Pancreatic adenocarcinoma prognosis risk assessment model and application thereof

Abstract

The invention relates to the field of biomarkers, in particular to a pancreatic adenocarcinoma prognosis risk assessment gene combination, a pancreatic adenocarcinoma prognosis risk assessment model and application, and the pancreatic adenocarcinoma prognosis risk assessment gene combination is composed of five genes of LRP3, TTLL6, TSGA13, PRKCG and SDK2. According to the biomarker obtained through screening and the pancreatic adenocarcinoma prognosis risk assessment model, the judgment result has high consistency with the prognosis data result in a pancreatic adenocarcinoma sample queue in the real world, and the prediction capacity of the model is proved.

Description

Technical Field

[0001] This invention relates to the field of biomarkers, specifically to a prognostic risk assessment model for pancreatic adenocarcinoma and its application. Background Technology

[0002] Pancreatic adenocarcinoma (PAAD) is one of the most aggressive gastrointestinal malignancies worldwide. Current treatment modalities for PAAD are limited; surgical resection is the only potential intervention, but only 10-15% of patients are resectable at diagnosis. Standard cytotoxic chemotherapy has extended median overall survival (OS) by 2-6 months in most advanced patients in clinical trials. The current lack of reliable biomarkers for early detection and treatment guidance further hinders the scientific management of PAAD. Therefore, identifying characteristic molecular biomarkers and exploring more precise potential therapeutic targets is essential.

[0003] Lactic acid accumulation contributes to the acidification of the tumor microenvironment (TME) and plays a direct role in the development of malignant tumor phenotypes through epigenetic regulation. Lactic acidification has been identified as a novel, highly conserved post-translational modification that influences gene regulation. Clinical studies have shown that elevated lactation levels may be an important cause of aggressive phenotypes and immune evasion in malignant tumors. Therefore, lactation-associated regulatory genes (LARGs) may have significant prognostic biomarker potential. Summary of the Invention

[0004] The purpose of this invention is to provide a gene combination for prognostic risk assessment of pancreatic adenocarcinoma, a prognostic risk assessment model, and its application.

[0005] In a first aspect, the present invention provides a gene combination for prognostic risk assessment of pancreatic adenocarcinoma, the gene combination comprising five genes: LRP3 (low density lipoprotein receptor-related protein 3), TTLL6 (tubulin tyrosine ligase-like family, member 6), TSGA13 (testis specific, 13), PRKCG (protein kinase C, gamma), and SDK2 (sidekick cell adhesion molecule 2).

[0006] In a second aspect, the present invention provides a detection product for a gene combination for prognostic risk assessment of pancreatic adenocarcinoma, comprising reagents for detecting the expression levels of the five genes described above in a biological sample.

[0007] Furthermore, the biological sample is pancreatic adenocarcinoma tumor tissue.

[0008] Furthermore, the expression level mentioned refers to the mRNA expression level of the gene.

[0009] Furthermore, the reagent includes: PCR primers with detection specificity for the above-mentioned genes, the nucleotide sequences of which are shown in SEQ ID NO.1 to SEQ ID NO.10, respectively.

[0010]

[0011] A third aspect of the present invention provides a gene combination for assessing the prognostic risk of pancreatic adenocarcinoma as described above, and the application of a detection product of the gene combination for assessing the prognostic risk of pancreatic adenocarcinoma in establishing a prognostic risk assessment model for pancreatic adenocarcinoma.

[0012] In a fourth aspect, the present invention provides the application of the pancreatic adenocarcinoma prognostic risk assessment gene combination and the detection product of the pancreatic adenocarcinoma prognostic risk assessment gene combination as described above in the preparation of a pancreatic adenocarcinoma prognostic risk assessment kit.

[0013] Furthermore, the prognostic risk assessment kit contains a combination of reagents for detecting the relative expression levels of LRP3, TTLL6, TSGA13, PRKCG, and SDK2 in biological samples.

[0014] In a fifth aspect, the present invention provides a prognostic risk assessment kit for pancreatic adenocarcinoma, the kit comprising reagents for detecting the relative expression levels of LRP3, TTLL6, TSGA13, PRKCG and SDK2 in biological samples.

[0015] Furthermore, the kit consists of a reverse transcription system, a primer system, and an amplification system, wherein the primer system includes PCR primers as shown in SEQ ID NO.1 to SEQ ID NO.10.

