CN109557311B

CN109557311B - Colorectal cancer diagnosis marker, colorectal cancer detection product and application thereof

Info

Publication number: CN109557311B
Application number: CN201811524741.4A
Authority: CN
Inventors: 陈永恒; 周敏泽; 李茂玉; 陈主初
Original assignee: Xiangya Hospital of Central South University
Current assignee: Xiangya Hospital of Central South University
Priority date: 2018-12-13
Filing date: 2018-12-13
Publication date: 2022-02-15
Anticipated expiration: 2038-12-13
Also published as: CN109557311A

Abstract

The invention relates to a colorectal cancer diagnosis marker, a colorectal cancer detection product and application thereof. The colorectal cancer diagnosis marker comprises a plurality of secretory proteins, wherein the secretory proteins comprise one or two of S100A9 protein and tenascin-c protein, and CEA protein. The kit has the characteristics of high sensitivity and strong specificity, the area under the roc curve of a diagnosis model is 0.902, the reliability of a diagnosis result is far higher than that of a diagnosis result obtained by adopting a single protein marker, and the kit has great significance for diagnosis and treatment of colorectal cancer. The colorectal cancer detection product comprises the colorectal diagnosis marker. The invention also provides application of the colorectal diagnosis marker in preparation of products for assisting in diagnosis of colorectal cancer.

Description

Colorectal cancer diagnosis marker, colorectal cancer detection product and application thereof

Technical Field

The invention relates to the technical field of medicines, in particular to a colorectal cancer diagnosis marker, a colorectal cancer detection product and application thereof.

Background

Colorectal cancer is one of the most common digestive tract malignant tumors all over the world, the morbidity and mortality of colon cancer are in an increasing trend in most countries in the world in the last 20 years, and most colon cancer patients are found to be in an advanced stage and have poorer prognosis. The current methods for screening and diagnosing colon cancer are invasive, and colonoscopy remains the gold standard for screening and diagnosis. In recent years, fecal occult blood test is the most common non-invasive test method used in colorectal cancer screening, but its sensitivity and specificity are low. If a simple serum test could be applied for its early diagnosis, it would be helpful for the prevention and prognosis of colon cancer. The colon tumor markers which are widely applied clinically at present greatly improve the diagnosis of colon cancer and reduce the death rate and the recurrence rate, but the sensitivity and the specificity are not high. Therefore, their role as markers for early diagnosis of colon cancer is limited. On the basis, the research and development of more tumor markers related to the colon cancer are expected.

Ulcerative colitis and inflammatory bowel polyps are often considered as one of the high risk factors for the onset of colorectal cancer, and the use of enteroscopy and review is an invasive test that is very inconvenient for the patient in detecting the progression of the disease. Therefore, there is a need for a detection method that can non-invasively track the progression of the disease, early detect and diagnose colorectal cancer, and guide the treatment of patients with colorectal cancer.

Disclosure of Invention

The invention aims to provide a colorectal cancer diagnosis marker, a colorectal cancer detection product and application thereof.

The invention provides a colorectal cancer diagnosis marker, which comprises one or two of S100A9 protein and tenascin-c protein (TNC protein for short) and CEA protein.

In addition, the invention also provides application of the colorectal diagnostic marker in preparing a detection product for detecting colorectal cancer.

In addition, the invention also provides a colorectal cancer detection product, which comprises the colorectal diagnosis marker.

Preferably, the test product is a diagnostic kit.

Preferably, the test product is used to distinguish between serum of at least one patient with colorectal cancer and serum of at least one patient with non-colorectal cancer, and/or the test product is used to distinguish between serum of at least one patient with colorectal cancer and serum of one healthy individual, and/or the test product is used to distinguish between serum of at least one patient with colorectal cancer and serum of at least one patient with ulcerative colitis.

Preferably, the test product further comprises a first regression model for differentiating between serum from at least one patient with colorectal cancer and serum from at least one patient with non-colorectal cancer, said first regression model establishing a discriminant function for P of P ═ 1/{1+ exp [ - (-4.3762+ 0.3X + 0.16Y + 0.46Z) ] }, wherein X is the concentration of CEA protein in ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL; wherein the critical value of the P value is 0.62;

and/or said test product further comprises a second regression model for differentiating between the serum of at least one patient with colorectal cancer and the serum of at least one healthy individual, said second regression model establishing a discriminant function for P of P ═ 1/{1+ exp [ - (-3.7801+ 0.22X + 0.17Y + 0.5Z) ] }, wherein X is the concentration of CEA protein in ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL; wherein the critical value of the P value is 0.71;

and/or, the test product further comprises a third regression model for differentiating between serum of at least one patient with colorectal cancer and serum of at least one patient with ulcerative colitis, said third regression model establishing a discriminant function for P of 1/{1+ exp [ - (-3.61+ 0.42X + 0.15Y + 0.42Z) ] }, wherein X is the concentration of CEA protein in ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL; wherein the critical value of P value is 0.67.

