CN113943801A - Application of biomarker in preparation or screening of colorectal cancer diagnostic reagent - Google Patents
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Abstract
Use of a biomarker for the preparation or screening of a diagnostic agent for colorectal cancer, the biomarker comprising at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam. The biomarker provided by the invention can be used for carrying out effective monitoring and early warning on patient prognosis.
Description
Technical Field
The invention relates to the field of medicine, in particular to application of biomarkers in preparing or screening colorectal cancer diagnostic reagents.
Background
Colorectal cancer (CRC) is one of the most common cancers worldwide, with an increase of 1 to 2 million diagnosed cases each year, making CRC the third most common cancer and the fourth most dying cancer. The current leading treatment modalities for colorectal cancer are surgery, adjuvant radiation therapy, and adjuvant chemotherapy (patients with stage III, IV, or high risk of stage II colon cancer). In terms of survival, 5-year survival rates for stage I patients can be more than 90%, while survival rates for stage IV patients are only about 10%.
Disclosure of Invention
According to a first aspect, in one embodiment, there is provided use of a biomarker comprising at least one of the following genes in the manufacture or screening of a cancer diagnostic agent: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
According to a second aspect, in an embodiment, there is provided use of a biomarker for the manufacture or screening of a medicament for the treatment of cancer, the biomarker comprising at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
According to a third aspect, in an embodiment, there is provided a method of calculating a cancer risk value, comprising:
an expression amount calculating step including calculating an expression amount of a target gene including at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
and calculating the risk value, wherein the risk value is calculated according to the expression quantity of the target gene.
According to a fourth aspect, in an embodiment, there is provided an apparatus for predicting cancer risk, comprising:
an expression amount calculation module for calculating an expression amount of a target gene, the target gene comprising at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
the risk value calculation module is used for calculating to obtain a risk value according to the expression quantity of the target gene;
and the judging module is used for predicting the cancer risk of the subject according to the risk value.
According to a fifth aspect, in an embodiment, there is provided an apparatus comprising:
a memory for storing a program;
a processor for implementing cancer risk prediction by executing the program stored in the memory, the prediction method comprising:
an expression amount calculating step including calculating an expression amount of a target gene including at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
calculating a risk value, namely calculating to obtain a risk value according to the expression quantity of the target gene;
a determining step comprising predicting the risk of cancer for the subject based on the risk value.
According to a sixth aspect, in an embodiment, there is provided a computer readable storage medium having a program stored thereon, the program being executable by a processor to implement the method of the third or fifth aspect.
According to the application of the biomarkers in the preparation or screening of colorectal cancer diagnostic reagents, the biomarkers provided by the invention can be used for effective monitoring and early warning of patient prognosis.
Drawings
FIG. 1 is a graph of the expression levels of predictors in situ tumors.
FIG. 2 is a diagram showing the analysis of the gene expression of the predictor in normal colorectal tissue (Colorectum), paracancerous tissue (Adjacent normal), and colorectal cancer (CRC).
FIG. 3 is an analysis graph of the survival curves of the predictor and the patients.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning.
As used herein, "Biomarker" (Biomarker) refers to a biochemical marker that can mark changes or changes that may occur in the structure or function of systems, organs, tissues, cells, and subcellular structures or functions, and has a very wide range of uses. Biomarkers can be used for disease diagnosis, to determine disease stage, or to evaluate the safety and effectiveness of new drugs or therapies in a target population. Herein, "biomarker", "predictor", "molecular marker" are used interchangeably. The biomarker may specifically be a specific gene expressing a specific protein.
For disease research, a biomarker generally refers to a biochemical indicator characteristic of a general physiological or pathological or therapeutic process that can be objectively measured and evaluated, and from its measurement, the progress of the biological process in which the body is currently located can be known. Examination of a disease-specific biomarker may be helpful in the identification, early diagnosis and prevention of disease, and monitoring of disease treatment. The search and discovery of valuable biomarkers has become an important hotspot of current research.
