CN116790754A

CN116790754A - Marker combination for prognosis prediction of thyroid undifferentiated carcinoma and application thereof

Info

Publication number: CN116790754A
Application number: CN202310797030.9A
Authority: CN
Inventors: 曹源; 刘肖; 冯超
Original assignee: 960th Hospital of the Joint Logistics Support Force of PLA
Current assignee: 960th Hospital of the Joint Logistics Support Force of PLA
Priority date: 2023-06-30
Filing date: 2023-06-30
Publication date: 2023-09-22
Anticipated expiration: 2043-06-30
Also published as: CN116790754B

Abstract

The application provides a marker combination for prognosis prediction of thyroid undifferentiated carcinoma and application thereof, and relates to the technical field of biological medicine and medical detection, wherein the marker combination comprises at least one of CD56, CD68, CD20, TIM3, LAG3, TIGIT, CTLA4 and PD1. The application is based on single cell sequencing and big data analysis, screens and obtains the 8 indexes, establishes a model through a bioinformatics analysis method and verifies the expression characteristics of the 8 indexes in thyroid undifferentiated carcinoma tumor tissues through an immunohistochemical method; further, the correlation between the above 8 indices and the prognosis of thyroid undifferentiated carcinoma was clarified by data analysis. The application has important significance for early evaluation of prognosis of thyroid undifferentiated carcinoma, assisting clinical decision and developing new thyroid undifferentiated carcinoma treatment targets.

Description

Marker combination for prognosis prediction of thyroid undifferentiated carcinoma and application thereof

Technical Field

The application relates to the technical field of biological medicine and medical detection, in particular to a marker combination for prognosis prediction of thyroid undifferentiated carcinoma and application thereof.

Background

Thyroid undifferentiated carcinoma (anaplastic thyroid carcinoma, ATC), also known as anaplastic thyroid carcinoma, progresses rapidly and has extremely high mortality rate, with more than 50% of thyroid carcinoma death cases being caused by ATC.

Histopathological examination is a gold standard for diagnosis of ATC. Surgical excision is the most commonly used treatment for ATC. In recent years, small molecule targeted drugs such as dasafil, qu Meiti, and PD-L1/PD-1 immunosuppressants have also been used for ATC treatment, but only some patients can obtain expected therapeutic effects, and the rest of patients die within 2 years after various treatments.

At present, biomarkers for ATC prognosis prediction are not available, and effective prompt cannot be given to whether more positive treatment measures should be taken clinically, so that a part of ATC patients with better prognosis can be given up to treat, and a part of ATC cancer patients with poor prognosis receive a large amount of useless treatment, so that the economic burden of families and society is greatly increased. Therefore, there is a need to develop a marker combination for ATC prognosis prediction to enable early assessment of ATC prognosis, and to timely administer specific treatments to patients, thereby improving ATC prognosis and avoiding unnecessary medical expenditure.

Disclosure of Invention

In order to solve the above problems, the present application provides a marker combination for ATC prognosis prediction and an application thereof, which can early evaluate the prognosis of ATC and timely perform specific treatment on a patient, thereby improving ATC prognosis and avoiding unnecessary medical expenditure.

In a first aspect, the application provides a marker combination for ATC prognosis prediction, the marker combination comprising at least one of CD56, CD68, CD20, TIM3, LAG3, TIGIT, CTLA4 and PD1.

Optionally, the marker combination comprises at least one of CD56, CD20, TIM3, LAG3, PD1, CD3, and CD 8.

In a second aspect, the application provides the use of a marker combination according to the first aspect for the preparation of a product for predicting ATC prognosis.

Optionally, the prognosis includes determining a treatment regimen and predicting the effectiveness of the treatment regimen, predicting patient survival, and/or assessing the infiltration rate of immune cells in ATC tumor tissue.

In a third aspect, the application provides a product for predicting ATC prognosis, comprising a product for detecting the expression level of a gene as follows, or a product for detecting the expression level of a protein encoded by a gene as follows, or a product for detecting the expression level of an mRNA transcribed by a gene as follows:

the gene comprises at least one of CD56, CD68, CD20, TIM3, LAG3, TIGIT, CTLA4, and PD 1; preferably, the gene comprises at least one of CD56, CD20, TIM3, LAG3, PD1, CD3 and CD 8.

