CN108753967B - Gene set for liver cancer detection and panel detection design method thereof - Google Patents
Gene set for liver cancer detection and panel detection design method thereof Download PDFInfo
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Abstract
The invention discloses a preparation method of a liver cancer gene detection panel, which comprises the following steps: screening liver cancer information and related genes thereof to obtain a gene set; selecting a gene target region; designing a panel probe; synthesizing an RNA single-stranded probe to obtain the gene detection panel. The gene detection panel designed by the invention can be used for diagnosis, parting and prognosis of liver cancer, has wide gene coverage and strong transportability, and is suitable for a newly developed detection platform.
Description
Technical Field
The invention belongs to the field of gene detection, and particularly relates to a gene set for liver cancer detection and a panel detection design method thereof, and further relates to application of the gene set in a gene chip.
Background
Liver cancer is one of the most common malignant tumors in clinic at present, and according to statistics, about 74 new liver cancer cases and 70 dead liver cancer cases occur in China every year in the global range, about half of which occur. Because of the high morbidity and mortality of liver cancer, liver cancer is particularly important worldwide, the morbidity and mortality of liver cancer in China also rise year by year, and the liver cancer is high in the second place of the death reasons related to various malignant tumors, seriously harms human health and becomes a global and serious public health problem. The development and progression of liver cancer is a complex biological process. In addition to the genetic factors that patients are susceptible to themselves, various extracellular factors can lead to abnormal proliferation and differentiation of hepatic stem cells, and ultimately to the development and progression of liver cancer. The rapid proliferation and metastasis of liver cancer are one of the important biological characteristics of liver cancer, and the rapid proliferation and metastasis of liver cancer are one of the important factors causing poor prognosis and low five-year survival rate of many liver cancer patients. Therefore, the research on the reasons causing the rapid proliferation and the metastasis of the liver cancer reveals that the molecular mechanism of liver cancer pathogenesis plays an extremely important role in improving the prognosis of liver cancer patients. The primary liver cancer does not generally have any symptom in the early stage, once clinical manifestations appear, most of the diseases enter the middle and late stages, and the malignancy degree is high, the progress is fast, the prognosis is poor, and the invasiveness is strong, so that the diagnosis, especially the early diagnosis of the liver cancer is the key of the clinical diagnosis and the prognosis, and the determination of the key mutant genes lays a foundation for the diagnosis, the typing and the targeted therapy of patients.
In recent years, with the rapid development of molecular biology and the emergence of high-throughput sequencing technologies, epigenetics is increasingly recognized and studied. The technology for diagnosing liver cancer by performing high-throughput sequencing on genome in a biological sample has been developed, but the existing liver cancer detection has the following defects: 1. the detection content is single, and the function is limited. At present, the detection only relates to part of the protein or gene to be observed. For gene detection, based on gene collection capability and sequencing cost, the number of covered genes is small, most of the genes only concern targeted drug treatment related sites, other related sites are small, and the functions of scientific research and exploration and discovery of new targets are limited. 2. The gene collection method is simple. Most current panel designs are directed only to drug guidelines, labeling, and limited literature collections, with less comprehensive analysis based on large-scale sequencing platform results, public databases, and text mining technologies. 3. At present, the technical process of a panel design synthesis method is single, and areas which can not be designed always exist according to a fixed algorithm, so that omission exists at some important sites. 4. The method has few applicable platforms, a set of detection system schemes exist on common platforms for developed panel detection, and no suitable detection scheme exists for a new detection platform such as BGISEQ-500. 5. The information analysis method has a complicated flow. The biological information analysis of off-line data needs to be evaluated step by step, results are completed step by step, the steps are complicated, the time is long, the transportability is not high, and the current analysis requirements cannot be met.
Disclosure of Invention
In order to make up for the above-mentioned deficiency of the existing liver cancer related gene detection technology, the inventor has developed a new liver cancer gene set panel detection design method, the gene panel contains all types of genes used for liver cancer detection, the gene coverage is wide, can be used for the liver cancer from liver cancer risk assessment to diagnosis, treatment, prognosis and other liver cancer development and development stages of liver cancer, and the portability is strong, is suitable for the present domestic market sequencing instrument or newly developed detection platform. Specifically, the technical solution of the present invention is as follows.