Biomarkers are signal indicators that are abnormal at different biological levels (molecules, cells, individuals, etc.) due to environmental contaminants before the organism is severely damaged. It can provide an early warning of severe toxic injury. The signal indicator can be the change of the molecular structure and function of the cell, the change of a certain biochemical metabolic process or the generation of abnormal metabolites or the content of the abnormal metabolites, the abnormal expression of a certain physiological activity or a certain physiologically active substance, the abnormal phenomenon shown by an individual, the abnormal change of a population or a community, and the abnormal change of an ecosystem.
As used herein, the term "expression level" is equivalent to an "expression profile," which refers to the expression level of one or more proteins (i.e., gene products) obtained from a test sample (e.g., tumor tissue). For gene products recovered from a test sample, the expression level can be expressed as the mass of a particular polypeptide per mL of test sample or any other suitable unit. Similar units can be used for gene products obtained from other test samples. Expression of the gene product can be determined using any suitable method (e.g., as described below). The measurements are typically normalized to account for variations in sample quantity and quality, reverse transcription efficiency, and the like. Where an expression profile reflects expression from a plurality of different gene products (e.g., RNA transcripts, primarily mRNA transcripts, of different genes), the gene products may be given different weights when generating or comparing the expression profile or reference profile.
As used herein, the Hmgb1 gene encodes a protein belonging to the box superfamily of high mobility groups. The encoded non-histone, nuclear DNA binding proteins regulate transcription and are involved in the organization of DNA. The protein plays a role in a variety of cellular processes, including inflammation, cell differentiation, and tumor cell migration. A number of pseudogenes for this gene have been identified. Alternative splicing results in multiple transcriptional variants encoding the same protein. (supplied by RefSeq, 9 months 2015)
As used herein, the Hmgb1 gene is a gene encoding the high mobility group protein B1, and the high mobility group protein B1 is a highly conserved nucleoprotein, widely distributed in mammalian cells.
As used herein, the SLC12a2 gene encodes a protein that mediates the transport and reabsorption of sodium and chloride. The encoded protein is a membrane protein and is important for maintaining proper ionic balance and cell volume. This protein is phosphorylated upon DNA damage. Three transcriptional variants have been found which encode two different subtypes of the gene. (supplied by RefSeq, month 1 2012)
As used herein, the protein encoded by the KRT15 gene is a member of the keratin gene family. Keratin is an intermediate filament protein responsible for the structural integrity of epithelial cells, and is divided into cytokeratin and hair keratin. Most type I cytokeratins consist of acidic proteins that are arranged into a pair of heterotypic keratin chains that aggregate in a region on chromosome 17q 21.2. (supplied by RefSeq, 7 months 2008)
As used herein, the KRT8 gene is a member of the type II keratin family that aggregates on the long arm of chromosome 12. Type I and type II keratins heteropolymerize in the cytoplasm of epithelial cells to form medium-sized filaments. The product of this gene typically dimerizes with keratin 18, forming intermediate filaments in simple, single-layered epithelial cells. The protein plays a role in maintaining the structural integrity of cells as well as in signal transduction and cell differentiation. The gene mutation causes cryptogenic cirrhosis. Alternatively spliced transcript variants of this gene have been found. (supplied by RefSeq, month 1 2012)
As used herein, the EphB2 gene encodes a member of the receptor tyrosine kinase transmembrane glycoprotein Eph receptor family. These receptors consist of an N-terminal glycosylated ligand binding domain, a transmembrane region, and an intracellular kinase domain. They bind a ligand called ephrins and are involved in a variety of cellular processes, including motility, division and differentiation. One significant feature of Eph-ephrin signaling is the ability of both the receptor and the ligand to transduce a signaling cascade, thereby producing a bidirectional signal. This protein belongs to a subgroup of Eph receptors known as EphB. Proteins of this subgroup differ from the other members of the family by sequence homology and preferential binding affinity of membrane-bound ephrin-B ligands. Allelic variation is associated with susceptibility to prostate and brain cancer. Alternative splicing results in multiple transcriptional variants. (supplied by RefSeq, 5 months 2015)
As used herein, the protein encoded by the Lgr5 gene is a leucine-rich repeat receptor (Lgr), a member of the G-protein coupled 7-transmembrane receptor (GPCR) superfamily. The encoded protein is a receptor of R-sponge protein and participates in a typical Wnt signal pathway. This protein plays a role in the formation and maintenance of adult intestinal stem cells during post-embryonic development. Several transcriptional variants have been found that encode different subtypes of the gene. (supplied by RefSeq, 9 months 2015)
