IncRNA molecular tag for predicting gastric cancer postoperative survival, detection kit and application

Abstract

The invention belongs to the technical field of biomedicine, and provides an lncRNA molecular tag for predicting the postoperative survival of gastric cancer patients, aiming at the technical problem that a molecular tag which is constructed based on m6A RNA modification and ferroptosis regulation related lncRNA and can be used for predicting the postoperative survival of the gastric cancer patients is not reported up to now, the invention provides the lncRNA molecular tag for predicting the postoperative survival of the gastric cancer, and the molecular tag consists of DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1 and Linc00900. The lncRNA molecular tag provided by the invention has the capability of remarkably evaluating the total lifetime and representing the tumor immune microenvironment, can be used as an effective method for risk hierarchical management, and provides valuable clues for selecting reasonable treatment strategies.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technologies, and particularly relates to an lncRNA molecular signature for predicting the survival after gastric cancer surgery, a detection kit and an application thereof. Background Art

[0002] In China, the incidence rate of gastric cancer ranks fifth among malignant tumors, while the mortality rate ranks third, bringing a heavy disease burden to society and families. Precision molecular typing of gastric cancer patients based on multi-omics data analysis helps to evaluate the prognosis of patients, implement hierarchical management, select appropriate prevention and treatment plans, and improve the survival rate of patients. However, there is currently a lack of an effective molecular model for evaluating the survival after gastric cancer surgery. Most of the currently established molecular models lack independent sample verification, and their effectiveness and robustness are difficult to determine.

[0003] Long non-coding RNA (lncRNA) is a class of RNAs with a length exceeding 200 nucleotides but without protein-coding functions. lncRNAs can bind to DNA, RNA, proteins, etc., regulate gene expression, affect protein stability and cellular localization, and alter epigenetic modifications. lncRNAs are a research hotspot in the field of tumors. lncRNAs are not easily degraded and can stably exist in body fluid specimens, having the clinical transformation potential as biomarkers and therapeutic targets. N6-methyladenosine (m6A) is the most abundant modification form of eukaryotic mRNA. It is jointly regulated by methyltransferases ("writers"), demethylases ("erasers") and RNA-binding proteins that recognize methylated modifications ("readers"). m6A modification can regulate RNA nucleo-cytoplasmic transport, alternative splicing, degradation and translation, etc. m6A RNA methylation modification is closely related to the occurrence and development of gastric cancer. Ferroptosis is a new type of cell death mode, and its mechanism is mainly related to the accumulation of iron ions in the body, redox status, generation of reactive oxygen species, and deposition of peroxidized lipids, playing an important role in the malignant progression of gastric cancer. To date, there has been no report on constructing a molecular signature based on m6A RNA modification and ferroptosis-regulated lncRNAs that can predict the survival after gastric cancer surgery. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide an lncRNA molecular signature for predicting the survival after gastric cancer surgery, a detection kit and an application thereof.

[0005] The first object of the present invention is to provide an lncRNA molecular signature for predicting the survival after gastric cancer surgery, and the molecular signature is composed of DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1 and Linc00900.

[0006] The second object of the present invention is to provide a method for screening an lncRNA molecular signature for predicting the survival after gastric cancer surgery, comprising the following steps:

[0007] S1. Obtain the lncRNA expression profile;

[0008] S2. Obtain the ferroptosis-related gene set;

[0009] S3. Screen out the lncRNAs in the lncRNA expression profile obtained in S1 that are related to the gene expression in the ferroptosis-related gene set obtained in S2 through correlation analysis to obtain ferroptosis-related lncRNAs;

[0010] S4. Combine the screened ferroptosis-related lncRNAs with m6A regulators in a positive-negative correlation two-way manner to obtain m6A modification-ferroptosis-related lncRNAs;

[0011] S5. Perform univariate Cox regression analysis on the obtained m6A modification-ferroptosis-related lncRNAs to obtain candidate lncRNAs related to the survival of gastric cancer patients after surgery;

[0012] S6. Perform Lasso regression analysis on the obtained candidate lncRNAs to obtain the lncRNA molecular signature;

[0013] The lncRNA molecular signature includes DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1, and Linc00900.

