CN117191759A - Brain glioma boundary identification technology based on Raman spectrum - Google Patents
Brain glioma boundary identification technology based on Raman spectrum Download PDFInfo
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Abstract
The invention discloses a glioma boundary identification technology based on Raman spectrum, and belongs to the field of tumor diagnosis. The invention is realized by the method that the catalyst is prepared by adding the catalyst in molybdenum disulfide (MoS 2 ) And synthesizing a gold nanoflower structure on the nanosheets in situ, and combining three kinds of aptamer capable of targeting glioma cells to prepare the glioma sensing SERS substrate. The proportion of tumor cells in glioma resected fragment samples is determined by machine learning, so that reference and calibration basis are provided for intra-operative real-time navigation. The method has the advantages of rapidness, accuracy and high sensitivity. The detection technology disclosed by the invention can obtain a result within 2 minutes, greatly improves the clinical diagnosis efficiency, realizes the molecular pathological diagnosis in operation, and has great clinical significance.
Description
Technical Field
The invention belongs to the field of tumor diagnosis, relates to a glioma boundary recognition technology based on Raman spectrum, and further relates to a method for determining the proportion of tumor cells in glioma resected fragment samples by combining a Surface Enhanced Raman Scattering (SERS) substrate with machine learning, so as to provide reference and calibration basis for intraoperative real-time navigation.
Background
Gliomas are the most common malignant neoplasms of the central nervous system, with "six high" characteristics of high proliferation, high invasion, high heterogeneity, high resistance to treatment, high recurrence, and high mortality. It is counted that the average median survival time of high grade glioma patients is often only 12 months. In addition, gliomas are common in children, and are the second leading cause of mortality in childhood malignant tumors.
Firstly, due to the diffuse infiltration growth characteristic of gliomas, the boundary between gliomas and normal brain tissues is not clear and imaging boundary is not clear, so that the surgical excision boundary based on an intraoperative navigation system is not clear, which is also an important cause of recurrence and deterioration of gliomas. At present, after the neuropathologist can simply perform fluorescent staining on the resected tissue, the existence of the tumor is observed through a laser confocal microscope, the navigation result in the operation is corrected, and the accuracy of the surgical resection is improved. However, the method has the defects of excessive dependence on the experience of pathologists, more subjective interference factors, low pathological feedback speed (more than 15 minutes), and the like, and further delays the treatment of patients. The detection results of related devices such as immunohistochemistry, immunofluorescence, mass spectrometry and the like are relatively accurate, but the time consumption is longer, and the method is not suitable for operation real-time guidance. Therefore, how to realize real-time rapid diagnosis of brain glioma tissue and further feedback reference of the surgical resection scope is a key problem to be solved by those skilled in the art.
Surface Enhanced Raman Scattering (SERS) is a non-elastic optical effect that occurs at the nanoparticle surface and interface. When molecules adsorb on rough noble metal surfaces, their raman signal can be enhanced by 10 6 ~ 10 14 Multiple times. The SERS technology has extremely high detection sensitivity, can even reach the level of single-molecule detection, and has the advantages of nondestructive detection, rapid detection, in-situ measurement and the like. The SERS spectrum has narrow characteristic band and can provide rich molecular fingerprint information. Thus, the spectroscopic technique is particularly applicable to biological systems and cell-related detection systems. In recent years, research on tumor analysis based on SERS technology has been a hot spot in the biomedical field, especially glueIdentification of a mass tumor boundary. For example, xu et al prepared a sensor chip by self-assembly of silver nanoparticles into a film and a responsive SERS reporter factor 4-mercaptopyridine, the SERS characteristic peak ratio of 4-mercaptopyridine was regularly varied under different pH conditions, and the boundary of glioma infiltration was determined by measuring interstitial fluid pH (Talanta 2022, 250:123750). However, the authors only carried out preliminary verification on the method in mouse glioma cells, and in a real sample, biological thiols such as glutathione and the like which are rich in biological environment can replace a thio ligand connected to the surface of noble metal through ligand exchange, so that SERS nano probes are damaged, and detection signals are distorted and quantification is inaccurate. In addition, this method of indirect detection is disadvantageous for the collection of tumor information. The human glioma was studied by Zhou et al using a portable raman analyzer and the glioma tissue was distinguished from normal tissue and glioma grade by a principal component analysis-support vector machine learning method. Compared with histopathology as a gold standard, the recognition accuracy of glioma by this technique is only 80% (cancer 2023, 15, 1752.). Besides the work, the current glioma identification method is limited to two classification methods, only the analysis of 'presence' or 'absence' is carried out, more accurate tumor infiltration proportion information cannot be provided, and reference and calibration basis are provided for intra-operative real-time navigation.
