CN117737237A - Kit for prognosis evaluation of prostate cancer and application thereof - Google Patents
Kit for prognosis evaluation of prostate cancer and application thereof Download PDFInfo
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Abstract
The invention relates to a kit for prognosis evaluation of prostate cancer and application thereof. The invention discloses the relativity between ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2, BMP6 and biochemical recurrence of prostate cancer, which has higher prediction accuracy than the common prognosis index in the prior art, and can more accurately carry out danger layering and prognosis prediction on BCR after RP of primary prostate cancer patients so as to accurately identify high-risk BCR patients needing positive intervention and avoid insufficient treatment or excessive treatment caused by judgment errors; meanwhile, the number of genes required to be detected is obviously reduced, the clinical detection process is greatly simplified, and the waste of medical resources can be effectively avoided. The invention fills the blank of realizing accurate prognosis evaluation of the prostate cancer, has important practical significance for better realizing individual accurate treatment of prostate cancer patients, and has extremely high social value and market application prospect.
Description
Technical Field
The invention belongs to the field of biological medicine, and relates to a kit for prognosis evaluation of prostate cancer and application thereof.
Background
Prostate cancer refers to an epithelial malignancy that occurs in the prostate. It is counted that the incidence of prostate cancer is 37.5 per 10 tens of thousands of people in developed countries, 11.3 per 10 tens of thousands of people in developing countries, and 8.1 per 10 tens of thousands of people in developed countries, 5.9 per 10 tens of thousands of people in developing countries. Currently about 1000 tens of thousands of men are diagnosed with prostate cancer. Prostate cancer causes more than 40 tens of thousands of deaths worldwide each year, and by 2040 years, mortality rates up to 80 tens of thousands of deaths each year are expected.
Androgen Receptor (AR) signaling plays an important role in prostate carcinogenesis and disease progression, and Androgen Deprivation Therapy (ADT) has become the primary means of treatment for patients with advanced disease. In general, the primary prostate cancer is treated by delayed therapy or active local therapy (e.g., radical prostatectomy or radiation therapy), with or without ADT. For metastatic prostate cancer, ADT using gonadotrophin releasing hormone (GnRH) antagonists/agonists followed by docetaxel prednisolone therapy and continued ADT following disease progression has become the standard treatment. However, patients respond differently to ADT, most of which eventually develop castration-resistant prostate cancer (CRPC).
For patients who do radical surgery for prostate cancer, or after radical radiotherapy achieves radical effect, if PSA exceeds 0.2ng/mL twice in succession, but no recurrence or metastasis is found in imaging, the biochemical recurrence of prostate cancer is indicated (BiochemicalRecurrence (BCR)). The data show that about 20-40% of prostate cancer patients experience biochemical recurrence within 10 years after RP. At this time, if no secondary treatment is performed, the median time from BCR to clinical progression of the patient is about 5-8 years, with 32-45% of patients developing prostate cancer-specific death within 15 years. For these high risk BCR patients, more aggressive follow-up and personalized treatments, such as Androgen Deprivation Therapy (ADT), radiation therapy, etc., should generally be taken clinically to improve patient prognosis.
There are a variety of indices already in the clinic for prognostic assessment of prostate cancer, such as PSA, gleason Score, tumor pathology stage and surgical margin, but due to the high heterogeneity of prostate cancer and the low sensitivity or specificity of the relevant clinical indices, accurate prognosis of prostate cancer BCR using these indices is not possible. However, indices such as Decipher, prolaris, oncotype _dx_gps can be used for prognosis prediction, but since the number of genes to be detected is at least 15 or more, there are drawbacks in clinical applications such as long detection time, high cost, and complicated detection procedures. Therefore, how to realize stable and accurate risk stratification and prognosis prediction of BCR of prostate cancer patients, especially of primary prostate cancer patients after RP, and at the same time, simplify detection procedures and reduce use cost are key problems to be solved in the clinical diagnosis and treatment of prostate cancer at present.
Disclosure of Invention
The invention aims to solve the technical problems that the biochemical recurrence of a prostate cancer patient is difficult to accurately predict and the clinical application is complicated in the prior art, thereby providing a novel detection kit for carrying out prognosis evaluation on the prostate cancer patient. The kit can stably and accurately reasonably predict the prognosis of the prostate cancer, especially the risk of BCR by detecting the expression levels of 9 biomarkers such as ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2, BMP6 and the like, greatly simplifies the clinical detection procedure, saves the use cost, can be used as a risk assessment tool for screening the prostate cancer patients with high risk of biochemical recurrence so as to reasonably prevent and treat in advance, guide the individual accurate treatment of the prostate cancer patients, further optimize the management of the prostate cancer patients, and avoid the disease progression caused by untimely treatment or the waste of medical data caused by excessive treatment.
In order to solve the technical problems, the invention is realized by the following technical scheme.
In a first aspect the invention provides a kit for prognosis prediction of prostate cancer comprising reagents for detecting the expression level of a biomarker consisting of ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2, BMP 6.
Preferably, the prostate cancer is primary prostate cancer.
Preferably, the prostate cancer is primary prostate cancer that has received radical treatment.
Preferably, the radical treatment is selected from one or more of radical prostatectomy treatment and radical radiation treatment.
Preferably, the reagent for detecting the expression level of the biomarker includes a primer pair for detecting the expression level of the biomarker gene and/or a reagent for detecting the content of a protein encoded by the biomarker gene.
