Evidence map›Paper›PMID 38071255›Full record

ArticleScientific reports2023

Exploration of the role of oxidative stress-related genes in LPS-induced acute lung injury via bioinformatics and experimental studies.

Yuanshui Liu, Huamei Li, Yanhong Ouyang, Yan Zhang, Pinhua Pan

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.5field-weighted citation impact, top 16% 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

9 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. 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

5 authors at 3 institutions in 1 country.

Yuanshui Liu *Department of Emergency Medicine, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, 570311, People's Republic of China. yuanshuiliu@hainmc.edu.cn.
Huamei Li *Department of Ultrasound, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, 570311, People's Republic of China. huameili@hainmc.edu.cn.
Yanhong OuyangDepartment of Emergency Medicine, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, 570311, People's Republic of China.
Yan ZhangDepartment of Respiratory Medicine, Key Cite of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China. zhangy4290@csu.edu.cn.
Pinhua PanDepartment of Respiratory Medicine, Key Cite of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China. pinhuapan668@csu.edu.cn.
Central South University · CNHainan Medical University · CNHainan General Hospital · CN

Funding

Hainan Provincial Medical and Health Research Project 20A200340Key R&D Program of Hunan Province 2022SK2038National Natural Science Foundation of China 81770080Peking Union Medical Foundation - Ruiyi Emergency Medical Research Fund R2021004Project Program of central south university graduate education teaching reform 2022JGB025Research Project on Education and Teaching Reform of Central South University 2021 jy139-2Xiangya Hospital-Beida Weiming Clinical Rehabilitation Research Fund XYWM2015I20
6 · The paper itself

Abstract

During the progression of acute lung injury (ALI), oxidative stress and inflammatory responses always promote each other. The datasets analyzed in this research were acquired from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-expression Network Analysis (WGCNA) and limma package were used to obtain the ALI-related genes (ALIRGs) and differentially expressed genes (DEGs), respectively. In total, two biological markers (Gch1 and Tnfaip3) related to oxidative stress were identified by machine learning algorithms, Receiver Operator Characteristic (ROC), and differential expression analyses. The area under the curve (AUC) value of biological markers was greater than 0.9, indicating an excellent power to distinguish between ALI and control groups. Moreover, 15 differential immune cells were selected between the ALI and control samples, and they were correlated to biological markers. The transcription factor (TF)-microRNA (miRNA)-Target network was constructed to explore the potential regulatory mechanisms. Finally, based on the quantitative reverse transcription polymerase chain reaction (qRT-PCR), the expression of Gch1 and Tnfaip3 was significantly higher in ALI lung tissue than in healthy controls. In conclusion, the differences in expression profiles between ALI and normal controls were found, and two biological markers were identified, providing a research basis for further understanding the pathogenesis of ALI.

Indexed as

Acute Lung InjuryLipopolysaccharidesBiomarkersComputational BiologyHumansOxidative StressBiomarkersLipopolysaccharides

Identifiers

PMID38071255
PMCPMC10710410
OpenAlexW4389508648

What Socratic 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 Socratic graph.