Analysis of the relationship between preoperative serum tumor marker levels and tumor immune scores in patients with colorectal cancer based on nano-electrochemiluminescence

Abstract

Surgery can be used to remove tumors from patients with colon cancer, but some patients have recurrence a short time after the tumor is surgically removed, and the disease-free survival (DFS) period is short. Therefore, if these high-risk patients can be identified as early as possible, appropriate treatment strategies can be formulated to reduce their postoperative recurrence rate. Tumor immune scores have been proven to be effective in predicting the prognosis of patients, but the scoring can only be performed after the tumor has been removed from the patient. There is still urgency to find indicators that can predict the prognosis of patients before surgery. We used electrochemiluminescence electrodes modified with nanomaterials to detect the expression of serum markers. After comparison, it was found that the contents of CEA, AFP, CA19-9, and CA125 in patients with CRC were substantially higher than those with benign colonic disease. Pearson correlation analysis showed that CEA, AFP, CA19-9, and CA125 contents are positive correlated to the patient’s tumor immune score. Further investigation found that patients with high expression of CEA, AFP, CA19-9, and CA125 had a three-year DFS rate, which was substantially lower than those with low expression. Therefore, our findings suggest that the use of the composite nano-modified electrode electrochemiluminescence method to detect the content of CEA, AFP, CA19-9, and CA125 in patients with colorectal cancer can predict DFS after surgery to a certain extent. Additionally, the perioperative data plan can be adjusted in time according to the expression of tumor markers before surgery.

Acute Toxicity, Synergistic and Antagonistic Effects of 12 Monoterpenoids Against the Poratrioza sinica Yang et Li (Hemiptera: Psyllidae)

“…High-throughput technology has facilitated largescale biological data research, with abundant genomic data have been extracted to identify new diagnostic biomarkers (Pan et al, 2021;Chaneton and Gottlieb, 2012;Li et al, 2014). Recent studies have constructed novel prognostic markers of gene expression levels or mutations based on RNA sequencing data, and identified them using Cox proportional hazards regression models (Ren et al, 2020;Lai et al, 2017).…”