Prediction model for endometrial cancer prognosis and endometrial cancer prognosis marker

Abstract

The invention discloses a prediction model for prognosis of endometrial cancer. The prediction model is a calculation formula as follows: Risk Score = (-0.000320197) * IL20RB + 0.000032075 * CXCL9 + 0.000039481 * CXCL9 + 0.000296397 * APLP1 + 0.000580471 * COL23A1 + (-0.00056117) * CCL20 +-0.297940274 * Methyllocanemia + (-0.326386905) * Pararhodospirillumm + (-0.212167955) * Saccharopolyspora + (-0.061136518) * Methth, and the calculation formula is as follows: Risk Score = (- The built prognosis evaluation model can predict the survival rates of the endometrial cancer patients in 1, 3 and 5 years, so that the accurate prognosis prediction of the endometrial cancer patients is realized, and the purpose of personalized diagnosis and treatment is achieved.

Description

Technical Field

[0001] The present invention relates to a prediction model for the prognosis of endometrial cancer and an endometrial cancer prognosis marker, belonging to the technical field of tumor prediction models and the technical field of gene detection. Background Art

[0002] Endometrial cancer is a cancer with a high incidence in women. It is a tumor that affects the endometrium, which is located in the innermost layer of the uterine epithelium, and the related mortality rate is on the rise. Most patients with endometrial cancer are diagnosed between the ages of 65 and 75. It is worth noting that the 5-year overall survival rates for stage IVB and stage IVA are only 15% and 17%, respectively. Although some patients can be diagnosed and treated in the early stages of the disease, some patients will still relapse after treatment. Therefore, it is crucial to effectively identify high-risk patients and tailor personalized adjuvant treatment plans to improve the accuracy of the prognostic evaluation system for endometrial cancer.

[0003] The tumor microbiome refers to the genomes of microorganisms (including bacteria, archaea, fungi, and viruses) present in the tumor parenchyma and the microenvironment surrounding the tumor. It has been shown that the intratumoral microbiome is more often parasitized by tumor cells and immune cells within the tumor microenvironment. Fu et al. used electron microscopy to find that about 3% of tumor cells contained bacteria, and about 97.25% of the bacteria were intracellular parasites, which could promote lung metastasis of breast cancer cells. The tumor microbiome is believed to be one of the most important characteristics of tumors, widely present in solid tumors, and affects the occurrence and progression of tumors by promoting host genome mutations and immune regulation. Summary of the invention

[0004] In view of the above-mentioned prior art, the present invention provides a prediction model for the prognosis of endometrial cancer and its application, and a group of prognosis markers for endometrial cancer and its application.

[0005] The present invention is achieved through the following technical solutions:

[0006] A prediction model for the prognosis of endometrial cancer is calculated as follows:

[0007] Risk Score(RS)=(-0.000320197)×IL20RB+0.000032075×CXCL3+0.000039481×CXCL9+0.000296397× APLP1+0.000580471×COL23A1+(-0.00056117)×CCL20+-0.297940274×Methyloceanibacter+0. 205386336×Candidatus_Contendobacter+(-0.326386905)×Pararhodospirillum+(-0.212167955)×Saccharopolyspora+(-0.061136518)×Methylosarcina+(-0.227380538)×Turicibacter;

[0008] Among them, Risk Score is the risk score of endometrial cancer, L20RB, CXCL3, CXCL9, APLP1, COL23A1 and CCL20 represent the expression matrix values ​​of the corresponding genes; Methyloceanibacter, Candidatus_Contendobacter, Pararhodospirillum, Saccharopolyspora, Methylosarcina and Turicibacter represent the abundance of the corresponding microorganisms; the constant is the regression coefficient of the corresponding marker; based on the RS score, patients can be divided into high-risk and low-risk groups.

[0009] The application of the prediction model for the prognosis of endometrial cancer in the prediction of the prognosis of endometrial cancer and in the preparation of a product for the prediction of the prognosis of endometrial cancer.

[0010] A set of prognostic markers for endometrial cancer includes 6 marker genes and 6 marker microorganisms. The names of the 6 marker genes are IL20RB, CXCL3, CXCL9, APLP1, COL23A1 and CCL20; the genera of the 6 marker microorganisms are Methyloceanibacter, Candidatus_Contendobacter, Pararhodospirillum, Saccharopolyspora, Methylosarcina and Turicibacter.

