CN116013403A - Construction method and application of cervical cancer methylation related long-chain non-coding RNA prognosis and immunotherapy curative effect prediction model - Google Patents
Construction method and application of cervical cancer methylation related long-chain non-coding RNA prognosis and immunotherapy curative effect prediction model Download PDFInfo
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Abstract
The invention develops and discloses a construction method and application of a cervical cancer methylation related long-chain non-coding RNA prognosis and immunotherapy curative effect prediction model. RNA methylation-associated lncRNAs were screened using Pearson correlation algorithm. And constructing a prognosis model of the cervical cancer RNA methylation related IncRNA by single-factor and multi-factor regression analysis. Drawing ROC and Kaplan-Meier survival curves, constructing a Nomo model, drawing a calization graph, and performing independent prognosis analysis to evaluate the accuracy of the model. And evaluating the correlation between the risk model and clinical characteristics, immune cell infiltration, tumor Mutation Burden (TMB), immune function and immune therapy related molecules. Finally, an online database is used for evaluating prognosis and immunotherapy value of methylation key lncRNAs, and IC50 is calculated to predict the effectiveness of the novel drug based on the target risk model. In short, the prognosis and immunotherapy efficacy prediction model can be used for guiding prognosis and making personalized treatment strategies, and cervical cancer diagnosis and prognosis related products can be further developed according to key molecules in the prognosis model.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a method for constructing a cervical cancer methylation related long-chain non-coding RNA prognosis and immunotherapy curative effect prediction model.
Background
Cervical Cancer (CC) is the fourth most common cancer in women, and recent research reports indicate about 57 tens of thousands of new cases and 31 tens of thousands of deaths per year. At present, surgery or chemoradiotherapy is the initial treatment mode of cervical cancer. In recent years, the clinical prognosis of patients with advanced cervical cancer has been significantly improved, but still not optimistic, in combination with comprehensive therapies (including surgery, chemo-radiotherapy and immunotherapy). The survival rate of patients with advanced cervical cancer is only 17% in 5 years. Given the serious threat to women's health and life posed by cervical cancer, it seems imperative to identify useful predictive biomarkers and therapeutic targets.
Prognosis of advanced or metastatic cervical cancer is poor. Previous studies have found that the presence of high risk HPV infection and elevated PD-L1 expression in pre-cancerous lesions and squamous cell carcinomas of cervical cancer are associated. Patients with high expression of PD-L1 have a poor prognosis. Several clinical studies demonstrate the primary safety and efficacy of PD-1/PD-L1 inhibitors. Currently in progress single and combination therapy trials, different immune checkpoint inhibitors, poly (apyrase) inhibitors, tumor angiogenesis inhibitors (i.e. bevacizumab), antibody drug conjugates, therapeutic vaccines and tumor infiltrating T lymphocytes (with immunotherapy) are used. Some of these new patterns are also being evaluated in combination with standard platinum-based chemotherapy regimens. Currently, pembrolizumab is approved for the treatment of programmed death ligand 1 (PD-L1) -positive cervical cancer that recurs or metastasizes following first-line chemotherapy. In summary, immunotherapy, particularly Immune Checkpoint Inhibitors (ICI), has achieved preliminary success in advanced solid tumors, while efficacy and safety in advanced or metastatic cervical cancer remain to be explored. The molecular marker capable of predicting the curative effect of the immunotherapy of cervical cancer patients is explored, and has important significance for improving the curative effect and safety of the immunotherapy.
N6-methyladenosine (m 6A), N1-methyladenosine (m 1A) and 5-methylcytosine (m 5C) and N7-methylguanosine (m 7G) are the most common types of RNA methylation. Alterations in RNA methylation affect specific biological processes through interactions with lncRNAs. On the other hand, lncRNAs can also affect the function of genes involved in RNA methylation modification. RNA modifications have important regulatory functions in cancer progression, and these changes may provide new biomarkers for cancer diagnosis and treatment, as recently studied indicate: RNA methylation is associated with promotion of cancer cell proliferation, invasiveness, and metastasis. M6A was found to be associated with proliferation, migration and resistance of tumors and was determined as a prognostic marker for tumors. In HCC tissues, abnormal changes in total m6A and its regulator levels are closely related to adverse survival. m6A and its regulator play the role of oncogene or anticancer gene in malignant tumor. The m5C modification is found to play an important role in tumor progression and can be used as an effective biomarker for cancer treatment. m1A methylation modifications accelerate tumor progression and affect prognosis. Currently, the impact of the long non-coding RNAs (lncRNAs) associated with RNA methylation described above on the prognostic value of treatment of cervical cancer and the effectiveness of immunotherapy remains unknown. Therefore, it is important to study the role of RNA methylation in cervical cancer and to initially investigate its prognostic value in cervical cancer.