Preferably, the primer pair for detecting the expression level of the biomarker gene is specifically as follows:
primer pair 1: the forward sequence is: 5'-ACACTGACCGATAAGTGGTATGG-3', the reverse sequence is: 5- 'GTTGT AGCTTTATGGCAAGGGA-3' for detecting ATAD1;
primer pair 2: the forward sequence is: 5'-CGCGGTCGCCAAAAAGAAAG-3', the reverse sequence is: 5- 'TACTTGCA GTCGGCTCCAAAC-3' for detecting MDK;
primer pair 3: the forward sequence is: 5'-AGCCATCTCCGACAATCTGTG-3', the reverse sequence is: 5- 'CCAACCA CGAGTCAGCAATG-3' for detecting SLC15A2;
primer pair 4: the forward sequence is: 5'-AGCGGGTGTATCTCAACAGC-3', the reverse sequence is: 5- 'TCTCTCCA CGTCTTTGGCTTC-3' for detecting PITPNC1;
Primer pair 5: the forward sequence is: 5'-CGAGCCAACGTGAAGAGTG-3', the reverse sequence is: 5- 'TCGAAGTTT TAAGCGATGCAGT-3' for detecting PPFIA2;
primer pair 6: the forward sequence is: 5'-CCAGATGGCTCTAACCTCAGT-3', the reverse sequence is: 5- 'AACTTCC ACGAAAAAGAGGCTT-3' for detecting CDK6;
primer pair 7: the forward sequence is: 5'-ATGCTGCCACAAATACCCTTT-3', the reverse sequence is: 5- 'GGTAGTG GGCCTTTTATGCCT-3' for detecting COL10A1;
primer pair 8: the forward sequence is: 5'-CCTTTCCGGCTAGTGCCTAAT-3', the reverse sequence is: 5- 'TTCTGCC CTCTGTAGTTGCAT-3' for detecting SH3RF2;
primer pair 9: the forward sequence is: 5'-TGTTGGACACCCGTGTAGTAT-3', the reverse sequence is: 5- 'AACCCAC AGATTGCTAGTGGC-3' for detecting BMP6.
Preferably, the reagent for detecting the protein content encoded by the biomarker gene is selected from one or more of monoclonal antibodies and/or polyclonal antibodies; most preferably, the reagent for detecting the protein content encoded by the biomarker gene is selected from one or more of ab155963 (Abcam for detecting BMP 6), 13306-1-AP (Proteintech for detecting SH3RF 2), A18604 (ABclonal for detecting COL10A 1), YT5884 (Immunoway for detecting CDK 6), YT6638 (Immunoway for detecting PPFIA 2), NBP2-97834 (Novus Biologicals, for detecting PITPNC 1), YN5137 (Immunoway for detecting SLC15A 2), YT5177 (Immunoway, for detecting MDK), ab229706 (Abcam, for detecting ATAD 1).
It is to be understood that, unless otherwise specified, in the context of the present invention, the primers and/or primer pairs refer to PCR primers used for synthesizing cDNA strands of each marker gene in PCR, thereby detecting the expression level of each marker gene. In addition to the primers and/or primer pairs listed in the present invention, it is fully within the ability of one skilled in the art to design corresponding primers and/or primer pairs based on the gene sequences of the respective markers by means conventional in the art, including but not limited to molecular biology, and to screen the designed primers and/or primer pairs by means of conventional experimentation, as long as specific detection of the expression levels of the respective markers is achieved. Meanwhile, for the antibody reagent for detecting the protein content encoded by each marker gene, a person skilled in the art can obtain the antibody reagent by market, and can also design the antibody reagent according to the sequence and/or the protein structure of each marker gene.
Preferably, the index of prognosis evaluation comprises one or more of biochemical recurrence rate, biochemical recurrence time, five-year survival rate, BCR SCR.
Preferably, the BCR SCR is calculated as follows:
BCR SCR＝predict(fit,newdata＝data)$predicted；
Wherein fit is a model that has been trained; newdata represents a data frame or matrix; the data format of the data is as follows: a. row name is the sample name; b. the list name is the target gene and the expression quantity in the BCR SCR model.
It will be appreciated that, unless otherwise specified, those skilled in the art will be aware that the "prediction" listed in the context of the present invention is a generic function in the R language for prediction from the results of fitting functions to various models. Wherein fit is a trained random living forest model, which can be directly provided for use. The newdata parameter represents a new data frame or matrix, used for prediction, and the data format of the data is mainly: the row name is the sample name and the column name is the related gene in the BCR SCR. In practical clinical application, the person skilled in the art can sort the collected patient information (including gene expression level) into a file conforming to the data format, and calculate the score by prediction (fit, newdata=data) $predicted.
Preferably, the kit further comprises one or more of PCR enzymes, PCR buffers, dNTPs, fluorogenic substrates.
Preferably, the fluorogenic substrate is selected from the group consisting of Syber Green or a fluorescently labeled probe.
In a second aspect, the invention provides the use of an agent for detecting the expression level of a biomarker consisting of ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK 6, COL10A1, SH3RF2, BMP6 in the manufacture of a product for prognostic assessment of prostate cancer.
Preferably, the prostate cancer is primary prostate cancer.
Preferably, the prostate cancer is primary prostate cancer that has received radical treatment.
Preferably, the radical treatment is selected from one or more of radical prostatectomy treatment and radical radiation treatment.
Preferably, the reagent for detecting the expression level of the biomarker includes a primer pair for detecting the expression level of the biomarker gene and/or a reagent for detecting the content of a protein encoded by the biomarker gene.
Preferably, the primer pair for detecting the expression level of the biomarker gene is specifically as follows:
primer pair 1: the forward sequence is: 5'-ACACTGACCGATAAGTGGTATGG-3', the reverse sequence is: 5- 'GTTGT AGCTTTATGGCAAGGGA-3' for detecting ATAD1;
primer pair 2: the forward sequence is: 5'-CGCGGTCGCCAAAAAGAAAG-3', the reverse sequence is: 5- 'TACTTGCA GTCGGCTCCAAAC-3' for detecting MDK;
Primer pair 3: the forward sequence is: 5'-AGCCATCTCCGACAATCTGTG-3', the reverse sequence is: 5- 'CCAACCA CGAGTCAGCAATG-3' for detecting SLC15A2;
primer pair 4: the forward sequence is: 5'-AGCGGGTGTATCTCAACAGC-3', the reverse sequence is: 5- 'TCTCTCCA CGTCTTTGGCTTC-3' for detecting PITPNC1;
primer pair 5: the forward sequence is: 5'-CGAGCCAACGTGAAGAGTG-3', the reverse sequence is: 5- 'TCGAAGTTT TAAGCGATGCAGT-3' for detecting PPFIA2;
primer pair 6: the forward sequence is: 5'-CCAGATGGCTCTAACCTCAGT-3', the reverse sequence is: 5- 'AACTTCC ACGAAAAAGAGGCTT-3' for detecting CDK6;
primer pair 7: the forward sequence is: 5'-ATGCTGCCACAAATACCCTTT-3', the reverse sequence is: 5- 'GGTAGTG GGCCTTTTATGCCT-3' for detecting COL10A1;
primer pair 8: the forward sequence is: 5'-CCTTTCCGGCTAGTGCCTAAT-3', the reverse sequence is: 5- 'TTCTGCC CTCTGTAGTTGCAT-3' for detecting SH3RF2;
primer pair 9: the forward sequence is: 5'-TGTTGGACACCCGTGTAGTAT-3', the reverse sequence is: 5- 'AACCCAC AGATTGCTAGTGGC-3' for detecting BMP6.
