A bladder cancer prognostic marker and its application

Abstract

The present invention discloses a bladder cancer prognostic marker, relating to the technical field of tumor prognosis assessment. The bladder cancer prognostic marker is composed of the following genes: PATZ1, TTC6, AEBP1, and MAOA. Also disclosed is the use of a reagent for detecting the expression level of the prognostic marker in preparing a bladder cancer prognosis detection product. The bladder cancer prognostic marker provided by the present invention is based on a FAM-related gene set, performs molecular subtype clustering on BLCA and obtains two subtypes. By performing univariate Cox regression analysis on the DEGs of the two subtypes, a model for BLCA prognosis prediction using four gene labels PATZ1, TTC6, AEBP1, and MAOA is established to evaluate the patient's prognosis, thereby helping to improve the survival rate of bladder cancer patients, providing new ideas for clinical precision treatment decisions for bladder cancer, and facilitating the selection of clinical treatment plans.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor prognosis assessment, and more particularly to a bladder cancer prognosis marker and its application. Background Art

[0002] Bladder cancer (BLCA) is one of the top ten cancers with the highest morbidity and mortality worldwide, placing a significant burden on healthcare systems. Bladder cancer can be divided into two main categories: muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC). MIBC refers to cells that have spread into or through the detrusor muscle and accounts for approximately 25% of newly diagnosed bladder cancer patients, while NMIBC is confined to the mucosa or submucosal connective tissue and accounts for approximately 75% of newly diagnosed bladder cancer patients. Patients with MIBC have a poor prognosis, with a 5-year overall survival rate of 49%. Patients with NMIBC have a good life expectancy, with 5-year bladder cancer-specific mortality rates of 0.5%, 1.7%, and 6.8% for grade 1, 2, and 3 tumors, respectively. However, patients with NMIBC have a high rate of disease recurrence at one and five years after transurethral resection of the bladder tumor (15-61% and 31-78%, respectively) and a high rate of progression to MIBC (10-40% for high-risk NMIBC). Frequent recurrence of NMIBC leads to lifelong cystoscopic surveillance and multiple therapeutic interventions, placing a heavy burden on the public health system. Therefore, it is necessary to elucidate prognostic molecular features to optimize clinical treatment.

[0003] Cancer cell growth and proliferation often involve metabolic disorders, which require fatty acids (FAs) for membrane synthesis, energy storage, and the production of signaling molecules. FAs consist of a terminal carboxyl group and a hydrocarbon chain of varying lengths and degrees of desaturation. FA synthesis is the process of converting nutrients into metabolic intermediates. Multiple studies have shown that FA metabolism (FAM) plays an important role in tumor cell proliferation. For example, ATP citrate lyase (ACLY) converts citrate into oxalacetate and diacetyl-CoA (a precursor for FA synthesis). Knockdown of ACLY can prevent human cancer cells from forming xenograft tumors. Acetyl-CoA carboxylase 1 (ACC1) carboxylates acetyl-CoA to form malonyl-CoA, a substrate for FA synthesis. Knockdown of ACC1 can induce apoptosis in prostate cancer cells. Given the crucial role of FAM in tumor proliferation and progression, targeting FAM may be a therapeutic strategy. For example, inhibition of sterol regulatory element binding protein 1 (SREBP-1), the master transcriptional regulator of fatty acid synthesis, significantly reduces cancer cell growth. Acyl-CoA synthetases (a family of enzymes responsible for activating free intracellular FAs) inhibit cardiolipin production, leading to cancer cell apoptosis. The synthetic compound C75, which inhibits the β-ketoacyl reductase activity of fatty acid synthase (FASN), induces apoptosis in several cancer cell lines and exhibits antitumor effects in mesothelioma, breast, kidney, lung, and prostate cancer xenograft models. Similar to tumor therapy, FAM has received considerable attention, but strategies targeting this process have yet to be translated into clinical practice. Specific and practical molecular signatures are urgently needed for accurate diagnosis, personalized treatment, and prognostic assessment of BLCA. Therefore, determining the expression and significance of FAM signature genes in BLCA and predicting prognosis holds great promise. Summary of the Invention

[0004] To this end, the technical problem to be solved by the present invention is to provide a bladder cancer prognostic marker and its application, construct a BLCA prognostic prediction model through four gene labels PATZ1, TTC6, AEBP1 and MAOA, and evaluate the patient's prognosis, thereby helping to improve the survival rate of bladder cancer patients, providing new ideas for clinical precision treatment decisions for bladder cancer, and facilitating the selection of clinical treatment plans.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A bladder cancer prognosis marker comprises the following genes: PATZ1, TTC6, AEBP1, and MAOA.

