WO2021154056A2 - Utilisation d'un pseudogène pour le diagnostic de la malignité d'un gliome - Google Patents

Utilisation d'un pseudogène pour le diagnostic de la malignité d'un gliome Download PDF

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WO2021154056A2
WO2021154056A2 PCT/KR2021/001271 KR2021001271W WO2021154056A2 WO 2021154056 A2 WO2021154056 A2 WO 2021154056A2 KR 2021001271 W KR2021001271 W KR 2021001271W WO 2021154056 A2 WO2021154056 A2 WO 2021154056A2
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pseudogene
hgnc
glioma
grade
group
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WO2021154056A3 (fr
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곽규범
임재준
안주원
박영준
심정민
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차의과학대학교 산학협력단
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    • 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
    • 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
    • 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
    • C12Q2600/136—Screening for pharmacological compounds
    • 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

  • Pseudogene biomarker for diagnosis and/or prognosis prediction of glioma and related to its use, and a composition for diagnosis and/or prognosis prediction of glioma comprising an agent for detecting the biomarker, glioma comprising the composition It relates to a kit for diagnosis and/or prognosis of glioma, and a method for providing information necessary for diagnosis and/or prognosis of glioma using the biomarker.
  • brain tumors Since the prognosis of brain tumors varies greatly depending on the location of the tumor and the size of the tumor at the time of discovery, an accurate diagnosis is very important. However, the symptoms of brain tumors are very diverse, such as headache, vomiting, speech disorders, memory loss, and decreased visual acuity.
  • biopsy-based histological examinations and morphological examination methods using magnetic resonance imaging (MRI) and/or computed tomography (CT) exist as methods for diagnosing the onset and malignancy of brain tumors, but radiotherapy and chemotherapy Additional studies and methods are still required to accurately evaluate and reflect factors related to clinical response and prognosis, and malignancy according to cancer progression.
  • MRI magnetic resonance imaging
  • CT computed tomography
  • Glioma is the most common primary malignant brain tumor and is an intractable malignant tumor accounting for about 60% of all brain tumors. Gliomas are classified according to the degree of undifferentiation and dysplasia according to the WHO criteria, from grade I, the lowest level of malignancy, to grade IV, the most severe. Although the average life expectancy of patients with low-grade glioma is around 10 years, patients with glioblastoma multiforme classified as grade IV have an average expected survival of only one year after diagnosis despite the use of surgery, chemotherapy, and radiation therapy. Research related to the diagnosis and progression of glioma is very important for the treatment and prolongation of survival of glioma patients.
  • TCGA Cancer Genome Atlas
  • SNP genotyping somatic mutation
  • CNV copy number mutation
  • microRNA profiling etc. It collects, standardizes, and integrates various cancer genomic data and clinical information of patients corresponding to it.
  • CGGA Chinese Glioma Genome Atlas
  • the study analyzing the TCGA and CGGA data sets is a good sample that can represent genomic changes in glioma patients, and provides a platform that can secure and analyze data for bioinformatics-based cancer genome analysis.
  • a pseudogene is characterized in that it has a DNA sequence homology similar to that of a generally known gene, but has lost its function in gene expression or the ability to encode the final product, protein.
  • the pseudogene was once classified as junk DNA, but since it was reported in 2011 that the 3'UTR region of PTENP1, a tumor suppressor gene, PTEN, regulates the expression of PTEN through complementary binding with miRNA. It is known that pseudogenes play a role in regulating gene expression at the transcript level in the form of ceRNA.
  • PTENP1 protects the suppression of PTEN expression from miR-19b by acting as a kind of transcript sponge, thereby inhibiting cell proliferation, metastasis, and invasion in cancer cells and promoting apoptosis.
  • the present application provides biomarkers and uses thereof for grade diagnosis and/or prognosis prediction of gliomas.
  • One example provides a biomarker for grade diagnosis and/or prognosis prediction of a glioma.
  • Another example provides a composition for diagnosing a grade of glioma and/or predicting a prognosis.
  • kits for glioma grade diagnosis and/or prognosis prediction including the composition for glioma grade diagnosis and/or prognosis prediction.
  • Another example provides a method for diagnosing and/or predicting the prognosis of a glioma using the composition for predicting the grade and/or prognosis of the glioma, or a method for providing information necessary for the diagnosis and/or prognosis.
  • Another example provides a method of screening for a therapeutic agent for glioma.
  • the present inventors analyzed the glioma data of TCGA (The Cancer Genome Atlas) and CGGA (Chinese Glioma Genome Atlas), and according to the malignancy of the glioma, lower grade glioma (lower grade glioma, grade II, III) and glioblastoma Pseudogenes differentially expressed in (glioblastoma, grade IV) were discovered, and through the detection of these pseudogenes, the diagnosis of glioma and the level of progression of glioma according to the malignancy could be diagnosed and predicted as a molecular diagnostic marker.
  • TCGA Cancer Genome Atlas
  • CGGA Choinese Glioma Genome Atlas
  • biomarkers and uses thereof for the grade diagnosis and/or prognosis prediction of gliomas.
  • One example provides a biomarker for grade diagnosis and/or prognosis prediction of a glioma.
  • Another example provides a composition for diagnosing a grade of glioma and/or predicting a prognosis.
