CN117153382A - Model construction method for predicting colorectal cancer prognosis risk - Google Patents
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Abstract
The invention relates to a model construction method for predicting colorectal cancer prognosis risks, which comprises the steps of screening colorectal cancer prognosis related genes by means of a training set GSE39582, constructing a prognosis label PSCRC and prognosis evaluation thereof, then carrying out PSCRC verification by using two external data sets GSE17536 and CRC_TCGA, and finally drawing a multi-factor prognosis model and evaluating the prognosis effect thereof. The invention integrates the newly found label PSCRC on the basis of the traditional colorectal cancer clinical index, builds a new colorectal cancer multi-factor prognosis model, improves the prognosis effect, and can discover the severity of the disease as soon as possible, thereby helping clinicians to make reasonable treatment schemes as soon as possible, realizing the optimal treatment effect, and having important guiding and reference significance for improving the survival rate and the life quality of patients.
Description
Technical Field
The invention belongs to the technical field of data analysis, and particularly relates to a model construction method for predicting colorectal cancer prognosis risks.
Background
Colorectal cancer (Colorectal cancer, CRC) refers to a cancerous change occurring in the colon or rectum, which can occur anywhere in the colorectal, but is especially common in the rectum, sigmoid colon. CRC places a heavy burden on the medical system and severely threatens the physical health of people. The current clinical methods for treating the CRC include operation, radiotherapy and chemotherapy, targeted therapy and immunotherapy, so that the survival rate of CRC cancer patients for 5 years is improved, but the diagnosis and treatment effects of the CRC are still influenced by heterogeneity. The CRC prognosis not only can help clinicians to formulate reasonable treatment schemes and realize optimal treatment effects, but also can help patients to know the illness state and better cooperate with doctors to diagnose. Therefore, the construction of a CRC prognosis model is clinically significant.
Disclosure of Invention
The invention aims to provide a model construction method for predicting colorectal cancer prognosis risks.
In order to solve the technical problems, the invention discloses a model construction method for predicting colorectal cancer prognosis risk, which comprises the following steps:
a. data downloading and processing: downloading colorectal cancer data set CRC_TCGA containing clinical and RNAseq data from a TCGA database, extracting TPM format data, and processing the data by a log2 method; the colorectal cancer data sets GSE39582 and GSE17536 are downloaded from the GEO database and subjected to id conversion and standardization treatment;
b. screening the relevant genes of prognosis in training set: adopting a training set GSE39582 as a processing object, integrating survival time, survival state and gene expression data by using an R software package 'survivinal', and evaluating the prognosis significance of each gene by using a cox method;
c. constructing a prognosis signature PSCRC and prognosis evaluation thereof by using a training set:
c1.Lasso-cox analysis
Establishment of a prognostic lasso variable trajectory: analyzing the GSE39582 data after cleaning by using glmnet of the R software package to obtain a variable coefficient value, a lambda pair value and an L1 regularization value, and visualizing the data; prognosis lasso coefficient screening: analyzing the washed GSE39582 data by using a glmnet package to obtain a variable lambda value, a maximum likelihood number or a C index and visualizing the data;
c2. evaluation of PSCRC prognostic Effect
Calculating a prognosis tag risk score according to a PSCRC risk score calculation formula, dividing patients in a GSE39582 dataset into two groups according to the risk score, analyzing prognosis differences of the two groups of patients by using a survivinfit function of a survivinal R package, and evaluating the difference significance by using a logrank test method; subsequently, ROC analysis was performed at three time points of 1 year, 3 years, and 5 years using the "ROC" function of R package "pROC" and the area under the curve and confidence interval were evaluated using the "ci" function, obtaining the final AUC results; finally, analyzing the relationship between PSCRC risk score and patient follow-up time, outcome and each gene expression change;
d. external data set verification PSCRC
d1. Calculating the risk scores of the PSCRC in the two external data sets GSE17536 and CRC_TCGA according to a PSCRC risk score calculation formula, and verifying the prognosis effect of the PSCRC;
d2.Kaplan-Meier survival curve plot: firstly, dividing patients into two groups according to a 50% percentile, further analyzing prognosis differences of the two groups by using a survivinfit function of a survivina 1R software package, and evaluating the significance of the prognosis differences among different groups of samples by using a logrank test method;
roc analysis: ROC analysis was performed using the R software package "pROC" to obtain AUC;
d4. risk heat map drawing: analyzing the relation between different risk scores and the follow-up time of a patient, the relation between the event and the expression change of each gene by using an R-package ggplot 2;
e. drawing a multi-factor prognosis model and evaluating a prognosis effect;
e1. drawing a prognosis model and a calibration curve: proportional risk hypothesis testing is carried out by using an R software package "survivinal" package, cox regression analysis is carried out, a nonogram related model is constructed by using an "rms" package, and calization analysis and visualization are carried out;
e2. and (3) analyzing and evaluating prognosis effect by a decision curve: the prognosis model was fitted by the "survivinal" package and the "stdca. R" file was used for decision curve analysis.
