In Vivo Staging the Progression of Colitis and Associated Cancer by Concurrent Microimaging of Key Biomarkers.
Zhihao HanYi LiXin WangChang LiChangsheng LiQiao LinEnping XuJinlong TangMaode LaiYi MaYueqing GuPublished in: Analytical chemistry (2023)
Currently colorectal cancer (CRC) staging (colitis, adenoma, and carcinoma) mainly relies on ex vivo pathologic analysis requiring an invasive surgical process with limited sample collection and increased metastatic risk. Thus, in vivo noninvasive pathological diagnosis is extremely demanded. By verifying the samples of clinical patients and CRC mouse models, it was found that vascular endothelial growth factor receptor 2 (VEGFR2) was barely expressed in the colitis stage and only appeared in adenoma and carcinoma stages with obvious elevation, while prostaglandin E receptor 4 (PTGER4) could be observed from colitis to adenoma and carcinoma stages with a gradient increase of expression. VEGFR2 and PTGER4 were further chosen as key biomarkers for molecular pathological diagnosis in vivo and corresponding molecular probes were constructed. The feasibility of in vivo noninvasive CRC staging by concurrent microimaging of dual biomarkers using confocal laser endoscopy (CLE) was verified in CRC mouse models and further confirmed by ex vivo pathological analysis. In vivo CLE imaging exhibited the correlation of severe colonic crypt structural alteration with a higher biomarker expression in adenoma and carcinoma stages. This strategy shows promise in benefiting patients undergoing CRC progression with in-time, noninvasive, and precise pathological staging, thus providing valuable guidance for selecting therapeutic strategies.
Keyphrases
- vascular endothelial growth factor
- lymph node
- pet ct
- ulcerative colitis
- mouse model
- poor prognosis
- patients undergoing
- end stage renal disease
- squamous cell carcinoma
- locally advanced
- chronic kidney disease
- newly diagnosed
- small cell lung cancer
- ejection fraction
- single molecule
- neoadjuvant chemotherapy
- small molecule
- papillary thyroid
- machine learning
- fluorescence imaging
- high speed
- big data
- mass spectrometry
- living cells
- early onset
- young adults