CN112813165A

CN112813165A - Lung squamous carcinoma prognosis prediction model and application thereof

Info

Publication number: CN112813165A
Application number: CN202110150019.4A
Authority: CN
Inventors: 乔田奎; 罗露梦; 武多娇; 庄喜兵
Original assignee: Jinshan Hospital of Fudan University
Current assignee: Jinshan Hospital of Fudan University
Priority date: 2021-02-03
Filing date: 2021-02-03
Publication date: 2021-05-18

Abstract

The invention relates to a lung squamous carcinoma prognosis prediction model and application thereof, which consists of biomarkers CFLAR, RGS19, PINK1, CTSD and related solutions. The invention also comprises the application of the reagent for detecting the expression quantity of the marker in the preparation of a kit for evaluating the anti-tumor immunotherapy reactivity and the prognosis survival of the lung squamous cell carcinoma. According to the invention, the lung squamous carcinoma sample of the large-sample anti-tumor immunotherapy is subjected to screening and construction after complete transcriptome sequencing and machine learning, so that the responsiveness of the lung squamous carcinoma patient receiving anti-tumor immunotherapy can be efficiently and accurately predicted, effective guidance opinions are provided for a clinician on the treatment decision of the lung squamous carcinoma patient, and the occurrence of ineffective treatment is reduced, thereby reducing the treatment cost and discomfort experience of the patient.

Description

Lung squamous carcinoma prognosis prediction model and application thereof

Technical Field

The invention relates to the technical field of biomedicine, in particular to a lung squamous carcinoma prognosis prediction model and application thereof.

Background

Lung cancer is the most common cause of cancer-related death in the world today, and 80% of them are non-small cell lung cancers (NSCLC). TNM staging is a currently widely accepted clinical staging system used to predict prognosis and to guide treatment of non-small cell lung cancer patients. However, the current TNM staging system is far from adequate to accurately predict prognosis in non-small cell lung cancer patients. For example, for lung cancer patients, the recurrence rate of lung cancer is as high as 35-50% even in the clinical stage I. In addition, a significant proportion of patients can be cured by surgery alone, and these patients should avoid the extremely strong side effects of adjuvant chemotherapy based on the current TNM system.

Squamous cell carcinoma of lung (also called squamous cell carcinoma of lung), accounts for 40% -51% of primary lung cancer, is commonly seen in middle-aged and elderly men, and has close relation with smoking. Is mainly formed by metaplasia of columnar epithelial cells of bronchial mucosa, including chronic stimulation and damage of bronchial epithelial cells, loss of cilia, squamous metaplasia or typical hyperplasia of basal cells and the like. Squamous cell lung cancer is common in central lung cancer, and tends to grow in the chest cavity, and early squamous cell lung cancer often causes bronchoconstriction or obstructive pulmonary inflammation. Squamous cell lung carcinoma has a large variation in malignancy, and generally, squamous cell carcinoma grows more slowly than other lung cancers, and the tumor grows larger when the squamous cell carcinoma is found.

The existing prognosis prediction model for squamous cell lung carcinoma mainly comprises single or multiple molecular marker combinations, including RNA-based, lncRNA-based or microRNA, and the prediction for the curative effect of the immune checkpoint inhibitor mainly comprises mutation load prediction, gene variation, such as INPP4B gene variation, FGFR4 point mutation, SWI/SNF complex related gene variation and the like. At present, the main methods and technologies for prognosis prediction of tumor therapy immunotherapy are as follows: detecting markers (such as PD-L1, TMB, dMMR and the like); ② immune function assessment, such as Tumor Infiltrating Lymphocytes (TILs). The existing lung squamous carcinoma related multi-gene prediction model mostly focuses on prediction of prognosis and prediction of treatment effect of an immune checkpoint inhibitor, mostly focuses on prediction of treatment effect through mutation state of a certain gene, has limited effect, and lacks comprehensive evaluation on different levels of tumor immune microenvironment including immune cell infiltration level, immune related pathway, immune molecules and the like. At present, the main technology has low specificity and sensitivity, and the detection method is unstable or has higher price, and has no clear guidance value for clinical tumor treatment. At present, clinical tumor immunity brings long-term benefit hope to partial patients, and the side effect and economic burden of immunotherapy limit clinical application. High specificity and sensitivity techniques are urgently needed in clinic.

The inventor of the patent screens out a diagnosis marker combination aiming at the defects and can be used in a risk prediction model, the risk model can predict the prognosis of the patient with squamous cell carcinoma, and meanwhile, the comprehensive evaluation of a tumor immune microenvironment is realized according to the relevance of the risk score, different immune cell infiltration levels, immune-related pathways, the expression level of a key immune checkpoint inhibitor and the like, so that guidance is provided for the immunotherapy selection of the patient with squamous cell carcinoma. The lung squamous carcinoma prognosis prediction model and the application thereof are not reported at present.