Disclosure of Invention
Aiming at the problems, the invention provides a method for enhancing the Raman signal intensity of a sample by utilizing a SERS substrate, combining machine learning, establishing a tumor proportion prediction scheme based on glioma cell characteristic spectrum, and providing reference and calibration basis for intraoperative real-time navigation.
In order to achieve the above purpose, the present invention provides the following technical solutions.
The invention provides a preparation method of a SERS reinforced substrate, which is characterized by comprising the following specific steps:
S1，MoS 2 preparing nano-sheets: the polymethyl methacrylate (PMMA) or polyethylene terephthalate (PET) adhesive tape is combined with MoS with smooth surface and no pollution 2 The monocrystalline blocks are tightly combined together, and the adhesive tape is lightly pressed; subsequently MoS is carried out 2 Separating from the adhesive tape to obtain a MoS layer 2 A nanosheet; clean silicon substrate is subjected to oxygen plasma cleaning for 30 minutes, and MoS is lightly touched by microscopic tweezers or microscopic needle tips 2 A nanosheet adhered to the tip of the microshutter or the microtip, and MoS adhered thereto 2 The micro tweezers or the micro needle points of the nano sheets are slightly moved to the position of the target substrate, so that the micro tweezers or the micro needle points are ensured to be lightly contacted with the surface of the substrate, and MoS is released at the contact position 2 The nanosheets slowly and carefully lift up the microshutter or the microshutter tip to make MoS 2 The nanoplatelets remain on the target substrate surface;
s2, preparing a SERS substrate: washing the MoS prepared in S1 with organic solvent acetone 2 The nano-sheets are dried by nitrogen; preparing 0.036% (w/v) chloroauric acid as a gold source, and using a mixed solution of 0.74% (w/v) hydroxylamine hydrochloride and 0.012% (w/v) trisodium citrate as a reducing solution; slowly dripping gold source solution into MoS at 90deg.C 2 The nano-sheet is fully covered; slowly dripping the reducing solution into MoS 2 Gold ions are reduced on the nano-sheet until the reduction solution is completely used, and then the gold ions are removed on MoS 2 Growing a gold nanoflower structure on the surface of the nanosheet; after the reaction is finished, carefully flushing the substrate with deionized water;
s3, preparing a SERS enhanced substrate: in order to further enhance the detection performance of SERS substrates on glioma cells, thiol-modified aptamers that can target binding to ADAM15, ARMC10 and ptpn proteins are used; adding 2-iminosulfane hydrochloride aqueous solution into the aptamer mixed solution, incubating for 1 hour at room temperature, and separating the sulfhydrylation aptamer by using a desalting centrifugal column; immersing the SERS substrate prepared in the S2 into the aptamer mixed solution, reacting for one hour, washing with deionized water, and preserving at 4 ℃ to obtain the SERS reinforced substrate.
Further, in step S3, the three aptamer solutions are mixed in a ratio of 1:1:1; the sequences of the three kinds of aptamer are shown as SEQ ID No. 1-SEQ ID No.3.
The invention also provides an application of the SERS enhanced substrate obtained by adopting the preparation method of any one of claims 1-3 in preparing a product for detecting glioma boundaries.
The invention also provides an application of the SERS enhanced substrate obtained by the preparation method of any one of claims 1-3 in preparation of products for predicting the proportion of tumor cells in glioma samples.