[0007] The prognostic markers provided by the present invention are based on the FAM-related gene set, and a model for BLCA prognosis prediction is established with four gene signatures PATZ1, TTC6, AEBP1, and MAOA. PATZ1, TTC6, and MAOA are negatively correlated with BLCA prognosis, while AEBP1 is positively correlated with BLCA prognosis. This confirms that FAMR is associated with the survival rate of BLCA, confirming the clinical application value of FAMR.

[0008] The invention relates to the use of a reagent for detecting the expression level of a prognostic marker in the preparation of a bladder cancer prognosis detection product, wherein the prognostic markers include the following genes: PATZ1, TTC6, AEBP1, and MAOA.

[0009] The technical solution of the present invention achieves the following beneficial technical effects:

[0010] Based on the FAM-related gene set, the present invention clustered BLCA into molecular subtypes and obtained two subtypes. By performing univariate Cox regression analysis on the DEGs of the two subtypes, a model for BLCA prognosis prediction using four gene signatures, PATZ1, TTC6, AEBP1, and MAOA, was established to evaluate the patient's prognosis, thereby helping to improve the survival rate of bladder cancer patients, providing new ideas for clinical precision treatment decision-making for bladder cancer, and facilitating the selection of clinical treatment options. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a bladder cancer prognostic marker and its application in BLCA clustering and prognostic analysis based on FAM-related genes;

[0012] Where A is the non-negative matrix factorization (NMF) clustering matrix diagram; B is the cluster distance diagram when the rank is 2-10, reflecting the stability of the clustering matrix; C is the residual sum of squares (RSS) when the rank is 2-10; D is the proportion of overall survival in the C1 and C2 subgroups; E represents the proportion of disease-specific survival in the C1 and C2 subgroups; F is the proportion of high grade and low grade in the C1 and C2 subgroups; G is the proportion of tumor T stage in the C1 and C2 subgroups; H is the proportion of five immune subtypes in the C1 and C2 subgroups; I is the overall survival curve of the C1 and C2 subgroups; J is the disease-specific survival curve;

[0013] Figure 2 A schematic diagram of a bladder cancer prognostic marker and its application in constructing a four-gene FAMR prognostic model;

[0014] Where A is the independent variable trajectory; B is the confidence interval of each λ; C is the KM survival curve of PATZ1 in the TCGA training set; D is the KM survival curve of TTC6 in the TCGA training set; E is the KM survival curve of AEBP1 in the TCGA training set; F is the KM survival curve of MAOA in the TCGA training set; G is the hazard index, survival time and the corresponding expression of the four genes in the TCGA training set; H is the receiver operating characteristic (ROC) curve of the four gene features in the TCGA training set; I is the KM survival curve based on FAMR in the TCGA training set;

[0015] Figure 3 A schematic diagram of the validation of a bladder cancer prognostic marker and a gene FAMR model used therein of the present invention;

[0016] A represents the risk index, survival time, and expression of four genes in the TCGA test dataset; D represents the risk index, survival time, and expression of four genes in the entire TCGA dataset; G represents the risk index, survival time, and expression of four genes in the ArrayExpress-mRNAseq-E-MTAB-4321 external validation dataset.

[0017] B is the receiver operating characteristic curve of the four gene features in the TCGA test dataset; E is the receiver operating characteristic curve of the four gene features in the entire TCGA dataset; H is the receiver operating characteristic curve of the four gene features in the ArrayExpress-mRNAseq-E-MTAB-4321 external validation dataset;

[0018] C is the FAMR-based KM survival curve in the TCGA test dataset; F is the FAMR-based KM survival curve in the entire TCGA dataset; I is the FAMR-based KM survival curve in the ArrayExpress-mRNAseq-E-MTAB-4321 external validation dataset;

[0019] Figure 4 A schematic diagram of the correlation analysis between a bladder cancer prognostic marker and its applied FAMR and biological function;