Alternatively, the product comprises a reagent, kit, chip or other diagnostic tool.

Alternatively, the product is a kit for detecting a protein comprising CD56, CD20, TIM3, LAG3, PD1, CD3, CD 8.

In a fourth aspect, the application provides the use of a marker combination according to the first aspect for constructing a model for predicting ATC prognosis and/or for constructing a method for predicting ATC prognosis.

In a fifth aspect, the present application provides a method of assessing ATC prognosis, the method comprising the steps of:

step one, detecting the expression of CD56, CD20, TIM3, LAG3, PD1, CD3 and CD8 in ATC tumor tissues by adopting an Immunohistochemical (IHC) technology;

determining the expression quantities of CD56, TIM3, LAG3 and PD1 in ATC tumor tissues by adopting an image analysis technology; and the number of infiltrating T cells (CD 3 positive cells), B cells (CD 20 positive cells), depleted T cells (TIM 3/CD8, LAG3/CD8, PD1/CD8 double positive cells) in tumor tissue;

step three, evaluating an image analysis result; if tumor tissue overexpresses TIM3, CD56, while depleting T cells in increased numbers, this ATC case is considered highly defective in prognosis.

Alternatively, an ATC case is considered highly adverse in prognosis if the proportion of TIM3, CD56 positive cells or TIM3/CD8 double positive depleted T cells in the tumor tissue is > 50%.

Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:

at present, biomarkers for ATC prognosis prediction are not available, and effective prompt cannot be given for whether more positive therapeutic measures should be taken clinically. The embodiment of the application provides a marker combination for ATC prognosis prediction, which is characterized in that a group of marker combinations are obtained by screening based on single-cell sequencing and big data analysis, and then a model is built by a bioinformatics analysis method and the expression characteristics of the group of markers in an ATC tumor tissue are verified by an IHC method; further, the correlation between the marker combination and the ATC prognosis was clarified by data analysis. The application has important significance for early evaluation of ATC prognosis, assisting clinical decision and developing new ATC treatment targets.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 shows the ratios of various populations of cells analyzed at the single cell level in ATC, papillary thyroid carcinoma (papillary thyroid carcinoma, PTC) and normal thyroid tissue (normal tyroid, nThtyroid) provided by the examples of the present application; wherein (a) sample composition, processing, bioinformatics analysis, and IHC validation workflow of the scRNA-seq dataset; (B) UMAP plot visualizes 11 cell clusters; (C) Bubble map of normalized expression level of cell lineage specific marker gene; color depth represents average expression value level, and dot size represents percentage of specific marker gene in each cluster;

FIG. 2 shows the ratio of various immune cells analyzed at single cell level in thyroid undifferentiated carcinoma, PTC, nThyroid; wherein, (a) a UMAP profile of immune cells in a tissue of class 3; (B) UMAP panels display 12 immune cell clusters; (C) Bubble diagram of standardized expression level of immune cell lineage specific marker gene, color depth represents average expression value level, and dot size represents percentage of specific marker gene in each cluster; (D) representative immune cell cluster duty cycle in each sample;

FIG. 3 shows the expression of CD8+ T cell depletion marker gene in ATC, PTC, nThyroid according to an embodiment of the present application; wherein, (a) the UMAP diagram shows 8 cd8+ T cell clusters; each color and number represents a respective cd8+ T cell cluster; (B) Bubble map of normalized expression levels of immune markers and depletion marker genes in cd8+ T cells. Color depth represents average expression value level, and dot size represents percentage of specific marker gene in each cluster; (C) the ratio of each cd8+ T cell cluster in each sample; (D) The expression pattern of cd8+ T cell clusters in class 3 tissues was mainly depleted of marker genes. Three colors represent three classes of tissue; (E) Comparison of expression of major depletion marker genes in group 3 tissues in Bulk RNA-seq dataset (GSE 33630); * P <0.05, < P <0.01, < P <0.001, < P <0.0001; (F) The Kaplan-Meier survival curve shows the difference in total survival between the high and low expression groups of TIM3 (also known as HAVCR 2), LAG3, TIGIT and PD1 (also known as PDCD 1) in ATC cases of GSE76039 dataset. (G) Expression of ENTPD1 in each cd8+ T cell cluster in class 3 tissues; (H) GSVA analysis of differences in the activity of the marker pathways between class 3 tissues; red represents a high degree of enrichment of the signal pathway, and blue represents a low degree of enrichment of the signal pathway;

