CN114317756A

CN114317756A - Use of markers

Info

Publication number: CN114317756A
Application number: CN202210018328.0A
Authority: CN
Inventors: 陈文标; 戴勇; 汤冬娥; 候显良; 徐慧旋; 郑凤屏; 张丰
Original assignee: Shenzhen Longhua Peoples Hospital
Current assignee: Shenzhen Longhua Peoples Hospital
Priority date: 2022-01-07
Filing date: 2022-01-07
Publication date: 2022-04-12

Abstract

The invention relates to the field of biotechnology, in particular to application of a marker. The invention provides application of gene combination as a marker in preparation of products for predicting tumors, predicting tumor stages and/or tumor prognosis. The gene combination 3-PPI-Mod (3NM) obtained by the invention is verified in two independent data sets, and a Kaplan-Meier curve shows that the total survival time of a patient classified as high-risk is obviously shorter than that of a patient classified as low-risk; and 3-PPI-Mod has a prognostic significance better than that of the 18-gene, showing a prognostic value better than that of the signature generated by gene expression alone. Furthermore, 3-PPI-Mod was significantly associated with intratumoral hypoxia score. These results indicate that 3-PPI-Mod is highly expressed in tumor exosomes of high clinical grade and can be used as a prognostic and predictive biomarker for hepatocellular carcinoma.

Description

Use of markers

Technical Field

The invention relates to the field of biotechnology, in particular to application of a marker.

Background

Extracellular exosomes (exosomes) are membrane particles released by cells that regulate intercellular communication by delivering functional molecules (e.g., proteins, nucleic acids, and lipids) to recipient cells. Cells can secrete exosomes of many different sizes and origins. The outer body has a diameter mainly between 50-150nm and is produced in multivesicular endosomes. Other exosomes (100-1000 nm diameter), such as Microbubbles (MVS), exosomes or microparticles, bud directly from the cell membrane.

In recent years, there has been increasing evidence that exosomes play an important role in the development of tumors. Cancer-derived exosomes are involved in a variety of canceration processes, including malignant transformation, angiogenesis, immunosuppression, invasion, and resistance to treatment. Exosomes released by the tumor microenvironment may also affect the properties of cancer cells. One common feature of the tumor microenvironment is the induction of metastatic invasive metastasis of tumor exosomes to recipient cells. In addition, exosomes released by the tumor can change the distant microenvironment to form a pre-metastatic niche and promote the formation of metastases. Suggesting that cancer cell exosomes may function both locally and remotely.

Cancer-derived exosomes detected in various body fluids are considered as a new non-invasive biomarker. For example, small exosomes (less than 200nm in diameter) in the circulation carry micrornas (mirnas) and proteins, which are promising diagnostic and prognostic biomarkers in tumors. However, exosomes identified in body fluids may represent a mixed population released by tumors and other tissues. To date, distinguishing tumor-specific exosomes from exosomes released by other tissues remains a significant challenge due to the lack of specific biomarkers. The levels of protein released into exosomes in body fluids are reported to be consistent with protein expression in NSCLC (non-small cell lung cancer) tissues. Therefore, the combined analysis of primary tumors and secreted exosomes may be a viable approach to identify tumor-specific exosomes-associated biomarkers.

Disclosure of Invention

In view of the above, the present invention provides the use of markers as prognostic and predictive biomarkers for hepatocellular carcinoma.

In order to achieve the above object, the present invention provides the following technical solutions:

the invention provides application of gene combination as a marker in preparing products for predicting tumors, predicting tumor stages and/or predicting tumor prognosis;

the gene combination comprises one or more of gene combination 1, gene combination 2 or gene combination 3;

the gene combination 1 comprises genes KRT1, HOXC13, KRT34, KRT2, KRT31, HOXC12, HOXC11 and HOXC 5;

the gene combination 2 comprises genes PAX2, GBX1, LHX1, WNT7A, SOST, SP7 and FGF 23;

the gene combination 3 comprises genes SPAG6, WDR63 and DNAH 9.

In some embodiments of the invention, the above gene combination 1 is associated with developmental differentiation of epidermal cells; the gene combination 2 is related to tumor-related pathways; the above gene combination 3 is involved in the constitution and/or activity of cell membrane components.

