KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33250051", "kind": "science-record", "revision": 6, "content_hash": "sha256:5fccdb695401497907e7b0ee94b5a2d255f070f984d982ecc76e195b7de99cc6", "created_at": "2026-11-01T15:41:17Z", "updated_at": "2026-11-01T21:24:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer.", "doi": "10.1186/s13046-020-01775-9", "pmid": "33250051", "authors": ["Marta De Donato", "Gabriele Babini", "Simona Mozzetti", "Marianna Buttarelli", "Alessandra Ciucci", "Gloria Arduini", "Maria Cristina De Rosa", "Giovanni Scambia", "Daniela Gallo"], "journal": "Journal of experimental & clinical cancer research : CR", "year": 2020, "abstract": "BACKGROUND: In spite of great progress in the surgical and clinical management, until now no significant improvement in overall survival of High-Grade Serous Ovarian Cancer (HGSOC) patients has been achieved. Important aspects for disease control remain unresolved, including unclear pathogenesis, high heterogeneity and relapse resistance after chemotherapy. Therefore, further research on molecular mechanisms involved in cancer progression are needed to find new targets for disease management. The Krüppel-like factors (KLFs) are a family of transcriptional regulators controlling several basic cellular processes, including proliferation, differentiation and migration. They have been shown to play a role in various cancer-relevant processes, in a context-dependent way.\nMETHODS: To investigate a possible role of KLF family members as prognostic biomarkers, we carried out a bioinformatic meta-analysis of ovarian transcriptome datasets in different cohorts of late-stage HGSOC patients. In vitro cellular models of HGSOC were used for functional studies exploring the role of KLF7 in disease development and progression. Finally, molecular modelling and virtual screening were performed to identify putative KLF7 inhibitors.\nRESULTS: Bioinformatic analysis highlighted KLF7 as the most significant prognostic gene, among the 17 family members. Univariate and multivariate analyses identified KLF7 as an unfavourable prognostic marker for overall survival in late-stage TCGA-OV and GSE26712 HGSOC cohorts. Functional in vitro studies demonstrated that KLF7 can play a role as oncogene, driving tumour growth and dissemination. Mechanistic targets of KLF7 included genes involved in epithelial to mesenchymal transition, and in maintaining pluripotency and self-renewal characteristics of cancer stem cells. Finally, in silico analysis provided reliable information for drug-target interaction prediction.\nCONCLUSIONS: Results from the present study provide the first evidence for an oncogenic role of KLF7 in HGSOC, suggesting it as a promising prognostic marker and therapeutic target.", "claims": [{"id": "claim-method", "statement": "Univariate and multiple Cox-regression analyses (based on gene, age, stage and residual tumor mass) for Overall Survival (OS) were performed on the eligible datasets", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33250051", "locator": "sections[3] \"KLF family expression data from publicly available databases\" paragraph 2", "quote": "Univariate and multiple Cox-regression analyses (based on gene, age, stage and residual tumor mass) for Overall Survival (OS) were performed on the eligible datasets"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in the Array Express repository (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-386/), GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and TCGA repository (https://portal.gdc.cancer.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33250051", "locator": "sections[34] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analysed during the current study are available in the Array Express repository (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-386/), GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and TCGA repository (https://portal.gdc.cancer.gov/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33250051"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE26712"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE26712"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33250051", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s13046-020-01775-9", "sha256": 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