CN110714073B - Kit for cancer prognosis detection - Google Patents

Abstract

The invention discloses a cancer prognosis detection kit, which comprises a detection reagent capable of detecting expression levels of ASPA, TMEM38B, PTGR2, TXLNB and ADGRE 2; the present invention provides a kit that can efficiently predict the prognosis of cancer.

Description

Kit for cancer prognosis detection

Technical Field

The invention relates to the field of medicine and health, in particular to a detection kit for cancer prognosis.

Background

Renal cancer is a malignant tumor that originates in the epithelial system of the urinary tubule of the renal parenchyma, and is known by the academic term as renal cell carcinoma. The kidney cancer accounts for about 2-3% of adult malignant tumor and 80-90% of adult malignant tumor. The incidence of diseases of countries or regions in the world is different, the incidence of diseases of developed countries is higher than that of developing countries in general, urban regions are higher than that of rural regions, more men are than women, the proportion of male patients to female patients is about 2: 1, the incidence of diseases can be found in all age groups, and the high incidence age is 50-70 years old. According to the statistics of the disease and death data of tumors in the trial-and-error cities and counties in China by the health statistical information center of the national cancer prevention and treatment research office and the Ministry of health, the disease rate of the kidney cancer in China is on the rising trend year by year, and the disease rate becomes the 10 th of the disease rate of the male malignant tumors in China by 2008.

Surgical treatment of early stage renal cancer is often the treatment of choice and is currently recognized as a cure for renal cancer. The kidney cancer patients in early stage can adopt nephron-sparing operation or radical nephrectomy. However, there is no recommendable adjuvant treatment regimen for patients with early and middle stage renal cancer after surgery to effectively prevent recurrence or metastasis. When patients develop relapse and metastasis, novel targeting regimens can be used for first or second line treatment of patients with metastatic renal cancer. However, this part of patients has a much poorer prognosis. In clinical work, how to distinguish high-risk renal cancer patients from low-risk renal cancer patients so as to purposefully adopt adjuvant therapy is still a great problem.

Functional genomics, also often referred to as post-genomics, makes use of the information and products provided by structural genomics to develop and apply new experimental approaches to switch biological research from the study of a single gene or protein to the systematic study of multiple genes or proteins simultaneously by comprehensively analyzing the functions of the genes at the genomic or systemic level. This is a biological functional study of genome dynamics, which was carried out after the nucleotide sequence of the genome was clarified in a static state. The research content comprises gene function discovery, gene expression analysis and mutation detection. The functions of the gene include biological functions, such as phosphorylation modification of specific proteins as protein kinases; cytological functions, such as involvement in intercellular and intracellular signaling pathways; developmental functions, such as participation in morphogenesis. The adopted means comprises classical subtractive hybridization, differential screening, cDNA representation differential analysis, mRNA differential display and the like, but the technologies can not carry out comprehensive systematic analysis on genes, and new technologies come into play and comprise Systematic Analysis of Gene Expression (SAGE), cDNA microarray (cDNA microarray), DNA chip (DNA chip) and sequence tagged fragment display (suggested by Zhongke institute Zengbon philosophy, 20th ICG German Berlin) technology, microfluidic chip laboratories and the like.

A large number of genes related to kidney cancer prognosis have been found in the existing research, but most of the existing research only has a single data set, such as a paper in 2016 (Oncotarget), A four-gene signature prediction in clear-cell carcinoma, only has a data set of TCGA, and the existing research lacks verification and has a large bias. A Five-Gene signatures prediction in Patients with Kidney fresh Cell Carcinoma, which discloses that the expression of 5 genes, CKAP4, SLC40A1, OTOF, MAN2A2 and ISPD, is used as a model for predicting the Prognosis of Renal Clear Cell Carcinoma. The paper also discloses the use of this model to predict the outcome of renal cancer: the concentrated HR values were found to be 0.37, 95% CI: 0.30-0.46, HR value in test set 0.52, 95% CI: 0.43-0.62.

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

Disclosure of Invention

In order to solve the technical problems, the invention provides a polygene kit.

In one aspect of the invention, a polygene detection kit for cancer prognosis is disclosed, and the kit comprises a detection reagent capable of detecting the expression levels of ASPA, TMEM38B, PTGR2, TXLNB and ADGRE 2.

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 renal cancer, preferably, the kit is for assessment of prognosis after radical renal cancer surgery.