In some embodiments of the present invention, the above gene combination 1 is in an interactive relationship with the above gene combination 2 via gene ISL 1; the gene combination 2 is in an interaction relationship with the gene combination 3 through genes PVALB, NPY, PF4 and SPAG 17.

In some embodiments of the invention, the expression of the above-described gene combination is up-regulated in high clinical staging tumors.

In some embodiments of the invention, the above-described combinations of genes are predictive of survival in cancer patients.

In some embodiments of the invention, the above gene combinations are significantly positively correlated with the ratio of immune cells.

In some embodiments of the present invention, the ratio of a portion of the genes in the above-described gene combinations to immune cells is significantly and positively correlated.

In some embodiments of the invention, the above-described gene combinations are associated with a tumor microenvironment.

In some embodiments of the invention, the combination of genes is positively correlated with tumor hypoxia.

In some embodiments of the invention, the hypoxia pathway related gene is significantly enriched in the high risk tumor predicted by the above gene combination.

In some embodiments of the invention, the score for tumor hypoxia is significantly positively correlated with the expression level of hypoxia inducible factor 1.

In some embodiments of the invention, the above gene combinations are significantly positively correlated with the expression level of hypoxia inducible factor 1 in the high risk subgroup.

The invention also provides a primer combination for amplifying the gene combination.

The invention also provides a reagent, a kit, a system or a device for predicting tumors, tumor stages and/or tumor prognosis, which is characterized by comprising an amplification primer of the gene combination or a product aiming at obtaining the sequence of the gene combination, and acceptable auxiliary materials, auxiliary agents, carriers, modules or components.

In some embodiments of the invention, the step of manipulating the reagent, kit, system or device comprises extracting exosome genes, sequencing, and/or analyzing the sequencing results using the combination of genes described above.

The invention also provides a detection method, which comprises the steps of using the gene combination as a template to carry out primer design and synthesis and/or using the gene combination as a reference to analyze the genes of a sample.

In some embodiments of the invention, the steps of the detection method include extracting exosome genes, sequencing, and/or analyzing sequencing results using the combination of genes.

The marker of the invention has the following effects:

1. expression of 3-PPI-Mod (3NM) was upregulated in tumors of the high clinical grade group. The patient's clinical characteristic risk was predicted by 3-PPI-Mod with an area under ROC of 0.7368. 79.74% (126/158) gave the correct classification when compared to the true clinical grade. The Kaplan-Meier curve demonstrates that 3-PPI-Mod can predict the overall survival of patients (p 0.0057), indicating that 3-PPI-Mod can be used to predict tumor grade and tumor prognosis.

2. In 3-PPI-Mod, Mod _5 is associated with developmental differentiation of epidermal cells; mod — 8 is significantly associated with tumor-associated pathways; the gene in Mod _15 is widely involved in the composition and activity of cell membrane components.

3. The same algorithm is used for establishing a prediction model based on gene expression, namely 18 genes, and the prognosis significance of the 18 genes is worse than that of 3-PPI-Mod, which shows that compared with the characteristics of pure gene expression, the exosome-related characteristics show better prognosis value.

4. 3 modules in the 3-PPI-Mod are positively correlated with hypoxia, and hypoxia pathway related genes are obviously enriched in high-risk tumors predicted by the 3-PPI-Mod. The fraction of hypoxia in tumor has obvious correlation with the expression level of hypoxia inducible factor 1(HIF1), and the HIF1 expression level is averagely up-regulated in the high-risk subgroup identified by 3-PPI-Mod, which indicates that the 3-PPI-Mod can be used for predicting and evaluating the tumor hypoxia microenvironment.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.

