Evidence map›Paper›PMID 38395907›Full record

ArticleJournal of ovarian research2024

Novel prognostic marker TGFBI affects the migration and invasion function of ovarian cancer cells and activates the integrin αvβ3-PI3K-Akt signaling pathway.

Hao Wang, Yin-Hai Xu, Yi Guo

Open access · goldAbstract read
In one paragraph

Article in Journal of ovarian research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.3field-weighted citation impact, top 8% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Tumor-associated macrophages and platelets in tumor microenvironment and its potential therapeutic role in ovarian cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  2. Review
  3. Smoking-Induced STC2Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  4. Review
  5. Article
  6. Article
  7. Processed Products ofAnti-cancer agents in medicinal chemistry · 2025
    Article
  8. 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

3 authors at 1 institution in 1 country.

Hao WangSchool of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Yin-Hai Xu *Department of Laboratory Medicine, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China. xyfyxyh@126.com.
Yi Guo *Department of Laboratory Medicine, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China. guoyi1227@qq.com.
Xuzhou Medical College · CN

Funding

Foundation Research Project (Natural Science Foundation) of Jiangsu Province BK20201013"Shuang‑Chuang Doctor" Project of Jiangsu Province 2020204003
6 · The paper itself

Abstract

backgroundIndividual patients with ovarian cancer show remarkably different prognosis. Present prognostic models for ovarian cancer mainly focus on clinico-pathological parameters, so quantifiable prognostic markers at molecular level are urgently needed. Platelets contribute to ovarian cancer progression, but have not been considered as biomarkers likely due to their instability. Here, we aimed to search for a stable prognostic marker from platelet-treated ovarian cancer cells, and explore its functions and mechanisms.

methodsMicroarrays analysis was done with platelet-treated SKOV-3 ovarian cancer cells. Relevant studies were searched in the Gene Expression Omnibus (GEO) database. The candidate genes were determined by differentially expressed genes (DEGs), Venn diagram drawing, protein-protein interaction (PPI) network, Cox proportional hazards model and Kaplan-Meier analysis. The expression of TGFBI in clinical samples was assessed by immunehistochemical staining (IHC), and the association of TGFBI levels with the clinic-pathological characteristics and prognosis in ovarian cancer patients was evaluated by univariate and multivariate analysis. The functions of TGFBI were predicted using data from TCGA, and validated by in vitro and in vivo experiments. The mechanism exploration was performed based on proteomic analysis, molecular docking and intervention study.

resultsTGFBI was significantly higher expressed in the platelet-treated ovarian cancer cells. An analysis of bioinformatics data revealed that increased expression of TGFBI led to significant decrease of overall survival (OS), progression-free survival (PFS) and post-progression survival (PPS) in ovarian cancer patients. Tissue microarray results showed that TGFBI was an independent factor for ovarian cancer, and TGFBI expression predict poor prognosis. Functionally, TGFBI affected the migration and invasion of ovarian cancer cells by regulation of epithelial mesenchymal transition (EMT) markers (CDH1 and CDH2) and extracellular matrix (ECM) degradation proteins (MMP-2). Mechanistically, TGFBI phosphorylated PI3K and Akt by combining integrin αvβ3.

conclusionsWe found out TGFBI as a novel prognostic indicator for ovarian cancer patients. TGFBI could promote metastasis in ovarian cancer by EMT induction and ECM remodeling, which might be associated with the activation of integrin αvβ3-PI3K-Akt signaling pathway.

Indexed as

Integrin alphaVbeta3Ovarian NeoplasmsTransforming Growth Factor betabetaIG-H3 ProteinExtracellular Matrix ProteinsFemaleHumansMolecular Docking SimulationPhosphatidylinositol 3-KinasesPrognosisProteomicsProto-Oncogene Proteins c-aktSignal TransductionbetaIG-H3 ProteinExtracellular Matrix ProteinsIntegrin alphaVbeta3Phosphatidylinositol 3-KinasesProto-Oncogene Proteins c-aktTransforming Growth Factor betaIntegrin αvβ3InvasionMetastasisMigrationOvarian cancerPI3K-Akt signaling pathwayPrognosisTGFBI

Identifiers

PMID38395907
PMCPMC10885438
OpenAlexW4392121243

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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.