CN111394456A - Early lung adenocarcinoma patient prognosis evaluation system and application thereof - Google Patents
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Abstract
The invention integrates disease-free survival data of 632 early-stage lung adenocarcinoma patients in three independent queues, and establishes and verifies an individualized IBRS model of L UAD patients in early stage, wherein the three independent queues comprise TCGA, GSE31210 and 68 frozen tissues, the IBRS model consists of nine genes including DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1, and the establishment of the IBRS model of the patients in early stage L UAD is not only beneficial to understanding a complex interaction mechanism among immune molecules, but also brings great help to relapse prediction and treatment scheme optimization of the patients in early stage L UAD.
Description
Technical Field
The invention belongs to the technical field of biomedicine, and particularly relates to an early lung adenocarcinoma patient prognosis evaluation system and application thereof.
Background
Lung cancer (L ung adenocarinoma, L UAD) is the most common histological subtype of lung cancer, accounting for about 40% of lung cancer, despite the continuous development of new therapies, such as molecular targeted drugs, immune checkpoint inhibitors, etc., the five-year survival rate of lung cancer patients remains around 17%, the high mortality rate of lung cancer can be attributed to two major causes, 66% of lung cancer patients have been diagnosed with advanced stages, the options for treatment of advanced stage lung cancer are limited, and prognosis is poor, even for early stage lung cancer patients, the five-year recurrence rate after surgery is as high as 30-45%, and the mortality risk is still high.
Although lung cancer has been previously considered to be a non-immunogenic disease, new evidence suggests that the lack of effective immune response is due to specific immune escape mechanisms, the disclosure of potential immune escape mechanisms opens a new chapter on lung cancer immunotherapy.immune checkpoint inhibitors such as targeting PD-1 and PD-L have been successfully applied clinically, revolutionizing the traditional lung cancer treatment modalities.
In view of the broad prospect of immunotherapy in lung cancer and the increasing proportion of L UAD patients at early stage of initial diagnosis, establishment of immune gene-based recurrence marker model (IBRS) in early stage lung cancer is urgently needed.
Disclosure of Invention
The technical problem to be solved by the invention is how to carry out prognosis on lung adenocarcinoma patients, in particular to early lung adenocarcinoma patients.
In order to solve the technical problems, the invention firstly provides a lung adenocarcinoma patient prognosis system.
The lung adenocarcinoma patient prognosis system provided by the invention comprises a system for detecting expression levels of nine genes including DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN 1.
In the lung adenocarcinoma patient prognosis system, the system for detecting expression levels of nine genes including DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1 comprises reagents and/or instruments required for detecting relative expression levels of the nine genes by a fluorescence quantitative PCR method.
Furthermore, the reagents and/or instruments required for detecting the relative expression amounts of the nine genes by the fluorescent quantitative PCR method comprise primer pairs for detecting the relative expression amounts of the nine genes, namely DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1, and the sequences of the primer pairs for detecting the genes are specifically shown in Table 2.
Furthermore, the reagent and/or the instrument for detecting the relative expression quantity of the nine genes by the fluorescent quantitative PCR method also comprise a primer pair for detecting the internal reference gene. The reference gene is GAPDH gene. The sequences of the primer pairs for detecting the reference genes are specifically shown in Table 2.
In the above lung adenocarcinoma patient prognosis system, the system further comprises a data processing device; the data processing device is internally provided with a module; the module has the functions as shown in (a1) and (a 2):
(a1) taking isolated lung adenocarcinoma tissues of a population to be detected consisting of lung adenocarcinoma patients as samples, measuring the relative expression amounts of the nine genes in each sample, and then calculating a risk value according to the relative expression amounts of the nine genes, namely (0.3987 × DYNC1I2 gene relative expression amount) - (0.5611 × THOC1 gene relative expression amount) + (0.2405 × 0ADAM10 gene relative expression amount) - (0.6713 × SERPINB6 gene relative expression amount) + (0.1268 × CC L20 gene relative expression amount) - (0.2408 × WNT2B gene relative expression amount) - (0.2255 × S L C11A1 gene relative expression amount) - (0.2034 × MAPT gene relative expression amount) + (0.6942 × PSEN1 gene relative expression amount), and dividing the population to be detected into a low risk group and a high risk group according to the risk value;
(a2) determining the prognostic risk and/or the prognostic relapse rate and/or the prognostic disease-free survival rate and/or the prognostic overall survival rate of a test patient from said test population according to the following criteria: "from the test patients in the high risk group" has a higher prognostic risk and/or prognostic relapse rate than or is candidate for higher than "from the test patients in the low risk group"; the prognostic disease-free survival and/or overall survival of "a test patient from the low risk group" is higher than or is candidate higher than "a test patient from the high risk group".