As used herein, the protein encoded by the E2F1 gene is a member of the E2F transcription factor family. The E2F family plays a key role in controlling the cell cycle and the role of tumor suppressor proteins, and is also a target for small DNA oncoviral transformation proteins. The E2F protein contains several evolutionarily conserved domains found in most members of the family. These domains include a DNA binding domain, a dimerization domain that determines interaction with a differentiation regulating transcription factor protein (DP), a transactivation domain enriched with acidic amino acids, and a tumor suppressor binding domain embedded within the transactivation domain. This protein and the other 2 members E2F2 and E2F3 have additional cyclin-binding domains. This protein preferentially binds to the retinoblastoma protein pRB in a cell cycle dependent manner. It may mediate cell proliferation and p 53-dependent/independent apoptosis. (supplied by RefSeq, 7 months 2008)
As used herein, the CD133 gene encodes a five-spanning transmembrane glycoprotein. This protein is localized to the membrane processes, is usually expressed on adult stem cells, and is thought to maintain stem cell characteristics by inhibiting differentiation. This gene mutation has been shown to cause retinitis pigmentosa and Stargardt disease. Expression of this gene is also associated with several types of cancer. The gene is expressed from at least five alternative promoters that are expressed in a tissue-dependent manner. Multiple transcriptional variants have been found that encode different subtypes of the gene. (supplied by RefSe q, 3 months 2009)
As used herein, the c-Myc gene is a proto-oncogene, encoding a nuclear phosphoprotein, which plays a role in cell cycle progression, apoptosis and cell transformation. The encoded protein forms a heterodimer with the relevant transcription factor MAX. This complex binds to the E-box D NA consensus sequence and regulates transcription of a specific target gene. Amplification of this gene is often observed in many human cancers. Translocations involving this gene are associated with human burkitt's lymphoma and multiple myeloma. There is evidence that translation initiation at upstream, intraframe non-AUG (cug) and downstream AUG initiation sites results in the production of two isoforms with different N-termini. (supplied by RefSeq, 8 months 2017)
The Epcam gene encodes a cancer-associated antigen and is a family member comprising at least two type I membrane proteins. This antigen is expressed on most normal epithelial cells and gastrointestinal cancers and functions as a homocalcium-independent cell adhesion molecule. Such antigens are being used as targets for immunotherapy of human cancers. This gene mutation results in congenital cluster bowel disease. (supplied by RefSeq, 12 months 2008)
The Gapdh gene encodes a member of the glyceraldehyde-3-phosphate dehydrogenase protein family. The encoded protein has been identified as a part-time protein based on its ability to perform different functions of the machine. The product of this gene catalyzes an important energy-generating step in carbohydrate metabolism, namely the reversible oxidative phosphorylation of glyceraldehyde-3-phosphate in the presence of inorganic phosphate and Nicotinamide Adenine Dinucleotide (NAD). The encoded protein was also identified as having uracil DNA glycosidase activity in the nucleus. In addition, the protein contains a peptide having antibacterial activity against Escherichia coli. Escherichia coli. Pseudomonas aeruginosa and C. Candida albicans. Studies of similar proteins in mice have conferred a variety of additional functions, including nitrosylation of nucleoproteins, modulation of mRNA stability, and as transferrin receptors on the cell surface of macrophages. There are many pseudogenes in the human genome that resemble this site, and alternative splicing results in multiple transcriptional variants. (supplied by RefSeq, 11 months 2014)
According to a first aspect, in one embodiment, there is provided use of a biomarker comprising at least one of the following genes in the manufacture or screening of a cancer diagnostic agent: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
By carrying out transcriptome sequencing and protein chip analysis on in-situ tumor tissues, the genes Hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam (transcription factors: Hmgb1, E2F1 and c-Myc, and the others are cell surface molecules) which are highly expressed in tumors are found to have significant correlation with the survival curves of patients. Therefore, the method can carry out targeted detection on key genes in tumor tissues of patients, and can effectively monitor and early warn prognosis of the patients. The 10 genes can be used as markers for assisting in predicting the postoperative survival time of colorectal cancer patients. A detection kit containing the factor 10 can be constructed and used for assisting the diagnosis of postoperative prognosis of colorectal cancer patients.
