CeRNA regulatory network for early diagnosis or prognosis analysis of breast cancer and application thereof

Abstract

The application provides a ceRNA regulation network for early diagnosis or prognosis analysis of breast cancer and an application thereof, and belongs to the technical field of auxiliary diagnosis of breast cancer.The application first discloses a ceRNA regulation network for early diagnosis or prognosis analysis of breast cancer, which is an lncRNAsAC145207.5 / hsa-miR-10b-5p / NUSAP1 regulation network.Among them, the AC145207.5 gene and the NUSAP1 gene are highly expressed in the serum of breast cancer patients, and the hsa-miR-10b-5p gene is lowly expressed in the serum of breast cancer patients.The mechanism may be that the lncRNA AC145207.5 may play a ceRNA role by competitively combining hsa-miR-10b-5p, regulate the expression of NUSAP1, and then affect the occurrence and development of breast cancer.Therefore, the detection kit based on the detection of the expression level of the ceRNA regulation network lncRNAsAC145207.5 / hsa-miR-10b-5p / NUSAP1 gene / protein can conveniently and quickly realize the detection of breast cancer at the molecular level, and the detection efficiency is high.

Description

Technical Field

[0001] This invention belongs to the field of breast cancer auxiliary diagnostic technology, specifically relating to a ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer and its application. Background Technology

[0002] Nucleolar and spindle-associated protein 1 (NUSAP1) is a microtubule-binding protein that plays a crucial regulatory role in mitosis, acting as a key agent in processes including cytoplasmic segregation, chromosome segregation, microtubule crosslinking, spindle assembly, and binding and attachment to chromosomes. SAPs (including SAF-A / B, Acinus, and PIAS), as domains of NUSAP1, regulate the interaction between NUSAP1 protein and chromatin, as well as the stability of microtubules around chromosomes. Overexpression of NUSAP1 can lead to abnormal microtubule binding and inhibit its in vitro depolymerization, resulting in spindle dysfunction and intracellular genomic imbalance. Furthermore, NUSAP1 deficiency leads to spindle disintegration, causing chromosome misalignment and cytokinesis defects. Currently, numerous studies have shown that abnormally high expression of NUSAP1 is a key factor contributing to cancer progression, including lung cancer, pancreatic cancer, bladder cancer, hepatocellular carcinoma, renal cell carcinoma, prostate cancer, and gastric cancer. However, research on the role of NUSAP1 in breast cancer remains relatively scarce. Although some previous studies have suggested that NUSAP1 is associated with tumor immune invasion

[29] , substantial evidence is still lacking. In lung adenocarcinoma, m6A modification is associated with NUSAP1 expression, but this association has not yet been confirmed in breast cancer. In addition, some studies have shown that the Wnt / β-catenin / EMT pathway may be a downstream pathway of NUSAP1, which can promote the proliferation and invasion of triple-negative breast cancer. However, the upstream regulatory mechanism of NUSAP1 remains unclear. Non-coding RNAs are a widely distributed class of RNA molecules with important biological functions such as regulating gene expression. Competing endogenous RNAs (ceRNAs) are a class of RNA molecules that can mutually regulate each other by competitively binding to the same miRNA. The ceRNA theory based on this mechanism has been widely used to predict and construct upstream pathways that regulate gene expression. Summary of the Invention

[0003] This invention uses a target gene prediction database to predict candidate miRNAs upstream of NUSAP1. Based on TCGA-BRCA transcriptome data, Spearman correlation analysis is used to assess the correlation between NUSAP1 expression and these potential miRNAs. Based on the ceRNA hypothesis, the most likely upstream miRNAs of NUSAP1 in breast cancer are screened. The starBase database is used to predict the lncRNAs most likely to bind to these miRNAs. After analyzing expression and survival correlations using TCGA-BRCA transcriptome data and clinical information, the upstream regulatory network most likely regulating NUSAP1 expression in breast cancer is constructed: lncRNAsAC145207.5 / hsa-miR-10b-5p / NUSAP1.

[0004] The technical solution adopted in this invention is as follows: In a first aspect, the present invention provides a ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer, wherein the ceRNA regulatory network is a lncRNAsAC145207.5 / hsa-miR-10b-5p / NUSAP1 regulatory network.

