CN110714072A - Cancer prognosis evaluation kit based on LncRNA detection - Google Patents

Abstract

The invention discloses a cancer prognosis evaluation kit based on LncRNA detection, which comprises a detection reagent capable of detecting expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE; the present invention provides a kit that can efficiently predict the prognosis of cancer.

Description

Cancer prognosis evaluation kit based on LncRNA detection

Technical Field

The invention relates to the field of medicine and health, in particular to a cancer prognosis evaluation kit based on LncRNA detection.

Background

Prostate cancer refers to an epithelial malignancy that occurs in the prostate. The incidence rate of prostate cancer in the tumor registration area of China in 2012 is 9.92/10 ten thousand, which is the 6 th of the incidence rate of malignant tumors in men. The onset age is at a low level before 55 years, the onset gradually increases after 55 years, the incidence rate increases with the increase of the age, and the peak age is 70-80 years. The onset age of the patients with familial hereditary prostate cancer is earlier, and 43 percent of the patients with the age less than or equal to 55 years are in the patients.

Radical prostate cancer is one of the important means for treating localized prostate cancer. However, after radical prostate cancer surgery, there are still considerable numbers of patients with biochemical recurrence. When a patient is in the event of a biochemical recurrence, it is necessary to ascertain whether a clinical recurrence has occurred, and whether it is a local recurrence, regional lymph node metastasis or distant metastasis. In clinical work, assessment of biochemical recurrence after radical prostate cancer is often painful, resulting in lack of precision in its treatment.

Long non-coding RNA (LncRNA) is a non-coding RNA greater than 200 nucleotides in length. Researches show that LncRNA plays an important role in a plurality of life activities such as dose compensation effect, epigenetic regulation, cell cycle regulation, cell differentiation regulation and the like, and becomes a genetic research hotspot. The research finds that the expression or function abnormality of the long-chain non-coding RNA is closely related to the occurrence of human diseases, wherein the human diseases comprise cancer, degenerative neurological diseases and a plurality of serious human health-endangering major diseases, and the serious human health-endangering major diseases are particularly represented by the abnormality of the long-chain non-coding RNA in sequence and space structure, the abnormality of expression level, the abnormality of interaction with binding protein and the like.

A large number of genes associated with prostate Cancer have been discovered in prior studies, a paper published in Cancer Research and Clinical Oncology in 2018, and the classification of a10-gene molecular signature for predicting biochemical and Clinical oncologic assays in localized protocol Cancer, FRZB, LEF1, SDCBP, WNT2, ING3, ANK3, MEIS2, ANXA4, PLA2G7 and CHD5 expression of 10 genes in total is used for biochemical models for predicting recurrence of prostate Cancer. The paper also discloses the use of this model to predict prostate cancer outcome: AUC value 0.65, HR value 0.24, 95% CI: 0.09-0.59.

Although the prior art discloses a large number of genes associated with prostate cancer, the use of prostate cancer-associated genes for assessing prostate cancer in practical applications is limited. The prediction effect of the existing model is still to be enhanced, and a model capable of more accurately predicting the recurrence risk of the prostate cancer needs to be established from a large number of prostate cancer genes.

Disclosure of Invention

In view of the above technical problems, the present invention provides a cancer prognosis evaluation kit based on the detection of LncRNA.

In one aspect of the invention, a cancer prognosis evaluation kit based on LncRNA detection is disclosed, and the kit comprises detection reagents capable of detecting the expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE.

Preferably, the detection reagent is a polynucleotide primer or probe.

Preferably, the sequence of the polynucleotide primer is shown in SEQ ID NO. 1-10.

Preferably, wherein the cancer is selected from prostate cancer, preferably, the kit is for the assessment of prognosis after radical prostate cancer therapy.

Preferably, the cancer prognosis method of the present invention comprises the steps of:

a) detecting mRNA expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE in the sample;

b) assessing the risk of cancer recurrence for the patient based on the expression level data obtained in step a).

