CN113444793B - Kit for detecting lung adenocarcinoma antioxidant stress pathway related gene mutation - Google Patents
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Abstract
The invention relates to a kit for detecting lung adenocarcinoma antioxidant stress pathway related gene mutation, which contains a detection reagent for detecting the following gene expression levels: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1. The specific gene mutation of the lung adenocarcinoma is predicted by utilizing the expression quantity of the Genome composition, and the prediction method is verified by a TCGA (the Cancer Genome atlas) database, experiments and a multimathematic database, so that the prediction method has the advantages of high accuracy and good specificity, and has good application prospect.
Description
Technical Field
The invention relates to the technical field of molecular diagnosis, in particular to a kit for detecting lung adenocarcinoma antioxidant stress pathway related gene mutation.
Background
Lung cancer is the most common malignant tumor, and the mortality rate of lung cancer is high in all tumor leaders worldwide. Lung adenocarcinoma is the most common subtype of lung cancer, accounting for over 40% of lung cancer cases. The occurrence of various targeted drugs aiming at lung adenocarcinoma obviously improves the prognosis of lung adenocarcinoma patients, so that the treatment of lung adenocarcinoma has entered the molecular era. How to accurately evaluate the molecular characteristics of the lung adenocarcinoma is of great significance to the accurate treatment of patients and the reduction of social burden.
The antioxidant stress pathway plays a great role in the occurrence and development of lung cancer, and although the pathway can resist the oxidative stress (ROS) of the external environment and the damage to the human body under normal physiological conditions, the excessive activation of the pathway in lung adenocarcinoma is of great significance in promoting the tumor progression, drug resistance and tumor heterogeneity formation. In lung adenocarcinoma, KEAP1, NFE2L2 and CUL3 genes have higher mutation rates and are highly correlated with antioxidant stress pathways. The mutation of the genes can lead to over activation of the oxidative stress state in tumor cells, and the high expression of the downstream genes of the pathway causes the metabolic reprogramming of tumors and the change of the internal immune microenvironment, thereby promoting the development of tumor, drug resistance and the formation of tumor heterogeneity, and leading to significantly poorer prognosis survival. The use of current targeted drug therapy against important mutant genes such as EGFR for lung adenocarcinoma has gained wide acceptance and benefit. Targeted drugs against antioxidant stress pathways related to KEAP1, NFE2L2, and CUL3 have also been in clinical trials, promising positive future prospects.
However, the detection of the related gene mutation of the antioxidant stress pathway of the lung adenocarcinoma is not a clinical routine item at present, and the detection alone is expensive.
Disclosure of Invention
Aiming at the current situations that the detection of the related gene mutation of the lung adenocarcinoma antioxidant stress pathway is poor in popularity and high in cost, the invention provides a composition and a kit for detecting the related gene mutation of the lung adenocarcinoma antioxidant stress pathway, and also provides an application of the composition and the kit for detecting the related gene mutation of the lung adenocarcinoma antioxidant stress pathway.
The purpose of the invention can be realized by the following technical scheme:
in a first aspect, the invention provides the use of a set of genetic compositions comprising RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, trppc 13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, 11-699a7.1, AL132671.1 for the preparation of a kit for assessing the presence or absence of mutations in the antioxidant stress pathway-associated genes KEAP1, NFE2L2, CUL3 of lung adenocarcinoma.
In a second aspect, the invention provides a composition for detecting the mutation of the lung adenocarcinoma antioxidant stress pathway related genes KEAP1, NFE2L2 and CUL3, wherein the composition consists of detection reagents for detecting the expression levels of the following genes: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1.
RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL132671.1, which are known in chromosomal location, the genome alignment information dataset used is GRGR38.2 (Release 22), and specific sequence information is generally downloaded from the Ensel website to the corresponding dataset mb.
TABLE 1 chromosomal regions of the genes
The research of the invention finds that the expression levels of genes such as RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1 and AL132671.1 can predict the mutation states of lung adenocarcinoma antioxidant stress pathway related genes CUL3, KEAP1 and NFE2L 2.
In a third aspect, the invention provides application of some detection reagents in preparing a kit for detecting the mutation of the lung adenocarcinoma antioxidant stress pathway related genes (KEAP1, NFE2L2 and CUL3), wherein the detection reagents comprise reagents for detecting the expression levels of the following genes: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1.
In one embodiment of the present invention, the detection reagent mainly comprises:
TRIzol (Life corporation, USA)
TruSeq SR Cluster kit 3-cBot-HS (Illumina, USA)
Chloroform (Shanghai Sheng Gong Co., Ltd.)
Isopropanol (Shanghai Sheng chemical company)
75% ethanol (Shanghai Sheng Gong Co., Ltd.)
LiCl (Shanghai Biotech).
In one embodiment of the invention, the detection reagent is used as a kit for realizing the only key component for evaluating whether the lung adenocarcinoma antioxidant stress pathway related gene is mutated.
In a fourth aspect, the invention provides a kit for detecting the mutation of the lung adenocarcinoma antioxidant stress pathway related genes (KEAP1, NFE2L2, CUL3), wherein the kit contains a detection reagent for detecting the expression level of the following genes: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1.
