CN114627970A - Prognosis model of scorching-related lncRNA of colon adenocarcinoma and construction method and application thereof - Google Patents
Prognosis model of scorching-related lncRNA of colon adenocarcinoma and construction method and application thereof Download PDFInfo
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Abstract
The invention discloses a prognosis model of scorch-related lncRNA of colon adenocarcinoma and a construction method and application thereof, wherein the construction method comprises the following steps: s1, acquiring transcriptome data, miRNA data and patient clinical data of the colon adenocarcinoma from the TCGA database; s2, screening to obtain DEmRNAs, DElncRNAs and DEmiRNAs; s3, screening out scorch related mRNAs and related scorch related miRNAs and scorch related lncRNAs according to the database, and constructing a scorch related ceRNA network; s4, integrating the IncRNAs related to the scorching and clinical data, and performing single-factor Cox regression analysis to obtain the IncRNAs related to the survival of the colon adenocarcinoma; s5, establishing a pre-model of the IncRNA related to the scorching according to LASSO regression analysis. The invention realizes the prognosis judgment of the colon adenocarcinoma through the apoptosis-related lncRNA.
Description
Technical Field
The invention relates to the technical field of tumor molecular biology and biomedical detection, in particular to a prognosis model of scorch-death-related lncRNA of colon adenocarcinoma and a construction method and application thereof.
Background
Colorectal cancer is a clinically common malignancy of the digestive system that occurs in the colon, is the third most common cancer type, and is the second leading cause of cancer-related mortality worldwide. Although modern research can reveal the pathogenesis of colorectal cancer and provide enhanced screening strategies, the prevalence of colorectal cancer is still rising, severely threatening human health. Colon adenocarcinoma is the most common type of pathology for colon cancer, a disease involving multiple etiologies, multiple stages, and multiple genes. The difficulty of early diagnosis is one of the major factors affecting the survival of patients with colon adenocarcinoma, and no effective method is available at present. Apoptosis is a programmed form of inflammatory cell death mediated by gasdermin and characterized by cellular swelling, pore formation and the release of a number of inflammatory factors, such as IL-1 β and IL-18. Due to the involvement of innate immunity and disease, there is increasing interest. Coke death is usually triggered by both classical and non-classical pathways. Over the past few years, more and more studies have shown that scorching is involved in the development of tumors. The main therapeutic strategy for tumors is to induce cell death, and several researchers are trying to find new targeted therapies for colon adenocarcinoma by activating the focal death pathway.
In recent years, non-coding rna (ncrna) has been shown to be involved in the development of colon cancer. It is well known that ncrnas belong to a class of transcripts that are not translated into proteins, but that they play important roles in a variety of cellular and physiological processes. Among these, long non-coding rnas (lncrnas) are ncrnas with a length of more than 200 nucleotides, often used as competitive endogenous rnas (cernas) to regulate the expression of specific mirnas, thereby targeting molecules downstream of these mirnas. The ceRNA hypothesis reveals a new mechanism of interaction between RNAs. These cerRNA molecules (mRNA, lncRNA, etc.) are able to compete with the binding of identical miRNAs by MiRNA Response Elements (MRE) to achieve a modulated level of expression from each other. In fact, lncRNA can interact with RNA, DNA and proteins to form RNA-RNA, RNA-DNA, RNA-protein complexes, and regulate gene expression by a variety of mechanisms, including regulation of transcription, mRNA stability and translation. Also lncrnas affect chromatin structure and regulate gene expression. It has now been found that abnormal expression of many lncRNA is associated with the clinical pathology of colon cancer.
However, there is currently no study of apoptosis-related lncRNA in colon adenocarcinoma, nor is there a clear reference standard for prognostic decisions with respect to the presence of predictive colon adenocarcinoma.
Disclosure of Invention
Technical problem to be solved
Based on the above problems, the present invention provides a prognosis model of tar-death related lncRNA of colon adenocarcinoma and a construction method and an application thereof, wherein lncRNA is a prognostic molecular marker, which solves the problems that no tar-death related lncRNA is researched in colon adenocarcinoma, and the tar-death related lncRNA has no clear reference standard for prognosis judgment of colon adenocarcinoma prediction, and the tar-death related molecular marker is screened out, and the prognosis model provides a reliable biomarker for prognosis evaluation of colon adenocarcinoma patients, so that the prognosis evaluation and prediction capability of colon adenocarcinoma patients is improved, colon adenocarcinoma patients with high prognosis risk can be effectively identified, and early intervention is realized, so as to improve the prognosis of patients.
