CN111763740A

CN111763740A - System for predicting treatment effect and prognosis of neoadjuvant radiotherapy and chemotherapy of esophageal squamous carcinoma patient based on lncRNA molecular model

Info

Publication number: CN111763740A
Application number: CN202010811273.XA
Authority: CN
Inventors: 赫捷; 孙楠; 张超奇; 张国超; 张志慧; 薛丽燕; 薛奇; 王雪霏; 骆玥君; 王�锋; 车云; 方凌凌; 王思慧
Original assignee: Cancer Hospital and Institute of CAMS and PUMC
Current assignee: Cancer Hospital and Institute of CAMS and PUMC
Priority date: 2020-08-13
Filing date: 2020-08-13
Publication date: 2020-10-13
Anticipated expiration: 2040-08-13
Also published as: CN111763740B

Abstract

The invention discloses a system for predicting the curative effect and prognosis of neoadjuvant chemoradiotherapy of an esophageal squamous carcinoma patient based on an lncRNA molecular model. The system for predicting the curative effect and/or prognosis of neoadjuvant radiotherapy and chemotherapy of esophageal squamous carcinoma patients comprises a system for detecting the expression levels of three lncRNA (triple-nucleotide polymorphism) of SCAT1, PRKAG2-AS1 and FLG-AS 1. The system can predict the curative effect of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient and also can predict the prognosis of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient, such as the total survival rate after the prognosis and the survival rate without relapse after the prognosis. The invention has important application value.

Description

System for predicting treatment effect and prognosis of neoadjuvant radiotherapy and chemotherapy of esophageal squamous carcinoma patient based on lncRNA molecular model

Technical Field

The invention belongs to the field of biomedicine, and particularly relates to a system for predicting the curative effect and prognosis of neoadjuvant radiotherapy and chemotherapy of an esophageal squamous carcinoma patient based on an lncRNA molecular model.

Background

Esophageal cancer is one of the most common malignant tumors, and esophageal adenocarcinoma and esophageal squamous cell carcinoma (hereinafter referred to as esophageal squamous cell carcinoma or ESCC) are two major histological types. Esophageal squamous carcinoma is the main pathological type of tumor, accounts for more than 90% of esophageal cancer cases, and accounts for almost half of the global esophageal disease burden. Because of the high postoperative recurrence rate and poor prognosis of esophageal cancer, it has been considered as a highly invasive malignant tumor.

Compared with simple esophagectomy, the newly-assisted chemoradiotherapy combined operation of locally advanced esophageal squamous cell carcinoma remarkably improves the survival rate of patients, the 5-year total survival rate is different from 47% to 60%, and the multidisciplinary comprehensive treatment scheme is recommended as a guideline for ESCC treatment. In fact, there is a great heterogeneity in the prognosis of patients with esophageal squamous carcinoma who received neoadjuvant chemotherapy. Only about one third of the patients showed a complete remission of the pathology (pCR) obtained in the postoperative course, i.e. no residual tumor cells in the primary foci and lymph nodes of the surgical specimens were examined pathologically, whereas pCR was associated with a significant improvement in long-term survival. In contrast, patients who do not obtain complete remission of the pathology do not benefit from neoadjuvant chemotherapy. In addition, they may be at risk for tumor progression and must withstand the unnecessary side effects of neoadjuvant chemotherapy. Therefore, determining which patients can benefit from neoadjuvant chemotherapy before treatment is very important for clinical decision making and helps in the personalized treatment of esophageal cancer.

Several studies have evaluated the accuracy of the examination methods in clinical applications, for example, examining the pCR status of esophageal squamous carcinoma by endoscopic ultrasound and 18 fluoro-deoxyglucose (18F-FDG) positron emission tomography (PET-CT) to detect tumor residues after neoadjuvant chemotherapy; however, a recent meta-analysis shows that these checks are less than satisfactory in accuracy. At present, with the progress of high-throughput sequencing technology, characteristic signal molecules of multiple transcriptomics, particularly multiple mRNAs or micrornas (mirnas), are proved to be powerful biomarkers, and can predict the pathological response of esophageal squamous carcinoma to neoadjuvant radiotherapy and chemotherapy. However, the previous studies have the defects of small sample size, lack of prognosis data and the like, and most of the previous studies are single-center studies, and the prediction value of the markers is limited, so that the research of novel markers based on the large sample data of multiple centers is urgently needed to predict the treatment effect and the long-term survival rate of the patient in the new adjuvant radiotherapy and chemotherapy.

