CN110706748A - Competitive endogenous RNA network regulation and analysis system and method - Google Patents

Competitive endogenous RNA network regulation and analysis system and method Download PDF

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CN110706748A
CN110706748A CN201910924249.4A CN201910924249A CN110706748A CN 110706748 A CN110706748 A CN 110706748A CN 201910924249 A CN201910924249 A CN 201910924249A CN 110706748 A CN110706748 A CN 110706748A
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夏昊强
周煌凯
高川
陶勇
罗玥
程祖福
邢燕
曾川川
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Guangzhou Kedior Technology Service Co Ltd
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Abstract

The invention provides a competitive endogenous RNA network regulation and analysis system and a method, wherein the system comprises: lncRNA sequencing analysis unit; a small RNA sequencing analysis unit; and a CERNA network regulation and analysis unit. The analysis method comprises the following steps: performing lncRNA sequencing on the sample to obtain mRNA, lncRNA and cicrRNA expression levels, and screening out mRNA, lncRNA and cicrRNA which are differentially expressed; sequencing the sample by small RNA to obtain miRNA expression quantity, and screening miRNA which are differentially expressed; and carrying out subsequent combined analysis on the differentially expressed mRNA, lncRNA, cicrRNA and miRNA obtained in the step. The invention well discloses the regulation and control relation among mRNA, lncRNA, cicrRNA and miRNA in the sample, and more comprehensively and accurately discloses biological phenomena.

Description

Competitive endogenous RNA network regulation and analysis system and method
Technical Field
The invention belongs to the technical field of biomedicine, and particularly relates to a competitive endogenous RNA network regulation and analysis system and method.
Background
Competitive endogenous RNA (ceRNA) does not mean a new RNA molecule, but means RNA in a complex transcriptional regulatory network in an organism, including protein-coding gene mRNA, long-chain non-coding RNA (incrna), pseudogenes (pseudogenes), circular RNA (circrna), and the like. The cepna hypothesis was proposed in 2011 by the research group of the Pier Paolo Pandolfi, Harvard medical institute, and is a brand-new gene expression regulation mode, and the hypothesis is that an endogenous RNA molecule has a miRNA action site and can be competitively combined with miRNA, so as to indirectly regulate the expression of miRNA target genes, and the action of the competitive combination of miRNA is also called miRNA sponges (miRNA sponges).
However, at present, sequencing means of various RNAs and small RNAs are mature, and when a single RNA is not enough to fully reveal a biological process, and multiple RNAs are analyzed simultaneously, only the sequencing means is used to find various transcripts with significantly different expressions in various samples, but the interrelationship among the transcripts cannot be identified, and the regulation mechanism among different transcripts cannot be accurately clarified. compared with the miRNA regulation network, the CERNA regulation network is more elaborate and complex, is a regulation mode among RNA molecules which relates to more gene types and has a larger regulation network, can help people to excavate gene functions and regulation mechanisms from deeper surfaces, and is convenient for more deeply and comprehensively disclosing biological phenomena.
In recent years, more and more researchers are aware of the importance of the cerana regulatory network in biological function, and there is a growing trend in papers on cerana research. Chinese patent publication No. CN 109637588A discloses a method for constructing a gene regulatory network based on whole transcriptome high throughput sequencing, which is to construct a competitive endogenous RNA regulatory network related to diseases based on whole transcriptome sequencing data, screen out key genes in the regulatory network according to a random walk algorithm and determine a key gene significant enrichment pathway, and analyze the biological mechanism of the key genes in disease regulation.
However, the research object of the cerRNA regulatory network of the patent has certain limitations. The research content of the current ceRNA relates to a plurality of fields such as cell development, virus and host interaction, cancer occurrence and development and the like, but the research aiming at the analysis of a certain key gene function mechanism is relatively less. In order to meet different research demands of people and help people to research and reveal biological phenomena better and more comprehensively, it is necessary to develop analysis technology for competitive endogenous RNA (cepRNA) network regulation.
