CN113862354B - System for predicting prognosis of patients with limited stage small cell lung cancer and application thereof - Google Patents
System for predicting prognosis of patients with limited stage small cell lung cancer and application thereof Download PDFInfo
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Abstract
The invention discloses a system for predicting prognosis of patients with limited-stage small cell lung cancer and application thereof. The invention provides application of five genes of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B as markers in preparation of products for evaluating prognosis of patients with limited small cell lung cancer. Also provided are systems for predicting the efficacy or prognosis of a treatment in a patient with limited small cell lung cancer, which can predict the efficacy and prognosis of a chemotherapy in a patient with limited small cell lung cancer, such as the efficacy, prognosis, and overall survival of the prognosis of the chemotherapy. The invention has important application value.
Description
Technical Field
The invention belongs to the field of biomedicine, and particularly relates to a system for predicting prognosis of a patient with limited-stage small cell lung cancer and application thereof.
Background
Small cell lung cancer (Small cell lung cancer, SCLC) is a highly lethal, high-grade neuroendocrine tumor, accounting for approximately 15% of lung cancer, with five-year survival rates of less than 7%. When neoplastic lesions are localized to the ipsilateral thorax, ipsilateral pleural effusion and lymph node metastasis can be combined, they are called limited stage small cell lung cancer. Despite the continued development of new therapeutic approaches such as molecular targeted drugs, immune checkpoint inhibitors, etc., therapeutic strategies for small cell lung cancer patients have not been significantly broken through for decades. Traditional radiation therapy remains the first line treatment regimen for small cell lung cancer patients. Studies have shown that many components of the immune system are key factors in the development of tumorigenesis. Immune checkpoint inhibitor therapy has made tremendous progress in a variety of tumors, and thus immunotherapy has also great potential in small cell lung cancer. However, a significant proportion of patients cannot benefit from immunotherapy, PD-L1 is a marker of classical immunotherapy, but in small cell lung cancer, PD-L1 expression is low or absent, and thus PD-L1 cannot be a predictive marker of therapeutic efficacy in small cell lung cancer immunotherapy. Thus, there is a need in the clinic to accurately screen suitable and beneficial small cell lung cancer patients, find specific methods to predict prognosis of small cell lung cancer patients, and to design the most appropriate treatment and management regimen for different subpopulations of small cell lung cancer patients.
Studies have shown that epigenetic dysregulation is closely related to tumor progression and therapeutic resistance in small cell lung cancer. N6-methyladenosine (m 6A) is the most widely occurring modification of RNA in eukaryotes. The modification mode can regulate and control various RNA related biological processes including RNA degradation, stabilization, translation, shearing and transportation, and finally regulate the expression of target genes. The m 6A-related biological processes are dynamic, multifaceted, reversible processes, mainly mediated by methylases, methyltransferases and binding proteins.
In view of the highly malignant and limited treatment measures of the small cell lung cancer, the establishment of a predictive marker model for predicting the prognosis of the limited stage small cell lung cancer and assisting chemotherapy has great significance.
Disclosure of Invention
It is an object of the present invention to provide the use of five genes G3BP1, METTL5, albh 5, IGF2BP3 and RBM15B as markers.
The application of five genes of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B provided by the invention as markers is any one of (B1) - (B4):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) Assessing prognosis of patients with limited stage small cell lung cancer;
(b3) Preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer;
(b4) The degree of chemotherapy benefit of patients with limited stage small cell lung cancer was assessed.
Another object of the present invention is to provide the use of a substance for detecting the expression levels of five genes, G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM 15B.
The application provided by the invention is any one of (b 1) - (b 4):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) Assessing prognosis of patients with limited stage small cell lung cancer;
(b3) Preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer;
(b4) The degree of chemotherapy benefit of patients with limited stage small cell lung cancer was assessed.
The prognosis is manifested in total survival, relapse-free survival, or relapse-free survival.
The chemotherapy is adjuvant chemotherapy.
The invention also provides the application of the substance and the data processing device for detecting the expression levels of the five genes, namely G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B, which are any one of (B1) to (B4):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) Assessing prognosis of patients with limited stage small cell lung cancer;
(b3) Preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer;
(b4) The degree of chemotherapy benefit of patients with limited stage small cell lung cancer was assessed.
