Exploring biomarkers and transcriptional factors in type 2 diabetes by comprehensive bioinformatics analysis on RNA-Seq and scRNA-Seq data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36267740", "kind": "science-record", "revision": 4, "content_hash": "sha256:c884be024073539608b2d8eef0012864eb959d2fe25188ee7eaf29886b3c6d58", "created_at": "2026-11-01T18:15:39Z", "updated_at": "2026-11-01T22:07:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring biomarkers and transcriptional factors in type 2 diabetes by comprehensive bioinformatics analysis on RNA-Seq and scRNA-Seq data.", "doi": "10.21037/atm-22-4303", "pmid": "36267740", "authors": ["Yalan Huang", "Linkun Cai", "Xiu Liu", "Yongjun Wu", "Qin Xiang", "Rong Yu"], "journal": "Annals of translational medicine", "year": 2022, "abstract": "Background: Type 2 diabetes (T2D) is a prevalent chronic disease with elusive. Combining transcriptome and single-cell sequencing data to explore biomarkers of T2D could provide new insights into the in-depth understanding of the molecular mechanisms and diagnosis of T2D.\nMethods: The GSE41762 dataset including RNA-seq data for healthy and T2D patients, was obtained from the Gene Expression Omnibus (GEO) database. The potential functions of the differentially expressed genes (DEGs) were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Moreover, biomarkers were screened out by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and receiver operating characteristic (ROC) analysis. Furthermore, single-cell RNA (sc-RNA)-seq data in the \"E-MTAB-5061\" dataset was downloaded from the ArrayExpress (European Bioinformatics Institute, EBI) database. Principal components analysis (PCA) and t-distributed stochastic neighbor embedding (tSNE) were used for dimensionality reduction analysis and cell clustering. The FindAllMarkers function was used annotate different cell clusters, and key cell clusters were screened by the expression levels of the biomarkers. Finally, the transcription factors (TFs) of the biomarkers were recognized.\nResults: A total of 111 DEGs were screened in the GSE41762 dataset, which were mainly related to hormone secretion, specialized postsynaptic membrane, pancreatic secretion, JAK-STAT signaling pathway, and Ras signaling pathway. In addition, SLC2A2, SERPINF1, RASGRP1, and CHL1 were screened out as biomarkers of T2D, which possessed potential diagnostic value as AUC value greater than 0.8. A total of 1,515 T2D group cells and 1,817 healthy cohort cells were screened as core cells in the \"E-MTAB-5061\" dataset. Following tSNE dimensionality reduction cluster analysis, the core cells were divided into 13 cell clusters. According to the marker genes, the 13 cell clusters were annotated into six types of cells. Notably, SERPINF1 was highly expressed in fibroblasts and might be regulated by NR2F2 (nuclear receptor subfamily2, group F, and member 2).\nConclusions: This study identified four biomarkers (SLC2A2, SERPINF1, RASGRP1, and CHL1) for T2D, which provided new markers for the clinical diagnosis of T2D. Among them, SERPINF1 might be regulated by NR2F2, which provides valuable insight into the pathogensis of T2D.", "claims": [{"id": "claim-method", "statement": "The LASSO algorithm in R language was used to screen biomarkers for T2D. Next, the ability of biomarkers to distinguish healthy from T2D patients was analyzed using receiver operating characteristic (ROC) curves of the “pROC” package by calculating the area under curve (AUC) values of the ROC curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36267740", "locator": "sections[6] \"Biomarkers screening\" paragraph 2", "quote": "The LASSO algorithm in R language was used to screen biomarkers for T2D. Next, the ability of biomarkers to distinguish healthy from T2D patients was analyzed using receiver operating characteristic (ROC) curves of the “pROC” package by calculating the area under curve (AUC) values of the ROC curves."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded the GSE41762 dataset containing RNA-seq data from the islet tissues of 57 healthy participants and 20 T2D patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41762).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36267740", "locator": "sections[3] \"Data source\" paragraph 2", "quote": "We downloaded the GSE41762 dataset containing RNA-seq data from the islet tissues of 57 healthy participants and 20 T2D patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41762)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study identified four biomarkers (SLC2A2, SERPINF1, RASGRP1, and CHL1) for T2D, which provided new markers for the clinical diagnosis of T2D.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36267740", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study identified four biomarkers (SLC2A2, SERPINF1, RASGRP1, and CHL1) for T2D, which provided new markers for the clinical diagnosis of T2D."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "This study identified four biomarkers (SLC2A2, SERPINF1, RASGRP1, and CHL1) for T2D, which provided new markers for the clinical diagnosis of T2D.", "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:36267740"], "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:E-MTAB-5061"], "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:E-MTAB-5061"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36267740", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-4303", "sha256": "sha256:c440af74c63948e1407e7813586f2dc3a08d168703fba5db9443d894122fb8f9", "bytes": 34496, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/atm-22-4303", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9577746", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:E-MTAB-5061", "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=E-MTAB-5061", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "E-MTAB-5061", "version": null, "note": "We downloaded the GSE41762 dataset containing RNA-seq data from the islet tissues of 57 healthy participants and 20 T2D patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41762).