circRNA-miRNA-mRNA Regulatory Network for Non-Small Cell Lung Cancer Diagnosis and Its Application

Abstract

The present invention discloses a circRNA-miRNA-mRNA regulatory network for the diagnosis of non-small cell lung cancer and its applications. The characteristics are that it is a circRNA-miRNA-mRNA regulatory network, the circRNA gene is hsa_circ_0061235, the miRNA gene is hsa-miR-3180-5p, and the mRNA gene is PPM1L; the application of the circRNA-miRNA-mRNA regulatory network in the preparation of early diagnosis or detection drugs and kits for non-small cell lung cancer; the drugs or kits promote the expression of hsa_circ_0061235 and / or PPM1L and simultaneously inhibit the expression of the hsa-miR-3180-5p gene in the early diagnosis or detection of lung cancer; the advantages are strong specificity and high sensitivity.

Description

Technical Field

[0001] The present invention relates to the technical field of lung cancer auxiliary diagnosis, in particular to a circRNA-miRNA-mRNA regulatory network for non-small cell lung cancer diagnosis and its application. Background Art

[0002] Global cancer data in 2020 showed that lung cancer is the cancer type with the highest mortality rate. Non-small cell lung cancer (NSCLC) is the main pathological type of lung cancer, accounting for about 85% of lung cancer cases. According to statistics, the five-year survival rate of NSCLC patients is only 20%-30%. Because NSCLC is often asymptomatic or has non-obvious symptoms in the early stage, patients are already in the advanced stage at the first diagnosis and lose the opportunity for surgery. At the same time, the treatment options for NSCLC are limited, resulting in poor prognosis for patients. Therefore, early screening of lung cancer is crucial for improving the survival rate and reducing the mortality rate of lung cancer.

[0003] Currently, non-invasive screening of early lung cancer can be based on low-dose computed tomography (CT) and liquid biopsy biomarkers. Among them, liquid biopsy has advantages in early screening, measuring treatment response, and predicting the prognosis of lung cancer due to its minimally invasive, real-time monitoring, and easy access. Clinically common tumor markers for liquid biopsy include not only carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 19 fragment (CYFRA21-1), but also circulating tumor cells (CTCs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and exosomes. In the past two decades, a large number of literatures have shown that circulating miRNAs, lncRNAs, and circRNAs have broad potential in the early detection, treatment stage, and prognosis detection of non-small cell lung cancer.

[0004] A large number of studies have shown that non-coding RNAs (such as miRNAs, lncRNAs, and circRNAs), as non-invasive biomarkers for liquid biopsy, play important roles in cancer development. Competing endogenous RNA (ceRNA), as a brand-new gene expression regulation mode, is more delicate and complex than other regulatory networks, involving more abundant RNA molecules, and plays important roles in various diseases, especially cancer. Recent studies have confirmed that the circRNA-miRNA-mRNA signal cascade based on the ceRNA mechanism is involved in tumorigenesis and development. Therefore, the circRNA-miRNA-mRNA regulatory network is considered to have the potential to be a new tumor biomarker, which has higher sensitivity and specificity compared with simple circRNAs or miRNAs. Recent studies have shown that circRNAs bind to target miRNAs through miRNA response elements (MREs) to de-repress miRNAs from their target tumor-related genes, playing important roles in the occurrence and development of NSCLC. For example, it has been reported that in NSCLC tissues, circ-CPA4 upregulates the expression of programmed cell death ligand-1 (PD-L1) by sponging let-7 miRNA. This regulatory network inactivates CD8 + T cells in the tumor microenvironment, affecting the growth, stem cell characteristics, and drug resistance of lung cancer cells. Thus, the circ-CPA4-let-7 miRNA-PD-L1 regulatory network plays an important role in lung cancer.