  • kits for glioma grade diagnosis and/or prognosis prediction including the composition for glioma grade diagnosis and/or prognosis prediction.
  • Another example provides a method for diagnosing and/or predicting the prognosis of a glioma using the composition for predicting the grade and/or prognosis of the glioma, or a method for providing information necessary for the diagnosis and/or prognosis.
  • Another example provides a method of screening for a therapeutic agent for glioma.
  • glioma means a tumor that occurs in glial cells, grade 1 (Grade I, G1) pilocytic astrocytoma, or subependymal giant astrocytoma (subependymal giant ell astrocytoma) , grade 2 (Grade II, G2) diffuse astrocytoma, grade 3 (Grade III, G3) anaplastic astrocytoma, and grade 4 (Grade IV, G4) glioblastoma includes all
  • grade 1 glioma In the case of grade 1 glioma, it is a benign tumor, showing a non-invasive tendency, and has a good prognosis, and grade 2 to 4 glioma corresponds to a malignant tumor.
  • glioma means a grade 1 to grade 4 broad glioma, and may preferably be a grade 2 to grade 4 malignant tumor type glioma.
  • grade of glioma is indicated by 'Grade', 'Grade' or 'G'.
  • Said glioma also includes both primary glioma and recurrent glioma.
  • the primary glioma is a tumor that arises from glial cells itself, and is formed in the cranial cavity from the beginning, and the recurrent glioma originates in an organ or organ other than the brain and moves to the glial cells of the brain to form a tumor. do.
  • risk of glioma may be used interchangeably with “hazard ratio”.
  • an increase in risk and/or hazard ratio may mean a decrease in survival rate and/or an increase in the size and/or number of tumors.
  • An example of the present invention relates to a composition for diagnosing and/or predicting the prognosis of a glioma and a method of providing information necessary for diagnosing the grade and/or predicting the prognosis of a glioma using the composition.
  • composition for glioma grade diagnosis and/or prognosis prediction includes an agent for detecting a pseudogene marker differentially expressed according to the grade of glioma.
  • a pseudogene is characterized in that it has a DNA sequence homology similar to that of a generally known gene, but has lost its function in gene expression or the ability to encode the final product, protein.
  • the pseudogene was once classified as junk DNA, but since it was reported in 2011 that the 3'UTR region of PTENP1, a tumor suppressor gene, PTEN, regulates the expression of PTEN through complementary binding with miRNA. It is known that pseudogenes play a role in regulating gene expression at the transcript level in the form of ceRNA.
  • the composition has the advantage of being able to diagnose the degree of progression of glioma beyond simple prognostic prediction and provide a potential therapeutic target compared to the conventional method of detecting somatic mutations by detecting a pseudogene marker.
  • the existing somatic mutation can be used as a simple diagnostic aid indicator according to the presence or absence of a disease, or can be used as a simple and one-time prediction of the prognosis of a disease, either positive or negative, after mutation. Diagnosis and prediction were difficult. In addition, it is very rare for a specific gene to have a high level of mutation frequency, and each specific gene is often representative of a specific subgroup of a specific disease, so it is very limited to use for diagnosis and/or prognosis prediction there was However, the expression level of the pseudogene marker provided by the present invention is a quantitative and continuous element, and has the advantage of being able to continuously and continuously track and/or confirm glioma diagnosis and/or prognosis prediction.
  • the pseudogene markers differentially expressed according to the grade of the glioma are: (1) HGNC:18611 pseudogene (GenBank Accession No. NR_001587.1), HGNC:13229 pseudogene (GenBank Accession No. NR_027795.1), HGNC:5240 pseudogene (GenBank Accession No. NR_024151.1), HGNC:7404 pseudogene (GenBank Accession No. NR_001447.2), HGNC:32523 pseudogene (GenBank Accession No. NR_003187.3), HGNC:30222 pseudogene (GenBank Accession No. NR_037144.1), HGNC:30080 pseudogene (GenBank Accession No.
  • NR_003714.1 HGNC:16797 pseudogene (GenBank Accession No. NR_024045.1), HGNC:30518 pseudogene (GenBank Accession No. NR_003714.1).
  • NR_026676.1 HGNC:33993 pseudogene (GenBank Accession No. NR_026749.2), HGNC:11987 pseudogene (GenBank Accession No. NR_002719.2), and HGNC:30806 pseudogene (GenBank Accession No. NR_027256.1)
  • HGNC:24976 pseudogene (GenBank Accession No. NR_002307.1), HGNC:30886 pseudogene (GenBank Accession No. NR_027141.2), and At least one pseudogene selected from the group consisting of HGNC:35196 pseudogene (GenBank Accession No. NR_026730.1); may be one or more pseudogene markers selected from the group consisting of.
  • the pseudogene marker is a pseudogene marker whose expression level increases as the risk ratio of glioma increases, and includes HGNC:5240 pseudogene (GenBank Accession No. NR_024151.1), HGNC:32523 pseudogene (GenBank Accession No. NR_003187.3), HGNC:30222 pseudogene (GenBank Accession No. NR_037144.1), and HGNC:30518 pseudogene (GenBank Accession No. NR_026676.1) may be one or more pseudogenes selected from the group consisting of, , HGNC:18611 pseudogene (GenBank Accession No. NR_001587.1), HGNC:13229 pseudogene (GenBank Accession No.