FIG. 1 illustrates an algorithm for module construction;

FIG. 2 shows a flow chart of a method of the present patent;

FIG. 3 illustrates PPI network construction; wherein the blue ellipse represents an exosome-associated gene; black lines represent interactions between blue ellipses;

FIG. 4a illustrates cluster mapping based on expression scores of profiles of 16 modules; the cluster map also shows the association between the clusters and the clinical pathology staging; T-N-M: tumor classification form represents tumor-lymph node-metastasis;

FIG. 4b illustrates the determination of an optimal module using an RF algorithm; the abscissa represents the number of modules of the study, and the ordinate represents the cross-validation of each prediction model;

FIG. 4c shows expression scores based on 3 modules; the abscissa indicates module 5, module 8, module 15; the ordinate represents the module score; red is high clinical tumor stage; blue is low clinical tumor stage;

FIG. 4d shows the validation of the 3NM prediction by ROC curve; the abscissa represents specificity; the ordinate represents sensitivity;

FIG. 4e shows survival analysis of TCGA-LIHC HCC patients based on 3NM signature using KM analysis; the abscissa represents time; the ordinate represents the survival probability;

FIG. 5a shows that exosome-associated genes from 3 modules interact through overlapping genes;

FIG. 5b shows GO and KEGG analysis for 3 modules;

figure 6 shows survival analysis of HCC patients on different data sets using KM analysis based on 3NM exosome-associated genes; wherein a is survival analysis of the GEO-GSE76427 data set; b is survival analysis of ICGC-LIHC-JP data set;

FIG. 7 shows survival analysis of HCC patients on TCGA-LIHC data set using KM analysis based on 3 NM-based exosome-associated genes;

figure 8a shows the correlation analysis of 3NM with tumor immune cells and tumor microenvironment status;

FIG. 8b shows tumor hypoxia status using GSEA analysis according to classification of groups by 3 NM;

FIG. 8c shows the analysis of the association of HIF1A with hypoxia using the Pearson correlation algorithm; the abscissa represents the hypoxia score; the ordinate represents HIF1A expression score;

figure 8d shows a comparison of high and low risk groups using a tumor microenvironment status score (hypoxia); the abscissa represents the grouping; the ordinate represents the hypoxia status score;

FIG. 8e shows a comparison of high and low risk groups using tumor microenvironment status score (HIF 1A); the abscissa represents the grouping; the ordinate represents the HIF1A expression score.

Detailed Description

The invention discloses the application of the marker, and the technical personnel can appropriately improve the technological parameters for realization by referring to the content. It is expressly intended that all such similar substitutes and modifications which would be obvious to one skilled in the art are deemed to be included in the invention. While the methods and applications of this invention have been described in terms of preferred embodiments, it will be apparent to those of ordinary skill in the art that variations and modifications in the methods and applications described herein, as well as other suitable variations and combinations, may be made to implement and use the techniques of this invention without departing from the spirit and scope of the invention.

Extracellular entities (exosomes) mediate cell-to-cell communication in the tumor microenvironment and are involved in tumor invasion. Although exosomes in body fluids are considered ideal biomarkers for tumor diagnosis and prognosis, it is still difficult to distinguish tumor-derived exosomes from exosomes released by other tissues. The invention supposes that analysis of exosomes-associated molecules in tumor tissues is helpful for estimating the prognostic value of tumor-specific exosomes, and analyzes the gene expression profile of exosomes protein coding genes in hepatocellular carcinoma in tumors. Based on a protein-protein interaction (PPI) network method, exosomes related gene characteristics are established, and 3 network modules (3-PPI-Mod) are determined to be used as characteristics for predicting the clinical grade of hepatocellular carcinoma. The predictive value of this feature in both retrospective datasets was further validated. In addition, the relationship of gene signatures to the tumor microenvironment, including hypoxic state and stromal cell abundance, was studied. These results indicate that the 3-PPI-Mod signal is highly expressed in tumor exosomes of high clinical grade, and can be used as a prognostic and predictive biomarker for hepatocellular carcinoma.

Hypoxia/anoxia as referred to in the present invention means a state in which the partial pressure of oxygen in the tissue falls below a critical value or oxygen is reduced effectively.

The microenvironment as referred to in the present invention refers to the intercellular matrix and the fluid components therein.

The tumor microenvironment refers to the occurrence, growth and metastasis of tumors and the internal and external environments of tumor cells, and not only includes the structure, function and metabolism of the tissues where the tumors are located, but also relates to the internal environments (nucleus and cytoplasm) of the tumor cells.

