Biomarker for predicting prognosis of BPA-induced intrahepatic cholangiocarcinoma, scoring model and application

Abstract

The invention discloses a biomarker for predicting prognosis of BPA-induced intrahepatic cholangiocarcinoma, a scoring model and application, and belongs to the technical field of biomedicine. The biomarker is composed of eight genes, namely, GAPDH (Glyceraldehyde-3-phosphate Dehydrogenase), HSP90AA1 (Hepatocyte Specific Protein 90AA1), CTNNB1 (Cytotoxic Nuclear Nuclear Nuclear Nuclear B1), NFKBIA (Nuclear Factor Kinase Kinase Institute), HSPA5 (Hepatocyte A risk scoring model is constructed by utilizing the biomarkers, and by calculating risk scores, clinical recognition of intrahepatic cholangiocarcinoma prognosis high-risk patients can be assisted in advance, so that an effective strategy can be adopted in an early stage of a disease, the recurrence probability is reduced, the lifetime of the patients is prolonged, and an accurate basis is provided for formulating an individualized treatment scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biomedical technology, in particular to a biomarker for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma, a scoring model and application thereof. BACKGROUND

[0002] Bisphenol A (BPA) is a compound first synthesized by Russian chemist Aleksandr P. Dianin in 1891. It is mainly used in some industries for the synthesis of polymers, and is widely used in the manufacture of industrial products such as thermal paper, food containers, electronic products and medical equipment, etc. The widespread application of BPA-containing products has led to the continuous release and widespread distribution of BPA in the natural environment, and we have detected high levels of BPA in many environments, such as river water and river sediments, and the atmosphere of many cities around the world. With the increasing demand for BPA and BPA materials, its release and accumulation in the natural environment may increase, and studies have shown that BPA can have harmful effects on the environment and exposed organisms. Specifically, BPA can interfere with the endocrine system of the human body because of its structural similarity to estrogen, thereby having harmful effects on the human body and causing genetic and cytotoxic, mutagenic and carcinogenic effects. It has been reported that BPA is significantly associated with some human diseases, including diabetes, cardiovascular disease, and various cancers (such as ovarian cancer, breast cancer, uterine cancer, testicular cancer, prostate cancer and liver cancer). It can be seen that the release and accumulation of BPA ultimately cause irreversible damage to the ecosystem and human society.

[0003] There is sufficient evidence that BPA has carcinogenic effects, but most studies have revealed the relationship between BPA and reproductive tumors and the underlying mechanisms, which are attributed to its estrogen-like and anti-androgenic properties. For example, BPA can stimulate the up-regulation of CXCR4 expression by irreversible binding to the estrogen receptor, promoting the invasion and migration of breast cancer cells. Similarly, BPA-mediated up-regulation of the CXCL12 gene can enhance the proliferative capacity of ovarian cells. Currently, there is still a significant knowledge gap in the study of the association between BPA exposure and other malignant tumors. Although studies have shown that dietary intake is the main route of human exposure to BPA, and that orally ingested BPA can be rapidly metabolized in the liver and intestinal first-pass effect, the carcinogenic effects that can occur during the continuous contact of the digestive tract have not been fully elucidated.

[0004] Network toxicology systematically constructs a multidimensional interaction network of chemical substances, biological targets, and toxic pathways through the integration and analysis of multi-omics data (including genomics, proteomics, and metabolomics), providing a panoramic research framework for analyzing toxic effects. Molecular docking technology, relying on quantum mechanical computational models, can accurately simulate the three-dimensional conformational matching characteristics of ligand-receptor complexes. It quantitatively assesses the affinity of small molecules for target proteins through combined free energy calculations and has become a key technology for validating toxic targets and elucidating structure-activity relationships. However, there are currently no reports on technologies that explore BPA-induced intrahepatic cholangiocarcinoma risk genes and construct prognostic prediction models based on this. Summary of the Invention

[0005] The purpose of the present invention is to provide a biomarker, scoring model and application for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma to address the problems existing in the above-mentioned prior art. The scoring model constructed by the biomarker containing the screened 8 genes can assist the clinician in early identification of high-risk patients, help to adopt effective strategies in the early stages of the disease, reduce the probability of recurrence, prolong patient survival, and provide an accurate basis for the formulation of individualized treatment plans.

