Method and application of pyroptosis-related lncrna prognostic model for colon adenocarcinoma

Abstract

The application discloses a pyroptosis-related lncRNA prognosis model for colon adenocarcinoma, and a construction method and application thereof. The construction method comprises the following steps: S1, obtaining the transcriptome data, miRNA data and patient clinical data of colon adenocarcinoma from a TCGA database; S2, screening DEmRNAs, DElncRNAs and DEmiRNAs; S3, screening pyroptosis-related mRNAs, associated pyroptosis-related miRNAs and pyroptosis-related lncRNAs according to the database, and constructing a pyroptosis-related ceRNA network; S4, integrating the pyroptosis-related lncRNAs and the clinical data, performing single-factor Cox regression analysis, and obtaining lncRNAs related to the survival of colon adenocarcinoma; and S5, establishing a pyroptosis-related lncRNA prognosis model according to LASSO regression analysis. The application realizes the prognosis determination of colon adenocarcinoma through the pyroptosis-related lncRNA.

Description

Technical Field

[0001] This invention relates to the fields of tumor molecular biology and biomedical detection technology, and in particular to a method for constructing and applying a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma. Background Technology

[0002] Colorectal cancer is a common malignant tumor of the digestive system occurring in the colon. It is the third most common type of cancer and the second leading cause of cancer-related mortality worldwide. Although modern research has revealed the pathogenesis of colorectal cancer and provided enhanced screening strategies, its incidence continues to rise, seriously threatening human health. Colonic adenocarcinoma is the most common pathological type of colon cancer, a disease involving multiple etiologies, stages, and genes. The difficulty in early diagnosis is one of the main factors affecting the survival of patients with colonic adenocarcinoma, and currently there is no effective treatment. Pyroptosis is a gasdermin-mediated inflammatory programmed cell death process characterized by cell swelling, porosity formation, and the release of large amounts of inflammatory factors, such as IL-1β and IL-18. It has received increasing attention due to its association with innate immunity and disease. Pyroptosis is typically triggered by both classical and non-classical pathways. In recent years, increasing research has shown that pyroptosis is involved in tumorigenesis and development. The main therapeutic strategy for tumors is to induce cell death, and some researchers are attempting to find novel targeted therapies for colonic adenocarcinoma by activating the pyroptotic pathway.

[0003] In recent years, non-coding RNAs (ncRNAs) have been shown to be involved in the development and progression of colorectal cancer. As is well known, ncRNAs belong to a class of transcripts that are not translated into proteins, but they play important roles in various cellular and physiological processes. Among them, long non-coding RNAs (lncRNAs) are ncRNAs longer than 200 nucleotides, and they typically act as competitive endogenous RNAs (ceRNAs) to regulate the expression of specific miRNAs, thereby targeting downstream molecules of these miRNAs. The ceRNA hypothesis reveals a new mechanism of RNA-RNA interactions. These ceRNA molecules (mRNA, lncRNAs, etc.) can competitively bind to the same miRNAs through miRNA response elements (MREs) to regulate each other's expression levels. In fact, lncRNAs can interact with RNA, DNA, and proteins to form RNA-RNA, RNA-DNA, and RNA-protein complexes, regulating gene expression through multiple mechanisms, including regulating transcription, mRNA stability, and translation. Simultaneously, lncRNAs also affect chromatin structure and regulate gene expression. Abnormal expression of many lncRNAs has now been found to be associated with the clinicopathological characteristics of colorectal cancer.

[0004] However, there are currently no studies on pyroptosis-related lncRNAs in colorectal adenocarcinoma, and there are no clear reference standards for predicting the prognosis of colorectal adenocarcinoma using pyroptosis-related lncRNAs. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the aforementioned issues, this invention provides a method for constructing and applying a pyroptosis-related lncRNA prognostic model for colorectal adenocarcinoma. lncRNAs are prognostic molecular markers, and this invention addresses the lack of research on pyroptosis-related lncRNAs in colorectal adenocarcinoma, as well as the absence of clear reference standards for predicting the prognosis of colorectal adenocarcinoma using pyroptosis-related lncRNAs. By screening for pyroptosis-related molecular markers and using pyroptosis-related lncRNAs to predict the prognosis of colorectal adenocarcinoma, the constructed prognostic model provides reliable biomarkers for the prognostic assessment of colorectal adenocarcinoma patients, improving the ability to predict the prognosis of colorectal adenocarcinoma patients and effectively identifying colorectal adenocarcinoma patients with high prognostic risk, enabling early intervention and thus improving patient prognosis.

