Cervical cancer biomarker FCGR3B and application thereof

Abstract

The invention belongs to the technical field of biological medicines, and aims to solve the problems of low popularization rate, poor diagnosis sensitivity and specificity, low prediction efficiency and the like of current cervical cancer screening means. The invention provides a cervical cancer biomarker FCGR3B and an application thereof, and further provides an application of the FCGR3B as a cervical cancer diagnosis biomarker in diagnosis of cervical cancer. The biomarker is FCGR3B, and the specific expression level of the FCGR3B in cancer tissues of a cervical cancer patient is up-regulated. The fact that FCGR3B has a good diagnostic value in cervical cancer is evaluated by drawing an ROC curve, and the correlation between SELL and immune cells is analyzed by using a CIBERSORT technology. According to the research, the expression of FCGR3B in cervical cancer is obviously higher than that in normal cervical tissues. FCGR3B may become a biomarker for diagnosis of cervical cancer and a key target for treatment, and provides a new idea and reference for prevention and treatment of cervical cancer.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to a cervical cancer biomarker FCGR3B and applications thereof. Background Art

[0002] Colposcopy and cervical biopsy are currently the primary diagnostic methods for cervical cancer. However, these methods are subject to significant subjective judgment, limited screening coverage, poor diagnostic sensitivity and specificity, and low predictive efficiency. Furthermore, cervical cancer often presents no obvious symptoms in its early stages, potentially missing the optimal treatment window. Late-stage cancer is prone to invasive disease, resulting in poor treatment outcomes and an increased risk of postoperative local metastasis and recurrence. Therefore, identifying an effective biomarker could significantly improve the diagnosis and treatment of cervical cancer patients.

[0003] It is known that the inflammatory response caused by HPV infection has been proven to be a determining factor in the development and progression of cervical cancer. Risk factors for cervical cancer include human papillomavirus (HPV), early sexual activity, multiple sexual partners, HIV positivity, and smoking. Among them, persistent infection with high-risk HPV is the primary factor leading to cervical cancer, with HPV16 being the most common, accounting for more than 50%. HPV is a DNA virus that specifically infects epithelial cells, which can disrupt normal cell cycle control, promote the accumulation of genetic damage and uncontrolled cell division. Most HPVs will be cleared by the immune system of the infected person, and only 1% of the infected people will progress to cervical cancer. Eliminating the inflammatory response caused by HPV infection is a normal human immune defense mechanism. Chronic inflammation is caused by immune disorders and autoimmunity.

[0004] Studies have shown that HPV infection promotes the interaction between chronic inflammation and the immune microenvironment in the body, playing a crucial role in the progression of precancerous lesions to invasive cancer. Therefore, the occurrence of cervical cancer is not only related to HPV infection, but also to the immune system, which plays a vital role in immune surveillance and immune clearance. The immune system has been shown to be a determinant of the occurrence and development of cancer, and immune cells are the main components of the human immune system. The types, distribution, and degree of infiltration of immune cells in different tumors show significant differences. However, no studies have been found to explore the combination of inflammation and immune infiltration in cervical cancer. Summary of the Invention

[0005] The present invention aims to address the current problems of low penetration of cervical cancer screening methods, poor diagnostic sensitivity and specificity, and low prediction efficiency. It provides a cervical cancer biomarker FCGR3B and its application, and further provides the application of FCGR3B as a cervical cancer diagnostic biomarker in the diagnosis of cervical cancer.

[0006] The present invention is achieved by the following technical solution: a biomarker for diagnosing cervical cancer, wherein the biomarker is FCGR3B, and the expression level of FCGR3B is specifically upregulated in cancer tissues of cervical cancer patients.

[0007] Use of the biomarker in developing and / or preparing a product for diagnosing cervical cancer.

[0008] Furthermore, the application is to detect the expression level of the biomarker.

[0009] A product for diagnosing cervical cancer, comprising: a reagent for detecting the expression level of the biomarker in a biological sample.

[0010] A detection kit for diagnosing cervical cancer, comprising the biomarker.

[0011] By integrating two GEO datasets, we identified 520 inflammatory DEGs associated with cervical cancer. Subsequently, we used CIBERSORT and WGCNA to identify seven immune infiltration-related gene modules. Correlation analysis revealed that the blue and brown modules were most highly correlated with cervical cancer. We further intersected the inflammatory DEGs with the key gene modules to identify 12 differentially expressed immune-related inflammation genes. By constructing a PPI network, we identified five hub genes associated with both inflammation and immune cell infiltration in cervical cancer. Ultimately, we discovered a novel inflammation- and immunity-related marker—FCGR3B.

