A biomarker, prediction model and application for predicting the prognosis of patients with intrahepatic cholangiocarcinoma

Abstract

The present invention discloses a biomarker, a prediction model and an application for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, belonging to the field of biomedicine. The present invention uses the BD Rhapsody platform to perform single-cell sequencing on matched intrahepatic cholangiocarcinoma and adjacent tissues, screens out a tumor cell subset represented by the gene SPINK1 using the single-cell sequencing data, performs differential expression analysis on this subset and other epithelial cell subsets, and obtains 249 differential genes. Machine learning is performed on these differential genes to select the GBM model with the optimal prediction efficiency, and a prediction model for predicting the prognosis of patients with intrahepatic cholangiocarcinoma containing 205 genes is established according to the GBM model. The prognosis prediction model improves the prediction efficiency for the prognosis of patients with intrahepatic cholangiocarcinoma, can more sensitively detect high-risk populations, and implement focused monitoring and more personalized treatment for this population to reduce the mortality rate of patients with intrahepatic cholangiocarcinoma and extend the survival period of patients.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and particularly to a biomarker for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, a prediction model and applications thereof. Background Art

[0002] Intrahepatic cholangiocarcinoma is the second most common primary liver cancer, accounting for about 20% of all liver malignancies. Intrahepatic cholangiocarcinoma has a very high fatality rate, with a 5-year overall survival rate of only 9%, and the incidence of intrahepatic cholangiocarcinoma is also increasing day by day. The poor curative effect of intrahepatic cholangiocarcinoma is because most patients have no symptoms in the early stage, so they are diagnosed at an advanced stage and miss the best surgical opportunity. The occurrence of intrahepatic cholangiocarcinoma is closely related to some risk factors, such as: primary sclerosing cholangitis, intrahepatic bile duct stones, liver cirrhosis, viral hepatitis, Clonorchis sinensis infection, etc., but most patients diagnosed with intrahepatic cholangiocarcinoma have no identified cause. At present, complete surgical resection is still the most effective method for treating intrahepatic cholangiocarcinoma, but only about 20-30% of patients can undergo complete surgical resection. However, even after complete surgical resection, the prognosis of patients is still not very optimistic.

[0003] Single-cell sequencing has become a powerful tool for revealing the interactions between tumor cells and the tumor microenvironment as well as different cell components in the tumor microenvironment at single-cell resolution, and has been applied in cancer types such as breast cancer, ovarian cancer, lung cancer, pancreatic cancer, etc. The application of this technology has improved our understanding of cell heterogeneity and the interactions between cells at single-cell resolution. However, in human intrahepatic cholangiocarcinoma, tumor heterogeneity and the interactions between tumor cells and tumor microenvironment components at single-cell resolution are still poorly understood. Therefore, it is of great significance to use single-cell sequencing to analyze cell subsets in intrahepatic cholangiocarcinoma tissues, explore the differentially expressed genes between malignant cells and benign cells, and establish a prognostic risk model for predicting patients using these genes.

[0004] SPINK1 is known as a tumor-associated trypsin inhibitor, and emerging evidence indicates a relationship between SPINK1 and cancer progression. In pancreatic cancer cell lines, SPINK1 co-precipitated with EGFR in immunoprecipitation experiments and triggered cancer cell proliferation by activating EGFR downstream signaling. In prostate cancer, EGFR was found to mediate the biological function of SPINK1 when triggering epithelial-mesenchymal transition. In addition, patients with lung adenocarcinoma with high SPINK1 levels were associated with poor overall survival and progression-free survival. It can be seen that SPINK1 plays a pro-cancer role in multiple cancers. However, the role of SPINK1 in intrahepatic cholangiocarcinoma has not been elucidated. Summary of the Invention

