CN111778336A - Gene marker combination and application for comprehensive quantitative assessment of tumor microenvironment - Google Patents

Abstract

The gene marker combination for quantitative evaluation of the tumor microenvironment disclosed by the invention can well carry out comprehensive quantitative evaluation on the tumor microenvironment, the prediction accuracy of the evaluation result on tumor metastasis is 100%, and the prediction accuracy on tumor recurrence is over 80%. The invention disassembles and refines the tumor microenvironment into four constituent factors, and searches related genes of each factor through NCBI. The genes are simplified and refined according to functions to form a gene marker combination for quantitative evaluation of the tumor microenvironment. The four components comprise a vascular proliferation index, a chemokine expression index, an immune cell infiltration index and a tumor growth and invasion index, thereby forming a gene marker combination for comprehensive quantitative evaluation of a tumor microenvironment.

Description

Gene marker combination and application for comprehensive quantitative assessment of tumor microenvironment

technical field

The invention belongs to the field of molecular biomedicine, and particularly relates to comprehensive quantitative assessment of tumor microenvironment.

Background technique

At present, the pathological research of tumors, the research on drugs and programs for the treatment of tumors are all aimed at the tumor cells themselves, such as staining of pathological sections for pathological research, drawing of tumor gene mutation maps, radiotherapy and chemotherapy, and targeted therapy for the treatment of tumors. The research on the tumor microenvironment on which survival depends is obviously lagging behind. British surgeon Stephen Paget put forward the "seed and soil" hypothesis of tumor metastasis in 1889, which can be described as the origin of the tumor microenvironment. However, this hypothesis has not been scientifically confirmed. It was not until the widespread clinical application of PD-1/PD-L1 immune checkpoint inhibitors and the review of anti-vascular targeted therapy that the research on the tumor microenvironment was re-emphasized. . Even now, the research on the tumor microenvironment is still not systematic. In fact, the current research on the tumor microenvironment is more at the conceptual level, unable to accurately describe and quantitatively evaluate it. Some studies only sporadically involve some factors related to the tumor microenvironment, such as inferring the composition of tumor immune cells based on the expression of tumor immune response-related genes. Such studies are too specific and do not comprehensively reflect the real state of the tumor microenvironment, resulting in inconsistent or even diametrically opposite research results, making it difficult to apply the tumor microenvironment as a target/indicator to clinical practice. The tumor microenvironment is a complex system (with complex molecular and cellular components and various factors involved). Whether it can be comprehensively and accurately evaluated and how to evaluate it is related to whether the tumor microenvironment can play a role in clinical research and clinical applications. There is no method and scheme for comprehensively evaluating the state of the tumor microenvironment. The present invention creatively proposes to decompose the tumor microenvironment into four constituent factors, and screen out a combination of gene markers to comprehensively and quantitatively evaluate the tumor microenvironment, so that we can A more specific understanding of the tumor microenvironment, more accurate measurement of the tumor microenvironment, and application of this to clinical practice, bringing clinical value.

SUMMARY OF THE INVENTION

The invention solves the problem of comprehensive evaluation and quantitative evaluation of the tumor microenvironment, so that the tumor microenvironment can play a better role in clinical practice as a measurable biomarker.

According to one aspect of the present invention, a combination of gene markers for comprehensive quantitative assessment of tumor microenvironment is provided, the combination of gene markers includes an angiogenesis index gene marker, a chemokine expression index gene marker, and an immune cell infiltration index Gene markers and tumor growth and invasion markers gene markers.

