Application of mRNAs marker in early diagnosis of signet ring cell carcinoma and kit

Abstract

The invention discloses application of an mRNAs marker in early diagnosis of lung signet-ring cell carcinoma and a kit. The marker is a combination of C1QA, CRISPLD2, ROBO4, SPARCL1, SFTPB, LAMC3, FN1, EMP1, CD68, THBS1, PRG4, ICAM1, PODXL, KDM6B, ENPP2, KLF10 and THBD (tetrahydrofuran). According to the invention, FFPE cancer tissue of a patient with primary pulmonary signet ring cell carcinoma diagnosed by an existing detection means in clinic is used as a sample, para-carcinoma tissue is used as a control sample, mRNA expression profiles of the cancer tissue and the control tissue are detected by adopting an RNA sealing technology, and 17 differentially expressed mRNA markers are found by applying statistics and a machine learning method. Based on the 17 found markers, a risk scoring model with high sensitivity and specificity is constructed, and the sensitivity gt of the model to early-stage lung signet-ring cell carcinoma diagnosis; the specificity is gt; 89%.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological detection, and specifically relates to the application of mRNAs markers in the early diagnosis of lung signet ring cell carcinoma and a diagnostic kit. Background Art

[0002] Lung signet ring cell carcinoma (LSRCC) is a rare and highly invasive subtype of poorly differentiated adenocarcinoma, which is characterized by a large amount of mucus in tumor cells, and these mucus push the cell nucleus to the cell edge, forming a "signet ring" - like appearance. In addition, cancer cells are arranged disorderly, showing sheet - like, island - like or diffuse distribution, and are often accompanied by significant pathological mitotic figures and necrosis foci. Its special pathological morphology is often associated with high malignancy and poor prognosis. Signet ring cell carcinoma usually has no obvious symptoms or signs in the early stage, and it is difficult to detect lesions by routine physical examination, making early diagnosis based on clinical manifestations extremely difficult. Therefore, exploring early - diagnosis biomarkers with high sensitivity and specificity helps to achieve early detection and intervention of the disease, thereby significantly reducing mortality, reducing medical resource consumption and patients' economic burden, and improving patients' quality of life.

[0003] Currently, endoscopic examination combined with biopsy technology is the main means for the clinical diagnosis of lung signet ring cell carcinoma, but its ability to identify early - stage micro - lesions is limited, and there are certain false - negative and false - positive problems. For example, in terms of cell morphology alone, cells of various benign and malignant lesions can present a "signet ring" - like morphology. Such as gastric xanthoma, pseudomembranous enteritis, etc. can all show cells similar to signet rings. Therefore, attention should be paid in differential diagnosis. In addition, the existing blood tumor markers such as CEA, CA19 - 9, etc. are not ideal for the diagnosis of signet ring cell carcinoma in terms of sensitivity and specificity. Although certain breakthroughs have been made in the fields of genomics, transcriptomics, proteomics, etc. in recent years, the research on early - diagnosis biomarkers for lung signet ring cell carcinoma is still relatively lagging behind, and no stable, highly sensitive and highly specific biomarker has been found.

[0004] At present, there are few reports on the research of molecular-level biomarkers in the early diagnosis of pulmonary signet-ring cell carcinoma. There are two relatively relevant research literatures: Literature 1 (PMID: 39910169): Researchers found the activation of ATR signal in LSRCC and the upregulation of DNA repair proteins such as FANCD2 and RECQL5 through deep vision proteomics, suggesting that replication stress can be used as a potential diagnostic biomarker. However, this study was only based on one patient, and the universality of its results needs to be further verified. Literature 2 (PMID: 20022810) focused on gastric signet-ring cell carcinoma. Researchers analyzed 353 gastric samples from two independent patient subgroups in Japan through microRNA microarray, compared the microRNA expression patterns between non-tumor mucosa and cancer samples. Among 160 non-tumor mucosa and cancer paired samples, 22 microRNAs were upregulated and 13 microRNAs were downregulated in gastric cancer, and 292 (83%) samples were correctly distinguished by this feature. However, this study mainly evaluated the relationship between microRNA expression and the progression and prognosis of gastric cancer, and did not further explore the potential of differentially expressed genes as diagnostic biomarkers.