Long non-coding RNA (IncRNA) is defined as RNA that is non-coding with a length of 200 nucleotides or more. Studies show that lncRNAs are hopefully novel biomarkers and therapeutic targets for cancers. However, no studies have been made to explore the correlation between cervical cancer and lncRNAs associated with RNA methylation modification. Second, the impact of RNA methylation-associated long non-coding RNAs (lncRNAs) on the prognostic value of cervical cancer and the effectiveness of immunotherapy remains unknown. Therefore, there is a great clinical need to construct key relevant lncRNAs models for risk stratification of cervical cancer, in the hope that these key RNA methylation lncRNAs models can predict prognosis, responsiveness to immunotherapy and drug sensitivity of cervical cancer patients, helping to formulate more accurate and personalized treatment strategies.
Disclosure of Invention
Technical problem to be solved
Based on the problems, the invention provides a method for constructing a cervical cancer RNA methylation related long-chain non-coding RNA prognosis and immunotherapy curative effect prediction model. Aims to solve the problem that RNA methylation related IncRNA has no accurate and reliable molecular index for predicting prognosis and immunotherapy curative effect of patients with cervical cancer. The development of the invention provides reliable biomarkers for prognosis of cervical cancer patients and evaluation of the curative effect of immunotherapy, thereby being beneficial to improving the prognosis of cervical cancer patients and the evaluation capability of the curative effect of immunotherapy, and realizing early intervention and establishment of personalized treatment strategies.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a model for prognosis of long-chain non-coding RNAs (incrnas) associated with cervical cancer (CervicalCancer, CC) RNA methylation and prediction of therapeutic efficacy of immunotherapy. The invention firstly collects transcriptome data, tumor mutation data and related clinical data of cervical cancer patients from a TCGA database. Further, 55 RNA methylation-critical molecules were obtained from the published study, and RNA methylation-related IncRNAs were screened using the Pearson correlation algorithm.
And constructing a prognosis model of the cervical cancer RNA methylation related IncRNA by adopting a single-factor regression analysis and a multi-factor regression method. And (3) evaluating the accuracy of the prognosis model by adopting an ROC curve and drawing a Kaplan-Meier survival curve. And constructing a co-expression network of the key RNA methylated IncRNAs and the methylated molecules. And (3) analyzing and evaluating the correlation between the risk model and clinical characteristics, immune cell infiltration, tumor mutation load (TMB), immune function and immune therapy related molecules. The TIDE database evaluates the difference of the immunotherapy curative effect of patients in high and low risk groups. Finally, the prognosis and immunotherapeutic value of the screened RNA methylation key lncRNAs were verified using GEPIA, lnc2Cancer3.0, GEPIA, GEPIA2021 and TIGER on-line databases. The effectiveness of the novel drug based on the target risk model is further predicted by calculating IC 50. The prognosis and immunotherapy prediction model of the invention can be used for guiding prognosis and making personalized treatment strategies, and products related to diagnosis and treatment of cervical cancer and prognosis can be further developed according to key molecules in the prognosis model.
Compared with the prior art, the invention adopting the technical scheme has the following beneficial effects: the technical scheme of the invention has the following characteristics:
1) Transcriptome data, tumor mutation data and related clinical data of cervical cancer patients are extracted from a TCGA database. 55 RNA methylation-critical molecules were obtained from published studies. RNA methylation-related IncRNAs were further screened using the Pearson correlation algorithm. And constructing a prognosis model of the cervical cancer RNA methylation related IncRNA by adopting a single-factor regression analysis and a multi-factor regression method. The accuracy of the prognostic model was assessed by using ROC curves, plotting Kaplan-Meier survival curves, and calculating the concordance index (C-index).
And constructing a co-expression network of the key RNA methylated IncRNAs and the methylated molecules. And (3) analyzing and evaluating the correlation between the risk model and clinical characteristics, immune cell infiltration, tumor mutation load (TMB), immune function and immune therapy related molecules. The TIDE database evaluates the difference of the immunotherapy curative effect of patients in high and low risk groups. The effectiveness of the novel drug based on the target risk model is predicted by calculating the IC 50. Finally, the prognostic value of key RNA methylation-associated lncRNAs was validated using GEPIA and lnc2cancer3.0 online databases. The development of the invention can reasonably and reliably screen out RNA methylation related IncRNAs molecular markers, and construct a risk scoring model of RNA methylation related lncRNAs, which has important significance for predicting prognosis of CC patients, reactivity and drug sensitivity to immunotherapy and assisting in formulating more accurate and personalized treatment strategies.
2) The RNA methylation related lncRNAs prognosis risk model constructed by the invention can accurately predict prognosis of cervical cancer patients of 1 year, 3 years and 5 years independently of other clinical factors (age, sex, stage, grade, T stage, N stage and the like).
3) Compared with the prior art, the invention considers the important significance of RNA methylation and lncRNAs in cervical cancer biology, and determines an lncRNAs risk model related to RNA methylation to predict prognosis, responsiveness to immunotherapy and drug sensitivity of CC patients.
The prognosis of the CC sample can be accurately judged; by extracting the lncRNAs associated with RNA methylation more accurately, the selected lncRNAs associated with RNA methylation can reflect the conditions of most cervical cancer patient samples, so that the final prognosis model is universal, and more cervical cancer patients benefit.