[0005] The gene ID for NUSAP1 in the NCBI database is NM_016359.5. 1 gtggcgccag ggatttgaac cgcgctgacg aagtttggtg atccatcttc cgagtatcgc61 cgggatttcg aatcgcgatg atcatcccct ctctagagga gctggactcc ctcaagtaca 121gtgacctgca gaacttagcc aagagtctgg gtctccgggc caacctgagg gcaaccaagt 181tgttaaaagc cttgaaaggc tacattaaac atgaggcaag aaaaggaaat gagaatcagg 241atgaaagtca aacttctgca tcctcttgtg atgagactga gatacagatc agcaaccagg 301aagaagctga gagacagcca cttggccatg tcaccaaaac aaggaagg tgcaagactg 361tccgtgtgga ccctgactca cagcagaatc attcagagat aaaaaataagt aatcccactg 421aattccagaa tcatgaaaag caggaaagcc aggatctcag agctactgca aaagttcctt 481ctccaccaga cgagcaccaa gaagctgaga atgctgtttc ctcaggtaac agagattcaa 541aggtacctt agaaggaaag aaatctctct acacagatga gtcatccaaa cctggaaaaa 601ataaaagaac tgcaatcact actccaaact ttaagaagct tcatgaagct cattttaagg 661aaatggagtc cattgatcaa tatattgaga gaaaaaagaa aacttttgaa gaacacatt 721ccatgaatga actgaagcag cagcccatca ataagggagg ggtcaggact ccagtacctc 781caagagaag actctctgtg gcttctactc ccatcagcca acgacgctcg caaggccggt 841cttgtggccc tgcaagtcag agtaccttgggtctgaaggg gtcactcaag cgctctgcta 901tctctgcagc taaaacgggt gtcaggtttt cagctgctac taaagataat gagcataagc 961gttcactgac caagactcca gccagaaagt ctgcacatgt gaccgtgtct gggggcaccc 102actg1gcgcgc gggacacaca aattaaagac catcacgggg aattctgctg 1081ctgttattac cccattcaag ttgacaactg aggcaacgca gactccagtc tccaataaga 1141aaccagtgtt tgatcttaaa gcaagttttgt ctcgtcccct caactatgaa ccacacag1201aggagga accatagga caatctaaag aaataatta tctaaatcaa catgtcaaca 1261gaattaactt ctacaagaaa acttacaaac aaccccatct ccagacaaag gaagagcaac 1321ggaagaaacg cgagcaagaa cgaaaggaga agaaagcaaa ggttttggga atgc38cctgagatt1 tataattt ttaacatctt gtaaatattc ctgtattctc 1441aactttttc cttttgtaaa tttttttttt ttgctgtcat ccccacttta gtcacgagat 1501ctttttctgc taactgttca tagtctgtgt agtgtccatg ggttcttcat gtgctct acgattct 1441aacttctct acgattctc515 cttaaagctc aaattctttg ggatggtttt tacttaagtc 1621cattaacaat tcaggtttct aacgagaccc atcctaaaat tctgtttcta gatttttaat 1681gtcaagttcc caagttcccc ctgctggttc tatattaac agaactgcagtcttctgcta 1741gccaatagca tttacctgat ggcagctagt tatgcaagct tcaggagaat ttgaacaata 1801acaagaatag ggtaagctgg gatagaaagg ccacctcttc actctctata gaatatagta 1861acctttatga aacggggcca tatagtttgg ttatgacatc aatattttac ctaggtgaaa 1921ttgtttaggc ttatgtacct tcgttcaaat atcctcatgt aattgccatc tgtcactcac 1981tatattcaca aaaataaaac tctacaactc attctaacat tgcttactta aaagctacat 2041agccctatcg aaatgcgagg attaatgctt taatgctttt agagacaggg tctcactgtg 2101ttgcccaggc tggtctcaaa ctccaccaaa tgtacttctt attcatttta tggaaaagac 2161taggctttgc ttagtatcat gtccatgttt ccttcacctc agtggagctt ctgagtttta 2221tactgctcaa gatcgtcata aataaaattt tttctcattg tca The gene ID for hsa-miR-10b-5p in the NCBI database is NC_000002.12: (176150303…176150412); AC145207.5 is named in the Ensembl database as: ENSG00000263731; Chromosome 17:81,878,425-81,881,106 reverse strand; GRCh38:CM000679.2.

[0006] Secondly, the present invention provides the application of a ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer in the preparation of drugs for early diagnosis or prognostic analysis of breast cancer.

[0007] In some embodiments, the drug promotes the expression of lncRNAs AC145207.5 and / or inhibits the expression of hsa-miR-10b-5p and / or promotes the expression of NUSAP1 in early diagnosis or prognostic analysis of breast cancer.

[0008] Thirdly, the present invention provides the application of a ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer in the preparation of a kit for early diagnosis or prognostic analysis of breast cancer.

[0009] In some embodiments, the kit includes a specific upstream primer for AC145207.5 gene real-time PCR: 5′-ATTGACTGGCCAAGCATTTG-3′; a specific downstream primer for AC145207.5 gene real-time PCR: 5′-ACATGATCACAGACAAGCTGA-3′; a specific upstream primer for NUSAP1 gene real-time PCR: 5′-CAACCTGAGGGCAACCAAGT-3′; and a specific downstream primer for NUSAP1 gene real-time PCR: 5′-CCACTTGCAGCTTCCTTTGAG-3′.

[0010] In some embodiments, the expression level of the hsa-miR-10b-5p gene is negatively correlated with the incidence of breast cancer, while the expression levels of the AC145207.5 gene and the NUSAP1 gene are positively correlated with the risk of breast cancer.

[0011] The beneficial effects of this invention are: This invention discloses for the first time a ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer. This ceRNA regulatory network is a lncRNAs AC145207.5 / hsa-miR-10b-5p / NUSAP1 regulatory network. Specifically, the AC145207.5 gene and the NUSAP1 gene are highly expressed in the serum of breast cancer patients, while the hsa-miR-10b-5p gene is lowly expressed in the serum of breast cancer patients. The mechanism may be that lncRNA AC145207.5 may exert its ceRNA effect by competitively binding to hsa-miR-10b-5p, regulating the expression of NUSAP1, and thus influencing the occurrence and development of breast cancer. Therefore, a detection kit based on detecting the expression level of the ceRNA regulatory network lncRNAsAC145207.5 / hsa-miR-10b-5p / NUSAP1 gene / protein can conveniently and quickly detect breast cancer at the molecular level, with high detection efficiency and strong specificity. At the same time, lncRNAsAC145207.5 / hsa-miR-10b-5p / NUSAP1 is an innovative application for the auxiliary diagnosis, detection and screening of breast cancer. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 NUSAP1 in expression and diagnostic analysis; (A) Expression level of NUSAP1 in 33 cancer and normal tissues from the TCGA database; (B) Expression level of NUSAP1 in TCGA-BRCA tumor tissues and paired adjacent normal tissues; (CD) mRNA expression level of NUSAP1 in breast cancer tissues and normal tissues from GSE42568 and GSE15852; (E) ROC curve analysis of NUSAP1 in TCGA-BRCA. (ns, meaningless;) * P-value < 0.05; ** P-value < 0.01; *** P-value < 0.001.

