Biomarker for predicting prognosis of intrahepatic cholangiocarcinoma patient, prediction model and application

Abstract

The invention discloses a biomarker for predicting prognosis of an intrahepatic cholangiocarcinoma patient, a prediction model and application, and belongs to the field of biomedicine. According to the invention, single cell sequencing is carried out on matched intrahepatic cholangiocarcinoma and para-carcinoma tissues by using a BD Rhapsody platform, a tumor cell subset taking SPINK1 as a representative gene is screened out by using single cell sequencing data, and differential expression analysis is carried out on the subset and other epithelial cell subpopulations to obtain 249 differential genes. The differential genes are subjected to machine learning, a GBM model with the optimal prediction efficiency is selected, and a prediction model which contains 205 genes and is used for predicting the prognosis of the intrahepatic cholangiocarcinoma patient is established according to the GBM model. According to the prognosis prediction model, the prognosis prediction efficiency of the intrahepatic cholangiocarcinoma patient is improved, high-risk people can be more sensitively found, and key monitoring and more personalized treatment are carried out on the people, so that the death rate of the intrahepatic cholangiocarcinoma patient is reduced, and the lifetime of the patient is prolonged.

Description

Technical Field

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

[0002] Intrahepatic cholangiocarcinoma is the second most common primary liver cancer, accounting for about 20% of all liver malignancies. The mortality rate of intrahepatic cholangiocarcinoma is extremely high, with a 5-year overall survival rate of only 9%, and the incidence of intrahepatic cholangiocarcinoma is still increasing. The poor efficacy of intrahepatic cholangiocarcinoma is because most patients are asymptomatic in the early stage, so they are already in the late stage when diagnosed and miss the best time for surgery. The occurrence of intrahepatic cholangiocarcinoma is closely related to some risk factors, such as primary sclerosing cholangitis, intrahepatic bile duct stones, cirrhosis, viral hepatitis, Clonorchis sinensis infection, etc., but most patients diagnosed with intrahepatic cholangiocarcinoma have no 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 optimistic.

[0003] Single-cell sequencing has become a powerful tool to reveal the interactions between tumor cells and the tumor microenvironment and between different cellular components in the tumor microenvironment at single-cell resolution. It has been used in breast cancer, ovarian cancer, lung cancer, pancreatic cancer and other cancers. The application of this technology has improved our understanding of cellular 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 perform cell subpopulation analysis on intrahepatic cholangiocarcinoma tissue, explore the differentially expressed genes between malignant cells and benign cells, and use these genes to establish a risk model to predict patient prognosis.

[0004] SPINK1 is known as a tumor-associated trypsin inhibitor, and emerging evidence suggests 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 had elevated SPINK1 levels and were associated with poor overall survival and progression-free survival. It can be seen that SPINK1 plays a pro-oncogenic role in a variety of cancers, but the role of SPINK1 in intrahepatic cholangiocarcinoma has not been elucidated. Summary of the invention

[0005] The purpose 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 intrahepatic cholangiocarcinoma epithelial cell subpopulations with high expression of SPINK1, thereby improving the predictive efficiency of the prognosis of patients with intrahepatic cholangiocarcinoma. The prediction model can be used to more sensitively detect high-risk patient groups, and key monitoring and more personalized treatment can be implemented for this patient group to reduce the mortality rate of patients with intrahepatic cholangiocarcinoma and prolong the survival 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, wherein 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, RP L36, KRT80, ZNHIT1, GNG12, PBX1, MRPL34, NUPR1, GPX4, XIST, TNFSF10, RPL18A, TNC, SLC11A2, MLF2, AZGP1, C12orf57, SLC44A2, MAN1A2, NDUFA4, DUSP1 1. 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, FAM3 B. 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 a reagent for detecting the gene expression amount contained in the biomarker.

[0009] The present invention also provides a reagent for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, wherein the reagent is used to detect the expression level of the gene contained in the biomarker.

