CN113234835A - Application of prognosis related gene and risk model in prediction of pancreatic cancer prognosis - Google Patents
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Abstract
The invention discloses a prognosis related gene and application of a risk model in predicting pancreatic cancer prognosis, and also discloses a judgment method for pancreatic cancer prognosis and a kit containing a detection reagent of the prognosis related gene. The kit can be used for predicting the prognosis of a pancreatic cancer patient and providing theoretical support for the formulation of a treatment scheme by a clinician.
Description
Technical Field
The invention belongs to the field of biological medicine, and relates to application of a prognosis related gene and a risk model in pancreatic cancer prognosis prediction.
Background
Pancreatic cancer is a high-incidence and refractory cancer in china, and statistics of chinese cancer data in 2015 show that the incidence rate of pancreatic cancer is ranked 9 th in the cancer field and the mortality rate is ranked 6 th. In order to improve the accurate diagnosis and treatment level and the cure rate of pancreatic cancer, it is important to determine the prognosis of a pancreatic cancer patient.
Currently, there are many technical solutions for prognosis of pancreatic cancer, but these technical solutions have different defects. Firstly, one kind of technical scheme is to judge the prognosis of pancreatic cancer patients based on a single clinical factor, including the ECOG expression state (Eastern Cooperative Oncology Group Performance Status), the tumor marker CA 19-9 level and age, but the prognosis judgment based on a single clinical factor is not sufficient in efficiency and is easily influenced by other factors. Secondly, one kind of technical scheme is to calculate a prognosis score based on a plurality of clinical factors and judge the prognosis of a patient according to the score, but the scheme has larger subjective factors in the setting of corresponding scores of different clinical factors and influences the accuracy of prognosis judgment. Therefore, the current technology for prognosis of pancreatic cancer is not ideal through the analysis of the prior art.
Disclosure of Invention
The technical problem is as follows: aiming at the problem of poor technology for pancreatic cancer prognosis judgment in the prior art, the application provides a marker molecule related to pancreatic cancer prognosis and a detection kit, so that accurate prognosis judgment can be performed on a pancreatic cancer patient.
One aspect of the present application provides a marker molecule associated with pancreatic cancer prognosis comprising at least one gene of CLCF1, MAPT, S100A3, SEMA7A, STAT 1.
In another aspect, the present application provides a test kit for evaluating the prognostic effect of pancreatic cancer, which comprises a reagent for detecting the marker molecule described above.
Further, the reagent for detecting the marker molecule includes a primer pair or a specific fluorescent probe for the marker molecule.
The primer of the present invention can be prepared by chemical synthesis, appropriately designed by referring to known information using a method known to those skilled in the art, and prepared by chemical synthesis.
The probe of the present invention may be prepared by chemical synthesis, by appropriately designing with reference to known information using a method known to those skilled in the art, and by chemical synthesis, or may be prepared by preparing a gene containing a desired nucleic acid sequence from a biological material and amplifying it using a primer designed to amplify the desired nucleic acid sequence.
The probe that hybridizes to the nucleic acid sequence of a gene may be DNA, RNA, a DNA-RNA chimera, PNA, or other derivatives. The length of the probe is not limited, and any length may be used as long as specific hybridization and specific binding to the target nucleotide sequence are achieved. The length of the probe may be as short as 25, 20, 15, 13 or 10 bases in length. Also, the length of the probe can be as long as 60, 80, 100, 150, 300 base pairs or more, even for the entire gene. Since different probe lengths have different effects on hybridization efficiency and signal specificity, the length of the probe is usually at least 14 base pairs, and at most, usually not more than 30 base pairs, and the length complementary to the nucleotide sequence of interest is optimally 15 to 25 base pairs. The probe self-complementary sequence is preferably less than 4 base pairs so as not to affect hybridization efficiency.
