Tumor cell metastasis capability detection method based on Raman spectrum

Abstract

The invention provides a tumor cell metastasis capability detection method based on Raman spectrum, and relates to the technical field of medicine, the single cell Raman technology and the machine learning method are combined, the tumor metastasis capability is rapidly and accurately predicted according to the Raman signal of the tumor cell, the cell source is not limited, and the tumor metastasis capability can be rapidly and accurately predicted according to the Raman signal of the tumor cell. Culture cells or tissue-derived cells can be detected, the use scene of detection is expanded, the method is simple to operate, the cells do not need to be treated too much, a single-cell Raman instrument can be directly used for detection, the detection of a single cell can be completed only in 5 seconds, the method is more efficient, and the result is more accurate; meanwhile, the method can be used for identifying the single cell level, so that the sensitivity is high, the metastasis capability of tumor tissues without metastasis can be predicted in advance, tumors with high metastasis potential can be intervened and treated in advance, and the cure rate is increased.

Description

Technical Field

[0001] The invention relates to the field of medical technology, and in particular to a method for detecting tumor cell metastasis ability based on Raman spectroscopy. Background Art

[0002] The high metastatic ability of tumor cells is one of the important signs of tumor deterioration, and it is the main problem faced in clinical tumor research for the treatment and recovery of patients. For the metastatic ability of tumor cells, the commonly used methods in laboratories and clinics include the following: 1) Comparison of the metastatic ability of cultured cells based on transwell, including cell invasion and migration. This method requires cells to be cultured in transwell chambers, and then the number of cells passing through the bottom of the chamber is compared. Therefore, the main disadvantage of this method is that it is not applicable to tissue cells or other cells that cannot be cultured in vitro, and cell culture takes time, usually 2-3 days, so this method is also time-consuming; 2) Judgment based on the expression of some proteins related to tumor metastasis that have been reported in the literature. Past studies on tumor metastasis have found that tumor metastasis is usually accompanied by the process of epithelial-mesenchymal transition (EMT). The current literature has reported that the expression of multiple EMT-related proteins is associated with tumor metastasis, but the disadvantage of this method is that current studies have shown that EMT is a complex process with different stages, and the metastatic ability of tumor cells at different stages shows significant differences. There is no significant correlation between the reported EMT-related proteins and the metastatic ability of tumor cells in the complex EMT process. Therefore, there is currently no reliable EMT-related protein that can characterize the metastatic ability of tumors; 3) Circulating tumor cells (CTC) in peripheral blood. Distal metastasis of tumors requires tumor cells to enter blood vessels, then reach the distal target organs through blood circulation, and then exit blood vessels and enter specific target organs. The part of tumor cells that enter blood vessels is called CTC. The detection of CTC in the blood indicates that the tumor has metastasized. The disadvantage of this method is that a complex enrichment method is required to enrich and identify CTC from the blood, which is costly and complicated to operate. Secondly, the CTC content in the blood is often low, resulting in insufficient sensitivity when using CTC as a detection indicator, which is prone to false negative results. 4) Diagnosis based on imaging. This method is one of the gold standards for clinical judgment of whether a tumor has metastasized. When tumor tissue appears in an organ through imaging, it can be determined that metastasis has occurred. However, its disadvantage is that it can only be observed through imaging after tumor metastasis has occurred and tumor cells have colonized and developed into tumor tissue in distant organs, so early prediction and intervention treatment of tumor metastasis cannot be achieved.

[0003] Therefore, there is an urgent need for a new, simpler and more accurate detection method to accurately predict the metastatic ability of tumors.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] One of the purposes of the present invention is to provide a method for detecting tumor cell metastasis ability based on Raman spectroscopy, so as to at least solve one of the technical problems existing in the prior art.

