Biomarker for specific diagnosis of high-grade serous ovarian cancer and application
By screening out JUP as a biomarker, the problem of insufficient sensitivity and specificity in HGSOC diagnosis was solved, early detection and treatment optimization were achieved, and the detection rate of HGSOC was improved.
Patent Information
- Application Number
- CN202510417208.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-03
- Publication Date
- 2025-07-11
AI Technical Summary
The existing high-grade serous ovarian cancer (HGSOC) diagnostic biomarkers lack sensitivity and specificity, and are difficult to detect early. The detection rate of existing technologies such as CA125 molecules in early patients is only 23%-50%, and cannot be detected in late patients.
JUP was used as a biomarker and screened through comprehensive bioinformatics analysis to verify that it is highly expressed in tumor-type epithelial cells and endothelial cells, thereby improving the detection rate of HGSOC.
It has improved the detection rate of HGSOC, promoted early disease detection and treatment optimization, and promoted the progress of health management.
Smart Images
Figure CN120290717A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of biological medicine technology, and specifically relates to a biomarker for specific diagnosis of high-grade serous ovarian cancer and its application. Background Art
[0002] Currently, the diagnostic methods for HGSOC include pelvic palpation, imaging examination, detection of serum specific protein levels, and surgery. However, the biomarkers for specific diagnosis of HGSOC at the present stage are still very limited. For example, the currently most widely used CA125 molecule has a detection rate of only 23%-50% in early HGSOC patients and cannot be detected in all advanced HGSOC patients, lacking sufficient sensitivity and specificity and being difficult to be used as a specific molecule for diagnosing HGSOC.
[0003] Therefore, the present invention aims to provide a biomarker for specific diagnosis of high-grade serous ovarian cancer and its application to solve the above problems. Summary of the Invention
[0004] The purpose of the present invention is to solve the above problems, and provide a biomarker for specific diagnosis of high-grade serous ovarian cancer and its application. The biomarker is JUP, which can be used to diagnose HGSOC, promoting early disease detection, treatment optimization and health management, and facilitating the progress of the medical field.
[0005] To achieve the above purpose, the technical solution of the present invention is as follows:
[0006]
[0007] The JUP can be used for diagnosing HGSOC, and the JUP is a specific molecule for diagnosing HGSOC.
[0008] Compared with the prior art, the beneficial effects of this solution are as follows:
[0009] The biomarker of the present invention is JUP, which is highly expressed in tumor - type epithelial cells and endothelial cells, improves the detection rate of HGSOC, promotes early disease detection, treatment optimization and health management, and contributes to the progress of the medical field. Brief Description of the Drawings
[0010] Figure 1 It is a schematic diagram of comprehensively analyzing bioinformatics to screen JUP as a biomarker for the progression of HGSOC in the embodiment of the present invention Figure 1 , where A is a volcano plot showing differentially expressed genes in HGSOC tissues and normal ovarian epithelial tissues, B is a Venn diagram showing the common differentially expressed genes of two GEO data sets, C is the KEGG pathway enrichment analysis and GO analysis of 520 common differentially expressed genes, D is the LASSO cox regression analysis to screen prognosis - related genes, E is the Kaplan - Meier survival analysis in the GSE63885 data set and the TCGA database to screen prognosis - related genes, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001;
[0011] Figure 2 It is a schematic diagram of comprehensively analyzing bioinformatics to screen JUP as a biomarker for the progression of HGSOC in the embodiment of the present invention Figure 2 , where A is the differential expression status of HEBP1, JUP, KIZ, and CLDN4 in multiple GSE data sets, B is the U_MAP algorithm dimensionality reduction of GSE184880, C is the annotation of each different cell cluster, D is the expression pattern of JUP between different cell clusters, E is the differential expression analysis of JUP in different cell clusters, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Detailed Embodiments
[0012] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the embodiments and drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0013] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.
[0014] Embodiment:
[0015] 1. Experimental method
[0016] 1.1 Differential analysis and intersection acquisition of the HGSOC dataset in the GEO database
[0017] The GEO dataset was obtained from NCBI (National Center for Biotechnology Information). The data type was the bulk-seq sequencing dataset of cancer and adjacent tissues with HGSOC, and the dataset numbers were GSE26712 and GSE54388. After cleaning and normalizing the data using the RStuido 4.2.1 version, differential analysis of the data was performed using the limma package. Genes with |Log2FC>1, P<0.05| were significant differential genes. Subsequently, the intersection of the differential genes in the two datasets was taken to obtain common differential genes.
[0018] 1.2 GO and KEGG pathway enrichment analysis of common differential genes
[0019] The clusterProfiler package of the RStuido 4.2.1 version was used to perform gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the common differential genes. The significant enrichment criterion was set as P<0.05. The ggplot2 package was used to visualize the results. The GO analysis results were shown as a bubble chart, and the KEGG analysis results were shown as a bar chart.