[0014] Preferably, step S4 includes the following steps:

[0015] S41. Screen the lncRNAs in the screened ferroptosis-related lncRNAs that are positively correlated with the expression of methyltransferases METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, and RBM15B in the m6A regulators and at the same time negatively correlated with the expression of demethylases FTO and ALKBH5 in the m6A regulators to obtain the first lncRNAs;

[0016] S42. Screen the lncRNAs in the screened ferroptosis-related lncRNAs that are negatively correlated with the expression of methyltransferases METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, and RBM15B in the m6A regulators and at the same time positively correlated with the expression of demethylases FTO and ALKBH5 in the m6A regulators to obtain the second lncRNAs;

[0017] S43. Combine the first lncRNA and the second lncRNA to obtain the m6A-modified ferroptosis-related lncRNA.

[0018] The third object of the present invention is to provide an application of the above lncRNA molecular tag in the preparation of a gastric cancer postoperative survival prediction product.

[0019] Preferably, the types of the products include a gastric cancer postoperative survival prediction model or a kit.

[0020] The fourth object of the present invention is to provide a kit for predicting the survival after gastric cancer surgery, and the kit includes reagents for detecting the above lncRNA molecular tag gene.

[0021] Preferably, the reagent for detecting the gene expression level of the lncRNA molecular tag is a primer and / or a probe that specifically binds to the gene.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] The lncRNA molecular tag provided by the present invention has the ability to significantly evaluate the overall survival period and characterize the tumor immune microenvironment. Specifically, the experimental results show that: in the modeling dataset TCGA-STAD, the areas under the ROC curves (AUC) of the 3-year and 5-year overall survival periods can reach 0.708 and 0.731, and in the validation dataset Huashan cohort, the areas under the ROC curves (AUC) of the 3-year and 5-year overall survival periods can still reach 0.605 and 0.672. Therefore, this molecular tag can be used as an effective method for risk stratification management. There are significant differences in the infiltration of immune cells in the high- and low-score groups of this molecular tag, which have been verified in the samples of the Huashan cohort. In addition, the drug sensitivities of the high- and low-score groups are quite different. Therefore, this molecular tag can also provide valuable clues for evaluating the characteristics of the tumor microenvironment and selecting a reasonable treatment strategy. Description of the Drawings

[0024] Figure 1Flow chart for establishing the lncRNA molecular signature for predicting the survival of gastric cancer patients after surgery and the efficacy diagram for predicting the prognosis of gastric cancer patients in the TCGA-STAD cohort; (A) Flow chart for establishing the m6A modification-ferroptosis-related lncRNA molecular signature; (B) Result diagram of Lasso regression analysis for 25 candidate lncRNAs obtained; (C) Heat map of the expression of 6 key lncRNA genes in the lncRNA molecular signature provided in the embodiments of the present invention; (D) Result diagram of univariate Cox regression analysis for 6 key lncRNA genes in the lncRNA molecular signature provided in the embodiments of the present invention; (E) Result diagram of the lncRNA molecular signature provided in the embodiments of the present invention for identifying patients in the low-risk and high-risk groups and the result diagram of the death risk in the two groups of patients; (F) Result diagram of principal component analysis (PCA) for patients in the high-risk group and the low-risk group; (G) Result diagram of the survival time of patients in the high-risk group and the low-risk group; (H) Survival prediction ROC curve of the lncRNA molecular signature provided in the embodiments of the present invention.

[0025] Figure 2 Diagram for verifying the prognostic prediction efficacy of the lncRNA molecular signature provided in the embodiments of the present invention through the Huashan gastric cancer cohort; (A) Result diagram of qPCR detection and analysis of 6 key lncRNA genes in the lncRNA molecular signature provided in the embodiments of the present invention in gastric cancer and paired adjacent cancer tissues; (B) Univariate Cox regression analysis confirms that the risk score obtained by this lncRNA molecular signature is related to the prognosis of gastric cancer patients; (C) Result diagram of the lncRNA molecular signature provided in the embodiments of the present invention for identifying the death risk of patients in the high-risk group and the low-risk group in the Huashan gastric cancer validation cohort; (D) Result diagram of the survival time of patients in the high-risk group and the low-risk group in the Huashan gastric cancer validation cohort; (E) Overall survival prediction ROC curve of the high-risk group and the low-risk group of patients in the Huashan gastric cancer validation cohort for the lncRNA molecular signature provided in the embodiments of the present invention; (F) Nomogram for the risk score obtained by the lncRNA molecular signature provided in the embodiments of the present invention for predicting the 3-year and 5-year survival rates of gastric cancer patients; (G) Calibration diagram for the lncRNA molecular signature provided in the embodiments of the present invention for predicting the 3-year and 5-year survival rates.

