Evidence map›Paper›PMID 40216864›Full record

ArticleScientific reports2025

In silico pan-cancer analysis of VRAC subunits and their prognostic roles in human cancers.

Alessandro Paolì, Soha Sadeghi, Giulia Battistello, Veronica Carpanese, Vanessa Checchetto

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alessandro Paolì *Department of Biology, University of Padova, Padua, Italy.
Soha Sadeghi *Department of Biology, University of Padova, Padua, Italy.
Giulia BattistelloDepartment of Biology, University of Padova, Padua, Italy.
Veronica CarpaneseDepartment of Biology, University of Padova, Padua, Italy.
Vanessa ChecchettoDepartment of Biology, University of Padova, Padua, Italy. vanessa.checchetto@unipd.it.

Funding

BIRD-PRID BIRD239198/23PRIN 2022ZY7ATN
6 · The paper itself

Abstract

The study focuses on the VRAC channel and its significant roles in cancer development. It addresses a research gap by conducting a pan-cancer analysis with multi-omics bioinformatics tools, integrating data from the Human Protein Atlas (HPA) and Genotype-Tissue Expression (GTEx) datasets to examine mRNA expression patterns of its Leucine Rich Repeat Containing 8 (LRRC8) subunits in various tissues and cancers. The study links variations in LRRC8s expression with patient outcomes and includes analyses of DNA and RNA methylation. The study reveals significant correlations between LRRC8s expression and immune cell infiltration, as well as a positive association with cancer-associated fibroblasts and key immune regulators such as major histocompatibility complex (MHCs) and chemokines. Furthermore, the research suggests that LRRC8s are involved in cancer-signalling pathways, which may offer new therapeutic targets. Additionally, a drug sensitivity analysis shows that LRRC8 subunits affect drug responses differently, supporting the use of personalized therapeutic strategies. In conclusion, the study emphasizes the significance of VRAC subunits in cancer biology and suggests their potential as biomarkers and targets in cancer immunotherapy and personalized medicine.

Indexed as

Membrane ProteinsNeoplasmsBiomarkers, TumorComputational BiologyComputer SimulationDNA MethylationGene Expression Regulation, NeoplasticHumansPrognosisBiomarkers, TumorMembrane ProteinsImmuno-oncologyLRRC8 subunitsPrognosisVRAC

Identifiers

PMID40216864
PMCPMC11992229

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.