[0012] The mechanism of action of FCGR3B in cervical cancer has not been reported in any domestic or international literature to date. There are three main types of Fcγ receptors: FcγRI, FcγRII, and FcγRIII. These are categorized by their affinity for IgG: FcγRI (high-affinity receptor), FcγRII, and FcγRIII (low-affinity receptor). Low-affinity Fc receptor genes include three FCGR2 genes (FCGR2A, FCGR2B, and FCGR2C) and two FCGR3 genes (FCGR3A and FCGR3B). FCGR3B is a member of the Fcγ receptor family, primarily expressed on human neutrophils at the 1q23.3 locus. This locus modulates the expression of the corresponding receptor and antigen-specific immunity. FCGR3B is also a key immune receptor controlling both humoral and innate immunity, playing a crucial role in maintaining autoimmune homeostasis and responses to infection. Disruption of this homeostasis can lead to increased susceptibility to autoimmunity and infection. Numerous previous studies have suggested that FCGR3B is a risk factor for a range of autoimmune diseases, such as systemic lupus erythematosus and rheumatoid arthritis. This study evaluated the diagnostic value of FCGR3B in cervical cancer by plotting receiver operating characteristic (ROC) curves and analyzing the correlation between SELLs and immune cells using CIBERSORT technology. This study showed that FCGR3B expression was significantly higher in cervical cancer than in normal cervical tissue. Validation of our clinical samples largely supported this conclusion. Therefore, FCGR3B has the potential to become a diagnostic biomarker for cervical cancer and a key target for treatment, providing new insights and insights for its prevention and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Figure 2 is the identification result of inflammation-related differentially expressed genes; (a) is the heat map of DEGs; (b) is the volcano map of DEGs; (c) is the Venn diagram of DEGs and inflammation-related genes; (d) is the heat map for identifying inflammation-related DEGs; Figure 2 The results of the identification of differentially expressed genes in immune-related inflammation are shown in the figure: (a) is the relative percentage of 22 immune cells in normal tissues and cervical cancer tissues; (b) is a heat map of the association between the WGCNA module and immune cells; (c) is a Venn diagram of inflammation-related DEGs and important module genes; (d) is a heat map for identifying immune-related inflammation DEGs; Figure 3 Middle: (a) protein-protein interaction network; (b) Hub genes extracted from the PPI network; Figure 4 ROC curve analysis for FCGR3B gene; Figure 5This is the result of the correlation analysis between FCGR3B gene and immune infiltrating cells; Figure 6 The expression of FCGR3B gene in CC was verified by PCR. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] Unless defined otherwise, 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 invention belongs, and the disclosure and materials cited therein are hereby incorporated by reference.

[0016] Technical equivalents to the specific embodiments described that are apparent to those skilled in the art using no more than routine experimentation are intended to be encompassed by this application.

[0017] The experimental methods in the following examples, unless otherwise specified, are all conventional methods. The instruments and equipment used in the following examples, unless otherwise specified, are all conventional laboratory instruments and equipment; the experimental materials used in the following examples, unless otherwise specified, are all purchased from conventional biochemical reagent stores.

[0018] 1. Experimental Methods 1. Data Processing and Screening of Differentially Expressed Genes: Gene expression microarrays for cervical cancer were downloaded from the GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ), including GSE39001 (CC = 28, control = 24) and GSE63514 (CC = 43, control = 12). Both datasets were normalized upon upload. When multiple probes corresponded to a common gene, the average expression value was taken. Furthermore, the Surrogate Variable Analysis (SVA) package in Bioconductor was used to eliminate batch effects and other undesirable variations between the three datasets. The processed data were screened for differentially expressed genes (DEGs) using the "limma" package in R software. The criteria for screening differentially expressed genes were: P.adjust (adjusted P value) < 0.05 and |log2FC| (fold change) > 1. R software was used to draw gene heat maps and volcano plots. The volcano plots used log2FC as the horizontal axis and -log10 (P.adj) as the vertical axis.

[0019] 2. Identification of inflammation-related differentially expressed genes: 320 inflammation-related genes with correlations greater than 6 were identified using the GeneCards database. The “VennDiagram” R package was used to intersect DEGs and inflammation-related differentially expressed genes, and these genes were defined as differentially expressed inflammation-related genes for subsequent analysis.

[0020] 3. Identification of differentially expressed genes associated with immune-related inflammation: Based on standardized gene expression patterns, CIBERSORT was used to quantify the relative proportion of infiltrating immune cells in the samples.

