Method for predicting risk of pulmonary fibrosis after lung adenocarcinoma treatment by integrating transcriptomic features and application thereof in preparation of kit

Abstract

The present application belongs to the field of biomedical technology, and particularly relates to a risk prediction method for lung fibrosis after anti-lung adenocarcinoma treatment by integrating transcriptome characteristics and application thereof in preparation of a kit. The risk prediction method comprises the following steps: (1) setting expression data retrieval strategy and inclusion and exclusion criteria of lung fibrosis and primary lung adenocarcinoma; (2) identifying differential genes of lung adenocarcinoma in TCGA; (3) obtaining molecular targets and constructing score and prognosis relationship; and (4) verifying the score system. The risk prediction method provided by the present application is used for assisting in identifying LUAD patients who may have lung fibrosis related molecular characteristics, and provides molecular basis for patient stratification in anti-tumor treatment and lung fibrosis related treatment intervention.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a method for predicting the risk of pulmonary fibrosis after anti-lung adenocarcinoma treatment by integrating transcriptome features and its application in the preparation of reagent kits. Background Technology

[0002] Lung adenocarcinoma (LUAD) is one of the most common histological types of lung cancer, exhibiting significant molecular heterogeneity. With the development of targeted therapy, immunotherapy, and other anti-tumor treatments, treatment options for LUAD patients are constantly increasing. However, some LUAD patients may also have molecular alterations related to pulmonary fibrosis, or have a potential risk of developing and progressing pulmonary fibrosis during anti-tumor treatment, thus affecting treatment tolerability and efficacy.

[0003] Pulmonary fibrosis is a chronic, progressive disease characterized by alveolar structural damage, abnormal fibroblast activation, excessive extracellular matrix deposition, and lung tissue remodeling. Lung fibrosis and pulmonary fibrosis (LUAD) may share some common molecular changes in abnormal cell proliferation, tissue damage repair, inflammatory responses, and extracellular matrix remodeling. Previous studies have found that some genes can be highly expressed in both LUAD and pulmonary fibrosis tissues, suggesting that these genes may possess both tumor-related and pulmonary fibrosis-related characteristics.

[0004] Currently, research on molecular biomarkers related to LUAD (pulmonary fibrosis) mainly focuses on tumor diagnosis, prognosis, and therapeutic target screening, while gene testing methods for the risk of pulmonary fibrosis in LUAD patients remain relatively limited. Existing single tumor-related gene markers usually only reflect molecular changes in the tumor itself and are difficult to simultaneously indicate abnormalities related to pulmonary fibrosis.

[0005] For example, Chinese patent CN116313062B discloses a prognostic model for lung adenocarcinoma, belonging to the field of tumor molecular biology. This prognostic model is constructed based on a combination of polyamine metabolism-related genes SMS, SMOX, GPC1, SLC47A1, AZIN2, and MAOB. The construction method involves collecting training and validation sets from cancer genome mapping databases and gene expression databases to construct the prognostic model; compiling polyamine metabolism-related genes from KEGG, GO databases, and literature; and constructing the prognostic model using LASSO-COX regression. This invention improves the C-index of the prognostic model by approximately 10.5%, achieving a synergistic effect. The predictive accuracy and universality of the prognostic model are improved, providing more accurate guidance for clinicians in making treatment decisions for lung adenocarcinoma patients. However, this invention still uses a single clinical indicator, making it difficult to accurately predict the risk of pulmonary fibrosis during lung adenocarcinoma treatment and assess the prognostic risk of the target population.

[0006] Therefore, screening for abnormally expressed genes that are involved in the development of both lung adenocarcinoma and pulmonary fibrosis, and predicting the prognostic risk of potential individuals with lung adenocarcinoma and pulmonary fibrosis, has significant research value and clinical translational implications. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention aims to provide a method for predicting the risk of pulmonary fibrosis after anti-lung adenocarcinoma treatment by integrating transcriptome features, and its application in a reagent kit. This invention screens expression data of LUAD and pulmonary fibrosis-related genes to obtain candidate genes that are highly expressed in both LUAD and pulmonary fibrosis tissues, and utilizes these candidate genes or combinations thereof to construct a molecular marker detection system. By detecting the expression levels of relevant genes in the test samples, a comprehensive assessment of LUAD-related characteristics and pulmonary fibrosis-related molecular characteristics can be performed.