Preferably, the risk of cancer recurrence in the patient is assessed in step b) by:

(0.0766 × RP11-783K16.13 expression level) + (0.2443 × RP11-727F15.11 expression level) + (0.0042 × PRKAG2-AS1 expression level) + (2.8117 × AC013460.1 expression level) + (0.0162 × CRNDE expression level), wherein said expression level is the mRNA expression level detected in step a).

Preferably, the method for detecting the expression level of mRNA comprises: Affymetrix/Illumina chip detection, whole transcriptome shotgun sequencing, RT-PCR.

In another aspect of the invention, the use of the detection reagent for detecting the expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE in the preparation of a product for cancer prognosis.

Preferably, the invention can be used for early prediction of biochemical recurrence of prostate cancer, and preferably, the invention can be used for evaluation of prognosis after radical prostate cancer surgery.

Preferably, the cancer prognosis method comprises the steps of:

a) detecting mRNA expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE in the sample;

b) assessing the risk of cancer recurrence for the patient based on the expression level data obtained in step a).

Preferably, the risk of cancer recurrence in the patient is assessed in step b) by:

(0.0766 × RP11-783K16.13 expression level) + (0.2443 × RP11-727F15.11 expression level) + (0.0042 × PRKAG2-AS1 expression level) + (2.8117 × AC013460.1 expression level) + (0.0162 × CRNDE expression level), wherein said expression level is the mRNA expression level detected in step a).

Preferably, the method for detecting the expression level of mRNA comprises: Affymetrix/Illumina chip detection, whole transcriptome shotgun sequencing, RT-PCR.

Compared with the prior art, the technical scheme of the invention has the following advantages: the kit and the method can accurately predict the prognosis of the cancer, and particularly have higher prediction value, important basis and clinical value and wide application prospect for diagnosing the biochemical recurrence of the prostate cancer.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a 5 LncRNA-based gene decision model according to an embodiment of the present invention;

FIG. 2 is a graph of 343 patients ROC curves for an embodiment of the present invention;

FIG. 3 is a graph of biochemical recurrence for 343 patients in accordance with an embodiment of the present invention;

FIG. 4 is a graph of ROC for 114 patients according to an embodiment of the present invention;

FIG. 5 is a graph of biochemical recurrence in 114 patients according to an embodiment of the present invention.

Detailed Description

The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.

The terms "cancer" and "carcinoma" refer to or describe the physiological state in mammals that is typically characterized by abnormal or uncontrolled cell growth. Cancer and cancer pathology can be accompanied by, for example, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immune responses, neoplasia, precancerous lesions, malignancy, infiltration of surrounding or distant tissues or organs, such as lymph nodes, and the like. Prostate cancer is especially included.

The term "prognosis" refers to the prediction of a medical outcome (medical output), such as poor or good outcome (e.g., likelihood of long-term survival); negative prognosis or adverse outcome includes the prediction of relapse, disease progression (e.g., tumor growth or metastasis or drug resistance), or death. Positive prognosis or good outcome includes prediction of disease improvement (e.g., disease-free state), improvement (e.g., tumor regression), or stabilization.

The RP11-783K16.13 of The invention is The same as RP11-783K16.13 in RNA-seq data in The Cancer Genome Atlas (TCGA) database.

The RP11-727F15.11 of The invention is The same as RP11-727F15.11 in RNA-seq data in The Cancer Genome Atlas (TCGA) database.

The PRKAG2-AS1 of The present invention is The same AS PRKAG2-AS1 in The RNA-seq data in The Cancer Genome Atlas (TCGA) database.

The AC013460.1 of The present invention is The same as AC013460.1 in RNA-seq data in The Cancer Genome Atlas (TCGA) database.