[0014] Figure 2 Analysis of NUSAP1 expression levels in different clinicopathological features based on the TCGA-BCRA cohort; (A) Age; (B) T stage; (C) N stage; (D) M stage; (E) Pathological stage; (F) ER status; (G) PR status; (H) HER2 status; (I) PAM50 classification. (*P value < 0.05; **P value < 0.01; ***P value < 0.001).

[0015] Figure 3 Survival analysis of NUSAP1 in breast cancer; Kaplan-Meier curve analysis of overall survival (OS) in breast cancer; (B) Kaplan-Meier curve analysis of recurrence-free survival (RFS) in breast cancer; (C) Kaplan-Meier curve analysis of distant metastasis-free survival (DMFS) in breast cancer; (D) Kaplan-Meier curve analysis of progression-free survival (PPS) in breast cancer.

[0016] Figure 4 qRT-PCR and immunohistochemical staining analysis of NUSAP1 expression in breast cancer (A) Comparison of NUSAP1 mRNA expression in normal human breast epithelial cell line (MCF-10A) and human breast cancer cell lines (including MB-231, BT549, T47D, and SKBR3); (B) Comparison of NUSAP1 expression in 113 breast cancer tissues and 27 normal breast tissues; (C) Survival analysis of NUSAP1 protein levels in 113 breast cancer tissues; (D) Detection of NUSAP1 protein expression in adjacent breast cancer tissues using IHC; (E) Detection of NUSAP1 protein expression in breast cancer tissues using IHC. * P-value < 0.05; ** P-value < 0.01; *** P-value < 0.001.

[0017] Figure 5 Mfuzz expression pattern clustering analysis; (A) Correlation analysis between NUSAP1 and 50 gene clustering modules; (B) ssGSEA scores of gene clustering modules in the normal group (C) and breast cancer group (P); (C) Correlation analysis between Cluster 48 gene expression and NUSAP1. * P-value < 0.05; ** P-value < 0.01; *** P-value < 0.001.

[0018] Figure 6 : Identify gene clustering modules most significantly associated with NUSAP1 in breast cancer; (A) Correlation analysis between NUSAP1 and 50 gene clustering modules; (B) ssGSEA scores of gene clustering modules in the normal group (C) and breast cancer group (P); (C) Correlation analysis between Cluster 48 gene expression and NUSAP1. * P-value < 0.05; ** P-value < 0.01; *** P-value < 0.001.

[0019] Figure 7 Enrichment analysis of NUSAP1-related genes (Cluster 48); (A) Bubble diagram of GO enrichment analysis; (B) Bubble diagram of DO enrichment analysis; (CD) Network diagram and pathway diagram of KEGG enrichment analysis.

[0020] Figure 8 Correlation analysis of NUSAP1 with 21 genes related to m6A modification in breast cancer; (A) The correlation between NUSAP1 expression and 21 m6A modification-related genes was studied using the TCGA-BRCA and GEPIA-BRCA cohorts; (B) A scatter plot of the expression relationship between NUSAP1 and the 10 most significantly associated m6A modification genes, based on the TCGA-BRCA cohort; (C) The expression levels of 21 m6A modification-related genes in the NUSAP1 high-expression and low-expression groups of the TCGA-BRCA cohort. (ns, meaningless;) * P-value < 0.05; ** P-value < 0.01; *** P-value < 0.001.

[0021] Figure 9 The relationship between NUSAP1 expression and tumor immune cell infiltration in breast cancer; (A) Correlation between NUSAP1 expression and infiltration levels of six different immune cells; (B) Immune cell infiltration levels in different NUSAP1 gene copy number variation (CNV) groups; (C) Enrichment scores of 24 immune cells in the NUSAP1 high-expression and low-expression groups in breast cancer. (ns, meaningless;) * P-value < 0.05; ** P-value < 0.01; *** P-value < 0.001.

[0022] Figure 10 The correlation between NUSAP1 and immune checkpoints and immunotherapy response (AH) Correlation analysis of NUSAP1 expression with immune checkpoints (CD274, HAVCR2, CTLA4, LAG3, PDCD1, PDCD1LG2, TIGIT, SIGLEC15) in breast cancer; (I) Expression levels of immune checkpoint genes in the NUSAP1 high-expression and low-expression groups in the TCGA-BRCA cohort; (J) Kaplan-Meier curve analysis of the NUSAP1 high / low expression groups in the IMvigor 210 cohort; (K) NUSAP1 expression levels in the CR / PR and SD / PD groups of the IMvigor 210 cohort; (L) Percentage of patients in the IMvigor 210 cohort who responded to and did not respond to antiPD-L1 immunotherapy in the NUSAP1 high-expression and low-expression groups. CR: Complete response; PR: Partial response; SD: Stable disease; PD: Progressive disease. (ns, meaningless;) * P-value < 0.05; ** P-value < 0.01; *** P-value < 0.001.

[0023] Figure 11 hsa-miR-10b-5p may be an upstream miRNA that regulates NUSAP1 expression in breast cancer; (A) Cytoscape visualization of the miRNAs-NUSAP1 regulatory network; (B) Predicted correlation between upstream miRNAs and NUSAP1 expression in breast cancer; (C) TCGA-BRCA cohort analysis of hsa-miR-10b-5p expression in unpaired breast cancer and normal breast tissues; (D) TCGA-BRCA cohort analysis of hsa-miR-10b-5p expression in paired breast cancer and normal breast tissues; (E) Kaplan-Meier survival analysis revealing the prognostic value of hsa-miR-10b-5p in breast cancer. * P-value < 0.05; ** P-value < 0.01; *** A p-value < 0.001; a p-value < 0.05 in the Log-rank test is considered statistically significant.

