Liver cancer biomarker and application thereof

Abstract

The invention discloses a liver cancer biomarker and application thereof. The liver cancer biomarker comprises MT1G, FOS, TXNRD1 and MYL6. The invention provides a group of liver cancer biomarkers, and a liver cancer risk assessment model is constructed based on the liver cancer biomarkers. The liver cancer biomarker is obtained by synthesizing gene expression level, protein level, single cell analysis and space transcriptome analysis, and the diversity and large sample size of a data set improve the reliability and universality of the liver cancer biomarker and the liver cancer risk assessment model; the sensitivity of the liver cancer risk assessment model in an independent verification data set exceeds 97%, the specificity exceeds 98%, and the liver cancer risk assessment model shows excellent classification performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of bioinformatics, and specifically relates to a liver cancer biomarker and application thereof. Background Art

[0002] Hepatocellular carcinoma (HCC) is the fourth most lethal tumor in the world and one of the malignant tumors with the highest morbidity and mortality. Although traditional treatments such as surgery, chemotherapy, and targeted therapy have achieved certain results in the treatment of liver cancer, its five-year overall survival rate is only 50-70%. In recent years, immunotherapy, especially immune checkpoint inhibitors such as PD-1 / PD-L1 inhibitors, have shown great potential in the treatment of various tumors, bringing new hope for the treatment of liver cancer. However, the liver cancer microenvironment has unique immunosuppressive properties, resulting in a low response rate to immunotherapy and limited clinical benefits. Therefore, it is of great significance to deeply understand the characteristics of the liver cancer immune microenvironment and discover markers that can predict the efficacy of immunotherapy to improve the level of liver cancer immunotherapy.

[0003] The development of multi-omics technologies such as genomics, transcriptomics, and proteomics has provided a new way to analyze the tumor immune microenvironment. Using new technologies such as single-cell sequencing and spatial transcriptome sequencing, tumor heterogeneity and immune cell infiltration can be studied at the single-cell level and spatial dimension, and new immunotherapy-related markers are expected to be discovered. Integrated multi-omics data analysis can also reveal the regulatory relationship between different omics and deepen the understanding of the immune regulatory mechanism of liver cancer. In addition, the combination of omics big data and machine learning methods provides new ideas for building efficient and accurate tumor diagnosis and prognosis prediction models. The diagnostic and prognostic models constructed based on key molecular markers are expected to achieve patient stratification and personalized treatment, and improve the accuracy of liver cancer immunotherapy. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0005] To this end, the present invention proposes a liver cancer biomarker, which can be used to construct a liver cancer risk assessment model, and further used for early diagnosis of liver cancer and promotion of precise immunotherapy for liver cancer.

[0006] The present invention also proposes a method for constructing a liver cancer risk assessment model.

[0007] The present invention also provides a liver cancer risk assessment system.

[0008] The invention also provides an electronic device.

[0009] The present invention also provides a storage medium.

[0010] According to a first aspect of the present invention, a liver cancer biomarker is provided, wherein the liver cancer biomarker comprises MT1G, FOS, TXNRD1 and MYL6.

[0011] According to a second aspect of the present invention, a method for constructing a liver cancer risk assessment model is proposed, the method comprising the following steps:

[0012] S1: Obtain a gene expression dataset of cancer tissues from patients with liver cancer who have received immunotherapy, and screen for differentially expressed genes between patients who respond to and do not respond to immunotherapy;

[0013] S2: Obtain a gene expression dataset of cancer tissues of patients with liver cancer who responded to immunotherapy before and after treatment, and screen for differentially expressed genes between cancer tissues before and after immunotherapy;

[0014] S3: comparing the differentially expressed genes obtained in step S1 and step S2 to obtain genes that are differentially expressed in both sets of gene expression data sets, i.e., immune response-related genes;

[0015] S4: Obtaining a gene expression dataset of cancerous tissues and adjacent normal tissues of liver cancer patients, performing univariate and multivariate logistic regression analysis on the immune response-related genes obtained in step S3 in the dataset to determine differentially expressed genes related to liver cancer, and establishing a risk model;

[0016] The differentially expressed genes associated with liver cancer include MT1G, FOS, TXNRD1 and MYL6.

