Biomarker COX6B2 for predicting recurrence risk of bile duct cancer and related products thereof

Abstract

The invention discloses a biomarker COX6B2 for predicting the recurrence risk of bile duct cancer and related products thereof, the biomarker COX6B2 has excellent prediction efficiency on the recurrence risk of bile duct cancer, the accuracy, the sensitivity and the specificity are high, the AUC value is up to 0.87, and the biomarker COX6B2 can be used for predicting the recurrence risk of bile duct cancer. The technical problem that a reliable biomarker for guiding personalized treatment of a bile duct cancer patient does not exist in clinic at present is effectively solved, a key tool is provided for accurate evaluation of the bile duct cancer recurrence risk, and important scientific significance and clinical application value are achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomedical technology, and in particular, relates to a biomarker COX6B2 for predicting the risk of recurrence of cholangiocarcinoma and related products thereof. BACKGROUND

[0002] Cholangiocarcinoma (CCA) is a highly invasive malignant tumor with a rising global incidence. Although surgical resection is still the main treatment, the high recurrence rate of 50%-70% after surgery seriously restricts the survival prognosis and quality of life of patients. The current clinical prognostic evaluation indicators, such as tumor size, lymph node metastasis, vascular invasion and CA19-9 level, have limited accuracy in predicting the risk of recurrence, which highlights the urgent need to explore more reliable biomarkers to guide individualized treatment strategies.

[0003] Cytochrome C oxidase subunit 6B2 (COX6B2) is a nuclear-encoded subunit of mitochondrial respiratory chain complex IV, which plays a key role in oxidative phosphorylation (OXPHOS) and cellular energy metabolism. It is not only a core component of complex IV assembly and function maintenance, but also participates in ATP generation and mitochondrial membrane potential stability by mediating the terminal step of the electron transport chain, and exhibits carcinogenic properties by virtue of its regulatory role in metabolic reprogramming. Related studies have shown that COX6B2 is abnormally expressed in various malignant tumors such as lung adenocarcinoma and pancreatic ductal adenocarcinoma, but there is no research or report on its correlation with CCA recurrence. SUMMARY

[0004] In order to solve the above technical problems existing in the prior art, the purpose of the present application is to provide a biomarker COX6B2 for predicting the risk of recurrence of cholangiocarcinoma and related products thereof.

[0005] The present application achieves the above-mentioned application purposes by adopting the following technical solutions:

[0006] The first aspect of the present application provides a biomarker for predicting the risk of recurrence of cholangiocarcinoma.

[0007] Further, the biomarker is COX6B2.

[0008] In the present application, the COX6B2 includes COX6B2 gene and COX6B2 protein. COX6B2 gene is transcribed and translated into COX6B2 protein product in the body of the research subject (subject). The COX6B2 gene has a Gene ID of 125965, and its full name is cytochrome c oxidase subunit 6B2 [Homo sapiens (human)]. Detailed information of the COX6B2 gene can be obtained in the NCBI database (https: / / www.ncbi.nlm.nih.gov / gene / ) based on the above-mentioned Gene ID.

[0009] In the present application, the biomarker refers to a molecular indicator with specific biological properties, biochemical characteristics or other aspects, which can be used to determine the presence or absence of a certain specific disease or condition and / or the severity of a certain specific disease or condition. In a specific embodiment of the present application, the biomarker is COX6B2.

[0010] The second aspect of the present application provides use of a reagent for detecting the expression level of COX6B2 in a sample in the preparation of a product for predicting the risk of recurrence of cholangiocarcinoma.

[0011] Further, the reagent includes:

[0012] a reagent for detecting the protein expression level of COX6B2 in the sample;

[0013] a reagent for detecting the DNA level of COX6B2 in the sample;

[0014] a reagent for detecting the RNA level of COX6B2 in the sample; or

[0015] a reagent for detecting the number of COX6B2 positive expression cells in the sample.

[0016] Further, the reagent for detecting the protein expression level of COX6B2 in the sample includes an antibody, an aptamer, an agglutination agent and / or a small molecule compound that specifically binds to the protein encoded by COX6B2;

[0017] Alternatively, the reagent for detecting the DNA level of COX6B2 in the sample includes a reagent for detecting the DNA expression level, DNA methylation level, DNA acetylation level, DNA phosphorylation level, DNA hydroxymethylation level and / or SNP site genotype of COX6B2 in the sample;

[0018] Alternatively, the reagent for detecting the RNA level of COX6B2 in the sample includes a reagent for detecting the mRNA, lncRNA and / or miRNA expression level of COX6B2 in the sample;

[0019] Optionally, the reagent for detecting the number of COX6B2 positive expression cells in the sample comprises a reagent for detecting the number of COX6B2 positive expression cells by immunohistochemical experiment.

[0020] Further, the reagent for detecting the DNA level of COX6B2 in the sample comprises a reagent for detecting the level by sequencing technology;

[0021] Optionally, the reagent for detecting the RNA level of COX6B2 in the sample comprises a primer for specifically amplifying COX6B2 and / or a probe for specifically recognizing COX6B2.