[0024] Figure 12AC145207.5 in breast cancer may be an upstream lncRNA of hsa-miR-10b-5p; (A) Expression of AC145207.5 in unpaired breast cancer and normal breast tissues based on TCGA-BRCA cohort analysis; (B) Expression of AC145207.5 in paired breast cancer and normal breast tissues based on TCGA-BRCA cohort analysis; (C) Correlation analysis of AC145207.5 expression with NUSAP1; (D) Correlation analysis of AC145207.5 expression with hsa-miR-10b-5p. * P-value < 0.05; ** P-value < 0.01; *** A p-value < 0.001; a p-value < 0.05 in the Log-rank test is considered statistically significant.

[0025] Figure 13 The difference in drug sensitivity between the NUSAP1 high expression group and the low expression group; IC50: half-maximal inhibitory concentration.

[0026] Figure 14 This chart shows the molecular docking of the NUSAP1 protein with tamoxifen, lapatinib, fulvestrant, and brilanestrant; green represents the drug; red represents the docking target; and yellow represents the binding bond.

[0027] Figure 15 : AC145207.5 / hsa-miR-10b-5p / NUSAP1 Schematic diagram of the mechanism of the regulatory axis in the occurrence and development of breast cancer. Detailed Implementation

[0028] 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 with reference to the accompanying drawings.

[0029] Unless otherwise specified, all experimental materials used in the following examples were purchased from conventional biochemical reagent stores.

[0030] 1. Materials and Methods 1.1 Data Acquisition and Preprocessing Researchers collected human cancer RNA-seq data in 33 FPKM formats and clinical data of breast cancer patients (including age, TNM stage, clinicopathological stage, histological type, PAM50 type, ER status, PR status, and HER2 status) from the Cancer Genome Atlas (TCGA) database. In addition, RNA-seq data from the GSE42568 dataset (containing 17 normal breast tissues and 104 breast cancer tissues) and the GSE15852 dataset (containing 43 paired breast cancer and adjacent normal tissues) were obtained from the GEO database. The FPKM format RNA-seq data were converted to TPM format, then log2 transformation [log2(TPM+1)] and normalization were performed.

[0031] 1.2 Expression analysis of NUSAP1 in breast cancer The expression levels of NUSAP1 in various normal and tumor tissues were analyzed using the TCGA database. Furthermore, the expression profiles of NUSAP1 in breast cancer were analyzed using the TCGA-BRCA, GSE42568, and GSE15852 datasets. Clinicopathological data of breast cancer patients in the TCGA-BRCA cohort were analyzed to explore the relationship between NUSAP1 expression levels and clinicopathological features of breast cancer patients. Receiver operating characteristic (ROC) curves were plotted using the R package pROC to further evaluate the diagnostic efficacy of NUSAP1 in breast cancer patients.

[0032] 1.3 Survival analysis of NUSAP1 in breast cancer The Kaplan-Meier plotter database was used to analyze the correlation between NUSAPI expression levels and overall survival (OS), recurrence-free survival (RFS), distant metastasis-free survival (DMFS), and post-progression survival (PPS) in breast cancer patients. Patients were divided into high-expression and low-expression groups based on the median NUSAPI expression level, and a statistical significance threshold of log-rank P-value less than 0.05 was set.

[0033] 1.4 RNA extraction and qRT-PCR detection The human breast cancer cells (MDA-MB-231, BT549, T47D, SK-BR-3) and normal human breast epithelial cells MCF-10A used in this experiment were all purchased from Wuhan Pronosei Life Sciences Co., Ltd., and each cell line was accompanied by an STR identification report. BT549 and T47D cells were cultured in RPMI-1640 medium (Gibco, USA); MDA-MB-231 and SK-BR-3 cells were cultured in DMEM high-glucose medium (Gibco, USA) supplemented with 10% FBS (Gibco, USA) and 1% antibiotics (penicillin-streptomycin, Gibco, USA); and MCF-10A cells were cultured in a dedicated culture medium (Pronosei, China). All cells were routinely cultured in a 37°C, 5% CO2 saturated humidity incubator. Total RNA was extracted from cell lines using Trizol reagent (Invitrogen, USA). Reverse transcription was performed using the HiScript II RT SuperMix kit (Novizan, China) with a gDNA wiper, followed by quantitative real-time polymerase chain reaction (qRT-PCR) using the ChamQ Universal SYBR qPCR MasterMix kit (Novizan, China). β-actin was used as an internal control gene, and its relative expression level was obtained using the 2-A4CT method. NUSAPI primer sequences: forward primer 5'-CAACCTGAGGGCAACCAAGT-3' and reverse primer 5'-CCACTTGCAGCTTCCTTTGAG-3'. β-actin primer sequences: forward primer 5'-CCACGAAACTACCTTCAACTCC-3' and reverse primer 5'-TCATACTCCTGCTGCTTGCTGATCC-3'.

[0034] 1.5 Immunohistochemical staining The human breast cancer tissue microarray (HBre-Duc159Sur-01) used in this experiment was purchased from Shanghai Chipchao Biotechnology Co., Ltd., and came with accompanying clinical information including patient age, tumor size, TNM stage, and pathological stage. A semi-quantitative scoring method was used to assess NUSAP1 protein levels: images were acquired under an optical microscope (10x and 20x magnification), and scores were assigned based on staining intensity and the percentage of positive cells. Staining intensity was scored as 0 (absent), 1 (weak), 2 (medium), and 3 (strong); the percentage of positive cells ranged from 0% to 100%. The total staining score ranged from 0 to 300 points, calculated by multiplying the staining intensity score by the percentage of positive cells. The Mann-Whitney U test was used to compare the total scores between tumor tissue and adjacent normal tissue, with a p-value <0.05 considered statistically significant.