[0026] Figure 3 Diagram for identifying gene feature differences and potential treatment strategies between high- and low-risk groups by the lncRNA molecular signature provided in the embodiments of the present invention; (A) Result diagram of GO and KEGG enrichment analysis of differential genes between high- and low-risk groups; (B) Molecular interaction network diagram of differential genes between high- and low-risk groups; (C) Result diagram of the sensitivity of patients in the high- and low-risk groups to different drugs.

[0027] Figure 4The figure showing the characterization results of the lncRNA molecular signature provided by the embodiments of the present invention for the gastric cancer immune microenvironment; (A) is the analysis result figure of the risk score and the ssGSEA scores of various immune cells in the microenvironment; (B) is the expression levels of different immune cell markers (such as CD8, CD206, and FOXP3) between the low-risk group and the high-risk group; (C) is the multiplex immunofluorescence staining result figure of the low-risk group and the high-risk group in the Huashan gastric cancer validation cohort. Detailed implementation manners

[0028] Next, in combination with the Figures 1 to 4 embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] Embodiment 1

[0030] The embodiments of the present invention provide an lncRNA molecular signature for predicting the survival after gastric cancer surgery, and this molecular signature is composed of DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1, and Linc00900.

[0031] Embodiment 2

[0032] As Figure 1 shown in (A) of

[0033] the embodiments of the present invention provide a screening method for the lncRNA molecular signature for predicting the survival after gastric cancer surgery described in Embodiment 1, which specifically includes the following steps:

[0034] S1. Obtain the lncRNA expression profile; specifically obtain the lncRNA expression profile from the TCGA-STAD dataset;

[0035] (http: / / www.zhounan.org / ferrdb) to obtain the ferroptosis-related gene set;

[0036] S3. Screen out the lncRNAs related to the gene expression in the ferroptosis-related gene set obtained in S2 from the lncRNA expression profile obtained in S1 through Spearman correlation analysis (R>0.3, p<0.05), and 403 ferroptosis-related lncRNAs are screened out;

[0037] S4. Combine the 403 ferroptosis-related lncRNAs obtained by screening with m6A regulators in a two-way positive-negative correlation manner to obtain m6A-modified ferroptosis-related lncRNAs, which specifically include the following steps:

[0038] S41. Screen the lncRNAs among the 403 ferroptosis-related lncRNAs obtained by screening that are positively correlated with the expression of methyltransferases METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, and RBM15B in m6A regulators and at the same time negatively correlated with the expression of demethylases FTO and ALKBH5 in m6A regulators to obtain the first lncRNA;

[0039] S42. Screen the lncRNAs among the 403 ferroptosis-related lncRNAs obtained by screening that are negatively correlated with the expression of methyltransferases METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, and RBM15B in m6A regulators and at the same time positively correlated with the expression of demethylases FTO and ALKBH5 in m6A regulators to obtain the second lncRNA;

[0040] S43. Combine the first lncRNA and the second lncRNA, that is, summarize the obtained first lncRNA and second lncRNA, and a total of 72 m6A-modified ferroptosis-related lncRNAs are screened.

[0041] S5. Perform univariate Cox regression analysis on the obtained 72 m6A-modified ferroptosis-related lncRNAs to evaluate the relationship between these lncRNAs and overall survival (OS), and obtain 25 candidate lncRNAs related to the postoperative survival of gastric cancer patients;

[0042] S6. Perform Lasso regression analysis on the obtained 25 candidate lncRNAs to obtain an lncRNA molecular signature, and evaluate the postoperative survival time of gastric cancer patients through the lncRNA molecular signature;

[0043] The lncRNA molecular signature includes DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1, and Linc00900, as shown in (B-D) of Figure 1 the figure.

[0044] Example 3

[0045] The embodiment of the present invention provides an application of the lncRNA molecular signature described in Example 1 in the preparation of a gastric cancer postoperative survival prediction product.

[0046] In the embodiments of the present invention, the types of the products include a gastric cancer postoperative survival prediction model or a kit.

[0047] Example 4

[0048] The embodiments of the present invention provide a kit for predicting the survival after gastric cancer surgery, and the kit includes a gene reagent for detecting the lncRNA molecular tag described in Example 1.

