Asian hepatocellular carcinoma prognosis model based on double-sulfur death related lncRNA and application of Asian hepatocellular carcinoma prognosis model

Abstract

The invention relates to an Asian hepatocellular carcinoma prognosis model based on lncRNA (long non-coding RNA) related to disulphide death and application of the Asian hepatocellular carcinoma prognosis model, and particularly relates to a diagnosis marker combination of long non-coding RNA (DRLRs) related to disulphide death, and the diagnosis marker combination comprises AC099850.3, ZNF337-AS1, LINC01138, AL031985.3 and AC131009.1. The invention also comprises application of the reagent for evaluating the combined expression quantity of the diagnostic marker in preparation of a kit for predicting prognosis survival of Asian HCC patients. A prognosis model including a risk score (Risk Score) is constructed to predict survival prognosis of the patient based on the sum of the expression level of the RNA and the product of the coefficient of the RNA and in combination with clinical characteristics of the patient. The model has the advantages of high efficiency and accuracy in the aspect of effectively predicting the total lifetime of the Asian HCC patient, and a practical guidance tool is provided for clinicians, so that the treatment decision is optimized, the invalid treatment is reduced, and the medical cost and uncomfortable experience of the patient are reduced.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology. Specifically, it is a prognostic model for Asian hepatocellular carcinoma based on disulfide bond-related lncRNA and its application. Background Art

[0002] Hepatocellular carcinoma (HCC) is a malignant tumor with a relatively high incidence worldwide, characterized by insidious onset, rapid progression, and poor prognosis. The high incidence of HCC in Asia is closely related to chronic hepatitis B and C virus infections. Although vaccination and antiviral therapy have achieved certain effects, the early diagnosis and prognostic evaluation of HCC remain challenging.

[0003] In 2023, Liu et al. from MD Anderson Cancer Center in the United States found that under glucose starvation conditions, cells with high expression of SLC7A11 accumulate abnormal amounts of disulfide bonds (such as cystine), thereby inducing disulfide bond stress. This leads to an increase in the content of disulfide bonds in the actin cytoskeleton, resulting in the contraction of actin filaments, the destruction of the cytoskeleton structure, and cell death. This mode of cell death is called "disulfide bond death" and is different from other known forms (such as apoptosis and ferroptosis). The formation and breakage of intracellular disulfide bonds are closely related to the development and progression of cancer. Currently, there are relatively few studies exploring the association mechanism between disulfide bond death and long non-coding RNA (lncRNA), especially for the Asian population, and there is no research yet. Currently, the prognosis prediction of HCC mainly relies on clinical pathological staging, such as TNM staging. However, the TNM staging system alone cannot clearly predict the different prognostic outcomes of patients with the same stage. Therefore, exploring molecular markers and prediction models that can more accurately predict the survival prognosis of patients has important clinical significance. Currently, there are still few studies on disulfide bond death-related genes, especially the important prognostic role of disulfide bond-related lncRNA in HCC in the Asian population. At the same time, most of the existing studies are limited to the single-gene level, lacking an overall understanding of the lncRNA characteristics related to HCC disulfide bond death.

[0004] Considering the above deficiencies, the present invention focuses on exploring the correlation between the lncRNA characteristics related to disulfide bond death in Asian HCC and the prognosis of patients, providing a basis for the prognosis prediction of Asian HCC patients clinically. There is currently no report on the prognostic prediction model of lncRNA related to disulfide bond death in Asian HCC of the present invention. Summary of the Invention

[0005] Object of the Invention: The technical problem to be solved by the present invention is to provide a prognostic model for Asian hepatocellular carcinoma based on disulfide bond-related lncRNA and its application in view of the deficiencies of the prior art.

[0006] To solve the above technical problems, the present invention discloses a prognostic model for Asian hepatocellular carcinoma based on ferroptosis-related lncRNAs and its application. The specific technical solutions are as follows:

[0007] A diagnostic marker combination for a prognostic model of Asian hepatocellular carcinoma based on ferroptosis-related lncRNAs, the diagnostic marker combination being AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, and AC131009.1. The lncRNAs are long non-coding RNAs.

[0008] In a second aspect, the present invention provides the use of the diagnostic marker combination described in the first aspect in constructing a prognostic model for Asian hepatocellular carcinoma based on ferroptosis-related lncRNAs.