[0035] 1.6Mfuzz temporal clustering and enrichment analysis Based on the NUSAPI co-expression network, this experiment uses the R package Mfuzz for expression pattern clustering analysis.

[0036] Subsequently, Spearman correlation analysis was used to assess the correlation between NUSAPI and the 50 co-expression modules obtained. The enrichment score of each module's samples was then calculated using single-sample gene-seen-richment analysis (SSGSEA) to identify the gene clustering modules most significantly associated with NUSAPI. A statistical significance threshold of P < 0.05 was set.

[0037] Using the R package clusterProfiler, the most significantly related gene clustering modules were analyzed to perform GeneOntology (GO), DiseaseOntology (DO), and Kyoto Gene and Genome Hundreds of Studies analyses. Enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed. Results were considered statistically significant if both the adjusted p-value was <0.05 and the q-value was <0.2.

[0038] 1.7 Association analysis between NUSAP1 and m6A modification-related genes The association between NUSAPI and the expression of m6A modification-related genes in the TCGA-BRCA and GEPIA databases was assessed using the R package ggplot2. Related genes were referenced from previous studies. Differences in m6A modification-related gene expression levels between high and low NUSAPI expression groups were assessed using the R packages ggplot2 and pheatmap. Statistical significance was defined as P < 0.05.

[0039] 1.8 Correlation analysis of tumor immune infiltration and immune checkpoints This study used the TIMER database to investigate the immune cell infiltration of NUSAPI in breast cancer and explored the relationship between NUSAPI copy number variation (CNV) and immune cell infiltration levels using the SCNA module. Based on TCGA-BRCA transcriptome data, the GSVA package was used to assess immune cell infiltration in high / low NUSAPI expression groups. Furthermore, GEPIA2 and TCGA-BRCA data were used to further analyze the correlation between NUSAPI expression levels and immune cell markers.

[0040] This experiment used the R package ggplot2 to assess NUSAPI expression and eight immune checkpoint genes (CD274, HAVCR2, CTLA4, LAG3, PDCD1, PDCD1LG2, TIGIT, and ) in TCGA-BRCA. The correlation between SIGLEC15 and NUSAPI expression levels was assessed using the R packages ggplot2 and pheatmap. Statistical significance was defined as P < 0.05.

[0041] 1.9 Prediction of Immunotherapy Response This study used the R package IMvigor210CoreBiologies to extract transcriptomic data and accompanying clinical information from the IMvigor210 cohort (anti-PD-L1 immunotherapy cohort). Patients were divided into high and low NUSAP1 expression groups based on the median cutoff value of NUSAP1 expression. The Kaplan-Meier method and log-rank test were used to assess the relationship between NUSAP1 expression and overall survival. In this study, patients with partial response (PR) or complete response (CR) were defined as the treatment-responsive group, and patients with progressive disease (PD) or stable disease (SD) were defined as the treatment-ineffective group. The Wilcoxon test was used to compare the differences in NUSAP1 expression between the two groups. The response rate and non-response rate of immunotherapy in the high and low NUSAP1 expression groups were calculated.

[0042] 1.10ceRNA Regulatory Network Construction This experiment provides a method for constructing a ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer, the process of which is as follows: This study used seven target gene prediction databases—PITA, RNA22, miRmap, Diana-microT, miRanda, PicTar, and TargetScan—to predict candidate miRNAs upstream of NUSAP1. Based on TCGA-BRCA transcriptome data, Spearman correlation analysis was used to assess the correlation between NUSAP1 expression and these potential miRNAs. Based on the ceRNA hypothesis, this study screened for the most likely upstream miRNAs of NUSAP1 in breast cancer. The starBase database was used to predict the lncRNAs most likely to bind to these miRNAs. After performing expression and survival correlation analysis using TCGA-BRCA transcriptome data and clinical information, this study constructed the upstream regulatory network most likely regulating NUSAP1 expression in breast cancer.

[0043] 1.11 Drug sensitivity analysis and molecular docking This study integrated transcriptomic data from the TCGA-BRCA cohort and drug-related data from the GDSC database. Data preprocessing included batch effect correction, normalization, and logarithmic transformation. Finally, the R package oncopredict was used to perform a differential analysis of the half-maximal inhibitory concentration (IC50) between the high and low expression groups of NUSAP1. Drugs approved by the US Food and Drug Administration (FDA) for breast cancer or under development, and whose differential expression was statistically significant, were screened and presented. Statistical significance was defined as P < 0.05.

[0044] To analyze the binding energy and interaction mechanism between the drug and NUSAP1, this study used Autodockvina 1.2.5 to perform docking between NUSAP1 and the drug. Human NUSAP1 was used as the receptor, and its three-dimensional structure was obtained from the AlphaFold protein structure database (https: / / alphafold.ebi.ac.uk / entry / Q9BXS6). Ligand files were obtained from the Pubchem database (https: / / pubchem.ncbi.nlm.nih.gov / ). All files were converted to PDBQT format, a grid covering the entire protein structure was set, and the molecular docking results were displayed using PyMOL 3.1. A molecular docking binding free energy less than -5 kcal / mol generally indicates a strong binding interaction.