[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 BPA-induced intrahepatic cholangiocarcinoma. The biomarker consists of eight genes: GAPDH, HSP90AA1, CTNNB1, NFKB1, NFKBIA, HSPA5, MAP1LC3B and CAV1.

[0008] The present invention also provides a use of a reagent for detecting the expression level of the biomarker in preparing a kit for predicting the prognosis of BPA-induced intrahepatic bile duct carcinoma.

[0009] The present invention also provides a kit for predicting the prognosis of BPA-induced intrahepatic bile duct carcinoma, wherein the kit comprises a reagent for detecting the expression level of the biomarker.

[0010] The present invention also provides a method for constructing a scoring model for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma, comprising the steps of multiplying the expression levels of the eight genes included in the biomarkers by the correlation coefficients and then summing them to obtain a score.

[0011] Preferably, the formula of the scoring model is: risk score = ∑ (gene expression level × gene correlation coefficient), wherein the gene correlation coefficient is shown in the following table:

[0012]

[0013] The present invention also provides an application of a scoring model constructed using the construction method in constructing a system for predicting the prognosis of intrahepatic bile duct carcinoma, wherein patients with intrahepatic bile duct carcinoma are grouped according to the scores calculated by the scoring model, thereby predicting the prognosis of intrahepatic bile duct carcinoma.

[0014] Preferably, when the risk score is ≥13.629, it is judged as a high-risk group, indicating that the patient has a poor prognosis; when the risk score is <13.629, it is judged as a low-risk group, indicating that the patient has a good prognosis.

[0015] The present invention also provides a system for predicting the prognosis of intrahepatic cholangiocarcinoma, which includes a scoring model for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma. The scoring model uses the biomarkers as input variables to predict the prognosis of intrahepatic cholangiocarcinoma. The scoring model calculates the risk score for the prognosis of intrahepatic cholangiocarcinoma using the following formula: risk score = ∑(gene expression level × gene correlation coefficient), where the gene correlation coefficient is shown in the following table:

[0016]

[0017]

[0018] Preferably, when the risk score is ≥13.629, it is judged as a high-risk group, indicating that the patient has a poor prognosis; when the risk score is <13.629, it is judged as a low-risk group, indicating that the patient has a good prognosis.

[0019] The present invention discloses the following technical effects:

[0020] This study identified BPA-induced intrahepatic cholangiocarcinoma risk genes by cross-analyzing a BPA target library with public datasets and differentially expressed genes from self-tested single-cell data. Machine learning was then performed on these risk genes. Using the Lasso algorithm with the highest C-index, a cholangiocarcinoma prognosis prediction model was constructed, encompassing eight genes (GAPDH, HSP90AA1, CTNNB1, NFKB1, NFKBIA, HSPA5, MAP1LC3B, and CAV1). This model can assist clinicians in identifying high-risk patients early, facilitating more frequent follow-up and interventions. This helps implement effective strategies early in the disease, reducing the probability of recurrence and prolonging patient survival. It also provides a precise basis for developing personalized treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. 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 any creative work.

[0022] Figure 1 This is a heat map of the machine learning C-index for 10 algorithm combinations;

[0023] Figure 2 This is a survival analysis of the model's risk score for patients with intrahepatic cholangiocarcinoma in the GEO and TCGA datasets;

[0024] Figure 3 It is a survival analysis of the model's risk score for patients with intrahepatic cholangiocarcinoma using a self-assessed proteomics dataset;

[0025] Figure 4 It is a schematic diagram of the area under the ROC curve of the internal validation of the model on GEO and TCGA datasets;

[0026] Figure 5 It is a diagram of the area under the ROC curve of the external validation of the model on the self-tested proteomics dataset. DETAILED DESCRIPTION

[0027] 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 rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0028] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. The intermediate value within any stated value or stated range, and each smaller range between any other stated value or intermediate value within the stated range, is also encompassed within the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0029] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein may also be used in the practice 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 any conflict with any incorporated document, the contents of this specification shall prevail.

