Immune cell–related gene signatures for diagnostic and prognostic stratification in thyroid cancer using machine learning analysis

作者：Kun Fang, Zhifang Zhang, Meng Luo, Xudong Niu · 发表于：Discover Oncology · 年份：2026 · DOI：10.1007/s12672-026-04667-0 · 被引用次数：1 · 研究领域：Ferroptosis and cancer prognosis、Thyroid Cancer Diagnosis and Treatment、Cancer Immunotherapy and Biomarkers

BACKGROUNDS: Immune cells play a crucial role in the tumor microenvironment (TME) by regulating the progression of cancer cells. However, the clinical relevance of immune cell infiltration-related mRNA in thyroid cancer (TC) remains uncertain. Current diagnostic methods, such as cytology and imaging, still face limitations in accurately assessing tumor behavior and prognosis, highlighting the need for more reliable molecular indicators. METHODS: Three cohorts (TCGA, GSE3678, and GSE33630) were included in the study to construct immune-related signatures for thyroid cancer (TC). The immune cell infiltration levels were quantified using single-sample gene set enrichment analysis (ssGSEA), followed by consensus clustering to identify immune cell-related molecular subtypes (IRMS). Subsequently, immune-related genes (IRGs) were selected via weighted gene co-expression network analysis (WGCNA). Based on these IRGs, we established two predictive models: an immune cell-related diagnostic signature (IRDS) was developed using a machine-learning framework with 113 combinations of 12 machine-learning algorithms, while an immune cell-related prognostic signature (IRPS) was constructed via LASSO regression algorithm. Finally, both signatures were systematically evaluated for their predictive performance. RESULTS: Through WGCNA analysis, key immune-related gene modules were identified in the TCGA-THCA cohort. From these modules, 28 immune-related genes were selected based on their expressio...