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Bulk RNA-seq, single-cell RNA-seq, gene set enrichment, immune infiltration, machine learning, Mendelian randomization, and molecular docking identify subtype-specific LNM drivers and a 17-gene predictive model.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/integrating-multi-omics-and-machine-learning-to-unravel-mechanisms-of-lymph-node-metastasis-in-papillary-carcinoma-with-and-without-thyroiditis/355208/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/integrating-multi-omics-and-machine-learning-to-unravel-mechanisms-of-lymph-node-metastasis-in-papillary-carcinoma-with-and-without-thyroiditis/355208.png","ImageObject",300,407,{"name":42,"@type":43},"Rowan","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-27","2026-09-22",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"How were PTC-thyroiditis and PTC-blank cases defined in the study?","Question",{"text":63,"@type":64},"Cases with coexisting thyroiditis were designated PTC-thyroiditis, and cases without thyroiditis were designated PTC-blank. The comparison then focused on molecular and cellular heterogeneity between these subtypes.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which computational and experimental approaches were used to identify LNM-related drivers?",{"text":68,"@type":64},"The study integrated bulk RNA-seq from TCGA and single-cell RNA-seq, then applied differential expression analysis, GSEA, and immune infiltration analysis. Machine learning (LASSO, random forest, KNN) was used to identify LNM-related genes and build a predictive model, while Mendelian randomization and molecular docking were used for causal validation and interaction assessment.",{"name":70,"@type":61,"acceptedAnswer":71},"What distinguishes the LNM mechanisms between PTC-blank and PTC-thyroiditis?",{"text":72,"@type":64},"In PTC-blank, LNM is associated with ECM remodeling and collagen fiber accumulation, linked to a distinct PI16+ fibroblast subcluster with active ECM organization functions. 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Front. Immunol. 17:1708330 .  \ndoi: 10.3389/fimmu.2026.1708330  \nCOPYRIGHT  \n© 2026 Chen, Yang, Liu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIntegrating multi-omics and machine learning to unravel mechanisms of lymph node metastasis in papillary carcinoma with and without thyroiditis  \nShuran Chen, Wencan Yang, Yanyan Liu and Angqing Li*  \nDepartment of General Surgery, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, China  \nBackground: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with lymph node metastasis (LNM) significantly influencing prognosis. PTC cases were categorized based on the presence or absence of coexisting thyroiditis: those with thyroiditis were designated as PTC-thyroiditis, and those without as PTC-blank. Although both subtypes exhibit distinct clinical and genomic profiles, the mechanisms underlying LNM—particularly the role of extracellular matrix (ECM) remodeling and cancer-associated fibroblasts (CAFs)—remain poorly understood. This study aimed to compare the molecular and cellular heterogeneity of PTC-blank and PTC-thyroiditis, identify key drivers of LNM, and develop a predictive model for clinical use.  \nMethods: We integrated bulk RNA-seq data from the TCGA database and singlecell RNA-seq (scRNA-seq) data from GSE184362 to analyze clinical, genomic, and tumor microenvironment (TME) differences between PTC-blank and PTCthyroiditis. Differential expression analysis, Gene Set Enrichment Analysis (GSEA), and immune cell infiltration analysis were performed. Fibroblast subpopulations were characterized at single-cell resolution. Machine learning algorithms (LASSO, random forest, KNN) were applied to identify LNM-related genes and construct a predictive model. Mendelian randomization (MR) and molecular docking were used to validate causal genes and potential drug interactions.  \nResults: PTC-blank exhibited higher T, N, and M stages and increased mutations in BRAF and MUC16 compared to PTC-thyroiditis. LNM in PTC-blank was associated with ECM remodeling and collagen fiber accumulation, associated with a distinct PI16+ fibroblast subcluster with active ECM organization functions. In contrast, LNM in PTC-thyroiditis involved immune-related pathways without significant fibroblast infiltration or ECM changes. A 17-gene predictive model for LNM was developed, with the KNN classifier demonstrating high accuracy. MR analysis identified SHISA5 as a causal risk gene for thyroid cancer, and molecular docking revealed strong binding affinity with acetaminophen, suggesting therapeutic potential.  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nConclusions: PTC-blank and PTC-thyroiditis exhibit distinct LNM mechanisms: ECM remodeling and fibroblast infiltration are associated with metastasis in PTCblank, while immune dysregulation ","cbCaiqBn1spkrn06","https://ap.wps.com/l/cbCaiqBn1spkrn06","pdf",6200513,15,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"How were PTC-thyroiditis and PTC-blank cases defined in the study?\",\"answer\":\"Cases with coexisting thyroiditis were designated PTC-thyroiditis, and cases without thyroiditis were designated PTC-blank. The comparison then focused on molecular and cellular heterogeneity between these subtypes.\"},{\"question\":\"Which computational and experimental approaches were used to identify LNM-related drivers?\",\"answer\":\"The study integrated bulk RNA-seq from TCGA and single-cell RNA-seq, then applied differential expression analysis, GSEA, and immune infiltration analysis. Machine learning (LASSO, random forest, KNN) was used to identify LNM-related genes and build a predictive model, while Mendelian randomization and molecular docking were used for causal validation and interaction assessment.\"},{\"question\":\"What distinguishes the LNM mechanisms between PTC-blank and PTC-thyroiditis?\",\"answer\":\"In PTC-blank, LNM is associated with ECM remodeling and collagen fiber accumulation, linked to a distinct PI16+ fibroblast subcluster with active ECM organization functions. In PTC-thyroiditis, LNM involves immune-related pathways with no significant fibroblast infiltration or ECM changes.\"}]","Integrating multi-omics and machine learning to unravel mechanisms of lymph node metastasis in papillary carcinoma with and without thyroiditis | PDF",1790116739,38]