Evidence map›Paper›PMID 39866667›Full record

ArticleComputational and structural biotechnology journal2025

Characterizing the pan-cancer role of exosomal miRNAs in metastasis across cancers.

Piyush Agrawal, Gulden Olgun, Arashdeep Singh, Vishaka Gopalan, Sridhar Hannenhalli

Abstract read
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Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Piyush AgrawalCancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
Gulden OlgunDepartment of Computer Engineering, Hacettepe University, Ankara 06800, Turkey.
Arashdeep SinghCancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
Vishaka GopalanCancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
Sridhar HannenhalliCancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exosomal microRNAs (exomiRs) play a critical role in intercellular communication, especially in cancer, where they regulate key cellular processes like proliferation, angiogenesis, and metastasis, highlighting their significance as potential diagnostic and therapeutic targets. Here, we aimed to characterize the role of exomiRs, derived from seven cancer types (four cell lines and three tumors), in influencing the pre-metastatic niche (PMN). In each cancer type we extracted high confidence exomiRs (LogFC >= 2 in exosomes relative to control), their experimentally validated targets, and the enriched pathways among those targets. We then selected the top100 high-confidence targets based on their frequency of appearance in the enriched pathways. We observed significantly higher GC content in exomiRs relative to genomic background. Gene Ontology analysis revealed both general cancer processes, such as wound healing and epithelial cell proliferation, as well as cancer-specific processes, such as "angiogenesis" in the kidney and "ossification" in the lung. ExomiR targets were enriched for cancer-specific tumor suppressor genes and downregulated in PMN formed in lungs compared to normal. Motif analysis showed high inter-cancer similarity among motifs enriched in exomiRs. Our analysis recapitulated exomiRs associated with M2 macrophage differentiation and chemoresistance, such as miR-21 and miR-222-3p, regulating signaling pathways like PTEN/PI3/Akt, NF-kB, etc. Additionally, Cox regression analysis in TCGA indicated that exomiR targets are significantly associated with better overall survival of patients. Lastly, support vector machine model using exomiR targets gene expression classified responders and non-responders to therapy with an AUROC ranging from 0.72 to 0.96, higher than previously reported gene signatures.

Indexed as

Cancer metastasisExosomal miRNAMachine LearningPre-metastatic NicheSurvival Analysis

Identifiers

PMID39866667
PMCPMC11763893

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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.