Integrated transcriptomic signatures for risk prediction of post-treatment pulmonary fibrosis in lung squamous carcinoma and their application in kits

Abstract

This invention belongs to the field of biomedical technology, specifically relating to a risk prediction method for pulmonary fibrosis after anti-squamous lung cancer treatment that integrates transcriptomic features and its application in a kit. The risk prediction includes the following steps: (1) collecting lung squamous cell carcinoma samples for mRNA sequencing; (2) retrieval strategy and inclusion / exclusion criteria for expression data of pulmonary fibrosis and primary lung squamous cell carcinoma; (3) identifying differentially expressed genes in lung squamous cell carcinoma from TCGA; (4) obtaining molecular targets and constructing a relationship between scoring and prognosis; (5) validating the scoring system. This invention incorporates key molecular targets from the transcriptomic features of pulmonary fibrosis, a lung disease, into the screening system for prognostic biomarkers of lung squamous cell carcinoma. Based on the key molecular alteration features common to both diseases, it screens prognostic-related genes, identifies key genes in the progression of pulmonary fibrosis in lung squamous cell carcinoma, and constructs a scoring system for predicting the risk of pulmonary fibrosis after anti-tumor treatment of lung squamous cell carcinoma in a kit.

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-squamous cell carcinoma treatment by integrating transcriptome features and its application in a kit. Background Technology

[0002] Lung cancer is one of the leading causes of cancer-related morbidity and mortality worldwide. It is the most common newly diagnosed malignant tumor globally and the leading cause of cancer-related death, seriously threatening human life and health. Squamous cell carcinoma of the lung (squamous carcinoma of the lung) is an important histological type of non-small cell lung cancer, characterized by biological features closely related to smoking, chronic airway damage, and an inflammatory microenvironment. Compared to adenocarcinoma of the lung, squamous cell carcinoma of the lung still faces significant limitations in molecular subtyping and targeted therapy; its pathogenesis and effective molecular markers remain to be further elucidated.

[0003] Pulmonary fibrosis is a chronic, progressive interstitial lung disease characterized by alveolar epithelial damage, abnormal fibroblast activation, excessive extracellular matrix deposition, and lung tissue remodeling. It can lead to progressive dyspnea, decreased lung function, and poor prognosis. Previous studies have shown that patients with pulmonary fibrosis, especially idiopathic pulmonary fibrosis, have a significantly increased risk of developing lung cancer, and the occurrence of lung cancer is often closely related to fibrotic areas, honeycomb-like changes, and the chronic tissue damage repair environment. Among lung cancers occurring in the context of pulmonary fibrosis, squamous cell carcinoma is one of the more common histological types, suggesting that the pulmonary fibrosis-related microenvironment may be associated with abnormal squamous epithelial proliferation, squamous metaplasia, and the development and progression of squamous cell carcinoma.

[0004] Currently, the key molecular mechanisms underlying squamous cell carcinoma of the lung complicated by pulmonary fibrosis remain incompletely understood. The repeated epithelial cell damage and repair, fibroblast activation, extracellular matrix remodeling, and chronic inflammatory responses that occur during pulmonary fibrosis may provide favorable conditions for the abnormal proliferation, invasion, and microenvironment adaptation of squamous cell carcinoma cells. Furthermore, the interaction between tumor cells and stromal cells during the development of squamous cell carcinoma may further promote local tissue remodeling and fibrosis-like changes. Because both share a common pathological basis in chronic inflammation, tissue damage repair, abnormal cell proliferation, and matrix remodeling, relying solely on traditional imaging, histopathology, or a single clinical indicator is insufficient to accurately predict the risk of pulmonary fibrosis during the treatment of squamous cell carcinoma of the lung and assess the prognostic risk of the affected population.

[0005] For example, Chinese patent CN116479126A discloses a biomarker and a method for constructing a scoring system to predict the efficacy of immunotherapy for lung cancer, relating to the field of biotechnology. The inventors discovered that CypB mRNA, RNA transcribed from the FAM83H-AS1 gene, and LncRNA VPS9D1-AS1 are associated with the prognosis of lung cancer, and that the infiltration of immune cells around lung cancer lesions is related to the treatment of lung cancer patients. Based on this, the invention uses CypB mRNA, RNA transcribed from the FAM83H-AS1 gene, LncRNA VPS9D1-AS1, panCK, CD3, CD4, CD8, and Foxp3 as biomarkers to provide a method for constructing a scoring system to predict the efficacy of immunotherapy for lung cancer. The scoring system constructed using this method can comprehensively assess the state of the tumor and its microenvironment, accurately predict the risk of lung cancer progression and prognosis, and better guide clinical medication. However, this invention still uses a single clinical indicator detection, making it difficult to accurately predict the risk of pulmonary fibrosis during the treatment of squamous cell carcinoma of the lung and assess the prognostic risk of the population.

