Evidence map›Paper›PMID 37051233›Full record

ArticleFrontiers in immunology2023

Analysis of necroptosis-related prognostic genes and immune infiltration in idiopathic pulmonary fibrosis.

Hongzuo Chen, Zhenkun Xia, Bei Qing, Wei Wang, Linguo Gu, Ying Chen, Juan Wang, Yunchang Yuan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
5.2field-weighted citation impact, top 4% 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

17 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. RIPK3 Orchestrates Scar-Associated Macrophage Dysfunction to Drive Pulmonary Fibrosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  5. Identification ofInternational journal of chronic obstructive pulmonary disease · 2026
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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

8 authors at 2 institutions in 1 country.

Hongzuo ChenDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Zhenkun XiaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Bei QingDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Wei WangDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Linguo GuDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Ying ChenDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Juan WangDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Yunchang YuanDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Central South University · CNSecond Xiangya Hospital of Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: IPF is an undetermined, progressive lung disease. Necroptosis is a type of programmed apoptosis, which involved in the pathogenesis of lung diseases like COPD and ARDS. However, necroptosis in IPF have not been adequately studied. This study aimed to investigate the necroptosis in IPF and the relationship between necroptosis and immune infiltration, to construct a prognostic prediction model of IPF based on necroptosis-related genes. Methods: GSE110147 was downloaded from the GEO database and utilized to analyze the expression of necroptosis-related differentially expressed genes (NRDEGs). Then NRDEGs were used to construct protein-protein interaction (PPI) networks in the STRING database, and Cytoscape software was used to identify and visualize hub genes. Necroptosis-related prognosticgenes were explored in GSE70866, and a prognostic prediction model was constructed. The ImmuCellAI algorithm was utilized to analyze the landscape of immune infiltration in GSE110147. The single-cell RNA sequencing dataset GSE122960 was used to explore the association between necroptosis and type II alveolar epithelial cells (AT II) in IPF. The GSE213001 and GSE93606 were used for external validation. The expression of prognostic genes was quantified using RT-qPCRin the IPF A549 cell model, and was further verified by western blotting in the bleomycin-induced pulmonary fibrosis mouse model. Results: It was observed that necroptosis-related signaling pathways were abundantly enriched in IPF. 29 NRDEGs were screened, of which 12 showed consistent expression trends in GSE213001. Spearman correlation analysis showed that the expression of NRDEGs was positively correlated with the infiltration of proinflammatory immune cells, and negatively correlated with the infiltration of anti-inflammatory immune cells. NRDEGs, including MLKL, were highly expressed in AT II of fibrotic lung tissue. A necroptosis-related prediction model was constructed based on 4 NRDEGsby the cox stepwise regression. In the validation dataset GSE93606, the prognostic prediction model showed good applicability. The verification results of RT-qPCR and western blotting showed the reliability of most of the conclusions. Conclusions: This study revealed that necroptosis existed in IPF and might occur in AT II. Necroptosis was associated with immune infiltration, suggesting that necroptosis of AT II might involve in IPF by activating immune infiltration and immune response.

Indexed as

Idiopathic Pulmonary FibrosisNecroptosisA549 CellsAnimalsHumansMicePrognosisReproducibility of Resultsbioinformatics analysisidiopathic pulmonary fibrosisimmune filtrationnecroptosisprognostic prediction model

Identifiers

PMID37051233
PMCPMC10083386
OpenAlexW4361011280

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.