Evidence map›Paper›PMID 35117293›Full record

ArticleTranslational cancer research2020

Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis.

Jinghang Li, Yanxiu Li, Min Jin, Lin Huang, Xiaowei Wang

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 76% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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.

Jinghang LiDepartment of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yanxiu LiDepartment of Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Min JinDepartment of Thoracic and Cardiovascular Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Lin HuangDepartment of Neurology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xiaowei WangDepartment of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jiangsu Province Hospital · CNNanjing Medical University · CNNanjing Drum Tower Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung adenocarcinoma (LUAD) is the most frequent histologic type of lung cancer and the morbidity of LUAD is increasing rapidly in the worldwide. But the mechanism of LUAD is still largely unknown.

methodsIn this study, we analyzed three microarrays of gene expression profiles, containing 196 LUAD samples and 137 normal samples, to explore the potential key candidate genes in LUAD by integrated bioinformatical analysis.

resultsA total of 240 shared differentially expressed genes (DEGs) were identified and pathways enrichment were analyzed. DEGs-associated protein-protein interaction (PPI) network was constructed and top 20 hub genes were established by calculating the degree of connectivity. We further validated these genes in TCGA and GTEx projects, and found all of these hub genes were differentially expressed in LUAD patients except

conclusionsOur study contributes to comprehend the role of genes in LUAD and provides possible therapeutic targets for further clinical application.

Indexed as

bioinformatical analysisdifferentially expressed genes (DEGs)Kaplan-Meier analysisLung adenocarcinoma (LUAD)

Identifiers

PMID35117293
PMCPMC8798186
OpenAlexW3104314089

What OpenQuestion holds

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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.