In fact, more than 98% of the human genome is transcribed into non-coding RNAs, and about 76% of the non-coding RNAs are transcribed into long non-coding RNAs (lncrnas). This suggests that lncRNAs may be important potential biomarkers in addition to mRNA and miRNAs. LncRNAs are transcripts of similar mRNAs without protein coding ability, ranging from 200 nucleotides to 100kb in length. More and more studies have shown that abnormal expression of lncRNAs is closely related to the occurrence and prognosis of human cancers, some of which are relevant to disease diagnosis.

Disclosure of Invention

The invention aims to predict the curative effect and prognosis of neoadjuvant radiotherapy and chemotherapy for patients with esophageal squamous carcinoma.

The invention firstly protects a system which can comprise a system for detecting the expression quantity of three lncRNA of SCAT1, PRKAG2-AS1 and FLG-AS 1; the system is used for predicting the curative effect of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient or predicting the prognosis of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient.

The system can specifically comprise a system for detecting the expression levels of three lncRNA of SCAT1, PRKAG2-AS1 and FLG-AS 1.

In any of the above systems, the system for detecting the expression levels of the three incRNAs, namely SCAT1, PRKAG2-AS1 and FLG-AS1, can comprise reagents and/or instruments required for detecting the relative expression levels of the three incRNAs by a fluorescent quantitative PCR method.

Further, the reagents and/or instruments required for detecting the relative expression amounts of the three lncRNAs by the fluorescent quantitative PCR method comprise primer pairs for detecting the relative expression amounts of the three lncRNAs such AS SCAT1, PRKAG2-AS1 and FLG-AS 1.

Furthermore, the reagents and/or instruments required for detecting the relative expression amounts of the three lncRNAs by the fluorescent quantitative PCR method also comprise a primer pair for detecting an internal reference gene. Namely, the relative expression amount of the three lncrnas may be specifically the expression amount of the three lncrnas relative to the reference gene.

The reference gene is GAPDH gene.

Any of the above systems may also include a data processing device; the data processing device is internally provided with a module; the module has the following functions (a1) and/or (a 2):

(a1) taking an isolated esophageal squamous carcinoma tissue of a population to be detected consisting of esophageal squamous carcinoma patients as a specimen, determining the relative expression quantity of the three lncRNA in each specimen, and then calculating the discriminant score according to the relative expression quantity of the three lncRNA according to the following formula: the discriminant score is 0.219+ (2.608 × SCAT1 relative expression) + (-0.685 × PRKAG2-AS1 relative expression) + (-0.542 × FLG-AS1 relative expression), and the population to be detected is divided into a low-grade group and a high-grade group according to the discriminant score;

(a2) determining the curative effect, the pathological complete remission rate, the prognosis recurrence-free survival rate and/or the prognosis overall survival rate of the patient to be tested from the population to be tested according to the following criteria:

"from patients to be tested in the high score cohort" is more effective or candidate higher than "from patients to be tested in the low score cohort";

a higher or more candidate rate of complete remission of pathology "from patients to be tested in the high scoring cohort" than "from patients to be tested in the low scoring cohort";

the overall survival prognosis for "patients tested from the low scoring cohort" is higher or is more candidate than for "patients tested from the high scoring cohort";

the prognostic recurrence-free survival "is higher or more candidate than" test patient from the high scoring cohort ".

The specific method for dividing the population to be detected into a low-score group and a high-score group according to the discriminant score is as follows: determining a threshold value through a 'pROC' software package of R language software, comparing the discriminant score of the patient to be predicted with the esophageal squamous carcinoma patient who receives the new auxiliary chemoradiotherapy to obtain pCR with the size of the threshold value, wherein the patient with the score larger than the threshold value is listed in a high-score group, and the patient with the score smaller than or equal to the threshold value is listed in a low-score group.

The method for determining the threshold value through the 'pROC' software package of the R language software is specifically as follows: and (3) inputting the score of the patient with esophageal squamous carcinoma to be predicted and the matched new auxiliary chemoradiotherapy curative effect information into R language software, and under the algorithm of a 'pROC' software package, automatically calculating a division point with the maximum AUC value by the software, wherein the division point is a threshold value of a high-score group and a low-score group.

