A method for constructing a prognostic risk prediction model for prostate cancer patients

Abstract

The present invention discloses a method for constructing a prostate cancer patient prognosis risk prediction model, the method comprising: obtaining a prostate puncture sample; performing MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing on the prostate puncture sample, and using a differential analysis method to analyze the gene related to prostate cancer prognosis in the prostate puncture sample, and obtaining several m6A modified genes related to prostate cancer prognosis; using all m6A modified genes as a training set, using regression analysis to screen out several key genes related to prostate cancer prognosis risk prediction; constructing a prostate cancer patient prognosis risk prediction model according to all key genes, and obtaining the prognosis risk situation of prostate cancer patients by the prognosis risk prediction model. The prediction model established by the present invention can more accurately predict the prognosis of the patient, better guide clinical decision-making, provide a more scientific reference basis for the treatment of prostate cancer, and is of great significance for the accurate diagnosis and treatment of prostate cancer.

Description

Technical Field

[0001] The present invention relates to the technical field of cancer prognosis risk assessment, and in particular to a method for constructing a prostate cancer patient prognosis risk prediction model. Background Art

[0002] Prostate cancer (PCA) is the most common cancer in men and one of the leading causes of cancer death in men. Prostate cancer ranks second in the incidence of male cancer worldwide, accounting for 14.1% of new cases of male cancer and 6.8% of all cancers. In my country, the incidence of prostate cancer has risen rapidly from 10% to 20% in the past 20 years. Although the overall survival rate of prostate cancer is better than that of other malignancies, its recurrence rate is very high, and most patients eventually develop into an advanced stage, known as castration-resistant prostate cancer (CRPC). In addition, the median survival of patients with cancer metastasis is very short, only 30 months. Therefore, further research on the molecular mechanisms of prostate cancer and finding effective screening and diagnostic methods are of great significance to improving the quality of life of patients.

[0003] There are several tumor markers used in clinical diagnosis of tumors, but most of them are single markers, and their sensitivity and specificity cannot meet clinical needs. In the face of the current diagnosis and treatment of prostate cancer, a good model is urgently needed to evaluate it in a timely manner and judge the prognosis in a timely manner. Summary of the invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a method for constructing a prognosis risk prediction model for prostate cancer patients. The established prediction model can more accurately predict the patient's prognosis, better guide clinical decision-making, and provide a more scientific reference basis for the treatment of prostate cancer, which is of great significance for the accurate diagnosis and treatment of prostate cancer.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for constructing a prostate cancer patient prognosis risk prediction model, comprising:

[0006] Obtaining prostate puncture samples, wherein the prostate puncture samples include N normal prostate puncture samples, N hormone-sensitive prostate cancer puncture samples, and N hormone-resistant prostate cancer puncture samples; wherein N is a positive integer greater than 1;

[0007] The prostate biopsy samples are subjected to MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing, and the genes related to prostate cancer prognosis in the prostate biopsy samples are analyzed by a differential analysis method to obtain several m6A modified genes related to prostate cancer prognosis;

[0008] All the m6A modified genes were used as training sets, and regression analysis was used to screen out several key genes related to the prediction of prostate cancer prognosis risk;

[0009] A prognostic risk prediction model for prostate cancer patients is constructed based on all the key genes, and the prognostic risk situation of the prostate cancer patients is obtained through the prognostic risk prediction model.

[0010] As an optional embodiment, obtaining the prognostic risk of the prostate cancer patient through the prognostic risk prediction model specifically includes:

[0011] The expression level and correlation coefficient of each of the key genes are obtained by the prognostic risk prediction model; wherein the correlation coefficient is the correlation coefficient between the key gene and the prognosis of the prostate cancer patient;

[0012] Calculating a risk score for the prognosis of the prostate cancer patient according to the expression level and the correlation coefficient;

[0013] The prognostic risk of the prostate cancer patient is obtained according to the risk score.

[0014] As an optional embodiment, the risk score calculation formula is:

[0015]

[0016] Among them, RS represents the risk score, n represents the number of key genes, and a i Represents the correlation coefficient of the i-th key gene, exp i Represents the expression level of the i-th key gene.

[0017] As an optional embodiment, the step of obtaining the prognostic risk of the prostate cancer patient according to the risk score specifically includes:

[0018] Determining whether the risk score exceeds a preset range;

[0019] If the risk score exceeds the preset range, it indicates that the prostate cancer patient has a higher prognostic risk;

[0020] If the risk score does not exceed the preset range, it means that the prognosis risk of the prostate cancer patient is low.

[0021] As an optional embodiment, the method further includes:

[0022] One or more of Kaplan–Meier survival analysis, ROC analysis and C-index are used to verify the predictive accuracy of the prognostic risk prediction model.

[0023] As an optional embodiment, the regression analysis is any one of ridge regression analysis, Cox regression analysis, Lasso regression analysis or ElasticNet regression analysis.