The method for classifying the population to be tested into a low risk group and a high risk group according to the risk Value can be referred to the method in the documents "1. L i X, Yuan Y, Ren J, Shi Y, Tao X.Incremental protective Value of applied Diffusion Coefficient Histogram Analysis in Head and N.k Squalmous cell Carcinoma.academic Radiology,2018Nov, (11):1433-1438.doi: 10.1016/j.a.acara.2018.02.017", and can be specifically carried out by determining a threshold Value through the "surv _ cutpoint" function of the "surfmer" software package of the R language software, comparing the risk Value of the patient to be predicted lung adenocarcinoma with the magnitude of the threshold Value, and the patient with the risk Value greater than the threshold Value is listed into the high risk group, and the patient with the risk Value less than or equal to the low risk group is listed into the low risk group.
The method for determining the threshold value through the surv _ cutoff of the survminer software package of the R language software is specifically as follows: the risk value of the lung adenocarcinoma patient to be predicted and the matched prognosis information are input into R language software, and under the algorithm of 'surv _ cutoff' of a 'survminer' software package, the software can automatically calculate a division point with the minimum P value, wherein the division point is a threshold value (optimal cutoff point) of a high risk group and a low risk group.
In order to solve the technical problems, the invention also provides a new application of the lung adenocarcinoma patient prognosis system.
The invention provides application of the lung adenocarcinoma patient prognosis system in the following (1) to (8):
(1) preparing a product for prognosis risk assessment of lung adenocarcinoma patients;
(2) assessing a risk of prognosis for a patient with lung adenocarcinoma;
(3) preparing a product for evaluating the recurrence rate of lung adenocarcinoma patients;
(4) assessing the prognostic recurrence rate of a patient with lung adenocarcinoma;
(5) preparing a product for prognosis disease-free survival rate evaluation of lung adenocarcinoma patients;
(6) assessing prognosis disease-free survival rate of the lung adenocarcinoma patient;
(7) preparing a product for prognosis of overall survival rate of a patient with lung adenocarcinoma;
(8) assessing the overall survival rate of a lung adenocarcinoma patient.
The application of the nine genes of DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1 as markers in the preparation of products for prognosis of patients with lung adenocarcinoma also belongs to the protection scope of the invention.
The application of the substance for detecting the expression quantity of nine genes including DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1 in preparing a product for prognosis of a patient with lung adenocarcinoma also belongs to the protection scope of the invention.
The invention also belongs to the protection scope of the invention, and the application of the substances for detecting the expression levels of nine genes including DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1 and the data processing device in the preparation of products for prognosis of patients with lung adenocarcinoma.
The application of the data processing device in preparing a product for prognosis of patients with lung adenocarcinoma also belongs to the protection scope of the invention.
In the above prognosis system or application for patients with lung adenocarcinoma, the patients with lung adenocarcinoma are early patients with lung adenocarcinoma; further, the early stage lung adenocarcinoma patient is a TNM staged stage I lung adenocarcinoma patient and/or a TNM staged stage II lung adenocarcinoma patient.
The isolated lung adenocarcinoma tissue can be a sample prepared by formalin-fixed paraffin embedding of the isolated lung adenocarcinoma tissue of the lung adenocarcinoma patient to be predicted or a frozen section of the isolated lung adenocarcinoma tissue of the lung adenocarcinoma patient to be predicted.
In the prognosis system or application for lung adenocarcinoma patients, the GenBank number of DYNC1I2 is NM _001271785.2, the GenBank number of THOC1 is NM _005131.3, the GenBank number of ADAM10 is NM _001320570.2, the GenBank number of SERPINB6 is NM _001195291.3, the GenBank number of CC L20 is NM _001130046.2, the GenBank number of WNT2B is NM _001291880.1, the GenBank number of S L C11a1 is NM _000578.4, the GenBank number of MAPT is NM _001123066.3, and the GenBank number of PSEN1 is NM _ 000021.4.