[0008] Terminology Explanation: Unless otherwise defined, all technical terms in this document have the same meanings as commonly understood by one of ordinary skill in the art to which the subject matter of the claims pertains. Unless otherwise stated, all patents, patent inventions, and publications cited in this document are incorporated herein by reference in their entirety. If multiple definitions exist for terms in this document, the definitions in this chapter shall prevail.

[0009] It should be understood that the above brief description and the following detailed description are exemplary and for illustrative purposes only, and do not limit the subject matter of the invention in any way. In this invention, the singular is used in conjunction with the plural unless otherwise specifically stated. It should also be noted that, unless otherwise stated, the use of “or” or “or” means “and / or”. Furthermore, the use of the term “comprising” and other forms such as “including,” “containing,” and “contains” are not limiting.

[0010] Unless specifically defined herein, the use of all commercially available products herein employs standard techniques. For example, it may be carried out using the manufacturer's instructions for use with the kit, or in accordance with methods known in the art or the description of this invention. The techniques and methods described herein can generally be implemented according to conventional methods well known in the art, based on the descriptions in the various summary and more specific documents cited and discussed in this specification.

[0011] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for predicting the risk of pulmonary fibrosis after anti-lung adenocarcinoma treatment by integrating transcriptome features, comprising the following steps: (1) Data retrieval strategy and inclusion / exclusion criteria for expression in pulmonary fibrosis and primary lung adenocarcinoma: Gene chip and RNA sequencing (RNA-seq) data from lung adenocarcinoma tissues were screened globally; inclusion and exclusion criteria for pulmonary fibrosis and primary lung adenocarcinoma were set; gene chip and RNA-seq data from the same platform were merged and batch effects were removed using the sva package; mRNA expression datasets for pulmonary fibrosis and primary lung adenocarcinoma were screened according to the inclusion and exclusion criteria; the standardized mean difference of each dataset was calculated, and a meta-analysis was performed using a random effects model to assess the overall expression trend of genes; samples with prognostic information of lung adenocarcinoma patients were collected and integrated according to the data description, and univariate Cox regression analysis was used to preliminarily screen prognostic genes, and genes significantly associated with overall survival (OS) of lung adenocarcinoma patients were screened with a P<0.05 threshold.

[0012] (2) Identification of differentially expressed genes in lung adenocarcinoma from The Cancer Genome Atlas (TCGA): To systematically identify key molecular changes in the development and progression of lung adenocarcinoma, lung adenocarcinoma data from TCGA was used, and the DESeq2 package was used to screen for differentially expressed genes (DEGs) in cancer and normal tissues; transcription factors were obtained by searching the Cistrome DB database. (3) Screening key targets of pulmonary fibrosis in lung adenocarcinoma and constructing the relationship between scores and prognosis: By intersecting differentially expressed genes (SMD>0) in lung adenocarcinoma, differentially expressed genes (SMD>0) in pulmonary fibrosis, differentially expressed genes (log2FoldChange>0) in lung adenocarcinoma in TCGA, genes associated with poor prognosis in lung adenocarcinoma (HR>1), and transcription factors; scoring the intersecting genes of key targets of pulmonary fibrosis in lung adenocarcinoma using ssGSEA, and using lung adenocarcinoma data from TCGA for prognostic analysis; (4) Scoring system verification: GSE30219, GSE42127 and GSE50081 were collected for scoring system verification.

[0013] Preferably, the inclusion criteria for primary lung adenocarcinoma in step (1) are as follows: S1, the research subjects are Homo sapiens; S2, the disease type is primary lung adenocarcinoma; S3, non-cancerous lung tissue is provided as a control; S4, the sample size for both lung adenocarcinoma and non-cancerous tissue is ≥3; the exclusion criteria are as follows: A1, the sample is from an animal; A2, the disease type is metastatic or recurrent lung adenocarcinoma; A3, duplicate samples.

[0014] Preferably, based on the inclusion and exclusion criteria for primary lung adenocarcinoma described in step (1), a total of 43 sets of mRNA expression datasets for lung adenocarcinoma were obtained.