[0007] (II) Technical Solution

[0008] To address the aforementioned technical problems, this invention provides a method for constructing a pyroptosis-related lncRNA prognostic model for colorectal adenocarcinoma, comprising the following steps:

[0009] S1. Obtain transcriptomic data, miRNA data, and patient clinical data for colon adenocarcinoma from the TCGA database;

[0010] S2. Differentially expressed mRNAs and differentially expressed lncRNAs, namely DEmRNAs and DElncRNAs, are obtained from the transcriptome data. Differentially expressed miRNAs, namely DEmiRNAs, are obtained from the miRNA data.

[0011] S3. Based on the DEmiRNAs, DEmRNAs and DElncRNAs, pyroptosis-related mRNAs are obtained from the miRcode, TargetScan, miRTarBase and miRDB databases and the GeneCards database, as well as pyroptosis-related miRNAs and pyroptosis-related lncRNAs associated with the pyroptosis-related mRNAs, and a pyroptosis-related ceRNA network is constructed.

[0012] S4. Integrate the pyroptosis-related lncRNAs with the patient's clinical data and perform univariate Cox regression analysis to obtain lncRNAs associated with colon adenocarcinoma survival;

[0013] S5. Incorporate the lncRNAs associated with colon adenocarcinoma survival into LASSO regression analysis to obtain the weight coefficient of each lncRNA associated with colon adenocarcinoma survival, and establish a pyroptosis-related lncRNA prognostic model based on ceRNA network: Prognostic score = ,in, The weighting coefficients of lncRNAs associated with colon adenocarcinoma survival in patients. The expression level of lncRNAs associated with colon adenocarcinoma survival in the patient is represented by n, where n is the number of lncRNAs associated with colon adenocarcinoma survival in the patient.

[0014] Furthermore, in step S1, the patient clinical data includes the patient's age, gender, tumor stage, and survival status, and the transcriptome data and miRNA data include the expression level of each RNA in each sample.

[0015] Furthermore, in step S2, the screening conditions are fdr < 0.05 and log|FC| > 1, where fdr is the false discovery rate and FC is the difference fold. The screening is performed using the edgeR software package in R. In step S4, a one-way Cox regression analysis is performed using the survival software package in R. The p < 0.05 in the one-way Cox proportional hazards regression analysis is used. In step S5, a LASSO regression analysis is performed using the glmnet software package.

[0016] Furthermore, step S3 includes the following steps:

[0017] S3.1 For the DEmiRNAs, DEmRNAs and DElncRNAs, obtain the DElncRNA-DEmiRNA relationship pairs according to the miRcode database, and obtain the mRNAs targeted by the DEmiRNAs according to the three databases TargetScan, miRTarBase and miRDB;

[0018] S3.2, Pyroptosis-related genes obtained from the GeneCards database;

[0019] S3.3. Take the intersection of the DEmiRNA-targeted mRNA, the DEmRNAs described in step S2, and the pyroptosis-related genes to obtain pyroptosis-related mRNAs;

[0020] S3.4. Based on the DElncRNA-DEmiRNA relationship pairs and the mRNAs targeted by DEmiRNAs, obtain the DEmiRNAs and DElncRNAs associated with the pyroptosis-related mRNAs, namely pyroptosis-related miRNAs and pyroptosis-related lncRNAs, and draw the pyroptosis-related ceRNA network.

[0021] Furthermore, step S2 yields 5373 DEmRNAs, 355 DEmiRNAs, and 1159 DElncRNAs; in step S3, the pyroptosis-related mRNAs include TXNIP, SESN2, CEBPB, ALK, and IL1B, and the pyroptosis-related ceRNA network includes 5 pyroptosis-related mRNAs, 7 pyroptosis-related miRNAs, and 132 pyroptosis-related lncRNAs.