In still another aspect, the present application provides an apparatus for evaluating a pancreatic cancer prognostic effect, the apparatus including:
the data input module is used for inputting the content detection result of the marker molecules related to pancreatic cancer prognosis into the model calculation module; the marker molecule comprises at least one gene of CLCF1, MAPT, S100A3, SEMA7A and STAT 1.
Further, the model calculation module is used for calculating and processing the input content detection result to obtain the prognosis effect data of the detected patient; the model calculation module adopts a model which specifically comprises the following steps:
risk Score 0.46322463 CLCF1 gene expression level + (-0.5672263) MAPT gene expression level + 0.22243289S 100A3 gene expression level + 0.34145487 SEMA7A gene expression level + 0.26932230 STAT1 gene expression level.
Further, the evaluation device also comprises a result output module which is used for evaluating the prognosis effect data of the tested patient according to the evaluation standard of the pancreatic cancer prognosis effect and outputting the evaluation result.
And the result output module is used for evaluating the Risk according to the Risk Score value, so as to evaluate the prognosis effect data of the tested patient.
Before clinical actual evaluation is carried out, a large amount of patient data can be collected in a big data mode, the median obtained by statistics after the large amount of data are collected is a cutoff value, samples are divided into high-risk groups and low-risk groups, and accordingly, the prognosis effect data of a detected patient are evaluated. I.e. high Risk if the patient's Risk Score value is above the cut-off value; if the RiskScore value of the patient is below the cut-off value, then there is a low risk.
In a further aspect, the present application provides a method of constructing the model described above, the method comprising the steps of:
(1) analysis of candidate genes: screening a cancer expression profile database for immune-related genes;
(2) screening for prognosis-related genes: obtaining immune related genes related to the survival of pancreatic cancer patients by using single-factor Cox analysis;
(3) establishing a model: constructing the model by analyzing the prognosis-related genes obtained in step (2) using LASSO Cox and finally determining the genes constituting the prognostic gene signature, the genes including at least one of CLCF1, MAPT, S100A3, SEMA7A, STAT 1.
Further, the construction method further comprises the following steps: verifying the effectiveness of the model, wherein the verification method comprises the following steps:
calculating the Risk Score of each patient based on the expression level of the marker molecules in the pancreatic cancer patient samples in the model and test set;
based on the Risk Score cut-off, test sets were divided into high Risk groups and low Risk groups and the prognosis differences between the two groups of patients were compared.
Yet another aspect of the application provides an application comprising any one of:
1) the application of the marker molecule in preparing a detection kit for evaluating pancreatic cancer prognosis effect;
2) the application of the marker molecule in preparing an evaluation device for pancreatic cancer prognosis effect;
3) use of a marker molecule as hereinbefore described in the preparation of a model as hereinbefore described;
4) use of a marker molecule as hereinbefore described in the manufacture of a computing device as hereinbefore described;
5) use of a marker molecule as hereinbefore described in the preparation of a computer readable storage medium as hereinbefore described;
6) the application of the model in preparing an evaluation device for pancreatic cancer prognosis effect;
7) use of the model described above in the preparation of a computing device as described above;
8) use of the model described above in the preparation of a computer readable storage medium as described above.
In one aspect, the present application provides a method for prognosis of pancreatic cancer, comprising
Detecting the expression level of the marker molecules in a sample of a patient with pancreatic cancer;
determining a cutoff value of a pancreatic cancer patient according to the gene expression level and the model;
and determining the prognosis of the pancreatic cancer patient according to the cutoff value.
Further, the method for determining the prognosis of a pancreatic cancer patient based on the cutoff value comprises:
if the risk score is greater than or equal to the cutoff value, judging that the pancreatic cancer patient has a poor prognosis;
and if the Risk Score is less than the Risk Score cutoff value, the pancreatic cancer patient is judged to have good prognosis.
Further, the method for determining the cutoff value of the Risk Score comprises the following steps:
calculating a risk score for each patient based on the model and the expression levels of the marker molecules in the samples in the training set;
selecting a value based on the Risk Score of each patient can classify the patients into a high Risk group and a low Risk group, and determining the selected value as the Risk Score cutoff value when the two groups of patients have the greatest prognosis difference.
