GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours

Zerui Wu; Changjun Rao; Yilin Xie; Zhen Ye; Yichao Zhang; Zengyi Ma; Zhipeng Su; Zhao Ye

doi:10.1016/j.gene.2025.149374

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours

Gene. 2025 May 20:950:149374. doi: 10.1016/j.gene.2025.149374. Epub 2025 Feb 28.

Authors

Zerui Wu¹, Changjun Rao², Yilin Xie¹, Zhen Ye¹, Yichao Zhang¹, Zengyi Ma¹, Zhipeng Su³, Zhao Ye⁴

Affiliations

¹ Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China; National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China.
² Department of Cell Biology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China.
³ Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China. Electronic address: drsuzhipeng@163.com.
⁴ Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China; National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China. Electronic address: yezhaozj663812@126.com.

PMID: 40024300
DOI: 10.1016/j.gene.2025.149374

Abstract

Background: Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, which increases the difficulty of surgery and indicates a poor prognosis. However, the molecular mechanism related to invasiveness remains to be further studied. This study is to screen and identify the characteristic biomarkers of invasive NFPitNETs.

Methods: Based on the data of 73 NFPitNETs microarray chips in the GSE169498 dataset, this study used weighted gene co-expression network (WGCNA), differential expression analysis, protein-protein interaction (PPI) network analysis and various machine learning methods (XGBOOST, LASSO regression, random forest, support vector machine) to screen candidate biomarkers for invasive NFPitNET. Then, using gene set enrichment analysis (GSEA) to explore the differences in biological activities and signaling pathways between invasive NFPitNET and non-invasive NFPitNET. Single-sample GSEA (ssGSEA) was used to analyze key biomarkers-related signaling pathways. Finally, the expression and function of the key biomarkers were verified by q-RT PCR, immunohistochemical (IHC) experiments and in vitro experiments.

Results: Combined with WGCNA and differential expression analysis, 128 high-expression and 85 low-expression candidate biomarkers were preliminarily obtained. PPI analysis and four machine learning algorithms further identified GALR1, PENK and HOXD9. The receiver operating characteristic (ROC) curve results showed that the three biomarkers had good predictive ability of invasiveness. After combining the validation set data, GALR1 and PENK were the final key biomarkers. Finally, PCR and IHC results verified the decreased expression of GALR1 and PENK in invasive NFPitNET and promotes proliferation and invasive ablity of pituitary tumor cells.

Conclusion: This study confirmed that the reduced expression of GALR1 and PENK is an important molecular feature of invasive NFPitNETs, which may play an important role in inhibiting the development of NFPitNET.

Keywords: GLAR1; Invasion; PENK; Pituitary tumors.

MeSH terms

Biomarkers, Tumor* / genetics
Biomarkers, Tumor* / metabolism
Cell Line, Tumor
Gene Expression Profiling / methods
Gene Expression Regulation, Neoplastic*
Gene Regulatory Networks
Humans
Machine Learning
Neoplasm Invasiveness
Neuroendocrine Tumors* / genetics
Neuroendocrine Tumors* / metabolism
Neuroendocrine Tumors* / pathology
Pituitary Neoplasms* / genetics
Pituitary Neoplasms* / metabolism
Pituitary Neoplasms* / pathology
Protein Interaction Maps / genetics
Signal Transduction

Substances

Biomarkers, Tumor