CN115232875A - Method and system for identifying different types of cancer cells of renal pelvis cancer patient based on urine - Google Patents
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Abstract
The invention relates to the field of cancer cell identification, in particular to a method and a system for identifying different types of cancer cells of a renal pelvis cancer patient based on urine, wherein the method comprises the following steps: collecting a urine sample, and obtaining a detection sample by using the urine sample; sequencing the test sample, thereby obtaining a cell expression profile of the test sample; using the cell expression profile to obtain cell expression profile data for mixed urothelium cell subpopulations including normal urothelium cells and cancerous urothelium cells; extracting different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation; analyzing the different protooncogene expression data to obtain a ratio of cancer cells and identifying different types of cancer cells. According to the method, different types of cancerous urothelial cells are rapidly and accurately obtained through proto-oncogene expression profile data in a non-invasive inspection mode, and reference and basis are provided for the pre-operative new auxiliary treatment of the renal pelvis cancer.
Description
Technical Field
The invention relates to the field of cancer cell identification, in particular to a method and a system for identifying different types of cancer cells of a renal pelvis cancer patient based on urine.
Background
Cancer is one of the most serious diseases threatening human health at present, and many medical workers are trying to overcome the problem. Renal Pelvis Cancer (RPC) refers to a malignant tumor that occurs in the Renal pelvis region, the cause of which is not clear, and the incidence of which is relatively low in urinary tract tumors, accounting for about 10% of all urothelial tumors. RPC has no specific clinical manifestations, most common is naked eye hematuria, other common clinical manifestations are pain and discomfort of waist and abdomen, lumbar mass, hydronephrosis and the like, and some patients are discovered accidentally during physical examination. At present, the clinical treatment modes for tumors mainly comprise operations, radiotherapy, chemotherapy and the like, but the tumor pathogenesis is complex, and the individual difference factors are large, so the treatment effect is different from person to person, and the overall effect is not optimistic. In recent years, with the progress of the technology of 'target drugs', more and more patients begin to benefit from the technology, the overall life cycle is effectively prolonged, and the quality of life is obviously improved. With the increasing advocated concept of "precision medicine", the therapeutic approach of using targeted drugs guided by genetic testing technology is being accepted and used by more clinicians, and brings more suitable therapeutic approaches and better therapeutic effects to patients with tumor cancer. Currently, for patients with preoperative diagnosis of renal pelvis cancer, RNU (radio Nephro-uretechnology) surgery is performed and postoperative adjuvant chemotherapy is given; on the other hand, many surgical groups now advocate the use of neoadjuvant chemotherapy for patients allowed by the physical situation, i.e. the treatment of patients with chemotherapy before surgery, the observation of the effect of the treatment in real time, and then the decision to maintain the treatment or to proceed with the surgery according to the specific situation. Generally, no matter operation or chemotherapy, great trauma or non-specific injury can be caused to the body of a patient, and a tumor living body sample cannot be taken out due to the deep renal pelvis, so that the optimal tumor target drug is judged by a second-generation sequencing technology. Therefore, there is a need for a non-invasive detection method capable of identifying the types of free cancer cells in an in vitro sample, and further providing support and basis for the search of a new adjuvant therapy method before renal pelvis cancer operation.
Disclosure of Invention
In order to overcome the defects in the prior art, the present invention provides a method and a system for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine, which aims to solve the above technical problems, and in a first aspect, the present invention provides a method for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine, comprising the following steps: collecting a urine sample, and obtaining a detection sample by using the urine sample; sequencing the detection sample to obtain a cell expression profile of the detection sample, and further optionally, sequencing the detection sample by a single-cell RNA sequencing technology; obtaining cell expression profile data of mixed urothelial cell subsets by using the cell expression profiles, wherein the mixed urothelial cell subsets comprise normal urothelial cells and cancerous urothelial cells; extracting different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subset; the different protooncogene expression data are analyzed to obtain a ratio of cancer cells and to identify different types of cancer cells. In conclusion, the method takes the urine sample of a patient confirmed to diagnose the renal pelvis cancer as a detection object, unifies the heterogeneity of cells by using a new index of cancer cell ratio through a single-cell RNA sequencing technology, obtains the cell expression profile data of the mixed urothelial cell subgroup, screens out different cancerization urothelial cells in the urine by different protooncogenes through data analysis and mining, and thus provides a reference basis based on a single-target medicine treatment mode for the pre-operative new auxiliary treatment of the renal pelvis cancer. According to the method, different types of cancerous urothelial cells are quickly and accurately obtained through proto-oncogene expression profile data in an atraumatic examination mode, and reference and basis are provided for the pre-operative new adjuvant therapy of the renal pelvis cancer.