In one embodiment, the biomarker comprises all of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
In one embodiment, the biomarker comprises the expression level of the gene in the test sample.
In one embodiment, the biomarker comprises the expression level of the gene in the test sample relative to a reference gene.
In one embodiment, the reference gene comprises a constitutively and stably expressed gene in the cell.
In one embodiment, the reference gene comprises an internal reference gene.
In one embodiment, the reference gene includes at least one of, but not limited to, Gapdh gene, Actin gene, and Tubulin gene.
In one embodiment, the cancer includes, but is not limited to, at least one of colon cancer, rectal cancer.
In one embodiment, a risk value is calculated based on the relative expression levels of the genes in the test sample, and the risk of cancer recurrence in the subject is assessed based on the risk value.
In one embodiment, the subject is assessed for risk of first cancer and/or relapse based on the magnitude of the risk value in relation to a threshold.
In one embodiment, a high risk is predicted if the risk value > the threshold, and a low risk is predicted if the risk value ≦ the threshold.
In an embodiment, the risk value is [ RQ ═ RQ(Hmgb1)*RQ(Slc12a2)*RQ(Krt15)*RQ(Krt8)*RQ(EphB2)*RQ(Lgr5)*RQ(E2F1)*RQ(CD133)*RQ(c-Myc)*RQ(Epcam)]And/alpha, alpha is a nonzero constant, and RQ represents the relative expression amount of a corresponding gene relative to a reference gene.
In one embodiment, RQ ═ 2-ΔΔCtAnd delta Ct (number of target gene PCR cycles in the test sample-number of reference gene PCR cycles) - (number of target gene PCR cycles in the control sample-number of reference gene PCR cycles).
In one embodiment, the number of PCR cycles is detected by Real-time fluorescent Quantitative PCR (Quantitative Real-time PCR).
Real-time fluorescent Quantitative PCR (Quantitative Real-time PCR) is a method for measuring the total amount of products after each Polymerase Chain Reaction (PCR) cycle by using fluorescent chemical substances in DNA amplification reaction. A method for quantitatively analyzing a specific DNA sequence in a sample to be detected by an internal reference method or an external reference method.
Real-time PCR is a Real-time detection of PCR progress by fluorescence signals during PCR amplification. In the exponential phase of PCR amplification, the Ct value of the template and the initial copy number of the template have a linear relationship, and therefore, the method becomes a basis for quantification.
In one embodiment, α may be 11.16. α is an empirical constant and can be derived from sample sequencing data from sequencing as well as sequencing data in the TCGA database.
In one embodiment, the test sample comprises a tumor tissue sample. The tumor tissue sample is typically solid tumor tissue.
In one embodiment, the control sample comprises a normal tissue sample.
In one embodiment, the normal tissue sample includes, but is not limited to, a tissue sample adjacent to cancer, other normal tissue samples not adjacent to cancer. The paracancerous tissue sample is a solid tissue mass.
According to a second aspect, in an embodiment, there is provided use of a biomarker for the manufacture or screening of a medicament for the treatment of cancer, the biomarker comprising at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
In one embodiment, the biomarker comprises all of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
In one embodiment, the biomarker comprises the expression level of the gene in the test sample.
In one embodiment, the biomarker comprises the relative expression level of the gene in the test sample relative to a reference gene.
In one embodiment, the cancer includes, but is not limited to, at least one of colon cancer, rectal cancer.
According to a third aspect, in an embodiment, there is provided a method of calculating a cancer risk value, comprising:
an expression amount calculating step including calculating an expression amount of a target gene including at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
and calculating the risk value, wherein the risk value is calculated according to the expression quantity of the target gene. The risk value is an intermediate result, and the sample to be tested is an ex vivo sample, so the method for calculating the cancer risk value does not belong to the method for diagnosing and treating diseases.
In one embodiment, the target gene comprises all of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
In one embodiment, the expression level of the target gene comprises the relative expression level of the target gene relative to a reference gene.