FIG. 4 shows the expression of CD20, CD3, CD68, CD56 in ATC and PTC tumor tissues in the marker combinations provided in the examples of the present application; wherein (A) is the detection result of single-color IHC of CD20, CD3 and CD 68; (B) is the detection result of multi-color IHC of CD3, CD8 and CD 56; (C) Analysis results (vs PTC, ×p <0.01, ×0.01< P < 0.05) for positive cell ratios of CD20, CD3, CD68, CD8, CD56 in ATC tumor tissues with PTC, total survival (OS) greater than 1year, ATC with OS less than 1 year.

FIG. 5 shows the expression of LAG3, PD1, TIM3, CD8 in ATC and PTC tumor tissues and the survival analysis result of ATC cases in the marker combination provided by the embodiment of the application; wherein (A) is the multicolor IHC detection result of LAG3, PD1, TIM3 and CD 8; (B) Analysis results for positive LAG3, PD1, TIM3, CD8 cells and double positive LAG3/CD8, PD1/CD8, TIM3/CD8 cell ratios in ATC tumor tissues with OS greater than 1year and ATC less than 1year (vs PTC, #0.01, # 0.01<P<0.05;vs ATC OS>1year, #0.01< P < 0.05); (C) Survival curves of ATC patients with a tumor area TIM3 positive cells ratio of greater than or less than 50% show that survival of TIM3 positive cells with a ratio of less than 50% is prolonged by greater than 50%; (D) Survival curves for ATC patients with tumor area TIM3/CD8 double positive cells at ratios greater than or less than 50% showed prolonged survival for those with TMI3/CD8 double positive cells at ratios less than 50% at ratios greater than 50%.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Unless otherwise specifically indicated, the various raw materials, reagents, instruments, equipment and the like used in the present application are commercially available or may be prepared by existing methods.

The inventive thinking of the application is:

at present, biomarkers which can be used for the prognosis prediction of ATC are not available, and effective prompt cannot be given for whether more positive treatment measures should be taken clinically, so that a part of ATC cancer patients which can obtain better prognosis after positive treatment are abandoned; meanwhile, a part of ATC patients with poor prognosis receive a large amount of useless treatment, and the economic burden of families and society is greatly increased.

Therefore, there is a need to develop a marker combination for ATC prognosis prediction to enable early assessment of ATC patient prognosis, thereby improving ATC prognosis and avoiding unnecessary medical expenditure.

In order to solve the above problems, the inventive concept of the present application includes the following processes:

the present application systematically compared the cellular component changes between 3 tissues by integrating the single cell sequencing data of ATC, PTC, nThyroid, and identified 11 major cell clusters, including 2 epithelium, 2 stroma, and 7 immune cell clusters, annotated by cell lineage specific marker genes.

The inventors reagglomerate immune cells, including T/NK cells (CD 3D, CD3E, CD3G, CD 247), macrophages (CD 14, FCGR3A, LYZ, S100A8, S100A 9), B cells and plasma cells (CD 79A, CD79B, MS A1, IGKC, IGHG1, MZB 1), and dendritic cells (HLA-DRA, HLA-DRB 1).

The data show that immune cell lineages present different enrichment preferences in class 3 tissues, with T cells (marker gene CD 3) and NK cells (marker gene CD 56) being the largest immune cell clusters, which account for significantly higher ratios in ATC than in other class 2 tissues; macrophages (labeled gene CD 68) are more abundant than nThyroid in ATC and PTC tissues; however, B cells (labeled gene CD 20) are more abundant in nThyroid than ATC and PTC, as opposed to macrophages.