Further, a computer readable carrier is included, having a program recorded thereon, which when executed by a processor, performs the steps of:

judging the concentration of CEA protein, the concentration of S100A9 protein and the concentration of tenascin-c protein in the peripheral blood plasma of the testee;

introducing the concentration of the CEA protein, the concentration of the S100a9 protein, and the concentration of the tenascin-c protein into the discriminant function P ═ 1/{1+ exp [ - (-4.3762+ 0.3X + 0.16Y + 0.46Z) ] }, wherein the P value cutoff value is 0.62;

and/or, introducing the concentration of the CEA protein, the concentration of the S100a9 protein, and the concentration of the tenascin-c protein into the discriminant function P ═ 1/{1+ exp [ - (-3.7801+ 0.22X + 0.17Y + 0.5Z) ] }, wherein the P value cutoff is 0.71;

and/or, introducing the concentration of the CEA protein, the concentration of the S100a9 protein, and the concentration of the tenascin-c protein into the discriminant function P ═ 1/{1+ exp [ - (-3.61+ 0.42X + 0.15Y + 0.42Z) ] }, wherein the P value cutoff value is 0.67; wherein, X is the concentration of the CEA protein, and the unit is ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL.

The invention also comprises the use of a product for the detection of the S100a9 protein and/or a product for the detection of the tenascin-c protein for the preparation of a product for the auxiliary diagnosis of colorectal cancer.

Further, the present invention also includes one or both of a product for detecting tenascin-c protein and/or a product for detecting S100a9 protein, and the use of a product for detecting CEA protein and a computer readable carrier in the manufacture of a product for aiding in the diagnosis of colorectal cancer, wherein the computer readable carrier has a program recorded therein which, when executed by a processor, carries out the steps of:

and/or, introducing the concentration of the CEA protein, the concentration of the S100a9 protein, and the concentration of the tenascin-c protein into the discriminant function P ═ 1/{1+ exp [ - (-3.61+ 0.42X + 0.15Y + 0.42Z) ] }, wherein the P value cutoff value is 0.67;

wherein, X is the concentration of the CEA protein, and the unit is ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL.

Compared with the prior art, the invention has the advantages that: the colorectal cancer diagnosis marker provided by the invention has the characteristics of high sensitivity and strong specificity in colorectal cancer diagnosis, the area under the roc curve (AUC) of a diagnosis model is 0.902, the reliability of a diagnosis result is far higher than that of a diagnosis result obtained by adopting a single protein marker, and the colorectal cancer diagnosis marker has great significance for colorectal cancer diagnosis and treatment. A colorectal cancer detection product comprising a colorectal diagnostic marker for distinguishing between serum of at least one colorectal cancer patient and serum of at least one non-colorectal cancer patient, and/or for distinguishing between serum of at least one colorectal cancer patient and serum of one healthy individual, and/or for distinguishing between serum of at least one colorectal cancer patient and serum of at least one ulcerative colitis patient.

Drawings

The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:

FIG. 1 is a ROC plot of the diagnosis of colorectal cancer for the three proteins alone and in combination.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. The following examples are given to facilitate a better understanding of the invention, but do not limit the invention. The experimental procedures in the following examples are conventional unless otherwise specified. The test materials used in the following examples were purchased from a conventional biochemical reagent store unless otherwise specified. In the quantitative tests in the following examples, two-well replicates were set up and the results averaged.

The gold standard for colorectal cancer diagnosis is the pathological detection result.

Examples

The peripheral blood serum sample collection method comprises the following steps: collecting 10mL peripheral blood vein specimen in early morning with empty stomach, standing at room temperature for two hours, centrifuging at 4 deg.C at 1000g for 15min, separating upper layer plasma, and storing at-80 deg.C for use.

The number of an Elisa kit for detecting Tenascoin-c is CSB-E13125 h;

the number of an Elisa kit for detecting S100A9 is CSB-E11834 h;

the number of an Elisa kit for detecting CEA is CSB-E04767 h.