[0020] A shows the top 10 pathways that are significantly positively and negatively correlated with FAMR; B shows the heat map of the top 10 pathways that are significantly positively and negatively correlated with FAMR; C shows the correlation between FAMR and StromalScore; D shows the correlation between FAMR and ImmuneScore; E shows the correlation between FAMR and ESTIMATEScore;

[0021] Figure 5A schematic diagram of the correlation analysis between a bladder cancer prognostic marker and its applied FAMR and clinical characteristics of the present invention;

[0022] A is the KM survival curve of male patients based on FAMR; B is the KM survival curve of female patients based on FAMR; C is the KM survival curve of patients aged ≥60 years based on FAMR; D is the KM survival curve of patients aged ≤60 years based on FAMR; E is the FAMR value of different gender groups; F is the FAMR value of different age groups; G is the FAMR value of tumor stage group; H is the forest plot multivariate Cox regression analysis. DETAILED DESCRIPTION

[0023] This example provides the construction and validation of a model that can predict the survival probability of bladder cancer patients.

[0024] 1. Determine BLCA molecular subtypes based on FAM-related genes and clinical features

[0025] First, the expression of 158 FAM-related genes was obtained from the TCGA (The Cancer Genome Atlas)-BLCA expression profile data. 18 genes associated with BLCA prognosis were identified by R univariate Cox analysis (p < 0.05). Based on the expression of these 18 genes, BLCA patients were clustered by non-negative matrix factorization (NMF). The optimal cluster with k = 2 was selected by synthesis and residual sum of squares (RSS), and two clusters were obtained, as shown in Figure 2. Figure 1 The clusters (C1 and C2) shown in AC were then compared with the clinical characteristics of the two clusters, as shown in Figure 1 As shown in Figures D and E, the survival rate of C1 was found to be higher than that of C2.

[0026] In addition, according to Figure 1 F, Compared with C1, High Grade tumors are more enriched in C2, indicating enhanced cancer progression. Compared with C2, C1 has more early-stage tumors, including stage I and stage II, and fewer late-stage tumors, including stage III and stage IV. Figure 1 G, These all indicate that C1 has a better prognosis.

[0027] Combine Figure 1 H, C1 was more strongly correlated with immune subtypes 1, 3, 4, and 5, while C2 was more strongly correlated with immune subtype 2, indicating that C1 had a better prognosis, which was consistent with the Kaplan-Meier (KM) curve, according to overall survival (OS) and disease-specific survival (DSS), combined with Figure 1 IJ in Figure 3, indicating that C1 showed a better prognosis.

[0028] 2. Constructing a prognostic risk model

[0029] A total of 359 eligible bladder cancer patient samples with prognostic information from the TCGA (The Cancer Genome Atlas)-BLCA cancer genome atlas database were randomly divided into a training group of 180 cases and a validation group of 179 cases to construct a prognostic risk model.

[0030] Combine Figure 2 Based on the survival data, 295 prognosis-related genes were identified by univariate regression Cox risk model analysis (P < 0.01 as the threshold), and then the R package glmnet was used to perform lasso cox regression analysis on the 295 prognosis-related genes. The results are as follows Figure 2 As shown in A, the number of coefficients of the independent variable gradually increases with the increase of λ. Cross-validation is used to calculate the confidence interval under each λ. The results are as follows Figure 2 As shown in B, the optimal model of 16 genes was found at λ=0.07290695. Four genes, PATZ1, TTC6, AEBP1 and MAOA, were found through Akaike information criterion (AIC) analysis.

[0031] Combine Figure 2 In Figures C, D, and F, PATZ1, TTC6, and MAOA were negatively correlated with the prognosis of BLCA, and the survival curve of the High expression group was poor. Figure 2 As can be seen in E, AEBP1 is positively correlated with the prognosis of BLCA, and the higher the expression level, the better the survival curve.

