CN116479123A - Application of m7G related lncRNA as biomarker in liver cancer prognosis or treatment response prediction, product and system - Google Patents
Application of m7G related lncRNA as biomarker in liver cancer prognosis or treatment response prediction, product and system Download PDFInfo
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Abstract
The application discloses application of m7G related lncRNA as a biomarker in liver cancer prognosis or treatment response prediction, and a product and a system thereof. In particular, the use of reagents for detecting a biomarker, which is lncRNA selected from one or more of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3, in the manufacture of a product for prognosis of liver cancer or prediction of therapeutic response is disclosed. The biomarker has differential expression between liver cancer patients and normal people and between different liver cancer patients, one or more of the biomarkers can be applied to prognosis of liver cancer or prediction of therapeutic response, and the biomarker has higher accuracy, sensitivity and specificity.
Description
Technical Field
The present application relates generally to the field of crossover technologies of tumor molecular biology and bioinformatics. More particularly, the application relates to application, products and systems of m7G related lncRNA as a biomarker in prognosis of liver cancer or prediction of therapeutic response.
Background
Liver cancer, especially hepatocellular carcinoma, is one of the most common malignant tumors, and is one of the major factors of cancer-related death worldwide. Currently, liver cancer accounts for about 90% of all primary hepatic malignancies, which becomes a serious global health problem as the incidence increases. Despite the considerable progress currently made in strategies for treating liver cancer, survival rates for liver cancer remain far from satisfactory due to low early detection rates, the propensity for recurrence, and resistance to chemotherapy. Thus, extensive and intensive research is required to determine new therapeutic strategies.
According to the mod omis study in 2021, more than 300 different forms of chemical changes have been detected after RNA transcription, with methylation being involved in almost every step of RNA metabolism. RNA methylation has been reported to be associated with a variety of physiology and diseases, while aberrant methylation can lead to diseases and cancers. N7-methylguanosine (m 7G) is the most common modification in the 5' end of mRNA, and m7G may be present not only at the mRNA end but also at several sites within mRNA, tRNA and rRNA. More and more studies have shown that m7G modification has a broad impact on mRNA, tRNA and rRNA and plays a key role in oncogenic mRNA translation and cancer progression. For example, the METTL1-m7G-EGFR/EFEMP1 signaling axis may enhance growth of bladder cancer, MYC-targeted WDR4 enhances growth, metastasis and sorafenib tolerance by increasing CCNB1 translation of liver cancer. In addition, a relationship between METTL1 mediated m7G tRNA changes, oncogenic mRNA translation regulation, and cancer progression was also found in intrahepatic cholangiocarcinoma.
Genomics studies have shown that about 1% of genes can be transcribed into RNA with protein-coding functions, while most are transcribed into RNA without protein-coding functions, i.e., non-coding RNA. Wherein lncRNA (long non-coding RNA) is a kind of RNA with transcripts longer than 200 nucleotides and little or no protein coding function. With the advancement of lncRNA functionality, more and more studies have shown that lncRNA is capable of performing a variety of cellular and physiological functions and is intimately involved in carcinogenesis and tumor development, and that RNA methylation of lncRNA has been studied to affect tumor development. For example, lncRNA THAP7-AS1 is activated by SP1 transcription and then stabilized by METTL3 mediated modification of m6A, which exerts oncogenic properties by improving CUL4B entry into the nucleus. In addition, 5-methylcytosine modified H19 lncRNA was found to increase the likelihood of recurrence of liver cancer and development of tumors. However, so far, little research has been done on the regulation of m7G in lncRNA, and no research has yet been done to indicate the relationship between m 7G-related lncRNA and liver cancer and its use in liver cancer prognosis and treatment strategies.
Disclosure of Invention
In order to solve at least one or more of the technical problems mentioned above, the application proposes in various aspects the use of m 7G-related lncRNA as biomarker in prognosis of liver cancer or prediction of treatment response, product for prognosis of liver cancer or prediction of treatment response, risk assessment model for prediction of prognosis of liver cancer, liver cancer prognosis system, etc.
In a first aspect, the present application provides the use of an agent for detecting a biomarker, which is lncRNA selected from one or more of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3, in the manufacture of a product for prognosis of liver cancer or prediction of therapeutic response.