Disclosure of Invention
In view of this, there is a need to provide a competitive endogenous RNA network regulation and analysis system and method, which employ a whole transcriptome sequencing strategy, and mainly detect mRNA, lncrrna, cicrRNA and miRNA contained in a sample by a combination of lncrrna sequencing and miRNA sequencing strategies, so as to perform a cerana network regulation and analysis. The technical scheme of the invention is as follows:
in a first aspect, the present invention provides a competitive endogenous RNA network regulation assay system, comprising:
lncRNA sequencing analysis unit: obtaining the expression quantity of mRNA, lncRNA and cicrRNA, thereby screening out mRNA, lncRNA and cicrRNA which are differentially expressed;
small RNA sequencing analysis unit: obtaining the expression quantity of miRNA, thereby screening miRNA with differential expression;
a cerRNA network regulation and analysis unit: and the differential expression mRNA, lncRNA, cicrRNA and miRNA obtained by the lncRNA sequencing analysis unit and the small RNA sequencing analysis unit are subjected to subsequent combined analysis.
In a second aspect, the present invention provides a method for analyzing the regulation and control of a competitive endogenous RNA network, which uses the above analysis system, and comprises the following steps:
step S1, carrying out lncRNA sequencing on the sample to obtain mRNA, lncRNA and cicrRNA expression quantities, and screening out mRNA, lncRNA and cicrRNA which are differentially expressed;
s2, sequencing the sample by small RNA to obtain miRNA expression quantity, and screening miRNA with differential expression;
and step S3, carrying out subsequent combined analysis on the differentially expressed mRNA, lncRNA, cicrRNA and miRNA obtained in the step.
Preferably, the step S1 is to screen mRNA, lncRNA and cicrRNA which are differentially expressed, wherein the screening condition of the mRNA and the lncRNA which are differentially expressed is FDR<0.05 and | log2FC|>1, the screening condition of miRNA and circRNA which are differentially expressed is p<0.05 and | log2FC>1。
Preferably, the conditions in which the mirnas are differentially expressed are selected in step S2 as p <0.05 and | log2FC | > 1.
Further, the step S3 of performing subsequent combined analysis on the differentially expressed mRNA, lncrrna, cicrRNA and miRNA obtained in the above steps specifically includes the following steps:
s3.1, constructing a CERNA regulation network;
step S3.2, carrying out functional analysis on the CERNA GO/Pathway;
step S3.3, analysis of the ceRNA connectivity.
Further, the construction of the ceRNA regulatory network in step S3.1 includes the following steps:
step S3.1.1, screening miRNA-target gene pairs with negative correlation of expression quantity;
step S3.1.2, screening ceRNA pairs with positive correlation of expression quantity;
step S3.1.3, screening to determine the final pair of ceRNA;
step S3.1.4, constructing a cerRNA regulatory network map.
Preferably, the step S3.1.1 of screening miRNA-target gene pairs with negative expression level correlation includes: 1) carrying out miRNA target gene prediction, using three software of mireap, miRanda and targetScan to carry out target gene prediction on an animal sample, and then taking the intersection of the obtained prediction results as the result of the miRNA target gene prediction; for plant samples, target gene prediction was performed using patmatch software; 2) and calculating the Spanisman grade correlation coefficient of the miRNA and the candidate ceRNA (including mRNA, lncRNA and circRNA) of the obtained target gene pair, screening miRNA-mRNA, miRNA-lncRNA and miRNA-cicrRNA pairs with the correlation coefficient less than or equal to-0.7, and respectively listing the miRNA-mRNA, miRNA-lncRNA and miRNA-cicrRNA pairs in a table form.
Further, the step S3.1.2 of screening the ceRNA pairs with positively correlated expression levels is to calculate the Pearson correlation coefficient for the expression levels between the miRNA-target gene pairs obtained in the step S3.1.1, and to use the ceRNA pairs with the correlation number of more than 0.9 as potential ceRNA pairs.
Further, the screening in step S3.1.3 determines the final pair of cenna, the cenna pair with the positively correlated expression level obtained in step S3.1.2 is reused for hyper-geometric distribution test, and the cenna pair with the P value less than 0.05 is screened as the final pair of cenna.
