Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38632516", "kind": "science-record", "revision": 3, "content_hash": "sha256:72abd407abfaea1fb0b4c0ac26f69b1365601652f0308076d8331835b7e25da9", "created_at": "2026-11-01T19:20:36Z", "updated_at": "2026-11-01T22:11:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma.", "doi": "10.1186/s12864-024-10283-5", "pmid": "38632516", "authors": ["Yueshu Wu", "Jun Yang", "Gang Xu", "Xiaolin Chen", "Xiaochen Qu"], "journal": "BMC genomics", "year": 2024, "abstract": "BACKGROUND: Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454.\nRESULTS: Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time.\nCONCLUSIONS: A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.", "claims": [{"id": "claim-method", "statement": "Using the intersection genes, univariate Cox regression analysis (survival package, v 3.2–13) [17] and least absolute shrinkage and selection operator (LASSO) regression analysis (glmnet package, v 4.1-3) [18] were utilized to select prognostic genes as biomarkers in the training set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38632516", "locator": "sections[5] \"Construction and validation of prognostic model\" paragraph 2", "quote": "Using the intersection genes, univariate Cox regression analysis (survival package, v 3.2–13) [17] and least absolute shrinkage and selection operator (LASSO) regression analysis (glmnet package, v 4.1-3) [18] were utilized to select prognostic genes as biomarkers in the training set."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed in this study are included in this article. The datasets generated and/or analyzed in this study are available online (The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38632516", "locator": "sections[25] \"notes\" paragraph 1", "quote": "All data generated or analyzed in this study are included in this article. The datasets generated and/or analyzed in this study are available online (The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38632516", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.", "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:38632516"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38632516", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12864-024-10283-5", "sha256": "sha256:30f4df66ddfc97506718ff893f098ca993a54494b1ee4e9651248bc403902fa3", "bytes": 37519, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12864-024-10283-5", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11022332", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Using the intersection genes, univariate Cox regression analysis (survival package, v 3.2–13) [17] and least absolute shrinkage and selection operator (LASSO) regression analysis (glmnet package, v 4.1-3) [18] were utilized to select prognostic genes as biomarkers in the training set."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "PCA"}}, {"id": "method-4", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "PPI"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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