[0005] The object of the present invention is to provide a biomarker, a prediction model and an application for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, so as to solve the problems existing in the above-mentioned prior art. The present invention constructs a prediction model for predicting the prognosis of patients with intrahepatic cholangiocarcinoma based on the differential genes of the intrahepatic cholangiocarcinoma epithelial cell subset with high SPINK1 expression, improves the prediction efficiency of the prognosis of patients with intrahepatic cholangiocarcinoma, and can more sensitively detect high-risk patient groups by using the prediction model, and can implement key monitoring and more personalized treatment for this patient group, so as to reduce the mortality rate of patients with intrahepatic cholangiocarcinoma and prolong the survival period of patients.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] The present invention provides a biomarker for predicting the prognosis of patients with intrahepatic cholangiocarcinoma. The biomarker comprises 205 genes, namely ATP1A1, CP, EIF4B, GBP1, CERS2, PKP4, SPINK1, ATP1B1, DHCR24, CANX, PAQR5, RABGGTB, UFC1, RAB3IP, LENG8, LTBP3, TAF3, SCGN, CPT1A, C4BPB, SNTB1, S100A1, FMNL2, CBX5, TGFBI, IFI16, CAPN1, ATP6AP2, PREPL, TMEM205, SLC38A1, CREBL2, IFIT2, RPL36, KRT80, ZNHIT1, GNG12, PBX1, MRPL34, NUPR1, GPX4, XIST, TNFSF10, RPL18A, TNC, SLC11A2, MLF2, AZGP1, C12orf57, SLC44A2, MAN1A2, NDUFA4, DUSP11, SLC40A1, C2CD4A, RPS17, RPS19, FIBP, BST2, TIMP1, FTH1, TNFSF14, DNAJC21, EFEMP1, PTGFR, NACA3P, GDF15, CREG1, NUP107, ZKSCAN1, CCND1, MAP2K7, NDUFS6, PTMS, SEC11C, RPS4X, PTPRF, RPS16, AHNAK, SCAF11, TMED9, RPS23, TRIM56, CLDN10, RPL37, RPS27L, SLPI, MTND2P28, EEF1B2P3, VDAC1, BCAM, BANF1, IFIT1, ZRANB2, DNAJC10, RPL18AP3, SKP1, CCT2, SH3GLB1, TPM4, EEF2, HINT1, RPS28, LAMTOR5, TECR, SSBP3, SLC35E3, OLFM4, SPINT2, SCP2, OS9, TMEM59, TMEM258, WFDC2, CAST, SLC39A6, PLEKHA5, MDM2, STRAP, CCT5, DDB1, GANAB, ARL4C, LGALS1, TAGLN2, CAMLG, DSTN, ZCRB1, CAND1, HPN, JAG1, FAM3B, SMARCC2, ZMAT2, SRP72, RPL8, R3HDM4, NTRK2, VPS13C, CD63, EEF1B2, PRKCSH, CDK2AP2, ARHGAP29, NDUFS5, RPL10P3, GAPDH, NDUFA2, UBL5, IK, UBA52,RAD23A, INSIG2, ESRP1, IGFBP1, ATP6V0E1, R3HDM2, BRK1, LTBR, DAP, PRDX6, RPL5, TM4SF18, PFDN5, BHLHE41, TNFRSF21, EEF1D, HM13, RPL41, FAU, YBX1, BUB3, ELOF1, CLDN1, CLPTM1L, HSD17B2, NEAT1, RPL37A, RAP1B, ZMPSTE24, RPS15, CS, FADS3, PDCD6, LRIG3, YBX3, SP100, PPIG, COPZ1, EIF3G, UBXN1, PA2G4, LMCD1, EXT1, NACA, RPS14, GRB14, RPS8, UQCRH, RPL31, ZFAS1, ESF1, EIF5B, MRPL51 and RPS26.,

[0008] The present invention also provides a kit for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, comprising reagents for detecting the gene expression levels of the biomarkers.

[0009] The present invention also provides a reagent for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, and the reagent is used for detecting the gene expression levels of the biomarkers.

[0010] The present invention also provides the use of reagents for detecting the gene expression levels of the biomarkers in the preparation of a kit for predicting the prognosis of patients with intrahepatic cholangiocarcinoma.

[0011] The present invention also provides a prediction model for predicting the prognosis of patients with intrahepatic cholangiocarcinoma. The prediction model takes the gene expression levels of the biomarkers as input variables, and predicts the prognosis of patients with intrahepatic cholangiocarcinoma by calculating a risk score. The calculation formula of the risk score is: Risk score = ∑(gene expression level * gene correlation coefficient).

[0012] Preferably, when the risk score ≥ 1.665, it is judged as the high-risk group; when the risk score < 1.665, it is judged as the low-risk group. The prognostic survival rate of patients in the high-risk group is significantly lower than that of patients in the low-risk group.