Preferably, the reagent for detecting the protein content encoded by the biomarker gene is selected from one or more of monoclonal antibodies and/or polyclonal antibodies; most preferably, the reagent for detecting the protein content encoded by the biomarker gene is selected from one or more of ab155963 (Abcam for detecting BMP 6), 13306-1-AP (Proteintech for detecting SH3RF 2), A18604 (ABclonal for detecting COL10A 1), YT5884 (Immunoway for detecting CDK 6), YT6638 (Immunoway for detecting PPFIA 2), NBP2-97834 (Novus Biologicals, for detecting PITPNC 1), YN5137 (Immunoway for detecting SLC15A 2), YT5177 (Immunoway, for detecting MDK), ab229706 (Abcam, for detecting ATAD 1).
Preferably, the index of prognosis prediction comprises one or more of biochemical recurrence occurrence rate, biochemical recurrence occurrence time, five-year survival rate, BCR SCR.
Preferably, the BCR SCR is calculated as follows:
BCR SCR＝predict(fit,newdata＝data)$predicted；
wherein fit is a model that has been trained; newdata represents a data frame or matrix; the data format of the data is as follows: a. row name is the sample name; b. the list name is the target gene and the expression quantity in the BCR SCR model.
The third aspect of the present invention provides a screening method for a potential therapeutic agent for prostate cancer, comprising the steps of:
(1) Detecting the expression level of a biomarker consisting of ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2, BMP6 in a prostate cancer patient;
(2) BCR SCR was calculated for prostate cancer patients according to the following formula:
BCR SCR＝predict(fit,newdata＝data)$predicted；
wherein fit is a model that has been trained; newdata represents a data frame or matrix; the data format of the data is as follows: a. row name is the sample name; b. the list name is the target gene and the expression quantity in the BCR SCR model;
(3) Calculating a survivin_setpoint function in an R language 'surviviner' package to obtain a cut-off value, dividing a prostate cancer patient into a BCR SCR high-score group (upper ten digits) and a BCR SCR low-score group (lower ten digits) according to upper ten digits, and respectively carrying out medicine reaction difference analysis in the two groups to determine medicines with lower AUC estimated values in the BCR SCR high-score group;
(4) Screening out drugs with significant negative correlation between AUC values and BCR SCR according to Spearman correlation analysis;
(5) And (3) screening the step (3) and the step (4) to obtain medicines, and taking an intersection.
Unlike many other types of tumors, prostate cancer, especially primary prostate cancer, progresses much more slowly, many patients show only prostate hyperplasia and even no obvious symptoms, and the 10-year cancer-specific survival rate of some patients is even more than 94%. Currently, radical therapy (Radical Prostatectomy (RP)) is commonly used in clinical treatment of primary prostate cancer, and the key to the choice of a specific therapeutic strategy is whether the risk of disease aggressiveness can be accurately predicted at diagnosis.
Biochemical recurrence is a key node of whether the current treatment strategy needs to be changed after radical treatment of primary prostate cancer. BCR patients will have more than 1/3 of their patients with distant metastasis and progress to clinical recurrence if they do not receive further curative radiation or endocrine therapy. At present, indexes such as PSA, gleason Score, surgical margin and lymph node metastasis are mainly relied on clinically to predict the risk of BCR after the primary prostate cancer patient RP, but researches show that even if the combination of the indexes is adopted, the accuracy of predicting the prognosis of the prostate cancer is still 75-85%, namely the existing clinical parameters are difficult to meet the requirements of accurate risk stratification and current individuation treatment on the BCR after the prostate cancer RP.
According to the invention, through a large number of researches, 9 biomarkers related to biochemical recurrence of the prostate cancer are obtained through screening, and through detecting the expression level of the 9 biomarkers in the body and calculating based on a specific model BCR SCR, reasonable prediction of biochemical recurrence of the prostate cancer and the like can be performed. By utilizing the 9 biomarkers, compared with the single prediction indexes or even the combination thereof in the prior art, the method has more accurate and stable prediction performance and precision; compared with the clinically used partial indexes (including a Decipher (22 genes need to be detected), a Prolaris (46 genes need to be detected) and an Oncotype_DX_GPS (17 genes need to be detected), the number of the genes needing to be detected is obviously reduced, the production, preparation and use costs are greatly reduced, the detection procedure is simplified, and the waste of medical resources is effectively avoided.
Compared with the prior art, the invention has the following technical effects:
(1) The invention carries out intensive research on the pathogenesis and development mechanism of the prostate cancer, discovers that the accurate prediction of the biochemical recurrence status of the prostate cancer patient has key significance for evaluating the survival rate and prognosis of the prostate cancer patient, and screens and obtains 9 biomarkers ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL 10A1, SH3RF2 and BMP6 related to the biochemical recurrence of the prostate cancer. By detecting the expression levels of 9 biomarkers in a subject and performing calculation and analysis based on a specific model BCR SCR, the prognosis and survival rate state of a prostate cancer patient, particularly a primary prostate cancer patient after radical treatment, can be reasonably predicted and estimated more accurately.
(2) The invention discloses the relativity between ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF 2, BMP6 and biochemical recurrence of prostate cancer, which has higher prediction accuracy than the common prognosis index in the prior art, and can more accurately carry out danger layering and prognosis prediction on BCR after RP of primary prostate cancer patients so as to accurately identify high-risk BCR patients needing positive intervention and avoid insufficient treatment or excessive treatment caused by judgment errors; meanwhile, the number of genes required to be detected is obviously reduced, the clinical detection process is greatly simplified, and the waste of medical resources can be effectively avoided. The invention fills the blank of realizing accurate prognosis evaluation of the prostate cancer, has important practical significance for better realizing individual accurate treatment of prostate cancer patients, and has extremely high social value and market application prospect.