[0022] Further, the sample comprises a tissue sample, a blood sample, a serum sample, a plasma sample, an exosome sample and / or a cell sample.

[0023] In some embodiments, the sequencing technology refers to any reagent capable of detecting the DNA expression level, DNA methylation level, DNA acetylation level, DNA phosphorylation level, DNA hydroxymethylation level and / or SNP site genotype of COX6B2 in the sample, exemplarily, the sequencing technology comprises but is not limited to: Sanger sequencing technology, Illumina sequencing technology, Ion Torrent sequencing technology, PacBio sequencing technology, Oxford Nanopore long read sequencing technology.

[0024] In the present application, the sample refers to a composition obtained or derived from a target subject, which comprises a cell entity and / or other molecular entity to be characterized and / or identified, for example, based on physical, biochemical, chemical and / or physiological characteristics.

[0025] In some embodiments, the sample can be obtained from a tissue sample, blood and other fluid samples of biological origin of a subject (the subject includes a human or a non-human mammal, preferably, the subject is a human), such as a biopsy tissue sample or a tissue culture or cells derived therefrom. The source of the tissue sample can be a solid tissue, such as from a fresh, frozen and / or preserved organ or tissue sample, a biopsy tissue or aspirate; blood or any blood component; a body fluid; cells from an individual at any time of gestation or development; or plasma. The term sample includes biological samples that have been treated in any way after their procurement, such as treatment with reagents, stabilization, or enrichment for certain constituents (such as proteins or polynucleotides), or embedding in a semi-solid or solid matrix for sectioning purposes. In the present application, the sample is not particularly limited, and the application related to detecting COX6B2 in any sample for the purpose of predicting the risk of recurrence of cholangiocarcinoma in a subject falls within the scope of the present application.

[0026] In some embodiments, the sample of the present application includes, but is not limited to, tissue, blood, tissue-derived cells, blood-derived cells, serum, plasma, lymph fluid, synovial fluid, exosomes, cell extracts, stool, urine, saliva, sputum, joint fluid, ascites, serosal fluid, lymph fluid, cerebrospinal fluid, uterine fluid, digestive fluid, bile, alveolar bronchial lavage fluid, organs, and any combination thereof, and in preferred embodiments, the sample is selected from the group consisting of tissue from a subject (e.g., a subject-derived cholangiocarcinoma tissue).

[0027] In some embodiments, the subject refers to any animal, and also refers to human and non-human animals, the term non-human animals includes all vertebrates, e.g., mammals, such as non-human primates, sheep, dog, rodent (e.g., mouse or rat), guinea pig, goat, pig, cat, rabbit, cow, and any livestock or pet animal; and non-mammals, such as chicken, amphibians, reptiles, etc., and in particular embodiments of the present application, the subject is preferably a human.

[0028] In the present application, the primer refers to a 7-50 nucleic acid sequence that is capable of forming base pairs complementary to the template strand, and plays a role as a starting point for copying the template strand. The primer is usually synthesized, but naturally occurring nucleic acids can also be used. The sequence of the primer does not necessarily need to be identical to the sequence of the template, as long as it is sufficiently complementary to hybridize with the template.

[0029] In the present application, the probe refers to a nucleic acid fragment, such as RNA or DNA, which is as short as several to as long as several hundred bases, which can establish specific binding with mRNA and can determine the presence of a specific mRNA due to Labeling. The probe can be prepared in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, and an RNA probe, etc.

[0030] In the present application, the antibody refers to a specific immunoglobulin against an antigenic site. The antibody of the present application refers to an antibody that specifically binds to the COX6B2 protein of the present application, and can be manufactured according to conventional methods in the art. The form of the antibody includes polyclonal or monoclonal antibodies, antibody fragments (such as Fab, Fab', F(ab')2, and Fv fragments), single-chain Fv (scFv) antibodies, multispecific antibodies (such as bispecific antibodies), monospecific antibodies, monovalent antibodies, chimeric antibodies, humanized antibodies, human antibodies, fusion proteins comprising an antigen-binding site of an antibody, and any other modified immunoglobulin molecule comprising an antigen-binding site, as long as the antibody exhibits the desired biological binding activity. In particular embodiments of the present application, the antibody is a rabbit-derived anti-COX6B2 monoclonal antibody (PA5-50213, ThermoFisher).

[0031] The third aspect of the present application provides a product for predicting the risk of recurrence of cholangiocarcinoma.

[0032] Further, the product comprises a reagent for detecting the expression level of COX6B2 in the sample.

[0033] Further, the reagent is the reagent described in the second aspect of the present application;

[0034] Optionally, the product comprises a kit, a chip or a test strip.

[0035] In some embodiments, the kit comprises, but is not limited to, an ELISA kit, a protein chip kit, an RT-PCR kit, a DNA chip kit, a rapid detection kit or an MRM (multiple reaction monitoring) kit.

[0036] In some embodiments, the kit comprises primers or probes that specifically bind to COX6B2. In some embodiments, the detection chip comprises a solid support, and probes that specifically recognize COX6B2 attached to the solid support.