[0009] In a third aspect, the present invention provides a prognostic model for Asian hepatocellular carcinoma based on ferroptosis-related lncRNAs constructed using the diagnostic marker combination described in the first aspect.

[0010] Wherein, the prognostic model includes a Risk Score system, and the Risk Score system includes the following content (the expression levels described below are the numerical values obtained by qPCR detection of the genes):

[0011] Risk Score = (0.00391 × AC099850.3 expression level) + (0.00510 × ZNF337-AS1 expression level) + (0.00102 × LINC01138 expression level) + (0.00438 × AL031985.3 expression level) + (0.02123 × AC131009.1 expression level); wherein, Risk Score is the risk value.

[0012] Wherein, when Risk Score ≥ median value, the patient is determined to be in a high-risk state, and when Risk Score < median value, the patient is determined to be in a low-risk state. The prognosis of patients in the high-risk group is worse than that of patients in the low-risk group. At the same time, Riskscore is positively correlated with the degree of immune cell infiltration and TMB mutation status of the patient. The median value is the value located in the middle position in the Risk Score data. After arranging the data in the database in ascending (or descending) order, the method for determining the median value is as follows: for an odd number of data: arrange the data in order, and the middle number is the median value; for an even number of data: first, arrange the data in order, and then take the average of the two middle numbers, which is the median value.

[0013] Among them, the prognostic model includes reagents or kits for detecting the expression levels of AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, and AC131009.1.

[0014] Among them, the prognostic model further includes combining the patient's Risk Score with age, gender, pathological grade, and TNM stage for integral scale scoring, calculating the total score, and using a conversion function to estimate the survival probabilities of each patient in the next 1 year, 3 years, and 5 years.

[0015] In a fourth aspect, the present invention provides the use of the diagnostic marker combination described in the first aspect or the prognostic model for Asian hepatocellular carcinoma based on disulfide death-related lncRNAs described in the third aspect in a product for evaluating the prognosis of Asian hepatocellular carcinoma.

[0016] In a fifth aspect, the present invention provides a product for evaluating the prognosis of Asian hepatocellular carcinoma. The product includes a reagent for detecting the expression levels of the diagnostic marker combination described in the first aspect, and evaluates the prognostic survival function of Asian hepatocellular carcinoma through the following content (the expression level is the numerical value detected by qPCR of the gene):

[0017] Risk Score = (0.00391 × expression level of AC099850.3) + (0.00510 × expression level of ZNF337-AS1) + (0.00102 × expression level of LINC01138) + (0.00438 × expression level of AL031985.3) + (0.02123 × expression level of AC131009.1); when Risk Score ≥ median value, the patient is determined to be in a high-risk state, and when Risk Score < median value, the patient is determined to be in a low-risk state. The reagent for detecting the expression levels of the diagnostic marker combination described in the first aspect is a key component for evaluating the prognostic survival function of HCC.

[0018] Among them, the sample detected by the product is a fresh tissue tumor sample, preferably a fresh tissue hepatocellular tumor sample.

[0019] Beneficial effects:

[0020] (1) The present invention utilizes an online database to construct a prognostic model for Asian hepatocellular carcinoma (HCC) based on ferroptosis-related lncRNAs and evaluate its clinical applicability. The RNA sequencing data (RNA-seq) of Asian HCC patients is downloaded and sorted, and the Wilcoxon rank-sum test is used to analyze the correlation between lncRNAs and ferroptosis-related genes (GYS1, NDUFS1, OXSM, LRPPRC, NDUFA11, NUBPL, NCKAP1, RPN1, SLC3A2, and SLC7A11). The selection threshold for this analysis is set as the correlation (|cor|) > 0.4 and p < 0.001. In the training set, univariate Cox regression analysis is first used for preliminary screening to determine the DRLRs (p < 0.05) related to the overall survival rate of Asian HCC patients. Then, the least absolute shrinkage and selection operator (LASSO) regression is applied for dimensionality reduction, and finally, multivariate Cox regression analysis (based on the Akaike information criterion) is used to determine the final DRLRs to construct a prognostic Risk Score system for Asian HCC patients. The final Risk Score system is internally validated using Bootstrap resampling. In the training cohort and the internal validation cohort, Kaplan-Meier (KM) survival curves are used for survival analysis, and time-dependent receiver operating characteristic curves (timeROC) are used to evaluate the predictive performance of the prognostic Risk Score. The concordance index (C-index) analysis is used to evaluate the consistency of the Risk Score, and univariate / multivariate regression is used for independent prognostic analysis. At the same time, a nomogram for predicting the 1-, 3-, and 5-year survival of Asian HCC patients is developed by combining the patient's age, gender, pathological grade, and TNM stage.