Example 1 establishment of an assessment model for prostate cancer recurrence and/or survival outcome or prognosis

343 prostate Cancer patients were screened as a discovery set by searching from The Cancer Genome Atlas (TCGA) database and by trend matching analysis, from which corresponding RNA-seq data were extracted, and lncrnas closely related to biochemical recurrence of these patients were predicted using Lasso Cox regression model analysis. Finally, 5 LncRNA-based gene decision models were determined and constructed (see FIG. 1). And according to the risk of the decision-making model, obtaining a risk value by adopting the following scoring formula, and dividing the patient into a high-risk group and a low-risk group for biochemical recurrence after the prostate cancer operation.

Risk value ═ (0.0766 × RP11-783K16.13 expression level) + (0.2443 × RP11-727F15.11 expression level) + (0.0042 × PRKAG2-AS1 expression level) + (2.8117 × AC013460.1 expression level) + (0.0162 × CRNDE expression level).

And substituting the LncRNA expression value into the model to calculate a risk value, judging the group with high risk of biochemical recurrence of the prostate cancer if the risk value is more than 0.55, and judging the group with low risk of biochemical recurrence of the prostate cancer if the risk value is less than 0.55.

The risk values of 343 prostate cancer patients are respectively calculated, and an ROC curve is drawn by combining clinical data of the patients, as shown in figure 2, the AUC value of early stage biochemical recurrence diagnosis of the prostate cancer patients in two years is 0.722, and the AUC value derived from five-year biochemical recurrence is 0.704. And dividing the prostate cancer postoperative biochemical recurrence high-risk group and the prostate cancer postoperative biochemical recurrence low-risk group according to the risk score, drawing a biochemical recurrence curve, as shown in fig. 3, it can be seen that there is a significant difference between the two groups of postoperative biochemical recurrence, HR is 0.44, 95% CI: 0.27-0.72, and the C-index value is 0.63.

Example 2 kit for prognosis evaluation of cancer

The kit for cancer prognosis in this embodiment includes:

extracting a reagent Trizol from the total RNA;

chloroform (trichloromethane);

isoamyl alcohol;

absolute ethyl alcohol;

DEPC water (DD 1005);

ethylene Pyrophosphate (DEPC);

anti-rnase solution (RNaseZap);

a reverse transcription kit;

iQ SYBR Green Supermix；

polynucleotide primer with sequence of SEQ ID NO. 1-10.

After real-time quantitative PCR (qRT-PCR) detection of each LncRNA is performed by using the kit, the initial data result is expressed by Ct value (cycle threshold), namely the cycle number required by the fluorescent signal in each reaction system to reach the set threshold. The Ct value of each sample has a linear relation with the logarithm of the initial copy number of the sample, and the more the initial copy number is, the smaller the Ct value is. The expression level of each LncRNA was calculated by the Δ Δ CT method and normalized.

Substituting the LncRNA expression levels obtained in the steps into the following prostate cancer biochemical recurrence prediction model to calculate the risk value of the biochemical recurrence of the prostate cancer, judging the group with high risk of biochemical recurrence of the prostate cancer when the risk value is more than 0.55, and judging the group with low risk of biochemical recurrence of the prostate cancer when the risk value is less than 0.55.

Risk value ═ (0.0766 × RP11-783K16.13 expression level) + (0.2443 × RP11-727F15.11 expression level) + (0.0042 × PRKAG2-AS1 expression level) + (2.8117 × AC013460.1 expression level) + (0.0162 × CRNDE expression level).

EXAMPLE 3 preparation and pretreatment of sample RNA

(1) The gun head and the tweezers for the tissue RNA extraction experiment are soaked in DEPC water overnight and then sterilized at high temperature and high pressure, and a precooled 4 ℃ centrifuge, a laboratory bench, a liquid transfer gun, gloves and the like are wiped by RNase Zap.

(2) Tissue homogenate 50-100 mg of a tissue sample is taken, slightly cut by a sterile scalpel blade and placed in a 1.5mL EP tube, 500 μ L of Trizol reagent is added, an electric tissue grinder is used for fully homogenizing, and then 500 μ L of Trizol reagent is additionally added.