[0037] In some embodiments, the kit further comprises one or more substances selected from the group consisting of a container, an instruction manual, a positive control, a negative control, a buffer, an adjuvant or a solvent, etc.

[0038] In some embodiments, the ELISA kit can further comprise elements necessary for performing ELISA. The ELISA kit can comprise an antibody specific to a protein (COX6B2 protein described in the present application). The antibody has high selectivity and affinity to the marker protein, has no cross-reactivity with other proteins, and can be a monoclonal antibody, a polyclonal antibody or a recombinant antibody. In addition, the ELISA kit can comprise an antibody specific to a control protein. In addition, the ELISA kit can further comprise a reagent capable of detecting the bound antibody, for example, a labeled secondary antibody, a chromophore, an enzyme (for example, conjugated with an antibody), a substrate thereof or a substance capable of binding to the antibody.

[0039] In some embodiments, the protein chip kit can comprise a chip carrier on which probes (such as antibodies or aptamers) that specifically bind to COX6B2 protein are immobilized, and can further comprise blocking solution, washing buffer, labeling reagent (such as fluorescently labeled secondary antibody) and substrate required for color development or detection, etc. elements, for high-throughput detection of the expression level of COX6B2 protein in the sample.

[0040] In some embodiments, the RT-PCR kit can further comprise elements necessary for reverse transcription polymerase chain reaction. The RT-PCR kit comprises a pair of primers specific to the gene encoding the marker protein. Each primer is a nucleotide with a nucleic acid sequence specific to the gene. The RT-PCR kit can also comprise test tubes or suitable vessels, reaction buffer (different pH and magnesium concentration), deoxynucleotides (dNTPs), enzymes (such as Taq polymerase and reverse transcriptase), deoxyribonuclease inhibitor, ribonuclease inhibitor, DEPC-water, and sterile water.

[0041] In some embodiments, the DNA chip kit can comprise a chip with COX6B2 gene specific probes immobilized thereon, as well as hybridization buffer, washing solution, labeling reagent (such as biotin-labeled cDNA), signal detection reagent, etc., to detect the expression or mutation of the COX6B2 gene through nucleic acid hybridization reaction, and indirectly correlate the protein function thereof.

[0042] In some embodiments, the rapid detection kit can comprise antibodies or other binding molecules that specifically recognize COX6B2 protein, color developing reagents (such as colloidal gold, latex particles, etc.), sample processing solution, reaction test strips or cartridges, etc., which are easy and fast to operate, can directly display the expression level of COX6B2 protein in the sample, and are suitable for on-site or bedside preliminary screening.

[0043] In some embodiments, the MRM (multiple reaction monitoring) kit can comprise COX6B2 protein specific peptide segment standard, isotope internal standard, proteolysis reagent, chromatographic column, mobile phase additive, etc., to quantitatively detect COX6B2 protein in the sample through mass spectrometry multiple reaction monitoring technology, with high specificity and sensitivity.

[0044] In some embodiments, the chip (microarray chip) refers to a solid support comprising linked nucleic acid or peptide probes. The array typically comprises a plurality of different nucleic acid or peptide probes linked to the surface of a substrate at different known locations. These arrays, also known as microarrays, can typically be produced using mechanical synthesis methods or light-directed synthesis methods, which incorporate a combination of photolithographic methods and solid-phase synthesis methods. The array can comprise a flat surface, or can be nucleic acids or peptides on beads, gels, polymeric surfaces, fibers such as optical fibers, glass, or any other suitable substrate. The array can be packaged in a manner that allows for diagnostic or other manipulations of a fully functional device.

[0045] In some embodiments, the chip comprises a gene chip, a protein chip; the gene chip comprises a solid phase carrier; and an oligonucleotide probe specifically corresponding to part or all of the sequence of COX6B2 is fixed on the solid phase carrier. The protein chip comprises a solid phase carrier, and a specific antibody or ligand of the protein encoded by COX6B2 fixed on the solid phase carrier. The solid phase carrier can adopt various commonly used materials in the chip field, for example, including but not limited to: plastic products, microparticles, membrane carriers, etc.

[0046] In some embodiments, the test strip can comprise a sample pad, a binding pad, a reaction membrane, a water absorption pad and a base plate, wherein the binding pad is fixed with a labeled anti-COX6B2 antibody (such as a colloidal gold labeled rabbit monoclonal antibody), the reaction membrane is provided with a detection line (coated with an anti-COX6B2 antibody) and a quality control line (coated with an anti-label antibody), and a sample processing liquid can also be matched. Through the specific binding reaction of antigen and antibody, the detection line and the quality control line develop color, and the expression level of COX6B2 protein in the sample can be directly judged.

[0047] The fourth aspect of the present application provides a system or device for predicting the risk of recurrence of cholangiocarcinoma.

[0048] Further, the system or device comprises a processor, an input module, an output module;

[0049] The processor is used for logical operation on the input information by bioinformatics method; the input module is used for inputting the expression level of COX6B2 in the sample of the subject; the computer readable medium containing instructions, which, when executed by the processor, executes the algorithm on the input expression level of COX6B2; and the output module is used for outputting the risk of recurrence of cholangiocarcinoma in the subject.

