Evidence map›Paper›PMID 32741804›Full record

ArticleCancer biomarkers : section A of Disease markers2020

Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma.

Dan Yang, Yang He, Bo Wu, Ruxi Liu, Nan Wang, Tieting Wang, Yannan Luo, Yunda Li, Yang Liu

Abstract readValidation Study
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2020. 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
0.8field-weighted citation impact, top 34% 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, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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.

Dan YangDepartment of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.
Yang HeMolecular Oncology Laboratory of Cancer Research Institute, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China.
Bo WuDepartment of Anus and Intestine Surgery, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China.
Ruxi LiuDepartment of Rheumatology and Immunology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China.
Nan WangDepartment of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.
Tieting WangDepartment of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.
Yannan LuoDepartment of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.
Yunda LiDepartment of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.
Yang LiuDepartment of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.
China Medical University · CNFirst Hospital of China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to construct a competing endogenous RNA (ceRNA) network to predict the progression in LUAD.

methodsDifferentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC|> 1.0 and a false discovery rate (FDR) < 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs.

resultsA total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were associated with the KEGG pathway terms "microRNAs in cancer", "pathways in cancer", "cell cycle", "HTLV-1 infection", and the "PI3K-Akt signalling pathway". K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P< 0.05). Furthermore, 15 LUAD drugs interacting with 29 significant DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, PRKCE, DLC1, LATS2, and DPY19L1 were incorporated into the risk score system, and the results suggested that LUAD patients who had the high-risk score always suffered from a poorer overall survival. Additionally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 significant DEL-DEG pairs, NAV2-AS2 - PRKCE (r= 0.430, P< 0.001) and NAV2-AS2 - LATS2 (r= 0.338, P< 0.001). And NAV2-AS2 - mir-31 - PRKCE and NAV2-SA2 - mir-31 - LATS2 were finally identified as ceRNA network involved in the progression of LUAD.

conclusionsThe lncRNA-miRNA-mRNA ceRNA network plays an essential role in predicting the progression of LUAD. These results may improve our understanding and provide novel mechanistic insights to explore prognosis and therapeutic drugs for LUAD patients.

Indexed as

Gene Expression Regulation, NeoplasticGene Regulatory NetworksAdenocarcinoma of LungBiomarkers, TumorDisease ProgressionFemaleHumansKaplan-Meier EstimateLung NeoplasmsMaleMicroRNAsMiddle AgedNeoplasm StagingPrognosisProtein Interaction MapsRisk AssessmentBiomarkers, TumorMicroRNAsRNA, Long NoncodingRNA, MessengerbioinformaticsceRNAlncRNAlung adenocarcinomamiRNArisk score

Identifiers

PMID32741804
PMCPMC12662519
OpenAlexW3046168225

What OpenQuestion holds

Textmetadata
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.