ArticleJournal of Cancer2019
High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer.
Article in Journal of Cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Tropomodulin 1 is essential for chemotherapy sensitivity and associated with better outcome in triple-negative breast cancer.BMC cancer · 2025Article
- Cancer-metastasis-on-a-chip reveals efficacy of bevacizumab and siRNA in overcoming carboplatin resistance in SKOV3 ovarian cancer within a fibrotic metastatic microenvironment.Materials today. Bio · 2025Article
- Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications.Translational oncology · 2025Article
- Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.Scientific reports · 2025Article
- Spatial transcriptomics reveal high T cell and monocyte status as predictive and prognostic markers in pancreatic cancer.Journal of translational medicine · 2025Article
- Proteomic analysis emphasizes the adaptation of energy metabolism in horses during endurance races.BMC veterinary research · 2025Article
- Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer.Open medicine (Warsaw, Poland) · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- FOXF1 inhibits invasion and metastasis of lung adenocarcinoma cells and enhances anti-tumor immunity via MFAP4/FAK signal axis.Scientific reports · 2024Article
- Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones.Nature communications · 2024Article
- Reclassify High-Grade Serous Ovarian Cancer Patients Into Different Molecular Subtypes With Discrepancy Prognoses and Therapeutic Responses Based on Cancer-Associated Fibroblast-Enriched Prognostic Genes.Biomedical engineering and computational biology · 2024Article
- Prediction of Chemoresistance-How Preclinical Data Could Help to Modify Therapeutic Strategy in High-Grade Serous Ovarian Cancer.Current oncology (Toronto, Ont.) · 2023Review
- Declined plasma microfibrillar-associated protein 4 levels in acute coronary syndrome.European journal of medical research · 2023Article
- Review
- KAZN as a diagnostic marker in ovarian cancer: a comprehensive analysis based on microarray, mRNA-sequencing, and methylation data.BMC cancer · 2022Article
- Article
- A risk model of gene signatures for predicting platinum response and survival in ovarian cancer.Journal of ovarian research · 2022Article
- Five immune-related genes as diagnostic markers for endometriosis and their correlation with immune infiltration.Frontiers in endocrinology · 2022Article
- Genomic Landscape of RTK/RAS Pathway and Tumor Immune Infiltration as Prognostic Indicator of Lung Adenocarcinoma.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Primary platinum-based chemoresistance occurs in approximately one-third of patients with serous ovarian cancer (SOC); however, traditional clinical indicators are poor predictors of chemoresistance. So we aimed to identify novel genes as predictors of primary platinum-based chemoresistance. Gene expression microarray analyses were performed to identify the genes related to primary platinum resistance in SOC on two discovery datasets (GSE51373, GSE63885) and one validation dataset (TCGA). Univariate and multivariate analyses with logistic regression were performed to evaluate the predictive values of the genes for platinum resistance. Machine learning algorithms (linear kernel support vector machine and artificial neural network) were applied to build prediction models. Univariate and multivariate analyses with Cox proportional hazards regression and log-rank tests were used to assess the effects of these gene signatures for platinum resistance on prognosis in two independent datasets (GSE9891, GSE32062). AGGF1 and MFAP4 were found highly expressed in patients with platinum-resistant SOC and independently predicted platinum resistance. Platinum resistance prediction models based on these targets had robust predictive power (highest AUC: 0.8056, 95% CI: 0.6338-0.9773; lowest AUC: 0.7245, 95% CI: 0.6052-0.8438). An AGGF1- and MFAP4-centered protein interaction network was built, and hypothetical regulatory pathways were identified. Enrichment analysis indicated that aberrations of extracellular matrix may play important roles in platinum resistance in SOC. High AGGF1 and MFAP4 expression levels were also related to shorter recurrence-free and overall survival in patients with SOC after adjustment for other clinical variables. Therefore, AGGF1 and MFAP4 are potential predictive biomarkers for response to platinum-based chemotherapy and survival outcomes in SOC.
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