System for determining whether thyroid tissue lesion is benign lesion or malignant lesion and application thereof

Abstract

The invention provides a system for determining whether thyroid tissue lesions are benign lesions or malignant lesions and application thereof, and the system is used for simultaneously detecting expression levels of MET genes, TIMP1 genes, TGFA genes, CITED1 genes and FN1 genes in thyroid tissues. And determining whether the thyroid tissue lesion is a benign lesion or a malignant lesion based on a model constructed by detection results of the five genes. On the basis of high expression of 15 genes in thyroid cancer tissue, mRNA expression of 5 genes is screened out for the first time, an arithmetic model for calculating the 5 mRNA is constructed, a scoring model for finally distinguishing malignant canceration and benign lesion of thyroid is obtained, and through scoring values, the method can be more accurate than cytology and gene mutation methods.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of disease detection, and particularly relates to a system for determining whether a thyroid tissue lesion is a benign lesion or a malignant lesion and application thereof. BACKGROUND

[0002] Thyroid cancer is a malignant tumor originating from thyroid follicular epithelium or parafollicular epithelial cells, and is also the most common malignant tumor in the head and neck. According to the origin and differentiation of the tumor, thyroid cancer is divided into: papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), poorly differentiated thyroid carcinoma (PDTC), and anaplastic thyroid cancer (ATC), among which PTC is the most common, accounting for about 80-90% of all thyroid cancers, and PTC and FTC are collectively referred to as differentiated thyroid carcinoma (DTC). The incidence of DTC accounts for more than 95% of thyroid cancer, and the remaining MTC, PDTC and ATC are relatively rare.

[0003] Most thyroid nodule patients have no clinical symptoms. Usually, thyroid palpation and neck ultrasound examination are found during physical examination. When thyroid nodules are found by ultrasound and other methods, biopsy puncture is often needed to take the cells of the nodules for observation in order to confirm the nature of the lesion. However, about 30% of the specimens observed by cytology and pathology of the biopsy specimens still cannot be identified as malignant or benign lesions. Therefore, molecular biology methods are applied to thyroid biopsy specimens to determine whether they are benign or malignant lesions. The first molecular biology method used includes detecting gene mutations or gene rearrangements at the DNA level in the puncture specimens, such as BRAF, RAS gene mutations, and RET gene rearrangements. With the accumulation of data, studies have found that the detection of gene mutations and gene rearrangements has insufficient sensitivity, and about 1 / 3 of the samples cannot detect these common gene mutations and gene rearrangements even if they are malignant carcinomas, so recently high-throughput techniques have been used to detect a large number of gene mutations or gene rearrangements, which greatly increases the detection cost.

[0004] Then there are clinical applications that use gene expression in cells from biopsy puncture to determine whether it is cancerous, including the American commercial company Afirma in California using microarray chip technology to detect the expression of 167 genes to diagnose whether the biopsy puncture specimen is a benign or malignant tumor, see Alexander EK, Kennedy GC, Baloch ZW, Cibas ES, Chudova D, Diggans J, Friedman L, Kloos RT, LiVolsi VA, Mandel SJ et al: Preoperative diagnosis of benign thyroid nodules with indeterminate cytology. N Engl J Med 2012, 367(8):705-715; there is also a commercial product that detects the expression of 40 genes to distinguish between benign and malignant thyroid biopsy tissues, see Pluciennik A, Placzek A, Wilk A, Student S, Oczko-Wojciechowska M, Fujarewicz K: Data Integration-Possibilities of Molecular and Clinical Data Fusion on the Example of Thyroid Cancer Diagnostics. Int J Mol Sci 2022, 23(19). But the number of genes used is too large, the cost is too high, and it is difficult to meet the needs of clinical application. SUMMARY

[0005] Therefore, in order to solve the above technical problems, the present application provides a system for determining whether a thyroid tissue lesion is a benign lesion or a malignant lesion, which is used to simultaneously detect the expression levels of MET gene, TIMP1 gene, TGFA gene, CITED1 gene and FN1 gene in thyroid tissue, and a model constructed based on the detection results of the above five genes to determine whether the thyroid tissue lesion is a benign lesion or a malignant lesion.

