2025-07-29 中国科学院(CAS)
Identification of cfDNA biomarkers for screening IPA from NPA. (Image by ZHAO Ningning)
<関連情報>
- https://english.cas.cn/newsroom/research_news/life/202508/t20250801_1048947.shtml
- https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-025-04164-1
侵襲性および非侵襲性下垂体腺腫の分子マーカー:DNAメチル化と遺伝子発現の包括的解析 Molecular signatures of invasive and non-invasive pituitary adenomas: a comprehensive analysis of DNA methylation and gene expression
Yike Chen,Ningning Zhao,Jiahao Zhang,Xinyi Wu,Jian Huang,Xiaohui Xu,Feng Cai,Sheng Chen,Liyin Xu,Wei Yan,Yuan Hong,Yunfei Wang,Hui Ling,Jianxiong Ji,Gao Chen,Hongcang Gu,Jianmin Zhang & Qun Wu
BMC Medicine
Abstract
Background
Pituitary adenomas (PAs) are benign tumors in the pituitary gland. However, 30–40% of these tumors are invasive, complicating diagnosis and treatment. Invasive pituitary adenomas (IPAs) often respond poorly to conventional therapies, emphasizing the need for better diagnostic and therapeutic strategies. Understanding DNA methylation patterns in IPAs may reveal new biomarkers and therapeutic targets, leading to more effective management of this challenging disease.
Methods
Reduced representation bisulfite sequencing (RRBS) and RNA sequencing (RNA-seq) were performed on 129 samples from the Second Affiliated Hospital of Zhejiang University, including 69 tissue samples from invasive and non-invasive pituitary adenomas (NPA) and 60 blood samples from IPA, NPA and healthy individuals. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were identified in tissues. Pearson correlation analysis was used to identify associations between DNA methylation status and gene expression, as well as the effect of methylation on gene expression at different sites. Blood samples were analyzed to detect DMRs and DEGs, correlating with tissue-derived findings. Finally, ROC analysis and a random forest model were used to identify biomarkers for discriminating invasive from non-invasive phenotypes.
Results
We identified 347 DMRs between IPA and NPA, of which 63% (219/347) were hypomethylated. Additionally, 543 mRNAs showed differential expression, with 350 upregulated and 193 downregulated. 17 genes demonstrated concurrent aberrant methylation and expression, primarily within introns, promoters, and CpG islands (CGIs). Notably, only protein tyrosine phosphatase receptor type T (PTPRT) exhibited a remarkably high correlation (r = 0.81) between its DNA methylation levels and mRNA expression levels. This correlation was observed within the intronic region/opensea of the gene’s CGIs. Plasma sample analysis revealed 852 DMRs between IPA and NPA, with 52% (447/852) being hypomethylated. Three tumor tissue-derived blood biomarkers (MIR4535, SLC8A1-AS1, and TTC34) accurately discriminated between IPA and NPA patients with a combined AUC of 0.980. These markers also differentiated NPA from healthy controls, though with different methylation patterns.
Conclusions
The relationship between DNA methylation and gene expression is complex. Plasma-based DNA methylation markers can effectively discriminate between IPA and NPA, as well as between NPA and healthy individuals (N group).
