WO2023246808A1 - 利用癌症中剪接异常的短外显子辅助癌症诊断和预后 - Google Patents

利用癌症中剪接异常的短外显子辅助癌症诊断和预后 Download PDF

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WO2023246808A1
WO2023246808A1 PCT/CN2023/101497 CN2023101497W WO2023246808A1 WO 2023246808 A1 WO2023246808 A1 WO 2023246808A1 CN 2023101497 W CN2023101497 W CN 2023101497W WO 2023246808 A1 WO2023246808 A1 WO 2023246808A1
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cancer
prognosis
risk
combination
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French (fr)
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王泽峰
张思蕊
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中国科学院上海营养与健康研究所
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    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to the field of biomedicine, and specifically to the use of short exons with abnormal splicing in cancer to assist cancer diagnosis and prognosis.
  • Cancer has become one of the most common diseases in China. Accurate judgment of cancer and cancer prognosis is very necessary for cancer treatment. The use of easily detectable biomarkers to judge cancer and predict the prognosis of cancer patients is of great significance. However, there are still insufficient effective indicators for predicting the prognosis of cancer patients.
  • the purpose of the present invention is to invent a more effective new method for predicting cancer and the prognosis of cancer patients based on RNA splicing changes in cancer.
  • a method of detecting abnormally spliced short exons (CASE) in cancer (cancer-associated short exons, CASE, or “cancer-associated short exons”) or a detection reagent thereof is provided.
  • CASE abnormally spliced short exons
  • CASE cancer-associated short exons
  • a detection reagent thereof is provided.
  • the aberrantly spliced short exons in the cancer include one or more of the short exons selected from Table 2A:
  • the abnormally spliced short exons in the cancer at least include all short exons selected from Table 2A.
  • the short exons with abnormal splicing in the cancer also include one or more short exons selected from Table 2B:
  • the abnormally spliced short exons in the cancer include at least 4 short exons selected from Tables 2A and 2B.
  • the abnormally spliced short exons in the cancer include at least 8 short exons selected from Tables 2A and 2B.
  • the short exons with abnormal splicing in the cancer also include one or more short exons selected from Table 2C:
  • the short exons with abnormal splicing in the cancer also include one or more short exons selected from Table 2D:
  • the judgment includes pre-judgment (prediction).
  • the reagents include primers, probes, sequencing libraries, and nucleic acid chips (such as DNA chips).
  • the short exons with abnormal splicing in cancer are derived from mammals, preferably from rodents (such as mice, rats), primates and humans, more preferably from For patients diagnosed with cancer or suspected of having cancer.
  • the cancer is selected from the group consisting of esophageal cancer, lung adenocarcinoma, or a combination thereof.
  • the detection is to measure tumor tissue or general tissue samples.
  • the general tissue includes paracancerous tissue.
  • the detection reagent includes specific amplification primers, probes or chips for short exons with abnormal splicing in cancer.
  • the detection reagent is selected from the following group: primers, probes, sequencing libraries, nucleic acid chips (such as DNA chips), or combinations thereof.
  • the judgment includes the steps:
  • the sample is selected from the following group: tissue samples of patients diagnosed with cancer or adjacent normal samples.
  • the level of one or more biomarkers selected from Table 2A meets the predetermined value, indicating that the subject to be tested has a risk of cancer or suffers from cancer.
  • the predetermined value is a PSI value.
  • the predetermined value is a score obtained by scoring the measured PSI value using a random forest algorithm.
  • the subject is a human.
  • the detection is for detection of ex vivo samples.
  • the in vitro sample is a tissue sample.
  • the method before step (1), further includes the step of processing the sample.
  • a second aspect of the present invention provides a biomarker combination, said combination comprising short exons with abnormal splicing in one or more cancers selected from Table 2A.
  • the combination further includes short exons with abnormal splicing in one or more cancers selected from Table 2B.
  • the abnormally spliced short exons in the cancer include at least 4 short exons selected from Tables 2A and 2B.
  • the abnormally spliced short exons in the cancer include at least 8 short exons selected from Tables 2A and 2B.
  • the abnormally spliced short exons in the cancer at least include all short exons selected from Table 2A.
  • the biomarker combination is used to determine whether a sample is cancer or to determine the risk of cancer or to predict cancer or to prepare a reagent or kit that is used to determine whether a sample is cancer. for cancer or to determine the risk of cancer or to predict cancer.
  • the biomarker or biomarker combination is derived from tissue samples of patients diagnosed with cancer or adjacent normal samples.
  • the level of one or more biomarkers selected from Table 2A, or Tables 2A and 2B meets the predetermined value, indicating that the subject to be tested has a risk of cancer or suffers from cancer.
  • the predetermined value is a PSI value.
  • the predetermined value is a predicted value obtained based on a model established by CASE.
  • the predetermined value is a score obtained by scoring the measured PSI value using a random forest algorithm.
  • each biomarker is identified by a method selected from the group consisting of: RT-PCR, RT-PCR chip, RNA sequencing or a combination thereof.
  • the combination is used to determine the risk of cancer in the subject to be tested or to determine whether the subject to be tested has cancer.
  • the third aspect of the present invention provides a reagent combination for determining the risk of cancer.
  • the reagent combination includes reagents for detecting each biomarker in the combination according to the second aspect of the present invention.
  • the reagents include substances for detecting each biomarker in the collection of the second aspect of the present invention using a method selected from the following group: RT-PCR, RT-PCR chip, RNA sequencing, or its combination.
  • the fourth aspect of the present invention provides a kit, which includes the combination described in the second aspect of the present invention and/or the reagent combination described in the third aspect of the present invention.
  • each marker in the combination described in the second aspect of the present invention is used as a standard.
  • the fifth aspect of the present invention provides a method for determining the risk of cancer in a subject to be tested or determining whether the subject to be tested has cancer, including the steps:
  • the combination further includes one or more short exons selected from Table 2B.
  • the level of one or more biomarkers selected from Table 2A, or Tables 2A and 2B meets the predetermined value, indicating that the subject to be tested has a risk of cancer.
  • the predetermined value is a PSI value.
  • the predetermined value is a predicted value obtained based on a model established by CASE.
  • the predetermined value is a score obtained by scoring the measured PSI value using a random forest algorithm.
  • a sixth aspect of the present invention provides a system for determining the risk of cancer in a subject to be tested or determining whether the subject to be tested has cancer.
  • the system includes:
  • a characteristic input module for the risk of cancer which is used to input the characteristics of the risk of cancer of the subject to be tested;
  • the characteristic of cancer risk includes one or more of the short exons selected from Table 2A;
  • a cancer risk identification and processing module performs scoring processing on the input cancer risk characteristics according to predetermined judgment standards, thereby obtaining a cancer risk score; and assigns the cancer risk score to Compare with a predetermined value to obtain an auxiliary diagnosis result, wherein when the score of the cancer risk meets the predetermined value, it is prompted that the subject is at risk of cancer or suffers from cancer; and
  • Auxiliary diagnosis result output module the output module is used to output the auxiliary diagnosis result.
  • the characteristics of cancer risk also include one or more short exons selected from Table 2B.
  • the object is a human.
  • the score includes (a) the score of a single feature; and/or (b) the sum of scores of multiple features.
  • the scoring includes calculating the PSI value of each short exon.
  • RT-PCR or RNA sequencing method is used to calculate the PSI value of each short exon.
  • the predetermined value is a PSI value.
  • the predetermined value is a score obtained by scoring the sample using a random forest algorithm.
  • the predetermined value is a predicted value obtained based on a model established by CASE.
  • the feature input module is selected from the following group: sample collector, sample storage tube, cell lysis and nucleic acid sample extraction kit, RNA nucleic acid reverse transcription and amplification kit, and second-generation sequencing library construction kit, library quantification kit, sequencing reaction kit, or a combination thereof.
  • the cancer risk determination processing module includes a processor and a storage, wherein the storage stores cancer risk scoring data based on cancer risk characteristics.
  • the output module includes a reporting system.
