Gene marker for predicting castration resistance and prognosis risk of hormone-sensitive prostate cancer and application of gene marker

Abstract

The invention discloses a gene marker for predicting castration resistance and prognosis risk of hormone-sensitive prostate cancer, the gene marker comprises at least one of PRR7, REXO2, SKP1, MXD3, SPAG5, DHX16, NASP, TSPAN6, CCDC127 and BCAS1, and the gene marker is also used for constructing an evaluation model (LLPS-related prostate risk score, LPRS) for predicting castration resistance and prognosis risk of hormone-sensitive prostate cancer. The model can accurately predict castration resistance and death risk of hormone-sensitive prostate cancer, multi-factor Cox regression analysis finds that LPRS is an independent prognosis index of three prostate cancer queues, and further meta analysis results combine HR to 1.44 (95% CI = 1.11-1.89), that is, the risk of disease progression, recurrence or death is increased by 44% every time the LPRS is increased by one unit.

Description

Technical Field

[0001] The present invention relates to the field of gene technology, and in particular to a gene marker combination and application for predicting castration resistance and prognostic risk of hormone-sensitive prostate cancer. Background Art

[0002] Prostate cancer (PCa) is a common malignant tumor of the male genitourinary system, with a worldwide incidence and mortality rate of 14.1% and 6.8%, respectively, ranking second and fifth among male malignant tumors. Androgen deprivation therapy (ADT) is the main treatment for advanced-stage PCa and is also the first-line treatment for patients with metastatic prostate cancer. ADT inhibits the growth and survival of prostate tumor cells by blocking the action of androgens, and is initially effective. However, with prolonged treatment, nearly all patients develop castration resistance after 18 to 36 months of ADT. Despite testosterone levels dropping to extremely low "castration levels" (<50 ng / dL), tumors continue to grow, progressing from hormone-sensitive prostate cancer (HSPC) to castration-resistant prostate cancer (CRPC). The emergence of CRPC marks the entry of the disease into a more malignant and refractory stage. The patient's survival is significantly shortened, with a median overall survival of less than 2 years. It is the main cause of death in prostate cancer patients. Its pathogenic mechanism is unclear, the related molecular network is complex, and multiple signaling pathways synergistically cause the disease.

[0003] Liquid-liquid phase separation (LLPS) is an intracellular phenomenon similar to oil-water separation. Through multivalent, low-affinity interactions, such as hydrophobic and electrostatic interactions, enzymes, their substrates, and nucleic acids that perform a specific function or class of functions spontaneously aggregate into molecular condensates at specific times and locations, forming relatively independent compartments. The formation of these molecular condensates can increase the local concentration of proteins, thereby activating cellular signaling, allowing them to sense and rapidly respond to environmental changes while avoiding interference from the large amount of irrelevant substances in the cytoplasm. Dysregulation of LLPS is closely associated with the development and progression of various diseases, particularly cancer, a disease characterized by genomic instability. Tumor-associated biomolecules (mRNA, lncRNA, and tumor-associated proteins) can undergo phase separation or aberrant phase separation, affecting transcription and translation, DNA damage repair, and regulating autophagy and ferroptosis, thereby modulating the development and progression of various tumors.

[0004] Clinicopathological features such as PSA (prostate-specific antigen) levels, Gleason score, pathological tumor stage, and lymph node metastasis are commonly used clinical indicators for monitoring PCa disease progression, but their predictive capabilities are significantly limited. With the rapid development of high-throughput sequencing technology, significant progress has been made in the study of molecular markers based on DNA and RNA levels, including single-gene and multi-gene models, as well as the miRNA-based MiCaP prediction model. However, existing studies are generally plagued by insufficient sample size, high population heterogeneity, and limited genomic coverage, which seriously restrict the clinical application of these molecular markers. Furthermore, currently developed prediction models primarily focus on the risk of PCa recurrence, and effective molecular models for predicting the progression of HSPC to CRPC are still lacking. Summary of the Invention

[0005] In response to the gaps in the prior art, the present invention provides a gene marker for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer, wherein the marker includes at least one of PRR7, REXO2, SKP1, MXD3, SPAG5, DHX16, NASP, TSPAN6, CCDC127, and BCAS1.

[0006] Furthermore, high expression of PRR7, REXO2, SKP1, MXD3, SPAG5, DHX16, and NASP increased the risk of progression to castration resistance; low expression of TSPAN6, CCDC127, and BCAS1 increased the risk of progression to castration resistance.