Drawings
FIG. 1 is an average C index of the overall prognostic model for 7 different training set constructs.
FIG. 2 shows 9 identical genes selected by the Lasso and CoxBoost algorithm in two different combinations.
FIG. 3 shows 9 genes selected according to Lasso.
FIG. 4 is a graph showing the effect of 9 genes on BCR in prostate cancer patients.
FIG. 5 is a schematic representation of scoring results for each gene sample using the prediction function.
FIG. 6 is a graph showing the effect of TCGA-PRAD cohort BCR SCR on BSC of prostate cancer patients.
Fig. 7 is a graph showing the effect of the cancer map queue BCR SCR on BSC in prostate cancer patients.
Fig. 8 is a graph showing the effect of CIT queue BCR SCR on BSC in prostate cancer patients.
Fig. 9 is a graph showing the effect of CPC queue BCR SCR on BSC in prostate cancer patients.
Fig. 10 is a graph showing the effect of GSE54460 cohort BCR SCR on BSC in prostate cancer patients.
Fig. 11 is a graph showing the effect of Stockholm cohort BCR SCR on BSC in prostate cancer patients.
Fig. 12 is a graph showing the effect of Taylor-queued BCR SCR on BSC in prostate cancer patients.
Fig. 13 is a schematic representation of ROC analysis results for BCR SCR in 9 queues.
Fig. 14 is a graph showing KM analysis, cox analysis, and ROC analysis results for BCR SCR in Cambridge queues.
Fig. 15 is a graph showing the results of KM analysis, cox analysis, and ROC analysis of BCR SCR in DKFZ cohorts.
FIG. 16 is a graph showing the results of KM analysis of BCR in different expression level groups of BCR SCR based on Shanghai cohort.
FIG. 17 is a graph showing the results of Cox analysis and ROC analysis of BCR SCR based on Shanghai queue.
FIG. 18 is a graph showing results of different predictor C index rankings in the TCGA and cancer map queues.
FIG. 19 is a graph showing the results of the different predictor C index rankings in the CIT and CPC queues.
Fig. 20 is a schematic diagram of results of different predictor C index ranking in GSE54460 and stock holm queues.
FIG. 21 is a graph showing results of different predictor C index rankings in the Taylor queue.
FIG. 22 is a graph showing the results of KM analysis of BCR in different expression level groups of BCR SCR based on Belfast queue.
FIG. 23 is a graphical representation of analysis of BCR SCR score for patients undergoing distant metastasis based on the Belfast cohort.
FIG. 24 is a graph showing analysis results of remote transfer rate conditions of BCR SCR different expression level groups based on a Belfast queue.
FIG. 25 is a graph showing the results of KM analysis of OS in BCR SCR different expression level groups based on Shanghai queue.
FIG. 26 is a graph showing the results of Cox analysis of BCR SCR differential expression level groups based on Shanghai cohort.
FIG. 27 is a graph showing the results of ROC analysis of BCR SCR differential expression level groups based on Shanghai cohorts.
FIG. 28 is a graph showing the AUC results of docetaxel in TNFAIP8 high and low expression groups.
FIG. 29 is a schematic representation of the results of immunohistochemical analysis of 9 genes in prostate cancer tissue.
Fig. 30 is a schematic diagram showing the results of screening for potential therapeutic drugs for prostate cancer by drug response differential analysis.
FIG. 31 is a graphical representation of the results of screening for potential therapeutic agents for prostate cancer by Spearman correlation analysis.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention more clear and clear, the present invention will be described in further detail with reference to examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
All reagents used in the context of the present invention are commercially available, unless otherwise specified. Clinical samples or data from studies or analyses are derived from primary prostate cancer patients who have received curative treatment (surgery or radiation therapy, etc.). For the use of clinical specimens, the patient signs informed consent, and related procedures and methods accord with medical ethics requirements and quality management specifications of clinical tests of medicines. Experimental methods used in the present invention, such as model construction, bioinformatics analysis, immunohistochemistry, and the like, are all conventional methods and techniques in the art.
Representative results of selection from the biological experimental replicates are presented in the context figures, and data are presented as mean±sd and mean±sem as specified in the figures. All experiments were repeated at least three times. Data were analyzed using GraphPad Prism 9.0 or R software (version 4.0.1). And comparing the average value difference of two or more groups by adopting conventional medical statistical methods such as t-test, chi-square test, analysis of variance and the like. p < 0.05 was considered a significant difference.
Example 1 screening and model construction of biomarkers
(1) Queues with transcriptomic and clinical information data for prostate cancer patients were downloaded from the PCaDB (http:// bioinfo. Jialab-ucr. Org/PCaDB /) website: TCGA-PRAD, cancerMap (GSE 94767), CIT (E-MTAB-6128), CPC (GSE 107299), GSE54460, stockholm (GSE 70769), taylor (GSE 21034), cambridge (GSE 70768), DKFZ and Belfast. At the same time, 1 cohort with chinese prostate cancer population RNA sequencing information and clinical data was retrospectively collected: shanghai queue. Furthermore, a tissue chip (Tissue Microarray, TMA) cohort of 1 prostate cancer was also collected based on immunohistochemistry. Based on the clinical cohort of global multicenter prostate cancer RNA sequencing samples described above, inclusion samples were screened according to the following criteria:
a. primary prostate cancer tissue; b. carrying RNA sequencing information; c. radical prostate cancer treatment is accepted; d. the follow-up time after operation is more than or equal to 30 days;
the samples which are screened and incorporated are divided into long-term queues (the follow-up time is more than or equal to 8 years), 5-year queues and Chinese queues according to the follow-up time and the race information; wherein the long-term queue consists of TCGA-PRAD, cancerMap, CIT, CPC, GSE54460, stockholm and Taylor; the 5-year queue consists of Cambridge and DKFZ; chinese queues consist of Shanghai and TMA.