A preparation method of a liver cancer gene detection panel comprises the following steps:
1) screening liver cancer information and related genes thereof, and establishing a local database: respectively collecting matched samples of the same liver cancer patient, wherein the matched samples are matched by solid tumor tissue samples/blood cells or normal tissue (tissue beside cancer), respectively carrying out gene detection on the matched samples by adopting an NGS sequencing technology, and selecting a gene region with mutation frequency of more than or equal to 5% through analysis and comparison;
2) selecting published liver cancer information and related genes thereof from published literature resources, wherein the published liver cancer information and related genes thereof comprise: high-frequency mutant genes in a public database, liver cancer treatment related genes in application standards, treatment guidelines, drug labels and a general database, and genes related to liver cancer molecular typing, treatment, prognosis and induction reported in published documents;
3) combining the liver cancer information and related genes, removing redundancy, determining a standard gene name through an NCBI office name and HGNC advanced office Symbol system, and obtaining a liver cancer detection chip gene set which can be used for guiding the preparation of a liver cancer detection chip;
4) selecting a gene target region for probe design: for the genes collected in step 1) and step 2), if specific variation positions are defined, selecting a target region according to the defined gene locus coverage region; selecting exons as target regions for gene regions with more concentrated or dense positions; selecting all regions with variable splicing types as target regions for important genes highly related to liver cancer;
5) extending two ends of the gene target region selected in the step 4), wherein the default length is 50bp, combining all the selected regions, removing redundancy, and finally forming a bed file for designing the probe, wherein the bed file comprises the chromosome number of the target region, the initial position of the target region, the termination position of the target region and self-defined information;
6) panel probe design: searching a design region in which a probe can be designed from the probe data set which can be designed in the human genome according to the target region selected in the step 4), and generating the probe according to the design region and the probe design parameter;
7) and (3) designing and evaluating a probe: comparing the screened important sites highly related to the liver cancer and all target regions with the probe design regions selected in the step 6), and calculating the coverage of the important sites covered by the probes and the coverage of all target regions, wherein the calculation formula is as follows: if the coverage meets the coverage requirements that the total coverage is more than or equal to 90 percent and the coverage of the important sites is more than or equal to 99 percent, the next step of panel synthesis can be carried out; if not, selecting a proper probe near the target area;
8) synthesis of panel: adding fixed amplification sequences at two ends of the probe designed in the step 7), synthesizing a DNA single chain, carrying out PCR amplification, transcribing into an RNA probe, and adding a biotin label to obtain the liver cancer gene detection panel.
The screening process of the liver cancer information and the related genes thereof can be used for inputting various data through perl script process, carrying out standardized filtering treatment and finally outputting an alternative gene list.
In one embodiment, the collection of the high-frequency mutant genes in the public database in step 2) is performed by filtering the detection data of the patients with liver cancer detected in the public database, selecting the high-frequency mutant genes, wherein the screening method is to download a TCGA COSMIC database, calculate the gene mutation frequency of the patients with liver cancer, calculate the gene mutation frequency, sort and filter, and the default parameters are the first 20 genes; in the same way, the detection data of liver cancer samples, including four data of LIRI-JP, LINV-JP, LIHC-US, LICA-FR and LIHM-FR, are selected from the ICGC database, the mutation frequency of the genes is calculated, and the genes are sorted and filtered, wherein the default parameters are the first 20 genes.
In one embodiment, the liver cancer treatment-related genes in the application standard, the treatment guideline, the drug label and the general database in step 2) are collected from databases PGKB, NCCN, us.fda drug, CFDA, PGKB application standard, treatment guideline, drug label and general database.
In one embodiment, the genes related to molecular typing, treatment, prognosis and induction of liver cancer reported in the published literature in step 2) are collected by screening important genes in the literature through text mining in the prior art, key parameters of the text mining include cancer type, pathogenesis, cancer risk, prognosis, detection method, molecular detection type and treatment method, word stock is established through keywords, and "and/or" connection among the keywords is selected, NCBI Pubmed literature is mined through scripts, and finally important genes related to molecular typing, treatment, prognosis and induction of liver cancer are screened from the selected literature subjected to text mining.
A gene may belong to multiple attributes associated with typing, treatment, prognosis, induction, and the same gene may have different names from different sources. The "redundancy removal" in the step 3) is to remove the duplication of the cases, and the genes in the finally screened gene list have one occurrence.
The "redundancy elimination" in the above step 5) is to eliminate duplication of a gene region, so that a non-duplicated region set located on a chromosome is finally obtained.
Preferably, the programmable probe dataset in step 6) is a dataset which is formed by removing regions with high repetition degree or low assembly quality in Alu repeat region (i.e. Alu repeat), tandem repeat region (i.e. tandamrepeat) and pseudogene (i.e. pesudo gene) of genome.