According to one aspect of the present invention, a combination of gene markers for comprehensive quantitative assessment of tumor microenvironment is also provided. FLT1, HIF1A, PDGFB, SERPINB5, TYMP, VEGFA, VEGFB and VEGFC total 16 genes;

The above-mentioned chemokine expression index gene markers include CCL1, CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL1, CXCL2, CXCL3, CXCL8, CXCL9, CXCL10, CXCL11, CXCL12, CXCL13 a total of 18 genes;

The above-mentioned immune cell infiltration index gene markers: IDO1, HLA-DRA, STAT1, IFNG, PRF1, GZMA, GZMB, NKG7, GZMH, KLRK1, KLRB1, KLRD1, CTSW, GNLY, CD14, CD15, CD19, CD68, CD163, CD33 , CEACAM8, CD80, CD86, BATF3, TNFRSF17, CD20, TNFRSF4, CD4, TNFRSF9, CD8A, CD8B, LAG3, CD39, CXCR5, TBX21, FOXP3, CD45RO a total of 37 genes;

The above tumor growth and invasion index gene markers: CDH1, CTNNB1, EPCAM, ITGAM, ITGAV, ITGAX, MACC1, MMP1, MMP2, MMP3, MMP9, MMP11, MMP13, MMP14, MKI67, MYC, PLAU, RAN, SNAI1, SNAI2, TIMP1, TNC, TWIST1, ZEB1, ZEB2 have a total of 25 genes.

According to one aspect of the present invention, an application of the above-mentioned combination of gene markers is also provided, and the above-mentioned combination of gene markers is applied to comprehensive quantitative assessment of tumor microenvironment.

According to one aspect of the present invention, there is also provided a kit for predicting the accuracy of tumor metastasis and/or tumor recurrence, the kit comprising probes for detecting the above-mentioned combination of gene markers.

According to one aspect of the present invention, there is also provided a method for establishing a comprehensive quantitative assessment model for tumor microenvironment. The above-mentioned method for establishing a comprehensive quantitative assessment model for tumor microenvironment uses the above-mentioned combination of gene markers including angiogenesis index gene markers Gene markers, chemokine expression index gene markers, immune cell infiltration index gene markers, and tumor growth and invasion index gene markers were used as markers for comprehensive quantitative assessment of tumor microenvironment.

According to one aspect of the present invention, the above-mentioned method for establishing a comprehensive quantitative assessment model for tumor microenvironment, the establishment method performs the gene expression data and clinical data (staging/recurrence/metastasis) in the GEO database including the above-mentioned combination of gene markers. For analysis, first select the first 150 samples in GSE62254[HG-U133_Plus_2] as the training set, use the MAS5 method for background correction and normalization, and obtain the gene expression values of 96 genes. When the gene has multiple probes, the maximum value is taken as the The expression value of its gene, log2 logarithm transformation of the gene expression value, using logistic regression to calculate the weight value.

According to one aspect of the present invention, in the above-mentioned method for establishing a comprehensive quantitative assessment model for tumor microenvironment, the weight values of the gene markers are as follows:

ANG weight value -20.1306

ANGPT1 weight value -34.0557

ANGPT2 weight value 28.6868

ANGPTL4 weight value -7.5101

BATF3 weight value -31.0749

CCL1 weight value 13.6034

CCL18 weight value 10.4873

CCL19 weight value -20.9738

CCL2 weight value 11.7576

CCL21 weight value -5.1514

CCL3 weight value 54.0877

CCL4 weight value - 53.5021

CCL5 weight value 27.601

CCL8 weight value -7.8325

CD14 weight value 49.8048

CD163 weight value 20.646

CD19 weight value 21.3692

CD33 weight value 4.0713

CD4 weight value 9.214

CD68 weight value - 24.3628

CD80 weight value 30.8515

CD86 weight value 70.5589

CD8A weight value 5.4726

CD8B weight value 7.7914

CDH1 weight value -5.3397

CEACAM8 weight value 0.21

CTNNB1 weight value -20.1479

CTSW weight value -6.4395

CXCL1 weight value -20.0591

CXCL10 weight value 29.703

CXCL11 weight value -13.3524

CXCL12 weight value 19.7961

CXCL13 weight value -20.5527

CXCL2 weight value 13.4255

CXCL3 weight value 23.3393

CXCL8 weight value - 43.1623

CXCL9 weight value 9.0169

CXCR5 weight value 5.4856

DLL4 weight value 17.0118

EDN1 weight value -4.8511

ENTPD1 weight value 3.4117

EPCAM weight value -33.8795

FGF1 weight value 25.9813

FGF2 weight value -12.3332

FLT1 weight value -31.7469

FOXP3 weight value 13.9705

FUT4 weight value 13.9706

GNLY weight value -10.0842

GZMA weight value 35.9977

GZMB weight value -9.9711

GZMH weight value 15.305

HIF1A weight value - 125.904

HLA-DRA weight value -51.5725

IDO1 weight value -7.3857

IFNG weight value -3.9568

ITGAM weight value -20.4378

ITGAV weight value 91.4138

ITGAX weight value -17.7399

KLRB1 weight value 6.4642

KLRD1 weight value -27.2995

KLRK1 weight value - 31.2776

LAG3 weight value 5.9364

MACC1 weight value 4.823

MKI67 weight value - 28.3144

MMP1 weight value - 19.0372

MMP11 weight value 9.8452

MMP13 weight value 3.3756

MMP14 weight value 29.6593

MMP2 weight value - 25.6822

MMP3 weight value 16.0266

MMP9 weight value 8.0522

MS4A1 weight value 1.1736

MYC weight value 20.084

NKG7 weight value -31.6593

PDGFB weight value 35.2784

PLAU weight value 20.9008

PRF1 weight value 46.0077

PTPRC weight value -108.985

RAN weight value 5.157

SERPINB5 weight value 18.3407

SNAI1 weight value -10.2615

SNAI2 weight value 7.6711

STAT1 weight value 18.7689

TBX21 weight value 7.2855

TIMP1 weight value 8.1053

TNC weight value -27.6527

TNFRSF17 weight value 28.0124

TNFRSF4 weight value -19.4462

TNFRSF9 weight value 7.2481

TWIST1 weight value 12.0057

TYMP weight value -15.5035

VEGFA weight value 15.2587

VEGFB weight value -15.0916

VEGFC weight value 1.8606

ZEB1 weight value 47.2097

ZEB2 weight value -15.7898.

According to one aspect of the present invention, the above-mentioned method for establishing a comprehensive quantitative assessment model for tumor microenvironment uses a logistic classifier, the logistic classifier is a classifier based on a generalized linear model, and the linear discriminant formula is TMEscore=∑ _i ω _i x _i , where ω _i is the weight value of each gene, _xi is the expression value of the gene, and TMEscore represents the quantitative evaluation value of the tumor microenvironment.

According to one aspect of the present invention, in the above-mentioned method for establishing a comprehensive quantitative assessment model of tumor microenvironment, if the TMEscore in the sample is greater than the threshold value, it is classified as metastasis, otherwise, it is classified as non-metastatic.

According to one aspect of the present invention, in the above-mentioned method for establishing a comprehensive quantitative assessment model for tumor microenvironment, the threshold of the logistic classifier is -427.9891.

The combination of gene markers for quantitative evaluation of tumor microenvironment disclosed in the present invention can well perform comprehensive quantitative evaluation of tumor microenvironment. The prediction accuracy is over 80%.

The above description is only a combination of gene markers for quantitative assessment of tumor microenvironment disclosed by the present invention, which can comprehensively and quantitatively assess the tumor microenvironment, and the assessment results can accurately predict tumor metastasis. The rate of tumor recurrence is 100%, and the prediction accuracy of tumor recurrence is more than 80%.

In order to have a clearer understanding of the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention and the accompanying drawings are described in detail below.

Description of drawings

Fig. 1 is ROC AUC in the embodiment 2 of the present invention;

Fig. 2 is ROC AUC in the embodiment of the present invention 3;

Fig. 3 is another ROC AUC in embodiment 3 of the present invention;

Fig. 4 is the transfer ROC diagram in the embodiment 4 of the present invention;

Fig. 5 is another ROC diagram in the embodiment 4 of the present invention;

Fig. 6 is GSE57303ROC diagram in the embodiment 4 of the present invention;

FIG. 7 is a ROC diagram of GSE8167 in Example 4 of the present invention.

Detailed ways

The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following examples are intended to illustrate the present invention, but not to limit the scope of the present invention.