[0022] Furthermore, the lncRNAs associated with colon adenocarcinoma survival include: HOTAIR, LINC00402, SFTA1P, ZRANB2-AS1, LINC00461, MYB-AS1, DSCR8, TP53TG1, CYP1B1-AS1, LINC00330, and ALMS1-IT1; in step S5, the pyroptosis-related lncRNA prognostic model is as follows:

[0023] Prognostic score = (0.0013 × HOTAIR) exp ) + (0.0174×LINC00402 exp ) + (0.0186×SFTA1P exp ) + (0.0373×LINC00461 exp ) + (-0.2108×ZRANB2-AS1 exp ) + (-0.0012×TP53TG1 exp ) + (-0.0647×MYB-AS1 exp ) + (0.0032×DSCR8 exp ) + (0.0084×LINC00330 exp )+ (0.0156×CYP1B1-AS1 exp ) + (0.0053×ALMS1-IT1 exp The subscript exp indicates the expression level of the corresponding lncRNA.

[0024] This invention also discloses a system for constructing a pyroptosis-related lncRNA prognostic model for colorectal adenocarcinoma, comprising:

[0025] At least one processor; and at least one memory communicatively connected to said processor, wherein:

[0026] The memory stores program instructions that can be executed by the processor. The processor can execute the construction method by calling the program instructions, which includes the following sequentially linked components:

[0027] The pyroptosis-related ceRNA network construction module performs steps S1-S3;

[0028] The lncRNA identification module associated with colon adenocarcinoma survival performs step S4;

[0029] The prognostic model building module executes step S5.

[0030] This invention also discloses a method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma, which provides a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma.

[0031] This invention also discloses an application of the aforementioned pyroptosis-related lncRNA prognostic model for colon adenocarcinoma, having one of the following applications:

[0032] lncRNAs associated with survival in colon adenocarcinoma were used as biomarkers to assess prognostic risk in patients.

[0033] The prognostic score of the pyroptosis-related lncRNA prognostic model was used to assess the prognostic risk of patients with colorectal adenocarcinoma.

[0034] This invention also discloses an evaluation system for a pyroptosis-related lncRNA prognostic model of colon adenocarcinoma, comprising the following components connected in sequence:

[0035] The pyroptosis-related ceRNA network construction module performs steps S1-S3;

[0036] The lncRNA identification module associated with colon adenocarcinoma survival performs step S4;

[0037] The prognostic model building module executes step S5;

[0038] And a prognostic module for prognostic assessment based on the pyroptosis-related lncRNA prognostic model.

[0039] The present invention also discloses a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the construction method described herein.

[0040] (III) Beneficial Effects

[0041] The above-described technical solution of the present invention has the following advantages:

[0042] (1) This invention extracts mRNA, miRNA and lncRNA data from the TCGA database, performs differential analysis, and obtains pyroptosis-related mRNAs by taking the intersection. Then, it combines the DElncRNA-DEmiRNA relationship pairs obtained through database analysis and the mRNAs targeted by DEmiRNA to obtain a pyroptosis-related ceRNA network. Based on clinical data, it uses univariate Cox regression analysis to explore the relationship between the expression and survival of pyroptosis-related lncRNAs, rationally and reliably screens out pyroptosis-related molecular markers, clarifies the potential molecular mechanism of pyroptosis in colorectal adenocarcinoma, and establishes a lncRNA prognostic model based on LASSO regression analysis. This identifies specific biomarkers to predict the prognostic risk of colorectal adenocarcinoma patients and provides reliable biomarkers for assessing the prognosis of colorectal adenocarcinoma patients through this prognostic model. This improves the ability to assess and predict the prognosis of colorectal adenocarcinoma patients, provides direction for the early diagnosis and treatment of colorectal adenocarcinoma, and can improve the prognosis of patients and improve the level of diagnosis and treatment to a certain extent.

[0043] (2) The pyroptosis-related lncRNA prognostic model constructed in this invention can use pyroptosis-related lncRNA as an independent prognostic factor, and the accuracy of the prognostic prediction is high, including high accuracy in predicting high risk and low risk, and high accuracy in predicting prognosis within 5 years.

[0044] (3) Compared with the prior art, the prognostic model and evaluation system based on the pyroptosis-related lncRNA model of colon adenocarcinoma according to the present invention, considering the important significance of lncRNA and pyroptosis in the biology of colon adenocarcinoma, determines a pyroptosis-related lncRNA model to predict the prognosis of colon adenocarcinoma samples, and makes a more accurate determination of the prognosis of colon adenocarcinoma samples; by extracting pyroptosis-related lncRNA more accurately, the selected pyroptosis-related lncRNA can reflect the situation of most colon adenocarcinoma samples, making the final prognostic model universal, benefiting more patients, and further improving the prognostic accuracy of colon adenocarcinoma. Attached Figure Description

[0045] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings:

[0046] Figure 1 This is a flowchart illustrating the method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma according to an embodiment of the present invention;

[0047] Figure 2The images shown are volcano plots and heatmaps of DEmRNAs, DEmiRNAs, and DElncRNAs according to embodiments of the present invention, wherein image a is a volcano plot of DEmRNAs, image b is a heatmap of DEmRNAs, image c is a volcano plot of DEmiRNAs, image d is a heatmap of DEmiRNAs, image e is a volcano plot of DElncRNAs, and image f is a heatmap of DElncRNAs.

