A system for identifying microvascular invasion of liver malignant tumors

Abstract

The present invention provides a system for identifying microvascular invasion of malignant liver tumors. First, a tissue sample is obtained, and transcriptome or proteome sequencing is performed to generate initial sequencing data; the gene expression values of the initial data are sorted to determine the sample sequencing data; differential genes are obtained through differential analysis, and these genes are selected as the sample set. The sample set is randomly divided into a training set and a validation set, and the training set is evaluated using a ten-fold cross-validation method to obtain the out-of-bag error rate to select appropriate model hyperparameters. On this basis, the target genes are selected and their expression values are sorted by a preset importance ranking, a random forest model is constructed, and the validation set is used for verification to obtain a recognition model; the sample to be tested is input into the recognition model to obtain the recognition result. The present invention is independent of a specific detection platform and means, and can integrate a variety of sequencing data to distinguish microvascular invasion, providing a basis for timely intervention and treatment for patients with a high clinical risk of metastasis.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, and in particular to a system for identifying microvascular invasion of malignant liver tumors.

[0002] For the diagnosis and treatment of liver malignancies, surgery is only suitable for a minority of patients, and the five-year survival rate remains unsatisfactory. Other treatments, including chemotherapy, targeted therapy, and immunotherapy, also have suboptimal results. This phenomenon is primarily due to metastasis and frequent recurrence. Therefore, indicators that reflect early metastasis and recurrence are needed and can serve as a basis for targeted treatment.

[0003] Vascular structure is a crucial component of the tumor microenvironment. Microvascular invasion, also known as microvascular cancer thrombus, refers to the nesting of cancer cells within vascular endothelial cells. The presence of microvascular invasion is a key factor in early cancer metastasis and recurrence. It is considered an independent prognostic factor for various cancers and serves as an important reference for treatment options such as chemotherapy, radiotherapy, transcatheter arterial chemoembolization, radionuclide therapy, and endoscopic resection.

[0004] Because microvascular invasion is a histopathological finding, it can only be diagnosed through postoperative surgical specimens. However, a considerable number of patients are not eligible for surgery at the time of initial onset, so specimens must be obtained through puncture to diagnose the disease. In addition, due to the limited information obtained from these specimens and the existence of sampling errors, assessing the microvascular invasion status of these patients is challenging, which ultimately affects treatment decisions and clinical outcomes, and has an unsatisfactory impact on the selection of therapeutic agents for patients receiving neoadjuvant therapy or advanced patients receiving radiotherapy, as well as the prediction of the efficacy of interventional therapies. Artificial intelligence is currently showing a trend of deep penetration into the medical field, playing an important auxiliary role. To overcome the limitations of microvascular invasion assessment in the above-mentioned situations, using artificial intelligence to construct models from molecular features related to microvascular invasion to predict the occurrence of microvascular invasion may be a better solution. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art, the present invention aims to provide a system for identifying microvascular invasion of malignant liver tumors.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for identifying microvascular invasion of malignant liver tumors, comprising:

[0008] Obtaining a tissue sample, and performing transcriptome sequencing or proteome sequencing on the tissue sample to obtain initial sequencing data, and sorting the initial sequencing data by gene expression value to obtain gene expression rank, and determining the gene expression rank or the original value of the gene expression data as the sample sequencing data;

[0009] Perform differential analysis on all gene expression ranks or gene expression data in the sample sequencing data to obtain differential genes, and then select data on differential genes from all genes in the sample sequencing data as a sample set;

[0010] Randomly allocating the sample set according to a preset ratio to obtain a training set and a validation set;

[0011] Performing a ten-fold cross validation on the training set to obtain an out-of-bag error rate result, and selecting corresponding model hyperparameters based on the out-of-bag error rate result;

[0012] Select variables according to the preset importance ranking to obtain the target gene;

[0013] Sorting the expression values of the target genes to obtain gene expression ranks;

[0014] A random forest model is constructed using the gene expression rank and the model hyperparameters, and the validation set is used to verify the results to obtain a validated recognition model;

[0015] The sample to be tested is input into the recognition model to obtain a recognition result.

