In one paragraphArticle in Cancer research communications, 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 itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
20 authors.
Jared Sipes *Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0009-0003-1065-3208 Didi Zha *Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois.ORCID 0000-0003-1335-1435 Sagar RayamajhiDepartment of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0001-5420-7873 Leonidas E BantisDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0002-2364-2562 Rashna MadanDepartment of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0009-0004-6492-4584 Amrita MitraDepartment of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0002-1936-2189 Rajni V PuriDepartment of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0002-0570-7350 Mohammod Mahmudur RahmanDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0009-0000-4659-9299 Foyez AhmmedDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0002-6107-6637 Harsh B PathakDepartment of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0003-4034-0520 Angela RussoDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois.ORCID 0000-0002-7322-4190 Mihaela SardiuDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0002-3987-7569 Pamoda M GalhenageCenter for Personalized Cancer Therapy, University of Massachusetts, Boston, Massachusetts.ORCID 0009-0000-5820-3255 Shailja PathaniaCenter for Personalized Cancer Therapy, University of Massachusetts, Boston, Massachusetts.ORCID 0000-0001-8974-6420 Shannon MacLaughlan DavidTranslational Oncology Program, University of Illinois Cancer Center, University of Illinois at Chicago, Chicago, Illinois.ORCID 0000-0001-7374-5773 Joanna E BurdetteDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois.ORCID 0000-0002-7271-6847 Andrew K GodwinDepartment of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas.ORCID 0000-0002-3987-9580 Funding
Transgenic & Gene-Targeting Shared ResourceP30CA168524 · NCI · UNIVERSITY OF KANSAS MEDICAL CENTER · PI NICOLE L NOLLEN · 2012 to 2026
$40.1MUsing Integrated Omics to Identify Dysfunctional Genetic Mechanisms Influencing Schizophrenia and Sleep DisturbancesP20GM130423 · NIGMS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI ANDREW K. GODWIN · 2019 to 2026
$21.5MPilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu, Gail S Prins · 2017 to 2026
$13.6MMicrofluidic Models of Ovarian Cancer Preneoplastic LesionsR01CA240301 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI Joanna E Burdette, Jonathan Coppeta · 2019 to 2026
$4.6MExtracellular Vesicle Proteomic Fingerprinting of Ovarian Cancer for Early Detection with a Nanoengineered MicrosystemR01CA260132 · NCI · UNIVERSITY OF KANSAS MEDICAL CENTER · PI GODWIN, ANDREW K., ZENG, YONG · 2021 to 2025
$3.4MJEOL JEM-1400 Transmission Electron Microscope with AMT-XR611M Digital Camera SysS10RR027564 · NCRR · UNIVERSITY OF KANSAS MEDICAL CENTER · PI ABRAHAMSON, DALE R · 2010 to 2010
$418kNCI NIH HHS P30 CA168524NCI NIH HHS R01 CA240301NCI NIH HHS R01 CA260132NCRR NIH HHS S10 RR027564NIEHS NIH HHS P30 ES027792NIGMS NIH HHS P20 GM130423
6 · The paper itselfAbstract
Serous tubal intraepithelial carcinomas (lesions) in the human fallopian tube epithelium (hFTE) are theorized to give rise to high-grade serous ovarian cancers. Small extracellular vesicles (sEV) are known to mediate key signaling in both normal and cancerous tissues, but few ex vivo systems exist for studying the impact of sEV on hFTE tissue. In this study, we present a microfluidic tissue culture platform with combined spatial transcriptomic and proteomic readouts that allows us to profile dual responses in tissue exposed to sEV "messages"-capturing both short-term transcriptomic shifts in the tissue and long-term changes in protein cargo of secreted EVs (the "reply"). Using spatial transcriptomics, we show that the short-term 1-day exposure to ovarian cancer-derived sEVs alters expression of 68 transcripts in secretory cells, the progenitor of high-grade serous ovarian cancer, notably upregulating immune-related mRNA, including CXCL family chemokines, VCAM1, and pro-inflammatory mediators (NFKB1, IL1B, and IFNA7/17). Additionally, we observed that the long-term 14-day exposure to sEVs alters the expression of seven transcripts and 25 EV cargo proteins of fallopian tube-derived EVs ("secondary release EVs") following stimulus from cancer EVs. Together, tissue transcriptomics and tissue-derived EV proteomics indicate that ovarian cancer-derived sEVs rewire target cell signaling to modify the tubal immune landscape. This study provides insights into the early molecular changes associated with the pathogenesis of ovarian cancer in its tissue of origin, providing a platform to study EV-tissue interactions and identify how sEVs drive cell signaling reprogramming in hFTE. SIGNIFICANCE: We model the fallopian tube preneoplastic landscape using a microfluidic platform to study EV-induced stress and show that cancer EVs promote immune signaling changes representing the earliest stages of ovarian cancer pathogenesis.
Indexed as
Extracellular VesiclesFallopian Tube NeoplasmsFallopian TubesOvarian NeoplasmsPrecancerous ConditionsEpitheliumFemaleGene Expression Regulation, NeoplasticHumansProteomicsTranscriptome
Identifiers
PMID40689422
PMCPMC12319521
What OpenQuestion holds
Textmetadata
LicenceCC BY
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