[0048] Figure 3 The intersection of the data in this embodiment of the invention yields a Venn diagram of pyroptosis-related mRNAs.

[0049] Figure 4 This is a pyroptosis-related ceRNA network according to an embodiment of the present invention;

[0050] Figure 5 Forest plot of univariate analysis of lncRNAs associated with colon adenocarcinoma survival in an embodiment of the present invention;

[0051] Figure 6 LASSO regression analysis for an embodiment of the present invention;

[0052] Figure 7 The Kaplan-Meier survival analysis curves are from an embodiment of the present invention.

[0053] Figure 8 ROC curves of the prognostic predictive ability of this invention at 1, 3, and 5 years are shown in the embodiments of the present invention.

[0054] Figure 9 The expression heatmap, risk score, and survival status distribution of colon adenocarcinoma samples according to embodiments of the present invention;

[0055] Figure 10 These are the results of univariate and multivariate Cox regression analyses in embodiments of the present invention;

[0056] Figure 11 The nomogram is used to verify the prognostic prediction ability of the model in this embodiment of the invention.

[0057] Figure 12 A calibration graph of 1-year survival is used to verify the prognostic predictive ability of the model in this embodiment of the invention.

[0058] Figure 13 A calibration graph of 3-year survival is used to verify the prognostic predictive ability of the model in this embodiment of the invention.

[0059] Figure 14 A calibration graph of 5-year survival is used to verify the prognostic predictive ability of the model in this embodiment of the invention.

[0060] Figure 15This invention provides an evaluation system for a pyroptosis-related lncRNA prognostic model of colon adenocarcinoma. Detailed Implementation

[0061] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0062] This invention provides a method for constructing a pyroptosis-related lncRNA prognostic model for colorectal adenocarcinoma, such as... Figure 1 As shown, it includes the following steps:

[0063] Step S1, Data Collection and Processing: Obtain transcriptomic data, miRNA data, and clinical data of colorectal adenocarcinoma from the TCGA database;

[0064] The TCGA (The Cancer Genome Atlas) database is a tumor genome mapping database; miRNAs (microRNAs) are a class of non-coding single-stranded RNA molecules, approximately 22 nucleotides in length, encoded by endogenous genes, which participate in post-transcriptional gene expression regulation in animals and plants; transcriptome data includes, but is not limited to, mRNA and lncRNA data; the patient clinical data includes the patient's age, sex, tumor stage, and survival status, and the transcriptome and miRNA data include the expression level of each RNA in each sample; statistical analysis was performed using the R programming language (version 4.1.0) and Perl (version v5.32.1).

[0065] Step S2: Screening differentially expressed genes: Using the edgeR software package in R language, differentially expressed mRNAs and differentially expressed lncRNAs (DEmRNAs and DElncRNAs) are extracted from the transcriptome data according to the screening criteria. Differentially expressed miRNAs (DEmiRNAs) are also extracted from the miRNA data.

[0066] lncRNAs are long non-coding RNAs, while mRNAs are messenger RNAs. mRNAs are single-stranded RNAs transcribed from one strand of DNA as a template, carrying genetic information and guiding protein synthesis. The selection criteria are fdr < 0.05 and log|FC| > 1. fdr (False discovery rate) is the false discovery rate, and FC (fold change) is the fold change. Genes that meet the threshold conditions are differentially expressed genes. Differentially expressed genes largely represent the complexity of tumor growth and metastasis.

[0067] Differential analysis in this invention yielded 5373 DEmRNAs (2886 upregulated, 2487 downregulated), 355 DEmiRNAs (217 upregulated, 138 downregulated), and 1159 DElncRNAs (819 upregulated, 340 downregulated). The volcano plots of DEmRNAs, DEmiRNAs, and DElncRNAs are shown below. Figure 2 As shown in Figures a, c, and e, the heatmaps of DEmRNAs, DEmiRNAs, and DElncRNAs are as follows: Figure 2 As shown in b, d, and f.