When the panel probe design is performed in the step 6), weighting the probe design coverage of the designed region, wherein the coverage weighting data is obtained by predicting the depth of some regions too high or too low according to the whole genome sequencing data, and quantitatively marking the regions so as to achieve the purpose that the finally designed probe can uniformly capture the target region, wherein the weighting formula is as follows:
in the formula, x1, x2 and x3 … xk are data higher than or lower than the average probe depth, f1, f2 and f3 … fk are corresponding weights of the data of the probe depth, and k is over/underThe statistical number of depths of (a) and (b),
refers to the average probe depth.
The depth is a probe coverage depth, and a small probe coverage means a low depth.
In a preferred embodiment, the RNA probe in step 8) above is a single-stranded RNA probe.
Preferably, the liver cancer gene detection panel is fixed on a silicon chip or a glass slide of a gene chip; or the liver cancer gene detection panel adopts a liquid phase reaction tank, and the RNA single-stranded probe can directly participate in subsequent reaction without being fixed on a solid phase carrier.
Another aspect of the present invention is to provide a gene chip, a liver cancer detection device or a kit, which comprises the liver cancer gene detection panel obtained by the above method, for example, consisting of hybridization probes.
The gene chip, the liver cancer detection device or the kit can be used for detecting genomes in biological samples so as to be used for diagnosis, typing and prognosis of liver cancer.
Another aspect of the present invention is to provide a liver cancer gene detection panel for detecting a gene set comprising 378 genes shown in Table 1.
Preferably, the gene set comprises the following genes: TP53, CTNNB1, AXIN1, ARID1A, APC, FAT3, NAV3, KMT2D, TAF1, NCOR1, RB1, SETD2, TSC1, TSC2, ALK, CARD11, CHD2, NFE2L2, NOTCH4, PIK3CG, SMARCA4, CCND1, PTEN.
Obviously, the gene set can be used as a target for detecting panel of liver cancer genes and used for preparing gene chips.
The liver cancer gene detection panel prepared by the method has the following advantages:
1. the detection performance is improved. The related data sources are wide, besides the autonomous sequencing detection result, the liver cancer detection data in the currently recognized common database are calculated and screened, the high-throughput detection data in published documents are screened by text mining, and finally the selected gene coverage is wide and the correlation with the liver cancer is high. The development of the technology is not limited to the treatment period of the liver cancer, and can comprise all types of genes for detecting the liver cancer from the liver cancer risk assessment to the diagnosis, treatment and prognosis of the liver cancer at each stage of the occurrence and development of the liver cancer.
2. Is more suitable for specific people. The invention contains the detection result of high-throughput sequencing, and the high-frequency mutant gene is screened out according to the gene mutation detection result of the patient in China, so the designed gene panel is more suitable for the detection of the Chinese.
3. The controllability is stronger, and the coverage is higher. The method has stronger adjustability to the design of the probe of the panel, enables the designed probe to better cover an important area through screening of different parameters, and has higher overall coverage of a target area.
4. The detection cost is low, and the application range is wider. Compared with the current monopolized detection method of commercial companies, the panel detection platform disclosed by the invention has lower cost for detecting and analyzing genes and has great popularization value.
5. The analysis is convenient, the transportability is strong, and the operability is strong. Software packages formed by information analysis in the technology can be installed on a plurality of platforms, data can be completed in one step from the off-line state to various variation results, the analysis time is greatly shortened, and precious detection and analysis time is strived for patients.
Drawings
FIG. 1 shows the gene mutation frequencies detected by the present invention for panel of 110 liver cancer patients.
FIG. 2 shows the mutation (variation) distribution of the panel test of the present invention for 110 liver cancer patients.
Detailed Description
The present invention will be described in further detail with reference to specific examples. It is to be understood that the following examples are illustrative of the present invention only and are not intended to limit the scope of the present invention.
The gene set (gene set) herein is the set or combination of all genes associated with liver cancer.
The gene detection panel is a word used after the development of high-throughput gene detection and gene sequencing, and refers to that probes corresponding to a plurality of genes are designed on the same capture chip in detection to capture target DNA and be used for subsequent gene sequencing. In the detection, not only one site and one gene but also a plurality of sites, a plurality of genes and a plurality of sites are detected simultaneously, and the sites and the genes need to be selected and combined according to a standard and a detection panel is needed.
As used herein, the terms "gene detection panel", "gene panel", or "panel" mean the same meaning and are used interchangeably, and particularly refer to a collection of RNA probes or a hybridization probe combination as a means for multiple gene detection.
The term "gene panel" is used herein to refer in particular to a probe capture scheme for the detection of a small number of genes or genomic regions. Compared with whole genome detection, the gene panel can only capture a small amount of target regions for detection, and relates to related genes and mutant genes thereof of each stage of liver cancer. The gene panel of the application consists of hybridization probes for detecting liver cancer information and related genes thereof, and common solid phase carriers can adopt silicon chips or glass slides of gene chips. Of course, the reaction cell may be a liquid-phase probe reaction cell, and various reactants and probes may be reacted in the reaction cell, which is not particularly limited. In one embodiment of the present application, the gene panel for liver cancer detection is a liquid phase reaction cell.