[0024] As one optional embodiment, the key genes are THSD7A, TNFRSF12A, CELSR3, NFIX, SYT7, GPRC5A, IGF1, HGF, ANK1, VCAN, CDH1, ADRB1, HDAC9, COL9A2, KITLG, LAMA3, TMCC3, LAMC2, GNA15, CDH3, GPC1, NGEF, SYT1, ATP11A, ADD2, FDFT1, OPRK1, RPS5, WNT11, LYZ, SLC5A1, NEFH, CHGA, MMP9, MYBL2, EEF1A2, PTK6, SMS, KLF5, MMP15 , SLC7A5, CPPED1, PLAT, EIF3E, NDRG1, NCALD, BNIP3L, MYH14, COMP, LFNG, CASP2, SFRP4, AEBP1, RLN1, SH3PXD2A, SOD3, FAM149A, CUX2, OAS3, RIC8B, CO L12A1, FRK, BVES, SLC16A10, PDE10A, MDFI, ENPP5, MRPS27, PCDHB3, PCDHB5, PCDHB7, QPCT, SDC1, ST6GALNAC5, GBP3, F3, SPP1, GALNT12, PLEKHG1, SOCS2 , GLIPR2, NUDT10, LRP1, HJURP, SDC4, SOX9, C3, IFI6, ZC4H2, HIP1, STEAP4, CDC42EP1, ISLR, PGAP6, PPP1R1B, MCCC2, SLC6A11, PPARG, VSTM2L, ATP8A2, CHI3L1, KL, AMPD3, BHLHE40, HMGCS2, LDHA, ANXA1, GOLM1, ELF5, CKAP2, TTYH3, DAB2IP, KIF12, MYC, RPS6, RNF144B, GGH, SORL1, ITGA11, CYP1B1, SLC39A 8. SORD, CPNE2, SLC39A6, MAPK4, PFKL, RPS8, ZNF697, GULP1, IL17RD, PLA1A, ADPRH, RPS3A, OTULINL, CXCL14, TNFRSF21, SDK1, CSGALNACT1, RPL7, CDKN 2A, LCN2, FAM171A1, FAM13C, ADD3, ADM, ITPR1, ANK3, PLEKHG4B, AK5, ANKRD29, PDLIM3, SLC16A1, MMP14, ERG, CLSTN2, RUNX1, SPON2, RPL8, DMKN, RPL26,CAMK2N1, C1orf115, PIGR, CLDN1, ELF3, RPL22L1, CDCP1, ARHGEF3, GUCY1A1, HHIP, FABP5, CTSB, WNK2, SLC16A9, GJB2, PKNOX2, GREM1, C19orf48, KRT80, DDIT4, ZNF608, VXN, PARM1, PFKFB3, KCNK3, KRT19, RRM2, RARG, DMXL1, AGR3, PTPRM, CHST2, UCP2, DMRTA1, RIMS2, RPS27, ADAMTSL1, VWA1, SLITRK4, ALOX1 5B, BACE2, CACNB4, ANXA2, CRIP2, CCSER1, SEPTIN9, SFTPA2, MUC1, THBS2, KRT10, ZNF395, ISG15, FYB2, AGRN, PLA2G2A, DPYD, APOD, NTNG2, ESRRG, SPTSS B, MME, TLE1, TMEM26, SERPINA1, S100A10, RPL12, ELOVL2, HLA-DRB5, RYR2, SCAMP5, COL5A2, VGLL3, LBH, S1PR3, EML6, RPS18, TMEM150C, RPL17, and PCDHB16. ,

[0025] Compared with the prior art, the beneficial effect of a method for constructing a prostate cancer patient prognosis risk prediction model provided by an embodiment of the present invention is: by obtaining prostate puncture samples, the prostate puncture samples include N normal prostate puncture samples, N hormone-sensitive prostate cancer puncture samples and N hormone-resistant prostate cancer puncture samples; MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing is performed on the prostate puncture samples, and a differential analysis method is used to analyze the genes related to prostate cancer prognosis in the prostate puncture samples to obtain several m6A modified genes related to prostate cancer prognosis; all the m6A modified genes are used as training sets, and regression analysis is used to screen out several key genes related to prostate cancer prognosis risk prediction; a prostate cancer patient prognosis risk prediction model is constructed based on all the key genes, and the prognosis risk situation of the prostate cancer patient is obtained through the prognosis risk prediction model. The prediction model established by the embodiment of the present invention can more accurately predict the patient's prognosis, better guide clinical decision-making, and help clinical physicians select postoperative patients with worse prognosis for intervention to prevent tumor recurrence and metastasis. At the same time, it also provides more evidence for the selection of adjuvant chemotherapy drugs, helps to formulate effective treatment strategies, reduce unnecessary treatments, and provide a more scientific reference basis for the treatment of prostate cancer, which is of great significance for the accurate diagnosis and treatment of prostate cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a preferred embodiment of a method for constructing a prostate cancer patient prognosis risk prediction model provided by the present invention;

[0027] Figure 2 It is a forest diagram of the univariate Cox analysis results comparing the prostate cancer patient prognosis risk prediction model provided by the present invention with other similar models in different data sets;

