Correlation model of PLEKHA4 gene expression level and low-grade glioma radiotherapy sensitivity and prediction method

Abstract

The invention provides an innovative model based on the correlation between the PLEKHA4 gene expression level and the low-grade glioma radiotherapy sensitivity and a prediction method. According to the model, a multi-factor Logistic regression analysis framework is constructed, and the PLEKHA4 gene expression level and key clinical pathological parameters are organically combined, so that a radiotherapy sensitivity prediction model is established. The method comprises a series of steps of sample collection, gene expression detection, clinical information collection, model calculation, result interpretation and the like, and can realize accurate prediction of radiotherapy response of low-grade glioma patients. Compared with the prior art, the method has the remarkable advantages of simplicity and convenience in operation, high prediction accuracy, high clinical transformability and the like. The method has great potential in the aspect of guiding individualized radiotherapy scheme formulation, can significantly improve the treatment effect, and has important value for medical application. Besides, the model can be optimized through further clinical verification, so that clinical practice can be better served, and the life quality and prognosis effect of patients are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of tumor molecular diagnosis and precision medicine, and particularly relates to a mathematical model for predicting radiotherapy sensitivity of low-grade glioma based on gene expression characteristics and an application method thereof, and is particularly suitable for a neural tumor treatment decision support system. BACKGROUND

[0002] Low-grade glioma (LGG) is a low-malignancy tumor originating from central nervous system glial cells, commonly seen in young adults. Although it grows slowly, it has an infiltrative growth characteristic, is prone to recurrence and may progress to high-grade glioma. Radiotherapy is an important adjuvant treatment after surgery, but there is significant difference in radiotherapy sensitivity among patients.

[0003] Currently, the commonly used clinical prognostic evaluation system mainly relies on histopathological grading, age, IDH mutation status and 1p / 19q co-deletion, and there is still a lack of efficient prediction tools that can dynamically reflect the biological behavior of tumors, especially the response ability to radiotherapy. In recent years, with the development of genomics, more and more studies have shown that abnormal expression of specific genes is closely related to radiotherapy resistance of tumor cells.

[0004] PLEKHA4 (Pleckstrin Homology Domain Containing A4) gene encodes a protein involved in cell membrane signal transduction and cytoskeleton regulation, which has been found to be related to invasiveness, migration ability and treatment resistance in various tumors. However, its expression pattern in low-grade glioma and its quantitative correlation with radiotherapy sensitivity have not been systematically revealed, and there is no related report on using it to construct a radiotherapy sensitivity prediction model.

[0005] Although there are prediction models based on multi-gene signature or imageomics in the prior art, there are generally problems such as complex modeling, high detection cost, and difficulty in clinical landing. Therefore, it is urgent to develop a prediction model with key single gene as the core, combined with clinical variables, good interpretability and generalizability to meet the needs of clinical precision radiotherapy decision support SUMMARY

[0006] The present application aims to provide a correlation model between PLEKHA4 gene expression level and radiotherapy sensitivity of low-grade glioma and a prediction method, which realizes accurate prediction of patient radiotherapy response by constructing a quantifiable statistical model, and provides a scientific basis for formulating individualized radiotherapy strategies in clinic.

[0007] The technical scheme of the present application is implemented as follows: a PLEKHA4 gene expression level and low-grade glioma radiotherapy sensitivity correlation model and prediction method, the model is a multi-factor logistic regression model, taking radiotherapy sensitivity as the dependent variable, and PLEKHA4 gene expression level as one of the independent variables, and combining at least two clinical covariates of age, IDH mutation status, and Ki-67 index, for predicting the sensitivity probability of patients after radiotherapy.

[0008] As a preferred embodiment, the PLEKHA4 gene expression level is detected by qRT-PCR, RNA sequencing or digital PCR technology, and is standardized to log2(FPKM+1), TPM value or ΔCt value.

[0009] As a preferred embodiment, the radiotherapy sensitivity is defined as: tumor volume reduction ≥30% or stable disease without progression, and no new lesions within 6 months after receiving standard radiotherapy (total dose ≥50Gy), which meets the RANO evaluation standard.

[0010] A model-based low-grade glioma radiotherapy sensitivity prediction method, comprising the following steps:

[0011] S1, obtaining a patient's tumor tissue sample and detecting the PLEKHA4 gene expression level;

[0012] S2, collecting the patient's age, IDH mutation status, Ki-67 index and tumor volume clinical information;

[0013] S3, inputting the above data into the Logistic model to calculate the radiotherapy sensitivity probability P;

[0014] S4, risk stratification according to P value: if P≥0.65, it is determined as "radiotherapy sensitive type"; if P<0.65, it is determined as "radiotherapy low sensitive type".

[0015] As a preferred embodiment, the total RNA is extracted in S1, and the PLEKHA4 gene expression level is detected by qRT-PCR or high-throughput sequencing technology.

