ArticleCancer control : journal of the Moffitt Cancer Center
Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer.
Article in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 24 citations in OpenAlex.
- A bibliometric analysis of artificial intelligence in ovarian cancer research from 2006 to 2025.Discover oncology · 2026Article
- Molecular Characteristics of the Endometrium in Polycystic Ovary Syndrome with Insulin Resistance.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- Review
- Spatial and single-cell analyses uncover links between ALKBH1 and tumor-associated macrophages in gastric cancer.Cancer cell international · 2024Article
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- Identification of Driver Genes and miRNAs in Ovarian Cancer through an Integrated In-Silico Approach.Biology · 2023Article
- cAMP-Dependent Signaling and Ovarian Cancer.Cells · 2022Review
- Integrated analysis of pivotal biomarker of LSM1, immune cell infiltration and therapeutic drugs in breast cancer.Journal of cellular and molecular medicine · 2022Article
- Expression status and prognostic significance of mitochondrial dynamics OPA3 in human ovarian cancer.Aging · 2022Article
- Integrative Multiomics Evaluation of IIDH1 Metabolic Enzyme as a Candidate Oncogene That is Correlated with Poor Prognosis and Immune Infiltration in Prostate Adenocarcinoma.Journal of oncology · 2022Article
- The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives.Biomedicines · 2021Review
- Article
- Construction of a Myc-associated ceRNA network reveals a prognostic signature in hepatocellular carcinoma.Molecular therapy. Nucleic acids · 2021Article
- Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects.International journal of environmental research and public health · 2021Review
- Identification of Novel Biomarkers and Candidate Drug in Ovarian Cancer.Journal of personalized medicine · 2021Article
- Identification and Analysis of An Epigenetically Regulated Five-lncRNA Signature Associated With Outcome and Chemotherapy Response in Ovarian Cancer.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
- Retracted
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein-protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the "Human Protein Atlas" database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.
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Registered trials
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.