[0006] In order to achieve the above-mentioned purpose of the present invention, the following technical solutions are particularly adopted:

[0007] Raman spectroscopy is a non-labeled scattering spectrum, which is the phenomenon that the frequency of the incident light changes after being scattered when the molecular bond is excited to the virtual energy state but has not yet recovered to the original state. Since different molecules can produce specific Raman scattering spectra, it can be used as a standard to distinguish different substances. Initially, Raman spectroscopy was mainly used to analyze the composition of chemical substances, but with the development of technology, Raman spectroscopy technology has gradually been applied to the identification of biological materials such as microorganisms, blood, cells, etc. The Raman spectrum of each single cell is a collection of Raman peaks (also called Raman groups, Ramanome) composed of more than 1500 Raman peaks corresponding to different types of chemical bonds, and these signals contain multidimensional information such as the composition and content of all substances in the cell, including DNA, RNA, proteins, lipids, etc., which can reflect the current life characteristics of the cell. Since Raman spectroscopy technology can realize detection at the single-cell level, and the detection time for cells is only about 1 second, it is gradually applied to the biological field. In the field of tumor research, there have been reports on the distinction of different types of tumor cells, tumors before and after drug induction, etc. based on tumor Raman spectra, and the direct Raman detection of tumor tissues for distinguishing different types of tumors. These existing studies have shown that Raman spectroscopy can effectively distinguish different types of tumor cells or the same type of cells that undergo EMT under drug induction. In particular, in skin cancer, tumor tissue in the epidermis and tumor tissue that has invaded the dermis can be distinguished under Raman spectroscopy detection. However, there are few reports on the correlation between the metastatic ability of tumor cells and their Raman signals. Based on this, in the first aspect, the present invention provides a method for detecting the metastatic ability of tumor cells based on Raman spectroscopy, comprising the following steps:

[0008] a) Single-cell Raman spectroscopy detection of tumor cell lines with different metastatic abilities;

[0009] b) Using machine learning algorithms, construct a classification model based on single-cell Raman signal data that can be used to distinguish different cell lines with different metastatic abilities;

[0010] c) using the Gini algorithm to select Raman signal peaks with high classification weight from the classification model;

[0011] d) calculating the Raman transfer index (RMI) according to the correlation between the Raman signal peak and the metastatic ability of the tumor cells and the Raman signal intensity.

[0012] Compared with the transwell method, the detection method provided by the present invention can be applied to tissue cells and other cells that cannot be cultured in vitro, and each cell detection only takes a few seconds, which is more time-saving and labor-saving. Moreover, since the detection is performed at the single-cell level, the required amount of cells is less, and the heterogeneity of the tumor can also be reflected. Compared with CTC cell detection, the detection method provided by the present invention does not require too many pre-treatment steps, and detects in situ tumor cells, which makes it easier to obtain samples. The CTC content in the blood is extremely low and requires a more complicated enrichment process. Compared with gold standard imaging, the detection method provided by the present invention can determine whether there is a greater risk of metastasis by detecting the in situ tumor before the formation of distal metastases, thereby intervening in advance.

[0013] Furthermore, in step a), the tumor cell lines with different metastatic abilities include the ASPC1 cell line with high metastatic ability, the PANC1 and BXPC3 cell lines with medium metastatic abilities, and the MIAPACA2 cell line with low metastatic ability.

[0014] It is to be understood that the present invention does not limit the specific selection of tumor cell lines with different metastatic abilities, and other tumor cells with known differences in metastatic abilities may also be selected as standards.

[0015] Using the above four tumor cells with verified tumor metastasis ability as standards, the obtained Raman transfer index RMI is more accurate.

[0016] Furthermore, in step a), the laser power of the Raman spectrum detection is 100 mW, the wavelength is 532 nm, and the acquisition time is 1 second.

[0017] Furthermore, in step b), the machine learning algorithm includes a random forest algorithm.

[0018] It is understandable that the present invention does not limit the specific type of machine learning algorithm, and other algorithms besides random forest may also be used for modeling, such as PC-LDA (linear principal component analysis) or SVM (support vector machine).