The invention also provides a method for identifying glioma boundaries based on Raman spectrum, which is characterized by comprising the following steps:
step 1, preparation of a SERS enhanced substrate:
S1，MoS 2 preparing nano-sheets: the polymethyl methacrylate (PMMA) or polyethylene terephthalate (PET) adhesive tape is combined with MoS with smooth surface and no pollution 2 The monocrystalline blocks are tightly combined together, and the adhesive tape is lightly pressed; subsequently MoS is carried out 2 Separating from the adhesive tape to obtain a MoS layer 2 A nanosheet; clean silicon substrate is subjected to oxygen plasma cleaning for 30 minutes, and MoS is lightly touched by microscopic tweezers or microscopic needle tips 2 A nanosheet adhered to the tip of the microshutter or the microtip, and MoS adhered thereto 2 The micro tweezers or the micro needle points of the nano sheets are slightly moved to the position of the target substrate, so that the micro tweezers or the micro needle points are ensured to be lightly contacted with the surface of the substrate, and MoS is released at the contact position 2 The nanosheets slowly and carefully lift up the microshutter or the microshutter tip to make MoS 2 The nanoplatelets remain on the target substrate surface;
s2, preparing a SERS substrate: washing the MoS prepared in S1 with organic solvent acetone 2 The nano-sheets are dried by nitrogen; preparing 0.036% (w/v) chloroauric acid as a gold source, and using a mixed solution of 0.74% (w/v) hydroxylamine hydrochloride and 0.012% (w/v) trisodium citrate as a reducing solution; slowly dripping gold source solution into MoS at 90deg.C 2 The nano-sheet is fully covered; slowly dripping the reducing solution into MoS 2 Gold ions are reduced on the nano-sheet until the reduction solution is completely used, and then the gold ions are removed on MoS 2 Growing a gold nanoflower structure on the surface of the nanosheet; after the reaction is finished, carefully flushing the substrate with deionized water;
s3, preparing a SERS enhanced substrate: in order to further enhance the detection performance of SERS substrates on glioma cells, thiol-modified aptamers that can target binding to ADAM15, ARMC10 and ptpn proteins are used; mixing the three aptamer solutions according to a ratio of 1:1:1, adding 2-iminosulfane hydrochloride aqueous solution into the aptamer mixed solution, incubating for 1 hour at room temperature, and separating the thiolated aptamer by using a desalting centrifugal column; immersing the SERS substrate prepared in the step S2 into the aptamer mixed solution, reacting for one hour, washing with deionized water, and preserving at 4 ℃ to prepare the SERS reinforced substrate;
step 2, glioma sample preparation:
adding 0.86% of ice physiological saline into a brain glioma sample obtained by clinical operation according to a proportion, homogenizing the sample by adopting an ultrasonic homogenizer, wherein the glioma sample presents a white uniform solution;
step 3, SERS signal acquisition and analysis:
dropwise adding the homogenate obtained in the step S2 onto the SERS reinforced substrate obtained in the step S1, stirring to uniformly distribute the homogenate, wherein the thickness of a liquid layer is about 50 mu m, incubating for 30-60S, using a laser with a wavelength of 532nm, the maximum excitation power of 14mW, the amplification factor of an objective lens of 20 times and the integration time of 10S, and collecting signals of a sample on the reinforced substrate; before spectrum acquisition, the spectrometer is calibrated by using a Raman spectrum band of 520cm < -1 > of the silicon chip;
step 4, training a prediction model:
taking characteristic peaks of signals at 1002, 1154, 1373 and 1519 cm-1 as nucleic acid and protein as input characteristic vectors, taking a tumor cell proportion range as a classification label, and designing a radial basis function support vector machine between any two types of samples to perform classifier training;
step 5, risk prediction:
in order to realize the distinction of k categories, a k (k-1)/2 similar vector machines are required to be designed, and when an unknown sample is classified, the category with the largest ticket is the category of the unknown sample.
Further, the three aptamer solutions in step 1 S3 are mixed according to a ratio of 1:1:1; the sequences of the three kinds of aptamer are shown as SEQ ID No. 1-SEQ ID No.3.
Further, the ratio of the gum sample to the ice physiological saline in the step 2 is 1g:9mL.
The invention also provides a glioma boundary recognition model which is characterized by being constructed by the glioma boundary recognition method based on the Raman spectrum.