  • the seventh aspect of the present invention provides the use of abnormally spliced short exons (CASE) in cancer (cancer-associated short exons, CASE) or its detection reagent, which can be used as a marker to judge the prognosis and survival time of cancer patients or for the preparation of A reagent or kit used to determine the prognosis and survival time of cancer patients.
  • the short exons with abnormal splicing in the cancer include those from PPP3CC, DST, MBNL1, MARK3, TPD52L2, and ARHGAP17 in Table A. , FLNA, and one or more abnormally spliced short exons of RPS24.
  • the cancer is selected from the group consisting of esophageal cancer, lung adenocarcinoma, or a combination thereof.
  • the judgment includes pre-judgment (prediction).
  • the judgment includes the steps:
  • the sample is selected from the following group: tissue samples of patients diagnosed with cancer or adjacent normal samples.
  • the predetermined value is a risk factor.
  • the risk factor is calculated using the following formula:
  • relative PSI is the relative value obtained by sorting according to the quantitative value of each biomarker (CASE) (see Figure 4 for details), and N is the total number of biomarkers (where N is any Positive integer, better Land, 4-100, preferably, 5-80, more preferably, 6-50, more preferably, N is 8), i is the i-th biomarker, where i ⁇ 100.
  • N is any positive integer, preferably, 4-100, preferably, 5-80, more Preferably, 6-50, more preferably, N is 8) the calculated risk factor (risk factor) of the level of the biomarker is higher than the predetermined value, indicating that the subject to be tested has a poor prognosis and a shortened survival time.
  • N is any positive integer, preferably, 4-100, preferably, 5-80, more Preferably, 6-50, more preferably, N is 8) the calculated risk factor (risk factor) of the biomarker level is lower than the predetermined value, indicating that the test subject has a good prognosis and an increased survival time.
  • the subject is a human.
  • the detection is for detection of ex vivo samples.
  • the in vitro sample includes a tissue sample.
  • the method before step (1), further includes the step of processing the sample.
  • the eighth aspect of the present invention provides a biomarker combination, which includes one or more short short genes with abnormal splicing selected from PPP3CC, DST, MBNL1, MARK3, TPD52L2, ARHGAP17, FLNA, and RPS24 in Table A. Exon.
  • the biomarker combination is used to determine the prognosis and survival time of cancer patients or is used to prepare a reagent or kit, and the reagent or kit is used to determine the prognosis and survival time of cancer patients.
  • the biomarker or biomarker combination is derived from tissue samples of patients diagnosed with cancer or adjacent normal samples.
  • the level of one or more biomarkers selected from Table A is higher than the predetermined value, indicating a poor prognosis and shortened survival time of the subject to be tested.
  • the level of one or more biomarkers selected from Table A is lower than the predetermined value, indicating that the test subject has a good prognosis and an increased survival time.
  • the predetermined value is a risk factor.
  • each biomarker is identified by a method selected from the group consisting of: RT-PCR, RT-PCR chip, RNA sequencing or a combination thereof.
  • the combination is used to determine the prognosis and survival time of cancer patients.
  • the ninth aspect of the present invention provides a reagent combination for judging the prognosis and survival time of cancer patients.
  • the reagent combination includes reagents for detecting each biomarker in the combination according to the eighth aspect of the present invention.
  • the reagents include substances for detecting each biomarker in the combination according to the eighth aspect of the present invention using a method selected from the following group: RT-PCR, RT-PCR chip, RNA sequencing or other combination.
  • a tenth aspect of the present invention provides a kit, which includes the combination of the eighth aspect of the present invention and/or the reagent combination of the ninth aspect of the present invention.
  • each marker in the combination described in the eighth aspect of the present invention is used as a standard.
  • the eleventh aspect of the present invention provides a method for judging the prognosis and survival time of cancer patients, including the steps:
  • the combination includes one or more biomarkers selected from Table A from PPP3CC, DST, MBNL1, MARK3, Abnormally spliced short exons of TPD52L2, ARHGAP17, FLNA, and RPS24;
  • the level of one or more biomarkers selected from Table A is higher than the predetermined value, indicating a poor prognosis and shortened survival time of the subject to be tested.
  • the level of one or more biomarkers selected from Table A is lower than the predetermined value, indicating that the test subject has a good prognosis and an increased survival time.
  • the predetermined value is a risk factor.
  • a twelfth aspect of the present invention provides a system for determining the prognosis and survival time of cancer patients, the system including:
  • a characteristic input module for the prognosis and survival period of cancer patients which is used to input the characteristics of the prognosis and survival period of the cancer patient of the subject to be tested;
  • the prognosis and survival characteristics of cancer patients include one or more abnormally spliced short exons selected from PPP3CC, DST, MBNL1, MARK3, TPD52L2, ARHGAP17, FLNA, and RPS24 in Table A;
  • a discrimination processing module for the prognosis and survival time of cancer patients performs scoring processing on the input characteristics of the prognosis and survival time of the cancer patient according to predetermined judgment standards, thereby obtaining the prognosis and survival time scores of the cancer patient; And compare the prognosis and survival time scores of the cancer patient with a predetermined value to obtain an auxiliary diagnosis result, wherein when the prognosis and survival time score of the cancer patient is higher than the predetermined value, the subject is prompted Poor prognosis and shortened survival. When the score of the cancer risk is lower than the predetermined value, it indicates that the subject has a good prognosis and increased survival; and
  • Auxiliary diagnosis result output module the output module is used to output the auxiliary diagnosis result.
  • the object is a human.
  • the score includes (a) the score of a single feature; and/or (b) the sum of scores of multiple features.
  • the scoring includes calculating the relative PSI value of each short exon.
  • the scoring includes calculating the PSI value of each short exon.
  • RT-PCR or RNA sequencing is used to calculate the PSI value of each short exon.
  • the predetermined value is a risk factor.
  • the risk factor is calculated using the following formula:
  • relative PSI is the relative value obtained by sorting according to the quantitative value of each biomarker (CASE) (see Figure 4 for details), N is the total number of biomarkers (where N is any Positive integer, preferably 4-100, preferably 5-80, more preferably 6-50, more preferably, N is 8), i is the i-th biomarker, where i ⁇ 100.
  • the feature input module is selected from the following group: sample collector, sample storage tube, cell lysis and nucleic acid sample extraction kit, RNA nucleic acid reverse transcription and amplification kit, and second-generation sequencing library construction kit, library quantification kit, sequencing reaction kit, or a combination thereof.
  • the discriminating processing module for the prognosis and survival time of cancer patients includes a processor and a storage, wherein the storage stores the cancer risk based on the prognosis and survival time of the cancer patient. rating data.
  • the output module includes a reporting system.
  • Figure 1 shows that principal component analysis (left) and partial least squares regression analysis (right) using CASE splicing can distinguish cancer tissue from adjacent normal tissue.
  • Figure 2 shows that principal component analysis (top) and partial least squares regression analysis (bottom) using CASE splicing in each individual cancer can differentiate cancer tissue from adjacent normal tissue.
  • Figure 3 shows (A) the flowchart of training a random forest model to predict cancer using PSI values of CASE as features. 4-fold cross-validation is used in 100 random trials, where 75% of the samples are randomly selected for training and the remaining 25% for testing. (B) ROC curve of random forest model. (C) AUC value of random forest model in each cancer type. (D) Performance of two random forest models using TCGA LUAD data as training set and Chinese LUAD data as test set (left), or using Chinese ESCC data for training and TCGA ESCA data for testing (right).
  • Figure 4 shows A) the process of calculating risk factors using the PSI values of eight CASEs. Sort the PSI values of each CASE in positive or negative order according to their correlation with survival time, give each patient a relative PSI score (an integer from 1 to 4) according to the order, and calculate the average of the eight relative PSIs. risk factors. Risk factors were ranked from low to high and the top and bottom 25 percent of patients were defined as Group 1 and Group 2. (B) Kaplan-Meier curves for cancer patients grouped by CASE-based risk factors.
  • Figure 5 shows the distribution of cancer patients in each type grouped by CASE-based risk factors.
  • Figure 6 shows the survival time distribution between the two groups of Chinese esophageal cancer patients grouped using eight CASE calculated risk factors.