[0049] In the embodiments of the present invention, the reagent for detecting the gene expression level of the lncRNA molecular tag is a primer and / or a probe that specifically binds to the gene.

[0050] Example 5

[0051] The embodiments of the present invention further provide an lncRNA model for predicting the survival after gastric cancer surgery, and the lncRNA model is constructed by the lncRNA molecular tag of Example 1.

[0052] The expression formula of the lncRNA model for predicting the survival after gastric cancer surgery provided by the embodiments of the present invention is as follows:

[0053] Score = LINC00900 * 0.055217346471299 + AC087392.1 * 0.139222141484366 + MSC-AS1 * 0.172808957538896 + AC129507.1 * 0.475604132944101 + AL049838.1 * 0.0577528302321133 - DNM3OS * 0.039242443394601

[0054] According to the expression levels of these 6 lncRNAs, the score of each patient can be obtained, and the score of each patient is used as the cut-off point for the high-risk group and the low-risk group, and the p-value under the current grouping is calculated. Then, the score of the patient with the smallest p-value obtained from the multiple p-values is selected as the cut-off point, and the gastric cancer patients are divided into the high-risk group and the low-risk group.

[0055] By analyzing the expression of these 6 genes, namely DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1, and Linc00900, in cancer and adjacent tissues in TCGA-STAD, the results are as Figure 1 shown in (C), and it can be seen from Figure 1 (C) that there are differences in the expression of the 6 key lncRNA genes.

[0056] After performing univariate Cox regression analysis on the 25 candidate lncRNAs related to the postoperative survival of TCGA-STAD gastric cancer patients obtained in step S6, the HR values and p-values of the 6 lncRNAs, namely DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1, and Linc00900, were displayed using a forest plot. The results are as Figure 1 shown in (D) below. It can be seen from Figure 1 (D) that these 6 lncRNAs have significant prognostic predictive value.

[0057] By ranking the risk scores of TCGA-STAD patients, in the upper part of the rectangular box in (E) below, the horizontal axis represents the risk score of each patient, sorted from smallest to largest. The blue dots represent low-risk patients, and the red dots represent high-risk patients. The dashed line is the boundary between low risk and high risk. In the lower part of the rectangular box in (E) below, the horizontal axis represents the risk score of each patient, and the vertical axis represents the survival time of the patient. The blue dots represent survival, and the red dots represent death. The dashed line is the boundary between low risk and high risk. It can be seen from Figure 1 (E) that the death risk of patients in the high-risk group obtained through this molecular signature is significantly higher than that in the low-risk group. PCA dimensionality reduction analysis was performed on the 6 lncRNAs, namely DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1, and Linc00900, of 336 TCGA-STAD patients. The results are as Figure 1 shown in (F) below. It can be seen from Figure 1 (F) that there are obvious clustering characteristics between the high-risk group and the low-risk group. Figure 1 Figure 1

[0058] Figure 1 Figure 1 According to the lncRNA molecular signature, the 336 TCGA-STAD patients included in the study were divided into a high-risk group and a low-risk group. Kaplan-Meier survival analysis was performed on the two groups of patients. The results are as Figure 1 shown in (G) below. It can be found from Figure 1 (G) that patients with high risk scores have shorter survival times, while patients with low risk scores have significantly longer survival times.

[0059] ROC curve analysis was performed on the scores of the 336 TCGA-STAD patients included in the study to detect the discrimination of this molecular signature. The results are as Figure 1 shown in (H) below. It can be seen from Figure 1 (H) that the AUC values of this molecular signature in 3-year and 5-year survival predictions reached 0.708 and 0.731 respectively, showing good prognostic predictive efficacy.