[0006] In an embodiment, the primers and probes for detecting MET gene are SEQ ID NO. 1, SEQ ID NO. 2 and SEQ ID NO. 3, respectively, the primers and probes for detecting TIMP1 gene are SEQ ID NO. 10, SEQ ID NO. 11 and SEQ ID NO. 12, respectively, the primers and probes for detecting TGFA gene are SEQ ID NO. 13, SEQ ID NO. 14 and SEQ ID NO. 15, respectively, the primers and probes for detecting CITED1 gene are SEQ ID NO. 19, SEQ ID NO. 20 and SEQ ID NO. 21, respectively, and the primers and probes for detecting FN1 gene are SEQ ID NO. 22, SEQ ID NO. 23 and SEQ ID NO. 24, respectively.

[0007] In an embodiment, the system is further used for detecting the expression level of the internal reference gene GAPDH, and the primers and probes for detecting the human internal reference gene GAPDH are SEQ ID NO. 46, SEQ ID NO. 47 and SEQ ID NO. 48, respectively.

[0008] In an embodiment, the system is used for preparing a product for determining whether a thyroid tissue lesion is a benign lesion or a malignant lesion.

[0009] In an embodiment, a primer, a probe or a combination thereof is provided, which is used for simultaneously detecting the expression levels of MET gene, TIMP1 gene, TGFA gene, CITED1 gene and FN1 gene in a thyroid tissue, and a model is constructed based on the detection results of the five genes to determine whether a thyroid tissue lesion is a benign lesion or a malignant lesion.

[0010] The present application is the first time to construct a scoring model for differentiating thyroid malignant cancer and benign lesion based on the high expression of 15 genes in thyroid cancer tissues, the mRNA expression of 5 genes, the construction of an arithmetic model for the 5 mRNAs, and the scoring value. The method is more accurate than cytology and gene mutation method, and the combination of the 5 genes (MET+TIMP1+TGFA+CITED1+FN1) achieves the best sensitivity, specificity and AUC area. DETAILED DESCRIPTION

[0011] From public database or published literature, 15 genes were found to be overexpressed in thyroid cancer tissue: MET, SERPINA1, KRT19, TIMP1, TGFA, QPCT, CITED1, FN1, EPS8, PROS1, LRP4, PSD3, SDC4, ETV5, CD44. Primers and probes were designed for each gene expression of mRNA, and at least one of the primers and probes was required to cross the intron of the genome, so that the detection of mRNA would not be affected by the residual genomic DNA in the extracted nucleic acid. Then the thyroid biopsy specimens diagnosed as malignant cancer and benign lesions by cytology or postoperative pathology were tested, and the mRNA of each gene and the mRNA of the internal reference gene GAPDH were detected at the same time, and finally 5 genes were screened out. mRNA expression, and we constructed an arithmetic model to calculate the 5 mRNA, and obtained a final score model to distinguish thyroid malignant cancer and benign lesions. Through the score value, it can be more accurate to distinguish malignant cancer and benign lesions from thyroid biopsy tissue than cytology and gene mutation method. The primers and probes designed for detecting the mRNA expression level of 15 genes and the internal reference gene are shown in Table 1 and Table 2.

[0012] Table 1

[0013] Table 2

[0014] The qPCR reaction system of the above primers and probes was a premixed qRT-PCR reaction system produced by Nuoyan Company. The specific reaction system is shown in Table 3.

[0015] Table 3

[0016] Add water to 20 μl.

[0017] The reaction conditions are shown in Table 4.

[0018] Table 4

[0019] 61 ℃ Collect fluorescence. Record the Ct values of the internal reference gene and the target gene.

[0020] The primers and probes of each gene and the internal reference gene GAPDH were detected, the probe of the target gene was labeled with FAM fluorescent dye, and the probe of the GAPDH gene was labeled with VIC fluorescent dye; according to the Ct values of the target gene and the GAPDH gene, the relative GAPDH expression value of the target gene = 2 （GAPDH的Ct-靶基因的Ct）*100, and then according to the relative abundance of each target gene, a score of expression is obtained.

