Evidence map›Paper›PMID 35675043›Full record

ArticleAging2022

Identification of prognostic candidate signatures by systematically revealing transcriptome characteristics in lung adenocarcinoma with differing tumor microenvironment immune phenotypes.

Qiang Chen, Jiakang Ma, Xiaoyi Wang, Xiangqing Zhu

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 43% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

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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

4 authors at 3 institutions in 1 country.

Qiang ChenFaculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China.
Jiakang MaDepartment of Medical Oncology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Xiaoyi WangFaculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China.
Xiangqing ZhuBasic Medical Laboratory, The 920th Hospital of Joint Logistics Support Force of PLA, Kunming, China.
Yunnan Agricultural University · CNKunming Medical University · CNSecond Affiliated Hospital of Zhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accumulated evidence shows that tumor microenvironment plays crucial roles in predicting clinical outcomes of lung adenocarcinoma (LUAD). The current study aimed to identify some potentially prognostic signatures by systematically revealing the transcriptome characteristics in LUADs with differing immune phenotypes. LUAD gene expression data were retrieved from the public TCGA and GEO databases, and the transcriptome characteristics were systematically revealed using a comprehensive bioinformatics method including single-sample gene set enrichment analysis, differentially expressed gene (DEG) analysis, protein and protein interaction (PPI) network construction, competitive endogenous RNA (ceRNA) network construction, weighted gene coexpression network analysis and prognostic model establishment. Finally, 1169 key DEGs associated with LUAD immune phenotype, including 88 immune DEGs, were excavated. Five essential and eight immune essential DEGs were separately identified by constructing two PPI networks based on the above DEGs. Totals of 1085 key DElncRNAs and 45 key DEmiRNAs were excavated and one ceRNA network consisting of 26 DEmRNAs, 3 DEmiRNAs and 57 DElncRNAs were established. The most significant gene coexpression module (cor=0.63 and

Indexed as

AdenocarcinomaLung NeoplasmsRNA, Long NoncodingBiomarkers, TumorGene Expression Regulation, NeoplasticHumansLungPhenotypePrognosisTranscriptomeTumor MicroenvironmentBiomarkers, TumorRNA, Long Noncodingbioinformaticslung adenocarcinomaprognosistranscriptometumor microenvironment

Identifiers

PMID35675043
PMCPMC9217709
OpenAlexW4281713295

What OpenQuestion 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 OpenQuestion graph.