A module is arranged in the data processing device; the module has functions as shown in (a 1) and (a 2) below:
(a1) Taking an isolated localized small cell lung cancer tissue of a group to be detected consisting of localized small cell lung cancer patients as a sample, measuring the expression amounts of the five genes in each sample, and calculating a risk value according to the five gene expression amounts by the following formula: risk value= (0.0906 ×g3bp1 gene expression amount) + (0.4096×mettl5 gene expression amount) - (0.6365 ×albh5 gene expression amount) - (0.0912 ×igf2bp3 gene expression amount) - (0.0660 ×rbm15B gene expression amount), and classifying the population to be tested into a low risk group and a high risk group according to the risk value;
(a2) Determining prognosis of a test patient from said test population according to the following criteria:
the prognosis of the patient under test in the low risk group is better or a candidate better than the patient under test in the high risk group;
or, the prognosis total survival of the patients under test in the low risk group is longer or a candidate longer than the patients under test in the high risk group;
or, the overall survival rate of prognosis of the patient under test in the low risk group is higher or the candidate is higher than that of the patient under test in the high risk group;
or, the prognosis of the patient to be tested in the low risk group has a relapse-free survival longer than or a candidate longer than the patient to be tested in the high risk group;
or, the prognosis relapse-free survival rate of the patient in the low risk group is higher or the candidate is higher than the patient in the high risk group;
or, the patients in the low risk group benefit from chemotherapy to a higher degree or candidate than the patients in the high risk group;
or, the total post-chemotherapy survival of the patients in the low risk group is longer or is a candidate longer than the patients in the high risk group;
or, the overall survival rate after chemotherapy of the patients under test in the low risk group is higher or the candidates are higher than the patients under test in the high risk group;
or, the post-chemotherapy relapse-free survival of the patient at risk group is longer or candidate for longer than the patient at risk group;
or, the survival rate of no recurrence after chemotherapy is higher or the candidate is higher for patients in the low risk group than for patients in the high risk group;
the substances for detecting the expression levels of the five genes of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B comprise the following c1 or c2:
(c1) A substance capable of specifically binding to the G3BP1, METTL5, albh 5, IGF2BP3 and RBM15B proteins or genes, respectively;
(c2) Primer pairs capable of specifically amplifying the G3BP1, METTL5, albh 5, IGF2BP3 and RBM15B genes, respectively.
Further, the substance includes reagents and/or instruments required for detecting the expression amounts of the five genes by a fluorescent quantitative PCR method.
Further, the reagents and/or instruments required for detecting the five gene expression levels by the fluorescent quantitative PCR method comprise primer pairs for detecting the five gene expression levels of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM 15B.
The primer sequences for detecting six genes of G3BP1, METTL5, ALKBH5, IGF2BP3, RBM15B and GAPDH are specifically shown in Table 5.
The prognosis is embodied by the length of the total survival time, the total survival rate, the length of the relapse-free survival time or the survival rate of relapse.
The benefit degree of the chemotherapy is embodied by the length of the total survival time, the total survival rate, the length of the relapse-free survival time or the relapse-free survival rate after the chemotherapy.
It is yet another object of the present invention to provide a system for predicting the efficacy or prognosis of a treatment for a patient with limited stage small cell lung cancer.
The invention provides a system for predicting the curative effect or prognosis of a patient with limited small cell lung cancer, which comprises a system for detecting the expression levels of five genes, namely G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM 15B.
In the above system, the system for detecting the expression levels of five genes of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B comprises the above substances for detecting the expression levels of five genes of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM 15B.
The system further comprises the data processing device in the application.
The application of the system is also within the scope of the present invention, and is any one of (b 1) to (b 4):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) Assessing prognosis of patients with limited stage small cell lung cancer;
(b3) Preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer;
(b4) The degree of chemotherapy benefit of patients with limited stage small cell lung cancer was assessed.
The application of the data processing device is also within the scope of the present invention, and is any one of (b 1) to (b 4):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) Assessing prognosis of patients with limited stage small cell lung cancer;
(b3) Preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer;
(b4) Evaluating the chemotherapy benefit degree of patients with limited stage small cell lung cancer;
the expression levels of the five genes may be specifically expression levels of the five genes relative to a reference gene.
The reference gene is specifically GAPDH gene.
The prognosis is manifested in the overall survival of the prognosis or in the survival without recurrence.
The isolated restricted small cell lung cancer tissue can be obtained from a sample prepared by formalin fixed paraffin embedding of the isolated restricted small cell lung cancer tissue of the patient to be predicted or from a frozen section of the isolated restricted small cell lung cancer tissue of the patient to be predicted.
In the prognosis system or the application of the patient with the limited small cell lung cancer, the G3BP1 has the GenBank number of NM_005754.3, the METTL5 has the GenBank number of NM_001293186.2, the ALKBH5 has the GenBank number of NM_017758.4, the IGF2BP3 has the GenBank number of NM_006547.3 and the RBM15B has the GenBank number of NM_013286.5.