[0015] Preferably, the inclusion criteria for pulmonary fibrosis in step (1) are as follows: B1, the research subjects are Homo sapiens; B2, the disease type is pulmonary fibrosis, mainly idiopathic pulmonary fibrosis (IPF); B3, the specimen type is fibrotic lung tissue; B4, normal lung tissue or non-fibrotic lung tissue is used as a control; B5, the sample size of both the pulmonary fibrosis group and the control group is ≥3; the exclusion criteria are as follows: C1, the sample comes from animals; C2, the disease type is other non-target interstitial lung diseases, secondary pulmonary fibrosis or in vitro induced fibrosis models; C3, the specimen type is blood, bronchoalveolar lavage fluid, single-cell sequencing sample or non-lung tissue; C4, duplicate samples.

[0016] Preferably, based on the inclusion and exclusion criteria for pulmonary fibrosis described in step (1), a total of 5 sets of mRNA expression datasets for pulmonary fibrosis were obtained.

[0017] Preferably, a total of 1773 transcription factors were obtained in step (2).

[0018] Preferably, the intersection genes of key targets in the process of pulmonary fibrosis in lung adenocarcinoma described in step (3) are TOP2A, HELLS, PTTG1, MYBL2, GAPDH, E2F8, HNF4G, TFAP2A, OTX1, MCM2, GMNN, PRAME, BARX2, PITX1, TERT, HMGA2, GRIP1, SHOX2, CDX2, HOXB7, HOXC6, HOXC8, SOX9, and TWIST1.

[0019] Preferably, the higher the score in the scoring system verification described in step (4), the worse the prognosis for patients with lung adenocarcinoma.

[0020] Secondly, the present invention also provides the application of the above-mentioned risk prediction method in assessing the state of tumors and their microenvironment.

[0021] Thirdly, the present invention also provides the application of the above-mentioned risk prediction method in a kit for predicting the risk of pulmonary fibrosis during antitumor treatment of lung adenocarcinoma.

[0022] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention screens LUAD and pulmonary fibrosis-related gene expression data to obtain candidate genes that are highly expressed in both LUAD and pulmonary fibrosis tissues, and uses these candidate genes or combinations thereof to construct a molecular marker detection system. By detecting the expression levels of related genes in the test samples, a comprehensive evaluation of LUAD-related characteristics and pulmonary fibrosis-related molecular characteristics can be performed.

[0023] (2) The risk prediction method provided by the present invention is used to help identify LUAD patients who may have molecular characteristics related to pulmonary fibrosis, and to provide molecular basis for patient stratification and intervention of pulmonary fibrosis-related treatments during anti-tumor treatment. Attached Figure Description

[0024] Figure 1 A graph showing the origin and prognostic relationship of 24 key molecular targets for pulmonary fibrosis progression in high-value lung adenocarcinoma (where A is the Venn diagram of intersecting genes and B is the TCGA prognostic analysis graph).

[0025] Figure 2 The diagram is for the validation of the scoring system (where A is GSE30219; B is GSE42127; and C is GSE50081). Detailed Implementation

[0026] The following non-limiting embodiments are intended to enable those skilled in the art to gain a more comprehensive understanding of the present invention, but do not limit the invention in any way. The following content is merely an exemplary description of the scope of protection claimed by the present invention, and those skilled in the art can make various changes and modifications to the present invention based on the disclosed content, and such changes should also fall within the scope of protection claimed by the present invention.

[0027] The present invention will be further described below by way of specific embodiments. Unless otherwise specified, all instruments, devices, equipment, reagents, products, etc., used in the embodiments of the present invention are obtained through conventional commercial means.

[0028] The GSE30219 dataset has been publicly used in the paper “Identification and validation of a novel prognostic model of inflammation-related gene signature of lungadenocarcinoma”. The GSE42127 dataset has been publicly used in the paper “Exploring diverse programmed cell-death patterns to develop a novel gene signature for predicting the prognosis of lung adenocarcinoma patients”; The GSE50081 dataset has been publicly available in the paper “Predicting lung adenocarcinoma prognosis, immune escape, and pharmacomic profile from arginine and proline-related genes”. Example 1: A method for predicting the risk of pulmonary fibrosis after anti-lung adenocarcinoma treatment by integrating transcriptome features Includes the following steps: (1) Data retrieval strategy and inclusion / exclusion criteria for pulmonary fibrosis and primary lung adenocarcinoma expression: Gene chip and RNA sequencing (RNA-seq) data from lung adenocarcinoma tissues were screened globally. The inclusion criteria for primary lung adenocarcinoma were set as follows: S1, the study subjects were Homo sapiens; S2, the disease type was primary lung adenocarcinoma; S3, non-cancerous lung tissue was provided as a control; S4, the sample size for both lung adenocarcinoma and non-cancerous tissues was ≥3. The exclusion criteria were set as follows: A1, the samples were from animals; A2, the disease type was metastatic or recurrent lung adenocarcinoma; A3, duplicate samples. Gene chip and RNA-seq data from the same platform were merged, and batch effects were removed using the sva package. Based on the above inclusion and exclusion criteria, the mRNA expression dataset of LUAD was obtained.

