Evidence map›Paper›PMID 40359002›Full record

ArticleeLife2025

Transcriptional pattern enriched for synaptic signaling is associated with shorter survival of patients with high-grade serous ovarian cancer.

Arkajyoti Bhattacharya, Thijs S Stutvoet, Mirela Perla, Stefan Loipfinger, Mathilde Jalving, Anna K L Reyners, Paola D Vermeer, Ronny Drapkin, Marco de Bruyn, Elisabeth G E de Vries and 2 more

Abstract read
In one paragraph

Article in eLife, 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

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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

12 authors.

Arkajyoti Bhattacharya *Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0003-1479-0739
Thijs S Stutvoet *Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Mirela Perla *Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Stefan LoipfingerDepartment of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0002-5571-0435
Mathilde JalvingDepartment of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Anna K L ReynersDepartment of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Paola D VermeerCancer Biology and Immunotherapies Group, Sanford Research, Sioux Falls, United States.ORCID https://orcid.org/0000-0003-2370-8223
Ronny DrapkinPenn Ovarian Cancer Research Center and Basser Center for BRCA, University of Pennsylvania, Perelman School of Medicine, Philadelphia, United States.
Marco de BruynDepartment of Obstetrics and Gynecology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0001-9819-9131
Elisabeth G E de VriesDepartment of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Steven de JongDepartment of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Rudolf S N FehrmannDepartment of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0002-7516-315X

Funding

Hanarth Fonds 2019N1552
6 · The paper itself

Abstract

Bulk transcriptomic analyses of high-grade serous ovarian cancer (HGSOC) so far have not uncovered potential drug targets, possibly because subtle, disease-relevant transcriptional patterns are overshadowed by dominant, non-relevant ones. Our aim was to uncover disease-outcome-related patterns in HGSOC transcriptomes that may reveal novel drug targets. Using consensus-independent component analysis, we dissected 678 HGSOC transcriptomes of systemic therapy naïve patients-sourced from public repositories-into statistically independent transcriptional components (TCs). To enhance c-ICA's robustness, we added 447 transcriptomes from non-serous histotypes, low-grade serous, and non-cancerous ovarian tissues. Cox regression and survival tree analysis were performed to determine the association between TC activity and overall survival (OS). Finally, we determined the activity of the OS-associated TCs in 11 publicly available spatially resolved ovarian cancer transcriptomes. We identified 374 TCs, capturing prominent and subtle transcriptional patterns linked to specific biological processes. Six TCs, age, and tumor stage stratified patients with HGSOC receiving platinum-based chemotherapy into ten distinct OS groups. Three TCs were linked to copy-number alterations affecting expression levels of genes involved in replication, apoptosis, proliferation, immune activity, and replication stress. Notably, the TC identifying patients with the shortest OS captured a novel transcriptional pattern linked to synaptic signaling, which was active in tumor regions within all spatially resolved transcriptomes. The association between a synaptic signaling-related TC and OS supports the emerging role of neurons and their axons as cancer hallmark-inducing constituents of the tumor microenvironment. These constituents might offer a novel drug target for patients with HGSOC.

Indexed as

Cystadenocarcinoma, SerousOvarian NeoplasmsSignal TransductionSynapsesTranscriptomeAgedFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiddle AgedSurvival Analysiscancer biologydecision tree analysisgeneticsgenomicshumanindependent component analysisserous ovarian cancertranscriptomics

Identifiers

PMID40359002
PMCPMC12074640

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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.