In this context, the term "local database" refers to a genetic information database created by an organization autonomously developing a genetic testing platform by self-collecting biological samples (including blood samples, tumor tissues, paracancerous tissues, normal tissues, etc. derived from liver cancer patients and healthy people at various stages), and performing genetic sequencing.
Herein, the term "liver cancer information and its related genes" includes not only genetic information such as base sequence, chromosome number, position, repetition rate, etc., but also information related to liver cancer such as liver cancer typing, stage of development, treatment, prognosis, sample type, etc.
As used herein, the term "important gene" refers to a gene highly correlated with liver cancer typing, treatment, prognosis, induction (or drive), especially a gene whose mutation frequency is detected in a matched sample to be 5% or more.
In this context, the term "important site" refers to a site that is well-defined for functional studies on liver cancer treatment, prognosis, pathways, frequency, etc.
The gene panel probe constructed by the invention has the advantages that the coverage of 378 important sites of liver cancer information and related genes thereof is more than 99 percent, and the coverage of all target regions is more than 90 percent.
In one embodiment, the number of probes for detecting 378 liver cancer information and related genes thereof is 3.9 × 104In the case of the kit, the coverage of the probe on 378 liver cancer information and important loci of relevant genes of the liver cancer information is 100 percent, and the coverage of all target regions is 95 percent.
The gene panel of the application covers all stages of liver cancer from liver cancer risk assessment to liver cancer diagnosis, treatment, prognosis and the like, so that the gene panel can be used for preparing gene chips, gene detection devices or kits of corresponding stages.
As a preferred embodiment, the method for preparing the gene panel of the present application mainly comprises the following steps:
1. screening liver cancer information and related genes thereof. The method comprises the steps of collecting liver cancer information and related genes thereof in at least three aspects, wherein in the first aspect, the liver cancer information and related genes thereof obtained by sequencing liver cancer patient samples in a local database are collected; in a second aspect, liver cancer information and related genes thereof disclosed in a public database are collected; in a third aspect, liver cancer information and related genes disclosed in the literature are collected; combining and removing redundancy of liver cancer information and related genes thereof collected in the three aspects, and determining a standard gene name through HGNCadvanced Official Symbol to form a gene set;
2. and selecting a detection target area. Selecting a target region based on the gene set obtained in step 1; extending 50-100bp at two ends of the selected target region, combining all the selected regions, removing redundancy, and finally forming a bed file for probe design, wherein the bed file comprises chromosome information, initial position information and termination position information; wherein, the extension at both ends of the selected target region is to ensure that the region at the two ends of the target region can be covered when designing a probe, and generally the extension is 50-100bp, the specific extension size is determined according to the size of the finally designed panel region, in one embodiment of the application, the gene panel for liver cancer detection is the extension of 50 bp;
in one embodiment, the method of selecting a target region specifically comprises, first, for a gene of a known mutation site, selecting a target region based on the coverage area of the known mutation site; secondly, for gene regions with more concentrated or dense positions, exons of the gene regions are selected as target regions; third, for the important gene, all regions of variable splicing types thereof were selected as the target region.
Panel probe design. Forming a database of probe design regions for capture probe design based on the human genome; searching a probe design area corresponding to each target area in the database according to the position information of each target area in the formed bed file; generating a probe according to the probe design area and the probe design parameters;
preferably, the design coverage of the generated probes is weighted, the weight of the design coverage is based on the depth of each probe design area predicted by the human whole genome sequencing data, specifically, the depth predicted by the sequencing data of each probe design area is quantitatively marked, and the probes of the probe design areas higher or lower than the average depth are weighted, so that the finally designed probes can uniformly capture each target area; for example, the weight of a probe design area with the depth lower than the average depth is increased, so that the probe can have better capture capacity and effect on an area with the low depth, and the effect of uniformly capturing each target area is achieved;
4. and (3) probe evaluation: comparing the important sites and all target regions with the selected probe design regions, and calculating the coverage of each probe on the important sites and the coverage on all target regions;
the formula is that the coverage is read length number on comparison/read length number of target sequencing,
selecting probes with the coverage of the important sites being more than or equal to 99 percent and the coverage of all target areas being more than or equal to 90 percent for the synthesis of the panel; if the probe coverage can not meet the requirements, selecting a proper probe near the probe design area again;
panel synthesis: adding fixed amplification sequences at two ends of the designed probe, synthesizing a DNA single chain, carrying out PCR amplification, transcribing into an RNA probe, and adding a biotin label on the RNA probe to obtain the gene panel for detecting the liver cancer.