The invention is further illustrated by the following examples:

example 1: establishing predictive signatures of clinical grade risk of cancer

1. Patient and clinical characteristics

The present invention uses data from 3 different databases as the study subject. These are LIHC (hepatocellular carcinoma) transcript sequencing data from TCGA (TCGA-LIHC), Japanese case transcript sequencing cohort for LIHC from ICGC (ICGC-LIHC-JP) and LIHC chip sequencing data from the Singapore bioinformatics center (GEO: GSE76427), respectively. These 3 different independent cohorts cover two different transcript quantification methods and different populations in asia and europe and america. Among them, the total number of samples of TCGA-LIHC was 356, and the number of available samples was 316 after removing OS (Total survival) for less than 30 days and cases of clinical data insufficiency. Similarly, 212 samples of ICGC-LIHC-JP data are collected, 161 samples of available samples are collected, 115 samples of GSE76427 data are collected, and 94 samples of available samples are collected. Sample information is detailed in table 1.

TABLE 1 clinical characteristics of patients in different LIHC databases

TCGA data was downloaded from:

https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/

ICGC data was downloaded from:

https://dcc.icgc.org/

GSE76427 data was downloaded from:

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgiacc＝GSE76427/

2. exoRBase exosome database

The exosome genes associated with hepatocellular carcinoma were derived from the exosome gene database exoRBase. The download address is http:// www.exorbase.org/exoRBase/browse/tomRNAIndex/. The database hepatocellular carcinoma exosome characteristic gene comprises 17221 genes. Further enrichment by bioinformatics means is required to obtain the exosome expression genes closely related to hepatocellular carcinoma.

3. Gene expression data processing

TCGA-LIHC data are read counts and FPKM data, ICGC-LIHC-JP data are read counts, and GSE76427 data extract probe values (log2 intensity) and probe annotations from raw files downloaded from a gene expression integrated database (GEO). The FPKM data of TCGA-LIHC, ICGC-LIHC-JP and GSE76427 data were Z-transformed to normalize the data. Differential expression analysis of tumor and normal paracarcinoma samples was performed using read counts data of TCGA-LIHC.

In differential expression analysis, EBSeq software was used to analyze the gene expression differences between tumor and paracancerous normal samples in the TCGA-LIHC cohort.

4. PPI data processing

The construction of human protein interaction networks PPI was performed using STRING (https:// STRING-db. org /). The build process uses default parameters.

Identification of PPI modules is performed using MCODE. The Degree cutoff value is 2, the node score cutoff value is 0.2, the K-score value is 2, and the Max.depth value is 100.

5. Identification of clinical grade-associated PPI modules

In order to further screen out modules with significant clinical grading prediction function from potential PPI modules. The PPI module obtained by the method is integrated with the expression data of TCGA-LIHC. The present invention first calculates an Expression Score (e) for each module. In a given module M with M genes, the expression score (e) for M in sample j is defined as:

wherein Z_ijIs the z-transformed gene expression value for gene i.

The discriminative score s (M) of the M module is then defined as the Mutual Information (MI) between the e' class and the clinical stratification class (c):

where e' represents a discrete form of e, the expression score e is discretized to 9 (log)₂(N) +1), N being the number of samples. The calculation process is shown in fig. 1. In clinical grading, Stage I and II were classified into the Low Stage group, and III and IV were classified into the High Stage group.

Then, the present invention calculates the MI value of the randomly selected "module" by randomly extracting the same number of genes as the M module genes from the PPI network genes. Each module was run 1000 times at random. And (4) performing statistical test by using the calculation result of the random sampling and the calculation result of the actual module, and selecting the module with a significant p value (p <0.0001) for further analysis.

6. Recursive predictive PPI module

The PPI network-based gene signature was constructed using the gene expression scores of the candidate modules as described previously. Feature selection and modeling are performed by using R-package randomForest based on a Random Forest (RF) algorithm, namely, the prediction importance of each candidate module repetition is estimated by using the initial RF of 5000 decision trees. And determining the optimal combination of the recursive prediction candidate modules by adopting a step-by-step backward selection method. In each iteration, 10% of the features are excluded and the remaining features are used to construct an RF containing 3000 decision trees. This procedure stops when only two functions remain. Among all the iteration results, the RF model with the smallest number of features is selected. In the final RF model, the clinical level risk of the patient is determined by oob (out-of-bag data) probabilities. Finally, the invention selects 3 required PPI modules.