[0068] Step S3: Construction of the pyroptosis-related ceRNA network: Based on the DEmiRNAs, DEmRNAs, and DElncRNAs, pyroptosis-related mRNAs are obtained from the miRcode, TargetScan, miRTarBase, and miRDB databases, as well as the GeneCards database. Pyroptosis-related miRNAs and pyroptosis-related lncRNAs associated with the pyroptosis-related mRNAs are then constructed to form a pyroptosis-related ceRNA network.

[0069] S3.1 For the DEmiRNAs, DEmRNAs and DElncRNAs, obtain the DElncRNA-DEmiRNA relationship pairs according to the miRcode database, and obtain the mRNA targeted by the DEmiRNA, i.e. miTG, according to the three databases TargetScan, miRTarBase and miRDB.

[0070] miRcode is a human miRNA binding map database, TargetScan is a miRNA target gene prediction database, miRTarBase is a database that collects microRNA-mRNA target interactions (MTI) supported by experimental evidence, and miRDB is an online miRNA database.

[0071] S3.2, Pyroptosis-related genes obtained from the GeneCards database;

[0072] The GeneCards database is a comprehensive searchable database that provides concise information on all known and predicted human genes in terms of genome, proteomics, transcriptomics, genetics, and function. In this embodiment of the invention, 155 pyroptosis-related genes were obtained.

[0073] S3.3. Take the intersection of the DEmiRNA-targeted mRNA, the DEmRNAs described in step S2, and the pyroptosis-related genes to obtain pyroptosis-related mRNAs;

[0074] In this embodiment of the invention, the intersection of 1533 miTGs, 5373 DEmRNAs obtained in step S2, and 155 pyroptosis-related genes obtained from the GeneCards database yielded 5 pyroptosis-related mRNAs, including TXNIP, SESN2, CEBPB, ALK, and IL1B, as shown below. Figure 3 As shown in the figure, PRGs: pyroptosis-related genes.

[0075] S3.4. Based on the DElncRNA-DEmiRNA relationship pairs and the mRNAs targeted by DEmiRNAs, obtain the DEmiRNAs and DElncRNAs associated with the pyroptosis-related mRNAs, namely pyroptosis-related miRNAs and pyroptosis-related lncRNAs, and draw the pyroptosis-related ceRNA network.

[0076] In this embodiment of the invention, 7 pyroptosis-related miRNAs and 132 pyroptosis-related lncRNAs were associated with 5 pyroptosis-related mRNAs. The pyroptosis-related ceRNA network drawn using Cytoscape software is shown below. Figure 4 As shown;

[0077] Step S4, Survival Analysis: The pyroptosis-related lncRNAs were integrated with the patient's clinical data, and univariate Cox regression analysis was performed using the R software survival package to obtain the lncRNAs associated with colon adenocarcinoma survival.

[0078] The lncRNAs in the pyroptosis-related ceRNA network were integrated with patient survival data, and univariate Cox proportional hazards regression analysis was performed using the "survival" and "survminer" software packages. In this embodiment of the invention, univariate Cox proportional hazards regression analysis was performed on 132 pyroptosis-related lncRNAs in the pyroptosis-related ceRNA network, and 11 lncRNAs associated with colorectal adenocarcinoma survival were obtained (p<0.05 in univariate Cox proportional hazards regression analysis). The 11 lncRNAs associated with colorectal adenocarcinoma survival are: HOTAIR, LINC00402, SFTA1P, ZRANB2-AS1, LINC00461, MYB-AS1, DSCR8, TP53TG1, CYP1B1-AS1, LINC00330, and ALMS1-IT1; among them, 8 lncRNAs (HOTAIR, LINC00402, SFTA1P, LINC00461, DSCR8, CYP1B1-AS1, ...) LINC00330 and ALMS1-IT1 were upregulated in colon adenocarcinoma tissues, while three lncRNAs (ZRANB2-AS1, MYB-AS1, and TP53TG1) were downregulated in colon adenocarcinoma tissues. Figure 5 As shown.