[0011] The use of the endometrial cancer prognosis marker in the preparation of an endometrial cancer prognosis prediction product.

[0012] Furthermore, the endometrial cancer prognosis prediction product is a detection reagent or kit for endometrial cancer prognosis markers; the detection reagent or kit for endometrial cancer prognosis markers includes: a reagent or kit for detecting the expression levels of 6 marker genes in endometrial cancer prognosis markers, and a reagent or kit for detecting the abundance of 6 marker microorganisms in endometrial cancer prognosis markers.

[0013] The use of a detection reagent or kit for endometrial cancer prognostic markers in the preparation of an endometrial cancer prognosis prediction product, wherein the detection reagent or kit for endometrial cancer prognostic markers includes: a reagent or kit for detecting the expression levels of 6 marker genes in the endometrial cancer prognostic markers, and a reagent or kit for detecting the abundance of 6 marker microorganisms in the endometrial cancer prognostic markers.

[0014] The prediction model for the prognosis of endometrial cancer and the prognostic markers for endometrial cancer of the present invention can be used for the prognosis evaluation of endometrial cancer patients, to accurately group endometrial cancer patients into high-risk and low-risk groups after clinical treatment, and to improve the overall survival rate of patients. The present invention is of great significance to the prognostic diagnosis, evaluation, and treatment of endometrial cancer.

[0015] The present invention utilizes the endometrial cancer transcriptome RNA-seq data and clinical information in the TCGA database to find the prognostic biomarkers of the differential genes and differential intratumoral microbial combinations between the long survival group and the short survival group in clinical practice, and constructs a prognostic evaluation model combining genes and microorganisms. The model covers 6 genes and 6 microorganisms, which can predict the 1-, 3-, and 5-year survival rates of endometrial cancer patients, thereby achieving accurate prognosis prediction for endometrial cancer patients and achieving the purpose of personalized diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 : Schematic diagram of the survival curve of the training set.

[0017] Figure 2 : Schematic diagram of the survival curve of the test set.

[0018] Figure 3 : Schematic diagram of the time-ROC curve of the training set.

[0019] Figure 4 : Schematic diagram of the time-ROC curve of the test set.

[0020] Figure 5 : Scatter plot of Riskscore and survival status.

[0021] Figure 6 : Schematic diagram of the nomogram model.

[0022] Figure 7: Calibration curve of the nomogram model. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with the embodiments. However, the scope of the present invention is not limited to the following embodiments. It will be appreciated by those skilled in the art that various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention.

[0024] The instruments, reagents, and materials involved in the following examples, unless otherwise specified, are all conventional instruments, reagents, and materials already available in the prior art and can be obtained through regular commercial channels. The experimental methods and detection methods involved in the following examples, unless otherwise specified, are all conventional experimental methods and detection methods already available in the prior art.

[0025] Example 1 Prediction model for prognosis of endometrial cancer

[0026] 1. Collect RNA-seq data and clinical data of endometrial cancer patients

[0027] The RNA expression profile, clinical information, and intratumoral microbiome characteristics of endometrial cancer were obtained from the "Endometrial Cancer (TCGA, PanCancer Atlas)" dataset of the cbioportal database.

[0028] 2. Screening for microbial markers significantly associated with survival

[0029] 2.1 Analysis of differential abundance of microorganisms

[0030] After excluding patients with incomplete clinical information, 522 samples remained. Patients were grouped according to the median survival time. Those with OS_MONTHS≥median were "high survivors" (HS), and those with OS_MONTHS<median were "low survivors" (LS). The patient ID lists of the "high survivors" and "low survivors" tumor groups were uploaded to the cbioportal platform. The "Compare" function under Groups can compare the microbiome characteristics between the groups. The original number of microorganisms was 1407, and 1048 microorganisms remained after decontamination. After screening the microorganisms with a difference in microbial abundance p-value <0.05 between the two groups of patients, 97 microorganisms were retained, as shown in Table 1.

[0031] Table 1 97 retained microorganisms

[0032]

[0033]

[0034] 2.2 Univariate Cox analysis of microbial markers

[0035] The R package “survival” was used to perform univariate Cox regression analysis on the normalized data of logCPM of 97 microorganisms, and 42 microorganisms with pvalue < 0.05 were retained for the next step of prognostic model construction, as shown in Table 2.