[0050] In the present application, the system or device is a method for distinguishing different components, elements, parts, portions or assemblies of different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions. Those skilled in the art of the technical field to which the present application pertains are familiar that the present application can be implemented as an apparatus, a method or a computer program product. Therefore, the disclosure of the present application can be embodied in the form of an entirely hardware, an entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some specific embodiments, the present application can also be embodied in the form of a computer program product in one or more computer readable media, which contains computer readable program code.

[0051] The present application also provides a computer readable storage medium.

[0052] Further, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the system or the device of the present application.

[0053] The fifth aspect of the present application provides any one of the following aspects:

[0054] (1) The reagent for detecting the expression level of COX6B2 in a sample is used in the preparation of a system or a device for predicting the risk of recurrence of cholangiocarcinoma.

[0055] (2) The biomarker COX6B2 described in the first aspect of the present application is combined with the tumor marker CA19-9, the tumor marker CEA, the blood vessel invasion index, and the tumor size index in the preparation of a product for predicting the risk of recurrence of cholangiocarcinoma or in the construction of a prediction model for predicting the risk of recurrence of cholangiocarcinoma.

[0056] In some embodiments, the tumor marker CA19-9 refers to carbohydrate antigen 19-9, which is a tumor marker commonly used for the auxiliary diagnosis and recurrence monitoring of diseases such as cholangiocarcinoma (CCA).

[0057] In some embodiments, the tumor marker CEA refers to carcinoembryonic antigen, which can be elevated in various malignant tumors. In some embodiments of the present application, it is used as one of the clinicopathological features related to the risk of recurrence of CCA to construct a composite risk stratification model.

[0058] In some embodiments, the blood vessel invasion index refers to the invasion of tumor cells to surrounding blood vessels, which is an important pathological feature for evaluating the invasiveness and prognosis of tumors. In some embodiments of the present application, it is included in the risk stratification model as a factor affecting the risk of recurrence of CCA.

[0059] In some embodiments, the tumor size index refers to the size of the detected cholangiocarcinoma tumor, which is a basic index for describing the degree of tumor progression. In some embodiments of the present application, it is used as one of the clinicopathological features for constructing a prediction model for the risk of recurrence of CCA.

[0060] The present application also provides a method for predicting the risk of recurrence of cholangiocarcinoma.

[0061] Further, the method comprises detecting the expression level of COX6B2 in a test sample from a subject, and predicting the risk of recurrence of cholangiocarcinoma according to the detected expression level of COX6B2.

[0062] In the present application, as long as the biomarker COX6B2 (or in combination with other indicators, for example, the biomarker COX6B2 is combined with tumor marker CA19-9, tumor marker CEA, vascular invasion indicator and tumor size indicator) is used to predict the risk of recurrence of cholangiocarcinoma, such methods fall within the scope of the present application, and are not limited to the specific use method of using the biomarker COX6B2, as long as the purpose of predicting the risk of recurrence of cholangiocarcinoma can be achieved or basically achieved, it falls within the protection scope of the present application.

[0063] Compared with the prior art, the present application has the advantages and beneficial effects as follows:

[0064] The present application first creatively discovers that COX6B2 can be used as an effective biomarker for predicting the risk of recurrence of cholangiocarcinoma. Through clinical verification of the training cohort (115 patients) and the verification cohort (48 patients), it is proved that COX6B2 has excellent prediction performance for the risk of recurrence of cholangiocarcinoma, and the accuracy, sensitivity and specificity are all high, and the AUC value is as high as 0.87. The present application effectively solves the technical problem that there is still a lack of reliable biomarkers to guide the individualized treatment of cholangiocarcinoma patients in the current clinical practice, and provides a key tool for the accurate assessment of the risk of recurrence of cholangiocarcinoma and a basis for the individualized treatment strategy for patients, which has important scientific significance and clinical application value. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 : Bioinformatics analysis results, wherein, A figure: comparison of differentially expressed genes (DEGs) distribution between recurrence group and non-recurrence group in TCGA-CHOL data set and GSE107943 data set, showing the number of DEGs significantly up-regulated in each of the two data sets (27 in TCGA-CHOL recurrence group and 393 in GSE107943 recurrence group); B figure: Kaplan-Meier curve of COX6B2 expression level and recurrence-free survival (RFS) in GSE107943 cohort, showing that high expression of COX6B2 is significantly associated with increased risk of recurrence (log-rank test); C figure: volcano plot of differentially expressed genes in recurrence group and non-recurrence group in GSE107943 data set, wherein COX6B2 is significantly up-regulated (annotated by red dots); D figure: volcano plot of differentially expressed genes in recurrence group and non-recurrence group in TCGA-CHOL data set, COX6B2 also shows an up-regulation trend (annotated by red dots);