Example 1 Selection of Gene Markers

The present invention defines four components of tumor microenvironment (TME) (the microenvironment that tumor cells rely on has an incalculable impact on tumor cells, such as blood oxygen provided by the vascular system affects the metabolism of tumor cells, the body Surveillance and tolerance of tumor cells by immune cells, restriction of tumor growth and metastasis by stromal cells and extracellular matrix, etc. In order to better grasp the tumor microenvironment, it was disassembled and refined into four factors, and searched through NCBI Relevant genes of each factor. These genes are simplified and refined according to their functions to form a combination of gene markers for quantitative assessment of tumor microenvironment. These four components include angiogenesis indicators, chemokine expression indicators, Immune cell infiltration indicators, tumor growth and invasion indicators, thereby forming a combination of gene markers for comprehensive quantitative assessment of tumor microenvironment, including the following gene markers:

I. Angiogenesis index gene markers: ANG, ANGPT1, ANGPT2, ANGPTL4, DLL4, EDN1, FGF1, FGF2, FLT1, HIF1A, PDGFB, SERPINB5, TYMP, VEGFA, VEGFB, VEGFC, a total of 16 genes

II. Chemokine expression index gene markers: CCL1 CCL2 CCL3 CCL4 CCL5 CCL8CCL18CCL19CCL21CXCL1 CXCL2 CXCL3 CXCL8 CXCL9 CXCL10 CXCL11 CXCL12 CXCL13 a total of 18 genes

III. Immune cell infiltration indicator gene markers: IDO1 HLA-DRASTAT1 IFNG PRF1GZMAGZMBNKG7 GZMHKLRK1 KLRB1 KLRD1 CTSW GNLY CD14 CD15 CD19 CD68 CD163CD33CEACAM8 CD80 CD86 BATF3TNFRSF17 CD20 TNFRSF4 CD4 TNFRSF9 CD8A CD8B LAG3 CD35 CD34 CXRO FO 7 TBX2 genes

IV. Tumor growth and invasion index gene markers: CDH1 CTNNB1 EPCAM ITGAM ITGAVITGAXMACC1 MMP1MMP2 MMP3 MMP9 MMP11 MMP13 MMP14 MKI67MYC PLAU RAN SNAI1 SNAI2TIMP1 TNC TWIST1 ZEB1 ZEB2 a total of 25 genes

Example 2 Construction of the classifier model

Through the combination of gene markers for each component of the immune microenvironment (hereinafter referred to as 96 genes), the following applications are carried out:

①Analyze the gene expression data and clinical data (staging/recurrence/metastasis) in the GEO database containing the above-mentioned combination of gene markers. First, the first 150 samples in GSE62254[Affymetrix HG-U133_Plus_2] were selected as the training set, and the MAS5 method was used for background correction and normalization to obtain the gene expression values of 96 genes. If a gene has multiple probes, take the maximum value as the expression value of its gene, and perform log2 log transformation on the gene expression value. Use logistic regression to calculate the weights.

②Because the clinical data of tumors, such as recurrence or metastasis, are either one or the other, the probability distribution conforms to Bernouli distribution, so we use a binary classifier—logistic classifier. This is a classifier based on a generalized linear model, and its linear discriminant formula TMEscore=∑ _i ω _i x _i , where ω _i is the weight value of each gene, _xi is the expression value of the gene, and TMEscore represents the tumor microenvironment. Quantitative evaluation value. A sample is classified as transferred if its TMEscore > threshold, otherwise it is classified as not transferred. The threshold for the logistic classifier was obtained by maximum likelihood estimation (MLE) and was -427.9891.

The judgment accuracy of the classifier on the training set is: 100%

Classifier Accuracy logistic classifier 100

The sensitivity and specificity by cross-validation are:

Explanation from the above table: For example, for transfer,

Sensitivity is the correct rate at which metastatic samples are judged to be metastatic.

Specificity is the correct rate at which non-transfer samples are judged as non-transfer.

PPV (Positive Predictive Value) is the probability that the samples that are judged to be transfer samples are actually transfer samples.