Optionally, the method for obtaining a test sample by collecting a urine sample comprises the following steps: collecting a urine sample, wherein the urine sample is a morning urine sample; classifying cells in the urine sample to obtain a cell population in the urine sample; analyzing and counting the cell populations to obtain the number of the cell populations and the total number of cells in each cell population; setting a detection standard; comparing the number of cell populations and the total number of cells in the cell populations to a detection standard; obtaining a test sample when the number of cell populations and the total number of cells in the population meet the test criteria.
Optionally, sequencing the test sample to obtain a cell expression profile of the test sample, comprising the steps of: washing and resuspending the detection sample; labeling and washing cells in the resuspended detection sample; constructing a sequencing library according to the cells; sequencing the cells on a computer to obtain a corresponding sequencing result; obtaining an accurate cell sequence by controlling the quality of a sequencing result; and filtering low-quality cell expression profile data by using sequence comparison and distribution so as to obtain high-quality cell expression profile data.
Optionally, using the cell expression profile, obtaining cell expression profile data of a mixed urothelial cell subpopulation comprising normal urothelial cells and cancerous urothelial cells, comprising the steps of: classifying said cells using cell expression profile data; performing type identification on the classified cells to obtain an epithelial cell population; confirming a urothelial cell marker gene; and screening out cell expression profile data of the mixed urothelial cell subset by utilizing the urothelial cell marker gene to combine with the epithelial cell population.
Optionally, extracting different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation, comprising the steps of: identifying different protooncogenes; and matching and extracting corresponding protooncogenes from the cell expression profile data of the mixed urothelial cell subset according to different protooncogenes.
Optionally, analyzing the different proto-oncogene expression data to obtain a ratio of cancer cells and identify different types of cancer cells, comprising the steps of: setting classification standards, wherein the classification standards comprise a high expression class and a normal expression class; randomly selecting N data from different protooncogene expression data as initial central points; comparing the protooncogene expression data with the initial central point by using the similarity of protooncogene expression to obtain a comparison result; dividing the protooncogene expression data into a high expression class and a normal expression class according to the comparison result and the classification standard; redefining the initial center by calculating the mean value by using protooncogene expression data in the high expression class and the normal expression class respectively; re-classifying the protooncogene expression data by using the redefined initial center in combination with the similarity of the protooncogene expression until the square sum of errors is globally minimum to obtain the unique classified expression N of the protooncogenes 1 And N 2 Wherein, N is 1 Group showing Normal expression of protooncogene, N 2 A group showing high expression of protooncogenes; expression of N by classification of protooncogenes 1 And N 2 Obtaining a ratio of cancer cells caused by the protooncogene; identifying the type of cancer cell using the ratio of cancer cells. The classification method can definitely screen different protooncogenes in a mixed urothelial cell population on the basis of the expression values of the different protooncogenes and the same termination conditionThe relative high expression group and the normal group simply and definitely meet the requirements, and the calculation efficiency is improved.
Alternatively, N 1 And N 2 All satisfy the following formula:
wherein i =1,2,i represents a class of classified expression of protooncogenes; k =2, representing the number of classes of classified expression, i.e. both the high expression class and the normal expression class; mu.s i Represents N i Mean value of primary oncogene expression data; σ represents the standard deviation of protooncogene expression data.
Alternatively, the cancer cell ratio satisfies the following formula:
wherein eta represents the cancer cell ratio, num (N) 2 ) Num (N) which is the number of cells in a group in which the protooncogene is highly expressed 1 +N 2 ) Indicates the total number of cells in the group in which the protooncogene is normally expressed and the group in which the protooncogene is highly expressed.
Optionally, identifying the type of cancer cell using the ratio of cancer cells, comprising the steps of: obtaining the existence probability of the corresponding protooncogene in the mixed urothelial cell subgroup through the cancer cell ratio; using the probability of presence, different types of cancer cells are identified in the urothelial cell subpopulation. The invention provides a reference basis for exploring a new auxiliary treatment mode based on tumor-targeted drugs before operation of the renal pelvis cancer by measuring the cancer cell ratio of a certain protooncogene in urothelial mixed cells.