(II) technical scheme
Based on the technical problems, the invention provides a construction method of a pre-prognosis model of scorching-related lncRNA of colon adenocarcinoma, which comprises the following steps:
s1, acquiring transcriptome data, miRNA data and patient clinical data of the colon adenocarcinoma from the TCGA database;
s2, screening and obtaining differentially expressed mRNAs and differentially expressed lncRNAs from the transcriptome data, namely DEmRNAs and DElncRNAs, and screening and obtaining differentially expressed miRNAs from the miRNA data, namely DEmiRNAs;
s3, for the DEmiRNAs, the DEmRNAs and the DElncRNAs, obtaining the scorch related mRNAs according to 4 databases of miRcode, TargetScan, miRTarBase and miRDB and a GeneCards database, and the scorch related miRNAs and the scorch related lncRNAs related to the scorch related mRNAs, and constructing a scorch related ceraRNAs network;
s4, integrating the IncRNAs related to the scorching and the clinical data of the patient, and carrying out single-factor Cox regression analysis to obtain IncRNAs related to the survival of the colon adenocarcinoma;
s5, carrying the lncRNA related to the survival of the colon adenocarcinoma into LASSO regression analysis to obtain the weight coefficient of each lncRNA related to the survival of the colon adenocarcinoma, and establishing a apoptosis related lncRNA prognosis model based on a cerRNA network:
wherein XiThe patient has a weight coefficient of lncRNA associated with survival of colon adenocarcinoma, YiIs the expression level of lncRNA associated with the survival of the colon adenocarcinoma possessed by the patient, and n is the number of lncRNA associated with the survival of the colon adenocarcinoma possessed by the patient.
Further, in step S1, the patient clinical data includes the patient' S age, sex, tumor stage, and survival or not, and the transcriptome data and miRNA data include the expression level of each RNA in each sample.
Further, in step S2, the screening conditions include fdr <0.05, log | FC | >1, fdr is a false discovery rate, FC is a multiple of difference, the screening is performed by an edgeR software package of the R language, in step S4, a one-factor Cox regression analysis is performed by using an R software survival software package, p in the one-factor Cox proportional risk regression analysis is <0.05, and in step S5, LASSO regression analysis is performed by using a glmnet software package.
Further, the step S3 includes the following steps:
s3.1, for the DEmiRNAs, the DEmRNAs and the DElncRNAs, obtaining a DElncRNA-DEmiRNA relational pair according to a miRcode database, and obtaining a DEmiRNA targeted mRNA according to targetScan, mirTarbabase and 3 miRDB databases;
s3.2, acquiring a tar death related gene from a GeneCards database;
s3.3, taking intersection of the DEmiRNA targeted mRNA, the DEmRNAs in the step S2 and the scorch related genes to obtain the scorch related mRNAs;
s3.4, obtaining DEMIRNAs and DElNCRNAs associated with the scorch related mRNAs according to the DElncRNA-DEmiRNA relation and the DEmiRNA targeted mRNA, namely the scorch related miRNAs and the scorch related lncRNAs, and drawing a scorch related ceRNA network.
Further, the step S2 obtains 5373 DEmRNAs, 355 DEmiRNAs and 1159 demncrnas; in step S3, the apoptosis-related mRNAs include TXNIP, SESN2, CEBPB, ALK, and IL1B, and the apoptosis-related ceRNA network includes 5 apoptosis-related mRNAs, 7 apoptosis-related miRNAs, and 132 apoptosis-related lncRNAs.
Further, the lncRNA related to the survival of the colon adenocarcinoma comprises: HOTAIR, LINC00402, SFTA1P, ZRANB2-AS1, LINC00461, MYB-AS1, DSCR8, TP53TG1, CYP1B1-AS1, LINC00330, ALMS1-IT 1; in step S5, the prognosis model of the apoptosis-related lncRNA is:
prognostic score ═ 0.0013 × hotaarrexp) + (0.0174 × LINC00402exp) + (0.0186 × SFTA1Pexp) + (0.0373 × LINC00461exp) + (-0.2108 × ZRANB2-AS1exp) + (-0.0012 × TP53TG1exp) + (-0.0647 × MYB-AS1exp) + (0.0032 × DSCR8exp) + (0.0084 × LINC 0130 exp) + (0.00556 × CYP1B1-AS1exp) + (0.0053 × ALMS1-IT1exp), said subscript exp indicating the expression level of the corresponding lncRNA.