[0010] The present invention also provides the use of a reagent for detecting the gene expression amount contained in the biomarker in preparing 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 uses the expression level of the gene contained in the biomarker as an input variable, and predicts the prognosis of patients with intrahepatic cholangiocarcinoma by calculating the risk score. The calculation formula of the risk score is: risk score = ∑ (gene expression level * gene correlation coefficient).

[0012] Preferably, when the risk score is ≥1.665, it is judged as a high-risk group, and when the risk score is <1.665, it is judged as a low-risk group, and the prognosis survival rate of patients in the high-risk group is significantly lower than the prognosis survival rate 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 obtain the gene expression level contained in the biomarker of claim 1;

[0015] A prognosis evaluation module, which predicts the risk score of the prognosis of patients with intrahepatic cholangiocarcinoma according to the gene expression amount 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 amount * gene correlation coefficient);

[0016] An output module outputs a prediction result according to 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 prognosis survival rate of patients in the high-risk group is significantly lower than that of patients in the low-risk group; wherein, when the risk score is ≥1.665, it is judged as a high-risk group, and when the risk score is <1.665, it is judged as a low-risk group.

[0018] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program can realize the functions of the system when executed by a processor.

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

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

[0021] (2) Application in the formulation of prognostic evaluation scheme 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 degree of epithelial cell malignancy, and uses machine learning to analyze the differential genes of this subpopulation and other epithelial cell subpopulations, and constructs a cholangiocarcinoma prognosis prediction model containing 205 genes based on the GBM algorithm with the highest C index. The cholangiocarcinoma prognosis prediction model can detect high-risk patients in advance, improve the predictive efficacy of the prognosis of intrahepatic cholangiocarcinoma patients, more sensitively detect high-risk patient groups, implement key monitoring of the patient group, and formulate more personalized treatment plans, reduce the mortality rate of intrahepatic cholangiocarcinoma patients, and prolong the survival of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 The CNV scoring results of the present invention for each epithelial cell subpopulation;

[0026] Figure 2 This is a heat map of the machine learning C index using 10 algorithm combinations in the present invention;

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

[0028] Figure 4 This is the result of survival analysis of the risk score of patients with intrahepatic cholangiocarcinoma in the TCGA dataset using the constructed model in the present invention. DETAILED DESCRIPTION

[0029] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0030] It should be understood that the terms described in the present invention are only for describing a particular embodiment and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. The intermediate value in any stated value or stated range, and each smaller range between any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0031] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may 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 associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0032] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present invention description and examples are exemplary only.

[0033] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0034] Example 1 Prognostic prediction model constructed based on differentially expressed genes in intrahepatic cholangiocarcinoma epithelial cell subpopulations with high expression of SPINK1

[0035] 1. Source of sample data

[0036] Transcriptome sequencing data of clinical samples of patients with intrahepatic cholangiocarcinoma were downloaded from the GEO database and the TCGA database, and patients who had not received non-surgical treatment such as radiotherapy and chemotherapy and had overall survival and disease-free survival data were screened out.

[0037] 2. Acquisition of differentially expressed genes

[0038] The BD Rhapsody platform was used to perform single-cell sequencing on tumor tissues and adjacent tissues of patients with intrahepatic cholangiocarcinoma. According to the classic single-cell sequencing analysis method, the single-cell sequencing analysis data was reduced in dimension and clustered, and each group of cells was manually annotated according to marker genes. Thus, a single-cell map of intrahepatic cholangiocarcinoma including cell subpopulations such as epithelial cells, T cells, B cells, fibroblasts and myeloid cells was obtained. The present invention extracted the epithelial cell subpopulations and used copy number variation analysis to distinguish the malignant cells in the epithelial cell subpopulations. Among them, the cell subpopulation with the highest degree of malignancy (assessed according to CNV score) in the malignant cells best represents the tumor cells. The results showed that the tumor cell subpopulation with the highest degree of malignancy in epithelial cells was the one represented by the gene SPINK1 (see Figure 1 ). Afterwards, this cell subset was used to perform differential analysis with other epithelial cell subsets, and 249 differentially expressed genes were obtained (see Table 1).