[0017] In some embodiments of the present invention, the gene expression dataset of cancer tissue of liver cancer patients after immunotherapy in step S1 includes a spatial transcriptome dataset.

[0018] In some embodiments of the present invention, the immunotherapy in step S1 and step S2 is immune checkpoint inhibitor therapy.

[0019] In some embodiments of the present invention, the immune checkpoint inhibitor therapy is selected from one or more of cabozantinib, nivolumab, pembrolizumab, tesimumab and durvalumab.

[0020] In some embodiments of the present invention, the risk model is a logistic regression model.

[0021] In some embodiments of the present invention, the calculation formula of the risk model is: risk score = -27.0469-0.8454×MT1G expression level-1.0688×FOS expression level+2.0823×TXNRD1 expression level+2.5147×MYL6 expression level.

[0022] In some embodiments of the present invention, the MT1G expression level, FOS expression level, TXNRD1 expression level and MYL6 expression level are all values ​​obtained by global scaling and normalization of gene expression levels using a function.

[0023] In some embodiments of the present invention, the global scaling normalization uses a NormalizeData function.

[0024] In some embodiments of the present invention, when the risk score is greater than or equal to 0.4, it is determined to be liver cancer, and when the risk score is less than 0.4, it is determined to be non-liver cancer.

[0025] According to a third aspect of the present invention, a liver cancer risk assessment system is provided, the liver cancer risk assessment system comprising:

[0026] Input unit: used to input the data to be evaluated;

[0027] Analysis unit: a liver cancer risk assessment model constructed by the construction method described in the second aspect of the present invention, used to analyze the data to be assessed in the input unit;

[0028] Evaluation unit: used to display the risk score of liver cancer risk evaluation model;

[0029] When the risk score is greater than or equal to 0.4, it is determined to be liver cancer, and when the risk score is less than 0.4, it is determined to be non-liver cancer.

[0030] According to a fourth aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the construction method described in the second aspect of the present invention is implemented.

[0031] According to a fifth aspect of the present invention, a storage medium is provided, wherein the storage medium stores processor-executable instructions, and the processor-executable instructions, when executed by the processor, are used to execute the construction method described in the second aspect of the present invention.

[0032] The present invention has at least the following beneficial effects:

[0033] The present invention provides a group of liver cancer biomarkers, and a liver cancer risk assessment model is constructed based on these liver cancer biomarkers; the above liver cancer biomarkers are obtained by comprehensive gene expression level, protein level, single cell analysis and spatial transcriptome analysis. The diversity of the data set and the large sample size improve the reliability and universality of the above liver cancer biomarkers and liver cancer risk assessment model; the liver cancer risk assessment model provided by the present invention has a sensitivity of more than 97% and a specificity of more than 98% in an independent validation data set, showing excellent classification performance. The liver cancer biomarkers and liver cancer risk assessment model provided by the present invention not only deepen our understanding of the mechanism of liver cancer immunotherapy, but also provide new ideas for screening patients suitable for immunotherapy and predicting treatment effects. In addition, combining the biomarkers provided by the present invention with existing biomarkers and clinical indicators is expected to develop more accurate diagnostic and prognostic assessment tools, thereby promoting the precision of liver cancer immunotherapy.

[0034] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein:

[0036] Figure 1 This is a schematic diagram of the distribution of various types of cells in the spatial transcriptome data in Example 1 of the present invention;

[0037] Figure 2 The results of the analysis of differentially expressed genes between the immunotherapy response group and the non-response group in the spatial transcriptome data in Example 1 of the present invention are shown in Figure A; wherein Figure A is a malignant cell obtained by merging 7 sample sources using the Harmony algorithm, Figure B is a heat map of the expression patterns of differentially expressed genes in the two groups of samples, Figure C is a GO gene enrichment result of the up-regulated genes in the response group, and Figure D is a GO gene enrichment result of the down-regulated genes in the response group;