Study object

258 examples of peripheral blood were collected from patients diagnosed with colorectal cancer by pathological examination in the xiangya hospital, university in south china, between 2017 and 2018, month 3. Of these, 148 cases were male, and 110 cases were female. The informed consent protocol and study procedure were approved by the ethics committee and all patients and healthy volunteers who agreed to participate in the study signed an informed consent.

Determination of protein markers

In this example, proteomics of colon cancer was performed in the early stage, matrix proteomes of different stages of colorectal cancer were studied by a combination of Laser Capture Microdissection (LCM), iTRAQ labeling and two-dimensional liquid chromatography-tandem mass spectrometry (2D LC-MS/MS), and 222 differentially expressed proteins were obtained by co-screening. Through analysis and immunohistochemical verification, S100A9 and tenascin-c are confirmed to be secretory proteins with large expression difference and can be used as alternative clinical tumor markers of colon cancer.

S100A9 (Chinese name calcium binding protein S100A9, English name protein S100-A9, gi | 4506773);

tenascin-c (Tenascin-c, Chinese name, protein Tenascin-c, gi |1842130, TNC for short);

the two markers and a clinical common tumor marker CEA are jointly detected.

ELISA detection verification

And (3) detecting 258 cases of the peripheral blood plasma of the colorectal cancer patients, 103 cases of the blood plasma of healthy people and 99 cases of the blood plasma of benign colorectal disease patients according to a kit instruction, and collecting the blood plasma by using EDTA or heparin as an anticoagulant. The plasma was collected and centrifuged at 1000 Xg for 15 minutes at 2-8 ℃ within 30 minutes and immediately assayed to obtain the concentrations of the three proteins in 460 samples. (instructions for the kit for the specific method)

Threshold determination

Logistic regression analysis was performed using the sps software, naming the colon cancer patient as "1" and the control group (healthy or/and benign colitis patients) as "0", with corresponding inputs of S100a9, TNC, CEA concentrations, setting the disease type as "dependent" and the concentrations of the three as "independent" in the sps, and three formulas for P-value were derived from the logistic analysis. After analyzing to obtain a P value (the P value is a value obtained by logical analysis, and the concentration value of CEA, S100a9, TNC is different for each case, and the P value is also different, and can be regarded as a "detection value" calculated according to our model), ROC curve creation is performed to obtain a critical value at the time of maximum diagnosis efficiency, the critical value is a specific P value, above which the value is regarded as "positive", and below which the value is regarded as "negative", and the P value critical values of cancer specimens and inflammation (benign colorectal disease patients, and can also be ulcerative colitis patients) specimens, the P value critical values of cancer specimens and healthy human specimens, and the P value critical values of cancer specimens and non-cancer specimens are analyzed respectively. If the cutoff value is greater than or equal to the cutoff value, the disease is detected (namely positive), and if the cutoff value is lower than the cutoff value, the negative result is obtained. Non-cancer includes healthy people and inflammation. Three equations for the P value are as follows:

the formula for P-value to distinguish healthy from cancer patients is:

p ═ 1/{1+ exp [ - (-3.7801+ 0.22X + 0.17Y + 0.5Z) ] } (cutoff: P ≧ 0.71); more than or equal to 0.71 is a cancer patient, and less than 0.71 is a healthy person;

the formula for P-value to distinguish inflammatory patients from cancer patients is:

p ═ 1/{1+ exp [ - (-3.61+ 0.42X + 0.15Y + 0.42Z) ] } (cutoff: P ≧ 0.67); cancer patients are treated with more than or equal to 0.67, and inflammation patients are treated with less than 0.67;

the formula for P-value to distinguish cancer patients from non-cancer is:

p ═ 1/{1+ exp [ - (-4.3762+ 0.3X + 0.16Y + 0.46Z) ] } (cutoff: P ≧ 0.62); a cancer patient is considered to be at least 0.62, while a patient with cancer is considered to be non-cancerous when the cancer is less than 0.62.