[0028] Figure 3 It is a C-index box plot comparing the prostate cancer patient prognosis risk prediction model provided by the present invention with other similar models in different data sets;

[0029] Figure 4 It is an AUC box plot comparing the prostate cancer patient prognosis risk prediction model provided by the present invention with other similar models in different data sets. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 , Figure 1 The present invention provides a method for constructing a prostate cancer patient prognosis risk prediction model. The method for constructing a prostate cancer patient prognosis risk prediction model comprises:

[0032] S1, obtaining prostate puncture samples, wherein the prostate puncture samples include N normal prostate puncture samples, N hormone-sensitive prostate cancer puncture samples, and N hormone-resistant prostate cancer puncture samples; wherein N is a positive integer greater than 1;

[0033] S2, performing MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing on the prostate biopsy sample, and using a differential analysis method to analyze genes related to prostate cancer prognosis in the prostate biopsy sample to obtain several m6A modified genes related to prostate cancer prognosis;

[0034] S3, using all the m6A modified genes as training sets, and using regression analysis to screen out several key genes related to the prediction of prostate cancer prognosis risk;

[0035] S4, constructing a prognosis risk prediction model for prostate cancer patients based on all the key genes, and obtaining the prognosis risk situation of the prostate cancer patients through the prognosis risk prediction model.

[0036] For example, in this embodiment, 4 normal prostate biopsy samples are first obtained, and then 8 patients are selected according to the preset patient selection criteria to obtain 4 hormone-sensitive prostate cancer biopsy samples and 4 hormone-resistant prostate cancer biopsy samples at the same position. After obtaining 4 normal prostate biopsy samples, 4 hormone-sensitive prostate cancer biopsy samples and 4 hormone-resistant prostate cancer biopsy samples, all prostate biopsy samples are subjected to MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing, and the genes related to prostate cancer prognosis in all prostate biopsy samples are analyzed by differential analysis method to obtain several m6A modified genes related to prostate cancer prognosis. Secondly, all m6A modified genes are used as training sets in the TCGA database, and several key genes related to prostate cancer prognosis risk prediction are screened out by regression analysis. A prostate cancer patient prognosis risk prediction model is constructed based on all key genes, and the prognosis risk of prostate cancer patients is obtained by the prognosis risk prediction model.

[0037] It should be noted that the principle of MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing is to use m6A-specific antibodies to immunoprecipitate RNA fragments with m6A modification in cells, perform high-throughput sequencing on the enriched RNA fragments, and combine it with bioinformatics analysis to systematically study m6A modification within the transcriptome.

[0038] The prediction model established in this embodiment has a good predictive value for the prognosis of prostate cancer, can more accurately predict the prognosis of patients, better guide clinical decision-making, and help clinical physicians select postoperative patients with worse prognosis for intervention to prevent tumor recurrence and metastasis. At the same time, it also provides more evidence for the selection of adjuvant chemotherapy drugs, helps to formulate effective treatment strategies, reduce unnecessary treatments, and provide a more scientific reference basis for the treatment of prostate cancer, which is of great significance for the accurate diagnosis and treatment of prostate cancer.

[0039] In another preferred embodiment, obtaining the prognostic risk of the prostate cancer patient by using the prognostic risk prediction model specifically includes:

[0040] The expression level and correlation coefficient of each of the key genes are obtained by the prognostic risk prediction model; wherein the correlation coefficient is the correlation coefficient between the key gene and the prognosis of the prostate cancer patient;

[0041] Calculating a risk score for the prognosis of the prostate cancer patient according to the expression level and the correlation coefficient;

[0042] The prognostic risk of the prostate cancer patient is obtained according to the risk score.

[0043] Specifically, this embodiment obtains the expression level and correlation coefficient of each key gene through the prognosis risk prediction model. The correlation coefficient refers to the correlation coefficient between the key gene and the prognosis of the prostate cancer patient. Then, the risk score of the prognosis of the prostate cancer patient is calculated according to the expression level and correlation coefficient of each key gene, and the prognosis risk of the prostate cancer patient is obtained according to the risk score.

[0044] As a preferred solution, the calculation formula of the risk score is:

[0045]

[0046] Among them, RS represents the risk score, n represents the number of key genes, and a i Represents the correlation coefficient of the i-th key gene, exp i Represents the expression level of the i-th key gene.

[0047] In another preferred embodiment, the obtaining the prognostic risk of the prostate cancer patient according to the risk score specifically includes:

[0048] Determining whether the risk score exceeds a preset range;

[0049] If the risk score exceeds the preset range, it indicates that the prostate cancer patient has a higher prognostic risk;

[0050] If the risk score does not exceed the preset range, it means that the prognosis risk of the prostate cancer patient is low.

[0051] Specifically, after calculating the risk score of the prognosis of the prostate cancer patient, it is determined whether the risk score exceeds the preset range. If the risk score exceeds the preset range, it means that the prognosis risk of the prostate cancer patient is high; if the risk score does not exceed the preset range, it means that the prognosis risk of the prostate cancer patient is low.