In one embodiment, the relative expression level RQ of the target gene is 2-ΔΔCtAnd delta Ct (number of target gene PCR cycles in the test sample-number of reference gene PCR cycles) - (number of target gene PCR cycles in the control sample-number of reference gene PCR cycles).
In one embodiment, the cancer includes, but is not limited to, at least one of colon cancer, rectal cancer.
In an embodiment, the risk comprises a risk of relapse.
In an embodiment, the risk value is [ RQ ═ RQ(Hmgb1)*RQ(Slc12a2)*RQ(Krt15)*RQ(Krt8)*RQ(EphB2)*RQ(Lgr5)*RQ(E2F1)*RQ(CD133)*RQ(c-Myc)*RQ(Epcam)]And/alpha, alpha is a nonzero constant, and RQ represents the relative expression amount of a corresponding gene relative to a reference gene.
In one embodiment, the method further comprises a signal acquisition step, wherein the signal acquisition step comprises acquiring a signal and calculating the PCR cycle number of the target gene and/or the reference gene according to the signal.
In one embodiment, the signal comprises a fluorescent signal.
According to a fourth aspect, in an embodiment, there is provided an apparatus for predicting cancer risk, comprising:
an expression amount calculation module for calculating an expression amount of a target gene, the target gene comprising at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
the risk value calculation module is used for calculating to obtain a risk value according to the expression quantity of the target gene;
and the judging module is used for predicting the cancer risk of the subject according to the risk value.
In one embodiment, the target gene comprises all of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
In one embodiment, the expression level of the target gene comprises the relative expression level of the target gene relative to a reference gene.
In one embodiment, the relative expression level RQ of the target gene is 2-ΔΔCtAnd delta Ct (number of target gene PCR cycles in the test sample-number of reference gene PCR cycles) - (number of target gene PCR cycles in the control sample-number of reference gene PCR cycles).
In one embodiment, the cancer includes, but is not limited to, at least one of colon cancer, rectal cancer.
In an embodiment, the risk comprises a risk of relapse.
In an embodiment, the risk value is [ RQ ═ RQ(Hmgb1)*RQ(Slc12a2)*RQ(Krt15)*RQ(Krt8)*RQ(EphB2)*RQ(Lgr5)*RQ(E2F1)*RQ(CD133)*RQ(c-Myc)*RQ(Epcam)]And/alpha, alpha is a nonzero constant, and RQ represents the relative expression amount of a corresponding gene relative to a reference gene.
In one embodiment, the risk of cancer in the subject is predicted based on the magnitude of the risk value in relation to a threshold.
In one embodiment, if the risk value > a threshold, then a high risk is predicted; if the risk value is less than or equal to the threshold value, low risk is predicted.
In one embodiment, the system further comprises a signal acquisition module for acquiring a signal and calculating the number of PCR cycles of the target gene and/or the reference gene according to the signal.
In one embodiment, the signal comprises a fluorescent signal.
According to a fifth aspect, in an embodiment, there is provided an apparatus comprising:
a memory for storing a program;
a processor for implementing cancer risk prediction by executing the program stored in the memory, the prediction method comprising:
an expression amount calculating step including calculating an expression amount of a target gene including at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
calculating a risk value, namely calculating to obtain a risk value according to the expression quantity of the target gene;
a determining step comprising predicting the risk of cancer for the subject based on the risk value.
In one embodiment, the target gene comprises all of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
In one embodiment, the expression level of the target gene comprises the relative expression level of the target gene relative to a reference gene.
In one embodiment, the relative expression level RQ of the target gene is 2-ΔΔCtAnd delta Ct (number of target gene PCR cycles in the test sample-number of reference gene PCR cycles) - (number of target gene PCR cycles in the control sample-number of reference gene PCR cycles).
In one embodiment, the cancer includes, but is not limited to, at least one of colon cancer, rectal cancer.
In an embodiment, the risk comprises a risk of relapse.
In an embodiment, the risk value is [ RQ ═ RQ(Hmgb1)*RQ(Slc12a2)*RQ(Krt15)*RQ(Krt8)*RQ(EphB2)*RQ(Lgr5)*RQ(E2F1)*RQ(CD133)*RQ(c-Myc)*RQ(Epcam)]And/alpha, alpha is a nonzero constant, and RQ represents the relative expression amount of a corresponding gene relative to a reference gene.
