Method for predicting liver cancer transfer relapse of primary liver cancer patient after operation

Abstract

The invention provides a method for forecasting metastasis and recurrence of liver cancer after the operation of a primary liver cancer patient which comprises the followig concrete steps: protein extraction reagent is prepared by dissolving 3-[(3-cholanidopropyl)dimethylammonio]-1-propanesulfonate, dithiothreitol, ethylene diamine tetraacetic acid, phenylmethylsulfonyl fluoride, gastric inhibitory polypeptide and leupeptin in phosphate buffered solution with a pH value ranging from 7.0 to 7.5; a frozen liver cancer surrounding tissue is put in liquid nitrogen and smashed into powder, and a protein extraction reagent is added into the mixture which is then blended in a mixer; ultrasound treatment and centrifugation are carried out on the mixture; supernate is sucked and stored at the temperature ranging from 20 DEG C to 80 DEG C; and the concentrations of interleukin-2, interleukin-15 and interleukin-5 of the supernate are detected, for purpose of forecasting the metastasis and recurrence as well as survival or death after the operation of a liver cancer patient. The concentrations of the interleukin-2, the interleukin-15 and the interleukin-5 are obviously superior to the existing clinical indicator that is used for forecasting the recurrence and the survival of the liver cancer patient.

Description

technical field [0001] The invention relates to a method for predicting the metastasis and recurrence of primary liver cancer patients after operation, and belongs to the field of biotechnology. Background technique [0002] Liver cancer is the second leading cause of cancer death in my country, and surgery is currently the main treatment. However, the high postoperative recurrence rate of metastasis (5-year recurrence rate reaches 70%, small liver cancer also reaches 50%) has become a bottleneck for further improving long-term efficacy, and is also an important key to conquer liver cancer. If it is possible to predict the early metastasis and recurrence of specific groups after radical resection of liver cancer, and to provide timely intervention and treatment, so as to achieve early detection and early treatment, the survival rate of patients with recurrence after radical resection will be further improved, and there will also be great social benefits and economic benefit...

AI Technical Summary

Problems solved by technology

Proteins cannot be replicated like genes, so the sensitivity of protein detection has become a key issue, especially the detection of low-abundance proteins has always been a problem. Previous studies have shown that cytokines such as: interferon gamma, interleukin 2, Tumor necrosis factor, etc. are difficult to detect in tissue extracts

Due to the formula and extraction procedures of the existing methods for extracting proteins from tissues, most of the proteins are not easy to dissolve, and will be lost in large quantities after extraction. In addition, the extracts contain a large amount of alkali, making the extracts unsuitable for antigen antibodies in downstream protein detection. binding reaction

[0004] At present, there are no sufficiently sensitive and specific predictors to predict the metastasis, recurrence and survival of patients after liver cancer surgery. Although alpha-fetoprotein, tumor size, and presence or absence of capsule are related to metastasis and recurrence, the overall prediction accuracy is still low. very low

Images