[0045] 2. Conclusion 2.1 NUSAP1 Expression Level and Diagnostic Efficacy To determine the role of NUSAP1 in cancer, this study compared the expression levels of NUSAP1 in 33 human cancers and normal tissues. Figure 1 As shown in Figure A, NUSAP1 expression was upregulated in 18 cancer types, including BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, THCA, and UCEC. Next, this study used transcriptomic data from the TCGA and GEO databases to further compare the expression differences of NUSAP1 between breast cancer tissue and normal breast tissue. Figure 1 As shown in Figure BD, the expression level of NUSAP1 in breast cancer tissue was significantly higher than that in normal tissue. This study also analyzed the efficacy of NUSAP1 as a diagnostic biomarker for breast cancer using ROC curve analysis. Figure 1 E), the AUC value of NUSAP1 was 0.964 (95% CI = 0.948–0.979), indicating high diagnostic efficacy. Finally, the researchers compared the differences in NUSAP1 expression levels among different clinicopathological feature groups in the TCGA-BRCA cohort. The results showed that NUSAP1 expression levels were associated with multiple clinicopathological features, including age, TNM stage, ER status, PR status, HER2 status, pathological stage, and PAM50 classification. Figure 2 ).

[0046] 2.2 Relationship between NUSAP1 expression and prognosis This study used the Kaplan-Meierplotter database to analyze the correlation between NUSAP1 expression and overall survival (OS), response rate of failure (RFS), direct facultative breast cancer (DMFS), and percentage point saturation (PPS) in breast cancer. Figure 3 As shown, breast cancer patients with high NUSAP1 expression had a poorer prognosis, with lower overall survival (OS) (HR=1.65; 95% CI: 1.36-2, P<0.05), lower renal survival (RFS) (HR=1.71; 95% CI: 1.54-1.89, P<0.05), lower DMFS (HR=1.8; 95% CI: 1.54-2.11, P<0.05), and lower prostate survival (PPS) (HR=1.29; 95% CI: 1.03-1.63, P<0.05). Breast cancer patients with low NUSAP1 expression had a better prognosis.

[0047] 2.3 qRT-PCR and immunohistochemical staining analysis of NUSAP1 in breast cancer tissues This study used real-time quantitative PCR to detect the NUSAP1 mRNA level in breast cancer cell lines and normal breast epithelial cell lines. Figure 4As shown in Figure A, NUSAP1 mRNA expression in breast cancer was significantly higher than in normal breast epithelial cells. This study used immunohistochemistry to label NUSAP1 in human breast cancer tissue microarrays to detect its level in breast cancer and adjacent tissues. This microarray contained 113 clinical breast cancer specimens and 27 normal breast tissue specimens. The results were scored based on the percentage of positive cells and staining intensity, and survival analysis was performed using accompanying clinical information. Figure 4 As shown in BD, NUSAP1 protein expression was significantly upregulated in breast cancer tissues, and breast cancer patients with high NUSAP1 levels had a poor prognosis (HR=2.07, 95% CI: 1.05-4.08, P=0.035).

[0048] 2.4Mfuzz clustering results and gene set functional enrichment analysis This study used Mfuzz expression pattern clustering analysis to evaluate the correlation between NUSAP1 and 50 co-expression clustering modules obtained in breast cancer. Figure 5 ).like Figure 6 As shown in Figure A, Cluster 8, Cluster 14, Cluster 34, Cluster 42, and Cluster 48 showed a significant positive correlation with NUSAP1; Cluster 20, Cluster 25, Cluster 27, Cluster 39, Cluster 35, and Cluster 40 showed a significant negative correlation with NUSAP1. Among them, Cluster 48 showed the strongest correlation. The enrichment of these gene clusters in breast cancer tissue and normal breast tissue was compared using ssGSEA scores. Figure 6 As shown in Figure B, compared with normal breast tissue, the relative abundance of NUSAP1 in Cluster 6, Cluster 15, Cluster 17, Cluster 26, Cluster 38, Cluster 41, Cluster 45, and Cluster 48 was significantly correlated with the relative abundance of NUSAP1 in breast cancer tissue. Next, this study also analyzed the correlation between NUSAP1 expression levels and the Cluster 48 gene cluster. Figure 3-6 The results showed that in breast cancer, the expression of the Cluster48 gene cluster was significantly correlated with the expression of NUSAP1 (P<0.01).

[0049] Subsequently, to better understand the biological function of NUSAP1 in breast cancer, this study performed an enrichment analysis on genes in Cluster48. For example... Figure 7As shown in Figure A, GO analysis indicates that NUSAP1-related genes (Cluster 48) are mainly enriched in the positive regulation of nuclear division, organelle division, and cell cycle in the biological process (BP) of breast cancer; mainly enriched in the spindle, mitochondrial matrix, and microtubules in the cellular component (CC); and mainly enriched in microtubule binding, ATP hydrolysis activity, and microtubule binding in the molecular function (MF). According to DO analysis results, these genes are associated with various cancers, including non-small cell lung cancer, female reproductive organ tumors, and breast cancer. Figure 7 B). KEGG analysis showed significant enrichment in the cell cycle, particularly in S and G2 phases. Figure 7 CD).

