ArticleMolecular medicine reports2016
Identification of potential therapeutic targets for lung cancer by bioinformatics analysis.
Article in Molecular medicine reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.BMC bioinformatics · 2017Pooled it
- Correlation ofBiomedicines · 2023Article
- Identification of potential key molecular biomarkers in lung adenocarcinoma by bioinformatics analysis.Translational cancer research · 2022Article
- Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis.Oncology letters · 2019Article
- Identification of differentially expressed genes and enriched pathways in lung cancer using bioinformatics analysis.Molecular medicine reports · 2019Article
- Article
- Adaptively capturing the heterogeneity of expression for cancer biomarker identification.BMC bioinformatics · 2018Article
- Comparative Network Analysis of Patients with Non-Small Cell Lung Cancer and Smokers for Representing Potential Therapeutic Targets.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to identify potential therapeutic targets for lung cancer and explore underlying molecular mechanisms of its development and progression. The gene expression profile datasets no. GSE3268 and GSE19804, which included five and 60 pairs of tumor and normal lung tissue specimens, respectively, were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between lung cancer and normal tissues were identified, and gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of the DEGs was performed. Furthermore, protein‑protein interaction (PPI) networks and a transcription factor (TF) regulatory network were constructed and key target genes were screened. A total of 466 DEGs were identified, and the PPI network indicated that IL‑6 and MMP9 had key roles in lung cancer. A PPI module containing 34 nodes and 547 edges was obtained, including PTTG1. The TF regulatory network indicated that TFs of FOSB and LMO2 had a key role. Furthermore, MMP9 was indicated to be the target of FOSB, while PTTG1 was the target of LMO2. In conclusion, the bioinformatics analysis of the present study indicated that IL‑6, MMP9 and PTTG1 may have key roles in the progression and development of lung cancer and may potentially be used as biomarkers or specific therapeutic targets for lung cancer.
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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.