Further, the method for determining the cutoff value of the Risk Score comprises the following steps:
before clinical actual evaluation is carried out, a large amount of patient data can be collected in a big data mode, the median obtained by statistics after the large amount of data are collected is a cutoff value, samples are divided into high-risk groups and low-risk groups, and accordingly, the prognosis effect data of a detected patient are evaluated. I.e. high Risk if the patient's Risk Score value is above the cut-off value; if the RiskScore value of the patient is below the cut-off value, then there is a low risk.
The invention firstly collects the pancreatic cancer public gene expression data in a gene expression integrated database (GEO) and a cancer genome map database (TCGA), determines the immune related genes existing in the TCGA and the GEO data set according to the immune related gene information in an immport database, performs single-factor Cox regression analysis on the immune related genes, and selects the related genes according to the single-factor P value to perform LASSO regression analysis. According to LASSO regression results, selecting genes to construct a survival-related linear risk assessment model, calculating risk values (risk score) of all samples, taking the median of the risk score as a cut-off value, and dividing the samples into high-risk groups and low-risk groups. And (3) evaluating the prediction capability of the survival periods of the models in 1, 3 and 5 years by adopting a time-dependent ROC curve, and analyzing the survival curves of the high and low groups.
Yet another aspect of the present application provides a computing device comprising a memory and a processor, wherein the memory stores a program, and the processor implements the model or the pancreatic cancer prognosis method when executing the program.
Yet another aspect of the present application provides a computer-readable storage medium, on which a program is stored, which when executed by a processor implements the aforementioned model or the aforementioned pancreatic cancer prognosis method.
The computing device of the present invention may be, but is not limited to, any terminal such as a personal computer, a server, etc. that can perform human-computer interaction with a user through a keyboard, a touch pad, a voice control device, etc. The computing device herein may also include a mobile terminal, which may be, but is not limited to, any electronic device capable of human-computer interaction with a user through a keyboard, a touch pad, or a voice control device, for example, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a smart wearable device, and other terminals. The Network in which the computing device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
The memory of the present invention includes non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Having stored thereon code for an operating system. For example, the memory may also have stored thereon code or instructions that, when executed, enable implementation of the pancreatic cancer prognosis model provided by embodiments of the disclosure. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Further, the computing device of the present invention may include a processor, memory, an external interface, a display, and an input device connected by a system bus. Wherein the processor is configured to provide computational and control capabilities. The display of the computing device may be a liquid crystal display or an electronic ink display, and the input device may be a touch layer covered on the display, or may be, for example, a key, a trackball, or a touch pad arranged on a casing of the computing device, or may be an external keyboard, a touch pad, or a mouse.
The processor may include one or more microprocessors, digital processors. The processor may call program code stored in the memory to perform the associated functions. The processor is also called a Central Processing Unit (CPU), and may be an ultra-large scale integrated circuit, which is an operation Core (Core) and a Control Core (Control Unit).
The computer-readable storage medium stores a program that, when executed by a processor, implements the model or the method for pancreatic cancer prognosis described above. Alternatively, the computer readable storage medium may be in a separate physical form, such as a U disk, and when connected to a processor, the program stored on the U disk is executed to implement the model or the pancreatic cancer prognosis method. The method of the invention can also be realized as an APP (application program) in apple or android application markets, and the APP is downloaded to respective mobile terminals by users for operation.
As described above, it can be understood by those skilled in the art that all or part of the processes in the above determination method can be implemented by instructing the related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium.
Drawings
FIG. 1 shows a survival graph for a GEO data set;
FIG. 2 shows the survival plots of TCGA;
FIG. 3 shows a ROC plot for a GEO data set;
FIG. 4 shows a ROC plot for TCGA.