Drawings
For further explanation of the invention, reference is made to the following further description, taken in conjunction with the accompanying drawings:
FIG. 1 is a flow chart of the invention;
FIGS. 2A to 2F are screens for RNA methylation-related lncRNAs in cervical cancer patients
FIGS. 3A-3E are graphs of KM curves for evaluation of prognostic value of 5 key RNA methylated lncRNAs;
3F-3I are ROC curve verification model accuracies;
3J-3K, single-factor and multiple-factor regression analysis of risk score independent prognostic signatures;
3L-3M construct a Nomogram model and draw a calization graph to further verify the model accuracy;
FIGS. 4A-4O are graphs showing correlation analysis of risk scores with clinical features;
FIGS. 5A-5I show correlations between risk scores and immune cell infiltration, tumor mutational burden and immune profile;
FIGS. 6A-6I are prognostic and efficacy assessment of RNA methylation-critical lncRNAs and immunotherapy;
FIGS. 7A-7K are diagrams of predicting the effectiveness of a novel drug based on a target risk model and validating the prognostic value of RNA methylation-related key lncRNAs;
FIG. 8 Table 1RNA methylation-associated genes;
FIG. 9 Table 2 single-element regression analysis screens 8 IncRNAs associated with RNA methylation;
FIG. 10 Table 3 multifactor regression analysis screens 5 IncRNAs associated with RNA methylation;
FIG. 11 Table 4FAM27E3 and SOX21-AS1 are related to the therapeutic efficacy of immunotherapy in CC patients.
Detailed Description
1) Data collection and preparation
The TCGA database (https:// portal. Gdc. Cancer. Gov /) downloads transcriptome data, TMB data, and corresponding clinical data for cervical cancer patients. Gene expression profiles of 306 cervical cancer samples and 3 normal persons were included. Data post-processing and clinical information extraction use programming language (strawberry-perl-5.32.0.1-64 bit. Msi, http:// www.perl.org). Protein coding genes and IncRNA were annotated and classified using Ensembl human genome browser GRCh38.p13 (http:// asia. Ensembl. Org/index. Html).
2) RNA methylation IncRNAs screening
Published literature has obtained 55 molecules associated with RNA methylation (table 1). RNA methylation-related IncRNAs were obtained by calculating Pearson correlation coefficients, and the selection criteria were correlation coefficients > 0.4 and P values < 0.001. 208 RNA methylation-related IncRNAs were selected according to the invention. The co-expression network of the interactions between the RNA methylation-associated gene and 208 IncRNAs is shown in FIG. 2A.
3) RNA methylation IncRNAs prognosis model construction
And running a survivinal package in the R language, carrying out univariate Cox regression analysis on methylation-related IncRNAs, and primarily screening prognostic methylation-related IncRNAs, wherein P is less than 0.05, so that the statistical significance is achieved. Multiplex Cox regression analysis was performed and the risk score for each patient was calculated. CC patients are divided into high-risk and low-risk groups. Calculate risk score = n1×coef1+n2×coef2+ … … nn×coefn; wherein, nn represents the expression level of the related differential gene, coefn represents the regression coefficient corresponding to the related differential gene.
In the present invention, 8 methylated IncRNAs associated with prognosis of cervical cancer were found by single factor regression analysis (Table 2). Multiple factor regression analysis found: FAM27E3, AC024270.3, AC096992.2, SOX21-AS1 and AC012306.2 are associated with prognosis of cervical cancer patients. Among them, FAM27E3, AC024270.3, AC096992.2, SOX21-AS1 are associated with a good prognosis for cervical cancer patients, and AC012306.2 is a poor prognosis factor for cervical cancer patients (table 3).
FIG. 2B shows the screening of 5 RNA methylation-related IncRNA for interactions with RNA methylation genes. Figures 2C-E show risk levels, case survival status, and model lncRNA expression levels. KM survival analysis found that the higher risk group had a poor prognosis than the lower risk group (fig. 2F).
4) Evaluating accuracy of a model
Kaplan-Meier survival curves for five RNA methylation-critical IncRNAs were plotted. The predictive power of the model was assessed by plotting ROC curves, calculated area under the curve (AUC) and C-index for 1, 3 and 5 years using the timeROC software package. AUC values range from 0.5 to 1.0. Auc=0.5, indicating no predictive value, AUC between 0.5-0.7 represents low accuracy of prediction, AUC between 0.7-0.9 represents moderate accuracy of prediction, AUC > 0.9 represents high accuracy of prediction.
The C-index value ranges between 0.5 and 1.0. Single and multiple factor Cox regression analysis was used to determine whether the risk score could accurately predict prognosis for 3-year and 5-year CC patients independent of other clinical factors (age, gender, stage, grade, etc.). HR > 1 and P values less than 0.05 represent a prognostic penalty. HR < 1 and P values less than 0.05 represent prognostic advantage. Building a Nomo model to predict the prognosis of patients 1, 3 and 5 years and drawing a nomination chart to evaluate the accuracy of the Nomo model.