[0021] (2) The prognostic prediction Risk Score system of the diagnostic marker combination obtained by the method of the present invention has the advantages of high sensitivity, good specificity, and high accuracy, and can provide effective guiding opinions for clinicians' treatment decisions for Asian HCC patients, reduce the occurrence of ineffective treatments, and thus reduce the patient's cost and discomfort experience. Brief Description of the Drawings

[0022] The following further specifically describes the present invention in conjunction with the drawings and specific embodiments, and the above and / or other advantages of the present invention will become clearer.

[0023] Figure 1 It is a prognostic Risk Score system for ferroptosis-related long non-coding RNAs (DRLRs) in Asian HCC patients constructed by the present invention. Figure 1 A is a Sankey diagram showing the association between ferroptosis-related genes and DRLRs, Figure 1 B and Figure 1C is the selection of the least absolute shrinkage and selection operator (LASSO) regression applied to DRLR, Figure 1 D is the forest plot representing the results of multivariate Cox regression analysis.

[0024] Figure 2 It is the Kaplan-Meier (KM) survival curve, time-dependent receiver operating characteristic (timeROC), concordance index (C-index), and independent prognostic analysis and validation of the Risk Score in the Asian HCC training cohort. Figure 2 A is the analysis of the difference in overall survival (OS) between the high-risk group and the low-risk group in the training cohort by KM survival curve, Figure 2 B is the timeROC curve in the training cohort predicting the 1-year, 3-year, and 5-year OS of Asian HCC patients, Figure 2 C is the C-index in the training cohort, Figure 2 D is the univariate Cox analysis in the training cohort, Figure 2 E is the multivariate Cox analysis in the training cohort.

[0025] Figure 3 It is the KM survival curve, timeROC, C-index, and independent prognostic analysis and validation of the Risk Score in the Asian HCC internal validation cohort. Figure 3 A is the analysis of the OS difference between the high-risk group and the low-risk group in the internal validation cohort by KM survival curve, Figure 3 B is the timeROC curve in the internal validation cohort predicting the 1-year, 3-year, and 5-year OS of Asian HCC patients, Figure 3 C is the C-index in the internal validation cohort, Figure 3 D is the univariate Cox analysis in the internal validation cohort, Figure 3 E is the multivariate Cox analysis in the internal validation cohort.

[0026] Figure 4 It is the timeROC curve comparing the Risk Score of the dithionite death-related lncRNA gene marker of the present invention with the Risk Score of other similar dithionite death-related lncRNA gene markers. Figure 4 A is the timeROC curve of the present invention, Figure 4The model shown in B is from the reference [Chao Chen, et al. Prognosis and chemotherapy drugsensitivity in liver hepatocellular carcinoma through a disulfidptosis-related lncRNA signature. Scientific Reports. 2024, 14 volumes, 1 issue, page 7157], Figure 4 The model shown in C is from the reference [Zhoubo Guo, et al. A novel disulfidptosis-related lncRNAssignature for predicting survival and immune response in hepatocellularcarcinoma. Aging(Albany NY). 2024, 16 volumes, 1 issue, 267 - 284 pages]. Figure 4 The model shown in D is from the reference [Xiao Jia, et al. Constructed Risk Prognosis Model Associated withDisulfidptosis lncRNAs in HCC. International Journal of MolecularSciences. 2023, 24 volumes, 24 issues, page 17626], Figure 4 The model shown in E is from the reference [Qian Wei, etal. Disulfidptosis-Associated lncRNAs are Potential Biomarkers for PredictingImmune Response and Prognosis Within Individuals Diagnosed withHepatocellular Carcinoma. Hepatic Medicine-Evidence and Research. 2023, 15 volumes, 1 issue, 249 - 264 pages].

[0027] Figure 5 It is the Gene Ontology GO analysis of the Risk Score of the present invention. Figure 5 A is a bar chart of the top 10 pathways of biological process (BP), cellular component (CC), and molecular function (MF) obtained from the GO analysis, Figure 5 B is a circular diagram of the GO enrichment analysis.

