Biomarker for histopathological subtype identification and prognosis prediction of lung adenocarcinoma and use thereof

Abstract

A composition for the histopathological subtype identification and prognosis prediction of lung adenocarcinoma, a kit, and a histopathological subtype identification method, according to an embodiment of the present invention, clearly distinguish histopathological subtypes of lung adenocarcinoma having undergone surgery at cellular and molecular levels, thereby not only enabling clear histopathological subtype identification and prognosis prediction of lung adenocarcinoma in patients, but also enabling effective subtype identification even for patients who have undergone biopsy alone. Therefore, through non-invasive and repeatable monitoring, plans for anticancer adjuvant therapy before and after surgery and post-surgical follow-up can be established more precisely, and the present invention can be effectively used to establish personalized therapeutic approaches for individual patients.

Description

Biomarkers for the Identification of Histopathological Subtypes and Prognosis Prediction of Lung Adenocarcinoma and Their Uses

[0001] The present invention relates to a biomarker for the identification of histopathological subtypes of lung adenocarcinoma and the prediction of prognosis, and the use thereof.

[0002] Lung cancer refers to histologically and molecularly heterogeneous malignant tumors that develop in the lungs, and it is the most common cancer worldwide. In 2020, it accounted for 18% of cancer-related deaths globally. Primary lung cancer is classified into non-small cell lung cancer and small cell lung cancer based on the size and morphology of the cancer cells; when viewed under a microscope, cells are classified as small cell lung cancer if they are small, and as non-small cell lung cancer if they are not small. Furthermore, non-small cell lung cancer is further divided into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma according to histological type.

[0003] Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer, accounting for approximately 40% of all lung cancer patients. Lung adenocarcinoma exhibits significant heterogeneity in clinical and radiological presentation, histological appearance, surgical outcomes, and molecular profile, and is a subtype that is difficult to treat, particularly in advanced stages. Invasive non-mucinous lung adenocarcinoma can be further classified according to extensive histological subdivisions into the major histological patterns of lepidic, acinar, papillary, micropapillary, and solid subtypes; however, most lung adenocarcinomas exhibit a mixture of these patterns. Among these, the micropapillary and solid subtypes are associated with a poor prognosis.

[0004] Previous studies have demonstrated that chromosomal instability and oncogenic gene mutations are associated with histological subtypes of lung adenocarcinoma. Furthermore, it has been reported that solid and micropapillary subtypes exhibit a greater accumulation of poorly differentiated cancer cells and immunosuppressive immune cells compared to other subtypes with better prognoses. However, despite the prognostic and predictive significance of histological subclassification in lung adenocarcinoma, there are currently no technologies available to identify detailed differences at the cellular and molecular levels within lung adenocarcinoma tumors.

[0005] In order to solve the aforementioned problems, the inventors completed the present invention by classifying molecular subtypes of lung adenocarcinoma and characterizing them through differentially expressed gene analysis (DEG analysis) and single-cell transcriptome analysis using the SCENIC algorithm, and by discovering HMGA1 as a cancer cell-specific marker for solid subtype lung adenocarcinoma.

[0006] Therefore, the objective of the present invention is to provide a composition for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma comprising a preparation that measures the expression level of HGMA1.

[0007] Another objective of the present invention is to provide a kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma containing HMGA1.

[0008] Another objective of the present invention is to provide a method for providing information on a kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma, comprising a preparation that measures the expression level of HMGA1.

[0009] Another objective of the present invention is to provide a method for identifying histopathological subtypes of lung adenocarcinoma including HMGA1.

[0010] The terms used in this specification are for illustrative purposes only and should not be interpreted as being limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "comprising" or "having" are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0011] Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0012] In the case of duplicate content or terms in this invention, their description has been omitted to avoid excessive complexity in this specification.

[0013]

[0014] To achieve the above objective, one aspect of the present invention provides a composition for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma comprising a preparation for measuring the expression level of HGMA1.