The invention prompts that: the Kaplan-Meier survival curve further demonstrates that FAM27E3, AC024270.3, AC096992.2, SOX21-AS1 are associated with a good prognosis for cervical cancer patients, and AC012306.2 is a poor prognostic factor for cervical cancer patients (fig. 3A-E). ROC curve results indicate that: AUCs of 1 year, 3 years, 5 years were 0.782 and 0.717 and 0.763, respectively (fig. 3F). ROC curve analysis found that risk-score could predict the 1, 3, 5 year prognosis of cervical cancer patients independent of other clinical factors (fig. 3G-I). Single and multi-factor independent prognostic analysis suggests: the prognosis risk model constructed according to the present invention can accurately assess patient prognosis independent of other prognostic factors (fig. 3J-K). Nomo model constructed based on riskscore and drawing a calization graph to find risk model can accurately predict 1, 3 and 5 year prognosis of patient (FIG. 3L-M).
5) Riskscore and clinical relevance analysis
The study analyzed risk score differences between different ages, rankings, stages, T and N stages based on limma and ggpubr packages in R language. The prognosis differences of cervical cancer patients of different ages, grades, stages, T and N stages were further analyzed. The results show that: no significant differences in risk scores were seen between age, grade, stage, T and N stages, but the trend was higher for StageIII-IV than StageI-II and T3-4 than T1-2 (fig. 4A-E). The prognosis of the higher risk CC patients and the lower risk CC patients among GradeI-IV and T1-2 was poor, whereas there was no difference in survival between the higher and lower risk groups of patients of different ages, gradeI-II, T3-4 and N-staging (FIG. 4F-O). These results further indicate that high risk scoring CC patients have a lower risk prognosis. 6) Correlation between risk score and immune cell infiltration, tumor mutational burden and immune profile
The cibelort algorithm was used to compare the differences in immune cell infiltration in high and low risk patients. Correlation between immune cell infiltration and the identified key RNA methylated lncRNAs was further analyzed using the "ggplot2", "ggpubr" and "ggExtra" R packages. The difference in survival of the infiltrated immune cells in the high and low risk groups was analyzed by means of an online database of GEPIA2021 (http:// GEPIA2021.Cancer-pku. Cn /). TMB was calculated using tumor specific gene mutations. Mutation data was calculated using the "maftools" package in the R language. All samples were divided into high TMB and low TMB groups and the survival of each group of patients was checked using these data and patient survival statistics. The relationship between risk score and patient survival was next studied using the survival of four subgroups (high tmb+ high risk, high tmb+ low risk, low tmb+ low risk score, low tmb+ high risk). Differences in immune-related functions between the high-risk and low-risk groups were compared by using the R packages "Limma", "GSVA", "GSEABase", "pheeatmap" and "reshape2" [22 ]. The relationship between RNA methylation-associated lncRNAs and typical immune checkpoint molecules (including CD70, TNFSF9, FGL1, CD276, NT5E, HHLA, VTCN1, TNFRSF18, CD274.Pdcdl1lg2, IDO1, CTLA4, ICOS, HAVCR, PDCD1, LAG3, SIGLEC15, TNFSF4, TNFRSF9, ENTPD1, VSIR, ICOSLG, TNFSF14, TMIGD2, CD27, NCR3, BTLA, CD40LG, CD40, and TNFRSF 4). The TIDE algorithm is used to calculate the likelihood of immunotherapy effectiveness. The correlation matrix of tumor immunotherapeutic gene expression resources (TIGER, http:// TIGER. Cancer. Org/#) was used to examine whether the determined RNA methylation-related lncRNAs were correlated with known immunotherapeutic response characteristics. We further used GEPIA2 (http:// GEPIA2.Cancer-pku. Cn /) to estimate the correlation between the RNA methylation-related lncRNAs we determined and the characteristics of the immunotherapeutic response.
Immune microenvironment analysis showed that naive B cells and cd4+ memory resting T cells were mainly enriched in low risk groups, while activated NK cells were mainly concentrated in high risk groups (fig. 5A). In addition, immune function analysis found that active cytolysis, pro-inflammatory responses, T cell-related stimulation, immune checkpoints, T cell-related inhibition, CCR, MCH-type I, side inflammatory responses, IFN-type I responses were different at high and low risk (fig. 5B). The 5 RNA methylation key IncRNAs screened were correlated with the presence of immune checkpoint inhibitors (fig. 5C).