[0028] Figure 6 The gene set enrichment analysis (GSEA) of the Risk Score of the present invention for the c2.all.v2022.1.Hs.symbols.gmt gene set Figure 6 A is the high-risk group Figure 6 B is the low-risk group

[0029] Figure 7 The gene set enrichment analysis (GSEA) of the Risk Score of the present invention for the c5.all.v2022.1.Hs.symbols.gmt gene set Figure 7 A is the high-risk group Figure 7 B is the low-risk group

[0030] Figure 8 The enrichment analysis (GSEA) of the Risk Score of the present invention in the high-risk group for the h.all.v2022.1.Hs.symbols.gmt gene set

[0031] Figure 9 It is the immune landscape analysis of Asian HCC patients of the present invention Figure 9 A is the proportion of 22 immune cells in the high-risk group and low-risk group calculated using the CIBERSORT algorithm Figure 9 B is the difference in immune function scores between the high-risk group and low-risk group

[0032] Figure 10 It is the relationship between the tumor mutational burden (TMB) and Risk Score of Asian HCC patients of the present invention Figure 10 A is the difference in TMB between the high-risk group and low-risk group Figure 10 B is the Kaplan-Meier survival curve between the high-TMB group and low-TMB group Figure 10 C is the Kaplan-Meier survival curve of the high-TMB + high-Risk Score group, high-TMB + low-Risk Score group, low-TMB + high-Risk Score group, and low-TMB + low-Risk Score group

[0033] Figure 11 It is the prognostic nomogram of disulfiram death-related lncRNA in Asian HCC patients Detailed implementation manners

[0034] The present invention will be elaborated in detail below in combination with specific implementation manners. It should be noted that these examples are only used to illustrate the present invention and are not limited to the scope of the present invention. Professionals in the technical field can make various improvements or modifications after reading the content of the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application

[0035] Example 1

[0036] 1. Method

[0037] 1) Data collection and screening: Downloaded RNA sequencing data of Asian patients with hepatocellular carcinoma (HCC) from The Cancer Genome Atlas (TCGA), and collected corresponding clinical data from the cBioPortal database. The screening criteria were clear Asian HCC classification and samples with complete lncRNA and mRNA sequencing data. Finally, 155 Asian HCC patients and 6 normal liver tissue samples were included. The Wilcoxon rank-sum test was used to analyze the correlation between lncRNAs and genes related to disulfide death (GYS1, NDUFS1, OXSM, LRPPRC, NDUFA11, NUBPL, NCKAP1, RPN1, SLC3A2, and SLC7A11). The selection threshold for this analysis was set as correlation |cor| > 0.4 and p < 0.001, and a total of 835 lncRNAs were obtained ( Figure 1 A).

[0038] 2) Construction of the Risk Score system: To identify disulfide-related lncRNAs (DRLRs) associated with HCC prognosis, first, univariate Cox regression analysis was used to screen out lncRNAs with significant prognostic value (p < 0.05), and 509 DRLRs were obtained. Subsequently, LASSO regression was used to further screen out key lncRNAs ( Figure 1 B and Figure 1 C). Finally, multivariate Cox regression analysis was applied to obtain five specific lncRNAs: AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, and AC131009.1 ( Figure 1 D), and the Risk Score system was constructed. The Risk Score calculation formula is as follows (the expression level is the value detected by qPCR for the gene):

[0039] Risk Score = (0.00391 × AC099850.3 expression level) + (0.00510 × ZNF337-AS1 expression level) + (0.00102 × LINC01138 expression level) + (0.00438 × AL031985.3 expression level) + (0.02123 × AC131009.1 expression level).

