Evidence map›Paper›PMID 38339391›Full record

ArticleCancers2024

Alternative Polyadenylation Characterizes Epithelial and Fibroblast Phenotypic Heterogeneity in Pancreatic Ductal Adenocarcinoma.

Swati Venkat, Michael E Feigin

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2024. 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, top 99% of its field
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, 0 citations in OpenAlex.

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

2 authors at 1 institution in 1 country.

Swati VenkatDepartment of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA.
Michael E FeiginDepartment of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA.
Roswell Park Comprehensive Cancer Center · US

Funding

American Cancer Society RSG-21-014-01
6 · The paper itself

Abstract

Human tumors are characterized by extensive intratumoral transcriptional variability within the cancer cell and stromal compartments. This variation drives phenotypic heterogeneity, producing cell states with differential pro- and anti-tumorigenic properties. While bulk RNA sequencing cannot achieve cell-type-specific transcriptional granularity, single-cell sequencing has permitted an unprecedented view of these cell states. Despite this knowledge, we lack an understanding of the mechanistic drivers of this transcriptional and phenotypic heterogeneity. 3' untranslated region alternative polyadenylation (3' UTR-APA) drives gene expression alterations through regulation of 3' UTR length. These 3' UTR alterations modulate mRNA stability, protein expression and protein localization, resulting in cellular phenotypes including differentiation, cell proliferation, and migration. Therefore, we sought to determine whether 3' UTR-APA events could characterize phenotypic heterogeneity of tumor cell states. Here, we analyze the largest single-cell human pancreatic ductal adenocarcinoma (PDAC) dataset and resolve 3' UTR-APA patterns across PDAC cell states. We find that increased proximal 3' UTR-APA is associated with PDAC progression and characterizes a metastatic ductal epithelial subpopulation and an inflammatory fibroblast population. Furthermore, we find significant 3' UTR shortening events in cell-state-specific marker genes associated with increased expression. Therefore, we propose that 3' UTR-APA drives phenotypic heterogeneity in cancer.

Indexed as

alternative polyadenyationgene regulationpancreatic cancer

Identifiers

PMID38339391
PMCPMC10854489
OpenAlexW4391485831

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.