[0015] The term "histopathology" as used in this invention refers to a field of medicine that studies the causes and nature of diseases by observing the microstructures of tissues and cells under a microscope. Histopathology is utilized to diagnose diseases, evaluate prognoses, and verify treatment responses by analyzing tissue samples obtained from individuals. Histopathology plays a crucial role in accurately diagnosing the type and stage of cancer, particularly in oncology, and can provide a basis for determining the direction of treatment through pathological findings. In this invention, histopathological analysis can be utilized in the process of identifying subtypes of lung adenocarcinoma.

[0016] As used in the present invention, the term “prognosis” refers to determining the presence of recurrence, metastasis, drug responsiveness, resistance, etc., before and after treatment for individuals who are not yet diagnosed, have been diagnosed, or whose histopathological subtypes have been identified. In the present invention, histopathological subtypes of patients with lung adenocarcinoma are identified using biomarkers, and thereby, it is possible to predict whether the future survival prognosis will be good.

[0017] Here, the term “biomarker” generally includes all organic biomolecules such as polypeptides, proteins, nucleic acids, genes, lipids, glycolipids, glycoproteins, sugars, etc., which are substances detectable in biological samples and capable of detecting biological changes. In the present invention, the HMGA1 gene or protein may be used as a biomarker for identifying histopathological subtypes of lung adenocarcinoma or for predicting the prognosis.

[0018] To detect such HMGA1 gene or protein, a reagent for measuring the expression level of the HMGA1 gene or protein is required, and said reagent may be one or more selected from the group consisting of primer pairs, probes, antisense nucleotides, antibodies, oligopeptides, ligands, RNA, and aptamers that specifically bind to HMGA1.

[0019]

[0020] Another aspect of the present invention provides a kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma comprising HMGA1, comprising the following configuration:

[0021] (a) an antibody or aptamer that specifically binds to the HMGA1 protein or its expression product; and

[0022] (b) A detection reagent for measuring the amount of HMGA1 bound to the antibody or aptamer.

[0023] The “kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma” used in the present invention refers to a substance capable of identifying tumor subtypes or predicting the prognosis through biological samples collected from a test subject, more specifically from an individual for whom a clear subtype of lung cancer or lung adenocarcinoma has not been identified, thereby enabling the rapid, accurate, and convenient determination of the test subject’s subtype.

[0024] The above kit may include, without limitation, kits based on conventional mRNA expression and protein quantification analysis.

[0025] In one embodiment of the present invention, the kit may be one or more selected from the group consisting of a PCR (polymerase chain reaction) kit, an RT-PCR (reverse transcription PCR) kit, an NGS (next generation sequencing) kit, a protein kit, and an array kit.

[0026] For example, when the above kit is applied to a PCR amplification process, the kit of the present invention may optionally include reagents required for PCR amplification, such as a buffer, DNA polymerase, DNA polymerase cofactor, and dNTPs, and when the above kit is applied to an immunoassay, the kit of the present invention may optionally include a secondary antibody and a substrate of a label. In addition, the kit according to the present invention may be manufactured into a plurality of separate packages or compartments containing the above-mentioned reagent components.

[0027] The term "antibody" as used in the present invention refers to a specific protein molecule directed toward an antigenic site. In the present invention, it refers to an antibody that specifically binds to each protein, and includes monoclonal antibodies, polyclonal antibodies, and recombinant antibodies.

[0028] Here, “specifically binding” means having a superior binding affinity to a target substance compared to other substances, to the extent that the presence of the target substance can be detected by binding. In addition, the antibody includes not only a complete form having two full-length light chains and two full-length heavy chains, but also functional fragments of the antibody molecule. A functional fragment of the antibody molecule refers to a fragment that possesses at least an antigen-binding function and may be Fab, F(ab'), F(ab')2, Fv, etc.

[0029]

[0030] Another aspect of the present invention provides a method for providing information on a kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma, comprising a preparation for measuring the expression level of HMGA1, comprising the following steps:

[0031] (a) A step of collecting a biological sample from an individual for which histopathological subtype identification is required;

[0032] (b) a step of measuring the expression level of the HMGA1 gene or protein with respect to the biological sample;

[0033] (c) a step of comparing the measured expression level of the HMGA1 gene or protein with a reference value; and

[0034] (d) A step of identifying solid subtype lung adenocarcinoma or determining that the prognosis of lung adenocarcinoma is poor when the measured expression level of the HMGA1 gene or protein is higher than the reference value.

