Proteomic Biomarkers for the Detection of Endometrial Cancer.
Kelechi NjokuDavide ChiasseriniAnthony D WhettonEmma J CrosbiePublished in: Cancers (2019)
Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.
Keyphrases
- endometrial cancer
- high throughput
- label free
- mass spectrometry
- early stage
- small molecule
- machine learning
- high resolution
- induced apoptosis
- metabolic syndrome
- liquid chromatography
- gene expression
- big data
- weight gain
- insulin resistance
- risk factors
- adipose tissue
- loop mediated isothermal amplification
- oxidative stress
- artificial intelligence
- squamous cell carcinoma
- photodynamic therapy
- high performance liquid chromatography
- signaling pathway
- rna seq
- body mass index
- squamous cell
- endoplasmic reticulum stress
- quantum dots
- free survival