Disclosure of Invention

The invention aims to provide a lung squamous carcinoma prognosis prediction model and application thereof aiming at the defects of the prior art.

In order to achieve the purpose, the invention adopts the technical scheme that:

in a first aspect, the invention provides an application of diagnostic markers, namely CFLAR, RGS19, PINK1 and CTSD, in constructing a lung squamous carcinoma prognosis prediction model.

Further, the prediction model further comprises reagents for detecting the expression levels of CFLAR, RGS19, PINK1 and CTSD.

Further, the model comprises the following:

Risk Score＝1.313*RGS19+1.161*PINK1+1.037*CTSD+1.098*CFLAR；

if Risk Score >44.754, it represents a high Risk group for predicting immunotherapy responsiveness and prognosis survival, and if Risk Score <44.754, it represents a low Risk group for predicting immunotherapy responsiveness and prognosis survival.

In a second aspect, the invention provides an application of a detection reagent in preparing a kit for evaluating the responsiveness of anti-tumor immunotherapy for lung squamous carcinoma and survival after prognosis, wherein the detection reagent consists of reagents for detecting the expression levels of the following four genes: CFLAR, RGS19, PINK1 and CTSD, the detection reagent is used as a kit to realize the evaluation of lung squamous carcinoma anti-tumor immunotherapy reactivity and the only key component of the survival function after prognosis, the kit also comprises an instruction book, and the instruction book is described as follows:

Risk Score＝1.313*RGS19+1.161*PINK1+1.037*CTSD+1.098*CFLAR；

Preferably, the sample detected using the kit is a fresh tissue tumor sample.

In a third aspect, the invention provides an application of an inhibitor in preparing a medicament for improving the anti-tumor immunotherapy reactivity and the prognosis survival of the lung squamous carcinoma, wherein the inhibitor is a substance for down-regulating the expression quantity of the following genes: CFLAR, RGS19, PINK1, and CTSD.

Preferably, the inhibitor is selected from a small molecule compound or a biological macromolecule.

Preferably, the medicament also comprises other medicaments which are compatible with the promoter and pharmaceutically acceptable carriers and/or auxiliary materials.

The invention has the advantages that:

according to the invention, the ARGs relevant to prognosis are screened out through large sample survival analysis, then the most key ARGs relevant to survival (CFLAR, RGS19, PINK1 and CTSD) are obtained through a random forest method, a risk prediction model based on the four genes is constructed through the depth of random forests and a learning method thereof, and patients are divided into a low risk group and a high risk group according to the risk score obtained by the model. The model proves the effectiveness of the risk score of the model on prognosis prediction of squamous cell lung carcinoma patients through ROC analysis of a working specificity curve of a subject, log-rank test verification of a KM survival curve, verification of multi-factor and single-factor Cox risk regression and verification of a GEO external data set. The kit has the advantages of high sensitivity, good specificity and high accuracy, and can provide effective guidance for the treatment decision of the patient with squamous cell lung carcinoma for clinicians, and reduce the occurrence of ineffective treatment, thereby reducing the treatment cost and discomfort experience of the patient.

Drawings

FIG. 1 is a 4 ARGs-based prediction model constructed from random forests. Figure 1A shows that with increased expression levels of 4 ARGs, the risk score increased, with worse prognosis. The risk groups are divided into high risk groups and low risk groups according to the risk score cut-off value. K-M survival analysis showed that the prognosis was significantly worse in the high risk group than in the low risk group (fig. 1B for TCGA training set data, fig. 1C for validation set data). FIGS. 1D-E are the validation results of the ROC curves in the two data sets for the predictive model, respectively, further examining the predictive efficacy of the risk score for reliability.

FIG. 2 is a graph showing that the risk score based on this model can be further validated as an independent prognostic indicator by single-factor COX regression and multi-factor COX regression for risk score and clinical profile (gender, age, T stage, N stage).

Fig. 3 is a Gene Set Enrichment Analysis (GSEA) of immune-related pathways by high and low risk groups, and the results show that in the high risk group, immunosuppression is significantly upregulated.

Fig. 4 is a graph showing the level of infiltration of 28 immune cells in the tumor microenvironment in the high and low risk groups. Immunosuppressive cells were enriched in the high risk group.

FIG. 5 is a graph showing immune cells associated with prognosis in patients with squamous cell lung carcinoma, with macrophages and regulatory T cells found to be significantly higher in the high risk group than in the low risk group (FIGS. 5A-C). Macrophage and regulatory T cell infiltration levels showed a significant negative correlation with prognosis (fig. 5D-E).

FIG. 6 shows the results of correlation analysis of the risk scores and immune cells for four predictor genes ARGs (FIG. 6A), and the results of correlation analysis of the expression levels of three key immune checkpoints with the risk scores (FIGS. 6B-D).