[0005] Therefore, considering the important role of the circRNA-miRNA-mRNA signal cascade in NSCLC, we believe that the circRNA-miRNA-mRNA regulatory axis reflects a more comprehensive gene regulatory network and may have higher sensitivity and specificity for early diagnosis of NSCLC. Currently, there are few research reports on ceRNA network analysis related to non-small cell lung cancer at home and abroad. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a circRNA-miRNA-mRNA regulatory network with strong specificity and high sensitivity for the diagnosis of non-small cell lung cancer and its application, wherein the expression level of hsa-miR-3180-5p is negatively correlated with the incidence of non-small cell lung cancer, and the expression levels of hsa_circ_0061235 and PPM1L are positively correlated with the risk of non-small cell lung cancer.

[0007] The technical solution adopted by the present invention to solve the above technical problems is as follows:

[0008] 1. A circRNA-miRNA-mRNA regulatory network for the diagnosis of non-small cell lung cancer, wherein the circRNA gene in the circRNA-miRNA-mRNA regulatory network is hsa_circ_0061235, the miRNA gene is hsa-miR-3180-5p, and the mRNA gene is Protein phosphatase Mg 2+ / Mn 2+ dependent1L (Protein phosphatase Mg 2+ / Mn 2+ dependent1L, PPM1L).

[0009] 2. Application of the above circRNA-miRNA-mRNA regulatory network for the diagnosis of non-small cell lung cancer in the preparation of early diagnosis or detection drugs for non-small cell lung cancer.

[0010] Furthermore, the drug promotes the expression of hsa_circ_0061235 and / or PPM1L and simultaneously inhibits the expression of the hsa-miR-3180-5p gene in the early diagnosis or detection of lung cancer.

[0011] 3. Application of the above circRNA-miRNA-mRNA regulatory network for the diagnosis of non-small cell lung cancer in the preparation of early diagnosis or detection kits for non-small cell lung cancer.

[0012] Furthermore, the kit includes

[0013] Specific forward primer for fluorescence quantitative PCR of hsa_circ_0061235: 5’- TGTTCACGCTAGCCAACCTA -3’;

[0014] Specific reverse primer for fluorescence quantitative PCR of hsa_circ_0061235: 5’- TCATCATTCACAGCTTCCCG -3’;

[0015] Specific stem-loop primer for reverse transcription of hsa-miR-3180-5p: 5’- CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCGACGTGG -3’;

[0016] Specific forward primer for fluorescence quantitative PCR of hsa-miR-3180-5p: 5’- TCGGCAGGCTTCCAGACGCTCC -3’;

[0017] PPM1L fluorescence quantitative PCR specific upstream primer: 5’- GATAGGAAGAATTCATGCAGGTGGA -3’;

[0018] PPM1L fluorescence quantitative PCR specific downstream primer: 5’- TCAGCTGCAGTTGTTGAAAAAG -3’.

[0019] Furthermore, the expression level of the hsa-miR-3180-5p gene is negatively correlated with the incidence of non-small cell lung cancer, and the expression levels of the hsa_circ_0061235 and PPM1L genes are positively correlated with the risk of lung cancer.

[0020] Compared with the prior art, the advantages of the present invention are as follows: The present invention discloses a circRNA-miRNA-mRNA regulatory network for the diagnosis of non-small cell lung cancer. This regulatory network consists of a circRNA (hsa_circ_0061235), a miRNA (hsa-miR-3180-5p), and an mRNA (PPM1L). hsa_circ_0061235 and PPM1L are highly expressed in the serum of non-small cell lung cancer patients, while hsa-miR-3180-5p is lowly expressed in the serum of non-small cell lung cancer patients. The mechanism may be that hsa_circ_0061235 acts as a molecular sponge for hsa-miR-3180-5p, specifically binds to hsa-miR-3180-5p, and then regulates the expression of the downstream target gene PPM1L of hsa-miR-3180-5p, thereby affecting the progression of non-small cell lung cancer. Therefore, the specific circRNA-miRNA-mRNA network, combined with hsa_circ_0061235, hsa-miR-3180-5p, and PPM1L, can conveniently and quickly detect non-small cell lung cancer at the molecular level, with high detection efficiency and strong specificity. The combination of circRNA, miRNA, and mRNA has higher sensitivity and specificity for the diagnosis of NSCLC, which is an innovative use for the auxiliary diagnosis, detection, and screening of non-small cell lung cancer. Description of the Drawings