Preferably, in c2, the PSCRC risk score calculation formula is:
PSCRC risk score = (0.34) × (ZEB 1-AS 1) + (0.08) × (PTPN 14) + (0.11) + (MYB) + (0.05) × (LINC 00973) + (0.03) × (GDI 1) + (0.04) × (SLC 2 A3) + (0.01) × (SIX 4) + (0.08) × (ACAT 2) + (0.04) × (KRT 6A) + (0.18) × (ZNF 552) + (0.06) × (SEMA 4C) + (0.29) × (KIF 7) + (0.09) × (GABRG 2) + (0.09) × (TNFRSF 14) + (0.09) × (LINC 00638) + (0.14) × (OIT 3) + (0.25) × (n 4) + (0.73) × (OFCC 1).
The model construction method for predicting colorectal cancer prognosis risks integrates newly discovered label PSCRC on the basis of traditional colorectal cancer clinical indexes, improves prognosis effects by constructing colorectal cancer prognosis label PSCRC, PSCRC risk score calculation formulas and multi-factor prognosis models, and can discover the severity of diseases early, thereby taking corresponding treatment measures early, helping clinicians to formulate reasonable treatment schemes, realizing optimal treatment effects, and having important guiding and reference significance for improving survival rate and life quality of patients.
Drawings
FIG. 1 shows a portion of the prognostic-related genes screened by a single factor survival assay.
FIG. 2 shows a colorectal cancer prognostic signature PSCRC formed by fitting a prognostic gene in a training set GSE39582, wherein A is a LASSO regression coefficient path map; b is LASSO regression cross-validation chart; c is a PSCRC risk score calculation formula; d is a survival curve; e is a time dependent ROC curve; f is a prognostic risk factor heat map.
FIG. 3 shows that 2 external datasets verify PSCRC prognostic effects, where A-C is a survival curve, time-dependent, ROC curve and prognostic risk factor heat map plotted based on PSCRC risk scores in a GSE17536 queue; D-F is a survival curve, a time dependent ROC curve and a prognosis risk factor heat map drawn based on PSCRC risk scores in the CRC_TCGA queue.
FIG. 4 shows a CRC multifactor prognostic model and prognostic evaluation, wherein A is a prognostic model; b is a prognosis calibration curve; c is prognostic DCA (decision curve analysis, DCA).
Detailed Description
The present invention is described in further detail below by way of examples to enable those skilled in the art to practice the same by reference to the specification.
It will be understood that terms, such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings.
Example 1
This example is used to illustrate the type of data, download and processing required to construct a prognosis model for colorectal cancer.
1. Data downloading and processing:
TABLE 1 colorectal cancer expression profiling and clinical follow-up data queues
CRC_TCGA clinical and RNAseq data are downloaded from a TCGA database (https:// portal. Gdc. Cancer. Gov), TPM format data are extracted, and the data are processed by a log2 (expressed value +1) method. Colorectal cancer data sets GSE39582 and GSE17536 were downloaded from a GEO (https:// www.ncbi.nlm.nih.gov/gds) database, subjected to id conversion and normalization.
Example 2
This example is used to illustrate screening of prognostic-related genes in a training set for construction of the prognostic signature PSCRC.