[0050] 2.5 Correlation between NUSAP1 and m6A modification-related genes To understand the correlation between NUSAP1 and m6A modification in breast cancer, this study conducted expression correlation analysis based on the BRCA cohorts in the TCGA and GEPIA databases. Figure 8 The results shown in Figure A indicate that NUSAP1 is significantly associated with multiple m6A modification-related genes, showing a positive correlation with METTL3, METTL14, ZC3H13, WTAP, RBM15, RBMX, YTHDC1, YTHDF2, YTHDF3, IGF2BP3, HNRNPC, and HNRNPA2B1, and a negative correlation with ALKBH3. Furthermore, analysis based on the GEPIA database showed a significant negative correlation between NUSAP1 expression and METTL16, while analysis based on the TCGA database showed a significant positive correlation between NUSAP1 expression and ALKBH5. This study used Person correlation analysis to assess the correlation between NUSAP1 and m6A modification-related genes, and the top 10 genes with the strongest correlations are presented in a scatter plot. Figure 8 B). Figure 8 The results shown in C illustrate the expression of 21 m6A modification-related genes in the TCGA-BRCA cohort NUSAP1 high / low expression group. Among them, METTL3, METTL14, ZC3H13, WTAP, RBM15, RBMX, YTHDC1, YTHDF2, YTHDF3, IGF2BP3, HNRNPC, and HNRNPA2B1 were upregulated in breast cancer, while ALKBH3 was downregulated.

[0051] 2.6 Correlation between NUSAP1 expression level and immune cell infiltration This study used the TIMER database to investigate the relationship between NUSAP1 expression levels and immune cell infiltration in breast cancer. Figure 9 As shown in Figure A, NUSAP1 expression levels were significantly positively correlated with the abundance of B cells (P=6.90×10⁻¹⁰), CD8+ T cells (P=2.44×10⁻⁶), neutrophils (P=5.29×10⁻¹⁰), and dendritic cells (P=1.93×10⁻⁷). Simultaneously, the results indicated that NUSAP1 copy number variation (CNV) was significantly correlated with the infiltration levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, dendritic cells, and neutrophils. Figure 9 B).

[0052] Furthermore, based on TCGA-BRCA transcriptome data, this study divided the cohort into NUSAP1 high / low expression groups, and then compared the infiltration levels of immune cells between the two groups. Figure 9 As shown in Figure C, the infiltration levels of 16 immune cells were statistically correlated with the expression levels of NUSAP1. In the NUSAP1 high-expression group, the enrichment levels of activated dendritic cells, macrophages, natural killer cell subset CD56dim cells, helper T cells, central memory T cells, Th1 cells, Th2 cells, and regulatory T cells were all significantly upregulated, while the enrichment levels of CD8 T cells, eosinophils, natural killer cells, natural killer cell subset CD56bright cells, plasmacytoid dendritic cells, immature dendritic cells, mast cells, and Th17 cells were all significantly downregulated.

[0053] This study further used the GEPIA2 database to investigate the correlation between NUSAP1 expression and immune cell biomarkers in breast cancer. As shown in Table 1, NUSAP1 expression levels were correlated with CD4+ T cell biomarkers (CD4), M1 macrophage biomarkers (NOS2, IRF5), M2 macrophage biomarkers (CD163, CD206, MS4A4A), dendritic cell biomarkers (HLA-DPB1, HLA-DQB1, CD1C, CD141, ITGAX), and natural killer cell biomarkers (KIR2DL1, KIR2DL3, KIR2DL4, KIR3DL1, KIR3DL2, KIR3). DL3, KIR2DS4), mast cell biomarkers (TPSB2, TPSAB1, CPA3, HDC, MS4A2), Th1 cell biomarkers (STAT1, IFN-γ, TNF-α), Th2 cell biomarkers (STAT6, STAT5A), Th17 cell biomarkers (STAT3, IL17A), follicular helper T cell biomarkers (IL21), and regulatory T cell biomarkers (FOXP3, CCR8, STAT5B, TGFβ) showed significant correlations.

[0054] Table 1. Correlation analysis of NUSAP1 expression level and immune cell biomarkers in breast cancer. 2.7 NUSAP1 Immunotherapy Response Prediction Analysis To further understand the role of NUSAP1 in immunotherapy, this study conducted a correlation analysis on the expression levels of NUSAP1 and immune checkpoint genes. Figure 10 As shown in the AH, the expression level of NUSAP1 was significantly positively correlated with CD274, HAVCR2, CTLA4, LAG3, PDCD1, PDCD1LG2, and TIGIT, and significantly negatively correlated with SIGLEC15. Figure 10 The results showed that in the TCGA-BRCA cohort, the expression of immune checkpoint genes such as CD274, HAVCR2, CTLA4, LAG3, PDCD1, PDCD1LG2, and TIGIT was significantly upregulated in the NUSAP1 high-expression group, while SIGLEC15 was significantly downregulated. Next, this study obtained transcriptomic data and clinical information from the IMvigor210 immunotherapy cohort to assess whether NUSAP1 expression levels affect the prognosis of patients receiving immunotherapy. Figure 10 As shown in Figure J, patients with high NUSAP1 expression had a better prognosis after receiving immunotherapy (HR=0.7, P=0.013). Furthermore, compared to the SD / PD group, patients in the CR / PR group had significantly higher NUSAP1 expression levels. Figure 10 K). The disease response rate (CR / PR) in patients with low NUSAP1 expression was 14.77%, lower than the 30.87% in the high expression group. Figure 10 The above results suggest that patients with high NUSAP1 expression may benefit from immunotherapy.

