← All .science files
pmid:40791155
Identification and validation of TUBB, CLTA, and FBXL5 as potential diagnostic markers of postmenopausal osteoporosis.
Yue Tan · Yujing Wang · Qin Zhu · Yan Xue · Xuhao Ji · Zhenkun Li · Jiawen Shen · Chengming Sun · Shiqi Ren · Chenlin Zhang · Jianfeng Chen
Biomolecules & biomedicine · 2025 doi:10.17305/bb.2025.12019
Paper
complete
Experiment
complete
Data
partial
Analysis
partial
Execution
absent
Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
paper object · 10 fields
authors array · 11 items
claims array · 3 items
[0] object · 6 fields
evidence array · 1 item
[0] object · 3 fields
[1] object · 6 fields
[2] object · 6 fields
evidence array · 1 item
[0] object · 3 fields
gaps array · 0 items
experiment object · 8 fields
steps array · 3 items
[0] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 0 items
data object · 3 fields
assets array · 3 items
[0] object · 15 fields
structure object · 4 fields
[1] object · 15 fields
structure object · 15 fields
sample_accessions array · 13 items
[2] object · 15 fields
structure object · 15 fields
sample_accessions array · 80 items
gaps array · 4 items
analysis object · 4 fields
methods array · 5 items
[0] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[1] object · 10 fields
dsl_operations array · 0 items
missing_operations array · 1 item
parameters object · 0 fields
data_shapes array · 1 item
[2] object · 10 fields
dsl_operations array · 0 items
missing_operations array · 1 item
parameters object · 0 fields
data_shapes array · 1 item
[3] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[4] object · 10 fields
dsl_operations array · 6 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
targets array · 0 items
gaps array · 1 item
capability object · 3 fields
requirements array · 1 item
[0] object · 6 fields
assay_families array · 9 items
gaps array · 0 items
readiness object · 8 fields
axes object · 3 fields
method object · 5 fields
required_operations array · 3 items
missing_operations array · 0 items
capability object · 4 fields
data object · 6 fields
accessions array · 2 items
resolved_accessions array · 2 items
statements array · 1 item
can_run object · 3 fields
dsl_operations array · 3 items
assets array · 2 items
steps array · 1 item
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
gaps array · 0 items
review object · 5 fields
machine_critique object · 5 fields
reader object · 5 fields
evidence_check object · 2 fields
usage object · 3 fields
stages array · 1 item
[0] object · 4 fields
gaps array · 3 items
provenance object · 7 fields
entities array · 3 items
activities array · 3 items
[0] object · 4 fields
used array · 3 items
[0] object · 3 fields
[1] object · 3 fields
[2] object · 3 fields
[1] object · 4 fields
agents array · 2 items
lineage object · 4 fields
gaps array · 9 items
assays array · 2 items
[0] object · 11 fields
required_operations array · 0 items
source_hashes object · 1 field
evidence array · 2 items
[1] object · 11 fields
required_operations array · 3 items
source_hashes object · 1 field
evidence array · 1 item
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40791155", "kind": "science-record", "revision": 4, "content_hash": "sha256:5e62ae37130d0d428b5023abcca91b69f773815d576527a184162f3dea15dc1e", "created_at": "2026-11-01T20:30:53Z", "updated_at": "2026-11-01T22:56:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of TUBB, CLTA, and FBXL5 as potential diagnostic markers of postmenopausal osteoporosis.", "doi": "10.17305/bb.2025.12019", "pmid": "40791155", "authors": ["Yue Tan", "Yujing Wang", "Qin Zhu", "Yan Xue", "Xuhao Ji", "Zhenkun Li", "Jiawen Shen", "Chengming Sun", "Shiqi Ren", "Chenlin Zhang", "Jianfeng Chen"], "journal": "Biomolecules & biomedicine", "year": 2025, "abstract": "Postmenopausal osteoporosis (PMOP) is recognized as the most prevalent bone disease worldwide. N6-methyladenosine (m6A) is one of the most common RNA modifications influencing the progression of various disorders; however, its specific role in PMOP remains unexplored. This study aims to investigate the expression profiles of m6A-related genes and their impact on the prognosis of PMOP patients. We utilized the GSE56815 expression analysis dataset obtained from the Gene Expression Omnibus (GEO) database and extracted m6A-related genes for further examination. Our analysis revealed that m6A-related genes exhibited differential expression between PMOP patients and healthy controls. We employed consensus clustering to identify subgroups within the PMOP cohort and conducted immunological analyses on these clusters. Additionally, we intersected the clusters to identify differentially expressed genes (DEGs) and analyzed potential diagnostic markers for PMOP using support vector machine recursive feature elimination (SVM-RFE), LASSO, and