The inventor further analyzes the surface markers of CD8 positive T cells (T cells with killing function) in 3 tissues, and discovers that 5 typical T cell depletion marker genes (including TIM3, LAG3, TIGIT, CTLA4 and PD 1) are remarkably increased in the ATC tissues compared with PTC and nThyroid. To this end, the inventors screened 8 indices of CD56, CD68, CD20, TIM3, LAG3, TIGIT, CTLA4, PD1 together, and speculated that they correlated with the prognosis of ATC.

Then, the application adopts IHC method to detect ATC and PTC tumor tissue, and carries out correlation analysis to the detection result and OS of the patient. The results show that the survival time of the ATC patient and the number of the infiltrated depleted T cells in the tumor tissues of the ATC patient and the expression amounts of LAG3, TIM3, PD1 and CD56 all show negative correlation trends; furthermore, compared to ATC cases with OS for more than 1year, the CD3 positive cells (T cells) were decreased in ATC case tumor tissues with OS for less than 1year, but the CD68, CD56, TIM3 positive cells and TIM3/CD8, PD1/CD8, LAG3/CD8 double positive cells (depleted T cells) were increased in proportion.

The above results demonstrate that decreased T cells, increased MC cells and depleted T cells in ATC tumor tissue, while increased CD56 expression, are indicative of a high level of ATC prognosis. Meanwhile, the results provide more references for in-depth understanding of the association of tumor immune microenvironment and ATC occurrence and clinical treatment strategies of thyroid undifferentiated carcinoma, and provide ideas for developing new ATC treatment targets.

Accordingly, the present application provides a marker combination for ATC prognosis prediction, the marker combination comprising at least one of CD56, CD68, CD20, TIM3, LAG3, TIGIT, CTLA4 and PD1. Preferably, the marker combination comprises at least one of CD56, CD20, TIM3, LAG3, PD1, CD3 and CD 8.

The application is based on single cell sequencing and big data analysis, and 8 genes of CD56, CD68, CD20, TIM3, LAG3, TIGIT, CTLA4 and PD1 are obtained by screening, and can be used as a marker combination for ATC prognosis prediction. Preferably, the marker combination comprises at least one of CD56, CD20, TIM3, LAG3, PD1, CD3 and CD 8.

Based on one general inventive concept, the present application provides the use of a marker combination according to the first aspect for the preparation of a product for predicting ATC prognosis.

In some alternative embodiments, the application provides a product for predicting ATC prognosis, such as a specific product, e.g., a reagent, kit, chip or other diagnostic tool, preferably a kit for detecting a protein comprising CD56, CD20, TIM3, LAG3, PD1, CD3, CD8, comprising a product for detecting the expression level of a gene, or a product for detecting the expression level of a protein encoded by a gene, or a product for detecting the expression level of an mRNA transcribed by a gene, e.g., a gene, comprising:

Based on one general inventive concept, the present application provides the use of a marker combination according to the first aspect for constructing a model for predicting an ATC prognosis and/or for constructing a method for predicting an ATC prognosis.

In some alternative embodiments, the application provides a method of assessing ATC prognosis, the method comprising the steps of:

step one, detecting CD56, CD20, TIM3, LAG3, PD1, CD3 and CD8 expression in ATC tumor tissues by adopting IHC;

The application will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present application and are not intended to limit the scope of the present application. The experimental procedures, which are not specified in the following examples, are generally determined according to national standards. If the corresponding national standard does not exist, the method is carried out according to the general international standard, the conventional condition or the condition recommended by the manufacturer.

