Research Paper Volume 16, Issue 3 pp 2934—2952

Investigating the clinical role and prognostic value of genes related to insulin-like growth factor signaling pathway in thyroid cancer

Junyan Liu1, *, , Xin Miao1, *, , Jing Yao1, , Zheng Wan1, , Xiaodong Yang1, , Wen Tian1, ,

  • 1 Department of General Surgery, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing 100853, China
* Equal contribution

Received: September 25, 2023       Accepted: December 27, 2023       Published: February 7, 2024
How to Cite

Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Thyroid cancer (THCA) is the most common endocrine malignancy having a female predominance. The insulin-like growth factor (IGF) pathway contributed to the unregulated cell proliferation in multiple malignancies. We aimed to explore the IGF-related signature for THCA prognosis.

Method: The TCGA-THCA dataset was collected from the Cancer Genome Atlas (TCGA) for screening of key prognostic genes. The limma R package was applied for differentially expressed genes (DEGs) and the clusterProfiler R package was used for the Gene Ontology (GO) and KEGG analysis of DEGs. Then, the un/multivariate and least absolute shrinkage and selection operator (Lasso) Cox regression analysis was used for the establishment of RiskScore model. Receiver Operating Characteristic (ROC) analysis was used to verify the model’s predictive performance. CIBERSORT and MCP-counter algorithms were applied for immune infiltration analysis. Finally, we analyzed the mutation features and the correlation between the RiskScore and cancer hallmark pathway by using the GSEA.

Result: We obtained 5 key RiskScore model genes for patient’s risk stratification from the 721 DEGs. ROC analysis indicated that our model is an ideal classifier, the high-risk patients are associated with the poor prognosis, immune infiltration, high tumor mutation burden (TMB), stronger cancer stemness and stronger correlation with the typical cancer-activation pathways. A nomogram combined with multiple clinical features was developed and exhibited excellent performance upon long-term survival quantitative prediction.

Conclusions: We constructed an excellent prognostic model RiskScore based on IGF-related signature and concluded that the IGF signal pathway may become a reliable prognostic phenotype in THCA intervention.


IGF: insulin-like growth factor; THCA: thyroid cancer; TCGA: The Cancer Genome Atlas; MSigDB: The Molecular Signatures Database; ssGSEA: single-sample gene set enrichment analysis; KM: Kaplan-Meier; PTC: papillary thyroid carcinoma; ATC: anaplastic thyroid cancer; IGFBP: IGF-binding protein; DEGs: differentially expressed genes; GSEA: Gene Set Enrichment Analysis; TMB: tumor mutation burden; MF: Molecular Function; BP: Biological Process; CF: Cellular Component; EGRs: early growth response proteins; ACBD: Acyl-CoA binding domain.