Method and device for detecting ATRX gene mutation in glioma tissue sample based on Raman spectrum
Through Raman spectroscopy-based methods, rapid detection of glioma tissue samples is solved, and the problems of high cost and low acceptance in the prior art are achieved, and low cost and rapid detection of ATRX gene mutation status is achieved.
Patent Information
- Application Number
- CN202411534400.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-30
- Publication Date
- 2025-02-25
AI Technical Summary
The methods used in the prior art to judge ATRX gene mutations in glioma tissue samples mainly rely on high-cost gene detection technology, and the patient acceptance is not high and lacks low-cost and rapid detection methods.
Raman spectroscopy was used to scan glioma tissue samples by Raman spectroscopy. By pre-processing and feature extraction of Raman spectroscopy data, classification model was input to assist in determining the mutation status of the ATRX gene.
It realizes low-cost and rapid detection of ATRX gene mutation status of glioma, which can partially replace the high-cost tumor gene detection and analysis function, and provides a more economical and efficient detection solution.
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Figure CN119510388A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of Raman spectroscopy detection, and in particular to a method and device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy. Background Art
[0002] Glioma is the most common primary malignant tumor of the central nervous system. In recent years, with the development of molecular technology, more and more gene and chromosomal changes have been found to be related to the occurrence, development and prognosis of glioma. Alphathalassemia retardation syndrome X-linked (ATRX) gene mutations are common in some glioma subtypes, and their loss may affect the patient's prognosis. Glioma patients with ATRX gene mutations have longer progression-free survival and overall survival. The results of bioinformatics analysis of the Cancer and Tumor Gene Atlas showed that glioblastoma patients with ATRX deletion had a longer overall survival time and may have a better response to temozolomide treatment. Therefore, the ATRX gene can be used as an indicator for clinicians to assess the prognosis of patients.
[0003] Currently, clinical judgment of ATRX gene mutations mainly relies on genetic testing technology, which is costly and has low patient acceptance. Therefore, a low-cost and rapid method for detecting ATRX gene mutations is urgently needed. Summary of the invention
[0004] The purpose of this application is to provide a method and device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy, which can realize low-cost and rapid detection of glioma ATRX gene mutation status.
[0005] To achieve the above objectives, this application provides the following solutions.
[0006] In a first aspect, the present application provides a method for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy, comprising: performing Raman spectroscopy scanning on the glioma tissue sample to be tested to obtain Raman spectroscopy data; preprocessing the Raman spectroscopy data to obtain normalized Raman spectroscopy data; extracting the intensity value at the characteristic displacement from the normalized Raman spectroscopy data; the characteristic displacement includes the Raman displacement of 701cm -1 、1592cm -1 、2861cm -1 、2869cm -1 、2878cm -1 、2924cm -1 、3168cm -1 、3172cm -1 and 3483cm-1 ; The intensity value at the characteristic displacement is input into the classification model to calculate the classification prediction value; the classification model characterizes the relationship between the classification prediction value and the intensity value at the characteristic displacement; the classification prediction value is used to assist in determining the ATRX gene mutation status of the glioma tissue sample to be tested; the ATRX gene mutation status includes ATRX gene mutation and ATRX gene non-mutation.
[0007] Optionally, the Raman spectrum data is preprocessed to obtain normalized Raman spectrum data, including: removing fluorescence background from the Raman spectrum data to obtain Raman spectrum data with fluorescence background removed; smoothing and denoising the Raman spectrum data with fluorescence background removed to obtain smoothed and denoised Raman spectrum data; and normalizing the smoothed and denoised Raman spectrum data to obtain normalized Raman spectrum data.
[0008] Optionally, the classification model is determined based on a logistic regression algorithm and a maximum relevance minimum redundancy algorithm; the determination process of the classification model includes: obtaining a number of Raman spectral samples marked with ATRX gene mutation status, and preprocessing and extracting characteristic spectral information to construct a sample data set; the sample data set includes characteristic spectral information and ATRX gene mutation status corresponding to a number of normalized Raman spectral samples; the characteristic spectral information includes intensity values at a number of Raman shifts; the ATRX gene mutation status includes 1 and 0, wherein 1 indicates that the ATRX gene is mutated and 0 indicates that the ATRX gene is not mutated; a linear regression model is constructed with the ATRX gene mutation status as the target variable and the characteristic spectral information as the characteristic variable; according to the sample data set, the maximum relevance minimum redundancy algorithm is used to screen the characteristic displacement, and the logistic regression algorithm is used to train the linear regression model to obtain a classification model.