  • Figure 7 shows our use of statistical algorithms to find the top 10 CASEs (cancer-associated short exons) that contribute most significantly to the random forest prediction model.
  • Figure 8 shows that the survival probability in all cancers can be effectively predicted using the PSI levels of the four single CASEs (MBNL1, MARK3, FLNA, RPS24) that contribute the most to the random forest model.
  • Figure 9 shows the use of Cox regression in statistical algorithms to analyze each CASE and cancer patient (pan-cancer analysis, all cancers put together) survival to find the hazard ratio (HR, hazard ratio) associated with each CASE and cancer survival. and estimated the corresponding P value. Under different P value cut-offs, find CASEs that are significantly related to survival, and use examples to show the predictive effect of a single CASE on cancer prognosis.
  • HR hazard ratio
  • Figure 9A Abnormally spliced short exons in cancer (CASE) have a stronger ability to predict cancer prognosis than cancer-associated long exons (CALE) in other cancers.
  • CASE cancer-associated long exons
  • the figure shows The ratio of exons related to cancer prognosis in CASE is significantly higher than that in CALE.
  • Figure 9B Genes that regulate cell junctions and cell migration are enriched in CASE related to cancer prognosis.
  • Figure 9C Examples of CASEs that are positively or negatively correlated with cancer prognosis.
  • the P value on the right shows the significance of different CASEs related to cancer prognosis.
  • FIG. 9D Two CASEs (PPP3CC and DST) whose PSI values are positively correlated with cancer prognosis, and two CASEs (TPD52L2 and ARHGAP17) with negative correlations are selected from Figure 9C, and their PSI values are used to classify cancers. , a single CASE can be used to predict cancer prognosis.
  • the inventor discovered for the first time that short exons with abnormal splicing can be used as markers to judge the risk of cancer and the prognosis and survival of cancer patients, and have the characteristics of high sensitivity and high specificity. . On this basis, the inventor completed the present invention.
  • the term "marker combination” refers to a combination of two or more markers.
  • the level of the marker substance is identified by methods such as RT-PCR or RNA sequencing.
  • biomarker also known as “biological marker”, “biomarker” refers to a measurable indicator of the biological state of an individual.
  • biomarkers can be any substances in an individual as long as they are related to a specific biological state (eg, disease) of the subject, for example, nucleic acid markers (eg, DNA), protein markers, cytokine markers , chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers (species/genus markers) and functional markers (KO/OG markers), etc.
  • Biomarkers are measured and evaluated, often to examine normal biological processes, pathogenic processes, or pharmacological responses to therapeutic interventions, and are useful in many scientific fields.
  • the term "individual” refers to an animal, in particular a mammal, such as a primate, preferably a human being.
  • the term "about” when used in reference to a specifically recited value means that the value may vary by no more than 1% from the recited value.
  • the expression “about 100” includes all values between 99 and 101 and between (eg, 99.1, 99.2, 99.3, 99.4, etc.).
  • the term “contains” or “includes” can be open, semi-closed and closed. In other words, the term also includes “consisting essentially of,” or “consisting of.”
  • Esophageal cancer also called esophageal cancer, is a malignant tumor that occurs in the esophageal epithelial tissue.
  • Lung adenocarcinoma is a type of lung cancer and is a non-small cell carcinoma that originates from the bronchial mucosal epithelium, and a few originates from the mucus glands of the large bronchi.
  • short exons with abnormal splicing in cancer were defined as exons with abnormal splicing in at least one cancer.
  • the substance of each biomarker in the combination of the invention is detected by a method selected from the group consisting of: RT-PCR, RT-PCR chip, RNA sequencing.
  • the kit of the present invention includes the combination described in the second aspect of the present invention or the eighth aspect of the present invention and/or the reagent combination described in the third aspect of the present invention or the ninth aspect of the present invention.
  • the predetermined value refers to the PSI value (used to determine the risk of cancer) or the risk factor (used to determine the prognosis and survival period of cancer patients).
  • the PSI value is a score obtained by quantifying alternative splicing events.
  • the PSI value generally refers to the proportion of splicing types retained by exons in this splicing event.
  • each CASE is ranked and each PSI quartile is assigned a relative score from [1,2,3,4] as the relative PSI value.
  • the risk factor is calculated by the following formula:
  • the relative PSI is the relative value obtained by sorting according to the quantitative value of each biomarker (CASE) (see Figure 4 for details), and N is the total number of biomarkers (where N is any positive integer, preferably (land, N is 4-100, preferably, 5-80, more preferably, 6-50, more preferably, N is 8), i is the i-th biomarker, where i ⁇ 100.
  • the present invention discovered for the first time that short exons with abnormal splicing can be used as markers to determine the risk of cancer and the prognosis and survival of cancer patients, and have the characteristics of high sensitivity and high specificity.
  • the present invention identified 494 abnormally spliced short exons (CASE, CASE, see Table A) in cancer, and established a model based on their splicing conditions that can be used to predict cancer. and prognosis for cancer patients.
  • CASE abnormally spliced short exons
  • CASE as a molecular marker for cancer prediction
  • all types of cancer were combined into a training set consisting of 6788 tumor and 705 normal samples.
  • Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) analyzes were performed based on the PSI value of CASE in each sample.
  • PCA Principal component analysis
  • PLS-DA partial least squares discriminant analysis
  • Example 1 Prediction of cancer samples using abnormally spliced short exons in cancer
  • PCA principal component analysis
  • PLSDA partial least squares regression analysis
  • Random forest modeling was performed using the lung adenocarcinoma data in the TCGA database with CASE splicing (CASE in Table A or Table 2A-2B) as input, and was tested on eight pairs of lung adenocarcinoma samples from the Chinese population.
  • the results showed that the AUC value was 0.881 ( Figure 3D); using Chinese population esophageal cancer samples for modeling and using esophageal cancer samples in the TCGA database for detection, the AUC value was 0.875 ( Figure 3D), proving that the use of CASE splicing for cancer prediction has a good effect.
  • RT-PCR can be directly performed on the RNA of tumor samples to calculate the PSI value of each splicing event (see Table 1 for primers), and input into the random forest model to determine cancer and non-cancer.
  • Example 2 Predicting the prognosis of cancer patients using the splicing status of eight cancer-related short exons
  • FIG. 3A The specific method is shown in Figure 3A.
  • the PSI values of eight exon splicing were calculated based on the transcriptome sequencing data in the TCGA database, and based on the PSI values Sorting, four of them are sorted in forward order (PPP3CC, DST, MBNL1, MARK3), and four are sorted in reverse order (TPD52L2, ARHGAP17, FLNA, RPS24), each of which is given a relative value from low to high according to its sorting result.
  • RT-PCR can be used to detect the splicing status of eight exons after extracting RNA from tumor samples (see Table 1 for primers), and perform PSI value calculation and group prediction.
  • Table 1 List of primers for predicting prognosis of cancer patients using CASE
  • Tables 2A-2D are examples of some CASE-related information.
  • Tables 2A and 2B are some examples of CASE that have not been reported before and are related to cancer prediction and prognosis.
  • Tables 2C and 2D are some examples of CASE that have been previously reported and are related to cancer prediction and prognosis.
  • Tables 2A-2D after extensive screening, it can be seen from the data in Tables 2A-2D that the weight of CASE in Table 2A is higher than the weight of CASE in Table 2B, and the weight of CASE in Table 2C is higher than the weight of CASE in Table 2D.
  • Tables 2A and 2B have higher priority than Tables 2C and 2D.
  • Figure 8 shows the prediction results using only one CASE
  • Figure 4B in the application shows the prediction results using 8 CASEs integrated.
  • CASE abnormally spliced short exons
  • CALE cancer-associated long exons
  • genes that regulate cell connection and cell migration are enriched in CASE related to cancer prognosis, indicating that they may regulate cancer metastasis.
  • CASE The alternative splicing variation of short exons reflected behind CASE is a basic feature of gene expression in rapidly dividing cancer cells and is determined by the growth characteristics of cancer cells. Therefore, although the specific CASEs vary slightly in different cancers, a common CASE set can be an important molecular marker for the diagnosis and prognosis prediction of all different cancers. Therefore, the prediction model and risk factors formed by multiple CASEs are integrated to have better prediction ability for different types of cancer.