[0066] Figure 2: COX6B2 expression and predictive performance in CCA, wherein, A-C: immunohistochemical staining results of COX6B2 in CCA tissues, showing weak (A), moderate (B), and strong (C) staining intensity (cytoplasmic expression as the main); D-E: ROC curve (D) and Bootstrap internal validation curve (E) of COX6B2 predicting CCA recurrence in the training cohort, AUC value is 0.87 (95% CI: 0.80-0.93), and the optimal cutoff value is 0.73; F-G: ROC curve (F) and validation results (G) of COX6B2 predicting CCA recurrence in the independent validation cohort, AUC value is 0.81 (95% CI: 0.69-0.93), confirming its cross-cohort reliability;

[0067] Figure 3 : Prognostic stratification and nomogram model based on COX6B2, wherein, A-B: Kaplan-Meier curves of recurrence-free survival (RFS, A) and overall survival (OS, B) of high and low expression groups of COX6B2 in the training cohort, showing significant differences between the two groups (log-rank P<0.01); C-D: Kaplan-Meier curves of RFS (C) and OS (D) of high and low expression groups of COX6B2 in the validation cohort, showing consistent prognostic stratification with the training cohort (log-rank P<0.01); E-F: nomogram model integrating COX6B2 and clinical pathological characteristics (CA19-9, CEA, etc.), directly showing the prediction weight of each factor on the risk of recurrence (E for the training cohort, F for the validation cohort);

[0068] Figure 4 : Predictive performance and clinical application of the composite risk stratification model, wherein, A-B: ROC curves of the composite model (COX6B2 + clinical pathological characteristics) in the training cohort (A) and the validation cohort (B), AUC is 0.93, which is significantly better than COX6B2 alone; C-D: RFS Kaplan-Meier curves of high-risk and low-risk groups divided by the composite model in the training cohort (C) and the validation cohort (D), showing significant differences between the two groups (log-rank P<0.01); E-F: Columnar comparison of 5-year recurrence rate (90.62%-95.59%) of the high-risk group and no recurrence rate (85.11%-87.50%) of the low-risk group in the training cohort (E) and the validation cohort (F), directly showing the risk stratification performance of the model. DETAILED DESCRIPTION

[0069] The application will be further described in connection with the following specific examples, which are merely illustrative of the application and are not to be construed as limiting the application. It will be obvious to a person skilled in the art that various changes, modifications, substitutions and alterations can be made to these examples without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0070] The reagents, raw materials and experimental consumables used in the present application are readily available to those skilled in the art, and can be obtained commercially unless otherwise specified. The experimental methods of the present application not specified in the present application are generally carried out according to conventional conditions or according to the conditions recommended by the manufacturer. In particular, the following examples are only used to illustrate the present application and should not in any way limit the scope of the present application. It should be noted that the experimental conditions described in the following examples and the results thereof are only used to illustrate the present application and should not and will not limit the present application as described in detail in the claims.

[0071] Example Screening and verification of biomarkers for predicting the risk of recurrence of cholangiocarcinoma

[0072] 1. Experimental methods

[0073] (1) Bioinformatics analysis

[0074] Transcriptomic analysis data with matched clinical annotation were obtained from two independent cholangiocarcinoma cohorts.

[0075] TCGA-CHOL dataset includes RNA-seq profiles of 36 tumors and recurrence status, which were retrieved from UCSC Xena platform (https: / / xena.ucsc.edu). Patients were stratified into recurrence group (n=10) and non-recurrence group (n=14).

[0076] GSE107943 dataset was obtained from Gene Expression Omnibus (https: / / www.ncbi.nlm.nih.gov / geo / ), including 21 cases of recurrence and 9 cases of non-recurrence selected by the same clinical criteria.

[0077] Differential expression analysis was performed using DESeq2 (v1.38.3) and significant differentially expressed genes (DEGs) were identified according to stringent double thresholds: absolute log2 fold change (|log2FC| > 1) and adjusted p-value (false discovery rate, FDR) < 0.05. Survival-associated genes were systematically identified through a three-step workflow: (1) batch log-rank test on recurrence-upregulated genes in GSE107943 (Kaplan-Meier stratification by median expression), (2) univariate Cox regression (proportional hazards model, P < 0.05 threshold), (3) cross-cohort validation by intersecting survival-significant genes in GSE107943 with recurrence-upregulated DEGs in TCGA-CHOL.

[0078] (2) Patient cohorts

[0079] Two independent cohorts were included in this study: a training cohort (comprising 115 patients who underwent curative resection at the Second Hospital of Hebei Medical University between July 2010 and August 2024) and a validation cohort (comprising 48 patients from Hengshui People's Hospital). Both cohorts met the standardized inclusion criteria. The Institutional Review Board approved the study protocol, and written informed consent was obtained from all participants. Tumor staging followed the 2017 UICC TNM classification (8th edition). Recurrence diagnosis was made by contrast-enhanced CT, MRI, or DSA imaging, combined with elevated serum CA19-9 (> 37 U / mL), and, where feasible, confirmed by histopathology. Disease-free survival (DFS) was calculated from surgery to the first recurrence (intrahepatic / extrahepatic), and overall survival (OS) was defined as the time from surgery to death or last follow-up (censored for surviving patients). Subsequent protocols were implemented in the manner previously described.