TMB is a predictive biomarker of response to immunotherapy. Thus, we compared the TMB differences between the high risk score and the low risk score and showed the first 20 genes with the greatest mutation rate (FIGS. 5D-E). However, there was no significant difference in TMB between the high risk score group and the low risk score group (fig. 5F). High TMB is associated with good OS for CC patients (fig. 5G), and high TMB is longest for CC patients with combined low risk score (fig. 5H). The low risk group immunotherapy was better (fig. 5I). 7) Prognosis of RNA methylation-critical lncRNAs and efficacy assessment of immunotherapy
The immune cell infiltration was analyzed for correlation with key IncRNAs associated with the methylation of the selected RNA using GEPIA2021 (GeneExpressionProfilingInteractionanalysis 2021, http:// GEPIA2021.Cancer-pku. Cn /), and the survival differences of the two groups were compared according to the proportion of infiltrated immune cells. The correlation between the key RNA methylation-related lncRNAs we determined and the response profile of immunotherapy was further estimated using gene expression analysis interaction analysis2 (GEPIA 2, http:// GEPIA2.Cancer-pku. Cn /). Correlation matrix of tumor immunotherapeutic Gene expression resources (TIGER, http:// TIGER. Cancer. Org/#) by applying published gene signatures to our provided lncRNAs for cervical squamous cell carcinoma and cervical endometrial adenocarcinoma (CESC), predicting the patient's response to immunotherapy and comparing our provided lncRNAs to known immunotherapeutic response signatures using gene expression data and clinical information for immunotherapy.
The patent of the invention shows that: AC024270.3, FAM27E3 and SOX21-AS1 were positively correlated with naive B cells. Naive B cells correlated with good overall survival of the patient, further demonstrating that patients highly expressing AC024270.3, FAM27E3 and SOX21-AS1 were well predicted (fig. 6A-D). We assessed the effect of key lncRNAs (FAM 27E3 and SOX21-AS 1) on the response of CC patients to immunotherapy. The results show that: FAM27E3 and SOX21-AS1 were correlated with the immunotherapeutic response of CC patients by application of published gene signatures (Table 4). Analysis according to TIGER database revealed that in melanoma-GSE 91061_anti-PD-1, FAM27E3 expression was higher in non-responders than in responders, and that FAM27E3 overexpression was associated with total survival (fig. 6E-F). According to the analysis result of GEPIA 2: FAM27E3 was inversely related to the T cell-related characteristics of failure, affecting Treg cell and resting Treg cell characteristics (fig. 6G-I). The invention further shows that: the low-cohort was more sensitive to immunotherapy, and FAM27E3 and SOX21-AS1 were clearly associated with the response of CC patient immunotherapy.
8) IC50 to predict the effectiveness of novel drugs based on target risk models
The present patent uses the "prrofetic" package in R software to calculate the IC50 of chemotherapeutic drugs to assess the effect of chemotherapy on cervical cancer patients, and the results can be used to guide specific treatments. IC50 analysis found: patients in the low risk group were more sensitive to abt.263, AKT inhibitor VIII and ATRA (fig. 7B, C, F). Patients with high risk scores were shown to be more sensitive to these five drugs by small IC50 values for a.770041, ap.24534, AS 60245, AUY922, AZ628, and azd.0530 (fig. 7a, d, e, g, h, i).
9) Verification of the prognostic value of key RNA methylation-associated lncRNAs
The on-line analytical databases GEPIA (http:// GEPIA. Cancer-pku. Cn /) and Lnc2cancer3.0 (http:// bio-bigdata. Hrbmu. Edu. Cn/lnc2 cancer) were used to confirm the prognostic value of RNA methylation-related lncRNAs in CC patients. The results further demonstrate that high expression of FAM27E3 and SOX21-AS1 is associated with good prognosis for CC patients (FIGS. 7J, K).
The invention has the beneficial effects that
The technical scheme of the invention has the following characteristics:
1) Transcriptome data, tumor mutation data and related clinical data of cervical cancer patients are extracted from a TCGA database. 55 RNA methylation-critical molecules were obtained from published studies. RNA methylation-related IncRNAs were further screened using the Pearson correlation algorithm. Cervical cancer RNA construction by adopting single-factor regression analysis and multi-factor regression method
Prognosis model for methylation-associated incrnas. The accuracy of the prognostic model was assessed by using ROC curves, plotting Kaplan-Meier survival curves, and calculating the concordance index (C-index).
And constructing a co-expression network of the key RNA methylated IncRNAs and the methylated molecules. And (3) analyzing and evaluating the correlation between the risk model and clinical characteristics, immune cell infiltration, tumor mutation load (TMB), immune function and immune therapy related molecules. The TIDE database evaluates the difference of the immunotherapy curative effect of patients in high and low risk groups. The effectiveness of the novel drug based on the target risk model is predicted by calculating the IC 50. Finally, the prognostic value of key RNA methylation-associated lncRNAs was validated using GEPIA and lnc2cancer3.0 online databases. The development of the invention can reasonably and reliably screen out RNA methylation related IncRNAs molecular markers, and construct a risk scoring model of RNA methylation related lncRNAs, which has important significance for predicting prognosis of CC patients, reactivity and drug sensitivity to immunotherapy and assisting in formulating more accurate and personalized treatment strategies.
2) The RNA methylation related lncRNAs prognosis risk model constructed by the invention can accurately predict prognosis of cervical cancer patients of 1 year, 3 years and 5 years independently of other clinical factors (age, sex, stage, grade, T stage, N stage and the like).