[0005] At present, there has been no report on a suitable biomarker for the early detection of pulmonary signet-ring cell carcinoma, which has high specificity while ensuring sensitivity. How to develop a biomarker for the early diagnosis of pulmonary signet-ring cell carcinoma has become one of the urgent problems to be solved in this field. Summary of the Invention

[0006] The purpose of the present invention is to provide an mRNAs marker and a kit for the early diagnosis of pulmonary signet-ring cell carcinoma.

[0007] The technical solution adopted by the present invention to achieve the above purpose is as follows:

[0008] The present invention provides an mRNAs marker for the early diagnosis of pulmonary signet-ring cell carcinoma, and the mRNAs marker is a combination of C1QA, CRISPLD2, ROBO4, SPARCL1, SFTPB, LAMC3, FN1, EMP1, CD68, THBS1, PRG4, ICAM1, PODXL, KDM6B, ENPP2, KLF10 and THBD.

[0009] The present invention also provides the application of a detection reagent for the expression level of the mRNAs marker in the preparation of a composition or a kit for evaluating, diagnosing or monitoring pulmonary signet-ring cell carcinoma.

[0010] On the other hand, the present invention provides a kit for the early diagnosis of pulmonary signet-ring cell carcinoma, and the kit includes a reagent for detecting the expression level of the mRNAs marker.

[0011] As a specific implementation, the expression values of the mRNAs markers of the cancer tissue and the control tissue are respectively obtained by the RNA sequencing technology.

[0012] As a specific implementation, the kit further includes a risk scoring model, and the formula of the model is as follows:

[0013]

[0014] X i represents the marker expression value of sample i, and P(X i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a patient with pulmonary signet-ring cell carcinoma; the sum of the probabilities of all classifications is equal to 1, and the classification with the highest probability is taken as the final prediction result of sample i.

[0015] As a specific implementation, the model algorithm adopted by the risk scoring model is selected from extremely randomized trees (Gini), extremely randomized trees (Entr), random forests (Entr), weighted ensemble models, and linear regression.

[0016] The present invention also provides a method for diagnosing early pulmonary signet-ring cell carcinoma using the kit described above, including the following steps:

[0017] Step 1, collect the cancer tissue of a patient with a clinical diagnosis suspected of having pulmonary signet-ring cell carcinoma, and use RNA sequencing to obtain the expression level of the mRNAs markers in the cancer tissue;

[0018] Step 2, substitute the obtained expression values of the mRNAs markers into the risk scoring model formula of claim 5 to calculate the probabilities of the patient being predicted as healthy and having pulmonary signet-ring cell carcinoma;

[0019] Step 3, select the output result with the highest probability and give the prediction result of the risk of the patient having pulmonary signet-ring cell carcinoma.

[0020] The English abbreviations involved in the present invention are annotated as follows:

[0021] C1QA: Complement C1qA chain

[0022] CRISPLD2: Cysteine-rich secretory protein LCCL domain-containing protein 2

[0023] ROBO4: Roundabout guidance receptor 4

[0024] SPARCL1: Cysteine-rich acidic secretory protein-like protein 1

[0025] SFTPB: Surfactant protein B

[0026] LAMC3: Laminin subunit gamma-3

[0027] FN1: Fibronectin 1

[0028] EMP1: Epithelial membrane protein 1

[0029] CD68: Cluster of differentiation 68

[0030] THBS1: Thrombospondin 1

[0031] PRG4: Proteoglycan 4

[0032] ICAM1: Intercellular adhesion molecule-1

[0033] PODXL: Podocalyxin

[0034] KDM6B: Lysine demethylase 6B

[0035] ENPP2: Ectonucleotide pyrophosphatase / phosphodiesterase 2

[0036] KLF10: Krüppel-like factor 10

[0037] THBD: Thrombomodulin

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] 1. The present invention uses RNA sequencing technology to detect the mRNA expression profiles of cancer tissues and control tissues of patients with pulmonary signet-ring cell carcinoma, and applies statistical and machine learning methods to discover 17 differentially expressed mRNA markers, specifically: C1QA, CRISPLD2, ROBO4, SPARCL1, SFTPB, LAMC3, FN1, EMP1, CD68, THBS1, PRG4, ICAM1, PODXL, KDM6B, ENPP2, KLF10, and THBD.