[0016] This invention also provides a method for assessing the prognostic risk of pancreatic adenocarcinoma using the above-mentioned prognostic risk assessment kit, specifically including the following steps:

[0017] (a) The tumor sample was reverse transcribed and amplified using the reagents in the kit to obtain the mRNA expression level of each gene;

[0018] (b) The prognostic risk value for pancreatic adenocarcinoma is calculated using the following formula:

[0019] Risk value = (LRP3 expression level × (-0.787)) + (TTLL6 expression level × (-0.739)) + (TSGA13 expression level × (-32.719)) + (PRKCG expression level × (-0.277)) + (SDK2 expression level × (-0.669)).

[0020] A sixth aspect of the present invention provides a prognostic risk assessment system for pancreatic adenocarcinoma, the system comprising:

[0021] A processor and a memory, the memory being coupled to the processor, the memory storing instructions that, when executed by the processor, use the following steps:

[0022] The mRNA expression levels of the gene combination for pancreatic adenocarcinoma prognostic risk assessment as described above are input into the following calculation formula to obtain the risk value; the calculation formula is as follows:

[0023] Risk value = (LRP3 expression level × (-0.787)) + (TTLL6 expression level × (-0.739)) + (TSGA13 expression level × (-32.719)) + (PRKCG expression level × (-0.277)) + (SDK2 expression level × (-0.669)); A risk value greater than -135 indicates a low risk of poor prognosis, and a risk value less than -135 indicates a high risk of poor prognosis.

[0024] A seventh aspect of the present invention provides the application of the prognostic risk assessment system described above in the preparation of products for predicting the prognostic risk of pancreatic adenocarcinoma.

[0025] Furthermore, in the product for predicting the prognostic risk of pancreatic adenocarcinoma, the mRNA expression level of the prognostic assessment gene combination is input into the calculation formula to obtain the risk value.

[0026] An eighth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, the computer program comprising the following steps when executed by a processor:

[0027] The mRNA expression levels of the gene combination for pancreatic adenocarcinoma prognostic risk assessment as described above are input into the following calculation formula to obtain the risk value; the calculation formula is as follows:

[0028] Risk value = (LRP3 expression level × (-0.787)) + (TTLL6 expression level × (-0.739)) + (TSGA13 expression level × (-32.719)) + (PRKCG expression level × (-0.277)) + (SDK2 expression level × (-0.669)).

[0029] A ninth aspect of the present invention provides a pancreatic adenocarcinoma prognostic risk prediction product, comprising:

[0030] The product for measuring the expression levels of the aforementioned gene combination for prognostic risk assessment of pancreatic adenocarcinoma;

[0031] And the pancreatic adenocarcinoma prognostic risk assessment system, or the computer-readable storage medium.

[0032] This invention also provides a system and method for screening prognostic risk predictive biomarkers for pancreatic adenocarcinoma. The biomarkers to be screened are lactation-related regulatory genes. The system includes a data acquisition module, a screening module, and a testing module.

[0033] The data acquisition module is used to download gene expression information and overall survival data of pancreatic adenocarcinoma and adjacent normal tissues from The Cancer Genome Atlas (TCGA) project.

[0034] The filtering module is as follows Figure 1 As shown, through comprehensive analysis of the TCGA-PAAD database, differentially expressed genes (DEGs) between PAAD tumors and normal tissues were identified. Subsequently, survival analysis using proportional hazards hypothesis testing and univariate Cox regression was used to screen for long-term prognostic risk genes. Additionally, a lactation-related gene module was established using WGCNA (Weighted Gene Co-expression Network Analysis). The intersection of these two gene sets was taken, and combined with prognostic data from corresponding samples, multivariate analysis using univariate Cox regression, LASSO regression, and stepwise regression was used to screen for core prognostic genes related to pancreatic adenocarcinoma.

[0035] The aforementioned testing module is used to validate the prognostic biomarker value of the above-mentioned pancreatic adenocarcinoma prognostic-related genes in a real-world pancreatic adenocarcinoma sample cohort.

[0036] The present invention also provides a method for constructing a prognostic risk assessment model for pancreatic adenocarcinoma, comprising: a training module and a testing module;

[0037] The training module uses gene expression data from pancreatic adenocarcinoma tissue samples in TCGA as the training set. Five prognostic-related genes were selected using multivariate analysis combining univariate analysis, LASSO regression, and stepwise regression to construct a prognostic risk scoring tool, riskScore (as shown below). The median risk score of all training set samples was selected as the threshold for classifying risk levels.