  • HGNC:7404 pseudogene (GenBank Accession No. NR_001447.2), HGNC:30080 pseudogene Gene (GenBank Accession No. NR_003714.1), HGNC:16797 pseudogene (GenBank Accession No. NR_024045.1), HGNC:33993 pseudogene (GenBank Accession No. NR_026749.2), HGNC:11987 pseudogene (GenBank Accession No. NR_002719.2) and HGNC:30806 pseudogene (GenBank Accession No. NR_027256.1) may further include one or more pseudogenes selected from the group consisting of.
  • the expression level of the pseudogene marker (high-expression-high-risk group pseudogene marker), whose expression increases as the risk ratio of the glioma increases, increases as the grade progresses from Grade II to Grade III and Grade IV in primary glioma patients. .
  • the pseudogene marker is a pseudogene marker whose expression level increases as the risk ratio of glioma decreases, and may be a HGNC:30486 pseudogene, and/or a HGNC:35196 pseudogene, and a HGNC:30886 pseudogene. It may further include a gene (GenBank Accession No. NR_027141.2), and/or a HGNC:24976 pseudogene.
  • Pseudogene markers (high-expression-low-risk group pseudogene markers) whose expression increases as the risk of the glioma decreases, as the grade progresses from Grade II to Grade III and Grade IV in patients with primary or recurrent glioma expression decreases.
  • TCGA and CGGA data sets were analyzed as gene expression data sets of glioma patients, and similar genes having statistical significance and showing differences in expression levels were selected according to the grade of glioma.
  • Pseudogenes showing a statistically significant difference in expression levels at all three malignancy levels of Grade II, III, and IV were 102 in the TCGA data set and 51 in the CGGA data set (Figs. 1 and 2).
  • There were a total of 22 common pseudogenes identified in both the TCGA and CGGA data sets (Fig. 3). Among the 22 genes, 17 pseudogenes except for 5 known pseudogenes were first selected.
  • the pseudogene markers having clinical significance were secondarily selected by calculating the relative hazard ratios for the primary and recurrent glioma patient groups for the 17 pseudogenes selected for the first time.
  • the relative risk ratio is normalized and converted to a value between 0 and 1 by normalizing the relative expression levels of the 17 first-selected pseudogenes, standardized, and calculated by performing a Cox proportional hazard model analysis according to the standardized expression level.
  • the analysis may be performed using any statistical program known in the art without limitation.
  • the relative expression level may be calculated by performing a normality test and/or an equal variance test, and one-way ANOVA, Tukey's post hoc analysis, Welch's ANOVA, GAMES_HOWELL method according to the results of the normality test and/or equal variance test.
  • the relative expression level can be calculated by performing one or more, or two or more analysis methods selected from the group consisting of a post-hoc analysis of , Kruskal-Wallis test, and Tukey and Kramer (Nemenyi) test.
  • one-way ANOVA' and/or 'Tukey's post-hoc analysis' may be performed, but the analysis method is not limited thereto.
  • Welch's ANOVA and/or 'GAMES-HOWELL post-hoc analysis' may be performed, but the analysis method is not limited thereto.
  • the nonparametric 'Kruskal-Wallis test' and/or the 'Tukey and Kramer (Nemenyi) test' may be performed, but the method is not limited thereto.
  • the 12 pseudogene markers are HGNC:18611 pseudogene, HGNC:13229 pseudogene, HGNC:5240 pseudogene, HGNC:7404 pseudogene, HGNC:32523 pseudogene, HGNC:30222 pseudogene, HGNC:30080 pseudogene, HGNC :16797 pseudogene, HGNC:30518 pseudogene, HGNC:33993 pseudogene, HGNC:11987 pseudogene, and HGNC:30806 pseudogene.
  • pseudogene markers for primary glioma, 4 types of pseudogene markers whose expression increases as the hazard ratio decreases were finally selected.
  • the four pseudogene markers are HGNC:30486 pseudogene, HGNC:24976 pseudogene, HGNC:30886 pseudogene, and HGNC:35196 pseudogene.
  • pseudogene markers for recurrent glioma, four types of pseudogene markers whose expression increased as the hazard ratio increased were finally selected.
  • the four pseudogene markers are HGNC:5240 pseudogene, HGNC:32523 pseudogene, HGNC:30222 pseudogene, and HGNC:30518 pseudogene.
  • pseudogene markers for recurrent glioma, two types of pseudogene markers whose expression increases as the hazard ratio decreases were finally selected.
  • the two pseudogene markers are the HGNC:30486 pseudogene, and the HGNC:35196 pseudogene.
  • pseudogene markers for recurrent glioma of the present invention may be used as pseudogene markers for primary glioma.
  • the expression level of the pseudogene refers to the transcription level of the pseudogene
  • a person skilled in the art uses an appropriate method based on common knowledge in the art within the target range for measuring the desired transcription level of the pseudogene. can do.
  • quantitative RNA sequencing, quantitative polymerase chain reaction (qPCR), microarray, and Northern blotting may be used.
  • An agent for detecting a genetic marker differentially expressed according to the grade of the glioma may be used without limitation within the range of purposes for detecting the expression level of the pseudogene.
  • it may be a primer set complementary to a pseudogene marker, a probe, an aptamer, or an antisense nucleic acid, but is not limited thereto.