Example 1

The single cell integration map of ATC, PTC, nThyroid was drawn in this example, and the ratio changes of various cells were compared. The specific method comprises the following steps:

1. single cell RNA expression matrices were downloaded from the GEO database of NCBI. The database access address is: https:// www.ncbi.nlm.nih.gov/geo/. The RNA sequencing dataset was retrieved in the GEO database as a search with ((scRNA) OR (single cell RNA) OR (scRNA-seq) OR (single cell RNAsequencing)) AND ((tyroid cancer) OR (thyroidcarcinoma)). Finally, a scRNA-seq dataset from 18 total samples of GEO: GSE184362 and GSE148673 was selected. Of these, 29110 cells were from 6 nttyroid cells, 46926 cells were from 7 PTC tumor tissues, 15565 were from 5 ATC tumor tissues, and 91601 cells in total. The 18 samples were prepared according to the user guidelines of the Single Cell 3' v2 kit of 10 x Genomics.

2. Quality control procedures were performed using the "setup" R software package (v4.1.1), and cells meeting any of the following criteria were filtered: (1) The number of genes observed in the cell (nFeature), i.e., the number <200 or >2500 of non-0 per column in the expression matrix. (2) The amount of gene expression in the cell (nCount), i.e., the sum of the columns in the expression matrix >25000. (3) A cell having a mitochondrial gene expression ratio (percentage. Mt) > 15%.

3. The characteristic expression of each cell was normalized using global-scaling normalization method "lognormal", processed to total expression, and multiplied by a scale factor (default 10,000), and the results were then logarithmically transformed using the normazedata () function in semoat. The normalized expression profiles of all samples were then combined using the merge () function in the R statistical programming language (v3.6.3).

4. Calculation was performed using a semat, and HVG screening was performed to obtain 3000 hypervariable genes.

5. The different data sets are integrated by using the harmony (v0.1.0) to identify the shared cell state existing between the different data sets, and the technical noise influence between the data sets and the cells is eliminated, so that the difference between genes mainly reflects biological heterogeneity, and the downstream analysis task is facilitated. The low-dimensional expression matrix after the batch effect correction of all samples is obtained, and after clustering, other cell groups are formed by cells derived from normal tissues and cells of tumor tissues together except for tumor cell groups, so that the batch effect removal effect is good, and the data can be used for subsequent cell type and molecular difference comparison.

6. PCA is carried out on the expression matrix of the screened high variable characteristic genes, the optimal number of main components are selected, original characteristic information is reserved as much as possible, the dimension of data used in subsequent analysis is reduced, and reasonable data compression is realized.

7. The sequential processing and analysis of the scRNA-seq data is performed using the Seurat package, including unsupervised graph-based clustering and unified manifold approximation and projection (uniform manifold approximation and projection, UMAP) dimension reduction visualization, etc.

8. Map-based unsupervised clustering of cells was performed using FindNeighbors () and FindClusters () functions in semat. The "cluster tree" based visualization method helps to find the best cluster resolution. Individual cells are divided into different cell groups by clustering, so that heterogeneity among cell types is found, and inherent biological rules are mined. FindClusters () function is then applied based on the shared nearest neighbor (shared nearest neighbor, SNN) algorithm to obtain the final clustering result. Finer sub-clusters in the main cell types were obtained by a multi-round clustering process.

9. Using UMAP nonlinear dimension reduction enables data to be visualized in a low-dimensional space.

10. Cell type specific Marker gene sets are constructed by consulting the literature, retrieving the corresponding Marker gene database, screening for Differentially Expressed Genes (DEG). Wherein the DEGs in each cell cluster were identified using findalmarkers () function in semat. The identification standard is as follows: (1) Expressed in more than 20% of cells in either or both groups; (2) |log2fc| >0.5; (3) Wilcoxon rank sum test corrected P <0.01. If the DEGs <10 between two clusters, the clusters are merged. The screened differential genes need to be analyzed one by one, and the current Marker gene verification is combined. If a Marker gene is differentially expressed in a cell cluster, the cell cluster is labeled as the cell type represented by the Marker gene.

11. The function module score of the cell was calculated using the AddModuleScore () function in the setup. The method was used to calculate the average abundance level of the published signature gene list versus the aggregate abundance level of the random control gene set.

12. The Hallmark gene set of the molecular characteristic database is used as a functional gene set of GSVA enrichment analysis, the enrichment difference of the gene sets in cells of different groups is analyzed, and the functional characteristics of the cells of different groups are read.

