{"doi":"10.3389/fmed.2026.1801737","title":"Identification and preliminary clinical validation of type 2 diabetes signature genes through machine learning analysis of scRNA-seq data","abstract":"<jats:sec>\n                    <jats:title>Objective</jats:title>\n                    <jats:p>Type 2 diabetes (T2DM) is a highly prevalent metabolic disorder with substantial molecular heterogeneity, and traditional bulk transcriptomic approaches often fail to capture cell-specific changes critical to disease pathogenesis. This study aims to identify and validate key signature genes for T2DM by integrating single-cell RNA sequencing (scRNA-seq) with machine learning, providing new insights into disease mechanisms and potential biomarkers.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We analyzed scRNA-seq data to characterize cellular heterogeneity across 10 distinct cell types. Differential expression analysis identified 455 candidate genes, which were refined using LASSO regression. The diagnostic potential of identified genes was evaluated using ROC curve analysis on an independent dataset. Functional enrichment and cell communication analyses were performed to elucidate biological processes and intercellular signaling networks. Finally, expression changes of the candidate genes were validated in peripheral blood from a separate clinical cohort (15 T2DM patients, 20 controls) using qRT-PCR.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>Four core genes (PNLIP, BUB1, CTSB, NAMPT) were identified as candidate signature genes. ROC analysis showed AUC values of 0.819, 0.931, 0.882, and 0.694, respectively, suggesting promising but variable diagnostic accuracy. Enrichment analyses indicated these genes participate in processes including extracellular matrix remodeling, digestion/absorption, and signal transduction. Cell communication analysis suggested a potential central role of Alpha and Beta cells in diabetic signaling networks, with the MK and SPP1 pathways showing complementary expression patterns. In addition, qRT-PCR confirmed significantly up-regulated expression of PNLIP, BUB1, and CTSB along with down-regulated NAMPT in T2DM patients, supporting their potential as circulating candidate biomarkers.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>This study integrates machine learning with scRNA-seq to identify PNLIP, BUB1, CTSB, and NAMPT as potential T2DM signature genes. These findings offer candidate diagnostic biomarkers and provide preliminary mechanistic insights into disease-associated pathways.</jats:p>\n                  </jats:sec>","journal":"Frontiers in Medicine","year":2026,"id":662711,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1730156,"name":"Xin Zuo","orcid":null,"position":1,"is_corresponding":false},{"id":1730157,"name":"Weiyan Wang","orcid":null,"position":2,"is_corresponding":false},{"id":1147764,"name":"Rui Sun","orcid":"0000-0003-2571-1012","position":3,"is_corresponding":false},{"id":1730158,"name":"Heng Cheng","orcid":null,"position":4,"is_corresponding":false},{"id":703581,"name":"Weihua Wu","orcid":"0000-0002-0642-5523","position":5,"is_corresponding":false},{"id":51686,"name":"Fang Tang","orcid":"0000-0003-2453-4878","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Identification and preliminary clinical validation of type 2 diabetes signature genes through machine learning analysis of scRNA-seq data","abstract":"<jats:sec>\n                    <jats:title>Objective</jats:title>\n                    <jats:p>Type 2 diabetes (T2DM) is a highly prevalent metabolic disorder with substantial molecular heterogeneity, and traditional bulk transcriptomic approaches often fail to capture cell-specific changes critical to disease pathogenesis. This study aims to identify and validate key signature genes for T2DM by integrating single-cell RNA sequencing (scRNA-seq) with machine learning, providing new insights into disease mechanisms and potential biomarkers.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We analyzed scRNA-seq data to characterize cellular heterogeneity across 10 distinct cell types. Differential expression analysis identified 455 candidate genes, which were refined using LASSO regression. The diagnostic potential of identified genes was evaluated using ROC curve analysis on an independent dataset. Functional enrichment and cell communication analyses were performed to elucidate biological processes and intercellular signaling networks. Finally, expression changes of the candidate genes were validated in peripheral blood from a separate clinical cohort (15 T2DM patients, 20 controls) using qRT-PCR.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>Four core genes (PNLIP, BUB1, CTSB, NAMPT) were identified as candidate signature genes. ROC analysis showed AUC values of 0.819, 0.931, 0.882, and 0.694, respectively, suggesting promising but variable diagnostic accuracy. Enrichment analyses indicated these genes participate in processes including extracellular matrix remodeling, digestion/absorption, and signal transduction. Cell communication analysis suggested a potential central role of Alpha and Beta cells in diabetic signaling networks, with the MK and SPP1 pathways showing complementary expression patterns. In addition, qRT-PCR confirmed significantly up-regulated expression of PNLIP, BUB1, and CTSB along with down-regulated NAMPT in T2DM patients, supporting their potential as circulating candidate biomarkers.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>This study integrates machine learning with scRNA-seq to identify PNLIP, BUB1, CTSB, and NAMPT as potential T2DM signature genes. These findings offer candidate diagnostic biomarkers and provide preliminary mechanistic insights into disease-associated pathways.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"42205842","pmcid":"PMC13201128","openalex_id":"https://openalex.org/W7160935172","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.50167273,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1801737/pdf","host_type":"journal"},{"url":"https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1801737/pdf","host_type":"publisher"},{"url":"https://www.frontiersin.org/articles/10.3389/fmed.2026.1801737/full","host_type":"publisher"},{"url":"https://doi.org/10.3389/fmed.2026.1801737","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/42205842","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13201128/","host_type":"repository"},{"url":"https://doaj.org/article/47700ff90eaf47f8ae668da140aa9802","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13201128/pdf/fmed-13-1801737.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC13201128","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC13201128?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Single-cell and spatial transcriptomics","Pancreatic function and diabetes","GDF15 and Related Biomarkers"],"mesh_terms":[],"keywords":["Candidate gene","Transcriptome","Gene","Identification (biology)","Independent component analysis","Receiver operating characteristic","Gene expression profiling","Gene expression","Disease","Type 2 diabetes","Machine Learning","Clinical Validation","Signature Genes","Single-cell Rna Sequencing"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"geo"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T17:04:01.025830Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}