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Example 1 pancreatic cancer prognostic score model construction
1. Data download
Public gene expression data and complete clinical annotations were searched in a gene expression integration database (GEO) and a cancer genomic profile database (TCGA). Patients without survival information were removed from further evaluation. A total of 5 eligible PDAC cohorts (GSE28735, GSE62452, GSE71729, GSE85916 and TCGA-PAAD) were collected for further analysis in this study. For Affymetrix microarray data, we downloaded the original "CEL" files and used RMA algorithm in the affy software package for background adjustment and quantile normalization. And directly downloading the normalized matrix file for the microarray data of other platforms. For the data set in TCGA, RNA sequencing data (FPKM values) and clinical information for gene expression were downloaded from UCSC Xena (https:// gdc. The FPKM values were then converted to million per kilobase (TPM) value transcripts. Batch effects due to non-biotechnological deviations are corrected using the "ComBat" algorithm of the sva software package. The information for all qualified PDAC datasets is summarized in table 1, with the GEO dataset as the training set and the TCGA dataset as the validation set.
TABLE 1 basic information of the data sets in this study
2. Immune related gene
The immune-related genes were from the immport database (https:// www.immport.org/home). We included a total of 1793 immune-related genes in the immport database. Of these, 1116 immune-related genes were present in the TCGA and GEO datasets.
3. One-factor Cox analysis
A one-way Cox analysis of 1116 immune-related genes was performed, and genes with P <0.01 were considered to have an effect on the survival of pancreatic cancer patients. Under this standard, there are 156 genes in the TCGA database and 100 genes in the GEO database. After the intersection treatment, the two genes have 26 genes in total.
4. LASSO Cox analysis
Note: and (3) a calculation formula of the risk score, wherein n is a prognostic factor, expi is an expression value of the gene i, and beta i is a regression coefficient of the gene i.
The genes identified by the final screening for constructing the risk score model include the following five genes: CLCF1, MAPT, S100A3, SEMA7A, STAT 1. Table 2 lists relevant information and parameters for the 5 genes used to construct the risk scoring model. HR in the one-factor cox regression analysis is used to characterize the relative risk, wherein an HR value greater than 1 indicates that the expression value of the corresponding gene is in a positive correlation with the risk score, such that the corresponding LASSO coefficient is greater than 0, and an HR value less than 1 indicates that the expression value of the corresponding gene is in a negative correlation with the risk score, such that the corresponding LASSO coefficient is less than 0. In table 2, 95% CI indicates a 95% Confidence interval (Confidence interval).
TABLE 25 genes in the Risk scoring model
From the results in table 2, the risk score models for 5 genes are shown as:
risk Score (= 0.46322463 × CLCF1 gene expression level + (-0.5672263) × MAPT gene expression level + 0.22243289 × S100A3 gene expression level + 0.34145487 × SEMA7A gene expression level + 0.26932230 × STAT1 gene expression level
Note: a calculation formula of risk score, wherein n is a prognostic factor, expi is an expression value of the gene i, and beta i is a regression coefficient of the gene i
The survival analysis results showed that the survival time of patients in the high risk score group was significantly shorter than in the low risk score group (fig. 1). To assess the accuracy of the prognostic models consisting of the above genes in predicting pancreatic cancer prognosis, we performed 1-year, 3-year and 5-year Receiver Operating Characteristic (ROC) curve analyses, comparing the respective AUC values. The results showed that the AUC for 1 year, 3 years and 5 years were 0.66, 0.75, 0.83, respectively (fig. 3). The AUC value shows that the prognostic model composed of the genes has better differentiation performance on the prognosis of pancreatic cancer patients. Next, we calculated the risk score of each sample using the same formula in the TCGA validation set, and performed survival analysis and Receiver Operating Characteristic (ROC) curve analysis, and the results showed the same trend as the training set (fig. 2 and 4). These results indicate that risk scores calculated based on 5 risk features can better predict the prognosis of pancreatic cancer patients.
