CN114934116A - Early gastric cancer prognosis differential gene and recurrence prediction model - Google Patents
Early gastric cancer prognosis differential gene and recurrence prediction model Download PDFInfo
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Abstract
The application relates to establishment of an early gastric cancer recurrence prediction model, 25 potential genes related to early gastric cancer recurrence are screened out by utilizing two batches of gene chip transcriptome data of GSE130823 and GSE55696, and a recurrence prediction model based on 8 genes of AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF3 is established. The model has excellent sensitivity, namely, all patients which are predicted not to relapse do not relapse, and the clinical suggestion means that the frequency of the follow-up and the follow-up of the part of patients can be adjusted according to the model.
Description
Technical Field
The application relates to the field of biological diagnosis, in particular to a differential gene of Early Gastric Cancer (EGC) prognosis, and a recurrence prediction model is established.
Background
Gastric cancer is one of the common tumors that have a great impact on human health. Many studies have shown that gastric cancer progression follows a clear multi-stage cascade progression from initial inflammation and atrophy, to precancerous lesions (including LGIN and HGIN), to early gastric cancer, and further to Advanced Gastric Cancer (AGC). Early stage gastric cancer refers to gastric cancer with or without lymph node metastasis, with lesions confined to the gastric mucosal layer or submucosa. Another clinically interesting prognostic indicator is tumor recurrence, since early gastric cancer patients have a long overall survival. The recurrence rate of EGC patients is between 3% and 9% 5 years after receiving ESD treatment. The judgment and prediction of the risk of relapse directly determines the follow-up visit scheme of different patients. Therefore, an efficient and accurate recurrence prediction model can effectively guide clinicians to construct individualized patient follow-up schemes, and has strong clinical value.
The risk factors associated with the recurrence of EGC patients mainly include the tumor lesion status (such as the size of the lesion tissue, the pathological type, the tumor infiltration depth, etc.) and the endoscope and surgical operation status (such as the amount of bleeding during the operation, the completeness of lesion excision, etc.). Patients with tumor lesions larger than 20mm are more prone to relapse. Patients with low differentiation have a higher risk of relapse than patients with high differentiation of tumor tissue. The operation or operation time is short, the bleeding condition during the operation is better, and patients with complete excision of the lesion have relatively lower recurrence probability. In addition, advanced age, complicated by helicobacter pylori (Hp) infection is an independent risk factor for sporadic recurrence in EGC patients. The research on early gastric cancer recurrence is mostly in the aspect of clinical pathological factors, the gene level research is less, and an accurate tumor recurrence prediction model is lacked.
Accordingly, the research screens the genes with monotonous increasing or decreasing expression and obvious difference change, namely mcDEGs, in the tumor evolution process (gastritis → LGIN → HGIN → EGC), by utilizing the whole transcriptome data of the EGC specimen, and takes recurrence as an outcome, three tumor recurrence prediction models are constructed by three methods, namely cluster analysis, risk score constructed based on multi-factor COX regression and decision tree analysis, and patient samples are collected in advance, corresponding gene expression is detected, the prediction efficiency of the models is verified, and meanwhile, the application value of the models in clinical follow-up and individualized treatment is further discussed.
Disclosure of Invention
In the present study, differential expression genes (mcDEGs) showing monotonous changes in expression of gastritis/control tissue → Low-grade intraepithelial neoplasia (LGIN) → High-grade intraepithelial neoplasia (HGIN) → EGC were first screened from two sets of gene chip transcriptome data (GSE130823 and GSE55696), and mcDEGs potentially associated with tumor recurrence were screened by two methods of T test and one-way COX regression analysis. And then taking the patients in the I/II stage in an external data set GSE62254 containing prognosis data as a training set, screening the obtained mcDEGs as training variables, and constructing a recurrence prediction model based on a decision tree algorithm, wherein the predicted outcome is the recurrence condition of the tumor. And further prospectively collecting 16 HGIN or EGC patients as a verification set (4 recurrent patients and 12 non-recurrent patients), detecting the expression quantity of corresponding mcDEGs by using a Quantitative real-time polymerase chain reaction (qRT-PCR), inputting the expression quantity into a model as a test set, and testing the prediction efficiency (sensitivity, specificity and the like) of the model.