In a second aspect, the present invention also provides a system for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine, the system being suitable for a method for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine, comprising an acquisition unit, a measurement unit and an analysis unit; the acquisition unit, the measurement unit and the analysis unit are connected with each other pairwise; the collection unit is used for collecting a urine sample and extracting cells through the urine sample so as to obtain a detection sample; the measuring unit is used for sequencing the detection sample so as to obtain a cell expression profile of the detection sample, screening cell expression profile data of a mixed urothelial cell subset from the cell expression profile, and obtaining different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subset; the analysis unit is used for analyzing different protooncogene expression data, thereby obtaining a cancer cell ratio and identifying different types of cancer cells. The system achieves the aim of identifying different types of cancer cells quickly and accurately by the interaction of the three functional units and the combination of a method for identifying different types of cancer cells of a renal pelvis cancer patient based on urine.
In a third aspect, the present invention also provides a system for identifying different types of cancer cells in a patient with renal pelvis cancer based on urine, comprising an input device, a processor, a memory, and an output device, the input device, the processor, the memory, and the output device being interconnected, wherein the memory is used for storing a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions to perform a method for identifying different types of cancer cells in a patient with renal pelvis cancer based on urine. The system has compact structure and strong applicability, greatly improves the operation efficiency, and provides an entity system model for identifying different types of cancer cells by combining the method for identifying different types of cancer cells of the renal pelvis cancer patient based on urine.
Drawings
FIG. 1 is a flow chart of a method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine according to the present invention;
FIG. 2 is a schematic view of a cell population in a urine sample according to the present invention;
FIG. 3 is a schematic diagram of cell classification after sequencing of a test sample according to the present invention;
FIG. 4 is a schematic representation of the cell subpopulation classification according to the present invention;
FIG. 5 is a schematic representation of mixed urothelial cell subpopulations according to the present invention;
FIG. 6 is a scatter plot of protooncogene expression of mixed urothelial cell subsets according to the present invention;
FIG. 7 is a schematic diagram showing the classification of various protooncogenes of the present invention;
FIG. 8 is a schematic diagram illustrating the classification principle of HER2 and EGFR expression in mixed urothelial cell subsets according to the present invention;
FIG. 9 is a schematic representation of different types of cells in a mixed urothelial cell subpopulation according to the present invention;
FIG. 10 is a schematic diagram of the system elements for identifying different types of cancer cells in a patient with renal pelvis cancer based on urine according to the present invention;
FIG. 11 is a schematic diagram of a system for identifying different types of cancer cells in a patient with renal pelvis cancer based on urine according to the present invention.
Detailed Description
Specific embodiments of the present invention will be described in detail below, and it should be noted that the embodiments described herein are merely illustrative and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known circuits, software, or methods have not been described in detail so as not to obscure the present invention.
Throughout the specification, reference to "one embodiment," "an embodiment," "one example," or "an example" means: the particular features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "one example" or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Further, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.
Referring to fig. 1, in one embodiment, the present invention provides a method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine, comprising the steps of: s1, collecting a urine sample, and obtaining a detection sample by using the urine sample.
In detail, in a further alternative embodiment, a urine sample is collected, and a test sample is obtained by using the urine sample, comprising the steps of: s11, collecting a urine sample, wherein the urine sample is a morning urine sample, and specifically, 5mL of the morning urine sample can be optionally sucked in the operation process.
In an optional embodiment, the collecting of the urine sample and the obtaining of the test sample by using the urine sample further comprises the following steps: s12, classifying the cells in the urine sample to obtain a cell population in the urine sample. In this example, a UF-5000 urine analyzer may be used to classify cells in a urine sample. In detail, the cell population includes red blood cells, white blood cells, epithelial cells, casts and bacteria. In further detail, in another alternative embodiment, please refer to FIG. 2, FIG. 2 is a cell population in a urine sample, the cell population includes C 1 (white blood cell) C 2 (Red blood cell), C 3 (squamous epithelial cell), C 4 (urothelial cells) C 5 (renal tubular epithelial cells).