[0040] 2. Based on the 17 markers discovered, the present invention constructs a risk scoring model with high sensitivity and specificity. The sensitivity of the model for the diagnosis of early pulmonary signet-ring cell carcinoma is >75%, and the specificity is >89%. Description of the Drawings

[0041] Figure 1 It is the immunohistochemical staining result diagram in Example 1.

[0042] Figure 2 It is the ROC curve of the training cohort of the extreme random tree model algorithm in Example 3.

[0043] Figure 3ROC curve of the validation cohort for the extreme random tree model algorithm in Example 3. Detailed implementation manners

[0044] The technical solutions of the present invention will be described in detail below in conjunction with the embodiments. The reagents and biological materials used below are all commercial products unless otherwise specified.

[0045] Example 1: Screening of mRNA markers

[0046] (1) Research cohort and clinical information

[0047] A total of 12 cases of cancer tissues of patients with pulmonary signet-ring cell carcinoma were included, and another 10 cases of adjacent tissues were collected as healthy controls. Finally, a total of 22 tissue samples were included in the study. 15 samples were randomly selected as the training cohort, including 8 cases of pulmonary signet-ring cell carcinoma tissues and 7 cases of adjacent tissues. The validation cohort included 7 cases, including 4 cases of cancer tissues and 3 cases of adjacent tissues. The specific grouping information is shown in Table 1.

[0048] Table 1

[0049]

[0050] (2) Preparation and sectioning of FFPE samples

[0051] The production of FFPE samples is completed by two parts: formaldehyde fixation and paraffin embedding. Put tissue samples of appropriate size into 10% neutral buffered formalin (Formalin, also known as formaldehyde) fixative, fix at room temperature for 18 - 24h, take out and rinse with running water for several minutes after fixation to remove residual fixative and possible crystals. After the tissue samples are fixed, the water in them needs to be removed so that the embedding medium can penetrate fully. Alcohol gradient dehydration: soak in 75% ethanol, 85% ethanol, 95% ethanol, absolute ethanol (I), and absolute ethanol (II) for 1 minute respectively. After dehydration, soak in xylene twice, 1 minute each time, and then put into melted paraffin. Put the wax-impregnated tissue samples into a mold, pour in melted paraffin, and then let the paraffin cool and solidify. In this way, the tissue samples are embedded in paraffin.

[0052] Fix the fully cooled embedded tissue block on the sample clamp of the microtome, adjust the section thickness of the microtome, usually 4 - 5μm thick, gently remove the cut tissue sections from the knife with a brush, and spread them in warm water (40°C). After the spread sections are firmly attached to the glass slides, bake at 60°C for 2h.

[0053] (3) Immunohistochemical detection

[0054] The sections were dewaxed with xylene and then successively placed in absolute ethanol (I), absolute ethanol (II), 95% ethanol, 85% ethanol, 70% ethanol and pure water, each immersion for 5 min. Antigen retrieval was performed with 10 mM sodium citrate (pH 6.0), and then the sections were treated with 3% hydrogen peroxide aqueous solution at room temperature for 10 min and blocked with 5% BSA at room temperature for 30 min. Primary antibody information: Napsin A (ab73021, abcam), CK7 (ab181598, abcam) and TTF-1 (ab76013, abcam). The antibodies were incubated at room temperature for 1 h, washed thoroughly with TBST, and then incubated with rabbit secondary antibody (A0208, Beyotime) or mouse secondary antibody (A0216, Beyotime) at room temperature for 30 min. The sections were washed four times with TBST for 10 min each time, sealed after color development, and observed under a microscope for the location and expression level of the antigen in cells or tissues. See Figure 1 , which is the immunohistochemical staining result diagram. The staining results showed that Napsin A and CK7 showed obvious brownish or yellowish brown staining. Specifically, diffuse brownish granular precipitates were visible in the cytoplasm, while there was no obvious staining in the intercellular substance, indicating that Napsin A and CK7 were mainly expressed in the cytoplasm of the tissue cells. TTF1 showed obvious punctate yellowish brown staining and was mainly expressed in the nucleus. Figure 1 The results showed that the alveolar epithelial markers Napsin A, CK7 and TTF1 in the patient's tumor cells were all positive, strongly suggesting that the patient had lung adenocarcinoma.