As in fig. 1, a portion of the prognosis-related genes in GSE39582 cohorts are shown.
1. Prognostic gene screening in training set GSE 39582:
the prognostic significance of each gene was assessed using the cox method using the R (version 4.2.1) software package "survivinal" [3.3.1] integrating time to live, state of live and gene expression data.
Example 3
This example is used to illustrate construction of a prognostic signature, PSCRC, using a training set and its prognostic evaluation.
As shown in fig. 2, a LASSO-cox regression analysis is shown to construct a prognostic signature, PSCRC, wherein (a) a map of LASSO regression coefficient paths; (B) LASSO regression cross-validation plots; (C) a PSCRC risk score calculation formula; (D) a survival curve; (E) a time dependent ROC curve; (F) prognosis risk factor heatmaps.
1.Lasso-cox analysis
Prognosis lasso (Construction of Prognostic Signature With least absolute shrinkage and selection operator) variable trajectory: the cleaned GSE39582 data was analyzed using R (version 4.2.1) R packets glmnet [4.1.4] to obtain variable coefficient values, lambda log values, L1 regularization values, and the data was visualized. Prognosis lasso coefficient screening: and analyzing the cleaned data by using a glmnet package to obtain a variable lambda value, a maximum likelihood number or a C index and visualizing the data.
2.PSCRC prognostic effect assessment
Patients were divided into two groups according to the prognostic signature (Prognostic signature of colorectal cancer, PSCRC) risk score percentile (50%), the two groups of patient prognosis differences were analyzed using the "survivinfit" function of the R-package "survivinal" and the significance of the differences was assessed using the logrank test method. Subsequently, ROC (Receiver operating characteristic curve, ROC) analysis was performed at three time points of 1 year, 3 years, and 5 years using the "ROC" function of R package "pROC", and the area under the curve (Area Under The Curve, AUC) and confidence interval were evaluated using the "ci" function, obtaining the final AUC results. Finally, the relationship between the PSCRC risk score and patient follow-up time, outcome, and each gene expression change was analyzed.
Example 4
This example is used to illustrate the validation of the prognostic signature PSCRC using 2 external data sets.
As in fig. 3, the external dataset verifies the prognostic signature PSCRC. Wherein, survival curves, time dependence, ROC curves and prognosis risk factor heatmaps drawn based on PSCRC risk scores in the (a-C) GSE17536 queue; (D-F) survival curves, time-dependent ROC curves and prognostic risk factor heatmaps plotted based on PSCRC risk scores in the crc_tcga cohort.
1. And calculating the risk scores of the PSCRC in the two external queues GSE17536 and CRC_TCGA according to a risk score formula, and verifying the prognosis effect of the PSCRC.
2.Kaplan-Meier survival curve drawing
Patients were first divided into two groups according to percentiles (50%), the prognosis differences of the two groups were further analyzed using the "survivinfit" function of the R software package "survivinal", and the significance of the prognosis differences between the different groups of samples was assessed using the logrank test method.
3.ROC analysis
ROC analysis was performed using the R software package "pROC" (version 1.17.0.1) to obtain AUC.
4. Risk thermal mapping
The relationship between different risk scores and the follow-up time of the patient, the event and the expression change of each gene is analyzed by using an R package 'ggplot 2' (version 3.3.3).
Example 5
The embodiment is used for describing the drawing of the multi-factor prognosis model and the prognosis effect evaluation.
As shown in fig. 4, a CRC prognosis model and prognosis evaluation is shown. Wherein, (a) a prognostic model; (B) a prognosis calibration curve; (C) prognosis DCA (decision curve analysis, DCA).
The embodiment is used for explaining the drawing of the prognosis model and the evaluation of the prognosis effect;
1. drawing a prognosis model and a calibration curve
Proportional risk hypothesis testing was performed using the R (version 4.2.1) package "survivinal" package [3.3.1], and Cox regression analysis was performed, and a nomogram correlation model was constructed using the "rms" package, and calization analysis was performed, and visualization was performed.