[0007] The second aspect of the present invention provides the use of the gene marker in any of the following aspects:

[0008] A1) Use in the preparation of a kit for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer;

[0009] A2) Application in the preparation of a system for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer.

[0010] In a third aspect, the present invention provides a kit for predicting castration resistance and prognostic risk in sensitive prostate cancer, wherein the kit comprises a reagent for detecting the gene marker.

[0011] Furthermore, the kit includes reagents for detecting mRNA expression and / or protein expression of a gene.

[0012] Furthermore, the kit includes any one of the following:

[0013] B1) a gene or its transcript or protein, wherein the sequence of the transcript or protein is the sequence of one or more or all transcripts or proteins of the corresponding gene;

[0014] B2) primer molecules and / or probe molecules that hybridize to the gene or its transcript;

[0015] B3) Immune molecules of the protein expression products of the genes.

[0016] Furthermore, the kit is suitable for samples containing nucleic acids, preferably blood samples, but tissue samples, body fluid samples, exfoliated cell samples, stool samples, and urine samples can also be selected.

[0017] In a fourth aspect, the present invention provides a system for predicting castration resistance and prognostic risk in sensitive prostate cancer, the system comprising at least the following modules:

[0018] A data acquisition module, the data acquisition module is at least used to obtain the relative expression level of the gene marker;

[0019] A data processing module, the data processing module is at least used to calculate a score according to a formula, wherein the formula is: risk score = (-0.0035)*log2(BCAS1 expression level)+(-0.1163)*log2(CCDC127 expression level)+0.1588*log2(MXD3 expression level)+0.0163*log2(PRR7 expression level)+0.0723*log2(SKP1 expression level)+0.2646*log2 (SPAG5 expression level) + 0.3878*log2 (NASP expression level) + (-0.1779)*log2 (TSPAN6 expression level) + 0.3566*log2 (DHX16 expression level) + 0.0582*log2 (REXO2 expression level); a judgment module, the judgment module is at least used to compare the score obtained by the data processing module with a threshold, and a score higher than the threshold is a high-risk group; a score lower than the threshold is a low-risk group, wherein the threshold is 2.1;

[0020] A data output module is used at least to output a result.

[0021] Furthermore, the system further comprises a sample extraction module, which is configured to extract nucleic acid from a sample. Preferably, the sample comprises at least one of a blood sample, a tissue sample, a body fluid sample, an exfoliated cell sample, a stool sample, and a urine sample.

[0022] The fifth aspect of the present invention provides the use of the system in preparing an instrument, device or kit for predicting the prognosis and progression risk of papillary thyroid cancer.

[0023] The beneficial effects of the present invention include but are not limited to:

[0024] The present invention discloses that 10 key genes, namely PRR7, REXO2, SKP1, MXD3, SPAG5, DHX16, NASP, TSPAN6, CCDC127, and BCAS1, were screened out through the weighted gene co-expression network analysis (WGCNA) algorithm and LASSOCox regression, and an LPRS model was constructed. The model can accurately predict the castration resistance and mortality risk of hormone-sensitive prostate cancer. Multivariate Cox regression analysis found that LPRS is an independent prognostic indicator for three prostate cancer cohorts. Further meta-analysis results showed a combined HR of 1.44 (95% CI = 1.11–1.89), that is, the risk of disease progression, recurrence, or death increases by 44% for each unit increase in LPRS, providing a new idea for preventing hormone-sensitive prostate cancer from progressing to castration-resistant prostate cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 Schematic diagram of the results of preliminary screening of differentially expressed genes in the embodiments of the present invention, wherein: Figure 1 A. Volcano plot shows differentially expressed genes between CRPC and HSPC samples, with blue representing low-expression genes and red representing high-expression genes. Figure 1 B. Venn diagram showing the intersection of LLPS-related differentially expressed genes and LLPS-related genes affecting prognosis obtained by univariate Cox regression analysis, screening out 498 LLPS-related differentially expressed genes affecting prognosis.

[0027] Figure 2 Schematic diagram of NMF classification parameters for Primary data in an embodiment of the present invention.