In one embodiment of the present invention, the kit further comprises an instruction manual, wherein the instruction manual describes the following formula:
Score＝(-0.1058×RP11-539L10.2)+(-0.1257×AKR1C2)+(-48.6069× RP11-572H4.1)+(-0.357×TRIM16L)+(0.0587×RARA)+(0.0244×SESN2) +(-19.895×RP5-827L5.2)+(-0.3798×CTD-2139B15.5)+(-0.4665×Metazoa_SRP) +(-0.2482×snoU13)+(-0.1185×RP11-545H22.1)+(-0.32×KRT8P30)+(-0.1907 ×TALDO1)+(-0.3089×TRAPPC13P1)+(-1.0802×GS1-388B5.2)+(-0.4102 ×RP11-267L5.1)+(-1.3136×TRAV11)+(-0.5642×RP11-699A7.1)+(-0.3232 ×AL132671.1)。
in the above formula, for example, RP11-539L10.2 represents the expression level of the gene RP11-539L10.2, and log2(FPKM +1) is used as the expression level of the gene RP11-539L10.2, wherein FPKM means Fragments Per Kilost Per Million plated reads, and KM FPcalculation formula is: FPKM ═ number of reads aligned per RNA)/(total number of samples aligned read x length of gene) x 10 6 . Other genes are expressed in the same manner.
In one embodiment of the present invention, the specification further describes: when the Score of the detection sample is smaller than-2.838, the sample is represented as the existence of the antioxidant stress pathway related gene mutation; when the Score of the detection sample is not less than or equal to-2.838, the sample is represented to have no antioxidant stress pathway related gene mutation.
In one embodiment of the present invention, the specification further describes: when the kit is used, the detection sample is a fresh tissue tumor sample.
When the kit provided by the invention is used for evaluating whether the lung adenocarcinoma antioxidant stress pathway related genes KEAP1, NFE2L2 and CUL3 are mutated or not, the cutoff value of the lung adenocarcinoma is-2.838.
In one embodiment of the invention, the kit further comprises instructions describing a method for evaluating mutations of lung adenocarcinoma antioxidant stress pathway related genes KEAP1, NFE2L2 and CUL3, comprising the following steps:
(1) detecting the following gene expression levels of the sample: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1;
(2) and (3) calculating: score (-0.1058 × RP11-539L10.2) + (-0.1257 × AKR1C2) + (-48.6069 × RP11-572H4.1) + (-0.357 × TRIM16L) + (0.0587 × RARA) + (0.0244 × SESN2) + (-19.895 × RP5-827L5.2) + (-5 × CTD-2139B15.5) + (-5 × Metazoa _ SRP) + (-5 × snoU 5) + (-5 × RP 5-545H 22.1) + (-0.32 × KRT8P 5) + (-5 × do 5) + (-0.3089 × trap C13P 5) + (-5 × 5 t 5-5.2B 5.2) + (-5 × KRT8P 5) + (-5 × talc 5 × 5) + (-5 × 5) + (5 × 5L 5) + (5 × 5L 3 × 5) + (5L 5) + (5);
(3) and (3) judging: when Score is < -2.838, the sample represents the existence of the antioxidant stress pathway related gene mutation; when the Score is more than or equal to-2.838, the sample is represented to have no gene mutation related to the antioxidant stress pathway.
The invention also provides a method for detecting whether the lung adenocarcinoma antioxidant stress pathway related gene is mutated or not based on a next-generation sequencing method, which comprises the following steps:
(1) RNA extraction is carried out on tumor sample tissues;
(2) construction of RNA library: adding a 3-end connector and a 5-end connector based on the extracted RNA of the tumor sample tissue, and then carrying out RT-PCR enrichment to obtain an RNA library;
(3) and (3) cluster generation: adding NaOH and TrisCl into an RNA library, uniformly mixing, adding a hybrid buffer, and starting cluster generation by using a CBOT cluster generator;
(4) sequencing by Illumina Hiseq2000, and converting the sequencing result raw data into a Fastq format;
(5) and (3) data analysis:
(5.1) removing primer and adaptor sequences from the original Fastq file data, checking the quality and length of the base of the sequencing fragment, and screening the sequencing fragment with reliable quality;
(5.2) comparing the sequencing results with each database and filtering to identify RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL132671.1 in the results;
(5.3) carrying out expression quantity statistics according to the identified RNA, wherein the RNA expression quantity is calculated by adopting FPKM (fragments Per Kilobase Million) calculation measurement indexes, and the FPKM calculation formula is as follows: FPKM ═ number of reads aligned per RNA)/(total number of samples aligned read x length of gene) × 10 6 ；
(5.4) obtaining the value of FPKM and converting the data into log2(FPKM +1) as a value for calculating the expression amount of score as a subsequent formula;
(5.5) calculating: score (-0.1058 × RP11-539L10.2) + (-0.1257 × AKR1C2) + (-48.6069 × RP11-572H4.1) + (-0.357 × TRIM16L) + (0.0587 × RARA) + (0.0244 × SESN2) + (-19.895 × RP5-827L5.2) + (-5 × CTD-2139B15.5) + (-5 × Metazoa _ SRP) + (-5 × snoU 5) + (-5 × RP 5-545H 22.1) + (-0.32 × KRT8P 5) + (-5 × do 5) + (-0.3089 × trap C13P 5) + (-5 × 5 t 5-5.2B 5.2) + (-5 × KRT8P 5) + (-5 × talc 5 × 5) + (-5 × 5) + (5 × 5L 5) + (5 × 5L 3 × 5) + (5L 5) + (5);
(5.6) judgment: when Score is < -2.838, the sample represents that the antioxidant stress pathway related gene mutation exists; when the Score is more than or equal to-2.838, the sample is represented to have no gene mutation related to the antioxidant stress pathway.