Preferably, the local database in step 1 refers to a database of liver cancer and related genes of a liver cancer patient sample obtained by sequencing, preferably a gene region with mutation frequency of more than or equal to 5%.
Preferably, the common database described in step 1 comprises: a TCGA COSMIC database and an ICGC database; application criteria, treatment guidelines, drug labeling, and/or a general database.
Preferably, at least the first 20 genes with the highest frequency of gene mutation in the TCGA COSMIC database are collected, and at least the first 20 genes with the highest frequency of gene mutation in the ICGC database are collected.
The application standards, treatment guidelines, drug labels, and universal databases include, but are not limited to, PGKB, NCCN, NCBI pubed, us.
In one embodiment, the method for collecting liver cancer and related genes disclosed in the literature in step 1 comprises: text mining and screening liver cancer and related genes thereof in the article, including but not limited to liver cancer molecular typing, treatment, prognosis and driving related genes; key parameters for text mining include, but are not limited to, cancer type, pathogenesis, cancer risk, prognosis, detection method, molecular detection type, treatment method; establishing a word bank through key parameters, selecting the relation among the key words, and mining the NCBIPubmed document; finally, relevant genes including but not limited to liver cancer molecular typing, treatment, prognosis and driving are obtained from the screened literature.
The gene panel applicable target obtained by the invention contains all types of genes for liver cancer detection, has wide gene coverage, and can be used for various stages of liver cancer occurrence and development from liver cancer risk assessment to diagnosis, treatment, prognosis and the like of liver cancer; moreover, the gene panel can be suitable for sequencing instruments in the current domestic market, and the cost of gene detection and analysis is lower. Lays a foundation for comprehensive and systematic detection and research of liver cancer.
The technical effect of the present invention was verified by the Panel sequencing and analysis example as follows. All percentages referred to in the examples refer to mass percentages unless otherwise indicated (e.g., by volume percentage or ratio).
Example 1 Panel sequencing
1) Sample collection
According to the collection standard of 'Chinese primary liver cancer diagnosis and treatment standard', a medical institution collects samples of 110 liver cancer patients.
2) Extraction of sample DNA, library preparation
In the liver cancer individualized gene detection, the FFPE of the liver cancer tumor is adopted as an experimental group in1 sample of the liver cancer individualized gene detection, the blood samples collected at the same time are used as a control group, the number of constructed banks is evaluated in consideration of the influence of label joints, hybridization and data volume utilization rate, and the optimal 4 pairs of samples in one-time bank construction are confirmed. The FFPE nucleic acid extraction kit and the blood cell extraction kit are respectively used for sample extraction, and the Qubit DNA fluorescence quantifier and the Qubit dsDNA HS Assay kit are used for quantitative detection, the extraction concentration is required to be evaluated, and the best result is confirmed that the yield is more than 200ng and 10 ng/mu L.
Each 200ng of DNA sample was taken and the break and end repair reaction mixture was added, reagent components: DNA fragmenting enzyme (0.4U/. mu.l) + Klenow fragment (5,000U/mL) + polymerase (10000U) +5:1 of dATP: dNTP (1mM) + DNA fragmenting enzyme buffer (10X), mixed well and centrifuged, and then reacted according to the following procedure: keeping at 37 deg.C for 20min, 65 deg.C for 15min, and 4 deg.C.
After the reaction is finished, adding a label joint and a connection reaction mixed solution into the original tube: ATP (100mM) + polyethylene glycol 8000 (50%) + T4 polynucleotide phosphokinase buffer (10X) + nuclease-free water + ligase (600U/. mu.l) to carry out the following reactions: keeping at 23 deg.C for 60min and 4 deg.C.
And after the reaction is finished, purifying, dissolving the obtained product in nuclease-free water, and directly carrying magnetic beads to perform PCR amplification reaction, wherein the amplification reaction adopts KAPA-PCR reaction liquid, and upstream and downstream PCR primers and samples are added to perform reaction. The reaction system comprises the following components: ammonium sulfate, magnesium chloride, dithiothreitol, trihydroxymethyl aminomethane and PCR enzyme.
The reaction process is firstly to react for 5min at 95 ℃; a re-entry recycling step: reacting at 98 ℃ for 15s, 60 ℃ for 10s and 72 ℃ for 40s, and circulating the steps for 7 times; then reacting for 5min at 72 ℃; finally, the temperature is kept at 4 ℃.
The PCR product is quantified by using the Qubit HS, and the PCR concentration is required to be more than 25 ng/muL, and the yield is required to be more than 550 ng.
