Evidence map›Paper›PMID 40822676›Full record

ArticleBiologics : targets & therapy2025

Investigating the Oncogenic and Immunological Implications of YTHDF1 in Ovarian Cancer.

Bo Yin, Huijuan Zhou

Abstract read
In one paragraph

Article in Biologics : targets & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Bo Yin *Department of Gynecology; Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.
Huijuan Zhou *Department of Gynecology; Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.ORCID 0000-0001-5358-8010

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Ovarian cancer (OC) ranks among the most lethal and aggressive gynecological malignancies. Identifying novel molecular targets is crucial for improving early diagnosis and developing effective therapies. Despite advancements in immunotherapy, its efficacy in OC remains limited due to the absence of well-defined immune-related molecular targets. Methods: This study offers a comprehensive analysis of YTHDF1, combining multi-omics-based bioinformatics approaches with in vitro and in vivo experimental validation to elucidate its functional role and significance in the progression and treatment of OC. Results: Our findings reveal that YTHDF1 is significantly upregulated in OC and correlates with poor clinical outcomes. Functional assays confirmed its oncogenic properties, while pathway analyses highlight its involvement in critical tumor-promoting signaling pathways. Importantly, we identified a potential link between YTHDF1 expression and the tumor immune landscape, suggesting its role in modulating immune cell infiltration and driving immunosuppression. Additionally, both computational and in vivo evidence underline the relevance of YTHDF1 in influencing immunotherapeutic responsiveness and chemosensitivity in OC. Mechanistically, we discovered for the first time that YTHDF1 can be encapsulated within tumor-derived exosomes, contributing to the polarization of macrophages toward the immunosuppressive M2a phenotype. Discussion: These findings position YTHDF1 as a promising prognostic biomarker and therapeutic target for OC. Its role in shaping an immunosuppressive microenvironment and mediating chemoresistance underscores its potential in enhancing immunotherapy and improving chemotherapy outcomes.

Indexed as

biomarkermacrophageovarian cancertherapeutic targetYTHDF1

Identifiers

PMID40822676
PMCPMC12357560

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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.