FIGS. 7A-F and 7G-L show the results of correlation analysis of the expression levels of four predictor genes ARGs with three key immune checkpoints.

Figure 8 is a graph showing the results of the correlation analysis of the immune and matrix scores with risk scores.

Detailed Description

The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, it should be understood that various changes and modifications can be made by those skilled in the art after reading the disclosure of the present invention, and equivalents fall within the scope of the appended claims.

Example 1 model construction and Effect verification

1. Method of producing a composite material

1.1 obtaining 326 lung squamous carcinoma RNA sequencing data and clinical data of TCGA database, obtaining Autophagy-Related gene (ARGs) expression profile, KM survival analysis to screen ARGs Related to prognosis, drawing Kaplan-Meier survival curve and log-rank test to calculate P value.

1.2 screening survival-related sARGs by a random forest machine learning method to obtain four prediction genes most related to survival, and constructing a prediction model based on the four genes. (Cox regression Risk ratio model formula: Risk Score 1.313 RGS19+1.161 PINK1+1.037 CTSD +1.098 CFLAR). The patient was classified into low Risk group and high Risk group according to the level of Risk Score obtained by the model. The model passes ROC analysis of the operating characteristic curve of a testee, log-rank test verification of a KM survival curve and verification of a GEO external data set.

1.3 Single and Multi-factor COX regression through risk score and clinical characteristics (gender, age, T stage, N stage).

1.4 Gene Set Enrichment Analysis (GSEA) of immune-related pathways, Clusterprofiler package, R language.

1.5(ssGSEA method): immune cells and corresponding gene lists were obtained in the literature and the GSVA implication scores of GSVA irradiation scales represent the infiltration levels of 28 immune cells were assessed according to RNA sequencing data, the R language GSVA package. The pheatmap package draws the heat map.

1.6 wilcoxon rank-sum test higher low risk group phagocytic and regulatory T cell differences; log-rank test of KM survival curves compares macrophage and regulatory T cell infiltration levels versus survival time.

1.7Pearson correlation analysis

1.8 matrix scoring and immune scoring method: obtaining immune scores and matrix scores of the tumors by an ESTIMATE algorithm based on RNA sequencing data; pearson correlation analysis.

2 results

2.1 four ARGs most relevant to prognosis (CFLAR, RGS19, PINK1 and CTSD) were obtained: the results showed that the RNA levels of the four genes were significantly negatively correlated with prognosis.

2.2 FIG. 1A shows that with increased expression levels of 4 ARGs, the risk score increased, with worse prognosis. The risk groups are divided into high risk groups and low risk groups according to the risk score cut-off value. K-M survival analysis showed that the prognosis was significantly worse in the high risk group than in the low risk group (fig. 1B for TCGA training set data, fig. 1C for validation set data). FIGS. 1D-E are the validation results of the ROC curves in the two data sets for the predictive model, respectively, further examining the predictive efficacy of the risk score for reliability.

2.3 FIG. 2 is a further demonstration that the risk score based on this model can be used as an independent prognostic indicator by single-and multi-factor COX regression of risk score and clinical profile (gender, age, T stage, N stage).

2.4 fig. 3 is a Gene Set Enrichment Analysis (GSEA) of immune-related pathways by high and low risk groups, and the results show that in the high risk group, the immune suppression is significantly upregulated.

2.5 fig. 4 shows the level of infiltration of 28 immune cells in the tumor microenvironment in the high and low risk groups. Immunosuppressive cells were enriched in the high risk group.

2.6 FIG. 5 shows that immune cells associated with prognosis in patients with squamous cell lung carcinoma, macrophages and regulatory T cells were found to be significantly higher in the high risk group than in the low risk group (FIGS. 5A-C). Macrophage and regulatory T cell infiltration levels showed a significant negative correlation with prognosis (fig. 5D-E).

2.7 FIG. 6 shows the results of correlation analysis of four predictor genes ARGs with risk scores and immune cells (FIG. 6A), and the results of correlation analysis of expression levels of three key immune checkpoints with risk scores (FIGS. 6B-D).

2.8 FIG. 7 shows the results of correlation analysis of the expression levels of four predictor genes ARGs with three key immune checkpoints.

2.9 FIG. 8 shows the results of the correlation analysis of the immune and stromal scores with the risk scores.

Example 2 control test

1. The kit comprises the following components:

[ kit I ]

Comprises a detection kit instruction, and the detection reagent is a reagent for detecting the expression level of CFLAR, RGS19, PINK1 and CTSD.

The description content of the specification is as follows:

Risk Score＝1.313*RGS19+1.161*PINK1+1.037*CTSD+1.098*CFLAR；

[ reagent kit II ]

Comprises a detection kit instruction, and the detection reagent is a reagent for detecting the expression quantity of the CFLAR.