  • antisense nucleic acid is sufficiently complementary to selectively hybridize to the BRAF mutant gene target under predetermined hybridization or annealing conditions, preferably physiological conditions, and is substantially complementary. ) and perfectly complementary, preferably completely complementary.
  • the 'antisense nucleic acid' refers to a nucleic acid-based molecule that has a sequence complementary to a target pseudogene and can form a dimer with the pseudogene marker, and can be used to detect the pseudogene marker herein. .
  • primer refers to a nucleic acid sequence having a short free 3' hydroxyl group, capable of forming a base pair with a complementary template and serving as a starting point for template strand copying. It means 7 to 50 nucleic acid sequences. Primers are usually synthesized but can also be used on naturally occurring nucleic acids. The sequence of the primer does not necessarily have to be exactly the same as the sequence of the template, but only if it is sufficiently complementary to hybridize with the template.
  • probe refers to a nucleic acid fragment such as RNA or DNA corresponding to several bases to several hundred bases in length that can form specific binding to mRNA, and is labeled to determine the presence or absence of a specific mRNA.
  • the probe may be manufactured in the form of an oligonucleotide probe, a single stranded DNA probe, a double stranded DNA probe, an RNA probe, or the like.
  • kits for glioma grade diagnosis and/or prognosis prediction comprising the composition for glioma grade diagnosis and/or prognosis prediction.
  • the term "kit” refers to a device for screening that includes the above-described pseudogene biomarker for glioma grade diagnosis and/or prognosis prediction, and provides information on glioma grade diagnosis and/or prognosis prediction. And, any form in which the pseudogene biomarker can be identified from a biological sample may be used without limitation.
  • the kit of the present invention can detect a pseudogene biomarker for grade diagnosis and/or prognosis prediction of glioma.
  • the kit of the present invention may include, in addition to the composition for detecting a pseudogene biomarker, one or more other component compositions, solutions, or devices suitable for the analysis method.
  • Another embodiment of the present invention provides a method of providing information necessary for glioma grade diagnosis and/or prognosis prediction.
  • diagnosis means identifying the presence or characteristics of a pathological condition. For the purposes of the present invention, diagnosis is to determine whether or not a glioma has developed, and the grade of the glioma that has occurred.
  • the prognosis of glioma refers to all symptoms and/or conditions associated with the progression of glioma, for example, changes in glioma symptoms according to anti-glioma treatment (improvement, alleviation, treatment (removal), or worsening, etc.) , glioma recurrence, etc., and a good prognosis means that glioma symptoms are improved, alleviated, or treated (removed), and/or the likelihood of glioma recurrence is low, and the prognosis is poor. It may mean that the symptoms of glioma are worsening or that there is a high possibility of recurrence of the glioma.
  • the recurrence of the glioma may include recurrence at the primary location, metastatic recurrence, and the like.
  • the likelihood of glioma recurrence can be determined based on approximately 5 or 6 years after glioma treatment (which may include surgical operation in one example), but is not limited thereto.
  • the method of providing information necessary for glioma grade diagnosis and/or prognosis prediction provided by the present invention may include identifying and/or measuring the expression level of a pseudogene marker in a biological sample isolated from a patient. .
  • the measurement of the expression level may be performed singly or plural times, and when the measurement is performed multiple times, the measurement may be performed with a time interval between the measurement cycles.
  • the time interval between fraud measurements is 1 day to 1 year, 1 day to 6 months, 1 day to 3 months, 1 day to 1 month, 1 day to 15 days, 1 day to 1 week, 1 week to 1 year, 1 week to 6 months, 1 week to 3 months, 1 week to 1 month, or 1 week to 15 days may be, but is not limited thereto, and a person skilled in the art may select an appropriate time interval and measure it if necessary.
  • the pseudogene marker is the same as described above in the pseudogene marker differentially expressed according to the above-mentioned grade of glioma.
  • the method for providing information necessary for glioma grade diagnosis and/or prognosis prediction comprises (1) HGNC:18611 pseudogene, HGNC:13229 pseudogene in a biological sample isolated from a patient , HGNC:5240 pseudogene, HGNC:7404 pseudogene, HGNC:32523 pseudogene, HGNC:30222 pseudogene, HGNC:30080 pseudogene, HGNC:16797 pseudogene, HGNC:30518 pseudogene, HGNC:33993 pseudogene, HGNC :11987 pseudogene, and HGNC:30806 pseudogene and/or (2) HGNC:30486 pseudogene, HGNC:24976 pseudogene, HGNC:30886 pseudogene, and HGNC:35196 pseudogene It may include the step of confirming and/or measuring the expression level of one or more pseudogene markers selected from the group consisting of pseudogenes.
  • the expression level of the pseudogene marker when the expression level of the pseudogene marker is measured multiple times, the expression level of the pseudogene marker on the first measurement date and/or on the previous (all or part of the measurement date prior to the measurement date) is the reference (
  • the relative pseudogene expression level may be calculated as the reference value), and in one embodiment, the relative pseudogene marker expression level may be expressed as a ratio to the reference value (the level of the pseudogene marker expression/reference value on the day of measurement).
  • the reference value when the measurement is made on a patient with recurrent glioma, the reference value is a sample at the time of confirmation of a primary cancer prior to recurrence and/or a grade I and/or grade II low-grade nerve in the recurrent cancer. It may be the expression level of the pseudogene marker measured in the glioma stage.
