Evidence map›Paper›PMID 37910672›Full record

ArticleAging2023

Identification of epithelial-mesenchymal transition-related biomarkers in lung adenocarcinoma using bioinformatics and lab experiments.

Yuanjun Cheng, Yumei Shen, Qianru Fang, Shanzhou Duan, Yifei Wang, Xiaoxiao Dai, Yongbing Chen

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

7 authors at 3 institutions in 1 country.

Yuanjun ChengDepartment of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yumei ShenDepartment of Operation, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Qianru FangDepartment of Obstetrics, People’s Hospital of Chizhou, Chizhou, China.
Shanzhou DuanDepartment of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yifei WangDepartment of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Xiaoxiao DaiDepartment of Pathology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yongbing ChenDepartment of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Soochow University · CNChizhou University · CNSecond Affiliated Hospital of Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung adenocarcinoma accounts for approximately 40% of lung cancer cases and poses a serious threat to human health. Therefore, there is an urgent need to identify central biomarkers in lung adenocarcinoma.

methodsWe first identified the EMT-associated genes in LUAD based on the TCGA cohort. Then we screened these 90 EMT-associated genes using univariate Cox regression analysis and LASSO regression analysis to develop a prognostic gene signature in the training set. The predictive performance of the gene signature was assessed in the validation set and multiple external test sets using the ROC cure, C index and log-rank tests. RT-PCR, western blot, wound healing assays, and siRNA methods were further used to investigate the role of PLEK2 in tumor behaviors.

resultsEight genes (CCNB1, PLEK2, DERL3, C1QTNF6, DLGAP5, HMMR, GJB3, and SPOCK1) were eventually selected to develop an eight-gene signature. The 5-year AUC of the gene signature has a robust predictive ability both for predicting overall survival (0.774, 0.756, and 0.669 in the external test sets, respectively), and for progression free survival (0.774, 0.746, and 0.755 in the external test sets, respectively). C-index of the gene signature was 0.961 ± 0.005, 0.916 ± 0.011, and 0.868 ± 0.234 in the external test sets, respectively. Four genes (C1QTNF6, DLGAP5, HMMR, and PLEK2) were identified as key genes in LUAD progression, which were upregulated in the cancerous tissue compared with in the normal tissue (P < 0.001), and correlated with an unwanted prognosis in lung cancer (P < 0.05). PLEK2 was used as an example to explore its effect on LUAD progression

conclusionsThis study developed a novel EMT-related gene signature benefiting precision medicine, and identified four pivotal genes which can serve as therapeutic targets in LUAD. Four key genes can serve as molecular targets for patients with LUAD; silencing of PLEK2 was shown to reduce proliferative and migrated ability of lung cancer cells via prohibition of autophagy.

Indexed as

Adenocarcinoma of LungLung NeoplasmsBiomarkersComputational BiologyEpithelial-Mesenchymal TransitionHumansMembrane ProteinsPrognosisProteoglycansBiomarkersMembrane ProteinsPLEK2 protein, humanProteoglycansSPOCK1 protein, humanautophagybiomarkersepithelial-mesenchymal transitionPLEK2prognosis

Identifiers

PMID37910672
PMCPMC10683598
OpenAlexW4388041310

What OpenQuestion holds

Textmetadata
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.