Evidence map›Paper›PMID 34391383›Full record

ArticleBMC cancer2021

Construction of a prognostic signature of autophagy-related lncRNAs in non-small-cell lung cancer.

Xinyang Zhang, Yu Cao, Li Chen

Abstract read
In one paragraph

Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

4 citing papers in PubMed.

  1. Article
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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

3 authors.

Xinyang ZhangDepartment of Pathology Anatomy, Medical College of Nantong University, Nantong University, Nantong, 226001, Jiangsu, China.
Yu CaoThird People's Hospital of Nantong, Nantong, 226001, Jiangsu, China.
Li ChenDepartment of Pathology Anatomy, Medical College of Nantong University, Nantong University, Nantong, 226001, Jiangsu, China. ntchenli1@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutophagy inhibits tumorigenesis by limiting inflammation. LncRNAs regulate gene expression at various levels as RNAs; thus, both autophagy and lncRNAs are closely related to the occurrence and development of tumours.

methodsA total of 232 autophagy-related genes were used to construct a coexpression network to extract autophagy-related lncRNAs. A prognostic signature was constructed by multivariate regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was applied to analyse enrichment in cancer-related pathways. Immune infiltration analysis was used to analyse the relationship between the prognostic signature and the tumour microenvironment.

resultsNine autophagy-related lncRNAs were used to construct a prognostic model for non-small-cell lung cancer. The median risk score was used to discriminate the high- and low-risk groups, and the low-risk group was found to have better survival. Because KEGG pathway analysis showed that the prognostic signature was enriched in some immune pathways, further analysis of immune infiltration was conducted, and it was found that the prognostic signature did play a unique role in the immune microenvironment. Additionally, the prognostic signature was associated with clinical factors.

conclusionWe constructed a prognostic model of autophagy-related lncRNAs that can predict the prognosis of non-small-cell lung cancer.

Indexed as

Biomarkers, TumorGene Expression Regulation, NeoplasticRNA, Long NoncodingAutophagyCarcinoma, Non-Small-Cell LungGene Expression ProfilingHumansLung NeoplasmsPrognosisProportional Hazards ModelsReproducibility of ResultsRNA, MessengerROC CurveTranscriptomeBiomarkers, TumorRNA, Long NoncodingRNA, MessengerAutophagyCancerImmune infiltrationlncRNAsSignature

Identifiers

PMID34391383
PMCPMC8364711

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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.