Evidence map›Paper›PMID 39487796›Full record

ArticleHuman molecular genetics2025

Alternative polyadenylation shapes the molecular and clinical features of lung adenocarcinoma.

Yipeng Gao, Vikram R Shaw, Christopher I Amos

Abstract read
In one paragraph

Article in Human molecular genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Yipeng GaoDepartment of Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, United States.
Vikram R ShawGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, United States.ORCID 0000-0002-1172-5686
Christopher I AmosDepartment of Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, United States.

Funding

High Memory High-Performance Computer Cluster for Biomedical ResearchS10OD032185 · OD · BAYLOR COLLEGE OF MEDICINE · PI HILSENBECK, SUSAN G. · 2022 to 2022
$596k
Cancer Prevention Research Institute of Texas RR170048NIH HHS S10 OD032185
6 · The paper itself

Abstract

Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation that affects mRNA stability, localization and translation efficiency. Previous pan-cancer studies have revealed that APA is frequently disrupted in cancer and is associated with patient outcomes. Yet, little is known about cancer type-specific APA alterations. Here, we integrated RNA-sequencing data from a Korean cohort (GEO: GSE40419) and The Cancer Genome Atlas (TCGA) to comprehensively analyze APA alterations in lung adenocarcinomas (LUADs). Comparing expression levels of core genes involved in polyadenylation, we find that overall, the set of 28 of 31 genes are upregulated, with CSTF2 particularly upregulated. We observed broad and recurrent APA changes in LUAD growth-promoting genes. In addition, we find enrichment of APA events in genes associated with known LUAD pathways and an increased heterogeneity in polyadenylation (polyA) site usage of proliferation-associated genes. Upon further investigation, we report smoking-specific APA changes are also highly relevant to LUAD development. Overall, our in-depth analysis reveals APA as an important driver for the molecular and clinical features of lung adenocarcinoma.

Indexed as

Adenocarcinoma of LungLung NeoplasmsPolyadenylationGene Expression Regulation, NeoplasticHumansRNA, MessengerRNA, Messenger3′ UTRlung adenocarcinomalung cancerpolyadenylation

Identifiers

PMID39487796
PMCPMC13031180

What OpenQuestion holds

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Registered trials

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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.