[0035] Step (a) above is the process of collecting biological samples from individuals or subjects requiring examination to determine the subtype of lung adenocarcinoma or to predict the prognosis.

[0036] According to one embodiment of the present invention, the individual in step (a) may be an individual that is not diagnosed with lung cancer or lung adenocarcinoma, or in which a subtype of lung cancer or lung adenocarcinoma has not been identified.

[0037] In addition, the biological sample of step (a) above may be one or more selected from the group consisting of blood, plasma, serum, lymph fluid, saliva, urine, and tissue.

[0038] In addition, according to one embodiment of the present invention, the expression level of the HMGA1 gene in step (b) may be measured by one or more methods selected from the group consisting of fluorescence in situ hybridization, next generation sequencing polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time PCR, real-time RT-PCR, nuclease protection assay, in situ hybridization, DNA microarray, and Northern blot.

[0039] In addition, the expression level of the HMGA1 protein in step (b) above may be measured by one or more methods selected from the group consisting of enzyme-linked immunosorbent assay (ELISA), western blot, immunoprecipitation, flow cytometry, immunohistochemistry, immunofluorescence, and protein microarray.

[0040] In addition, according to one embodiment of the present invention, the control group in step (c) may be normal tissue without lung cancer lesions, normal tissue adjacent to the tumor of a lung adenocarcinoma patient, or normal tissue of other organs.

[0041] In addition, the reference value of step (c) above may be the expression level of HMGA1 in normal tissue without lung cancer lesions, normal tissue adjacent to the tumor of a patient with lung adenocarcinoma (tumor adjacent normal tissue), and normal tissue of other organs.

[0042] In addition, according to one embodiment of the present invention, in step (d), the case where the expression level of the gene and protein of HMGA1 is higher than the reference value is a case where the numerical and quantitative changes are upexpressed compared to the control group.

[0043] In addition, the method for comparing the HMGA1 gene expression value higher than the reference value in step (d) above may be to perform a comparative analysis using one or more methods selected from the group consisting of fluorescence in situ hybridization, next generation sequencing polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time PCR, real-time RT-PCR, nuclease protection assay, in situ hybridization, DNA microarray, and Northern blot.

[0044] In addition, the method for comparing HMGA1 protein values ​​higher than the reference value in step (d) above may be to perform comparative analysis using one or more methods selected from the group consisting of fluorescence in situ hybridization, chromatin immunoprecipitation, and Western blot.

[0045]

[0046] Another aspect of the present invention provides a method for identifying histopathological subtypes of lung adenocarcinoma comprising the following steps:

[0047] (a) A step of analyzing single-cell transcriptome data obtained from a sample taken from a patient with lung adenocarcinoma;

[0048] (b) a step of analyzing the expression level of the HMGA1 gene in the above sample; and

[0049] (c) A step of classifying the histopathological subtypes of lung adenocarcinoma based on the above analysis results.

[0050] The term "histopathological subtype identification" as used in this invention refers to distinguishing various subtypes of a specific disease, particularly complex diseases such as cancer.

[0051] The term "single-cell transcriptome analysis" as used in this invention refers to an analytical method that identifies the gene expression profile of each cell by analyzing transcriptomes at the individual cell level, and is used to elucidate cell types, states, and functional diversity within heterogeneous cell populations. Single-cell transcriptome analysis is primarily utilized in oncology, immunology, and developmental biology, and in this invention, it is used to define specific cell subtypes of lung adenocarcinoma or to study intercellular interactions within tissues.

[0052] The term "SCENIC algorithm" as used in this invention refers to an analytical method that reconstructs intracellular gene-regulatory networks based on single-cell transcriptome data and infers the regulatory status in each cell. SCENIC utilizes single-cell transcriptome sequencing data to analyze correlations between gene expression patterns and identify activated transcription factors to construct regulatory networks. This enables an understanding of differences between cell types and the identification of important transcription factors in specific cell states. In this invention, the SCENIC algorithm is utilized to analyze gene regulatory network activity in patients with lung adenocarcinoma, and based on this, it can classify and characterize molecular subtypes of lung adenocarcinoma.

