ArticleBiochemistry and biophysics reports2025
KRT6A, KRT6B, PKP1, and PKP3 as key hub genes in esophageal cancer: A combined bioinformatics and experimental study.
Article in Biochemistry and biophysics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Article
- A 12-gene immune signature predicts prognosis and identifies KRT6B as a therapeutic target in lung adenocarcinoma.Frontiers in immunology · 2026Article
- Investigating the expression of hsa_circ_0036722/hsa-miR-503-5p/PDCD4 axis in esophageal cancer.Discover oncology · 2025Article
- Expression patterns, regulatory interactions, and diagnostic potential of LINC00839 and LINC01605 in esophageal cancer.Biochemistry and biophysics reports · 2025Article
- Comprehensive pan-cancer analysis of KRT6A as a prognostic and immune biomarker.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal cancer (EC) is the eighth most common cancer in the world. Due to poor survival rates and severe side effects of current therapies, there is a need for a better understanding of the mechanisms and signaling pathways involved in EC. In this study, we downloaded the microarray datasets GSE157808 and GSE92396 from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using R software and validated through the GEPIA and TIMER databases. CytoHubba was used to extract hub genes from the overlapping DEGs. The TIMER database was employed to assess correlations between gene expression and immune infiltration. Additionally, hub gene expression was analyzed in 20 pairs of EC tissue samples through RT-qPCR and Western blot. We evaluated clinicopathological correlations and diagnostic potential. We identified 83 overlapping DEGs across the datasets. Subsequently, based on the highest number of degrees in the hub gene network, the
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.