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

(a) detecting the mRNA expression levels of ASPA, TMEM38B, PTGR2, TXLNB, ADGRE2 in the sample;

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

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

(1.6631 × ASPA expression level) + (0.2026 × TMEM38B expression level) + (0.1667 × PTGR2 expression level) + (-2.3969 × TXLNB expression level) + (-2.0618 × ADGRE2 expression level), wherein said expression level is the mRNA expression value 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 ASPA, TMEM38B, PTGR2, TXLNB and ADGRE2 in the preparation of a product for cancer prognosis.

Preferably, the present invention can be used for prognosis prediction of kidney cancer, and preferably, the present invention can be used for the evaluation of prognosis after radical treatment of kidney cancer.

Preferably, the cancer prognosis method comprises the steps of:

(a) detecting the mRNA expression levels of ASPA, TMEM38B, PTGR2, TXLNB, ADGRE2 in the sample;

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

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

the risk value is (1.6631 × ASPA expression level) + (0.2026 × TMEM38B expression level) + (0.1667 × PTGR2 expression level) + (-2.3969 × TXLNB expression level) + (-2.0618 × ADGRE2 expression level), wherein said expression level is the mRNA expression value detected in step (a).

Preferably, the method for detecting the expression level of mRNA comprises: chip detection of Affymetrix/Illumina, sequencing by a complete transcriptome shotgun method and 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 consequences of the cancer, and particularly have higher prediction value, important basis and clinical value and wide application prospect for high-risk diagnosis of the kidney 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-gene based gene decision model according to an embodiment of the present invention;

FIG. 2 is a ROC graph of 80 patients according to an embodiment of the present invention;

FIG. 3 is a graph of 80 patient survival plots for an embodiment of the present invention;

FIG. 4 is a graph of the ROC of 515 patients according to an embodiment of the invention;

FIG. 5 is a graph of 515 patient survival curves for 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. Particularly included are kidney cancers.

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 ASPA of the invention is the same as the ASPA in SCI paper (Expression of aspartic enzyme (ASPA) and Canavan disease journal book: Gene 2012 Sep 01; 505 (2)).

TMEM38B described in the present invention is similar to TMEM38B in SCI paper (Absence of the ER catalysis Channel TMEM38B/TRIC-B dispersions across cellular Calcium and Dys rules Collagen Synthesis in Recessed Osteogenesis Imperfect, journal book PLoS Gene.201607; 12(7))

The PTGR2 described in the present invention is identical to PTGR2 in SCI paper (Targeting the 15-keto-PGE2-PTGR2axis models systems and subvalin experimental sessions, journal of Free Radic, biol, Med.2018 Feb 01; 115).

The TXLNB of the invention is TXLNB in the same SCI (RNA interference targeting CUG repeats in a mouse model of myonic dynophy. journal volume: mol. the. 2013 Feb; 21(2))

ADGRE2 of the present invention, is ADGRE2 in the SCI paper (Membrane-association of EMR2/ADGRE2-NTF is regulated by site-specific N-glycosylation, journal book, Sci Rep 2018Mar 14; 8 (1)).

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

80 renal cancer patients were screened as a discovery set from the subsidiary tumor hospital of the university of Compound Dane, corresponding gene expression data were extracted therefrom by RNA-seq, i.e., transcriptome sequencing technology, and genes closely related to biochemical recurrence of these patients were predicted using Lasso Cox regression model analysis. Finally, a gene decision model based on 5 genes was determined and constructed (see FIG. 1). And according to the risk of the decision model, a risk value is obtained by adopting the following scoring formula, and the patients are divided into a kidney cancer high-risk group and a kidney cancer low-risk group.

Risk value ═ (1.6631 × ASPA expression level) + (0.2026 × TMEM38B expression level) + (0.1667 × PTGR2 expression level) + (-2.3969 × TXLNB expression level) + (-2.0618 × ADGRE2 expression level.

And substituting the mRNA expression value of the decision gene into the model to calculate a risk value, judging the high-risk group of the kidney cancer if the risk value is more than 0, and judging the low-risk group of the kidney cancer if the risk value is less than 0.

The risk values of 80 renal 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 the five-year overall survival is 0.828. And according to the renal cancer high-risk group and the renal cancer low-risk group divided by the risk score, a survival curve is drawn, as shown in fig. 3, it can be seen that the two groups have significant difference in overall survival, HR is 0.05, 95% CI: 0.01-0.24.