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Abstract

本发明提供了利用癌症中剪接异常的短外显子辅助癌症诊断和预后预测。具体地，本发明提供了一种癌症中剪接异常的短外显子(cancer-associated short exons,CASE)或其检测试剂的用途，用作判断组织样本是否为癌症的标志物或癌症病人预后效果预测，可用于制备试剂或试剂盒，所述试剂或试剂盒用于判断癌症的发生风险和癌症病人的预后情况，所述癌症中剪接异常的短外显子如表A所列，本发明将剪接异常的短外显子作为判断癌症的发生风险和判断癌症患者预后和生存期的标志物，具有高灵敏性、高特异性的特点。

Description

利用癌症中剪接异常的短外显子辅助癌症诊断和预后 技术领域
本发明涉及生物医药领域，具体地涉及利用癌症中剪接异常的短外显子辅助癌症诊断和预后。
背景技术
癌症现已成为中国高发疾病之一，精准判断癌症以及癌症的预后对于癌症治疗是非常必要的，而利用易检测的生物标志物对癌症进行判断以及癌症病人的预后进行预测则具有重要的意义，但是对癌症病人预后进行预测的有效指标尚有不足。
因此，本领域迫切需要开发一种更有效的预测癌症以及癌症患者的预后的新方法。
发明内容
本发明的目的在于，根据癌症中RNA剪接变化，发明一种更有效的预测癌症以及癌症患者的预后的新方法。
在本发明的第一方面，提供了一种癌症中剪接异常的短外显子(cancer-associated short exons,CASE,或称“癌相关短外显子”)或其检测试剂的用途，用作判断样本是否为癌症或判断癌症的发生风险或预测癌症的标志物或用于制备一试剂或试剂盒，所述试剂或试剂盒用于判断样本是否为癌症或判断癌症的发生风险或预测癌症，所述癌症中剪接异常的短外显子包括选自表2A的一个或多个所述短外显子：
表2A(基于人类基因组hg19)
在另一优选例中，所述癌症中剪接异常的短外显子至少包括选自表2A的全部短外显子。
在另一优选例中，所述癌症中剪接异常的短外显子还包括选自表2B的一个或多个短外显子：
表2B(基于人类基因组hg19)
在另一优选例中，所述癌症中剪接异常的短外显子至少包括选自表2A和2B的4个短外显子。
在另一优选例中，所述癌症中剪接异常的短外显子至少包括选自表2A和2B的8个短外显子。
在另一优选例中，所述癌症中剪接异常的短外显子还包括选自表2C的一个或多个短外显子：
表2C(基于人类基因组hg19)
在另一优选例中，所述癌症中剪接异常的短外显子还包括选自表2D的一个或多个短外显子：
表2D(基于人类基因组hg19)
在另一优选例中，所述判断包括预先判断(预测)。
在另一优选例中，所述试剂包括引物、探针、测序文库、核酸芯片(如DNA芯片)。
在另一优选例中，所述的癌症中剪接异常的短外显子来源哺乳动物，较佳地来源于啮齿动物(如小鼠、大鼠)、灵长动物和人，更佳地，来源于被诊断患有癌症的患者或癌症疑似患者。
在另一优选例中，所述癌症选自下组：食道癌、肺腺癌、或其组合。
在另一优选例中，所述的检测是测定肿瘤组织、一般组织样品。
在另一优选例中，所述的一般组织包括癌旁组织。
在另一优选例中，所述检测试剂包括癌症中剪接异常的短外显子的特异性扩增引物、探针或芯片。
在另一优选例中，所述检测试剂选自下组：引物、探针、测序文库、核酸芯片(如DNA芯片)、或其组合。
在另一优选例中，所述的判断包括步骤：
(1)提供一来源于待测对象的样品，对样品中所述组合中各个生物标记物的水平进行检测；
(2)将步骤(1)测得的水平与预定值进行比较。
在另一优选例中，所述的样品选自下组：被诊断患有癌症的患者组织样品或癌旁正常样品。
在另一优选例中，与预定值进行比较，一个或多个选自表2A的生物标志物的水平符合所述预定值，表明待测对象具有癌症发生风险或患有癌症。
在另一优选例中，所述预定值为PSI值。
在另一优选例中，所述预定值是用随机森林算法对测得的PSI值进行评分获得的评分分值。
在另一优选例中，所述对象为人。
在另一优选例中，所述检测是针对离体样本的检测。
在另一优选例中，所述的离体样本为组织样本。
在另一优选例中，在步骤(1)之前，所述的方法还包括对样品进行处理的步骤。
本发明第二方面提供了一种生物标志物组合，所述的组合包括选自表2A的一个或多个癌症中剪接异常的短外显子。
在另一优选例中，所述组合还包括选自表2B的一个或多个癌症中剪接异常的短外显子。
在另一优选例中，所述癌症中剪接异常的短外显子至少包括选自表2A和2B的4个短外显子。
在另一优选例中，所述癌症中剪接异常的短外显子至少包括选自表2A和2B的8个短外显子。
在另一优选例中，所述癌症中剪接异常的短外显子至少包括选自表2A的全部短外显子。
在另一优选例中，所述生物标志物组合用于判断样本是否为癌症或判断癌症的发生风险或预测癌症或用于制备一试剂或试剂盒，所述试剂或试剂盒用于判断样本是否为癌症或判断癌症的发生风险或预测癌症。
在另一优选例中，所述的生物标志物或生物标志物组合来源被诊断患有癌症的患者组织样品或癌旁正常样品。
在另一优选例中，与预定值进行比较，一个或多个选自表2A、或表2A和2B的生物标志物的水平符合所述预定值，表明待测对象具有癌症发生风险或患有癌症。
在另一优选例中，所述预定值为PSI值。
在另一优选例中，所述预定值为根据CASE建立的模型所获得的预测值。
在另一优选例中，所述预定值是用随机森林算法对测得的PSI值进行评分获得的评分分值。
在另一优选例中，通过选自下组的方法对各个生物标志物进行鉴定：RT-PCR，RT-PCR芯片，RNA测序或其组合。
在另一优选例中，所述的组合用于判断待测对象的癌症发生风险或判断待测对象是否患有癌症。
本发明第三方面提供了一种用于判断癌症的发生风险的试剂组合，所述试剂组合包括用于检测本发明第二方面所述的组合中各个生物标志物的试剂。
在另一优选例中，所述的试剂包括用选自下组的方法检测本发明第二方面所述的集合中各个生物标志物的物质：RT-PCR，RT-PCR芯片，RNA测序、或其组合。
本发明第四方面提供了一种试剂盒，所述的试剂盒包括本发明第二方面所述的组合和/或本发明第三方面所述的试剂组合。
在另一优选例中，本发明第二方面所述的组合中各个标志物用作标准品。
本发明第五方面提供了一种判断待测对象的癌症发生风险或判断待测对象是否患有癌症的方法，包括步骤：
(1)提供一来源于待测对象的样品，对样品中集合中各个生物标记物的水平进行检测，所述组合包括一个或多个选自表2A的短外显子；
(2)将步骤(1)测得的水平与一预定值进行比较。
在另一优选例中，所述组合还包括一个或多个选自表2B的短外显子。
在另一优选例中，与预定值进行比较，一个或多个选自表2A、或表2A和2B的生物标志物的水平符合所述预定值，表明待测对象具有癌症发生风险。
在另一优选例中，所述预定值为PSI值。
在另一优选例中，所述预定值为根据CASE建立的模型所获得的预测值。
在另一优选例中，所述预定值是用随机森林算法对测得的PSI值进行评分获得的评分分值。
本发明第六方面提供了一种判断待测对象的癌症发生风险或判断待测对象是否患有癌症的系统，所述系统包括：
(a)癌症发生风险的特征输入模块，所述输入模块用于输入待测对象的癌症发生风险的特征；
其中所述的癌症发生风险的特征包括一个或多个选自表2A的所述短外显子；
(b)癌症发生风险的判别处理模块，所述处理模块对于输入的癌症发生风险的特征，按预定的判断标准进行评分处理，从而获得癌症发生风险的评分；并且将所述癌症发生风险的评分与预定值进行比较，从而得出辅助诊断结果，其中，当所述癌症发生风险的评分符合所述预定值时，则提示该对象具有癌症发生风险或患有癌症；和
(c)辅助诊断结果输出模块，所述输出模块用于输出所述的辅助诊断结果。
在另一优选例中，所述的癌症发生风险的特征还包括一个或多个选自表2B的短外显子。
在另一优选例中，所述的对象是人。
在另一优选例中，所述的评分包括(a)单个特征的评分；和/或(b)多个特征的评分之和。
在另一优选例中，所述评分包括计算各短外显子的PSI值。
在另一优选例中，用RT-PCR或RNA测序方法计算各短外显子的PSI值。
在另一优选例中，所述预定值为PSI值。
在另一优选例中，所述预定值是用随即森林算法对样本进行评分获得的评分分值。