[0060] Next, 60 pairs of gastric cancer patient specimens collected by this center were used to detect and verify the model provided in Example 6 of the present invention. The results are as Figure 2 shown. The qPCR test analysis results of 60 pairs of gastric cancer patients in this hospital are as Figure 2 shown in (A) of Figure 2 . It can be seen from (A) of

[0061] Table 1 Detection primers for 6 key lncRNAs

[0062] AC087392.1-F CTGTGGGCTAACAGACAGGG AC087392.1-R TCCGCTTCTTCTCTCTCCGA AC129507.1-F TAAACACAGACTGCGCCCTT AC129507.1-R TTGCCTTCCCTGCTTTTCCA DNM3OS-F TGGTACACACGCCCACATTT DNM3OS-R AGTTTGGACAGACCCTGAAACA AL049838.1-F ATGCATCTTCCAAGTGCTCCA AL049838.1-R AGTGTTTACATGGCCACAGGT LINC00900-F AGATGGCGTGTTTGGACTGT LINC00900-R GGCTGTGATGGGAATGTGGA MSC-AS1-F TCCTTGGGGCCAAAATTGTAA MSC-AS1-R TGGGTGTTATTTGGGGGTGG

[0063] that the expression levels of 6 key lncRNAs (lnc-AC087392.1, lnc-MSC-AS1, lnc-TNC00900, lnc-DNM3OS, lnc-AL049838.1, and lnc-AC129507.1) in gastric cancer tissues are significantly higher than those in adjacent para-cancerous tissues. The detection primers for the 6 key lncRNAs are shown in Table 1 below. Figure 2 It can be seen from the univariate Cox regression analysis in (B) of Figure 2 that the risk score obtained from the lncRNA molecular signature is related to the prognosis of gastric cancer patients, and patients with higher risk scores have a worse prognosis. By ranking the risk scores of the patients in this hospital, in the upper part of the rectangular box in (C) of Figure 2 , the horizontal axis represents the risk score of each patient, and the scores are sorted from small to large. Among them, the blue dots represent low-risk patients, while the red dots represent high-risk patients. The dashed line is the dividing line between low risk and high risk. In the lower part of the rectangular box in (C) of Figure 2 , the horizontal axis represents the risk score of each patient, and the vertical axis represents the survival time of the patient. The blue dots represent survival, and the red dots represent death. The dashed line is the dividing line between low risk and high risk. It can be seen from (C) of Figure 2 that the death risk of patients in the high-risk group obtained from this molecular signature is significantly higher than that in the low-risk group. The 60 patients in this hospital included in the study were divided into high- and low-risk groups according to the lncRNA molecular signature, and Kaplan-Meier survival analysis was performed on the two groups of patients. It can be found from (D) of Figure 2 that patients with high risk scores have a shorter survival time, while patients with low risk scores have a significantly longer survival time. Figure 2 (E) The ROC curve analysis was performed on the scores of 60 patients in this hospital included in the study to detect the discrimination of this molecular signature. The results are as Figure 2 shown in (E) of Figure 2The (E) therein shows that the AUC values in the 3-year and 5-year survival predictions are 0.605 and 0.672 respectively, indicating that the lncRNA molecular signature established by the present invention for predicting the survival after gastric cancer surgery has good sensitivity and specificity, and verifying its prediction accuracy. Figure 2 The (F) therein constructs a nomogram based on the risk scores of 60 patients in this hospital included in the study, and can predict the 3-year and 5-year survival probabilities of gastric cancer patients with different scores. Figure 2 The (G) therein conducts a calibration analysis on the nomogram of the lncRNA molecular signature, and finds that the 3-year and 5-year calibration curves calculated by this molecular signature are close to the diagonal line (Ideal line).

[0064] Perform GO and KEGG enrichment analysis on the differentially expressed genes between the high- and low-risk groups of TCGA-STAD patients. The results are as Figure 3 shown in (A) therein. Through Figure 3 shown in (A) therein, it is shown that the differentially expressed genes are significantly enriched in pathways related to oxidative stress response, regulation of RNA metabolic process, etc. The molecular characteristics of the differentially expressed genes mainly focus on oxidative stress, ROS production, RNA metabolism, glutathione metabolism, and T cell signal transduction. Figure 3 The (B) therein shows the complex interaction between the enriched pathways of the differentially expressed genes through a molecular network diagram. The pRRophetic platform was used to evaluate the sensitivity of high-risk and low-risk patients to different drugs. The results are as Figure 3 shown in (C) therein. Through Figure 3 shown in (C) therein, it can be seen that the IC50 values of PDK1 inhibitor (BX-795), tyrosine kinase inhibitors (Imatinib and AP.24534), PI3K inhibitor (GDC0941), and HIF prolyl hydroxylase inhibitor (DMOG) are significantly lower in high-risk patients, indicating that high-risk patients are more sensitive to these inhibitors. While the IC50 values of ErbB inhibitor (BIBW2992), TrkA inhibitor (GW441756), PARP inhibitor (ABT-888), and ribosomal S6K kinase inhibitor (SL.001.1) are significantly lower in low-risk patients, indicating that low-risk patients are more sensitive to these inhibitors.