[0013] The present invention also provides a system for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, comprising:[[]]

[0014] A data acquisition module, which is used to acquire the gene expression levels of the biomarkers described in claim 1;

[0015] A prognosis evaluation module that predicts the risk score of the prognosis of patients with intrahepatic cholangiocarcinoma based on the gene expression levels obtained by the acquisition module and outputs the risk score; the prognosis evaluation module includes a risk score model for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, and the formula of the risk score model is: risk score = ∑(gene expression level * gene correlation coefficient);

[0016] An output module that outputs a prediction result based on the risk score.

[0017] Preferably, the prediction result is: patients with intrahepatic cholangiocarcinoma are divided into a high-risk group and a low-risk group according to the risk score, and the prognostic survival rate of patients in the high-risk group is significantly lower than that of patients in the low-risk group; among them, when the risk score ≥ 1.665, it is judged as the high-risk group, and when the risk score < 1.665, it is judged as the low-risk group.

[0018] The present invention also provides a computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor, the functions of the system can be realized.

[0019] The present invention also provides the application of the prediction model or the system or the computer-readable storage medium in any one of the following:

[0020] (1) Application in screening patients with poor prognosis of intrahepatic cholangiocarcinoma;

[0021] (2) Application in formulating a prognosis evaluation plan for intrahepatic cholangiocarcinoma.

[0022] The present invention discloses the following technical effects:

[0023] The present invention uses single-cell sequencing to analyze the subpopulation with the highest malignancy degree of epithelial cells, and performs machine learning on the differential genes between this subpopulation and other epithelial cell subpopulations. According to the GBM algorithm with the highest C-index, a cholangiocarcinoma prognosis prediction model containing 205 genes is constructed. This cholangiocarcinoma prognosis prediction model can discover high-risk patients in advance, improve the prediction efficiency of the prognosis of patients with intrahepatic cholangiocarcinoma, can more sensitively discover high-risk patient groups, and implement key monitoring and formulate more personalized treatment plans for this patient group, reduce the mortality rate of patients with intrahepatic cholangiocarcinoma, and extend the survival period of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 CNV score results of each epithelial cell subset in the present invention;

[0026] Figure 2 Machine learning C-index heatmap of the combination of 10 algorithms in the present invention;

[0027] Figure 3 Results of survival analysis of the risk scores of patients with intrahepatic cholangiocarcinoma in the GEO dataset using the model constructed in the present invention;

[0028] Figure 4 Results of survival analysis of the risk scores of patients with intrahepatic cholangiocarcinoma in the TCGA dataset using the model constructed in the present invention. Detailed implementation manners

[0029] Now, various exemplary implementation manners of the present invention will be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, characteristics, and implementation schemes of the present invention.

[0030] It should be understood that the terms described in the present invention are only for describing specific implementation manners and are not used to limit the present invention. Additionally, for the numerical ranges in the present invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any intermediate value within any stated value or stated range, as well as each smaller range between any other stated value or intermediate value within the stated range, is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded from the range.

[0031] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention pertains. Although the present invention only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein can also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In case of conflict with any incorporated document, the content of this specification shall prevail.

[0032] Without departing from the scope or spirit of the present invention, various improvements and changes can be made to the specific implementation manners of the present invention specification, which are obvious to those skilled in the art. Other implementation manners obtained from the present invention specification are also obvious to those skilled in the art. The present invention specification and examples are only exemplary.

[0033] Regarding the terms "comprising", "including", "having", "containing", etc. used herein, they are all open-ended terms, meaning including but not limited to.

[0034] Prognostic Prediction Model Constructed Based on Differentially Expressed Genes in Intrahepatic Cholangiocarcinoma Epithelial Cell Subpopulation with High SPINK1 Expression

[0035] 1. Source of Sample Data

[0036] Download the transcriptome sequencing data of clinical samples of patients with intrahepatic cholangiocarcinoma from the GEO database and the TCGA database, and screen out patients who have not received non-surgical treatments such as radiotherapy and chemotherapy and have data on overall survival and disease-free survival.