[0038] Figure 3 It is a graph of differentially expressed gene analysis results between samples before and after immunotherapy in single-cell transcriptome data in Example 1 of the present invention; wherein, Figure A is the single-cell annotation results of H68a (before treatment) and H68b (after treatment) samples, Figure B is the GO gene enrichment results of up-regulated genes in samples after treatment, and Figure C is the GO gene enrichment results of down-regulated genes in samples after treatment;

[0039] Figure 4It is a graph of the analysis results of differentially expressed genes between samples before and after immunotherapy in the single-cell transcriptome data in Example 1 of the present invention; wherein, Figure A is a heat map of the expression patterns of differentially expressed genes in the two groups of samples, and Figure B is the expression levels of the two genes MT1E and SPP1 in the samples before and after treatment;

[0040] Figure 5 This is a graph showing the expression difference of MT1E in spatial transcriptome data in Example 1 of the present invention;

[0041] Figure 6 This is a graph showing the expression difference of SPP1 in spatial transcriptome data in Example 1 of the present invention;

[0042] Figure 7 The PCA dimensionality reduction analysis results of the four data sets used in Examples 1 and 2 of the present invention are shown in Figure 1; Figure A is GSE14520, Figure B is GSE36376, Figure C is GSE25097, and Figure D is PXD006512;

[0043] Figure 8 The results of using LASSO regression to screen liver cancer biomarkers in Example 1 of the present invention are shown in Figure A. The LASSO regression coefficient of the GSE14520 dataset varies with the penalty parameter, and Figure B shows the LASSO regression coefficient of the GSE36376 dataset varies with the penalty parameter.

[0044] Fig. 9 The results of the analysis of the expression levels of liver cancer biomarkers in different data sets in Example 2 of the present invention are shown in Figure A, Figure B, Figure C, Figure D, and Figure PXD006512.

[0045] Fig.10 This is a graph showing the results of the correlation analysis between TCGA prognosis data and the expression levels of liver cancer biomarkers in Example 2 of the present invention;

[0046] Fig.11 This is a graph showing the results of the correlation analysis between TCGA prognosis data and the expression levels of liver cancer biomarkers in Example 2 of the present invention;

[0047] Fig.12 The results of the association analysis between liver cancer biomarkers and immune microenvironment scores in Example 2 of the present invention are shown in Figure A; Figure A is a heat map of the correlation between liver cancer biomarkers and Stromal score, Immune score and ESTIMATE score, and Figure B is a heat map of the correlation between liver cancer biomarkers and 24 immunotherapy response-related scores;

[0048] Fig.13The ROC curve diagram of the liver cancer risk assessment model verified by four data sets in Example 4 of the present invention; wherein, Figure A is GSE14520, Figure B is GSE36376, Figure C is GSE25097, and Figure D is PXD006512;

[0049] Fig.14 The ROC curve diagram of the liver cancer risk assessment model verified by three data sets in Example 4 of the present invention; wherein, Figure A is GSE76297, Figure B is GSE57957, and Figure C is GSE76427;

[0050] Fig.15 The result diagrams of verifying the risk scores of the liver cancer risk assessment model by using samples in four data sets in Example 4 of the present invention; wherein, Figure A is GSE14520, Figure B is GSE36376, Figure C is GSE25097, and Figure D is PXD006512;

[0051] Fig.16 The result graph of correlation analysis of the risk score of the liver cancer risk assessment model based on the patient prognosis data of the TCGA database in Example 4 of the present invention; wherein, Figure A is the patient survival status, Figure B is the metastasis risk, Figure C is the gender, Figure D is the AJCC staging, Figure E is the age, Figure F is the main tumor size, Figure G is whether there is liver cirrhosis, Figure H is the BCLC staging, Figure I is the CLIP staging, Figure J is the alpha-fetoprotein concentration, and Figure K is the chronic carriers with active viral replication (AVR-CC) and common chronic carriers;

[0052] Fig.17 This is a result diagram of verifying the risk score of the liver cancer risk assessment model based on self-collected clinical liver cancer samples in Example 4 of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the embodiments to clearly and completely describe the concept of the present invention and the technical effects produced, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0054] In the description of the present invention, "several" means more than one, "many" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0055] The following will be combined with the embodiments to clearly and completely describe the concept of the present invention and the technical effects produced, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0056] When a numerical range is disclosed herein, the above range is considered to be continuous and includes the minimum and maximum values ​​of the range, as well as every value between such minimum and maximum values. Further, when a range refers to an integer, every integer between the minimum and maximum values ​​of the range is included. In addition, when multiple ranges are provided to describe features or characteristics, the ranges can be combined. In other words, unless otherwise indicated, all ranges disclosed herein should be understood to include any and all subranges included therein.