[0016] As a preferred embodiment, the model parameters are determined by maximum likelihood estimation method fitting, and are optimized by 10-fold cross-validation, and the AUC value of the model in the independent validation set is not less than 0.80.

[0017] As a preferred embodiment, it further includes outputting the prediction result in the form of a visual report, which includes prediction probability, risk level, treatment recommendation and model confidence information.

[0018] Use of a correlation model in preparation of a kit for predicting radiotherapy sensitivity of low-grade glioma, the kit comprising primers, probes or sequencing library construction components for detecting PLEKHA4 gene expression.

[0019] As a preferred embodiment, it further comprises enzymes and buffer solutions for signal amplification to improve detection sensitivity.

[0020] After adopting the above technical solution, the model and prediction method have multiple advantages, the prediction accuracy is greatly improved by combining key gene expression and clinical characteristics, the operability is strong, only routine gene detection and clinical data are needed without additional expensive equipment, the clinical guidance value is clear, the output result is directly related to treatment decision, and individualized medical treatment is assisted, the scalability is good, the model framework supports subsequent incorporation of new biomarkers for iterative upgrading, the model is consistent with the development trend of precision oncology, and the transformation from "experience treatment" to "prediction driven treatment" is promoted. These characteristics make the method have important value and broad prospects in clinical application. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0022] Figure 1 The ROC curve and AUC value of the prediction model of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] As Figure 1 shown, a correlation model of PLEKHA4 gene expression level and radiotherapy sensitivity of low-grade glioma, the model is a trained and optimized multifactor Logistic regression prediction model, the expression is as follows:

[0025] wherein,

[0026] P: represents the probability of the patient being sensitive to radiotherapy;

[0027] Xi: represents the normalized expression level of PLEKHA4 gene (log2(FPKM+1) value obtained by qRT-PCR or RNA-seq);

[0028] X2,..., Xn: selected from at least one of the following clinical covariates: age, gender, tumor volume, IDH mutation status, 1p / 19q co-deletion status, Ki-67 index;

[0029] β0: model intercept term;

[0030] β1,..., βn: regression coefficients of each variable, determined by maximum likelihood estimation method;

[0031] The model is verified by receiver operating characteristic curve (ROC) analysis, with an AUC value not less than 0.82, a sensitivity ≥ 80%, and a specificity ≥ 75%.

[0032] Example 1: Model construction and verification

[0033] (1) Research object:

[0034] A total of 210 patients diagnosed with WHO grade II astrocytoma or oligodendroglioma in a certain third-grade hospital from 2016 to 2023 were retrospectively collected, all of whom received postoperative adjuvant radiotherapy (total dose of 50-54 Gy, fractionated irradiation). Exclusion criteria: combined with serious systemic diseases, radiotherapy interruption, loss of follow-up.

[0035] (2) Gene expression detection:

[0036] The PLEKHA4 expression level in all samples was detected by RNA-seq, and the data came from TCGA-LGG database and local queue, which were uniformly normalized.

[0037] (3) Variable screening and model construction:

[0038] Single factor Logistic regression was used to screen variables significantly related to radiotherapy sensitivity (P<0.1);

[0039] LASSO regression was used to further select the optimal feature combination;

[0040] A multivariate Logistic regression model was constructed to determine the final coefficients:

[0041] β 0 = -2.15;

[0042] β 1 (PLEKHA4 expression) = 1.32;

[0043] β 2 (age) = 0.04;

[0044] β 3 (IDH wild type) = -1.05;

[0045] β 4 (IKi-67≥5%)=0.88。

[0046] (4) Model performance evaluation:

[0047] Internal validation (cross-validation): AUC = 0.85, sensitivity 83.2%, specificity 78.6%;

[0048] External validation (independent cohort n = 60): AUC = 0.82, good consistency.

[0049] Example 2: Clinical prediction application example

[0050] A 45-year-old male patient diagnosed with WHO grade II astrocytoma, IDH mutant, Ki-67 index 4%, tumor volume about 3.5 cm 3 , PLEKHA4 expression level log2(FPKM+1) = 8.2.

[0051] Substitute the model calculation:

[0052]

[0053] Predicted result: probability of radiotherapy sensitivity is 71%, judged as "sensitive type", and standard external beam radiotherapy is recommended.

[0054] A method for predicting radiotherapy sensitivity of low-grade glioma based on a model, comprising the following steps:

[0055] Step S1: sample collection and gene detection;

[0056] Obtain fresh frozen tumor tissue or paraffin-embedded tissue of low-grade glioma patients before or after operation;

[0057] Extract total RNA and detect PLEKHA4 gene expression level by qRT-PCR or high-throughput sequencing technology;

[0058] Standardize the data and convert to log2(FPKM+1) or ΔCt value.