The invention provides a model for predicting early gastric cancer recurrence: differentially expressed genes (mcDEGs) whose expression is Monotonically changing in gastritis/control tissue → Low Grade Intraepithelial Neoplasia (LGIN) → High Grade Intraepithelial Neoplasia (HGIN) → EGC were screened in gene chip transcriptome data, and the mcDEGs potentially associated with tumor recurrence were screened by both T-test and one-way COX regression analysis. And then taking the patients in the I/II stage in an external data set GSE62254 containing prognosis data as a training set, taking the screened mcDEGs as training variables, taking the predicted outcome as the tumor recurrence condition, and constructing a recurrence prediction model based on a decision tree algorithm. And after the model is screened and pruned according to factors such as parameter importance, collinearity and the like, selecting genes as final prediction indexes.
A gene combination for assessing risk of early gastric cancer recurrence comprising AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2, BATF 3.
The gene combination is applied to the preparation of a kit for evaluating the recurrence risk of early gastric cancer.
Use of a reagent for detecting AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF3 gene expression changes in preparation of a kit for predicting gastric cancer recurrence risk, wherein gastric cancer is early gastric cancer, and the reagent is a PCR detection reagent.
The invention also provides a kit for predicting the recurrence risk of early gastric cancer, which comprises the following components: the kit is characterized by comprising reagents for detecting expression changes of AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF 3.
The invention also provides a device, system and/or model for determining early gastric cancer recurrence, comprising an AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2, BATF3 assessment.
The present invention also provides a gene that can be used for independently predicting the risk of relapse of early gastric cancer: any of the following genes may be selected: FOS, ARGE, SNCA, MMD, CH3L1, KCNMB4, CHN1, BATF3, LOC 100507520.
A kit for predicting risk of early gastric cancer recurrence: comprises a reagent for detecting expression changes of one or more genes of FOS, ARGE, SNCA, MMD, CH3L1, KCNMB4, CHN1, BATF3, LOC100507520 and AP1G 1.
The invention also provides a device, system and/or model for determining early gastric cancer recurrence, which comprises FOS, ARGE, SNCA, MMD, CH3L1, KCNMB4, CHN1, BATF3, LOC100507520 and AP1G1 evaluation.
The gene and the combined model thereof have excellent sensitivity, namely, all patients which are predicted not to relapse do not relapse, and the clinical suggestion means that the frequency of the follow-up and the follow-up of the part of patients can be adjusted according to the frequency.
Drawings
Figure 1 study flow chart: mcDEGs: differentially expressed genes that vary monotonically; HGIN: high grade intraepithelial neoplasia; EGC: early stage gastric cancer
FIG. 2 screening of the GSE130823 dataset for monotonically varying differentially expressed genes from the GSE55696 dataset
FIG. 3 forest map of single-factor COX regression analysis of differentially expressed genes with monotonic variation
FIG. 4 decision tree prediction model based classification result dendrogram for external data set
The first row of numbers in each ellipse represents recurrence, 0 represents no recurrence, and 1 represents recurrence; the second row of numbers is gini coefficients; the third row of numbers is the percentage of patients in the total under this classification.
FIG. 5 ROC curves predicted by the decision tree model for the external data set
FIG. 6 validation set ROC curves predicted using decision tree models
Detailed Description
Example 1: procedure of this study
In this study, differential expression genes (mcDEGs) showing monotonous changes in expression in gastritis/control tissue → Low Grade Intraepithelial Neoplasia (LGIN) → High Grade Intraepithelial Neoplasia (HGIN) → EGC were first screened from two sets of gene chip transcriptome data (GSE130823 and GSE55696), and mcDEGs potentially associated with tumor recurrence were screened by two methods of T-test and one-way COX regression analysis. And then taking the patients in the I/II stage in an external data set GSE62254 containing prognosis data as a training set, screening the obtained mcDEGs as training variables, and constructing a recurrence prediction model based on a decision tree algorithm, wherein the predicted outcome is the recurrence condition of the tumor. And further prospectively collecting 16 HGIN or EGC patients as a verification set (4 recurrent patients and 12 non-recurrent patients), detecting the expression quantity of corresponding mcDEGs by using a Quantitative real-time polymerase chain reaction (qRT-PCR), inputting the expression quantity into a model as a test set, and testing the prediction efficiency (sensitivity, specificity and the like) of the model. The flow chart of this study is shown in figure 1.
