Evidence map›Paper›PMID 41588537›Full record

ArticleCancer cell international2026

Single-cell and machine learning approaches reveal METTL14-mediated autophagy via PI3K/AKT signaling in invasive PitNET.

Shuangjian Yang, Changqin Pu, Congcong Deng, Xuexue Bai, Chenxin Tian, Wentai Zhang, Kan Deng, Lian Duan, Lin Lu, Huijuan Zhu and 4 more

Abstract read
In one paragraph

Article in Cancer cell international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

14 authors.

Shuangjian Yang *Department of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Changqin Pu *Department of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Congcong DengDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Xuexue BaiDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Chenxin TianDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Wentai ZhangDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Kan DengDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Lian DuanKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Lin LuKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Huijuan ZhuKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Yong YaoDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Renzhi WangDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China. wangrz@126.com.ORCID http://orcid.org/0000-0002-1666-2717
Mengqi ChangDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China. changmengqi@pumch.cn.ORCID http://orcid.org/0000-0002-9588-953X
Ming FengDepartment of Neurosurgery, Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China. jackietz@163.com.ORCID http://orcid.org/0000-0003-2080-5474

Funding

the Beijing Municipal Natural Science Foundation M2201the CAMS Innovation Fund for Medical Sciences CIFMS 2021-I2M-1-003the Key Area Research and Development Program of Guangdong Province 2021B0101420005the National High Level Hospital Clinical Research Funding 2022-PUMCH-C-012the National Natural Science Foundation of China 82103302 to MC
6 · The paper itself

Abstract

objectiveThis study investigates the role of m6A regulators in invasive pituitary neuroendocrine tumors (PitNETs) by defining m6A-related molecular subtypes, constructing a nomogram, and elucidating the mechanistic role of METTL14 in PitNET progression.

methodsGEO datasets were analyzed for m6A regulatory gene expression. Machine learning approaches were used to develop a nomogram. m6A molecular and gene subtypes were identified, and scRNA-seq from tumors characterized intratumoral subpopulations. Functional validation was performed using METTL14 overexpression and knockdown in PitNET cells, followed by proliferation, invasion, RT-qPCR, Western blotting, m6A-RIP, RIP, RNA stability, and luciferase reporter assays.

resultsSeven key m6A regulators were selected for the nomogram. Two m6A subtypes were identified, with Cluster B showing lower immune infiltration and higher m6A scores. Further classification revealed two gene-based subtypes with consistent patterns. scRNA-seq defined four PitNET clusters, including a proliferative TPC population with the highest invasion scores, strongly correlated with METTL14. Functional experiments confirmed that METTL14 promotes proliferation, invasion, and autophagy via PI3K/AKT activation. Mechanistically, IGF2 was identified as a novel downstream effector of METTL14, as METTL14 enhanced IGF2 expression through m6A modification, thereby activating PI3K/AKT signaling.

conclusionThis study provides a comprehensive characterization of m6A methylation in invasive PitNET, integrating bulk and single-cell transcriptomics. The nomogram offers clinical potential, while the discovery of TPCs and the identification of the METTL14–IGF2–PI3K/AKT regulatory axis highlight novel mechanistic insights and therapeutic targets for personalized invasive PitNET management.

Indexed as

AutophagyInvasive pituitary neuroendocrine tumorMachine learningRNA m6A methylationSingle-Cell gene expression analysis

Identifiers

PMID41588537
PMCPMC12879320

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.