ReviewCellular signalling2020
Pathway identification through transcriptome analysis.
Review in Cellular signalling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 16 citations in OpenAlex.
- Comparative Transcriptomics Reveals the Molecular Basis for Inducer-Dependent Efficiency in Gastrodin Propionylation byBiomolecules · 2025Article
- Translational regulation of PKD1 by evolutionarily conserved upstream open reading frames.RNA biology · 2025Review
- GLIS3: A novel transcriptional regulator of mitochondrial functions and metabolic reprogramming in postnatal kidney and polycystic kidney disease.Molecular metabolism · 2024Article
- Role of NHERF1 in MicroRNA Landscape Changes in Aging Mouse Kidneys.Biomolecules · 2024Article
- Kidney transcriptome and cystic kidney disease genes in zebrafish.Frontiers in physiology · 2023Article
- Exposure to Veterinary Antibiotics via Food Chain Disrupts Gut Microbiota and Drives IncreasedPathogens (Basel, Switzerland) · 2022Article
- Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues.International journal of molecular sciences · 2022Review
- Multiomic identification of factors associated with progression to cystic kidney disease in mice with nephron Ift88 disruption.American journal of physiology. Renal physiology · 2022Article
- Caspase-1 and the inflammasome promote polycystic kidney disease progression.Frontiers in molecular biosciences · 2022Article
- Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia.Scientific reports · 2021Article
- Deciphering Intercellular Communication of the Immune Landscape within Autosomal Dominant Polycystic Kidney Disease Microenvironment at Single-Cell Resolution.Kidney diseases (Basel, Switzerland)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
3 authors at 1 institution in 1 country.
Funding
Abstract
Systems-based, agnostic approaches focusing on transcriptomics data have been employed to understand the pathogenesis of polycystic kidney diseases (PKD). While multiple signaling pathways, including Wnt, mTOR and G-protein-coupled receptors, have been implicated in late stages of disease, there were few insights into the transcriptional cascade immediately downstream of Pkd1 inactivation. One of the consistent findings has been transcriptional evidence of dysregulated metabolic and cytoskeleton remodeling pathways. Recent technical developments, including bulk and single-cell RNA sequencing technologies and spatial transcriptomics, offer new angles to investigate PKD. In this article, we review what has been learned based on transcriptional approaches and consider future opportunities.
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.