[0057] Unless otherwise specified, the reagents, methods and equipment used in the present invention are conventional reagents, methods and equipment in the art.

[0058] Example 1 Screening of liver cancer biomarkers

[0059] In this example, a set of liver cancer biomarkers was obtained by integrating and screening the spatial transcriptome dataset, the single cell transcriptome dataset and the gene expression dataset. The dataset information used is shown in Table 1;

[0060] Table 1 Dataset information used for liver cancer biomarker screening

[0061]

[0062]

[0063] The specific screening methods are as follows:

[0064] 1. Identification of gene sets that are responsive to immunotherapy based on spatial transcriptome data:

[0065] 1) Preprocessing of spatial transcriptome data:

[0066] In order to exclude the influence of other non-malignant cells on the results, the spots of the spatial transcriptome were first screened, and only those spots annotated as malignant tumor cells were retained. The immune infiltration areas in the local microenvironment of the tumor were identified for subsequent screening of differentially expressed genes. The screening results are as follows: Figure 1 As shown; among them, HCC01-HCC04 are samples that respond to immunotherapy, and HCC05-HCC07 are samples that do not respond to immunotherapy;

[0067] 2) Analysis of spatial transcriptome data:

[0068] The data of GSE238264 were normalized using the SCTransform method of the R package 'Seurat'. This method can not only standardize the data, but also detect highly variable genes and store the results in a specific assay (Spatial). The Harmony method was used to integrate the SCT data of 7 patient samples. This method effectively corrected the technical differences between samples and ensured the comparability of the data. The Harmony algorithm was used to unify the data of 4 samples that responded to immunotherapy and 3 samples that did not respond to immunotherapy into the same reference space (e.g. Figure 2 A);

[0069] On this basis, we further identified 566 genes with significant differences in expression between patients who responded to immunotherapy and those who did not respond to immunotherapy, namely the gene set of immunotherapy response based on spatial transcriptome (e.g. Figure 2 In the above immunotherapy response gene set, 534 (94.34%) genes were up-regulated in the response group, and only 32 (5.65%) genes were down-regulated.

[0070] Functional enrichment analysis was performed on the above 566 immunotherapy-responsive genes, and the results were as follows: Figure 2 C and Figure 2 As shown in D;

[0071] Depend on Figure 2 C shows that the up-regulated genes are enriched in biological processes such as leukocyte-mediated immune response, cell adhesion, and T cell activation; Figure 2 D shows that the down-regulated genes are significantly enriched in pathways such as humoral immune response and complement activation.

[0072] 2. Identify gene sets that respond to immunotherapy based on single-cell transcriptome data:

[0073] 1) Preprocessing of single-cell transcriptome data:

[0074] The R package 'Seurat' was used to process the data in 10X Genomics format. After the data were imported, quality control was performed, including detecting the UMI count, gene count, and proportion of mitochondrial genes for each cell to identify and remove low-quality cells;

[0075] Then, the NormalizeData function was used to normalize the data and perform global scaling normalization on the gene expression levels of each cell. The 'FindVariableFeatures' method was used to identify highly variable genes (HVGs), which is also an important step in single-cell preprocessing and helps to highlight biological signals in subsequent analysis.

[0076] Finally, use tSNE or UMAP method to perform linear or nonlinear dimensionality reduction on the data to visualize the results.