[0055] (4) RNA extraction from FFPE samples

[0056] Refer to the product instruction manual and use the ReadiMag FFPE RNA Kit (3DMed, Shanghai) to extract total RNA from FFPE samples. Finally, elute the RNA with 60 μL RNase-free Water. Use NanoDrop to detect the purity of RNA, Qubit to quantify the RNA concentration, and use the Agilent 2100 bioanalyzer and the supporting RNA analysis kit (5067-1514, Agilent) to detect the fragment distribution of mRNA (evaluate the DV200 value).

[0057] (5) Detection of mRNA expression level

[0058] Refer to the product manual, use the KAPA RiboErase HMR Kit (KK8481, Kapa) to remove ribosomal RNA, and use the KAPA RNA Hyper Kit (KK8542, Kapa) for mRNA library construction. Load 400 ng of total RNA for each sample, and then perform rRNA removal, DNase digestion, first and second strand synthesis, adapter ligation, and library amplification. Use Agencourt Ampure XP-PCR purification beads (A63881, Beckman) to purify the PCR enrichment product, and finally elute the library DNA with 20 μL of nuclease-free water. Use the Invitrogen Qubit 4.0 fluorometer and the accompanying reagent Qubit dsDNA HS Assay Kit (Q32854, Thermofisher) to quantify the DNA concentration. Use the Agilent 4150 Bioanalyzer and the accompanying chip and reagent High Sensitivity D1000 ScreenTape & Reagents (5067-5584 & 5067-5585, Agilent) to detect the distribution of library DNA fragments. Sequence using the Illumina NovaSeq platform with a sequencing strategy of PE150 and a sequencing data volume of 12 G for each library.

[0059] (6) Sequencing data analysis process

[0060] Based on the RNA sequencing detection technology, obtain the expression levels of mRNA in the cancer tissues and adjacent tissues of patients with pulmonary signet ring cell carcinoma. The analysis process of the sequencing data is as follows:

[0061] 1) Alignment of sequencing data. After removing the sequencing adapters from the RNA sequencing data using the fastp software (version: 0.23.4), use the STAR software (version: 2.7.11b) to align the sequencing data to the human reference genome hg38 (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg38 / bigZips / )

[0062] 2) mRNA expression quantification. Use the RSEM software (version: 1.3.3) to quantitatively count the number of reads aligned to different mRNAs.

[0063] 3) mRNA annotation. Use the Gencode v43 database to annotate the mRNAs, and retain the mRNAs annotated as known for subsequent analysis.

[0064] 4) mRNA filtering. For the training cohort, mRNAs that were aligned to at least 10 reads in all training cohort samples were retained for subsequent analysis. For the validation cohort, the mRNAs selected from the training cohort were retained for subsequent analysis.

[0065] 5) mRNA expression normalization. The raw mRNA expression levels of the training cohort samples were normalized using the trimmed mean of M-values (TMM) method, and the validation cohort samples were processed with the same parameters.

[0066] (7) Discovery of biomarkers

[0067] Samples were grouped according to the pathological examination results. Based on the mRNA expression levels in the training cohort, statistical and machine learning methods were used to discover mRNAs that could be used to distinguish patients with pulmonary signet-ring cell carcinoma from healthy controls as biomarkers. The process is as follows:

[0068] 1) Grouping of the training cohort. According to the pathological examination results of the samples, the samples in the training cohort were divided into two groups: healthy controls and pulmonary signet-ring cell carcinoma.

[0069] 2) Screening of biomarkers by statistical methods. The U-test and T-test statistical methods were used to calculate the significance of the differences in the expression of all mRNAs between the two groups. Statistical significance was evaluated using the P-value, and a P-value ≤ 0.05 was considered statistically significant. First, mRNAs with both median and mean expression levels greater than 5 in the two groups were selected. Subsequently, mRNAs that met at least one of the following two screening criteria were retained for subsequent analysis: (a) according to the statistical test results of the U-test, the absolute value of the logarithm of the fold change in median expression (logFC median) > 1, and the U-test P-value ≤ 0.05; (b) according to the statistical test results of the T-test, the absolute value of the logarithm of the fold change in mean expression (logFC mean) > 1, and the T-test P-value ≤ 0.05.