[0019] Furthermore, in step c), the screening condition of the Gini algorithm is that the Gini score is greater than 0.75.

[0020] The higher the Gini score, the more effective the corresponding Raman characteristic peak is in distinguishing tumor metastasis, that is, the stronger its correlation with tumor metastasis. Therefore, the higher the Gini score, the more accurate the calculation of the Raman peak for RMI. However, the higher the Gini score, the fewer the number of Raman characteristic peaks. When considering the accuracy of the calculation of the Raman peak for RMI, the diversity of the Raman characteristic peaks must also be considered to prevent overfitting, thereby reducing the scope of application. Therefore, after multiple attempts, 0.75 is the most appropriate screening condition.

[0021] Furthermore, in step c), the Raman signal peaks with high classification weights include 1465cm -1 、1129cm -1 、1435cm -1 、854cm -1 、1433cm -1 、1699cm -1 、1078cm -1 、1302cm -1 、1449cm -1 、1461cm -1 、1559cm -1 、1352cm -1 、1065cm -1 、1668cm -1 and 1799cm -1 .

[0022] In addition to the Raman signal peaks listed above, other Raman signal peaks other than these 15 Raman peaks, or a part of these 15 Raman peaks, and a method of adding other new Raman peaks can also be used to calculate RMI.

[0023] Furthermore, the tumor cells include pancreatic cancer cells, cervical cancer cells and choroidal melanoma cells.

[0024] Furthermore, the tumor cells include natural cells, gene-edited cells and drug-treated cells.

[0025] The inventors verified the effect of RMI through in vivo and in vitro experiments, and proved that it can effectively characterize the metastatic ability of in vivo and in vitro cells, as well as different types of tumor cell lines, including pancreatic cancer cell lines (ASPC1, P1E6, P1G8, P2D6, P2G8), cervical cancer cell lines (Siha) and choroidal melanoma cell lines (MUM2B). In addition, the present method also found that whether it is for differences in tumor metastatic ability caused by gene editing methods, such as ASPC1-KO and -NC, Siha-OE and -NC, or differences in metastatic ability caused by drug treatment, such as MUM2B-ART and -NC (ART represents artesunate treatment), as well as natural cells without any treatment (P1G8, P1E6, P2G8, P2D6), the metastatic ability can be accurately indicated, indicating that the present method is not affected by tumor types and has better practicality.

[0026] Furthermore, the Raman metastasis index RMI is the abbreviation of Ramanome-based metastasis index, that is, a coefficient calculated based on a single-cell Raman signal for characterizing the metastatic ability of tumor cells, and its calculation method is the following formula, wherein w is the true Raman signal of each Raman peak in the cell in claim 6, and I is the correlation between each Raman peak and the tumor metastasis ability. If the correlation is positive, the I value is 1, and if the correlation is negative, it is -1.

[0027] RM=w(1).