Example 2 basic clinical information for study patients
The first batch of biochip specimens was included in 94 samples. The make internal disorder or usurp subjects were identified as LGIN, HGIN or EGC in Beijing-collaborated with the hospital department of gastroenterology during 2011 to 2015, and the results were stored in a gene expression database under the accession number GSE 130823. The second batch of biochip specimens was included in 77 samples. The study subjects were identified as LGIN, HGIN, EGC and gastritis patients in Beijing coordination hospital digestive system during the period from 3 months to 5 months in 2010 to 2013, and the registration number of the study subjects was GSE 55696. The third validation set of patients included 16 patients, 32 specimens. The patients are treated in Beijing cooperative hospital digestive system department from 1 month in 2018 to 6 months in 2019 and are followed regularly. In the last year, clear biopsy reexamination results are obtained. The biopsy specimens were obtained and pathologically interpreted in the same two previous batches. Finally, 16 patients (HGIN6, EGC 10) were included, 32 samples. The patients who had relapsed were 4, and 12 patients who had not relapsed. The standard of recurrence refers to the consensus opinion on screening of early gastric cancer and endoscopic diagnosis and treatment in China (Changsha, 2014). The samples were detected using a LightCycler480qRT-PCR instrument (Roche, Switzerland).
Example 3 training and validation of Risk score prediction models based on Multi-factor C0X regression analysis
In the early stage of screening the mcDEGs, the data of the patients in the I/II stage in the external data set GSE62254 are utilized by single-factor COX analysis, and the mcDEGs which are obviously related to the outcome are screened by taking relapse as the outcome. Further correlation tests were performed on each mcDEGs by the R language corrplot package (0.88) and mcDEGs associated with recurrence were included in the LASSO regression analysis using the glmnet package (4.1.1) to knock out non-essential or multiple co-linear genes. The remaining genes were then subjected to a multifactorial C0X regression analysis to determine whether the genes had a significant effect on recurrence and to formulate the risk score for the patient. The risk score calculation used in the study was as follows, risk score ∑ (X J @ coef J), where X J is the gene expression level after normalization of mcDEGs incorporated into the multifactorial COX regression analysis and coefJ is the coefficient of the corresponding gene in the multifactorial C0X regression analysis. Calculating the risk score of each patient according to the constructed formula, determining an optimal Cut-off value (Cut-off value) by using X-tile software, classifying the patients with the risk score higher than the Cut-off value into a relapse high risk group, and classifying the patients with the risk score lower than the Cut-off value into a relapse low risk group. Finally, comparing whether the recurrence outcome of two groups of patients has obvious difference by utilizing Log-rank test and Kaplan-Meier survival analysis, comparing the model grouping with the actual recurrence condition, and calculating the accuracy of model prediction.
After a risk score formula is established through external data set training and a critical value is determined, a verification sample is used as a verification set, the expression conditions of corresponding mcDEGs are input, the risk score of each patient is calculated, whether the survival curves of the patients in a relapse high-risk group and the relapse low-risk group are obviously different or not is compared, and the sensitivity and the specificity of the model are calculated.
Example 4 training and validation of recurrence prediction model based on Cluster analysis
In order to obtain the mcDEGs combinations with the highest accuracy, a traversing mode is adopted, all the permutation combinations of the mcDEGs are exhausted to carry out clustering analysis successively, the accuracy of classification is calculated, and the mcDEGs combinations with the highest accuracy are selected to serve as the input parameters of the final clustering analysis model. And after obtaining the gene expression value of the normalized verification sample, selecting a single verification sample, mixing the single verification sample with the training samples in the previous stage, and constructing a new clustering object. And (3) performing unsupervised clustering analysis by taking the gene expression value of the mcDEGs combination with the highest accuracy as a parameter, and obtaining the classification of the patient corresponding to the sample in the model. All the verification samples are checked one by one, whether the classification condition of the verification samples passing through the clustering model is matched with the actual recurrence condition is observed, and the final Sensitivity (Sensitivity) and Specificity (Specificity) of the model are calculated.
The calculation formula of accuracy, sensitivity and specificity is as follows:
accuracy (TP + TN)/(TP + FP + TN + FN)
Sensitivity TP/(TP + FN)
Specificity TN/(FP + TN).