在另一优选例中，所述预定值为根据CASE建立的模型所获得的预测值。
在另一优选例中，所述的特征输入模块选自下组：样本采集器、样本保存管、细胞裂解与核酸样本提取试剂盒、RNA核酸逆转录与扩增试剂盒、二代测序文库构建试剂盒、文库定量试剂盒、测序反应试剂盒、或其组合。
在另一优选例中，所述的癌症发生风险的判别处理模块包括一处理器，以及一储存器，其中所述的储存器中存储有基于癌症发生风险特征的癌症发生风险的评分数据。
在另一优选例中，所述的输出模块包括报告系统。
本发明第七方面提供了一种癌症中剪接异常的短外显子(cancer-associated short exons,CASE)或其检测试剂的用途，用作判断癌症患者预后和生存期的标志物或用于制备一试剂或试剂盒，所述试剂或试剂盒用于判断癌症患者预后和生存期，所述癌症中剪接异常的短外显子包括表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的一种或多种剪接异常的短外显子。
在另一优选例中，所述癌症选自下组：食道癌、肺腺癌、或其组合。
在另一优选例中，所述判断包括预先判断(预测)。
在另一优选例中，所述的判断包括步骤：
(1)提供一来源于待测对象的样品，对样品中所述组合中各个生物标记物的水平进行检测；
(2)将步骤(1)测得的水平与预定值进行比较。
在另一优选例中，所述的样品选自下组：被诊断患有癌症的患者组织样品或癌旁正常样品。
在另一优选例中，所述预定值为风险因子。
在另一优选例中，所述风险因子用如下公式计算：
其中，相对PSI(relative PSI)为根据每个生物标志物(CASE)的量化值进行排序而得到的相对值(详见图4)，N为生物标志物的总个数(其中N为任意的正整数，较佳 地，4-100个，较佳地，5-80个，更佳地，6-50个更佳地，N为8)，i为第i个生物标记物，其中i≤100。
在另一优选例中，与预定值进行比较，通过对选自表A的N个(其中N为任意的正整数，较佳地，4-100个，较佳地，5-80个，更佳地，6-50个，更佳地，N为8)生物标志物的水平计算的风险因子(risk factor)高于所述预定值，表明待测对象预后不良和生存期缩短。
在另一优选例中，与预定值进行比较，通过对选自表A的N个(其中N为任意的正整数，较佳地，4-100个，较佳地，5-80个，更佳地，6-50个，更佳地，N为8)生物标志物的水平计算的风险因子(risk factor)低于所述预定值，表明待测对象预后好和生存期增加。
在另一优选例中，所述对象为人。
在另一优选例中，所述检测是针对离体样本的检测。
在另一优选例中，所述的离体样本包括为组织样本。
在另一优选例中，在步骤(1)之前，所述的方法还包括对样品进行处理的步骤。
本发明第八方面提供了一种生物标志物组合，所述的组合包括一个或多个选自表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的剪接异常的短外显子。
在另一优选例中，所述生物标志物组合用于判断癌症患者预后和生存期或用于制备一试剂或试剂盒，所述试剂或试剂盒用于判断癌症患者预后和生存期。
在另一优选例中，所述的生物标志物或生物标志物组合来源被诊断患有癌症的患者组织样品或癌旁正常样品。
在另一优选例中，与预定值进行比较，一个或多个选自表A的生物标志物的水平(比如相对PSI值)高于所述预定值，表明待测对象预后不良和生存期缩短。
在另一优选例中，与预定值进行比较，一个或多个选自表A的生物标志物的水平(比如相对PSI值)低于所述预定值，表明待测对象预后好和生存期增加。
在另一优选例中，所述预定值为风险因子。
在另一优选例中，通过选自下组的方法对各个生物标志物进行鉴定：RT-PCR，RT-PCR芯片，RNA测序或其组合。
在另一优选例中，所述的组合用于判断癌症患者预后和生存期。
本发明第九方面提供了一种用于判断癌症患者预后和生存期的试剂组合，所述试剂组合包括用于检测本发明第八方面所述的组合中各个生物标志物的试剂。
在另一优选例中，所述的试剂包括用选自下组的方法检测本发明第八方面所述的组合中各个生物标志物的物质：RT-PCR，RT-PCR芯片，RNA测序或其组合。
本发明第十方面提供了一种试剂盒，所述的试剂盒包括本发明第八方面所述的组合和/或本发明第九方面所述的试剂组合。
在另一优选例中，本发明第八方面所述的组合中各个标志物用作标准品。
本发明第十一方面提供了一种判断癌症患者预后和生存期的方法，包括步骤：
(1)提供一来源于待测对象的样品，对样品中集合中各个生物标记物的水平进行检测，所述组合包括一个或多个选自表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的剪接异常的短外显子；
(2)将步骤(1)测得的水平与一预定值进行比较。
在另一优选例中，与预定值进行比较，一个或多个选自表A的生物标志物的水平(比如相对PSI值)高于所述预定值，表明待测对象预后不良和生存期缩短。
在另一优选例中，与预定值进行比较，一个或多个选自表A的生物标志物的水平(比如相对PSI值)低于所述预定值，表明待测对象预后良好和生存期增加。
在另一优选例中，所述预定值为风险因子。
本发明第十二方面提供了一种判断癌症患者预后和生存期的系统，所述系统包括：
(a)癌症患者预后和生存期的特征输入模块，所述输入模块用于输入待测对象的癌症患者预后和生存期的特征；
其中所述的癌症患者预后和生存期的特征包括一个或多个选自表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的剪接异常的短外显子；
(b)癌症患者预后和生存期的判别处理模块，所述处理模块对于输入的癌症患者预后和生存期的特征，按预定的判断标准进行评分处理，从而获得癌症患者预后和生存期的评分；并且将所述癌症患者预后和生存期的评分与预定值进行比较，从而得出辅助诊断结果，其中，当所述症患者预后和生存期的评分高于所述预定值时，则提示该对象预后不良和生存期缩短，当所述癌症发生风险的评分低于所述预定值时，则提示该对象预后良好和生存期增加；和
(c)辅助诊断结果输出模块，所述输出模块用于输出所述的辅助诊断结果。
在另一优选例中，所述的对象是人。
在另一优选例中，所述的评分包括(a)单个特征的评分；和/或(b)多个特征的评分之和。
在另一优选例中，所述评分包括计算各短外显子的相对PSI值。
在另一优选例中，所述评分包括计算各短外显子的PSI值。
在另一优选例中，用RT-PCR或RNA测序的方法计算各短外显子的PSI值。
在另一优选例中，所述预定值为风险因子。
在另一优选例中，所述风险因子(risk factor)用如下公式计算：
其中，相对PSI(relative PSI)为根据每个生物标志物(CASE)的量化值进行排序而得到的相对值(详见图4)，N为生物标志物的总个数(其中N为任意的正整数，较佳地为4-100个，较佳地，5-80个，更佳地，6-50个，更佳地，N为8)，i为第i个生物标记物，其中i≤100。
在另一优选例中，所述的特征输入模块选自下组：样本采集器、样本保存管、细胞裂解与核酸样本提取试剂盒、RNA核酸逆转录与扩增试剂盒、二代测序文库构建试剂盒、文库定量试剂盒、测序反应试剂盒、或其组合。
在另一优选例中，所述的癌症患者预后和生存期的判别处理模块包括一处理器，以及一储存器，其中所述的储存器中存储有基于癌症患者预后和生存期的癌症发生风险的评分数据。
在另一优选例中，所述的输出模块包括报告系统。
应理解，在本发明范围内中，本发明的上述各技术特征和在下文(如实施例)中具体描述的各技术特征之间都可以互相组合，从而构成新的或优选的技术方案。限于篇幅，在此不再一一累述。
附图说明
图1显示了利用CASE的剪接进行主成分分析(左)和偏最小二乘回归分析(右)可以区分癌症组织与癌旁正常组织。
图2显示了利用CASE的剪接在每种单独的癌症中进行主成分分析(上)和偏最小二乘回归分析(下)可以区分癌症组织与癌旁正常组织。