[0030] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from the description of the invention. The description and examples are intended to be illustrative only.

[0031] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0032] Through analysis of the NHANES database, the inventors of the present invention found that the concentration of BPA metabolites in urine was significantly positively correlated with the risk of intrahepatic bile duct cancer. This epidemiological evidence suggests that BPA may induce malignant transformation of specific organs through local exposure mechanisms during digestive tract metabolism. Therefore, the present invention first screened a core target group related to BPA and intrahepatic bile duct cancer based on network toxicology, and then used molecular docking technology to verify the specific binding mode of BPA with key target proteins. This multi-scale research method can not only systematically analyze the carcinogenic molecular mechanism of BPA, improve the systematic understanding of the toxicological properties of BPA, break through the traditional framework of existing research limited to endocrine disruption effects, but also provide a theoretical basis for establishing toxicity intervention strategies based on key regulatory nodes, which has important public health significance for improving the risk management strategy of chemical pollutants. The following is a further explanation of this research strategy in the form of specific embodiments.

[0033] Example 1 Prognostic prediction model based on BPA-induced intrahepatic cholangiocarcinoma risk genes

[0034] 1. Multi-source data integration and key gene acquisition

[0035] Sample selection: All patients with intrahepatic cholangiocarcinoma in this study were primary, non-metastatic, and untreated. All patients underwent urine BPA testing before surgery. All sequencing tissues were sequenced after surgical resection.

[0036] Key gene identification methods: Using bioinformatics correction techniques, the GSE107943 dataset (27 pairs of intrahepatic cholangiocarcinoma / normal tissues and 3 single intrahepatic cholangiocarcinoma samples) and the TCGA-CHOL dataset (7 pairs of intrahepatic cholangiocarcinoma / normal tissues and 25 intrahepatic cholangiocarcinoma samples and 1 normal tissue sample) were combined to construct the GT integrated cohort (n=97) after batch effect correction using the ComBat algorithm. Differentially expressed genes were screened using the limma package. Six surgical specimens (paired tumor / adjacent tissue) from patients with intrahepatic cholangiocarcinoma and high urine BPA concentrations at Nanfang Hospital were sequenced using the BD Rhapsody platform. Tissue-type-specific differentially expressed genes were identified after quality control using the Seurat pipeline. Furthermore, 281 potential molecular targets of BPA were identified by systematic screening of the SwissTarget Prediction database and the ChEMBL compound library, thus establishing a library of BPA targets. By cross-analyzing the BPA target library with the two differentially expressed gene datasets, 23 BPA-intrahepatic cholangiocarcinoma co-regulated targets were identified. Furthermore, the CytoHubba plug-in in the Cytoscape platform was used to identify hub genes. Multidimensional network analysis, integrating eight topological algorithms (such as Degree, MCC, and MNC), identified the top 10 targets with the highest degree. Cross-validation ultimately identified eight core regulatory factors: GAPDH, HSP90AA1, CTNNB1, NFKB1, NFKBIA, HSPA5, MAP1LC3B, and CAV1.

[0037] 2. Use machine learning to select the best model to build a predictive model

[0038] In this study, we used intrahepatic cholangiocarcinoma samples from the GEO and TCGA databases as training sets, and self-tested proteomic data from 75 cases of intrahepatic cholangiocarcinoma from Nanfang Hospital as external validation sets. We used 10 machine learning algorithms, including RSF, Enet, StepCox, CoxBoost, plsRcox, superpc, GBM, survivalsvm, Ridge, and Lasso, and their combinations, to build models and evaluate their performance (see Figure 5). Figure 1 By comparing the predictive performance of each model, the model constructed by the Lasso algorithm was determined to have the best performance. Based on this, a BPA-induced prognosis prediction model for intrahepatic cholangiocarcinoma patients based on 8 key genes was established.