3) Compared with the prior art, the invention considers the important significance of RNA methylation and lncRNAs in cervical cancer biology, and determines an lncRNAs risk model related to RNA methylation to predict prognosis, responsiveness to immunotherapy and drug sensitivity of CC patients.
The prognosis of the CC sample can be accurately judged; by extracting the lncRNAs associated with RNA methylation more accurately, the selected lncRNAs associated with RNA methylation can reflect the conditions of most cervical cancer patient samples, so that the final prognosis model is universal, and more cervical cancer patients benefit.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the foregoing embodiments, which have been described in the foregoing description merely illustrates the principles of the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (7)
1. The construction method of cervical cancer methylation related long-chain non-coding RNA prognosis and immunotherapy curative effect prediction model is characterized by comprising the following steps,
1) Respectively acquiring transcriptome data, tumor mutation data and corresponding clinical data of cervical cancer samples and normal sample tissues from a TCGA database; summarizing and downloading RNA methylation-related molecules from published articles;
2) Pearson correlation analysis (Pearson's scientific oeffects) screening for RNA methylation-related IncRNAs, corranationalcoeffects > 0.4 and p-values <0.001 as screening criteria;
3) Integrating the RNA methylation-related lncRNAs with clinical data of a corresponding patient, and performing single-factor Cox regression analysis to preliminarily obtain RNA methylation lncRNAs related to cervical cancer survival;
4) Further incorporating the survival-related key lncRNAs screened by the single-factor regression analysis into the multi-factor regression analysis to screen key RNA methylation lncRNAs, wherein the screening condition is set to be P < 0.05;
5) Establishing a prognostic risk score model for drawing, and calculating a risk score = n1×coef1+n2×coef2+ … … nn×coefn; wherein, nn represents the expression level of the related differential gene, coefn represents the regression coefficient corresponding to the related differential gene;
6) Evaluating the accuracy of a prognosis model by drawing an ROC curve and a Kaplan-Meier survival curve;
7) Single and multi-factor Cox independent prognostic assays are used to determine whether a risk score can accurately predict prognosis for cervical cancer patients for 1 year, 3 years, and 5 years, independent of other clinical factors (age, stage, grade, T and N stage, etc.);
8) Building a Nomo model and drawing a calication graph to further evaluate the accuracy of the model;
9) Analyzing the correlation between the risk score and molecules related to clinical characteristics, immune cell infiltration, tumor mutation load (TMB), immune function and immune treatment of cervical cancer patients;
10 The TIDE database evaluates the difference of the immunotherapy efficacy of patients in high and low risk groups;
11 Using GEPIA, lnc2Cancer3.0, GEPIA, GEPIA2021 and TIGER on-line database to evaluate prognosis and immunotherapeutic value of the screened RNA methylation key lncRNAs;
12 Predicting the effectiveness of a novel drug based on a target risk model by calculating IC 50.
2. The method for constructing a cervical cancer methylation related long-chain non-coding RNA prognosis efficacy prediction model according to claim 1, wherein,
1) Data collection and preparation
A TCGA database (https:// portal. Gdc. Cancer. Gov /) downloads transcriptome data, TMB data, and corresponding clinical data for cervical cancer patients; the transcriptome data contains 306 cervical cancer samples and gene expression profiles of 3 normal people; data post-processing and clinical information extraction use programming language (strawberry-perl-5.32.0.1-64 bit. Msi, http:// www.perl.org); protein coding genes and incrnas were annotated and classified using the Ensembl human genome browser grch38.p13 (http:// asia. Ensembl. Org/index. Html);
2) RNA methylation IncRNAs screening
Published literature has acquired 55 molecules associated with RNA methylation; obtaining RNA methylation related IncRNAs by calculating Pearson correlation coefficients, wherein the screening standard is that the correlation coefficients are more than 0.4 and the P value is less than 0.001; 208 RNA methylation related IncRNAs are screened out; the interaction co-expression network between the RNA methylation related genes and 208 IncRNAs;
3) RNA methylation IncRNAs prognosis model construction
Running a survivinal package in the R language, carrying out univariate Cox regression analysis on methylation-related IncRNAs, and primarily screening prognostic methylation-related IncRNAs, wherein P is less than 0.05, so that the statistical significance is achieved; performing a multiplex Cox regression analysis and calculating a risk score for each patient; dividing cervical cancer patients into a high risk group and a low risk group; calculate risk score = N 1 ×coef 1 +N 2 ×coef 2 +……N n ×coef n The method comprises the steps of carrying out a first treatment on the surface of the Wherein N is n Representing the expression level of the related differential gene, coef n Representing regression coefficients corresponding to the related differential genes;