[0080] wherein, in the training cohort, cholangiocarcinoma tissue samples from cholangiocarcinoma patients with recurrence: cholangiocarcinoma tissue samples from cholangiocarcinoma patients without recurrence = 72: 43.

[0081] wherein, in the validation cohort, cholangiocarcinoma tissue samples from cholangiocarcinoma patients with recurrence: cholangiocarcinoma tissue samples from cholangiocarcinoma patients without recurrence = 31: 17.

[0082] (3) Immunohistochemical analysis

[0083] Immunohistochemical staining for COX6B2 expression was performed according to standard protocol. After antigen retrieval, tissue sections were incubated with rabbit-derived anti-COX6B2 monoclonal antibody (PA5-50213, ThermoFisher; 1:100 dilution), followed by incubation with horseradish peroxidase-conjugated secondary antibody (PV-9000, ZSGB-BIO). 3,3'-diaminobenzidine was used for staining, and hematoxylin was used for counterstaining. Staining assessment was performed according to established semi-quantitative criteria, evaluating staining intensity (0 = none; 1 = weak cytoplasmic staining; 2 = moderate membranous / cytoplasmic staining; 3 = strong diffuse staining) and cell distribution (0 = ≤10%; 1 = 11-25%; 2 = 26-50%; 3 = 51-75%; 4 = >75% positive cells). The Immune Response Score (IRS) is the product of the intensity and distribution scores, and the final classification is defined as: negative (IRS≤3), weak (4-6), moderate (7-9), or strong (>9). All assessments were performed by two independent pathologists unaware of the clinical outcomes. For cases with inconsistencies (IRS score difference >2 points), consensus review was conducted using a multi-observer microscopy system.

[0084] (4) Statistical analysis

[0085] Continuous variables were classified into two categories using a cohort-specific median threshold. Between-group comparisons (relapsed vs. non-relapsed) were performed using the χ² method. 2 Tests were performed (categorical variables) and the Mann-Whitney U test (continuous variables were tested for Shapiro-Wilk normality). The discriminative power of the Cox model was quantified using time-dependent receiver operating characteristic (ROC) analysis. For the training cohort, internal validation was performed using 1000 guided resampling samples to compute adjusted area under the curve (AUC) estimates and bias-adjusted 95% confidence intervals. The DeLong test was used for paired ROC curve comparisons. Survival endpoints (OS / RFS) were calculated from surgical intervention to event occurrence or last follow-up and analyzed using Kaplan-Meier estimates and log-rank tests. A phased Cox regression approach was performed: (1) univariate screening (P < 0.1 inclusion threshold), (2) multivariate analysis with backward elimination (P < 0.05 retained). All analyses were performed using SPSS 29.0 and R 4.4.2, as well as the survival, pROC, and survminer packages.

[0086] 2. Experimental Results

[0087] (1) Identification of COX6B2 as a potential biomarker for predicting recurrence of cholangiocarcinoma

[0088] Analysis of the TCGA-CHOL dataset showed that there were 27 significantly up-regulated differentially expressed genes in the recurrence group of cholangiocarcinoma, while there were 393 significantly up-regulated genes in the GSE107943 dataset Figure 1 A). Batch log-rank test identified 206 genes significantly associated with recurrence, univariate Cox regression analysis identified 252 candidate genes associated with cholangiocarcinoma recurrence time, and cross comparison found 166 overlapping genes. Subsequent cross analysis with the up-regulated genes in the TCGA-CHOL recurrence group identified COX6B2 as a key overlapping gene.

[0089] In the GSE107943 cohort, log-rank test Figure 1 B) showed that COX6B2 expression level was significantly associated with recurrence-free survival. Univariate Cox regression analysis also confirmed that COX6B2 overexpression was significantly associated with increased risk of cholangiocarcinoma recurrence (HR = 1.351, 95% CI = 1.023-1.783, p = 0.034). These results showed that for every one unit increase in COX6B2 expression, the risk of cholangiocarcinoma recurrence increased by 35.1%.

[0090] In addition, volcano plot intuitively depicts the distribution pattern of differentially expressed genes between the cholangiocarcinoma recurrence group and the cholangiocarcinoma non-recurrence group in the GSE107943 Figure 1 C) and TCGA-CHOL Figure 1 D) datasets, COX6B2 showed a consistent up-regulation trend in the cholangiocarcinoma recurrence group of both cohorts.

[0091] Cross-database bioinformatics analysis showed that COX6B2 exhibited an overexpression pattern in patients with cholangiocarcinoma recurrence. Its expression level was significantly negatively correlated with recurrence-free survival, indicating that it could be an independent molecular biomarker for predicting postoperative recurrence of cholangiocarcinoma.