In another optional embodiment, the collecting urine sample, and the obtaining test sample using the urine sample, further comprises the following steps: and S13, analyzing and counting the cell populations, and obtaining the number of the cell populations and the total number of the cells in each cell population. In detail, a UF-5000 urine analyzer may be used to analyze statistics for cell populations.
In another optional embodiment, the collecting urine sample, and the obtaining test sample using the urine sample, further comprises the following steps: and S14, setting a detection standard. Specifically, the detection criterion is determined according to actual conditions.
In yet another alternative embodiment, the collecting of the urine sample and the obtaining of the test sample by using the urine sample further comprises the following steps: and S15, comparing the number of the cell population and the total number of the cells in the cell population with the detection standard.
In an optional embodiment, the collecting of the urine sample and the obtaining of the test sample by using the urine sample further comprises the following steps: and S16, obtaining a detection sample when the number of the cell population and the total number of the cells in the population meet the detection standard.
Referring to fig. 1, in one embodiment, the present invention provides a method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine, further comprising the steps of: s2, sequencing the detection sample to obtain a cell expression profile of the detection sample. Specifically, a single cell RNA sequencing technology can be selected for sequencing a detection sample, the single cell RNA sequencing technology can be used for measuring cell expression profile data of each cell in the sample, namely, the expression profile condition in the whole detection sample is represented by the resolution ratio of a single cell in the detection sample, all numerical values can be fused and used as a tissue transcriptome sequencing result, individual comparison and analysis can also be carried out on individual cells in similar cells from the same sample, and individual differences among different individuals are completely avoided; the cell expression profile data includes a complete expression profile of the cell.
In detail, in this embodiment, sequencing the test sample to obtain the cell expression profile of the test sample comprises the following steps: s21, washing and resuspending the detection sample.
In one embodiment, sequencing the test sample to obtain a cell expression profile of the test sample further comprises the steps of: and S22, labeling and washing cells in the detection sample after the resuspension.
In yet another embodiment, sequencing the test sample to obtain a cell expression profile of the test sample further comprises the steps of: s23, constructing a sequencing library according to the cells.
In yet another embodiment, sequencing the test sample to obtain a cell expression profile of the test sample further comprises the steps of: and S24, sequencing the cell on a computer to obtain a corresponding sequencing result. Specifically, the sequencing result includes transcriptional information such as high expression of protooncogenes or low expression of cancer suppressor genes.
In another embodiment, sequencing the test sample to obtain a cell expression profile of the test sample further comprises the steps of: and S25, controlling the quality of the sequencing result to obtain an accurate cell sequence. The data are screened through the step S25 to obtain effective readable information, so that the accuracy of the data is guaranteed.
In one embodiment, sequencing the test sample to obtain a cell expression profile of the test sample further comprises the steps of: and S26, filtering low-quality cell expression profile data by using sequence comparison and distribution so as to obtain high-quality cell expression profile data. The data are compared and distributed through the step S26, so that the accuracy of the data is further improved.
In one embodiment, the present invention provides a method for identifying different types of cancer cells in a patient with renal pelvis cancer based on urine, further comprising the steps of: and S3, obtaining cell expression profile data of the mixed urothelium cell subset by using the cell expression profile, wherein the mixed urothelium cell subset comprises normal urothelium cells and cancerous urothelium cells.
Specifically, in this embodiment, using the cell expression profile, cell expression profile data of a mixed urothelial cell subpopulation including normal urothelial cells and cancerous urothelial cells is obtained, including the steps of: and S31, classifying the cells by using the cell expression profile data. Specifically, referring to fig. 3, in an alternative embodiment, through steps S21 to S26, high-quality cell expression profile data after sequencing of the detection sample is obtained, and according to the high-quality cell expression profile data, clustering analysis and dimension reduction visualization are performed on the cells by using a Seurat software, so as to obtain a cell classification schematic diagram after sequencing of the detection sample, more specifically, 11780 cells are captured together after sequencing of the detection sample, 5048 high-quality cells are captured together after filtering of low-quality cell data, each point in the diagram represents one cell, cell clustering is performed according to the expression profile of each cell, the expression profiles of the cells contained in each cell subset are similar, and 15 cell subsets, that is, cell subsets corresponding to the 0 to 14 labels on fig. 3, are obtained through sequencing in this embodiment.