[0040] 3) Clinical application, survival analysis, and Risk Score system model evaluation: The median is the value in the middle position of the Risk Score data. After arranging the data in ascending (or descending) order, the method for determining the median is as follows: For an odd number of data: Arrange the data in order, and the middle number is the median. For an even number of data: First, arrange the data in order, and then take the average of the two middle numbers as the median. Patients were divided into high-risk and low-risk groups according to the median Risk Score. An internal validation cohort was obtained by using bootstrap resampling to draw with replacement the same number of data as the training cohort from the original sample of the training cohort. The Kaplan-Meier method was used to compare the overall survival of Asian HCC patients in the training cohort and the internal validation cohort. The Kaplan-Meier analysis results showed that whether in the training cohort ( Figure 2 A) or the internal validation cohort ( Figure 3 A), the overall survival (OS) advantage of patients in the low-risk group was higher than that of patients in the high-risk group (both P < 0.001). The 1-, 3-, and 5-year timeROC curves were used to evaluate the prediction accuracy of Risk Score in the training cohort and the internal validation cohort. The results showed that the areas under the curve (AUC) of the 1-, 3-, and 5-year curves in the training cohort were 0.837, 0.794, and 0.783, respectively ( Figure 2 B). The 1-, 3-, and 5-year AUCs in the internal validation cohort were 0.859, 0.809, and 0.832, respectively ( Figure 3 B). C-index analysis showed that in the training cohort and the internal validation cohort, Risk Score had higher predictive ability compared with other clinical feature models ( Figure 2 C and Figure 3 C). To verify the effectiveness of Risk Score as an independent prognostic factor, in the training cohort and the internal validation cohort, Risk Score and clinical features were included together for univariate and multivariate Cox regression analysis. The results showed that in the training cohort, univariate analysis ( Figure 2 D) showed that TNM stage (Stage) and Risk Score were statistically significant, and it was found that Risk Score (P < 0.001) and AJCC TNM stage (P < 0.001) were important risk factors affecting the prognosis of Asian HCC patients. Multivariate analysis ( Figure 2 E) showed that TNM stage and Risk Score were statistically significant, confirming that Risk Score (P < 0.001) and AJCC TNM stage (P < 0.001) were independent risk factors for predicting OS of Asian HCC. In the internal validation cohort, univariate analysis ( Figure 3D) showed that age, TNM stage, and Risk Score were statistically significant. It was found that age (P = 0.033), Risk Score (P < 0.001), and AJCC TNM stage (P < 0.001) were important risk factors affecting the prognosis of Asian HCC patients. Multivariate analysis ( Figure 3 E) indicated that TNM stage and Risk Score were statistically significant, confirming that Risk Score (P < 0.001) and AJCC TNM stage (P < 0.001) were independent risk factors for predicting OS in Asian HCC.

[0041] In addition, the present invention also provides the comparison results of the accuracy or specificity between the Risk Score of other similar disulfidptosis-related lncRNA gene markers and the Risk Score of the disulfidptosis-related lncRNA marker described in the present invention, as shown in Figure 4 , and the results showed that: when compared with other similar disulfidptosis-related lncRNA gene markers (markers not protected by the present invention) Risk Score ( Figure 4 B - E)), the area under the timeROC curve corresponding to the Risk Score of the disulfidptosis-related lncRNA gene marker of the present invention ( Figure 4 A) had higher accuracy or specificity. Among them Figure 4 A was the timeROC curve established by the model described in the present invention, Figure 4 The model shown in B was from the reference [Chao Chen, et al. Prognosis and chemotherapy drugsensitivity in liver hepatocellular carcinoma through a disulfidptosis-related lncRNA signature. Scientific Reports. 2024, 14th volume, 1st issue, page 7157], Figure 4 The model shown in C was from the reference [Zhoubo Guo, et al. A novel disulfidptosis-related lncRNAssignature for predicting survival and immune response in hepatocellularcarcinoma. Aging (Albany NY). 2024, 16th volume, 1st issue, 267 - 284 pages], Figure 4The model shown in D is from the reference [Xiao Jia, et al. Constructed Risk Prognosis Model Associated with Disulfidptosis lncRNAs in HCC. International Journal of Molecular Sciences. 2023, 24 volumes, 24 issues, page 17626]. Figure 4 The model shown in E is from the reference [Qian Wei, et al. Disulfidptosis - Associated lncRNAs are Potential Biomarkers for Predicting Immune Response and Prognosis Within Individuals Diagnosed with Hepatocellular Carcinoma. Hepatic Medicine - Evidence and Research. 2023, 15 volumes, 1 issue, pages 249 - 264].

