CN110714072B - 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 detection reagents capable of detecting the expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1 and CRNDE; the invention provides a kit for efficiently predicting 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 the prostate cancer in the tumor registration area of China in 2012 is 9.92/10 ten thousand, and the incidence rate of the malignant tumor of the prostate is 6 th. The onset age is at a lower level before the age of 55 years, gradually increases after the age of 55 years, increases with the increase of the incidence rate with the increase of the age, and has a peak age of 70 to 80 years. Patients with familial hereditary prostate cancer have a slightly earlier onset age, and 43% of patients with ages less than or equal to 55 years old.

Radical treatment of prostate cancer is one of the important means for treating localized prostate cancer. However, after radical prostate cancer surgery, there are still significant patients who experience biochemical recurrence. When a patient is undergoing 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 surgery of prostate cancer is often headache-demanding, leading to lack of precision in its treatment.

Long non-coding RNAs (LncRNA) are non-coding RNAs greater than 200 nucleotides in length. Studies show that LncRNA plays an important role in a plurality of life activities such as dose compensation effect, epigenetic regulation and control, cell cycle regulation and control, cell differentiation regulation and the like, and becomes a genetic research hotspot. It has been found that expression or dysfunction of long-chain non-coding RNAs is closely related to the occurrence of human diseases, wherein various serious diseases seriously endangering human health, including cancer and degenerative neurological diseases, are specifically expressed by anomalies in sequence and spatial structure, anomalies in expression level, anomalies in interactions with binding proteins, and the like of long-chain non-coding RNAs.

A number of genes related to prostate cancer have been found in prior studies, a paper published in journal of cancer research and clinical oncology (Cancer Research and Clinical Oncology) 2018, and expression of 10 genes FRZB, LEF1, SDCBP, WNT2, ING3, ANK3, MEIS2, ANXA4, PLA2G7 and CHD5 has been disclosed as a model for predicting biochemical recurrence of prostate cancer. The paper also discloses the use of the model to predict the outcome of prostate cancer: AUC value is 0.65, hr value is 0.24, 95% ci:0.09-0.59.

Although the prior art discloses a large number of genes related to prostate cancer, the use of the genes related to prostate cancer 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

Aiming at the technical problems, the invention provides a cancer prognosis evaluation kit based on LncRNA detection.

In one aspect of the invention, a cancer prognosis evaluation kit based on detection of LncRNA is disclosed, the kit comprising 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 polynucleotide primers have the sequences shown in SEQ ID NOS.1-10.

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

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, CRNDE in the sample;

b) Assessing the risk of cancer recurrence in 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:

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), wherein said expression level is the mRNA expression value detected in step a).

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

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

Preferably, the invention is useful for the early prediction of biochemical recurrence of prostate cancer, preferably, the invention is useful for the assessment 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, CRNDE in the sample;

b) Assessing the risk of cancer recurrence in 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:

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), wherein said expression level is the mRNA expression value detected in step a).

Preferably, the method for detecting the mRNA expression level comprises: chip detection of Affymetrix/Illumina, 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 foundation 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 invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.

FIG. 1 is a model of a gene decision based on 5 LncRNAs of an embodiment of the present invention;

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

FIG. 3 is a graph of biochemical recurrence in 343 patients according to an embodiment of the present invention;

FIG. 4 is a graph of ROC for 114 patients in accordance with an embodiment of the invention;

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

Detailed Description

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

The terms "cancer" and "cancer" refer to or describe physiological states in mammals that are generally characterized by abnormal or uncontrolled cell growth. Cancers and cancer pathologies may be accompanied by, for example, metastasis, interference with normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, inhibition or exacerbation of inflammatory or immune responses, neoplasia, precancerous lesions, malignant tumors, infiltration of surrounding or distant tissues or organs such as lymph nodes, and the like. Especially included are prostate cancers.

The term "prognosis" refers to the prediction of a medical outcome (medical outcome), e.g., poor or good outcome (e.g., likelihood of long-term survival); negative prognosis or adverse outcome includes prognosis of recurrence, disease progression (e.g., tumor growth or metastasis or drug resistance), or prediction of death. Positive prognosis or good outcome includes disease improvement (e.g., no disease state), improvement (e.g., tumor regression), or stable prediction.

RP11-783K16.13 and The Cancer Genome Atlas (TCGA) database, RP11-783K16.13 in RNA-seq data.

RP11-727F15.11 and The Cancer Genome Atlas (TCGA) database, RP11-727F15.11 in RNA-seq data.

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

AC013460.1 and The Cancer Genome Atlas (TCGA) according to the present invention AC013460.1 in RNA-seq data in database.

