178", "locator": "sections[21] \"qPCR validation of biomarkers expressions\" paragraph 2", "quote": "The expression levels of all the 3 biomarkers were significantly higher in relapse samples (P < 0.05), which were consistent with the previous bioinformatics differential analysis in GEO ( Table 2 and Figure 8 )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified NADSYN1, SIRT3, and PARP6 as biomarkers associated with B-ALL and showed that their expression was higher in relapse samples.", "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:36439178"], "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:GSE130116"], "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:GSE130116"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36439178", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.1043111", "sha256": "sha256:cf7ad448754023af97b0b72d45da58b67f24570a3c42022794be134e5fbf3366", "bytes": 41211, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.1043111", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9691973", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE130116", "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=GSE130116", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE130116", "version": null, "note": "Publicly available datasets were analyzed in this study. 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Furthermore, single cell RNA sequencing was conducted in the GSE130116 dataset to fi", "ntary Figure 5Quality control of single-cell RNA-seq (scRNA-seq) data in GSE130116.Click here for", ") in B-ALL relapse remains unclear. Methods: Transcriptome data (GSE3912), and single-cell sequencing d"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Integrating RNA-seq and scRNA-seq to explore the biological significance of NA", "le. Supplementary Figure 5Quality control of single-cell RNA-seq (scRNA-seq) data in GSE130116.Click here for", "ure 5Quality control of single-cell RNA-seq (scRNA-seq) data in GSE130116.Click here for additional"], "order_basis": "paper_text_order:0", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["data file. 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