Evidence map›Paper›PMID 39730475›Full record

ArticleScientific reports2024

KDELR3 overexpression as a novel prognostic and diagnostic biomarker in glioma: comprehensive bioinformatic analysis insights.

Jing Feng, Lin Zhao, Liyuan Fu, Xinpeng Wang, Danyu Ma, Mingchao Shang, Baoqing Xu, Jinping Zhou, Zhonghua Chen, Hu Zhao

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

10 authors.

Jing Feng *Department of Radiation Oncology, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China.
Lin Zhao *Department of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China.
Liyuan Fu *Department of Diagnostic Radiology, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China.
Xinpeng WangDepartment of Radiation Oncology, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China.
Danyu MaDepartment of Radiation Oncology, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China.
Mingchao ShangDepartment of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China.
Baoqing XuDepartment of Pathology, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China.
Jinping ZhouDepartment of Clinical Quality Management, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China. zhoujinping2003@163.com.
Zhonghua ChenDepartment of Radiation Oncology, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China. zyczhonghua@163.com.
Hu ZhaoDepartment of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, China. zhaohubear@163.com.

Funding

Medical Key Specialty (Neurosurgery) Project of PLA Joint Logistics Team LQZD-SWResearch Project of the 900th Hospital of the Joint Logistic Support Force 2022MS12Science and Technology Innovation Platform Project of Fujian Science and Technology Plan 2022Y2017Science and Technology Program of Fujian Province 2022J011094Science and Technology Program of Fujian Province 2023J011368Science and Technology Program of Fujian Province 2024J011155
6 · The paper itself

Abstract

Glioma, the most prevalent primary intracranial malignancy among adults, is distinguished by its high morbidity and recurrence rates, posing a considerable threat to patients' quality of life and survival prospects. Consequently, the pursuit of efficacious molecular prognostic markers holds paramount importance. The exploration of the role of the KDELR3 kinase family in various neoplastic conditions constitutes an emerging area of research. However, the biological functions of KDELR3 and its prognostic implications in brain tumors remain largely undocumented. This study endeavored to ascertain the potential of KDELR3 as a prognostic indicator for glioma. We integrated a comprehensive dataset encompassing 1127 glioma samples, sourced from our cohort, The Cancer Genome Atlas (TCGA), and the Chinese Glioma Genome Atlas (CGGA), to delve into the expression patterns of KDELR3 in glioma and their associated implications. Notably, KDELR3 was markedly overexpressed in glioma and demonstrated a positive correlation with clinical progression. By utilizing Kaplan-Meier survival analysis and the Cox proportional hazards regression model, we evaluated the prognostic significance of KDELR3, revealing it as an independent predictor of adverse outcomes in glioma patients. Furthermore, gene set enrichment analysis unveiled potential signaling pathways associated with KDELR3 expression in glioma, primarily encompassing Cytokine-cytokine receptor interaction, extracellular matrix (ECM)-receptor interaction, and complement and coagulation cascades. In summation, our findings provide profound insights into the potential role of KDELR3 and its application as a novel and promising prognostic biomarker for glioma.

Indexed as

Biomarkers, TumorBrain NeoplasmsComputational BiologyGliomaFemaleGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisBiomarkers, TumorBioinformaticsBiomarkerGliomaKDELR3Prognosis

Identifiers

PMID39730475
PMCPMC11681132

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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.