[0021] Figure 1 shows the top 5 up-regulated miRNAs screened from non-small cell lung cancer patients by Small RNA deep sequencing;

[0022] Figure 2 shows the construction of the circRNA-miRNA-mRNA network using Cytoscape software;

[0023] Figure 3 shows the diagnostic ability of different combinations of serum hsa_circ_0061235, hsa-miR-3180-5p, and PPM1L to distinguish healthy individuals from non-small cell lung cancer through ROC analysis;

[0024] Figure 4 shows the diagnostic ability of different combinations of serum hsa_circ_0061235, hsa-miR-3180-5p, and PPM1L to distinguish benign lung tumors from non-small cell lung cancer through ROC analysis;

[0025] Figure 5 shows the diagnostic ability of traditional tumor markers (CEA, NSE, and CYFRA21-1) to distinguish benign lung tumors and non-small cell lung cancer through ROC analysis;

[0026] Figure 6 shows the diagnostic ability of hsa_circ_0061235, hsa-miR-3180-5p, and their combinations to distinguish non-small cell lung cancer with or without lymph node metastasis through ROC analysis;

[0027] Figure 7 shows the diagnostic ability of hsa_circ_0061235, hsa-miR-3180-5p, and their combinations to distinguish non-small cell lung cancer with or without distant metastasis through ROC analysis;

[0028] Figure 8 shows the diagnostic ability of hsa_circ_0061235, hsa-miR-3180-5p, and PPM1L to distinguish stage I-II and III-IV through ROC analysis;

[0029] Figure 9 shows the diagnostic ability of all combinations of hsa_circ_0061235, hsa-miR-3180-5p, and PPM1L to distinguish stage I-II and III-IV through ROC analysis;

[0030] Figure 10 shows the diagnostic ability of traditional tumor markers (CEA, NSE, and CYFRA21-1) to distinguish stage I-II and III-IV through ROC analysis. Detailed implementation method

[0031] The present invention will be further described in detail below in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0032] 1. Collection of research subjects

[0033] Serum samples were randomly selected from patients with NSCLC (n = 200), benign lung tumors (n = 30), and pneumonia (n = 50) before surgery or treatment in the clinical laboratories of Affiliated Hospital of Ningbo University School of Medicine and Li Huili Hospital Affiliated to Ningbo University, as well as from healthy human serum samples without a history of tumors (n = 50). In this study, each subject filled out an informed consent form, agreeing to use their blood for research. All blood collections were approved by the Clinical Research Ethics Committee of Ningbo University School of Medicine and complied with the principles of the Declaration of Helsinki. The clinical characteristics (age, gender, smoking status, histological subtype, TNM stage, lymph node metastasis, distant metastasis, tumor size, etc.) of all patients participating in the study are shown in Table 1.

[0034] Table 1 Serum samples and corresponding clinical parameters in this study

[0035]

[0036] 。

[0037] 2. Construction of circRNA-miRNA-mRNA network

[0038] To screen NSCLC-related miRNAs, we used Trizol reagent (Invitrogen, USA) to isolate total RNA (concentration ≥ 200 ng / μL, total amount ≥ 10 μg, OD260 / 280 between 1.8 - 2.2) from serum samples of 4 healthy individuals and 4 NSCLC patients. Subsequently, the 8 isolated RNA samples were sequenced on a small RNA deep sequencing platform (NovaSeq, Shanghai, China). The original sequencing sequences obtained through high-throughput sequencing were called raw sequencing data (RawReads). Data (reads) containing 5' primers and poly (A) tails, data without 3' adapters and tag sequences, and data with a length less than 15 nt or greater than 41 nt were removed. Further, low-quality data were filtered to obtain filtered sequencing data (clean reads). Subsequently, according to the length distribution of the filtered sequencing data in the reference genome, the sequences were aligned with the Rfam v10.1 database (http: / / www.sanger.ac.uk / software / Rfam) using Bowtie software to annotate and filter sequences such as rRNA, scRNA, Cis-reg, snRNA, tRNA, etc. Then, they were aligned and annotated with cDNA sequences, the species repetitive sequence library Repbase database, and the miRBase database (http: / / www.mirbase.org / ) in turn to remove degraded transcript sequences and repetitive sequences, and to identify and annotate known miRNAs. At the same time, the expression patterns of known miRNAs in different samples were analyzed.

