Evidence map›Paper›PMID 40598468›Full record

ArticleBMC medicine2025

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 and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Yike Chen *Department of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Ningning Zhao *Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China.
Jiahao ZhangDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Xinyi WuSchool of Medicine, Hangzhou City University, Hangzhou, Zhejiang, 310000, P. R. China.
Jian HuangAnhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China.
Xiaohui XuDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Feng CaiDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Sheng ChenDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Liyin XuNursing Department, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Wei YanDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Yuan HongDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Yunfei WangHangzhou ShengTing Medical Technology Co., Ltd, Zhejiang Province, Hangzhou, 310018, China.
Hui LingDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Jianxiong JiDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Gao ChenDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China.
Hongcang GuAnhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China. gu_hongcang@cmpt.ac.cn.
Jianmin ZhangDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China. zjm135@zju.edu.cn.
Qun WuDepartment of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, P. R. China. wuqun2192010@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPituitary 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.

methodsReduced 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.

resultsWe 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.

conclusionsThe 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).

Indexed as

AdenomaDNA MethylationGene Expression Regulation, NeoplasticPituitary NeoplasmsAdultBiomarkers, TumorFemaleGene Expression ProfilingHumansMaleMiddle AgedNeoplasm InvasivenessBiomarkers, TumorCell-free DNADNA methylationInvasive pituitary adenoma (IPA)Pituitary adenoma (PA)Reduced representation bisulfite sequencing (RRBS)

Identifiers

PMID40598468
PMCPMC12217985

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.