CN113151460A - Gene marker for identifying lung adenocarcinoma tumor cells and application thereof - Google Patents
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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; wherein, 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, the invention also establishes a risk assessment model by a Logistic regression method so as to more accurately identify the lung adenocarcinoma tumor cells with single cell type, and the specificity and the sensitivity of the model are both close to 90 percent. 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
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 first for all tumors. 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 may still exist a gene which is convenient, 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 the 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 the single cell type, and the specificity and the sensitivity of the risk assessment model are close to 90 percent.
Drawings
FIG. 1 is a diagram of a single cell sample of lung adenocarcinoma and a result of cell type clustering;
FIG. 2 is a ROC plot of 11 genes such as ERRFI1, wherein the abscissa represents specificity and the ordinate represents sensitivity, in distinguishing 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 to detect the difference of multiple mRNA expression of 11 genes such as ERRFI1 in tumor cell samples and non-tumor cell samples, wherein the ordinate represents the difference of multiple 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 sample of lung adenocarcinoma and the cell type clustering results are shown in FIG. 1.
1.2 Using the R language Seurat package, the gene expression differences between the tumor and non-tumor cells were analyzed after normalization of the expression values of each gene of each cell in the single-cell sample by the "ScaleData" function, and. + -. 0.5 was chosen as log2And 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 existing genes were deleted, eleven genes, ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22, and TSPAN3, were selected and further studied, and the data in Table 1 revealed that the ROC curves of these eleven genes all had an area of 0.80 or more. The ROC curves of erfi 1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1a1, SMIM22, and TSPAN3 genes for distinguishing between tumor cells and non-tumor cells are shown in fig. 2, and the distribution of the expression levels of each of them in the respective 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 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 gene alone, and the ROC curve is shown in fig. 4, therefore the prediction model has further improved prediction performance compared to the single gene, and can more accurately distinguish tumor cells from non-tumor cells. Thus, the risk assessment model can be applied to the analysis of cell types of any lung adenocarcinoma single cell type: according to the above 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 above model, and the obtained P value is closer to 1, and it is proved that the higher the probability that the P value is a tumor cell, the closer to 0 the P value, the lower the probability that the P value is a tumor cell, and the higher the probability that the P value is a non-tumor cell.
Example 3
Flow cytometry sorting of tumor cells and non-tumor cells to verify gene expression:
3.1 flow assay validation of tumor and normal tissues of 5 patients with lung adenocarcinoma was performed by using flow cytometric sorting. EPCAM, FLOR1 and CD45 are used as surface markers of tumor cells, normal epithelial cells and lymphocytes respectively, 5 pairs of sample tissues are dissociated into cells, the cells are stained by corresponding fluorescein-coupled antibodies respectively, and the tumor cells of EPCAM + CD 45-and the non-tumor cells of EPCAM-FLOR 1+/CD45+ are separated by a flow cytometry method, and the result is shown in FIG. 5.
3.2 detection of fold difference in mRNA expression of these 11 genes in sorted tumor and non-tumor cells by RT-qPCR method, the results are shown in FIG. 6. From the results in fig. 6, it is understood that the expression levels of 11 genes of erfi 1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1a1, SMIM22, and TSPAN3 in tumor cells are higher than those of non-tumor cells, particularly three genes of MAL2, CYB5A, and ATP11A, and the expression levels in tumor cells are 3 to 6 times higher than those 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 various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (6)
1. A genetic marker that recognizes lung adenocarcinoma tumor cells, selected from at least one of the genes erfi 1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1a1, SMIM22, and TSPAN 3.
2. The genetic marker for identifying lung adenocarcinoma tumor cells according to claim 1, which is a combination of genes ERRFI1, MAL2, RNASE1, ATP11A, CYB5A, LGALS3BP, LPCAT1, SPINT2, ATP1A1, SMIM22 and TSPAN 3.
3. The genetic marker for identifying lung adenocarcinoma tumor cells according to claim 1, wherein the gene is selected from one of the genes MAL2, CYB5A and ATP 11A.
4. Use of a genetic marker for the identification of lung adenocarcinoma tumor cells according to any one of claims 1 to 3, characterized in that said use is in addition to diagnosis and therapy.
5. The use according to claim 4, wherein the use comprises use in the manufacture of a medicament for the treatment of lung adenocarcinoma.
6. The use according to claim 4, wherein said use comprises use in the preparation of a kit for the diagnosis of lung adenocarcinoma.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113755602A (en) * | 2021-10-20 | 2021-12-07 | 复旦大学附属中山医院 | A gene marker for identifying lung adenocarcinoma tumor stem cells and its application |
| WO2023004624A1 (en) * | 2021-07-28 | 2023-02-02 | 北京沙东生物技术有限公司 | Myeloma biomarker lgals3bp and use thereof |
| CN115993453A (en) * | 2022-07-22 | 2023-04-21 | 四川大学 | Methods and kits for the diagnosis and treatment of RDAA-positive diseases |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103874770A (en) * | 2011-08-08 | 2014-06-18 | 卡里斯生命科学卢森堡控股有限责任公司 | Biomarker compositions and methods |
| WO2014151117A1 (en) * | 2013-03-15 | 2014-09-25 | The Board Of Trustees Of The Leland Stanford Junior University | Identification and use of circulating nucleic acid tumor markers |
| US20180106806A1 (en) * | 2016-10-13 | 2018-04-19 | Regents Of The University Of Minnesota | Tumor Analytical Methods |
-
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- 2021-01-29 CN CN202110127761.3A patent/CN113151460B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103874770A (en) * | 2011-08-08 | 2014-06-18 | 卡里斯生命科学卢森堡控股有限责任公司 | Biomarker compositions and methods |
| WO2014151117A1 (en) * | 2013-03-15 | 2014-09-25 | The Board Of Trustees Of The Leland Stanford Junior University | Identification and use of circulating nucleic acid tumor markers |
| US20180106806A1 (en) * | 2016-10-13 | 2018-04-19 | Regents Of The University Of Minnesota | Tumor Analytical Methods |
Non-Patent Citations (4)
| Title |
|---|
| CHUNHUA WEI ET AL.: ""LPCAT1 promotes brain metastasis of lung adenocarcinoma by up-regulating PI3K/AKT/MYC pathway"", 《JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH》 * |
| JIAQI LIANG ET AL.: ""Signatures of malignant cells and novel therapeutic targets revealed by single-cell sequencing in lung adenocarcinoma"", 《CANCER MEDICINE》 * |
| YING LIU ET AL.: ""Identifcation of feature genes for smoking-related lung adenocarcinoma based on gene expression profle data"", 《ONCOTARGETS AND TERAPY》 * |
| Z-H YANG ET AL.: ""Abnormal gene expression and gene fusion in lung adenocarcinoma with high-throughput RNA sequencing"", 《CANCER GENE THERAPY》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023004624A1 (en) * | 2021-07-28 | 2023-02-02 | 北京沙东生物技术有限公司 | Myeloma biomarker lgals3bp and use thereof |
| CN113755602A (en) * | 2021-10-20 | 2021-12-07 | 复旦大学附属中山医院 | A gene marker for identifying lung adenocarcinoma tumor stem cells and its application |
| CN115993453A (en) * | 2022-07-22 | 2023-04-21 | 四川大学 | Methods and kits for the diagnosis and treatment of RDAA-positive diseases |
| CN115993453B (en) * | 2022-07-22 | 2025-03-25 | 四川大学 | Methods and kits for diagnosis and treatment of RDAA-positive diseases |
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