In addition, a Belfast radiotherapy cohort was used to assess the predictive value of BCR SCR response to prostate cancer treatment. Meanwhile, RNA-seq and clinical information data of 31 solid tumors other than prostate cancer recorded in the TCGA database are also downloaded from UCSC Xena (University of California Santa Cruz Xena, UCSC Xena, https:// xen abrowser. Net/datapages /) website. The RNA-seq data from TCGA and SRA were processed using M-value weighted truncated mean (Trimmed Mean of M values, TMM) in the "edge" package. Affymetrix chip data with raw CEL files in GEO/Arrayexpress was processed using the robust multichip averaging method (Robust Multichip Average, RMA) in the R software "oligo" package. The processed normalized expression data spectrum is downloaded and used for other data sets acquired from GEO and cbioPortal databases. The gene expression values were further log2 transformed.
(2) Screening potential genes related to the BCR of the prostate cancer from intersection genes of 7 long-term queues by using single factor Cox analysis, wherein the potential genes simultaneously meet the following conditions: (1) The Hazard Ratio (HR) value is simultaneously greater than or less than 1 in at least 6 queues; (2) p < 0.05; in this regard, a total of 11 potential genes were screened. Then, sequentially taking one of 7 long-term queues as a training set, taking the other 6 queues as verification sets, respectively calculating average C indexes of the overall prognosis model under the condition that 7 different training sets form based on 10-fold cross verification, and taking the training set with the highest average C index as the training set when the BCR SCR model of the prostate cancer is finally constructed; as a result, it was found that the average C index of the overall model was highest at 0.700 when the TCGA-PRAD cohort was used as the training set and the remaining 6 as the validation set (see fig. 1).
(3) Based on the 11 BCR related potential genes obtained by screening in the step (2) and the TCGA-PRAD training set with the highest average C index, 10 machine learning algorithms are used for integrating the potential genes into 101 algorithm combinations to fit a prognosis model based on 10-fold cross validation, and the C index of each prognosis model in each long-term queue is calculated respectively, so that the algorithm combination with the highest C index and the best prediction performance is screened out, and two algorithm combinations with the highest average C index are obtained; the 10 machine learning algorithms are specifically: random Survival Forest (RSF), survival Support Vector Ma chine (Survval-SVM), least absolute shrinkage and selection operator (Lasso), stepwise Cox, ridge, gradient Boosting Machine (GBM), elastic network (Enet), coxBoost, partial least squares Regression for Cox (plsRcox), supervised Princ ipal Components (SuperPC), wherein algorithms such as RSF, lasso, stepwise Cox and CoxBoost have feature selection capability; as a result, the average C index of the prognosis model constructed by the combination of the Lasso+RSF and the CoxBoost+RSF is 0.743, wherein the Lasso and the CoxBoost are mainly used for screening characteristic variables, and the RSF in the combination is used for constructing the model. Meanwhile, it was also found that the characteristic variables screened by the Lasso and CoxBoost algorithms in two different combinations are 9 identical genes, and the gene types obtained by taking intersections of the genes screened by the two algorithms are target genes, namely, ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2 and BMP6 (see fig. 2-3). By Kaplan-Meier analysis of these 9 genes, it was found that in the BMP6, COL10A1, PPFIA2 and MDK highly expressed groups, the BCR occurrence time of the patients was significantly advanced, whereas in the SH3RF2, CDK6, PI TPNC1, SCL15A2 and ATAD1 highly expressed groups, the BCR occurrence time of the patients was significantly prolonged (see FIG. 4).
Example 2 biomarker prognostic function verification
From example 1 above, it was found that there was a certain correlation between the 9 genes of ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH 3RF2 and BMP6 and the occurrence of prostate cancer BCR. In this regard, the predictive performance of the above biomarkers needs to be further clarified. In order to integrate the different expression level values of the 9 genes into an index form which can be clinically compared and objectively evaluated, and improve operability, a prognosis model, namely BCR SCR, is constructed by adopting RSF, meanwhile, each sample is scored by using a prediction function (see FIG. 5), and the calculation of the BCR SCR value is carried out according to the following formula:
BCR SCR＝predict(fit,newdata＝data)$predicted；
the "prediction ()" function is a generic function in the R language that is commonly used to apply a trained model to a new dataset and generate a prediction result. Wherein fit is a model that has been trained; newdata represents a data frame or matrix; the data format of the data is as follows: a. row name is the sample name; b. the list name is the target gene and the expression quantity in the BCR SCR model. The value of the model prediction in the new dataset, here the score of the model, can be extracted using the $predicted expression to further analyze and interpret the model prediction effect.
To assess the prognostic value of BCR SCR, the optimal cut-off value of the model score was obtained using the surviving_cut point function in the R language "surviviner" package, and prostate cancer patients were classified into BCR SCR high score (upper ten) and BCR SCR low score group (lower ten) according to the upper and lower ten digits. The specific method comprises the following steps: res.cut<Surv_cutpoint (data, # the first two columns of this dataset are biochemical recurrence time and status, respectively, the third column is the score of model. Time= "BCR. Time", # biochemical recurrence time. Event= "BCR", # biochemical recurrence status. Variables= "riskscore" # score of model). Analysis of survival by Kaplan-Meier showed (see fig. 6-12) that in the TCGA-PRAD training set (n=460, p < 0.0001) andthe remaining 6 validation sets cancelmap (n=99, p=0.00024), CIT (n=77, p=0.0012), CPC (n=99, p=0.00044), GSE54460 (n=89, p < 0.0001), stock holm (n=90, p < 0.0001) and Taylor (n=131, p=9e) -04 ) In the middle, the occurrence time of BCR of patients with high BCR SCR is obviously earlier than that of patients with low BCR SCR. Furthermore, single-and multifactorial Cox regression analysis, including clinical factors such as age, prostate-Specific Antigen (PS a), prostate cancer glisen Score (Gleason Score) and TNM staging, showed that in 7 long-term cohorts, model BCR SCR constructed from 9 gene expression levels was considered an independent risk factor (p < 0.05) for Prostate cancer BCR, except CIT and Taylor cohorts.