[0052] In another preferred embodiment, the method further comprises:

[0053] One or more of Kaplan–Meier survival analysis, ROC analysis and C-index are used to verify the predictive accuracy of the prognostic risk prediction model.

[0054] Specifically, one or more of Kaplan–Meier survival analysis, ROC analysis and C-index are used to verify the prediction accuracy of the prognostic risk prediction model. Among them, Kaplan–Meier survival analysis can obtain the patient's survival rate, survival time, etc. ROC analysis can determine whether a certain factor has diagnostic value for the diagnosis of a certain disease. C-index, C index, or consistency index, is used to evaluate the predictive ability of the model. C index refers to the proportion of pairs of all patient pairs whose predicted results are consistent with the actual results. It estimates the probability that the predicted results are consistent with the actual observed results. The calculation method of C index is: all research subjects in the studied data are randomly paired. Taking survival analysis as an example, for a pair of patients, if the predicted survival time of the one with a longer survival time is also longer than the predicted survival time of the other, or the predicted survival time of the one with a higher survival probability is longer than the other with a lower survival probability, then the predicted result is said to be consistent with the actual result.

[0055] In order to evaluate the accuracy of the prognostic risk prediction model, the prognostic risk prediction model can also be evaluated in multiple public data sets. Data sets containing overall survival (OS) statistical information (GSE54460, Stockolm, Belfast, DKFZ, Tayloe, CancerMap, CPC-Gene, Cambridge, CIT) are selected to evaluate the prognostic risk prediction model and compare it with other similar models. For example, see Figures 2 to 4 , Figure 2 It is a forest diagram of the univariate Cox analysis results comparing the prostate cancer patient prognosis risk prediction model provided by the present invention with other similar models in different data sets, Figure 3 is a C-index box plot comparing the prostate cancer patient prognosis risk prediction model provided by the present invention with other similar models in different data sets, Figure 4It is an AUC box plot comparing the prostate cancer patient prognosis risk prediction model provided by the present invention with other similar models in different data sets. In the figure, N refers to the number of samples in each data set. Hazard Ratio refers to the risk ratio. When the 95% CI contains 0, that is, when the horizontal line segment in the forest plot intersects with the invalid line, it can be considered that the mean difference between the two groups is not statistically significant, and it cannot be considered that the means of the two groups of outcome indicators are unequal. If the HR value is larger, it means that the predictive performance of the model in the data set is better. The predictive performance of the model is detected by observing the P.value. If the more data sets with P.value<0.05, the more accurate the predictive performance of the model. It can be seen from the figure that the prognosis risk prediction model provided by the present invention has a larger HR value than other similar models, indicating that the predictive performance of the prognosis risk prediction model is better than other similar models.

[0056] The above results show that this prognostic risk prediction model has potential predictive value in all data sets, and this prognostic risk prediction model is better than other similar models in predicting prognosis.

[0057] As a preferred solution, the regression analysis is any one of ridge regression analysis, Cox regression analysis, Lasso regression analysis or ElasticNet regression analysis.

[0058] Specifically, the regression analysis used in this embodiment when screening several key genes related to the prediction of prostate cancer prognosis risk is any one of Ridge regression analysis, Cox regression analysis, Lasso regression analysis or ElasticNet regression analysis.

[0059] As a preferred embodiment, the key genes are THSD7A, TNFRSF12A, CELSR3, NFIX, SYT7, GPRC5A, IGF1, HGF, ANK1, VCAN, CDH1, ADRB1, HDAC9, COL9A2, KITLG, LAMA3, TMCC3, LAMC2, GNA15, CDH3, GPC1, NGEF, SYT1, ATP11A, ADD2, FDFT1, OPRK1, RPS5, WNT11, LYZ, SLC5A1, NEFH, CHGA, MMP9, MYBL2, EEF1A2, PTK6, SMS, KLF5, MMP15, SLC7A 5. CPPED1, PLAT, EIF3E, NDRG1, NCALD, BNIP3L, MYH14, COMP, LFNG, CASP2, SFRP4, AEBP1, RLN1, SH3PXD2A, SOD3, FAM149A, CUX2, OAS3, RIC8B, COL12A1, F RK, BVES, SLC16A10, PDE10A, MDFI, ENPP5, MRPS27, PCDHB3, PCDHB5, PCDHB7, QPCT, SDC1, ST6GALNAC5, GBP3, F3, SPP1, GALNT12, PLEKHG1, SOCS2, GLIPR2 , NUDT10, LRP1, HJURP, SDC4, SOX9, C3, IFI6, ZC4H2, HIP1, STEAP4, CDC42EP1, ISLR, PGAP6, PPP1R1B, MCCC2, SLC6A11, PPARG, VSTM2L, ATP8A2, CHI3L1, KL, AMPD3, BHLHE40, HMGCS2, LDHA, ANXA1, GOLM1, ELF5, CKAP2, TTYH3, DAB2IP, KIF12, MYC, RPS6, RNF144B, GGH, SORL1, ITGA11, CYP1B1, SLC39A8, SORD, CPNE2, SLC39A6, MAPK4, PFKL, RPS8, ZNF697, GULP1, IL17RD, PLA1A, ADPRH, RPS3A, OTULINL, CXCL14, TNFRSF21, SDK1, CSGALNACT1, RPL7, CDKN2A, LCN2, FAM171A1, FAM13C, ADD3, ADM, ITPR1, ANK3, PLEKHG4B, AK5, ANKRD29, PDLIM3, SLC16A1, MMP14, ERG, CLSTN2, RUNX1, SPON2, RPL8, DMKN, RPL26, CAMK2N1,C1orf115, PIGR, CLDN1, ELF3, RPL22L1, CDCP1, ARHGEF3, GUCY1A1, HHIP, FABP5, CTSB, WNK2, SLC16A9, GJB2, PKNOX2, GREM1, C19orf48, KRT80, DDIT4, ZNF608, VXN, PARM1, PFKFB3, KCNK3, KRT19, RRM2, RARG, DMXL1, AGR3, PTPRM, CHST2, UCP2, DMRTA1, RIMS2, RPS27, ADAMTSL1, VWA1, SLITRK4, ALOX15B, B ACE2, CACNB4, ANXA2, CRIP2, CCSER1, SEPTIN9, SFTPA2, MUC1, THBS2, KRT10, ZNF395, ISG15, FYB2, AGRN, PLA2G2A, DPYD, APOD, NTNG2, ESRRG, SPTSSB, MME, TLE1, TMEM26, SERPINA1, S100A10, RPL12, ELOVL2, HLA-DRB5, RYR2, SCAMP5, COL5A2, VGLL3, LBH, S1PR3, EML6, RPS18, TMEM150C, RPL17 and PCDHB16. ,

