Evidence map›Paper›PMID 36686480›Full record

ArticleFrontiers in endocrinology2022

A novel risk model based on the correlation between the expression of basement membrane genes and immune infiltration to predict the invasiveness of pituitary adenomas.

Zheng Chen, Xin Sun, Yin Kang, Jian Zhang, Fang Jia, Xiyao Liu, Hongwei Zhu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.4field-weighted citation impact, top 37% of its field
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

5 citing papers in PubMed, 2 citations in OpenAlex.

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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

7 authors at 2 institutions in 2 countries.

Zheng ChenDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Xin SunDepartment of Immunology, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
Yin KangDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Jian ZhangDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Fang JiaDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Xiyao LiuDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Hongwei ZhuDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
First Affiliated Hospital of Xiamen University · CNHokkaido University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Invasive pituitary adenomas (IPAs) are common tumors of the nervous system tumors for which invasive growth can lead to difficult total resection and a high recurrence rate. The basement membrane (BM) is a special type of extracellular matrix and plays an important role in the invasion of pituitary adenomas (PAs). The aim of this study was to develop a risk model for predicting the invasiveness of PAs by analyzing the correlation between the expression of BM genes and immune infiltration. Methods: Four datasets, featuring samples IPAs and non-invasive pituitary adenomas (NIPAs), were obtained from the Gene Expression Omnibus database (GEO). R software was then used to identify differentially expressed genes (DEGs) and analyze their functional enrichment. Protein-protein interaction (PPI) network was used to screen BM genes, which were analyzed for immune infiltration; this led to the generation of a risk model based on the correlation between the expression of BM genes and immunity. A calibration curve and receiver operating characteristic (ROC) curve were used to evaluate and validate the model. Subsequently, the differential expression levels of BM genes between IPA and NIPA samples collected in surgery were verified by Quantitative Polymerase Chain Reaction (qPCR) and the prediction model was further evaluated. Finally, based on our analysis, we recommend potential drug targets for the treatment of IPAs. Results: The merged dataset identified 248 DEGs that were mainly enriching in signal transduction, the extracellular matrix and channel activity. The PPI network identified 11 BM genes from the DEGs: Conclusion: Using a variety of bioinformatics methods, we developed a novel risk model to predict the probability of PAs invasion based on the correlation between 11 BM genes and immune infiltration. These findings may facilitate closer surveillance and early diagnosis to prevent or treat IPAs in patients and improve the clinical awareness of patients at high risk of IPAs.

Indexed as

AdenomaPituitary NeoplasmsADAMTS ProteinsGenes, RegulatorGlypicansHumansLamininProtein Interaction MapsReproducibility of ResultsADAMTS8 protein, humanADAMTS ProteinsGlypicansGPC3 protein, humanLAMC3 protein, humanLamininbasement membrane genesbioinformaticsGEOimmune infiltrationinvasive pituitary adenomasPPItumor microenvironment

Identifiers

PMID36686480
PMCPMC9846255
OpenAlexW4313483105

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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.