Scholarlink

Objective <p>The aim of this study is to reveal key genes related to sphingolipid metabolism for distinguishing the invasive characteristics of pituitary adenomas (PA).</p> Methods <p>Bulk-RNA sequencing datasets related to the invasive pituitary adenomas (IPA) were retrieved from the GEO database and integrated after batch effect correction. IPA-related genes were identified through weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEGs) analysis. Sphingolipid metabolism-related genes were sourced from the GeneCards database and intersected with IPA-related genes to identify IPA-specific metabolism genes (SMGs). Machine learning, including Lasso and SVM-RFE, was used to identify the key genes incorporated into a diagnostic model. The tumor immune microenvironment was analyzed, exploring correlations with key genes. Finally, key genes were validated through immunohistochemistry (IHC) and RT-qPCR in the IPA and PA samples.</p> Results <p>Four datasets were included. A total of 228 IPA-related genes were identified, with 10 IPA-SMGs obtained by intersecting with SM-related genes. Machine learning revealed three key genes, SLC27A2, EHD3, and SV2B. A logistic regression model was constructed, and after internal validation with 1000 bootstrap iterations, the ROC curve achieved an AUC of 0.767. Immune infiltration analysis showed that the ratio of endothelial cells and fibroblasts was significantly reduced in the IPA group compared to controls. IHC and RT-qPCR confirmed that SLC27A2 expression was significantly reduced, while EHD3 and SV2B were significantly increased in IPA tissues, which was consistent with the bioinformatics findings.</p> Conclusion <p>This study identifies key genes potentially involved in the development and progression of IPA, offering new insights into the molecular mechanisms underlying IPA pathogenesis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Revealing sphingolipid metabolism genes as biomarkers for the diagnosis of invasive pituitary adenomas in silico and in vivo

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

查看全文
  • Zhi-jie Xu,
  • Ruo-tong Zhang,
  • Nan Tian,
  • Ning Gan

摘要

Objective

The aim of this study is to reveal key genes related to sphingolipid metabolism for distinguishing the invasive characteristics of pituitary adenomas (PA).

Methods

Bulk-RNA sequencing datasets related to the invasive pituitary adenomas (IPA) were retrieved from the GEO database and integrated after batch effect correction. IPA-related genes were identified through weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEGs) analysis. Sphingolipid metabolism-related genes were sourced from the GeneCards database and intersected with IPA-related genes to identify IPA-specific metabolism genes (SMGs). Machine learning, including Lasso and SVM-RFE, was used to identify the key genes incorporated into a diagnostic model. The tumor immune microenvironment was analyzed, exploring correlations with key genes. Finally, key genes were validated through immunohistochemistry (IHC) and RT-qPCR in the IPA and PA samples.

Results

Four datasets were included. A total of 228 IPA-related genes were identified, with 10 IPA-SMGs obtained by intersecting with SM-related genes. Machine learning revealed three key genes, SLC27A2, EHD3, and SV2B. A logistic regression model was constructed, and after internal validation with 1000 bootstrap iterations, the ROC curve achieved an AUC of 0.767. Immune infiltration analysis showed that the ratio of endothelial cells and fibroblasts was significantly reduced in the IPA group compared to controls. IHC and RT-qPCR confirmed that SLC27A2 expression was significantly reduced, while EHD3 and SV2B were significantly increased in IPA tissues, which was consistent with the bioinformatics findings.

Conclusion

This study identifies key genes potentially involved in the development and progression of IPA, offering new insights into the molecular mechanisms underlying IPA pathogenesis.