[0060] For example, after using regression analysis to screen out 239 key genes related to the prognosis risk prediction of prostate cancer, a prognosis risk prediction model for prostate cancer patients was constructed based on these 239 key genes. The expression level and correlation coefficient of each key gene were obtained through the prognosis risk prediction model.

[0061]

[0062] Calculate the risk score for prognosis of prostate cancer patients. That is:

[0063] RS=(0.007504351*THSD7A expression)+(0.007693094*TNFRSF12Aexpression)+(0.011321329*CELSR3 expression)+(0.006320433*NFIX expression)+(0.012705622*SYT7 expression)+(-0.004443684*GPRC5A expression)+(-0.011202961*IGF1 expression)+(-0.002334165*HGF expression)+(-0.016740373*ANK1 expression)+(0.010877237*VCAN expression)+(-0.006349589*CDH1 expression)+(-0.004700945*ADRB1 expression)+(0.010546944*HDAC9 expression)+(0.009547606*COL9A2expression)+(-0.007795567*KITLG expression)+(0.017646452*LAMA3 expression)+(0.028478225*TMCC3 expression)+(0.000485882*LAMC2 expression)+(0.017040573*GNA15 expression)+(-0.005289582*CDH3 expression)+(0.067995837*GPC1expression)+(0.011571604*NGEF expression)+(0.011708155*SYT1 expression)+(-0.003753929*ATP11A expression)+(-0.001618912*ADD2 expression)+(-0.012911473*FDFT1 expression)+(0.012458775*OPRK1 expression)+(-0.01155524*RPS5expression)+(0.017133406*WNT11 expression)+(0.005050671*LYZ expression)+(0.001267791*SLC5A1 expression)+(-0.00090857*NEFH expression)+(0.00051433*CHGA expression)+(0.004896006*MMP9 expression)+(0.023654154*MYBL2 expression)+(0.006447066*EEF1A2 expression)+(0.007366339*PTK6 expression)+(0.005652202*SMS expression)+(-0.035751961*KLF5 expression)+(0.00868379*MMP15 expression)+(0.005801663*SLC7A5 expression)+(-0.019277967*CPPED1 expression)+(-0.000343958*PLAT expression)+(-0.04247478*EIF3E expression)+(-0.016524064*NDRG1 expression)+(0.008547397*NCALD expression)+(-0.036257595*BNIP3Lexpression)+(0.003782912*MYH14 expression)+(0.01395105*COMP expression)+(-0.005797147*LFNG expression)+(0.001266466*CASP2 expression)+(0.012520192*SFRP4 expression)+(0.028236328*AEBP1 expression)+(-0.001065632*RLN1expression)+(0.008104515*SH3PXD2A expression)+(0.002905381*SOD3 expression)+(0.013298104*FAM149A expression)+(-0.004810167*CUX2 expression)+(0.011224827*OAS3 expression)+(0.016388934*RIC8B expression)+(-0.000863843*COL12A1expression)+(-0.010206964*FRK expression)+(-0.029129371*BVES expression)+(-0.025856311*SLC16A10 expression)+(0.002817365*PDE10A expression)+(0.004632818*MDFI expression)+(-0.015506554*ENPP5 expression)+(-0.05917483*MRPS27 expression)+(0.009160749*PCDHB3 expression)+(-0.007616622*PCDHB5expression)+(0.003409693*PCDHB7 expression)+(-0.004754711*QPCT expression)+(0.003471704*SDC1 expression)+(0.003841769*ST6GALNAC5 expression)+(0.006928534*GBP3 expression)+(-0.002963971*F3 expression)+(0.012747075*SPP1expression)+(-0.019699127*GALNT12 expression)+(0.001566326*PLEKHG1expression)+(-0.015629088*SOCS2 expression)+(-0.010716566*GLIPR2 expression)+(-0.004905162*NUDT10 expression)+(0.003448292*LRP1 expression)+(0.029133393*HJURP expression)+(0.014237949*SDC4 expression)+(-0.003740534*SOX9expression)+(0.002256374*C3 expression)+(0.007002996*IFI6 expression)+(-0.014060999*ZC4H2 expression)+(-0.008826645*HIP1 expression)+(-0.004645191*STEAP4 expression)+(0.021878466*CDC42EP1 expression)+(0.010534287*ISLRexpression)+(0.007172494*PGAP6 expression)+(-0.021299856*PPP1R1B expression)+(4.94281E-05*MCCC2 expression)+(0.00285947*SLC6A11 expression)+(0.00569595*PPARG expression)+(0.008603476*VSTM2L expression)+(0.004654592*ATP8A2expression)+(-0.003165385*CHI3L1 expression)+(-0.003781093*KL expression)+(-0.003729622*AMPD3 expression)+(-0.005630169*BHLHE40 expression)+(-0.010653297*HMGCS2 expression)+(0.021316583*LDHA expression)+(-0.017045848*ANXA1 expression)+(-0.016802703*GOLM1 expression)+(-0.001664143*ELF5expression)+(-0.030076085*CKAP2 expression)+(0.053963468*TTYH3 expression)+(0.00671962*DAB2IP expression)+(0.00705406*KIF12 