The invention also provides an electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that the processor, when executing the program, implements the steps of the method for identifying glioma boundaries based on raman spectroscopy as described in any one of the above.
The present invention also provides a computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor performs the steps of the method for identifying glioma boundaries based on raman spectroscopy as defined in any one of the above.
Compared with the prior art, the invention has the beneficial effects.
The invention is realized by the method that the catalyst is prepared by adding the catalyst in molybdenum disulfide (MoS 2 ) And synthesizing a gold nanoflower structure on the nanosheets in situ, and combining three kinds of aptamer capable of targeting glioma cells to prepare the glioma sensing SERS substrate. The proportion of tumor cells in glioma resected fragment samples is determined by machine learning, so that reference and calibration basis are provided for intra-operative real-time navigation.
The invention provides a method for predicting the existence and proportion of brain glioma cells in human brain tissues by means of a preferred SERS substrate material, a characteristic spectrum and a classification method, and determining whether a tumor boundary exists or not.
The technical scheme provided by the invention fully utilizes the advantages of Raman spectrum liquid phase detection, high sensitivity and fingerprint spectrum detection, is beneficial to identifying tumor boundaries in surgery, and provides effective guidance for surgery.
The technique can obtain results within 2 minutes, greatly improves the clinical diagnosis efficiency, and realizes the real-time feedback of navigation in operation.
Drawings
FIG. 1 is a scanning electron microscope image of a SERS substrate.
FIG. 2 SERS signal comparison of samples before and after modification of the SERS substrate.
Figure 3 average raman spectra of brain glioma samples, brain glioma cells and normal tissues and their differential sites.
Fig. 4 is a classification result based on an unmodified SERS substrate.
Fig. 5 is based on the classification results of modified SERS substrates.
Detailed Description
The following examples will aid in the understanding of the present invention, but are merely illustrative of the invention and the invention is not limited thereto. The methods of operation in the examples are all conventional in the art.
Example 1 preparation of SERS enhanced substrate.
Firstly, polymethyl methacrylate (PMMA) or polyethylene terephthalate (PET) adhesive tape is combined with MoS with smooth surface and no pollution 2 The monocrystalline pieces were tightly bonded together and the tape was gently pressed. Subsequently MoS is carried out 2 Separating from the adhesive tape to obtain a MoS layer 2 A nano-sheet. Clean silicon substrates were subjected to oxygen plasma cleaning for 30 minutes. Lightly touching MoS with micro tweezers or micro needle tip 2 A nanoplatelet adhered to the tip of a microshutter or a microtip. Will be adhered with MoS 2 The micro tweezers or the micro needle points of the nano sheets are slightly moved to the position of the target substrate, so that the micro tweezers or the micro needle points are ensured to be lightly contacted with the surface of the substrate, and MoS is released at the contact position 2 The nanosheets slowly and carefully lift up the microshutter or the microshutter tip to make MoS 2 The nanoplatelets remain on the target substrate surface.
Subsequently, the MoS is washed with the organic solvent acetone 2 And (3) nanometer sheets, and drying with nitrogen. 0.036% (w/v) chloroauric acid was prepared as a gold source, and a mixture of 0.74% (w/v) hydroxylamine hydrochloride and 0.012% (w/v) trisodium citrate was used as a reducing solution. Slowly dripping gold source solution into MoS at 90deg.C 2 The nano-sheet is fully covered. Slowly dripping the reducing solution into MoS 2 Gold ions are reduced on the nano-sheet until the reduction solution is completely used, and then the gold ions are removed on MoS 2 Gold grown on the surface of the nanosheetA popcorn structure. After the reaction was completed, the substrate was carefully rinsed with deionized water.
To further enhance the detection performance of SERS substrates against glioma cells (see fig. 2), they were modified with thiolated aptamers that could target binding to ADAM15, ARMC10 and ptpn proteins. After mixing the three aptamer solutions at a ratio of 1:1:1, 46. Mu.L of 2-iminothiolane hydrochloride aqueous solution (14 mM) was added to the 1 mL aptamer mixture (10 g/L), and after incubation at room temperature for 1 hour, the thiolated aptamer was separated by desalting centrifugation. The prepared SERS substrate is immersed in the aptamer mixed solution, and after one hour of reaction, the SERS substrate is washed by deionized water and stored at 4 ℃. The prepared SERS enhanced substrate electron microscope chart is shown in figure 1.