[0077] 2) Analysis of single-cell transcriptome data:

[0078] Since the H68 patient in the single-cell dataset GSE151530 responded to immunotherapy, only two samples of the H68 patient were retained, one from before treatment (H68a) and one from after treatment (H68b). The results after dimensionality reduction analysis are as follows: Figure 3 As shown in A;

[0079] By comparing H68a and H68b, 593 significantly differentially expressed genes were found, i.e., the gene set of immunotherapy response based on single-cell transcriptome; among them, 406 genes were upregulated after treatment, and 187 genes were downregulated after treatment;

[0080] GO functional enrichment analysis showed that the up-regulated genes were mainly enriched in the biological processes of terpenoid, fatty acid and alcohol metabolism (such as Figure 3 B), while the down-regulated genes were mainly enriched in biological processes such as cytoplasmic translation, ribosome biogenesis and ATP synthesis (such as Figure 3 C);

[0081] Comparing the gene set of immunotherapy response identified in the single-cell data with the gene set of immunotherapy response identified in the spatial transcriptome data, we found that 51 genes showed significant differential expression in both datasets (e.g. Figure 4 These common differentially expressed genes include SPP1 and MT1E. The differential expression of SPP1 and MT1E was verified in spatial transcriptome data and single-cell transcriptome data. The results are shown in Figure 2. Figure 4 B. Figure 5 and Figure 6 Among them, MT1E was the most significantly differentially expressed gene in the spatial transcriptome, while SPP1 was significantly upregulated in the malignant cells of the four responding samples in the spatial transcriptome.

[0082] 3. Screening of liver cancer biomarkers based on immunotherapy response gene sets:

[0083] The two data sets GSE14520 and GSE36376 involved in this embodiment each contain a different number of samples. Specifically, the GSE14520 data set contains 247 cancer tissue samples and 239 adjacent normal samples; the GSE36376 data set contains 240 cancer tissue samples and 193 adjacent normal samples.

[0084] This example uses the PCA method to show the structure of the above data set (such as Figure 7 As shown in A and B in Figure 7 The displayed results show that there is a clear separation between cancer tissue and adjacent normal samples, indicating that the data of the two data sets are representative. In the future, liver cancer biomarkers can be further screened based on these two data sets.

[0085] 1) Data preprocessing: remove samples of benign liver diseases (such as cirrhosis) and only keep samples of cancer tissue and adjacent adjacent tissue.

[0086] 2) LASSO regression analysis to screen liver cancer biomarkers:

[0087] LASSO regression technology was applied to the GSE14520 and GSE36376 datasets to screen the aforementioned 51 immunotherapy response gene sets. The LASSO regression coefficients of the two datasets changed with the penalty parameters as shown in the figure below: Figure 8 A and Figure 8 As shown in B; this step significantly reduced the number of candidate genes and screened out the genes most relevant to liver cancer classification. Through this process, four key genes MT1G, FOS, TXNRD1 and MYL6 that play an important role in HCC classification were successfully identified in the spatial transcriptome data GSE238264 and the single-cell transcriptome data GSE151530, namely the liver cancer biomarkers provided by the present invention.

[0088] Example 2 Validation of liver cancer biomarkers

[0089] This example uses four gene expression datasets and prognostic data from the TCGA and ICGC databases to verify the biomarkers provided in Example 1. The data information used is shown in Table 2. Among them, the PCA dimensionality reduction results of the datasets GSE25097 and PXD006512 are shown in Table 2. Figure 7 As shown in C and D, it can be seen that there is an obvious separation between cancer tissue and adjacent normal samples, indicating that the data of the two data sets are well representative.

[0090] Table 2 Dataset information used for liver cancer biomarker validation

[0091]

[0092] 1. Validation of liver cancer biomarkers in different datasets:

[0093] The expression of liver cancer biomarkers provided in Example 1 in different samples was analyzed in the data sets GSE14520, GSE36376, GSE25097 and PXD006512. The results are as follows: Fig. 9 shown.

[0094] Depend on Fig. 9 It can be seen that the expression levels of MT1G and FOS were significantly downregulated in the cancer tissues of the above four data sets, while the expression levels of TXNRD1 and MYL6 were significantly upregulated.