Further, the step S3.1.4 is to construct a cerRNA regulatory network map by using Cytoscape after obtaining the above information on the interaction relationship of the cerRNA.
Further, in the step S3.2, the functional analysis of the ceRNA GO/Pathway is to perform functional annotation on the ceRNA in the ceRNA regulatory network constructed in the step S3.1, wherein the functional annotation of the non-coding RNA is determined by the function of the mRNA directly connected with the non-coding RNA, and then perform GO/KEGG functional enrichment analysis on the ceRNA to obtain the gene functions and pathways significantly enriched in the ceRNA regulatory network.
Furthermore, in the step S3.3, the analysis of the cenna connectivity, in the regulatory network, the nodes with high connectivity often have important biological significance. In a cerRNA regulatory network, the connectivity of a certain RNA molecule refers to the number of miRNA molecules with which a targeted regulatory relationship exists. The higher the connectivity of the RNA molecule, the stronger its potential regulatory capacity. The analysis results can be presented in the form of a statistical table and a cenna connectivity profile can also be drawn, where the abscissa is the connectivity of the nodes and the ordinate is the number of nodes containing a particular connectivity.
In a third aspect, the invention provides applications of the competitive endogenous RNA network regulation and analysis system and method in analyzing gene interaction relationship, identifying disease key genes or constructing disease key gene regulation and control networks.
Compared with the prior art, the invention provides a competitive endogenous RNA (cepRNA) network regulation and analysis method and a competitive endogenous RNA (cepRNA) network regulation and analysis system, wherein a complete transcriptome sequencing strategy is adopted, lncRNA sequencing and miRNA sequencing are combined to obtain mRNA, lncRNA, cicrRNA and miRNA contained in a sample, various differentially expressed RNAs are screened out for joint analysis, a cepRNA network regulation and control graph is drawn, and functional enrichment analysis and connectivity analysis are carried out on the cepRNA. The invention well reveals the regulation and control relation among mRNA, lncRNA, cicrRNA and miRNA in a sample, more comprehensively and more accurately reveals biological phenomena, provides a complete research idea for researchers to better understand the biological phenomena on the one hand, and lays a reliable analysis foundation for deeper research on the functional mechanism of a certain key gene and the like on the other hand.
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FIG. 1 is a diagram of the architecture of a competitive endogenous RNA (cepRNA) network regulation assay system according to the present invention.
FIG. 2 is a flow chart of the steps of a competitive endogenous RNA (cepRNA) network regulation analysis method of the present invention.
FIG. 3 is a bar graph of GO enrichment analysis of mRNA in functional assays of CERNA in an embodiment of the invention.
FIG. 4 is a graph of KEGG pathway enrichment analysis bubbles for mRNA in a functional assay for CERNA in an example of the invention.
FIG. 5 is a graph showing the connectivity profile of circ RNA in cerRNA in an example of the present invention.
Detailed Description
Other advantages and capabilities of the present invention will be readily apparent to those skilled in the art from the present disclosure by describing the embodiments of the present invention with specific embodiments thereof in conjunction with the accompanying drawings. The invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the spirit and scope of the present invention.
As shown in fig. 1, the embodiment of the present invention provides a competitive endogenous RNA network regulation analysis system, which includes:
lncRNA sequencing analysis unit 201: obtaining the expression quantity of mRNA, lncRNA and cicrRNA, thereby screening out mRNA, lncRNA and cicrRNA which are differentially expressed;
specifically, screening differentially expressed mRNA, lncRNA and cicrRNA, wherein the screening condition of the differentially expressed mRNA and lncRNA is FDR<0.05 and | log2FC|>1, differential expression of circRNA screening conditions p<0.05 and | log2FC>1. For multiple sets of samples, the differentially expressed genes were pooled for subsequent joint analysis.
Small RNA sequencing analysis unit 202: obtaining the expression quantity of miRNA, thereby screening miRNA with differential expression;
specifically, miRNA which are differentially expressed in a sample are screened out, and the screening conditions are as follows: p <0.05 and | log2FC | > 1. For multiple sets of samples, the differentially expressed genes were pooled for subsequent joint analysis.