13. Converting the gene expression matrix into a gene set variation score matrix (comprehensive expression values of all genes in a gene set), obtaining GSVA enrichment scores of all the channels in each cell, judging the enrichment degree of all the channels, and then analyzing by using a limma package to obtain the channels with obvious differences among all the groups.

The results were as follows:

1. as shown in fig. 1, 11 major cell clusters, including 2 epithelium, 2 stroma and 7 immune cell clusters, were identified in total by the system comparing cell composition changes between 3 tissues by integrating single cell sequencing data of ATC, PTC and nThyroid tissues, annotated by cell lineage specific marker genes, according to an embodiment of the present application.

2. As shown in fig. 2, immune cell lineages exhibit different enrichment preferences in ATC, PTC, and nThyroid tissues, wherein T cells (labeled with CD 3) and NK cells (labeled with CD 56) are the largest immune cell clusters, which account for significantly higher ratios in ATC than in other class 2 tissues, according to an embodiment of the application; macrophages (labeled gene CD 68) are more abundant in ATC and PTC tissues than the nThyroid tissues; however, B cells (labeled gene CD 20) are more abundant in the nThyroid than ATC and PTC tissues, as opposed to macrophages.

3. As shown in FIG. 3, according to the embodiment of the present application, there was a difference in the surface markers of CD8 positive T cells (i.e., killer T cells) in ATC, PTC and nThyroid tissues, and it was found that 5 typical T cell depletion marker genes (including TIM3, LAG3, TIGIT, CTLA4, PD 1) were expressed higher in ATC tissues than in PTC and nThyroid tissues.

Example 2

In this example, IHC was used to detect expression of a combination of markers (CD 56, CD20, TIM3, LAG3, PD1, CD3, CD 8) in ATC and PTC tumor tissues and the differences were compared. The specific method comprises the following steps:

1. the positive ATC cases are confirmed by the pathological morphological detection after the group is put into the group, and because the ATC cases are rare, the PTC cases which are equivalent to the ATC cases in gender, age and tumor volume are further put into the group by adopting a one-by-one pairing mode after the ATC cases are put into the group.

2. And taking paraffin embedded tumor tissues remained after clinical pathology detection of the group of cases as samples, and performing conventional paraffin flaking on the samples.

3. HE staining was performed on the paraffin sections of the samples.

4. Samples paraffin sections were subjected to IHC single staining for CD20, CD3, CD8, and IHC multiple staining for CD56, CD8, LAG3, TIM3, PD1.

5. Scanning the slice by adopting a pathological full-slice scanning system; IHC single-staining slices directly generate images, IHC multiple-staining slices generate images with superimposed colors of indexes.

6. The image is evaluated according to the following steps:

step one, a positive signal with any intensity exists in a cell membrane or a cell plasma, and the cell is considered to express an index corresponding to the positive signal.

Step two, analyzing the number of cells with a certain positive signal in all images by adopting Image J software; the duty cycle of a cell is then calculated according to the formula in table 1.

And thirdly, interpreting images by two pathologists, and evaluating the duty ratio of certain cells according to a formula shown in the table 1.

Step four, comparing the average value of the evaluation results of the two doctors with the evaluation result of Image J software to determine the consistency of the evaluation results; and if the two steps are inconsistent, repeating the second step and the third step.

And fifthly, taking the average value of the evaluation results of two doctors as the ratio of certain cells in the image, and gathering data for statistical analysis.

TABLE 1 IHC dyeing index and calculation formula thereof

T T cells, B B cells, MC macrophages, NK natural killer cells, exT depleted T cells.

The results were as follows:

1. the co-administration group ATC (n=8) and PTC (n=8) cases 16.

2. According to an embodiment of the present application, the expression of the marker combinations (CD 56, CD20, TIM3, LAG3, PD1, CD3, CD 8) in ATC and PTC tumor tissues is shown in fig. 4, 5 and table 2. The results show that the expression level of the index in the ATC tumor tissue is obviously higher than that of the PTC tumor tissue. The results suggest that ATC and PTC tumor cells have different immunophenotype characteristics.

