Mapping the genetic-transcriptional landscape of thyroid irAEs in sintilimab therapy: toward biomarker-guided immunotoxicity prediction.

OBJECTIVE

By integrating whole-genome resequencing (WGR) with longitudinal transcriptomic profiling, this study aimed to unravel the genetic-transcriptional regulatory network underlying thyroid immune-related adverse events (irAEs) in non-small cell lung cancer (NSCLC) patients treated with sintilimab. A key objective was to identify molecular biomarkers with predictive and therapeutic relevance.

METHODS

This prospective study included NSCLC patients receiving sintilimab, from whom peripheral blood samples were collected at three time points: baseline, post-first treatment, and post-second treatment. RNA sequencing (RNA-seq) and 30× WGR were performed. Differential gene expression analysis was conducted on the RNA-seq data, followed by longitudinal consistency filtering using the Longitudinal Concordant Gene Intersection (LCGI) algorithm to identify robust differentially expressed genes (DEGs). These DEGs underwent downstream integration with protein-protein interaction (PPI) network analysis and cis-expression quantitative trait loci (cis-eQTL) mapping to pinpoint key genes and regulatory single-nucleotide polymorphisms (SNPs) associated with thyroid irAEs.

RESULTS

The LCGI algorithm identified 13 DEGs exhibiting sustained directional shifts across treatment timepoints. Integration with conventional DEG signatures revealed a functionally cohesive module, with C1QA/B/C, FLT1, TEK, PDGFRB, SPP1, and HLA-DPB1 emerging as central regulators of thyroid irAEs. Cis-eQTL mapping identified 500 SNPs with significant cis-regulatory effects on 153 genes. A "C3 complement-matrix axis" was uncovered as a pivotal node, promoting macrophage polarization toward a pro-inflammatory phenotype. Based on the refined PPI network, we proposed a cascading pathological model in which a self-sustaining feedback loop drove progressive and irreversible thyroid autoimmunity.

CONCLUSION

This study established a genetic-transcriptional regulatory framework for sintilimab-induced thyroid irAEs and identified a candidate gene set with biomarker potential. Our findings highlighted the central role of complement-driven mechanisms, providing a foundation for precision risk prediction and targeted intervention strategies that preserve antitumor efficacy while mitigating autoimmune toxicity.