The invention also protects the application of any one of the systems, which is any one of (b1) - (b 8):

(b1) preparing a product for evaluating the curative effect of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b2) evaluating the curative effect of the new adjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient;

(b3) preparing a product for evaluating the complete remission rate of the pathology of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b4) evaluating the complete pathological remission rate of the new adjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient;

(b5) preparing a product for evaluating the prognosis relapse-free survival rate of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b6) evaluating the prognosis recurrence-free survival rate of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b7) preparing a product for evaluating the total survival rate of new auxiliary chemoradiotherapy of an esophageal squamous carcinoma patient;

(b8) and (3) evaluating the overall survival rate of the prognosis of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient.

The invention also protects the application of three lncRNAs of SCAT1, PRKAG2-AS1 and FLG-AS1 AS markers, which can be any one of (b1) - (b 10):

(b8) evaluating the overall survival rate of the prognosis of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b9) preparing a product for the prognosis of new adjuvant radiotherapy and chemotherapy of patients with esophageal squamous carcinoma;

(b10) prognosis is carried out on the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient.

The invention also protects the application of the substances for detecting the expression quantity of three lncRNA of SCAT1, PRKAG2-AS1 and FLG-AS1, which can be any one of (b1) - (b 10):

The invention also protects a substance for detecting the expression quantity of three lncRNA of SCAT1, PRKAG2-AS1 and FLG-AS1 and the application of any one of the data processing devices, which can be any one of (b1) to (b 10):

The present invention also protects the application of any of the above data processing devices, which may be any of (b1) - (b 10):

In any of the above applications, the substance for detecting the expression levels of the three incrnas SCAT1, PRKAG2-AS1 and FLG-AS1 may include reagents and/or instruments for detecting the relative expression levels of the three incrnas by a fluorescent quantitative PCR method.

The reference gene is GAPDH gene.

The primer sequences for detecting three lncRNAs of SCAT1, PRKAG2-AS1 and FLG-AS1 in any one of the above tests are specifically shown in Table 2.

Any one of the patients with esophageal squamous carcinoma can be patients with local middle and late esophageal squamous carcinoma.

Any one of the patients with locally advanced esophageal squamous carcinoma can be patients with esophageal squamous carcinoma with stage II or stage III of TNM.

Any one of the above new auxiliary chemoradiotherapy can be synchronous new auxiliary chemoradiotherapy (preoperative synchronous chemoradiotherapy) or sequential new auxiliary chemoradiotherapy (preoperative sequential chemoradiotherapy).

Any one of the isolated esophageal squamous carcinoma tissues can be a sample prepared by embedding the separated esophageal squamous carcinoma tissues of the patient to be predicted with esophageal squamous carcinoma through formalin fixation and paraffin or a frozen section of the separated esophageal squamous carcinoma tissues of the patient to be predicted with esophageal squamous carcinoma.

The GenBank number of any SCAT1 is NR _ 110848.1. Any one of the NR _038926.1 described above has GenBank number NM _ 002426.6. The GenBank number of any FLG-AS1 is NR _ 103778.1.

Based on 28 cases of gene chip results of ESCC endoscope samples before new adjuvant therapy is received in Guangzhou queue, 12 lncRNAs molecules with differential expression more than 2 times are screened out through bioinformatics analysis. Next, qPCR verification was performed on these 12 lncRNAs molecules in the beijing cohort discovery group, and 6 lncRNAs molecules stably and differentially expressed were confirmed. Further, the 6 lncRNAs molecules were subjected to qPCR detection using 67 pre-treatment endoscopic samples from the Beijing cohort training group, and an individualized prediction model based on 3 lncRNAs molecules (SCAT1, PRKAG2-AS1 and FLG-AS1) was successfully constructed, which reached 0.952 in the predicted AUC for pCR in the training group. Meanwhile, the model was successfully validated in ESCC queues from multi-center of beijing, zheng, angyang, with predicted AUCs for pCR in internal and external validation queues (zheng and angyang queues) of 0.856 and 0.817, respectively. Multifactorial analysis shows that the molecular model of lncRNAs is the only independent predictor of pCR. In addition, at the same time, the model can effectively predict the overall survival and disease-progression-free survival of the patients receiving nCRT treatment, and the overall survival and relapse-free survival of the patients in the low discriminant score group are remarkably prolonged (P < 0.05). This is the first established and verified by multicenter verification individualized lncRNAs molecular model that can be used for clinical prediction of esophageal squamous carcinoma neoadjuvant chemoradiotherapy pCR and prognosis. The successful construction of the model can greatly improve the diagnosis and treatment level of ESCC and practically and powerfully promote the implementation of ESCC accurate medical treatment. The invention has important application value.