3. The case was considered positive when the positive cells in the tumor tissue accounted for > 10%. IHC staining results show that the positive rates of LAG3, TIM3 and PD1 in the ATC tumor tissues are respectively 100%, 100% and 87.5%, and 25%, 50% and 12.5% in the PTC tumor tissues. In ATC tumor tissue, TIM3 expression was higher than LAG3 and PD1. The result shows that the T cell depletion related index in tumor tissue is highly expressed and is one of the pathological characteristics of ATC.

4.T cells are significantly increased in ATC tumor tissue, wherein T cells expressing CD8 with killing function are also significantly increased in ATC tumor tissue. However, killer T cells in ATC tumor tissue express mostly depletion indicators (LAG 3, TIM3 or PD 1). Wherein the TIM3/CD8 double-positive cells are the most, and the LAG3/CD8 double-positive cells are the next least, and the PD1/CD8 double-positive cells are the least. This result suggests that although T cell infiltration is increased in ATC tumor tissue, most are in a depleted T state, which may be associated with poor ATC prognosis.

CD56 expressed negative in all PTC tumor tissues but positive in 75% of ATC tumor tissues. This result further demonstrates that there is significant biospecificity between ATC and PTC.

TABLE 2IHC staining image analysis results

**vs PTC P<0.01,*vs PTC 0.01<P<0.05,#vs ATC(OS>1year)0.01<P<0.05.

Example 3.

This example evaluates the effect of predicting ATC prognosis by detecting expression of marker combinations (CD 56, CD20, TIM3, LAG3, PD1, CD3, CD 8) in tumor tissue of ATC cases. The specific method comprises the following steps:

1. the ATC cases confirmed by the pathomorphology detection are put into groups, then the residual tumor tissue wax blocks of the put-into-groups cases after the clinical detection are collected, and the clinical information of the gender, age, tumor tissue size, whether metastasis exists, death time and the like of the put-into-groups cases are collected, so that the cases which cannot obtain complete clinical information are removed.

2. And taking paraffin embedded tumor tissues remained after clinical detection of the group of cases as samples, and performing conventional paraffin flaking.

3. Samples of paraffin sections were subjected to IHC single staining for CD20, CD3, CD8, and IHC double staining for CD56/CD68, CD4/CD3, LAG3/CD8, HAVCR2 (also known as TIM 3)/CD 8, PD1/CD 8.

4. Scanning the slice by adopting a pathological full-slice scanning system; IHC single-staining slices directly generate images, IHC multiple-staining slices generate images with superimposed colors of indexes.

5. The image is evaluated according to the following steps:

6. The cell ratios of the various types in the tissue were calculated by the formula (shown in table 1).

7. The ATC cases in the group are divided into two groups with the OS less than 1year and the OS greater than 1year according to the OS, and the difference of the proportion of various cells in the tumor tissues of the ATC cases in the two groups is compared.

8. The correlation of the cell ratios of the various classes with the OS of the ATC patients in the group was analyzed.

The results were as follows:

1. the total group has complete clinical information and 8 ATC cases with definite death time.

The number of ExT cells infiltrated in tumor tissues of the ATC patient OS and the expression amounts of LAG3, TIM3, PD1 and CD56 all show negative correlation trend.

3. The CD3 positive cells (T cells) were decreased in tumor tissues of ATC cases with OS less than 1year, but the CD68, CD56, TIM3 positive cells and TIM3/CD8, PD1/CD8, LAG3/CD8 double positive cells (depleted T cells) were increased compared to ATC cases with OS for more than 1 year.

4. The result shows that ExT quantity and related specific marker expression quantity in tumor tissues are related to the ATC lifetime.

5. When the proportion of TIM3, CD56 positive cells or TIM3/CD8 double positive depleted T cells in ATC tumor tissue is >50%, the prognosis for this ATC case is highly poor, and the survival is usually less than 1year after such cases are positively treated.