The predicted performance of the 9 gene expression levels and their constructed model BCR SCR was then further verified by ROC analysis and the results are shown in fig. 13. The results showed that the AUC values for 3, 5 and 8 years in TCGA-PRAD were 0.983, 0.949 and 0.975, respectively; AUC values for 3 years, 5 years, and 8 years in cancer map were 0.763, 0.711, and 0.711, respectively; AUC values for 3, 5 and 8 years in CIT were 0.882, 0.764 and 0.703, respectively; AUC values in CPC for 3 years, 5 years and 8 years were 0.705, 0.723 and 0.676, respectively; AUC values in GSE54460 for 3 years, 5 years and 8 years are 0.756, 0.703 and 0.654, respectively; AUC values in stock holm for 3 years, 5 years and 8 years were 0.763, 0.711 and 0.711, respectively; AUC values in Taylor for 3 years, 5 years and 8 years were 0.737, 0.727 and 0.512, respectively. Overall, the average AUC values for 3 years, 5 years, and 8 years were 0.798, 0.755, and 0.706, respectively, in 7 long-term queues, indicating that 9 gene expression levels and their constructed model BCR SCR had reliable and stable predictive performance in most long-term queues.
Further, external validation in 2 5 year cohorts Cambridge and DKFZ, and results from Kaplan-Meier survival analysis, single, multifactorial Cox analysis, and ROC analysis showed that 9 gene expression levels and their constructed model BCR SCR also had similar predictive power in patients with shorter follow-up time, even in the population with shorter follow-up time (see fig. 14-15).
Since the above-mentioned adopted queue is mainly limited to the European and American population, it is not clear whether it has the same predictive performance in the Chinese and Asian population. In this regard, the Shanghai (Shanghai) cohort was used to evaluate 9 gene expression levels and their constructed model BCR SCR performance in the Chinese population. According to Kaplan-Meier survival analysis, it was found that in Shanghai (n=117, p < 0.0001) cohorts, BCR SCR high-scoring patients had significantly advanced BCR occurrence times, while BCR SCR low-scoring patients had significantly retarded BCR occurrence times (see fig. 16). The results of single and multi-factor Cox analysis showed that the 9 gene expression levels and the model BCR SCR constructed therefrom were used as independent risk factors for prostate cancer BCR with AUC values of 0.711, 0.727 and 0.900 for 1, 3 and 6 years, respectively (see fig. 17).
The results show that the 9 gene expression levels and the model BCR SCR constructed by the gene expression levels not only have good prediction performance in European and American populations, but also have the same stable prediction capability and potential clinical transformation performance in Chinese populations, namely, the gene expression level has universality for the prediction of BCR prognosis of prostate cancer patients.
Example 3 biomarker prognostic reliability verification
It has been clarified in the previous examples that 9 gene expression levels and the model BCR SCR model constructed thereof show reliable and stable prediction and external generalization ability in different queues. To further verify whether it still maintains beneficial predictive performance compared to the numerous known prostate cancer prognostic indicators disclosed and used in the prior art, 102 recently disclosed prostate cancer prognostic indicators have been organized, each of which is closely related to a different biological characteristic, such as lipid metabolism, glycolysis, apoptosis, iron death, hypoxia, inflammatory response, and the like. Then, C indices of 102 prognostic indicators in 7 long-term cohorts were calculated sequentially and compared with 9 gene expression levels and their constructed model BCR SCR, respectively, and the prognostic indicators of top 20 of C indices in 7 cohorts are shown in fig. 18-21, respectively. The results show that in the TCGA-PRAD and cancer map queues, the C index of the BCR SCR is obviously higher than other prognosis indexes; in the GSE54460 queue, the C index of BCRSCR is only lower than wu.29.Pnas; in the CIT queue, the C index row name of the BCR SCR is the 3 rd bit; in the CP C queue, the C index row name of the BCR SCR is the 5 th bit; whereas in the Stockholm and Taylor queues, the C-exponential ordering of BCR SCRs is 10 th and 16 th bits, respectively. And the overall performance of the BCR SCR ranks 1 st in terms of comprehensive average ranking and C index, which is obviously superior to other prognostic indexes. Further, repeating the above comparative analysis in Shanghai's cohort found that the C index of BCR SCR was highest. This suggests that the 9 gene expression levels and their constructed model BCR SCR remain relatively robust predictive and exogenously able, even when compared to other commonly known prognostic indicators.
To investigate the prognostic value of 9 gene expression levels for prostate cancer patients after radical radiotherapy treatment, the kalfast cohort was selected for Kaplan-Meier survival analysis to find that BCR SCR high-score patients were significantly advanced in both BCR onset time and tumor distant metastasis time (see fig. 22), and BCR SCR was significantly higher in distant metastasis patients than in non-distant metastasis patients (p < 0.05) (see fig. 23), and BCR SCR high-score patients had significantly higher BCR incidence and prostate cancer distant metastasis incidence than in BCR SCR low-score patients (see fig. 24).
Considering that high quality mRNA is often difficult to obtain in clinical samples of prostate cancer, paraffin sections of pathological samples are relatively easy to obtain. Thus, BCR SCR potential clinical conversion capacity was further validated using immunohistochemistry in TMA cohorts. Kap lan-Meier Survival analysis showed that Overall Survival (OS) of BCR SCR high-score patients was significantly shortened (n=60, p=0.0057) (see fig. 25). Single and multifactorial Cox analysis results showed that BCR SCR is an independent risk factor for prostate cancer OS (see fig. 26). ROC analysis showed AUC values of 0.695,0.792 and 0.897 for 6, 7 and 8 years, respectively (see fig. 27).
To further evaluate the clinical applicability of BCR SCR, a validation was performed using a prostate cancer tissue chip containing 60 cases of primary prostate cancer tissue, and the inclusion of clinical samples required the following conditions: a. prostate cancer patients; b. radical prostatectomy was performed. Immunohistochemistry was used to measure the expression levels of BMP6, SH3RF2, COL10A1, CDK6, PPFIA2, PITPNC1, SLC15A2, MDK, and ATAD1 proteins in the above prostate cancer patients, and the immune response score of each tissue was calculated as follows:
(1) Paraffin sections are dewaxed by using dimethylbenzene, and then subjected to concentration gradient ethanol rehydration, and then citrate buffer solution is added for antigen retrieval.