[0021] First, nucleic acids extracted from 159 biopsy specimens of thyroid cancer and 58 biopsy specimens of benign lesions were used as training data. The expression abundance of the above 15 genes was detected by reverse transcriptional fluorescent quantitative PCR, and the relative expression value of each gene was obtained by comparing with the internal reference gene GAPDH. The training data was input into a logistic regression calculation model, different genes were combined, and each gene was assigned a judgment parameter to distinguish cancer and benign lesion specimens in the training data with the maximum accuracy. According to the score, the probability p of sample cancer was obtained p = 1-1 / (1+EXP(-score)); if the p value is <0.50, it is judged as a benign lesion; if the p value is >=0.50, it is judged as a malignant cancer.

[0022] Then, the gene combination and judgment parameter obtained from the training data were used to judge a new set of test specimens (134 thyroid cancer and 38 benign lesions). In the test specimens, the sensitivity and specificity of the top 6 gene combinations in accuracy ranking were listed as follows.

[0023] Here, we list the score calculation formula of the top 6 gene combinations in accuracy ranking. The number in front of each target gene in the calculation formula is calculated by the logistic regression model according to the known clinical specimen grouping. For example, according to the Ct value, the relative expression abundance of FN1 gene is relatively high, and the logistic regression model finds that the coefficient in front of FN1 gene is relatively small during the calculation process, so that the score can distinguish between malignant cancer and benign lesion group, and the p value is calculated through the score, and then the sensitivity and specificity of thyroid cancer are judged according to the p value.

[0024] Combination 1: MET+TIMP1+TGFA+CITED1+FN1 combination; Score=2.96-0.056*MET-0.0012*TIMP1+0.02*TGFA-0.062*CITED1-0.0002*FN1 Combination 2: KRT19+TIMP1+TGFA+QPCT+CITED1+FN1+EPS8 combination: Score= 2.96-0.002*KRT19-0.0018*TIMP1+0.02*TGFA+0.06*QPCT-0.05*CITED1-0.001*FN1+0.01*EPS8 Combination 3: SERPINA1+TIMP1+TGFA+CD44+FN1 combination: Score = 2.96 - 0.02* SERPINA1 - 0.0012* TIMP1 + 0.018* TGFA + 0.03* CD44 - 0.00018* FN1 Combination 4: CITED1 + EPS8 + PSD3 + SDC4 + ETV5 combination Score = 2.96 - 0.08* CITED1 + 0.02* EPS8 + 0.011* PSD3 + 0.01* SDC4 - 0.04* ETV5 Combination 5: MET + CITED1 + FN1 + EPS8 + PROS1 + LRP4 combination Score = 2.96 - 0.04* MET - 0.05* CITED1 - 0.001* FN1 + 0.034* EPS8 + 0.02* PROS1 + 0.03* LRP4 Combination 6: SERPINA1 + KRT19 + QPCT + TGFA + LRP4 + SDC4 Score = 2.96 - 0.02* SERPINA1 - 0.001* KRT19 + 0.05* QPCT + 0.02* TGFA + 0.028* LRP4 + 0.01* SDC4 The score value, p value and the consistency with pathology of 134 cases of clinically diagnosed thyroid cancer and 38 cases of benign lesion samples in combination 1 are calculated, see Table 5, Table 6, Table 7, Table 8, Table 9 and Table 10.

[0025] Table 5

[0026] Table 6

[0027] Table 7

[0028] Table 8

[0029] Table 9

[0030] Table 10

[0031] The sensitivity and specificity of the combination 1 detection were calculated according to the Score score and p-value calculation data in the above table, as shown in the following table; similarly, for combination 2, combination 3, combination 4, combination 5 and combination 6, the Score score and p-value are calculated (the original data for calculating the Score score and p-value are not shown in this application), so as to obtain the corresponding sensitivity and specificity data, see Table 11 for specific data.

[0032] Table 11

[0033] The sensitivity and specificity of the thyroid cancer according to the p-value are as follows: only the combination of 5 genes in combination 1 (MET+TIMP1+TGFA+CITED1+FN1 combination) reached the highest sensitivity and specificity.