An aptamer to ADAM15 protein, SEQ ID No.1:
5’-SH-C6-ATAAATGGCGTCACTAGACATGGAATTGACATCATCACCG-3’
an aptamer to the ARMC10 protein, SEQ ID No.2:
5’-SH-C6-ACAATTGGCGTCACCCGACGGGGACTTGACGGATGAAAG-3’
an aptamer to the ptpn protein, SEQ ID No.3:
5’-SH-C6-AATCCGCCGATGCGCCGAAGGCGACTTGACATTATGAGAG-3’。
example 2 glioma sample preparation.
Brain glioma samples obtained in clinical surgery weighed about 10mg according to 1g:9mL of ice normal saline with the concentration of 0.86% is added, an ultrasonic homogenizer is adopted to homogenize a sample, and parameters are set as follows: 100% power, 3s ultrasound, 5s interval; ultrasonic crushing is carried out for 3 times, so that glioma samples are in white uniform solution.
The gun head is stirred uniformly so as to make the signal as uniform as possible, and the thickness of the liquid layer reduces the loss of Raman signal caused by sample accumulation; the weight of the glioma sample can ensure the acquisition of enough SERS signals; in contrast to PBS and ultrapure water, ice saline (8.6 g of sodium chloride dissolved in 1L of ultrapure water and precooled to 4 ℃) ensures that the sample is kept as stable as possible without affecting the SERS signal of the sample.
Example 3 SERS signal acquisition and analysis.
The obtained homogenate is dripped on an SERS substrate, a gun head is stirred to be evenly distributed at 1.0 mL, the thickness of a liquid layer is about 50 mu m, after incubation is carried out for 30-60 s, an adopted instrument is a Renishaw inVia confocal Raman spectrometer, the adopted laser wavelength is 532nm, the maximum excitation power is 14mW, the amplification factor of an objective lens is 20 times, the integration time is 10s, and signal acquisition is carried out on a sample on the enhanced substrate. 520cm Using silicon chip before Spectrum acquisition -1 The raman bands of (c) calibrate the spectrometer. At least 5 random spots on each sample were selected for scanning to obtain an average raman signal.
Incubation for 30s-60s is the sufficient reaction time between the SERS substrate and the homogenized sample, which is verified by experiments, and too long incubation time can lead to larger sample volatilization and change the original signal. The selected wavelength is the wavelength with lower noise in detection, and the power and the integration time are used for ensuring the signal strength. The intensity of the characteristic peak can be characterized by its height. Judging whether tumor cells exist or not according to the peak intensity change, and predicting the tumor proportion.
Preferably, the focused attention is directed to 1002, 1154, 1373 and 1519 cm -1 The signal at the position is one of characteristic peaks of nucleic acid and protein, the characteristic peaks are used as input characteristic vectors, a tumor cell proportion range (10% "," 25% "," 50% "," 75% ", and" 90% ") is used as a classification label, and a radial basis function support vector machine is designed between any two types of samples to perform classifier training.
To achieve the purposes ofkDifferentiation of individual categories, requiring designk(k-1)/2 homogeneous vector machines, when classifying an unknown sample, the class with the highest score finally being the class of the unknown sample.
According to the obtained prediction results, the tumor cell ratios were classified into five different ranges of "10%", "25%", "50%", "75%" and "90%".
Example 4 tissue samples and glioma cell samples were tested.
1. SERS enhanced substrates were prepared as in example 1.
2. Obtaining 50 brain glioma tissues5 normal brain tissue samples and total 10 7 Is prepared by ultrasonic homogenization of a sample of glioma cells (cell line from clinical patient, hospital supplied cell line name GSC 4) in accordance with the procedure of example 2, and is maintained in a mixed state.
3. And (3) carrying out modified SERS substrate signal acquisition (shown in figure 3) on the sample, and carrying out statistical prediction by adopting machine learning.
4. And comparing the obtained prediction result with a tumor proportion result obtained by actually measuring DNA sequencing, wherein the result shows that 100% of the prediction of the proportion interval is correct.