Referring to fig. 4, in yet another alternative embodiment, the cell expression profile is used to obtain cell expression profile data of a mixed urothelial cell subpopulation, further comprising the steps of: and S32, carrying out type identification on the classified cells to obtain an epithelial cell population, wherein the epithelial cell population comprises a mixed urinary tract epithelial cell sub-population and a renal tubular epithelial cell sub-population. Specifically, in this embodiment, the cell subsets corresponding to the above 0-14 labels are automatically identified by using SingleR software, and the cell types include Epithelial cells, T cells, macrophages, monocytes, B cells, neutroppieces, DCs, and CMP.
In yet another optional embodiment, the cell expression profile is used to obtain cell expression profile data for a mixed urothelial cell subpopulation, further comprising the steps of: s33, confirming the urothelial cell marker gene. More specifically, the urothelial cell marker gene may be selected as KRT8/PSCA and the tubular Epithelial cell marker gene may be selected as KRT8, and Epithelial cells composed of the cell subsets numbered 9, 10, 11, and 13 in fig. 3 are further divided into two groups, i.e., a mixed urothelial cell subset numbered 9, 10, and 11 and a tubular Epithelial cell subset numbered 13.
Referring to fig. 5, in yet another alternative embodiment, the cell expression profile is used to obtain cell expression profile data of a mixed urothelial cell subpopulation, further comprising the steps of: and S34, screening out cell expression profile data of the mixed urothelial cell subset by utilizing the urothelial cell marker gene to combine with the epithelial cell population. In fig. 5, the cell population in the coil is a sub-population of urothelial cells.
Referring to fig. 1, in one embodiment, the present invention provides a method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine, further comprising the steps of: and S4, extracting different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subset.
Specifically, in this embodiment, the step of extracting different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation includes the steps of: s41, determining different protooncogenes; and S42, matching and extracting corresponding protooncogene expression data from the cell expression profile data of the mixed urothelial cell subset according to different protooncogenes. More specifically, in this example, please refer to fig. 6, fig. 6 is a proto-oncogene expression scattergram of mixed urothelial cell subsets obtained using steps S41-S42, wherein the genes include ALK, EGFR, FGFR2, FLT3, HER2, INSR, KIT, NTRK1, PDGFRA, PD-L1, PIK3CA, and SRC, the ordinate represents the expression value of the corresponding gene, and the abscissa represents the number of cells.
In one embodiment, the present invention provides a method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine, further comprising the steps of: and S5, analyzing the expression data of different protooncogenes so as to obtain the ratio of the cancer cells and identify different types of cancer cells.
Specifically, in an alternative embodiment, different protooncogene expression data is analyzed to obtain a ratio of cancer cells and identify different types of cancer cells, comprising the steps of: s51, setting classification standards, wherein the classification standards comprise a high expression class and a normal expression class. Specifically, the expression value in the gene expression data of the high expression class is higher than that of the normal expression class.
In an alternative embodiment, analyzing the different protooncogene expression data to obtain a ratio of cancer cells and identify different types of cancer cells, further comprises the steps of: s52, randomly selecting N data from different protooncogene expression data as initial central points.
In an alternative embodiment, the different protooncogene expression data is analyzed to obtain a ratio of cancer cells and identify different types of cancer cells, further comprising the steps of: and S53, comparing the protooncogene expression data with the initial central point by using the similarity of protooncogene expression to obtain a comparison result.
In an alternative embodiment, the different protooncogene expression data is analyzed to obtain a ratio of cancer cells and identify different types of cancer cells, further comprising the steps of: and S54, dividing the protooncogene expression data into a high expression class and a normal expression class according to the comparison result and the classification standard.
In an alternative embodiment, analyzing the different protooncogene expression data to obtain a ratio of cancer cells and identify different types of cancer cells, further comprises the steps of: and S55, redefining the initial center by calculating the mean value by using the expression data of the protooncogenes in the high expression class and the normal expression class respectively.