图3显示了(A)使用CASE的PSI值作为特征训练随机森林模型来预测癌症的流程图。在100次随机试验中使用4倍交叉验证，其中75％的样本被随机选择用于训练，其余 25％用于测试。(B)随机森林模型的ROC曲线。(C)随机森林模型在每种癌症类型中的AUC值。(D)使用TCGA LUAD数据作为训练集和中国LUAD数据作为测试集(左)，或使用中国ESCC数据进行训练使用TCGA ESCA数据进行测试(右)的两个随机森林模型的性能。
图4显示了A)利用八个CASE的PSI值计算风险因子的流程。将每个CASE的PSI值根据其与生存时间的相关性进行正序或者倒序排序，按照其顺序给予每个患者一个相对PSI评分(1到4的整数)，计算八个相对PSI的平均值得到风险因子。将危险因子从低到高进行排序并且将前百分之二十五和后百分之二十五的患者定义为组1和组2。(B)按基于CASE的风险因子进行分组的癌症患者的Kaplan-Meier曲线。
图5显示了按基于CASE的风险因子进行分组的每种类型中的癌症患者的
Kaplan-Meier曲线。
图6显示了利用八个CASE计算的风险因子对中国食道癌患者进行分组，两组间的生存时间分布情况。
图7显示了我们利用统计算法，找到对随机森林预测模型的贡献最明显的前10个CASE(癌相关短外显子)。
图8显示了利用对随机森林模型的贡献最大的4个单一的CASE(MBNL1、MARK3、FLNA、RPS24)的PSI高低，可以对所有癌症中的生存几率进行有效预测。
图9显示了利用统计算法中的Cox回归对每个CASE和癌症病人(泛癌分析，所有癌症放在一起)生存进行分析，找到每个CASE与癌症生存相关的风险比(HR，hazard ratio)并估算了对应的P值。在不同的P值cut-off下，找出与生存显著相关的CASE，并用示例显示单一CASE对癌症预后的预测效果。
其中，图9A.癌症中剪接异常的短外显子(CASE)比其余的癌症中剪接异常的外显子(CALE，cancer-associated long exons)有更强的对癌症预后的能力，图中显示了与癌症预后相关的外显子在CASE中的比率比起CALE来显著升高。
图9B.与癌症预后相关的CASE中富集了可调节细胞连接和细胞迁移的基因。
图9C.与癌症预后正相关或副相关的CASE示例，右边的P值显示了不同CASE与癌症预后相关的显著性。
图9D.从图9C中选取了其PSI值与癌症预后正相关的两个CASE(PPP3CC和DST)，和负相关的两个CASE(TPD52L2和ARHGAP17)，并利用其PSI值对癌症进行分类后，可以利用单一CASE来预测癌症预后效果。
具体实施方式
本发明人经过广泛而深入地研究，首次发现，剪接异常的短外显子可作为判断癌症的发生风险和判断癌症患者预后和生存期的标志物，并且具有高灵敏性、高特异性的特点。在此基础上，发明人完成了本发明。
术语
本发明所用术语具有相关领域普通技术人员通常理解的含义。然而，为了更好地理解本发明，对一些定义和相关术语的解释如下：
根据本发明，术语“标志物组合”是指两种及两种以上标志物的组合。
根据本发明，标志物质的水平通过RT-PCR或RNA测序等方法进行鉴定。
根据本发明，术语“生物标志物”，也称为“生物学标志物”、“生物标记物”，是指个体的生物状态的可测量指标。这样的生物标记物可以是在个体中的任何物质，只要它们与被检个体的特定生物状态(例如，疾病)有关系，例如，核酸标志物(例如DNA)，蛋白质标志物，细胞因子标记物，趋化因子标记物，碳水化合物标志物，抗原标志物，抗体标志物，物种标志物(种/属的标记)和功能标志物(KO/OG标记)等。生物标记物经过测量和评估，经常用以检查正常生物过程，致病过程，或治疗干预药理响应，而且在许多科学领域都是有用的。
根据本发明，术语“个体”指动物，特别是哺乳动物，如灵长类动物，最好是人。
根据本发明，术语如“一”、“一个”和“这”不仅指单数的个体，而是包括可以用来说明特定实施方式的通常的一类。
如本文所用，在提到具体列举的数值中使用时，术语“约”意指该值可以从列举的值变动不多于1％。例如，如本文所用，表述“约100”包括99和101和之间的全部值(例如，99.1、99.2、99.3、99.4等)。
如本文所用，术语“含有”或“包括(包含)”可以是开放式、半封闭式和封闭式的。换言之，所述术语也包括“基本上由…构成”、或“由…构成”。
需要说明的是，在此提供术语的解释仅为了使本领域技术人员更好地理解本发明，并非对本发明限制。
食道癌
食道癌又叫食管癌，是发生在食管上皮组织的恶性肿瘤。
肺腺癌
肺腺癌是肺癌的一种，属于非小细胞癌，起源于支气管粘膜上皮，少数起源于大支气管的粘液腺。
剪接异常的短外显子
利用TCGA数据库中18种癌症类型，将癌症中剪接异常的短外显子定义为至少在一种癌症中发生剪接异常的外显子。
检测方法
在本发明中，通过选自下组的方法检测本发明的组合中各个生物标志物的物质：RT-PCR，RT-PCR芯片，RNA测序。
试剂盒
在本发明中，本发明的试剂盒包括本发明第二方面或本发明第八方面所述的组合和/或本发明第三方面或本发明第九方面所述的试剂组合。
预定值
在本发明中，预定值是指PSI值(用于判断癌症风险)或风险因子(用于判断癌症患者预后和生存期)。
其中，PSI值是对选择性剪接事件进行定量得到的分值，PSI值一般指这个剪接事件中外显子保留的剪接型所占的比例。
对每个CASE的PSI值进行排名，并为每个PSI四分位数分配一个来自[1,2,3,4]的相对分数作为相对PSI值。
其中，风险因子通过如下公式计算：
其中，相对PSI为根据每个生物标志物(CASE)的量化值进行排序而得到的相对值(详见图4)，N为生物标志物的总个数(其中N为任意正整数，较佳地，N为4-100个，较佳地，5-80个，更佳地，6-50个，更佳地，N为8)，i为第i个生物标记物，其中i≤100。
本发明的主要优点包括：
(1)本发明首次发现，剪接异常的短外显子可作为判断癌症的发生风险和判断癌症患者预后和生存期的标志物，并且具有高灵敏性、高特异性的特点。
(2)本发明首次在癌症中鉴定出了494个癌症中剪接异常的短外显子(cancer-associated short exons,CASE，见表A)，并根据他们的剪接情况建立了模型可用于预测癌症以及癌症患者的预后。
(3)本发明建立的模型与需要大量RNA测序结果的方法相比，只需要对几十个CASE进行定量即可，在操作上更加简单可靠，并可以对90％的样本进行预后预测，弥补了一些方法由于单一变量的数据缺失而造成的只能预测少数样本的问题。
下面结合具体实施例，进一步阐述本发明。应理解，这些实施例仅用于说明本发明而不用于限制本发明的范围。下列实施例中未注明具体条件的实验方法，通常按照常规条件，例如Sambrook等人，分子克隆：实验室手册(New York:Cold Spring Harbor Laboratory Press,1989)中所述的条件，或按照制造厂商所建议的条件。除非另外说明，否则百分比和份数是重量百分比和重量份数。
如无特别说明，本发明实施例中所用的试剂和材料均为市售产品。
通用方法
为了检测CASE作为癌症预测的分子标记物的能力，将所有类型的癌症组合成一个由6788个肿瘤和705个正常样本组成的训练集。我们主要考虑了在至少5500个肿瘤样本和450个正常样本中均可检测到的60个CASE，并使用了至少检测到20个CASE的样本用于我们的分析。根据每个样本中CASE的PSI值进行主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)分析。我们进一步训练了一个基于60个CASE的PSI值来预测癌症的随机森林模型，以对肿瘤和正常样本进行分类。我们进行了100次随机取样，每次利用4倍交叉验证以评估预测准确性，其中每次随机取75％的样本被随机选择用于训练，其余25％用于测试。我们还使用来自中国LUAD和ESCC患者与西方TCGA患者的RNA-seq数据进行了不同数据来源的交叉验证来评估性能。
在预后相关预测分析中，对于每个CASE，我们对所有患者的PSI值进行排序，并将顶部和底部四分位数的患者分为“PSI高”和“PSI低”两组并对这两组之间的总生存率进行比较。为了提高预测的敏感性，我们接下来选择了8个CASE来对每个患者的风险值 进行打分。我们对每个CASE的PSI值进行排名，并为每个PSI四分位数分配一个来自[1,2,3,4]的相对分数。然后由所选8个CASE的相对分数的平均值定义了基于CASE的风险因素，并对每个患者进行计算。我们使用该风险因素对患者进行排序，将上下四分之一定义为高风险组和低风险组。