[0039] Table 1 Differential genes in epithelial cell subpopulations with high SPINK1 expression

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[0046] 3. Use machine learning to select the best model to build a predictive model

[0047] The GEO dataset (GSE107943) was used as the training set and the TCGA dataset was used as the validation set. The 249 differentially expressed genes were respectively constructed based on the combination 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 efficiency was selected according to the size of the C index (see Figure 2 ), and a prediction model containing 205 genes for predicting the prognosis of patients with intrahepatic cholangiocarcinoma was established based on the GBM model.

[0048] 4. Test of prediction effect of prediction model

[0049] The prediction model was constructed based on the GBM algorithm, which automatically calculated the correlation coefficient of each gene and calculated the patient's risk score based on the gene expression and the gene correlation coefficient. The formula for calculating the risk score is: risk score = ∑ (gene expression * gene correlation coefficient). After obtaining the patient's risk score, the "surv_cutpoint" function in the "survminer" package of the R language was used to determine the optimal cutoff value (1.655) to divide the patients in the GEO and TCGA data sets into high-risk and low-risk groups. The "survival" package in the R language was used for survival analysis to test the predictive effectiveness of the prediction model. The results are shown in Figure 3 and Figure 4 As shown, the survival rate of patients in the high-risk group scored by the model was significantly lower than that of low-risk patients, and the 2-year survival rate of the low-risk group could reach more than 60%.

[0050] Table 2 Prediction model gene correlation coefficient

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[0056] Example 2 Application of the prognostic prediction model constructed based on differentially expressed genes in intrahepatic cholangiocarcinoma epithelial cell subpopulations with high expression of SPINK1

[0057] Clinically, samples of patients with intrahepatic cholangiocarcinoma are collected after puncture or surgery for RNA sequencing, and the risk score of the patient is calculated using the risk score formula in Example 1. According to the risk score results, patients with a risk score higher than 1.655 are in the high-risk group, and patients with a risk score lower than 1.655 are in the low-risk group. The clinical prognosis and survival time of the patient are then predicted based on the survival curve.

[0058] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all 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 biomarkers include 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, PBX 1. MRPL34, NUPR1, GPX4, 19. 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, S LC39A6, 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: Comprising a reagent for detecting the gene expression level contained in the biomarker of claim 1.

3. A reagent for predicting the prognosis of patients with intrahepatic bile duct cancer, characterized in that: The reagent is used to detect the expression level of the gene contained in the biomarker according to claim 1.

4. Use of a reagent for detecting the gene expression level contained in the biomarker according to claim 1 in preparing 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 expression level of the gene contained in the biomarker of claim 1 as an input variable, and predicts the prognosis of patients with intrahepatic cholangiocarcinoma by calculating the risk score. The calculation formula of the risk score is: risk score = ∑ (gene expression level * gene correlation coefficient).

6. The prediction model according to claim 5, characterized in that When the risk score is ≥1.665, it is judged as a high-risk group, and when the risk score is <1.665, it is judged as a 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.

7. A system for predicting the prognosis of patients with intrahepatic cholangiocarcinoma, characterized in that: include: A data acquisition module, which is used to obtain the gene expression level contained in the biomarker of claim 1; A prognosis evaluation module, which predicts the risk score of the prognosis of patients with intrahepatic cholangiocarcinoma according to the gene expression amount 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 amount * gene correlation coefficient); An output module outputs a prediction result according to the risk score.

8. The system according to claim 7, characterized in that The prediction result is: according to the risk score, patients with intrahepatic cholangiocarcinoma are divided into a high-risk group and a low-risk group, and 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 is ≥1.665, it is judged as a high-risk group, and when the risk score is <1.665, it is judged as a low-risk group.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the functions of the system as claimed in claim 7 or 8 can be realized.

10. Application of the prediction model according to any one of claims 5 to 6, the system according to any one of claims 7 to 8, or the computer-readable storage medium according to claim 9 in any of the following: (1) Application in screening patients with poor prognosis for intrahepatic cholangiocarcinoma; (2) Application in the formulation of prognostic evaluation scheme for intrahepatic cholangiocarcinoma.

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