TWI615472B - Gene marker and method for predicting breast cancer recurrence - Google Patents

Description

Gene marker and method for predicting breast cancer recurrence

The invention relates to a gene marker and a method for predicting recurrence of breast cancer, which particularly relates to detecting the expression amount of at least one gene of LMCD1, DEAF1, ABCC1, PLOD2, LARS2 and AACS in a breast cancer tissue of a patient, and thereby predicting the recurrence of breast cancer. Sex.

Breast cancer is the most common cancer among women worldwide, and there are a few male cases. According to the World Health Organization, more than half a million people worldwide die from breast cancer every year. According to a cancer research report published by the American Cancer Society in 2013, the association estimates that in 2013, 232,340 new cases of women with invasive breast cancer will be diagnosed in the United States, and 2,240 new male cases will be generated. The association estimates that 39,620 women and 410 men will die of breast cancer in the United States in 2013. In the United States, breast cancer is the second leading cause of cancer death in women, second only to lung cancer.

In Taiwan, breast cancer is the fourth leading cause of cancer death in women. According to information released by the Ministry of Health and Welfare of the Executive Yuan in 2013, 1,912 women died of breast cancer in Taiwan in 2012. In terms of incidence, according to the statistics of the Ministry of Health and Welfare in 2013, in 2011, there were 9,655 new cases of female breast cancer in Taiwan (another 39 cases of male breast cancer) were diagnosed with an incidence of 77.73 (per 100,000). People), the highest incidence of cancer in women. In addition, more than 40% of breast cancer patients in Taiwan occur before the age of 50. Compared with women in Europe and the United States, breast cancer occurs in postmenopausal women. Cancer has a younger trend in Taiwan and is a major threat to the health of young women.

In recent years, due to advances in diagnosis and treatment, the mortality rate of breast cancer has gradually declined. Overall, the survival rate of breast cancer patients in Taiwan after five years of treatment can reach 83%. However, there are still some patients with recurrence and metastasis, which are often difficult to detect in the early stage of cancer recurrence, and have been difficult to treat when they are in advanced medical treatment, and may be transferred to distant tissues and organs, and the prognosis is poor. Therefore, the follow-up of breast cancer surgery And recurrence assessment is even more important.

Traditionally, the study of breast cancer prognostic factors is based on the clinical diagnosis of tumor behavior such as tumor size, stage, and degree of invasion of lymph as prognostic factors to predict the prognosis and recurrence of breast cancer patients, but the above-mentioned tumor behavior is equivalent to breast cancer. There was still a significant difference in recurrence rate and survival rate between patients. In other words, tumor size, stage, and degree of invasion of lymph as predictors of breast cancer recurrence did not completely predict the possibility of breast cancer recurrence.

Scientists used microarray technology to analyze the differences in gene expression in tumor tissues, and found that the expression of some genes was significantly different between patients without recurrence and recurrence, and could be used as a better predictor of breast cancer recurrence. In 2002, the National Cancer Center of the Netherlands used microarray technology to analyze the gene expression of breast cancer cells in 117 breast cancer patients with axillary lymph nodes not affected by cancer cells. 70 different genes were used in the study, and 83% accuracy can predict which Patients with breast cancer have a good prognosis and do not need to undergo systemic chemotherapy to avoid breast cancer recurrence. In addition, the Harvard research team used microarray wafers to analyze 60 tumor cells that were estrogen receptor-positive and received Tamoxifen drug treatment. The tumor samples were found to be HOXB13 and IL17BR. The relative ratio of gene expression can be used as a molecular marker to predict the success or failure of Tamoxifen .

At present, most of the research results published by the world's major breast cancer research teams need to analyze the expression of dozens or nearly 100 target genes in breast cancer tumor tissues in order to accurately predict the possibility of breast cancer recurrence. As well as the primers used to analyze each gene, the cost of the sample is also large, and it is difficult to reduce the cost.

Therefore, the inventors of the present invention used logistic regression analysis, decision trees, and neural networks to find 10 predictable uses of breast cancer microarray wafer data (a total of 757 valid samples) published by various research teams. The target genes for breast cancer recurrence are LMCD1, DEAF1, ZFP36L2, AP2A2, ABCC1, PLOD2, LARS2, AACS, CDCA3 and LMNB1. Among them, six target genes such as LMCD1, DEAF1, ABCC1, PLOD2, LARS2 and AACS are other studies. The team has not been a novel target gene for breast cancer recurrence predictors. The gene marker for predicting breast cancer recurrence proposed by the present invention, that is, selecting at least one of the above six novel target genes, or selecting at least two of the ten target genes, and analyzing the expression amount of the target gene in the breast cancer tissue of the patient And use the amount of performance or related data to predict the likelihood of breast cancer recurrence in patients.