In an alternative embodiment, analyzing the different protooncogene expression data to obtain a ratio of cancer cells and identify different types of cancer cells, further comprises the steps of: s56, classifying the protooncogene expression data again by using the redefined initial center and the similarity of the protooncogene expression until the square sum of errors of the initial center is global minimum to obtain the unique classified expression N of the protooncogene 1 And N 2 Wherein, N is 1 Group showing Normal expression of protooncogenes, N 2 A group showing high expression of protooncogenes; in particular, N 1 And N 2 All satisfy the following formula:
wherein i =1,2,i represents a class of classified expression of protooncogenes; k =2, representing the classification number of classified expression, namely a high expression class and a normal expression class; mu.s i Represents N i Mean value of primary oncogene expression data; σ represents the standard deviation of protooncogene expression data. The classification method is not limited by the sample data distribution mode and the number of samples, and simultaneously considers the possible influence of the mean value. Specifically, please refer to fig. 7 and 8, wherein fig. 7 shows a schematic diagram of classification of different proto-oncogenes obtained through steps S51-S56, and fig. 8 shows a schematic diagram of classification principle of HER2 and EGFR expression in mixed urothelial cell subpopulations, respectively. In FIG. 8, the abscissa represents the number of cells, the ordinate represents the expression value, and the a-line represents the overall highest expressionThe value, line b is the overall lowest expression value, line c is the overall intercept line, and N is above line c 1 Class, bottom is N 2 And (4) classification. When the c-line is cycled to the optimal position, the whole body has the global 'least square sum of errors', namely
Wherein, | N 1 |、|N 2 I is the number of samples in the class, σ 2 N 1 、σ 2 N 2 The square of the standard deviation of the samples contained in the category; referring to fig. 9, fig. 9 is a schematic diagram showing different types of cells in a mixed urothelial cell subpopulation, wherein each circle represents a urothelial cell, blue letters K and P represent marker genes KRT8 and PSCA of the urothelial cell, and the rest letters represent different proto-oncogenes, wherein the type of expression is represented by font color, specifically, black font can be selected to represent normal expression, and red font can represent high expression; E. h, P, F, and B represent proto-oncogenes EGFR, HER2, PD-L1, FGFR2, and BRAF, respectively, letters on the circle represent membrane proteins, and letters on the circle represent membrane proteins. When a urothelial cell has only one protooncogene expressed to a higher degree than that of a normal cell, the cell is expressed as a single mutation, and so on, i.e., in FIG. 8, T 0 Normal cells, T1 but mutant cells, T2 double mutant cells, T3 triple mutant cells, T4 quadruple mutant cells, and T5 pentamutant cells. Each mutation type corresponds to the gray scale image block on the right side, the higher the mutation degree of the cells is, the darker the color of the image block is, and the combined gray scale ladder diagram indicates that the number of the cells of the corresponding mutation type is in a descending state along with the increase of the mutation degree. N in dotted circle after EGFR Gene Classification 2 A group of cells representing the sum of different mutant type cells containing EGFR mutations. The classification method can definitely screen out relative high expression groups and normal groups of different protooncogenes based on the expression values of different protooncogenes in a mixed urothelial cell population by using the same termination condition, simply and definitely meets the requirements, and the calculation efficiency is improved.
In an alternative embodiment, the different protooncogene expression data is analyzed to obtain a ratio of cancer cells and identify different types of cancer cells, further comprising the steps of: s57, differential expression of N by protooncogenes 1 And N 2 Obtaining a ratio of cancer cells caused by the protooncogene; specifically, the cancer cell ratio satisfies the following formula:
wherein eta represents the cancer cell ratio, num (N) 2 ) Num (N) which is the number of cells in a group in which the protooncogene is highly expressed 1 +N 2 ) Indicates the total number of cells in the group in which the protooncogene is normally expressed and the group in which the protooncogene is highly expressed. The heterogeneity of the cells is eliminated through the cancer cell ratio, a unified evaluation index is found, the cancer cell distinguishing accuracy is greatly improved, and particularly, the evaluation index is N 2 Cells of the group, due to their significantly higher protooncogene expression values than N 1 Group of cells, N can be considered 2 The phenotype of the cancer cells such as proliferation, migration, and invasion of the cells in the group is enhanced, and thus it is considered that the phenotype of the cancer cells is caused by overexpression of the corresponding protooncogenes; therefore, a larger cancer cell ratio of a protooncogene means that more cancer cells are generated due to the overexpression of the protooncogene in the mixed urothelial cell subpopulation. The invention provides a reference basis for exploring a new auxiliary treatment mode based on tumor-targeted drugs before operation of the renal pelvis cancer by measuring the cancer cell ratio of a certain protooncogene in urothelial mixed cells.
In an alternative embodiment, the different protooncogene expression data is analyzed to obtain a ratio of cancer cells and identify different types of cancer cells, further comprising the steps of: and S58, identifying the type of the cancer cell by using the cancer cell ratio.