[0028] Furthermore, it is only necessary to perform simple and rapid Raman signal detection on the cultured cells through a single-cell Raman instrument, and then use the constructed RMI algorithm to calculate the RMI value based on the signal intensity of the 15 Raman characteristic peaks and their correlation with tumor metastasis. The RMI value represents the metastatic ability of tumor cells. The larger the RMI value, the stronger the metastatic ability of tumor cells, thereby achieving rapid and accurate prediction of tumor metastasis ability.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention combines single-cell Raman technology with machine learning methods to quickly and accurately predict the metastatic ability of tumors based on the Raman signals of tumor cells. The advantages are: 1) Compared with transwell, this method can achieve no restrictions on the source of cells, and cells of cultured cells or tissue sources can be detected, which expands the use scenarios of detection. In addition, this method is simple to operate and does not require excessive cell processing. It can be directly detected using a single-cell Raman instrument, and a single cell can be detected in just 5 seconds, which is more efficient. 2) Compared with EMT-related proteins, this method analyzes the overall Raman signals of cells. Analysis is not just a few proteins, so it can more accurately reflect the cell's metastatic ability and the results are more accurate; 3) Compared with CTC, this method can achieve identification at the single cell level, so the sensitivity is higher than CTC, and this method can directly detect tumor cells in situ tissues, and quantify the tumor's metastatic ability through the calculated RMI value, so as to predict earlier whether it will metastasize; 4) Compared with imaging methods, this method can predict the metastatic ability of tumor tissues that have not metastasized in advance, and intervene in those tumors with high metastatic potential in advance to improve the cure rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 Verification of the accuracy of RMI of different tumor cell types provided in the embodiments of the present invention. Wherein, A) Comparison of RMI of ASPC1-hnRNPAB-NC and ASPC1-hnRNPAB-KO cells with cell invasion and migration assays based on transwell experiments. The "Raman" histogram represents the RMI calculated based on Raman spectroscopy, and the y-axis on the right represents the RMI value. B) Comparison of RMI between four monoclonal cells isolated from PANC-1 cells. C) Comparison of RMI between MUM2B cells treated with and without ART and between SiHa cells with or without overexpression of the S100A7 gene. D) Comparison of RMI with cell invasion and migration assays in ASPC1-NC-RFP and ASPC1-KO-RFP cells. ns means p≥0.05, * means p<0.05, ** means p<0.01, and *** means p<0.001. Columns and error bars represent SD±mean. N represents the number of cells, and n represents the number of biological replicates.

[0033] Figure 2 The experimental results of the metastatic ability of the naturally screened cell lines P1G8, P2G8, P1E6 and P2D6 provided in the embodiments of the present invention are shown;

[0034] Figure 3 Verification of the accuracy of RMI in pancreatic tumor tissue provided by an embodiment of the present invention. Wherein, A) a schematic diagram of the process of establishing an orthotopic model of PDAC mice, in which ASPC1-NC-RFP and ASPC1-KO-RFP cells are inoculated into the pancreas of mice, respectively. B) Pancreatic tumor tissue is dissected from mice injected with ASPC-NC-RFP cells and ASPC1-KO-RFP cells. The light white tissue on the right represents the pancreas containing tumor cells, while the dark red tissue on the left represents the spleen. C) Comparison of RMI between pancreatic tumor cells derived from mice injected with ASPC1-NC-RFP cells and ASPC1-KO-RFP cells. D) H&E staining of liver tissue obtained from mice injected with ASPC1-NC-RFP cells and ASPC1-KO-RFP cells is used as a standard for whether pancreatic cancer liver metastasis occurs. The proportion of mice with liver metastasis is calculated by dividing the number of mice with liver metastasis by the total number of mice in the corresponding group. DETAILED DESCRIPTION

[0035] Unless otherwise defined herein, scientific and technical terms used in conjunction with the present invention shall have the meanings commonly understood by those of ordinary skill in the art. The meaning and scope of the terms should be clear, however, in the case of any potential ambiguity, the definitions provided herein take precedence over any dictionary or external definitions. In this application, unless otherwise stated, the use of "or" means "and / or". In addition, the use of the term "including" and other forms is non-limiting.

[0036] Generally, the nomenclature and its technology used in conjunction with cell and tissue culture, molecular biology, immunology, microbiology, genetics, and protein and nucleic acid chemistry and hybridization described herein are those well-known and commonly used in the art. Unless otherwise indicated, the methods and techniques of the present invention are generally carried out according to conventional methods as well-known in the art, and as described in various general and more specific references, which are cited and discussed throughout this specification. Enzymatic reactions and purification techniques are carried out according to the manufacturer's specifications, as generally achieved in the art, or as described herein. The nomenclature and its laboratory procedures and technology used in conjunction with analytical chemistry, synthetic organic chemistry, and medical and pharmaceutical chemistry described herein are those well-known and commonly used in the art.