The main object of the present invention is to provide a genetic marker for predicting breast cancer recurrence, which is selected from at least one of six novel target genes and analyzed for its performance in breast cancer tissue cells to predict the possibility of breast cancer recurrence.

A secondary object of the present invention is to provide a genetic marker for predicting recurrence of breast cancer, which is selected from at least two of ten target genes and analyzed in a breast cancer tissue cell. The current amount can predict the possibility of breast cancer recurrence.

Another object of the present invention is to provide a method for predicting breast cancer recurrence, analyzing the amount of gene labeling in breast cancer tissue cells to predict the likelihood of breast cancer recurrence.

In order to achieve the above-mentioned various purposes and effects, the present invention discloses a genetic marker for predicting breast cancer recurrence, comprising at least one target gene selected from six targets: LMCD1, DEAF1, ABCC1, PLOD2, LARS2, AACS, and the like. A group of genes. Among them, patients with higher expression of LMCD1 or DEAF1 in breast cancer tumors are less likely to predict recurrence of breast cancer; patients with higher expression of ABCC1, PLOD2, LARS2 or AACS in breast cancer tumors may predict the recurrence of breast cancer Higher sex.

The invention further discloses a gene marker for predicting breast cancer recurrence, which comprises at least two target genes selected from ten target genes including LMCD1, DEAF1, ABCC1, PLOD2, LARS2, AACS, LMNB1, CDCA3, AP2A2, ZFP36L2 and the like. Which constitutes a group. Among them, patients with higher expression of LMCD1, DEAF1, ZFP36L2 or AP2A2 in breast cancer tumors are less likely to predict the recurrence of breast cancer; the expression of ABCC1, PLOD2, LARS2, AACS, CDCA3 or LMNB1 is higher in breast cancer tumors. Patients are more likely to predict a recurrence of breast cancer.

The invention also discloses a method for predicting breast cancer recurrence, firstly providing a breast cancer tumor sample, and detecting at least one performance quantity of at least one target gene in the breast cancer tumor sample, and the breast cancer tumor sample can be predicted from the patient by the performance amount. The possibility of breast cancer recurrence. The target gene used in the method for predicting breast cancer recurrence is taken from the group revealed by the above two types of breast cancer recurrence gene markers.

First Figure: It is a flow chart of the steps of a preferred embodiment of the present invention.

In order to enable the reviewing committee to have a better understanding and understanding of the features and effects of the present invention, the preferred embodiments and detailed descriptions are provided to illustrate the following: the genetic markers and methods for predicting breast cancer recurrence of the present invention. It is characterized in that at least one of six target genes, such as LMCD1, DEAF1, ABCC1, PLOD2, LARS2, and AACS, is selected, or at least two of ten target genes, such as ZFP36L2, AP2A2, CDCA3, and LMNB1, are selected. As a genetic marker for predicting breast cancer recurrence, by analyzing the amount of gene marker expression in a patient's breast cancer tissue cells, it is possible to effectively predict the likelihood of breast cancer recurrence in a patient.

Please refer to the first figure, which is a flow chart of the steps of the first embodiment of the present invention; as shown in the figure, the method for predicting breast cancer recurrence of the present invention comprises the following steps: Step S10: providing a breast cancer tumor sample; Step S20: testing At least one performance amount of at least one target gene in the breast cancer tumor sample; and step S30: predicting, by the performance amount, the breast cancer tumor sample is taken from a patient whose breast cancer has a possibility of recurrence.

In step S10, a breast cancer tumor sample is obtained, which can be obtained from the breast cancer tumor tissue of the patient, frozen by dry ice or liquid nitrogen, and stored in an environment of -70 degrees Celsius.

In step S20, at least one target quantity of at least one target gene in the breast cancer tumor sample is examined, and the target gene(s) should be selected from the group consisting of LMCD1, DEAF1, ABCC1, PLOD2, LARS2, AACS, ZFP36L2, AP2A2, CDCA3, and LMNB1. set. The invention discloses two selection criteria for selecting a genetic marker for predicting breast cancer recurrence, and selecting at least one target gene from LMCD1, DEAF1, ABCC1, PLOD2, LARS2, and AACS, or LMCD1, DEAF1, ABCC1, PLOD2, LARS2, AACS, At least two target genes are selected from ZFP36L2, AP2A2, CDCA3, and LMNB1, and the amount of expression of the target gene(s) in the breast cancer tumor sample is examined.