[0070] 3) Screening biomarkers using machine learning methods. To determine the final markers for constructing the risk score model, multiple machine learning algorithms were used for biomarker screening, including 6 machine learning algorithms: Linear Model, randomforest, extremely randomized trees, neuralnetwork, Gradient Boosting Machine (GBM), and Ensemble methods. The main process is as follows: (a) Using the mRNAs screened in 2) above as the initial biomarkers, evaluate the baseline performance of the biomarkers; (b) Evaluate the feature importance of each biomarker in the model, retain the biomarkers with importance > 0, and retrain the model; (c) Evaluate the feature importance of the biomarkers in the new model again, and repeat the process in (b) until the feature importance of the biomarkers is > 0 in each model. These biomarkers were used for subsequent analysis.

[0071] Example 2: Construction of the risk score model

[0072] Taking healthy individuals and pulmonary signet-ring cell carcinoma as the classification prediction targets, using the biomarkers discovered in Example 1, 4 machine learning algorithms, namely Extremely Randomized Trees, Linear Model, Random Forest, and Ensemble methods, were used. Different hyper-parameters were preset for each algorithm, and diagnostic models of 6 different machine learning algorithms were trained on the sample data of the entire training cohort. The formula for the final risk score model is as follows:

[0073]

[0074] X i represents the biomarker expression value of sample i, and P(X i ) is the probability of the classification corresponding to sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a patient with pulmonary signet-ring cell carcinoma. The sum of the probabilities of all classifications is equal to 1, and the classification with the highest probability is taken as the final prediction result of sample i. The trained model is saved on the hard disk in the form of a file, and the model prediction result can be obtained by inputting the biomarker expression value of the sample when calling the model.

[0075] Example 3: Evaluation and validation of the performance of the risk score model

[0076] In the training cohort, a 5-fold cross-validation method was used to evaluate the classification performance and feature weights of the model, and the generalization performance of the model was evaluated in the validation cohort. The model evaluation and validation metrics included the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1), accuracy (ranging from 0 to 1), positive predictive value (ranging from 0 to 1), and negative predictive value (ranging from 0 to 1). The specificity (ranging from 0 to 1) and sensitivity (ranging from 0 to 1) of the model were evaluated, and higher values indicated better classification performance of the model.

[0077] For the 17 markers C1QA, CRISPLD2, ROBO4, SPARCL1, SFTPB, LAMC3, FN1, EMP1, CD68, THBS1, PRG4, ICAM1, PODXL, KDM6B, ENPP2, KLF10, and THBD, the evaluation results of individual markers in the training cohort and the validation cohort are shown in Table 2.

[0078] Table 2

[0079]

[0080] The data results in Table 2 showed that the evaluation effects of individual markers were poor and the generalization performance was relatively poor. A high value in either the training cohort alone or the validation cohort alone indicated poor generalization performance. It was better that the AUC values were close in the training cohort and the validation cohort.

[0081] The 17 markers were combined, and a 5-fold cross-validation method was used in the training cohort to evaluate the classification performance and feature weights of the model. Combining the pathological test results, the prediction results of the risk of pulmonary signet-ring cell carcinoma for each patient were given according to the model formula and program. The model evaluation metrics included the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1), accuracy (ranging from 0 to 1), positive predictive value (ranging from 0 to 1), negative predictive value (ranging from 0 to 1), sensitivity (ranging from 0 to 1), and specificity (ranging from 0 to 1). The evaluation results of the combination of 17 markers in the training cohort are shown in Table 3. The results showed that in the training cohort, this risk score model had high AUC, accuracy, positive predictive value, negative predictive value, specificity, and sensitivity, and the model had excellent prediction performance. See Figure 2 , which is the ROC curve of the training cohort for the extreme random tree model algorithm.

[0082] Table 3

[0083]

[0084] To verify the performance of the risk score model in predicting pulmonary signet ring cell carcinoma, another independent cohort was selected as the validation cohort to evaluate the classification effect and feature weights of the model. Using the pathological test results as the ground truth, the model evaluation metrics included AUC (value range 0-1), accuracy (value range 0-1), positive predictive value (value range 0-1), negative predictive value (value range 0-1), sensitivity (value range 0-1), and specificity (value range 0-1). The evaluation results of the combination of 17 markers used in the validation cohort are shown in Table 4. The results showed that in the validation cohort, this risk score model had high AUC, accuracy, positive predictive value, negative predictive value, specificity, and sensitivity, and the model had excellent predictive performance. See Figure 3 , which is the ROC curve of the validation cohort for the extreme random tree model algorithm.