  • the reference value is a non-patient (or normal), a measurement value (a level of pseudogene expression) in the low-grade glioma stage of the patient (a sample providing patient, a patient to be diagnosed), and/or a patient providing the sample
  • a measurement value a level of pseudogene expression in the low-grade glioma stage of the patient
  • a sample providing patient, a patient to be diagnosed a patient providing the sample
  • it may be a measurement value from a primary cancer sample isolated from the patient with recurrent cancer, but is not limited thereto, and may be appropriately selected within the range of purposes for determining a high-expression group and a low-expression group.
  • the primary cancer sample isolated from the recurrent cancer patient may be a sample isolated before and/or after recurrence.
  • the expression level of the pseudogene marker in the measurement target is compared with the expression level of the pseudogene marker in the control group, and the pseudogene in the control group is compared.
  • the expression level of the pseudogene marker of the patient to be measured compared to the expression level of the target patient may be determined as the relative expression level of the pseudogene marker.
  • the "low-expression group” can be decided with
  • the diagnostic and/or predictive method provided herein may further include, after confirming and/or measuring the expression level of the pseudogenic marker, comparing with the expression level of the pseudogenic marker in a control group. there is.
  • control refers to non-patients, i.e., patients not suffering from gliomas or normal persons or cells isolated therefrom; a patient with or isolated from a low-grade glioma stage glioma (eg, a glioma cell isolated from a low-grade glioma stage of a patient being diagnosed); And when the patient to be diagnosed is a patient with recurrent cancer, it may be selected from the group consisting of a primary cancer patient or cells isolated therefrom (eg, glioma cells isolated in the primary cancer stage prior to recurrence of the patient to be diagnosed).
  • the isolated cell may be, for example, a glioma cell.
  • the "high expression group” means that the relative expression level of the pseudogene marker (the level of expression of the pseudogene marker on the measurement day/reference value) is 1 or more. it could be In another example, the "high expression group” for a specific pseudogene is an expression level higher than the average value of the expression level of the pseudogene in the database compared with the expression level of the pseudogene in the glioma patient database, the expression of the pseudogene in the database Expression level higher than the median value of the amount of pseudogene expression in the database means the case of having the expression level of the pseudogene within the top 45%, within the top 30%, within the top 15%, or within the top 10% in the normal distribution of the expression level of the pseudogene in the database. can The glioma patient database can be used regardless of type as long as the database can be accessed by those of ordinary skill in the art to which the present invention pertains.
  • the "low expression group” refers to a case in which the relative expression level of the pseudogene marker (the level of expression of the pseudogene marker on the measurement day / reference value) is 1 or less.
  • the "low expression group" for a specific pseudogene is an expression level lower than the average value of the expression level of the pseudogene in the database compared with the expression level of the corresponding gene in the glioma patient database, the corresponding similarity in the database
  • the expression level is lower than the median value of the gene expression level, and the expression level of the pseudogene within the lower 45%, within the lower 30%, within the lower 15%, or within the lower 10% in the normal distribution of the expression level of the pseudogene in the database.
  • (2) HGNC:30486 pseudogene, HGNC:24976 pseudogene, HGNC:30886 pseudogene, and HGNC:35196 It may include the step of classifying the expression level of one or more pseudogenes selected from the group consisting of pseudogenes into a high-expression group or a low-expression group, and when the expression level belongs to the high-expression group, it is determined that the risk of glioma is low.
  • the glioma risk when it is determined that the glioma risk is high, after the determining step, the glioma risk is high (high grade and performing glioma treatment to the patient determined to be glioma).
  • the treatment of the glioma consists of surgical operation (eg, resection of the lesion site (tumor site)), radiation therapy (eg, high-energy radiation), and administration of an anticancer agent (eg, temozolomide, etc.) for high-grade glioma treatment.
  • the method may further include performing glioma treatment on a patient determined to have a low glioma risk.
  • the treatment of the glioma is observation, surgical operation (eg, lesion site (tumor site) resection), radiation therapy (eg, low-energy (compared to high-grade glioma) irradiation), low-grade glioma treatment.
  • anticancer agents eg, temozolomide, etc.
  • one or two of all therapies applicable to the treatment of low-grade glioma It can be performed by selecting more than one branch. Accordingly, appropriate personalized treatment according to the patient's risk is possible.
  • Another embodiment of the present invention relates to a screening method for a therapeutic agent for glioma.
  • the screening method for a glioma therapeutic agent comprises the steps of (i) contacting a candidate substance with a glioma sample, and (ii) the candidate substance-treated group (contacting the candidate substance) in the glioma sample and the untreated control sample. and analyzing the expression level of the pseudogene marker.
  • the pseudogene marker is the same as described above.
  • the glioma sample may be a sample isolated from a glioma patient, an animal inducing a glioma, or a sample isolated from the animal.
  • the sample may include (isolated) cells, tissues, body fluids, or cultures thereof obtained from all animals, including mammals, including rodents such as primates such as humans and mice, and in one example, glioma It may include cancer cells or tumor tissue including cells, but is not limited thereto.
  • the candidate material may be selected from the group consisting of various compounds, such as proteins, polypeptides, oligopeptides, polynucleotides, oligonucleotides, other small molecule compounds, natural products, and extracts of natural products, but is not limited thereto. .