TP, TN, FP and FN refer to true positive rate, true negative rate, false positive rate and false negative rate, respectively.
Example 5 training and validation of recurrence prediction model based on decision Tree and random forest model construction
The study was first subjected to decision tree analysis using the R language rpart package (4.1.15). The input variable parameters are expression levels of mcDEGs obtained by early-stage screening, data of patients in the I/II stage in an external data set of GSE62254 are used as a training set, data of 16 patients with verification samples are used as a verification set, a corresponding decision tree model is constructed, a confusion matrix of the verification set is output, an R0CR package (1.0.11) is adopted to draw an R0C curve, an area value under the R0C curve is calculated, and the sensitivity and the specificity of the model are calculated.
The study used a T-test for the comparison between continuous variables and a Fisher exact test or Chi-squared analysis for the comparison between discontinuous variables. The significance judgment standard takes a Z3 value <0.05 as a threshold value, multiple groups of comparisons are used for correction, and the correction method adopts FDR (pulse discovery rate) value correction.
Example 6 differential Gene screening results
The number of genes with significant expression and gradually monotonically increasing levels in the lesion tissues screened according to the GSE130823 data set is 75, and the number of genes monotonically decreasing levels is 4. The number of genes with significant expression and gradually monotonically increasing level by level in the lesion tissues screened according to the GSE55696 data set is 40, and the number of genes with gradually monotonically decreasing level by level is 4. Taking intersection of the difference genes obtained by the two batches of data sets to obtain 32 genes in total; the union set was taken to obtain 91 genes in total, as shown in FIG. 2.
After the two batches of genes are subjected to union, clinical data of patients in the I/II phase in an external data set GSE62254 containing prognosis data are selected, recurrence is taken as an outcome, single-factor COX regression analysis is performed on each mcDEGs one by one, the result shows that 21 genes are the independent influence factors related to the recurrence outcome of the patients, and the drawn forest is shown in figure 3.
In order to avoid the omission of gene screening as much as possible, the same external data set patient information is selected, the patients are divided into a relapse group and a non-relapse group according to the relapse condition, 91 genes obtained in the early stage are subjected to line-by-line T test, and finally 22 genes in two groups of patients are shown to have significant difference in expression, wherein 18 genes are consistent with the genes obtained by the single-factor C0X regression screening.
A total of 25 mcdees (see table 1) that were significantly associated with early gastric cancer recurrence were selected by combining the single factor C0X regression analysis with the T test results. Thus serving as an input gene for subsequent model training and validation.
TABLE 1 McDEGs in the Extrinsic dataset that exhibit significant P-values by one-way COX regression analysis or T-test
GO analysis and KEGG analysis were performed on these 25 mcDEGs. The GO analysis result shows that the main enriched functions are mostly related to immune regulation, and the related inherent immune functions are more. Wherein the CTSC, SNCA, PLA2G7, S100A8 and other genes play effects in a plurality of physiological functions. KEGG result analysis shows that 4 genes including CXCR2, TNFSF15, IL13RA2 and CCL20 are located in a cytokine interaction pathway, and S100A8, CCL20 and FOS are also involved in the conduction of an IL-17 signal pathway.
Example 7 demonstration of mcDEGs expression profiles in a sample
After the screening of the mcDEGs was completed, lesion tissue specimens and paracancer background mucosa specimens (4 recurrent and 12 non-recurrent) of 16 patients with HGIN or EGC were prospectively collected in the study, and the expression of 25 mcDEGs was detected by qRT-PCR technology.
Example 8 training and validation results of predictive models constructed based on unsupervised clustering analysis Algorithm
And (3) taking the expression condition of the screened mcDEGs as an input variable, taking the expression condition and the recurrence condition of the corresponding genes of the patients in the stage I/II in the external data GSE62254 as a training set, performing unsupervised clustering analysis based on Ward.D algorithm, and dividing the patients into two groups. And performing Log-rank test on the two groups of patients, and drawing a Kaplan-Meier survival curve. In the case of significant survival differences between the two groups of patients, the accuracy of the model, i.e., the ratio of the sum of the number of true positive patients (classified as high risk group, with actual recurrence) and true negative patients (classified as low risk group, with actual recurrence not), to the total sample, was calculated.