[0009] The expression of the classification model is: z=1.2259+0.5528×I 701 +(-0.4440×I 1592 )+0.5846×I 2861 +(-0.7022×I 2869 )+(-0.9595×I 2878 )+(-0.9046×I 2895 )+0.6754×I 2924 +0.8655×I 3168 +1.2689×I 3172 +(-0.6224×I 3483); P represents the classification prediction value. When P ≥ 0.5, it is judged as a glioma with ATRX gene mutation, and when P < 0.5, it is judged as a glioma without ATRX gene mutation; z is the output value of the linear regression model; I 701 Indicates the Raman shift of 701 cm -1 The intensity value at I 1592 Indicated at Raman shift 1592cm -1 The intensity value at I 2861 Indicated at Raman shift 2861cm -1 The intensity value at I 2869 Indicated at Raman shift 2869cm -1 The intensity value at I 2878 Indicated at Raman shift 2878cm -1 The intensity value at I 2895 Indicated at Raman shift 2895cm -1 The intensity value at I 2924 Indicated at Raman shift 2924cm -1 The intensity value at I 3168 Indicates that the Raman shift is 3168cm -1 The intensity value at I 3172 Indicated at Raman shift 3172cm -1 The intensity value at I 3483 Indicates that the Raman shift is 3483cm -1 The intensity value at .
[0010] Optionally, the Raman spectrum data is processed to remove the fluorescence background to obtain the Raman spectrum data with the fluorescence background removed, including: using an adaptive iterative reweighted penalized least squares method to process the Raman spectrum data to remove the fluorescence background to obtain the Raman spectrum data with the fluorescence background removed.
[0011] Optionally, performing smoothing and denoising processing on the Raman spectrum data from which the fluorescence background is removed to obtain smoothed and denoised Raman spectrum data includes: performing smoothing and denoising processing on the Raman spectrum data from which the fluorescence background is removed using a Savitzky-Golay smoothing algorithm to obtain smoothed and denoised Raman spectrum data.
[0012] Optionally, normalizing the smoothed and denoised Raman spectrum data to obtain normalized Raman spectrum data includes: normalizing the smoothed and denoised Raman spectrum data using a maximum-minimum normalization algorithm to obtain normalized Raman spectrum data.
[0013] In a second aspect, the present application provides a device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy, using the above-mentioned method for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy, including: a laser for emitting laser to the glioma tissue sample to be tested; a spectrum analyzer for collecting Raman light scattered by the glioma tissue sample to be tested to obtain Raman spectrum data, preprocessing the Raman spectrum data to obtain normalized Raman spectrum data, extracting intensity values at characteristic displacements from the normalized Raman spectrum data, and inputting the intensity values at the characteristic displacements into a classification model to calculate a classification prediction value.
[0014] Optionally, the device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy also includes: an optical microscopy platform for placing the glioma tissue sample to be tested, and adjusting the position and height of the glioma tissue sample to be tested.
[0015] Optionally, the device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy also includes: a display connected to the spectrometer, for displaying the classification prediction value and the auxiliary determination result; the auxiliary determination result is determined based on the classification prediction value.
[0016] Optionally, the device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy also includes: an optical fiber probe, connected to the laser and the spectrometer, respectively, for transmitting the laser emitted by the laser to the surface of the glioma tissue sample to be tested, and transmitting the Raman light scattered by the surface of the glioma tissue sample to be tested to the spectrometer.
[0017] Optionally, the excitation wavelength of the laser is 532 nm, and the integration time is 1 to 3 seconds.