In a second aspect, the present application provides a product for prognosis of liver cancer or prediction of therapeutic response, the product comprising an agent for detecting a biomarker as described in the above technical scheme.
In some embodiments, the product detects the expression level of the biomarker by reverse transcription PCR, real-time quantitative PCR, or high throughput sequencing platform.
In some embodiments, the product is a chip or a kit.
In some embodiments, the product is used to predict prognosis in a liver cancer patient.
In some embodiments, the prognosis is 1-5 years survival. In some embodiments, the prognosis is 1 year survival, 2 years survival, 3 years survival, 4 years survival, or 5 years survival.
In a third aspect, the present application provides a risk assessment model for predicting prognosis of liver cancer, the risk assessment model comprising 6 lncRNA of biomarkers AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC 099850.3; the risk assessment model is as follows:
risk score = 0.706658 x AC009403.1+0.246817 x casc19+
(-0.35061)×AC103760.1+0.617499×AL117336.3+
(-0.49976)×AC015908.3+0.269682×AC099850.3
AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3 in the risk assessment model are the expression levels of each lncRNA; wherein the risk score is lower than or equal to a preset median risk, and higher than the median risk.
In a fourth aspect, the present application provides a liver cancer prognosis system, which comprises a detection unit, wherein the detection unit is used for obtaining detection information of a subject, and the detection information comprises the expression amounts of lncRNA such as AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, AC099850.3 and the like according to the above technical scheme.
In some embodiments, the system further includes a prediction unit, configured to substitute the expression level obtained by the detection unit into the risk assessment model described in the foregoing technical solution, and output a risk score.
In some embodiments, the system further comprises a prediction result obtaining module for obtaining a risk score output by the prediction unit, to obtain a prognosis prediction result of the subject.
The application provides application of an agent for detecting a biomarker in preparation of a product for prognosis of liver cancer or prediction of therapeutic response, wherein the biomarker is one or more of lncRNAs (ribonucleic acids) such as AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3. The biomarker provided by the application has differential expression between a liver cancer patient and a normal person and different liver cancer patients, and one or more of the biomarkers can be applied to prediction of prognosis of liver cancer. The biomarker provided by the application is used for predicting prognosis of liver cancer or response to treatment, and has higher accuracy, sensitivity and specificity.
The application further provides a risk assessment model for predicting liver cancer prognosis, wherein the model comprises the lncRNAs such as biomarkers AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, AC099850.3 and the like. The model can provide reliable prognosis prediction for liver cancer patients, improves the assessment prediction capability of prognosis of liver cancer patients, can effectively identify liver cancer patients with high risk prognosis, and can monitor and effectively intervene in clinic at early stage, thereby reducing the poor prognosis occurrence rate and death rate of liver cancer patients, improving the prognosis of liver cancer patients, and has wide clinical application prospect.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which:
FIG. 1 is a graph showing the survival rate of liver cancer patients in the high risk group and the low risk group in the examples of the present application;
FIG. 2A shows prognosis indices of liver cancer patients in the high risk group and the low risk group in the embodiment of the present application, and FIG. 2B shows survival of liver cancer patients in the embodiment of the present application;
FIG. 3 shows ROC curves of prognosis, clinical and pathological factors of liver cancer patients in the examples of the present application;
FIG. 4 shows a predicted ROC curve for survival of 1 to 5 years for liver cancer patients in the examples of the present application;
FIG. 5A shows a Kaplan-Meier analysis graph in inner group 1 in an embodiment of the present application, FIG. 5B shows a Kaplan-Meier analysis graph in inner group 2 in an embodiment of the present application, FIG. 5C shows ROC curves for 1 year, 3 years, and 5 years of survival in inner group 1 in an embodiment of the present application, and FIG. 5D shows ROC curves for 1 year, 3 years, and 5 years of survival in inner group 2 in an embodiment of the present application;
FIG. 6A shows a Noman plot based on clinical variables and risk score predictions in an embodiment of the present application, and FIGS. 6B-6D show calibration curves of predicted model survival versus actual survival, respectively, in an embodiment of the present application;
7A-7C illustrate the correlation between risk score and risk scores for survival, clinical stage, and T stage, respectively, in an embodiment of the present application;
FIG. 8A shows the ssGSEA scores for the immunoinfiltrates of various immune cells of the low and high risk groups in the examples of the present application, wherein the abscissa is the different immune cells, respectively, and the ordinate is the corresponding ssGSEA score; FIG. 8B illustrates ssGSEA scores for various immune functions in low risk and high risk groups, respectively, in embodiments of the present application, wherein the abscissa is the different immune functions and the ordinate is the corresponding ssGSEA score;
fig. 9A shows the TIDE scores for the high and low risk groups in the examples of the present application, fig. 9B shows the T cell rejection scores for the high and low risk groups in the examples of the present application, fig. 9C shows the T cell dysfunction scores for the high and low risk groups in the examples of the present application, and fig. 9D-9J show the IC50 values for the chemotherapeutic drugs docetaxel, bleomycin, doxorubicin, gemcitabine, lenalidomide, dasatinib, erlotinib in the examples of the present application, respectively.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments, based on the embodiments herein, which are within the scope of the present application, will be within the skill of the art without undue effort.