Drawings

FIG. 1 is a flow chart of research for constructing lncRNA prediction model for efficacy and prognosis of neoadjuvant chemoradiotherapy of esophageal squamous carcinoma. The study is a national multi-center study, including Guangzhou (Zhongshan university tumor center), Beijing (national cancer center), Zhengzhou (affiliated tumor hospital of Zhengzhou university), and Anyang (tumor hospital of Anyang city). pCR represents complete remission of pathology, < pCR represents complete remission of nonpathology, qPCR represents real-time fluorescent quantitative PCR, and FLDA represents Fisher's linear discriminant analysis for step-by-step variable screening.

FIG. 2 shows the overall survival rate and recurrence-free survival rate of two groups of patients with pCRs and < pCRs after neoadjuvant radiotherapy and chemotherapy for esophageal squamous carcinoma. The overall survival rates of pCRs and < pCRs in the beijing queue discovery group (a), the beijing queue training group (B), the beijing internal validation queue (C), the entire beijing queue (D), and the external validation queue (E) were compared. The no recurrence survival of pCRs and < pCRs in the beijing cohort discovery group (F), the beijing cohort training group (G), the beijing internal validation cohort (H), and the entire beijing cohort (I) were compared.

Figure 3 is the lncRNA molecular expression profile of endoscopic samples differential expression before guangzhou cohort treatment. A is volcano graph of differentially expressed lncRNAs molecules, and all qPCR results are processed by log2(X + 1); b is a heatmap of pCR and 12 lncRNAs differentially expressed by < pCR patients.

FIG. 4 shows the expression levels of SCAT1(A), H19(B), LINC00592(C), PRKAG2-AS1(D), FLG-AS1(E), GAS6-AS1(F), SYNPR-AS1(G), ZNF503-AS1(H), LINC00960(I), LINC00551(J), LOC349160(K) and SOX2-OT (L) detected in the Beijing cohort discovery group using qPCR technology. P <0.05, P <0.01, qPCR represents real-time fluorescent quantitative PCR.

FIG. 5 is the construction of 3 lncRNA molecular spectra of a prediction model of esophageal squamous carcinoma neoadjuvant chemoradiotherapy. A is the heat map of the spectra of the 3 lncRNA molecules screened and the corresponding discriminant scores. And B is characteristic operating curve analysis (ROC) of the testees of 3 lncRNA molecular spectrums in the Beijing cohort training group. C. The distribution of the scores of the pCRs and the discriminant of < pCRs in the Beijing cohort training set. Denotes P < 0.0001.

FIG. 6 shows ROC curves of 3 IncRNA molecules SCAT1(A), PRKAG2-AS1(B) and FLG-AS1(C) for pCR prediction using qPCR in Beijing cohort training set.

FIG. 7 shows the ROC curves for predicting pCR in Guangzhou cohort (A), Beijing cohort discovery group (B), Zhengzhou cohort (C) and Anyang cohort (D) and the discriminant scores for pCRs and < pCRs for Guangzhou cohort (E), Beijing cohort discovery group (F), Zhengzhou cohort (G) and Anyang cohort (H). Represents P <0.05, represents P <0.01, represents P < 0.001.

FIG. 8 shows the predicted effect of 3 IncRNA molecular profiles in the internal validation cohort, the entire Beijing cohort and the external validation cohort. Characteristic working curve analysis (ROC) of the subjects in the internal validation cohort (a), the entire beijing cohort (C) and the external validation cohort (E) were performed for 3 lncRNA molecular profiles, respectively. And discriminant scores for pCRs and < pCRs in the internal validation queue (B), the entire Beijing queue (D), and the external validation queue (F). And represents P <0.0001 and P <0.01, respectively.

FIG. 9 is an analysis of Overall Survival (OS) of the prediction model of lncRNA molecules for predicting prognosis of neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma. Respectively are Kaplan-Meier survival curves of the OS obtained based on discriminant scores in a Beijing queue training group (A), an internal verification queue (B), a whole Beijing queue (C) and an external verification queue (D).

FIG. 10 is a recurrence-free survival Rate (RFS) analysis of prediction model of lncRNA molecules for prognosis of neoadjuvant chemoradiotherapy of esophageal squamous carcinoma patients. Respectively are Kaplan-Meier survival curves of RFS obtained based on discriminant scores of a Beijing queue training group (A), an internal verification queue (B) and the whole Beijing queue (C).

