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<p>Thyroid carcinoma (THCA) exhibits molecular heterogeneity, necessitating robust biomarkers for precise diagnosis and mechanistic insights. In this study, we integrated the transcriptomic data of THCA and normal thyroid tissues from The Cancer Genome Atlas and Genotype-Tissue Expression (GTEx) databases using ComBat-based batch correction, identifying 5,573 differentially expressed genes. Functional enrichment showed that these genes were associated with dysregulation in immune-microenvironment interactions and post-transcriptional regulatory pathways. After a comparison of seven machine-learning algorithms, LightGBM was selected for its high discrimination performance and interpretability. Using this algorithm, we obtained an 8-gene diagnostic panel comprising a glycosyltransferase (<i>GALNT7</i>), pseudogenes (<i>HIRAP1</i>, <i>SKP1P1</i>), miRNAs (<i>MIR331</i>,<i> MIR93</i>), and novel transcripts. siRNA-mediated knockdown of <i>GALNT7</i> and <i>SKP1P1</i> significantly attenuated the proliferative and migratory abilities and clonogenicity of the THCA cell line TPC-1 in vitro. Immune correlation analysis revealed the tumor-specific suppression of stromal components by <i>GALNT7</i> and normal tissue-specific lymphoid regulation by <i>SKP1P1</i>, suggesting the different roles of these components in microenvironment remodeling. Knockdown of <i>HIRAP1</i>, <i>MIR331</i>, and <i>MIR93</i> had no statistically significant effects on these oncogenic phenotypes. However, their high predictive value in the computational model suggests they may serve as effective biomarkers for tumor classification, independent of their functional roles in tumorigenesis. These findings highlight the importance of orthogonal experimental validation in linking computational biomarker discovery with biological causality. This study suggests that <i>GALNT7</i> and <i>SKP1P1</i> are promising diagnostic biomarkers and therapeutic targets for THCA, with dual roles in tumor-specific signaling and immune microenvironment modulation.</p>

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LightGBM-guided discovery of mechanistic biomarkers in thyroid cancer: GALNT7 and SKP1P1 emerge as therapeutic targets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月7日

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  • Juntong Liu,
  • Xin Liu,
  • Jun Li,
  • Guilin Feng,
  • Fang Fang,
  • Zhiyong Zhao,
  • Di Hu

摘要

Thyroid carcinoma (THCA) exhibits molecular heterogeneity, necessitating robust biomarkers for precise diagnosis and mechanistic insights. In this study, we integrated the transcriptomic data of THCA and normal thyroid tissues from The Cancer Genome Atlas and Genotype-Tissue Expression (GTEx) databases using ComBat-based batch correction, identifying 5,573 differentially expressed genes. Functional enrichment showed that these genes were associated with dysregulation in immune-microenvironment interactions and post-transcriptional regulatory pathways. After a comparison of seven machine-learning algorithms, LightGBM was selected for its high discrimination performance and interpretability. Using this algorithm, we obtained an 8-gene diagnostic panel comprising a glycosyltransferase (GALNT7), pseudogenes (HIRAP1, SKP1P1), miRNAs (MIR331, MIR93), and novel transcripts. siRNA-mediated knockdown of GALNT7 and SKP1P1 significantly attenuated the proliferative and migratory abilities and clonogenicity of the THCA cell line TPC-1 in vitro. Immune correlation analysis revealed the tumor-specific suppression of stromal components by GALNT7 and normal tissue-specific lymphoid regulation by SKP1P1, suggesting the different roles of these components in microenvironment remodeling. Knockdown of HIRAP1, MIR331, and MIR93 had no statistically significant effects on these oncogenic phenotypes. However, their high predictive value in the computational model suggests they may serve as effective biomarkers for tumor classification, independent of their functional roles in tumorigenesis. These findings highlight the importance of orthogonal experimental validation in linking computational biomarker discovery with biological causality. This study suggests that GALNT7 and SKP1P1 are promising diagnostic biomarkers and therapeutic targets for THCA, with dual roles in tumor-specific signaling and immune microenvironment modulation.