Identification of lactylation-related biomarkers in HNSC by integrating machine learning and Spatial transcriptomics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41191207", "kind": "science-record", "revision": 3, "content_hash": "sha256:752a8cad2893837631ef269438d8f52e99f10e07b8d33080a42dd0ed1dabc2e3", "created_at": "2026-11-01T20:31:13Z", "updated_at": "2026-11-01T22:14:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of lactylation-related biomarkers in HNSC by integrating machine learning and Spatial transcriptomics analysis.", "doi": "10.1007/s12672-025-03849-6", "pmid": "41191207", "authors": ["Pu-Yu Wang", "Yi-Ping Zheng", "Yu-Lin Zhao"], "journal": "Discover oncology", "year": 2025, "abstract": "OBJECTIVE: Head and neck squamous cell carcinoma (HNSC) is a common malignant tumor with a 5-year survival rate of less than 60% and lactylation modification plays a key role in its occurrence and progression. This study aims to identify key lactylation-related genes (LRGs) in HNSC and their prognostic impact by combining machine learning methods and spatial transcriptomics analysis.\nMETHODS: High-throughput gene expression data of HNSC were obtained from the TCGA database. The \"Limma\" R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) was employed to identify disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via the \"RcisTarget\" package identified transcription factors associated with motifs exceeding NES 3.5. Single-cell and spatial transcriptomic analysis was performed, which involved dimensionality reduction, clustering via UMAP, cell type annotation and the plotting of spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and then the correlations between key genes and immune-related genes were analyzed.\nRESULTS: Our study identified three key LRGs (MSN, KIF2C, and RFC4), which exhibited strong diagnostic performance, with AUC values of 0.986 for KIF2C, 0.964 for RFC4, and 0.89 for MSN in the TCGA-HNSC cohort. These findings were validated in the GSE6631 dataset. Western blotting showed a significant upregulation of the expression of these genes in the HNSC group, which was consistent with the RT-qPCR results. Survival analysis showed that the high expression of key genes was closely associated with a poorer overall survival (p < 0.05). Spatial transcriptomics analysis revealed that KIF2C, RFC4, and MSN were preferentially expressed in tumor core regions, indicating their involvement in the malignant microenvironment. Additionally, we discovered that 10 types of immune cell infiltrations were significantly changed in HNSC and well correlated with key genes.\nCONCLUSION: MSN, KIF2C and RFC4 may serve as potential biomarkers for diagnosing and predicting the prognosis of HNSC, providing new insights into targeted therapy.", "claims": [{"id": "claim-method", "statement": "Kaplan-Meier method was used to plot survival curves to visualize differences in overall survival between the two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41191207", "locator": "sections[6] \"Survival analysis\" paragraph 2", "quote": "Kaplan-Meier method was used to plot survival curves to visualize differences in overall survival between the two groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study are available in the following open access repositories: TCGA, (https://portal.gdc.cancer.gov/). GEO (https://www.ncbi.nlm.nih.gov/geo/). The 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:41191207", "locator": "sections[27] \"notes\" paragraph 1", "quote": "The datasets analyzed in this study are available in the following open access repositories: TCGA, (https://portal.gdc.cancer.gov/). GEO (https://www.ncbi.nlm.nih.gov/geo/). The data that support the findings of this study are available from the corresponding author, upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our study identified three key LRGs (MSN, KIF2C, and RFC4), which exhibited strong diagnostic performance, with AUC values of 0.986 for KIF2C, 0.964 for RFC4, and 0.89 for MSN in the TCGA-HNSC cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41191207", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Our study identified three key LRGs (MSN, KIF2C, and RFC4), which exhibited strong diagnostic performance, with AUC values of 0.986 for KIF2C, 0.964 for RFC4, and 0.89 for MSN in the TCGA-HNSC cohort."