Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Genome-Wide Identification and Validation of Gene Expression Biomarkers in the Diagnosis of Ovarian Serous Cystadenocarcinoma

2 Aug 2022

Abstract excerpt

Ovarian cancer is the second most prevalent gynecologic malignancy, and ovarian serous cystadenocarcinoma (OSCA) is the most common and lethal subtype of ovarian cancer. Current screening methods have strong limits on early detection, and the majority of OSCA patients relapse. In this work, we developed and cross-validated a method for detecting gene expression biomarkers able to discriminate OSCA tissues from...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer-related molecular mechanisms research
Share to Ferroptosis and cancer prognosis
Share to Ovarian cancer diagnosis and treatment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35954427
DOI: 10.3390/cancers14153764

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Genome-Wide Identification and Validation of Gene Expression Biomarkers in the Diagnosis of Ovarian Serous CystadenocarcinomaPMID 35954427Read ↗

Identification of potential biomarkers in subtypes of epithelial ovarian cancerSemantic proximity Read ↗

Prognostic and predictive value of a mRNA signature in high-grade serous ovarian carcinoma with an integrated computation analysisSemantic proximity Read ↗

Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis.Semantic proximity Read ↗

Identification of potential biomarkers in ovarian carcinoma and an evaluation of their prognostic valueSemantic proximity Read ↗

The molecular prognostic score, a classifier for risk stratification of high-grade serous ovarian cancerSemantic proximity Read ↗

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.Semantic proximity Read ↗

Identifying multi-omics biomarkers for ovarian cancer survival estimationSemantic proximity Read ↗

Screening of Prognostic Molecular Markers and Establishment of Prognostic Model for G-protein Coupled Receptor-Related Genes in Epithelial Ovarian Serous Cancer Based on Machine Learning MethodSemantic proximity Read ↗

Identification of clinical trait–related lncRNA and mRNA biomarkers with weighted gene co-expression network analysis as useful tool for personalized medicine in ovarian cancerSemantic proximity Read ↗

Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian CancerSemantic proximity Read ↗

A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAsSemantic proximity Read ↗

Molecular Subtyping of Serous Ovarian Cancer Based on Multi-omics DataSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.