[0018] According to the specific embodiments provided by the present application, the present application discloses the following technical effects: The present application provides a method and device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy, firstly performing Raman spectroscopy scanning on the glioma tissue sample to be tested to obtain Raman spectroscopy data, then preprocessing and feature extraction of the Raman spectroscopy data to obtain the intensity value at the characteristic displacement, and finally inputting the intensity value at the characteristic displacement into a pre-trained classification model to calculate the classification prediction value to assist in determining the ATRX gene mutation status of the glioma tissue sample to be tested. Compared with the prior art, the present application utilizes Raman spectroscopy technology, performs Raman spectroscopy analysis on the glioma tissue sample to be tested, and combines the trained classification model to achieve low-cost and rapid detection of glioma ATRX gene mutation status, which can partially replace the high-cost tumor gene detection and analysis function. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0020] Figure 1 A flow chart of a method for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy provided in this application.
[0021] Figure 2 This is a graph showing the predictive efficacy curve of the ATRX gene mutation status of the classification model provided in this application.
[0022] Figure 3 A structural diagram of a device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy provided in this application.
[0023] Reference numerals: laser-1, spectrum analyzer-2, display-3, optical microscopy platform-4. DETAILED DESCRIPTION
[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0025] The present application relates to a method and device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy. The method uses Raman spectroscopy technology to identify the ATRX gene mutation status by performing Raman spectroscopy analysis on glioma tissue samples, thereby achieving rapid detection of gliomas with ATRX gene mutations and guiding the formulation of treatment plans.
[0026] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.
[0027] like Figure 1 As shown, the method for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy provided in the present application includes the following steps S1 to S4.
[0028] Step S1: Perform Raman spectrum scanning on the glioma tissue sample to be tested to obtain Raman spectrum data.
[0029] Among them, the glioma tissue samples to be tested can be collected directly during the operation without any slicing processing. Tissue fragments with a long diameter of about 2 to 20 mm can be taken and placed on a glass slide.
[0030] Exemplarily, a Raman spectrum excitation device (such as a laser with an excitation wavelength of 532 nm) is used to perform Raman spectrum scanning on the glioma tissue sample to be tested, and a spectrum analyzer is used to collect Raman spectrum data. The integration time of the Raman spectrum scanning is controlled to be 1 to 3 seconds.
[0031] Step S2: preprocessing the Raman spectrum data to obtain normalized Raman spectrum data.
[0032] Exemplarily, the Raman spectrum data is first processed to remove the fluorescence background to obtain the Raman spectrum data with the fluorescence background removed; secondly, the Raman spectrum data with the fluorescence background removed is smoothed and denoised to obtain the smoothed and denoised Raman spectrum data; finally, the smoothed and denoised Raman spectrum data is normalized to obtain the normalized Raman spectrum data.
[0033] Preferably, the fluorescence background removal process uses the built-in background removal function in the Python RamPy library - background removal based on polynomial fitting, which removes the background by fitting the background part of the spectrum (ie, the non-main peak region).
[0034] Preferably, the smoothing and noise reduction process uses a Savitzky-Golay smoothing algorithm, and smoothing is achieved by fitting a polynomial to the data in a local window. The Python function is named savgol_filter.
[0035] The formula of the Savitzky-Golay smoothing algorithm can be expressed as: Among them, b u is the smoothed data point, a u is the original data point, a u+v is the data point after moving v units, m is half the window width (that is, the window width is 2m+1), c v is the coefficient of the polynomial fitting. In this embodiment, the window width is 7 and the polynomial fitting coefficient is 1.
[0036] Preferably, the normalization process adopts the maximum-minimum normalization algorithm. The formula is: x'=(x-min(x)) / (max(x)-min(x)); wherein x' is the normalized Raman spectrum data, x is the smoothed and denoised Raman spectrum data, min(x) is the minimum value of the smoothed and denoised Raman spectrum data, and max(x) is the maximum value of the smoothed and denoised Raman spectrum data.
[0037] Step S3: Extract the intensity value at the characteristic shift from the normalized Raman spectrum data. The characteristic shift includes the Raman shift of 701 cm -1 、1592cm -1 、2861cm -1 、2869cm -1 、2878cm -1 、2924cm -1 、3168cm -1 、3172cm -1 and 3483cm -1 .