Although the invention has been described in detail hereinabove by way of general description, specific embodiments and experiments, it will be apparent to those skilled in the art that many modifications and improvements can be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. Marker molecules associated with the prognosis of pancreatic cancer, characterized in that said marker molecules comprise CLCF1, MAPT, S100A3, SEMA7A, STAT 1.
2. A test kit for assessing the prognostic effect of pancreatic cancer, which comprises a reagent for detecting the marker molecule according to claim 1.
3. The kit according to claim 2, wherein the reagent for detecting the marker molecule comprises a primer pair or a specific fluorescent probe for the marker molecule.
4. An apparatus for evaluating a pancreatic cancer prognostic effect, comprising:
the data input module is used for inputting the content detection result of the marker molecules related to pancreatic cancer prognosis into the model calculation module; the marker molecules comprise CLCF1, MAPT, S100A3, SEMA7A and STAT 1.
5. The evaluation device according to claim 4, wherein the model calculation module is configured to perform calculation processing on the input content detection result to obtain the prognosis effect data of the patient to be tested; the risk scoring model adopted by the model calculation module specifically comprises the following steps:
risk Score 0.46322463 CLCF1 gene expression level + (-0.5672263) MAPT gene expression level + 0.22243289S 100A3 gene expression level + 0.34145487 SEMA7A gene expression level + 0.26932230 STAT1 gene expression level.
6. The evaluation apparatus according to claim 4 or 5, further comprising a result output module for evaluating the prognostic effect data of the patient to be tested according to the assessment criteria of the prognostic effect of pancreatic cancer and outputting the result of the evaluation.
7. The method for constructing a risk score model used by the model calculation module in the evaluation apparatus according to claim 5, wherein the method for constructing the risk score model comprises the steps of:
(1) analysis of candidate genes: screening a cancer expression profile database for immune-related genes;
(2) screening for prognosis-related genes: obtaining immune related genes related to the survival of pancreatic cancer patients by using single-factor Cox analysis;
(3) establishing a model: and (3) analyzing the prognosis-related genes obtained in the step (2) by using LASSO Cox, and finally determining the genes forming the prognostic gene label to construct the model, wherein the genes comprise CLCF1, MAPT, S100A3, SEMA7A and STAT 1.
8. A computing device comprising a memory and a processor, the memory storing a program, wherein the processor implements a risk scoring model employed by the model calculation module in the evaluation apparatus of claim 5 when executing the program.
9. A computer-readable storage medium on which a program is stored, the program, when executed by a processor, implementing a risk scoring model employed by the model calculation module in the evaluation apparatus of claim 5.
10. An application, characterized in that the application comprises any of the following:
1) use of the marker molecule of claim 1 in the preparation of a test kit for assessing the prognostic effect of pancreatic cancer;
2) use of the marker molecule of claim 1 for the preparation of a device for assessing the prognostic effect of pancreatic cancer;
3) use of a marker molecule according to claim 1 for the preparation of a risk scoring model for use by the model calculation module in the evaluation device according to claim 5;
4) use of the marker molecule of claim 1 in the preparation of a computing device according to claim 8;
5) use of the marker molecule of claim 1 in the preparation of a computer readable storage medium according to claim 9;
6) the use of the risk scoring model used by the model calculation module in the evaluation device of claim 5 in an evaluation device for predicting the effect of pancreatic cancer;
7) use of a risk scoring model employed by the model calculation module in the evaluation apparatus of claim 5 in the manufacture of the computing device of claim 8;
8) use of a risk scoring model employed by the model calculation module in the evaluation device of claim 5 in the preparation of the computer-readable storage medium of claim 9.
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| CN114317757A (en) * | 2022-01-10 | 2022-04-12 | 广东省人民医院 | Evaluation gene set, kit, application and system for predicting pancreatic cancer prognosis |
| CN114317757B (en) * | 2022-01-10 | 2024-02-23 | 广东省人民医院 | Evaluation gene set, kit, application and system for predicting prognosis of pancreatic cancer |
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