[0065] In the embodiments of the present invention, the results of the study on the immune microenvironment of high- and low-risk group patients are as Figure 4 shown. By performing ssGSEA analysis on the differentially expressed genes between the high- and low-risk groups of TCGA-STAD patients, the scores of various immune cell infiltrations are obtained, and their correlation analyses are respectively performed with the risk scores and the 6 lncRNAs of DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1, and Linc00900. The results are as Figure 4As shown in (A) and (B) therein, through Figure 4 The results of (A) therein show that high risk scores are positively correlated with immunosuppressive cells such as Tregs and M2 macrophages, and negatively correlated with CD56dim natural killer cells. Through Figure 4 It can be seen from (B) therein that there are differences in the expression of different immune cell markers CD8, CD206 and FOXP3 between the high- and low-risk groups, and there are significant differences in the expression levels of CD206 and FOXP3 between the low-risk and high-risk groups. Among them, high risk scores are significantly positively correlated with the expression of the M2 macrophage marker CD206 and the Treg marker FOXP3. The tumor tissues of gastric cancer patients with high and low risks were detected by multiplex immunofluorescence, and the detection results are as Figure 4 shown in (C) therein, through Figure 4 It can be seen from the results of (C) therein that CD8+ T cells (magenta) are abundant in low-risk samples, while M2 macrophages (green, CD206) and Tregs (white, FOXP3) are less; while in high-risk samples, the infiltration of CD8+ T cells is reduced, and the infiltration of tumor-associated M2 macrophages and Treg cells with immunosuppressive functions is more obvious.

[0066] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A lncRNA molecular signature for predicting postoperative survival of gastric cancer, characterized in that: The molecular signature consists of DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1 and Linc00900.

2. A method for screening lncRNA molecular signatures for predicting postoperative survival of gastric cancer according to claim 1, characterized in that: The following steps are involved: S1. Obtain lncRNA expression profile; S2. Obtain ferroptosis-related gene sets; S3, screening out lncRNAs in the lncRNA expression profile obtained in S1 that are correlated with the gene expression in the ferroptosis-related gene set obtained in S2 through correlation analysis, and obtaining ferroptosis-related lncRNAs; S4, the ferroptosis-related lncRNA screened in S3 is bidirectionally combined with the m6A regulatory factor for positive-negative correlation to obtain the m6A modification-ferroptosis-related lncRNA; S5. Perform univariate Cox regression analysis on the m6A modification-ferroptosis-related lncRNAs obtained in S4 to obtain candidate lncRNAs associated with postoperative survival of gastric cancer patients; S6, performing Lasso regression analysis on the candidate lncRNA obtained in S5 to obtain the lncRNA molecular signature; The lncRNA molecular signatures include DNM3OS, AL049838.1, AC129507.1, MSC-AS1, AC087392.1 and Linc00900.

3. The screening method according to claim 2, characterized in that Step S4 comprises the following steps: S41. Among the screened ferroptosis-related lncRNAs, lncRNAs that are positively correlated with the expression of methyltransferases METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, and RBM15B among m6A regulatory factors and negatively correlated with the expression of demethylases FTO and ALKBH5 among m6A regulatory factors are screened to obtain the first lncRNA; S42, among the screened ferroptosis-related lncRNAs, the lncRNAs that are negatively correlated with the expression of methyltransferases METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, and RBM15B among the m6A regulatory factors and positively correlated with the expression of demethylases FTO and ALKBH5 among the m6A regulatory factors are screened to obtain the second lncRNA; S43. Combine the first lncRNA and the second lncRNA to obtain the m6A modification-ferroptosis related lncRNA.

4. Use of the lncRNA molecular signature according to claim 1 in preparing a product for predicting postoperative survival of gastric cancer.

5. The use according to claim 4, characterized in that: The types of products include gastric cancer postoperative survival prediction models or kits.

6. A kit for predicting postoperative survival of gastric cancer, characterized in that: The kit comprises a gene reagent for detecting the lncRNA molecular tag according to claim 1.

7. The kit according to claim 6, characterized in that The reagent for detecting the gene expression level of the lncRNA molecular tag is a primer and / or a probe that specifically binds to the gene.

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