The invention integrates disease-free survival data of 632 early-stage lung adenocarcinoma patients in three independent queues, establishes and verifies an individualized IBRS model of L UAD patients in early stage, wherein the three independent queues comprise TCGA, GSE31210 and 68 frozen tissues, and the establishment of the IBRS model of L UAD patients in early stage is not only beneficial to understanding the complex interaction mechanism among immune molecules, but also brings great help to relapse prediction and treatment scheme optimization of L UAD patients in early stage.
Drawings
FIG. 1 is a flow chart of construction and verification of early stage lung adenocarcinoma recurrence marker model IBRS.
FIG. 2 is a graph of early stage lung adenocarcinoma recurrence marker model IBRS.A, distribution of risk values, recurrence status and gene expression, B, Kaplan-Meier curves of RFS of total early stage L UAD patients based on risk values, C, ROC analysis of immune-related gene markers to predict recurrence risk of TCGA cohorts at 1, 3 and 5 years, D, Kaplan-Meier curves of RFS of stage I L UAD patients based on risk values, E, Kaplan-Meier curves of RFS of stage II L UAD patients based on risk values.
FIG. 3 is a survival curve grouped based on risk values in a TCGA cohort A-the Kaplan-Meier curve for the OS of all early L UAD patients based on risk values B-the Kaplan-Meier curve for the OS of L UAD patients in phase I based on risk values C-the Kaplan-Meier curve for the OS of L UAD patients in phase II based on risk values.
FIG. 4 is a graph of the prognostic power of the early lung adenocarcinoma recurrence marker model IBRS verified in GSE 31210A. distribution of risk values, recurrence status and gene expression B. Kaplan-Meier curves for RFS of total early L UAD patients based on risk values, C. Kaplan-Meier curves for RFS of stage I L UAD patients based on risk values, D. Kaplan-Meier curves for RFS of stage II L UAD patients based on risk values, E. Kaplan-Meier curves for OS of total early L UAD patients based on risk values.
FIG. 5 is a Kaplan-Meier plot of OS for stage I L UAD patients based on risk values in GSE 31210.
FIG. 6 is a Kaplan-Meier curve of RFS for an early L UAD population of males (A), females (B), smokers (C), non-smokers (D), older (E) and younger (F) patients based on risk values.
FIG. 7 is a Kaplan-Meier plot of OS for all early L UAD patients stratified by gender, smoking history, and age in the TCGA cohort, based on risk values, for early L UAD populations of men (A), women (B), smokers (C), non-smokers (D), elderly (E), and young (F).
FIG. 8 is a Kaplan-Meier curve of RFS for a population of early L UAD based on risk values in males (A), females (B), smokers (C), non-smokers (D), older (E) and younger (F) patients demonstrating the prognostic performance of the early lung adenocarcinoma recurrence marker model IBRS in a GSE31210 cohort of different molecular subtypes.
FIG. 9 is a validation of prognostic power of early lung adenocarcinoma recurrence marker model IBRS stratified by gender, smoking history and age in GSE31210 cohort the Kaplan-Meier curves for OS of early L UAD populations of males (A), females (B), smokers (C), non-smokers (D), older (E) and younger (F) patients based on risk values.
FIG. 10 is a graph of the validation of prognostic power of early lung adenocarcinoma recurrence marker model IBRS in patients carrying either wild-type or mutant KRAS or EGFR genes in the TCGA cohort A. KRAS and EGFR wild-type (WT) and Mutant (MUT) ratios in the TCGA cohort at High Risk (HR) or low risk (L R.) Kaplan-Meier curves of RFS in patients with EGFR/KRAS-WT (F) based on risk values.
FIG. 11 is a Kaplan-Meier plot of OS for EGFR-WT (A), EGFR-MUT (B), KRAS-WT (C), KRAS-MUT (D), and EGFR/KRAS-WT (E) early L UAD patients based on risk values for survival analysis of all L UAD early patients carrying wild-type or mutant KRAS or EGFR genes in the TCGA cohort.