[0036] Table 2 42 retained microorganisms

[0037]

[0038]

[0039] 3. Screening of genetic markers significantly associated with survival

[0040] The gene expression matrix of 522 samples was sorted, and differential gene analysis was performed using the R package “edgeR”. The thresholds were set at p-value < 0.05 and |log2FC| > 0.5. The survival curve was used to screen genes with p-value < 0.05, and 57 key differential genes related to survival in endometrial cancer were obtained, as shown in Table 3.

[0041] Table 3 57 key differentially expressed genes

[0042] CTCFL EFEMP1 NBEA GALNTL1 NPR1 C8orf85 TRIL TMEM171 APLP1 MSX1 UPK3B PLAC8 BNC2 CILP2 CCL20 SNCA PNMA2 LILRB5 CXCL3 SLC7A2 POU2F3 SSTR1 HERC5 CXCL9 SERPINA5 ZFHX4 CLDN11 ONECUT2 RARRES1 C4BPA PON3 TLL1 RNF128 C9orf125 PLA2G4D ZBTB32 IL20RB ERN2 COL23A1 TFF3 ELOVL4 CST7 SYT13 KCNT2 COMP AGT COLEC12 GNLY ADRA1B KHK MMP9 KCNK3 C11orf86 TMEM169 TUBB4 BEND4 OVGP1

[0043] 4. Build and validate risk scoring models

[0044] To establish a prognostic scoring system for endometrial cancer, 99 variables, including the above 42 microbial markers and the above 57 gene markers were used as predictors, and patients were randomly divided into a training set (n = 365) and a test set (n = 157) at a ratio of 7:3. LASSO regression analysis was performed on the training set, and the best model was determined based on the model coefficient graph and parameter graph. This method generated a prediction model consisting of 6 microbial groups and 6 genes, which had good prediction accuracy for the survival time of patients with endometrial cancer (C-index = 0.75).

[0045] Table 4 gives the coefficient values ​​of each variable in the LASSO regression analysis.

[0046] Table 4

[0047]

[0048]

[0049] The risk score for each sample is calculated based on the following formula:

[0050] Riskscore i=∑Ni(expresion / abundance of variables j*coefβj);

[0051] Among them, expression / abundance of variables represents the gene expression of the corresponding predictor or the abundance of the microbial marker, coefβ represents the regression coefficient of the corresponding marker in the lasso regression result, Riskscore represents the sum of the gene expression of the predictor or the abundance of the microbial marker in each sample multiplied by the corresponding regression coefficient, i represents the sample, and j represents the marker.

[0052] Substituting each regression coefficient into the calculation formula, we get the following prediction model:

[0053] Risk Score=(-0.000320197)×IL20RB+0.000032075×CXCL3+0.000039481×CXCL9+0.000296397×APL P1+0.000580471×COL23A1+(-0.00056117)×CCL20+-0.297940274×Methyloceanibacter+0.20 5386336×Candidatus_Contendobacter+(-0.326386905)×Pararhodospirillum+(-0.212167955)×Saccharopolyspora+(-0.061136518)×Methylosarcina+(-0.227380538)×Turicibacter;

[0054] Among them, Risk Score is the risk score of endometrial cancer, L20RB, CXCL3, CXCL9, APLP1, COL23A1 and CCL20 represent the expression matrix values ​​of the corresponding genes; Methyloceanibacter, Candidatus_Contendobacter, Pararhodospirillum, Saccharopolyspora, Methylosarcina and Turicibacter represent the abundance of the corresponding microorganisms; the constant is the regression coefficient of the corresponding marker; based on the RS score, patients can be divided into high-risk and low-risk groups.

[0055] According to the risk score, the patients were divided into HRG (high-risk group) and LRG (low-risk group). The survival curve of the training set is as follows: Figure 1 As shown, the survival curve of the test set is Figure 2As shown, the survival curve analysis showed that the prognosis of HRG was significantly worse than that of LRG in both the training set and the test set.

[0056] The time-ROC curve of the training set is as follows Figure 3 As shown in Figure 2, the areas under the ROC curve for the 1-year, 3-year, and 5-year OS rates in the training set were 0.74, 0.71, and 0.72, respectively; the time-ROC curve of the test set was as follows: Figure 4 As shown in the figure, the areas under the ROC curve of the test set were 0.73, 0.68, and 0.73, respectively, indicating the effectiveness of the model in predicting the prognosis of BS.