The invention also provides a method for detecting whether the lung adenocarcinoma antioxidant stress pathway related gene is mutated or not based on a next-generation sequencing method, which comprises the following detailed steps:
firstly, RNA extraction:
(1) and (3) homogenizing treatment: tumor sample tissues were ground in liquid nitrogen, 1ml of TRIzol was added to 50-100mg of the tissues, and homogenized with a homogenizer.
(2) The homogenate was left at room temperature (15-30 ℃) for 5 minutes to completely separate the nucleic acid-protein complex.
(3) Centrifuge at 12000g for 5 minutes at 4 ℃ and collect the supernatant.
(4) 0.2ml of chloroform was added to 1ml of TRIzol, and the mixture was vigorously shaken for 15 seconds and left at room temperature for 3 minutes.
(5) Centrifuge at 12000g for 15 min at 4 ℃.
(6) The aqueous phase was transferred to a new tube and the RNA in the aqueous phase was precipitated with isopropanol. 0.5ml of isopropyl alcohol was added to 1ml of TRIzol, and the mixture was left at low temperature for 30 minutes.
(7) After centrifugation at 12000 Xg for 10 minutes at 4 ℃ the RNA precipitate was visualized and the supernatant was removed.
(8) The RNA pellet was washed with 75% ethanol. 1ml of 75% ethanol was added. Centrifugation at 8000g for 5 minutes at 4 ℃ removed the supernatant and the excess ethanol blotted.
(9) 50-100. mu.l of RNase-free water was added and dissolved by pipetting several times with a pipette tip.
(10) Half the volume of 8M LiCl solution was added, mixed well and left on ice for 1 hour.
(11) 13000g was centrifuged at 4 ℃ for 15 minutes to visualize the RNA precipitation and the supernatant was removed.
(12) The RNA pellet was washed with 75% ethanol. 1ml of 75% ethanol was added. Centrifugation at 8000g for 5 minutes at 4 ℃ removed the supernatant and the excess ethanol blotted.
(13) 20-50. mu.l of RNase-free water was added and dissolved by pipetting several times with a pipette tip.
(14) The Nanodrop measures the RNA concentration.
Secondly, constructing an RNA library: (completed with TruSeq RNA Sample Prep Kitv2 kit)
(1) Adding a 3-end connector: 1ug total RNA was taken, adjusted to 5ul in water, added 1ul of 3' adapter and mixed well in PCR instrument for 2 min at 70 ℃ and immediately put on ice. Add 1ul RNase Inhibitor and 1ul T4 RNA strain 2, mix well. PCR apparatus 28 ℃ for 1 hour. Adding 1ul STP, mixing well, and heating at 28 deg.C for 15 min
(2) Adding 5 end connectors: 1.1ul of RNA 5' adapter was taken. The PCR instrument was set at 70 ℃ for 2 minutes and immediately placed on ice. Add 1.1ul 10mM dATP and mix well. Add 1.1ul of T4 RNA ligase and mix well. Add 3ul of the mix to the tube in step 1. After 1 hour at 28 ℃ the mixture was kept on ice.
(3) RT-PCR enrichment: dilute dNTPs, add 0.5ul 25mM dNTP mix to 25ul water, mix well. 6ul of the adaptor-ligated RNA was added to 1ul of RTP and mixed well. The PCR instrument was set at 70 ℃ for 2 minutes and immediately placed on ice. 2ul of 5 Xfirst strand buffer, 0.5ul of 12.5mM dNTPs, 1ul of 100mM DTT, 1ul of RNase Inhibitor, 1ul of SSII were added. After mixing well, the mixture was subjected to PCR at 50 ℃ for 1 hour. 12.5ul RT product was mixed well with 8.5ul water, 25ul PML, 2ul RP1 and 2ul RPIX (where PCRmix is PML, RNAPPRImer is RP1, RNAPPRIMERIndex is RPX, all from Illumina RNA library construction kit). 30 seconds at 98 ℃ and 11 cycles (10 seconds at 98 ℃, 30 seconds at 60 ℃ and 10 seconds at 72 ℃) for 5 minutes at 72 ℃.
Thirdly, cluster generation: (completed with TruSeq SR Cluster kit 3-cBot-HS kit)
(1) 4ul of the 10nM RNA library was taken, 1ul of 2N NaOH and 15ul of TrisCl were added, mixed well and left at room temperature for 5 minutes.
(2) 6ul of the above solution was taken and 994ul of cold hybridization buffer was added. 140ul of the clusters were collected and placed in 8-tube tubes, and cluster formation was started using a CBOT cluster formation apparatus.
Fourthly, Illumina Hiseq2000 sequencing:
(1) hiseq2000 was programmed, and the sequencing results raw data were converted to Fastq format.
Fifthly, data analysis:
(1) primer and adaptor sequences are removed from the original Fastq file data, the quality and length of the base of the sequencing fragment are checked, and the sequencing fragment with reliable quality is screened.
(2) The sequencing results were filtered against each database to identify the RNAs in the results (i.e., RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11 RP11-699A7.1, AL132671.1 as described earlier in this invention)
(3) Performing expression quantity statistics according to the identified RNA, wherein the RNA expression quantity is calculated by adopting FPKM (fragments Per Kilost Million) calculation measurement index, and the FPKM calculation formula is as follows: FPKM ═ number of reads aligned per RNA)/(total number of samples aligned read x length of gene) × 10 6 。
(4) Values for FPKM were obtained and the data were converted to log2(FPKM +1) as a value for the expression of score calculated as a subsequent formula.