FIG. 12 is a survival analysis of all L UAD early stage patients carrying wild-type or mutant KRAS or EGFR genes in the GSE31210 cohort A: the ratio of KRAS to EGFR wild-type (WT) and Mutant (MUT) in the GSE31210 cohort as found at High Risk (HR) or low risk (L R), the Kaplan-Meier curves for RFS in EGFR-WT (B), EGFR-MUT (C), KRAS-WT (D), KRAS-MUT (E), and EGFR/KRAS-WT (F) early stage L UAD patients based on risk values.
FIG. 13 is a Kaplan-Meier plot of OS for EGFR-WT (A), EGFR-MUT (B), KRAS-WT (C), KRAS-MUT (D), and EGFR/KRAS-WT (E) early L UAD patients based on risk scores for survival analysis of all L UAD early patients carrying wild-type or mutant KRAS or EGFR genes in the TCGA cohort.
FIG. 14 is a graph demonstrating prognostic power of the early lung adenocarcinoma recurrence marker model IBRS in a separate set of 68 cases of L UAD early stage patient frozen tissue.A: distribution of risk values, recurrence status and gene expression.B: Kaplan-Meier curves for RFS of all early stage L UAD patients based on risk values.C: Kaplan-Meier curves for RFS of L UAD patients in stage I based on risk values: Kaplan-Meier curves for RFS of L UAD patients in stage II based on risk values.E: Kaplan-Meier curves for OS of all early stage L UAD patients based on risk values.
Detailed Description
The following examples are given to facilitate a better understanding of the invention, but do not limit the invention. The experimental procedures in the following examples are conventional unless otherwise specified. The test materials used in the following examples were purchased from a conventional biochemical reagent store unless otherwise specified. The quantitative tests in the following examples, all set up three replicates and the results averaged.
The recurrence rate in the following examples is defined as the proportion of patients who have undergone in situ recurrence, distant metastasis, in the patients who have entered the cohort.
The Overall Survival (OS) in the following examples is defined as the time from enrollment to death or last follow-up due to any cause.
The overall survival rate in the following examples is defined as the probability that a patient will survive from a particular time point to a particular time.
The disease free survival (RFS) in the examples below is defined as the time from enrollment to local recurrence, or distant metastasis, or death from any cause, or last follow-up.
Disease-free survival in the following examples is defined as the probability that a patient will not have had a local recurrence or metastasis by a certain time since the patient was followed up from a certain time point.
The early stage lung adenocarcinoma patients in the following examples refer to TNM staged stage I-II lung adenocarcinoma patients.
Example 1 early stage lung adenocarcinoma recurrence marker model IBRS established based on immune gene and model verification
The invention constructs an early lung adenocarcinoma recurrence marker model IBRS from a TCGA early lung adenocarcinoma cohort consisting of 338 lung adenocarcinoma patients, and verifies the constructed model by a GSE31210 early lung adenocarcinoma cohort consisting of 226 lung adenocarcinoma patients and an independent group consisting of frozen tissues of 68 early lung adenocarcinoma patients. Clinical features of early stage lung adenocarcinoma patients are shown in table 1.
TABLE 1 clinical characteristics of patients with early stage lung adenocarcinoma
Note: WT (wild-type) represents a wild type; MUT (mutation) represents a mutant type; NA (not available) represents unavailable.
Method for constructing model IBRS (infectious bronchitis syndrome) by TCGA (TCGA) early lung adenocarcinoma cohort and prognosis method
1. Construction of early lung adenocarcinoma recurrence marker model IBRS
A flowchart for constructing early stage lung adenocarcinoma recurrence marker model IBRS is shown in table 1. The method comprises the following specific steps:
1) 553 patients with primary lung adenocarcinoma (L UAD) from a human cancer genomic map (TCGA) were taken as the TCGA training set.475 unmatched genes and 142 low-expressing genes (half or more than half of which were expressed at 0) were knocked out, and 2487 of 3104 immune-related genes of AmiGO2 were matched to the TCGA training set.
2) 338 early stage (stage I-II patients) lung adenocarcinoma patients with recurrence data were selected from 553 patients on the TCGA training set for pre-recurrence analysis.