TABLE 1 specificity and sensitivity for the diagnosis of colorectal cancer for the three proteins alone and in combination

	CEA	S100A9	TNC	Diagnostic model
					VS healthy person of colon cancer patient	3.23	18.74	3.87	-
Joden index	0.35	0.56	0.5	0.69
					Sensitivity (%)	44.2	64.7	57	78.3
Degree of specificity (%)	90.3	91.3	93.2	90.3
					Benign colorectal carcinoma patient of carcinoma of colon patient vs	3.36	17.06	2.43	-
Joden index	0.39	0.56	0.5	0.68
					Sensitivity (%)	44.2	68.2	70.9	81.8
Degree of specificity (%)	94.9	87.9	79.8	85.9

TABLE 2 area under ROC Curve for the diagnosis of colorectal cancer for the three proteins alone and in combination

Variable	AUC	SEa	95％Clb
				CEA	0.736	0.0227	0.693 to 0.776
S100A9	0.831	0.0186	0.793 to 0.864
				TNC	0.728	0.0232	0.685 to 0.769
model	0.902	0.0142	0.872 to 0.928

As can be seen from Table 1, the sensitivity and specificity of the diagnostic model established by combining the three proteins were 81.9% and 85.9%, respectively. Is superior to the sensitivity of the single detection of the three proteins. The sensitivity and the specificity are difficult to be considered clinically, the specificity is reduced while the sensitivity is improved, the johnson index can be used as a comprehensive standard for measuring the diagnosis efficiency, and the johnson index of a diagnosis model established by combining the three proteins is the highest as can be seen from the table 1. From fig. 1 and table 2, it can be known that the AUC value of the diagnostic model established by the combination of the three proteins is 0.902, which is obviously higher than the AUC value of the three proteins detected separately, and the reliability of the diagnostic result is far higher than that of the diagnostic model established by the use of the separate protein markers, and the diagnostic model has great significance for the diagnosis and treatment of colorectal cancer.

Therefore, this example proposes a diagnostic marker for colorectal cancer, comprising one or both of the S100a9 protein and tenascin-c protein, and the CEA protein.

In addition, a colorectal cancer detection product is provided, which comprises the colorectal diagnosis marker.

Preferably, the test product is a diagnostic kit.

The test product further comprises a first regression model for differentiating between serum of at least one patient with colorectal cancer and serum of at least one patient with non-colorectal cancer, said first regression model establishing a discriminant function for P of P ═ 1/{1+ exp [ - (-4.3762+ 0.3X + 0.16Y + 0.46Z) ] }, wherein X is the concentration of CEA protein in ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL; wherein the critical value of the P value is 0.62;

and/or, the test product further comprises a third regression model for differentiating between serum of at least one patient with colorectal cancer and serum of at least one patient with ulcerative colitis, said third regression model establishing a discriminant function for P of 1/{1+ exp [ - (-3.61+ 0.42X + 0.15Y + 0.42Z) ] }, wherein X is the concentration of CEA protein in ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL; wherein the critical value of P value is 0.67. The detection product can be distinguished through one or more of the three models to obtain a distinguishing result or a plurality of distinguishing results.

The present example also includes the use of a colorectal diagnostic marker in the preparation of a test product for detecting colorectal cancer.

In addition, the present example also proposes the use of a product for the detection of the S100a9 protein and/or a product for the detection of the tenascin-c protein for the preparation of a product for the auxiliary diagnosis of colorectal cancer.

Finally, this example also proposes the use of one or both of a product for the detection of tenascin-c protein and/or a product for the detection of the S100a9 protein, and a product for the detection of CEA protein and a computer-readable vector in the manufacture of a product for aiding in the diagnosis of colorectal cancer, wherein said computer-readable vector has a program recorded therein which, when executed by a processor, carries out the steps of:

and/or, introducing the concentration of the CEA protein, the concentration of the S100a9 protein, and the concentration of the tenascin-c protein into the discriminant function P ═ 1/{1+ exp [ - (-3.61+ 0.42X + 0.15Y + 0.42Z) ] }, wherein the P value cutoff value is 0.67; wherein, X is the concentration of the CEA protein, and the unit is ng/mL; y is the concentration of S100A9 protein, and the unit is ng/mL; z is the concentration of tenascin-c protein in ng/mL. The concentrations of the three proteins can be determined by one or more of the three determination functions to obtain a single determination result or multiple determination results.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A test product for colorectal cancer comprising a colorectal diagnostic marker comprising S100a9 protein, tenascin-c protein and CEA protein; further comprising a computer readable carrier having a program recorded thereon which, when executed by a processor, performs the steps of:

2. The detection product for colorectal cancer according to claim 1, wherein the detection product is a diagnostic kit.

3. Use of a product for the detection of tenascin-c protein and a product for the detection of the S100a9 protein, as well as a product for the detection of CEA protein and a computer-readable vector in the manufacture of a product for aiding in the diagnosis of colorectal cancer, wherein said computer-readable vector bears a program which, when executed by a processor, carries out the steps of: