A novel diagnostic marker for neuroendocrine prostate cancer and application thereof

Abstract

The application provides a novel diagnostic marker for neuroendocrine prostate cancer and application thereof, and relates to the technical field of genetic engineering. The novel diagnostic marker is PCSK1N and / or C4orf48. The application overcomes the defects of the prior art, verifies the high expression of PCSK1N and / or C4orf48 in neuroendocrine prostate cancer, and based on this, the PCSK1N and / or C4orf48 can be used as a novel diagnostic marker for neuroendocrine prostate cancer.

Description

Technical Field

[0001] This invention relates to the field of genetic engineering technology, specifically to a novel diagnostic biomarker for neuroendocrine prostate cancer and its application. Background Technology

[0002] Prostate cancer (PCa) can undergo histological transformation into neuroendocrine prostate cancer (NEPC) during castration-resistant progression, a highly aggressive and poorly prognostic subtype of CRPC. Unlike adenocarcinoma, NEPC is typically independent of androgen receptor (AR) signaling and is insensitive to standard endocrine therapy. NEPC diagnosis lacks specific biomarkers, and its progression mechanism remains unclear. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a novel diagnostic biomarker for neuroendocrine prostate cancer and its application. It verifies the high expression of PCSK1N and / or C4orf48 in neuroendocrine prostate cancer, and based on this, it can be used as a novel diagnostic biomarker for neuroendocrine prostate cancer.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A novel diagnostic biomarker for neuroendocrine prostate cancer, wherein the diagnostic biomarker is PCSK1N and / or C4orf48, wherein the nucleotide sequence and amino acid sequence of PCSK1N are shown in SEQ ID NO.1 and SEQ ID NO.2, respectively; and the nucleotide sequence and amino acid sequence of C4orf48 are shown in SEQ ID NO.3 and SEQ ID NO.4, respectively.

[0005] The aforementioned diagnostic markers PCSK1N and / or C4orf48 can be used to prepare products for diagnosing neuroendocrine prostate cancer.

[0006] A diagnostic kit for neuroendocrine prostate cancer, the kit comprising reagents for detecting the expression levels of PCSK1N and / or C4orf48.

[0007] The detection reagents for the above-mentioned biomarkers PCSK1N and / or C4orf48 can be used to prepare auxiliary diagnostic products for the diagnosis of neuroendocrine prostate cancer.

[0008] This invention provides a novel diagnostic biomarker for neuroendocrine prostate cancer and its application, which has the following advantages compared with the prior art: This invention identifies the most representative malignant subgroup of CRPC, collects gene features related to CRPC and NEPC from four independent studies, and uses an addmodulescore-based assessment to identify CRIP1, PCSK1N, C4orf48, TUBB2B, and TFF3 as NEPC-related genes. Among them, PCSK1N and C4orf48 showed excellent discriminative performance, and PCSK1N and C4orf48 co-localize with SYP or CHGA in NEPC tissues. That is, the upregulation or enrichment of PCSK1N and / or C4orf48 can be used as one of the diagnostic criteria for neuroendocrine prostate cancer. Attached Figure Description

[0009] Figure 1 This is a schematic diagram illustrating the single-cell transcriptome correlation of prostate cancer progression according to the present invention. In diagram a, scRNA-seq is performed on tumor specimens from patients with PCa, mHSPC, and CRPC, combined with spatial transcriptomics, Xenium in situ sequencing, and extensive transcriptomics analysis. In vitro and in vivo experiments were used to functionally validate the role of POSTN+ cancer-associated fibroblasts in regulating immune and epithelial cell function. Diagram b shows the uniform manifold approximation and projection (UMAP) of all single cells in PCa, mHSPC, and CRPC, colored by annotated cell types. Major cell types include epithelial cells, fibroblasts (including CAFs), endothelial cells, immune cells, and proliferating cells, showing efficient cross-cohort integration and overlap at disease stages. Diagram c shows a dot plot of typical marker genes used for lineage and subtype annotation, where the size of the dots represents the proportion of cells expressing each gene, and the color intensity represents the average normalized expression level within each population. Figure 2 This is a schematic diagram of the marker gene expression and functional annotation of the main cell types in this invention; where a is a representative differentially expressed gene dot plot of different annotated cell populations, the size of the dot represents the proportion of cells expressing each gene, and the color represents the scaled average expression level; b is the first two gene set enrichment analysis (GSEA) items for each main cell population; Figure 3 A schematic diagram illustrating the identification and staging distribution of malignant epithelial cells as defined by CNV; where a is an InferCNV heatmap showing the chromosome copy number of epithelial cells relative to normal epithelial cells; b is the UMAP projection of the five malignant epithelial subclusters (MaligC0-C4); c is the R-axis of the malignant subpopulations in PCa, mHSPC, and CRPC. o / e Enrichment score; d is a CNV score heatmap for different disease stages, showing chromosome gain and loss patterns.

[0010] Figure 4A schematic diagram of different transcriptional programs for CNV-defined malignant epithelial cells; where a) is a pseudo-temporal trajectory of CNV-defined malignant epithelial cells reconstructed using Monocle and projected onto a UMAP embedding, the trajectory starting from MaligC0 and MaligC1 and diverging into three terminal states corresponding to MaligC2, MaligC3, and MaligC4; b) is a heatmap showing the pseudo-time-dependent gene module enrichment along the malignant epithelial trajectory, with early states enriched in antigen processing / presentation and cytoskeleton maintenance programs; intermediate states enriched in cell fate regulation, cytoskeleton remodeling, androgen receptor signaling, and activation of translational activity; and late states dominated by cell cycle and neuronal development-related programs; c) representative cell cycle regulators (TOP2A, CENPF, BIRC5, ... The feature map of UBE2C highlights their preferential enrichment in proliferative malignant states, especially in the MaligC4 subcluster; d shows the spatial expression patterns of typical neuroendocrine markers (CHGA, SYP, NEFL, CHGB, ASCL1) in SYP samples, validating stage-related transcriptional programs at the tissue level; e shows the expression violin plots of established and newly discovered NEPC-related genes (CRIP1, PCSK1N, C4orf48, TUBB2B, TFF3), in which PCSK1N and C4orf48 show stronger discriminatory expression; f is a schematic diagram of multiplex immunofluorescence staining of NEPC tissue, showing that PCSK1N and C4orf48 partially co-localize with neuroendocrine markers SYP or CHGA, supporting their association with neuroendocrine-like malignant states. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Example 1: 1. Single-cell transcriptome diagram of prostate cancer progression 1.1 Method 1.1.1 Single-celled organisms Ethical Statement The mHSPC single-cell cohort and AHMU-CPRC cohort have been approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (Approval No.: PJ2025-01-42). All patients signed informed consent forms. This study was conducted in strict accordance with the Declaration of Helsinki and the Good Clinical Practice (GCP) guidelines. For the publicly available datasets (PRJNA699369 and GSE137829 cohorts), the ethical approval and patient informed consent details are described in the original literature.

[0013] 1.1.2 Sample Collection and Data Sources Single-cell RNA sequencing (scRNA-seq) data from three independent cohorts were integrated, encompassing 29 prostate cancer samples. Public Dataset Cohorts: This study included two published single-cell RNA sequencing datasets. The first dataset, from the NCBI database (accession number: PRJNA699369), included 3 primary prostate cancer (PCa) samples and 3 castration-resistant prostate cancer (CRPC) samples. The second dataset, from the Gene Expression Comprehensive Database (GEO, accession number: GSE137829), contained 6 CRPC samples. Anhui Medical University First Affiliated Hospital (AHMU) mHSPC Single-Cell Cohort (AHMU-mHSPC): This study included fresh tissue samples from 17 patients with metastatic hormone-sensitive prostate cancer (mHSPC), all of whom underwent biopsy at Anhui Medical University First Affiliated Hospital. Tumors were graded according to the Gleason scoring system. None of the patients had received androgen deprivation therapy (ADT), chemotherapy, radiotherapy, or any other antitumor treatment prior to surgery. Patient clinical characteristics are shown in Table 1. In addition, 46 patients diagnosed with mHSPC (n=32) and CPRC (n=14) were collected from the First Affiliated Hospital of Anhui Medical University, and a new CPRC cohort AHMU-CRPC was established, as detailed in Table 2.

[0014] Table 1 Summary of Public Datasets

[0015] Table 2. Clinicopathological characteristics of patients in the mHSPC and AHMU-CRPC cohorts

[0016] 1.1.3 Collection and preparation of single-cell suspension samples Prostate biopsy was performed under ultrasound guidance using an 18G needle. The biopsy sample was immediately placed in MACS tissue storage solution (catalog number: 130-100-008, Miltenyi Biotec) and transferred to the laboratory for processing within 30 minutes. First, the fresh biopsy sample was gently washed with cryo-phosphate-buffered saline (PBS) to remove blood and adipose tissue. Due to the small size of the biopsy sample (approximately 1-2 mm × 15-20 mm), no further cutting was required; the entire biopsy tissue was used directly for single-cell dissociation. Using a human tumor tissue dissociation kit (catalog number: 130-095-929, Miltenyi Biotec), the tissue was dissociated for 50 minutes in a 37°C incubator containing RPMI 1640 medium (catalog number: C11875500BT, Gibco). Gentle mixing was performed every 15 minutes to promote tissue dissociation; if necessary, the dissociation time was extended to 60 minutes.

[0017] 1.1.4 Quality Control of Single-Cell Suspensions The resulting suspension was filtered through a 70 μm filter (Corning) and centrifuged at 300 × g for 5 min at 4 °C. To remove erythrocyte contamination, the pellet was treated with 1x erythrocyte lysis buffer (Solarbio) for 5 min, followed by the addition of 10 volumes of cold PBS to terminate lysis. The final cell pellet was washed twice with PBS containing 2% fetal bovine serum (FBS). Viability and single-cell percentage were rigorously quantified using an AO / PI staining kit (Nexcelom Bioscience) on a Cellometer Auto 2000. Only cell suspensions with viability exceeding 85% and single-cell percentage exceeding 80% were used for subsequent library construction.

[0018] 1.1.5 Multi-platform library preparation and sequencing Two complementary scRNA-seq strategies were employed to balance high-throughput capture efficiency and sensitive gene detection.

[0019] ① Droplet-based scRNA-seq (10x genomics): A library was constructed using the Chromium Next GEM Single Cell 3' Kit v3.1. The cell concentration was adjusted to 700–1200 cells / µL. Approximately 10,000–16,000 cells were loaded into the Chromium Next GEM ChipG chip for each reaction, with a target recovery of 6,000–10,000 cells. After GEM generation, intradrop reverse transcription and subsequent cDNA amplification were performed (12–14 cycles).

[0020] ② Combinatorial Barcoding scRNA-seq (Parse Biosciences): Approximately 100,000–200,000 cells were immobilized in 96-well plates using the Evercode™ WT Mini v2 platform (Parse Biosciences). Cell identity, including cell permeability, mRNA hybridization, and adapter ligation, was determined through three consecutive rounds of split-and-pool bar coding. This combined indexing method achieved barcoding within the entire cell, followed by template conversion and library modeling. Clinical information is listed in Table 3.

[0021] Table 3 Detailed clinical information used

[0022] 1.1.6 Single-cell RNA library preparation and sequencing Sequencing on the 10x Genomics platform: Single-cell RNA library preparation was performed strictly according to the manufacturer's instructions for the 10x Genomics Chromium Single-Cell 3' Platform (v3.1 Chemistry). In short, single-cell gel bead emulsions (GEMs) were prepared using the 10x Chromium controller (model: GCG-SR-1, 10x Genomics), the Chromium Next GEM Single-Cell 3' Kit v3.1 (catalog number: PN-1000123, 10x Genomics), and the Chromium Next GEM Chip G Single-Cell Kit (catalog number: PN-1000120, 10x Genomics). Approximately 10,000–16,000 cells were loaded per reaction, with a target recovery rate of 6,000–10,000 cells.

[0023] 1.1.7 Computational Pipeline and Data Integration Raw sequencing data from the 10x platform were processed using Cell Ranger (v7.0.0), while ParseBiosciences data were processed using Parse Analysis Pipeline (v1.0.3). Sequencing fragments were aligned with the GRCh38 human reference genome (Ensembl v104).

[0024] 1.1.8 Single-cell RNA-seq data processing and annotation Single-cell RNA sequencing data representing PCa, mHSPC, and CRPC were analyzed using the Seurat framework (v4.4.1) in R (v4.2.3)

[21] (Table 1). Initial quality control was performed to filter low-quality cells based on strict thresholds: <200 or >8,000 detected genes, <500 or >100,000 UMI counts, and excessive expression of mitochondria (>20%), ribosomes (>50%), or hemoglobin (>5%). In addition, genes detected in fewer than 3 cells were excluded from downstream analysis.

[0025] To ensure the integrity of the single-cell atlas, potential double cells were identified and removed using DoubletFinder (v2.0.4). This was achieved by generating artificial double cells and sorting individual cells according to the proportion of their artificial k-nearest neighbors (pANN). Following double filtering, technical variation was modeled using the SCTransform (SCT) workflow, which performs regularized negative binomial regression for standardization and variance stabilization.

[0026] Considering batch effects across different datasets and patient cohorts, the Harmony (v0.1.1) ensemble algorithm was employed. This process projects cells into a shared embedding space, ensuring that clustering is driven by biological characteristics rather than technological artifacts. In UMAP at a resolution of 0.5 and graph-based Louvain clustering, the first 30 principal components were used for nonlinear dimensionality reduction.

[0027] Cell types were manually labeled based on typical biomarkers: epithelial cells (EPCAM, KRT8, KRT18), endothelial cells (PECAM1, VWF, CD34), fibroblasts (COL1A1, DCN), cancer-associated fibroblasts (CAFs, FAP, ACTA2), and POSTN. + Mycafa-like cells (POSTN, ACTA2, FAP), T cells (CD3D, CD3E), CD8 + T cells (CD8A, CD8B), B cells (CD79A, MS4A1), myeloid cells (CD68, CD14), and tumor-associated macrophages (tam, CD68, CD163). To characterize the tumor microenvironment at higher resolution, further sub-clustering and fine-grained annotation of T cell, CAF, and myeloid cell populations were performed with reference to the comprehensive cell atlas. Gene sets for different T cell states were referenced to the TcellSI framework (including cytotoxicity, exhaustion, and exhaustion scores).

[0028] 1.1.9. InterCNV calculations and malignant clone identification To identify malignant epithelial cells and characterize their genomic instability, large-scale chromosomal copy number variation (CNV) inference was performed on single-cell gene expression data using intercnv (v1.12.0). Normal epithelial cells were used as a reference background, and HMMi6 (Hidden Markov Model) was used to predict discrete CNV states. One hundred genes were selected to smooth expression signals across genomic coordinates. Subsequently, unsupervised clustering of CNV profiles identified malignant subclones with significant genomic characteristics.

[0029] To elevate inferred gene segment variations to the structural features at the chromosome arm level, cytogenetic band coordinate data obtained from the UCSC Genome Explorer (hg38 cytoBand) were integrated. All gene variation regions were precisely located to the corresponding chromosome p-arms or q-arms by identifying centromere positions as cleavage points. To ensure the robustness of the biological analysis and eliminate false positives caused by local transcriptional fluctuations, a strict arm-level filtering criterion was implemented: gain or loss events were only recorded as valid arm-level CNV events when the cumulative length on a chromosome arm exceeded 30% of the total length of that arm (ratio = 0.3). Rare variations occurring in less than 5% of all cells were excluded to focus on driving changes at the clonal level. The ratio of observed to predicted values ​​(R0.05) was used. o / e This study analyzed the enrichment of different subclones in prostate cancer at different stages (PCa, mHSPC, CRPC). To quantitatively assess the extent of genomic remodeling, a copy number variation (CNV) score was calculated for each chromosome arm. This score, based on the coverage depth and frequency of specific variants in the cell population, aims to capture the genomic instability accumulated from the progression of in situ lesions to the castration-resistant phase.

[0030] 1.2 Results To define the cellular landscape of prostate cancer progression, scRNA-seq data from local PCa, mHSPC, and CRPC were integrated, combining newly generated mHSPC samples from the AHMU-PC cohort with primary and CRPC tumor datasets from our cohort and previous studies. Figure 1 a). Through rigorous quality control and cross-cohort integration, a single-cell atlas consisting of 208,382 high-quality cells was generated, exhibiting minimal batch effects observed across different disease stages. Figure 1 b).