Among the 10 target genes, LMCD1, DEAF1, ZFP36L2 and AP2A2 belong to the breast cancer protection gene, which means that in patients with breast cancer, LMCD1, DEAF1, ZFP36L2 or AP2A2 have higher expression, and the possibility of breast cancer recurrence after treatment Lower, while ABCC1, PLOD2, LARS2, AACS, CDCA3, and LMNB1 are risk genes. In this breast cancer tumor sample, patients with higher expression of ABCC1, PLOD2, LARS2, AACS, CDCA3, or LMNB1 have a higher likelihood of breast cancer recurrence. high.

In step S20, the more than one of the target genes is selected as a predictor of breast cancer recurrence, the more accurate the prediction effect can be achieved, and the cost of the detection is relatively increased, and the less the selection of the target genes is selected. As a predictor of the likelihood of breast cancer recurrence, the cost of testing can be reduced, but the accuracy of the prediction is sacrificed.

In order to test the amount of expression of the target gene(s), a sample of ribonucleic acid (RNA) should be taken from the breast cancer tumor sample, and the RNA sample is used to break the cell, separate the protein, and The steps of deoxyribonucleic acid (DNA) separation and the like are all known in the art, and are not described in the specification.

After the RNA sample is extracted from the breast cancer tumor sample, the DNA microarray technique can be used to quantify the message transcribed from the target gene in the RNA sample (messenger RNA, mRNA). That is, the amount of expression of the target gene(s), the DNA microarray may also be referred to as a gene chip, and the density chip in the gene chip is suitable for quantifying mRNA, The mRNA transcribed from the target gene and the probe on the gene chip will be subjected to hybridization reaction, and the mRNA and the transcript of the target gene(s) will be determined by the cursor. A hybrid of a needle, and the mRNA transcribed from the target gene(s) can be quantified by the intensity of fluorescence.

Since the RNA is unstable and easily degraded, the RNA sample can be extracted by reverse transcriptase, and the mRNA contained in the RNA sample is reverse transcribed to produce a complementary DNA (cDNA) sample. And quantizing the sequence of the target gene(s) in the cDNA sample, and obtaining the amount of performance data of the target gene(s).

In order to quantify the sequence of the target gene(s), a real-time polymerase chain reaction (real-time PCR or RT-PCR) technique, also known as quantitative real-time polymerase chain reaction (quantitative real time polymerase chain reaction, Q-PCR), using a primer that can amplify the number of target gene sequences and labeled with (various) fluorescence, and use the polymerase chain reaction in the process ( Various) changes in fluorescence brightness quantify the sequence of the target gene(s).

In addition, DNA microarray technology can also be used to quantify the sequence of the target gene(s), and an intensity chip is also suitable for quantifying DNA sequences. The target gene sequence(s) and the (various) probes on the gene chip will be heterozygous, and the target gene sequence(s) and the (various) probe hybrids will be fluorescing, and the fluorescence can be transmitted through the fluorescent light. The strength of the target gene(s) is quantified.

In addition to the above three methods, the data of the target gene(s) in the RNA sample or the cDNA sample can be obtained in the S20 by other conventional techniques in the art for quantifying RNA or DNA. I will not list them here.

In step S30, the breast cancer tumor sample is predicted from the amount of breast cancer recurrence in one patient, and in order to achieve the effect of predicting the recurrence of breast cancer, LMCD1, DEAF1, ZFP36L2, ABCC1, PLOD2, and LARS2 must be established. , AACS. CDCA3, LMNB1 or a combination thereof is used as a predictive model for breast cancer recurrence predictors.

To establish a predictive model, a logistic regression model, a decision tree model, an artificial neural network model, a decision tree compound logistic regression model, or a decision tree composite neural network model can be selected. To track one batch of case samples (the number of samples is better than 400) in the past is used to train the prediction model, that is, to determine the parameter settings in the model, and to test the prediction model through another batch of case samples. The accuracy of the prediction model (accuracy, ACC) and the receiver operating characteristic (ROC) area under the curve (AUC) can be evaluated to evaluate the effect of the prediction model prediction.