It should be understood that the terms "comprises" and "comprising," when used in this specification and in the claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only, and is not intended to be limiting of the application. As used in the specification and claims of this application, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the term "and/or" as used in the present specification and claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In the prior art, the application of N7-methylguanosine related lncRNA in liver cancer is not found, and the relation between the N7-methylguanosine related lncRNA and prognosis of the liver cancer is not found. Based on this, the present application provides in a first aspect the use of an N7-methylguanosine-associated lncRNA selected from one or more of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3 as a biomarker in the prognosis of liver cancer or the prediction of therapeutic response. In a further embodiment, there is provided the use of an agent for detecting a biomarker, which is lncRNA selected from one or more of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3, in the manufacture of a product for prognosis of liver cancer or prediction of therapeutic response. Specifically, the biomarker may be one of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, and AC099850.3, or a combination of the foregoing. More specifically, the biomarker may be a combination of 6 lncRNA of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, and AC099850.3. In some embodiments, the reagent is used to detect the amount of expression of the lncRNA described above. In further embodiments, the reagents include, but are not limited to, primers or probes, such as primers capable of specifically amplifying the lncRNA or probes that specifically hybridize to the lncRNA. In some embodiments, detection of the biomarker is achieved by means of nucleic acid amplification techniques, nucleic acid hybridization techniques, sequencing techniques, and the like, using the reagent.
Biomarkers described herein, such as AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3, are N7-methylguanosine-associated lncRNA known in the art, the detailed information and sequences of which are publicly available.
In a second aspect, the present application provides a product for prognosis of liver cancer or prediction of therapeutic response, the product comprising an agent capable of detecting lncRNA as described above for biomarkers AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3. In some embodiments, the reagent is used to detect the amount of expression of the lncRNA described above. In some embodiments, the reagents include, but are not limited to, primers or probes. In certain embodiments, the primer or probe is a primer capable of specifically amplifying the lncRNA or a probe that specifically hybridizes to the lncRNA. In some embodiments, the product utilizes the reagents to detect the expression level of the biomarker by reverse transcription PCR, real-time quantitative PCR, or high throughput sequencing platform techniques, among others. The product may be a chip or a kit. The chip or the kit can comprise a reagent capable of detecting the expression quantity of the biomarker, and the reagent has the characteristics of strong specificity and high sensitivity, so that the chip or the kit can provide higher prediction accuracy and reliability when being used for predicting prognosis of a liver cancer patient and predicting response of the liver cancer patient to treatment to be adopted or already adopted.
In a third aspect, the present application provides a risk assessment model for predicting liver cancer prognosis, the risk assessment model comprising lncRNA such as biomarkers AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, and AC 099850.3;
the risk assessment model is:
risk score = 0.706658 x AC009403.1+0.246817 x casc19+
(-0.35061)×AC103760.1+0.617499×AL117336.3+
(-0.49976)×AC015908.3+0.269682×AC099850.3
AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3 in the risk assessment model are the expression levels of each lncRNA; wherein the risk score is low when the risk score is less than or equal to a threshold value, and high when the risk score is greater than the threshold value. In some embodiments, the threshold is a median of risk scores obtained when constructing the risk assessment model.
The risk assessment model provided by the application can be used for not only predicting prognosis of a liver cancer patient, but also predicting response of the liver cancer patient to treatment to be adopted or already adopted. Preferably, the treatment is a drug treatment, such as chemotherapy or immunotherapy.