[0079] Step S5: Construction of the pyroptosis-related lncRNA prognostic model: The lncRNAs associated with colon adenocarcinoma survival are included in LASSO regression analysis to obtain the weight coefficient of each lncRNA, and a pyroptosis-related lncRNA prognostic model based on ceRNA network is established: the risk score of the lncRNA associated with colon adenocarcinoma survival of the patient is the sum of the risk scores of the lncRNAs associated with colon adenocarcinoma survival, and the risk score of each lncRNA associated with colon adenocarcinoma survival is the weight coefficient of the lncRNA multiplied by the corresponding lncRNA expression level;

[0080] LASSO regression analysis was performed using the glmnet software package, such as... Figure 6 As shown, the constructed prognostic model is the sum of risk scores for lncRNAs associated with colon adenocarcinoma survival in patients. The risk score is the weight coefficient of each lncRNA multiplied by its corresponding lncRNA expression level. The formula for calculating the risk score is: Prognostic Score = ,in, The weighting coefficients of lncRNAs associated with colon adenocarcinoma survival in patients. The expression level of lncRNAs associated with colon adenocarcinoma survival in the patient is denoted as n, where n is the number of lncRNAs associated with colon adenocarcinoma survival in the patient. The weighting coefficients of lncRNAs associated with colon adenocarcinoma survival are obtained from LASSO regression, and the expression levels of lncRNAs are known in TCGA. In this embodiment of the invention, the risk score for colon adenocarcinoma = (0.0013 × HOTAIR) exp ) + (0.0174×LINC00402 exp ) + (0.0186×SFTA1P exp ) + (0.0373×LINC00461 exp ) + (-0.2108×ZRANB2-AS1 exp ) + (-0.0012×TP53TG1 exp ) + (-0.0647× MYB-AS1 exp ) + (0.0032×DSCR8 exp ) + (0.0084×LINC00330 exp ) + (0.0156×CYP1B1-AS1 exp ) + (0.0053×ALMS1-IT1 exp ).

[0081] Evaluation of the pyroptosis-related lncRNA prognostic model:

[0082] Patients were divided into high-risk and low-risk groups based on the median risk score. Kaplan-Meier survival curve analysis was used to compare the prognostic differences between the two groups. Survival analysis showed that the overall survival (OS) of patients in the high-risk group was worse than that in the low-risk group, and the difference between the two groups was statistically significant (p<0.001). Figure 7 As shown in the figure. The accuracy of the model was evaluated using ROC curves. The AUC values ​​for 1, 3, and 5 years were 0.744, 0.696, and 0.623, respectively, indicating that the model has good predictive ability. Figure 8 As shown in the diagram, the risk graph indicates that as the risk score increases, the patient's survival time gradually decreases, such as... Figure 9As shown. To verify whether the model can act as an independent prognostic factor independent of other clinical characteristics, univariate and multivariate Cox regression analyses were performed. Univariate Cox regression analysis showed 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) were significantly different from other clinical characteristics. The results of multivariate Cox regression analysis confirmed the independence of the PRlncRNA risk model in predicting COAD prognosis. Figure 10 As shown. To further verify the good prognostic predictive ability of this pyroptosis-related lncRNA model, the patients' age, gender, and tumor stage data were collected, and 1-, 3-, and 5-year nomograms were plotted as shown. Figure 11 As shown, the 1-, 3-, and 5-year nomogram correction charts are as follows: Figure 12 , 13 As shown in Figures 1 and 14, this further demonstrates that the model can predict the 1-, 3-, and 5-year survival of patients with colon adenocarcinoma relatively well.

[0083] Finally, it should be noted that the above-described construction method can be converted into software program instructions. It can be implemented using a construction system including a processor and memory, or it can be implemented using computer instructions stored in a non-transitory computer-readable storage medium. The integrated unit implemented as a software functional unit can be stored in a computer-readable storage medium. This software functional unit, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The application of the pyroptosis-related lncRNA prognostic model for colon adenocarcinoma has one of the following applications: using lncRNAs associated with colon adenocarcinoma survival as biomarkers to assess the prognostic risk of patients, note that they are not biomarkers for the diagnosis or treatment efficacy of colon adenocarcinoma; using the prognostic score of the pyroptosis-related lncRNA prognostic model to assess the prognostic risk of patients with colon adenocarcinoma.