[0092] (2) Training and validation of COX6B2 in predicting cholangiocarcinoma recurrence

[0093] Immunohistochemical analysis showed that COX6B2 was mainly expressed in the cytoplasm of cholangiocarcinoma tissues, with staining intensity classified as weak, moderate, and strong Figure 2 A-C). We established a training cohort of 115 patients with a recurrence rate of 62.6%, and a validation cohort of 48 patients with a recurrence rate of 64.6%, and the clinical and pathological characteristics of the two groups of patients were similar to verify its clinical predictive value. The median age of the training cohort and the validation cohort was 61 years and 63.5 years, respectively (Table 1). There was no significant difference in the recurrence rate and baseline characteristics between the two groups of patients (P>0.05), thereby minimizing the risk of selection bias.

[0094] Table 1. Clinicopathological characteristics of the clinical cohorts

[0095]

[0096]

[0097] Cox proportional hazards regression analysis showed that COX6B2 had excellent discriminative ability for recurrence in the training cohort (AUC = 0.87, sensitivity 88%, specificity 71%, 95% CI: 0.80-0.93; optimal cutoff = 0.73) Figure 2 D-E). Bootstrap internal validation (1000 resamplings) further enhanced its robustness (AUC = 0.859, sensitivity 80.7%, specificity 81.1%, 95% CI: 0.827-0.865). COX6B2 maintained consistent diagnostic performance in the independent validation cohort (AUC = 0.81, sensitivity 84%, specificity 71%, 95% CI: 0.69-0.93) Figure 2 F-G), confirming its cross-cohort reliability. In summary, COX6B2 demonstrated excellent and stable predictive performance for recurrence through multi-stage clinical validation. Its high AUC value and cross-cohort reproducibility highlight its translational potential as a non-invasive diagnostic biomarker for postoperative CCA recurrence.

[0098] (3) Development and validation of COX6B2-based prognostic nomogram

[0099] Semi-quantitative immunohistochemical analysis classified CCA tissues into four expression levels: negative, weak, moderate, and strong. ROC curve analysis determined the optimal cutoff value as 0.73 (Youden index: sensitivity 88%, specificity 71%), which enabled the dichotomization of the four-level classification into a clinically operable binary risk model (low-risk group vs. high-risk group). The validation cohort was stratified using the same Youden index threshold as in the training cohort to ensure the reproducibility of the model. The median follow-up time was 21.2 months (range: 4.2-55.8 months) in the training cohort and 21 months (range: 5.2-36.2 months) in the validation cohort. Survival analysis showed that in the training cohort, the high-risk group had significantly shorter recurrence-free survival (RFS; log-rank test, P < 0.01; Figure 3 A) and overall survival (OS; log-rank test, P < 0.01; Figure 3 B) in the training cohort. Similar prognostic stratification was observed in the independent validation cohort (RFS: Figure 3 C; OS: Figure 3 D, log-rank test, P < 0.01).

[0100] We utilized data from the two independent cohorts (training cohort, validation cohort) to integrate the novel biomarker COX6B2 with established pathological risk indicators, including CA19-9, CEA, vascular invasion, and tumor size, to construct a prognostic nomogram to enhance clinical application Figure 3 E-F). Traditional risk factors contributed little to the predictive model, while COX6B2 had a significant predictive weight, highlighting its key role in prognostic evaluation.

[0101] By integrating variables preliminarily screened by univariate analysis (P < 0.1, Table 2, where CI is confidence interval; HR is hazard ratio; *, P < 0.05), multivariate Cox regression analysis showed that CEA, CA19-9, and COX6B2 were independent predictors of prognosis in both cohorts. Among them, COX6B2 became the most robust predictor, with an HR of 4.94 (95% CI = 2.6-9.4, P < 0.01) in the training cohort and an HR of 4.38 (95% CI = 1.52-12.62, P < 0.05) in the validation cohort.

[0102] Table 2 Univariate and multivariate Cox proportional hazards regression analysis of recurrence

[0103]

[0104]

[0105] This nomogram not only intuitively highlights the better prognostic value of COX6B2 compared to traditional biomarkers, but also demonstrates cross-cohort robustness through multivariate analysis. These results highlight the potential of the model provided by the present application in clinical translation.

[0106] (4) Risk stratification model integrating COX6B2 and clinicopathological features enhances the prediction of CCA recurrence

[0107] To overcome the limitations of traditional clinicopathological indicators in assessing the risk of CCA recurrence, we constructed a comprehensive risk stratification model that combined COX6B2 with mature clinicopathological features such as CA19-9, CEA, vascular invasion, and tumor size. This model showed better predictive performance than single markers. In the training cohort, the AUC of this model reached 0.929 Figure 4 A), and in the validation cohort, it reached 0.928 Figure 4B), both of which outperformed the AUC of single marker A. Moreover, Delong test showed that the diagnostic accuracy of this combined model was significantly higher than that of COX6B2 alone, both in the training cohort and in the validation cohort (P<0.05). Table 3 lists the advantages of this combined approach in detail and presents comprehensive performance metrics, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). These findings highlight the potential of this composite model as a valuable clinical tool that enables more accurate risk stratification and supports personalized treatment strategies for CCA patients.