(5) And (3) calculating: score (-0.1058 × RP11-539L10.2) + (-0.1257 × AKR1C2) + (-48.6069 × RP11-572H4.1) + (-0.357 × TRIM16L) + (0.0587 × RARA) + (0.0244 × SESN2) + (-19.895 × RP5-827L5.2) + (-5 × CTD-2139B15.5) + (-5 × Metazoa _ SRP) + (-5 × snoU 5) + (-5 × RP 5-545H 22.1) + (-0.32 × KRT8P 5) + (-5 × do 5) + (-0.3089 × trap C13P 5) + (-5 × 5 t 5-5.2B 5.2) + (-5 × KRT8P 5) + (-5 × talc 5 × 5) + (-5 × 5) + (5 × 5L 5) + (5 × 5L 3 × 5) + (5L 5) + (5);
(6) and (3) judging: when Score is < -2.838, the sample represents the existence of the antioxidant stress pathway related gene mutation; when the Score is larger than or equal to-2.838, the sample is represented to have no antioxidant stress pathway related gene mutation.
Compared with the prior art, the invention has the advantages that:
1. the invention firstly proposes the idea that the genome composition can be used for evaluating whether the lung adenocarcinoma has the mutation of the antioxidant stress pathway related genes (KEAP1, NFE2L2 and CUL3), can effectively guide the individualized treatment of the lung adenocarcinoma patients, improves the clinical benefit, and simultaneously avoids the unnecessary waste of medical resources.
2. The lung adenocarcinoma antioxidant stress pathway related gene mutation belongs to an unconventional detection project clinically at present, and the cost for singly detecting the gene mutation is high. The invention can judge whether the lung adenocarcinoma patients have the gene mutation or not by the RNA sequencing technology which is widely applied clinically, and has the advantages of economy, high accuracy, good sensitivity and good specificity.
3. The invention screens genes related to the antioxidant stress pathway by RNA sequencing, LASSO and binary Logistic regression, constructs Score, obtains a cut-off value of a corresponding Score in the lung adenocarcinoma by ROC curve analysis, and can be used for prediction of the mutation of the antioxidant stress pathway related genes (KEAP1, NFE2L2 and CUL3) in the lung adenocarcinoma. The specific gene mutation of the lung adenocarcinoma is predicted by utilizing the expression quantity of the Genome composition, and the prediction method is verified by a TCGA (the Cancer Genome atlas) database, experiments and a multimathematic database, so that the prediction method has the advantages of high accuracy and good specificity and has good application prospect. The kit for detecting the lung adenocarcinoma antioxidant stress pathway related gene mutation is not reported at present.
Drawings
FIG. 1 is a Receiver Operating Characteristic (ROC) curve of the predictive model of the invention in TCGA data.
FIG. 2 is a plot of mean square error versus log (. lamda.) in LASSO regression.
FIG. 3 is a comparison of ROC curves for two lambda values for LASSO regression screening features.
Detailed Description
The invention provides a kit for detecting the mutation of lung adenocarcinoma antioxidant stress pathway related genes (KEAP1, NFE2L2 and CUL3), which comprises a detection reagent for detecting the expression quantity of the following genes: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1.
In one embodiment of the present invention, the kit further comprises an instruction manual, wherein the instruction manual describes the following formula:
Score＝(-0.1058×RP11-539L10.2)+(-0.1257×AKR1C2)+(-48.6069× RP11-572H4.1)+(-0.357×TRIM16L)+(0.0587×RARA)+(0.0244×SESN2) +(-19.895×RP5-827L5.2)+(-0.3798×CTD-2139B15.5)+(-0.4665×Metazoa_SRP) +(-0.2482×snoU13)+(-0.1185×RP11-545H22.1)+(-0.32×KRT8P30)+(-0.1907 ×TALDO1)+(-0.3089×TRAPPC13P1)+(-1.0802×GS1-388B5.2)+(-0.4102 ×RP11-267L5.1)+(-1.3136×TRAV11)+(-0.5642×RP11-699A7.1)+(-0.3232 ×AL132671.1)。
in the above formula, for example, RP11-539L10.2 represents the expression level of the gene RP11-539L10.2, and log2(FPKM +1) represents the expression level of the gene RP11-539L10.2, wherein FPKM means Fragments Per Kilobase Per Million mapped reads, and the calculation formula of FPKM is: FPKM ═ number of reads aligned per RNA)/(total number of samples aligned read x length of gene) × 10 6 . Other genes are expressed in the same manner.
In one embodiment of the present invention, the specification further describes: when the Score of the detection sample is smaller than-2.838, the sample is represented as the existence of the antioxidant stress pathway related gene mutation; when the Score of the detection sample is not less than or equal to-2.838, the sample is represented to have no antioxidant stress pathway related gene mutation.
In one embodiment of the present invention, the specification further describes: when the kit is used, the detection sample is a fresh tissue tumor sample.
The detection reagents described in the following examples are mainly:
TRIzol (Life corporation, USA)
TruSeq SR Cluster kit 3-cBot-HS (Illumina, USA)
Chloroform (Shanghai Sheng Gong Co., Ltd.)
Isopropanol (Shanghai Sheng chemical company)
75% ethanol (Shanghai Sheng Gong Co., Ltd.)