[0059] Step S2: collection of clinical information;

[0060] Collect patient basic information: age, gender;

[0061] Imaging data: tumor location, maximum diameter, volume;

[0062] Pathology report: histological type, IDH mutation status, 1p / 19q co-deletion status, Ki-67 proliferation index;

[0063] Radiotherapy regimen and follow-up records: radiotherapy dose, fractionation mode, 6-month post-treatment MRI evaluation of efficacy (determined as "sensitive" or "resistant" according to RANO criteria).

[0064] Step S3: model input and operation;

[0065] Substitute the normalized PLEKHA4 expression value and other clinical variables into the Logistic model;

[0066] Calculate the probability P of radiotherapy sensitivity for the patient.

[0067] Step S4: result interpretation and clinical recommendations

[0068] If P≥0.65, it is determined as "radiotherapy sensitive type", and it is recommended to actively implement standard radiotherapy regimen;

[0069] If P<0.65, it is determined as "low sensitivity / resistant to radiotherapy type", and it is recommended to adjust the treatment strategy, such as combined chemotherapy, targeted therapy or participation in clinical trials;

[0070] Output the prediction report, including the prediction probability, risk stratification and individualized treatment recommendations.

[0071] The application of a correlation model in the preparation of a kit for predicting the radiotherapy sensitivity of low-grade glioma, the kit comprising primers, probes or sequencing library construction components for detecting PLEKHA4 gene expression, further comprising enzymes and buffer solutions for signal amplification to improve detection sensitivity.

[0072] The present application provides a correlation model based on PLEKHA4 gene expression level and radiotherapy sensitivity of low-grade glioma and a prediction method. The model can quantitatively analyze the relationship between PLEKHA4 expression level and radiotherapy efficacy, and is used to predict the radiotherapy response probability of individual patients, providing a scientific basis for formulating individualized treatment strategies in clinic.

[0073] The above is only a preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A model of the correlation between the expression level of the PLEKHA4 gene and the radiotherapy sensitivity of low-grade glioma, characterized by, The model is a multi-factor Logistic regression model, taking radiotherapy sensitivity as the dependent variable, PLEKHA4 gene expression level as one of the independent variables, and combining at least two clinical covariates of age, IDH mutation status, and Ki-67 index, to predict the sensitivity probability of patients after radiotherapy. 2.The model of correlation between PLEKHA4 gene expression level and radiotherapy sensitivity of low-grade glioma according to claim 1, wherein, The PLEKHA4 gene expression level is detected by qRT-PCR, RNA sequencing or digital PCR technology, and is standardized to log2(FPKM+1), TPM value or ΔCt value. 3.The model of correlation between PLEKHA4 gene expression level and radiotherapy sensitivity of low-grade glioma according to claim 1, wherein, The radiotherapy sensitivity is defined as: within 6 months after receiving standard radiotherapy (total dose ≥ 50 Gy), the tumor volume is reduced by ≥ 30% or the disease is stable without progression, and no new lesions are found, which meets the RANO evaluation standard.

4. A method for predicting radiotherapy sensitivity of low-grade glioma based on the model of any one of claims 1-3, characterized in that, The method comprises the following steps: S1, obtaining a tumor tissue sample of a patient and detecting the PLEKHA4 gene expression level; S2, collecting the age, IDH mutation status, Ki-67 index and tumor volume clinical information of the patient; S3, inputting the above data into the Logistic model to calculate the radiotherapy sensitivity probability P; S4, risk stratification according to the P value: if P≥0.65, it is determined as "radiotherapy sensitive type"; if P<0.65, it is determined as "radiotherapy low sensitive type".

5. The method of claim 4, wherein the method is for predicting radiotherapy sensitivity of a low-grade glioma. In S1, total RNA is extracted, and the PLEKHA4 gene expression level is detected by qRT-PCR or high-throughput sequencing technology.

6. The method of claim 4, wherein the method is for predicting radiotherapy sensitivity of a low-grade glioma. The model parameters are determined by maximum likelihood estimation method, and are optimized by 10-fold cross-validation, and the AUC value of the model in the independent validation set is not less than 0.

80.

7. The method of claim 4, wherein the method is for predicting radiotherapy sensitivity of a low-grade glioma. It also includes outputting the prediction results in the form of a visual report, which includes prediction probability, risk level, treatment recommendation and model confidence information.

8. Use of the correlation model according to claim 1 in the preparation of a kit for predicting the radiotherapy sensitivity of low-grade glioma, which comprises primers, probes or sequencing library construction components for detecting the expression of PLEKHA4 gene.

9. Use of the kit for predicting the radiotherapy sensitivity of low-grade glioma according to claim 7, which further comprises enzymes and buffer solutions for signal amplification to improve detection sensitivity.

Images