  • the above (1) HGNC:18611 pseudogene, HGNC:13229 pseudogene, HGNC:5240 pseudogene, HGNC:7404 pseudogene, HGNC:32523 pseudogene, HGNC: 30222 pseudogene, HGNC:30080 pseudogene, HGNC:16797 pseudogene, HGNC:30518 pseudogene, HGNC:33993 pseudogene, HGNC:11987 pseudogene, and HGNC:30806 pseudogene
  • the candidate material may be determined (selected) as a glioma therapeutic agent.
  • the candidate material may be determined (selected) as a glioma therapeutic agent.
  • the screening method for a glioma therapeutic agent provided by the present invention may further include the step of determining whether the glioma sample is primary or recurrent.
  • the present invention relates to a pseudogene marker for diagnosing glioma malignancy and/or prognosis, a composition for diagnosing glioma malignancy and/or prognosis prediction for detecting the marker, and use thereof,
  • the shortcomings of the morphological diagnosis method can be supplemented. More specifically, it is possible to accurately predict the malignancy of gliomas through the analysis of the expression level of pseudogene markers and to predict the survival rate of the patient to provide appropriate treatment according to the patient's condition.
  • the diagnosis and prediction of the progression level of glioma can be made more accurately by confirming the similar gene expression pattern along with the diagnostic method using the genetic mutation of glioma using the existing cancer panel.
  • FIG. 1 is a Venn diagram showing the distribution of pseudogenes showing a significant difference in expression levels between malignant grades of gliomas in the TCGA data set. It stands for G2: Grade II, G3: Grade III, and G4: Grade IV, where each red text is a region separator in the Venn diagram.
  • FIG. 2 is a Venn diagram showing the distribution of pseudogenes showing a significant difference in expression levels between malignant grades of gliomas in the CGGA data set. It stands for G2: Grade II, G3: Grade III, and G4: Grade IV, where each red text is a region separator in the Venn diagram.
  • FIG. 3 is a Venn diagram showing the distribution of pseudogenes that showed a common significant difference in expression in all grades of glioma malignancy in the TCGA dataset and the CGGA dataset.
  • Each red text (from left to right, s15, s16, and s17) is a region separator in the Venn diagram.
  • 4A to 4L are survival plots according to the expression level of the pseudogene marker confirmed that the higher the expression level, the higher the risk ratio in the primary tumor patient group.
  • the left graph is the TCGA dataset analysis result
  • the right graph is the CGGA data set analysis result
  • the Y-axis indicates survival probability
  • 5A to 5D are survival plots according to the expression level of the pseudogene marker confirmed that the higher the expression level, the lower the risk ratio in the primary tumor patient group.
  • the graph on the left is the TCGA data set analysis result
  • the right graph is the CGGA data set analysis result.
  • 6A to 6D are graphs of survival analysis according to the expression level of pseudogene markers confirmed to increase the risk ratio as the expression level increases in the recurrent tumor patient group.
  • the graph on the left is the TCGA data set analysis result
  • the right graph is the CGGA data set analysis result.
  • 7A to 7B are graphs of survival analysis according to the expression level of pseudogene markers confirmed to decrease the risk ratio as the expression level increases in the recurrent tumor patient group.
  • the graph on the left is the TCGA data set analysis result
  • the right graph is the CGGA data set analysis result.
  • 8A to 8L are Violin plots showing the expression patterns and distribution densities of pseudogenes confirmed to increase the hazard ratio as the expression level increases in the primary glioma patient group.
  • the graph on the left is the analysis result of the TCGA data set and the right side is the analysis result of the CGGA data set.
  • Grade 2 (Grade II, G2) is gray
  • Grade 3 (Grade III, G3) is blue
  • Grade 4 Grade IV, G4 of gliomas are indicated in yellow.
  • 9A to 9D are Violin plots showing the expression patterns and distribution densities of pseudogenes confirmed that the higher the expression level, the lower the risk ratio in the primary glioma patient group.
  • the graph on the left is the analysis result of the TCGA data set and the right side is the analysis result of the CGGA data set.
  • Grade 2 (Grade II, G2) is gray
  • Grade 3 (Grade III, G3) is blue
  • Grade 4 Grade IV, G4 of gliomas are indicated in yellow.
  • 10A to 10D are Violin plots showing the expression patterns and distribution densities of pseudogenes confirmed to increase the hazard ratio as the expression level increases in the recurrent glioma patient group.
  • the graph on the left is the analysis result of the TCGA data set and the right side is the analysis result of the CGGA data set.
  • Grade 2 (Grade II, G2) is gray
  • Grade 3 (Grade III, G3) is blue
  • Grade 4 Grade IV, G4 of gliomas are indicated in yellow.
  • 11A to 11B are Violin plots showing the expression patterns and distribution densities of pseudogenes confirmed that the higher the expression level, the lower the risk ratio in the recurrent glioma patient group.
  • the graph on the left is the analysis result of the TCGA data set and the right side is the analysis result of the CGGA data set.
  • Grade 2 (Grade II, G2) is gray
  • Grade 3 (Grade III, G3) is blue
  • Grade 4 Grade IV, G4 of gliomas are indicated in yellow.
  • Example 1 Identification of biomarkers of pseudogenes related to malignancy of gliomas
  • the TCGA and CGGA databases were used.