The invention also discloses a construction system of the pre-prognosis model of the scorching-related lncRNA of the colon adenocarcinoma, which comprises the following steps:
at least one processor; and at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the construction method, which comprises the following steps connected in sequence:
a tar death related cerana network construction module for executing the steps S1-S3;
an lncRNA determination module associated with survival of colon adenocarcinoma, performing said step S4;
and a prognostic model building module executing the step S5.
The invention also discloses a pancreatic death-related lncRNA prognosis model of the colon adenocarcinoma, which is constructed by the construction method of the pancreatic death-related lncRNA prognosis model of the colon adenocarcinoma.
The invention also discloses an application of the pre-model of the scorching-related lncRNA of the colon adenocarcinoma, which has one of the following applications:
taking lncRNA related to survival of the colon adenocarcinoma as a biomarker for evaluating the prognosis risk of the patient;
using the prognostic score of the prognostic model of the apoptosis-related lncRNA to assess the prognostic risk of a patient with colon adenocarcinoma.
The invention also discloses an evaluation system of the pre-model of the IncRNA related to the scorching of the colon adenocarcinoma, which comprises the following components in sequential connection:
a tar death related ceRNA network construction module for executing the steps S1-S3;
an lncRNA determination module associated with survival of colon adenocarcinoma, performing said step S4;
a prognosis model building module for executing the step S5;
and a prognosis module for carrying out prognosis evaluation based on the IncRNA prognosis model related to the scorching.
A non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the construction method is also disclosed.
(III) advantageous effects
The technical scheme of the invention has the following advantages:
(1) the method extracts mRNA, miRNA and IncRNA data from a TCGA database, performs differential analysis, obtains apoptosis-related mRNAs in an intersection mode, combines a DEIncRNA-DEmiRNA relation pair obtained through database analysis and DEmiRNA-targeted mRNA to obtain an apoptosis-related ceRNA network, utilizes single-factor Cox regression analysis to discuss the relation between expression and survival of apoptosis-related IncRNA according to clinical data, reasonably and reliably screens out apoptosis-related molecular markers, determines a molecular mechanism of potential apoptosis in colon adenocarcinoma, and establishes an IncRNA prognosis model according to LASSO regression analysis, thereby determining the definite biomarkers to predict the prognosis risk of the colon adenocarcinoma patient, providing reliable biomarkers for the prognosis evaluation of the colon adenocarcinoma patient through the prognosis model, improving the evaluation and prediction capacity of the colon adenocarcinoma patient, and performing early diagnosis, early diagnosis and early diagnosis on colon adenocarcinoma, The treatment provides directions, and can improve the prognosis of patients and improve the diagnosis and treatment level to a certain extent;
(2) the prognosis prediction of the constructed tar death related lncRNA prognosis model can take the tar death related lncRNA as an independent prognosis factor, the accuracy of the prognosis prediction is high, the prediction accuracy including high risk and low risk is high, and the accuracy of the prognosis prediction capability within 5 years is high;
(3) compared with the prior art, according to the prognosis model and the evaluation system based on the IncRNA model related to the apoptosis of the colon adenocarcinoma, an IncRNA model related to the apoptosis is determined to predict the prognosis of a colon adenocarcinoma sample in consideration of the important significance of the IncRNA and the apoptosis in the biology of the colon adenocarcinoma, and the prognosis of the colon adenocarcinoma sample is accurately judged; by extracting the apoptosis-related lncRNA more accurately, the selected apoptosis-related lncRNA can reflect the conditions of most colon adenocarcinoma samples, so that the final prognosis model has universality, more patients benefit, and the prognosis accuracy of the colon adenocarcinoma can be further improved.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a flowchart of a method for constructing a prognosis model of IncRNA associated with apoptosis of colon adenocarcinoma according to an embodiment of the present invention;