[0039] When calculating differentially expressed miRNAs, a threshold of P value < 0.05 and fold change > 2 was used for filtering and screening to analyze the differentially expressed miRNAs between healthy and NSCLC subgroups. As shown in Figure 1, we screened the top 5 significantly upregulated serum miRNAs in NSCLC by small RNA deep sequencing (according to the principle of the smallest P value and the largest fold change). Then we predicted the circRNAs that interacted with these 5 serum miRNAs (miR-196a-3p, miR-3180-5p, miR-592, miR-203a-3p, and miR-551b-5p) through circbank (http: / / www.circbank.cn / ). Next, using the above 5 NSCLC-related miRNAs as target nodes and their corresponding target circRNAs as source nodes, a circRNA-miRNA network was constructed by CytoScape v3.9.1. As shown in Figure 2, we found that the optimal target circRNA was hsa_circ_0061235, and among the 5 upregulated miRNAs, 3 miRNAs had binding sites for hsa_circ_0061235: hsa-miR-203a-3p, hsa-miR-3180-5p, and hsa-miR-196a-3p. Subsequently, we searched for the target mRNAs that might interact with the above miRNAs through the miRDB bioinformatics database and determined the common target mRNAs of the three miRNAs. Through cross-analysis, 16 target mRNAs (ELAVL4, NUFIP2, ZNF704, BACH2, RAD18, SH3TC2, SLC1A2, CREB1, FAM78B, MEF2C, RNF38, CAMK4, SCML4, PPM1L, CACNA2D1, LPP) were found.

[0040] 3. Primer Design

[0041] Design of circRNA primers: First, we obtained the circRNA 5'-3' sequences and circRNA IDs through circBase (http: / / circbase.org / ). To amplify the circular structure of circRNA rather than the linear structure, we transformed the circRNA sequences as follows: Cut off the 150-bp sequence at the 3' end and place it in front of the 5' end to form a new sequence, and the junction of the new sequence is the splicing site. Then, import the new sequence into the Primer Premier 5.0 software and select specific primers under the restrictive conditions (primer length 15 - 30 nt, G + C content 40 - 60%, qRT-PCR amplification product about 100 bp). The primers for exon-circularized circRNA are designed at the splicing site, and the primers for intron-circularized circRNA are designed at the splicing site or around the intron region. Then, use the BLAST primer tools in NCBI and circPrimer to detect the specificity of the designed primers. At the same time, perform melting curve, agarose gel electrophoresis after circRNA amplification, and real-time quantitative polymerase chain reaction (qRT-PCR) to detect the specificity of the circRNA products.

[0042] The specific primer sequences for circRNA fluorescence quantitative PCR are as follows:

[0043] Specific upstream primer for hsa_circ_0061235 fluorescence quantitative PCR: 5’- TGTTCACGCTAGCCAACCTA -3’;

[0044] Specific downstream primer for hsa_circ_0061235 fluorescence quantitative PCR: 5’- TCATCATTCACAGCTTCCCG -3’;

[0045] Design of mRNA primers: First, we obtained the mRNA 5'-3' sequences through NCBI (https: / / www.ncbi.nlm.nih.gov / ). Import the obtained sequences into the primer design interface of NCBI and select specific primers under the restrictive conditions (primer length 15 - 30 nt, G + C content 40 - 60%, qRT-PCR amplification product 100 - 200 bp).