[0037] 2. Obtaining Differentially Expressed Genes

[0038] Perform single-cell sequencing on tumor tissues and adjacent tissues of patients with intrahepatic cholangiocarcinoma using the BD Rhapsody platform. According to the classical single-cell sequencing analysis method, perform dimensionality reduction and clustering on the single-cell sequencing analysis data, and manually annotate each group of cells based on marker genes. Thus, a single-cell atlas of intrahepatic cholangiocarcinoma containing cell subpopulations such as epithelial cells, T cells, B cells, fibroblasts, and myeloid cells was obtained. The present invention extracted the epithelial cell subpopulation and used copy number variation analysis to distinguish malignant cells in the epithelial cell subpopulation. Among them, the cell subpopulation with the highest degree of malignancy (evaluated according to the CNV score) in malignant cells best represents tumor cells. The results showed that the tumor cell subpopulation represented by SPINK1 had the highest degree of malignancy among epithelial cells (see Figure 1 ). Subsequently, differential analysis was performed between this cell subpopulation and other epithelial cell subpopulations, and 249 differentially expressed genes were obtained (see Table 1).

[0039] Table 1 Differentially Expressed Genes in the Epithelial Cell Subpopulation with High SPINK1 Expression

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[0046] 3. Select the Best Model to Construct a Prediction Model Using Machine Learning

[0047] Using the GEO dataset (GSE107943) as the training set and the TCGA dataset as the validation set, 249 differential genes were used to construct models based on the combinations of 10 algorithms, namely RSF, Enet, StepCox, CoxBoost, plsRcox, superpc, GBM, survivalsvm, Ridge, and Lasso, for machine learning. The GBM model with the best prediction performance was selected according to the C-index value (see Figure 2 ), and a prediction model for predicting the prognosis of patients with intrahepatic cholangiocarcinoma containing 205 genes was established based on the GBM model.

[0048] 4. Verification of the prediction effect of the prediction model

[0049] A prediction model was constructed according to the GBM algorithm. This algorithm automatically calculates the correlation coefficient of each gene, and the risk score of the patient is calculated based on the gene expression level and the gene correlation coefficient. The formula for calculating the risk score is: Risk score = ∑(gene expression level * gene correlation coefficient). After obtaining the risk score of the patient, the "surv_cutpoint" function in the "survminer" package of R language was used to determine the optimal cut-off value (1.655) to divide the patients in the GEO and TCGA datasets into high-risk and low-risk groups. The "survival" package in R language was used for survival analysis to test the prediction performance of the prediction model. The results are as shown in Figure 3 and Figure 4 . The survival rate of patients in the high-risk group with high model scores is significantly lower than that of low-risk patients. The 2-year survival rate of the low-risk group can reach more than 60%.

[0050] Table 2 Gene correlation coefficients of the prediction model

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[0056] Example 2 Application of the prognosis prediction model constructed based on the differential genes of the intrahepatic cholangiocarcinoma epithelial cell subset with high SPINK1 expression

[0057] Clinically, samples are taken from patients with intrahepatic cholangiocarcinoma after puncture or surgery for RNA sequencing. The risk score of the patient is calculated using the risk score formula in Example 1. According to the risk score result, if the risk score is higher than 1.655, it is the high-risk group; if it is lower than 1.655, it is the low-risk group. Then, the survival time of the patient's clinical prognosis is predicted based on the survival curve.