[0038] Step S4: Input the intensity value at the characteristic displacement into the classification model to calculate the classification prediction value. The classification model is determined based on machine learning or pattern recognition algorithm, and is used to characterize the relationship between the classification prediction value and the intensity value at the characteristic displacement. The classification prediction value is used to assist in determining the ATRX gene mutation status of the glioma tissue sample to be tested. The ATRX gene mutation status includes ATRX gene mutation and ATRX gene non-mutation.
[0039] Exemplarily, the present application determines a classification model based on a logistic regression (LR) algorithm and a maximal relevance and minimal redundancy (mRMR) algorithm, and the determination process includes: obtaining a number of Raman spectral samples annotated with ATRX gene mutation status, and preprocessing and extracting characteristic spectral information to construct a sample data set; the sample data set includes characteristic spectral information and ATRX gene mutation status corresponding to a number of normalized Raman spectral samples; the characteristic spectral information includes intensity values at a number of Raman shifts; the ATRX gene mutation status includes 1 and 0, wherein 1 indicates that the ATRX gene is mutated and 0 indicates that the ATRX gene is not mutated; a linear regression model is constructed with the ATRX gene mutation status as the target variable and the characteristic spectral information as the characteristic variable; according to the sample data set, the mRMR algorithm is used to screen the characteristic displacement, and the LR algorithm is used to train the linear regression model to obtain a classification model.
[0040] In this embodiment, training was performed based on 2606 spectral data of 572 patients retrospectively included (the total data was randomly divided into a training group and a validation group at a ratio of 8:2, the training group contained 2084 spectra, of which 1658 were ATRX gene mutation spectra, and the SMOTE oversampling algorithm was used before training to balance the data classification, so that the number of ATRX gene mutation spectra and ATRX gene non-mutation spectra reached 1:1), the mRMR algorithm and the LR algorithm were used to generate a classification model, and the machine learning model parameters were optimized to predict the glioma ATRX gene mutation status, and the area under the curve (area under the curve, AUC) of the receiver operating characteristic curve (ROC) of the classification model obtained by training could reach up to 95.6%, the accuracy could reach 87.9%, and the sensitivity could reach 82.5%, as shown in Figure 2. Figure 2 shown.
[0041] In the process of spectral feature screening using the mRMR algorithm, the possible main characteristic peaks involved are as follows.
[0042] 701cm -1 :This peak position is related to the effects of lipids such as cholesterol and phospholipids.
[0043] 1592cm -1 :This peak may be related to Amide I, or it may correspond to the characteristic vibration of tryptophan, which is a synthetic precursor of many biologically active molecules.
[0044] 2861-2878cm -1 : This peak is caused by the stretching vibration of the CH bond in the protein methyl group.
[0045] 2924cm -1 :The peak position originates from the change of lipid content.
[0046] 3168~3172cm -1 :The peak position in this region is related to the characteristics of the protein secondary structure and involves the stretching of free NH bonds.
[0047] 3483cm -1 :The peak in this area is likely to come from the hydroxyl (OH) effect of water molecules.
[0048] Specifically, the classification model is expressed as: z=1.2259+0.5528×I 701 +(-0.4440×I 1592 )+0.5846×I 2861 +(-0.7022×I 2869)+(-0.9595×I 2878 )+(-0.9046×I 2895 )+0.6754×I 2924 +0.8655×I 3168 +1.2689×I 3172 +(-0.6224×I 3483 ).
[0049] Wherein, P represents the classification prediction value. When P≥0.5, it is judged as a glioma with ATRX gene mutation, and when P<0.5, it is judged as a glioma without ATRX gene mutation; z is the output value of the linear regression model; I 701 Indicates the Raman shift of 701 cm -1 The intensity value at I 1592 Indicated at Raman shift 1592cm -1 The intensity value at I 2861 Indicated at Raman shift 2861cm -1 The intensity value at I 2869 Indicated at Raman shift 2869cm -1 The intensity value at I 2878 Indicated at Raman shift 2878cm -1 The intensity value at I 2895 Indicated at Raman shift 2895cm -1 The intensity value at I 2924 Indicated at Raman shift 2924cm -1 The intensity value at I 3168 Indicates that the Raman shift is 3168cm -1 The intensity value at I 3172 Indicated at Raman shift 3172cm -1 The intensity value at I 3483 Indicates that the Raman shift is 3483cm -1 The intensity value at .