expression)+(0.004661172*MYCexpression)+(-0.010767545*RPS6 expression)+(-0.017777612*RNF144B expression)+(-0.011823067*GGH expression)+(-0.007796183*SORL1 expression)+(0.027373892*ITGA11 expression)+(-0.003987088*CYP1B1 expression)+(-0.007798628*SLC39A8expression)+(0.028344061*SORD expression)+(0.001523816*CPNE2 expression)+(-0.004782978*SLC39A6 expression)+(-0.004000066*MAPK4 expression)+(0.033678661*PFKL expression)+(-0.003731153*RPS8 expression)+(-0.016467133*ZNF697 expression)+(0.019440753*GULP1 expression)+(-0.032477947*IL17RD expression)+(-0.003934871*PLA1A expression)+(-0.009030139*ADPRH expression)+(0.007939271*RPS3A expression)+(0.022059307*OTULINL expression)+(0.009205976*CXCL14 expression)+(-0.002312314*TNFRSF21 expression)+(-0.01156888*SDK1 expression)+(-0.020599379*CSGALNACT1 expression)+(0.011890553*RPL7 expression)+(0.007489474*CDKN2A expression)+(-0.009933257*LCN2 expression)+(-0.005623597*FAM171A1 expression)+(-0.017531854*FAM13C expression)+(0.00276541*ADD3expression)+(0.001453243*ADM expression)+(-0.003975657*ITPR1 expression)+(-0.00275687*ANK3 expression)+(0.020030361*PLEKHG4B expression)+(0.014868484*AK5 expression)+(0.002272239*ANKRD29 expression)+(-0.01328461*PDLIM3expression)+(0.003037262*SLC16A1 expression)+(-1.93891E-05*MMP14 expression)+(-0.002982007*ERG expression)+(0.006982483*CLSTN2 expression)+(0.011178474*RUNX1 expression)+(0.003049329*SPON2 expression)+(-0.012995876*RPL8expression)+(0.012455394*DMKN expression)+(0.006479689*RPL26 expression)+(0.027435608*CAMK2N1 expression)+(-0.021219082*C1orf115 expression)+(-0.00116332*PIGR expression)+(-0.003260426*CLDN1 expression)+(-0.014051339*ELF3 expression)+(-0.021015573*RPL22L1 expression)+(-0.011220377*CDCP1expression)+(0.010145017*ARHGEF3 expression)+(0.002144298*GUCY1A1 expression)+(-0.000906904*HHIP expression)+(0.001551873*FABP5 expression)+(-0.008399676*CTSB expression)+(0.023156179*WNK2 expression)+(-0.020040077*SLC16A9expression)+(0.007134669*GJB2 expression)+(-0.013424073*PKNOX2 expression)+(0.009264182*GREM1 expression)+(0.014407947*C19orf48 expression)+(0.010652029*KRT80 expression)+(0.006231977*DDIT4 expression)+(0.003217265*ZNF608 expression)+(-0.014627587*VXN expression)+(-0.013149102*PARM1expression)+(-0.010964747*PFKFB3 expression)+(-0.002053606*KCNK3 expression)+(-0.009600293*KRT19 expression)+(0.02874047*RRM2 expression)+(0.008158444*RARG expression)+(-0.01433756*DMXL1 expression)+(-0.000732696*AGR3expression)+(-0.015404508*PTPRM expression)+(-0.001963153*CHST2 expression)+(-0.00759168*UCP2 expression)+(-0.038032177*DMRTA1 expression)+(0.001068461*RIMS2 expression)+(0.05776341*RPS27 expression)+(0.014616294*ADAMTSL1expression)+(-0.00830028*VWA1 expression)+(-0.00771616*SLITRK4 expression)+(-0.005479024*ALOX15B expression)+(-0.02217418*BACE2 expression)+(-0.001587175*CACNB4 expression)+(-0.012695914*ANXA2 expression)+(0.034423277*CRIP2expression)+(-0.008857745*CCSER1 expression)+(-0.008557276*SEPTIN9expression)+(-0.011678707*SFTPA2 expression)+(0.000508612*MUC1 expression)+(0.021744272*THBS2 expression)+(0.016554305*KRT10 expression)+(-0.038992581*ZNF395 expression)+(0.012745296*ISG15 expression)+(0.00763276*FYB2expression)+(0.033307688*AGRN expression)+(-0.010348683*PLA2G2A expression)+(-0.000390125*DPYD expression)+(-0.000967959*APOD expression)+(0.012250713*NTNG2 expression)+(-0.016549915*ESRRG expression)+(0.000677609*SPTSSBexpression)+(-0.000735276*MME expression)+(0.024452032*TLE1 expression)+(0.001883995*TMEM26 expression)+(-0.011390935*SERPINA1 expression)+(0.023098407*S100A10 expression)+(0.002736218*RPL12 expression)+(-0.004012199*ELOVL2 expression)+(0.023778683*HLA-DRB5 expression)+(-0.015285502*RYR2 expression)+(0.019565591*SCAMP5 expression)+(0.021017311*COL5A2 expression)+(-0.00806649*VGLL3 expression)+(0.013984564*LBHexpression)+(-0.002908948*S1PR3 expression)+(-0.005903768*EML6 expression)+(-0.002990156*RPS18 expression)+(0.003791268*TMEM150C expression)+(0.015810863*RPL17 expression)+(0.002941848*PCDHB16 expression).