The invention integrates the total life cycle data of 265 cases of the limited small cell lung cancer in 3 different queues, and establishes and verifies an individualized prediction model of the chemotherapy curative effect and prognosis of the limited small cell lung cancer patient, namely an m6A molecular model based on the m6A regulating element spectrum. The 3 independent queues included 68 international queue data (GSE 40275), 47 GEO microarray data (GSE 60052), and 150 FFPE organized national cancer center queue (National Cancer Centre, NCC) data. The method is a first reliable m6A prediction model for predicting the chemotherapy curative effect and prognosis of the local stage small cell lung cancer, can be used for predicting the chemotherapy benefit degree and prognosis, can become a clinically useful tool, and is helpful for promoting the accurate prediction and the personalized comprehensive treatment of the prognosis of the local stage small cell lung cancer patient. The invention has important application value.
The experiments of the present invention demonstrate the importance of m6A regulatory element-related genes in SCLC and develop the first and most comprehensive prognostic signature based on multicenter m6A regulatory elements for-SCLC patients. This m6A prediction model can accurately predict OS and RFS. The m6A risk value may also predict the benefit of chemotherapy in SCLC patients. The m6A predictive model is a prognostic and predictive tool for SCLC. Further prospective verification of the predictive ability of m6A risk values would aid in the ability to effectively treat SCLC patients.
Drawings
FIG. 1 is the clinical significance of m6A regulatory elements in restricted small cell lung cancer. A is the interaction between each m6A regulatory element in localized small cell lung cancer, the negative correlation is shown in blue and the positive correlation is shown in red. The scatter plot represents the group with the highest correlation coefficient (YTHDF 3 and KIAA1429, pearson r=0.820); b is the protein interaction between m6A regulatory elements; c is a forest map between m6A regulatory elements and prognosis of small cell lung cancer patients.
Figure 2 is the distribution of m6A scores in international cohorts and patient survival. A. B is the distribution of m6A scores in the international queue and the survival state of patients; c is the Kaplan-Meier curve of the total survival of 68 patients in the International cohort; d is the ROC curve for m6A risk value prediction for survival for 1, 3 and 5 years; e is an ROC curve for predicting 5 years of survival by m6A risk values and clinical pathological parameters; f is the distribution of m6A risk values and C index values of clinical pathology parameters for total survival in the training cohort.
Figure 3 is a m6A risk value distribution and patient survival in a multi-validated group. A is m6A risk value and patient survival condition in Shanghai queue; b is a Kaplan-Meier curve of the total survival time of 47 patients in the Shanghai cohort; c is an ROC curve for predicting survival of 1 year, 3 years and 5 years by m6A risk values in an Shanghai queue; d is the distribution of m6A risk values of total survival time in the Shanghai queue and C index values of clinical pathological parameters; e is the m6A risk value and patient survival in the NCC queue; f is a Kaplan-Meier curve of the total survival of 150 patients in the NCC queue; g is an ROC curve for predicting survival of 1 year, 3 years and 5 years of m6A risk values in an NCC queue; h is the distribution of m6A risk values for total survival in NCC cohorts and C index values for clinical pathology parameters; i is the m6A risk value in NCC queue and the recurrence condition of the patient; j is Kaplan-Meier curve of recurrence of 150 patients in NCC cohort; k is an ROC curve for predicting recurrence of m6A risk values in NCC cohorts for 1 year, 3 years, and 5 years; l is the distribution of m6A risk values and C index values of clinical pathology parameters for recurrence in NCC cohorts.
Figure 4 is a graph showing the predictive value of m6A risk values versus the degree of benefit of adjuvant chemotherapy in different cohorts. A is a Kaplan-Meier curve of the total survival of auxiliary chemotherapy in an international queue; b is ROC curve in international cohorts that aid chemotherapy patients in predicting m6A risk value for survival for 1, 3 and 5 years; c is the distribution of m6A risk values and C index values of clinical pathology parameters of adjuvant chemotherapy patients in the international cohort; d is a Kaplan-Meier curve of the total survival of the NCC queue-assisted chemotherapy patients; e is a ROC curve that predicts survival for 1 year, 3 years, and 5 years for m6A risk values of adjuvant chemotherapy patients in NCC cohorts; f is the distribution of m6A risk values and C index values of clinical pathology parameters in the NCC cohort for adjuvant chemotherapy patients; g is a Kaplan-Meier curve of the recurrence survival of NCC-queue-assisted chemotherapy patients; h is ROC curve of adjuvant chemotherapy patients m6A risk value prediction for 1, 3 and 5 years of recurrent survival in NCC cohorts; i is the distribution of m6A risk values and C index values of clinical pathology parameters in NCC cohorts that aid in the recurrent survival of chemotherapy patients.
Detailed Description
The experimental methods used in the following examples are conventional methods unless otherwise specified.
The following examples facilitate a better understanding of the present invention, but are not intended to limit the same.