In the present application, the "expression level" of a biomarker or lncRNA refers to the expression level or amount of said biomarker or lncRNA obtained by any means. The "expression level" may be absolute or relative and may be detected by methods well known to those skilled in the art, for example by techniques such as reverse transcription PCR, real-time quantitative PCR or high throughput sequencing. The "expression level" may be FPKM data obtained by sequencing, transformation and calculation as described in example 1 of the present application.
The fourth aspect of the present application provides a liver cancer prognosis system, which may include a detection unit, where the detection unit is configured to obtain detection information of a subject, where the detection information may include expression amounts of lncRNA such as AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, and AC099850.3 in the above technical solutions. In some application scenarios, the detection unit may use reverse transcription PCR, real-time quantitative PCR, or a high throughput sequencing platform to obtain the expression level of the lncRNA.
In some embodiments, the foregoing system may further include a prediction unit configured to substitute the expression level obtained by the detection unit into the risk assessment model, and output a risk score. The risk model can adopt the median of the risk scores obtained during model construction as a critical value, and when the risk model is used for prognosis prediction of a subject, the expression amounts of lncRNAs such as AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3 of the subject are substituted into the risk assessment model to obtain corresponding risk scores. A risk score is lower than or equal to the median and is higher than the median.
In other embodiments, the foregoing system may further include a prediction result obtaining module configured to obtain a risk score output by the prediction unit, to obtain a prognosis prediction result of the subject. In clinical practice, there are many ways to perform prognosis prediction, if it is desired to obtain the prognosis survival rate of the subject, a TNM stage alignment chart may be selected and combined with the risk score obtained in the present application, and by determining whether or not T clinical stage in the TNM stage, M, and N, lymph node metastasis (0, 1, and 1) occur respectively, the corresponding score is calculated and added together with the risk score obtained in the present application to obtain the survival rate of the subject for 1 year, 3 years, and 5 years.
In a further preferred embodiment, the product for prognosis of liver cancer or prediction of therapeutic response according to the first and second aspects of the present application achieves prediction of prognosis of liver cancer or prediction of therapeutic response by: detecting a sample of a subject by using the reagent for detecting the biomarker to obtain the expression quantity of the biomarker; substituting the expression quantity into a risk assessment model according to the third aspect of the application to obtain a risk score of the subject; and comparing the risk score with a preset critical value to obtain a prediction result of liver cancer prognosis or a prediction result of response to treatment. In some embodiments, the threshold is a median of risk scores obtained when constructing the risk assessment model.
As used herein, "subject" refers to humans and other mammals, including, but not limited to, rats, mice, guinea pigs, apes, monkeys, cats, dogs, pigs, cows, sheep, horses, rabbits, etc. Preferably, the subject is a human subject. In some embodiments, the subject is a patient having or at risk of having liver cancer. In a preferred embodiment, the liver cancer described herein is hepatocellular carcinoma. In some embodiments, the subject is a liver cancer patient who has received treatment or is ready to receive treatment. Preferably, the treatment is a drug treatment, such as chemotherapy or immunotherapy.
As used herein, "sample" includes in vitro, in vivo, ex vivo samples obtained from a subject, such as whole blood, peripheral blood, serum, plasma, biopsies, urine, saliva, and other body fluids, cells, tissues, extracts, cultures, and the like.
In still other embodiments, the product for prognosis of liver cancer or prediction of therapeutic response according to the first and second aspects of the present application further comprises instructions for use. In some embodiments, the instructions for use describe a prediction of liver cancer prognosis or a prediction of response to treatment by the risk assessment model of the third aspect of the present application based on the amount of expression of the biomarker detected using the reagent for detecting a biomarker.
Examples
In order to facilitate understanding of the technical solutions of the present application, the following description will be made with reference to specific embodiments. The following examples are provided herein for illustrative purposes only and should not be construed as limiting the invention in any way.