[0029] The inclusion criteria for pulmonary fibrosis were set as follows: B1. The study subjects were Homo sapiens; B2. The disease type was pulmonary fibrosis, mainly idiopathic pulmonary fibrosis (IPF); B3. The specimen type was fibrotic lung tissue; B4. Normal lung tissue or non-fibrotic lung tissue was provided as a control; B5. The sample size of both the pulmonary fibrosis group and the control group was ≥3. The exclusion criteria were set as follows: C1. The samples were from animals; C2. The disease type was other non-target interstitial lung diseases, secondary pulmonary fibrosis, or in vitro induced fibrosis models; C3. The specimen type was blood, bronchoalveolar lavage fluid, single-cell sequencing samples, or non-lung tissue; C4. Duplicate samples. Based on the above inclusion and exclusion criteria, a total of 5 sets of pulmonary fibrosis mRNA expression datasets were obtained.

[0030] mRNA sequencing of integrated lung adenocarcinoma samples was used to further calculate the standardized mean difference (SMD) of each dataset, and a meta-analysis was performed using a random-effects model to assess the overall gene expression trend. A p-value < 0.05 was considered statistically significant.

[0031] Based on the data description, samples containing prognostic information of lung adenocarcinoma patients were collected and integrated. Univariate Cox regression analysis was used to conduct preliminary screening of prognostic-related genes. Genes significantly associated with overall survival (OS) of lung adenocarcinoma patients were screened with a P<0.05 threshold.

[0032] (2) Identification of differentially expressed genes in TCGA-LUAD: In order to identify key molecular changes that may be involved in the development of LUAD, this study used the DESeq2 package to compare the gene expression differences between LUAD tissue and normal lung tissue based on TCGA-LUAD data and screened differentially expressed genes (DEGs); a total of 1773 transcription factors were obtained by searching the Cistrome DB database.

[0033] (3) Screening key molecular targets of pulmonary fibrosis progression in lung adenocarcinoma and constructing a relationship between scores and prognosis: The intersection of differentially expressed genes in lung adenocarcinoma (SMD>0), differentially expressed genes in pulmonary fibrosis (SMD>0), differentially expressed genes in lung adenocarcinoma from TCGA (log2FoldChange>0), and genes with poor prognosis in lung adenocarcinoma (HR>1) was analyzed. The intersection genes of key targets of pulmonary fibrosis progression in lung adenocarcinoma were scored using ssGSEA, and finally, prognostic analysis was performed using lung adenocarcinoma data from TCGA.

[0034] (4) Scoring system validation: To validate the association between the constructed scoring system and the prognosis of LUAD patients, three independent datasets, GSE30219, GSE42127 and GSE50081, were further included, and the scoring system was validated in each cohort.

[0035] The intersection yielded 24 high-value genes (TOP2A, HELLS, PTTG1, MYBL2, GAPDH, E2F8, HNF4G, TFAP2A, OTX1, MCM2, GMNN, PRAME, BARX2, PITX1, TERT, HMGA2, GRIP1, SHOX2, CDX2, HOXB7, HOXC6, HOXC8, SOX9, TWIST1). Figure 1 A).

[0036] Prognostic analysis showed that in the constructed prognostic model, the higher the score, the worse the patient's prognosis. Figure 1 B).

[0037] The scoring system constructed using 24 high-value genes was further validated using GSE30219, GSE42127, and GSE50081. The results showed that higher scores correlated with worse prognosis for LUAD patients. Figure 2 ).