FIG. 2 is a volcanic and thermographic map of DEmRNAs, DEmRNAs and DElncRNAs of an embodiment of the present invention, wherein a is a volcanic map of DEmRNAs, b is a thermographic map of DEmRNAs, c is a volcanic map of DEmiRNAs, d is a thermographic map of DEmiRNAs, e is a volcanic map of DElncRNAs, and f is a thermographic map of DElncRNAs;
FIG. 3 is a Venn diagram of focus-death-related mRNAs obtained by intersection set according to an embodiment of the present invention;
FIG. 4 is a pyro-death-related ceRNA network according to an embodiment of the present invention;
FIG. 5 is a forest diagram of univariate analysis of lncRNA associated with survival of colon adenocarcinoma according to an embodiment of the present invention;
FIG. 6 is a LASSO regression analysis of an embodiment of the invention;
FIG. 7 is a Kaplan-Meier survival analysis curve according to an embodiment of the present invention;
FIG. 8 is a ROC curve showing the prognostic power for 1, 3 and 5 years for an example of the present invention;
FIG. 9 is an expression heat map, risk score and survival status distribution of a colon adenocarcinoma sample in accordance with an embodiment of the present invention;
FIG. 10 shows the results of single-factor and multi-factor Cox regression analysis according to embodiments of the present invention;
FIG. 11 is a nomogram that verifies the predictive power of the model after prediction, according to an embodiment of the present invention;
FIG. 12 is a calibration graph for 1 year survival to verify the predictive power of the model after prediction, in accordance with an embodiment of the present invention;
FIG. 13 is a calibration graph for 3-year survival to verify the predictive power of the model prediction in anticipation, in accordance with an embodiment of the present invention;
FIG. 14 is a calibration graph for 5-year survival to verify the predictive power of the model after prediction, in accordance with an embodiment of the present invention;
fig. 15 is an evaluation system of the prognosis model of the tar death-related lncRNA of colon adenocarcinoma according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The embodiment of the invention relates to a construction method of a pre-prognosis model of tar death related lncRNA of colon adenocarcinoma, which comprises the following steps as shown in figure 1:
step S1, data collection and processing: acquiring transcriptome data, miRNA data and clinical data of the colon adenocarcinoma from a TCGA database;
the TCGA (the Cancer Genome atlas) database is a tumor Genome map database; miRNA (MicroRNA) is a non-coding single-stranded RNA molecule with the length of about 22 nucleotides, which is coded by endogenous genes and is involved in the regulation and control of the expression of genes after transcription in animals and plants; transcriptome data includes, but is not limited to, mRNA data and lncRNA data; the patient clinical data includes the age, sex, tumor stage, survival or not of the patient, and the transcriptome data and miRNA data include the expression level of each RNA in the respective sample; statistical analysis was performed using the R programming language (version 4.1.0) and Perl (version v5.32.1).
Step S2, screening the differential expression genes: using an edgeR software package of an R language, extracting differentially expressed mRNAs and differentially expressed lncRNAs (namely DEmRNAs and DElncRNAs) from the transcriptome data according to screening conditions, and extracting differentially expressed miRNAs (namely DEmiRNAs) from the miRNA data;
the lncRNAs are long-chain non-coding RNAs, the mRNAs are messenger RNAs, and the mRNAs are single-stranded RNAs which are transcribed by taking one strand of DNA as a template and can carry genetic information to guide protein synthesis; the screening conditions are fdr <0.05, log | FC | >1, fdr (false discovery rate), FC (fold change) is the difference multiple, and the genes meeting the threshold condition are differential expression genes which represent the complexity of tumor growth and metastatic spread to a great extent.
The example of the present invention obtained 5373 DEmRNAs (2886 up-regulated and 2487 down-regulated), 355 DEmRNAs (217 up-regulated and 138 down-regulated) and 1159 DElncRNAs (819 up-regulated and 340 down-regulated) by differential analysis, and the volcanic plots of the DEmRNAs, the DEmiRNAs and the DElncRNAs are shown as a, c and e in fig. 2, and the heat plots of the DEmRNAs, the DEmiRNAs and the DElncRNAs are shown as b, d and f in fig. 2.