Example 1 establishment of a model for evaluation of the recurrence and/or survival outcome or prognosis of prostate cancer

343 prostate cancer patients were screened as a discovery set by searching from the The Cancer Genome Atlas (TCGA) database and by trend matching analysis, from which corresponding RNA-seq data was extracted, and LncRNA closely related to biochemical recurrence of these patients was predicted using Lasso Cox regression model analysis. Finally, a gene decision model based on 5 LncRNAs was determined and constructed (see FIG. 1). And according to the risk of the decision model, the following scoring formula is adopted to obtain a risk value, and patients are divided into a high-risk group and a low-risk group for biochemical recurrence after 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 decision LncRNA expression value into the model to calculate a risk value, wherein the risk value is larger than 0.55, and the decision LncRNA expression value is judged to be a high-risk group for biochemical recurrence of the prostate cancer, and the risk value is smaller than 0.55, and the decision LncRNA expression value is judged to be a low-risk group for biochemical recurrence of the prostate cancer.

The risk values of 343 cases of prostate cancer patients are respectively calculated, and in combination with clinical data of the patients, an ROC curve is drawn, as shown in fig. 2, and it can be seen that the AUC value of early biochemical recurrence diagnosis of the prostate cancer patients is 0.722 within two years, and the AUC value of biochemical recurrence deduced to five years is 0.704. And according to the high-risk group and the low-risk group of the postoperative biochemical recurrence of the prostate cancer divided by the risk score, a biochemical recurrence curve is drawn, and as shown in fig. 3, a significant difference exists between the two groups of postoperative biochemical recurrence, hr=0.44, 95% ci:0.27-0.72, C-index value of 0.63.

Example 2 kit for prognosis evaluation of cancer

The kit for cancer prognosis in this embodiment includes:

total RNA extraction reagent Trizol;

chloroform (chloroform);

isoamyl alcohol;

absolute ethyl alcohol;

DEPC water (DD 1005);

ethylene Pyrophosphate (DEPC);

an anti-rnase solution (RNaseZap);

a reverse transcription kit;

iQ SYBR Green Supermix；

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

After the real-time quantitative PCR (qRT-PCR) detection of each LncRNA is carried out 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 relationship with the logarithm of the starting copy number of the sample, and the more the starting copy number is, the smaller the Ct value is. The expression level of each LncRNA was calculated by the DeltaCT method, and normalized.

And substituting the LncRNA expression levels obtained in the steps into the following prostate cancer biochemical recurrence prediction model to calculate a risk value of prostate cancer biochemical recurrence, wherein the risk value is greater than 0.55, and the risk value is less than 0.55, so that the risk value is judged as a high risk group of prostate cancer biochemical recurrence, and the risk value is less than 0.55, and the risk value is judged as a low risk group of prostate cancer biochemical recurrence.

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 tissue RNA extraction experiment is performed by immersing gun head and forceps in DEPC water overnight, sterilizing at high temperature under high pressure, pre-cooling in a centrifuge at 4deg.C, and wiping experiment table, pipette, glove, etc. with RNase resisting liquid (RNase Zap).

(2) Tissue homogenate 50-100 mg of tissue samples were taken, minced with a sterile scalpel blade, placed in a 1.5mL EP tube, added with 500 μl of Trizol reagent, homogenized thoroughly with an electric tissue grinder, and then supplemented with 500 μl of Trizol reagent.

(3) 0.2mL of chloroform was added to each 1mL of Trizol reagent homogenate sample, and the EP tube cover was closed. Shaking vigorously for 15 seconds, and standing at room temperature for 3min. Centrifuge at 12,000rpm at 4℃for 15min (pre-chill centrifuge). After centrifugation the mixture was separated into an upper colorless aqueous phase, a middle protein phase and a lower red phenol chloroform phase. DNA was dissolved in chloroform and distributed in the lower layer, and RNA was dissolved in the aqueous phase and distributed in the upper layer. The volume of the aqueous phase was about 60% of the volume of Trizol reagent added during homogenization.

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

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

(6) Dissolving RNA, slightly discarding ethanol solution, and drying RNA precipitate in air for about 5-10 min. Note that the RNA precipitate is not completely dried, otherwise the solubility of RNA will be greatly reduced. When the RNA is dissolved, a proper amount of DEPC water without RNase is added, and the mixture is repeatedly blown by a pipette to ensure that the RNA is fully dissolved, and then the RNA solution is stored in a refrigerator at-80 ℃.

(7) RNA concentration determination and quality control, using

ND-2000 ultraviolet spectrophotometer for determining concentration of RNA solutionAnd purity.