random forest (RF) algorithms, which were subsequently validated in the GSE56116 dataset. The receiver operating characteristic (ROC) curve was employed to assess the diagnostic significance of these markers. Furthermore, quantitative PCR (qPCR) was performed to validate the expression of the identified genes. In the GSE56815 dataset, we identified three subtypes associated with m6A modifications, leading to the identification of 302 shared DEGs among these subtypes. Gene ontology (GO) analysis indicated that the DEGs were predominantly enriched in nuclear specks, the nuclear envelope, and nucleocytoplasmic transport processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis further revealed that DEGs were primarily associated with endocytosis and nucleocytoplasmic transport pathways. Through the application of SVM, LASSO, and RF algorithms, we identified three potential diagnostic markers: TUBB, CLTA, and FBXL5, which demonstrated promising diagnostic capabilities when tested against an independent dataset. qPCR validation confirmed significant expression differences of these genes between the control and PMOP groups. The genetic markers identified in this study hold potential for accurately predicting the risk of PMOP in patients. The findings contribute to understanding the underlying molecular mechanisms of CLTA, TUBB, and FBXL5 in PMOP and may facilitate the development of novel therapeutic strategies and improved monitoring of the disease.", "claims": [{"id": "claim-method", "statement": "Three types of intersection genes, A-B, B-C, and C-A, were selected for algorithm screening, including minimum absolute contraction and selection operator (LASSO) logistic regression, SVM-RFE, and RandomForest (RF) [14, 17, 18].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40791155", "locator": "sections[8] \"Acquisition of diagnostic markers\" paragraph 2", "quote": "Three types of intersection genes, A-B, B-C, and C-A, were selected for algorithm screening, including minimum absolute contraction and selection operator (LASSO) logistic regression, SVM-RFE, and RandomForest (RF) [14, 17, 18]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE56815 contains 80 samples, including 40 postmenopausal osteoporosis samples and 40 premenopausal samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40791155", "locator": "sections[3] \"Data retrieval\" paragraph 2", "quote": "GSE56815 contains 80 samples, including 40 postmenopausal osteoporosis samples and 40 premenopausal samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified TUBB, CLTA, and FBXL5 as potential diagnostic markers for PMOP.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40791155", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Through the application of SVM, LASSO, and RF algorithms, we identified three potential diagnostic markers: TUBB, CLTA, and FBXL5, which demonstrated promising diagnostic capabilities when tested against an independent dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified TUBB, CLTA, and FBXL5 as potential diagnostic markers for PMOP.", "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:40791155"], "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:GSE56116"], "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:GSE56116"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40791155", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.17305/bb.2025.12019", "sha256": "sha256:d9bbb9016177be4fcde681aa0aed32122871738eb75552a50aa0c5aade8c018d", "bytes": 50632, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.17305/bb.2025.12019", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12505522", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE56116", "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=GSE56116", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE56116", "version": null, "note": "GSE56815 contains 80 samples, including 40 postmenopausal osteoporosis samples and 40 premenopausal samples.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GSE56815 contains 80 samples, including 40 postmenopausal osteoporosis samples and 40 premenopausal samples.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM1356155", "GSM1356156", "GSM1356157", "GSM1356158", "GSM1356159", "GSM1356160", "GSM1356161", "GSM1356162", "GSM1356163", "GSM1356164", "GSM1356165", "GSM1356166", "GSM1356167"], "sample_count": 13, "group_sizes": null, "group_sizes_basis": null, "platform": "4133", "taxon": "Homo sapiens", "title": "Genes expression profiles of postmenopausal osteoporosis with kidney Yin deficiency", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE56815", "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=GSE56815", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE56815", "version": null, "note": "GSE56815 contains 80 samples, including 40 postmenopausal osteoporosis samples and 40 premenopausal samples.