7. Other statistical methods

The invention uses R packet clusterirprofiler to perform GO and KEGG path enrichment analysis on the PPI module. GSEA was used to compare the gene set of interest to a subset of patients classified by 3-PPI-Mod. The PPI module's correlation to hypoxia, angiogenesis and inflammation scores and stromal cell abundance was calculated by pearson correlation coefficients. The multiplex test was adjusted by the False Discovery Rate (FDR) using the method of Benjamini-Hochberg. The Kaplan-Meier curve and log-rank test were used to compare patient survival in the low-risk and high-risk groups assigned with 3-PPI-Mod. Multivariate cox models were used to assess the prognostic and predictive value of the 3-PPI-Mod signal. All statistical analyses were performed using R software (version 3.3.1). p <0.05 indicates significant difference.

8. Results and analysis

Fig. 2 depicts an overall flow diagram of the present invention.

The present invention has been analyzed to obtain 1134 specific tumor-to-paracarcinoma differential expression genes (PPDE > 0.99). On the basis, the invention carries out intersection operation by utilizing differential expression genes and specific exosome expression genes of hepatocellular carcinoma in an exoRBase database to obtain 437 specific expression genes of the hepatoma exosome.

Based on the obtained 437 genes specifically expressed by the liver cancer exosomes, a PPI network constructed by STRING is used. The network contains 321 nodes, 938 interacting (fig. 3).

For specific proteins in hepatocellular carcinoma exosomes, 321 proteins were mapped onto the reference PPI network. Exosome-associated PPI networks are integrated with the gene expression profiles of the training dataset.

Identification of PPI modules using MCODE resulted in 16 potential PPI modules. The set of genes comprised by the module is as follows:

module 1: PYY, PF4, NPY4R, CCR9, ADCY2, OPRD1, NMU, GRM3, CNR2, NPY, PPBP, PPY, CHRM4, GALR1, OXGR1, HTR1F, HTR 5A;

and (3) module 2: MYH7, MYL2, ACTN3, MYBPC3, MYH2, MYH6, MYBPC2, TNNT3

And a module 3: GYPA, GYPB, RHAG, GATA1, AHSP, ALAS2, HBA1, HBQ 1;

and (4) module: HBD, HBE1, SCN2A, CALB2, BDNF, CARTPT, SLC17A8, SLC18A3, NTSR1, GHSR, CHRM1, KISS1R, QRFPR;

and a module 5: KRT1, HOXC13, KRT34, KRT2, KRT31, HOXC12, HOXC11, HOXC 5;

and a module 6: ALPL 2, CEACAM5, RAET1L, LYPD2, NTNG1, NLGN1, SLC17A7, OLFM3, GABRG2, RBFOX 1;

and a module 7: UCP1, HCRT, LEP, NR5a 1;

and a module 8: PAX2, GBX1, LHX1, WNT7A, SOST, SP7, FGF 23;

and a module 9: CATSPERD, CACNG3, CACNG 6;

the module 10: AZU1, DNTT, DEFA3, MPO, CD 1A;

module 11: CHRNA2, PCDH19, LGI 1;

the module 12: PVALB, NCAN, GRIA 2;

module 13: EMX2, HOXD13, IRX 6;

the module 14: CLCA2, KLK5, DSG 3;

module 15: SPAG6, WDR63, DNAH 9;

the module 16: FBXO40, RNF182, FBXL 13.

The significance of the discrimination score of each module is estimated using a random sampling method. Finally, the results show that the recurrence discrimination score for 16 modules is significantly higher than that of chance by random forest modeling analysis (p <0.001, table 2). Details of the 16 candidate modules are shown in table 2.