Step S3, construction of a apoptosis-related ceRNA network: for the DEmiRNAs, the DEmRNAs and the DElncRNAs, acquiring scorch related mRNAs according to 4 databases of miRcode, TargetScan, miRTarBase and miRDB and a GeneCards database, and constructing a scorch related CERNA network by using the scorch related mRNAs and the scorch related lncRNAs associated with the scorch related mRNAs;
s3.1, for the DEmiRNAs, the DEmRNAs and the DElncRNAs, obtaining a DElncRNA-DEmiRNA relational pair according to a miRcode database, and obtaining a DEmiRNA targeted mRNA (namely miTG) according to targetScan, mirtarBase and 3 miRDB databases;
the miRcode database is a human miRNA combined map database, the TargetScan is a miRNA Target gene prediction database, the miRTarBase is a database specially collected with microRNA-mRNA targeting relations (MTI, MicroRNA-Target Interactions) supported by experimental evidence, and the mirDB is an online miRNA database;
s3.2, acquiring a tar death related gene from a GeneCards database;
the GeneCards database is a searchable comprehensive database and provides concise genome, proteome, transcriptomics, heredity and function of all known and predicted human genes, and 155 apoptosis-related genes are obtained in the embodiment of the invention;
s3.3, taking intersection of the DEmiRNA targeted mRNA, the DEmRNAs in the step S2 and the scorch related genes to obtain the scorch related mRNAs;
in the embodiment of the present invention, 1533 miTG, 5373 DEmRNAs obtained in step S2 and 155 joule-death related genes obtained from GeneCards database are intersected to obtain 5 joule-death related mRNAs, including TXNIP, SESN2, CEBPB, ALK, IL1B, as shown in fig. 3, where PRGs: a scorch-associated gene.
S3.4, obtaining DEMIRNAs and DElNCRNAs associated with the scorch related mRNAs according to the DElncRNA-DEmiRNA relation and the DEmiRNA targeted mRNA, namely the scorch related miRNAs and the scorch related lncRNAs, and drawing a scorch related ceRNA network.
The embodiment of the invention obtains 7 scorch related miRNAs and 132 scorch related lncRNAs which are related to 5 scorch related mRNAs, and a scorch related ceraRNA network drawn by utilizing Cytoscape software is shown in figure 4;
step S4, survival analysis: integrating the IncRNAs related to the scorching with clinical data of the patient, and performing single-factor Cox regression analysis by adopting an R software survival software package to obtain IncRNAs related to the survival of the colon adenocarcinoma;
integrating lncRNA in the apoptosis-related ceRNA network with survival data of the patient, and performing single-factor Cox proportional risk regression analysis on the lncRNA by using a software package of 'survival' and 'surviviner'; in the embodiment of the present invention, a single-factor Cox proportional hazards regression analysis is performed on 132 loncrnas related to apoptosis in a apoptosis-related cerana network, so as to obtain 11 loncrnas related to colon adenocarcinoma survival (p in the single-factor Cox proportional hazards regression analysis is less than 0.05), where the 11 loncrnas related to colon adenocarcinoma survival are specifically: HOTAIR, LINC00402, SFTA1P, ZRANB2-AS1, LINC00461, MYB-AS1, DSCR8, TP53TG1, CYP1B1-AS1, LINC00330, ALMS1-IT 1; of these, 8 lncRNAs (HOTAIR, LINC00402, SFTA1P, LINC00461, DSCR8, CYP1B1-AS1, LINC00330, ALMS1-IT1) were up-regulated in colon adenocarcinoma tissue, and 3 lncRNAs (ZRANB2-AS1, MYB-AS1, TP53TG1) were down-regulated in colon adenocarcinoma tissue, AS shown in FIG. 5.