Detailed Description

The present invention is described in further detail below with reference to specific embodiments, which are given for the purpose of illustration only and are not intended to limit the scope of the invention. The examples provided below serve as a guide for further modifications by a person skilled in the art and do not constitute a limitation of the invention in any way.

The experimental procedures in the following examples, unless otherwise indicated, are conventional and are carried out according to the techniques or conditions described in the literature in the field or according to the instructions of the products. Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.

In the following examples, each esophageal squamous carcinoma patient is informed of consent.

The quantitative tests in the following examples, all set up three replicates and the results averaged.

Complete remission of pathology (pCR) was defined as the primary lesion of esophageal squamous carcinoma and no invasive tumor cell residue in the pathological examination of lymph node surgical specimens.

Overall Survival (OS) is defined as the time from group entry to death or last follow-up due to any cause.

Overall survival is defined as the probability that a patient will survive from a particular time point to a particular time.

Recurrence Free Survival (RFS) is defined as the time from entry to local recurrence, or distant metastasis, or death from any cause, or last follow-up.

Recurrence-free survival is defined as the probability that a patient will have not had a local recurrence or metastasis by a certain time since the patient's follow-up from a certain time point.

The middle-stage and late-stage esophageal squamous carcinoma patients in the following examples refer to TNM staged II-III esophageal squamous carcinoma patients.

In the examples below, the clinical pathology of each cohort of patients is shown in table 1.

TABLE 1 clinical pathological characteristics of the cohorts of patients

Examples

I, constructing lncRNA model of new adjuvant chemoradiotherapy curative effect and prognosis of esophageal squamous carcinoma

The flow chart of the lncRNA model for constructing the curative effect and prognosis of the esophageal squamous cell carcinoma neoadjuvant chemoradiotherapy is shown in figure 1. The method comprises the following specific steps:

1. discovery stage-screening of lncRNA molecular expression profile of endoscope sample of patients with different curative effects

A total of 244 patients with esophageal squamous carcinoma who received neoadjuvant chemotherapy and surgical resection were included in this study. 164 patients in the Beijing cohort were divided into 3 groups, 30 in the Beijing cohort discovery group, 67 in the Beijing cohort training group, and 67 in the Beijing cohort internal validation group. And 52 cases of multi-center external validation queues. Guangzhou queue 28 cases. The clinical pathology of each cohort of patients is shown in table 1. Pathological examination of esophageal resection specimens after treatment showed that the pCR positive rate was 34.4% (84 cases/244 cases). Differences in OS and RFS between pCR groups and < pCR group were assessed using Kaplan-Meier survival analysis. The results show (as shown in fig. 2) that both OS and RFS were worse than pCR group in each queue.

Dividing patients into treatment responsive patients (namely pCR patients) and treatment non-responsive patients (namely < pCR patients) according to the outcome of nCRT treatment, and then screening and verifying the differential expression lncRNA molecules by analyzing the lncRNA molecule expression profile panorama of the endoscope samples of the pCR patients and the < pCR patients before treatment, thereby providing a basis for further constructing lncRNA molecule models.

(1) Screening of lncRNA molecular expression profiles of endoscope samples of patients with different curative effects

To investigate the expression profile of lncRNA in the pCRs and < pCRs pre-biopsy specimens from the neoadjuvant chemoradiotherapy patients, the inventors of the present invention investigated the differences between pCR and < pCR in the Guangzhou cohort of GSE45670 chips. 4187 lncRNAs were identified after mapping Affymetrix human genome U133 Plus 2.0 array Probe set ID to annotation files. In 28 tumor specimens in the GSE45670 chip, lncRNAs were expressed at a lower level than mRNAs. After log2 transformation, the mean expression values of lncRNAs were 5.33 and the mean expression values of mRNAs were 7.97. In order to make the analysis more suitable for clinical application, the invention only incorporates lncRNAs with high expression level, and eliminates lncRNAs with average expression value lower than 5.33. Finally, 1878 lncRNAs were screened for further analysis. There were 12 lncRNAs differentially expressed between pCR and < pCR (P <0.05), of which 3 lncRNAs (SCAT1, H19 and LINC00592) were up-regulated and 9 lncRNAs (PRKAG2-AS1, FLG-AS1, GAS6-AS1, SYNPR-AS1, ZNF503-AS1, LINC00960, LINC00551, LOC349160 and SOX2-OT) were down-regulated in pCR (AS shown in FIG. 3).