[0028] Figure 3 4 LLPS-related subtypes and verification results in the embodiment of the present invention, wherein: Figure 3 A is a schematic diagram of NMF clustering, which divides 492 PCa patients into four LLPS-related subtypes; Figure 3 The BtSNE plot showed that the four LLPS subtypes formed clearly separated clusters in two-dimensional space; Figure 3 C Heat map showing the expression levels of 498 LLPS-related DEGs affecting prognosis in different LLPS-related subtypes; Figure 3 DK-M survival analysis showed significant differences in DFS among different LLPS-related subtypes.

[0029] Figure 4Schematic diagram of the comparison results of LLPS-related subtype cancer characteristics in the embodiment of the present invention, wherein Figure 4 A is a heat map showing the ssGSEA Z scores of 50 cancer signature markers among LLPS-related subtypes, with red indicating high scores and blue indicating low scores; Figure 4 B is a volcano plot showing the DEGs in the LS3 subtype, with the screening criteria of P < 0.05 and |log2FC| > 1.0; Figure 4 CD. GO enrichment and KEGG analysis results were performed on the differentially expressed genes in LS3 subtypes.

[0030] Figure 5 Schematic diagram of the results of constructing the LLPS-related prognostic risk scoring model in an embodiment of the present invention, wherein: Figure 5 A. shows the gene dendrogram and module colors; Figure 5 B shows the correlation between the 10 modules and LLPS-related subtypes, with the turquoise modules shown in red boxes; Figure 5 C is a schematic diagram of the LASSO cox regression analysis results; Figure 5 D is the LASSO coefficient of LLPS-related genes.

[0031] Figure 6 Schematic diagram of the prognostic value of LPRS scores in multiple PCa cohorts in the embodiment of the present invention, wherein Figure 6 A is the Kaplan-Meier survival curve of LPRS scores in the TCGA, SU2C / PCF Dream Team, and MSKCC cohorts; Figure 6 B is the random-effects meta-analysis showing the pooled HR of LPRS score.

[0032] Figure 7 Schematic diagram of the correlation results between LPRS scores and clinical pathological characteristics and cancer characteristics in the embodiment of the present invention, wherein: Figure 7 A is a schematic diagram of the corresponding relationship between LPRS scores and clinical characteristics of PCa patients; Figure 7 B is a schematic diagram of the correlation results between LPRS scores and cancer characteristics.

[0033] Figure 8 Schematic diagram of the expression levels of 10 core genes in HSPC and CRPC in the examples of the present invention.

[0034] Figure 9 These are the KM curves of the 10 core genes in the examples of the present invention.

[0035] Figure 10 Schematic diagram of the expression of DHX16 and AR in PCa patients progressing from HSPC to CRPC. DETAILED DESCRIPTION

[0036] The present invention is described in detail below with reference to the examples, but the present invention is not limited to these examples. Unless otherwise specified, the raw materials and catalysts in the examples of the present invention are purchased through commercial channels.

[0037] Example 1: Screening of differentially expressed genes and identification of LLPS-associated subtypes

[0038] 1. Data download:

[0039] (1) Prostate cancer-related genes: Clinical information data of hormone-sensitive primary prostate adenocarcinoma (HSPC) as of July 26, 2023 were obtained from the Cancer Genome Atlas (TCGA-PRAD) database. Gene expression data of 502 primary PCa tumor samples were downloaded, 1 metastatic cancer sample was deleted, and samples with a survival time of less than 1 month were excluded. Finally, expression data of 492 samples were retained. At the same time, corresponding clinical information of the samples, including general information, tumor stage, and prognostic status, were obtained as HSPC data. Clinical and transcriptome data of patients with metastatic castration-resistant prostate cancer (mCRPC) were obtained from the SU2C / PCF Dream Team dataset. 329 metastatic CRPC samples were downloaded, 222 samples without clinical information were removed, and samples with a survival time of less than 1 month were removed. Finally, expression data of 106 samples were retained for subsequent analysis.

[0040] (2) Genes related to liquid-liquid phase separation: Human data from three databases, CD-CODE, DrLLPS, and PhaSepDB, were downloaded and merged to obtain a total of 5595 human genes related to phase separation for subsequent analysis.

[0041] (3) The MSKCC (MSKCC / Broad, Nat Genet 2010) dataset was downloaded as a validation dataset. Data from 156 samples were downloaded from the omics data analysis platform cBioportal. After removing samples without clinical information and samples with a survival time of less than 30 days, a total of 122 expression data sets were used for subsequent analysis.