The test materials used in the examples described below, unless otherwise specified, were purchased from conventional Biochemical reagent companies.
The quantitative tests in the following examples were all set up in triplicate and the results averaged.
The total lifetime (OS) in the following examples is defined as the time from group entry to death or last follow-up from any cause.
The Relapse-free Survival (RFS) is defined in the examples below as the time from the day after surgery to the time of Relapse, metastasis or last follow-up.
The overall survival rate in the examples below is defined as the probability that a patient will survive from a particular point in time to a particular point in time.
Patient efficacy assessment during treatment was assessed according to solid tumor efficacy assessment standard version 1.1 (Response Evaluation Criteria in Solid Tumors, RECIST version 1.1). Efficacy evaluation criteria included complete remission (complete response, CR), partial Remission (PR), disease Stabilization (SD), and disease progression (progressive disease, PD).
Prognosis in the following examples refers to the effect of patient treatment, embodied as the length of OS and RFS.
In the following examples, the results of univariate and multivariate Cox regression analysis of the m6A risk values, clinical pathology and recurrence free survival for localized small cell lung cancer are shown in table 1.
Table 1 shows univariate and multivariate Cox regression of small cell lung cancer m6A risk values, clinical pathology and recurrence-free survival
Example 1, treatment efficacy and prognosis model for m 6A-based chemotherapy of restricted small cell lung cancer and model validation
The international cohort consisting of 68 patients with restricted small cell lung cancer was used to construct a restricted small cell lung cancer prognostic marker model, and the constructed model was validated by a separate set consisting of 47 patients with restricted small cell lung cancer in Shanghai cohort and 150 patients with small cell lung cancer in FFPE tissue. The clinical characteristics of all patients are shown in table 2, and all patients were subjected to surgical treatment.
TABLE 2 clinical characterization of lung cancer patients
Note that: NA represents unavailable.
1. Construction and verification of international queue construction limited small cell lung cancer prognosis marker model
1. Construction of prognosis prediction model of m6A regulatory element of limited stage small cell lung cancer
The specific steps for constructing the prediction model of the curative effect and prognosis of the limited small cell lung cancer m6A are as follows:
(1) Taking 68 patients with restricted small cell lung cancer from GEO as a training set, downloading and obtaining m6A related expression of the patients from Gene Expression Omnibus (GEO, http:// www.ncbi.nlm.nih.gov/GEO, GSE 40275), and collecting expression quantity data of all m6A regulating elements.
(2) In order to establish the therapeutic effect of the patients with limited stage small cell lung cancer and the prognosis prediction model of the m6A regulatory element, a single factor Cox proportional regression model is adopted to study the influence of the related genes of the m6A regulatory element on the prognosis index of the total survival (OS).
The results indicate that among all m6A regulatory elements, clearly related regulatory elements were determined (fig. 1A), and that the protein interaction network relationship between all m6A elements is shown in fig. 1B. By analyzing the relationship between all regulatory elements of m6A and survival, the results showed that multiple regulatory elements were closely related to survival (fig. 1C).
(3) In order to make the prognosis model more optimal and practical, a stepwise Cox proportional-risk regression model is adopted, and finally a prognosis model comprising the following 5 genes is constructed: genebank number NM-005754.3 for G3BP1 (Update: PRI 30-JUN-2021), genebank number NM-001293186.2 for METTL5 (Update: PRI 9-JAN-2021), genebank number NM-017758.4 for ALKBH5 (Update: PRI 1-AUG-2021), genebank number NM-006547.3 for IGF2BP3 (Update: PRI 3-DEC-2021), genebank number NM-013286.5 for RBM15B (Update: PRI 2-FEB-2021).
(4) Firstly, log2 processing is carried out on the data in the step (1), and if one gene corresponds to one probe, the gene expression value is the probe value for the processed data; if one gene corresponds to 2 or more than 2 probes, the average value of the probes is the gene expression value, so that the standardized expression quantity of the target gene of each patient is obtained, and the m6A risk value of each patient is calculated by using the following formula through LASSO analysis.
m6a risk value= (0.0906 ×g3bp1 gene expression amount) + (0.4096×mettl5 gene expression amount) - (0.6365 ×alkbh5 gene expression amount) - (0.0912 ×igf2bp3 gene expression amount) - (0.0660 ×rbm15b gene expression amount).
The results of detecting the expression levels and risk values of the 5 genes corresponding to each patient are shown in Table 3.
Table 3 shows the results of detecting the expression levels and risk values of 5 genes in the International queuing machine for limiting the period of small cell lung cancer
In the above table, 1 in the death state in column 3 indicates death in the 2 nd column follow-up time, and 0 indicates no death or no visit in the 2 nd column follow-up time.
