ArticleExperimental and therapeutic medicine2018
Transcriptional profiling analysis predicts potential biomarkers for glaucoma: HGF, AKR1B10 and AKR1C3.
Article in Experimental and therapeutic medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 4 citations in OpenAlex.
- The role of prostanoids in the retina and optic nerve in health and disease.Progress in retinal and eye research · 2026Review
- Candidate SNP Markers Significantly Altering the Affinity of the TATA-Binding Protein for the Promoters of Human Genes Associated with Primary Open-Angle Glaucoma.International journal of molecular sciences · 2024Article
- Article
- Updates on Genes and Genetic Mechanisms Implicated in Primary Angle-Closure Glaucoma.The application of clinical genetics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glaucoma results in damage to the optic nerve and vision loss. The aim of this study was to screen more accurate biomarkers and targets for glaucoma. The datasets E-GEOD-7144 and E-MEXP-3427 were screened for differently expressed genes (DEGs) by significance analysis of microarrays. Functional and pathway enrichment analysis were processed. Pathway relationship networks and gene co-expression networks were constructed. DEGs of disease and treatment with the same symbols were of interest. RT-qPCR was processed to verify the expression of key DEGs. A total of 1,019 DEGs of glaucoma were identified and 93 DEGs in transforming growth factor-β1 (TGF-β1) and TGF-β1-2 treatment cases compared with the normal control group. Pathway relationship network of glaucoma was constructed with 25 nodes. The pathway relationship network of TGF-β1 and -2 treatment groups was constructed with 11 nodes. Glaucoma-related DEGs in GO terms and pathways were inserted and 180 common DEGs were obtained. Then, gene co-expression network of glaucoma-related DEGs was constructed with 91 nodes. Furthermore, DEGs of TGF-β1 and -2 treated glaucoma in GO terms and pathways were inserted, and 29 common DEGs were identified. Based on these DEGs, gene co-expression network was constructed with 12 nodes and 16 edges. Finally, a total of 6 important DEGs of disease and treatment were inserted and obtained. They were HGF, AKR1B10, AKR1C3, PPAP2B, INHBA and BCAT1. The expression of HGF, AKR1B10 and AKR1C3 was decreased in glaucoma samples and treatment samples. In conclusion, HGF, AKR1B10 and AKR1C3 may be key genes for glaucoma diagnosis and treatment.
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.