Identification of biomarkers related to lymph node metastasis and prognosis in thyroid cancer based on integrated bioinformatics exploration.
- 1Department of Endocrinology and Metabolism, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, Gansu, China.
- 2Department of Endocrinology and Metabolism, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, Gansu, China. Electronic address: [email protected].
Abstract
Objective
Thyroid Carcinoma (THCA) is a common endocrine malignancy, and Lymph Node Metastasis (LNM) is a critical determinant of poor prognosis. This study aimed to identify LNM-related biomarkers and establish a prognostic model for THCA patients using integrated bioinformatics approaches.Methods
Clinical data from SEER were analyzed to identify prognostic factors in THCA. Transcriptomic data from TCGA were used to identify differentially expressed genes between LNM and non-LNM samples. Survival analysis, machine learning (Boruta and LASSO), and functional enrichment were applied to screen candidate biomarkers. Immune infiltration and a ceRNA network were also explored.Results
Six independent prognostic factors were identified from SEER (n = 38,955): age, gender, marital status, race, pathologic T-stage, and pathologic M-stage. A nomogram incorporating these factors showed good predictive performance (AUC = 0.744). Transcriptomic analysis revealed 332 LNM-related genes. Through integrative machine learning, six biomarkers ‒ CLDN10, OTOS, NEXMIF, ACOT6, ASXL3, and SLC4A4 ‒ were selected. These genes were predominantly enriched in immune response and metabolic pathways. M0 macrophages and resting dendritic cells showed significant abundance differences between LNM and NLNM samples, with CLDN10 positively correlated with resting dendritic cells (R = 0.47, adjusted p < 0.01). A ceRNA network involving 14 miRNAs and 4 lncRNAs was constructed, with hsa-miR-15a-5p predicted to regulate SLC4A4, ASXL3, and NEXMIF.Conclusion
We identified six candidate LNM-related biomarkers and clinical factors associated with THCA prognosis. These findings provide a basis for further investigation into LNM mechanisms and may offer new perspectives for prognostic assessment, though independent validation is required.
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BibTeX
@article{hu2026identification,
title = {{Identification of biomarkers related to lymph node metastasis and prognosis in thyroid cancer based on integrated bioinformatics exploration.}},
author = {Xuejian Hu and Dan Ge and Xiaolan Ma and Yan Yang},
journal = {Clinics (Sao Paulo, Brazil)},
year = {2026},
month = oct,
doi = {10.1016/j.clinsp.2026.101201},
url = {https://europepmc.org/article/MED/42880131},
note = {CC BY},
}
RIS
TY - JOUR
TI - Identification of biomarkers related to lymph node metastasis and prognosis in thyroid cancer based on integrated bioinformatics exploration.
AU - Xuejian Hu
AU - Dan Ge
AU - Xiaolan Ma
AU - Yan Yang
PY - 2026
DA - 2026/10/15
JO - Clinics (Sao Paulo, Brazil)
DO - 10.1016/j.clinsp.2026.101201
UR - https://europepmc.org/article/MED/42880131
AB - Objective Thyroid Carcinoma (THCA) is a common endocrine malignancy, and Lymph Node Metastasis (LNM) is a critical determinant of poor prognosis. This study aimed to identify LNM-related biomarkers and establish a prognostic model for THCA patients using integrated bioinformatics approaches.Methods Clinical data from SEER were analyzed to identify prognostic factors in THCA. Transcriptomic data from TCGA were used to identify differentially expressed genes between LNM and non-LNM samples. Survival analysis, machine learning (Boruta and LASSO), and functional enrichment were applied to screen candidate biomarkers. Immune infiltration and a ceRNA network were also explored.Results Six independent prognostic factors were identified from SEER (n = 38,955): age, gender, marital status, race, pathologic T-stage, and pathologic M-stage. A nomogram incorporating these factors showed good predictive performance (AUC = 0.744). Transcriptomic analysis revealed 332 LNM-related genes. Through integrative machine learning, six biomarkers ‒ CLDN10, OTOS, NEXMIF, ACOT6, ASXL3, and SLC4A4 ‒ were selected. These genes were predominantly enriched in immune response and metabolic pathways. M0 macrophages and resting dendritic cells showed significant abundance differences between LNM and NLNM samples, with CLDN10 positively correlated with resting dendritic cells (R = 0.47, adjusted p < 0.01). A ceRNA network involving 14 miRNAs and 4 lncRNAs was constructed, with hsa-miR-15a-5p predicted to regulate SLC4A4, ASXL3, and NEXMIF.Conclusion We identified six candidate LNM-related biomarkers and clinical factors associated with THCA prognosis. These findings provide a basis for further investigation into LNM mechanisms and may offer new perspectives for prognostic assessment, though independent validation is required.
SN - 1807-5932
SN - 1980-5322
ER -