[0095] 2. Validation of liver cancer biomarkers in TCGA data:

[0096] The TCGA HCC dataset provides very comprehensive clinical characteristics of patients, which helps to study the relationship between genes and clinical characteristics. This example analyzes the relationship between the expression level of liver cancer biomarkers provided in Example 1 and the status (dead or alive), T / N / M stage, pathological stage and grade of HCC patients based on TCGA data. The results are as follows: Fig.10 and Fig.11 shown.

[0097] Depend on Fig.10 It can be seen that although some analysis results are not statistically significant (p>0.05), MYL6 and TXNRD1 are positively correlated with (dead or alive), T / N / M stage, pathological stage (Stage), and grade; MT1G and FOS are negatively correlated with (dead or alive), T / N / M stage, pathological stage (Stage), and grade. The above results directly or indirectly indicate the availability and accuracy of the liver cancer biomarkers provided in Example 1.

[0098] Depend on Fig.11 It can be seen that MT1G, FOS, MYL6 and TXNRD1 have no statistical significance with the patient's stage, but the up-regulated expression of MYL6 and TXNRD1 is gradually up-regulated with the progression of the disease stage (positive correlation); the down-regulated expression of MT1G and FOS is gradually down-regulated with the progression of the disease stage (negative correlation). The above results directly or indirectly indicate the availability and accuracy of the liver cancer biomarkers provided in Example 1.

[0099] 3. Correlation analysis between liver cancer biomarkers and immune microenvironment:

[0100] The ESTIMATE algorithm can provide researchers with scores for tumor purity, stromal cell levels, and immune cell infiltration levels in tumor tissues based on expression data. This example analyzes the correlation between MT1G, FOS, MYL6, and TXNRD1 and Stromalscore, Immune score, and ESTIMATE score. The results are as follows: Fig.12 As shown in A.

[0101] Depend on Fig.12 A shows that MYL6 and TXNRD1 are significantly positively correlated with Stromal score, Immune score and ESTIMATE score, while MT1G and FOS are negatively correlated. The above results indicate that these genes may play an important role in regulating the infiltration of stromal cells and immune cells in the tumor microenvironment.

[0102] Subsequently, this example evaluated the relationship between MT1G, FOS, MYL6 and TXNRD1 and 24 immunotherapy response-related signatures (the 24 immunotherapy response-related signature data are from reference Thorsson, Vésteinn et al. "The Immune Landscape of Cancer." Immunity vol. 48, 4 (2018): 812-830.e14.doi: 10.1016 / j.immuni.2018.03.023). The results are as follows: Fig.12 As shown in B.

[0103] Depend on Fig.12 B shows that the expression of MYL6 and TXNRD1 is significantly positively correlated with these signature scores, while MT1G and FOS are significantly negatively correlated. The above results reveal that these genes may play an important role in regulating the tumor immune microenvironment and affecting the effect of immunotherapy.

[0104] Example 3 Establishment of liver cancer risk assessment model

[0105] This example establishes a liver cancer risk assessment model based on the liver cancer biomarkers provided in Example 1 and the data sets GSE14520 and GSE36376. The specific method is as follows:

[0106] This example uses the R software package glmnet to implement LASSO analysis, and uses the cv.glmnet function for cross validation, which automatically performs LASSO regression and fits multiple models on a series of regularization parameters λ; cross validation can evaluate the performance of the model under different λ values, and select the λ that minimizes the mean square error (MSE) as the optimal regularization parameter; finally, after determining the optimal λ value, the glmnet function is used again, this time specifying the optimal λ value obtained by cross validation, to obtain the final LASSO regression model, and the risk score calculation formula of the obtained liver cancer risk assessment model is as follows:

[0107] HCC risk score = -27.0469 - 0.8454 × MT1G expression level - 1.0688 × FOS expression level + 2.0823 × TXNRD 1 expression level + 2.5147 × MYL6 expression level.

[0108] The expression levels of the above four genes are all global normalized data.

[0109] Example 4 Validation of liver cancer risk assessment model

[0110] Based on the liver cancer risk assessment model provided in Example 3, this example uses different data sets and clinical data to verify it. The information of the data used is shown in Table 3.