[0034] In the field of medical diagnosis, machine learning model evaluation, etc., sensitivity, specificity and AUC area are important indicators for measuring the performance of models or diagnostic methods. Sensitivity refers to the proportion of individuals who are actually ill (or positive) and are correctly identified as ill (positive) by the model. Reflects the ability of the model to not miss diagnosis. The higher the sensitivity, the better the ability to identify individuals who are actually ill. Specificity refers to the proportion of individuals who are not actually ill (or negative) and are correctly identified as not ill (negative) by the model. Reflects the ability of the model to not misdiagnosis. The higher the specificity, the better the ability to exclude individuals who are not actually ill (such as in scenarios where over-treatment needs to be avoided, high specificity can reduce unnecessary intervention). AUC area (Area Under the ROC Curve) AUC is the area under the ROC curve (Receiver Operating Characteristic Curve, Receiver Operating Characteristic Curve), and the ROC curve is a curve drawn with the false positive rate (FPR) as the horizontal axis and the true positive rate (TPR, i.e. sensitivity) as the vertical axis. The AUC value ranges from 0 to 1, representing the overall ability of the model to distinguish between "positive" and "negative". The closer the AUC is to 1, the stronger the model's ability to distinguish between positive and negative. AUC is not affected by the diagnostic threshold (the critical value for determining "positive / negative"), and can comprehensively reflect the overall performance of the model at different thresholds, suitable for comparing the pros and cons of different models. In summary, sensitivity and specificity are indicators for a specific threshold, focusing on "not missing diagnosis" and "not misdiagnosis" respectively. AUC is a comprehensive indicator that reflects the overall discrimination ability of the model at all possible thresholds.

[0035] The sensitivity and specificity of the thyroid cancer according to the p-value are as follows: only the combination of 5 genes in combination 1 (MET+TIMP1+TGFA+CITED1+FN1 combination) reached the best sensitivity, specificity and AUC area.

[0036] Those skilled in the art will readily understand that the above-mentioned advantageous modes can be freely combined and superimposed without conflict.

[0037] The above merely describes the preferred embodiments of the present application, and should not be used to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application. The above merely describes the preferred embodiments of the present application, and should not be used to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A system for determining whether a thyroid tissue lesion is benign or malignant, characterized in that: The system is used to simultaneously detect the expression levels of MET, TIMP1, TGFA, CITED1 and FN1 genes in thyroid tissue, and to determine whether thyroid tissue lesions are benign or malignant based on the model constructed based on the detection results of the above five genes.

2. The system according to claim 1, wherein: The upstream and downstream primers and probes used to detect the MET gene are SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3 respectively, the upstream and downstream primers and probes used to detect the TIMP1 gene are SEQ ID NO.10, SEQ ID NO.11 and SEQ ID NO.12 respectively, the upstream and downstream primers and probes used to detect the TGFA gene are SEQ ID NO.13, SEQ ID NO.14 and SEQ ID NO.15 respectively, the upstream and downstream primers and probes used to detect the CITED1 gene are SEQ ID NO.19, SEQ ID NO.20 and SEQ ID NO.21 respectively, and the upstream and downstream primers and probes used to detect the FN1 gene are SEQ ID NO.22, SEQ ID NO.23 and SEQ ID NO.24 respectively.

3. The system according to claim 1, wherein: The system is also used to detect the expression level of the internal reference gene GAPDH, and the upstream and downstream primers and probes used to detect the human internal reference gene GAPDH are SEQ ID NO.46, SEQ ID NO.47 and SEQ ID NO.48 respectively.

4. Use of the system according to any one of claims 1 to 3 in the preparation of a product for determining whether a thyroid tissue lesion is a benign lesion or a malignant lesion.

5. A primer, a probe or a combination thereof, characterized in that: It is used to simultaneously detect the expression levels of MET gene, TIMP1 gene, TGFA gene, CITED1 gene and FN1 gene in thyroid tissue, and to determine whether the thyroid tissue lesions are benign or malignant based on the model constructed based on the test results of the above five genes.

6. The primer, probe or combination thereof according to claim 5, characterized in that: The upstream and downstream primers and probes used to detect the MET gene are SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3, respectively; the upstream and downstream primers and probes used to detect the TIMP1 gene are SEQ ID NO.10, SEQ ID NO.11 and SEQ ID NO.12, respectively; the upstream and downstream primers and probes used to detect the TGFA gene are SEQ ID NO.13, SEQ ID NO.14 and SEQ ID NO.15, respectively; the upstream and downstream primers and probes used to detect the CITED1 gene are SEQ ID NO.19, SEQ ID NO.20 and SEQ ID NO.21, respectively; and the upstream and downstream primers and probes used to detect the FN1 gene are SEQ ID NO.22, SEQ ID NO.23 and SEQ ID NO.24, respectively.

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