3) In order to establish a model of recurrence markers for patients with early stage lung adenocarcinoma, a univariate Cox proportional regression model was used to study the effect of immune-related genes on disease-free survival (RFS) prognostic indicators. The results show that: 232 key genes of 2487 immune-related genes were statistically correlated with disease-free survival (RFS). GO and KEGG analysis suggest biological processes and related pathways in which these key genes are involved. GO analysis indicates that these key genes are involved in innate immune responses, leukocyte migration, and T cell receptor signaling pathways. The KEGG pathway shows that these key genes are involved in cancer-related processes, inflammatory pathways.
4) In order to better establish a model of recurrence markers for patients with early stage lung adenocarcinoma, L ASSO Cox regression model was used to evaluate the most useful prognostic genes, and 11 genes of TGFBR1, DYNC1I2, L GR4, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1 were initially included.
5) In order to make the relapse marker model more optimized and practical, a prognosis model comprising 9 genes including DYNC1I2 (NM-001271785.2), THOC1 (NM-005131.3), ADAM10 (NM-001320570.2), SERPINB6 (NM-001195291.3), CC L20 (NM-001130046.2), WNT2B (NM-001291880.1), S L C11A1 (NM-000578.4), MAPT (NM-001123066.3), PSEN1 (NM-000021.4) is finally constructed by adopting a stepwise Cox proportional risk regression model, and nine genes including DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PS 1 constitute the relapse marker IBRS model of the lung adenocarcinoma patients in the early stage of the invention and are marked as the relapse marker IBRS model.
2. Prognosis method of early lung adenocarcinoma recurrence marker model IBRS
1) The relative expression levels of nine genes in lung adenocarcinoma tissue of each lung adenocarcinoma patient in the TCGA early stage lung adenocarcinoma cohort (338 early stage lung adenocarcinoma patients) were examined. The specific detection method comprises the following steps: RNA extraction is carried out on the obtained frozen tissue; reverse transcribing the extracted RNA into corresponding cDNA; and (3) performing fluorescence quantitative PCR by using the reverse transcribed cDNA as a template. Taking GAPDH as an internal reference gene, recording the Ct value of each reaction, and expressing the detection result as delta Ct, wherein the delta Ct is CtGene-CtGAPDH. The detection primer sequences of the respective target genes and GAPDH genes are shown in Table 2.
TABLE 2 primer sequences
| Name of Gene | Upstream primer | Downstream primer |
| GAPDH | 5'-GGAGCCAAAAGGGTCATCATCTC-3' | 5'-GAGGGGCCATCCACAGTCTTCT-3' |
| DYNC1I2 | 5'-TAGGACGCTGCATTGGGATAC-3' | 5'-TCGACTTGCGTGATTTTAGCC-3' |
| THOC1 | 5'-GAAAAATGAAGGTTGCCCAAGTT-3' | 5'-TTGTCTCTGATTTACAGGCTTCC-3' |
| ADAM10 | 5'-TTTCAACCTACGAATGAAGAGGG-3' | 5'-TAAAATGTGCCACCACGAGTC-3' |
| SERPINB6 | 5'-TCACCGAAGTGAACAAGACTGG-3' | 5'-GCTTGGTAGAATTTTTGGCAGG-3' |
| CCL20 | 5'-TGCTGTACCAAGAGTTTGCTC-3' | 5'-CGCACACAGACAACTTTTTCTTT-3' |
| WNT2B | 5'-CGGGACCACACCGTCTTTG-3' | 5'-GCGAGTAATAGCGTGGACTAC-3' |
| SLC11A1 | 5'-CGTGGCGGGATTCAAACTTCT-3' | 5'-CACCTTAGGGTAGTAGAGATGGC-3' |
| MAPT | 5'-CCAAGTGTGGCTCATTAGGCA-3' | 5'-CCAATCTTCGACTGGACTCTGT-3' |
| PSEN1 | 5'-ACAGGTGCTATAAGGTCATCCA-3' | 5'-CAGATCAGGAGTGCAACAGTAAT-3' |
2) And calculating the risk value of each patient according to the following formula according to the relative expression result of each patient, wherein the risk value is (0.3987 × DYNC1I2 gene relative expression amount) - (0.5611 × THOC1 gene relative expression amount) + (0.2405 × 0ADAM10 gene relative expression amount) - (0.6713 × SERPINB6 gene relative expression amount) + (0.1268 × CC L20 gene relative expression amount) - (0.2408 × WNT2B gene relative expression amount) - (0.2255 × S L C11A1 gene relative expression amount) - (0.2034 × MAPT gene relative expression amount) + (0.6942 × PSEN1 gene relative expression amount).