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our study identified three key LRGs (MSN, KIF2C, and RFC4), which exhibited strong diagnostic performance, with AUC values of 0.986 for KIF2C, 0.964 for RFC4, and 0.89 for MSN in the TCGA-HNSC cohort.", "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:41191207"], "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:GSE6631"], "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:GSE6631"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41191207", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-025-03849-6", "sha256": "sha256:3d9dc495af82a76ba8f84265199340f18bcb4f5efbdd030f52e382a5eaa185a0", "bytes": 35497, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-025-03849-6", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12589766", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE6631", "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=GSE6631", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE6631", "version": null, "note": "The datasets analyzed in this study are available in the following open access repositories: TCGA, (https://portal.gdc.cancer.gov/). GEO (https://www.ncbi.nlm.nih.gov/geo/). The 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 datasets analyzed in this study are available in the following open access repositories: TCGA, (https://portal.gdc.cancer.gov/). GEO (https://www.ncbi.nlm.nih.gov/geo/). The data that support the findings of this study are available from the corresponding author, upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM153813", "GSM153814", "GSM153815", "GSM153816", "GSM153817", "GSM153818", "GSM153819", "GSM153820", "GSM153821", "GSM153822", "GSM153823", "GSM153824", "GSM153825", "GSM153826", "GSM153827", "GSM153828", "GSM153829", "GSM153830", "GSM153831", "GSM153832", "GSM153833", "GSM153834", "GSM153835", "GSM153836", "GSM153837", "GSM153838", "GSM153839", "GSM153840", "GSM153841", "GSM153842", "GSM153843", "GSM153844", "GSM153845", "GSM153846", "GSM153847", "GSM153848", "GSM153849", "GSM153850", "GSM153851", "GSM153852", "GSM153853", "GSM153854", "GSM153855", "GSM153856"], "sample_count": 44, "group_sizes": null, "group_sizes_basis": null, "platform": "8300", "taxon": "Homo sapiens", "title": "Expression data from head and neck squamous cell carcinoma", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE6631 not downloaded", "asset:data:geo:GSE6631 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Kaplan-Meier method was used to plot survival curves to visualize differences in overall survival between the two groups."}}, {"id": "method-2", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.7, "band": "medium", "quote": "Limma"}}], "targets": [], "gaps": []}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE6631", "GSE252265-GSM7998252"], "resolved_accessions": ["GSE6631"], "statements": ["The datasets analyzed in this study are available in the following open access repositories: TCGA, (https://portal.gdc.cancer.gov/). GEO (https://www.ncbi.nlm.nih.gov/geo/). The data that support the findings of this study are available from the corresponding author, upon reasonable request."], "basis": "The paper names GSE6631 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["survival_km"], "assets": ["source:pmid:41191207", "data:geo:GSE6631"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE6631"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:04:54Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9778, "completion_tokens": 1746, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:41191207", "type": "paper", "label": "pmid:41191207"}, {"id": "source:pmid:41191207", "type": "manuscript", "label": "PMID 41191207 full text"}, {"id": "data:geo:GSE6631", "type": "dataset", "label": "GEO GSE6631"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41191207", "type": "paper", "label": "pmid:41191207"}, {"id": "source:pmid:41191207", "type": "manuscript", "label": "PMID 41191207 full text"}, {"id": "data:geo:GSE6631", "type": "dataset", "label": "GEO GSE6631"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41191207", "type": "extraction", "used": ["source:pmid:41191207"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE6631", "type": "accession_resolution", "used": ["data:geo:GSE6631"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:41191207", "parent_hash": "sha256:0fcd23ebcecc36349694b4dbce7db256cd64f824dd614b392147111a5c476d16", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE6631 licence not read", "asset:data:geo:GSE6631 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["dings were validated in the GSE6631 dataset. Western blotting showed a significant upregulation of the exp", "Rs in HNSC, we further performed qRT-PCR and Western blotting. qRT-PCR results showed that the mRNA expres", "ignificantly high in HNSC group (Fig. 8A–C). Western blotting results showed that the protein levels of MS", "n levels of three key genes were detected by Western blotting. E Detection of lactylation modification in"], "order_basis": "paper_text_order:1890", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["he HNSC group, which was consistent with the RT-qPCR results. Survival analysis showed that the h", "HNSC group, which was consistent with the RT-qPCR results. Survival analysis showed that the h"], "order_basis": "paper_text_order:2023", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Kaplan-Meier method was used to plot survival curves to visualize differences in overall survival between the two groups."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}