Various embodiments of the application may exist in a range of forms; it should be understood that the description in a range format is merely for convenience and brevity and should not be construed as a rigid limitation on the scope of the application; it is therefore to be understood that the range description has specifically disclosed all possible sub-ranges and individual values within that range. For example, it should be considered that a description of a range from 1 to 6 has specifically disclosed sub-ranges, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as single numbers within the range, such as 1, 2, 3, 4, 5, and 6, wherever applicable. In addition, whenever a numerical range is referred to herein, it is meant to include any reference number (fractional or integer) within the indicated range.

In the present application, unless otherwise specified, terms such as "upper" and "lower" are used specifically to refer to the orientation of the drawing in the figures. In addition, in the description of the present specification, the terms "include", "comprising" and the like mean "including but not limited to". Relational terms such as "first" and "second", and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Herein, "and/or" describing an association relationship of an association object means that there may be three relationships, for example, a and/or B, may mean: a alone, a and B together, and B alone. Wherein A, B may be singular or plural. Herein, "at least one" means one or more, and "a plurality" means two or more. "at least one", "at least one" or the like refer to any combination of these items, including any combination of single item(s) or plural items(s). For example, "at least one (individual) of a, b, or c," or "at least one (individual) of a, b, and c," may each represent: a, b, c, a-b (i.e., a and b), a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple, respectively.

The foregoing is only a specific embodiment of the application to enable those skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A marker combination for prognosis prediction of thyroid undifferentiated carcinoma, wherein the marker combination includes at least one of CD56, CD68, CD20, TIM3, LAG3, TIGIT, CTLA4, and PD1.

2. The marker combination of claim 1, wherein the marker combination comprises at least one of CD56, CD20, TIM3, LAG3, PD1, CD3, and CD 8.

3. Use of a marker combination according to claim 1 or 2 for the preparation of a product for predicting the prognosis of thyroid undifferentiated carcinoma.

4. The use of claim 3, wherein the prognosis comprises determining a treatment regimen and predicting the effectiveness of the treatment regimen, predicting patient survival, and/or assessing the level of infiltration of immune cells in thyroid undifferentiated carcinoma tumor tissue.

5. A product for predicting prognosis of thyroid undifferentiated carcinoma, comprising a product for detecting the expression level of a gene as follows, or detecting the expression level of a protein encoded by a gene as follows, or detecting the expression level of mRNA transcribed by a gene as follows:

6. The product of claim 5, wherein the product comprises a reagent, kit, chip or other diagnostic tool.

7. The product of claim 5 or 6, wherein the product is a kit for detecting a protein comprising CD56, CD20, TIM3, LAG3, PD1, CD3, CD 8.

8. Use of a marker combination according to claim 1 or 2 for constructing a model for predicting the prognosis of thyroid undifferentiated carcinoma and/or for constructing a method for predicting the prognosis of thyroid undifferentiated carcinoma.

9. A method of assessing the prognosis of thyroid undifferentiated carcinoma, the method comprising the steps of:

step one, detecting the expression of CD56, CD20, TIM3, LAG3, PD1, CD3 and CD8 in thyroid undifferentiated carcinoma tumor tissues by adopting an immunohistochemical technology;

determining the ratio of CD8, CD56, TIM3, LAG3 and PD1 positive cells, T cells (CD 3 positive cells), B cells (CD 20 positive cells), MC cells (CD 68 positive cells) and depleted T cells (TIM 3/CD8, LAG3/CD8 and PD1/CD8 double positive cells) in thyroid undifferentiated cancer tumor tissues by adopting an image analysis technology;

step three, evaluating the ratio of various cells; if the ratio of TIM3 to CD56 positive cells to depleted T cells in the thyroid undifferentiated carcinoma tumor tissue is increased; the case of thyroid undifferentiated carcinoma is considered to have a highly adverse prognosis.

10. The method of claim 9, wherein the case of thyroid undifferentiated carcinoma is considered highly prognostic if the proportion of TIM3, CD56 positive cells or TIM3/CD8 double positive depleted T cells in the tumor tissue is > 50%.