The results of the measurement of the relative expression levels and risk values of the nine genes for each patient are shown in Table 3.
TABLE 3 results of the measurement of the relative expression levels and risk values of TCGA early stage lung adenocarcinoma cohort patients
3) Patients of the TCGA training set (338 patients with early stage lung adenocarcinoma) were classified into a high risk group (N-148) and a low risk group (N-190) according to the risk value of each patient. The specific method comprises the following steps:
and determining a threshold value through a 'surv _ cutoff' function of a 'survminer' software package of the R language software, comparing the risk value of the patient with the lung adenocarcinoma to be predicted with the threshold value, wherein the patient with the risk value larger than the threshold value is listed in a high risk group, and the patient with the risk value smaller than or equal to the threshold value is listed in a low risk group. The specific method for determining the threshold value through the 'surv _ cutpoint' of the 'survminer' software package of the R language software is as follows: inputting the risk value of the lung adenocarcinoma patient to be predicted and the matched prognosis information into R language software, and under the algorithm of 'surv _ cutoff' of a 'survminer' software package, automatically calculating a division point with the minimum P value by the software, wherein the division point is the threshold value (optimal cutoff point) of a high risk group and a low risk group,
the threshold value determined according to the above method was-0.7963, and early stage lung adenocarcinoma patients with a risk value of more than-0.7963 were included in the high risk group, and early stage lung adenocarcinoma patients with a risk value of less than or equal to-0.7963 were included in the low risk group.
3. Validity verification of early lung adenocarcinoma recurrence marker model IBRS
1) Kaplan-Meier analysis of patients (338 patients with early stage lung adenocarcinoma) was used to predict disease-free survival RFS. Kaplan-Meier survival analysis results showed that disease-free survival was significantly lower in patients in the high risk group than in patients in the low risk group (FIG. 2B), P < 0.0001).
2) To evaluate the predictive efficacy of the early lung adenocarcinoma recurrence marker model IBRS of the present invention, the areas under the ROC curve were further calculated, and in the TCGA training set (338 early lung adenocarcinoma patients), the areas under the curve of the early lung adenocarcinoma recurrence marker model IBRS were 0.775, 0.789, and 0.789 for the 1-year, 3-year, and 5-year recurrence predictions, respectively (fig. 2C).
3) Selecting stage I patients out of the TCGA training set patients (338 patients with early stage lung adenocarcinoma), the early stage lung adenocarcinoma recurrence marker model IBRS can classify patients into a significantly different subgroup of RFS (fig. 2D). similarly, similar effects are achieved in stage II patients (fig. 2E). at the same time, the survival rate is significantly lower in the high risk group than in the low risk group (fig. 3) regardless of whether in the overall or stage I/II L UAD patients.
Secondly, verifying the model in GSE31210 queue
1. Validating models in GSE31210 cohorts
To verify whether the early stage lung adenocarcinoma recurrence marker model IBRS of the present invention works in other populations, 226 cases of early stage L UAD patients from lung cancer genechip data (GSE31210) were used as a validation set, the relative expression levels of nine genes in each patient in GSE31210 were measured, risk values were calculated, and the patients were divided into high risk group (N-61) and low risk group (N-165) (fig. 4A) (threshold of-1.3408), the relative expression levels and risk values of the patients in the GSE31210 cohort were shown in table 4.
TABLE 4 results of measurement of relative expression levels and risk values in GSE31210 cohort patients
The difference between disease-free survival rate RFS and overall survival rate OS of the patients in the high-risk group and the low-risk group is analyzed by using a Kaplan-Meier survival analysis method.
Kaplan-Meier survival analysis showed that disease-free survival was significantly higher in patients in the low risk group than in patients in the high risk group (FIG. 4B, p < 0.0001). In phase I patients, there was also a significant difference in RFS in patients in the high risk or low risk group (fig. 4C, p < 0.0001). However, in phase II patients, the immune-related gene markers only showed marginal difference RFS, probably due to limited sample size (fig. 4D, p ═ 0.16).