(2) Validation of differentially expressed lncRNA molecules

To validate the lncRNA molecules screened earlier, the inventors retrospectively collected pre-treatment endoscopic FFPE samples (11 pCR patients and 19 < pCR patients) of 30 ESCC patients receiving ncr treatment from 2007 to 2018 in the national cancer center/chinese medical academy of sciences oncology hospital (beijing cohort discovery group).

The FFPE sample RNA extraction technology is utilized to separate and prepare total RNA for paraffin coils with the diameter of 10 mu m of each sample, the qPCR technology is further adopted to detect the relative expression quantity of 12 lncRNA molecules in esophageal squamous cell carcinoma tissues of patients with esophageal squamous cell carcinoma, and the results show that 6 lncRNA molecules are in pCRs and pCRs in total<There was significant differential expression between pCRs (P <0.05), including SCAT1, LINC00592, PRKAG2-AS1, FLG-AS1, SYNPR-AS1 and SOX2-OT (AS shown in FIG. 4). The specific detection method comprises the following steps: performing RNA extraction on the obtained FFPE sample; 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 Ct_Gene-Ct_GAPDH. The primer sequences for detecting each lncRNA molecule of interest and GAPDH gene are shown in Table 2.

TABLE 2 primer sequences

2. Construction of lncRNA molecular model for training stage-esophagus squamous carcinoma new auxiliary radiotherapy and chemotherapy curative effect and prognosis prediction

In order to establish an lncRNA molecular model for predicting pCRs, the inventor of the invention detects the expression conditions of the 6 lncRNA molecules in 67 samples of a Beijing cohort training set by using a qPCR technology. Based on the qPCR result of the verified differential expression lncRNA molecules, and by utilizing a Fisher's linear discriminant analysis method of step-by-step variable screening, a prediction model based on SCAT1, PRKAG2-AS1 and FLG-AS1 is constructed, and the specific formula of the model discriminant is AS follows: and the discriminant score is 0.219+ (2.608 × SCAT1 relative expression) + (-0.685 × PRKAG2-AS1 relative expression) + (-0.542 × FLG-AS1 relative expression), and the population to be detected is divided into a low-grade group and a high-grade group according to the discriminant score.

The specific method comprises the following steps: determining a threshold value through a 'pROC' software package of R language software, comparing the discriminant score of the patient to be predicted with the esophageal squamous carcinoma patient who receives the new auxiliary chemoradiotherapy to obtain pCR with the size of the threshold value, wherein the patient with the score larger than the threshold value is listed in a high-score group, and the patient with the score smaller than or equal to the threshold value is listed in a low-score group.

The threshold value determined according to the above method was 0.065, and patients with esophageal squamous carcinoma who received neoadjuvant radiotherapy and chemotherapy with a score of more than 0.065 were included in the high-score group, and patients with esophageal squamous carcinoma who received neoadjuvant radiotherapy and chemotherapy with a score of less than or equal to 0.065 were included in the low-score group.

The heatmap and discriminant scores for the 3 lncRNA molecules selected in this model are shown in a of fig. 5.

In the Beijing cohort training group, with 0.065 as the diagnostic cutoff, 22 of 22 pCR patients were found to be correctly predicted (sensitivity 100%), 37 of 45 < PCR patients were correctly predicted (specificity 82.2%), overall accuracy was 88.1% (59/67), and the area under the receiver operating characteristic curve (ROC) was 0.952[ P <0.001, 95% confidence interval 0.906-0.997] (as shown in B and C in FIG. 5).

Further analysis revealed that the predictive power of the model consisting of 3 lncRNA molecules was superior to the independent predictive effect of any single molecule [ independent diagnostic AUC values for SCAT1, PRKAG2-AS1 and FLG-AS1 were 0.779(P <0.001, 95% confidence interval 0.663-0.894), 0.873(P <0.001, 95% confidence interval 0.791-0.955) and 0.808(P <0.001, 95% confidence interval 0.704-0.912), respectively ] (AS shown in FIG. 6).

To verify the predictive power of the predictive model consisting of 3 lncRNA molecules, the model was examined in a cohort of people in the guangzhou state. The results indicated a diagnostic AUC value of 0.791(P ═ 0.010, 95% confidence interval of 0.619-0.964, as shown in figure 7). Meanwhile, in 30 patients in the beijing cohort discovery group, the diagnostic AUC value for the model to predict the effect of neoadjuvant chemoradiotherapy was 0.885(P ═ 0.001, 95% confidence interval 0.721-1.000) (as shown in fig. 7). These results preliminarily confirm that the prediction model for lncRNA molecules is reliable.