[0006] Therefore, screening for abnormally expressed genes that are involved in the development of both squamous cell carcinoma and pulmonary fibrosis, and predicting the prognostic risk of potential individuals with squamous cell carcinoma 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 system integrating key molecular targets and prognostic scoring from transcriptomic features of pulmonary fibrosis and squamous cell carcinoma of the lung, along with its clinical value. This invention integrates transcriptomic expression data from multiple independent datasets, and through systematic screening and validation, obtains a set of gene biomarkers significantly associated with overall survival in patients with squamous cell carcinoma of the lung. These biomarkers can be used to predict pulmonary fibrosis in squamous cell carcinoma of the lung, and based on this, a prognostic scoring model is constructed to achieve accurate assessment of the prognostic risk for patients with squamous cell carcinoma of the lung complicated by pulmonary fibrosis.

[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-squamous cell carcinoma treatment by integrating transcriptome features, comprising the following steps: (1) Collect lung squamous cell carcinoma samples for mRNA sequencing: extract total RNA from lung squamous cell carcinoma tissue and control samples, and evaluate the purity and integrity of RNA; synthesize cDNA samples and label them; detect the efficiency of fluorescent labeling; hybridize the chip with the labeled probe under standard conditions; after scanning, convert the results into digital data as an in-house microarray matrix for storage and subsequent analysis. (2) Data retrieval strategy and inclusion / exclusion criteria for expression data of pulmonary fibrosis and primary squamous cell carcinoma of the lung: Gene chip and RNA sequencing (RNA-seq) data from squamous cell carcinoma tissues were screened globally; inclusion and exclusion criteria for pulmonary fibrosis and primary squamous cell carcinoma of the lung 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 squamous cell carcinoma of the lung were screened according to the inclusion and exclusion criteria; the standardized mean difference of each dataset was calculated by integrating mRNA sequencing data of squamous cell carcinoma samples, and a meta-analysis was performed using a random effects model to assess the overall expression trend of genes; samples with prognostic information of squamous cell carcinoma 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 squamous cell carcinoma patients were screened with a threshold of P<0.05.

[0012] (3) Identification of differentially expressed genes in squamous cell carcinoma of the lung in The Cancer Genome Atlas (TCGA): In order to systematically identify key molecular changes in the development and progression of squamous cell carcinoma of the lung, we used squamous cell carcinoma data from TCGA and combined it with the DESeq2 package to screen for differentially expressed genes (DEGs) in cancer and normal tissues. (4) Screening key targets of pulmonary fibrosis in squamous cell carcinoma and constructing the relationship between scores and prognosis: By intersecting differentially expressed genes (SMD>0) in squamous cell carcinoma, differentially expressed genes (SMD>0) in pulmonary fibrosis, differentially expressed genes (log2FoldChange>0) in squamous cell carcinoma in TCGA, and genes with poor prognosis (HR>1) in squamous cell carcinoma; the intersecting genes of key targets of pulmonary fibrosis in squamous cell carcinoma are scored using ssGSEA, and prognostic analysis is performed using squamous cell carcinoma data from TCGA; (5) Scoring system verification: GSE157011, GSE4573, GSE19188, GSE42127 and GSE81089 were collected for scoring system verification.

[0013] Preferably, in step (1), the total number of RNAs extracted from the lung squamous cell carcinoma tissue and control sample is 6, and the extraction is performed using TRIzol®Regent, which is purchased from Ingenium Life Sciences, Inc.

[0014] Preferably, the method for assessing the purity and integrity of RNA in step (1) is as follows: measuring the A260 / A280 ratio and the standard range of 1.8-2.1. When A260 / A280 is greater than 1.8, it indicates that the RNA has not been degraded.

[0015] Preferably, the efficiency of detecting fluorescent labels in step (1) is achieved using Nanodrop ND-1000, purchased from Agilent Technologies, California, USA.

[0016] Preferably, the chip mentioned in step (1) is purchased from Spectrum (Shanghai) Biotechnology Co., Ltd.

[0017] Preferably, the scanning in step (1) is performed using an Agilent microarray scanner (Agilent p / n G2565BA).