[0042] 4) Biological pathway and immune analysis: Perform differential gene expression analysis on the high - risk and low - risk groups in the training cohort, and conduct Gene Ontology (GO) ( Figure 5 A and Figure 5 B) and Gene Set Enrichment Analysis (GSEA) ( Figure 6 , Figure 7 and Figure 8 ) to explore potential biological function pathways. GO enrichment analysis ( Figure 5 A and Figure 5B) showed that in the biological process (BP), differential genes were significantly enriched in molecular functions such as proximal / distal pattern formation, embryonic skeletal system development, and embryonic skeletal system morphogenesis. In the cellular component (CC) part, differential genes were significantly enriched in pathways such as presynapse, synaptic membrane, and postsynaptic membrane. Additionally, in the molecular function (MF), significant enrichment of differential genes was observed in pathways related to gated channel activity, ion channel activity, and channel activity. Subsequently, GSEA analysis was performed on the high-risk and low-risk groups. The results showed that for the c2.all.v2022.1.Hs.symbols.gmt gene set, the high-risk group was mainly enriched in pathways such as basaki ybx1 targets up, blanco melo bronchial epithelial cells influenza a del ns1 infection dn, and blum response to salirasib dn ( Figure 6 A). While the low-risk group was significantly enriched in pathways such as boyault liver cancer subclass g123 dn, boyault liver cancer subclass g3 dn, and kim liver cancer poor survival dn ( Figure 6 B). For the c5.all.v2022.1.Hs.symbols.gmt gene set, the high-risk group was mainly related to pathways such as gocc collagen containing extracellular matrix, gocc intrinsic component of synaptic membrane, and gocc postsynaptic membrane ( Figure 7 A). In contrast, the low-risk group was mainly involved in pathways such as gobp mitochondrial electron transport nadh to ubiquinone, gobp nadh dehydrogenase complex assembly, and gocc nadh dehydrogenase complex ( Figure 7B). For the h.all.v2022.1.hs.symbols.gmt gene set, the high-risk group was significantly enriched mainly in pathways such as hallmark e2f targets, hallmark g2m checkpoint, and hallmark kras signaling dn ( Figure 8 ). In contrast, there was no significant pathway enrichment in the low-risk group in the h.all.v2022.1.hs.symbols.gmt gene set.

[0043] The abundance of immune cell subsets was analyzed using the CIBERSORT platform, and the immune microenvironment characteristics of each group were evaluated in combination with tumor mutation burden (TMB) data. The CIBERSORT results showed that compared with the low-risk group, the plasma cell infiltration was lower in the high-risk group, and a series of immune functions, such as APC_co_inhibition, B_cells, CCR, CD8+_T_cells, Check-point, Cytolytic_activity, HLA, Inflammation-promoting, Neutrophils, NK_cells, Paraininflammation, pDCs, T_cell_co-inhibition, T_cell_co-stimulation, T_helper_cells, Th1_cells, TIL, and Type_I_IFN_Response were lower ( Figure 9 A and Figure 9 B), indicating that both specific and non-specific immune responses were inhibited in the high-risk group compared with the low-risk group. The TMB was significantly higher in the high-risk group compared with the low-risk group ( Figure 10 A). The Kaplan-Meier survival curve confirmed that the overall survival of the high-TMB subgroup of Asian HCC patients was significantly lower than that of the low-TMB subgroup ( Figure 10 B). The combined survival curve analysis of TMB and Risk Score showed that the low-TMB + low-Risk Score group had the best overall survival and the most obvious survival advantage. The high-TMB + high-Risk Score and high-TMB + low-Risk Score groups had the worst overall survival ( Figure 10 C).

[0044] 5) Construction of a nomogram for overall prediction: Combining the age (Age), gender (Gender), pathological grade (Grade), TNM stage (Stage) of Asian HCC patients and the Risk Score of lncRNAs related to disulfiram death, a nomogram for overall prediction of prognosis of Asian HCC was constructed. The covariates were scored on an integral scale. The total score of the samples was calculated, and the survival probabilities of each sample at three time points (1 year, 3 years, and 5 years) were estimated using a transformation function. For example, the corresponding score of the 20th patient in the sequence was 195, and the predicted 1-year, 3-year, and 5-year survival rates were 73.3%, 60.8%, and 50.5% respectively ( Figure 11 ). This nomogram can guide the clinical prognosis assessment of Asian HCC patients.

[0045] 2. Results

[0046] Diagnostic markers: AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, AC131009.1.

[0047] The age, gender, pathological grade, TNM stage of Asian HCC patients were collected, and the expression levels of AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, and AC131009.1 in patient samples were examined using probes.

[0048] The Risk Score of the patients was calculated using the following formula:

[0049] Risk Score = (0.0039 × expression level of AC099850.3) + (0.00510 × expression level of ZNF337-AS1) + (0.00102 × expression level of LINC01138) + (0.00438 × expression level of AL031985.3) + (0.02123 × expression level of AC131009.1).