[0064] After calculating the risk score of the prognosis of the prostate cancer patient, it is determined whether the risk score exceeds the preset range. If the risk score exceeds the preset range, it means that the prognosis risk of the prostate cancer patient is high; if the risk score does not exceed the preset range, it means that the prognosis risk of the prostate cancer patient is low.

[0065] The embodiment of the present invention provides a method for constructing a prostate cancer patient prognosis risk prediction model, by obtaining prostate puncture samples, wherein the prostate puncture samples include N normal prostate puncture samples, N hormone-sensitive prostate cancer puncture samples and N hormone-resistant prostate cancer puncture samples; MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing is performed on the prostate puncture samples, and a differential analysis method is used to analyze the genes related to prostate cancer prognosis in the prostate puncture samples to obtain several m6A modified genes related to prostate cancer prognosis; all the m6A modified genes are used as a training set, and regression analysis is used to screen out several key genes related to prostate cancer prognosis risk prediction; a prostate cancer patient prognosis risk prediction model is constructed based on all the key genes, and the prognosis risk situation of the prostate cancer patient is obtained through the prognosis risk prediction model. The prediction model established by the embodiment of the present invention can more accurately predict the patient's prognosis, better guide clinical decision-making, and help clinical physicians select postoperative patients with worse prognosis for intervention to prevent tumor recurrence and metastasis. At the same time, it also provides more evidence for the selection of adjuvant chemotherapy drugs, helps to formulate effective treatment strategies, reduce unnecessary treatments, and provide a more scientific reference basis for the treatment of prostate cancer, which is of great significance for the accurate diagnosis and treatment of prostate cancer.