[0037] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of the present invention. Unless otherwise specified, the materials in the embodiments are prepared according to existing methods or directly purchased from the market.

[0038] Example 1 Preparation of cell suspension

[0039] 1. Preparation of single cell suspension of in vitro cultured cells:

[0040] Commercial reagents used for routine in vitro cell culture: DMEM medium, 1640 medium, fetal bovine serum FBS, penicillin-streptomycin-amphotericin B antibiotic solution, PBS buffer, trypsin solution;

[0041] Commercial reagents that need to be prepared additionally in this example: 4% tissue fixative;

[0042] Specific experimental steps:

[0043] a) Culture the cells in vitro in a suitable culture medium, and generally collect the cells after 24-48 hours of culture. For adherent cells, wash off the residual culture medium solution with PBS buffer, add an appropriate amount of trypsin for digestion (react in a 37°C incubator for 1 minute) to release the adherent cells from the bottom of the culture dish, then collect the cells in a 1.5mL EP tube, wash off the trypsin and culture medium solution with PBS buffer again, and discard the supernatant solution after centrifugation; for suspended cells, the trypsin digestion process can be omitted, collect the cells in a 1.5mL EP tube, wash off the culture medium solution with PBS, and discard the supernatant solution after centrifugation;

[0044] b) Add 1 mL of 4% tissue fixative to the EP tube containing the cells, react at room temperature for 15 minutes, centrifuge at 1000 rpm for 5 minutes, discard the supernatant, then add 1 mL of PBS buffer to resuspend the cells, centrifuge at 1000 rpm for 5 minutes, discard the supernatant, ensure that the residual fixative is washed away, and resuspend the cells in 200 μL PBS solution. At this point, the preparation of single-cell suspension is completed.

[0045] 2. Preparation of single cell suspension of tumor tissue:

[0046] Required commercial reagents: PBS buffer, collagenase IV solution (2 mg / mL), 100 μm cell strainer, 4% tissue fixative;

[0047] Specific experimental steps: the tumor tissue was cut into pieces with scissors and placed in a 1.5mL EP tube. The residual blood was washed away with 1mL PBS, and then the supernatant was discarded after centrifugation at 1000rpm for 10min. Then 1mL collagenase IV solution was added to resuspend the cells and reacted at 37℃ for 30min. After the reaction, the suspension was filtered with a 100μm cell filter to remove tissue blocks that were not digested into single cells. Then 4% tissue fixative was added to react at room temperature for 15min, and the supernatant was discarded after centrifugation at 1000rpm at room temperature for 5min. 1mL PBS buffer was added to resuspend the cells again, and the supernatant was discarded again after centrifugation at 1000rpm at room temperature for 5min to ensure that the residual fixative was washed away. Finally, the cells were resuspended in 200μL PBS solution to obtain the prepared tissue single cell suspension.

[0048] Example 2 Single Cell Raman Identification

[0049] Commercial instruments: Horiba LabRam HR Raman spectrometer, 532nm Nd:YAG laser, 60× / 1.00NA water immersion objective lens.

[0050] Detection method: drop 100μL of single-cell cell suspension on a CaF2 slide, then place it under a microscope equipped with a 60× water mirror, slowly adjust the distance between the water mirror and the slide, and when the cells on the slide can be clearly seen through the microscope, select different positions of the cells through the software to collect Raman signals: use a laser with a wavelength of 532nm to select 5 points for data collection for each cell, namely the top, bottom, left, right, and middle positions of the cell, with a power of 100mW and a collection time of 1 second. At least 25 cells are collected for each group of samples.