[0032] Based on the expression levels of 4 genes in the ggRISK package, the Risk Score of the samples in the 180 training cohorts was calculated. Figure 2 G, The Risk Score of the High group indicates a poor prognosis. A higher FAM-Risk Score (FAMR) is associated with lower PATZ1, TTC6, and MAOA expressions, and higher AEBP1 expression, which is consistent with the KM curve results. The 1-year, 3-year, and 5-year prognostic prediction efficacy was evaluated by r-packaging time ROC curve analysis. The results are as follows Figure 2 H shows that the area under the curve (AUC) values ​​of each indicator model are all higher than 0.65; the KM results are as follows Figure 2 As shown in Figure I, the FAMR-Low group had a better prognosis than the FAMR-High group, suggesting the clinical application value of FAMR.

[0033] 3. Validation of risk model

[0034] To validate the FAMR model, all 359 patients in the TCGA test set and 179 patients were analyzed separately. Figure 3 As shown in Figures A and D, higher FAMR was associated with poor prognosis, that is, lower expression of PATZ1, TTC6, and MAOA, and higher expression of AEBP1, in the 179 validation cohort and 359 validation cohort, as shown in Figures A and D. Figure 3 As shown in B and E in Figure 1, the AUCs of the 1-year, 3-year, and 5-year prognostic prediction efficacies were all higher than 0.65. Figure 3 C and F, KM results also showed that the FAMR-Low group was associated with a better prognosis compared with the FAMR-High group. The FAMR model was further validated using an external validation cohort (476 patients). Figure 3 As shown in G, higher FAMR is associated with poor prognosis, combined with Figure 3 H, the AUCs of 1-year, 3-year, and 5-year prognostic prediction efficiency were all higher than 0.75. Figure 3 KM analysis of I showed that the prognosis of the FAMR-Low group was significantly better than that of the FAMR-High group. The FAMR model was verified by using the internal validation cohort and the external validation cohort, and the results showed the stability and universality of the prognostic risk model.

[0035] 4. Analysis of the correlation between FAMR and biological functions and clinical characteristics

[0036] The correlation between FAMR and biological function was analyzed by fundus sum enrichment analysis (GSEA), the ssGSEA score of each sample was calculated, and the correlation between FAMR and biological function was further analyzed, such as Figure 4 Figures A and B show the top 10 significantly positively and negatively correlated pathways. The positively correlated pathways include chemotaxis, cytoskeleton, infection, and inflammation-related pathways, while the negatively correlated pathways include fat metabolism and metabolic-related disease-related pathways.

[0037] In addition, by evaluating the correlation between FAMR and immune scores, combined Figure 4 In the CE, it was found that the immune score, matrix score and ESTIMATEScore were positively correlated with FAMR.

[0038] In addition, the KM of male, female, >60 and ≤60 groups was calculated to verify whether gender, age and clinical characteristics (such as cancer stage) affect FAMR. Figure 5 It can be seen from the AD in the indicated 4 groups that the FAMR-Low group had a better prognosis than the FAMR-High group, indicating that the indicative ability of the FAMR model is stable; further analysis of the FAMR of gender, age and stage groups, combined with Figure 5 E, FAMR was found to be higher in the female group, indicating that women have a worse prognosis, which is consistent with the fact that female bladder cancer is often diagnosed at a higher stage and has a worse prognosis; Figure 5In F, the FAMR of the group >60 was higher, which was consistent with the fact that the elderly were more susceptible to bladder cancer. Figure 5 As can be seen in G, FAMR increases from stage II to stage IV, indicating that the prognosis is poorer as the cancer progresses. Multivariate COX regression analysis combined with Figure 5 H, FAMR was found to be associated with survival in BLCA.

[0039] In summary, the expression of PATZ1, TTC6, AEBP1 and MAOA genes can be used to construct a prognostic risk model for the prognostic diagnosis of BLCA, providing a reference for the formulation of clinical treatment strategies.

[0040] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the claims of this patent application.

Claims

1. A bladder cancer prognostic marker, characterized in that: The bladder cancer prognostic markers consist of the following genes: PATZ1, TTC6, AEBP1, and MAOA.

2. Use of a reagent for detecting the expression level of the prognostic marker according to claim 1 in the preparation of a bladder cancer prognosis detection product.

3. The use according to claim 2, characterized in that The product includes one or more of a kit, a chip, and a system.

4. The use according to claim 2, characterized in that The reagents include one or more of a primer that specifically amplifies the prognostic marker, a probe that specifically recognizes the prognostic marker, and a binding agent that specifically binds to a protein encoded by the prognostic marker.

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