The description content of the specification is as follows: when the CFLAR expression level is higher than 12.37775, it represents that the immunotherapy responsiveness and prognosis survival is predicted to be a high-risk group, and when the CFLAR expression level is lower than 12.37775, it represents that the immunotherapy responsiveness and prognosis survival is predicted to be a low-risk group.

[ reagent kit III ]

Including the instructions of the detection kit, and the detection reagent is a reagent for detecting the expression level of RGS 19.

The description content of the specification is as follows: when the expression level of RGS19 is higher than 9.98, it represents a group predicted to be at high risk for immunotherapy responsiveness and prognosis survival, and when the expression level of RGS19 is lower than 9.98, it represents a group predicted to be at low risk for immunotherapy responsiveness and prognosis survival.

[ reagent kit IV ]

Comprises a detection kit instruction, and the detection reagent is a reagent for detecting the expression level of PINK 1.

The description content of the specification is as follows: when the expression level of PINK1 is higher than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a high-risk group, and when the expression level of PINK1 is lower than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a low-risk group.

[ kit five ]

Comprises a detection kit instruction, and the detection reagent is a reagent for detecting the expression quantity of CTSD.

The description content of the specification is as follows: when the expression level of CTSD is higher than 15.29668, it represents the prediction of immunotherapy responsiveness and prognosis survival as a high-risk group, and when the expression level of CTSD is lower than 15.29668, it represents the prediction of immunotherapy responsiveness and prognosis survival as a low-risk group.

2. Method of producing a composite material

2.1 patients with squamous cell lung carcinoma who received anti-tumor immunotherapy at Jinshan Hospital affiliated at the university of Compound Dane, the inclusion and exclusion criteria for the patients were as follows:

(1) patients with squamous cell lung carcinoma receiving immunotherapy with tumors;

(2) complete curative effect information and clinical follow-up information are provided;

(3) having whole transcriptome RNA sequencing data;

(4) patients with unknown tumor immunotherapy results or incomplete survival data were excluded.

2.2 the 203 patients meeting the above standard are taken into the study and randomly divided into five groups, wherein the five groups are respectively recorded by a person in the specification by using a kit I, a kit II, a kit III, a kit IV and a kit V.

3. Results

The result shows that the prediction accuracy rate is 75.6% by using the first kit, 59.3% by using the second kit, 69.3% by using the third kit, 68.7% by using the fourth kit and 63.2% by using the fifth kit.

4. Conclusion

The results show that the marker combined prognosis prediction accuracy is higher, the inventor selects the optimal index combination based on abundant clinical and research experiences and a large number of cases in hospital for years, and proves that the marker combined prognosis prediction method has an excellent evaluation effect, can provide effective guidance for a clinician to treatment decision of a patient with squamous cell lung carcinoma, reduces the occurrence of ineffective treatment, reduces the treatment cost and discomfort experience of the patient, and has strong practicability.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and additions can be made without departing from the principle of the present invention, and these should also be considered as the protection scope of the present invention.

Claims

1. The application of the diagnostic marker combination in constructing a lung squamous carcinoma prognosis prediction model is characterized in that the diagnostic marker combination is CFLAR, RGS19, PINK1 and CTSD.

2. The use of claim 1, wherein the predictive model further comprises reagents for detecting the expression levels of CFLAR, RGS19, PINK1 and CTSD.

3. The application of claim 1, wherein the model comprises the following:

Risk Score＝1.313*RGS19+1.161*PINK1+1.037*CTSD+1.098*CFLAR；

4. The application of the detection reagent in preparing the kit for evaluating the lung squamous cancer anti-tumor immunotherapy reactivity and the prognosis survival is characterized in that the detection reagent consists of reagents for detecting the following four gene expression levels: CFLAR, RGS19, PINK1 and CTSD, the detection reagent is used as a kit to realize the evaluation of lung squamous carcinoma anti-tumor immunotherapy reactivity and the only key component of the survival function after prognosis, the kit also comprises an instruction book, and the instruction book is described as follows:

Risk Score＝1.313*RGS19+1.161*PINK1+1.037*CTSD+1.098*CFLAR；

5. The use of claim 4, wherein the sample to be tested using the kit is a fresh tissue tumor sample.

6. The application of an inhibitor in preparing a medicament for improving the anti-tumor immunotherapy reactivity and the prognosis survival of squamous cell lung carcinoma is characterized in that the inhibitor is a substance for down-regulating the expression quantity of the following genes: CFLAR, RGS19, PINK1, and CTSD.

7. The use according to claim 6, wherein the inhibitor is selected from a small molecule compound or a biological macromolecule.

8. The use of claim 6, wherein the medicament further comprises other drugs compatible with the enhancer and pharmaceutically acceptable carriers and/or excipients.