[0038]

[0039] The aforementioned testing module is used to detect the expression levels of five prognosis-related genes and calculate the riskScore in real-world pancreatic adenocarcinoma samples. All samples are grouped using the threshold values ​​of the model obtained from the training set, and survival analysis is used to verify the value of this threshold value in evaluating poor prognosis in pancreatic adenocarcinoma patients.

[0040] The advantages of this invention are:

[0041] This invention provides a screening, prognostic risk assessment model, and system for biomarkers related to lactation in pancreatic adenocarcinoma. The screening and classification methods are based on bioinformatics and statistics, which can improve the accuracy of prognostic prediction and treatment selection. The prognostic risk assessment tool riskScore and the prognostic risk assessment model for pancreatic adenocarcinoma obtained using the screening and classification methods described in this invention show a high degree of consistency with the prognostic data of real-world pancreatic adenocarcinoma sample cohorts, demonstrating the predictive ability of the model. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the screening process for prognostic biomarkers for pancreatic adenocarcinoma according to the present invention.

[0043] Figure 2 This is a Kaplan-Meier analysis of the prognostic risk score (riskScore) of this invention with overall survival in pancreatic adenocarcinoma within the training set.

[0044] Figure 3 This is a Kaplan-Meier analysis of the prognostic risk score (riskScore) of this invention with overall survival in the test set for pancreatic adenocarcinoma. Detailed Implementation

[0045] The specific implementation methods provided by the present invention will be described in detail below with reference to the embodiments.

[0046] Example 1:

[0047] 1. Screening methods for pancreatic adenocarcinoma tumor markers (e.g.) Figure 1 (As shown):

[0048] Data Collection: Gene expression data from pancreatic adenocarcinoma and adjacent normal tissue specimens were downloaded from the TCGA database. Analysis showed differential expression of five lactation-related regulatory genes between pancreatic adenocarcinoma and adjacent normal tissue. A p-value < 0.05 was defined as statistically significant.

[0049] Screening: Differential gene expression analysis was performed on pancreatic adenocarcinoma and adjacent normal tissue samples from TCGA to construct differentially expressed gene sets. The screening criteria were |log2 fold change| > 1 and P < 0.05. Separately, correlation analysis was performed on all genes in TCGA pancreatic adenocarcinoma tissue samples with lactation-related regulatory genes to construct a set of lactation-related regulatory genes for pancreatic adenocarcinoma. The selection criteria were |scale-free topological fit index| > 0.85 and P < 0.05. The intersection of the two sets of genes was taken, and combined with the prognostic data of the corresponding samples, multivariate analysis using univariate analysis, LASSO regression combined with stepwise regression was used to screen and identify five core prognostic-related genes: LRP3, TTLL6, TSGA13, PRKCG, and SDK2.

[0050] Testing: The prognostic biomarker value of the above 5 pancreatic adenocarcinoma prognostic-related genes was validated in a real-world pancreatic adenocarcinoma sample cohort.

[0051] 2. Construction of a prognostic risk assessment system for pancreatic adenocarcinoma

[0052] Provides an assessment model and thresholds for classifying risk levels, including: a training module and a testing module;

[0053] The training module used the aforementioned poor prognostic biomarkers to score the prognosis of pancreatic adenocarcinoma patients, obtaining a risk score: risk score = (LRP3 expression level × (-0.787)) + (TTLL6 expression level × (-0.739)) + (TSGA13 expression level × (-32.719)) + (PRKCG expression level × (-0.277)) + (SDK2 expression level × (-0.669)). The median of the risk scores of all training set samples was selected as the cutoff value for classifying risk levels (a risk score greater than -135 was considered low-risk with poor prognosis, and a risk score less than -135 was considered high-risk with poor prognosis). Kaplan-Meier survival analysis was performed to assess the correlation between risk score and prognosis; P < 0.05 was considered statistically significant. The difference in overall survival between high-risk and low-risk patients with poor prognosis in the training module is shown below. Figure 2 As shown in Figure A, a statistically significant difference was revealed between the groups (P < 0.05), with high-risk patients exhibiting shorter overall survival. Figure 2 B's risk model demonstrated predictive accuracy, with the area under the operating characteristic (ROC) curve (AUC) exceeding 0.7 for all subjects.

[0054]

[0055] The test module was used to validate the value of the risk assessment system's cutoff value in evaluating prognostic risk levels in an online data cohort sample of pancreatic adenocarcinoma from our institution. The data sample came from the First Affiliated Hospital of Naval Medical University, and the study was conducted with patient consent; the cohort included 102 pancreatic adenocarcinoma tissue samples. The expression levels of five prognostic-related genes were detected, and risk values ​​were calculated. After risk grouping using the cutoff values, Kaplan-Meier survival analysis was performed; P < 0.05 was considered statistically significant.