[0039] 3. Test of the prediction effect of the prediction model

[0040] A prognostic prediction model was constructed based on the Lasso algorithm, which automatically calculates the correlation coefficient for each gene. Patient risk scores were calculated based on gene expression and the correlation coefficient using the following formula: risk score = ∑(gene expression * gene correlation coefficient). After obtaining each patient's risk score, the "surv_cutpoint" function in the "survminer" R package was used to determine the optimal cutoff value (13.629) to stratify samples from the GEO and TCGA datasets into high-risk and low-risk groups. Subsequently, Kaplan-Meier survival analysis was performed using the "survival" R package to assess the model's predictive performance in distinguishing patients at different risk levels.

[0041] Table 1 Gene correlation coefficients of prediction models

[0042]

[0043] The analysis results showed that when the risk score was ≥13.629, it was judged as a high-risk group, and when the risk score was <13.629, it was judged as a low-risk group. The prognosis survival rate of patients in the high-risk group was significantly lower than that of patients in the low-risk group, verifying the effectiveness of the model in prognosis prediction (such as Figure 2 This model has also achieved significant results in external validation of self-assessed intrahepatic cholangiocarcinoma proteomics (as shown in Figure 3 The results of the training set are consistent with those of the validation set. In both the training set and the validation set, the 5-year survival rate of the high-risk group was only about 30%, while that of the low-risk group was about 70%. At the same time, the AUC value of the prediction model in the public dataset was 0.911, and the AUC value in the self-tested proteomics dataset was 0.874 (as shown in Figure 2). Figure 4-Figure 5 The results are relatively ideal, indicating that the cholangiocarcinoma prognosis prediction model containing 8 genes of the present invention can be used to predict the prognosis of intrahepatic cholangiocarcinoma, and provides an accurate basis for the formulation of individualized treatment plans.

[0044] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A biomarker for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma, characterized in that: The biomarkers consist of eight genes: GAPDH, HSP90AA1, CTNNB1, NFKB1, NFKBIA, HSPA5, MAP1LC3B and CAV1.

2. Use of a reagent for detecting the expression level of the biomarker according to claim 1 in preparing a kit for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma.

3. A kit for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma, characterized in that: The kit comprises reagents for detecting the expression level of the biomarker according to claim 1.

4. A method for constructing a scoring model for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma, characterized in that: The method comprises the steps of multiplying the expression levels of the eight genes included in the biomarker according to claim 1 by the correlation coefficient and then adding the results to obtain a score.

5. The construction method according to claim 4, wherein: The formula of the scoring model is: risk score = ∑ (gene expression level × gene correlation coefficient), where the gene correlation coefficient is shown in the following table:

6. Use of a scoring model constructed using the construction method according to claim 4 or 5 in constructing a system for predicting the prognosis of intrahepatic bile duct carcinoma, characterized in that: Patients with intrahepatic bile duct carcinoma are grouped according to the scores calculated by the scoring model, thereby predicting the prognosis of intrahepatic bile duct carcinoma.

7. The use according to claim 6, characterized in that When the risk score is ≥13.629, it is judged as a high-risk group, indicating that the patient has a poor prognosis; when the risk score is <13.629, it is judged as a low-risk group, indicating that the patient has a good prognosis.

8. A system for predicting the prognosis of intrahepatic bile duct carcinoma, characterized in that: The invention includes a scoring model for predicting the prognosis of BPA-induced intrahepatic cholangiocarcinoma, wherein the scoring model uses the biomarker according to claim 1 as an input variable to predict the prognosis of intrahepatic cholangiocarcinoma; the scoring model calculates the risk score for the prognosis of intrahepatic cholangiocarcinoma using the following formula: risk score = ∑(gene expression level × gene correlation coefficient), wherein the gene correlation coefficient is shown in the following table:

9. The system for predicting the prognosis of intrahepatic bile duct carcinoma according to claim 8, wherein: When the risk score is ≥13.629, it is judged as a high-risk group, indicating that the patient has a poor prognosis; when the risk score is <13.629, it is judged as a low-risk group, indicating that the patient has a good prognosis.

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