[0055] 2.8ceRNA Regulatory Network Construction To investigate whether NUSAP1 is regulated by certain ncRNAs in breast cancer, this study first predicted 25 upstream miRNAs that may bind to NUSAP1 using multiple public databases. The results were visualized using Cytoscape software, and a miRNA-NUSAP1 regulatory network was constructed. Figure 11 A). Based on the negative feedback regulatory mechanism of ncRNAs, upstream miRNAs should negatively regulate NUSAP1 expression post-transcriptionally. Therefore, this study further analyzed the correlation between the above 25 miRNAs and NUSAP1 expression ( Figure 11B). Combining the expression and prognostic analysis of the above miRNAs in breast cancer ( Figure 11 Of the CE (ceRNA) candidates, only hsa-miR-10b-5p met the criteria of being negatively correlated with NUSAP1 expression, meaning its expression was significantly downregulated in breast cancer, and patients with downregulated expression had a poor prognosis. Next, this study used the starBase database to predict upstream lncRNAs that might regulate hsa-miR-10b-5p, obtaining a total of 20 candidate lncRNAs. According to the ceRNA hypothesis, lncRNAs can regulate mRNA expression by competitively binding to specific miRNAs. Based on this hypothesis, only AC145207.5 among the aforementioned subsequent lncRNAs met the criteria. Figure 12 AB studies indicate that AC145207.5 expression is significantly upregulated in breast cancer. Figure 3-12 As shown in CD and Table 2, AC145207.5 was significantly positively correlated with NUSAP1 expression levels and significantly negatively correlated with hsa-miR-10b-5p. In summary, the most likely upstream ncRNA regulatory mechanism of NUSAP1 in breast cancer is the AC145207.5 / hsa-miR-10b-5p / NUSAP1 axis.

[0056] Table 2. Correlation analysis of the expression levels of AC145207.5 with hsa-miR-10b-5p and NUSAPI in breast cancer (using starBase database). 2.9 Drug sensitivity analysis and molecular docking This study further conducted a drug sensitivity analysis on NUSAP1, screened and presented drugs with FDA-approved indications or those under development for breast cancer, and aims to provide a possible basis for future precision treatment selection. Figure 13The IC50 differences between the high-expression and low-expression NUSAP1 groups were shown. The results indicated that the high-expression group was sensitive to tamoxifen, lapatinib, fulvestrant, brilanestrant (GDC0810), cyclophosphamide, and cisplatin. The low-expression group was sensitive to capapasetinib (AZD5363), ribociclib (LEE011), afuresertib (LAE002), buparilisib (BKM120), ipatacrylobalaminib (GDC-0068), palbociclib, AZD8186, and pictilisib.

[0057] This study used AutodockVinav.1.2.4 to conduct molecular docking studies to evaluate the binding affinity of these sensitive drugs to the NUSAP1 protein target, focusing on tamoxifen, lapatinib, fulvestrant, brinestren, cyclophosphamide, and cisplatin. As shown in Table 3, brinestren (-6.871 kcal / mol), tamoxifen (-6.139 kcal / mol), fulvestrant (-9.723 kcal / mol), and lapatinib (-6.342 kcal / mol) all indicated good binding energies and stable docking results. Figure 14 The three-dimensional structures of the docking of these four drugs with the NUSAP1 protein are shown.

[0058] Table 3. Binding energy of drugs targeting NUSAP1 protein 3. Conclusion This study suggests that NUSAP1 is upregulated in various cancers. In breast cancer, upregulated NUSAP1 expression is associated with poor prognosis. This study found that NUSAP1 is associated with immune cell infiltration in breast cancer, and it holds promise as a potential biomarker for predicting the efficacy and prognosis of immunotherapy. Furthermore, this study predicted and constructed the AC145207.5 / hsa-miR-10b-5p / NUSAP1 regulatory axis. The lncRNA AC145207.5 may exert its ceRNA effect by competitively binding to hsa-miR-10b-5p, thereby regulating NUSAP1 expression and influencing the development and progression of breast cancer. Combining the analysis of NUSAP1 in tumor immune infiltration and its correlation with the expression of m6A modification-related genes, this study systematically elucidates the potential mechanisms by which NUSAP1 affects the development and progression of breast cancer (e.g., Figure 15 As shown in the figure, this provides potential targets for future precision treatment of breast cancer. Finally, tamoxifen, lapatinib, fulvestrant, and bulinstrant have good affinity binding properties with NUSAP1, and treatment regimens based on these drugs targeting breast cancer subtypes with high NUSAP1 expression hold promise as a choice for precision treatment.

[0059] The above-described embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited thereto. Any simple changes or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the scope of the technology disclosed in the present invention shall fall within the scope of protection of the present invention.

Claims

1. A ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer, characterized in that, The ceRNA regulatory network is the lncRNAsAC145207.5 / hsa-miR-10b-5p / NUSAP1 regulatory network.

2. The application of the ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer as described in claim 1 in the preparation of drugs for early diagnosis or prognostic analysis of breast cancer.

3. The application as described in claim 2, characterized in that, The drug promotes the expression of lncRNAs AC145207.5 and / or inhibits the expression of hsa-miR-10b-5p and / or promotes the expression of NUSAP1 in the early diagnosis or prognostic analysis of breast cancer.

4. The application of the ceRNA regulatory network for early diagnosis or prognostic analysis of breast cancer as described in claim 1 in the preparation of a kit for early diagnosis or prognostic analysis of breast cancer.

5. The application as described in claim 4, characterized in that, The kit includes a specific upstream primer for AC145207.5 gene real-time PCR: 5′-ATTGACTGGCCAAGCATTTG-3′; a specific downstream primer for AC145207.5 gene real-time PCR: 5′-ACATGATCACAGACAAGCTGA-3′; a specific upstream primer for NUSAP1 gene real-time PCR: 5′-CAACCTGAGGGCAACCAAGT-3′; and a specific downstream primer for NUSAP1 gene real-time PCR: 5′-CCACTTGCAGCTTCCTTTGAG-3′.

6. The application according to claim 5, characterized in that: The expression levels of the hsa-miR-10b-5p gene are negatively correlated with the incidence of breast cancer, while the expression levels of the AC145207.5 gene and the NUSAP1 gene are positively correlated with the risk of breast cancer.

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