Example 1
Screening of molecular markers related to liver cancer and establishment of evaluation model for prognosis prediction of liver cancer
1.1 data download and processing
RNA-seq expression and clinical information of liver cancer patients are downloaded and extracted from a cancer genome map (TCGA, https:// cancerationome. Nih. Gov /), and 374 liver cancer samples and 50 normal liver tissues are obtained in total, wherein 343 liver cancer samples contain complete clinical information and follow-up time. The N7-methylguanosine (m 7G) related gene which is differentially expressed in the liver cancer group and the normal group is obtained through a limma package in R (4.0.3), and the m7G related gene is highly expressed in the liver cancer group than in the normal group, wherein the screening standard of the differentially expressed gene is as follows: log (Log) 2 |FC|>1 and FDR<0.05. A total of 47 m 7G-related genes (mRNA) were collected from Gene Cards (https:// www.genecards.org /), the correlation between the m 7G-related genes and differentially expressed m7G lncRNA was calculated using pearson correlation of the "limma" software package, and the screening criteria were |R 2 |>0.3 and P < 0.001. Based on the above procedure, 91 m 7G-related differentially expressed lncRNAs were determined to be highly correlated with prognosis of liver cancer.
Then, firstly screening the prognosis variables of the liver cancer by single factor cox regression analysis, wherein the screening condition is P < 0.05. And then performing lasso regression to reduce the dimension to obtain 6 m7G related lncRNAs, screening the lasso to obtain 6 genes, incorporating the 6 genes into a multi-factor cox model, and constructing a liver cancer prognosis model, wherein the 6 m7G related lncRNAs are AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3.
Further, constructing a model of liver cancer prognosis according to a cox proportional risk regression model proposed by D.R.Cox, wherein the cox proportional risk regression model is as follows:
h(t,x)＝h 0 (t)exp(β 1 X 1 +β 2 X 2 +…+β n X n ) (1)
wherein beta is 1 ，β 2 ...β n The partial regression coefficient, which is an independent variable, is a parameter that has to be estimated from sample data; h is a 0 (t) is the reference risk of h (t, X) when the X vector is 0, which is the quantity to be estimated from the sample data. Obtaining a partial regression coefficient beta of the response gene according to the formula (1), and constructing a risk scoring formula:
risk score = Σβ i ×geneExpression i (2)
geneExpression in equation (2) i The gene expression level for each gene.
Combining the obtained 6 m7G related lncRNAs can further obtain a liver cancer prognosis model-risk assessment model, namely a risk scoring formula is as follows:
risk score = 0.706658 x AC009403.1+0.246817 x casc19+
(-0.35061)×AC103760.1+0.617499×AL117336.3+
(-0.49976)×AC015908.3+0.269682×AC099850.3(3)
AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, and AC099850.3 in the risk assessment model of formula (3) are the expression amounts of each lncRNA, and the coefficient before each lncRNA is the partial regression coefficient.
Gene expression refers to the process of synthesizing functional gene products from genetic information of genes, and the existing sequencing technology quantifies the gene products, and the obtained product quantification data is the expression quantity of the genes. The gene expression level used in this example may be FPKM data downloaded from the TCGA database. FPKM (Fragments Per Kilobase of exon model per Million mapped fragments), i.e. per million copies of each kilobase, maps the read fragment, which can be calculated by equation (4):
wherein reads (read length) is a sequencing sequence obtained by one reaction in high-throughput sequencing, which is a base sequence obtained by single sequencing by a sequencer, and the lengths of reads are different by different sequencers. The reads number is calculated as the quotient of the number of bases and the read length. Exon Fragment is the number of fragments (or reads) mapped to an Exon, mapped reads is the total number of reads mapped to the gene, and Exon length is the full length of the gene Exon. That is, the number of reads of the target gene response can be obtained by the sequencing instrument, and then the FPKM data of the target gene can be obtained by transformation.
The related information of 343 liver cancer samples in the TCGA is substituted into the formula (3), so as to correspondingly obtain 343 risk scores. With the median score of the 343 risk scores as a threshold, liver cancer patients were divided into two groups, 174 low risk groups and 169 high risk groups. The inventors analyzed the two groups according to the Kaplan-Meier survival analysis, and as shown in fig. 1, the survival rate of patients in the high risk group was significantly lower than that in the low risk group. Next, risk scores (i.e., prognostic index, fig. 2A) and survival (fig. 2B) were evaluated for different groups of liver cancer patients, as shown by the greater risk of mortality in the high risk group than in the low risk group. The result shows that the liver cancer prognosis model established by the application can accurately distinguish a high-risk group from a low-risk group of a liver cancer patient.