[0057] The scatter plot of Riskscore and survival status is as follows Figure 5 As shown, it can be seen that the higher the Riskscore, the higher the mortality rate and the shorter the patient's survival time.

[0058] 5. Construction and validation of multifactorial clinical nomogram model

[0059] In order to verify whether Riskscore can be used as an independent prognostic indicator for patients with endometrial cancer, the present invention combines relevant clinical parameters (age, stage) to evaluate the significance of Riskscore.

[0060] A prognostic nomogram model was constructed to predict the 1-, 3-, and 5-year OS of patients with endometrial cancer. Figure 6 As shown, the calibration curve of the nomogram model is as follows Figure 7 As shown in the figure, the calibration curve shows the accurate prediction ability of the nomogram model for 1, 3, and 5-year survival rates. The results show that the prediction of the multifactorial clinical nomogram has more clinical guiding significance for the screening and treatment of high-risk populations for endometrial cancer.

[0061] The above examples are provided to those skilled in the art to fully disclose and describe how to make and use the claimed embodiments, and are not intended to limit the scope of the disclosure herein. Modifications that are obvious to those skilled in the art will fall within the scope of the appended claims.

Claims

1. A prediction model for the prognosis of endometrial cancer, characterized in that: The calculation formula is as follows: Risk Score=(-0.000320197)×IL20RB+0.000032075×CXCL3+0.000039481×CXCL9+0.000296397×APL P1+0.000580471×COL23A1+(-0.00056117)×CCL20+-0.297940274×Methyloceanibacter+0.20 5386336×Candidatus_Contendobacter+(-0.326386905)×Pararhodospirillum+(-0.212167955)×Saccharopolyspora+(-0.061136518)×Methylosarcina+(-0.227380538)×Turicibacter; Among them, Risk Score is the risk score of endometrial cancer, L20RB, CXCL3, CXCL9, APLP1, COL23A1 and CCL20 represent the expression matrix values ​​of the corresponding genes; Methyloceanibacter, Candidatus_Contendobacter, Pararhodospirillum, Saccharopolyspora, Methylosarcina and Turicibacter represent the abundance of the corresponding microorganisms; the constant is the regression coefficient of the corresponding marker; based on the RS score, patients can be divided into high-risk and low-risk groups.

2. Use of the prediction model for endometrial cancer prognosis according to claim 1 in the prediction of endometrial cancer prognosis, or in the preparation of an endometrial cancer prognosis prediction product.

3. The use according to claim 2, characterized in that: The endometrial cancer prognosis prediction product is a detection reagent or kit for endometrial cancer prognosis markers; the detection reagent or kit for endometrial cancer prognosis markers includes: a reagent or kit for detecting the expression levels of 6 marker genes in endometrial cancer prognosis markers, and a reagent or kit for detecting the abundance of 6 marker microorganisms in endometrial cancer prognosis markers.

4. A group of prognostic markers for endometrial cancer, characterized in that: It includes 6 marker genes and 6 marker microorganisms. The names of the 6 marker genes are IL20RB, CXCL3, CXCL9, APLP1, COL23A1 and CCL20; the genera of the 6 marker microorganisms are Methyloceanibacter, Candidatus_Contendobacter, Pararhodospirillum, Saccharopolyspora, Methylosarcina and Turicibacter.

5. Use of the endometrial cancer prognosis marker according to claim 4 in the preparation of an endometrial cancer prognosis prediction product.

6. The use according to claim 5, characterized in that: The endometrial cancer prognosis prediction product is a detection reagent or kit for endometrial cancer prognosis markers; the detection reagent or kit for endometrial cancer prognosis markers includes: a reagent or kit for detecting the expression levels of 6 marker genes in endometrial cancer prognosis markers, and a reagent or kit for detecting the abundance of 6 marker microorganisms in endometrial cancer prognosis markers.

7. Use of the detection reagent or kit for endometrial cancer prognosis markers according to claim 4 in the preparation of an endometrial cancer prognosis prediction product, characterized in that: The detection reagent or kit for endometrial cancer prognosis markers includes: a reagent or kit for detecting the expression levels of 6 marker genes in endometrial cancer prognosis markers, and a reagent or kit for detecting the abundance of 6 marker microorganisms in endometrial cancer prognosis markers.

Images