[0051] Example 3 Construction of a prediction model based on single-cell Raman technology and tumor metastasis ability

[0052] According to existing literature reports, four cell lines that have been shown to have significant differences in metastatic ability were selected, namely, the ASPC1 cell line with high metastatic ability, the PANC1 and BXPC3 cell lines with medium metastatic ability, and the MIAPACA2 cell line with low metastatic ability. The methods of cell culture and single cell suspension preparation were the same as in Example 1, and the method of collecting cell Raman signals was the same as in Example 2. After multiple single-cell Raman detections were performed, the Random Forest algorithm was used to construct a classification model based on single-cell Raman signals for distinguishing different cell lines with high, medium and low metastatic abilities, wherein 70% of the data was used to build the model and 30% was used to verify the classification accuracy of the model.

[0053] Example 4 Construction of Raman Index RMI (Ramanome-based metastasis index) for indicating tumor cell metastasis ability

[0054] After successfully building a classification model that can be used to distinguish different metastatic abilities of tumors in Example 3, the Gini algorithm is used to screen out Raman signal peaks with higher classification weights from the model. Here, each Raman signal peak has a corresponding Gini score. The higher the Gini score, the greater the weight of the Raman peak in the classification model. After testing, the method provided in this embodiment finally screened out 15 Raman peaks with a Gini score greater than 0.75 (1465, 1129, 1435, 854, 1433, 1699, 1078, 1302, 1449, 1461, 1559, 1352, 1065, 1668, 1799 cm- 1 ), according to the correlation between these 15 Raman peaks and tumor metastasis ability and their own Raman signal intensity, a cell metastasis index RMI based on Raman signals was constructed. Its calculation method is as follows: w is the real Raman signal of each Raman peak in the cell in right 6, I is the correlation between each Raman peak and tumor metastasis ability, and the I value is 1 for positive correlation and -1 for negative correlation.

[0055] RM=w(1).

[0056] Example 5 Verification of the effect of RMI on tumor metastasis ability

[0057] Verification at the in vitro culture cell level: This example uses 12 cell lines that have been screened, gene-edited or drug-treated, verified by transwell experiments, or reported in the literature, and confirmed to have significant differences in metastatic ability, namely Figure 1 The transfer results of 6 gene-edited cell lines, including ASPC1-hnRNPAB-NC and -KO, are shown in Figure 1 A; Siha-S100A7-NC and -OE, the transfer results of which are shown in the literature: Tian Tian, ​​Xukun Li, Zhen Hua, Jianlin Ma, Xiaowei Wu, Zhihua Liu, Hongyan Chen, Zhumei Cui, Oncotarget 2017, 8, 24964-24977; and ASPC1-NC-RFP and -KO-RFP, the transfer results of which are shown in Figure 1 D. The four naturally screened cell lines are P1G8, P2G8, P1E6 and P2D6. The experimental screenshots of their metastatic abilities are shown below. Figure 2Two drug-treated cell lines, MUM2B-NC and MUM2B-ART, the effect of ART on the metastatic ability of tumor cells can be found in the literature SAKRasheed, T.Efferth, IAAsangani, H.Allgayer, International Journal of Cancer 2010, 127, 1475-1485. Single-cell Raman data were collected for each of them, and then the corresponding RMI values ​​were calculated, proving that the corresponding RMI values ​​of cell lines with high metastatic ability were significantly higher than those of cell lines with low metastatic ability, such as Figure 1 shown.

[0058] Figure 1 ASPC1-hnRNPAB-KO in middle A, Figure 1 Siha-S100A7-OE in C, and Figure 1 ASPC1-KO-RFP in D all use gene editing technology to knock out (KO) or overexpress (OE) the corresponding genes in the corresponding cells; Figure 1 P1G8, P2G8, P1E6, and P2D6 in B are naturally grown tumor cells that have not undergone any gene editing or drug treatment; Figure 1 MUM2B-ART in C is the experimental group in which MUM2B cells were treated with artesunate (ART). Figure 1 The results show that regardless of the cell type, the tumor metastasis ability predicted by RMI calculated based on single-cell Raman technology is consistent with its transwell results or the tumor metastasis ability reported in existing literature.