3) Specific hybridization capture of liver cancer gene probe
A total of 2000ng of 500ng of blood cells in a whole blood sample were added to each of 4 FFPE samples as a set, 4 blood samples, that is, 4 blood samples of different sources as a set, and 2 sets of fragment screening were performed, and the screened product was dissolved in 16. mu.L of nuclease-free water to a concentration of more than 55 ng/. mu.L. Then 700ng of FFPE group and 300ng of blood group are merged and 1000ng is subjected to hybridization experiment. The kit adopted in the hybridization experiment is a 'target region capture universal reagent' of Shenzhen Huazhizhi manufacturing science and technology Limited.
The hybridization procedure was as follows: and (2) fixing the volume of 1000ng of fragment screening products to 18 mu L, adding 8 mu L of hybridization reaction solution 1, fully mixing uniformly, and putting the mixture into a PCR instrument to run the following program: keeping at 95 deg.C for 5min and 65 deg.C. After the temperature is reduced to 65 ℃, the sample is quickly and fully mixed with the hybridization reaction liquid 2 and the hybridization reaction liquid 3. The hybridization reaction was carried out in a PCR apparatus at a constant temperature of 65 ℃ for 16 hours.
Wherein, the main component of the hybridization reaction solution 1 is the combination of oligodeoxynucleotide, tris (hydroxymethyl) aminomethane and ethylenediamine tetraacetic acid; the main components of the hybridization reaction liquid 2 are the combination of sodium chloride, sodium dihydrogen phosphate, ethylene diamine tetraacetic acid, bovine serum albumin, ficoll 400, polyvinylpyrrolidone and lauryl sodium sulfate; the main components of the hybridization reaction liquid 3 are the combination of a liver cancer gene probe, 4-hydroxyethyl piperazine ethanesulfonic acid, potassium hydroxide, potassium iodide, dithiothreitol and glycerol; see Shenzhen Hua Dazhi Zhi science and technology Limited 'target region capture universal reagent' specification.
After the reaction is finished, transferring the product to streptavidin magnetic beads which are cleaned by using a binding solution and contain the binding solution, vertically and uniformly mixing for 30min, respectively washing by using a washing solution 1 and a washing solution 2, dissolving the product in 45 mu L of nuclease-free water, carrying out PCR reaction with the magnetic beads, after the reaction is finished, purifying by using purified magnetic beads, finally dissolving in 40 mu L of nuclease-free water, and transferring to a new 1.5mL centrifuge tube. QubitHS is adopted for quantification, and the PCR concentration is required to be more than 4 ng/muL, and the yield is required to be more than 160 ng.
In this example, Dynabeads M-280 elution was used as the streptavidin magnetic bead product, and the binding solution, washing solution 1, and washing solution 2 were referred to the magnetic bead product manual. The PCR reaction with the magnetic beads is a conventional PCR reaction, and the reaction system comprises: ammonium sulfate, magnesium chloride, dithiothreitol, tris (hydroxymethyl) aminomethane, and PCR enzyme; the dosage of each component refers to the conventional PCR reaction. The reaction condition is pre-denaturation at 95 ℃ for 5 min; then 14 cycles are entered: denaturation at 98 ℃ for 15s, annealing at 60 ℃ for 10s, and extension at 72 ℃ for 40 s; after circulation is finished, extension is carried out for 5min at 72 ℃; finally, the temperature is kept at 4 ℃. And purifying by adopting a purified magnetic bead, wherein the purified magnetic bead is a mixture of nano magnetic beads and sodium azide, and the product name of the purified magnetic bead is AgencourtAMPuXP-medium.
4) Sequencing on machine
160ng of the obtained hybridization product is cyclized, 20 mu L of the hybridization product is subjected to DNB preparation, and the DNA concentration can be subjected to machine sequencing until reaching the requirement, and if BGISEQ-500 requires that the DNB concentration is more than 10 ng/mu L, the sequencing can be performed. Wherein, the cyclization, DNB preparation, the sequencing on the computer and other steps are explained by referring to a gene sequencer of a sequencing reaction universal kit (combined probe anchoring polymerization sequencing method) produced by Wuhan Hua Dazhiji scientific and technological Limited.
Example 2 off-line data analysis
The information analysis process is used for analyzing matched samples of liver cancer projects, takes off-line Fastq files of a sequencing platform (such as BGI-Seq500 in the example) as input, and obtains final mutation results and basic statistical information of data through steps of filtering, comparison, de-duplication, repeated comparison, mutation detection and the like.