2. Predictive value of m6A risk value on prognosis in International queue
(1) The threshold is determined by "survivin_cutpoint" of "surviviner" package of R language software, and the specific method is as follows: and (3) inputting the m6A risk value of the patient with the limited stage small cell lung cancer to be predicted and matched prognosis information into R language software, and automatically calculating a segmentation point with the minimum P value by the software under the algorithm of the survivin-cutpoint of the surviviner software package, wherein the segmentation point is the threshold value (optimal cutoff point) of the high risk group and the low risk group.
The results showed that in the international cohort, the calculated threshold was-1.2708, the patient m6A risk value was greater than and/or equal to-1.2708 as the high risk group, and the patient m6A risk value was less than-1.2708 as the low risk group (fig. 2A). The expression of 5 genes per patient is shown in FIG. 2B.
(2) Analysis of Total survival OS Difference in patients in high-risk and Low-risk groups Using Kaplan-Meier survival analysis
The m6A risk values and survival data of the high risk group and the low risk group obtained in the above (1) were analyzed by Kaplan-Meier. The Kaplan-Meier survival analysis results showed that the OS of the high risk group of patients in the international cohort was shorter than the low scoring patients (fig. 2c, p < 0.001). The area under the ROC curve was calculated and AUCs for OS in international queues 1, 3 and 5 years were 0.672, 0.812 and 0.793, respectively (fig. 2D). Further ROC analysis showed that the AUC values for the m6A risk values (auc=0.791) were all higher than other common clinical parameters, such as gender (auc=0.745), age (auc=0.512), smoking status (auc=0.548) and SCLC stage (auc=0.529) (fig. 2E). Further, the C-index analysis results show that the prediction effect of the m6A risk value is significantly better than that of other common clinical parameters (fig. 2F).
Thus, the m6A risk value of each patient can be calculated according to the following formula by using the m6A risk value to predict prognosis of the limited small cell lung cancer, and detecting the expression level of G3BP1 (nm_ 005754.3), METTL5 (nm_ 001293186.2), alk bh5 (nm_ 017758.4), IGF2BP3 (nm_ 006547.3), RBM15B (nm_ 013286.5) in the tumor tissue of the patient with pre-operative limited small cell lung cancer:
m6a risk value= (0.0906 ×g3bp1 gene expression amount) + (0.4096×mettl5 gene expression amount) - (0.6365 ×alkbh5 gene expression amount) - (0.0912 ×igf2bp3 gene expression amount) - (0.0660 ×rbm15b gene expression amount);
patients with low m6A risk values have a better prognosis or are candidates for patients with high m6A risk values.
Or, patients with low m6A risk values have a longer overall survival than or candidates for patients with high m6A risk values.
Or, in the same follow-up time, patients with low m6A risk values have a total survival rate greater than or a candidate greater than those with high m6A risk values.
2. External validation of a restricted stage small cell lung cancer prognostic marker model
To verify if the limited stage small cell lung cancer m6A prediction model was functional in other populations, 2 independent cohorts, 197 samples were used as a validation set, 47 for Shanghai cohorts and 150 for NCC cohorts.
1. Verification of Shanghai queues
(1) With 47 patients with restricted small cell lung cancer from GEO as training set, the relative expression level of m6A of the patients was obtained from Gene Expression Omnibus (GEO, http:// www.ncbi.nlm.nih.gov/GEO, GSE 60052).
(2) And (3) detecting the expression levels of five genes of 47 patients suffering from the restricted small cell lung cancer according to the method of the step (1) and calculating the m6A risk value.
The results of detecting the expression levels and risk values of 5 genes for each patient in the Shanghai cohort are shown in Table 4.
TABLE 4 detection results of target Gene expression level and Risk value for Shanghai patients
In the above table, 1 in the death state in column 3 indicates death in the 2 nd column follow-up time, and 0 indicates no death or no visit in the 2 nd column follow-up time.
(2) According to the method 2 (1) in the step one, an OS threshold is determined.
The results showed that in the Shanghai cohort, the threshold for OS was calculated as-3.1199, and based on the threshold, patients were classified into high risk groups and low risk groups, with patients m6A risk value greater than and/or equal to-3.1199 being the high risk group and patients m6A risk value less than-3.1199 being the low risk group (FIG. 3A)
(3) Analysis of Total survival OS Difference in patients in high-risk and Low-risk groups Using Kaplan-Meier survival analysis
The m6A risk values and survival data of the high risk group and the low risk group obtained in the above (1) were analyzed by Kaplan-Meier. Kaplan-Meier survival analysis showed that the OS of the high risk group of patients in the Shanghai cohort was shorter than the low scoring patients (fig. 3b, p=0.006). The area under the ROC curve was calculated and AUCs of OS in the Shanghai cohort for 1, 3 and 5 years were 0.652, 0.733 and 0.731, respectively (fig. 3C). Further, the results of the C-index analysis showed that the predictive effect of m6A risk values was significantly better than other common clinical parameters such as gender, age, smoking status and SCLC stage (fig. 3D).