[0018] Preferably, the inclusion criteria for primary squamous cell carcinoma of the lung in step (2) are as follows: S1, the research subject is Homo sapiens; S2, the disease type is primary squamous cell carcinoma of the lung; S3, the specimen type is squamous cell carcinoma tissue or cell line of the lung; S4, non-cancerous lung bronchus is used as a control; S5, the sample size of both squamous cell carcinoma and non-cancerous lung samples is ≥3; the exclusion criteria are as follows: A1, the sample comes from an animal; A2, the disease type is metastatic or recurrent squamous cell carcinoma of the lung; A3, duplicate samples.

[0019] Preferably, based on the inclusion and exclusion criteria for primary squamous cell carcinoma of the lung described in step (2), a total of 22 sets of mRNA expression datasets for squamous cell carcinoma of the lung were obtained.

[0020] Preferably, the inclusion criteria for pulmonary fibrosis in step (2) 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.

[0021] Preferably, based on the inclusion and exclusion criteria for pulmonary fibrosis described in step (2), a total of 5 sets of mRNA expression datasets for pulmonary fibrosis were obtained.

[0022] Preferably, the intersection genes of key targets in the process of pulmonary fibrosis in squamous cell carcinoma of the lung described in step (4) are ITPA, IER5L, ADAMTS16, STX1A, SLC35F2, HOXC4, SPINK1, COL22A1, MYEOV and FAM83A.

[0023] Preferably, the higher the score in the scoring system verification described in step (5), the worse the prognosis for patients with squamous cell carcinoma of the lung.

[0024] Secondly, the present invention also provides the application of the above-mentioned risk prediction in assessing the state of tumors and their microenvironment.

[0025] Thirdly, the present invention also provides the application of the above-mentioned risk prediction in a kit for predicting the risk of pulmonary fibrosis during antitumor treatment of squamous cell carcinoma of the lung.

[0026] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention integrates 22 sets of lung squamous cell carcinoma mRNA expression datasets (including public databases and sequencing data of collected samples) and 5 sets of lung fibrosis mRNA expression datasets, combined with lung squamous cell carcinoma data from the TCGA database, and after multi-step systematic screening, finally obtains 10 high-value prognostic-related genes. This screening process has a large sample size and a wide range of data sources, overcoming the shortcomings of unstable results from a single dataset.

[0027] (2) This invention is the first to incorporate key molecular targets in the transcriptome features of pulmonary fibrosis, a lung disease, into the screening system for prognostic markers of squamous cell carcinoma of the lung. Based on the molecular alteration features common to the two diseases, prognostic-related genes are screened, and key genes in the process of pulmonary fibrosis in squamous cell carcinoma of the lung are discovered, providing key targets for the treatment and anti-fibrosis of squamous cell carcinoma of the lung in subsequent clinical treatment.

[0028] (3) This invention simultaneously considers the high expression of genes in squamous cell carcinoma of the lung (SMD>0), high expression in pulmonary fibrosis (SMD>0), and differential expression in the TCGA database (log2FoldChange>0), and combines them with prognostic analysis (HR>1) to ensure from multiple dimensions that the key molecular targets of pulmonary fibrosis process in the screened squamous cell carcinoma of the lung are closely related to the prognosis of squamous cell carcinoma of the lung. Attached Figure Description

[0029] Figure 1 A graph showing the origin and prognostic relationship of 10 key molecular targets for pulmonary fibrosis progression in high-value squamous cell carcinoma of the lung (where A is the Venn diagram of the intersection genes and B is the TCGA prognostic analysis graph).

[0030] Figure 2 The graph is used to verify the scoring system (where A is GSE157011; B is GSE4573; C is GSE19188).

[0031] Figure 3 Validation diagram of the scoring system (where A is GSE42127; B is GSE81089). Detailed Implementation

[0032] 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.

[0033] 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.

[0034] The GSE157011 dataset has been publicly used in the paper “Identification of a cancer-associated fibroblast classifier for predicting prognosis and therapeutic response inlung squamous cell carcinoma”.

[0035] The GSE4573 dataset has been publicly used in the paper “Machine learning constructs a ferroptosis-related signature for predicting prognosis and drug sensitivity in lung cancer”.

[0036] The GSE19188 dataset has been publicly available in the literature “Prediction of immune subtypes and overall survival in lung squamous cell carcinoma”.

[0037] The GSE42127 dataset has been publicly used in the paper “Construction and validation of an oxidative phosphorylation-related gene signature in lung squamous cell carcinoma patients”.

[0038] The GSE81089 dataset has been publicly used in the paper “LINC02678 as a Novel Prognostic Marker Promotes Aggressive Non-small-cell Lung Cancer”.