The process is used for the paired sample analysis of the liver cancer project. For fastq after BGI-Seq500 is off-line, firstly, conducting SOAPnuke filtering treatment, filtering out read lengths (reads) with low-quality base (less than or equal to 10) proportion higher than 50% or N (undetermined base) proportion larger than 10%, and counting relevant indexes before and after filtering, wherein the relevant indexes comprise initial data volume, effective data volume, CG content, N content, Q20, Q30 and the like; aligning the filtered clean fastq to a human reference genome hg19, wherein the step is realized by paligner; obtaining an original bam file after comparison, and carrying out merging and de-duplication by using picard so as to remove a repeated sequence caused by PCR; and finally, performing data quality control on the obtained bam, including calculation of coverage, capture rate, repetition rate, effective depth and the like.
After quality control, bam will undergo mutation detection including somatic SNV, atomic InDel, CNV, Fusion and chemotherapy-related Germline sites. Using Varscan to roughly detect pairing mutation by using Somatic SNV and Somatic InDel, setting the minimum depth to be 8 and the minimum mutation frequency to be 0.5%, and filtering the obtained original result by adopting a self-made filtering script to obtain a final mutation list; the CNV detection uses the cnvkit to carry out rough detection of copy number variation to obtain the copy number (copy number) value of each gene region, and respectively sets threshold values of Gain and Loss, wherein the Gain refers to the increase of the copy number and the Loss refers to the decrease of the copy number, the Gain threshold value is 1.1, and the Loss threshold value is < -1.25 to obtain a final CNV result; the Fusion detection adopts independently researched detection software, judges the Fusion type supported by the reads pair by collecting the reads pair (paired read length) of the long insertion fragment, and estimates the breakpoint interval of the reads pair, wherein if the breakpoint interval is concentrated enough, the Fusion result is a credible Fusion result; the chemotherapy detection adopts GATK4 to detect the genotype of chemotherapy-related locus, and the effect of chemotherapeutic drug is determined according to the genotype of each locus.
Example 3 Probe Capture efficiency assessment and Gene set acquisition
And evaluating the capture efficiency of the probe according to the proportion of the target area to the total sequencing quantity after sequencing analysis. According to the difference of the actual design area, the capture efficiency is generally between 30% and 60%, and then the probe design is qualified.
Through NGS gene detection of 110 liver cancer patients matched samples, for genes with mutation frequency more than or equal to 5%, important genes screened through gene panel detection comprise: TP53, CTNNB1, AXIN1, ARID1A, APC, FAT3, NAV3, KMT2D, TAF1, NCOR1, RB1, SETD2, TSC1, TSC2, ALK, CARD11, CHD2, NFE2L2, NOTCH4, PIK3CG, SMARCA4, CCND1, PTEN.
In addition, a gene set for liver cancer detection is finally obtained by screening databases such as TCGA, ICGC and the like and mining published literature texts, and the gene set relates to 378 genes. The list of genes obtained after final redundancy removal is shown in table 1.
TABLE 1 Gene set for detection of liver cancer
Downloading a human genome annotation file from UCSC, screening out all variably cut transcripts of 378 genes, extracting exon regions of the transcripts, integrating, and taking an overlap region to obtain a target region for liver cancer detection, wherein the size of the region is 1.54 Mb.
Extending the two ends of the fragment of the target region by 50bp respectively, removing the overlap region, reserving the chromosome number, the initial position and the termination position, and obtaining a bed file of the target region, wherein the coverage area is 1.59M.
Comparing the bed file area with the database of the designable probe area, and screening the covered areaDesigning synthetic probe, and finally designing 3.9X 10 probe4Strip, significant site coverage 100%, total target area coverage 95%.
In the embodiment, matched samples of 110 liver cancer patients are selected for detection, wherein positive samples of the patients are tumor tissues, and negative samples of the patients are blood/paracarcinoma. According to the method, DNA extraction, breaking, end repairing, first PCR, specific capture of liver cancer gene panel and second PCR amplification are carried out to obtain a DNA sample to be sequenced. And (3) performing machine sequencing after the quality of the DNA sample meets the machine requirement of BGISEQ-500.
The results of the assay were evaluated and the capture efficiency of the panel assay was between 30-60% in 110 patients. The gene mutation frequencies detected are shown in FIG. 1, and the mutation distribution is shown in FIG. 2. The detection result is consistent with the actual situation, and the gene panel for liver cancer detection prepared by the embodiment is proved to be capable of accurately detecting liver cancer patients at various stages.
The gene panel detection scheme of the present invention was verified in the above examples, and it should be noted that the gene panel covering 378 pieces of liver cancer information and related genes of the present invention can be used for risk assessment, diagnosis, treatment and prognosis of liver cancer at various stages. It is understood that, according to different requirements of detection or research design, the preparation method of the present application can be used to obtain more types or special purpose gene panel, even not limited to the liver cancer detection gene panel, and is not limited specifically herein. Without departing from the spirit of the invention, it is intended that various changes and modifications of the invention shall fall within the scope of the invention.
