ArticleAnnals of translational medicine2022
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 11 citations in OpenAlex.
- Article
- C/EBPβ Regulates HIF-1α-Driven Invasion of Non-Small-Cell Lung Cancer Cells.Biomolecules · 2024Article
- Pan-cancer analysis of SLC2A family genes as prognostic biomarkers and therapeutic targets.Heliyon · 2024Article
- Identification and validation of ferroptosis-related gene SLC2A1 as a novel prognostic biomarker in AKI.Aging · 2024Article
- A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses.Frontiers in immunology · 2022Article
- Cell death-related biomarker SLC2A1 has a significant role in prognosis prediction and immunotherapy efficacy evaluation in pan-cancer.Frontiers in genetics · 2022Article
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
Background: The treatment of lung adenocarcinoma (LUAD) has been stuck in a bottleneck due to a number of factors. There is a pressing need for research into potential genetic markers to help drug development and improve the prognosis of patients. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were used to acquire the samples. We used R to perform statistical analysis, Gene Set Enrichment Analysis (GSEA), immune infiltration and immune cell correlation analysis, drug sensitivity analysis, and visualization. The immune cell score was calculated using the Timer2.0 database. Prognostic analysis was performed using R, Gene Expression Profiling Interactive Analysis (GEPIA), and the Kaplan-Meier Plotter. Overall survival and progression free survival were the main outcome of prognosis analysis. Protein-protein interaction, disease-genetics analysis, and tissue-specific enrichment analyses were performed using Metascape. Results: Conclusions: The high expression of
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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.