[0050] like Figure 3 As shown, the device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy provided by the present application includes: a laser 1 and a spectrum analyzer 2. Among them, the laser 1 is used to emit laser (i.e., 532nm excitation light) to the glioma tissue sample to be tested; the spectrum analyzer 2 is used to collect Raman light (i.e., scattered light) scattered by the glioma tissue sample to be tested, obtain Raman spectrum data, pre-process the Raman spectrum data to obtain normalized Raman spectrum data, extract the intensity value at the characteristic displacement from the normalized Raman spectrum data, and input the intensity value at the characteristic displacement into the classification model to calculate the classification prediction value.
[0051] Preferably, the excitation wavelength of the laser 1 is 532 nm, and the integration time is 1 to 3 seconds.
[0052] Furthermore, the above-mentioned device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy also includes: an optical microscopic platform 4. The optical microscopic platform 4 is used to place the glioma tissue sample to be tested, and to adjust the position and height of the glioma tissue sample to be tested.
[0053] Furthermore, the device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy also includes: a display 3. The display 3 is connected to the spectrum analyzer 2 and is used to display the classification prediction value and the auxiliary determination result; the auxiliary determination result is determined based on the classification prediction value.
[0054] Furthermore, the above-mentioned device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy also includes: an optical fiber probe. The optical fiber probe is connected to the laser 1 and the spectrum analyzer 2 respectively, and is used to transmit the laser emitted by the laser 1 to the surface of the glioma tissue sample to be tested, and transmit the Raman light scattered by the surface of the glioma tissue sample to be tested to the spectrum analyzer 2.
[0055] The method and device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy provided in the present application have the following advantages.
[0056] 1. Fast: Using Raman spectroscopy technology, the detection time is short and the results are rapid.
[0057] 2. Accurate: Analyze the characteristic spectral information to accurately identify the ATRX gene mutation status.
[0058] 3. Non-destructive: No additional processing or staining of tissue samples is required, maintaining the integrity of the samples.
[0059] 4. Simple: easy to operate, no need for complicated experimental conditions and professional skills.
[0060] In summary, the present application provides a method and device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy, which has the advantages of being fast, accurate, and non-destructive, and is suitable for clinical medicine and scientific research fields.
[0061] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0062] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy, characterized in that: include: Performing Raman spectrum scanning on the glioma tissue sample to be tested to obtain Raman spectrum data; Preprocessing the Raman spectrum data to obtain normalized Raman spectrum data; Extract the intensity value at the characteristic shift from the normalized Raman spectrum data; the characteristic shift includes the Raman shift of 701cm -1 、1592cm -1 、2861cm -1 、2869cm -1 、2878cm -1 、2924cm -1 、3168cm -1 、3172cm -1 and 3483cm -1 ; Inputting the intensity value at the characteristic displacement into the classification model to calculate the classification prediction value; The classification model characterizes the relationship between the classification prediction value and the intensity value at the characteristic displacement; the classification prediction value is used to assist in determining the ATRX gene mutation status of the glioma tissue sample to be tested; the ATRX gene mutation status includes ATRX gene mutation and ATRX gene non-mutation.
2. The method for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 1, characterized in that: Preprocessing the Raman spectrum data to obtain normalized Raman spectrum data includes: Performing fluorescence background removal processing on the Raman spectrum data to obtain Raman spectrum data with fluorescence background removed; Performing smoothing and noise reduction processing on the Raman spectrum data after the fluorescence background is removed to obtain smoothed and noise-reduced Raman spectrum data; The smoothed and denoised Raman spectrum data is normalized to obtain normalized Raman spectrum data.