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GSE56815 contains 80 samples, including 40 postmenopausal osteoporosis samples and 40 premenopausal samples.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM1369756", "GSM1369757", "GSM1369758", "GSM1369759", "GSM1369760", "GSM1369761", "GSM1369762", "GSM1369763", "GSM1369764", "GSM1369765", "GSM1369766", "GSM1369767", "GSM1369768", "GSM1369769", "GSM1369770", "GSM1369771", "GSM1369772", "GSM1369773", "GSM1369774", "GSM1369775", "GSM1369776", "GSM1369777", "GSM1369778", "GSM1369779", "GSM1369780", "GSM1369781", "GSM1369782", "GSM1369783", "GSM1369784", "GSM1369785", "GSM1369786", "GSM1369787", "GSM1369788", "GSM1369789", "GSM1369790", "GSM1369791", "GSM1369792", "GSM1369793", "GSM1369794", "GSM1369795", "GSM1369796", "GSM1369797", "GSM1369798", "GSM1369799", "GSM1369800", "GSM1369801", "GSM1369802", "GSM1369803", "GSM1369804", "GSM1369805", "GSM1369806", "GSM1369807", "GSM1369808", "GSM1369809", "GSM1369810", "GSM1369811", "GSM1369812", "GSM1369813", "GSM1369814", "GSM1369815", "GSM1369816", "GSM1369817", "GSM1369818", "GSM1369819", "GSM1369820", "GSM1369821", "GSM1369822", "GSM1369823", "GSM1369824", "GSM1369825", "GSM1369826", "GSM1369827", "GSM1369828", "GSM1369829", "GSM1369830", "GSM1369831", "GSM1369832", "GSM1369833", "GSM1369834", "GSM1369835"], "sample_count": 80, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Gene expression study of blood monocytes in pre- and postmenopausal females with low or high bone mineral density (HG-U133A)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE56116 not downloaded", "asset:data:geo:GSE56116 licence not read", "asset:data:geo:GSE56815 not downloaded", "asset:data:geo:GSE56815 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Three types of intersection genes, A-B, B-C, and C-A, were selected for algorithm screening, including minimum absolute contraction and selection operator (LASSO) logistic regression, SVM-RFE, and RandomForest (RF) [14, 17, 18]."}}, {"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": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-4", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "logistic regression"}}, {"id": "method-5", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}], "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: support_vector_machine."}, "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": ["GSE56815", "GSE56116"], "resolved_accessions": ["GSE56815", "GSE56116"], "statements": ["GSE56815 contains 80 samples, including 40 postmenopausal osteoporosis samples and 40 premenopausal samples."], "basis": "The paper names GSE56815, GSE56116 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:40791155", "data:geo:GSE56116"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE56116"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:59:02Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 14774, "completion_tokens": 1610, "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:40791155", "type": "paper", "label": "pmid:40791155"}, {"id": "source:pmid:40791155", "type": "manuscript", "label": "PMID 40791155 full text"}, {"id": "data:geo:GSE56116", "type": "dataset", "label": "GEO GSE56116"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40791155", "type": "paper", "label": "pmid:40791155"}, {"id": "source:pmid:40791155", "type": "manuscript", "label": "PMID 40791155 full text"}, {"id": "data:geo:GSE56116", "type": "dataset", "label": "GEO GSE56116"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40791155", "type": "extraction", "used": ["source:pmid:40791155"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE56116", "type": "accession_resolution", "used": ["data:geo:GSE56116"], "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:40791155", "parent_hash": "sha256:305e5bb90cbee1b99c0f1bc0aaa90704ef1b324f565a86eacc3e63bb5b1c848e", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE56116 licence not read", "asset:data:geo:GSE56116 not downloaded", "asset:data:geo:GSE56815 licence not read", "asset:data:geo:GSE56815 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["hese markers. Furthermore, quantitative PCR (qPCR) was performed to validate the expression of", "when tested against an independent dataset. qPCR validation confirmed significant expression"], "order_basis": "paper_text_order:1359", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Three types of intersection genes, A-B, B-C, and C-A, were selected for algorithm screening, including minimum absolute contraction and selection operator (LASSO) logistic regression, SVM-RFE, and RandomForest (RF) [14, 17, 18]."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}