[0085] Table 4

[0086]

[0087] Example 4: Application of the risk score model

[0088] 1) Collect the cancer tissues of patients with clinically diagnosed suspected pulmonary signet ring cell carcinoma, and use RNA sequencing to obtain the expression levels of biomarkers in the cancer tissues;

[0089] 2) Substitute the obtained expression values of C1QA, CRISPLD2, ROBO4, SPARCL1, SFTPB, LAMC3, FN1, EMP1, CD68, THBS1, PRG4, ICAM1, PODXL, KDM6B, ENPP2, KLF10, and THBD into the trained model formula to calculate the probabilities of each patient being predicted as healthy and having pulmonary signet ring cell carcinoma;

[0090] 3) Select the output result with the highest probability and give the prediction result of the risk of each patient having pulmonary signet ring cell carcinoma.

[0091] In summary, the present invention uses RNA sequencing technology to detect the mRNA expression profiles of cancer tissues and healthy control tissues of patients with pulmonary signet ring cell carcinoma, and applies statistical and machine learning methods to discover 17 differentially expressed mRNA markers, namely C1QA, CRISPLD2, ROBO4, SPARCL1, SFTPB, LAMC3, FN1, EMP1, CD68, THBS1, PRG4, ICAM1, PODXL, KDM6B, ENPP2, KLF10, and THBD. Based on the discovered 17 markers, a risk score model with high sensitivity and specificity was constructed. The sensitivity of the model for the diagnosis of early pulmonary signet ring cell carcinoma > 75%, and the specificity > 89%.

[0092] The above are only some preferred embodiments of the present invention, and the present invention is not limited to the content of the embodiments. For those skilled in the art, various changes and modifications can be made within the scope of the concept of the technical solution of the present invention, and any changes and modifications made are within the protection scope of the present invention.

Claims

1. An mRNA marker for early diagnosis of signet ring cell carcinoma of the lung, characterized in that: The mRNAs markers are a combination of C1QA, CRISPLD2, ROBO4, SPARCL1, SFTPB, LAMC3, FN1, EMP1, CD68, THBS1, PRG4, ICAM1, PODXL, KDM6B, ENPP2, KLF10 and THBD.

2. Use of a detection reagent for obtaining the expression level of the mRNAs marker according to claim 1 in the preparation of a composition or a kit for evaluating, diagnosing or monitoring pulmonary signet ring cell carcinoma.

3. A kit for early diagnosis of pulmonary signet ring cell carcinoma, characterized in that: The kit comprises a reagent for detecting the expression level of the mRNAs marker according to claim 1.

4. The kit for early diagnosis of pulmonary signet ring cell carcinoma according to claim 3, characterized in that: The reagent obtains the expression values ​​of the mRNAs markers of cancer tissue and control tissue respectively through RNA sequencing technology.

5. The kit for early diagnosis of pulmonary signet ring cell carcinoma according to claim 3, characterized in that: The kit also includes a risk scoring model, the formula of which is as follows: represents the marker expression value of sample i, is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a patient with pulmonary signet ring cell carcinoma; the sum of the probabilities of all classifications is equal to 1, and the classification with the largest probability is taken as the final prediction result of sample i.

6. The kit for early diagnosis of pulmonary signet ring cell carcinoma according to claim 5, characterized in that: The model algorithm used by the risk scoring model is selected from extreme random trees (Gini), extreme random trees (Entr), random forest (Entr), weighted ensemble model, and linear regression.

7. The method for diagnosing early pulmonary signet ring cell carcinoma using the kit of claim 3, comprising the following steps: Step 1: Collect cancer tissue from patients with suspected pulmonary signet ring cell carcinoma in clinical diagnosis, and use RNA sequencing to obtain the expression level of mRNA markers in the cancer tissue; Step 2, bringing the expression values ​​of the obtained mRNAs markers into the risk scoring model formula described in claim 5 to calculate the probability of predicting the patient to be healthy and to have signet ring cell carcinoma of the lung; Step 3: Select the output result with the highest probability and give the predicted result of the patient's risk of developing signet ring cell carcinoma of the lung.

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