[0108] Table 3 Performance of the model in assessing the risk of recurrence

[0109]

[0110] The present invention aims to develop a precise and clinically operable risk stratification tool to identify high-risk CCA patients who need postoperative intensification therapy, while preventing over-treatment of low-risk CCA patients. The final model has been applied to the training cohort (n=115) and the validation cohort (n=48) to classify patients into high-risk and low-risk groups. In the training cohort, 59.1% (68 / 115) of patients were classified as high-risk and 40.9% (47 / 115) of patients were classified as low-risk; in the validation cohort, 66.7% (32 / 48) of patients were classified as high-risk and 33.3% (16 / 48) of patients were classified as low-risk.

[0111] The analysis showed that the difference in RFS between the high-risk and low-risk groups in both cohorts was statistically significant (P<0.01), demonstrating the reliability and consistency of the model Figure 4 C-D). The recurrence rate was significantly higher in the high-risk subgroup, with 95.59% (65 / 68) of patients in the training cohort and 90.62% (29 / 32) of patients in the validation cohort relapsing within 5 years Figure 4 E-F). These results highlight the urgent need for adjuvant chemotherapy to prevent recurrence in this population. In contrast, in the low-risk patients, 85.11% (40 / 47) of patients in the training cohort and 87.5% (14 / 16) of patients in the validation cohort remained recurrence-free during the same period Figure 4 E-F), indicating that these individuals can safely forego unnecessary treatment and its associated toxicity and cost. Moreover, the composite model outperformed COX6B2 alone, with an 11% increase in NPV. A higher NPV significantly reduces the likelihood of misdiagnosing low-risk patients as high-risk patients, thereby optimizing clinical decision-making. In summary, the risk stratification model effectively distinguishes between patients who need adjuvant chemotherapy and those who do not, thereby optimizing treatment strategies and improving patient outcomes.

Claims

1. A biomarker for predicting the risk of recurrence of cholangiocarcinoma, characterized by, The biomarker is COX6B2.

2. Use of a reagent for detecting the expression level of COX6B2 in a sample in the preparation of a product for predicting the risk of recurrence of cholangiocarcinoma.

3. Use according to claim 2, characterized in that, The reagent comprises: a reagent for detecting the protein expression level of COX6B2 in a sample; a reagent for detecting the DNA level of COX6B2 in a sample; a reagent for detecting the RNA level of COX6B2 in a sample; or a reagent for detecting the number of COX6B2-positive expression cells in a sample.

4. Use according to claim 3, characterized in that, The reagent for detecting the protein expression level of COX6B2 in a sample comprises an antibody, an aptamer, an agglutination agent and / or a small molecule compound that specifically binds to a protein encoded by COX6B2; Optionally, the reagent for detecting the DNA level of COX6B2 in a sample comprises a reagent for detecting the DNA expression level, DNA methylation level, DNA acetylation level, DNA phosphorylation level, DNA hydroxymethylation level and / or SNP site genotype of COX6B2 in a sample; Optionally, the reagent for detecting the RNA level of COX6B2 in a sample comprises a reagent for detecting the mRNA, lncRNA and / or miRNA expression level of COX6B2 in a sample; Optionally, the reagent for detecting the number of COX6B2-positive expression cells in a sample comprises a reagent for detecting the number of COX6B2-positive expression cells by immunohistochemical experiment.

5. Use according to claim 4, characterized in that, The reagent for detecting the DNA level of COX6B2 in a sample comprises a reagent for detecting the level by sequencing technology; Optionally, the reagent for detecting the RNA level of COX6B2 in a sample comprises primers that specifically amplify COX6B2 and / or probes that specifically recognize COX6B2.

6. Use according to claim 2, characterized in that, The sample comprises a tissue sample, a blood sample, a serum sample, a plasma sample, an exosome sample and / or a cell sample.

7. A product for predicting the risk of recurrence of cholangiocarcinoma, characterized by, The product comprises a reagent for detecting the expression level of COX6B2 in a sample.

8. The product of claim 7, wherein, The reagent is the reagent described in any one of claims 2-5; Optionally, the product comprises a kit, a chip or a test strip.

9. A system or apparatus for predicting the risk of recurrence of cholangiocarcinoma, comprising: The system or device comprises a processor, an input module and an output module; wherein the processor is configured to perform logical operations on the input information using bioinformatics methods; the input module is configured to input the expression level of COX6B2 in the sample of the subject, the computer readable medium containing instructions which, when executed by the processor, perform an algorithm on the input expression level of COX6B2; and the output module is configured to output the risk of recurrence of cholangiocarcinoma in the subject.

10. Use of any of the following aspects: (1) use of a reagent for detecting the expression level of COX6B2 in a sample in the preparation of a system or device for predicting the risk of recurrence of cholangiocarcinoma; (2) use of the biomarker COX6B2 described in claim 1 in combination with the tumor marker CA19-9, the tumor marker CEA, the blood vessel invasion index and the tumor size index in the preparation of a product for predicting the risk of recurrence of cholangiocarcinoma or in the construction of a prediction model for predicting the risk of recurrence of cholangiocarcinoma.

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