[0021] Data were downloaded from the CIBERSORT website (http: / / CIBERSORT.stanford.edu / ). CIBERSORT was used to analyze the combined expression data and calculate immune cell infiltration. Samples were filtered based on a p < 0.05. The percentage of each immune cell type in the sample was calculated. Results were visualized using the corplot and vioplot packages in R. Weighted Gene Co-expression Network Analysis (WGCNA) Construction and Module Identification To explore gene interactions, the systems biology method WGCNA was applied to construct a gene co-expression network.

[0022] First, the processed data were imported into WGCNA. Second, to ensure the reliability of the network construction results, outlier samples were removed. Third, a soft power of β = 2 was selected using the pick soft threshold function to construct a scale-free network. Then, the adjacency was transformed into a topological overlap matrix (TOM), and the corresponding dissimilarity (1-TOM) was calculated. Fourth, modules were detected using hierarchical clustering and dynamic tree cutting. To classify genes with similar expression profiles into gene modules, average linkage hierarchical clustering was performed based on the TOM dissimilarity metric, with a minimum genome size of 60 for the gene dendrogram. Fifth, the correlation between modules and differentially infiltrating immune cells was calculated using the WGCNA software package. Modules with high correlation coefficients were considered candidate modules associated with differentially infiltrating immune cells and were selected for subsequent analysis. The intersection of inflammation-related differentially expressed genes and genes in the significant modules, as shown by Venn diagrams, was identified as immune-associated inflammation differentially expressed genes (DGEs).

[0023] 4. PPI Network Construction and Key Gene Screening: A PPI network was constructed for the target genes using the STRING database (https: / / www.string-db.org / ), with an interaction score > 0.4 as the screening criterion. The PPI network generated from the STRING database analysis was visualized using Cytoscape software (http: / / www.cytoscape.org / ). Five key hub genes were identified using the MCC algorithm using the cytoHubba plugin in Cytoscape.

[0024] 5. ROC Curve Analysis and Expression Analysis: Receiver Operating Characteristic (ROC) curve analysis was performed on the selected genes to verify their accuracy. The "pROC" software package was used for ROC curve analysis. Hub genes with an AUC > 0.7 were considered useful for disease diagnosis.

[0025] 6. Correlation Analysis between Infiltrating Immune Cells and Diagnostic Genes: Immune infiltration analysis was performed using the CIBERSORT algorithm. Correlation analysis was performed in R to examine the relationship between the selected genes and the number of infiltrating immune cells. The correlation between diagnostic genes and immune cells was visualized using lollipop plots.

[0026] 7. PCR Verification of Expression: Expression of the selected genes was examined in normal tissues and cervical cancer tissues positive for HPV16 alone, with 5-6 samples per group. Complementary DNA synthesis and real-time PCR reverse transcription were performed under appropriate conditions. The real-time fluorescence quantitative PCR protocol was as follows: initial denaturation at 95°C for 30 seconds, 40 cycles of PCR at 95°C for 3 seconds, and at 60°C for 30 seconds. Cycle threshold (Ct) values were recorded, and expression of each gene was calculated using the 2-ΔΔCt function. 18S rRNA or GAPDH was used as an internal reference control for normalization. The primer sequences used are shown in Table 1.

[0027] Table 1 Primers used in PCR assay 2. Experimental Results 1. Identification of inflammation-related differentially expressed genes: Figure 1 As shown in Figure 2, after analyzing the processed data, we identified 520 DEGs, including 240 up-regulated genes and 280 down-regulated genes. We intersected the selected DEGs with inflammation-related genes to obtain 37 inflammation-related differentially expressed genes.

[0028] 2. Identification of differentially expressed genes in immune-related inflammation: 22 types of immune cell profiles were identified from CC and control samples. WGCNA was used to analyze the expression correlation of all genes, and a total of 1445 genes were found to have expression correlations with each other. The soft threshold of gene expression correlation was set to β = 6, and a weighted gene co-expression network was constructed. Figure 2 As shown, cluster analysis was performed on the network, setting the minimum module size to 60. Gene modules and a hierarchical clustering tree were constructed. The clustering tree was then pruned at a similarity coefficient of 0.25. Ultimately, seven gene modules were obtained. Furthermore, the blue and brown modules showed the strongest correlation with the signature (infiltrating immune cells in cervical cancer). We intersected the selected immune-related genes with the genes in the important modules to obtain 12 differentially expressed immune-associated inflammation genes (DEGs).

