ArticleTranslational cancer research2022
Identification of potential key molecular biomarkers in lung adenocarcinoma by bioinformatics analysis.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 2 citations in OpenAlex.
- Quantitative transcriptomic and metabolic analyses reveal the roles of RpoS and Crp in the acid resistance system 1 inMicrobiology spectrum · 2026Article
- Integrative stemness- and EMT-related gene signatures associated with prognosis and the immune microenvironment in lung adenocarcinoma.Discover oncology · 2025Article
- Comprehensive bioinformatics analysis on exportins in lung adenocarcinoma and lung squamous cell carcinoma.Journal of thoracic disease · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung cancer is one of the most common malignant tumors in the world, of which the rate of incidence has continuously increased over recent years. Lung adenocarcinoma (LUAD) is the most frequent pathological type of lung cancer. Methods: In order to discover the key markers for the occurrence and development of LUAD, we collected messenger RNA (mRNA) expression datasets in the Gene Expression Omnibus (GEO), namely, GSE2514, GSE7670, and GSE40275. The differentially expressed genes (DEGs) were screened using the online interface between GEO and R (GEO2R). Then, DEGs were functionally annotated in the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Next, a protein-protein interaction (PPI) network was drawn by using the Search Tool for the Retrieval of Interacting Genes (STRING) web tool and Cytoscape software. Finally, Kaplan-Meier plotter was utilized to analyze the overall survival (OS) of the hub genes. The correlation between fibroblast growth factor 2 ( Results: In this study, we obtained a total of 284 DEGs through the intersection of 3 datasets, and found that DEGs were highly related to biological processes such as "cell adhesion", "cell differentiation", and "cell proliferation". After that, the hub genes were obtained by analyzing the PPI network. Finally, we found that the abnormal expression of hub genes is obviously related to poor prognosis in LUAD patients. The expression level of Conclusions: In general, the DEGs and hub genes can provide new research targets for the development of LUAD, as well as potential diagnosis and treatment strategies for disease treatment. In particular,
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.