the single factor regression analysis finds 8 methylated IncRNAs which are related to cervical cancer prognosis, and the multi-factor regression analysis finds: FAM27E3, AC024270.3, AC096992.2, SOX21-AS1 and AC012306.2 are associated with prognosis of cervical cancer patients; wherein FAM27E3, AC024270.3, AC096992.2, SOX21-AS1 are associated with a good prognosis for cervical cancer patients, and AC012306.2 is a poor prognosis factor for cervical cancer patients;
interactions exist between 5 RNA methylation-associated incrnas and RNA methylation genes; FIGS. 2C-E show risk levels, case survival status, and model lncRNA expression levels; KM survival analysis found that the prognosis of the higher risk group was poor for the lower risk group;
4) Evaluating accuracy of a model
Drawing Kaplan-Meier survival curves of five RNA methylation-critical IncRNAs; drawing ROC curves of 1 year, 3 years and 5 years by adopting a timeROC software package, and calculating the area under the curve (AUC) and the predictive capability of a C-index evaluation model; AUC values range from 0.5 to 1.0; auc=0.5, indicating no predictive value, AUC between 0.5-0.7 representing low accuracy of prediction, AUC between 0.7-0.9 representing moderate accuracy of prediction, AUC > 0.9 representing higher accuracy of prediction;
the C-index value ranges from 0.5 to 1.0; single and multi-factor Cox regression analysis is used to determine whether the risk score can accurately predict prognosis for 3-year and 5-year CC patients independent of other clinical factors (age, gender, stage, grade, etc.); HR > 1 and P values less than 0.05 represent adverse prognosis factors; HR < 1 and P values less than 0.05 represent prognostic advantage; constructing a Nomo model to predict the prognosis of patients 1, 3 and 5 years, and drawing a nomination chart to evaluate the accuracy of the Nomo model;
the Kaplan-Meier survival curve further demonstrates that FAM27E3, AC024270.3, AC096992.2, SOX21-AS1 are associated with a good prognosis for cervical cancer patients, AC012306.2 being a poor prognostic factor for cervical cancer patients; ROC curve results indicate that: AUC for 1 year, 3 years, 5 years are 0.782 and 0.717 and 0.763, respectively; ROC curve analysis found that risk-score could predict the 1, 3, 5 year prognosis of cervical cancer patients independent of other clinical factors; single and multi-factor independent prognostic analysis suggests: the prognosis risk model constructed by the invention can accurately evaluate the prognosis of the patient independently of other prognosis factors; nomo model constructed based on riskscore and drawing a calization graph to find a risk model, so that prognosis of patients 1, 3 and 5 years can be accurately predicted;
5) Riskscore and clinical relevance analysis
The study analyzed risk score differences between different ages, classifications, stages, T and N stages based on limma and ggpubr packages in the R language; further analyzing prognosis differences of cervical cancer patients of different ages, grades, stages, T and N stages; the results show that: no obvious difference in risk scores among different ages, grades, stages, T and N stages is seen, but the StageIII-IV has a trend of higher risk scores than StageI-II and T3-4 than T1-2; the prognosis of the higher risk CC patients among CC patients of GradeIII-IV and T1-2 was poor, while the survival of the higher and lower risk group patients among the different ages, gradeI-II, T3-4 and N stages was not different; further suggesting that high risk CC patients have a lower prognosis than those with lower risk;
6) The correlation between the risk score and immune cell infiltration, tumor mutation load and immune characteristics adopts CIBERSORT algorithm to compare the medium immune cell infiltration difference of patients with high risk and low risk; further using the "ggplot2", "ggpubr" and "ggExtra" R packages to analyze the correlation between immune cell infiltration and the identified key lncRNAs; analyzing the survival difference of the infiltrated immune cells in the high-low risk group through a GEPIA2021 (http:// GEPIA2021.Cancer-pku. Cn /) online database; TMB was calculated using tumor specific gene mutations; calculating mutation data using the "maftools" package in the R language; all samples were divided into high TMB and low TMB groups and the survival of each group of patients was checked using these data and patient survival statistics; the relationship between risk score and patient survival was next studied using four subgroups of high tmb+ high risk, high tmb+ low risk, low tmb+ low risk score, low tmb+ high risk survival; comparing the differences in immune-related functions between the high-risk group and the low-risk group by using the R packages "Limma", "GSVA", "GSEABase", "pheeatmap" and "reshape 2"; RNA methylation-associated lncRNAs and typical immune checkpoint molecules including CD70, TNFSF9, FGL1, CD276, NT5E, HHLA2, VTCN1, TNFRSF18, CD274; PDCDL1LG2, IDO1, CTLA4, ICOS, HAVCR, PDCD1, LAG3, SIGLEC15, TNFSF4, TNFRSF9, ENTPD1, VSIR, ICOSLG, TNFSF, TMIGD2, CD27, NCR3, BTLA, CD40LG, CD40, and TNFRSF 4); calculating the effective possibility of immunotherapy by using a TIDE algorithm; checking whether the determined RNA methylation-related lncRNAs are associated with known immunotherapeutic response signatures using a correlation matrix of tumor immunotherapeutic gene expression resources (TIGER, http:// TIGER. Cancer. Org/#); we further used GEPIA2 (http:// GEPIA2.Cancer-pku. Cn /) to estimate the correlation between the RNA methylation-related lncRNAs we determined and the immunotherapeutic response profile;