2. Decision Curve Analysis (DCA) to assess prognostic effects
The prognosis model was fitted by the "survivinal" package and the "stdca. R" file was used for decision curve analysis (decision curve analysis, DCA).
Claims (2)
1. A model construction method for predicting colorectal cancer prognosis risk, comprising the steps of:
a. data downloading and processing: downloading colorectal cancer data set CRC_TCGA containing clinical and RNAseq data from a TCGA database, extracting TPM format data, and processing the data by a log2 method; the colorectal cancer data sets GSE39582 and GSE17536 are downloaded from the GEO database and subjected to id conversion and standardization treatment;
b. screening the relevant genes of prognosis in training set: adopting a training set GSE39582 as a processing object, integrating survival time, survival state and gene expression data by using an R software package 'survivinal', and evaluating the prognosis significance of each gene by using a cox method;
c. constructing a prognosis signature PSCRC and prognosis evaluation thereof by using a training set:
c1.Lasso-cox analysis
Establishment of a prognostic lasso variable trajectory: analyzing the GSE39582 data after cleaning by using glmnet of the R software package to obtain a variable coefficient value, a lambda pair value and an L1 regularization value, and visualizing the data; prognosis lasso coefficient screening: analyzing the washed GSE39582 data by using a glmnet package to obtain a variable lambda value, a maximum likelihood number or a C index and visualizing the data;
c2. evaluation of PSCRC prognostic Effect
Calculating a prognosis tag risk score according to a PSCRC risk score calculation formula, dividing patients in a GSE39582 dataset into two groups according to the risk score, analyzing prognosis differences of the two groups of patients by using a survivinfit function of a survivinal R package, and evaluating the difference significance by using a logrank test method; subsequently, ROC analysis was performed at three time points of 1 year, 3 years, and 5 years using the "ROC" function of R package "pROC" and the area under the curve and confidence interval were evaluated using the "ci" function, obtaining the final AUC results; finally, analyzing the relationship between PSCRC risk score and patient follow-up time, outcome and each gene expression change;
d. external data set verification PSCRC
d1. Calculating the risk scores of the PSCRC in the two external data sets GSE17536 and CRC_TCGA according to a PSCRC risk score calculation formula, and verifying the prognosis effect of the PSCRC;
d2.Kaplan-Meier survival curve plot: firstly, dividing patients into two groups according to a 50% percentile, further analyzing prognosis differences of the two groups by using a survivinfit function of a survivinal R software package, and evaluating the significance of the prognosis differences among samples of different groups by using a logrank test method;
roc analysis: ROC analysis was performed using the R software package "pROC" to obtain AUC;
d4. risk heat map drawing: analyzing the relation between different risk scores and the follow-up time of a patient, the relation between the event and the expression change of each gene by using an R-package ggplot 2;
e. drawing a multi-factor prognosis model and evaluating a prognosis effect;
e1. drawing a prognosis model and a calibration curve: proportional risk hypothesis testing is carried out by using an R software package "survivinal" package, cox regression analysis is carried out, a nonogram related model is constructed by using an "rms" package, and calization analysis and visualization are carried out;
e2. and (3) analyzing and evaluating prognosis effect by a decision curve: the prognosis model was fitted by the "survivinal" package and the "stdca. R" file was used for decision curve analysis.
2. The method for constructing a model for predicting colorectal cancer prognostic risk according to claim 1, wherein in c2, the PSCRC risk score calculation formula is:
PSCRC risk score = (0.34) × (ZEB 1-AS 1) + (0.08) × (PTPN 14) + (0.11) + (MYB) + (0.05) × (LINC 00973) + (0.03) × (GDI 1) + (0.04) × (SLC 2 A3) + (0.01) × (SIX 4) + (0.08) × (ACAT 2) + (0.04) × (KRT 6A) + (0.18) × (ZNF 552) + (0.06) × (SEMA 4C) + (0.29) × (KIF 7) + (0.09) × (GABRG 2) (-0.09) × (TNFRSF 14) + (0.09) × (LINC 00638) + (0.14) × (OIT 3) + (0.25) (HCN 4) (0.73) × (OFCC 1).
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