The ceRNA network regulation analysis unit 203: and the differential expression mRNA, lncRNA, cicrRNA and miRNA obtained by the lncRNA sequencing analysis unit and the small RNA sequencing analysis unit are subjected to subsequent combined analysis.
In a specific embodiment of the invention, the differentially expressed mRNA, lncrrna, cicrRNA and miRNA obtained in the above steps are subjected to subsequent combined analysis. The method specifically comprises the following steps:
(1) constructing a cerana regulation network; (2) functional analysis of the CERNA GO/Pathway; (3) and (4) carrying out connectivity analysis on the CERNA. See the description of the analytical methods for details.
As shown in fig. 2, the embodiment of the present invention provides a method for analyzing the regulation of a competitive endogenous RNA network, which uses the above analysis system, and comprises the following steps:
step S1, carrying out lncRNA sequencing on the sample to obtain mRNA, lncRNA and cicrRNA expression quantities, and screening out mRNA, lncRNA and cicrRNA which are differentially expressed;
s2, sequencing the sample by small RNA to obtain miRNA expression quantity, and screening miRNA with differential expression;
and step S3, carrying out subsequent combined analysis on the differentially expressed mRNA, lncRNA, cicrRNA and miRNA obtained in the step.
Screening mRNA, lncRNA and cicrRNA which are differentially expressed in the step S1, wherein the screening condition of the mRNA and the lncRNA which are differentially expressed is FDR<0.05 and | log2FC|>1, the screening condition of miRNA and circRNA which are differentially expressed is p<0.05 and | log2FC>1. For multiple sets of samples, the differentially expressed genes were pooled for subsequent analysis.
The conditions in which differentially expressed mirnas were screened in step S2 were p <0.05 and | log2FC | > 1.
In the step S3, the subsequent combined analysis of the differentially expressed mRNA, lncrrna, cicrRNA and miRNA obtained in the above steps includes the following steps:
s3.1, constructing a CERNA regulation network;
step S3.2, carrying out functional analysis on the CERNA GO/Pathway;
step S3.3, analysis of the ceRNA connectivity.
In the step S3.1, the construction of the CERNA regulatory network, namely, firstly, the analysis of the CERNA regulatory network is carried out, and the CERNA which potentially has interaction is filtered and screened according to the following 3 aspects, 1) the targeting relationship between miRNA and candidate CERNA and the negative correlation relationship of expression quantity are carried out; 2) positive correlation between expression levels of candidate ceRNAs; 3) and combining the candidate ceRNA with the enrichment degree of the same miRNA, and then constructing a network.
The construction of the cerRNA regulation network specifically comprises the following steps:
step S3.1.1, screening miRNA-target gene pairs with negative correlation of expression level, wherein the specific screening results are shown in Table 1. The method comprises the following specific steps: first, miRNA target gene prediction is performed. For an animal sample, carrying out target gene prediction by using three software of mireap, miRanda and targetScan, and then taking the intersection of the obtained prediction results as the result of miRNA target gene prediction; for plant samples, target gene prediction was performed using patmatch software. Then, the spearman grade correlation coefficient of miRNA and candidate ceRNA (including mRNA, lncRNA and circRNA) is calculated for the obtained target gene pair, and miRNA-mRNA, miRNA-lncRNA and miRNA-cicrRNA pairs with the correlation coefficient less than or equal to-0.7 are screened out and listed in a table form respectively.
TABLE 1 statistical Table of the screened differential miRNA-differential target gene pairs
Figure BDA0002218443400000071
Step S3.1.2, screening the ceRNA pairs with positive correlation of expression quantity: and (3) calculating a Pearson correlation coefficient according to the expression quantity between the ceRNA pairs obtained in the last step, taking the ceRNA pairs with the correlation coefficient more than 0.9 as potential ceRNA pairs, and obtaining a Pearson correlation statistical table among the ceRNA pairs as shown in a table 2.