[0042] 2. Screening of differentially expressed genes and identification of LLPS-related subtypes:

[0043] (1) The RNA-seq expression data of HSPC and CRPC were normalized by log2(FPKM+1) transformation and further differential expression analysis was performed ( Figure 1A), with |log2FC|>0.5, FC as the fold change, and p<0.05 after correction as the standard. By using R language to screen differentially expressed LLPS-related genes, and performing univariate Cox regression analysis, the coxph function in the Survival package was used to perform regression analysis on each LLPS-related gene, and its impact on disease-free survival (DFS) was calculated. The screening criteria were genes with a Cox regression analysis p value less than 0.05 and a significant hazard ratio (HR). By taking the intersection with the differentially expressed genes, 498 LLPS-related differentially expressed genes involved in the progression of HSPC to CRPC and affecting prognosis were systematically screened ( Figure 1 B).

[0044] (2) The expression profiles of these 498 prognosis-related LLPS differentially expressed genes (DEGs) were used to further identify LLPS-related subtypes in prostate cancer (PCa) patients in the TCGA cohort. First, the pre-processed transcriptome data were subjected to dimensionality reduction and feature extraction using the R package "NMF" clustering algorithm. The optimal number of clusters was selected as 4 based on the evaluation of cophenetic, dispersion, and silhouette indicators. Figure 2 Subsequently, 492 PCa patients were divided into four LLPS-related subtypes: LS1 (n = 224), LS2 (n = 63), LS3 (n = 81), and LS4 (n = 124) ( Figure 3 A). Visual validation using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm showed significant differences in distribution among the four LLPS-related subtypes ( Figure 3 B). In addition, the heat map showed that the expression of DEGs in the four subtypes was significantly different ( Figure 3 C). To further evaluate the prognostic differences among different LLPS-related subtypes of PCa, patients were grouped according to different LLPS-related subtypes, and Kaplan-Meier (KM) curves were drawn for each group. The log-rank test was used to evaluate the survival differences among the subtypes, indicating that there were significant survival differences among the four LLPS-related subtypes ( Figure 3 D) Among them, LS3 has the worst prognosis, while LS2 and LS4 have a better prognosis.

[0045] (3) To further explore the possible molecular mechanisms of different LLPS-related subtypes of PCa, this study used transcriptome data of 50 cancer marker signature gene sets from the Molecular Signatures Database (MSigDB) to perform single-sample gene set enrichment analysis. The levels of 50 cancer markers were quantified by ssGSEA Z-score and visualized by heatmap ( Figure 4 A). Compared with LS1, LS2, and LS4, the LS3 subtype showed significantly upregulated cancer markers including angiogenesis, coagulation, apical junction, EMT, Wnt / β-catenin signaling pathway, Notch signaling pathway, Hedgehog signaling pathway, TGF-β signaling pathway, downregulated UV response, adipogenesis, xenobiotic metabolism, early estrogen response, late estrogen response, p53 pathway, NF-κB / TNFα signaling pathway, hypoxia, apoptosis, myogenesis, apical surface, and downregulated Kras signaling pathway, indicating that LS3 has more significant changes in the tumor microenvironment and stronger characteristics of proliferation, migration, and invasion.

[0046] To further verify the molecular mechanism characteristics of LLPS-related subtypes, 5858 upregulated genes (log2FC>1 and P<0.05) and 3953 downregulated genes (log2FC<-1 and P<0.05) in LS3 were screened out compared with LS1, LS2, and LS4 ( Figure 4 B), and GO enrichment and KEGG analysis were performed on these genes. The GO analysis results ( Figure 4 C) showed that in the LS3 transcriptome, multiple biological processes related to the tumor microenvironment, such as the composition of the extracellular matrix, the composition of the extracellular structure, the composition of the tissue outer packaging structure, cell adhesion, and cell connection, were significantly enriched. KEGG analysis results ( Figure 4 D) Further supported the findings of GO analysis, showing that key pathways of tumor microenvironment, cell proliferation, and tumor migration, such as focal adhesion, TGF-β signaling pathway, glycoprotein glycans, extracellular matrix-receptor interaction, cAMP signaling pathway, RAS signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, were significantly enriched in LS3.