[0038] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for predicting the risk of pulmonary fibrosis after anti-lung adenocarcinoma treatment by integrating transcriptome features, characterized in that, Includes the following steps: (1) Data retrieval strategy and inclusion / exclusion criteria for expression of pulmonary fibrosis and primary lung adenocarcinoma: Gene chip and RNA sequencing data from lung adenocarcinoma tissues were screened globally; inclusion and exclusion criteria for pulmonary fibrosis and primary lung adenocarcinoma were set; gene chip and RNA-seq data from the same platform were merged and batch effects were removed using the sva package; mRNA expression datasets of pulmonary fibrosis and primary lung adenocarcinoma were screened according to the inclusion and exclusion criteria; the standardized mean difference of each dataset was calculated, and a meta-analysis was performed using a random effects model to assess the overall expression trend of genes; samples with prognostic information of lung adenocarcinoma patients were collected and integrated according to the data description, and univariate Cox regression analysis was used to preliminarily screen prognostic genes, and genes significantly related to the overall survival of lung adenocarcinoma patients were screened with P<0.05 as the threshold; (2) Identification of differentially expressed genes in lung adenocarcinoma in cancer genome atlas: In order to systematically identify key molecular changes in the development and progression of lung adenocarcinoma, lung adenocarcinoma data from TCGA were used, and differentially expressed genes in cancer and normal tissues were screened using the DESeq2 package; transcription factors were obtained by searching the Cistrome DB database. (3) Screening key targets of pulmonary fibrosis process in lung adenocarcinoma and constructing the relationship between scores and prognosis: By intersecting differentially expressed genes in lung adenocarcinoma, differentially expressed genes in pulmonary fibrosis, differentially expressed genes in lung adenocarcinoma in TCGA, genes related to poor prognosis of lung adenocarcinoma, and transcription factors; scoring the intersecting genes of key targets of pulmonary fibrosis process in lung adenocarcinoma using ssGSEA, and using TCGA lung adenocarcinoma data for prognostic analysis; (4) Scoring system verification: GSE30219, GSE42127 and GSE50081 were collected for scoring system verification.

2. The risk prediction method according to claim 1, characterized in that, The inclusion criteria for primary lung adenocarcinoma set in step (1) are as follows: S1, the study subjects are Homo sapiens; S2, the disease type is primary lung adenocarcinoma; S3, non-cancerous lung tissue is provided as a control; S4, the sample size of both lung adenocarcinoma and non-cancerous tissue is ≥3.

3. The risk prediction method according to claim 1, characterized in that, The exclusion criteria for primary lung adenocarcinoma set in step (1) are as follows: A1, the sample comes from an animal; A2, the disease type is metastatic or recurrent lung adenocarcinoma; A3, duplicate samples.

4. The risk prediction method according to claim 1, characterized in that, The inclusion criteria for pulmonary fibrosis set in step (1) are as follows: B1, the study subjects are Homo sapiens; B2, the disease type is pulmonary fibrosis, mainly idiopathic pulmonary fibrosis; B3, the specimen type is pulmonary fibrosis lung tissue; B4, normal lung tissue or non-fibrosis lung tissue is used as a control; B5, the sample size of both the pulmonary fibrosis group and the control group is ≥3.

5. The risk prediction method according to claim 1, characterized in that, The exclusion criteria for pulmonary fibrosis set in step (1) are as follows: C1, the sample comes from an animal; C2, the disease type is other non-target interstitial lung disease, secondary pulmonary fibrosis or in vitro induced fibrosis model; C3, the specimen type is blood, bronchoalveolar lavage fluid, single-cell sequencing sample or non-lung tissue; C4, duplicate sample.

6. The risk prediction method according to claim 1, characterized in that, Based on the inclusion and exclusion criteria for pulmonary fibrosis described in step (1), a total of 5 sets of mRNA expression datasets for pulmonary fibrosis were obtained.

7. The risk prediction method according to claim 1, characterized in that, In step (2), a total of 1773 transcription factors were obtained.

8. The risk prediction method according to claim 1, characterized in that, The genes that intersect with the key targets of pulmonary fibrosis in lung adenocarcinoma described in step (3) are TOP2A, HELLS, PTTG1, MYBL2, GAPDH, E2F8, HNF4G, TFAP2A, OTX1, MCM2, GMNN, PRAME, BARX2, PITX1, TERT, HMGA2, GRIP1, SHOX2, CDX2, HOXB7, HOXC6, HOXC8, SOX9, and TWIST1.

9. The application of the risk prediction method according to any one of claims 1-8 in assessing the state of tumors and their microenvironment.

10. The use of the risk prediction method according to any one of claims 1-8 in the preparation of a kit for predicting the risk of pulmonary fibrosis during antitumor treatment of lung adenocarcinoma.

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