Specifically, the cancer cell ratio is used for identifying the type of the cancer cell, and the method comprises the following steps: s581, obtaining the existence probability of the corresponding protooncogene in the mixed urothelial cell subgroup according to the cancer cell ratio; s582, identifying different types of cancer cells in the urothelial cell subpopulation by using the existence probability.
In conclusion, the method takes a urine sample of a patient confirmed to be diagnosed with the renal pelvis cancer as a detection object, obtains cell expression profile data of a mixed urothelial cell subgroup through a single-cell RNA sequencing technology, eliminates cellular heterogeneity through data analysis and mining by using a new index of cancer cell ratio, screens out different types of cancerous urothelial cells in urine through corresponding protooncogenes, and thus provides a reference basis for exploring a new auxiliary treatment mode based on a tumor-targeted medicine before the renal pelvis cancer operation. According to the method, different types of cancerous urothelial cells are quickly and accurately obtained through proto-oncogene expression profile data in an atraumatic examination mode, and reference and basis are provided for the pre-operative new adjuvant therapy of the renal pelvis cancer.
In a second aspect, referring to fig. 10, in an alternative embodiment, the present invention further provides a system for identifying different types of cancer cells of a renal pelvis cancer patient based on urine, the system being adapted for use in a method for identifying different types of cancer cells of a renal pelvis cancer patient based on urine, comprising an acquisition unit, a measurement unit and an analysis unit; the acquisition unit, the measurement unit and the analysis unit are connected with each other in pairs; the collection unit is used for collecting a urine sample, and extracting cells through the urine sample so as to obtain a detection sample; the measuring unit is used for sequencing the detection sample so as to obtain a cell expression profile of the detection sample, screening cell expression profile data of a mixed urothelial cell subset from the cell expression profile, and obtaining different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subset; the analysis unit is used for analyzing different protooncogene expression data, thereby obtaining a cancer cell ratio and identifying different types of cancer cells. The system achieves the aim of identifying different types of cancer cells quickly and accurately by the interaction of the three functional units and the combination of a method for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine.
In a third aspect, referring to fig. 11, in an alternative embodiment, the present invention further provides a system for identifying different types of cancer cells in a renal pelvis cancer patient based on urine, comprising an input device, a processor, a memory, and an output device, the input device, the processor, the memory, and the output device being interconnected, wherein the memory is used for storing a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions to perform a method for identifying different types of cancer cells in the renal pelvis cancer patient based on urine. The system has compact structure and strong applicability, greatly improves the operation efficiency, and provides an entity system model for realizing different types of cancer cells by combining the method for identifying different types of cancer cells of the renal pelvis cancer patient based on urine.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A method for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine, comprising the steps of:
collecting a urine sample, and obtaining a detection sample by using the urine sample;
sequencing the test sample, thereby obtaining a cell expression profile of the test sample;
obtaining cell expression profile data of mixed urothelial cell subsets by using the cell expression profiles, wherein the mixed urothelial cell subsets comprise normal urothelial cells and cancerous urothelial cells;
extracting different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation;
analyzing the different protooncogene expression data to obtain a ratio of cancer cells and identifying different types of cancer cells.
2. The method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine as claimed in claim 1, wherein the step of collecting a urine sample and obtaining a test sample by using the urine sample comprises the following steps:
collecting a urine sample, wherein the urine sample is a morning urine sample;
classifying cells in the urine sample to obtain a cell population in the urine sample;
analyzing and counting the cell populations to obtain the number of the cell populations and the total number of cells in each cell population;
setting a detection standard;
comparing the number of the cell population and the total number of cells in the cell population to the detection standard;
obtaining the test sample when the number of the cell population and the total number of cells in the population meet the test criteria.
3. The method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine as claimed in claim 1, wherein the step of sequencing the test sample to obtain the cell expression profile of the test sample comprises the steps of:
washing and resuspending the detection sample;
labeling and washing the cells in the detection sample after resuspension;
constructing a sequencing library from said cells;
performing on-machine sequencing on the cells to obtain corresponding sequencing results;
obtaining a precise sequence of the cell by controlling the quality of the sequencing result;
and filtering the cell expression profile data of the cells with low quality by utilizing the sequence alignment and distribution so as to obtain the cell expression profile data with high quality.