[0053] The composition, kit, and method for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma comprising a preparation for measuring the expression level of HGMA1 according to the present invention can not only more clearly identify histopathological subtypes and predict the prognosis of lung adenocarcinoma in patients who have undergone surgery at the cellular and molecular levels, but also effectively identify subtypes even in patients who have undergone only a biopsy. Therefore, plans for pre- and post-operative adjuvant chemotherapy and post-operative follow-up examinations can be established more precisely, and can be usefully utilized to establish personalized treatment approaches for each individual patient through non-invasive and repeatable monitoring.

[0054] Figure 1 is a diagram schematically illustrating the research design process of the present invention.

[0055] Figure 2 is a UMAP plot showing sub-clusters and sub-groups within cancer cells classified according to the present invention.

[0056] Figure 3 is a heatmap showing the signature scores of cancer cell-related signaling pathways for each cluster and cancer cell group.

[0057] Figure 4 is a violin plot comparing the differences in signature scores of cancer-related signaling pathways according to histological subtypes (A / P, MP, solid type).

[0058] Figure 5 shows the levels of HMGA1 protein in serum according to histological subtypes (A / P, MP, solid type).

[0059] Figure 6 is a heatmap of the results of analyzing the activity of gene regulatory networks in cancer cells by patient.

[0060] Figure 7 shows the difference in patient survival rates according to the degree of HMGA1 gene expression in lung adenocarcinoma.

[0061] The following examples are solely for the purpose of explaining the invention more specifically, and it will be obvious to those skilled in the art that the scope of the invention is not limited by these examples according to the gist of the invention.

[0062] Example 1. Sample preparation and sequencing for single-cell transcriptome analysis

[0063] 1-1. Sample Acquisition and Preparation

[0064] Among patients who had their primary cancer tissue dissected via surgical resection, 18 patients with lung adenocarcinoma who had no history of neoadjuvant therapy were selected. Subsequently, the 18 selected patients were classified according to histological subtypes into the A / P subtype (n=11), in which acinar and papillary patterns were dominant and solid and micropapillary patterns accounted for less than 20%; the MP subtype (n=3), in which micropapillary patterns accounted for more than 20%; and the solid subtype (n=4), in which solid patterns accounted for more than 20%. The above process is illustrated in Figure 1.

[0065] Subsequently, cancer tissue was collected from each patient via surgical resection. The collected tissue was transported in a postoperative preservation solution and prepared into a single-cell suspension following enzymatic treatment. The cell viability and quality of the suspension were then evaluated to confirm its suitability for sequencing.

[0066]

[0067] 1-2. Single-cell RNA sequencing

[0068] The 10x Genomics Chromium platform was used for single-cell RNA sequencing. Approximately 10,000 cells per sample were loaded onto the platform, and each cell was assigned a unique barcode. Subsequently, high-throughput sequencing was performed following cDNA synthesis, amplification, and library construction to obtain raw sequencing data (fastq format).

[0069]

[0070] 1-3. Data Processing and Integration

[0071] Raw sequencing data was preprocessed using Cell Ranger software. Subsequently, for data processing and integration, the standardized advanced analysis pipeline, Seurat R library, was applied to perform quality checks, normalization, screening of highly-variable genes (HVGs), and dimensionality reduction on single-cell transcriptome data, while the harmony R library was used to remove batch effects. Finally, the processed data were integrated to generate a single dataset. Based on the above data, cell type classification analysis was performed, followed by an analysis of changes in transcriptome patterns for each cell type. In particular, to identify differences among tumor subtypes within the cancer cell population, the activity of gene-regulatory networks was predicted at the single-cell level using the SCENIC algorithm. Furthermore, to characterize tumor properties according to tissue subtypes, signature scores were calculated for gene groups associated with various known molecular characteristics of cancer cells to identify the molecular phenotypes of tissue-specific cancer cells.

[0072]

[0073] Example 2: Molecular Subtype Classification and Characterization of Lung Adenocarcinoma

[0074] 2-1. Methods for Identifying Cancer Cell Subgroups

[0075] Based on the data obtained in Examples 1-3, epithelial cell clusters positive for the epithelial cell adhesion molecule (EPCAM+) were selectively extracted. Next, the Numbat R library was applied to specifically select only cancer cells. Re-clustering analysis was performed on the extracted cancer cell clusters through transcriptome profiling. Based on transcriptome heterogeneity, the cells were initially classified into six major cancer cell clusters, followed by an in-depth analysis of the molecular phenotypes of each cluster. Based on the results of the molecular phenotype analysis, they were finally reclassified into three distinct molecular subtypes. Through this stepwise classification method, cancer cell subgroups were systematically identified and characterized.

