Multi-Omics Analysis and Metastasis Risk Factor Prediction in N1b Stage PTMC: Insights into Immune Infiltration and Therapeutic Implications

Hao Dai¹Qian Zhao¹Wanli Ren¹Qian Chen¹Bei Pei¹Wenyan Wang¹Zhiqian Liu¹Zhihan Liu¹Jinzi Guo¹Shao Yuan¹Xiang Li^1,2Yanxia Bai^1*

• ¹Department of Otorhinolaryngology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
• ²Center for Gut Microbiome Research, Med-X Institute Centre, The First Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China

Background: Papillary thyroid microcarcinoma (PTMC) with lateral neck lymph node involvement exhibits a deceptively indolent yet highly invasive phenotype, characterized by early dissemination and slow tumor growth. A comprehensive understanding of integrating multiomics landscapes, circulating immune profiles, and tumor immune microenvironment is essential for more accurate surveillance and tailored therapeutic strategies.Methods: Clinical profile and circulating immune-inflammatory markers from 638 PTMC patients were analyzed using multivariate and least absolute shrinkage and selection operator (LASSO) regression to recognize N1b-associated risk indicators. Eight supervised machine learning models were trained via 10-fold cross-validation to select the optimal classifier. Weighted gene coexpression network analysis (WGCNA) alongside machine learning identified metastasis-related gene modules from the integrated RNA-seq profile, leading to a multilayer perceptron gene classifier. Genomic profiling was employed to investigate mutations, copy number alterations, and methylation modifications in signature genes, followed by screening of antineoplastic drugs and docking simulations to explore their therapeutic potential. CIBERSORT, combined with immunohistochemistry, was used to investigate immune infiltration and functional changes in N1b-stage PTMC lesions.: Two clinical metastasis risk models were developed, with Model A based on the neutrophil-to-lymphocyte ratio (NLR) and Model B on lymphocyte and neutrophil counts, where Model A showed superior generalization (AUC = 0.852) and discriminative performance. NLR was an independent risk determinant for N1b-stage PTMC (OR = 2.12, p < 0.01). Transcriptomic profiling revealed a molecular signature (ALDH1A3, CTXN1, MGAT3, and TMEM163) of occult lateral lymph node metastasis, exhibiting strong robustness (AUC = 0.857). Signature genes were predominantly associated with cell adhesion, intercellular signaling, and KRAS dysregulation pathways. Hypomethylation of CTXN1, MGAT3, and TMEM163 may underlie transcriptional 1 Hao et al. N1b PTMC Metastasis and Immunity activation. N1b-stage tumors exhibited reduced CD8+ T and T follicular helper cell infiltration but increased dendritic, γδ T, and activated CD4+ memory T cells, suggesting immune evasion and compensatory immune activation.Discussion: This study constructed a robust metastasis prediction nomogram for N1b-stage PTMC and identified metastasis-associated molecular drivers through integrative multiomics analysis. Comprehensive profiling of systemic and tumor-infiltrating immunity revealed key antitumor immune alterations. These findings establish a framework for early metastatic phenotype detection, potentially inspiring relevant immunotherapeutic hypotheses.