[0029] 3. Protein interaction network construction and hub gene screening: In order to screen the most important core genes from the above 15 genes, we used String to construct a protein interaction network for these genes and displayed the results using Cytoscape. Figure 3 As shown, we also used the Cytoscape software package "CytoHubba" to identify five hub genes at the center of the interaction network: CXCL8, CXCL10, CX3CR1, FCGR3B, and SELL. Furthermore, the correlations among these five hub genes are shown. These five genes are at the core of the protein-protein interaction network. Not only do their own expression levels vary significantly in tumor cells, but they also interact with most other differentially expressed genes.

[0030] 4. ROC curve analysis of FCGR3B gene: The ROC curve was drawn to evaluate the diagnostic value of FCGR3B as a biomarker for CC diagnosis. Figure 4 As shown in the figure, a gene with an AUC > 0.7 can be used as a diagnostic marker. Its AUC is 0.798. These results indicate that the FCGR3B gene has good diagnostic value.

[0031] 5. Correlation analysis between FCGR3B gene and immune cells: CIBERSORT technology was used to study the correlation between FCGR3B expression and infiltrating immune cells in CC. Figure 5 As shown in Figure 3, FCGR3B was highly positively correlated with neutrophils and negatively correlated with memory resting CD4 T cells.

[0032] 6. Expression level of FCGR3B gene in tissues: The expression of each molecule in normal tissues and cervical cancer tissues was detected. Figure 6 As shown, the FCGR3B level was higher than that in the control group, which was consistent with our prediction.

[0033] Inflammation and immune-related biomarkers can not only directly detect and monitor the occurrence and development of the disease, but also provide new perspectives for the targeted treatment of cervical cancer, achieve early detection and early treatment, and further improve the prognosis of cervical cancer patients.

[0034] By integrating two GEO datasets, we identified 520 inflammatory DEGs associated with cervical cancer. Subsequently, we used CIBERSORT and WGCNA to identify seven immune infiltration-related gene modules. Correlation analysis revealed that the blue and brown modules were most highly correlated with cervical cancer. We further intersected the inflammatory DEGs with the key gene modules to identify 12 differentially expressed immune-related inflammation genes. By constructing a PPI network, we identified five hub genes associated with both inflammation and immune cell infiltration in cervical cancer. Ultimately, we discovered a novel inflammation- and immunity-related marker—FCGR3B.

[0035] The mechanism of action of FCGR3B in cervical cancer has not been reported in any domestic or international literature to date. There are three main types of Fcγ receptors: FcγRI, FcγRII, and FcγRIII. These are categorized by their affinity for IgG: FcγRI (high-affinity receptor), FcγRII, and FcγRIII (low-affinity receptor). Low-affinity Fc receptor genes include three FCGR2 genes (FCGR2A, FCGR2B, and FCGR2C) and two FCGR3 genes (FCGR3A and FCGR3B). FCGR3B is a member of the Fcγ receptor family, primarily expressed on human neutrophils at the 1q23.3 locus. This locus modulates the expression of the corresponding receptor and antigen-specific immunity. FCGR3B is also a key immune receptor controlling both humoral and innate immunity, playing a crucial role in maintaining autoimmune homeostasis and responses to infection. Disruption of this homeostasis can lead to increased susceptibility to autoimmunity and infection. A large number of previous studies have suggested that FCGR3B is a risk factor for a series of autoimmune diseases, such as systemic lupus erythematosus and rheumatoid arthritis.

[0036] This study evaluated the diagnostic value of FCGR3B in cervical cancer using receiver operating characteristic (ROC) curves and analyzed the correlation between SELLs and immune cells using CIBERSORT technology. This analysis showed that FCGR3B expression was significantly higher in cervical cancer than in normal cervical tissue. Validation of our clinical samples largely supported this conclusion. PCR analysis revealed that the relative expression of FCGR3B in cervical cancer tissue was significantly higher than that in normal cervical tissue, with statistically significant differences (P < 0.05). Therefore, FCGR3B has the potential to become a diagnostic biomarker for cervical cancer and a key therapeutic target, providing new insights and insights for its prevention and treatment.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A biomarker for diagnosing cervical cancer, characterized by: The biomarker is FCGR3B, and the expression level of FCGR3B is specifically upregulated in cancer tissues of cervical cancer patients.

2. Use of the biomarker according to claim 1 in the development and / or preparation of a product for diagnosing cervical cancer.

3. The use according to claim 2, characterized in that: The application is to detect the expression level of the biomarker described in claim 1.

4. A product for diagnosing cervical cancer, characterized in that: The product includes: a reagent for detecting the expression level of the biomarker according to claim 1 in a biological sample.

5. A detection kit for diagnosing cervical cancer, characterized in that: The kit comprises the biomarker according to claim 1.

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