The prediction result of the cluster analysis model depends on the number and combination of genes selected. In order to obtain the optimal gene combination, an exhaustive principle is adopted, all permutation and combination of 25 mcDEGs are subjected to cluster analysis one by one, and the accuracy of the model is compared. The total number of all permutation and combination is 33554431 groups.
The clustering analysis-survival analysis-calculation accuracy of 33554431 gene combinations showed that there was a tendency of increasing model accuracy as the number of genes was gradually increased from a single gene. When the number of genes reaches a certain number, the accuracy of the model cannot be improved and tends to be reduced with the increase of the number of genes. Comparing the accuracy of 33554431 gene combinations, three groups of gene combinations with the highest accuracy are screened, wherein the three groups of gene combinations are the combinations of 10 genes in two groups and the combinations of 11 genes in 1 group, and the accuracy is 77.17% (see table 2).
TABLE 2 combination of genes incorporated into the best model of the three cluster analyses
Example 9 training and validation effects for construction of predictive models based on decision Trees
And performing decision tree analysis by using an R language RPART packet, taking the expression values of the 25 screened monotonously-changing differential expression genes of the patients in the I/II stage of the GSE62254 data set as predicted variables, and taking the recurrence outcome as a classification result, and performing model training. To avoid overfitting, the parameter minsplit is set to 10, and function defaults are used for the rest. The importance scores of the 25 genes in the decision tree analysis are shown in table 3, wherein the genes with the top five importance levels are MMP12, AREG, CCL20, CHI3L1, FOS, respectively.
TABLE 325 significance scores of monotonically varying differentially expressed genes in a decision Tree model
After the model is screened and pruned according to factors such as parameter importance, collinearity and the like, only 8 genes such as AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF3 are finally reserved as final prediction indexes, and a drawn classification tree diagram is shown in FIG. 4.
And (3) drawing an ROC curve according to the trained model, wherein the result is shown in FIG. 5, the AUC is calculated to be 0.895, and the model training effect is good.
The gene expression condition and the recurrence condition of 16 verification patients are used as a verification set for verification, the model prediction result and the actual recurrence condition are compared, the result shows that 5 patients in 12 patients without recurrence are predicted to be recurrent, 7 patients are predicted to be non-recurrent, and the misclassification probability is 41.7%; all 4 relapsing patients were correctly predicted with a 0% probability of misclassification (as shown in table 4). The sensitivity of the model as a whole was 100%, the specificity was 58.3%, and the AUC value was 0.792 (as shown in fig. 6).
In the research, 25 potential genes related to early gastric cancer relapse are screened out by using two batches of gene chip transcriptome data of GSE130823 and GSE55696, and a relapse prediction model based on 8 genes of AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF3 is established. The expression quantity of the genes is detected by taking 16 patients as a validation set and a qRT-PCR method, and the tumor recurrence prediction model trained according to a machine learning algorithm shows good prediction efficiency in the training set and the validation set. The sensitivity of the prediction model trained according to the decision tree algorithm in the validation set is up to 100%, the specificity is 58.3%, and the Area under the curve (AUC) is 0.792. This suggests to some extent that expression of these differentially expressed genes, which exhibit monotonic changes in the different stages of gastric cancer evolution, can predict the risk of tumor recurrence in patients. The prediction model constructed based on the machine learning algorithm can discover complex potential relations between genes and tumor recurrence, has excellent prediction efficiency, and can provide guiding significance when a clinician formulates an individualized follow-up scheme for EGC patients, namely mapping of a decision tree can be carried out on expression quantities of 8 genes such as AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF3 in future exploration to preliminarily predict recurrence probability of the patients.
TABLE 4 decision tree-based prediction model validation results and confusion matrix
The sensitivity of the patient is close to 100 percent, which means that after the early-stage gastric cancer patient receives treatment, all patients with high risk of relapse can be discovered through gene detection, for the patients, a clinician can improve the frequency of follow-up diagnosis of the patient, and timely monitors the relapse condition of the patient through means such as an endoscope, and if the relapse occurs, the accurate diagnosis and treatment can be ensured at the first time, and the life cycle of the patient can be improved. On the other hand, a sensitivity of 100% means that the probability of a subsequent relapse is very low for patients classified by the model into a relapse low risk group. Clinically, HGIN or EGC patients are usually followed endoscopically at a frequency of from half a year to one year after receiving ESD treatment. For the patients of which the prediction models are classified into the relapse low risk group, the follow-up time of the patients can be properly prolonged, the times of endoscope reexamination are reduced, the treatment and follow-up comfort of the patients are improved, the treatment cost is reduced, and the economic burden of the patients is relieved.