In one embodiment, the risk of cancer in the subject is predicted based on the magnitude of the risk value in relation to a threshold.
In one embodiment, if the risk value > a threshold, then a high risk is predicted; if the risk value is less than or equal to the threshold value, low risk is predicted.
In one embodiment, the method further comprises a signal acquisition step, wherein the signal acquisition step comprises acquiring a signal and calculating the PCR cycle number of the target gene and/or the reference gene according to the signal.
In one embodiment, the signal comprises a fluorescent signal.
According to a sixth aspect, in an embodiment, there is provided a computer readable storage medium having a program stored thereon, the program being executable by a processor to implement the method of the third or fifth aspect.
Example 1
The kit used in this example comprises:
trizol lysate (Invitrogen, 15596-026);
RNAse-free homogenizer;
RNAse-free 1.5 mM tube;
chloroform;
isopropyl alcohol;
washing solution (75% V/V ethanol prepared by RNAse-free water);
RNAse-free water;
reverse transcription reagent mixture (10. mu.L/tube, TAKARA, RR 047A);
Q-PCR reagent mixture (TAKARA, 639676);
primer dilution (aqueous solution containing primer).
The operation method comprises the following steps:
1. preparing a tumor sample: for patients with multiple colorectal cancer foci, one tumor (50 mg/portion) was taken per focus for a total of 3 foci; for patients with less than 3 colorectal foci, 3 tumor masses (50 mg/serving) were randomized; tumor tissue markers are CRC1, CRC2, CRC 3. 3 pieces of para-carcinoma tissue (50 mg/serving) were randomly selected and labeled with Nor1, Nor2, and Nor 3.
2. The tissue was cut as much as possible with sterile scissors and 200 microliters of Trizol lysate was added.
3. The above tissue fragment lysate mixture was transferred to an RNAse-free homogenizer using a pipette gun and the tissue fragments were ground until no particulate tissue was evident.
4. Adding 800 microliters of Trizol lysate, transferring the tissue lysate to a new RNAse-free 1.5 milli-tube, repeatedly blowing and beating for 10 times by using a pipette gun, uniformly mixing, and standing for 5 minutes at normal temperature.
5. Adding 200 microliters of chloroform into the tissue lysate, covering, shaking up and down forcibly to mix the liquid uniformly, and standing for 5 minutes at normal temperature.
6. The above tissue lysate was centrifuged at 12000 Xg for 20 minutes at 4 ℃.
7. After centrifugation was complete, 200. mu.l of supernatant was pipetted into a fresh RNAse-free 1.5 ml tube.
8. Adding 200 microliters of isopropanol into the supernatant, covering, shaking up and down to mix the liquid uniformly, and standing for 10 minutes at normal temperature.
9. The liquid was centrifuged at 12000 Xg for 10 minutes at 4 ℃.
10. The supernatant was removed and a white precipitate was visible at the bottom of the tube, 500. mu.l of washing solution was added to the precipitate, the lid was closed and the tube was shaken vigorously up and down 3 times.
11. The liquid was centrifuged at 6000 Xg for 5 minutes at 4 ℃.
12. The supernatant was removed and the tube was placed in a fume hood with the lid open for 10 minutes.
13. Adding 50-100 microliter of RNAse-free water, and carrying out water bath at 60 ℃ for 5 minutes.
14. The tube was centrifuged at high speed for 1 minute and the liquid collected at the bottom of the tube.
15. The RNA concentration is measured and placed in an ice box or a refrigerator at 4 ℃ for standby.
The steps 1-15 can be completed by using the existing full-automatic nucleic acid extraction workstation on the market, and the full-automatic nucleic acid extraction workstation is not used in the embodiment and is manually completed.
16. For the same patient or the same batch of detection, reverse transcription is performed using the same mass of RNA (total RNA) (the amount of template may be 100ng to 1000ng, specifically 500ng in this example).