[0031] Unsupervised clustering and subsequent label-based annotation resolved all major cellular compartments, including epithelial cells, endothelial cells, fibroblasts, mast cells, monocytes / macrophages, lymphocytes, and proliferating cell populations. Figure 1(b, c) Epithelial cells differentiated into luminal, intermediate / basal, and rod-shaped structures. Fibroblasts exhibited marked heterogeneity, including multiple CAF subtypes, such as CXCL1. + myCAFs, POSTN + myCAFs、rbm47 + Mesothelial-like CAFs and pericyte-like CAFs. The immune compartment is composed of different bone marrow and lymphocyte populations, including multiple tumor-associated macrophages and CD8+ cells. + T cell subsets, all of which are supported by typical lineage markers ( Figure 1 c). This shows differentially expressed genes among all cell subtypes ( Figure 2 a) The corresponding GSEA results are shown below. Figure 2 b.

[0032] Next, to ensure comparability, epithelial-biased datasets were excluded, and stage-dependent changes in cellular composition were quantified. The stromal population gradually expanded with disease progression, highlighting the extensive remodeling of the tumor microenvironment in CRPC. Within the CAF subset, CXCL1... + myCAFs were significantly enriched in CRPC (p = 0.0048), and the global proliferating population also increased (p = 0.015). In comparison, proflif myCAFs and POSTN... + While myCAFs did not reach statistical significance, they showed a consistent and significant increase in CRPC samples, occupying a relatively high proportion of the interstitial compartments and exhibiting stable directional changes. Simultaneously, significant changes were also observed in the immune compartments.

[0033] In summary, this integrated single-cell atlas depicts coordinated stromal expansion and selective immune reprogramming during prostate cancer progression, establishing a cellular framework to capture key microenvironmental shifts associated with castration-resistant disease.

[0034] 2. CNV-defined malignant epithelial cells differentiate into proliferative and neuroendocrine-like forms. 2.1 Methods 2.1.1 Trajectory Reasoning The developmental trajectory of malignant epithelial cells was reconstructed using Monocle3. Cells were aligned along a pseudo-time axis based on global gene expression profiles. Spatial autocorrelation tests (graph_test; q < 0.01) were used to identify trajectory-dependent genes, and branch points were analyzed to determine lineage fate determination during tumor progression.

[0035] 2.1.2 Spatial transcriptome analysis using the BayesSpace algorithm and 10x Visium. Formalin-fixed paraffin-embedded tissue sections from neuroendocrine prostate cancer and castration-resistant prostate cancer underwent 10x Genomics Visium spatial transcriptome sequencing. Libraries were prepared according to standard procedures and sequenced on the Illumina NovaSeq platform. Space Ranger (v1.3.0) was used to align to the GRCh38 reference genome and generate the raw expression matrix. To improve spatial resolution, the BayesSpace algorithm (v1.4) was used to cluster spots and infer sub-spot expression to obtain enhanced spatial coordinates.

[0036] 2.2 Results To reconstruct the evolutionary trajectory of prostate cancer epithelial cells, a malignant epithelial cell population was defined based on large-scale copy number variation (CNV) profiles inferred from single-cell transcriptomes. InferCNV analysis, using normal epithelial cells as a reference, identified epithelial cells with extensive chromosomal aberrations, thus enabling a clear distinction between malignant and non-malignant populations. Figure 3 a). Subsequent pedigree reconstruction and pseudo-time series analysis were limited to malignant epithelial cells as defined by CNV.

[0037] Unsupervised clustering identified five transcriptionally distinct malignant subsets (MaligC0-MaligC4), which showed different distributions in disease staging. Figure 3 b). R o / e Enrichment analysis revealed a stage-specific pattern: MaligC2 and MaligC4 were enriched in CRPC, MaligC1 and MaligC3 were enriched in localized PCa and mHSPC, and enriched in CRPC. MaligC0 was consistently detected in all disease stages, indicating the presence of a persistent malignant population. Figure 3 c). CNV burden analysis showed that genomic instability increased with disease progression: primary PCa exhibited limited chromosomal alterations, mHSPC exhibited expanded chromosomal alterations, and CRPC exhibited the most complex CNV structure with extensive amplification and relapse loss (c). Figure 3 d).