(3) 0.2mL of chloroform was added to each 1mL Trizol reagent homogenate and the EP tube cap was closed. Shake vigorously for 15 seconds and leave at room temperature for 3 min. Centrifuge at 12,000rpm for 15min at 4 ℃ (precool centrifuge in advance). After centrifugation, the mixed system will be divided into an upper colorless aqueous phase, a middle protein phase and a lower red phenol chloroform phase. The DNA is dissolved in chloroform and distributed in the lower layer, and the RNA is dissolved in water and distributed in the upper layer. The volume of the aqueous phase was about 60% of the volume of the Trizol reagent added during homogenization.

(4) RNA precipitation, the upper aqueous phase was transferred to a new EP tube. 0.5mL of isopropanol was added to each 1mL Trizol reagent homogenate sample. After mixing, the mixture was left at room temperature for 10min and centrifuged at 12,000rpm at 4 ℃ for 10 min. After centrifugation, a white precipitate will be visible on the bottom and side walls of the tube.

(5) RNA washing, gently discarding the supernatant, adding 1mL of 75% ethanol to each 1mL Trizol reagent homogenate sample, and washing the RNA pellet. Shaking, and centrifuging at 10,000rpm for 5min at 4 ℃.

(6) Dissolving the RNA, slightly removing the ethanol solution, and drying the RNA precipitate in the air for about 5-10 min. Note that the RNA pellet is never completely dried, otherwise the solubility of the RNA is greatly reduced. When dissolving RNA, adding a proper amount of DEPC water without RNase, repeatedly blowing and beating by using a pipette gun to ensure that the RNA is fully dissolved, and storing the RNA solution in a refrigerator at the temperature of-80 ℃.

(7) Determination and quality control of RNA concentration, use

The concentration and purity of the RNA solution were determined by ND-2000 UV spectrophotometer.

1) Before measurement, DEPC water for dissolving RNA is used for zero setting;

2) sucking 2 mu L of RNA sample by using a pipette and dripping the RNA sample on the surface of the measuring base;

3) after the base is lightly closed, the liquid drops can automatically form a liquid column between the upper base and the lower base, and various parameters of the RNA solution, including RNA concentration, purity and the like, can be displayed in a computer after the determination is finished. The ratio of A260/A280 is a commonly used parameter for evaluating RNA purity, and the ratio in the range of 1.8-2.1 is generally considered to indicate that the RNA purity is better.

4) After the detection is finished once, the liquid on the surfaces of the upper base and the lower base is slightly wiped off by using a piece of lens wiping paper, and then the detection of the next sample can be carried out

ii) agarose gel electrophoresis

1) Preparing glue: weighing 1g of agarose, adding the agarose into 100mL of 1 XTAE buffer, placing the agarose in a microwave oven, heating the agarose until the agarose is boiled, pouring a gel plate, taking down a comb after the agarose is gelled, placing the gel plate in an electrophoresis tank, and adding a proper amount of 1 XTAE buffer until the surface of the agarose completely covers the surface of the agarose.

2) Preparation of RNA samples: 3 mu g of RNA is taken, formaldehyde loading dye liquor with 3 times volume is added, EB is added into the formaldehyde loading dye liquor until the final concentration of EB is 10ug/mL, and the system is heated to 70 ℃ and incubated for 5min to denature the sample.

3) Electrophoresis: and (4) after sample loading, carrying out electrophoresis at a voltage of 5-6V/cm until the bromophenol blue indicator enters the gel for at least 2-3 cm.

4) Observation results in ultraviolet transmitted light: after denatured RNA electrophoresis, three bands of 28SrRNA, 18S rRNA and 5S rRNA were visualized on a gel imaging system. The intensity of the 28S rRNA band was observed to be about 2 times that of the 18S rRNA, and the 5S rRNA band was weak, indicating that no significant degradation of total RNA occurred.