[0039] Example 1: A method for predicting the risk of pulmonary fibrosis after anti-squamous cell carcinoma treatment by integrating transcriptome features. Includes the following steps: (1) Collection of lung squamous cell carcinoma samples for mRNA sequencing: Total RNA was extracted from six internal lung squamous cell carcinoma tissues and control samples using TRIzol® Regent (Agilent Technologies, Inc., USA). RNA purity and integrity were assessed by measuring the A260 / A280 ratio within a standard range of 1.8–2.1. An A260 / A280 greater than 1.8 indicated that the RNA was not degraded. Subsequently, cDNA samples were synthesized and labeled. The efficiency of fluorescent labeling was detected using a Nanodrop ND-1000 (Agilent Technologies, California, USA). Under standard conditions, the chip [Shanghai Shupu Biotechnology Co., Ltd.] was hybridized with the labeled probes. Finally, after scanning using an Agilent microarray scanner (Agilent p / n G2565BA), the results were converted into digital data as an in-house microarray matrix for storage and subsequent analysis.

[0040] (2) Data retrieval strategy and inclusion / exclusion criteria for pulmonary fibrosis and primary squamous cell carcinoma of the lung: Gene chip and RNA sequencing (RNA-seq) data from squamous cell carcinoma tissues were screened globally. The inclusion criteria for primary squamous cell carcinoma of the lung were as follows: S1, the study subjects were Homo sapiens; S2, the disease type was primary squamous cell carcinoma of the lung; S3, the specimen type was squamous cell carcinoma tissue or cell line; S4, non-cancerous lung bronchial tissue was used as a control; S5, the sample size for both squamous cell carcinoma and non-cancerous samples was ≥3. The exclusion criteria were as follows: A1, the sample was from an animal; A2, the disease type was metastatic or recurrent squamous cell carcinoma of the lung; 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, a total of 22 sets of mRNA expression datasets for squamous cell carcinoma of the lung were obtained.

[0041] 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. Gene chip and RNA-seq data from the same platform were merged separately, and batch effects were removed using the sva package. Based on the above inclusion and exclusion criteria, a total of 5 sets of pulmonary fibrosis mRNA expression datasets were obtained.

[0042] mRNA sequencing of integrated lung squamous cell carcinoma samples was further performed to calculate the standardized mean difference (SMD) of each dataset, and a meta-analysis using a random-effects model was conducted to assess the overall gene expression trend. A p-value < 0.05 was considered statistically significant.

[0043] Based on the data description, samples with prognostic information of patients with squamous cell lung cancer were collected and integrated. Univariate Cox regression analysis was used to conduct preliminary screening of prognostic-related genes. Genes that were significantly associated with overall survival (OS) of patients with squamous cell lung cancer were screened with a P<0.05 threshold.

[0044] (3) Identification of differentially expressed genes in squamous cell carcinoma of the lung in TCGA: In order to systematically identify key molecular changes in the development of squamous cell carcinoma of the lung, squamous cell carcinoma of the lung in TCGA was used, and the DESeq2 package was used to screen for differentially expressed genes (DEGs) in cancer and normal tissues.

[0045] (4) Screening key molecular targets for pulmonary fibrosis progression in squamous cell carcinoma of the lung and constructing a relationship between scoring and prognosis: The intersection of differentially expressed genes (SMD>0) in squamous cell carcinoma of the lung, differentially expressed genes (SMD>0) in pulmonary fibrosis, differentially expressed genes (log2FoldChange>0) in squamous cell carcinoma of the lung from TCGA, and genes with poor prognosis in squamous cell carcinoma of the lung (HR>1) was performed. The intersection genes were scored using ssGSEA, and finally, prognostic analysis was performed using squamous cell carcinoma data from TCGA.

[0046] (5) Scoring system validation: In order to validate the relationship between the constructed scoring system and the prognosis of squamous cell carcinoma of the lung, GSE157011, GSE4573, GSE19188, GSE42127 and GSE81089 were collected for scoring system validation.

[0047] The intersection analysis ultimately yielded 10 high-value key molecular targets for pulmonary fibrosis progression in squamous cell carcinoma of the lung (ITPA, IER5L, ADAMTS16, STX1A, SLC35F2, HOXC4, SPINK1, COL22A1, MYEOV, and FAM83A) (e.g. Figure 1 A).

[0048] Prognostic analysis showed that in the constructed prognostic model, the higher the score, the worse the patient's prognosis (e.g., ...). Figure 1 B).

[0049] The scoring system constructed using 10 high-value molecular targets for pulmonary fibrosis progression in squamous cell carcinoma of the lung (GSE157011, GSE4573, GSE19188, GSE42127, and GSE81089) was further validated, revealing that higher scores correlated with worse prognosis for patients with squamous cell carcinoma of the lung. Figure 2 and Figure 3 ).