4. The method for identifying different types of cancer cells in a renal pelvis cancer patient based on urine as claimed in claim 1, wherein the step of using the cell expression profile to obtain cell expression profile data of mixed urothelium cell subsets comprising normal urothelium cells and cancerous urothelium cells comprises the steps of:
classifying said cells using said cell expression profile data;
performing type identification on the classified cells to obtain an epithelial cell population;
confirming a urothelial cell marker gene;
and screening out cell expression profile data of the mixed urothelial cell subset by combining the urothelial cell marker gene with the epithelial cell population.
5. The method for urine-based identification of different types of cancer cells in renal pelvis cancer patients according to claim 1, wherein the step of extracting different proto-oncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation comprises the steps of:
identifying different proto-oncogenes;
and matching and extracting corresponding protooncogene expression data according to different protooncogenes from the cell expression profile data of the mixed urothelial cell subpopulation.
6. The method for identifying different types of cancer cells in a patient with renal pelvis cancer based on urine as claimed in claim 1, wherein the analyzing the expression data of different proto-oncogenes to obtain a ratio of cancer cells and identify different types of cancer cells comprises the steps of:
setting classification criteria, wherein the classification criteria comprise a high expression class and a normal expression class;
randomly selecting N data from different protooncogene expression data as initial central points;
comparing the protooncogene expression data with the initial central point by using the similarity of the protooncogene expression to obtain a comparison result;
classifying the protooncogene expression data into a high expression class and a normal expression class according to the comparison result in combination with a classification criterion;
redefining an initial center by averaging using the proto-oncogene expression data in the high expression class and the normal expression class, respectively;
re-classifying the proto-oncogene expression data by using the redefined initial center in combination with the similarity of the proto-oncogene expression until the square sum of errors is globally minimum to obtain unique classified expression N of the proto-oncogenes 1 And N 2 Wherein N is 1 Group showing normal expression of the protooncogenes, N 2 A group showing high expression of the protooncogenes;
expression of N by classification of the protooncogenes 1 And N 2 Obtaining a ratio of cancer cells caused by the protooncogene;
identifying the type of cancer cell using the ratio of cancer cells.
7. The method for urine-based identification of different types of cancer cells in a renal pelvis cancer patient according to claim 6, wherein N is 1 And N 2 All satisfy the following formula:
wherein i =1,2,i represents a class of classified expression of the protooncogene; k =2, representing the number of classes of classified expression, i.e. both the high expression class and the normal expression class; mu.s i Represents N i The mean of said protooncogene expression data in (1); σ represents a standard deviation of the protooncogene expression data.
8. The method for urine-based identification of different types of cancer cells in a renal pelvis cancer patient according to claim 6, wherein the cancer cell ratio satisfies the following formula:
wherein η represents the cancer cell ratio, num (N) 2 ) Number of cells in the group in which the protooncogene is highly expressed, num (N) 1 +N 2 ) Represents the total number of cells in the group in which the protooncogene is normally expressed and the group in which the protooncogene is highly expressed.
9. A system for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine, the system being adapted to the method for identifying different types of cancer cells of a patient with renal pelvis cancer based on urine according to any one of claims 1 to 8, comprising an acquisition unit, a measurement unit and an analysis unit;
the acquisition unit, the measurement unit and the analysis unit are connected with each other in pairs;
the collection unit is used for collecting a urine sample and extracting cells through the urine sample so as to obtain a detection sample;
the measuring unit is used for sequencing the detection sample so as to obtain a cell expression profile of the detection sample, screening cell expression profile data of a mixed urothelial cell subset from the cell expression profile, and obtaining different protooncogene expression data from the cell expression profile data of the mixed urothelial cell subset;
the analysis unit is adapted to analyze the different protooncogene expression data to obtain a cancer cell ratio and to identify different types of cancer cells.
10. A system for identifying different types of cancer cells in a patient with renal pelvis cancer based on urine, comprising an input device, a processor, a memory and an output device, the input device, the processor, the memory and the output device being interconnected, wherein the memory is for storing a computer program comprising program instructions, the processor being configured for invoking the program instructions for performing the method according to any one of claims 1 to 8.
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| CN116825206B (en) * | 2023-08-30 | 2023-11-03 | 四川大学华西医院 | Method, device and equipment for exploring FH-defect type kidney cancer key cell subgroup |
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