2. Verification of NCC queues
(1) The NCC cohort was 150 SCLC patients from the national cancer center, RNA was extracted from paraffin specimens of the patients, and the expression level of the gene was detected by PCR.
The specific detection method comprises the following steps: extracting RNA from the obtained tissue of the limited-period small cell lung cancer; reverse transcription of the extracted RNA into corresponding cDNA; performing fluorescent quantitative PCR by taking the cDNA after reverse transcription as a template; the GAPDH is used as an internal reference gene, the Ct value of each reaction is recorded, the relative expression quantity of the target gene is expressed as delta Ct, and delta Ct=Ct Target gene -Ct GAPDH 。
The primer sequences for detecting the target gene and the GAPDH gene when fluorescent quantitative PCR was performed are shown in Table 5.
TABLE 5 primer sequences for the genes of interest
The primer sequences in the table are numbered from top to bottom and from left to right as sequence 1 to sequence 12.
(2) The m6A risk value was calculated according to the method of step 1 (4) for the expression levels of the five genes of NCC patients in the series.
The results of detecting the expression levels and risk values of 5 genes for each patient in the NCC array are shown in Table 6.
TABLE 6 detection results of target Gene expression level and Risk value for NCC patients
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In the above table, 1 in the recurrent state in column 3 indicates recurrence in the 2 nd column follow-up time, 0 indicates no recurrence or no visit in the 2 nd column follow-up time; a death status of 1 in column 5 indicates death within column 4 follow-up time, and 0 indicates no death or no follow-up within column 4 follow-up time.
(3) Prognosis prediction of NCC queue OS by m6A prediction model
1) According to the method 2 (1) in the step one, an OS threshold is determined. The result determined a threshold of 0.1054, and based on the threshold, patients were classified into high risk groups and low risk groups, with patients having m6A risk values greater than and/or equal to 0.1054 being the high risk group and patients having m6A risk values less than 0.1054 being the low risk group (FIG. 3E)
2) Analysis of Total survival OS Difference in patients in high-risk and Low-risk groups Using Kaplan-Meier survival analysis
The m6A risk values and survival data of the high risk group and the low risk group obtained in the above (1) were analyzed by Kaplan-Meier. OS was analyzed by Kaplan-Meier survival and showed that the OS of the high risk group of patients in NCC cohort was shorter than that of the low scoring patients (fig. 3f, p < 0.001). Further, the area under the ROC curve was calculated, with AUCs of OS in international queues of 1, 3 and 5 years being 0.794, 0.691 and 0.686, respectively (fig. 3G). Further, the results of the C-index analysis showed that the predictive effect of m6A risk values was significantly better than other common clinical parameters such as gender, age, smoking status and SCLC stage (fig. 3H).
(4) Prognosis prediction of NCC queue RFS by m6A prediction model
1) The RFS threshold is determined according to the method 2 (1) in step (one). As a result, a threshold value of 0.1026 was determined, and based on the threshold value, patients were classified into a high risk group and a low risk group, with the patient m6A risk value greater than and/or equal to 0.1026 being the high risk group and the patient m6A risk value less than 0.1026 being the low risk group (FIG. 3I)
2) The m6A risk values and survival data of the high risk group and the low risk group obtained in 1) above were analyzed using Kaplan-Meier.
RFS was analyzed by Kaplan-Meier survival, and the results showed that the RFS of the high risk group of patients in the NCC cohort was shorter than that of the low scoring patients (FIG. 3J, P < 0.001). The area under the ROC curve was calculated and AUCs for RFS in international queues 1, 3 and 5 years were 0.713, 0.662 and 0.695, respectively (fig. 3K). Further, the results of the C-index analysis showed that the predictive effect of m6A risk values was significantly better than other common clinical parameters such as gender, age, smoking status and SCLC stage (fig. 3L).
Patients with low m6A risk values have a relapse free period greater than or a candidate greater than those with high m6A risk values.
Patients with low m6A risk values have a relapse free survival greater than or a candidate greater than patients with high m6A risk values during the same follow-up time.
3. m6A model predicts curative effect of adjuvant chemotherapy for patients with limited stage small cell lung cancer
In the international and NCC cohorts, 42 and 129 patients received adjuvant chemotherapy after surgery, respectively, and in the 2 cohorts, the m6A model predicted and validated the adjuvant chemotherapy benefit, with OS and/or RFS after patient receiving adjuvant chemotherapy as an indicator of the evaluation benefit.