NPV (Negative Predictive Value) is the probability that the samples judged to be non-metastatic are truly non-metastatic.

The corresponding logistic classifier ROC AUC reaches 1.0, as shown in Figure 1.

Example 3 Validation of the classifier model

In order to test the stability of the 96-gene markers used to construct the classifier, and to prevent the problem of over-fitting of the markers, another part of the dataset of GSE62254 [HG-U133_Plus_2] was used to perform prediction validation on the markers.

The judgment accuracy rate for the prediction set GSE62254 is 100%

Classifier Accuracy logistic classifier 100

The sensitivity and specificity by cross-validation are:

The corresponding logistic classifier ROC AUC reaches 1.0, as shown in Figure 2.

In order to further test the stability of the marker used to construct the classifier, and also to prevent the problem of over-fitting of the marker, the GSE57303 dataset and its own 22 tumor samples (clinically diagnosed as gastric cancer / gastroesophageal cancer, of which 9 13 cases were transferred, 13 cases were not transferred) data to verify the marker.

The judgment accuracy rate for the prediction set GSE57303 and its own 22 tumor samples is 100%

Classifier Accuracy logistic classifier 100

The sensitivity and specificity by cross-validation are:

The corresponding logistic classifier ROC AUC reaches 1.0, as shown in Figure 3.

Example 4

As a comparison, the 25-gene tumor microenvironment comprehensive quantitative assessment model made by the inventors earlier

The first 150 samples of GSE62254 were also used as the training set, and the MAS5 method was used for background correction and normalization to obtain the gene expression values of 25 genes. If a gene has multiple probes, take the maximum value as the expression value of its gene, and perform log2 log transformation on the gene expression value. Use logistic regression to calculate weights and build a model. Predict other prediction sets through the established model to obtain the prediction accuracy. Through the above verification, a grouped ROC chart was obtained to evaluate its diagnostic sensitivity and specificity.

Transfer:

Screening of training set markers

According to the expression value and grouping situation in the GSE62254[HG-U133_Plus_2] sample, using logistic, the training set is classified into two groups of transfer and non-transfer, and its weight value coefficient is obtained:

Set the weight value of each gene as ω _i , and the expression value of the gene as _xi , then a sample whose ∑ _i ω _i x _i > the threshold value is classified as transfer. The threshold for the classifier is -0.735.

Among them, the judgment accuracy of these markers on the training set is:

Classifier Accuracy Classifier 94

The sensitivity and specificity by cross-validation are:

Explanation from the above table: For example, for transfer,

Sensitivity is the correct rate at which metastatic samples are judged to be metastatic.

Specificity is the correct rate at which non-transfer samples are judged as non-transfer.

PPV (Positive Predictive Value) is the probability that the samples that are judged to be transfer samples are actually transfer samples.

NPV (Negative Predictive Value) is the probability that the samples judged to be non-metastatic are truly non-metastatic.

The corresponding logistic classifier ROC diagram is shown in Figure 4.

Validation of prediction set markers

In order to test the stability of the selected markers, and to prevent the problem of over-fitting of the markers, another part of the data set is used to verify the markers.

For the prediction set GSE62254, the judgment accuracy is

Classifier Accuracy logistic classifier 80

The sensitivity and specificity by cross-validation are:

The corresponding logistic classifier ROC diagram is shown in Figure 5.

In order to check the stability of the selected markers and to prevent the problem of over-fitting of the markers, the GSE57303 dataset was used to verify the markers.

For the prediction set GSE57303, the judgment accuracy is

Classifier Accuracy logistic classifier 79

The sensitivity and specificity by cross-validation are:

The corresponding logistic classifier ROC diagram is shown in Figure 6.

GSE8167

In order to check the stability of the selected markers, and to prevent the problem of over-fitting of the markers, the GSE8167 dataset was used to verify the markers.

For the prediction set GSE8167, the judgment accuracy is

Classifier Accuracy logistic classifier 79

The sensitivity and specificity by cross-validation are:

The corresponding logistic classifier ROC diagram is shown in Figure 7.