LiCl (Shanghai Biotech).
The invention is described in detail below with reference to the figures and specific embodiments.
Example 1 screening of Gene set and Effect verification
(I) construction of grading model of lung adenocarcinoma antioxidant stress pathway related gene mutation
1. Method of producing a composite material
First 493 lung adenocarcinoma sample data were obtained from the TCGA database, samples were classified into a mutation group (n 111) and a wild group (n 382) according to the presence or absence of mutations in genes associated with cell cycle progression (KEAP1, NFE2L2, CUL3), and by performing LASSO regression analysis on the expression amount data of all genes in the whole RNA expression profile of 493 samples in the form of [ log2(FPKM +1) ], a curve (fig. 2) was obtained in which the binomial deviation in the LASSO regression of fig. 2 varies with log (λ), and the prediction effect of the obtained model of the characteristic variable was different for different λ values, as shown in fig. 3, and when the model of the minimum mean square difference in fig. 2 was selected, the AUC value was larger and was 0.936. Therefore, 19 characteristic variables which enable the whole model to obtain the minimum mean square error are selected to construct a prediction model, that is, 19 genes which are obviously related to the mutation state of the antioxidant stress pathway related gene are obtained, and a LASSO model with a score value is constructed according to the expression quantity and the determined coefficient value, and finally the 19 genes are obtained: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1.
RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL132671.1, which are known in chromosomal location, the genome alignment information dataset used is GRGR38.2 (Release 22), and specific sequence information is generally downloaded from the Ensel website to the corresponding dataset mb.
TABLE 1 chromosomal regions of the genes
Then, after determining the lambda value and the characteristic variable, the coefficient of each variable (namely the gene expression quantity) in the model is also determined, thus, the Score is determined by the following formula of Score (-0.1058 × RP11-539L10.2) + (-0.1257 × AKR1C2) + (-48.6069 × RP11-572H4.1) + (-0.357 × TRIM16L) + (0.0587 × RARA) + (0.0244 × SESN2) + (-19.895 × RP5-827L5.2) + (-5 × CTD-2139B15.5) + (-5 × Metazoa _ SRP) + (-5 × snoU 5) + (-5 × 5-545H 22.1) + (-0.32 × KRT8P 5) + (-5 × TAL 5) + (-3672(-5) + (-5. 20.3672) + (-3672.3089 × TRARP 5) + (-3672.3672 × 5) + (-3672.3672.3.3 × KR 5) + (-3672.3.3.3 × 5) + (-5).
In the above formula, for example, RP11-539L10.2 represents the expression level of the gene RP11-539L10.2, and log2(FPKM +1) represents the expression level of the gene RP11-539L10.2, wherein FPKM means Fragments Per Kilobase Per Million mapped reads, and the calculation formula of FPKM is: FPKM ═ number of reads aligned per RNA)/(total number of samples aligned read x length of gene) × 10 6 。
When the Score of the sample is detected to be Score of < -2.838, the sample represents that the anti-oxidative stress pathway related gene mutation exists; when the Score of the detection sample is not less than or equal to-2.838, the sample is represented to have no antioxidant stress pathway related gene mutation.
And finally, calculating the score of each sample based on the scoring model, and establishing a Logistic regression model by using the score as a prediction variable to evaluate the prediction capability of the lung adenocarcinoma antioxidant stress pathway mutation. To determine the optimal cut-off value for score, the optimal threshold point, i.e., cut-off value, is obtained by ROC curve, the cut-off value is-2.838, and the specific cut-off value is determined as shown in fig. 1. The TCGA samples were divided into two groups based on cutoff values, above which the cut-off values were assigned to the mutant group and below which the cut-off values were assigned to the wild group.
The experimental results show that: the sensitivity of the prediction model is 0.908, and the specificity is 0.901.
In this embodiment, the method for detecting whether the lung adenocarcinoma antioxidant stress pathway related gene is mutated or not based on the next-generation sequencing method includes the following detailed steps:
firstly, RNA extraction:
(1) and (3) homogenizing treatment: tumor sample tissues were ground in liquid nitrogen, 1ml of TRIzol was added to 50-100mg of the tissues, and homogenized with a homogenizer.
(2) The homogenate was allowed to stand at room temperature (15-30 ℃) for 5 minutes to completely separate the nucleic acid-protein complex.
(3) Centrifuge at 12000g for 5 minutes at 4 ℃ and collect the supernatant.
(4) 0.2ml of chloroform was added to 1ml of TRIzol, and the mixture was vigorously shaken for 15 seconds and left at room temperature for 3 minutes.
(5) Centrifuge at 12000g for 15 min at 4 ℃.
(6) The aqueous phase was transferred to a new tube and the RNA in the aqueous phase was precipitated with isopropanol. 0.5ml of isopropyl alcohol was added to 1ml of TRIzol, and the mixture was left at low temperature for 30 minutes.
(7) After centrifugation at 12000 Xg for 10 minutes at 4 ℃ the RNA precipitate was visualized and the supernatant was removed.
(8) The RNA pellet was washed with 75% ethanol. 1ml of 75% ethanol was added. Centrifuge at 8000g for 5 minutes at 4 deg.C, discard the supernatant, and blot off excess ethanol.
(9) 50-100. mu.l of RNase-free water was added and dissolved by pipetting several times with a pipette tip.
(10) Half the volume of 8M LiCl solution was added, mixed well and left on ice for 1 hour.
