CircRNA markers for early diagnosis of lung adenocarcinoma and application thereof

[0020] Example 1

[0021] 1 Clinical sample collection:

[0022] Sixty pairs of paraffin tissue specimens of early (stage IA) lung adenocarcinoma and their paired normal lung tissues were collected from Zhongda Hospital Affiliated to Southeast University during 2013-2014. All patients signed an informed consent to participate in the scientific research.

[0023] 2 circRNA quantitative PCR experiment:

[0024] 2.1 RNA extraction and quantitative PCR

[0025] Total RNA was extracted from tissues using Trizol, and RNA was quantified using a microvolume spectrophotometer.

[0026] 2.2 Primers

[0027] A set of circRNAs provided by the present invention includes 8 circRNAs including circRNA_100323, circRNA_69774, circRNA_403738, circRNA_404449, circRNA_104855, circRNA_100983, circRNA_1011969, and circRNA_006718. They were all selected from the results of circRNA chip detection in patients with early lung adenocarcinoma, with GAPDH as the internal reference gene (SEQ ID No: 27), and the primer sequences are shown in Table 1:

[0028] Table 1 Primer sequences used in quantitative PCR

[0029]

[0030]

[0031] 2.3 Reverse transcription reaction

[0032] The TakaRa PrimeScript RT reagent Kit kit was used for reverse transcription. The initial amount of RNA was 1 μg. The amount of reverse transcription reaction components was shown in Table 2. The reaction conditions were: 37°C for 15 minutes, 85°C for 5 sec, and then stored at low temperature for later use.

[0033] Table 2 Reverse transcription reaction system composition

[0034]

[0035] 2.4 Real-time quantitative PCR reaction

[0036] The real-time quantitative PCR used the SYBR Premix Ex Taq (Tli RnaseH Plus) kit from TaKaRa Company, and the quantitative instrument was CFX96 real-time PCR instrument from BioRad Company. The reaction system is shown in Table 3, the reaction conditions are as shown in Table 5, and each real-time quantitative PCR reaction was repeated three times.

[0037] Table 3 Real-time quantitative PCR reaction system

[0038]

[0039] The reaction program is: 95°C for 2min; 95°C for 30sec, 56°C for 50sec, 40 cycles.

[0040] The data are expressed in the form of mean ± standard deviation, and SPSS 18.0 software was used for statistical analysis of the data. Unless otherwise specified. The paired sample t-test method was used to analyze the differences in the expression of circRNAs between lung adenocarcinoma and its paired normal lung tissues. All P values ​​were two-sided, and P<0.05 was considered statistically significant.

[0041] 2.5 Evaluation of the ability of 8 circRNAs to distinguish lung adenocarcinoma and its paired normal lung tissue

[0042]We utilized ROC curves to evaluate the ability of these 8 to distinguish lung adenocarcinoma from its paired normal lung tissue. The AUC of a single circRNA for the diagnosis of lung adenocarcinoma was 0.53-0.75, the sensitivity was 54.4%-77.4%, and the specificity was 49.1%-77.5% (Table 4). We combined these 8 circRNAs for the diagnosis of 30 pairs of lung adenocarcinoma samples, and the AUC value can reach 0.854 ( figure 1 ), after choosing a suitable threshold, the sensitivity and specificity were 80.1% and 83.3%, respectively.

[0043] Table 4 The expression of 8 circRNAs in the training set (30 pairs of samples)

[0044]

[0045] 2.6 Evaluation of the ability of the four preferred circRNAs to distinguish lung adenocarcinoma and its paired normal lung tissue

[0046] Through linear logistic regression analysis, we analyzed and optimized 4 circRNAs from 8 circRNAs. We used ROC curves to evaluate the ability of 4 to distinguish lung adenocarcinoma from its paired normal lung tissue in the training set (the same 30 pairs of samples used as in 2.5). The AUC of these four circRNAs combined to distinguish early lung adenocarcinoma from paired normal lung tissue can reach 0.820 ( figure 2 shown), close to the combined detection level of 8 circRNAs, and significantly higher than the AUC of these 4 circRNAs alone in the diagnosis of early lung adenocarcinoma (P<0.05). After selecting an appropriate threshold, the ROC curve showed that the sensitivity and specificity of the four circRNAs in the diagnosis of early lung adenocarcinoma were 83.3% and 80.0%, which were significantly higher than the sensitivity and specificity of a single circRNA for the diagnosis of lung adenocarcinoma (P <0.05).

[0047] Table 5 Diagnostic results of 4 circRNAs in the validation set (30 pairs of samples)

[0048]

[0049] We verified the detection characteristics of these four circRNAs screened from the training set in another set of 30 lung adenocarcinoma samples and their paired normal lung tissues (validation set). These four circRNAs were significantly differentially expressed in lung adenocarcinoma and its paired normal lung tissues (P<0.001). The AUC of a single circRNA for the detection of lung adenocarcinoma was 0.73-0.82, the sensitivity was 65.6%-79.3%, and the specificity was 67.9%-76.3% (Table 5). The AUC of 60 pairs of lung adenocarcinoma samples jointly detected by these 4 circRNAs can reach 0.841 ( image 3 ), after selecting an appropriate threshold, the ROC curve showed that its sensitivity was 83.4%, and its specificity was 73.5%, which were significantly higher than the detection ability of any single circRNA (P<0.05). Moreover, this group of circRNAs has similar detection capabilities in two independent samples (training set and validation set), indicating that this group of circRNAs can be used as biomarkers for early detection of lung adenocarcinoma.