Molecular model for judging prognosis of ovarian cancer patient and application

Abstract

The invention provides a molecular model for judging prognosis of an ovarian cancer patient and application. The molecular model comprises the steps of: comparing data of an ovarian cancer sample and data of a normal ovarian tissue sample to obtain metabolic genes with differences; shortening the range of the metabolic genes through a regression model to obtain a seven-gene combination model; and carrying out detection, verification and analysis and finally, obtaining a result that a risk score is related to the lifetime, and a patient with a high risk score has short lifetime and is poor in prognosis. Therefore, according to the invention, by constructing the molecular combination model of seven genes, prognosis of the ovarian cancer patient is evaluated, and the ovarian cancer patient who is poor in prognosis can be distinguished and selected, i.e., ovarian cancer crowds are layered, the ovarian cancer patients with high risk and poor prognosis are selected, clinicians are guided to provide more positive treatment schemes for the patients, and meanwhile, the clinicians can also be guided to avoid excessive treatment on the low-risk ovarian cancer patient.

Description

technical field [0001] The invention belongs to the technical field of biomedicine, and in particular relates to a molecular model for judging the prognosis of ovarian cancer patients and its application. Background technique [0002] Ovarian cancer is the most lethal gynecological malignancy. The death rate of ovarian cancer ranks seventh among all malignant tumors in women, with a death rate as high as 60%, which is much higher than other gynecological tumors. Ovarian cancer patients have a good initial treatment effect, but they are prone to relapse or drug resistance, resulting in a very low survival rate within 5 years for ovarian cancer patients. One of the main reasons is the lack of effective markers for judging the prognosis of ovarian cancer patients, the inability to stratify the risk of ovarian cancer patients, and the inability to guide clinicians in early intervention and early treatment of high-risk ovarian cancer patients. Contents of the invention [000...

AI Technical Summary

Problems solved by technology

Ovarian cancer patients have a good initial treatment effect, but they are prone to relapse or drug resistance, resulting in a very low survival rate within 5 years for ovarian cancer patients

One of the main reasons is the lack of effective markers for judging the prognosis of ovarian cancer patients, the inability to stratify the risk of ovarian cancer patients, and the inability to guide clinicians in early intervention and early treatment of high-risk ovarian cancer patients

Images