3. The method for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 1, characterized in that: The classification model is determined based on a logistic regression algorithm and a maximum relevance and minimum redundancy algorithm; the determination process of the classification model includes: Acquire several Raman spectrum samples annotated with ATRX gene mutation status, perform preprocessing and extract characteristic spectrum information, and construct a sample data set; the sample data set includes characteristic spectrum information and ATRX gene mutation status corresponding to several normalized Raman spectrum samples; the characteristic spectrum information includes intensity values at several Raman shifts; the ATRX gene mutation status includes 1 and 0, wherein 1 indicates that the ATRX gene is mutated, and 0 indicates that the ATRX gene is not mutated; A linear regression model is constructed using the ATRX gene mutation status as the target variable and the characteristic spectrum information as the characteristic variable; According to the sample data set, the maximum relevance minimum redundancy algorithm is used to screen the feature displacement, and the linear regression model is trained using a logistic regression algorithm to obtain a classification model.
4. The method for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 1, characterized in that: The expression of the classification model is: from=1.2259+0.5528×I 701 +(-0.4440×I 1592 )+0.5846×I 2861 +(-0.7022×I 2869 )+(-0.9595×I 2878 )+(-0.9046×I 2895 )+0.6754×I 2924 +0.8655×I 3168 +1.2689×I 3172 +(-0.6224×I 3483 ); Wherein, P represents the classification prediction value. When P≥0.5, it is judged as a glioma with ATRX gene mutation, and when P<0.5, it is judged as a glioma without ATRX gene mutation; z is the output value of the linear regression model; I 701 Indicates the Raman shift of 701 cm -1 The intensity value at I 1592 Indicated at Raman shift 1592cm -1 The intensity value at I 2861 Indicated at Raman shift 2861cm -1 The intensity value at I 2869 Indicated at Raman shift 2869cm -1 The intensity value at I 2878 Indicated at Raman shift 2878cm -1 The intensity value at I 2895 Indicated at Raman shift 2895cm -1 The intensity value at I 2924 Indicated at Raman shift 2924cm -1 The intensity value at I 3168 Indicated at Raman shift 3168cm -1 The intensity value at I 3172 Indicated at Raman shift 3172cm -1 The intensity value at I 3483 Indicates that the Raman shift is 3483cm -1 The intensity value at .
5. The method for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 2, characterized in that: Adopting adaptive iterative reweighted penalty least squares method to remove fluorescence background from the Raman spectrum data; The Savitzky-Golay smoothing algorithm is used to perform smoothing and noise reduction processing on the Raman spectrum data after removing the fluorescence background; and the maximum and minimum normalization algorithm is used to perform normalization processing on the smoothed and noise-reduced Raman spectrum data.
6. A device for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy, using the method for detecting ATRX gene mutations in glioma tissue samples based on Raman spectroscopy as claimed in claim 1, characterized in that: include: A laser, used for emitting laser light to the glioma tissue sample to be tested; The spectrum analyzer is used to collect Raman light scattered by the glioma tissue sample to be tested to obtain Raman spectrum data, pre-process the Raman spectrum data to obtain normalized Raman spectrum data, extract the intensity value at the characteristic displacement from the normalized Raman spectrum data, and input the intensity value at the characteristic displacement into a classification model to calculate a classification prediction value.
7. The device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 6, characterized in that: Also includes: The optical microscopy platform is used to place the glioma tissue sample to be tested, and to adjust the position and height of the glioma tissue sample to be tested.
8. The device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 6, characterized in that: Also includes: A display, connected to the spectrum analyzer, for displaying the classification prediction value and the auxiliary determination result; The auxiliary determination result is determined based on the classification prediction value.
9. The device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 6, characterized in that: Also includes: The optical fiber probe is connected to the laser and the spectrum analyzer respectively, and is used to transmit the laser light emitted by the laser to the surface of the glioma tissue sample to be tested, and transmit the Raman light scattered by the surface of the glioma tissue sample to be tested to the spectrum analyzer.
10. The device for detecting ATRX gene mutation in glioma tissue samples based on Raman spectroscopy according to claim 6, characterized in that: The excitation wavelength of the laser is 532 nm, and the integration time is 1 to 3 seconds.