TABLE 2 Module MI values and significance

Module	MI value	p value
			Module
8	0.06861101	7.18×10^-28
			Module 15	0.06685537	1.55×10^-38
Module 5	0.06665415	1.37×10^-11
			Module 16	0.06464605	5.05×10^-10
Module 1	0.06455428	1.66×10^-10
			Module 6	0.06433018	5.85×10^-10
Module 4	0.06269076	3.76×10^-7
			Module 10	0.06220704	4.32×10^-7
Module 11	0.06148547	1.07×10^-6
			Module 7	0.0605967	1.01×10^-18
Module 2	0.06023881	1.31×10^-22
			Module 12	0.06015081	1.00×10^-14
Module 9	0.06011182	2.96×10^-17
			Module 14	0.05942734	7.16×10^-25
Module 3	0.05891626	5.58×10^-32
			Module 13	0.05820099	2.46×10^-30

The expression score distribution for the 16 PPI modules is shown in fig. 4a, the cluster map showing the association between clusters and clinical pathology stages. Next, greedy search programs and random sampling found 13 modules that could significantly distinguish between clinical classifications (P < 0.001). The cluster map of expression scores for 16 modules divides the modules into two broad categories (clusters 1, 2). Cluster 1 contained 3 modules (including module 1, module 4 and module 6) of genes up-regulated in stage I and II of tumors, and cluster 2 contained 13 modules up-regulated in stage III + IV of tumors (P ═ 0.033). In addition, the clinicopathological classification of HCC, such as T (P ═ 0.043) or grade stage (P ═ 0.046), is associated with these 16 modules.

The present invention then contemplates using 13 PPI modules to establish an optimal model for predicting the risk of the clinical grade of the patient. By using a Random Forest (RF) algorithm, the present invention finds that the combination of the three modules (Mod _5, Mod _8, Mod _15) achieves the optimal prediction accuracy by the RF algorithm (fig. 4 b). 3 modules were upregulated in High stage group tumors (FIG. 4 c). A predictive model, referred to as the 3-PPI-Mod signature, is then constructed using these 3 PPI modules. The patient's clinical characteristic risk was predicted by the 3-PPI-Mod characteristic, with an Area Under ROC (AUROC) of 0.7368 (fig. 4 d). And taking the median of the predicted risk coefficients as a critical value, and dividing the patients into a high clinical grade risk group and a low clinical grade risk group. Overall, 79.74% (126/158) patients were correctly classified when compared to the true clinical grade. The Kaplan-Meier curve demonstrates that the 3-PPI-Mod signature can predict the overall survival of the patient (p 0.0057, fig. 4 e). Multivariate cox regression showed that 3-PPI-Mod was characterized as an independent prognostic factor for OS (corrected risk ratio [ HR ] ═ 2.7, 95% CI, 1-7.2, p ═ 0.045, table 3).

TABLE 3 multivariate Cox regression of OS in training dataset

In the 3-PPI-Mod marker, 8, 7 and 3 genes are contained in module 5(Mod _5), module 8(Mod _8) and module 15(Mod _15), respectively. Mod _5 is connected to Mod _8 and Mod _15 in the PPI network (fig. 5 a). GO and pathway enrichment analysis showed that Mod _5 is involved in the developmental differentiation of epidermal cells, Mod _8 is significantly involved in tumor-associated pathways, and the genes in Mod _15 are widely involved in the organization and activity of cell membrane components (fig. 5 b).

Example 2: verification of 3-PPI-Mod features in independent queues

The 3-PPI-Mod prognosis was validated using two independent validation datasets using the statistical method described in example 1. Prediction of patient risk of relapse was performed on each validation dataset using the 3-PPI-Mod signature established with the training dataset. The Kaplan-Meier curve shows that the OS time for patients classified as high risk is significantly shorter than for patients classified as low risk (log-rank test: GSE76427, P0.0039; ICGC-LIHC-JP, P <0.0001) (FIG. 6a, FIG. 6 b).

Example 3: comparison of prognostic value of exosome-associated signals with simple gene expression signals

Using statistical methods as described in example 1, exosome-associated 3-PPI-Mod markers were compared to 18-gene markers (constructed based on gene expression only) (FIG. 7). The 18 gene signature was established using the same algorithm as the 3-PPI-Mod signature. The 18 gene signature showed similar predictive performance as the 3-PPI-Mod signature in differentiating between clinical grade and OS, whether in the training dataset or in the validation set (fig. 6, fig. 7). However, to the extent that the significance of the prognosis of the 18-gene signature is less significant than that of the 3-PPI-Mod signature, it indicates that the exosome-associated signature shows a better prognostic value than the signature of gene expression alone.