(2) After rinsing with PBS, goat serum was added for blocking at room temperature, and after rinsing with PBS, the corresponding primary antibody was added for incubation at 4℃overnight.
(3) Then, after rinsing by PBS, adding secondary antibody, incubating for 30min at room temperature, adding DAB for color development, then dripping hematoxylin for counterstaining, namely gradient ethanol dehydration, xylene transparency and sealing. Each sample was scored microscopically by two pathologists.
The detection results are shown in FIG. 28. Wherein the immunoreactivity of BMP6, SH3RF2, COL10A1, CDK6, PPFIA2, PITPNC1, SLC15A2, MDK, and ATAD1 was graded according to staining intensity (negative=0, weak=1, medium=2, strong=3) and percentage of immunoreactive cells (< 5% =0, 5-25% =1, 25-50% =2, 50-75% =3, > 75% =4). The final immune response score for each case is the sum of the percent immune response cell score and the intensity score; the results of the quantitative analysis of immunohistochemistry are shown in table 1 below:
Table 1 results of immunohistochemical intensity analysis of 9 markers in different patients
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From the immunohistochemical results of the prostate cancer tissue chip, the positive rate of BMP6 was 68.3% (41/60), the positive rates of SH3RF2, CDK6, PPFIA2, PITPNC1, MDK and ATAD1 were 98.3% (59/60), the positive rate of COL10A1 was 96.7% (58/60), and the positive rate of SLC15A2 was 90% (54/60), indicating that the above 9 markers were expressed higher in prostate cancer. Subsequently, another 30 normal male subjects were evaluated for BCR SCR, and immunohistochemical analysis of their normal prostate tissues revealed that the above 9 biomarkers all exhibited significantly low expression levels, and that BCR SCR average scores were significantly smaller than those of the above primary prostate cancer patients (not shown in the figure). It follows that BCR SCR of the present invention and the above 9 biomarkers can be used as reliable indicators for prediction and assessment of prostate cancer.
EXAMPLE 4 screening of potential therapeutic drugs for prostate cancer
In the above examples, it was clarified that ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2 and BMP6 and BCR SCR models obtained based on the 9 genes can be used for prognosis prediction of prostate cancer, that is, the 9 genes have a high correlation with BCR of prostate cancer and the like, and in this regard, screening of potential therapeutic drugs for prostate cancer based on the 9 genes and BCR SCR models was considered.
First, in order to verify the reliability of the prediction result, the sensitivity relationship between TNFAIP8 and docetaxel is verified. Prior studies showed that high expression of TNFAIP8 reduced sensitivity of prostate cancer cells to docetaxel, for which higher AUC values were found for patients in the TNFAIP8 high expression group, indicating reduced sensitivity to docetaxel, as found by analysis of the difference in sensitivity between the TNFAIP8 high expression and the TNFAIP8 low expression groups, consistent with the findings of prior studies (see fig. 28). Subsequently, based on gene expression profile data for hundreds of CCLs in the CTRP and PRISM databases, the "prrofetic" package with built-in Ridge Regression Model was used to predict drug response for clinical samples, resulting in an estimate of AUC for each drug in each clinical sample. Next, a drug response differential analysis was performed between BCR SCR high-score (top score) and BCR SCR low-score (bottom score) to determine drugs with lower AUC estimates in BCR SCR high-score (lo g2FC > 0.1) (see fig. 29). Meanwhile, a drug with a significant negative correlation between AUC values and BCR SCR was screened according to Spearman correlation analysis (Spearman's R < -0.2) (see fig. 30). Finally, 9 potential therapeutic drugs for prostate cancer, namely leptiomycin B, paclitaxel, parbendazole, bl-2536, SB-743921, methotrexate, vincristine and irinotecan, are obtained by intersection of drug screening results obtained by the drug differential analysis and the Spearman correlation analysis. The 9 drugs have low AUC estimation values in the BCR SCR high-score groups and have negative correlation with the BCR SCR, so that the drugs can be used for treating prostate cancer patients, especially prostate cancer patients with high scores of the BCR SCR.
Unlike many other types of tumors, primary prostate cancer progresses slowly in large numbers of patients, even showing no obvious symptoms or only symptoms of prostatic hyperplasia, and the 10-year cancer-specific survival rate of some patients is even more than 94%. At present, most of clinically treating primary prostate cancer adopts RP, and the key of specific treatment strategy selection is whether the risk of disease invasiveness can be accurately predicted in diagnosis. BCR is an important turning point of whether the current treatment strategy needs to be changed after RP of the primary prostate cancer patient. BCR patients, if not further receiving radical or endocrine therapy, develop distant metastasis in about 34% of patients, and progress to clinical recurrence. Currently, clinical dependence on these indices, PSA, gleason Score, surgi cal margin and lymph node metastasis, is mainly used to predict BCR risk after RP in primary prostate cancer patients, but studies have shown that even with a combination of these indices, the accuracy of predicting prostate cancer prognosis is only 75-85%, which can lead to over-or under-treatment in some patients. Therefore, there is an urgent need to establish accurate and reliable prognostic biomarkers for risk stratification of post-RP BCR in primary prostate cancer patients in order to accurately identify high risk BC R patients in time that require positive intervention.
Through a great deal of research, 9 biomarkers related to prostate cancer are defined, namely ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2 and BMP6. There is a high correlation between the 9 biomarker expression levels and the prognosis of prostate cancer, especially BCR. In order to improve the convenience and operability of clinical application, the invention constructs a prostate cancer prognosis prediction model, namely BCR SCR, based on the expression level of the 9 biomarkers in vivo, and based on the model, accurate and reasonable prognosis evaluation can be carried out on prostate cancer patients more stably and objectively. And according to the study of the invention, the 9 biomarker expression levels and the model BCR SCR constructed by the biomarker expression levels show reliable and robust prediction performance in a long-term queue or a five-year queue. In addition, compared with 102 published prostate cancer prognosis indexes, for example, clinically used partial indexes (Decipher (22 genes to be detected), prol aris (46 genes to be detected) and Oncotype_DX_GPS (17 genes to be detected) and the like, the number of the genes to be detected is remarkably reduced, the production and preparation cost and the use cost are greatly reduced, the detection procedure is simplified, and the waste of medical resources is effectively avoided.