Example 2 Multi-Gene kit for prognosis of renal cancer

The multi-gene kit for the prognosis of kidney cancer 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 gene 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 mRNA expression level of each gene was calculated by the Δ Δ CT method and normalized.

And substituting the mRNA expression level of each gene obtained in the steps into the following renal cancer biochemical recurrence prediction model to calculate the biochemical recurrence risk value of the renal cancer, judging that the renal cancer biochemical recurrence high-risk group is judged if the risk value is greater than 0, and judging that the renal cancer biochemical recurrence low-risk group is judged if the risk value is less than 0.

The risk value ═ (1.6631 × ASPA expression level) + (0.2026 × TMEM38B expression level) + (0.1667 × PTGR2 expression level) + (-2.3969 × TXLNB expression level) + (-2.0618 × ADGRE2 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 beforehand). After centrifugation, the mixed system will be divided into a colorless aqueous phase at the upper layer, a protein at the middle layer and a red phenol chloroform phase at the lower layer. 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 per 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 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, and washing the RNA pellet by adding 1mL of 75% ethanol to each 1mL Trizol reagent homogenate sample. Shaking, and centrifuging at 10,000rpm for 5min at 4 ℃.

(6) Dissolving the RNA, slightly discarding 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 will be 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 using

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 gun 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 28S rRNA, 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 Gene mRNA detection

(1) First Strand cDNA was synthesized using TransScript 1st Strand cDNA Synthesis SuperMix from all-purpose gold, 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 the ASPA gene are as follows: 1 and 2 of SEQ ID NO;

the amplification primers of the TMEM38B gene are as follows: 3 and 4, respectively;

the amplification primers of the PTGR2 gene are as follows: 5 and 6 of SEQ ID NO;

the TXLNB gene amplification primers are as follows: 7 and 8 are SEQ ID NO;

the amplification primers of the ADGRE2 gene are as follows: SEQ ID NO 9 and SEQ ID NO 10.

(4) Reaction procedure

(5) Quantification of mRNA expression of each Gene

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 mRNA of each gene was calculated by the Δ Δ CT method.

Example 5 analysis of the correlation between the renal cancer prognosis prediction model and the overall survival of renal cancer

The kit is adopted for prediction, kidney cancer patients in 515 verification sets are verified, risk values of verification population are calculated and divided into a high-risk group and a low-risk group by the method of the embodiment, an ROC curve and a biochemical recurrence curve are drawn, the result is shown in fig. 4 and fig. 5, the result shows that the method has strong prediction value on the overall prognosis of the kidney cancer patients in the verification population, the ACU value of the five-year overall survival is 0.671, the HR is 0.37, and the CI is 95 percent: 0.27-0.52.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations can be devised by those skilled in the art in light of the above teachings. 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 (8)

1. A kit for kidney cancer prognosis detection, which is characterized in that the kit comprises a detection reagent capable of detecting the expression level of ASPA, TMEM38B, PTGR2, TXLNB and ADGRE 2.

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 determination of the prognosis of renal cancer comprises the steps of:

a) detecting the mRNA expression level of ASPA, TMEM38B, PTGR2, TXLNB, ADGRE2 of the sample;

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

5. The kit of claim 4, wherein in step b) the patient is assessed for risk of recurrence of renal cancer by:

risk value ═ (1.6631 × ASPA expression level) + (0.2026 × TMEM38B expression level) + (0.1667 × PTGR2 expression level) + (-2.3969 × TXLNB expression level) + (-2.0618 × ADGRE2 expression level.

6. The application of the detection reagent for detecting the expression levels of ASPA, TMEM38B, PTGR2, TXLNB and ADGRE2 in the preparation of products for the prognosis of kidney cancer.

7. The use according to claim 6, wherein the determination of the prognosis of renal cancer comprises the steps of:

a) detecting the mRNA expression level of ASPA, TMEM38B, PTGR2, TXLNB, ADGRE2 of the sample;

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

8. The use according to claim 7, wherein in step b) the patient is assessed for risk of recurrence of renal cancer by:

risk value ═ (1.6631 × ASPA expression level) + (0.2026 × TMEM38B expression level) + (0.1667 × PTGR2 expression level) + (-2.3969 × TXLNB expression level) + (-2.0618 × ADGRE2 expression level.

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