Kaplan-Meier survival analysis showed that overall survival was also significantly higher in patients in the low risk group than in patients in the high risk group (fig. 4E, p < 0.0001). Overall survival was also significantly higher in patients in the low risk group than in patients in the high risk group in patients in phase I (figure 5, p < 0.0001).
2. Validating models in different clinical subgroups
Considering that smoking is one of the greatest risk factors for developing lung adenocarcinoma, but other factors including gender and age also play a role, L UAD patients were stratified according to three clinical characteristics (gender, smoking history and age) in the TCGA training set (338 early lung adenocarcinoma patients) or the GSE31210 validation set (226 early L UAD patients), then Kaplan-Meier survival analysis was used to estimate disease-free survival RFS and overall survival OS differences between the high-risk and low-risk groups.
The results of the patient stratification analysis in the TCGA training set showed that in all subgroups (male and female, smokers and non-smokers, older (age > 60) and younger (age < 60)), the high risk group patients had shorter RFS (fig. 6, p <0.0001) and the low risk group patients also had significantly higher OS than the high risk group (fig. 7, p <0.05) compared to the low risk group patients.
The results of the patient stratification analysis in the GSE31210 validation set showed that, consistent with the TCGA training set, all six subgroups of low risk group patients had significantly higher RFS than the high risk group patients (fig. 8, p < 0.05). Meanwhile, Kaplan-Meier survival analysis shows that the high-risk group patients have shorter OS (figure 9, p <0.05), and the prediction capability of the early lung adenocarcinoma recurrence marker model IBRS in the clinical subgroup is fully proved.
3. Validation of models under different EGFR or KRAS mutation states
L UAD patients were stratified as wild-type and mutant versions of the EGFR gene or KRAS gene in the TCGA training set (338 early lung adenocarcinoma patients) or the GSE31210 validation set (226 early L UAD patients). Kaplan-Meier survival analysis was then used to estimate disease-free survival RFS and overall survival OS differences between the high-risk and low-risk groups.
The results of the distribution (based on the optimized risk values for all patients) of the patients in the high-risk group and the low-risk group at different mutation states in the TCGA training set show that the EGFR mutation (EGFR-MUT) group shows a higher proportion of low-risk patients compared to the EGFR wild-type (EGFR-WT) group. In contrast, the KRAS mutant (KARS-MUT) group showed a higher proportion of high risk patients compared to the KRAS wild type (KRAS-WT) group (fig. 10A). In the different mutational states, RFS was significantly higher in patients in the low risk group than in the high risk group (fig. 10B, C, D and E, p <0.05), and OS was also significantly higher in patients in the low risk group than in patients in the high risk group (fig. 11A, B, C, D and E, p < 0.05).
GSE31210 validation focused, and high risk patients also focused more on the EGFR-WT group or EGFR-MUT group (fig. 12A). In addition, in the validation set of GSE31210, the prognostic prediction effect of early stage lung adenocarcinoma recurrence marker model IBRS in different mutation states was also well demonstrated. In different stratified groups based on mutation status, patients in the high risk group had shorter RFS (fig. 12, C, D and E, p <0.05) and OS (fig. 13A, B, C, D and E), p <0.05) than patients in the low risk group.
Third, validation was performed in an independent cohort of 68 early lung adenocarcinoma frozen tissues
To assess the accuracy of IBRS in predicting the risk of tumor recurrence in the early L UAD patient in clinical practice, validation was performed in a separate cohort containing 68 frozen tissues of early lung adenocarcinoma.
The relative expression levels of nine genes in each of the 68 early stage lung adenocarcinoma frozen tissues were detected, the risk values were calculated, and the patients were classified into a high risk group (N ═ 34) and a low risk group (N ═ 34) according to the method in 2 of step one (fig. 14A) (threshold-1.8481). The results of measuring the relative expression levels of nine genes in frozen tissues of 68 patients with early stage lung adenocarcinoma and the results of calculating the risk values are shown in Table 5.
