阅读说明：本技术 肿瘤标志物截断值联合模型及其应用 (Tumor marker cutoff value combined model and application thereof ) 是由 曾凡新 王家驷 李洁 李诗林 于 2020-05-12 设计创作，主要内容包括：本发明公开肿瘤标志物截断值联合模型,采用回归计算获得肿瘤标志物的截断(Cut-off)值；肿瘤标志物包括CEA,CYFRA,NSE,CA125,CA153,CA199以及CA724；肿瘤标志物截断值联合模型建立方法包括以下步骤：(1)对患者各肿瘤标志物进行含量的测定；(2)Logistic回归分析获得肿瘤标志物的截断(Cut-off)值；(3)筛选与肿瘤转移相关的高危因素；(4)比较单一生物标志物参考上限值与截断值在评估肿瘤转移中的性能；(5)建立截断值(comb-cut-off)联合模型。一种上述肿瘤标志物截断值联合模型的应用,用作新诊断肺癌患者肿瘤转移诊断的工具。本发明通过检测肺癌患者血清多种肿瘤标志物含量,比较分析建立肿瘤标志物截断值联合模型,用作诊断肺癌肿瘤转移诊断的工具,准确性高。(The invention discloses a tumor marker cutoff value combined model, which adopts regression calculation to obtain the cutoff (Cut-off) value of a tumor marker; tumor markers include CEA, CYFRA, NSE, CA125, CA153, CA199, and CA 724; the tumor marker cutoff value combined model establishing method comprises the following steps: (1) measuring the content of each tumor marker of the patient; (2) obtaining a Cut-off (Cut-off) value of a tumor marker by Logistic regression analysis; (3) screening high-risk factors related to tumor metastasis; (4) comparing the performance of the single biomarker reference upper limit value with a cut-off value in assessing tumor metastasis; (5) and establishing a joint model of cut-off value (comb-cut-off). The application of the tumor marker cutoff value combined model is used as a tool for diagnosing the tumor metastasis of a patient with new lung cancer diagnosis. The invention establishes a tumor marker cutoff value combined model through detecting the contents of various tumor markers in the serum of a lung cancer patient and comparing and analyzing, is used as a tool for diagnosing lung cancer tumor metastasis, and has high accuracy.)

1. Tumor marker cutoff value combined model, its characterized in that: calculating to obtain a Cut-off (Cut-off) value of the tumor marker by adopting logistic regression;

the tumor markers include CEA, CYFRA, NSE, CA125, CA153, CA199, and CA 724;

the method for establishing the tumor marker cutoff value model comprises the following steps:

(1) measuring the content of each tumor marker in the serum of a patient;

(2) obtaining a Cut-off (Cut-off) value of each tumor marker by logistic regression analysis;

(3) screening high-risk factors related to tumor metastasis;

(4) comparing the performance of a single biomarker reference upper limit (URL) value with a Cut-off (Cut-off) value in assessing tumor metastasis;

(5) and establishing a tumor marker cutoff value (comb-cut-off) combined model.

2. The use of the tumor marker cutoff combination model of claim 1, wherein: used as a tool for diagnosing the tumor metastasis of a patient with the new diagnosis of the lung cancer.

Technical Field

The invention relates to the field of biotechnology, in particular to a tumor marker cutoff value combined model and application thereof.

Background

Lung cancer is the most common cause of death among all cancers worldwide. The two major types of lung cancer are Small Cell Lung Cancer (SCLC) and non-small cell lung cancer (NSCLC). Overall survival is dependent on the stage of lung cancer, with patients with advanced lung cancer generally having a poorer prognosis. Evidence suggests that tumor metastasis reflects a relatively advanced stage of lung cancer, with over 70% of deaths resulting from tumor metastasis.

Recurrence and metastasis are reported to significantly increase the risk of death in lung cancer patients. The 5-year overall survival rate for patients with stage IB non-small cell lung cancer is 68%, while the 5-year overall survival rate for patients with stage IVA-IVB is less than 10%. The median survival time of patients with extensive small cell lung cancer is 10-12 months. One previous study report showed that in non-small cell patients with no more than 5 metastases, adequate treatment resulted in no progression in 13% of patients within 3 years, and even stage IV patients could benefit from curative treatment. Therefore, the identification of the metastasis has important guiding value for the selection and prognosis of the clinical treatment scheme of a newly diagnosed lung cancer patient.