[0038] Using Monocle for quasi-temporal reconstruction, a continuous evolution trajectory from MaligC0 to MaligC1 was revealed, and it was divided into three terminal states: MaligC2, MaligC3, and MaligC4. Figure 4 a). The early stages are enriched by antigen processing and cytoskeleton maintenance programs; the intermediate stages activate programs related to cell fate regulation, androgen receptor signaling, and translational activity; while the late stages are mainly dominated by cell cycle, mitosis, and neuronal development programs. Figure 4b). Proliferative malignant states, particularly MaligC4, exhibit strong upregulation of typical cell cycle regulators, including TOP2A, CENPF, BIRC5, and UBE2C. Figure 4 c), indicating the transcriptional state associated with CRPC. In contrast, MaligC2 is enriched in neuroendocrine-related programs, with selective upregulation of neuroendocrine-related genes such as RBP1, TUBB2B, IFI27, CHGA, and SCG2.

[0039] To identify the most representative malignant subgroup of CRPC, genetic signatures associated with CRPC and NEPC were collected from four independent studies, and an addmodulescore-based assessment was applied: ①Beltran H, Prandi D, Mosquera JM, Benelli M, Puca L, Cyrta J, Marotz C, Giannopoulou E, Chakravarthi BV, Varambally S, Tomlins SA, NanusDM, Tagawa ST, Van Allen EM, Elemento O, Sboner A, Garraway LA, Rubin MA, Demichelis F. Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat Med. 2016 Mar;22(3):298-305. doi: 10.1038 / nm.4045. Epub 2016 Feb 8. PMID:26855148; PMCID: PMC4777652. ②Zhang X, Coleman IM, Brown LG, True LD, Kollath L, Lucas JM, LamHM, Dumpit R, Corey E, Chéry L, Lakely B, Higano CS, Montgomery B, Roudier M, Lange PH, Nelson PS, Vessella RL, Morrissey C. SRRM4 Expression and the Lossof REST Activity May Promote the Emergence of the Neuroendocrine Phenotype in Castration-Resistant Prostate Cancer. Clin Cancer Res. 2015 Oct 15;21(20):4698-708. doi: 10.1158 / 1078-0432.CCR-15-0157. Epub 2015 Jun 12. PMID: 26071481; PMCID: PMC4609255. ③Labrecque MP, Coleman IM, Brown LG, True LD, Kollath L, Lakely B, Nguyen HM, Yang YC, da Costa RMG, Kaipainen A, Coleman R, Higano CS, Yu EY, Cheng HH, Mostaghel EA, Montgomery B, Schweizer MT, Hsieh AC, Lin DW, Corey E, Nelson PS, Morrissey C. Molecular profiling stratifies diverse phenotypes of treatment-refractory metastatic castration-resistant prostate cancer. J Clin Invest. 2019 Jul30;129(10):4492-4505. doi: 10.1172 / JCI128212. PMID: 31361600; PMCID:PMC6763249. ④Tsai HK, Lehrer J, Alshalalfa M, Erho N, Davicioni E, Lotan TL. Gene expression signatures of neuroendocrine prostate cancer and primary small cell prostatic carcinoma. BMC Cancer. 2017 Nov 13;17(1):759. doi: 10.1186 / s12885-017-3729-z.PMID: 29132337; PMCID: PMC5683385. Spatially, NEPC samples highly expressed neuroendocrine markers (CHGA, SYP, NEFL, CHGB, ASCL1). Figure 4 d). In addition, CRIP1, PCSK1N, C4orf48, TUBB2B, and TFF3 were identified as NEPC-related genes, among which PCSK1N and C4orf48 showed excellent distinguishing performance ( Figure 4e). Multiplex immunofluorescence confirmed that PCSK1N and C4orf48 co-localized with SYP or CHGA in NEPC tissues. Figure 4 f). These results indicate that CNV-defined malignant epithelial cells follow a bifurcated evolutionary trajectory during prostate cancer progression, differentiating into either proliferative CRPC-like or neuroendocrine-like terminal states. In different independent cohorts, this lineage bifurcation is accompanied by increased genomic instability and reproducible transcriptional programs, highlighting epithelial plasticity as a key feature of advanced prostate cancer and treatment resistance.