[0016] Preferably, the target genes include: RANBP1, ARMCX3, RAMP3, CDH17, CTHRC1, TARS, MET, ASRGL1, SLC16A2, NDRG1, COG1, VPS45, PPP1R14A, RAN, TAF9, PCK1, CPXM2, ZNF280D, ST6GAL1, PCM1, CSTA, JMJD6, RCL1, UCK2, ZNF687, GTF2F2, RCCD1, CCDC127, FRK, NAT10, C1orf43, FRYLDUS1L, TTC7B.

[0017] Preferably, the preset ratio is 7:3.

[0018] A system for identifying microvascular invasion of malignant liver tumors, comprising:

[0019] A sample acquisition module is used to obtain a tissue sample and perform transcriptome sequencing or proteome sequencing on the tissue sample to obtain initial sequencing data, and to sort the initial sequencing data by gene expression value to obtain gene expression rank, and to determine the gene expression rank or the original value of the gene expression data as the sample sequencing data;

[0020] A differential analysis module is used to perform differential analysis on all gene expression ranks or gene expression data in the sample sequencing data to obtain differential genes, and then select data of differential genes from all genes in the sample sequencing data as a sample set;

[0021] A data set allocation module is used to randomly allocate the sample set according to a preset ratio to obtain a training set and a validation set;

[0022] A parameter selection module performs a ten-fold cross validation on the training set to obtain an out-of-bag error rate result, and selects corresponding model hyperparameters according to the out-of-bag error rate result;

[0023] Gene acquisition module, used to select variables according to preset importance ranking to obtain target genes;

[0024] A gene ranking module is used to sort the expression values of the target genes to obtain gene expression ranks;

[0025] A model construction module is used to construct a random forest model using the gene expression rank and the model hyperparameters, and verify the results using the validation set to obtain a verified recognition model;

[0026] The recognition module is used to input the sample to be tested into the recognition model to obtain the recognition result.

[0027] An electronic device, comprising:

[0028] at least one processor; and

[0029] a memory communicatively connected to the at least one processor; wherein,

[0030] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned method for identifying microvascular invasion of liver malignant tumors.

[0031] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the above-mentioned method for identifying microvascular invasion of liver malignant tumors.

[0032] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0033] The present invention provides a system for identifying microvascular invasion of malignant liver tumors, comprising: obtaining a tissue sample, and performing transcriptome sequencing or proteome sequencing on the tissue sample to obtain initial sequencing data, and sorting the initial sequencing data according to gene expression values to obtain gene expression ranks, and determining the gene expression ranks or the original values of the gene expression data as sample sequencing data; performing differential analysis on all gene expression ranks or gene expression data in the sample sequencing data to obtain differential genes, and then selecting data of differential genes from all genes in the sample sequencing data as a sample set; randomly allocating the sample set according to a preset ratio to obtain a training set and a validation set; performing ten-fold cross-validation on the training set to obtain an out-of-bag error rate result, and selecting corresponding model hyperparameters based on the out-of-bag error rate result; selecting variables based on a preset importance ranking to obtain target genes; sorting the expression values of the target genes to obtain gene expression ranks; constructing a random forest model using the gene expression ranks and the model hyperparameters, and performing result verification using the validation set to obtain a verified recognition model; and inputting the sample to be tested into the recognition model to obtain a recognition result. The present invention is independent of specific detection platforms and methods, and can therefore be used to calculate microvascular invasion discrimination using expression data from multiple gene panels obtained using different sequencing methods. This provides a basis for timely intervention and treatment for patients at high risk of metastasis. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a method for identifying microvascular invasion of liver malignant tumors, comprising:

[0040] Step 100: Obtain a tissue sample, and perform transcriptome sequencing or proteome sequencing on the tissue sample to obtain initial sequencing data, and sort the initial sequencing data by gene expression value to obtain gene expression rank, and determine the gene expression rank or the original value of the gene expression data as the sample sequencing data;

[0041] Step 200: performing differential analysis on all gene expression ranks or gene expression data in the sample sequencing data to obtain differential genes, and then selecting differential genes from all genes in the sample sequencing data as a sample set;

[0042] Step 300: Randomly distribute the sample set according to a preset ratio to obtain a training set and a validation set;

[0043] Step 400: performing a ten-fold cross validation on the training set to obtain an out-of-bag error rate result, and selecting corresponding model hyperparameters based on the out-of-bag error rate result;

[0044] Step 500: Select variables according to the preset importance ranking to obtain target genes;

[0045] Step 600: sorting the expression values of the target genes to obtain gene expression ranks;

[0046] Step 700: constructing a random forest model using the gene expression rank and the model hyperparameters, and verifying the results using the validation set to obtain a verified recognition model;

[0047] Step 800: Input the sample to be tested into the recognition model to obtain a recognition result.

[0048] The present invention provides a multi-gene combination for identifying microvascular invasion of liver malignant tumors and its application, the multi-gene combination comprising the following genes:

[0049] RANBP1, ARMCX3, RAMP3, CDH17, CTHRC1, TARS, MET, ASRGL1, SLC16A2, NDRG1, COG1, VPS45, PPP1R14A, RAN, TAF9, PCK1, CPXM2, ZNF280D, ST6GAL1, PCM1, CSTA, JMJD6, RCL1, UCK2, ZNF687, GTF2F2, RCCD1, CCDC127, FRK, NAT10, C1orf43, FRYLDUS1L, TTC7B.

[0050] The present invention further provides a specific method for obtaining the expression level of this multi-gene combination in a patient's tissue specimen: a fresh tissue sample of the tumor area obtained by surgery or puncture, or a paraffin tissue section sample or paraffin-embedded sample of the patient's tumor site, and the sample is subjected to transcriptome sequencing or proteome sequencing. Model construction process: All samples are randomly distributed in a ratio of 7:3, with most samples used as training sets and other samples used as validation sets. According to the out-of-bag error rate results of the ten-fold cross-validation, the corresponding parameters (such as the depth of the tree, the number of trees, and the feature selection strategy) are selected, and the importance of the sample features is obtained according to the feature importance assessment, and the variables, i.e., the genes in the above table, are selected based on the importance ranking. The expression values of these genes are sorted to obtain the gene expression rank. A random forest model was constructed using the gene expression rank and parameters, and the validation set was used to verify the results.

[0051] The present invention also provides a device for identifying and applying microvascular invasion of liver malignant tumors, which includes (1) providing a gene expression level analysis module. The expression value of the above-mentioned gene detected in the sample is obtained; (2) providing a sample discrimination module. The above-mentioned gene expression levels are sorted to obtain gene expression ranks, which are then incorporated into a random forest model to obtain a prediction result, which ultimately determines whether microvascular invasion exists or not.

[0052] The present invention also provides a memory for storing a computer program.

[0053] The present invention also provides a processor for executing a computer program, which can implement the above technical content.

[0054] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the content of the above technology can be implemented.

[0055] The present invention also provides devices that can be used to detect the expression of these genes, including detection devices targeting transcriptomes and proteomes.