Claims (6)
1. A preparation method of a liver cancer gene detection panel comprises the following steps:
1) screening liver cancer related genes, and establishing a local database: respectively collecting matched samples of the same liver cancer patient, wherein the matched samples are matched by a solid tumor tissue sample and blood cells or a tissue sample beside the cancer, respectively carrying out gene detection on the matched samples by adopting an NGS sequencing technology, selecting a gene region with mutation frequency of more than or equal to 5% through analysis and comparison, inputting various data through a perl script process, carrying out standardized filtration treatment, and finally outputting an alternative gene list;
2) selecting published liver cancer related genes from published literature resources, wherein the published liver cancer related genes comprise: high-frequency mutant genes in a public database, liver cancer treatment related genes in application standards, treatment guidelines, drug labels and a general database, and genes related to liver cancer molecular typing, treatment, prognosis and induction reported in published documents;
3) combining the related genes of the liver cancer, removing redundancy, determining a standard gene name through an NCBI office name or HGNC advanced office Symbol system, and obtaining a gene set of a liver cancer detection chip;
4) selecting a gene target region for probe design: for the genes collected in step 1) and step 2), if specific variation positions are defined, selecting a target region according to the defined gene locus coverage region; selecting exons as target regions for gene regions with more concentrated or dense positions; for important genes highly related to liver cancer, all regions of variable splicing types are selected as target regions;
5) extending the gene target region selected in the step 4) to two ends, wherein the length is 50bp, combining all the selected regions, removing redundancy, and finally forming a bed file for designing the probe, wherein the bed file comprises the chromosome number of the target region, the initial position of the target region, the termination position of the target region and self-defined information;
6) panel probe design: searching a design region in which a probe can be designed from a probe data set which can be designed in a human genome according to the position information of each target region in the bed file formed in the step 5), and generating the probe according to the design region and the probe design parameter, wherein the probe data set which can be designed is a data set which can be used for capturing probe design and is formed after eliminating an Alu repeat region, a tandem repeat region and a region with high repetition degree or low assembly quality in a pseudogene of the genome;
7) and designing and evaluating a probe: comparing the screened important sites highly related to the liver cancer and all target regions with the probe design region selected in the step 6), and calculating the coverage of the important sites covered by the probe and the coverage of all target regions, wherein the calculation formula is as follows: coverage = number of read length on comparison/number of read length of target sequencing, if the coverage requirement that the coverage of all target regions is more than or equal to 90% and the coverage of important sites is more than or equal to 99% is met, the next step of panel synthesis can be carried out; if not, selecting a proper probe near the target area;
8) and panel synthesis: adding fixed amplification sequences at two ends of the probe designed in the step 7), synthesizing a DNA single chain, carrying out PCR amplification, transcribing into an RNA probe, and adding a biotin label to obtain the liver cancer gene detection panel.
2. The method of claim 1, wherein the collection of the high frequency mutant genes in the public database in step 2) is performed by filtering the detection data of the patients with liver cancer in the public database, selecting the high frequency mutant genes, wherein the screening method comprises downloading TCGA and COSMIC databases, calculating the gene mutation frequency of the patients with liver cancer, sorting and filtering, and selecting the first 20 genes; in the same way, the detection data of liver cancer samples, including five data of LIRI-JP, LINV-JP, LIHC-US, LICA-FR and LIHM-FR, are selected from ICGC database, the mutation frequency of the genes is calculated, and the first 20 genes are selected after sorting and filtering.
3. The method of claim 1, wherein the collection of liver cancer treatment-related genes in the application criteria, treatment guidelines, drug signatures, and general database in step 2) is from the databases PGKB, NCCN, us.
4. The method of claim 1, wherein the genes reported in the published literature in step 2) are collected by screening important genes in the literature through prior art text mining, key parameters of the text mining comprise cancer type, pathogenesis, cancer risk, prognosis, detection method, molecular detection type and treatment method, word stock is established through keywords, and "and/or" connection among the keywords is selected, NCBI Pubmed literature is mined through scripts, and finally important genes related to molecular typing, treatment, prognosis and induction of liver cancer are screened from the selected literature subjected to text mining.
5. The method of claim 1, wherein the RNA probe in step 8) is a single-stranded RNA probe.
6. The method of claim 1, wherein the liver cancer gene detection panel is immobilized on a silicon wafer or glass slide of a gene chip; or a liquid phase reaction tank is adopted, and the RNA single-stranded probe is not fixed on a solid phase carrier.
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