Assessment of Epinephrine and Norepinephrine in Gastric Carcinoma.
Alina Maria MehedințeanuVeronica SfredelPuiu Olivian StovicekMichael SchenkerGeorgică Costinel TârteaOctavian IstrătoaieAna-Maria CiureaCristin Constantin VerePublished in: International journal of molecular sciences (2021)
The aim of our study was to assess the sympathetic nervous system's involvement in the evolution of gastric carcinoma in patients by analyzing the mediators of this system (epinephrine and norepinephrine), as well as by analyzing the histological expression of the norepinephrine transporter (NET). We conducted an observational study including 91 patients diagnosed with gastric carcinoma and an additional 200 patients without cancer between November 2017 and October 2018. We set the primary endpoint as mortality from any cause in the first two years after enrolment in the study. The patients were monitored by a 24-h Holter electrocardiogram (ECG) to assess sympathetic or parasympathetic predominance. Blood was also collected from the patients to measure plasma free metanephrine (Meta) and normetanephrine (N-Meta), and tumor histological samples were collected for the analysis of NET expression. All of this was performed prior to the application of any antineoplastic therapy. Each patient was monitored for two years. We found higher heart rates in patients with gastric carcinoma than those without cancer. Regarding Meta and N-Meta, elevated levels were recorded in the patients with gastric carcinoma, correlating with the degree of tumor differentiation and other negative prognostic factors such as tumor invasion, lymph node metastasis, and distant metastases. Elevated Meta and N-Meta was also associated with a poor survival rate. All these data suggest that the predominance of the sympathetic nervous system's activity predicts increased gastric carcinoma severity.
Keyphrases
- prognostic factors
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- lymph node metastasis
- squamous cell carcinoma
- type diabetes
- poor prognosis
- long non coding rna
- bone marrow
- heart rate variability
- risk factors
- deep learning
- atrial fibrillation
- binding protein
- data analysis
- replacement therapy