1. In the international queue, the m6A model predicts the curative effect of adjuvant chemotherapy for patients with limited stage small cell lung cancer
1) 42 patients in international cohorts receiving adjuvant chemotherapy were selected (see table 4 for specific information and table 7 for patient numbers), and an OS threshold was determined according to the method 2 (1) in step one, with a threshold of-1.2708.
Table 7 patient numbers on international cohorts receiving adjuvant therapy
| Sequence number | Patient numbering |
| 1 | sclc_ucologne_2015_S00022 |
| 2 | sclc_ucologne_2015_S00050 |
| 3 | sclc_ucologne_2015_S00356 |
| 4 | sclc_ucologne_2015_S00472 |
| 5 | sclc_ucologne_2015_S00825 |
| 6 | sclc_ucologne_2015_S00827 |
| 7 | sclc_ucologne_2015_S00829 |
| 8 | sclc_ucologne_2015_S00830 |
| 9 | sclc_ucologne_2015_S00832 |
| 10 | sclc_ucologne_2015_S00837 |
| 11 | sclc_ucologne_2015_S00838 |
| 12 | sclc_ucologne_2015_S01366 |
| 13 | sclc_ucologne_2015_S01494 |
| 14 | sclc_ucologne_2015_S01512 |
| 15 | sclc_ucologne_2015_S01524 |
| 16 | sclc_ucologne_2015_S01578 |
| 17 | sclc_ucologne_2015_S01728 |
| 18 | sclc_ucologne_2015_S01864 |
| 19 | sclc_ucologne_2015_S02093 |
| 20 | sclc_ucologne_2015_S02120 |
| 21 | sclc_ucologne_2015_S02163 |
| 22 | sclc_ucologne_2015_S02234 |
| 23 | sclc_ucologne_2015_S02246 |
| 24 | sclc_ucologne_2015_S02248 |
| 25 | sclc_ucologne_2015_S02255 |
| 26 | sclc_ucologne_2015_S02286 |
| 27 | sclc_ucologne_2015_S02287 |
| 28 | sclc_ucologne_2015_S02288 |
| 29 | sclc_ucologne_2015_S02289 |
| 30 | sclc_ucologne_2015_S02290 |
| 31 | sclc_ucologne_2015_S02291 |
| 32 | sclc_ucologne_2015_S02294 |
| 33 | sclc_ucologne_2015_S02296 |
| 34 | sclc_ucologne_2015_S02297 |
| 35 | sclc_ucologne_2015_S02298 |
| 36 | sclc_ucologne_2015_S02299 |
| 37 | sclc_ucologne_2015_S02322 |
| 38 | sclc_ucologne_2015_S02328 |
| 39 | sclc_ucologne_2015_S02347 |
| 40 | sclc_ucologne_2015_S02353 |
| 41 | sclc_ucologne_2015_S02375 |
| 42 | sclc_ucologne_2015_S02378 |
2) Based on the threshold, the patients were divided into 23 high risk groups and 19 low risk groups, and m6A risk values and survival data for the high risk groups and low risk groups obtained in 1) above were analyzed using Kaplan-Meier. The results show that patients tested in the high risk group have lower OS or lower candidates than patients tested in the low risk group (fig. 4A). Further, the area under the ROC curve was calculated, with AUCs of the OS in international queues of 1, 3 and 5 years being 0.768, 0.901 and 0.82, respectively (fig. 4B). Further, the results of the C-index analysis showed that the predictive effect of m6A risk values was significantly better than other common clinical parameters such as gender, age, smoking status and SCLC stage (fig. 4C).
2. Prediction of OS in NCC cohorts by m6A model for adjuvant chemotherapy patients receiving restricted stage small cell lung cancer patients
1) 129 patients in the NCC cohort receiving adjuvant chemotherapy were selected (see table 5 for specific information and table 8 for patient numbers), and an OS threshold was determined according to the method 2 (1) in step one, at 0.1054.
Table 8 patient numbers in NCC cohorts with adjuvant therapy
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2) Based on the threshold, patients were classified into 75 out of 129 patients into high risk groups, 54 patients into low risk groups, and m6A risk values and survival data for the high risk and low risk groups obtained in 1) above were analyzed using Kaplan-Meier. The results show that patients under test in the high risk group have lower OS or lower candidates than patients under test in the low risk group (fig. 4D). Further, the area under the ROC curve was calculated, with AUCs of OS of 0.807, 0.68 and 0.67 in international queues for 1, 3 and 5 years, respectively (fig. 4E). Further, the results of the C-index analysis showed that the predictive effect of m6A risk values was significantly better than other common clinical parameters such as gender, age, smoking status and SCLC stage (fig. 4F).
