CN113151460B

CN113151460B - Gene marker for identifying lung adenocarcinoma tumor cells and application thereof

Info

Publication number: CN113151460B
Application number: CN202110127761.3A
Authority: CN
Inventors: 梁嘉琪; 陈振淙; 郑元生; 黄宜炜; 卞赟艺; 毕国澍; 詹成; 王群
Original assignee: Zhongshan Hospital Fudan University
Current assignee: Zhongshan Hospital Fudan University
Priority date: 2021-01-29
Filing date: 2021-01-29
Publication date: 2022-10-18
Anticipated expiration: 2041-01-29
Also published as: CN113151460A

Abstract

The invention relates to a gene marker for identifying lung adenocarcinoma tumor cells and application thereof, belonging to the technical field of molecular biology. The gene marker is selected from at least one of genes ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22 and TSPAN 3; the expression quantity of three genes of MAL2, CYB5A and ATP11A in lung adenocarcinoma tumor cells is 3-6 times of that of normal cells, and the risk assessment model is established by a Logistic regression method to more accurately identify the lung adenocarcinoma tumor cells of single cell type, wherein the specificity and the sensitivity of the model are close to 90%. The gene marker for identifying lung adenocarcinoma tumor cells provides a new molecular target for clinically developing a method for diagnosing lung adenocarcinoma and a medicament for treating lung adenocarcinoma.

Description

Gene marker for identifying lung adenocarcinoma tumor cells and application thereof

Technical Field

The invention relates to a gene marker for identifying lung adenocarcinoma tumor cells and application thereof, belonging to the technical field of molecular biology.

Background

Lung cancer is the most common and most fatal tumor, with morbidity and mortality ranking all first. In recent years, the advent of single cell RNA sequencing has enabled researchers to analyze tumors with higher resolution, examine gene expression of each single cell, and map tumor views including the surrounding environment, thus uncovering a rich tumor ecosystem. However, without a reliable calculation method, it is difficult to distinguish between tumor cells and normal cells. Currently, in single cell data analysis, a mainstream method is to annotate cells by using a database such as CellMarker and the like through a cell type and cell marker gene table comparison mode, namely, according to the relationship between the marker gene table and an analyzed difference gene table and the abundance of genes, to infer which cell type a certain cell cluster belongs.

However, different databases or annotation methods can generally only distinguish cell types with significant differences, and for cell types with certain similarities and subtypes in the same category, the difficulty in correctly predicting and distinguishing the cell types is high, and at present, a relatively perfect method is not available for realizing high accuracy in different data sets. Therefore, there still exists a gene which is convenient and accurate, and has higher sensitivity, specificity and application value, and can be used for distinguishing tumor cells from non-tumor cells.

Disclosure of Invention

The technical problem solved by the invention is as follows: the technical problem of how to accurately distinguish tumor cells from non-tumor cells in lung adenocarcinoma.

In order to solve the above problems, the present invention provides a gene marker for identifying lung adenocarcinoma tumor cells, selected from at least one of genes erfi 1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22, and TSPAN 3; the gene is differentially expressed in lung adenocarcinoma tumor cells and non-tumor cells.

Preferably, the gene marker for identifying lung adenocarcinoma tumor cells is a combination of genes ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22 and TSPAN 3.

Preferably, the gene marker for identifying lung adenocarcinoma tumor cells is selected from one of genes MAL2, CYB5A and ATP 11A.

The invention also provides application of the gene marker for identifying lung adenocarcinoma tumor cells, and the application is application other than diagnosis and treatment.

Preferably, the use comprises use in the manufacture of a medicament for the treatment of lung adenocarcinoma.

Preferably, the use comprises use in the manufacture of a kit for diagnosing lung adenocarcinoma.

Compared with the prior art, the invention has the following beneficial effects:

1. the gene marker for identifying lung adenocarcinoma tumor cells can accurately identify the lung adenocarcinoma tumor cells, and provides a new molecular target for clinically developing a method for diagnosing lung adenocarcinoma and a medicament for treating lung adenocarcinoma.

2. The risk assessment model established by the Logistic regression method is used for identifying the lung adenocarcinoma tumor cells of single cell type, and the specificity and the sensitivity of the risk assessment model are close to 90%.

Drawings

FIG. 1 is a diagram of the result of single cell sample and cell type clustering of lung adenocarcinoma;

FIG. 2 is a ROC plot of 11 genes such as ERRFI1, wherein the abscissa represents specificity and the ordinate represents sensitivity, in differentiating tumor cells from non-tumor cells;

FIG. 3 shows the expression of 11 genes such as ERRFI1 in different cell types;

FIG. 4 is a ROC plot of a risk assessment model constructed based on 11 genes such as ERRFI1, wherein the abscissa represents specificity and the ordinate represents sensitivity;

FIG. 5 is a schematic representation of the results of flow cytometry validation of cell surface markers and sorting of tumor and non-tumor cells;

FIG. 6 is a graph showing the results of RT-qPCR method for detecting the fold difference of mRNA expression of 11 genes such as ERRFI1 in tumor cell samples and non-tumor cell samples, wherein the ordinate represents the fold difference of mRNA expression.