3. Verification stage-effectiveness verification of esophageal cancer neoadjuvant chemoradiotherapy curative effect and prognosis lncRNA molecular prediction model

(1) Validation of pCR prediction capability in Peking cohorts validation groups and entire Peking cohorts

In order to verify the curative effect of the esophageal squamous carcinoma neoadjuvant chemoradiotherapy and the prediction performance of the prognosis lncRNA molecular model, the inventor retrospectively collects 67 pre-treatment endoscope FFPE samples (23 pCR patients and 44 pCR patients) of ESCC patients receiving nCRT treatment in the national cancer center/Chinese medical academy of sciences tumor hospital (Beijing cohort verification group) from 2007 to 2018, also utilizes the FFPE sample RNA extraction technology to separate and prepare total RNA, and further adopts the qPCR technology to detect the target difference lncRNA molecules. The discriminant score for each sample was calculated based on the model discriminants, the predicted value of the model was evaluated with the therapeutic effect pCR as the end event and the discriminant scores for pCR patients and < pCR patients were compared.

The results show that the model can still stably predict the neoadjuvant chemoradiotherapy curative effect of the patient, the sensitivity is 95.7%, the specificity is 72.7%, the total prediction accuracy of the model is 80.6%, and the AUC value is 0.856(P <0.001, and the 95% confidence interval is 0.764-0.947) (as shown in A in figure 8). In addition, the predicted effect of the model was further evaluated in the entire Beijing cohort (including the Beijing cohort discovery group, the training group, and the internal validation group). The results show that the model exhibits very stable predictive performance with overall accuracy of 79.3% and AUC values of 0.800(P <0.001 with 95% confidence interval of 0.729-0.871) (as shown in C in fig. 8).

Discriminant score distributions between pCRs and < pCRs in the beijing queue validation group and the entire beijing queue are shown in fig. 8 as B and D (P < 0.05).

(2) Validation of pCR predictive Capacity in external validation set

To further evaluate the reproducibility and stability of the lncRNA molecular prediction model in the chinese population, two independent institutions, zheng state cohort and angyang cohort from the high incidence area of esophageal squamous cell carcinoma (south river, china), were used as the external multicenter cohort validation group. Similarly, total RNA was prepared by FFPE sample RNA extraction techniques and qPCR techniques were used to detect gene expression incorporated into the model, and the score for each sample was calculated using model discriminants to further validate the predictive power of the model for pCR.

In the external cohort, the model was found to successfully predict 14 of 17 pCR patients (sensitivity 82.4%) and 27 of 35 < pCR patients (specificity 77.1%) with a total accuracy of 78.8%. The AUC values for the three lncRNA molecular models in the outer cohort were 0.817(P <0.001, 95% confidence interval 0.700-0.933) (as shown in E in fig. 8). There was a significant difference in the discrimination scores between pCRs and < pCRs in the external cohort (P ═ 0.0012, shown as F in fig. 8).

In addition, the inventors of the present invention also verified model efficacy in zheng state cohort and angyang cohort, respectively. In these two separate cohorts, the AUC values for the model were 0.783(P ═ 0.016, 95% confidence interval 0.613-0.954) and 0.850(P ═ 0.007, 95% confidence interval 0.683-1.000), respectively (as shown in fig. 7C and D). These data indicate that the lncRNA molecular model can stably predict pCR of esophageal squamous cell carcinoma patients receiving new adjuvant radiotherapy and chemotherapy in people in different areas of China.

Secondly, verifying the evaluation capability of the model constructed in the first step on long-term prognosis

To explore the long-term prognostic assessment ability of the model for patients, the present invention will further assess the predictive ability of the model for patients' Overall Survival (OS) and Recurrence Free Survival (RFS).

Collecting age, sex, tumor part, tumor differentiation degree, clinical TNM stage, chemotherapy scheme and lncRNA molecule of a patient, and analyzing influence factors of pCR after neoadjuvant chemoradiotherapy of esophageal squamous carcinoma by applying single-factor logistic regression.