Example 10 establishment of independent prediction index for early gastric cancer prognosis
Further analysis of these 3 combinations of genes revealed that the degree of gene coincidence was high. Only 1 gene in the gene combination A and the gene combination B is different, and only 1 gene in the gene combination C is more than that in the gene combination B, which suggests that complex and nonlinear relations exist among the genes, and that the genes possibly have stronger correlation to relapse of early gastric cancer. Therefore, it was further verified whether the gene having a high degree of overlap can be used as an independent prediction index for determining the prognosis of early gastric cancer.
FOS, ARGE, SNCA, MMD, KCNMB4, CHN1, BATF3, LOC100507520, MICALL2, CTSC, AP1G1 are differential expression genes which are simultaneously included in a plurality of prediction models. The SNCA belongs to synuclein family, and is mostly related to nervous system diseases such as Parkinson's disease, Alzheimer's disease and the like. MMD is a gene associated with the differentiation of monocytes into macrophages (Monocyte to macrophage differentiated associated gene). CHN1 encodes a gtpase activator protein, which is primarily involved in neurotransmission. The protein encoded by BATF3 is an AP-1 family transcription factor, and is involved in regulating the differentiation of dendritic cells in the immune system. LOC100507520 belongs to non-coding RNA, and related researches report a little. The protein encoded by MICALL2 is a cytoskeletal regulatory protein. CTSC, cathepsin C, has been shown in several studies to promote the progression and metastasis of tumors such as breast cancer, liver cancer, etc. AP1G1 is a gamma-adaptin protein, belonging to the large subunit family of adaptor complexes. However, the above genes still lack studies related to prognosis of early gastric cancer.
The expression of FOS, ARGE, SNCA, MMD, CH3L1, KCNMB4, CHN1, bat 3, LOC100507520, AP1G1 in 40 cases of EGC relapsed patients was examined by immunohistochemical method and mRNA expression of each gene in cancer tissues was examined by quantitative real-time polymerase chain reaction (qRT-PCR). The correlation between gene expression and clinical and pathological factors was analyzed by chi-square test or Fisher test. By univariate analysis, we assessed correlations between clinical pathology factors including FOS, ARGE, SNCA, MMD, KCNMB4, CHN1, BATF3, LOC100507520, miall 2, CTSC, AP1G1 and EGC recurrence. By multivariate analysis, we determined independent risk factors for relapse in EGC patients.
Trizol was used for mRNA extraction, and the StepOnePelus real-time PCR system and SYBRGreen method were used for cDNA synthesis and quantitative PCR. GAPDH served as an internal control for 2- Δ Δ CT. The mean mRNA level of the tissue adjacent to the tumor was set at 1.0 and other mRNA levels were normalized to this baseline. GAPDH and detection primer sequences of the respective genes are as follows:
GAPDH upstream primer: 5'-tggagaatgagaggtgggatg-3', respectively;
GAPDH downstream primer: 5'-gagcttcacgttcttgtatctg-3', respectively;
FOS upstream primer: 5'-actctcatagtttcttccctaag-3', respectively;
FOS downstream primer: 5'-ttccactgagggcttgggc-3', respectively;
ARGE upstream primer: 5'-cacatcttttacgcttgtcaa-3';
ARGE downstream primer: 5'-caggatgagtggctgtccc-3', respectively;
SNCA upstream primer: 5'-tgtattcatgaaaggac-3', respectively;
SNCA downstream primer: 5'-ttcaggttcgtagtcttga-3', respectively;
MMD upstream primer: 5'-atgtgtgatagaatggttatctatt-3', respectively;
MMD downstream primer: 5'-gaacacagcctttatact-3';
CH3L1 upstream primer: 5'-gttgatgataagttcacgggt-3', respectively;
downstream primer of CH3L 1: 5'-tgtaataatatttaattgtgc-3', respectively;
KCNMB4 upstream primer: 5'-ctcggcttgtttctcatcatct-3', respectively;
KCNMB4 downstream primer: 5'-ttgggtaagagaacttgcgc-3', respectively;
CHN1 upstream primer: 5'-agtattatggaagagag-3', respectively;
CHN1 downstream primer: 5'-agccatcttgacatcttcaat-3', respectively;
BATF3 upstream primer: 5'-tcctgcagaggagcgtcg-3', respectively;
BATF3 downstream primer: 5'-ttcatcggggcaagcagccg-3';
LOC100507520 upstream primer: 5'-tgagaactccgagatgcattag-3';
LOC100507520 downstream primer: 5'-gctagttgagatgtcgatagtgc-3';
AP1G1 upstream primer: 5'-ttacagacaaacgcattggctatt-3', respectively;
AP1G1 downstream primer: 5'-agctatgaatgatatattagcac-3' are provided.