In general, the invention carries out intensive research on pathogenesis and development mechanism of the prostate cancer, screens and obtains 9 biomarkers highly correlated with the prostate cancer based on analysis of a plurality of biomarkers in a prostate cancer patient, constructs a quantifiable model-BCR SCR (binary sequence-based control) capable of being used for systematically evaluating the biochemical recurrence risk of the prostate cancer patient, and can more accurately carry out risk stratification and prognosis prediction on the BCR of the primary prostate cancer patient RP so as to accurately identify a high-risk BCR patient needing positive intervention and avoid insufficient treatment or excessive treatment caused by wrong judgment; meanwhile, the quantity of genes to be detected by the BCR SCR provided by the invention is obviously reduced, the clinical detection process is greatly simplified, and the waste of medical resources can be effectively avoided. The invention fills the blank of realizing accurate prognosis evaluation of the prostate cancer, has important practical significance for better realizing individual accurate treatment of prostate cancer patients, and has extremely high social value and market application prospect.
The above detailed description describes the analysis method according to the present invention. It should be noted that the above description is only intended to help those skilled in the art to better understand the method and idea of the present invention, and is not intended to limit the related content. Those skilled in the art may make appropriate adjustments or modifications to the present invention without departing from the principle of the present invention, and such adjustments and modifications should also fall within the scope of the present invention.
Claims (10)
1. Use of an agent for detecting the expression level of a biomarker consisting of ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2, BMP6 for the preparation of a product for prognostic assessment of prostate cancer.
2. The use according to claim 1, wherein the reagent for detecting the expression level of the biomarker comprises a primer pair for detecting the expression level of the biomarker gene and/or a reagent for detecting the amount of protein encoded by the biomarker gene.
3. The use of claim 1, wherein the prognostic assessment is an indicator comprising one or more of a biochemical recurrence rate, a biochemical recurrence time, a five-year survival rate, BCR SCR.
4. The use according to claim 3, wherein the BCR SCR is calculated as follows:
BCR SCR＝predict(fit,newdata＝data)$predicted；
wherein fit is a model that has been trained; newdata represents a data frame or matrix; the data format of the data is as follows: a. row name is the sample name; b. the list name is the target gene and the expression quantity in the BCR SCR model.
5. A kit for prognosis prediction of prostate cancer, comprising a reagent for detecting the expression level of a biomarker, characterized in that the biomarker consists of ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2, BMP 6.
6. The kit of claim 5, wherein the reagent for detecting the expression level of the biomarker comprises a primer pair for detecting the expression level of the biomarker gene and/or a reagent for detecting the protein content encoded by the biomarker gene.
7. The kit according to claim 6, wherein the primer pair for detecting the expression level of the biomarker gene is specifically as follows:
primer pair 1: the forward sequence is: 5'-ACACTGACCGATAAGTGGTATGG-3', the reverse sequence is: 5- 'GTTGTAGCTTTATGGCAAGGGA-3' for detecting ATAD1;
primer pair 2: the forward sequence is: 5'-CGCGGTCGCCAAAAAGAAAG-3', the reverse sequence is: 5- 'TACTTGCAGTCGGCTCCAAAC-3' for detecting MDK;
primer pair 3: the forward sequence is: 5'-AGCCATCTCCGACAATCTGTG-3', the reverse sequence is: 5- 'CCAACCACGAGTCAGCAATG-3' for detecting SLC15A2;
primer pair 4: the forward sequence is: 5'-AGCGGGTGTATCTCAACAGC-3', the reverse sequence is: 5- 'TCTCTCCACGTCTTTGGCTTC-3' for detecting PITPNC1;
primer pair 5: the forward sequence is: 5'-CGAGCCAACGTGAAGAGTG-3', the reverse sequence is: 5- 'TCGAAGTTTTAAGCGATGCAGT-3' for detecting PPFIA2;
Primer pair 6: the forward sequence is: 5'-CCAGATGGCTCTAACCTCAGT-3', the reverse sequence is: 5- 'AACTTCCACGAAAAAGAGGCTT-3' for detecting CDK6;
primer pair 7: the forward sequence is: 5'-ATGCTGCCACAAATACCCTTT-3', the reverse sequence is: 5- 'GGTAGTGGGCCTTTTATGCCT-3' for detecting COL10A1;
primer pair 8: the forward sequence is: 5'-CCTTTCCGGCTAGTGCCTAAT-3', the reverse sequence is: 5- 'TTCTGCCCTCTGTAGTTGCAT-3' for detecting SH3RF2;
primer pair 9: the forward sequence is: 5'-TGTTGGACACCCGTGTAGTAT-3', the reverse sequence is: 5- 'AACCCACAGATTGCTAGTGGC-3' for detecting BMP6.
8. The kit of any one of claims 5-7, further comprising one or more of PCR enzymes, PCR buffers, dNTPs, fluorogenic substrates.
9. The kit of claim 8, wherein the fluorogenic substrate is selected from the group consisting of Syber Green and a fluorescently labeled probe.
10. A method for screening a potential therapeutic agent for prostate cancer, comprising the steps of:
(1) Detecting the expression level of a biomarker consisting of ATAD1, MDK, SLC15A2, PITPNC1, PPFIA2, CDK6, COL10A1, SH3RF2, BMP6 in a prostate cancer patient;
(2) BCR SCR was calculated for prostate cancer patients according to the following formula:
BCR SCR＝predict(fit,newdata＝data)$predicted；
wherein fit is a model that has been trained; newdata represents a data frame or matrix; the data format of the data is as follows: a. row name is the sample name; b. the list name is the target gene and the expression quantity in the BCR SCR model.
(3) Calculating a survivin_setpoint function in an R language 'surviviner' package to obtain a cut-off value, dividing a prostate cancer patient into a BCR SCR high-score group (upper ten digits) and a BCR SCR low-score group (lower ten digits) according to upper ten digits, and respectively carrying out medicine reaction difference analysis in the two groups to determine medicines with lower AUC estimated values in the BCR SCR high-score group;
(4) Screening out drugs with significant negative correlation between AUC values and BCR SCR according to Spearman correlation analysis;
(5) And (3) screening the step (3) and the step (4) to obtain medicines, and taking an intersection.
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