[0020] 1.3 Batch univariate cox regression and LASSO cox regression analysis
[0021] The survival package of the RStuido 4.2.1 version was used to perform batch univariate cox regression analysis. The significant criterion for the regression analysis was set as P<0.05. The glmnet package was used to perform LASSO cox regression analysis, and the ggplot2 package was used to visualize the results.
[0022] 1.4 Kaplan-Meier survival analysis
[0023] Kaplan-Meier survival analysis was performed using the survival package in RStudio version 4.2.1. The data type included 365 HGSOC samples from the GEO dataset and the TCGA (GDC Data Portal Homepage) database. The significant criterion for Kaplan-Meier survival analysis was set at P < 0.05. The ggplot2 package was used for result visualization.
[0024] 1.5 U_MAP Clustering, Cell Annotation, and Differential Analysis
[0025] The Seurat package in RStudio version 4.2.1 was used for U_MAP clustering of single-cell sequencing datasets, the SingleR package was used for cell cluster annotation, and the Seurat package was used for differential analysis of gene expression levels. The significant criterion for differential analysis was set at P < 0.05. The ggplot2 package was used for result visualization.
[0026] 1.6 Statistical Analysis
[0027] All experiments were independently repeated at least 3 times. The t-test was used for comparison between two groups of data, and one-way ANOVA was used for comparison among multiple groups of data. GraphPad Prism 9.5.1 software was used for plotting and statistical analysis. P < 0.05 was considered statistically significant.
[0028] 2. Research Results
[0029] 2.1 Comprehensive Bioinformatics Analysis to Screen JUP as a Biomarker for HGSOC Progression
[0030] First, the GEO dataset was downloaded from NCBI, and differential analysis was performed using the GEO dataset (Table 1). The results showed that in the dataset GSE26712, there were 1,280 differentially expressed genes in HGSOC samples compared with normal ovarian epithelium, including 755 upregulated genes and 525 downregulated genes (|LOG2FC| > 1, P < 0.05, Figure 1 A); in the dataset GSE54388, there were 11,697 differentially expressed genes in HGSOC samples compared with normal ovarian epithelium, including 6,156 upregulated genes and 5,541 downregulated genes (|LOG2FC| > 1, P < 0.05, Figure 1 A). Subsequently, the intersection of the differentially expressed genes in the above two datasets was taken, and finally 520 common differentially expressed genes were obtained ( Figure 1 B), and the above 520 common differential genes were subjected to GO analysis and KEGG pathway enrichment analysis ( Figure 1C). The above 520 common differential genes were subjected to batch univariate cox regression analysis. After excluding insignificant genes, 38 prognosis-related genes (P < 0.05) were obtained. Subsequently, the above 38 genes were subjected to LASSO cox regression analysis, and 24 screened prognosis-related genes were obtained after dimensionality reduction ( Figure 1 D). Kaplan-Meier survival analysis was performed on the above 24 genes in the dataset GSE63885 and the TCGA database. After excluding insignificant genes, 4 further prognosis-related genes were obtained (P < 0.05, Figure 1 E), namely genes such as JUP, HEBP1, KIZ, and CLDN4.
[0031] Table 1 Comprehensive bioinformatics analysis screened JUP as a biomarker for HGSOC progression
[0032]
[0033] To further screen and validate biomarkers for HGSOC progression, the differential expression patterns of JUP, HEBP1, KIZ, and CLDN4 genes were explored in multiple GEO datasets (Table 2). The results showed that CLDN4 was highly expressed in GSE26712, GSE14407, and GSE18520, and was not statistically significant in GSE36668. HEBP1 was lowly expressed in all 4 datasets, JUP was highly expressed in all 4 datasets, and KIZ was lowly expressed in all 4 datasets. Figure 2 A). Based on the research goal of screening biomarkers affecting HGSOC progression, the JUP gene was selected for further validation. Subsequently, we used the single-cell dataset GSE184880 to validate the expression level of JUP. First, the U_MAP algorithm was used to reduce the dimensionality of GSE184880 ( Figure 2 B), and then the cells of each cluster were annotated ( Figure 2 C). Subsequently, visualization and differential expression analysis of JUP were performed, and the results showed that JUP was highly expressed in tumor epithelial cells and endothelial cells compared with normal epithelial and endothelial cells. Figure 2 D - E). Finally, according to the results of bioinformatics analysis, JUP was selected as the target gene.
[0034] Table 2 Comprehensive bioinformatics analysis screened JUP as a biomarker for HSGOC progression
[0035]
[0036] The above specific embodiments are merely explanations of the present invention, and they do not limit the present invention. After reading this specification, those skilled in the art can make modifications to these embodiments that do not contribute creatively as needed, but as long as they are within the scope of the claims of the present invention, they are protected by the patent law.