TABLE 5 comparison of differential expression of mcDEGs in diseased versus control tissues in patients with early stage gastric cancer recurrence
The study showed that: the mRNA levels of FOS, ARGE, SNCA, MMD, CH3L1, KCNMB4, CHN1, BATF3, LOC100507520 and AP1G1 in early gastric cancer tissues are obviously higher than those of paracancer tissues, and are only highly expressed in cancer tissues. The gene expression is significantly related to early gastric cancer recurrence (P ═ 0.002), and the gene can be identified as an independent biomarker for early gastric cancer diagnosis (P ═ 0.001).
Sequence listing
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cacatctttt acgcttgtca a 21
<210> 6
<211> 19
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 6
caggatgagt ggctgtccc 19
<210> 7
<211> 17
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 7
<210> 8
<211> 19
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 8
ttcaggttcg tagtcttga 19
<210> 9
<211> 25
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 9
atgtgtgata gaatggttat ctatt 25
<210> 10
<211> 18
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 10
<210> 11
<211> 21
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 11
gttgatgata agttcacggg t 21
<210> 12
<211> 21
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 12
tgtaataata tttaattgtg c 21
<210> 13
<211> 22
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 13
ctcggcttgt ttctcatcat ct 22
<210> 14
<211> 20
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 14
ttgggtaaga gaacttgcgc 20
<210> 15
<211> 17
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 15
<210> 16
<211> 21
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 16
agccatcttg acatcttcaa t 21
<210> 17
<211> 18
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 17
<210> 18
<211> 20
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 18
ttcatcgggg caagcagccg 20
<210> 19
<211> 22
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 19
tgagaactcc gagatgcatt ag 22
<210> 20
<211> 23
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 20
gctagttgag atgtcgatag tgc 23
<210> 21
<211> 24
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 21
ttacagacaa acgcattggc tatt 24
<210> 22
<211> 23
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 22
agctatgaat gatatattag cac 23
Claims (8)
1. A gene for assessing the risk of relapse of early gastric cancer, said gene being selected from the group consisting of AP1G1, KCNMB4, FOS, ARGE, SNCA, MMD, CHN1, BATF3, LOC100507520, MICALL2, CTSC.
2. Use of the gene according to claim 1 in the preparation of a kit for assessing the risk of early gastric cancer recurrence.
3. Use of a reagent for detecting a change in the gene of claim 1 for the preparation of a kit for predicting the risk of gastric cancer recurrence.
4. The use according to claim 3, wherein the gastric cancer is early stage gastric cancer.
5. The use of claim 3, wherein the reagent is a PCR detection reagent.
6. Use according to claim 5, characterized in that: the reagent is a PCR detection reagent and comprises:
AP1G1 upstream primer: 5'-ttacagacaaacgcattggctatt-3', respectively;
AP1G1 downstream primer: 5'-agctatgaatgatatattagcac-3'
KCNMB4 upstream primer: 5'-ctcggcttgtttctcatcatct-3';
KCNMB4 downstream primer: 5'-ttgggtaagagaacttgcgc-3', respectively;
FOS upstream primer: 5'-actctcatagtttcttccctaag-3', respectively;
FOS downstream primer: 5'-ttccactgagggcttgggc-3', respectively;
ARGE upstream primer: 5'-cacatcttttacgcttgtcaa-3';
ARGE downstream primer: 5'-caggatgagtggctgtccc-3', respectively;
SNCA upstream primer: 5'-tgtattcatgaaaggac-3';
SNCA downstream primer: 5'-ttcaggttcgtagtcttga-3', respectively;
MMD upstream primer: 5'-atgtgtgatagaatggttatctatt-3', respectively;
MMD downstream primer: 5'-gaacacagcctttatact-3';
CH3L1 upstream primer: 5'-gttgatgataagttcacgggt-3';
CH3L1 downstream primer: 5'-tgtaataatatttaattgtgc-3', respectively;
CHN1 upstream primer: 5'-agtattatggaagagag-3', respectively;
CHN1 downstream primer: 5'-agccatcttgacatcttcaat-3';
BATF3 upstream primer: 5'-tcctgcagaggagcgtcg-3', respectively;
BATF3 downstream primer: 5'-ttcatcggggcaagcagccg-3', respectively;
LOC100507520 upstream primer: 5'-tgagaactccgagatgcattag-3';
LOC100507520 downstream primer: 5'-gctagttgagatgtcgatagtgc-3', respectively;
AP1G1 upstream primer: 5'-ttacagacaaacgcattggctatt-3', respectively;
AP1G1 downstream primer: 5'-agctatgaatgatatattagcac-3' are provided.