Example 5 modified and unmodified SERS substrate classification results.
1. SERS enhanced substrates were prepared by the procedure in example 1.
2. Obtaining 50 brain glioma tissues, 5 normal brain tissue samples and total 10 7 Is prepared by ultrasonic homogenization of a sample of glioma cells (cell line from clinical patient, hospital supplied cell line name GSC 4) in accordance with the procedure of example 2, and is maintained in a mixed state.
3. The sample raman spectra were examined as in example 3 and tumor proportion predictions were made by the protocol described above.
4. Based on the obtained prediction, it was compared with the tumor ratio obtained by the actual measurement of DNA sequencing (see FIGS. 4 and 5).
Claims (10)
1. A preparation method of a SERS reinforced substrate is characterized by comprising the following specific steps:
S1，MoS 2 preparing nano-sheets: the polymethyl methacrylate (PMMA) or polyethylene terephthalate (PET) adhesive tape is combined with MoS with smooth surface and no pollution 2 The monocrystalline blocks are tightly combined together, and the adhesive tape is lightly pressed; subsequently MoS is carried out 2 Separating from the adhesive tape to obtain a MoS layer 2 A nanosheet; clean silicon substrate is subjected to oxygen plasma cleaning for 30 minutes, and MoS is lightly touched by microscopic tweezers or microscopic needle tips 2 A nano-sheet, which is adhered to the tip of a micro tweezer or a micro needle point, and then adheredWith MoS attached 2 The microsieve or the microsieve tip of the nanosheet is gently moved to the position of the target substrate, so that the microsieve or the microsieve tip is ensured to be lightly contacted with the surface of the substrate, and the MoS2 nanosieve is released at the contact position, and the microsieve or the microsieve tip is slowly and carefully lifted up to enable MoS to be realized 2 The nanoplatelets remain on the target substrate surface;
s2, preparing a SERS substrate: washing the MoS prepared in S1 with organic solvent acetone 2 Preparing 0.036% (w/v) chloroauric acid as a gold source, and using a mixed solution of 0.74% (w/v) hydroxylamine hydrochloride and 0.012% (w/v) trisodium citrate as a reducing solution; slowly dripping gold source solution into MoS at 90deg.C 2 Slowly dripping the reducing solution onto MoS 2 Gold ions are reduced on the nano-sheet until the reduction solution is completely used, and then the gold ions are removed on MoS 2 Growing a gold nanoflower structure on the surface of the nanosheet; after the reaction is finished, carefully flushing the substrate with deionized water;
s3, preparing a SERS enhanced substrate: in order to further enhance the detection performance of SERS substrates on glioma cells, thiol-modified aptamers that can target binding to ADAM15, ARMC10 and ptpn proteins are used; adding 2-iminosulfane hydrochloride aqueous solution into the aptamer mixed solution, incubating for 1 hour at room temperature, and separating the sulfhydrylation aptamer by using a desalting centrifugal column; immersing the SERS substrate prepared in the S2 into the aptamer mixed solution, reacting for one hour, washing with deionized water, and preserving at 4 ℃ to obtain the SERS reinforced substrate.
2. The method of claim 1, wherein in step S3 the three aptamer solutions are mixed in a ratio of 1:1:1; the sequences of the three kinds of aptamer are shown as SEQ ID No. 1-SEQ ID No.3.
3. Use of a SERS enhanced substrate obtained by the method of any one of claims 1 to 3 for the manufacture of a product for detecting glioma boundaries.
4. Use of a SERS enhanced substrate obtained by the method of any one of claims 1 to 3 for the preparation of a product for predicting the proportion of tumour cells in a glioma sample.