(11) 13000g was centrifuged at 4 ℃ for 15 minutes, RNA precipitation was visualized, and the supernatant was removed.
(12) The RNA pellet was washed with 75% ethanol. 1ml of 75% ethanol was added. Centrifugation at 8000g for 5 minutes at 4 ℃ removed the supernatant and the excess ethanol blotted.
(13) 20-50. mu.l of RNase-free water was added and dissolved by pipetting several times with a pipette tip.
(14) Nanodrop measures RNA concentration.
Secondly, constructing an RNA library: (completed with TruSeq RNA Sample Prep Kitv2 kit)
(1) Adding a 3-end connector: 1ug total RNA was taken, adjusted to 5ul in water, added 1ul of 3' adapter and mixed well in PCR instrument for 2 min at 70 ℃ and immediately put on ice. Add 1ul RNase Inhibitor and 1ul T4 RNA strain 2, mix well. PCR instrument for 1 hour at 28 ℃. Adding 1ul STP, mixing well, and keeping at 28 deg.C for 15 min
(2) Adding 5 ends of connectors: 1.1ul of RNA 5' adapter was taken. The PCR instrument was set on ice at 70 ℃ for 2 minutes. Add 1.1ul 10mM dATP and mix well. Add 1.1ul of T4 RNA ligase and mix well. Add 3ul of the mix to the tube in step 1. After 1 hour at 28 ℃ the mixture was kept on ice.
(3) RT-PCR enrichment: the dNTPs were diluted, 0.5ul of 25mM dNTP mix was added with 25ul of water, and mixed well. 6ul of the adaptor-ligated RNA was added to 1ul of RTP and mixed well. The PCR instrument was set at 70 ℃ for 2 minutes and immediately placed on ice. 2ul of 5 Xfirst strand buffer, 0.5ul of 12.5mM dNTPs, 1ul of 100mM DTT, 1ul of RNase Inhibitor, 1ul of SSII were added. After mixing well, the mixture was subjected to PCR at 50 ℃ for 1 hour. 12.5ul RT product was taken, added with 8.5ul water, 25ul PML, 2ul RP1 and 2ul RPIX (wherein, PCRmix is PML, RNAPCPrimer is RP1, RNAPCPrimerIndex is RPX, all from Illumina RNA library construction kit), and mixed well. 30 seconds at 98 ℃ and 11 cycles (10 seconds at 98 ℃, 30 seconds at 60 ℃, 10 seconds at 72 ℃) for 5 minutes at 72 ℃.
Thirdly, cluster generation: (completed with TruSeq SR Cluster kit 3-cBot-HS kit)
(1) 4ul of the 10nM RNA library was taken, 1ul of 2N NaOH and 15ul of TrisCl were added, mixed well and left at room temperature for 5 minutes.
(2) Add 994ul of cold hybridization buffer to 6ul of the above solution. 140ul of the clusters were collected and placed in 8-tube tubes, and cluster formation was started using a CBOT cluster formation apparatus.
Fourthly, Illumina Hiseq2000 sequencing:
(1) hiseq2000 was programmed, and the sequencing results raw data were converted to Fastq format.
Fifthly, data analysis:
(1) and removing primer and adapter sequences from the original Fastq file data, checking the quality and length of the base of the sequencing fragment, and screening the sequencing fragment with reliable quality.
(2) The sequencing results were filtered against each database to identify the RNAs in the results (i.e., RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11 RP11-699A7.1, AL132671.1 as described earlier in this invention)
(3) Performing expression quantity statistics according to the identified RNA, wherein the RNA expression quantity calculation adopts FPKM (fragments Per Kilobase Million) to calculate a measurement index, and the FPKM formula is (number of reads aligned to each RNA)/(total sample alignment read number multiplied by length of gene) × 10 6 。
(4) Values for FPKM were obtained and the data were converted to log2(FPKM +1) as a value for the expression of score calculated as a subsequent formula.
(5) And (3) calculating: score (-0.1058 × RP11-539L10.2) + (-0.1257 × AKR1C2) + (-48.6069 × RP11-572H4.1) + (-0.357 × TRIM16L) + (0.0587 × RARA) + (0.0244 × SESN2) + (-19.895 × RP5-827L5.2) + (-5 × CTD-2139B15.5) + (-5 × Metazoa _ SRP) + (-5 × snoU 5) + (-5 × RP 5-545H 22.1) + (-0.32 × KRT8P 5) + (-5 × do 5) + (-0.3089 × trap C13P 5) + (-5 × 5 t 5-5.2B 5.2) + (-5 × KRT8P 5) + (-5 × talc 5 × 5) + (-5 × 5) + (5 × 5L 5) + (5 × 5L 3 × 5) + (5L 5) + (5);
(6) and (3) judging: when Score is < -2.838, the sample represents the existence of the antioxidant stress pathway related gene mutation; when the Score is more than or equal to-2.838, the sample is represented to have no gene mutation related to the antioxidant stress pathway.
(II) Effect verification
37 lung adenocarcinoma samples collected from thoracic surgery of Zhongshan Hospital, affiliated to the university of Fudan were RNA sequenced and tested for mutations in genes associated with antioxidant stress pathway (KEAP1, NFE2L2, CUL3), RNA expression data were introduced into the prediction model to obtain Score for each sample, with mutants scoring below cut-off-2.838 and wildtype scoring above cut-off-2.838.