immune microenvironment analysis showed that naive B cells and cd4+ memory resting T cells were mainly enriched in low risk groups, while activated NK cells were mainly concentrated in high risk groups; in addition, immune function analysis found that active cytolysis, pro-inflammatory responses, T cell-related stimulation, immune checkpoints, T cell-related inhibition, CCR, MCH-type I, side inflammatory responses, IFN-type I responses were differentiated at high and low risk; the screened 5 RNA methylation key IncRNAs have correlation with immune checkpoint inhibitors;
TMB is a predictive biomarker for response to immunotherapy; thus, we compared the TMB differences between the high risk score and the low risk score and showed the first 20 genes with the greatest mutation rate; however, there was no significant difference in TMB between the high risk score group and the low risk score group; high TMB is associated with good OS for CC patients, and high TMB combined with low risk score has the longest OS for CC patients; the low risk assessment group immunotherapy effect is better;
7) Prognosis of RNA methylation-critical lncRNAs and efficacy assessment of immunotherapy
The immune cell infiltration was analyzed for correlation with key IncRNAs associated with the methylation of the screened RNA using GEPIA2021 (GeneExpressionProfilingInteractionanalysis 2021, http:// gepia2021.Cancer-pku. Cn /), and the survival differences of the two groups were compared according to the proportion of infiltrated immune cells; further using gene expression analysis interaction analysis2 (GEPIA 2, http:// GEPIA2.Cancer-pku. Cn /) to estimate the correlation between key RNA methylation-related lncRNAs as we determined and the response profile of immunotherapy; correlation matrix of tumor immunotherapeutic Gene expression resources (TIGER, http:// TIGER. Cancer. Org/#) by applying published gene signatures to our provided lncRNAs for cervical squamous cell carcinoma and cervical endometrial adenocarcinoma (CESC), predicting the patient's response to immunotherapy and comparing our provided lncRNAs to known immunotherapeutic response signatures using gene expression data and clinical information for immunotherapy.
3. The method for constructing the cervical cancer methylation related long-chain non-coding RNA prognosis efficacy prediction model according to claim 2, which is characterized in that,
the method also comprises the following steps of,
8) IC50 to predict the effectiveness of novel drugs based on target risk models
The present patent uses the "prrophilic" package in R software to calculate the IC50 of a chemotherapeutic drug to assess the effect of chemotherapy on cervical cancer patients, and the results can be used to guide specific treatments; IC50 analysis found: patients in the low risk group are more sensitive to abt.263, AKT inhibitor VIII and ATRA; patients with high risk scores were shown to be more sensitive to these five drugs by small IC50 values for a.770041, ap.24534, AS 60245, AUY922, AZ628, and azd.0530.
4. The method for constructing a cervical cancer methylation related long-chain non-coding RNA prognostic efficacy prediction model according to claim 3, wherein,
the method also comprises the following steps of,
9) Verification of the prognostic value of key RNA methylation-associated lncRNAs
The prognostic value of RNA methylation-related lncRNAs in CC patients was confirmed using the on-line analytical databases GEPIA (http:// GEPIA. Cancer-pku. Cn /) and Lnc2Cancer3.0 (http:// bio-bigdata. Hrbmu. Edu. Cn/lnc2 cancer); the results further demonstrate that high expression of FAM27E3 and SOX21-AS1 is associated with a good prognosis for CC patients.
Use of ac0244270.3, FAM27E3 and SOX21-AS1, and any one or a combination of the three and their expressed substances for the preparation of a medicament for treating a tumor patient or AS a biomarker.
6. The use according to claim 6, wherein the neoplasm is cervical cancer.
Use of any one or a combination of fam27e3 and SOX21-AS1 and expressed substances thereof for the preparation of a medicament for treating a patient with CC or AS a biomarker.
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| CN117316291A (en) * | 2023-10-20 | 2023-12-29 | 南方医科大学南方医院 | Immunoregulation gene classification method based on relationship between therapeutic effect and toxicity of immunotherapy |
| CN117334347A (en) * | 2023-12-01 | 2024-01-02 | 北京大学 | Method, device, equipment and storage medium for evaluating treatment effect |
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| CN116844685B (en) * | 2023-07-03 | 2024-04-12 | 广州默锐医药科技有限公司 | Immunotherapeutic effect evaluation method, device, electronic equipment and storage medium |
| CN117316291A (en) * | 2023-10-20 | 2023-12-29 | 南方医科大学南方医院 | Immunoregulation gene classification method based on relationship between therapeutic effect and toxicity of immunotherapy |
| CN117334347A (en) * | 2023-12-01 | 2024-01-02 | 北京大学 | Method, device, equipment and storage medium for evaluating treatment effect |
| CN117334347B (en) * | 2023-12-01 | 2024-03-22 | 北京大学 | Method, device, equipment and storage medium for evaluating treatment effect |
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