Using the predicted model established, the likelihood of recurrence of breast cancer in the patient can be predicted from the amount of expression of the target gene(s) in the breast cancer tumor sample. As mentioned above, in the breast cancer tumor sample, patients with higher expression of LMCD1, DFAF1, ZFP36L2 or AP2A2 have lower likelihood of recurrence of breast cancer after treatment, and the breast cancer is swollen. In tumor samples, patients with higher expression of ABCC1, PLOD2, LARS2, AACS, CDCA3 or LMNB1 have a higher probability of recurrence of breast cancer. When the prediction model is established, the target model genes in the prediction model can be determined by the sample training prediction model. To predict the weight or judgment order of the possibility of recurrence of breast cancer, and to achieve a more accurate prediction effect.

In summary, the present invention provides a genetic marker and method for predicting recurrence of breast cancer, wherein at least one target gene is selected from the group consisting of LMCD1, DEAF1, ABCC1, PLOD2, LARS2, and AACS, or LMCD1, DEAF1, and ABCC1. Selecting at least two target genes from the group consisting of PLOD2, LARS2, AACS, LMNB1, CDCA3, AP2A2, and ZFP36L2, and analyzing at least one target gene of the target gene(s) in one breast cancer tumor sample of one patient, As a predictor of breast cancer recurrence, the amount of performance predicts the likelihood of subsequent recurrence of breast cancer after treatment. The six or ten target genes proposed in the present invention are used as genetic markers for predicting breast cancer recurrence, and the target genes used are novel and less in quantity than other existing genetic markers for predicting breast cancer recurrence, and the analysis of the expression(s) can be reduced. The cost of the quantity can be compared to the existing gene chip for predicting the likelihood of recurrence of breast cancer by analyzing tens to hundreds of target genes by dynamic analysis, and the novel gene wafer which is cheaper but accurate in prediction can be produced by the technique of the present invention.

The above is only the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and the variations, modifications, and modifications of the shapes, structures, features, and spirits described in the claims of the present invention. All should be included in the scope of the patent application of the present invention.

The invention is a novel, progressive and available for industrial use, and should conform to the patent application requirements stipulated by the Chinese Patent Law. Please, pray that the bureau will grant patents as soon as possible.

Claims (10)

A method for predicting breast cancer recurrence, the method comprising: testing a breast cancer tumor sample using at least two expression levels of at least two target genes selected from the group consisting of DEAF1, ABCC1, LARS2, AACS, and LMNB1 group. The method for predicting breast cancer recurrence as described in claim 1, wherein the group further comprises LMCD1, PLOD2, CDCA3, AP2A2, and ZFP36L2. A method for predicting recurrence of breast cancer, the method comprising: providing a breast cancer tumor sample; testing at least two expression levels of at least two target genes in the breast cancer tumor sample; and predicting the breast cancer tumor sample from the patient by the performance amount The possibility of recurrence of breast cancer; wherein the target genes are selected from the group consisting of DEAF1, ABCC1, LARS2, AACS, and LMNB1. A method for predicting breast cancer recurrence as described in claim 3, wherein the group further comprises LMCD1, PLOD2, CDCA3, AP2A2, and ZFP36L2. A method for predicting recurrence of breast cancer as described in claim 3, wherein the expression amount is a nucleic acid (mRNA) which is one of the transcriptions of the target gene or a complementary deoxyribonucleic acid obtained by reverse transcription of the message ribonucleic acid. (cDNA) obtained. A method for predicting breast cancer recurrence as described in claim 5, wherein the performance amount is quantified by a gene chip or an instant polymerase chain reaction to quantify the message ribonucleic acid or the complementary deoxyribonucleic acid. A method for predicting breast cancer recurrence as described in claim 3, wherein the performance amount predicts the likelihood of breast cancer recurrence in the patient through a predictive model. The method for predicting breast cancer recurrence as described in claim 7 wherein the predictive model is a logistic regression model, a decision tree model, a neural network model, a decision tree complex logistic regression model, or a decision Tree composite neural network model. The method for predicting breast cancer recurrence as described in claim 3, wherein the expression amount of DEAF1, ABCC1, LARS2, AACS or LMNB1 in the breast cancer tumor sample predicts the likelihood of breast cancer recurrence in the patient. The method for predicting breast cancer recurrence as described in claim 4, wherein the expression amount of LMCD1, ZFP36L2, AP2A2, PLOD2 or CDCA3 in the breast cancer tumor sample predicts the possibility of recurrence of breast cancer in the patient.