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We downloaded the GSE41762 dataset containing RNA-seq data from the islet tissues of 57 healthy participants and 20 T2D patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41762).", "access_statement_kind": "repository_route_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-11-01T18:46:41Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE41762", "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=GSE41762", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE41762", "version": null, "note": "We downloaded the GSE41762 dataset containing RNA-seq data from the islet tissues of 57 healthy participants and 20 T2D patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41762).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We downloaded the GSE41762 dataset containing RNA-seq data from the islet tissues of 57 healthy participants and 20 T2D patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41762).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM1023655", "GSM1023656", "GSM1023657", "GSM1023658", "GSM1023659", "GSM1023660", "GSM1023661", "GSM1023662", "GSM1023663", "GSM1023664", "GSM1023665", "GSM1023666", "GSM1023667", "GSM1023668", "GSM1023669", "GSM1023670", "GSM1023671", "GSM1023672", "GSM1023673", "GSM1023674", "GSM1023675", "GSM1023676", "GSM1023677", "GSM1023678", "GSM1023679", "GSM1023680", "GSM1023681", "GSM1023682", "GSM1023683", "GSM1023684", "GSM1023685", "GSM1023686", "GSM1023687", "GSM1023688", "GSM1023689", "GSM1023690", "GSM1023691", "GSM1023692", "GSM1023693", "GSM1023694", "GSM1023695", "GSM1023696", "GSM1023697", "GSM1023698", "GSM1023699", "GSM1023700", "GSM1023701", "GSM1023702", "GSM1023703", "GSM1023704", "GSM1023705", "GSM1023706", "GSM1023707", "GSM1023708", "GSM1023709", "GSM1023710", "GSM1023711", "GSM1023712", "GSM1023713", "GSM1023714", "GSM1023715", "GSM1023716", "GSM1023717", "GSM1023718", "GSM1023719", "GSM1023720", "GSM1023721", "GSM1023722", "GSM1023723", "GSM1023724", "GSM1023725", "GSM1023726", "GSM1023727", "GSM1023728", "GSM1023729", "GSM1023730", "GSM1023731"], "sample_count": 77, "group_sizes": null, "group_sizes_basis": null, "platform": "6244", "taxon": "Homo sapiens", "title": "Expression data from human pancreatic islets", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:E-MTAB-5061 not downloaded", "asset:data:geo:E-MTAB-5061 licence not read", "asset:data:geo:GSE41762 not downloaded", "asset:data:geo:GSE41762 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 0.7, "band": "medium", "quote": "Principal components analysis"}}, {"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": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-3", "family": null, "specific": null, "label": "LASSO algorithm and receiver operating characteristic (ROC) analysis", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The LASSO algorithm in R language was used to screen biomarkers for T2D. Next, the ability of biomarkers to distinguish healthy from T2D patients was analyzed using receiver operating characteristic (ROC) curves of the “pROC” package by calculating the area under curve (AUC) values of the ROC curves."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "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": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "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": ["GSE41762", "E-MTAB-5061"], "resolved_accessions": ["GSE41762", "E-MTAB-5061"], "statements": ["We downloaded the GSE41762 dataset containing RNA-seq data from the islet tissues of 57 healthy participants and 20 T2D patients from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41762)."], "basis": "The paper names GSE41762, E-MTAB-5061 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": ["pca_fit"], "assets": ["source:pmid:36267740", "data:geo:E-MTAB-5061"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:E-MTAB-5061"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:12:05Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 10623, "completion_tokens": 1283, "cached_tokens": 264}}, "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:36267740", "type": "paper", "label": "pmid:36267740"}, {"id": "source:pmid:36267740", "type": "manuscript", "label": "PMID 36267740 full text"}, {"id": "data:geo:E-MTAB-5061", "type": "dataset", "label": "GEO E-MTAB-5061"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36267740", "type": "paper", "label": "pmid:36267740"}, {"id": "source:pmid:36267740", "type": "manuscript", "label": "PMID 36267740 full text"}, {"id": "data:geo:E-MTAB-5061", "type": "dataset", "label": "GEO E-MTAB-5061"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36267740", "type": "extraction", "used": ["source:pmid:36267740"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-E-MTAB-5061", "type": "accession_resolution", "used": ["data:geo:E-MTAB-5061"], "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:36267740", "parent_hash": "sha256:6622bf5514f45ab746c94e3895c3bdbd3aa31729a46735d39491e51051dd80e5", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:E-MTAB-5061 licence not read", "asset:data:geo:E-MTAB-5061 not downloaded", "asset:data:geo:GSE41762 licence not read", "asset:data:geo:GSE41762 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["nsive bioinformatics analysis on RNA-Seq and scRNA-Seq data."], "order_basis": "paper_text_order:79", "id": "assay:1"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["Principal components analysis"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}