[0111] Table 3 Data used to validate the liver cancer risk assessment model

[0112]

[0113] When the optimal cutoff value of the performance of the model was 0.4 in the validation set GSE36376 and GSE25097, the sensitivity and specificity of the model type were the largest using the liver cancer risk assessment model calculation formula provided in Example 3. When the calculated risk score was ≥ 0.4, it was determined to be liver cancer, and < 0.4 was determined to be non-liver cancer.

[0114] Table 4 Performance verification of liver cancer risk assessment model in different datasets

[0115]

[0116] In the dataset GSE14520, the AUC of the liver cancer risk assessment model reached 0.99 [95% CI: 0.99-1], and the optimal sensitivity and specificity were 97.3% and 97.3%, respectively; in the dataset GSE36376, the AUC reached 0.99 [95% CI: 0.98-1], and the optimal sensitivity and specificity were 97.9% and 98.4%, respectively, showing excellent classification performance; the ROC curve is shown in Figure 2. Fig.13 A and Fig.13As shown in B.

[0117] To further verify the generalization ability of the model, the liver cancer risk assessment model was validated on the independent dataset GSE25097. The results showed that the model still maintained good performance on this dataset, with an AUC of 0.99 [95% CI: 0.98-1], and the optimal sensitivity and specificity were 93.3% and 98.8%, respectively. The ROC curve is shown in Figure 2. Fig.13 As shown in C.

[0118] In addition, this example also validated the liver cancer risk assessment model on the proteomics dataset PXD006512, and found that the AUC value of the model was 0.79 [95% CI: 0.72-0.85], the optimal sensitivity and specificity were 60.6% and 87.5%, respectively, and the corresponding ROC curve was as follows: Fig.13 As shown in D; the results show that the liver cancer risk assessment model provided by the present invention also has good classification performance at the protein level, further confirming the potential of MT1G, FOS, MYL6 and TXNRD1 as liver cancer diagnostic markers.

[0119] This embodiment also verifies the liver cancer risk assessment model in independent data sets GSE76297, GSE57957 and GSE76427; in the data set GSE76297, the AUC of the liver cancer risk assessment model reaches 0.96 (0.94-0.98), and the optimal sensitivity and specificity are 95.4% and 94.3%, respectively; in the data set GSE57957, the AUC reaches 0.97 (0.93-1), and the optimal sensitivity and specificity are 97.4% and 92.3%, respectively; in the data set GSE76427, the AUC reaches 0.97 (0.95-0.99), and the optimal sensitivity and specificity are 94.2% and 93.9%, respectively; the corresponding ROC curves are shown in FIG. Fig.14 As shown, the results show excellent classification performance.

[0120] This example also verifies the clinical significance of this diagnostic model by performing correlation analysis on the liver cancer risk assessment model and four data sets. The results are as follows: Fig.15 As shown. Fig.15 It can be seen that the risk score calculated by the liver cancer risk assessment model is significantly increased in HCC patients, and the risk score is positively correlated with liver cancer patients.

[0121] This example further associates the liver cancer risk assessment model with TCGA clinical data (including sample type, gender, age, and stage), and the results are as follows: Fig.16 As shown. Fig.16 It can be seen that the risk score of HCC samples is not affected by factors such as patient age, gender, and tumor stage. Fig.16 It can also be seen that there is almost no difference in risk scores among samples with different clinical characteristics, indicating that the model has high efficiency in clinical applications.

[0122] Finally, this example also collected 80 liver cancer surgical samples from Shenzhen People's Hospital for experimental verification, extracted the total RNA of the liver cancer samples and performed subsequent reverse transcription and qPCR detection, and verified the model based on the expression levels of MT1G, FOS, MYL6 and TXNRD1 and the liver cancer risk assessment model calculation formula provided in Example 3. The results are as follows: Fig.17 As shown; the results showed that the diagnostic model constructed based on these four genes has a good efficacy in diagnosing liver cancer, with an AUC of 0.89, and the optimal sensitivity and specificity of 88.3% and 83.6%, respectively, which further verified the reliability of this model.