[0056] Corresponding to the above method, such as Figure 2As shown, this embodiment also provides a system for identifying microvascular invasion of malignant liver tumors, comprising:

[0057] A sample acquisition module is used to obtain a tissue sample and perform transcriptome sequencing or proteome sequencing on the tissue sample to obtain initial sequencing data, and to sort the initial sequencing data by gene expression value to obtain gene expression rank, and to determine the gene expression rank or the original value of the gene expression data as the sample sequencing data;

[0058] A differential analysis module is used to perform differential analysis on all gene expression ranks or gene expression data in the sample sequencing data to obtain differential genes, and then select data of differential genes from all genes in the sample sequencing data as a sample set;

[0059] A data set allocation module is used to randomly allocate the sample set according to a preset ratio to obtain a training set and a validation set;

[0060] A parameter selection module performs a ten-fold cross validation on the training set to obtain an out-of-bag error rate result, and selects corresponding model hyperparameters according to the out-of-bag error rate result;

[0061] Gene acquisition module, used to select variables according to preset importance ranking to obtain target genes;

[0062] A gene ranking module is used to sort the expression values of the target genes to obtain gene expression ranks;

[0063] A model construction module is used to construct a random forest model using the gene expression rank and the model hyperparameters, and verify the results using the validation set to obtain a verified recognition model;

[0064] The recognition module is used to input the sample to be tested into the recognition model to obtain the recognition result.

[0065] Specifically, this embodiment uses gene expression data of liver malignant tumors with microvascular invasion and non-microvascular invasion to obtain differential genes, and further incorporates them into subsequent analysis steps. The differential genes are used to perform ten-fold cross-validation on the training set to obtain the out-of-bag error rate results, and the corresponding model hyperparameters are selected based on the out-of-bag error rate results.

[0066] Furthermore, the gene expression data for microvascular invasion and non-microvascular invasion samples are the aforementioned sequencing data. This involves performing differential analysis on the expression values or expression ranks of all genes to identify differentially expressed genes. The data (gene expression values or re-ranked gene expression ranks) for the selected differentially expressed genes from all genes in the sample are then incorporated into subsequent steps.

[0067] This invention provides a multi-gene panel for predicting microvascular invasion in patients with liver malignancies. This panel utilizes gene expression data from patient tissue samples and a random forest algorithm based on gene expression rankings to predict microvascular invasion in liver malignancies. This method is independent of a specific detection platform or method, allowing it to calculate microvascular invasion prediction using expression data from multi-gene panels obtained using different sequencing methods. This invention provides a basis for timely intervention and treatment for patients at high risk of metastasis.

[0068] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0069] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A system for identifying microvascular invasion of liver malignant tumors, characterized in that: include: A sample acquisition module is used to obtain a tissue sample and perform transcriptome sequencing or proteome sequencing on the tissue sample to obtain initial sequencing data, and to sort the initial sequencing data by gene expression value to obtain gene expression rank, and to determine the gene expression rank or the original value of the gene expression data as the sample sequencing data; A differential analysis module is used to perform differential analysis on all gene expression ranks or original values of gene expression data in the sample sequencing data to obtain differential genes, and then select data of differential genes from all genes in the sample sequencing data as a sample set; A data set allocation module is used to randomly allocate the sample set according to a preset ratio to obtain a training set and a validation set; A parameter selection module performs a ten-fold cross validation on the training set to obtain an out-of-bag error rate result, and selects corresponding model hyperparameters based on the out-of-bag error rate result. Specific model hyperparameters include tree depth, number of trees, and feature selection strategy; A gene acquisition module is used to select variables according to a preset importance ranking to obtain target genes; the target genes include: RANBP1, ARMCX3, RAMP3, CDH17, CTHRC1, TARS, MET, ASRGL1, SLC16A2, NDRG1, COG1, VPS45, PPP1R14A, RAN, TAF9, PCK1, CPXM2, ZNF280D, ST6GAL1, PCM1, CSTA, JMJD6, RCL1, UCK2, ZNF687, GTF2F2, RCCD1, CCDC127, FRK, NAT10, C1orf43, FRYLDUS1L, TTC7B; A gene ranking module is used to sort the expression values of the target genes to obtain the final gene expression rank; A model construction module is used to construct a random forest model using the final gene expression rank and the model hyperparameters, and verify the results using the validation set to obtain a verified recognition model; The recognition module is used to input the sample to be tested into the recognition model to obtain the recognition result.

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