The invention finds that a prediction model established based on 3 lncRNA molecules is the only factor (P <0.05, shown in Table 3) which is obviously related to pCR in a Beijing queue training group, a Beijing queue verification group, a whole Beijing queue and an external multi-center queue verification group. Furthermore, multifactorial logistic regression analysis showed that the predictive model built based on 3 lncRNA molecules was the only independent factor significantly associated with pCR in the multicenter cohort after correction of other parameters (P <0.05, as shown in table 3).

TABLE 3 analysis of single and multifactorial components of patients with esophageal squamous cell carcinoma who have completely relieved their pathology after new adjuvant radiotherapy and chemotherapy in different cohorts

Note:^achi-square test or Fisher's exact test,^blogistic stepwise regression analysis, c1, platinum drug in combination with paclitaxel; 2, platinum drug combined with fluorouracil; 3,platinum drugs are combined with other drugs.

Since patients who acquired pCR after neoadjuvant chemoradiotherapy of esophageal squamous carcinoma had a significant survival benefit compared to < pCR patients, it is speculated that 3 lncRNA molecular models might also be used to predict patient survival. To verify this hypothesis, the Kaplan-Meier survival analysis was first used to estimate the relationship between the discriminant score for the lncRNA prediction and the OS of the Beijing cohort training set. Patients were classified into high-scoring and low-scoring groups based on discriminant scores of 3 lncRNA molecular models with 0.065 as a cutoff value. The OS of the high score group is significantly lengthened (P ═ 0.0072, HR 0.2858, 95% confidence interval 0.1302-0.6270 as shown in a in fig. 9).

In order to verify the prediction effect of the model, the relationship between discriminant scores predicted by the 3 lncRNA molecular models and survival data of the validation cohort was further analyzed. When the cutoff value was 0.622, the patients with low predicted scores were worse than the OS of the patients with high predicted scores in the inside-cohort validation group of beijing (P ═ 0.0260, HR 0.3853, 95% confidence interval 0.1600-0.9280, as shown in fig. 9B). Similar results were observed for the entire beijing cohort population (P ═ 0.0144, HR 0.3854, 95% confidence intervals 0.2120-0.7007, as shown by C in figure 9). In the external validation queue, liveness analysis also confirmed that the OS of the high discriminant packets were significantly longer than the low discriminant packets (P ═ 0.0144, HR 0.3100, 95% confidence interval 0.1258-0.7636, as shown in D in fig. 9). Subsequently, the model was evaluated for its ability to predict RFS in the beijing cohort. Patients in the high scoring cohort showed significantly better RFS than patients in the low scoring cohort (P <0.05, as shown in figure 10), whether in the beijing cohort training group, the internal validation group, or in the entire beijing cohort.

The present invention has been described in detail above. It will be apparent to those skilled in the art that the invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with reference to specific embodiments, it will be appreciated that the invention can be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The use of some of the essential features is possible within the scope of the claims attached below.

Claims

1. A system which comprises a system for detecting the expression level of three lncRNA of SCAT1, PRKAG2-AS1 and FLG-AS 1; the system is used for predicting the curative effect of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient or predicting the prognosis of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient.

2. The system of claim 1, wherein: the system for detecting the expression quantity of the three lncRNAs such AS SCAT1, PRKAG2-AS1 and FLG-AS1 comprises reagents and/or instruments required for detecting the relative expression quantity of the three lncRNAs by a fluorescent quantitative PCR method.

3. The system of claim 1 or 2, wherein: the system also includes a data processing device; the data processing device is internally provided with a module; the module has the following functions (a1) and/or (a 2):

4. Use of the system of any one of claims 1 to 3, being any one of (b1) - (b 8):

5. The system of any one of claims 1 to 3 or the use of claim 4, wherein: the esophageal squamous carcinoma patient is a local middle and late esophageal squamous carcinoma patient.

6. The system or use of claim 5, wherein: the locally advanced esophageal squamous carcinoma patient is an esophageal squamous carcinoma patient with TNM stage II or stage III.

The application of three lncRNAs of SCAT1, PRKAG2-AS1 and FLG-AS1 AS markers is any one of (b1) - (b 10):

8. The application of the substance for detecting the expression quantity of three lncRNA of SCAT1, PRKAG2-AS1 and FLG-AS1 is any one of (b1) to (b 10):

9. The substance for detecting the expression level of three lncRNA of SCAT1, PRKAG2-AS1 and FLG-AS1 and the application of the data processing device described in claim 3 are any one of (b1) - (b 10):

10. The use of the data processing apparatus as claimed in claim 3, being any one of (b1) - (b 10):