TABLE 5 and 68 test results of relative expression level and risk value of early stage lung adenocarcinoma patients
The difference between disease-free survival rate RFS and overall survival rate OS of the patients in the high-risk group and the low-risk group is analyzed by using a Kaplan-Meier survival analysis method.
Kaplan-Meier survival analysis showed significant differences in RFS between the high risk group and low risk group patients (FIG. 14B, p < 0.0001). at the same time, early stage lung adenocarcinoma recurrence marker model IBRS could classify stage I, II L UAD patients into high risk and low risk groups with significantly different RFS (FIGS. 14C and D, p < 0.05). high risk patients had significantly lower OS than low risk patients (FIG. 14E, p < 0.0001).
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the technical principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A lung adenocarcinoma patient prognosis system comprises a system for detecting expression levels of nine genes, namely DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN 1.
2. The system according to claim 1, wherein the system for detecting the expression levels of nine genes, namely DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1, comprises reagents and/or instruments required for detecting the relative expression levels of the nine genes by a fluorescence quantitative PCR method.
3. The system according to claim 1 or 2, characterized in that: the system also includes a data processing device; the data processing device is internally provided with a module; the module has the functions as shown in (a1) and (a 2):
(a1) taking isolated lung adenocarcinoma tissues of a population to be detected consisting of lung adenocarcinoma patients as samples, measuring the relative expression amounts of the nine genes in each sample, and then calculating a risk value according to the relative expression amounts of the nine genes, namely (0.3987 × DYNC1I2 gene relative expression amount) - (0.5611 × THOC1 gene relative expression amount) + (0.2405 × 0ADAM10 gene relative expression amount) - (0.6713 × SERPINB6 gene relative expression amount) + (0.1268 × CC L20 gene relative expression amount) - (0.2408 × WNT2B gene relative expression amount) - (0.2255 × S L C11A1 gene relative expression amount) - (0.2034 × MAPT gene relative expression amount) + (0.6942 × PSEN1 gene relative expression amount), and dividing the population to be detected into a low risk group and a high risk group according to the risk value;
(a2) determining the prognostic risk and/or the prognostic relapse rate and/or the prognostic disease-free survival rate and/or the prognostic overall survival rate of a test patient from said test population according to the following criteria: "from the test patients in the high risk group" has a higher prognostic risk and/or prognostic relapse rate than or is candidate for higher than "from the test patients in the low risk group"; the prognostic disease-free survival and/or overall survival of "a test patient from the low risk group" is higher than or is candidate higher than "a test patient from the high risk group".
4. Use of the system of any one of claims 1 to 3 in (1) to (8) below:
(1) preparing a product for prognosis risk assessment of lung adenocarcinoma patients;
(2) assessing a risk of prognosis for a patient with lung adenocarcinoma;
(3) preparing a product for evaluating the recurrence rate of lung adenocarcinoma patients;
(4) assessing the prognostic recurrence rate of a patient with lung adenocarcinoma;
(5) preparing a product for prognosis disease-free survival rate evaluation of lung adenocarcinoma patients;
(6) assessing prognosis disease-free survival rate of the lung adenocarcinoma patient;
(7) preparing a product for prognosis of overall survival rate of a patient with lung adenocarcinoma;
(8) assessing the overall survival rate of a lung adenocarcinoma patient.
5. The system according to any one of claims 1-3 or the use according to claim 4, wherein: the lung adenocarcinoma patient is an early lung adenocarcinoma patient.
6. The system or use according to claim 5, wherein: the early stage lung adenocarcinoma patient is a stage I lung adenocarcinoma patient and/or a stage II lung adenocarcinoma patient.
Application of nine genes, namely DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1, as markers in preparation of products for prognosis of lung adenocarcinoma patients.
8. The application of substances for detecting expression levels of nine genes, namely DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1, in preparing a product for prognosis of a patient with lung adenocarcinoma.
9. The application of the substance for detecting the expression levels of nine genes, namely DYNC1I2, THOC1, ADAM10, SERPINB6, CC L20, WNT2B, S L C11A1, MAPT and PSEN1, and the data processing device as described in claim 3 in the preparation of a product for the prognosis of a patient with lung adenocarcinoma.
10. Use of a data processing device as claimed in claim 3 for the preparation of a product for prognosis of a patient with lung adenocarcinoma.
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