[0047] Example 2: Construction and verification of LPRS scoring model

[0048] (1) Construction of LPRS scoring model

[0049] To identify the core LLPS genes that play a key role in the progression of HSPC to CRPC, this study performed WGCNA using transcriptome data of 498 LLPS genes that affect prognosis. We selected an appropriate soft-thresholding power to construct a weighted co-expression network that conforms to the scale-free network characteristics. First, the optimal soft threshold of 0.9 was selected based on the standard scale-free model fit index R2R^2R2. The expression profiles of LLPS differentially expressed genes (DEGs) related to prognosis were analyzed using the R package "WGCNA" ( Figure 5 A), 5 gene modules were obtained. These modules represent different gene expression patterns and may be associated with different biological characteristics of tumors. According to the previous results, there are similarities between LS1 and LS3, and between LS2 and LS4 in survival, genomic changes and immune characteristics. Therefore, LS1 and LS3, and LS2 and LS4 were merged. Among the 5 modules, the turquoise module contains 225 genes, showing a moderate positive correlation with LS2 and LS4, and a moderate negative correlation with LS1 and LS3 ( Figure 5 B) Therefore, these 225 genes were defined as LLPS-related core genes and used for subsequent analysis.

[0050] Next, these 225 core genes were included in the LASSOCox regression analysis ( Figure 5 C), by shrinking the regression coefficients, we found that 10 genes performed well in constructing the LLPS-related prognostic risk model ( Figure 5 D). Among them, 3 genes are protective genes and 7 genes are risk genes. Based on the results of the LASSO regression model, we calculated the prognostic risk score (LPRS) as follows:

[0051]

[0052] Among them, xi is the expression level of the selected key gene, and Coefi is the corresponding LASSO regression coefficient. That is, the LPRS score of each PCa patient = (-0.0035)*log2(BCAS1 expression level) + (-0.1163)*log2(CCDC127 expression level) + 0.1588*log2(MXD3 expression level) + 0.0163*log2(PRR7 expression level) + 0.0723*log2(SKP1 expression level) + 0.2646*log2(SPAG5 expression level) + 0.3878*log2(NASP expression level) + (-0.1779)*log2(TSPAN6 expression level) + 0.3566*log2(DHX16 expression level) + 0.0582*log2(REXO2 expression level). The expression levels of LLPS-related core genes were multiplied by the corresponding LASSO coefficients and summed to evaluate the patient's castration resistance and prognostic risk score.

[0053] (2) Validation of LPRS scoring model

[0054] ① Survival and prognosis verification

[0055] In the TCGA cohort, the median LPRS score was used to divide patients into a high LPRS score group and a low LPRS score group. The KM survival curve showed that patients with a high LPRS score had a shorter disease-free survival (DFS). This result was also verified in two other cohorts ( Figure 6 A). According to multivariate Cox regression analysis, LPRS score was significantly positively correlated with the prognosis of prostate cancer patients in all cohorts, and LPRS score was an independent prognostic indicator for all cohorts. Further meta-analysis showed that the overall combined hazard ratio (HR) of LPRS score was 1.44 (95% CI: 1.44-1.44).

[0056] CI = 1.12–1.86; Figure 6 B), further supporting the reliability of the LPRS score prognostic risk model.

[0057] ②Verification of correlation between clinical characteristics

[0058] The relationship between LPRS scores and clinical and pathological features commonly used in the TCGA cohort was further verified. LPRS was sorted from low to high to show the relationship between LPRS and clinical pathological features ( Figure 7A) Patients were divided into high LPRS group and low LPRS group by median (median value = 2.1). In terms of Gleason score group, lymph node stage (Node Stage) and clinical stage (Tumor Stage), the high LPRS group had higher Gleason score, lymph node stage (Node Stage) and clinical stage (Tumor Stage), which were significantly different from those in the low LPRS group. The heat map of the correlation between LPRS and cancer characteristics showed that LPRS was significantly positively correlated with many known cancer markers, including tumor microenvironment, proliferation, migration and invasion characteristics ( Figure 7 B). These results suggest that patients with higher LPRS have a higher risk of cancer progression, which is associated with key biological processes such as alterations in the tumor microenvironment.

[0059] In summary, the LPRS model was proven to be reliable and robust in terms of prognosis, clinical pathological characteristics, and cancer characteristics.

[0060] Example 3: Specific sample evaluation

[0061] (1) To further analyze the 10 LLPS-related hub genes in LPRS scores, we first analyzed their expression in PCa. By comparing the transcriptome data of HSPC and CRPC, we found that the expression levels of DHX16, MXD3, NASP, PRR7 and SPAG5 in CRPC tissues were significantly higher than those in HSPC tissues ( Figure 8 ).