实施例1利用癌症中异常剪接的短外显子对癌症样本进行预测
利用癌症基因组图谱(The cancer genome atlas，TCGA)数据库中的数据，通过主成分分析(PCA)和偏最小二乘回归分析(PLSDA)结果显示利用我们鉴定出的短外显子剪接的情况可以将肿瘤组织和癌旁正常组织显著分开(图1)，并且每个单独的癌症中肿瘤组织也均可与癌旁正常组织显著分开(图2)，预示着其具有预测癌症的能力。
利用CASE(表A)的剪接情况，对TCGA数据库中的癌症患者进行随机森林建模(图3A，参数为mtry＝22,ntree＝1300)，采用100次随机取样及四倍交叉验证，模型预测能力达到曲线下面积(AUC)为0.913(图3B)，在每种单一癌症样本中也达到较好的预测效果(图3C)。
使用TCGA数据库中肺腺癌的数据以CASE剪接(表A或表2A-2B中的CASE)为输入进行随机森林建模，并以中国人群八对肺腺癌样本进行测试，结果显示AUC值为0.881(图3D)；使用中国人群食道癌样本进行建模并用TCGA数据库中食道癌样本进行检测AUC值为0.875(图3D)，证明利用CASE的剪接做癌症预测具有很好的效果。
在实际临床中，对于肿瘤样本的RNA可直接进行RT-PCR来计算每个剪接事件的PSI值(引物见表1)，并输入到随机森林模型中进行癌症及非癌症的判断。
表A(基于人类基因组hg19)










上述基因名称均唯一确定地与NCBI网站数据库中的基因相对应(https://www.ncbi.nlm.nih.gov/)。
实施例2利用八个癌症相关的短外显子的剪接情况预测癌症患者预后
我们发现利用八个CASE的剪接情况可以很好的预测癌症病人的预后，具体方法如图3A，根据TCGA数据库中转录组测序数据计算了八个外显子剪接的PSI值，并根据PSI值进行排序，其中四个正序排序(PPP3CC,DST,MBNL1,MARK3)，四个倒序排序(TPD52L2,ARHGAP17,FLNA,RPS24)，其中每一个都根据其排序结果从低到高给定一个相对的值(1，2，3，4)，最终计算八个剪接事件的相对值的平均数作为风险因子(图4A)，也就是预测患者预后的指标。根据此风险因子进行生存分析(取上下四分之一进行分组)，结果显示风险因子高和低两组间的生存时间具有非常显著的差异(图4B)。这个结果不仅在所有癌症的集合中出现，在每个单独的癌症中也具有优秀的预测效果(图5)。
我们应用此种方法对中国人群食道癌患者进行打分，结果显示根据此风险因子进行分类的两组癌症患者生存时间具有显著差异(图6)，证实了此方法的稳定性和可应用的价值。在实际应用中，肿瘤样本提取RNA后可用RT-PCR的方法检测八个外显子的剪接情况(引物见表1)，并进行PSI值计算及分组预测。
表1利用CASE做癌症患者预后预测引物列表
表2A
表2B
表2C
表2D
表2A-2D为部分CASE相关信息的示例，表2A和2B为之前无报道与癌症预测以及预后有关的部分CASE示例，表2C和2D为之前有报道的与癌症预测以及预后有关的部分CASE示例，其中经过大量筛选，从表2A-2D中的数据中可以看出，表2A中的CASE的权重高于表2B的CASE的权重，表2C中的CASE的权重高于表2D的CASE的权重，表2A和2B的优先级高于表2C和2D。
我们利用所有的CASE，建立了一个随机森林预测模型来对癌症和正常组织进行预测，达到了较好的预测效果(图3B)。接下来，为了测量每个CASE对随机森林预测模型的贡献，我们在模型中人为减掉每一个CASE，然后利用剩余的CASE进行随机森林模型预测。这样就根据去掉每一个CASE对预测准确率的减少程度，来估算每个CASE对模型的贡献度。
不同的单一CASE对随机森林预测模型的贡献(图3)。我们利用两种统计方法(平均准确度下降mean decrease accuracy，和平均基尼系数下降mean decrease in Gini)，找到对随机森林预测模型的贡献最明显的前10个CASE(癌相关短外显子)。因为这些CASE对预测模型的贡献较大，所以它们在后续选取具体CASE进行癌症预后风险评估时应予以优先考虑(图7)。
我们利用每个单一CASE的PSI值，对所有类型的癌症病人(泛癌分析，所有癌症放到一起)进行分组(高PSI组，和低PSI组)，然后利用Kaplan-Meier生存曲线来分析不同分组的病人间的生存预期。
结果如图8所示，利用对随机森林模型的贡献最大的4个单一的CASE(MBNL1、MARK3、FLNA、RPS24)的PSI高低，可以对所有癌症中的生存几率进行有效预测。
图8显示的是只用一个CASE的预测结果，申请中的图4B显示的是用8种CASE整合后进行预测的结果。
我们利用统计算法中的Cox回归对每个CASE和所有类型的癌症病人生存进行分析，找到每个CASE与癌症生存相关的风险比(HR，hazard ratio)并估算了对应的P值。在不同的P值cut-off下，找出与生存显著相关的CASE。利用同样的方法，我们也找出了 与生存显著相关的CALE，然后比较了与生存显著相关的CASE和CALE在所有的CASE和CALE中所占的比例(图9A)，发现CASE比CALE有更强的对癌症预后生存的预测能力。
接下来，对于与生存显著相关的CASE，我们用Gene Ontology方法进行了功能富集分析，发现这些CASE中富集了调控细胞连接和细胞迁移的基因，说明这些CASE的剪接和癌症的转移密切相关(图9B)。
接下来，我们根据不同CASE的风险比(HR)和对应的P值对所有CASE排序(图9C)，利用cox回归的P值小于10-10做cutoff，找到了PSI值和癌症生存显著相关的CASE。
对于上述的CASE,我们选取了相关性最大的四个CASE，利用单一的CASE的PSI值，对所有类型的癌症病人进行分组(高PSI组，和低PSI组)，然后利用Kaplan-Meier生存曲线来分析不同分组的病人间的生存预期(图9D)。
如图9A所示，癌症中剪接异常的短外显子(CASE)比其余的癌症中剪接异常的外显子(CALE，cancer-associated long exons)有更强的对癌症预后的能力，图中显示了与癌症预后相关的外显子在CASE中的比率比起CALE来显著升高。
如图9B所示，与癌症预后相关的CASE中富集了可调节细胞连接和细胞迁移的基因，说明它们可能调节癌症转移。
如图9C所示，与癌症预后正相关或副相关的CASE示例，右边的P值显示了不同CASE与癌症预后相关的显著性。
如图9D所示，从图9C中选取了其PSI值与癌症预后正相关的两个CASE(PPP3CC和DST)，和负相关的两个CASE(TPD52L2和ARHGAP17)，并利用其PSI值对癌症进行分类后，可以利用单一CASE来预测癌症预后效果。
CASE背后反映出的短外显子的可变剪接异变，是快速分裂的癌症细胞中的基因表达的基本特征，是由癌细胞生长特征决定的。因此尽管具体的CASE在不同癌症中略有不同，但一个共性的CASE集合，可以是对所有不同癌症的诊断和预后预测都有重要的指导意义的分子标记物。因此，整合了多个CASE形成的预测模型和风险因子，对不同种类的癌症都具有较好的预测能力。
在本发明提及的所有文献都在本申请中引用作为参考，就如同每一篇文献被单独引用作为参考那样。此外应理解，在阅读了本发明的上述讲授内容之后，本领域技术人员可以对本发明作各种改动或修改，这些等价形式同样落于本申请所附权利要求书所限定的范围。

Claims (12)

  1. 一种癌症中剪接异常的短外显子(cancer-associated short exons,CASE,或称“癌相关短外显子”)或其检测试剂的用途，其特征在于，用作判断样本是否为癌症或判断癌症的发生风险或预测癌症的标志物或用于制备一试剂或试剂盒，所述试剂或试剂盒用于判断样本是否为癌症或判断癌症的发生风险或预测癌症，所述癌症中剪接异常的短外显子包括选自表2A的一个或多个所述短外显子：
    表2A(基于人类基因组hg19)
  2. 一种生物标志物组合，其特征在于，所述的组合包括选自表2A的一个或多个癌症中剪接异常的短外显子。