[0040] The nucleotide sequence (SEQ ID NO.1) of PCSK1N is as follows: gcagcctcgc cagctcgccc cggcactgcg cacttgccag ccagtccgcc cgtccggagcccggctcgct ggggcagcat ggcggggtcg ccgctgctct gggggccgcg ggccgggggc gtcggccttttggtgctgct gctgctcggc ctgtttcggc cgccccccgc gctctgcgcg cggccggtaa aggagccccgcggcctaagc gcagcgtctc cgcccttggc tgagactggc gctcctcgcc gcttccggcg gtcagtgccccgaggtgagg cggcgggggc ggtgcaggag ctggcgcggg cgctggcgca tctgctggag gccgaacgtcaggagcgggc gcgggccgag gcgcaggagg ctgaggatca gcaggcgcgc gtcctggcgc agctgctgcgcgtctggggc gccccccgca actctgatcc ggctctgggc ctggacgacg accccgacgc gcctgcagcgcagctcgctc gcgctctgct ccgcgcccgc cttgaccctg ccgccctagc agcccagctt gtccccgcgcccgtccccgc cgcggcgctc cgaccccggc ccccggtcta cgacgacggc cccgcgggcc cggatgctgaggaggcaggc gacgagacac ccgacgtgga ccccgagctg ttgaggtact tgctgggacg gattcttgcgggaagcgcgg actccgaggg ggtggcagcc ccgcgccgcc tccgccgtgc cgccgaccac gatgtgggctctgagctgcc ccctgagggc gtgctggggg cgctgctgcg tgtgaaacgc ctagagaccc cggcgccccaggtgcctgca cgccgcctct tgccaccctg agcactgccc ggatcccgtg caccctggga cccagaagtgcccccgccat cccgccaccaggactgctcc ccgccagcac gtccagagca acttaccccg gccagccagccctctcaccc gaggatccct accccctggc cccacaataa acatgatctg aagca; The amino acid sequence of PCSK1N (SEQ ID NO.2) is as follows: magspllwgp raggvgllvl lllglfrppp alcarpvkep rglsaasppl aetgaprrfrrsvprgeaag avqelarala hlleaerqer araeaqeaed qqarvlaqll rvwgaprnsd palgldddpdapaaqlaral lrarldpaal aaqlvpapvp aaalrprppv yddgpagpda eeagdetpdv dpellryllgrilagsadse gvaaprrlrr aadhdvgsel ppegvlgall rvkrletpap qvparrllpp; The nucleotide sequence of C4orf48 (SEQ ID NO.3) is as follows: atgcgcgttg cgcgccggac gcggaacgtc tgccggtgtc cccgcgctgc tggtcccggggtccctgaac cgcggtaagg gcggtggtgc gggcgtccga atgggcgttt tctagatacg gggcgcggactagaggctcg ctgggcccgg agaccggcgg actggagtcg gggaaccgga gggcggcccc gctccctctgctggccatgg cccccccgcc cgcgtgccgg tccccgatgt caccgccgcc gccgccgctg ctgctgctgctgctgagtct ggcgctgctg ggcgcccggg cccgcgccga gcccgccggg agtgccgtcc ccgcgcagagccgcccatgc gtggactgcc acgccttcga gttcatgcag cgcgccctgc aggacctgcg gaagacagcctgcagcctgg acgcgcggac ggagacccta ctgctgcagg cagagcgccg tgccctgtgt gcctgctggccagcggggca ctgaggacca cgctgctccg tgtgaataaa tgcccagtggca; The amino acid sequence of C4orf48 (SEQ ID NO.4) is as follows: mapppacrsp msppppplll lllslallga raraepagsa vpaqsrpcvd chafefmqraqdlrktacs ldartetllll qaerralcac wpagh.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A novel diagnostic biomarker for neuroendocrine prostate cancer, characterized in that, The diagnostic biomarkers are PCSK1N and / or C4orf48, wherein the nucleotide and amino acid sequences of PCSK1N are shown in SEQ ID NO.1 and SEQ ID NO.2, respectively; and the nucleotide and amino acid sequences of C4orf48 are shown in SEQ ID NO.3 and SEQ ID NO.4, respectively.

2. The use of the diagnostic markers PCSK1N and / or C4orf48 as described in claim 1 in the preparation of products for diagnosing neuroendocrine prostate cancer.

3. A diagnostic kit for neuroendocrine prostate cancer, characterized in that: The kit contains reagents for detecting the expression levels of PCSK1N and / or C4orf48.

4. The use of a detection reagent for detecting the diagnostic biomarkers PCSK1N and / or C4orf48 as described in claim 1 in the preparation of an auxiliary diagnostic product for the diagnosis of neuroendocrine prostate cancer.

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