[0062] (2) To evaluate the prognostic value of the 10 hub genes, PCa patients were divided into high expression group and low expression group according to the median value of gene expression level. Kaplan-Meier survival analysis showed that in the TCGA cohort, patients with high expression of NASP or DHX16 had significantly shorter DFS ( Figure 9 This result suggests that NASP and DHX16 may serve as potential molecular markers for poor prognosis of prostate cancer.

[0063] (3) The data of PCa patients in our hospital were collected and retrospectively analyzed, and 5 cases that met the following criteria were selected: they had a clear clinical course of progression from hormone-sensitive prostate cancer (HSPC) to castration-resistant prostate cancer (CRPC); and their tumor tissue specimens at both the HSPC and CRPC stages were completely preserved in the hospital's pathology library. The detailed clinical characteristics of the selected cases are shown in Table 1. Immunohistochemistry was used to detect the expression changes of DHX16 and AR in these 5 paired specimens. The results showed that compared with the HSPC stage, the expression levels of DHX16 and AR in CRPC tissues were significantly upregulated ( Figure 10 ). This finding further verified the above results at the clinical sample level.

[0064] Table 1 Clinical characteristics of 5 cases of HSPC progressing to CRPC

[0065]

[0066] The foregoing is merely an embodiment of the present invention. The scope of protection of the present invention is not limited by these specific embodiments but is determined by the claims of the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the technical concepts and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A gene marker for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer, characterized in that: The markers include at least one of PRR7, REXO2, SKP1, MXD3, SPAG5, DHX16, NASP, TSPAN6, CCDC127, and BCAS1.

2. The gene marker according to claim 1, characterized in that High expression of PRR7, REXO2, SKP1, MXD3, SPAG5, DHX16, and NASP increases the risk of progression to castration resistance; low expression of TSPAN6, CCDC127, and BCAS1 increases the risk of progression to castration resistance.

3. Use of the gene marker according to claims 1-2 in any of the following aspects: A1) Use in the preparation of a kit for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer; A2) Application in the preparation of a system for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer.

4. A kit for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer, characterized in that: The kit comprises a reagent for detecting the gene marker according to claim 1 or 2.

5. The kit according to claim 4, characterized in that The kit includes reagents for detecting the mRNA expression and / or protein expression of a gene.

6. The kit according to claim 4, characterized in that The kit includes any one of the following: B1) a gene or its transcript or protein, wherein the sequence of the transcript or protein is the sequence of one or more or all transcripts or proteins of the corresponding gene; B2) primer molecules and / or probe molecules that hybridize to the gene or its transcript; B3) Immune molecules of the protein expression products of the genes.

7. The kit according to claim 4, characterized in that The kit is suitable for samples containing nucleic acid, preferably blood samples, and may also be one of tissue samples, body fluid samples, exfoliated cell samples, feces samples, and urine samples.

8. A system for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer, characterized in that: The system includes at least the following modules: A data acquisition module, the data acquisition module is at least used to obtain the expression level of the gene marker according to claim 1; a data processing module, the data processing module is at least used to calculate a score according to a formula, wherein the formula is: LPRS risk score = (-0.0035)*log2(BCAS1 expression level) + (-0.1163)*log2(CCDC127 expression level) + 0.1588*log2(MXD3 expression level) + 0.0163*log2(PRR7 expression level) + 0.0723*log2(SKP1 expression level) + 0.2646*log2(SPAG5 expression level) + 0.3878*log2(NASP expression level) + (-0.1779)*log2(TSPAN6 expression level) + 0.3566*log2(DHX16 expression level) + 0.0582*log2(REXO2 expression level); a judgment module, the judgment module being at least used to compare the score obtained by the data processing module with a threshold, where a score higher than the threshold is a high-risk group; a score lower than the threshold is a low-risk group, wherein the threshold is 2.1; A data output module is used at least to output a result.

9. The system according to claim 8, characterized in that The system further comprises a sample extraction module, which is at least used for extracting nucleic acid from a sample.

10. Use of the system according to any one of claims 8 to 9 in the preparation of an instrument, device or kit for predicting castration resistance and prognostic risk in hormone-sensitive prostate cancer.

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