Evidence map›Paper›PMID 34257566›Full record

SynthesisPathology oncology research : POR2021

Identification of Key Genes Involved in Pancreatic Ductal Adenocarcinoma with Diabetes Mellitus Based on Gene Expression Profiling Analysis.

Weiyu Zhou, Yujing Wang, Hongmei Gao, Ying Jia, Yuanxin Xu, Xiaojing Wan, Zhiying Zhang, Haiqiao Yu, Shuang Yan

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Pathology oncology research : POR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Gene Dysregulation and Islet Changes in PDAC-Associated Type 3c Diabetes.International journal of molecular sciences · 2025
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. HighFrontiers in oncology · 2021
    Article
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

9 authors at 2 institutions in 1 country.

Weiyu ZhouDepartment of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Yujing WangDepartment of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Hongmei GaoDepartment of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Ying JiaDepartment of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Yuanxin XuDepartment of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Xiaojing WanDepartment of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Zhiying ZhangDepartment of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Haiqiao YuDepartment of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Shuang YanDepartment of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Harbin Medical University · CNFourth Affiliated Hospital of Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

Indexed as

Diabetes MellitusBiomarkers, TumorCarcinoma, Pancreatic DuctalDiabetes ComplicationsGene Expression ProfilingHumansPancreatic NeoplasmsBiomarkers, Tumordiabetes mellitusmeta-analysispancreatic ductal adenocarcinomasupport vector machinesurvival analysis

Identifiers

PMID34257566
PMCPMC8262175
OpenAlexW3155544973

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.