From Examples 1 to 4, it can be seen that 96 genes of the present invention were selected for evaluation, and the results showed that the prediction accuracy rate for tumor metastasis was 100%, and the prediction accuracy rate for tumor recurrence exceeded 80%. It is not necessary to randomly select some relevant genes for accurate quantitative evaluation.

The above embodiments are only preferred embodiments of the present invention and are not intended to limit the present invention. It should be pointed out that for those of ordinary skill in the art, some improvements can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention.

Claims (10)

1. A combination of gene markers for comprehensive quantitative assessment of tumor microenvironment, characterized in that, the combination of gene markers comprises angiogenesis index gene markers, chemokine expression index gene markers, and immune cell infiltration index gene markers and tumor growth and invasion marker gene markers. 2. The combination of gene markers for comprehensive quantitative assessment of tumor microenvironment according to claim 1, characterized in that, The angiogenesis index gene markers include a total of 16 genes including ANG, ANGPT1, ANGPT2, ANGPTL4, DLL4, EDN1, FGF1, FGF2, FLT1, HIF1A, PDGFB, SERPINB5, TYMP, VEGFA, VEGFB and VEGFC; The chemokine expression index gene markers include a total of 18 genes of CCL1, CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL1, CXCL2, CXCL3, CXCL8, CXCL9, CXCL10, CXCL11, CXCL12, and CXCL13; The immune cell infiltration index gene markers: IDO1, HLA-DRA, STAT1, IFNG, PRF1, GZMA, GZMB, NKG7, GZMH, KLRK1, KLRB1, KLRD1, CTSW, GNLY, CD14, CD15, CD19, CD68, CD163, CD33, CEACAM8, CD80, CD86, BATF3, TNFRSF17, CD20, TNFRSF4, CD4, TNFRSF9, CD8A, CD8B, LAG3, CD39, CXCR5, TBX21, FOXP3, CD45RO a total of 37 genes; The tumor growth and invasion index gene markers: CDH1, CTNNB1, EPCAM, ITGAM, ITGAV, ITGAX, MACC1, MMP1, MMP2, MMP3, MMP9, MMP11, MMP13, MMP14, MKI67, MYC, PLAU, RAN, SNAI1, SNAI2 , TIMP1, TNC, TWIST1, ZEB1, ZEB2, a total of 25 genes. 3 . The application of the gene marker combination according to claim 1 , wherein the gene marker combination is used for comprehensive quantitative assessment of tumor microenvironment. 4 . 4. A kit for predicting the accuracy of tumor metastasis and/or tumor recurrence, wherein the kit comprises a probe for detecting the combination of gene markers of claim 2. 5. A method for establishing a comprehensive quantitative assessment model for a tumor microenvironment, wherein the method for establishing a comprehensive quantitative assessment model for the tumor microenvironment uses the gene marker combination of claim 1 to include angiogenesis Index gene markers, chemokine expression index gene markers, immune cell infiltration index gene markers, and tumor growth and invasion index gene markers were used as markers for comprehensive quantitative assessment of tumor microenvironment. 6. a kind of establishment method for tumor microenvironment comprehensive quantitative assessment model according to claim 5, is characterized in that, described establishment method comprises the gene expression of the gene marker combination described in claim 1 in GEO database Data and clinical data (staging/recurrence/metastasis) were analyzed. First, the first 150 samples in GSE62254[HG-U133_Plus_2] were selected as the training set, and the MAS5 method was used for background correction and normalization to obtain the gene expression values of 96 genes. When a gene has multiple probes, the maximum value is taken as the expression value of the gene, log2 logarithm transformation is performed on the gene expression value, and logistic regression is used to calculate the weight value. 