5. A method for identifying glioma boundaries based on raman spectroscopy, comprising the steps of:
step 1, preparation of a SERS enhanced substrate:
S1，MoS 2 preparing nano-sheets: the polymethyl methacrylate (PMMA) or polyethylene terephthalate (PET) adhesive tape is combined with MoS with smooth surface and no pollution 2 The monocrystalline blocks are tightly combined together, and the adhesive tape is lightly pressed; then separating MoS2 from the adhesive tape to obtain a layer of MoS 2 A nanosheet; clean silicon substrate is subjected to oxygen plasma cleaning for 30 minutes, and MoS is lightly touched by microscopic tweezers or microscopic needle tips 2 A nanosheet adhered to the tip of the microshutter or the microtip, and MoS adhered thereto 2 The micro tweezers or the micro needle points of the nano sheets are slightly moved to the position of the target substrate, so that the micro tweezers or the micro needle points are ensured to be lightly contacted with the surface of the substrate, and MoS is released at the contact position 2 The nanosheets slowly and carefully lift up the microshutter or the microshutter tip to make MoS 2 The nanoplatelets remain on the target substrate surface;
s2, preparing a SERS substrate: washing the MoS prepared in S1 with organic solvent acetone 2 Preparing 0.036% (w/v) chloroauric acid as a gold source, and using a mixed solution of 0.74% (w/v) hydroxylamine hydrochloride and 0.012% (w/v) trisodium citrate as a reducing solution; slowly dripping gold source solution onto MoS2 nanosheets at 90deg.C to fully cover the nanosheets, slowly dripping reducing solution onto MoS 2 Reducing gold ions on the nanosheets until the reducing solution is completely used, and growing gold nanoflower structures on the surfaces of the MoS2 nanosheets; after the reaction is finished, carefully flushing the substrate with deionized water;
s3, preparing a SERS enhanced substrate: in order to further enhance the detection performance of SERS substrates on glioma cells, thiol-modified aptamers that can target binding to ADAM15, ARMC10 and ptpn proteins are used; mixing the three aptamer solutions according to a ratio of 1:1:1, adding 2-iminosulfane hydrochloride aqueous solution into the aptamer mixed solution, incubating for 1 hour at room temperature, and separating the thiolated aptamer by using a desalting centrifugal column; immersing the SERS substrate prepared in the step S2 into the aptamer mixed solution, reacting for one hour, washing with deionized water, and preserving at 4 ℃ to prepare the SERS reinforced substrate;
step 2, glioma sample preparation:
adding 0.86% of ice physiological saline into a brain glioma sample obtained by clinical operation according to a proportion, homogenizing the sample by adopting an ultrasonic homogenizer, wherein the glioma sample presents a white uniform solution;
step 3, SERS signal acquisition and analysis:
dropwise adding the homogenate obtained in the step S2 onto the SERS reinforced substrate obtained in the step S1, stirring to uniformly distribute the homogenate, wherein the thickness of a liquid layer is about 50 mu m, incubating for 30-60S, using a laser with a wavelength of 532nm, the maximum excitation power of 14mW, the amplification factor of an objective lens of 20 times and the integration time of 10S, and collecting signals of a sample on the reinforced substrate; before spectrum acquisition, the spectrometer is calibrated by using a Raman spectrum band of 520cm < -1 > of the silicon chip;
step 4, training a prediction model:
taking characteristic peaks of signals at 1002, 1154, 1373 and 1519 cm-1 as nucleic acid and protein as input characteristic vectors, taking a tumor cell proportion range as a classification label, and designing a radial basis function support vector machine between any two types of samples to perform classifier training;
step 5, risk prediction:
in order to realize the distinction of k categories, a k (k-1)/2 similar vector machines are required to be designed, and when an unknown sample is classified, the category with the largest ticket is the category of the unknown sample.
6. The method of claim 5, wherein the three aptamer solutions of step 1 S3 are mixed in a ratio of 1:1:1; the sequences of the three kinds of aptamer are shown as SEQ ID No. 1-SEQ ID No.3.
7. The method of claim 5, wherein the ratio of gum sample to ice physiological saline in step 2 is 1g:9mL.
8. A glioma boundary recognition model, characterized in that the glioma boundary recognition model is constructed by the method of the raman spectrum-based glioma boundary recognition according to any one of claims 5 to 7.
9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, wherein the processor, when executing the program, performs the steps of the method for identifying glioma boundaries based on raman spectroscopy as claimed in any one of claims 5 to 7.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor performs the steps of the method for identifying glioma boundaries based on raman spectroscopy according to any one of claims 5 to 7.
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