[0050] According to Figure 11 , the age, gender, pathological grade, TNM stage of the patients, and the Risk Score were scored on an integral scale. The total score of the samples was calculated, and the survival probabilities of each patient at three time points (1 year, 3 years, and 5 years) were estimated using a transformation function.

[0051] 3. Conclusions

[0052] The present invention utilizes an online database to construct a ferroptosis-related long non-coding RNA prognostic model and evaluate its clinical applicability. The RNA sequencing data (RNA-seq) of Asian HCC patients is downloaded and sorted. The Wilcoxon rank-sum test is used to analyze the correlation between lncRNAs and ferroptosis-related genes (GYS1, NDUFS1, OXSM, LRPPRC, NDUFA11, NUBPL, NCKAP1, RPN1, SLC3A2, and SLC7A11) among them. The selection threshold for this analysis is set as the correlation (|cor|) > 0.4 and p < 0.001. In the training set, univariate Cox regression analysis is first used for preliminary screening to determine the DRLRs (P < 0.05) related to the overall survival rate of Asian HCC patients. Then LASSO regression is applied for dimensionality reduction, and finally multivariate Cox regression analysis (based on the Akaike information criterion) is used to determine the final DRLRs to construct the Risk Score for Asian HCC patients. The Bootstrap resampling method is used for internal validation of the final Risk Score system. In the training set and internal validation set, survival analysis, timeROC, and independent prognostic analysis of univariate / multivariate regression are used to evaluate the predictive performance of the Risk Score, and the concordance index (C-index) analysis is used to evaluate the consistency of the Risk Score. At the same time, a nomogram for predicting the 1-, 3-, and 5-year survival of Asian HCC patients is developed in combination with patient age, gender, pathological grade, and TNM stage.

[0053] The present invention provides an idea and method for a prognostic model of Asian hepatocellular carcinoma based on ferroptosis-related lncRNA and its application. There are many methods and ways to specifically implement this technical solution. The above description is only the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be implemented by existing technologies.

Claims

1. A diagnostic marker combination for an Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA, characterized in that: The diagnostic marker combination is AC099850.3, ZNF337-AS1, LINC01138, AL031985.3 and AC131009.

1.

2. Use of the diagnostic marker combination according to claim 1 in constructing an Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA.

3. An Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA constructed using the diagnostic marker combination described in claim 1.

4. The Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA according to claim 3, characterized in that: The prognostic model includes a Risk Score system, and the Risk Score system includes the following content: RiskScore=(0.00391×AC099850.3 expression level)+(0.00510×ZNF337-AS1 expression level)+(0.00102×LINC01138 expression level)+(0.00438×AL031985.3 expression level)+(0.02123×AC131009.1 expression level).

5. The Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA according to claim 4, characterized in that: When Risk Score ≥ median value, the patient is judged as high risk; when Risk Score < median value, the patient is judged as low risk.

6. The Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA according to claim 3, characterized in that: The prognostic model includes reagents or kits for detecting the expression levels of AC099850.3, ZNF337-AS1, LINC01138, AL031985.3 and AC131009.

1.

7. The Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA according to claim 4, characterized in that: The prognostic model includes combining the patient's Risk Score with age, gender, pathological grade and TNM stage to score the integral scale, calculate the total score, and use a conversion function to estimate the survival probability of each patient in the next 1 year, 3 years and 5 years.

8. Use of the diagnostic marker combination of claim 1 or the Asian hepatocellular carcinoma prognosis model based on disulfide death-related lncRNA according to any one of claims 3 to 7 in a product for evaluating the prognosis of Asian hepatocellular carcinoma.

9. A product for assessing the prognosis of Asian hepatocellular carcinoma, characterized in that The product includes a reagent for detecting the expression level of the diagnostic marker combination described in claim 1, and evaluates the prognosis survival function of Asian hepatocellular carcinoma by the following content: RiskScore = (0.00391×AC099850.3 expression level) + (0.00510×ZNF337-AS1 expression level) + (0.00102×LINC01138 expression level) + (0.00438×AL031985.3 expression level) + (0.02123×AC131009.1 expression level).

10. The product according to claim 9, characterized in that The sample detected by the product is a fresh tissue tumor sample, preferably a fresh tissue hepatoma sample.

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