Example 4 decision LncRNA assay

(1) Using the whole formula of gold1st Strand cDNA Synthesis SuperMix was used to synthesize first Strand cDNA, and the reaction system was prepared on ice as follows:

(2) real-time quantitative PCR reaction was performed using iQ SYBR Green Supermix from Bio-Rad. The final concentration of the primer is generally 0.2. mu.M, so that a better result can be obtained, and when the reaction performance is poor, the concentration of the primer can be adjusted within the range of 0.1-1.0. mu.M.

(3) Reaction system

The PCR primers are respectively: the amplification primers of RP11-783K16.13 are: 1 and 2 of SEQ ID NO;

the amplification primers of RP11-727F15.11 are: 3 and 4, respectively;

the amplification primers of PRKAG2-AS1 are: 5 and 6 SEQ ID NO;

amplification primers for AC013460.1 were: 7 and 8 are SEQ ID NO;

amplification primers for CRNDE were: SEQ ID NO 9 and SEQ ID NO 10.

(4) Reaction procedure

(5) Quantification of expression of each LncRNA

And (3) after the Real-time quantitative PCR reaction is finished, performing data analysis processing on the Real time PCR amplification curve and the melting curve, wherein the initial data result is expressed by a Ct value (cycle threshold), namely the number of cycles required for the fluorescence signal in each reaction system to reach a set threshold value. The Ct value of each sample has a linear relation with the logarithm of the initial copy number of the sample, and the more the initial copy number is, the smaller the Ct value is. The expression level of each LncRNA was calculated by the Δ Δ CT method.

Example 5 correlation analysis of biochemical recurrence model of prostate cancer and biochemical recurrence of prostate cancer

The kit is adopted for prediction, 114 verification sets of prostate cancer patients are verified, risk values of verification populations are calculated and divided into a high-risk group and a low-risk group through the method of the embodiment, ROC curves and biochemical recurrence curves are drawn, the ROC curves and the biochemical recurrence curves are shown in fig. 4 and fig. 5, and the result shows that the method has strong prediction value on the biochemical recurrence of the prostate cancer postoperative patients in the verification populations, ACU values of biochemical recurrence in two years and five years are 0.680 and 0.702 respectively, HR is 0.22, and 95% CI: 0.09-0.56, and the C-index value is 0.65.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

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Claims (10)

1. A cancer prognosis evaluation kit based on LncRNA detection, which is characterized by comprising detection reagents capable of detecting the expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE.

2. The kit of claim 1, wherein the detection reagent comprises a polynucleotide primer or probe.

3. The kit of claim 2, wherein the polynucleotide primer has a sequence as shown in SEQ ID NOs 1-10.

4. The kit of claim 1, wherein the cancer is selected from prostate cancer.

5. The kit of claim 1, wherein said determination of the prognosis of cancer comprises the steps of:

a) detecting mRNA expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE of the sample;

b) assessing the risk of cancer recurrence for the patient based on the expression level data obtained in step a).

6. The kit of claim 5, wherein in step b) the patient's risk of cancer recurrence is assessed by:

risk value ═ (0.0766 × RP11-783K16.13 expression level) + (0.2443 × RP11-727F15.11 expression level) + (0.0042 × PRKAG2-AS1 expression level) + (2.8117 × AC013460.1 expression level) + (0.0162 × CRNDE expression level).

7. Use of detection reagent for detecting expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE in preparation of products for cancer prognosis.

8. The use of claim 7, wherein the cancer is prostate cancer.

9. The use according to claim 7, wherein said determination of the prognosis of cancer comprises the steps of:

a) detecting mRNA expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE of the sample;

b) assessing the risk of cancer recurrence for the patient based on the expression level data obtained in step a).

10. The use of claim 9, wherein in step b) the patient is assessed for risk of cancer recurrence by:

risk value ═ (0.0766 × RP11-783K16.13 expression level) + (0.2443 × RP11-727F15.11 expression level) + (0.0042 × PRKAG2-AS1 expression level) + (2.8117 × AC013460.1 expression level) + (0.0162 × CRNDE expression level).

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