7. A kit for predicting risk of early gastric cancer recurrence: the kit is characterized by comprising a reagent for detecting the change of KCNMB.
8. The kit of claim 7, wherein: the reagent for detecting KCNMB change comprises detection
KCNMB4 upstream primer: 5'-ctcggcttgtttctcatcatct-3', respectively;
KCNMB4 downstream primer: 5'-ttgggtaagagaacttgcgc-3', respectively;
FOS upstream primer: 5'-actctcatagtttcttccctaag-3', respectively;
FOS downstream primer: 5'-ttccactgagggcttgggc-3', respectively;
ARGE upstream primer: 5'-cacatcttttacgcttgtcaa-3', respectively;
ARGE downstream primer: 5'-caggatgagtggctgtccc-3', respectively;
SNCA upstream primer: 5'-tgtattcatgaaaggac-3', respectively;
SNCA downstream primer: 5'-ttcaggttcgtagtcttga-3', respectively;
MMD upstream primer: 5'-atgtgtgatagaatggttatctatt-3', respectively;
MMD downstream primer: 5'-gaacacagcctttatact-3';
CH3L1 upstream primer: 5'-gttgatgataagttcacgggt-3', respectively;
CH3L1 downstream primer: 5'-tgtaataatatttaattgtgc-3';
CHN1 upstream primer: 5'-agtattatggaagagag-3', respectively;
CHN1 downstream primer: 5'-agccatcttgacatcttcaat-3';
BATF3 upstream primer: 5'-tcctgcagaggagcgtcg-3', respectively;
BATF3 downstream primer: 5'-ttcatcggggcaagcagccg-3', respectively;
LOC100507520 upstream primer: 5'-tgagaactccgagatgcattag-3', respectively;
LOC100507520 downstream primer: 5'-gctagttgagatgtcgatagtgc-3' are provided.
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| WO2013147330A1 (en) * | 2012-03-26 | 2013-10-03 | 연세대학교 산학협력단 | Prognosis prediction system of locally advanced gastric cancer |
| CN103459597A (en) * | 2010-12-13 | 2013-12-18 | 社会福祉法人三星生命公益财团 | Marker for predicting stomach cancer prognosis and method for predicting stomach cancer prognosis |
| WO2020163639A1 (en) * | 2019-02-07 | 2020-08-13 | University Of Florida Research Foundation, Incorporated | Methods for targeted treatment and prediction of patient survival in cancer |
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| NZ562237A (en) * | 2007-10-05 | 2011-02-25 | Pacific Edge Biotechnology Ltd | Proliferation signature and prognosis for gastrointestinal cancer |
| US8741581B2 (en) * | 2009-04-27 | 2014-06-03 | Technion Research And Development Foundation Ltd. | Markers for cancer detection |
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| CN103459597A (en) * | 2010-12-13 | 2013-12-18 | 社会福祉法人三星生命公益财团 | Marker for predicting stomach cancer prognosis and method for predicting stomach cancer prognosis |
| WO2013147330A1 (en) * | 2012-03-26 | 2013-10-03 | 연세대학교 산학협력단 | Prognosis prediction system of locally advanced gastric cancer |
| WO2020163639A1 (en) * | 2019-02-07 | 2020-08-13 | University Of Florida Research Foundation, Incorporated | Methods for targeted treatment and prediction of patient survival in cancer |
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