Step S5, constructing a prognosis model of the scorch-related lncRNA: bringing the lncRNA related to the survival of the colon adenocarcinoma into LASSO regression analysis to obtain a weight coefficient of each lncRNA, and establishing a tar death related lncRNA prognosis model based on a cerRNA network: (ii) summing the risk scores for the lncrnas associated with colon adenocarcinoma survival that the patient has, each risk score for the lncrnas associated with colon adenocarcinoma survival being a weight coefficient of the lncrnas multiplied by the corresponding lncRNA expression level;
performing LASSO regression analysis by using a glmnet software package, as shown in fig. 6, a constructed prognosis model is the sum of risk scores of lncrnas related to survival of colon adenocarcinoma possessed by a patient, and the risk score is obtained by multiplying a weight coefficient of each lncRNA by a corresponding lncRNA expression quantity, that is, a calculation formula of the risk score is as follows:
wherein, XiThe patient has a weight coefficient of lncRNA associated with survival of colon adenocarcinoma, YiThe patient has an expression level of lncRNA associated with survival of colon adenocarcinoma, n is the patient's expression level of lncRNA associated with survival of colon adenocarcinomaThe number of lncrnas associated with survival of colon adenocarcinoma, and the weight coefficient of lncrnas associated with survival of colon adenocarcinoma were obtained by LASSO regression, and the lncRNA expression level was known in TCGA. In the present example, the risk score of colon adenocarcinoma ═ 0.0013 × hotaarrexp) + (0.0174 × LINC00402exp) + (0.0186 × 0SFTA1Pexp) + (0.0373 × LINC00461exp) + (-0.2108 × ZRANB2-AS1exp) + (-0.0012 × TP53TG1exp) + (-0.0647 × MYB-AS1exp) + (0.0032 × DSCR8exp) + (0.0084 × LINC00330exp) + (0.0156 × CYP1B1-AS1exp) + (0.0053 × ALMS1-IT1 exp).
Evaluation of the pre-treatment model for IncRNA associated with apoptosis:
patients were divided into two groups of high risk and low risk according to median risk score, and prognosis differences between the two groups of higher risk and low risk were analyzed using Kaplan-Meier survival curves, survival analysis showed that Overall Survival (OS) was inferior to that of the high risk group patients, and differences between the two groups were significant (p <0.001), as shown in fig. 7. The accuracy of the model is evaluated by using the ROC curve, and the AUC values of the ROC curve in 1 year, 3 year and 5 year are 0.744, 0.696 and 0.623 respectively, which shows that the model has good prediction capability, and is shown in FIG. 8. The risk profile indicates that the survival time of the patient gradually decreased with increasing risk score, as shown in figure 9. To verify whether this model could be used as an independent prognostic factor independently of other clinical features, one-factor and multi-factor Cox regression analyses were performed, showing that age (P ═ 0.018), clinical stage (P <0.01), T stage (P <0.001), risk score (P <0.001), N stage (P <0.001) and M stage (P <0.028), and the results of the multi-factor Cox regression analyses demonstrated the independence of the PRlncRNA risk model in predicting the prognosis of COAD, as shown in fig. 10. In order to further verify that the apoptosis-related lncRNA model has good prognosis prediction capability, age, sex and tumor stage data of the patient are collected, 1, 3 and 5-year nomogram graphs are drawn and shown in figure 11, and 1, 3 and 5-year nomogram correction graphs are drawn and shown in figures 12, 13 and 14 respectively, so that the model can be further proved to be capable of better predicting 1, 3 and 5-year survival of the colon adenocarcinoma patient.
Finally, it should be noted that the above construction method can be converted into software program instructions, and can be implemented by using a construction system comprising a processor and a memory, or by using computer instructions stored in a non-transitory computer readable storage medium. The integrated unit implemented in the form of a software functional unit may be stored in a computer-readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The application of the prognosis model of lncRNA related to the tar death of the colon adenocarcinoma has one of the following applications: taking lncRNA related to the survival of the colon adenocarcinoma as a biomarker for evaluating the prognosis risk of the patient, and taking no biomarker for the diagnosis or treatment effect of the colon adenocarcinoma; using the prognostic score of the prognostic model of the apoptosis-related lncRNA to assess the prognostic risk of a patient with colon adenocarcinoma.
Based on the same inventive concept, the invention also provides an evaluation system of the delayed colon adenocarcinoma apoptosis-related lncRNA model, as shown in fig. 15, which comprises the following components connected in sequence: a tar death related cerana network construction module for executing the steps S1-S3; an lncRNA determination module associated with survival of colon adenocarcinoma, executing the step S4, for determining lncRNA information associated with survival of colon adenocarcinoma from the RNA information of the plurality of colon adenocarcinoma samples and the RNA information of the plurality of normal colon samples; a prognosis model establishing module, executing the step S5, for analyzing the lncRNA information related to the survival of the colon adenocarcinoma and the clinical data of the plurality of colon adenocarcinoma samples, and establishing a lncRNA prognosis model related to the tar death; and a prognosis module for carrying out prognosis evaluation based on the IncRNA prognosis model related to the scorching.