[0085] Based on the same inventive concept, this invention also provides an evaluation system for pyroptosis-related lncRNA models in colorectal adenocarcinoma, such as... Figure 15As shown, the evaluation system for the pyroptosis-related lncRNA prognostic model of colorectal adenocarcinoma includes, in sequence: a pyroptosis-related ceRNA network construction module, which executes steps S1-S3; a lncRNA identification module related to colorectal adenocarcinoma survival, which executes step S4 to identify lncRNA information related to colorectal adenocarcinoma survival based on RNA information from multiple colorectal adenocarcinoma samples and RNA information from multiple normal colon samples; a prognostic model establishment module, which executes step S5 to analyze the lncRNA information related to colorectal adenocarcinoma survival and the clinical data of the multiple colorectal adenocarcinoma samples to establish a pyroptosis-related lncRNA prognostic model; and a prognostic module for prognostic assessment based on the pyroptosis-related lncRNA prognostic model.

[0086] In summary, the construction and application of the above-mentioned method for a pyroptosis-related lncRNA prognostic model for colorectal adenocarcinoma has the following beneficial effects:

[0087] (1) This invention extracts mRNA, miRNA and lncRNA data from the TCGA database, performs differential analysis, and obtains pyroptosis-related mRNAs by taking the intersection. Then, it combines the DElncRNA-DEmiRNA relationship pairs obtained through database analysis and the mRNAs targeted by DEmiRNA to obtain a pyroptosis-related ceRNA network. Based on clinical data, it uses univariate Cox regression analysis to explore the relationship between the expression and survival of pyroptosis-related lncRNAs, rationally and reliably screens out pyroptosis-related molecular markers, clarifies the potential molecular mechanism of pyroptosis in colorectal adenocarcinoma, and establishes a lncRNA prognostic model based on LASSO regression analysis. This identifies specific biomarkers to predict the prognostic risk of colorectal adenocarcinoma patients and provides reliable biomarkers for assessing the prognosis of colorectal adenocarcinoma patients through this prognostic model. This improves the ability to assess and predict the prognosis of colorectal adenocarcinoma patients, provides direction for the early diagnosis and treatment of colorectal adenocarcinoma, and can improve the prognosis of patients and improve the level of diagnosis and treatment to a certain extent.

[0088] (2) The pyroptosis-related lncRNA prognostic model constructed in this invention can use pyroptosis-related lncRNA as an independent prognostic factor, and has high accuracy in prognostic prediction, including high accuracy in predicting high risk and low risk, and high accuracy in predicting prognosis within 5 years.

[0089] (3) Compared with the prior art, the prognostic assessment method and system based on the pyroptosis-related lncRNA model of colon adenocarcinoma according to the present invention, considering the important significance of lncRNA and pyroptosis in the biology of colon adenocarcinoma, determines a pyroptosis-related lncRNA model to predict the prognosis of colon adenocarcinoma samples, and makes a more accurate determination of the prognosis of colon adenocarcinoma samples; by extracting pyroptosis-related lncRNA more accurately, the selected pyroptosis-related lncRNA can reflect the situation of most colon adenocarcinoma samples, making the final prognostic model universal, benefiting more patients, and further improving the prognostic accuracy of colon adenocarcinoma.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and 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 present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma, characterized in that, Includes the following steps: S1. Obtain transcriptomic data, miRNA data, and patient clinical data for colon adenocarcinoma from the TCGA database; S2. Differentially expressed mRNAs and differentially expressed lncRNAs, namely DEmRNAs and DElncRNAs, are obtained from the transcriptome data. Differentially expressed miRNAs, namely DEmiRNAs, are obtained from the miRNA data. S3. Based on the DEmiRNAs, DEmRNAs and DElncRNAs, pyroptosis-related mRNAs are obtained from the miRcode, TargetScan, miRTarBase and miRDB databases and the GeneCards database, as well as pyroptosis-related miRNAs and pyroptosis-related lncRNAs associated with the pyroptosis-related mRNAs, and a pyroptosis-related ceRNA network is constructed. S3.1 For the DEmiRNAs, DEmRNAs and DElncRNAs, obtain the DElncRNA-DEmiRNA relationship pairs according to the miRcode database, and obtain the mRNAs targeted by the DEmiRNAs according to the three databases TargetScan, miRTarBase and miRDB; S3.2, Pyroptosis-related genes obtained from the GeneCards database; S3.

3. Take the intersection of the DEmiRNA-targeted mRNA, the DEmRNAs described in step S2, and the pyroptosis-related genes to obtain pyroptosis-related mRNAs; S3.