3. In NCC cohorts, m6A model predicts RFS in patients receiving adjuvant chemotherapy for patients with restricted stage small cell lung cancer
1) 129 patients in international cohorts receiving adjuvant chemotherapy were selected (see table 5 for specific information and table 8 for patient numbers), and the RFS threshold was determined as 0.1054 according to the method 2 (1) in step one.
2) Based on the threshold, the patients were divided into 129 patients into high-risk and low-risk groups, and m6A risk values and survival data for the high-risk and low-risk groups obtained in 1) above were analyzed using Kaplan-Meier. The results show that patients in the high risk group have RFS lower or candidates lower than patients in the low risk group (fig. 4G). Further, the area under the ROC curve was calculated, with AUCs for RFS in international queues of 1, 3 and 5 years being 0.708, 0.683 and 0.66, respectively (fig. 4H). Further, the results of the C-index analysis showed that the predictive effect of m6A risk values was significantly better than other common clinical parameters such as gender, age, smoking status and SCLC stage (fig. 4I).
SEQUENCE LISTING
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<120> System for predicting prognosis of patients with restricted stage small cell lung cancer and use thereof
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Claims (8)
1. The use of five genes consisting of G3BP1, METTL5, albh 5, IGF2BP3 and RBM15B as markers is any one of (B1) - (B2):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) And (3) preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer.
2. The use of a panel for detecting the expression levels of five genes consisting of G3BP1, METTL5, albh 5, IGF2BP3 and RBM15B, which is any one of (B1) to (B2):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) And (3) preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer.
3. The use of a panel of five gene expression levels consisting of G3BP1, METTL5, albh 5, IGF2BP3, and RBM15B, and a data processing device, as any one of (B1) to (B2):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) Preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer;
a module is arranged in the data processing device; the module has functions as shown in (a 1) and (a 2) below:
(a1) Taking an isolated localized small cell lung cancer tissue of a group to be detected consisting of localized small cell lung cancer patients as a sample, measuring the expression amounts of the five genes in each sample, and calculating a risk value according to the five gene expression amounts by the following formula: risk value= (0.0906 ×g3BP1 gene expression amount) + (0.4096×mettl5 gene expression amount) - (0.6365 ×albh 5 gene expression amount) - (0.0912 ×igf2BP3 gene expression amount) - (0.0660 ×rbm15B gene expression amount), and classifying the population to be tested into a low risk group and a high risk group according to the risk value;
(a2) Determining prognosis of a test patient from said test population according to the following criteria:
the prognosis of the patient under test in the low risk group is higher or a candidate is higher than the patient under test in the high risk group;
or, the prognosis total survival of the patients under test in the low risk group is longer or a candidate longer than the patients under test in the high risk group;
or, the overall survival rate of prognosis of the patient under test in the low risk group is higher or the candidate is higher than that of the patient under test in the high risk group;
or, the prognosis of the patient to be tested in the low risk group has a relapse-free survival longer than or a candidate longer than the patient to be tested in the high risk group;
or, the prognosis relapse-free survival rate of the patient in the low risk group is higher or the candidate is higher than the patient in the high risk group;
or, the patients in the low risk group benefit from chemotherapy to a higher degree or candidate than the patients in the high risk group.
4. A use according to any one of claims 1-3, characterized in that:
the substance group for detecting the expression levels of five genes consisting of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B consists of c1 or c2:
(c1) A substance capable of specifically binding to the G3BP1, METTL5, albh 5, IGF2BP3 and RBM15B proteins or genes, respectively;
(c2) Primer pairs capable of specifically amplifying the G3BP1, METTL5, albh 5, IGF2BP3 and RBM15B genes, respectively.
5. A system for predicting the therapeutic efficacy or prognosis of patients with restricted stage small cell lung cancer, comprising a system for detecting the expression levels of five genes consisting of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM 15B.
6. The system according to claim 5, wherein: the system for detecting the expression levels of five genes consisting of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B comprises the substance group for detecting the expression levels of five genes consisting of G3BP1, METTL5, ALKBH5, IGF2BP3 and RBM15B in the application of any one of claims 1 to 4.
7. The system of claim 6, wherein: the system further comprises the set of data processing devices in the application of any of claims 3-4.
8. Use of the system of any one of claims 5 to 7, being any one of (b 1) - (b 2):
(b1) Preparing a product for evaluating prognosis of patients with limited stage small cell lung cancer;
(b2) And (3) preparing a product for evaluating the chemotherapy benefit degree of patients with limited small cell lung cancer.
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| Prophylactic cranial radiotherapy improves survival in extensive small cell lung cancer.《BMJ》.2007,第368页. * |
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