The specific model prediction cases are as follows: the sensitivity and specificity are shown in table 1 below:
| positive mutation in clinical test | Clinical detection of mutation negativity | |
| Prediction of mutation positivity | 9 | 4 |
| Predicting mutation negativity | 1 | 23 |
The above results show that: the sensitivity of the prediction model was 0.900, and the specificity was 0.851.
The embodiments described above are described to facilitate an understanding and use of the invention by those skilled in the art. It will be readily apparent to those skilled in the art that various modifications to these embodiments may be made, and the generic principles described herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make modifications and alterations without departing from the scope of the present invention.
Claims (9)
1. The application of the detection reagent in preparing the kit for detecting the gene mutation related to the oxidative stress resistance pathway of the lung adenocarcinoma is characterized in that the detection reagent consists of reagents for detecting the expression quantity of the following genes: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1; the antioxidant stress pathway related genes are KEAP1, NFE2L2 and CUL 3.
2. The kit for detecting the gene mutation related to the oxidative stress resistance pathway of the lung adenocarcinoma is characterized by comprising a detection reagent for detecting the expression quantity of the following genes: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1; the antioxidant stress pathway related genes are KEAP1, NFE2L2 and CUL 3.
3. The kit for detecting the mutation of the gene related to the antioxidant stress pathway of lung adenocarcinoma as claimed in claim 2, wherein the detection reagent is used as the only key component for evaluating whether the gene related to the antioxidant stress pathway of lung adenocarcinoma is mutated or not.
4. The kit for detecting the mutation of the gene related to the oxidative stress resistance pathway of lung adenocarcinoma as claimed in claim 2, further comprising an instruction manual, wherein the instruction manual records the following formula:
Score＝(-0.1058×RP11-539L10.2)+(-0.1257×AKR1C2)+(-48.6069×RP11-572H4.1)+(-0.357×TRIM16L)+(0.0587×RARA)+(0.0244×SESN2)+(-19.895×RP5-827L5.2)+(-0.3798×CTD-2139B15.5)+(-0.4665×Metazoa_SRP)+(-0.2482×snoU13)+(-0.1185×RP11-545H22.1)+(-0.32×KRT8P30)+(-0.1907×TALDO1)+(-0.3089×TRAPPC13P1)+(-1.0802×GS1-388B5.2)+(-0.4102×RP11-267L5.1)+(-1.3136×TRAV11)+(-0.5642×RP11-699A7.1)+(-0.3232×AL132671.1)；
in the above formula, RP11-539L10.2 indicates the expression level of the gene RP11-539L10.2, and the other genes also indicate the expression levels of the respective genes.
5. The kit for detecting the mutation of the gene related to the oxidative stress resistance pathway of lung adenocarcinoma as claimed in claim 4, wherein RP11-539L10.2 represents the expression level of the gene RP11-539L10.2, and log2(FPKM +1) is used as the geneThe expression value of RP11-539L10.2 is shown in the formula of FPKM: FPKM ═ number of reads aligned per RNA)/(total number of samples aligned read x length of gene) × 10 6 The same algorithm is used for other gene expression levels.
6. The kit for detecting the mutation of the gene related to the oxidative stress pathway of lung adenocarcinoma according to claim 4, wherein the instructions further recite: when the Score of the detection sample is Score < -2.838, the sample represents that the antioxidant stress pathway related gene mutation exists; when the Score of the detection sample is not less than or equal to-2.838, the sample is represented to have no antioxidant stress pathway related gene mutation.
7. The kit for detecting the mutation of the gene related to the oxidative stress pathway of lung adenocarcinoma according to claim 4, wherein the instructions further recite: when the kit is used, the detection sample is a fresh tissue tumor sample.
8. The kit for detecting the mutation of the genes related to the oxidative stress resistance pathway of lung adenocarcinoma as claimed in claim 2, further comprising instructions describing the method for evaluating the mutation of the genes related to the oxidative stress resistance pathway of lung adenocarcinoma KEAP1, NFE2L2 and CUL3, comprising the following steps:
(1) detecting the following gene expression levels of the sample: RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL 132671.1;
(2) and (3) calculating: score (-0.1058 × RP11-539L10.2) + (-0.1257 × AKR1C2) + (-48.6069 × RP11-572H4.1) + (-0.357 × TRIM16L) + (0.0587 × RARA) + (0.0244 × SESN2) + (-19.895 × RP5-827L5.2) + (-5 × CTD-2139B15.5) + (-5 × Metazoa _ SRP) + (-5 × snoU 5) + (-5 × RP 5-545H 22.1) + (-0.32 × KRT8P 5) + (-5 × do 5) + (-0.3089 × trap C13P 5) + (-5 × 5 t 5-5.2B 5.2) + (-5 × KRT8P 5) + (-5 × talc 5 × 5) + (-5 × 5) + (5 × 5L 5) + (5 × 5L 3 × 5) + (5L 5) + (5);
(3) and (3) judging: when Score is < -2.838, the sample represents the existence of the antioxidant stress pathway related gene mutation; when the Score is more than or equal to-2.838, the sample is represented to have no gene mutation related to the antioxidant stress pathway.