17. To the reverse transcription reagent mixture (10. mu.L/tube) was added the RNA to be detected and RNAse-free water, made up to 20. mu.L. Volume of supplemented RNAse-free water (μ L) ═ 10-RNA mass (ng)/RNA concentration (ng/μ L).
18. The reverse transcription reaction was performed in a PCR instrument under the following conditions:
10 minutes at 25 ℃;
30 minutes at 42 ℃;
5 minutes at 85 ℃;
and (6) ending.
19. Q-PCR reaction
The following reaction system was prepared: mu.L of the reverse transcription reaction solution obtained in step 18, 0.5. mu. L, ROX of the primer mixture, 0.5. mu. L, Q of the PCR reaction mixture, 8. mu.L. Each reaction was set up in 3 replicates.
The Q-PCR reaction process is as follows:
(A) at 95 ℃ for 3 minutes;
(B)95 ℃ for 5 seconds;
(C) 30 seconds at 60 ℃; step B, C is repeated 40 times;
(D)72 ℃, 30 seconds (signal collection);
(E)72 ℃ for 5 minutes;
(F) melting curve (Melting curve) generated the reaction.
And after the reaction is finished, entering a subsequent calculation step.
20. Computing
The calculation is carried out according to the following formula: RQ ═ 2-ΔΔCt。
RQ: relative expression amount.
Δ Ct ═ tumor sample (CRC) target gene PCR cycle-internal reference PCR cycle.
Δ Δ Ct ═ tumor sample (CRC) target gene PCR cycle-reference PCR cycle ] - [ control sample (Nor) target gene PCR cycle-reference PCR cycle ].
21. Recurrence risk value:
Risk＝RQ(Hmgb1)×RQ(Slc12a2)×RQ(Krt15)×RQ(Krt8)×RQ(EphB2)×RQ(Lgr5)×RQ(E2F1)×RQ(CD133)×RQ(c-Myc)×RQ(Epcam)/11.16。
22. determination method
If Risk > 100, there is a high Risk, indicating that the prognosis may be less than optimistic, requiring aggressive review, with close attention to the tumor status. If Risk ≦ 100, then there is low Risk.
23. Primer sequences
The primer sequences are shown in Table 1.
TABLE 1
Gapdh is the reference gene.
FIG. 1 is a graph of the expression level of a predictor in situ tumors with Log as ordinate2(TPM +1), TPM: transitions per million reads, it can be seen that the expression levels of each factor were significantly different in Tumor tissue samples (Tumor), and in paracarcinoma tissue samples (Normal).
FIG. 2 is a dimension reduction analysis chart of gene expression of the predictor in normal colorectal tissue (Colorectum, data in TCGA database), paracancer tissue (Adjacent normal) and colorectal cancer (CRC), and it can be seen that the expression of the predictor selected by us in colorectal cancer is significantly different from the expression in normal colorectal tissue and paracancer tissue.
Figure 3 is a graph of survival curve analysis of predictors versus patients (273 samples, 50 of which were self-sequencing and 223 other data from the TCGA database) plotted on the abscissa for Months (Months), on the ordinate for survival (Percent survivor), on the solid line near the abscissa for ten-factor high expression panels and on the solid line far from the abscissa for ten-factor low expression panels, showing that five-year survival for patients with high expression of the predictor we selected was significantly lower than for patients with low expression of the predictor.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
SEQUENCE LISTING
<110> Shenzhen Shenzhou Longhua central hospital
<120> use of biomarker for preparing or screening colorectal cancer diagnostic reagent
<130> 21I32338
<160> 22
<170> PatentIn version 3.3
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Claims (10)
1. Use of a biomarker for the preparation or screening of a cancer diagnostic agent, wherein the biomarker comprises at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
2. The use of claim 1, wherein the biomarkers comprise all of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
3. The use of claim 1, wherein the biomarker comprises the expression level of the gene in the test sample;
and/or, the biomarker comprises the relative expression level of the gene in the test sample relative to a reference gene;
and/or, the reference gene comprises a gene constitutively and stably expressed in the cell;
and/or, the reference gene comprises an internal reference gene;
and/or the reference gene comprises at least one of a Gapdh gene, an Actin gene and a Tubulin gene.