7. The method for establishing a comprehensive quantitative assessment model for tumor microenvironment according to claim 6, wherein the weight values of the gene markers are respectively as follows: ANG weight value -20.1306 ANGPT1 weight value -34.0557 ANGPT2 weight value 28.6868 ANGPTL4 weight value -7.5101 BATF3 weight value -31.0749 CCL1 weight value 13.6034 CCL18 weight value 10.4873 CCL19 weight value -20.9738 CCL2 weight value 11.7576 CCL21 weight value -5.1514 CCL3 weight value 54.0877 CCL4 weight value - 53.5021 CCL5 weight value 27.601 CCL8 weight value -7.8325 CD14 weight value 49.8048 CD163 weight value 20.646 CD19 weight value 21.3692 CD33 weight value 4.0713 CD4 weight value 9.214 CD68 weight value - 24.3628 CD80 weight value 30.8515 CD86 weight value 70.5589 CD8A weight value 5.4726 CD8B weight value 7.7914 CDH1 weight value -5.3397 CEACAM8 weight value 0.21 CTNNB1 weight value -20.1479 CTSW weight value -6.4395 CXCL1 weight value -20.0591 CXCL10 weight value 29.703 CXCL11 weight value -13.3524 CXCL12 weight value 19.7961 CXCL13 weight value -20.5527 CXCL2 weight value 13.4255 CXCL3 weight value 23.3393 CXCL8 weight value - 43.1623 CXCL9 weight value 9.0169 CXCR5 weight value 5.4856 DLL4 weight value 17.0118 EDN1 weight value -4.8511 ENTPD1 weight value 3.4117 EPCAM weight value -33.8795 FGF1 weight value 25.9813 FGF2 weight value -12.3332 FLT1 weight value -31.7469 FOXP3 weight value 13.9705 FUT4 weight value 13.9706 GNLY weight value -10.0842 GZMA weight value 35.9977 GZMB weight value -9.9711 GZMH weight value 15.305 HIF1A weight value - 125.904 HLA-DRA weight value -51.5725 IDO1 weight value -7.3857 IFNG weight value -3.9568 ITGAM weight value -20.4378 ITGAV weight value 91.4138 ITGAX weight value -17.7399 KLRB1 weight value 6.4642 KLRD1 weight value -27.2995 KLRK1 weight value - 31.2776 LAG3 weight value 5.9364 MACC1 weight value 4.823 MKI67 weight value - 28.3144 MMP1 weight value - 19.0372 MMP11 weight value 9.8452 MMP13 weight value 3.3756 MMP14 weight value 29.6593 MMP2 weight value - 25.6822 MMP3 weight value 16.0266 MMP9 weight value 8.0522 MS4A1 weight value 1.1736 MYC weight value 20.084 NKG7 weight value -31.6593 PDGFB weight value 35.2784 PLAU weight value 20.9008 PRF1 weight value 46.0077 PTPRC weight value -108.985 RAN weight value 5.157 SERPINB5 weight value 18.3407 SNAI1 weight value -10.2615 SNAI2 weight value 7.6711 STAT1 weight value 18.7689 TBX21 weight value 7.2855 TIMP1 weight value 8.1053 TNC weight value -27.6527 TNFRSF17 weight value 28.0124 TNFRSF4 weight value -19.4462 TNFRSF9 weight value 7.2481 TWIST1 weight value 12.0057 TYMP weight value -15.5035 VEGFA weight value 15.2587 VEGFB weight value -15.0916 VEGFC weight value 1.8606 ZEB1 weight value 47.2097 ZEB2 weight value -15.7898. 8. A method for establishing a comprehensive quantitative assessment model for tumor microenvironment according to claim 5, characterized in that, a logistic classifier is used, and the logistic classifier is a classifier based on a generalized linear model, and its linear discrimination The formula is TMEscore=∑ _i ω _i x _i , where ω _i is the weight value of each gene, _xi is the expression value of the gene, and TMEscore represents the quantitative evaluation value of the tumor microenvironment. 9 . The method for establishing a comprehensive quantitative assessment model for tumor microenvironment according to claim 8 , wherein if the TMEscore of the tumor sample is greater than the threshold, it is classified as metastasis, otherwise it is classified as non-metastasized. 10 . 10 . The method for establishing a comprehensive quantitative assessment model for tumor microenvironment according to claim 8 , wherein the threshold of the logistic classifier is -427.9891. 11 .

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