[0050] 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-squamous cell carcinoma treatment by integrating transcriptome features, characterized in that, Includes the following steps: (1) Collect lung squamous cell carcinoma samples for mRNA sequencing: extract total RNA from lung squamous cell carcinoma tissue and control samples, and evaluate the purity and integrity of RNA; synthesize cDNA samples and label them; detect the efficiency of fluorescent labeling; hybridize the chip with the labeled probe under standard conditions; After scanning, the results are converted into digital data as an in-house microarray matrix for storage and subsequent analysis. (2) Data retrieval strategy and inclusion / exclusion criteria for expression data of pulmonary fibrosis and primary squamous cell carcinoma of the lung: Gene chip and RNA sequencing data from squamous cell carcinoma tissues were screened globally; inclusion and exclusion criteria for pulmonary fibrosis and primary squamous cell carcinoma of the lung 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 squamous cell carcinoma of the lung were screened according to the inclusion and exclusion criteria; the standardized mean difference of each dataset was calculated by integrating mRNA sequencing data of squamous cell carcinoma samples, and a meta-analysis was performed using a random effects model to assess the overall expression trend of genes; samples with prognostic information of squamous cell carcinoma 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 squamous cell carcinoma patients were screened with P<0.05 as the threshold; (3) Identification of differentially expressed genes in squamous cell carcinoma of the lung in TCGA: In order to systematically identify key molecular changes in the development and progression of squamous cell carcinoma of the lung, we used squamous cell carcinoma data from TCGA and combined it with the DESeq2 package to screen for differentially expressed genes in cancer and normal tissues. (4) Screening key targets of pulmonary fibrosis process in squamous cell carcinoma of the lung and constructing the relationship between scores and prognosis: By intersecting differentially expressed genes in squamous cell carcinoma of the lung, differentially expressed genes in pulmonary fibrosis, differentially expressed genes in squamous cell carcinoma of the lung in TCGA, and genes with poor prognosis in squamous cell carcinoma of the lung; scoring the intersecting genes of key targets of pulmonary fibrosis process in squamous cell carcinoma of the lung using ssGSEA, and using squamous cell carcinoma data from TCGA of the lung for prognosis analysis; (5) Scoring system verification: GSE157011, GSE4573, GSE19188, GSE42127 and GSE81089 were collected for scoring system verification.

2. The risk prediction according to claim 1, characterized in that, In step (1), the total number of RNAs extracted from the lung squamous cell carcinoma tissue and control sample was 6, and the extraction was performed using TRIzol®Regent, which was purchased from Ingen Life Technologies Inc., USA.

3. The risk prediction according to claim 1, characterized in that, The method for assessing the purity and integrity of RNA described in step (1) is as follows: measure the A260 / A280 ratio and the standard range of 1.8-2.

1. When A260 / A280 is greater than 1.8, it indicates that the RNA has not been degraded.

4. The risk prediction according to claim 1, characterized in that, The efficiency of detecting fluorescent labels in step (1) was measured using a Nanodrop ND-1000, purchased from Agilent Technologies, California, USA.

5. The risk prediction according to claim 1, characterized in that, The inclusion criteria for primary squamous cell carcinoma of the lung described in step (2) are as follows: S1, the research subjects are Homo sapiens; S2, the disease type is primary squamous cell carcinoma of the lung; S3, the specimen type is squamous cell carcinoma tissue or cell line of the lung; S4, non-cancerous lung bronchial tissue is used as a control; S5, the sample size of both squamous cell carcinoma and non-cancerous lung samples is ≥3; the exclusion criteria are as follows: A1, the sample comes from an animal; A2, the disease type is metastatic or recurrent squamous cell carcinoma of the lung; A3, duplicate samples.

6. The risk prediction according to claim 1, characterized in that, The inclusion criteria for pulmonary fibrosis set in step (2) are as follows: B1. The research 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. 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.

7. The risk prediction according to claim 1, characterized in that, The overlapping genes of key targets in the process of pulmonary fibrosis in squamous cell carcinoma of the lung described in step (4) are ITPA, IER5L, ADAMTS16, STX1A, SLC35F2, HOXC4, SPINK1, COL22A1, MYEOV and FAM83A.

8. The risk prediction according to claim 1, characterized in that, In the scoring system verification described in step (5), the higher the score, the worse the prognosis for patients with squamous cell carcinoma of the lung.

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 a kit for predicting the risk of pulmonary fibrosis during antitumor treatment of squamous cell carcinoma of the lung.

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