4. The use of claim 1, wherein the cancer comprises at least one of colon cancer and rectal cancer.
5. The use of claim 1, wherein a risk value is calculated based on the relative expression level of said gene in the test sample, and the risk of relapse in the subject is assessed based on said risk value;
and/or, assessing the risk of cancer recurrence in the subject based on the magnitude of the risk value in relation to a threshold;
and/or predicting high risk if the risk value is greater than the threshold, and predicting low risk if the risk value is less than or equal to the threshold;
and/or, the risk value ═ RQ(Hmgb1)*RQ(Slc12a2)*RQ(Krt15)*RQ(Krt8)*RQ(EphB2)*RQ(Lgr5)*RQ(E2F1)*RQ(CD133)*RQ(c-Myc)*RQ(Epcam)]A, a is a non-zero constant, RQ represents the relative expression quantity of a corresponding gene relative to a reference gene;
and/or, RQ ═ 2-ΔΔCtDelta Ct ═ (number of target gene PCR cycles in the sample to be tested-number of reference gene PCR cycles) - (number of target gene PCR cycles in the control sample-number of reference gene PCR cycles);
and/or the PCR cycle number is obtained by real-time fluorescent quantitative PCR detection;
and/or, the reference gene comprises an internal reference gene and/or an external reference gene;
and/or, α is 11.16;
and/or, the sample to be tested comprises a tumor tissue sample;
and/or, the control sample comprises a normal tissue sample;
and/or the normal tissue sample comprises a cancer side tissue sample and other normal non-cancer side tissue samples.
6. Use of a biomarker for the manufacture or screening of a medicament for the treatment of cancer, wherein the biomarker comprises at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam.
7. A method of calculating a cancer risk value, comprising:
an expression amount calculating step including calculating an expression amount of a target gene including at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
and calculating the risk value, wherein the risk value is calculated according to the expression quantity of the target gene.
8. An apparatus for predicting cancer risk, comprising:
an expression amount calculation module for calculating an expression amount of a target gene, the target gene comprising at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
the risk value calculation module is used for calculating to obtain a risk value according to the expression quantity of the target gene;
and the judging module is used for predicting the cancer risk of the subject according to the risk value.
9. An apparatus, comprising:
a memory for storing a program;
a processor for implementing cancer risk prediction by executing the program stored in the memory, the prediction method comprising:
an expression amount calculating step including calculating an expression amount of a target gene including at least one of the following genes: hmgb1, Slc12a2, Krt15, Krt8, EphB2, Lgr5, E2F1, CD133, c-Myc and Epcam;
calculating a risk value, namely calculating to obtain a risk value according to the expression quantity of the target gene;
a determining step comprising predicting the risk of cancer for the subject based on the risk value.
10. A computer-readable storage medium, characterized in that the medium has stored thereon a program which is executable by a processor to implement the method as claimed in claim 7 or the prediction method as claimed in claim 9.
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| CN107447033A (en) * | 2017-09-15 | 2017-12-08 | 东南大学 | A kind of diagnosis of colorectal carcinoma biomarker and its application |
| US20190192691A1 (en) * | 2016-04-11 | 2019-06-27 | Obsidian Therapeutics, Inc. | Regulated biocircuit systems |
| CN112725456A (en) * | 2021-03-08 | 2021-04-30 | 温州医科大学 | Tumor marker related to colorectal cancer, application thereof and corresponding kit |
| CN113462775A (en) * | 2021-06-21 | 2021-10-01 | 华中农业大学 | Gene marker for colorectal cancer prognosis evaluation |
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Patent Citations (4)
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| US20190192691A1 (en) * | 2016-04-11 | 2019-06-27 | Obsidian Therapeutics, Inc. | Regulated biocircuit systems |
| CN107447033A (en) * | 2017-09-15 | 2017-12-08 | 东南大学 | A kind of diagnosis of colorectal carcinoma biomarker and its application |
| CN112725456A (en) * | 2021-03-08 | 2021-04-30 | 温州医科大学 | Tumor marker related to colorectal cancer, application thereof and corresponding kit |
| CN113462775A (en) * | 2021-06-21 | 2021-10-01 | 华中农业大学 | Gene marker for colorectal cancer prognosis evaluation |
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