Detailed Description

In order to make the invention more comprehensible, preferred embodiments are described in detail below with reference to the accompanying drawings.

Example 1

Single cell sequencing result analysis of lung adenocarcinoma tissue:

1.1 by analyzing the single cell RNA sequencing data results of 17 tumor tissue samples of lung adenocarcinoma and 12 normal lung tissue samples, 204157 single cell gene expression data were obtained, wherein 22491 tumor cells and 181666 non-tumor cells (alveolar cells, B cells, endothelial cells, epithelial cells, fibroblasts, mast cells, myeloid cells, and T cells) were included, and the single cell samples and cell type clustering results of lung adenocarcinoma are shown in FIG. 1.

1.2 Using the R language Seurat package, the differences in gene expression between the tumor and non-tumor cells were analyzed and. + -. 0.5 log was chosen, after normalization of the expression values of each gene in each cell in the single cell sample by the "ScaleData" function ₂ And F, FC threshold value, and finally screening 1655 genes with differential expression, wherein 949 genes with expression remarkably higher than that of non-tumor cells in tumor cells and 706 genes with expression remarkably higher than that of tumor cells in non-tumor cells. Then, 949 genes which were selected to be significantly highly expressed in tumor cells were analyzed by using a ROC curve, and the results of 51 gene expression cases with an area under the ROC curve (AUC) value of more than 0.80 in tumor cell differentiation and ROC analysis are shown in Table 1.

TABLE 1 Gene expression profiles and ROC analysis for specific high expression in lung adenocarcinoma tumor cells

Since some of the 51 genes have been used as markers for identifying tumor cells, and some genes have been studied in the field of lung cancer, and the expression of the genes has been deleted, eleven genes ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22, and TSPAN3 were screened and further studied, and it was found from the data in Table 1 that the areas under the ROC curve of the eleven genes were all 0.80 or more. The ROC curves of erfi 1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22, and TSPAN3 genes in distinguishing between tumor cells and non-tumor cells are shown in fig. 2, and the distribution of their expression levels in each type of cells in the single-cell cluster map is shown in fig. 3.

Example 2

A Logistic regression method is applied to establish a risk assessment model to distinguish single tumor cells from non-tumor cells:

2.1 the ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22 and TSPAN3 genes are included in a Logistic regression model, and the obtained risk assessment model is as follows:

wherein a, b, c, d, e, f, g, h, i, j, and k represent the normalized expression values of mRNA of eleven genes, i.e., ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22, and TSPAN3, respectively.

2.2 the results of the above model were verified using the gene expression data of the single cell of 1.1 in example 1, and the results showed that the specificity and sensitivity of the model were close to 90% compared to the single gene, and the ROC curve is shown in fig. 4, so that the prediction model was further improved in prediction performance compared to the single gene, and tumor cells and non-tumor cells could be more accurately distinguished. Therefore, the risk assessment model can be applied to the analysis of cell types of any lung adenocarcinoma single cell type: according to the model, the expression values normalized by the mrnas of the erfi 1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22, and TSPAN3 genes in the single-cell data are substituted into the model, and the P value obtained is closer to 1, and the higher the probability of being a tumor cell, the closer to 0 the P value, the lower the probability of being a tumor cell, and the higher the probability of being a non-tumor cell.

Example 3

Flow cytometry sorting of tumor cells and non-tumor cells to verify gene expression:

3.1 flow analysis of tumor and normal tissues of 5 patients with lung adenocarcinoma was verified by using flow cytometric sorting. Firstly, EPCAM, FLOR1 and CD45 are respectively used as surface markers of tumor cells, normal epithelial cells and lymphocytes, 5 pairs of sample tissues are dissociated into cells, the cells are respectively stained by corresponding fluorescein coupled antibodies, and then the tumor cells of EPCAM + CD 45-and the non-tumor cells of EPCAM-FLOR1+/CD45+ are separated by adopting a flow cytometry method, and the result is shown in figure 5.

3.2 detection of fold difference in mRNA expression of these 11 genes by RT-qPCR method in the sorted tumor cells and non-tumor cells, the results are shown in FIG. 6. From the results shown in FIG. 6, it is understood that 11 genes of ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22 and TSPAN3 are expressed in tumor cells at a higher level than in non-tumor cells, particularly three genes of MAL2, CYB5A and ATP11A, and that the expression level in tumor cells is 3 to 6 times higher than that in non-tumor cells. Therefore, 11 genes such as ERRFI1 can be used for distinguishing lung adenocarcinoma tumor cells from non-tumor cells. Especially three genes of MAL2, CYB5A and ATP11A, if one or more of the three genes are expressed in high amount in the lung cancer sample, the sample is tumor.

While the invention has been described with respect to a preferred embodiment, it will be understood by those skilled in the art that the foregoing and other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.

Claims

1. A genetic marker for identifying lung adenocarcinoma tumor cells, which is a combination of genes erfi 1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22 and TSPAN 3.

2. Use of a reagent for detecting a genetic marker for identifying lung adenocarcinoma tumor cells according to claim 1 in the preparation of a kit for diagnosing lung adenocarcinoma.