Evidence map›Paper›PMID 33870858›Full record

ArticleBioengineered2021

The screening of immune-related biomarkers for prognosis of lung adenocarcinoma.

Zhonghui Liu, Dan Sun, Qing Zhu, Xinmin Liu

Abstract read
In one paragraph

Article in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
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

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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

23 citing papers in PubMed.

  1. Review
  2. Article
  3. FMO2Journal for immunotherapy of cancer · 2025
    Article
  4. Journal of inflammation research · 2025
    Article
  5. Article
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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Zhonghui LiuDepartment of Geriatrics, Peking University First Hospital, Beijing, China.
Dan SunDepartment of Geriatrics, Peking University First Hospital, Beijing, China.
Qing ZhuInstitutes of Biomedical Sciences, Shanghai Medical College of Fudan University, Shanghai, China.
Xinmin LiuDepartment of Geriatrics, Peking University First Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) accounts for a frequently seen non-small cell lung cancer (NSCLC) histological subtype, and it is associated with dismal prognostic outcome. However, the benefits of traditional treatment are still limited, and the efficacies of immunotherapy are quite different. Therefore, it is of great significance to identify novel immune-related therapeutic targets in lung adenocarcinoma. In this study, we identified a set of immune-related biomarkers for prognosis of lung adenocarcinoma, which could provide new ideas for immunotherapy of lung adenocarcinoma. Datasets related to LUAD were filtered from the GEO database. The appropriate packages were used to identify differentially expressed genes (DEGs) and to carry out enrichment analysis, followed by the construction of prognostic biomarkers. The Kaplan-Meier (K-M) curves were plotted to analyze patient survival based on hub genes. Associations between the expression of selected biomarkers and six types of tumor-infiltrating immune cells were evaluated based on the online tool TIMER. After analyzing five GEO datasets(GSE32867, GSE46539, GSE63459, GSE75037 and GSE116959), we discovered altogether 67 DEGs, among which, 15 showed up-regulation while 52 showed down-regulation. Enrichments of integrated DEGs were identified in the ontology categories. CAV1, CFD, FMO2 and CLEC3B were eventually selected as independent prognostic biomarkers, they were correlated with clinical outcomes of LUAD patients. Moreover, a positive correlation was observed between biomarker expression and all different types of immune infiltration, and the expression level of the four biomarkers was all positively related to macrophage.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorDatabases, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticGene OntologyHumansLung NeoplasmsPrognosisReproducibility of ResultsBiomarkers, Tumorbioinformatics analysisimmune infiltrationLung adenocarcinoma (LUAD)

Identifiers

PMID33870858
PMCPMC8806236

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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.