Example 4: 3-PPI-Mod reflects tumor stromal interaction and hypoxic tumor microenvironment

1. Identification of tumor microenvironment

Hypoxic metabolites of different cancer types were obtained from previous studies (Buffa F. M. Harris A. L. West C. M. Miller C. J. Large. Meta-analysis of multiple cancer cells a common, compact and high purity viral hypoxia metal. Br. J. cancer. 2010; 102: 428-. The core angiogenic marker of the primary tumor was obtained from (Masiero M.

Han.h.d.snell c.peterkin t.bridge e.mangal l.s.wu s.y.praadep s.li d.et al.a core human primary vector machinery orientation signatures of the endethil alpha receptor ELTD1 as a key regulator of orientation cell.cancer cell.2013; 24:229-241). Inflammatory cytokinins were used to estimate intratumoral inflammation levels (Saloura V.Zuo Z.Koeppen H.Keck M.K.Khattri A.Boe M.Hegde P.S.Xiao Y.Nakamura Y.Vokes E.E.et al.correlation of T-cell-expressed phenylalanine with mesenchyme subtype, expression of PD-L1, and other immune responses in head and neck cancer. J.Clin. Oncol.2014; 32(6009 + 6009)). The hypoxia, angiogenesis and inflammation scores of tumor cells were calculated by averaging the Z-normalized expression values of the corresponding marker genes. The inventionThe abundance of immune and non-immune cells in the tumor microenvironment was calculated for the tissue infiltrating cell population by gene expression profiling using CIBERSORT.

2. Results and analysis

Non-cancerous cells within the tumor play an important role in the establishment of the tumor microenvironment, particularly the infiltration of immune cells. It was therefore hypothesized that exosome-specific 3-PPI-Mod might be associated with the tumor microenvironment. For this purpose, the identification of the proportion of cell subtypes which are immune-related was first carried out in the TCGA dataset using CIRBERSORT. Interestingly, partial gene expression in 3-PPI-Mod was significantly positively correlated with the proportion of immune cells, but 3-PPI-Mod itself was not significantly correlated.

Further, the invention analyzes the relation between the 3-PPI-Mod signal and the tumor microenvironment state. The results showed that 3 mods were positively correlated with hypoxia, and not only did Mod _5 correlate with angiogenesis and inflammation relative to Mod _8 and Mod _15 (fig. 8 a). Hypoxia pathway-associated genes were significantly enriched in 3-PPI-Mod predicted high-risk tumors (fig. 8 b). The intratumoral hypoxia score has a significant correlation with the expression level of hypoxia inducible factor 1(HIF1), and the HIF1 expression level is averagely up-regulated in the high-risk subgroup identified by the 3-PPI-Mod marker (FIG. 8c, FIG. 8d, FIG. 8 e).

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. The gene combination is used as a marker in the preparation of products for predicting tumors, predicting tumor stages and/or predicting tumor prognosis;

the gene combination 3 comprises genes SPAG6, WDR63 and DNAH 9.

2. The use according to claim 1, wherein gene combination 1 is associated with developmental differentiation of epidermal cells; the gene combination 2 is related to tumor-associated pathways; the gene combination 3 is involved in the formation and/or activity of cell membrane components.

3. The use of claim 1 or 2, wherein the expression of the combination of genes is up-regulated in a high clinical stage tumor.

4. The use of any one of claims 1 to 3, wherein the combination of genes is predictive of survival in cancer patients.

5. The use of any one of claims 1 to 4, wherein the combination of genes is significantly positively correlated with the proportion of immune cells.

6. The use of any one of claims 1 to 5, wherein the combination of genes is associated with a tumor microenvironment.

7. The use of any one of claims 1 to 6, wherein the combination of genes is positively correlated with tumor hypoxia.

8. The use of claim 7, wherein the score for tumor hypoxia is significantly positively correlated with the expression level of hypoxia inducible factor 1.

9. A primer set for amplifying the combination of genes of claim 1.

10. Reagent, kit, system or device for the prediction of tumors, the prediction of the staging and/or prognosis of tumors, comprising amplification primers of a gene combination according to claim 1 or comprising a product aimed at obtaining the sequences of a gene combination according to claim 1, together with acceptable adjuvants, carriers, modules or components.