Evidence map›Paper›PMID 33732284›Full record

ArticleFrontiers in genetics2021

Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients.

Jun Tan, Hecheng Zhu, Guihua Tang, Hongwei Liu, Siyi Wanggou, Yudong Cao, Zhaoqi Xin, Quanwei Zhou, Chaohong Zhan, Zhaoping Wu and 6 more

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In one paragraph

Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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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

12 citing papers in PubMed.

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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

16 authors.

Jun TanDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Hecheng ZhuChangsha Kexin Cancer Hospital, Changsha, China.
Guihua TangDepartment of Clinical Laboratory, Hunan Provincial People's Hospital (First Affiliated Hospital of Hunan Normal University), Changsha, China.
Hongwei LiuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Siyi WanggouDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Yudong CaoDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Zhaoqi XinDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Quanwei ZhouDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Chaohong ZhanDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Zhaoping WuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Youwei GuoDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Zhipeng JiangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Ming ZhaoChangsha Kexin Cancer Hospital, Changsha, China.
Caiping RenKey Laboratory for Carcinogenesis of Chinese Ministry of Health, School of Basic Medical Science, Cancer Research Institute, Central South University, Changsha, China.
Xingjun JiangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Wen YinDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.

Indexed as

gliomaimmune infiltrationmolecular subtypesprognostic signaturestemness index

Identifiers

PMID33732284
PMCPMC7957071

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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.