4. Based on the DElncRNA-DEmiRNA relationship pair and the mRNA targeted by DEmiRNA, obtain the DEmiRNAs and DElncRNAs associated with the pyroptosis-related mRNAs, namely pyroptosis-related miRNAs and pyroptosis-related lncRNAs, and draw the pyroptosis-related ceRNA network. S4. Integrate the pyroptosis-related lncRNAs in the pyroptosis-related ceRNA network with the patient's clinical data, and perform univariate Cox regression analysis to obtain lncRNAs related to colon adenocarcinoma survival. S5. Incorporate the lncRNAs associated with colon adenocarcinoma survival into LASSO regression analysis to obtain the weight coefficient of each lncRNA associated with colon adenocarcinoma survival, and establish a pyroptosis-related lncRNA prognostic model based on ceRNA network: Prognostic score = ,in, The weighting coefficients of lncRNAs associated with colon adenocarcinoma survival in patients. The expression level of lncRNAs associated with colon adenocarcinoma survival in the patient is represented by n, where n is the number of lncRNAs associated with colon adenocarcinoma survival in the patient.

2. The method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma according to claim 1, characterized in that, In step S1, the patient clinical data includes the patient's age, gender, tumor stage, and survival status, and the transcriptome data and miRNA data include the expression level of each RNA in each sample.

3. The method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma according to claim 1, characterized in that, In step S2, the screening conditions are fdr < 0.05 and log|FC| > 1, where fdr is the false discovery rate and FC is the difference fold. The screening is performed using the edgeR software package in R. In step S4, a one-way Cox regression analysis is performed using the survival software package in R. The p < 0.05 in the one-way Cox proportional hazards regression analysis is used. In step S5, a LASSO regression analysis is performed using the glmnet software package.

4. The method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma according to claim 1, characterized in that, Step S2 yields 5373 DE mRNAs, 355 DE miRNAs, and 1159 DE lncRNAs; in step S3, the pyroptosis-related mRNAs include TXNIP, SESN2, CEBPB, ALK, and IL1B, and the pyroptosis-related ceRNA network includes 5 pyroptosis-related mRNAs, 7 pyroptosis-related miRNAs, and 132 pyroptosis-related lncRNAs.

5. The method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma according to claim 1, characterized in that, In step S4, the lncRNAs associated with colon adenocarcinoma survival include: HOTAIR, LINC00402, SFTA1P, ZRANB2-AS1, LINC00461, MYB-AS1, DSCR8, TP53TG1, CYP1B1-AS1, LINC00330, and ALMS1-IT1; in step S5, the pyroptosis-related lncRNA prognostic model is: Prognostic score = (0.0013 × HOTAIR) exp ) + (0.0174×LINC00402 exp ) + (0.0186×SFTA1P exp )+ (0.0373×LINC00461 exp ) + (-0.2108×ZRANB2-AS1 exp ) + (-0.0012×TP53TG1 exp ) + (-0.0647×MYB-AS1 exp ) + (0.0032×DSCR8 exp ) + (0.0084×LINC00330 exp ) + (0.0156×CYP1B1-AS1 exp ) + (0.0053×ALMS1-IT1 exp The subscript exp indicates the expression level of the corresponding lncRNA.

6. A system for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma, characterized in that, include: At least one processor; and at least one memory communicatively connected to the processor, wherein: The memory stores program instructions executable by the processor, which, when invoked, can perform the construction method as described in any one of claims 1 to 5, comprising the following sequentially connected components: The pyroptosis-related ceRNA network construction module performs steps S1-S3; The lncRNA identification module associated with colon adenocarcinoma survival performs step S4; The prognostic model building module executes step S5.

7. The application of a method for constructing a pyroptosis-related lncRNA prognostic model for colon adenocarcinoma according to any one of claims 1-5, characterized in that, It has one of the following applications: lncRNAs associated with survival in colon adenocarcinoma were used as biomarkers to assess prognostic risk in patients. The prognostic score of the pyroptosis-related lncRNA prognostic model was used to assess the prognostic risk of patients with colorectal adenocarcinoma.

8. An evaluation system comprising the pyroptosis-related lncRNA prognostic model construction system for colon adenocarcinoma as described in claim 6, characterized in that, Including those connected sequentially: The pyroptosis-related ceRNA network construction module performs steps S1-S3; The lncRNA identification module associated with colon adenocarcinoma survival performs step S4; The prognostic model building module executes step S5; And a prognostic module for prognostic assessment based on the pyroptosis-related lncRNA prognostic model.

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