[0123] Example 5 A liver cancer risk assessment system

[0124] This embodiment provides a liver cancer risk assessment system, which specifically includes the following four units:

[0125] Input unit: used to input the data to be evaluated;

[0126] An analysis unit: analyzing the data to be evaluated in the input unit based on the liver cancer risk assessment model established in Example 3;

[0127] Evaluation unit: used to display the risk score of liver cancer risk evaluation model;

[0128] When the risk score is greater than or equal to 0.4, it is determined to be liver cancer, and when the risk score is less than 0.4, it is determined to be non-liver cancer.

[0129] Embodiment 6 An electronic device

[0130] This embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor;

[0131] When the processor executes the computer program, the method for establishing a liver cancer risk assessment model provided in Example 3 is implemented.

[0132] Embodiment 7 A storage medium

[0133] This embodiment provides a storage medium storing instructions executable by a processor, and the instructions executable by the processor are used to execute the method for establishing a liver cancer risk assessment model provided in Example 3 when executed by the processor. In summary, the present invention focuses on multi-omics data related to immunotherapy, discovers a batch of new immunotherapy-related genes, and constructs a liver cancer risk assessment model. These findings deepen the understanding of the response mechanism of liver cancer immunotherapy, provide new ideas for screening patients suitable for immunotherapy and predicting efficacy, and are expected to promote the precision of liver cancer immunotherapy.

[0134] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the purpose of the present invention. In addition, the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

Claims

1. A liver cancer biomarker, characterized in that: The liver cancer biomarkers include MT1G, FOS, TXNRD1 and MYL6.

2. A method for constructing a liver cancer risk assessment model, characterized in that: The construction method comprises the following steps: S1: Obtain a gene expression dataset of cancer tissues from patients with liver cancer who have received immunotherapy, and screen for differentially expressed genes between patients who respond to and do not respond to immunotherapy; S2: Obtain a gene expression dataset of cancer tissues of patients with liver cancer who responded to immunotherapy before and after treatment, and screen for differentially expressed genes between cancer tissues before and after immunotherapy; S3: comparing the differentially expressed genes obtained in step S1 and step S2 to obtain genes that are differentially expressed in both sets of gene expression data sets, i.e., immune response-related genes; S4: Obtaining a gene expression dataset of cancerous tissues and adjacent normal tissues of liver cancer patients, performing univariate and multivariate logistic regression analysis on the immune response-related genes obtained in step S3 in the dataset to determine differentially expressed genes related to liver cancer, and establishing a risk model; The differentially expressed genes associated with liver cancer include MT1G, FOS, TXNRD1 and MYL6.

3. The construction method according to claim 2, characterized in that: The gene expression dataset of cancer tissues of liver cancer patients after immunotherapy described in step S1 includes a spatial transcriptome dataset.

4. The construction method according to claim 3, characterized in that: The immunotherapy described in step S1 and step S2 is immune checkpoint inhibitor therapy.

5. The construction method according to claim 2, characterized in that: The calculation formula of the risk model is: risk score = -27.0469 - 0.8454 × MT1G expression - 1.0688 × FOS expression + 2.0823 × TXNRD1 expression + 2.5147 × MYL6 expression.

6. The construction method according to claim 5, characterized in that: The MT1G expression level, FOS expression level, TXNRD1 expression level and MYL6 expression level are all values ​​obtained by global scaling and normalization of gene expression levels using a function.

7. The construction method according to claim 6, characterized in that: When the risk score is greater than or equal to 0.4, it is determined to be liver cancer, and when the risk score is less than 0.4, it is determined to be non-liver cancer.

8. A liver cancer risk assessment system, characterized in that: The liver cancer risk assessment system comprises: Input unit: used to input the data to be evaluated; Analysis unit: a liver cancer risk assessment model constructed by the construction method according to any one of claims 2 to 7, used for analyzing the data to be assessed in the input unit; Evaluation unit: used to display the risk score of liver cancer risk evaluation model; When the risk score is greater than or equal to 0.4, it is determined to be liver cancer, and when the risk score is less than 0.4, it is determined to be non-liver cancer.

9. An electronic device, characterized in that: The electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the construction method as claimed in any one of claims 2 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores processor-executable instructions, and the processor-executable instructions are used to execute the construction method described in any one of claims 2 to 7 when executed by the processor.

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