1) Zeroing with DEPC water for dissolving RNA before measurement;

2) Sucking 2 mu L of RNA sample by a pipette and dripping the RNA sample onto the surface of a measuring base;

3) After the base is gently closed, the liquid drop automatically forms 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 are displayed in a computer after the measurement is completed. The ratio A260/A280 is a commonly used parameter for assessing RNA purity, and it is generally believed that a ratio in the range of 1.8 to 2.1 indicates that RNA purity is good.

4) After one detection is completed, the liquid on the surfaces of the upper base and the lower base is gently wiped by using mirror wiping paper, so that the detection of the next sample can be carried out

ii) agarose gel electrophoresis

1) And (3) glue preparation: weighing 1g agarose, adding into 100mL 1 xTAE buffer, heating to boil in a microwave oven, pouring a gel plate, taking off a comb after the gel plate is gelled, placing the gel plate into an electrophoresis tank, and adding a proper amount of 1 xTAE buffer until the liquid level completely covers the gel surface.

2) Preparing an RNA sample: taking 3 mug of RNA, adding 3 times of formaldehyde loading dye liquor, adding EB into the formaldehyde loading dye liquor until the final concentration of the EB is 10ug/mL, and heating the system to 70 ℃ for incubation for 5min to denature the sample.

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

4) Observation under ultraviolet transmitted light: three bands of 28S rRNA, 18S rRNA and 5S rRNA were visualized on a gel imaging system after denaturing RNA electrophoresis. The 28S rRNA band was observed to be about 2 times stronger than the 18S rRNA band, and the 5S rRNA band was weaker, indicating no significant degradation of total RNA.

Example 4 decision LncRNA detection

(1) Using the whole gold company

1st Strand cDNA Synthesis SuperMix first strand cDNA was synthesized, and the reaction system was prepared on ice as follows:

(2) Real-time quantitative PCR reactions were performed using iQ SYBR Green Supermix from Bio-Rad. In general, a final primer concentration of 0.2. Mu.M gives a good result, and when the reaction performance is poor, the primer concentration can be adjusted within the range of 0.1 to 1.0. Mu.M.

(3) Reaction system

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

the amplification primers of RP11-727F15.11 are: SEQ ID NO. 3, SEQ ID NO. 4;

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

the amplification primers for AC013460.1 were: SEQ ID NO. 7, SEQ ID NO. 8;

the amplification primers of CRNDE are: SEQ ID NO. 9, SEQ ID NO. 10.

(4) Reaction procedure

(5) Quantification of expression of LncRNA

After the Real-time quantitative PCR reaction is finished, the Real-time PCR amplification curve and the melting curve are subjected to data analysis, and initial data results are expressed by Ct values (cycle threshold), namely the number of cycles required for the fluorescent signals in each reaction system to reach a set threshold. The Ct value of each sample has a linear relationship with the logarithm of the starting copy number of the sample, and the more the starting copy number is, the smaller the Ct value is. The expression level of each LncRNA was calculated using the ΔΔct method.

EXAMPLE 5 analysis of correlation between prostate cancer Biochemical recurrence model and prostate cancer Biochemical recurrence

By adopting the kit for prediction, the prostate cancer patients in 114 cases of verification sets are verified, the risk values of verification groups are calculated and divided into a high-risk group and a low-risk group by the method of the embodiment, ROC curves and biochemical recurrence curves are drawn, see fig. 4 and 5, and the results show that the method has strong prediction value for the biochemical recurrence of patients after the prostate cancer operation in the verification groups, and the two-year and five-year biochemical recurrence ACU values are 0.680 and 0.702 respectively, hr=0.22 and 95% CI:0.09-0.56, C-index value 0.65.

The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.

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Claims (6)

1. A prostate cancer prognosis evaluation kit based on detection of LncRNA, characterized in that the kit comprises detection reagents capable of detecting expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1, 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 set forth in SEQ ID NO. 1-10.

4. The kit of claim 1, wherein the determination of the prognosis of prostate cancer comprises the steps of:

a) Detecting the expression level of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1, CRNDE of the sample;

b) Assessing the risk of cancer recurrence in the patient based on the expression level data obtained in step a);

assessing the risk of cancer recurrence in the patient in step b) 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).

5. Use of a detection reagent for detecting expression levels of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1, CRNDE in the preparation of a product for prognosis of prostate cancer.

6. The use of claim 5, wherein the determination of the prognosis of prostate cancer comprises the steps of:

a) Detecting the expression level of RP11-783K16.13, RP11-727F15.11, PRKAG2-AS1, AC013460.1, CRNDE of the sample;

b) Assessing the risk of cancer recurrence in the patient based on the expression level data obtained in step a);

assessing the risk of cancer recurrence in the patient in step b) 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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