Further, the application adopts ROC curve to further analyze the obtained liver cancer prognosis model. As a result, as shown in fig. 3, the AUC value of the risk score as a factor for prognosis of liver cancer for predicting the total survival of liver cancer patients was 0.791, and higher AUC values, such as the AUC value of age was 0.517 and the AUC value of gender was 0.528, were compared to other clinical pathological variables. Meanwhile, the inventor also draws ROC curves from time to time, as shown in fig. 4, wherein AUC values of survival periods of 1 year, 3 years and 5 years are respectively 0.803, 0.730 and 0.710, which have higher accuracy, and further proves that the liver cancer prognosis model can be used for predicting survival states of liver cancer patients and has higher prognosis prediction accuracy.
Example 2
Effect verification of predictive models
2.1 internal authentication
To further evaluate the stability of the liver cancer prognosis model obtained in example 1, the inventors performed internal validation on the entire dataset, specifically, randomly dividing 343 liver cancer patients into two groups, 172 being internal group 1 and 171 being internal group 2. And substituting the internal group 1 and the internal group 2 into the liver cancer prognosis model obtained in the embodiment 1, namely the formula (3), so as to obtain the risk score of the internal verification group. The results are shown in fig. 5A-5D, with the Kaplan-Meier analysis, the results of the internal validation are highly consistent with the results of the entire dataset. The liver cancer prognosis model established by the method can accurately distinguish the high risk group and the low risk group of the liver cancer patient. In the internal cohort, the overall survival of the high-risk cohort patients is significantly shorter than that of the low-risk cohort patients, and the ROC curves of the two internal cohorts have good predictive value for survival.
2.2 development and validation of nomann diagrams
To create nomograms based on risk scores and clinical pathology parameters, the present application creates nomans using R-packets "survivinal" and "rms" and draws calibration charts to evaluate the degree of matching of actual and predicted survival.
The results are shown in fig. 6A-6D, where fig. 6A is a noman plot predicted based on clinical variables and risk scores, where T is clinical stage, M is whether a tumor metastasizes, N is lymph node metastasis, 0 is no metastasis, and 1 is metastasis has occurred. Survival rates of HCC patients were estimated from risk scores and clinical features at 1, 3 and 5 years to learn more about the predictive performance of the 6 m 7G-related lncRNAs obtained in example 1. Fig. 6B-6D are calibration curves of the predicted model survival rate and the actual survival rate, respectively, the calibration curves show that the actual survival rate results of 1 year, 3 years and 5 years are highly consistent with the model predicted survival rate results, and further demonstrate the reliability of the liver cancer prognosis prediction model of the present application.
Example 3
Correlation between risk score and clinical variable
This example further evaluates the correlation between the risk score obtained in example 1 and each clinical feature. As a result, as shown in fig. 7A to 7C, the risk scores of the high risk group and the low risk group were scatter-mapped to the survival status fustat (p < 0.001), the clinical stage (p < 0.001) and the T stage (p < 0.001), and it was confirmed that the risk scores obtained according to the liver cancer prognosis model of the present application were related to the survival status (0 is life, 1 is death), the clinical stage and the T stage. As can be seen from the figure, the lower the risk score, the more survivors, the more clinical patients with low stage, and the more T-patients with low stage. Conversely, the higher the risk score, the more deaths, the more clinically staged, and the more T staged. It can be further shown that there is a correlation between the expression levels of the 6 m 7G-related lncRNAs obtained in example 1 and clinical variables.
Example 4
Correlation between risk score and immunological infiltration, immune function
To better investigate the relationship between risk scores and immunological infiltration, related functions, the inventors calculated the enrichment scores of various immune cells using ssGSEA. As a result, as shown in fig. 8A, B cells (B-cells), macrophages (Macrophages), mast cells (post-cells), neutrophils (neutroples), NK cells, and TILs in the low risk group have higher enrichment scores than in the high risk group, and the immune cells have a larger difference between the high risk group and the low risk group. In FIG. 8B, the inventors also found that the immune functions of the high-risk group and the low-risk group were significantly different in APC co-inhibition (APC-co-inhibition), cytolytic activity (Cytolytic-activity), MHC class I (MHC-class I), parainflammation (paraignition), type I IFN response (Type-I-IFN-response), and Type II IFN response (Type-II-IFN-response). In summary, the above results indicate that the low risk group and the high risk group of risk scores are associated with immunological infiltration and immune function in liver cancer patients, i.e. m 7G-related lncRNAs are associated with immunological infiltration and immune function in liver cancer patients.