Clinically, metastasis is determined by a combination of clinical symptoms and imaging evidence (computed tomography (CT), chest X-ray (CXR), positron emission tomography (PET-CT), and Magnetic Resonance Imaging (MRI), etc.) when complete pathological evidence is available for the diagnosis of lung cancer. However, factors such as high examination costs may place a significant economic burden on the patient, preventing clinical monitoring and early detection of lung cancer metastasis. In addition, patients may develop metastases in certain areas, with insignificant clinical symptoms, which are easily overlooked by patients and physicians. Therefore, there is a clinical urgent need for an economical and simple diagnostic technique to determine whether metastasis has occurred, which helps prompt a doctor to determine whether a lung cancer patient suspected of having metastatic symptoms needs to be examined in more detail. Blood-based biomarkers can be readily, rapidly, and economically obtained, and thus they have the potential to greatly improve the efficiency of assessment. The traditional and commonly used tumor markers for clinically assisting the diagnosis of lung cancer tumor include carcinoembryonic antigen (CEA), cytokeratin antigen 19 fragment (CYFRA), neuron-specific enolase (NSE), Carbohydrate Antigen (CA) series, such as CA125, CA153, CA199, CA724, etc.

However, the metastasis of tumor patients cannot be accurately determined by directly using the reference upper limit (URL) value of the tumor marker currently set clinically.

Disclosure of Invention

In order to solve the existing problems, the invention provides a tumor marker cutoff value model and an application thereof. The invention is realized by the following technical scheme.

A tumor marker cutoff value combined model, which adopts logistic regression calculation to obtain the cutoff (Cut-off) value of the tumor marker;

the tumor markers include CEA, CYFRA, NSE, CA125, CA153, CA199, and CA 724;

the method for establishing the tumor marker cutoff value model comprises the following steps:

(1) measuring the content of each tumor marker in the serum of a patient;

(2) logistic regression analysis to obtain a Cut-off (Cut-off) value of each tumor marker;

(3) screening high-risk factors related to tumor metastasis;

(4) comparing the performance of a single tumor marker reference upper limit (URL) value with a cutoff (Cut-off) value in assessing tumor metastasis;

(5) and establishing a comb-cut-off (comb-cut-off) joint model based on multiple swelling marks.

The application of the tumor marker cutoff value combined model is used as a tool for diagnosing the tumor metastasis of a patient newly diagnosed with lung cancer.

The invention has the beneficial effects that:

the invention relates to a tumor marker cutoff value combined model and application thereof

The invention carries out content determination on 7 tumor markers (CEA, CA125, CA153, CA199, CA724, CYFRA and NSE) in the serum of a patient through quantitative analysis of the tumor markers in the serum and a kit of Roche and a Roche 601 system. Compared with the prior art, the 7 tumor markers have obvious diagnosis effect on the occurrence of non-small cell lung cancer (NSCLC) and Small Cell Lung Cancer (SCLC), and compared with the prior art, the invention establishes a tumor marker cutoff value (comb-cut-off) combined model by detecting the content of a plurality of tumor markers in the serum of a lung cancer patient and performing comparative analysis, can be used as a tool for diagnosing the metastasis of lung cancer tumors, and has high accuracy.

Drawings

FIG. 1 is a graph of tumor marker levels for the metastatic and non-metastatic groups in a lung cancer patient; the horizontal dotted line is the reference upper limit value of each tumor marker;

FIG. 2 is a graph showing age, sex and the ratio of ratios (OR) of biomarkers grouped by Cut-off value;

FIG. 3 is a comparison of the logistic regression model based on the tumor marker measurement (Level) (A), the reference upper limit value (URL) (B) and the cutoff (Cut-off) value (C); wherein (D) comparing the comb-cut-off model with a logistic regression model for a single tumor marker adjusted without regard to gender and age factors; (E) comparing the comb-cut-off model with a logistic regression model of a single tumor marker adjusted by factors of gender and age;

FIG. 4 is a Nomogram graph and an example application display of regression models predicting tumor metastasis;

FIG. 5 shows the performance of the decision tree model. (A) The rules of the decision tree model are based on the measured values of the single tumor markers and the performance of the logistic regression model compared with the reality; (B) performance of the decision tree model.

Detailed Description

The technical scheme of the invention is more fully explained in detail by combining the attached drawings.