[0058] The above-described embodiments are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A biomarker for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, characterized in that, The biomarker consists of 205 genes, namely ATP1A1, CP, EIF4B, GBP1, CERS2, PKP4, SPINK1, ATP1B1, DHCR24, CANX, PAQR5, RABGGTB, UFC1, RAB3IP, LENG8, LTBP3, TAF3, SCGN, CPT1A, C4BPB, SNTB1, S100A1, FMNL2, CBX5, TGFBI, IFI16, CAPN1, ATP6AP2, PREPL, TMEM205, SLC38A1, CREBL2, IFIT2, RPL36, KRT80, ZNHIT1, GNG12, PBX1, MRPL34, NUPR1, GPX4, XIST, TNFSF10, RPL18A, TNC, SLC11A2, MLF2, AZGP1, C12orf57, SLC44A2, MAN1A2, NDUFA4, DUSP11, SLC40A1, C2CD4A, RPS17, RPS19, FIBP, BST2, TIMP1, FTH1, TNFSF14, DNAJC21, EFEMP1, PTGFR, NACA3P, GDF15, CREG1, NUP107, ZKSCAN1, CCND1, MAP2K7, NDUFS6, PTMS, SEC11C, RPS4X, PTPRF, RPS16, AHNAK, SCAF11, TMED9, RPS23, TRIM56, CLDN10, RPL37, RPS27L, SLPI, MTND2P28, EEF1B2P3, VDAC1, BCAM, BANF1, IFIT1, ZRANB2, DNAJC10, RPL18AP3, SKP1, CCT2, SH3GLB1, TPM4, EEF2, HINT1, RPS28, LAMTOR5, TECR, SSBP3, SLC35E3, OLFM4, SPINT2, SCP2, OS9, TMEM59, TMEM258, WFDC2, CAST, SLC39A6, PLEKHA5, MDM2, STRAP, CCT5, DDB1, GANAB, ARL4C, LGALS1, TAGLN2, CAMLG, DSTN, ZCRB1, CAND1, HPN, JAG1, FAM3B, SMARCC2, ZMAT2, SRP72, RPL8, R3HDM4, NTRK2, VPS13C, CD63, EEF1B2, PRKCSH, CDK2AP2, ARHGAP29, NDUFS5, RPL10P3, GAPDH, NDUFA2, UBL5, IK, UBA52, RAD23A, INSIG2, ESRP1, IGFBP1,ATP6V0E1, R3HDM2, BRK1, LTBR, DAP, PRDX6, RPL5, TM4SF18, PFDN5, BHLHE41, TNFRSF21, EEF1D, HM13, RPL41, FAU, YBX1, BUB3, ELOF1, CLDN1, CLPTM1L, HSD17B2, NEAT1, RPL37A, RAP1B, ZMPSTE24, RPS15, CS, FADS3, PDCD6, LRIG3, YBX3, SP100, PPIG, COPZ1, EIF3G, UBXN1, PA2G4, LMCD1, EXT1, NACA, RPS14, GRB14, RPS8, UQCRH, RPL31, ZFAS1, ESF1, EIF5B, MRPL51 and RPS26., 2. A kit for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, characterized in that, A reagent for detecting the gene expression level of the biomarker described in claim 1.

3. A reagent for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, characterized in that, The reagent is used for detecting the gene expression level of the gene contained in the biomarker described in claim 1.

4. Use of a reagent for detecting the gene expression level of the gene contained in the biomarker described in claim 1 in the preparation of a kit for predicting the prognosis of patients with intrahepatic cholangiocarcinoma.

5. A prediction model for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, characterized in that, The prediction model uses the gene expression level of the gene contained in the biomarker described in claim 1 as an input variable, and predicts the prognosis of patients with intrahepatic cholangiocarcinoma by calculating a risk score. The formula for calculating the risk score is: Risk score = ∑(gene expression level * gene correlation coefficient); The gene correlation coefficients are shown in the following table: When the risk score ≥ 1.665, it is judged as the high-risk group; when the risk score < 1.665, it is judged as the low-risk group. The prognosis survival rate of patients in the high-risk group is significantly lower than that of patients in the low-risk group.

6. A system for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, characterized in that, Comprising: A data acquisition module for acquiring the gene expression level of the gene contained in the biomarker described in claim 1; A prognosis evaluation module for predicting the risk score of the prognosis of patients with intrahepatic cholangiocarcinoma based on the gene expression level obtained by the acquisition module and outputting the risk score; the prognosis evaluation module includes a risk score model for predicting the prognosis of patients with intrahepatic cholangiocarcinoma. The formula of the risk score model is: Risk score = ∑(gene expression level * gene correlation coefficient); An output module for outputting a prediction result based on the risk score; The gene correlation coefficients are shown in the following table: The prediction result is: patients with intrahepatic cholangiocarcinoma are divided into a high-risk group and a low-risk group according to the risk score. The prognosis survival rate of patients in the high-risk group is significantly lower than that of patients in the low-risk group; among them, when the risk score ≥ 1.665, it is judged as the high-risk group, and when the risk score < 1.665, it is judged as the low-risk group.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it can realize the functions of the system described in claim 6.

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