Evidence map›Paper›PMID 30664219›Full record

ArticleMolecular medicine reports2019

Identification of differentially expressed genes and enriched pathways in lung cancer using bioinformatics analysis.

Tingting Long, Zijing Liu, Xing Zhou, Shuang Yu, Hui Tian, Yixi Bao

Open access · bronzeAbstract read
In one paragraph

Article in Molecular medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

19 citing papers in PubMed, 39 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

6 authors at 4 institutions in 1 country.

Tingting LongDepartment of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, P.R. China.
Zijing LiuDepartment of Clinical Medicine, Xinjiang Medical University, Urumqi, Xinjiang 830054, P.R. China.
Xing ZhouDepartment of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, P.R. China.
Shuang YuDepartment of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, P.R. China.
Hui TianDepartment of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, P.R. China.
Yixi BaoDepartment of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, P.R. China.
Dalian Medical University · CNSecond Affiliated Hospital of Chongqing Medical University · CNChongqing Medical University · CNXinjiang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is the leading cause of cancer‑associated mortality worldwide. The aim of the present study was to identify the differentially expressed genes (DEGs) and enriched pathways in lung cancer by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid microarray data of 31 pairs of lung cancer tissues and matched normal samples (GSE19804) were obtained from the Gene Expression Omnibus database. Significance analysis of the gene expression profile was used to identify DEGs between cancer tissues and normal tissues, and a total of 1,970 DEGs, which were significantly enriched in biological processes, were screened. Through the Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, 77 KEGG pathways associated with lung cancer were identified, among which the Toll‑like receptor pathway was observed to be important. Protein‑protein interaction network analysis extracted 1,770 nodes and 10,667 edges, and identified 10 genes with key roles in lung cancer with highest degrees, hub centrality and betweenness. Additionally, the module analysis of protein‑protein interactions revealed that 'chemokine signaling pathway', 'cell cycle' and 'pathways in cancer' had a close association with lung cancer. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of lung cancer, and certain genes (including advanced glycosylation end‑product specific receptor and epidermal growth factor receptor) may be used as candidate target molecules to diagnose, monitor and treat lung cancer.

Indexed as

Biomarkers, TumorComputational BiologyGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansLung NeoplasmsNeoplasm ProteinsProtein Interaction MappingSignal TransductionTranscriptomeBiomarkers, TumorNeoplasm Proteins

Identifiers

PMID30664219
PMCPMC6390056
OpenAlexW2911065812

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.