[0076]

[0077] 2-2. Cancer Cell Re-colonization via Transcriptome Profiling

[0078] To define and characterize molecular subtypes of lung adenocarcinoma, signaling pathway activity analysis of cancer cells and gene regulatory network activity analysis using the SCENIC algorithm were performed. Cancer cells exhibiting transcriptional characteristics of ciliated and goblet cells were excluded from subsequent analyses aimed at identifying lung adenocarcinoma-specific cancer cells, as these are cell types primarily distributed in the airway. Subsequently, the remaining 32,527 cancer cells were re-clustered into six distinct clusters that could be classified into three groups based on molecular phenotype, and the results are shown in Figure 2.

[0079]

[0080] 2-3. Reclassification of Molecular Subtypes and Analysis of Characteristics for Each Molecular Subtype

[0081] Six initial cancer cell clusters were systematically reclassified into three distinct molecular subtypes (Group 1 to Group 3), and the signature scores of cancer cell-related signaling pathways for each cluster and cancer cell group are shown in Figure 3.

[0082] As shown in Figure 3, among the three defined molecular subtypes, Group 1 was confirmed to preserve AT2 characteristics through increased expression of standard alveolar type 2 epithelial cell (AT2) markers (including ABCA3, ETV5, and SFTPC). In contrast, Groups 2 and 3 exhibited a loss of AT2 characteristics and high malignancy-related traits, as AT2 marker gene expression was observed to gradually decrease along with increased glycolysis and MYC target activity; furthermore, Group 3 was found to additionally display high cell division activity in addition to the characteristics of Group 2.

[0083] 2-4. Analysis of Characteristics by Histological Subtype

[0084] Differences in signature scores of cancer-related signaling pathways according to each histological subtype (A / P, MP, solid type) were compared, and the results are shown in Figure 4.

[0085] As shown in Figure 4, the solid subtype showed a significantly reduced AT2 signature score compared to other subtypes, and upregulation of hypoxia, glycolysis, MYC, and mesenchymal transition (EMT) signals in the epithelium was observed, confirming that cell plasticity increased in the solid subtype.

[0086]

[0087] Example 3: Discovery of HMGA1, a Blood-Based Biomarker for Solid Subtype Lung Adenocarcinoma, and Verification of Clinical Utility

[0088] To search for biomarkers for solid subtype lung adenocarcinoma, genes specifically activated in solid subtype lung adenocarcinoma compared to other subtypes were identified through SCENIC analysis and TCGA dataset analysis as in Examples 1-3, and regulon (HMGA1), which exhibits specifically high activity in cancer cells of the solid subtype among lung adenocarcinomas, was discovered. Subsequently, to verify the utility of HMGA1, serum HMGA1 protein levels were compared according to histological subtypes (A / P, MP, solid subtype), and the results are shown in Figure 5.

[0089] As shown in Figure 5, it was confirmed that the expression of HMGA1 protein in serum was increased in patients with solid subtype lung adenocarcinoma compared to A / P and MP types, thus confirming that HMGA1 is a useful biomarker for identifying solid subtype lung adenocarcinoma.

[0090] In addition, the results of analyzing patient serum samples of each histological subtype are shown in Figure 5.

[0091] As shown in Figure 6, when compared with the gene-regulatory network activation patterns of cancer cells by patient, it was confirmed that the activity of HMGA1 regulon was specifically higher in solid subtype lung adenocarcinoma compared to other subtypes.

[0092] In addition, to determine the clinical significance of the regulon, the difference in survival rates according to the expression of HMGA1, a transcription factor of regulon, was compared using TCGA (The Cancer Genome Atlas), the most widely used public database in the field of oncology, and the results are shown in Figure 7.

[0093] As shown in Figure 7, it was confirmed that the high expression pattern of the HMGA1 gene showed a statistically significant correlation with the low survival rate of patients.