  • the TCGA glioma data set the gene expression RNAseq data set (version; 2017-09-08) of TCGA lower grade glioma and glioblastoma (TCGA-GBMLGG) was obtained from UCSC XENA (https:// xena.ucsc.edu/).
  • the TCGA-GBMLGG data set is RNAseq gene expression data generated with the Illumina HiSeq 2000 platform, and all data values are RSEM (RNA-seq by expectation Maximization) normalized and log2 transformed state.
  • 'HiSeqV2' is a tab-separated file with expression values of 20,531 genes for 702 glioma patients
  • 'GBMLGG_clinicalMatrix' is a file for 1,148 patients including 'HiSeqV2' glioma patients. It is a file containing various clinical information.
  • CGGA's RNA-Seq data set of glioma patients was downloaded from GLIOMASdb (http://cgga.org.cn:9091/gliomasdb/download.jsp).
  • the CGGA data set is also RNAseq gene expression data generated with the Illumina HiSeq 2000 platform, normalized to RSEM and TMM.
  • 'CGGA_Gene_Expression_Profile_FPKM' is a tab-separated file with the expression values of 17,527 genes for 325 glioma patients, and 'Patient_Characteristics. A file that contains information.
  • the 'GBMLGG_clinicalMatrix' file containing the clinical information of TCGA-GBMLGG extracted only the necessary information and reconstructed the columns into 'sampleID', 'neoplasm_histologic_grade', 'primary_disease', and 'sample_type', and the clinical information of CGGA In 'Patient_Characteristics.txt' containing
  • all data was processed using a script written using R (version; 3.5.2, The R Foundation, https://www.r-project.org/). A detailed data processing process will be described later.
  • Sample IDs were classified according to categories based on various clinical information of glioma patients, and then, expression values of 20,530 genes corresponding to Sample IDs were extracted.
  • Table 1 shows the clinical pathological characteristics of glioma patients in the TCGA-GBMLGG data set and CGGA data set after pretreatment.
  • the 1,142 patients included in the TCGA data file can be classified into primary tumor and recurrent tumor patient groups. Among them, data from 695 patients with gene expression data were selected. Of the 695 data, 668 patients were classified as primary tumors, accounting for about 96% of the TCGA analysis data, and 27 patients classified as recurrent tumors, accounting for about 4%. As a result of classifying the data of the 695 patients according to the degree of malignancy, 258 patients with Grade II (G2) values and 270 and 167 patients with Grade III (G3) and Grade IV (G4) samples, respectively. became
  • HGNC HUGO Gene Nomenclature Committee
  • the additional filtering process excludes all genes matching the 'gene name overlapping with the protein expression gene' from the list of 12,963 pseudogenes using the 'gene name overlapping with the protein expression gene' provided by the HGNC. was performed.
  • the total number of pseudogenes provided by HGNC was 12,963, and the total number of genes in the TCGA data set was 20,531, of which 450 were pseudogenes, corresponding to about 2.19% of the total genes.
  • 450 were pseudogenes, corresponding to about 2.19% of the total genes.
  • Example 1-2 With respect to the data prepared in Example 1-2 to compare the difference in expression levels of similar genes between grade II, grade III, and grade IV groups classified according to the malignancy of glioma with respect to the TCGA and CGGA pseudogene sets Analysis of variance (ANOVA analysis) and post hoc analysis were performed.
  • ANOVA analysis Analysis of variance
  • Venn diagram analysis was performed. Venn diagram analysis was performed using the website tool Venny (version 2.1.0; BioinfoGP Service, CNB-CSIC, http://bioinfogp.cnb.csic.es/tools/venny/).
  • FIG. 3 shows a Venn diagram showing the distribution of pseudogenes (s5 in FIG. 1 and s12 in FIG. 2) that showed a significant difference in expression in all grades of glioma malignancy in the TCGA data set and the CGGA data set.
  • Table 3 shows the meaning of each section of the Venn diagram of FIGS. 1 to 3 .
  • a total of 338 genes passed the analysis of variance showing a statistically significant difference in expression level in at least one glioma malignancy (Grade, G) among the pseudogenes of the TCGA data set.
  • G glioma malignancy
  • 102 pseudogenes showed a significant difference in expression levels in all grades II to IV malignancies (region s5 in FIG. 1).
  • a total of 255 genes that passed the analysis of variance showed a statistically significant difference in expression level, and a total of 51 pseudogenes that showed a significant difference in expression level in all grades II to IV malignancies. (region s12 in Fig. 2).
  • the subjects of the post hoc test were Grade II and Grade III, Grade II and Grade IV, and Grade III and Grade IV, and the same method as TCGA was used as the test method.
  • Normality test and equal variance test were performed through Shapiro-Wilk test and Bartlett test. analysis' was carried out.
  • 'Welch's ANOVA' and 'GAMES-HOWELL method post hoc analysis' were performed.
  • the nonparametric 'Kruskal-Wallis test' and 'Tukey and Kramer (Nemenyi) test' were performed.
  • pseudogene markers of primary glioma were selected, and 12 pseudogenes with higher risk as the relative expression level was higher (high-risk group, HGNC:18611, HGNC:13229, HGNC:5240, HGNC:7404, HGNC:32523, HGNC:30222, HGNC:30080, HGNC:16797, HGNC:30518, HGNC:33993, HGNC:11987, HGNC:30806) and four pseudogenes with a lower risk as the relative expression level is higher (high primary carcinogenic expression) -Low risk group, HGNC:30486, HGNC:24976, HGNC:30886, HGNC:35196) were selected.