TABLE 2 statistical table of Pearson correlations between pairs of cerRNAs
Figure BDA0002218443400000072
Figure BDA0002218443400000081
Step S3.1.3, screening to determine the final pair of cernas: and (4) the ceRNA with the positively correlated expression quantity obtained in the step is detected by using hyper-geometric distribution, and the ceRNA pair with the P value less than 0.05 is screened to be the final ceRNA pair. When the species studied is an animal, since miRNA binds loosely (i.e. allows more mismatches) to the 3' UTR region of mRNA, a large number of target genes may be obtained, false positives exist, and the number of cernas interacting with miRNA is easily overestimated. Therefore, the cepRNA pairs with P value less than 0.05 were screened as final pairs of cepRNAs using hyper-geometric distribution function test (hyper-geometric distribution test). When the species to be studied is a plant, since miRNA is closely associated with ORF region of mRNA and the prediction parameters of the target gene are more stringent than those of animal, the number of target genes having negative correlation in expression level obtained in most cases is not so large, and thus the test can be omitted. For a given RNA pair (a-B), the mirnas that each contain can bind are defined as set C and set D. The significant overlap between set C and set D is judged using hyper-geometric cumulative distribution function test, as follows:
Figure BDA0002218443400000082
wherein N is a bindable miRNA shared by the two RNAs, U is the number of all miRNAs, and M and N are the number of miRNAs contained in set C and set D respectively. The resulting statistical table of the final pairs of cerRNAs is shown in Table 3.
TABLE 3 statistical Table of the final CERNA results selected
Figure BDA0002218443400000083
Figure BDA0002218443400000091
Step S3.1.4, constructing a cerRNA regulatory network map: and constructing a cerRNA regulation network map by using Cytoscape according to the obtained cerRNA interaction relation information.
And step S3.2, performing the function analysis of the CERNA GO/Pathway, namely performing function annotation on the built CERNA in the CERNA regulatory network, determining the function annotation of non-coding RNA by the function of mRNA directly connected with the non-coding RNA, and performing GO/KEGG function enrichment analysis on the CERNA to obtain the gene function and Pathway remarkably enriched in the CERNA regulatory network. The GO enrichment analysis result of the ceRNA can be displayed by a GO enrichment histogram, the KEGG enrichment analysis result of the ceRNA can be displayed by a bubble chart and other forms, wherein the enrichment analysis results of the mRNA GO and the KEGG are respectively shown in FIGS. 3 and 4.
In the step S3.3, the analysis of the cenRNA connectivity shows that the nodes with high connectivity often have important biological significance in the regulation network. In a cerRNA regulatory network, the connectivity of a certain RNA molecule refers to the number of miRNA molecules with which a targeted regulatory relationship exists. The higher the connectivity of the RNA molecule, the stronger its potential regulatory capacity. The results of the analysis may be presented in the form of a statistical table, as shown in Table 4 below, and a graph of the connectivity of the CERNA may also be plotted, as shown in FIG. 5, where the abscissa is the connectivity of the nodes and the ordinate is the number of nodes containing a particular connectivity.
TABLE 4 connectivity Table for CERNA
Figure BDA0002218443400000092
Figure BDA0002218443400000101
In summary, the competitive endogenous RNA (ceRNA) network regulation and analysis method and system of the present invention adopt a whole transcriptome sequencing strategy to combine lncRNA sequencing with miRNA sequencing to obtain mRNA, lncrrna, cicrRNA and miRNA contained in a sample, and screen out various differentially expressed RNAs for joint analysis, drawing a ceRNA network regulation and control graph, and performing function enrichment analysis and connectivity analysis on the ceRNA. The method well reveals the regulation and control relationship among mRNA, lncRNA, cicrRNA and miRNA in the sample, more comprehensively and more accurately reveals the biological phenomenon, provides a complete research idea for researchers to better understand the biological phenomenon, and lays a reliable analysis foundation for deeper research on the functional mechanism of a certain key gene and the like.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A competitive endogenous RNA network regulation analysis system is characterized in that: the method comprises the following steps:
lncRNA sequencing analysis unit: obtaining the expression quantity of mRNA, lncRNA and cicrRNA, thereby screening out mRNA, lncRNA and cicrRNA which are differentially expressed;
small RNA sequencing analysis unit: obtaining the expression quantity of miRNA, thereby screening miRNA with differential expression;
a cerRNA network regulation and analysis unit: and the differential expression mRNA, lncRNA, cicrRNA and miRNA obtained by the lncRNA sequencing analysis unit and the small RNA sequencing analysis unit are subjected to subsequent combined analysis.