[0056] The difference in overall survival between high-risk and low-risk individuals with poor prognosis is as follows: Figure 3 As shown, the results indicate that the mortality rate of the high-risk group increased and the overall survival rate decreased significantly, demonstrating the predictive ability of the model of this invention.

[0057] Example 2:

[0058] The specific usage procedure of the model of this invention in clinical prognostic risk assessment is as follows:

[0059] 1) Take tumor tissue surgically removed from patients with pancreatic adenocarcinoma, rinse with physiological saline, and extract RNA using an RNA extraction kit via the Trizol method;

[0060] 2) PCR detection of mRNA expression levels of 5 genes;

[0061] 3) Risk scoring was performed on the prognosis of patients with pancreatic adenocarcinoma to obtain the risk score:

[0062] Risk value = (LRP3 expression level × (-0.787)) + (TTLL6 expression level × (-0.739)) + (TSGA13 expression level × (-32.719)) + (PRKCG expression level × (-0.277)) + (SDK2 expression level × (-0.669));

[0063] 4) A risk value greater than -135 indicates a low risk of poor prognosis, while a risk value less than -135 indicates a high risk of poor prognosis.

[0064] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A combination of genes for evaluating the prognosis risk of pancreatic adenocarcinoma, characterized in that, The prognostic risk evaluation gene combination comprises five genes of LRP3, TTLL6, TSGA13, PRKCG and SDK2.

2. A detection product for a combination of pancreatic adenocarcinoma prognosis risk assessment genes, characterized by, The reagent comprises reagents for detecting the expression amounts of the five genes of LRP3, TTLL6, TSGA13, PRKCG and SDK2 in the biological sample.

3. The test product according to claim 2, characterized in that The biological sample is a pancreatic adenocarcinoma tumor tissue; and the expression amount is the mRNA expression amount of the gene.

4. Use of the pancreatic adenocarcinoma prognostic risk evaluation gene combination of claim 1 or the detection product of the pancreatic adenocarcinoma prognostic risk evaluation gene combination of claim 2 in establishing a prognostic risk evaluation model of pancreatic adenocarcinoma.

5. Use of the pancreatic adenocarcinoma prognostic risk evaluation gene combination of claim 1 or the detection product of the pancreatic adenocarcinoma prognostic risk evaluation gene combination of claim 2 in preparing a pancreatic adenocarcinoma prognostic risk evaluation kit.

6. A kit for assessing the prognosis risk of pancreatic adenocarcinoma, characterized by, The kit comprises reagents for detecting the relative expression amounts of LRP3, TTLL6, TSGA13, PRKCG and SDK2 in the biological sample.

7. A pancreatic adenocarcinoma prognosis risk assessment system, characterized by, The system comprises: a processor and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, use the following steps: input the mRNA expression amounts of the pancreatic adenocarcinoma prognostic risk evaluation gene combination of claim 1 into the following calculation formula to obtain a risk value; the calculation formula is as follows: Risk value = (LRP3 expression amount × (-0.787)) + (TTLL6 expression amount × (-0.739)) + (TSGA13 expression amount × (-32.719)) + (PRKCG expression amount × (-0.277)) + (SDK2 expression amount × (-0.669)); when the risk value is greater than -135, it is a poor prognosis low risk, and when the risk value is less than -135, it is a poor prognosis high risk.

8. Use of the prognostic risk evaluation system of claim 7 in preparing a product for predicting the prognosis risk of pancreatic adenocarcinoma.

9. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium and is executed by the processor using the following steps: input the mRNA expression amounts of the pancreatic adenocarcinoma prognostic risk evaluation gene combination of claim 1 into the following calculation formula to obtain a risk value; the calculation formula is as follows: Risk value = (LRP3 expression amount × (-0.787)) + (TTLL6 expression amount × (-0.739)) + (TSGA13 expression amount × (-32.719)) + (PRKCG expression amount × (-0.277)) + (SDK2 expression amount × (-0.669)).

10. A pancreatic adenocarcinoma prognosis risk prediction product, characterized by, The kit comprises: an expression amount determination product of the pancreatic adenocarcinoma prognostic risk evaluation gene combination of claim 1; and the pancreatic adenocarcinoma prognostic risk evaluation system of claim 7 or the computer readable storage medium of claim 9.

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