  • the 6 types of finally selected recurrent glioma pseudogene markers were 4 pseudogenes with a higher risk as the relative expression level was higher (high-risk group for recurrent carcinogenesis, HGNC:5240, HGNC:32523, HGNC:30222, HGNC:30518) and two pseudogenes with a lower risk as the relative expression level was higher (high-risk group of recarcinogenic expression-low risk, HGNC: 30486, HGNC: 35196) were selected.
  • the median value was calculated when the expression values of each pseudogene in each patient group (primary cancer group, recurrent cancer group) were sorted by expression size.
  • a patient group with a higher expression level of the pseudogene than the intermediate expression level of the pseudogene is classified as a high expression group, and a patient group with a low expression level is classified as a low expression group.
  • Kaplan-Meier survival analysis was performed. 4a to 4l show survival plots of the primary tumor patient group (high-risk group with high primary cancer expression) according to the expression level of each pseudogene marker, which increases the risk as the expression level increases.
  • the graph on the left is a survival analysis graph for the patient group of the TCGA data set
  • the graph on the right is a graph of survival analysis for the patient group of the CGGA data set.
  • 5A to 5D show graphs of survival analysis of the primary glioma patient group (high-risk group with high primary cancer expression) according to the expression level of each pseudogene marker, which has a lower risk as the expression level increases.
  • the graph on the left is a graph of survival analysis for the patient group of the TCGA data set
  • the graph on the right is a graph of survival analysis for the patient group of the CGGA data set.
  • the survival analysis graphs of FIGS. 5a to 5d showed that the decrease in survival rate in the high-expression group (red line) indicated as HIGH was delayed compared to the low-expression group (cyan line) indicated by LOW. .
  • FIGS. 6A to 6L show graphs of survival analysis of the recurrent tumor patient group (high-risk group with high recurrent cancer expression) according to the expression level of each pseudogene marker, which increases the risk as the expression level increases.
  • the graph on the left is a graph of survival analysis for the patient group of the TCGA data set
  • the graph on the right is a graph of survival analysis for the patient group of the CGGA data set.
  • the decrease in survival rate in the high-expression group (red line) indicated by HIGH was faster than that in the low-expression group (cyan line) indicated by LOW.
  • FIGS. 7A to 7D show graphs of survival analysis of the recurrent glioma patient group (high-recurrence-low-risk group) according to the expression level of each pseudogene marker, in which the risk decreases as the expression level increases.
  • the graph on the left is a graph of survival analysis for the patient group of the TCGA data set
  • the graph on the right is a graph of survival analysis for the patient group of the CGGA data set.
  • the decrease in survival rate in the high-expression group (red line) indicated by HIGH was slower than that in the low-expression group (cyan line) indicated by LOW.
  • the survival analysis rate analysis it was confirmed that the survival rate change is large regardless of whether the data set or the primary or recurrent glioma patient according to the relative expression level of each similar gene, and the difference in the expression level of the similar gene marker It was confirmed that the risk change can be predicted.
  • the primary and recurrent patient groups of the TCGA and CGGA datasets were further subdivided into Grade II (G2) to Grade IV (G4) according to the degree of malignancy, and the expression pattern of each pseudogene marker was shown as a violin graph. .
  • 8A to 8L show the expression pattern and distribution density of pseudogenes (high-risk group with high primary oncology) whose hazard ratio increases as the expression level of 12 types determined in Example 2 increases in the primary glioma patient group.
  • a Violin plot is shown. All graphs are TCGA data set on the left side, and analysis result graph of CGGA data set on the right side. Grade 2 (Grade II, G2) is gray, Grade 3 (Grade III, G3) is blue, and Grade 4 (Grade IV, G4) of gliomas are indicated in yellow.
  • the normalized value of the normalized expression level also increased, and the density showed the highest density near the median value.
  • the expression level decreased as progressed from G2 to G3, and then the expression level increased rapidly as it progressed to G4.
  • the expression level of the HGNC:30222 pseudogene increased as the glioma grade increased.
  • the overall expression level increased as the glioma grade increased (stage progression).

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Abstract

La présente invention concerne un biomarqueur de pseudogène pour le diagnostic et/ou la prédiction pronostique d'un gliome, et l'invention concerne une composition pour le diagnostic et/ou la prédiction pronostique d'un gliome comprenant une formulation pour la détection du biomarqueur, un kit pour le diagnostic et/ou la prédiction pronostique d'un gliome comprenant la composition, et un procédé pour fournir des informations requises pour le diagnostic et/ou la prédiction pronostique d'un gliome par l'utilisation du biomarqueur. Le biomarqueur de pseudogène peut clairement diagnostiquer le grade de gliome en tant que grade 2 à grade 4, et le pronostic peut être prédit en fonction de la différence de taux d'expression, ce qui permet un diagnostic précis et/ou une prédiction précise du gliome.
PCT/KR2021/001271 2020-01-31 2021-02-01 Utilisation d'un pseudogène pour le diagnostic de la malignité d'un gliome WO2021154056A2 (fr)

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