In conclusion, the prognosis model of the scorching-related lncRNA of the colon adenocarcinoma and the construction method and the application thereof have the following beneficial effects:
(1) the method extracts mRNA, miRNA and lncRNA data from a TCGA database, performs differential analysis, obtains apoptosis-related mRNAs by taking an intersection mode, then obtains an apoptosis-related ceRNA network by combining a DElncRNA-DEmiRNA relation pair obtained by database analysis and the DEmiRNA targeted mRNA, discusses the relation between expression and survival of apoptosis-related lncRNA by using single-factor Cox regression analysis according to clinical data, reasonably and reliably screens out apoptosis-related molecular markers, defines a molecular mechanism potential in colon adenocarcinoma by apoptosis, and establishes an lncRNA prognosis model according to LASSO regression analysis, so that the clear biomarkers are determined to predict the prognosis risk of the colon adenocarcinoma patient, and the prognosis model provides reliable biomarkers for evaluating the prognosis of the colon adenocarcinoma patient, thereby improving the evaluation and prediction capability of the colon adenocarcinoma patient, and early diagnosis, early diagnosis and early diagnosis of colon adenocarcinoma, The treatment provides directions, and can improve the prognosis of patients and improve the diagnosis and treatment level to a certain extent;
(2) the prognosis prediction of the constructed tar death related lncRNA prognosis model can take the tar death related lncRNA as an independent prognosis factor, the accuracy of the prognosis prediction is high, the prediction accuracy including high risk and low risk is high, and the accuracy of the prognosis prediction capability within 5 years is high;
(3) compared with the prior art, according to the prognosis evaluation method and system based on the IncRNA model related to the apoptosis of the colon adenocarcinoma, an IncRNA model related to the apoptosis is determined to predict the prognosis of the colon adenocarcinoma sample in consideration of the important significance of the IncRNA and the apoptosis in the colon adenocarcinoma biology, and the prognosis of the colon adenocarcinoma sample is accurately judged; by extracting the apoptosis-related lncRNA more accurately, the selected apoptosis-related lncRNA can reflect the conditions of most colon adenocarcinoma samples, so that the final prognosis model has universality, more patients benefit, and the prognosis accuracy of the colon adenocarcinoma can be further improved.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A construction method of a pre-model of apoptosis-related lncRNA of colon adenocarcinoma is characterized by comprising the following steps:
s1, acquiring transcriptome data, miRNA data and patient clinical data of the colon adenocarcinoma from the TCGA database;
s2, screening and obtaining differentially expressed mRNAs and differentially expressed lncRNAs from the transcriptome data, namely DEmRNAs and DElncRNAs, and screening and obtaining differentially expressed miRNAs from the miRNA data, namely DEmiRNAs;
s3, for the DEmiRNAs, the DEmRNAs and the DElncRNAs, obtaining the scorch related mRNAs according to 4 databases of miRcode, TargetScan, miRTarBase and miRDB and a GeneCards database, and the scorch related miRNAs and the scorch related lncRNAs related to the scorch related mRNAs, and constructing a scorch related ceraRNAs network;
s4, integrating the IncRNAs related to the scorching and the clinical data of the patient, and carrying out single-factor Cox regression analysis to obtain IncRNAs related to the survival of the colon adenocarcinoma;
s5, carrying the lncRNA related to the survival of the colon adenocarcinoma into LASSO regression analysis to obtain the weight coefficient of each lncRNA related to the survival of the colon adenocarcinoma, and establishing a apoptosis related lncRNA prognosis model based on a cerRNA network:
wherein, XiThe patient has a weight coefficient of lncRNA associated with survival of colon adenocarcinoma, YiIs the expression level of lncRNA associated with the survival of the colon adenocarcinoma possessed by the patient, and n is the number of lncRNA associated with the survival of the colon adenocarcinoma possessed by the patient.
2. The method of claim 1, wherein the patient clinical data includes the patient' S age, sex, tumor stage, and survival, and the transcriptome data and the miRNA data include the expression level of each RNA in each sample in step S1.