[0059] also, Figure 1 In C, MUM2B and Siha are choroidal melanoma cells and cervical cancer cells, respectively. Artesunate (ART in the figure) has been reported in the literature to reduce the metastatic ability of tumor cells, and overexpression of the gene S100A7 (S100A7-OE in the figure) has also been reported in the literature to promote the metastatic ability of Siha. Figure 1 From the results in Figure C, we can see that the prediction of the tumor metastasis ability of MUM2B and Siha under ART treatment and S100A7 overexpression by RMI calculated based on single-cell Raman technology is consistent with existing literature reports, indicating that RMI can be applied to tumor cell types other than pancreatic cancer cells.

[0060] Verification at the tumor tissue cell level: After successful verification in in vitro cultured cells, this example constructed a mouse pancreatic cancer in situ tumor liver metastasis model, and ASPC1-NC-RFP cells and ASPC1-KO-RFP cells were injected into the pancreas of 7 and 8 mice, respectively. After the mice continued to grow for 6 weeks, the mice were killed, and the pancreatic cancer tissue grown in the pancreas of the mice was taken. The single cell suspension was prepared using the method in Example 2, and then single cell Raman detection was performed to calculate the RMI values ​​of the two groups. At the same time, the liver tissue of each mouse was collected and HE staining was used to determine whether liver metastasis occurred as a positive control. The results showed that the RMI value was consistent with the proportion of mice with liver metastasis in the two groups, as shown in Figure 2. Figure 3 shown.

[0061] The above data prove that the Raman-based metastasis ability index RMI proposed in this method can quickly and accurately predict the metastasis ability of tumor cells cultured in vitro and tumor tissue cells grown in vivo.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting tumor cell metastasis ability based on Raman spectroscopy, characterized in that: The steps include: a) Single-cell Raman spectroscopy detection of tumor cell lines with different metastatic abilities; b) Using machine learning algorithms, construct a classification model based on single-cell Raman signal data that can be used to distinguish different cell lines with different metastatic abilities; c) using the Gini algorithm to select Raman signal peaks with high classification weight from the classification model; d) calculating the Raman transfer index (RMI) according to the correlation between the Raman signal peak and the metastatic ability of the tumor cells and the Raman signal intensity.

2. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 1, characterized in that: In step a), the tumor cell lines with different metastatic abilities include ASPC1 cell line with high metastatic ability, PANC1 and BXPC3 cell lines with medium metastatic abilities, and MIAPACA2 cell line with low metastatic ability.

3. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 1, characterized in that: In step a), the laser power of the Raman spectrum detection is 100 mW, the wavelength is 532 nm, and the acquisition time is 1 second.

4. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 1, characterized in that: In step b), the machine learning algorithm includes a random forest algorithm.

5. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 1, characterized in that: In step c), the screening condition of the Gini algorithm is that the Gini score is greater than 0.

75.

6. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 6, characterized in that: In step c), the Raman signal peaks with high classification weights include 1465cm -1 、1129cm -1 、1435cm -1 、854cm -1 、1433cm -1 、1699cm -1 、1078cm -1 、1302cm -1 、1449cm -1 、1461cm -1 、1559cm -1 、1352cm -1 、1065cm -1 、1668cm -1 and 1799cm -1 .

7. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 1, characterized in that: The tumor cells include pancreatic cancer cells, cervical cancer cells and choroidal melanoma cells.

8. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 1, characterized in that: The tumor cells include natural cells, gene-edited cells and drug-treated cells.

9. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to claim 1, characterized in that: The Raman transfer index RMI calculation method is as follows: formula (1), wherein w is the real Raman signal of each Raman peak in the cell, I is the correlation between each Raman peak and the tumor metastasis ability, and the I value is 1 for positive correlation and -1 for negative correlation. RMI=w·I (1).

10. The method for detecting tumor cell metastasis ability based on Raman spectroscopy according to any one of claims 1 to 9, characterized in that: The larger the RMI value, the stronger the metastatic ability of tumor cells.

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