ArticleOncology letters2019
Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed, 32 citations in OpenAlex.
- 18β-glycyrrhetinic acid inhibits lung adenocarcinoma progression by targeting the MET/AKT signaling axis.Frontiers in cell and developmental biology · 2026Article
- The miRNA-mRNA Regulatory Network in Human Hepatocellular Carcinoma by Transcriptomic Analysis From GEO.Cancer reports (Hoboken, N.J.) · 2025Article
- Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.PloS one · 2025Article
- NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence.Clinical and translational medicine · 2024Article
- NCAPH Drives Breast Cancer Progression and Identifies a Gene Signature that Predicts Luminal A Tumor Recurrence.Research square · 2023Article
- A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer.Scientific reports · 2023Article
- A Unique Gene Signature Predicting Recurrence Free Survival in Stage IA Lung Adenocarcinoma.The Journal of thoracic and cardiovascular surgery · 2023Article
- TMEM139 prevents NSCLC metastasis by inhibiting lysosomal degradation of E-cadherin.Cancer science · 2022Article
- The Roles of EXO1 and RPA1 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy.Disease markers · 2022Article
- Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.Bioinformatics and biology insights · 2022Article
- The role of RHAMM in cancer: Exposing novel therapeutic vulnerabilities.Frontiers in oncology · 2022Review
- Article
- Upregulation of CENPF is linked to aggressive features of osteosarcoma.Oncology letters · 2021Article
- Exonuclease 1 (EXO1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma.OncoTargets and therapy · 2021Article
- Maternal embryonic leucine zipper kinase: A novel biomarker and a potential therapeutic target in lung adenocarcinoma.Oncology letters · 2020Article
- Identification of drug targets and potential molecular mechanisms for Wantong Jingu Tablet extract in treatment of rheumatoid arthritis: bioinformatics analysis of fibroblast-like synoviocytes.Chinese medicine · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is one of the most widespread neoplasms worldwide. To identify the key biomarkers in its carcinogenesis and development, the mRNA microarray datasets GSE102287, GSE89047, GSE67061 and GSE74706 were obtained from the Gene Expression Omnibus database. GEO2R was used to identify the differentially expressed genes (DEGs) in lung cancer. The Database for Annotation, Visualization and Integrated Discovery was used to analyze the functions and pathways of the DEGs, while the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape were used to obtain the protein-protein interaction (PPI) network. Kaplan Meier curves were used to analyze the effect of the hub genes on overall survival (OS). Module analysis was completed using Molecular Complex Detection in Cytoscape, and one co-expression network of these significant genes was obtained with cBioPortal. A total of 552 DEGs were identified among the four microarray datasets, which were mainly enriched in 'cell proliferation', 'cell growth', 'cell division', 'angiogenesis' and 'mitotic nuclear division'. A PPI network, composed of 44 nodes and 886 edges, was constructed, and its significant module had 16 hub genes in the whole network: Opa interacting protein 5, exonuclease 1, PCNA clamp-associated factor, checkpoint kinase 1, hyaluronan-mediated motility receptor, maternal embryonic leucine zipper kinase, non-SMC condensin I complex subunit G, centromere protein F, BUB1 mitotic checkpoint serine/threonine kinase, cyclin A2, thyroid hormone receptor interactor 13, TPX2 microtubule nucleation factor, nucleolar and spindle associated protein 1, kinesin family member 20A, aurora kinase A and centrosomal protein 55. Survival analysis of these hub genes revealed that they were markedly associated with poor OS in patients with lung cancer. In summary, the hub genes and DEGs delineated in the research may aid the identification of potential targets for diagnostic and therapeutic strategies in lung cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.