[0066] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for constructing a prostate cancer patient prognosis risk prediction model, characterized in that: include: Obtaining prostate puncture samples, wherein the prostate puncture samples include N normal prostate puncture samples, N hormone-sensitive prostate cancer puncture samples, and N hormone-resistant prostate cancer puncture samples; wherein N is a positive integer greater than 1; The prostate biopsy samples are subjected to MeRIP-Seq methylated RNA immunoprecipitation combined with high-throughput sequencing, and the genes related to prostate cancer prognosis in the prostate biopsy samples are analyzed by a differential analysis method to obtain several m6A modified genes related to prostate cancer prognosis; All the m6A modified genes were used as training sets, and regression analysis was used to screen out several key genes related to the prediction of prostate cancer prognosis risk; Constructing a prognosis risk prediction model for prostate cancer patients based on all the key genes, and obtaining the prognosis risk of the prostate cancer patients through the prognosis risk prediction model; Among them, the key genes are respectively THSD7A, TNFRSF12A, CELSR3, NFIX, SYT7, GPRC5A, IGF1, HGF, ANK1, VCAN, CDH1, ADRB1, HDAC9, COL9A2, KITLG, LAMA3, TMCC3, LAMC2, GNA15, CDH3, GPC1, NGEF, SYT1, ATP11A, ADD2, FDFT1, OPRK1, RPS5, WNT11, LYZ, SLC5A1, NEFH, CHGA, MMP9, MYBL2, EEF1A2, PTK6, SMS, KLF5, MMP15, SLC7A5, CPPED1, PLAT, EIF3E, NDRG1, NCALD, BNIP3L, MYH14, COMP, LFNG, CASP2, SFRP4, AEBP1, RLN1, SH3PXD2A, SOD3, FAM149A, CUX2, OAS3, RIC8B, COL12A1, FRK, BVES, SLC16A10, PDE10A, MDFI, ENPP5, MRPS27, PCDHB3, PCDHB5, PCDHB7, QPCT, SDC1, ST6GALNAC5, GBP3, F3, SPP1, GALNT12, PLEKHG1, SOCS2, GLIPR2, NUDT10, LRP1, HJURP, SDC4, SOX9, C3, IFI6, ZC4H2, HIP1, STEAP4, CDC42EP1, ISLR, PGAP6, PPP1R1B, MCCC2, SLC6A11, PPARG, VSTM2L, ATP8A2, CHI3L1, KL, AMPD3, BHLHE40, HMGCS2, LDHA, ANXA1, GOLM1, ELF5, CKAP2, TTYH3, DAB2IP, KIF12, MYC, RPS6, RNF144B, GGH, SORL1, ITGA11, CYP1B1, SLC39A8, SORD, CPNE2, SLC39A6, MAPK4, PFKL, RPS8, ZNF697, GULP1, IL17RD, PLA1A, ADPRH, RPS3A, OTULINL, CXCL14, TNFRSF21, SDK1, CSGALNACT1, RPL7, CDKN2A, LCN2, FAM171A1, FAM13C, ADD3, ADM, ITPR1, ANK3, PLEKHG4B, AK5, ANKRD29, PDLIM3, SLC16A1, MMP14, ERG, CLSTN2, RUNX1, SPON2, RPL8, DMKN, RPL26, CAMK2N1,<h2 style=";text-align:left;direction:ltr">C1orf115、PIGR、CLDN1、ELF3、RPL22L1、CDCP1、ARHGEF3、GUCY1A1、HHIP、F ABP5、CTSB、WNK2、SLC16A9、GJB2、PKNOX2、GREM1、C19orf48、KRT80、DDIT4、 ZNF608、VXN、PARM1、PFKFB3、KCNK3、KRT19、RRM2、RARG、DMXL1、AGR3、PTPRM 、CHST2、UCP2、DMRTA1、RIMS2、RPS27、ADAMTSL1、VWA1、SLITRK4、ALOX15B、B ACE2, CACNB4, ANXA2, CRIP2, CCSER1, SEPTIN9, SFTPA2, MUC1, THBS2, KRT10, ZNF395, ISG15, FYB2, AGRN, PLA2G2A, DPYD, APOD, NTNG2, ESRRG, SPTSSB, M ME、TLE1、TMEM26、SERPINA1、S100A10、RPL12、ELOVL2、HLA-DRB5、RYR2、SCA MP5、COL5A2、VGLL3、LBH、S1PR3、EML6、RPS18、TMEM150C、RPL17、PCDHB16。、 2. The method for constructing a prostate cancer patient prognosis risk prediction model according to claim 1, characterized in that: The step of obtaining the prognostic risk of the prostate cancer patient by using the prognostic risk prediction model specifically includes: The expression level and correlation coefficient of each of the key genes are obtained by the prognostic risk prediction model; wherein the correlation coefficient is the correlation coefficient between the key gene and the prognosis of the prostate cancer patient; Calculating a risk score for the prognosis of the prostate cancer patient according to the expression level and the correlation coefficient; The prognostic risk of the prostate cancer patient is obtained according to the risk score.

3. The method for constructing a prostate cancer patient prognosis risk prediction model according to claim 2, characterized in that: The risk score is calculated as follows: Among them, RS represents the risk score, n represents the number of key genes, and a i Represents the correlation coefficient of the i-th key gene, exp i Represents the expression level of the i-th key gene.

4. The method for constructing a prostate cancer patient prognosis risk prediction model according to claim 2, characterized in that: The prognostic risk of the prostate cancer patient obtained according to the risk score specifically includes: Determining whether the risk score exceeds a preset range; If the risk score exceeds the preset range, it indicates that the prostate cancer patient has a higher prognostic risk; If the risk score does not exceed the preset range, it means that the prognosis risk of the prostate cancer patient is low.

5. The method for constructing a prostate cancer patient prognosis risk prediction model according to claim 1, characterized in that: The method further comprises: One or more of Kaplan-Meier survival analysis, ROC analysis and C-index are used to verify the prediction accuracy of the prognostic risk prediction model.

6. The method for constructing a prostate cancer patient prognosis risk prediction model according to claim 1, characterized in that: The regression analysis is any one of ridge regression analysis, Cox regression analysis, Lasso regression analysis or ElasticNet regression analysis.

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