Example 5
Liver cancer prognosis model for predicting patient response to immunotherapy and identifying potential chemotherapeutic drugs
In view of the significance of immune checkpoint therapy, further studies were conducted on the variation in immune checkpoint expression between the high and low risk groups. Tumor immune dysfunction and rejection (TIDE) is a computational method developed by the university of haverse for predicting the primary mechanism based on mimicking tumor immune evasion, and can provide a predictive outcome of immunotherapy. The potential efficacy of immunotherapy in high and low risk groups was further evaluated using the tid algorithm, in which the expression levels of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3 were independent. The results show that the high risk group exhibited a lower TIDE score than the low risk group, indicating that the patients of the high risk group responded more strongly to immunotherapy, as shown in fig. 9A. It was further found that the T cell rejection score was higher in the high risk group and lower in the low risk group, as shown in fig. 9B, 9C, indicating that the immunotherapeutic response in the low risk group patients may be related to immune evasion and T cell dysfunction.
In addition, this example also investigated the relationship between risk scores and the effect of conventional chemotherapy on liver cancer. The sensitivity test of liver cancer to conventional chemotherapy and the calculation of IC50 are carried out by adopting conventional technology and method in the field, for example, the prediction sensitivity of liver cancer to conventional chemotherapy and the corresponding IC50 value can be obtained by adopting R package pRRophic based on the expression level of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3. The results show that, as shown in fig. 9D-9J, the IC50 values of Bleomycin (Bleomycin), doxorubicin (dorabixin), gemcitabine (Gemcitabine), lenalidomide (Lenalidomide) are relatively low in the high risk group, meaning that the high risk group patients are more sensitive to drugs such as Bleomycin, gemcitabine, etc., while the IC50 values of Dasatinib (Dasatinib), erlotinib (Erlotinib), docetaxel (Docetaxel) are relatively high in the high risk group, which is advantageous for the accurate and personalized treatment of the high risk group patients. Based on the experimental data, researchers can provide strategies for optimizing chemotherapy and immunotherapy regimens for liver cancer patients with different risk scores, thereby improving and improving prognosis of liver cancer patients.
While various embodiments of the present application have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, changes, and substitutions will occur to those skilled in the art without departing from the spirit and scope of the invention. It should be understood that various alternatives to the embodiments of the present application described herein may be employed in practicing the application. The appended claims are intended to define the scope of the application and are therefore to cover all equivalents and alternatives falling within the scope of these claims.
Claims (10)
1. Use of an agent for detecting a biomarker in the manufacture of a product for prognosis of liver cancer or prediction of therapeutic response, wherein the biomarker is lncRNA selected from one or more of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3.
2. A product for prognosis of liver cancer or prediction of therapeutic response, comprising an agent for detecting the biomarker of claim 1.
3. The product of claim 2, wherein the product detects the expression level of the biomarker by reverse transcription PCR, real-time quantitative PCR, or high throughput sequencing platform.
4. The product of claim 2, wherein the product is a chip or a kit.
5. The product of any one of claims 2 to 4, wherein the product is used to predict prognosis in a liver cancer patient.
6. The product of claim 5, wherein the prognosis is survival rate of 1-5 years.
7. A risk assessment model for predicting prognosis of liver cancer, characterized in that the risk assessment model comprises 6 lncRNA of biomarkers AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC 099850.3;
the risk assessment model is as follows:
risk score = 0.706658 x AC009403.1+0.246817 x casc19+
(-0.35061)×AC103760.1+0.617499×AL117336.3+
(-0.49976)×AC015908.3+0.269682×AC099850.3
AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3 and AC099850.3 in the risk assessment model are the expression levels of each lncRNA;
wherein the risk score is lower than or equal to a preset median risk, and higher than the median risk.
8. A liver cancer prognosis system comprising a detection unit for acquiring detection information of a subject, the detection information comprising the expression levels of AC009403.1, CASC19, AC103760.1, AL117336.3, AC015908.3, and AC099850.3 according to claim 1.
9. The system according to claim 8, further comprising a prediction unit for substituting the expression level obtained by the detection unit into the risk assessment model according to claim 7, and outputting a risk score.
10. The system of claim 9, further comprising a predictor acquisition module for acquiring a risk score output by the predictor unit to obtain a prognosis predictor for the subject.
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