3. The method of claim 1, wherein in step S2, the screening conditions include fdr <0.05, log | FC | >1, fdr being a false discovery rate, FC being a multiple of difference, and the screening is performed by using an edgeR software package in R language, in step S4, the R software survival software package is used for single-factor Cox regression analysis, p in the single-factor Cox proportional risk regression analysis is <0.05, and in step S5, the glmnet software package is used for LASSO regression analysis.
4. The method for constructing a prognostic model of apoptosis-related lncRNA according to claim 1, wherein said step S3 includes the steps of:
s3.1, for the DEmiRNAs, the DEmRNAs and the DElncRNAs, obtaining a DElncRNA-DEmiRNA relational pair according to a miRcode database, and obtaining a DEmiRNA targeted mRNA according to targetScan, mirTarbabase and 3 miRDB databases;
s3.2, acquiring a tar death related gene from a GeneCards database;
s3.3, taking intersection of the DEmiRNA targeted mRNA, the DEmRNAs in the step S2 and the scorching related genes to obtain the scorching related mRNAs;
s3.4, obtaining DEMIRNAs and DElNCRNAs associated with the scorch related mRNAs according to the DElncRNA-DEmiRNA relation and the DEmiRNA targeted mRNA, namely the scorch related miRNAs and the scorch related lncRNAs, and drawing a scorch related ceRNA network.
5. The method for constructing a prognosis model of IncRNA associated with focal death of colon adenocarcinoma according to claim 1, wherein said step S2 provides 5373 DEmRNAs, 355 DEmRNAs and 1159 DElNCRNAs; in step S3, the apoptosis-related mRNAs include TXNIP, SESN2, CEBPB, ALK, and IL1B, and the apoptosis-related cerana network includes 5 apoptosis-related mRNAs, 7 apoptosis-related miRNAs, and 132 apoptosis-related lncRNAs.
6. The method for constructing a prognostic model of IncRNA associated with apoptosis of colon adenocarcinoma according to claim 1, wherein the IncRNA associated with survival of colon adenocarcinoma in step S4 comprises: HOTAIR, LINC00402, SFTA1P, ZRANB2-AS1, LINC00461, MYB-AS1, DSCR8, TP53TG1, CYP1B1-AS1, LINC00330, ALMS1-IT 1; in step S5, the tar death-related lncRNA prognosis model is:
prognostic score ═ 0.0013 × hotaarrexp) + (0.0174 × LINC00402exp) + (0.0186 × SFTA1Pexp) + (0.0373 × LINC00461exp) + (-0.2108 × ZRANB2-AS1exp) + (-0.0012 × TP53TG1exp) + (-0.0647 × MYB-AS1exp) + (0.0032 × DSCR8exp) + (0.0084 × LINC 0130 exp) + (0.00556 × CYP1B1-AS1exp) + (0.0053 × ALMS1-IT1exp), said subscript exp indicating the expression level of the corresponding lncRNA.
7. A pre-model construction system for IncRNA related to tar death of colon adenocarcinoma is characterized by comprising the following steps:
at least one processor; and at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the construction method of any of claims 1 to 6, comprising in series:
a tar death related cerana network construction module for executing the steps S1-S3;
an lncRNA determination module associated with survival of colon adenocarcinoma, performing said step S4;
and a prognostic model building module executing the step S5.
8. A Joule death-related IncRNA prognosis model for colon adenocarcinoma, constructed according to the Joule death-related IncRNA prognosis model construction method for colon adenocarcinoma of any one of claims 1 to 6.
9. Use of the prognostic model of apoptosis-related lncRNA according to claim 8, characterized by the use of one of the following:
taking lncRNA related to the survival of the colon adenocarcinoma as a biomarker for evaluating the prognosis risk of the patient;
using the prognostic score of the prognostic model of the apoptosis-related lncRNA to assess the prognostic risk of a patient with colon adenocarcinoma.
10. An assessment system according to claim 8, comprising in series:
a tar death related cerana network construction module for executing the steps S1-S3;
an lncRNA determination module associated with survival of colon adenocarcinoma, performing said step S4;
a prognosis model building module for executing the step S5;
and a prognosis module for carrying out prognosis evaluation based on the IncRNA prognosis model related to the scorching.
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