[0094]

[0095] Through these results, it was confirmed that the molecular subtype classification method can provide an important foundation for establishing personalized treatment strategies and predicting prognosis by systematically identifying the heterogeneity of lung adenocarcinoma and clearly defining the characteristics of each subtype.

[0096] The composition, kit, and method for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma comprising a preparation for measuring the expression level of HGMA1 according to the present invention can not only more clearly identify histopathological subtypes and predict the prognosis of lung adenocarcinoma in patients who have undergone surgery at the cellular and molecular levels, but also effectively identify subtypes even in patients who have undergone only a biopsy. Therefore, plans for pre- and post-operative adjuvant chemotherapy and post-operative follow-up examinations can be established more precisely, and since it can be usefully utilized to establish personalized treatment approaches for individual patients through non-invasive and repeatable monitoring, it has industrial applicability.

Claims

1. A composition for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma comprising a preparation for measuring the expression level of HGMA1.

2. In Paragraph 1, The agent for confirming the expression level of the above HMGA1 is a composition for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma consisting of a gene or protein.

3. In Paragraph 1, A composition for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma, comprising a preparation for measuring the expression level of the above-mentioned HMGA1 selected from the group consisting of primer pairs, probes, antisense nucleotides, antibodies, oligopeptides, ligands, RNA, and aptamers that specifically bind to HMGA1.

4. A kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma containing HMGA1, (a) an antibody or aptamer that specifically binds to the HMGA1 protein or its expression product; and (b) a detection reagent for measuring the amount of HMGA1 bound to the antibody or aptamer; A kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma, including 5. In Paragraph 4, The above-mentioned kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma is a kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma selected from the group consisting of PCR kits, RT-PCR kits, protein kits, and array kits.

6. A method for providing information on a kit for identifying histopathological subtypes or predicting the prognosis of lung adenocarcinoma comprising a preparation for measuring the expression level of HMGA1; (a) A step of collecting a biological sample from an individual for which histopathological subtype identification is required; (b) a step of measuring the expression level of the HMGA1 gene or protein with respect to the biological sample; (c) a step of comparing the measured expression level of the HMGA1 gene or protein with a reference value; and (d) A step of diagnosing solid subtype lung adenocarcinoma or determining that the prognosis of lung adenocarcinoma is poor when the measured expression level of the HMGA1 gene or protein is higher than the reference value; A method of providing information including 7. In Paragraph 6, A method for providing information, wherein the biological sample of step (a) above is one or more selected from the group consisting of blood, plasma, serum, lymph fluid, saliva, urine, and tissue.

8. In Paragraph 6, A method for providing information, wherein the expression level of the HMGA1 gene in step (b) above is measured by one or more methods selected from the group consisting of fluorescence in situ hybridization, next generation sequencing, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time PCR, real-time RT-PCR, nuclease protection assay, in situ hybridization, DNA microarray, and Northern blot.

9. In a method for identifying histopathological subtypes of patients with lung adenocarcinoma, (a) A step of analyzing single-cell transcriptome data obtained from a sample taken from a lung adenocarcinoma patient; (b) a step of analyzing the expression level of the HMGA1 gene in the above sample; and (c) A step of classifying the histopathological subtypes of lung adenocarcinoma based on the above analysis results; A method for identifying histopathological subtypes of lung adenocarcinoma including 10. In Paragraph 9, A method for identifying histopathological subtypes, wherein the biological sample of step (a) above is one or more selected from the group consisting of blood, plasma, serum, lymph fluid, saliva, urine, and tissue.

11. A method for predicting the prognosis of lung adenocarcinoma comprising a preparation for measuring the expression level of HMGA1; (a) A step of collecting a biological sample from an individual for which histopathological subtype identification is required; (b) a step of measuring the expression level of the HMGA1 gene or protein with respect to the biological sample; (c) a step of comparing the measured expression level of the HMGA1 gene or protein with a reference value; and (d) A step of diagnosing solid subtype lung adenocarcinoma or determining that the prognosis of lung adenocarcinoma is poor when the measured expression level of the HMGA1 gene or protein is higher than the reference value; A prognosis prediction method including

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