2. A competitive endogenous RNA network regulation and analysis method is characterized in that: the system of claim 1, comprising the steps of:
step S1, carrying out lncRNA sequencing on the sample to obtain mRNA, lncRNA and cicrRNA expression quantities, and screening out mRNA, lncRNA and cicrRNA which are differentially expressed;
s2, sequencing the sample by small RNA to obtain miRNA expression quantity, and screening miRNA with differential expression;
and step S3, carrying out subsequent combined analysis on the differentially expressed mRNA, lncRNA, cicrRNA and miRNA obtained in the step.
3. The method for competitive endogenous RNA network regulation analysis of claim 2, wherein: screening mRNA, lncRNA and cicrRNA which are differentially expressed in the step S1, wherein the screening condition of the mRNA and the lncRNA which are differentially expressed is FDR<0.05 and | log2FC|>1, the screening condition of miRNA and circRNA which are differentially expressed is p<0.05 and | log2FC>1。
4. The method for competitive endogenous RNA network regulation analysis of claim 2, wherein: the conditions in which differentially expressed mirnas were screened in step S2 were p <0.05 and | log2FC | > 1.
5. The method for competitive endogenous RNA network regulation analysis of claim 2, wherein: in the step S3, the subsequent combined analysis of the differentially expressed mRNA, lncrrna, cicrRNA and miRNA obtained in the above steps includes the following steps:
s3.1, constructing a CERNA regulation network;
step S3.2, carrying out functional analysis on the CERNA GO/Pathway;
step S3.3, analysis of the ceRNA connectivity.
6. The method for competitive endogenous RNA network regulation analysis of claim 5, wherein: the construction of the cerRNA regulation network in the step S3.1 comprises the following steps:
step S3.1.1, screening miRNA-target gene pairs with negative correlation of expression quantity;
step S3.1.2, screening ceRNA pairs with positive correlation of expression quantity;
step S3.1.3, screening to determine the final pair of ceRNA;
step S3.1.4, constructing a cerRNA regulatory network map.
7. The method for competitive endogenous RNA network regulation analysis of claim 6, wherein: the screening of the miRNA-target gene pairs with the negative correlation of the expression level in the step S3.1.1 comprises the following steps: 1) carrying out miRNA target gene prediction, using three software of mireap, miRanda and targetScan to carry out target gene prediction on an animal sample, and then taking the intersection of the obtained prediction results as the result of the miRNA target gene prediction; for plant samples, target gene prediction was performed using patmatch software; 2) and calculating the Spanisman grade correlation coefficient of the miRNA and the candidate ceRNA (including mRNA, lncRNA and circRNA) of the obtained target gene pair, screening miRNA-mRNA, miRNA-lncRNA and miRNA-cicrRNA pairs with the correlation coefficient less than or equal to-0.7, and respectively listing the miRNA-mRNA, miRNA-lncRNA and miRNA-cicrRNA pairs in a table form.
8. The method for competitive endogenous RNA network regulation analysis of claim 6 or 7, wherein: and (3) screening the ceRNA pairs with positively correlated expression levels in the step S3.1.2, namely calculating a Pearson correlation coefficient of the expression levels between the miRNA-target gene pairs obtained in the step S3.1.1, and taking the ceRNA pairs with the correlation number of more than 0.9 as potential ceRNA pairs.
9. The method for competitive endogenous RNA network regulation analysis of claim 8, wherein: and (3) screening and determining a final CERNA pair in the step S3.1.3, reusing the CERNA pair with the expression quantity positively correlated obtained in the step S3.1.2 for detection by super-geometric distribution, and screening the CERNA pair with the P value less than 0.05 to obtain the final CERNA pair.
10. Use of the system according to claim 1 or the method according to any one of claims 2 to 9 for analyzing gene interaction relationships, identifying disease-critical genes, or constructing disease-critical gene regulatory networks.
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