  3. 一种用于判断癌症的发生风险的试剂组合，其特征在于，所述试剂组合包括用于检测权利要求2所述的组合中各个生物标志物的试剂。
  4. 一种试剂盒，其特征在于，所述的试剂盒包括权利要求2所述的组合和/或权利要求3所述的试剂组合。
  5. 一种判断待测对象的癌症发生风险或判断待测对象是否患有癌症的方法，其特征在于，包括步骤：
    (1)提供一来源于待测对象的样品，对样品中集合中各个生物标记物的水平进行检测，所述组合包括一个或多个选自表2A的短外显子；
    (2)将步骤(1)测得的水平与一预定值进行比较。
  6. 一种判断待测对象的癌症发生风险或判断待测对象是否患有癌症的系统，其特征在于，所述系统包括：
    (a)癌症发生风险的特征输入模块，所述输入模块用于输入待测对象的癌症发生风险的特征；
    其中所述的癌症发生风险的特征包括一个或多个选自表2A的所述短外显子；
    (b)癌症发生风险的判别处理模块，所述处理模块对于输入的癌症发生风险的特征，按预定的判断标准进行评分处理，从而获得癌症发生风险的评分；并且将所述癌症发生风险的评分与预定值进行比较，从而得出辅助诊断结果，其中，当所述癌症发生风险的评分符合所述预定值时，则提示该对象具有癌症发生风险或患有癌症；和
    (c)辅助诊断结果输出模块，所述输出模块用于输出所述的辅助诊断结果。
  7. 一种癌症中剪接异常的短外显子(cancer-associated short exons,CASE)或其检测试剂的用途，其特征在于，用作判断癌症患者预后和生存期的标志物或用于制备一试剂或试剂盒，所述试剂或试剂盒用于判断癌症患者预后和生存期，所述癌症中剪接异常的短外显子包括表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的一种或多种剪接异常的短外显子。
  8. 一种生物标志物组合，其特征在于，所述的组合包括一个或多个选自表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的剪接异常的短外显子。
  9. 一种用于判断癌症患者预后和生存期的试剂组合，其特征在于，所述试剂组合包括用于检测权利要求8所述的组合中各个生物标志物的试剂。
  10. 一种试剂盒，其特征在于，所述的试剂盒包括权利要求8所述的组合和/或权利要求9所述的试剂组合。
  11. 一种判断癌症患者预后和生存期的方法，其特征在于，包括步骤：
    (1)提供一来源于待测对象的样品，对样品中集合中各个生物标记物的水平进行检测，所述组合包括一个或多个选自表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的剪接异常的短外显子；
    (2)将步骤(1)测得的水平与一预定值进行比较。
  12. 一种判断癌症患者预后和生存期的系统，其特征在于，所述系统包括：
    (a)癌症患者预后和生存期的特征输入模块，所述输入模块用于输入待测对象的癌症患者预后和生存期的特征；
    其中所述的癌症患者预后和生存期的特征包括一个或多个选自表A中的来自PPP3CC、DST、MBNL1、MARK3、TPD52L2、ARHGAP17、FLNA、RPS24的剪接异常的短外显子；
    (b)癌症患者预后和生存期的判别处理模块，所述处理模块对于输入的癌症患者预后和生存期的特征，按预定的判断标准进行评分处理，从而获得癌症患者预后和生存期的评分；并且将所述癌症患者预后和生存期的评分与预定值进行比较，从而得出辅助诊断结果，其中，当所述症患者预后和生存期的评分高于所述预定值时，则提示该对象预后不良和生存期缩短，当所述癌症发生风险的评分低于所述预定值时，则提示该对象预后良好和生存期增加；和
    (c)辅助诊断结果输出模块，所述输出模块用于输出所述的辅助诊断结果。
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105067822A (zh) * 2015-08-12 2015-11-18 中山大学附属肿瘤医院 用于食管癌诊断的标志物
US20160258026A1 (en) * 2013-11-04 2016-09-08 The University Of British Columbia Cancer biomarkers and classifiers and uses thereof
US20160266126A1 (en) * 2013-03-15 2016-09-15 Metamark Genetics, Inc. Compositions and methods for cancer prognosis

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160266126A1 (en) * 2013-03-15 2016-09-15 Metamark Genetics, Inc. Compositions and methods for cancer prognosis
US20160258026A1 (en) * 2013-11-04 2016-09-08 The University Of British Columbia Cancer biomarkers and classifiers and uses thereof
CN105067822A (zh) * 2015-08-12 2015-11-18 中山大学附属肿瘤医院 用于食管癌诊断的标志物

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
LI SONGWEI, HUANG JIAN, YANG FAN, ZENG HAIPING, TONG YUYUN, LI KEJIA: "High expression of PARD3 predicts poor prognosis in hepatocellular carcinoma", SCIENTIFIC REPORTS, NATURE PUBLISHING GROUP, US, vol. 11, no. 1, US , XP093119308, ISSN: 2045-2322, DOI: 10.1038/s41598-021-90507-w *
TSAI YIHSUAN S., DOMINGUEZ DANIEL, GOMEZ SHAWN M., WANG ZEFENG: "Transcriptome-wide identification and study of cancer-specific splicing events across multiple tumors", ONCOTARGET, IMPACT JOURNALS LLC, UNITED STATES, vol. 6, no. 9, 30 March 2015 (2015-03-30), United States , pages 6825 - 6839, XP093119306, ISSN: 1949-2553, DOI: 10.18632/oncotarget.3145 *
ZHANG SIRUI, MAO MIAOWEI, LV YUESHENG, YANG YINGQUN, HE WEIJING, SONG YONGMEI, WANG YONGBO, YANG YUN, AL ABO MUTHANA, FREEDMAN JEN: "A widespread length-dependent splicing dysregulation in cancer", SCIENCE ADVANCES, AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, US, vol. 8, no. 33, 19 August 2022 (2022-08-19), US , XP093119303, ISSN: 2375-2548, DOI: 10.1126/sciadv.abn9232 *

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