9. The kit for detecting the mutation of the genes related to the oxidative stress resistance pathway of lung adenocarcinoma as claimed in claim 2, further comprising instructions describing the method for evaluating the mutation of the genes related to the oxidative stress resistance pathway of lung adenocarcinoma KEAP1, NFE2L2 and CUL3, comprising the following steps:
(1) RNA extraction is carried out on tumor sample tissues;
(2) construction of RNA library: adding a 3 'end connector and a 5' end connector based on the extracted RNA of the tumor sample tissue, and then carrying out RT-PCR enrichment to obtain an RNA library;
(3) cluster generation: adding NaOH and TrisCl into an RNA library, uniformly mixing, adding a hybrid buffer, and starting cluster generation by using a CBOT cluster generator;
(4) sequencing by Illumina Hiseq2000, and converting the sequencing result raw data into a Fastq format;
(5) and (3) data analysis:
(5.1) removing primer and adaptor sequences from the original Fastq file data, checking the quality and length of the base of the sequencing fragment, and screening the sequencing fragment with reliable quality;
(5.2) comparing the sequencing results with each database and filtering to identify RP11-539L10.2, AKR1C2, RP11-572H4.1, TRIM16L, RARA, SESN2, RP5-827L5.2, CTD-2139B15.5, Metazoa _ SRP, snoU13, RP11-545H22.1, KRT8P30, TALDO1, TRAPPC13P1, GS1-388B5.2, RP11-267L5.1, TRAV11, RP11-699A7.1, AL132671.1 in the results;
(5.3) carrying out expression quantity statistics according to the identified RNA, wherein the RNA expression quantity is calculated by using FPKM (fragments Per Kilobase Million) as a measurement index, and the formula of FPKM (reads number aligned by each RNA)/(total sample aligned read number multiplied by the length of gene) is multiplied by 10 6 ；
(5.4) obtaining the value of FPKM and converting the data into log2(FPKM +1) as a value for calculating the expression amount of score as a subsequent formula;
(5.5) calculating: score (-0.1058 × RP11-539L10.2) + (-0.1257 × AKR1C2) + (-48.6069 × RP11-572H4.1) + (-0.357 × TRIM16L) + (0.0587 × RARA) + (0.0244 × SESN2) + (-19.895 × RP5-827L5.2) + (-5 × CTD-2139B15.5) + (-5 × Metazoa _ SRP) + (-5 × snoU 5) + (-5 × RP 5-545H 22.1) + (-0.32 × KRT8P 5) + (-5 × do 5) + (-0.3089 × trap C13P 5) + (-5 × 5 t 5-5.2B 5.2) + (-5 × KRT8P 5) + (-5 × talc 5 × 5) + (-5 × 5) + (5 × 5L 5) + (5 × 5L 3 × 5) + (5L 5) + (5);
(5.6) judgment: when Score is < -2.838, the sample represents the existence of the antioxidant stress pathway related gene mutation; when the Score is more than or equal to-2.838, the sample is represented to have no gene mutation related to the antioxidant stress pathway.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008079269A2 (en) * | 2006-12-19 | 2008-07-03 | Genego, Inc. | Novel methods for functional analysis of high-throughput experimental data and gene groups identified therfrom |
| CN104698191A (en) * | 2015-03-16 | 2015-06-10 | 复旦大学附属中山医院 | Applications of CALML3 (Calmodulin-like 3), MLPH (Melanophilin), TMC5 (Transmembrane Channel-like) and SFTA3 (Surfactant Associated 3) in pathological diagnosis of squamous cell lung carcinoma and adenocarcinoma |
| CN109890982A (en) * | 2016-07-08 | 2019-06-14 | 基因泰克公司 | Pass through the method for the expression status and mutation status diagnosing and treating cancer of NRF2 and its downstream targets gene |
| CN110499364A (en) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | A kind of probe groups and its kit and application for detecting the full exon of extended pattern hereditary disease |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012031008A2 (en) * | 2010-08-31 | 2012-03-08 | The General Hospital Corporation | Cancer-related biological materials in microvesicles |
-
2021
- 2021-05-31 CN CN202110597293.6A patent/CN113444793B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008079269A2 (en) * | 2006-12-19 | 2008-07-03 | Genego, Inc. | Novel methods for functional analysis of high-throughput experimental data and gene groups identified therfrom |
| CN104698191A (en) * | 2015-03-16 | 2015-06-10 | 复旦大学附属中山医院 | Applications of CALML3 (Calmodulin-like 3), MLPH (Melanophilin), TMC5 (Transmembrane Channel-like) and SFTA3 (Surfactant Associated 3) in pathological diagnosis of squamous cell lung carcinoma and adenocarcinoma |
| CN109890982A (en) * | 2016-07-08 | 2019-06-14 | 基因泰克公司 | Pass through the method for the expression status and mutation status diagnosing and treating cancer of NRF2 and its downstream targets gene |
| CN110499364A (en) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | A kind of probe groups and its kit and application for detecting the full exon of extended pattern hereditary disease |
Non-Patent Citations (3)
| Title |
|---|
| "Impacts of NRF2 activation in non–small-cell lung cancer cell lines on extracellular metabolites";Daisuke Saigusa等;《Cancer Science》;20201231;第111卷;第667-678页 * |
| "Role of KEAP1/NFE2L2 Mutations in the Chemotherapeutic Response of Patients with Non–Small Cell Lung Cancer";Youngtae Jeong等;《Clin Cancer Res》;20190923;第26卷(第1期);第274-281页 * |
| "核转录因子E2相关因子2和Keap1的分子结构和功能及其信号通路调控分子机制研究进展";王朝阳等;《中国药理学与毒理学杂志》;20160531;第30卷(第05期);第598-604页 * |
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