[0046] The specific primer sequences for mRNA fluorescence quantitative PCR are as follows:

[0047] Specific upstream primer for PPM1L fluorescence quantitative PCR: 5’- GATAGGAAGAATTCATGCAGGTGGA -3’

[0048] PPM1L Fluorescent Quantitative PCR Specific Downstream Primer: 5’- TCAGCTGCAGTTGTTGAAAAAG -3’

[0049] Design of miRNA primers: To quantify mature miRNAs using qRT-PCR technology, reverse transcription stem-loop primers (RT) and qRT-PCR primers are required. The miRNA RT primer consists of a regular 5'-end stem-loop structure (5'-ctcaactggtgtcgtggagtcggcaattcagttag-3') with a length of 36 bp and a 3'-end miRNA-specific sequence. The 3'-end specific miRNA sequence is obtained by reverse complementarity with 6-8 bases at the 3'-end of the mature miRNA. The qRT-PCR forward primer consists of a universal 5'-end sequence (5'-GCCGAG-3' or 5'-TCGGCAGG-3') and a 3'-end miRNA-specific sequence, which has 13-16 bases at the 5'-end of the mature miRNA. The qRT-PCR reverse primer is a universal primer (5'-CTCAACTGGTGTCGTGGA-3'). All primers were synthesized by BGI (Beijing Genomics institute, China).

[0050] The miRNA-specific primer sequences are as follows:

[0051] Specific reverse transcription stem-loop primer for hsa-miR-203a-3p: 5’-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCCTAGTGG-3’;

[0052] Specific upstream primer for hsa-miR-203a-3p in fluorescent quantitative PCR: 5’-GCCGAGGTGAAATGTTTAGG-3’;

[0053] Specific reverse transcription stem-loop primer for hsa-miR-3180-5p: 5’-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCGACGTGG-3’;

[0054] Specific upstream primer for hsa-miR-3180-5p in fluorescent quantitative PCR: 5’-TCGGCAGGCTTCCAGACGCTCC-3’

[0055] Specific reverse transcription stem-loop primer for hsa-miR-196a-3p: 5’-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCTCAGGCA-3’;

[0056] Specific upstream primer for hsa-miR-196a-3p fluorescence quantitative PCR: 5’-GCCGAGCGGCAACAAGAAAC-3’;

[0057] The downstream primers for the above miRNAs are all universal primers: 5'-CTCAACTGGTGTCGTGGA-3'.

[0058] 4. RNA extraction and cDNA synthesis

[0059] To extract total RNA from serum, 400 µL of cell-free serum was taken into a nuclease-free EP tube, and then 1.2 mL of Trizol reagent (Thermo Fisher Scientific, USA) was added. Next, 240 μL of chloroform (ChemMall, China) was added to the mixture and left to stand at 4 °C for 5 minutes. After centrifugation at 12,000×g for 15 minutes, the supernatant was transferred to a new nuclease-free EP tube and incubated with 600 µL of isopropanol (ChemMall, China) at 4 °C for 10 minutes, followed by centrifugation at 12,000×g at 4 °C for 10 min. Then it was washed twice with 1.2 mL of 75% ethanol at 4 °C, and finally dissolved in 50 µL of nuclease-free water and stored at -80 °C for later use. The RNA concentration was measured on a NanoDrop spectrophotometer (NanoDrop™ One, Thermo Fisher Scientific, USA). 4 µL of the serum RNA solution was used for cDNA synthesis. According to the manufacturer's recommendations, ReverTra Ace qPCR RT Master Mix and gDNA Remover (TOYOBO, Japan) were used to synthesize cDNA on the Life Touch TC-96 / G / H(b) b (Bioer, China) PCR equipment described above.

[0060] 5. qRT-PCR

[0061] The miRNAs, circRNAs, and mRNAs selected in this study were quantified by qRT-PCR. The 10-μL qRT-PCR reaction system included 5 μL of SYBR buffer (Yeasen Biotech, China), 3 μL of nuclease-free water, 0.5 μL of forward primer, 0.5 μL of reverse primer, and 1 μL of cDNA solution. The PCR cycling program was as follows: hot start at 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s, 60 °C for 30 s, and 72 °C for 30 s, and finally held at 4 °C on a Mastercycler gradient (Vaudaux-Eppendorf, Germany). Each reaction was performed in triplicate. The specificity of the qRT-PCR products was evaluated using melting curves. GAPDH and U6 were selected as internal reference genes to normalize the mRNA, circRNA, and miRNA data, respectively. The relative levels of mRNA, circRNA, and miRNA were calculated using the ΔCq method as follows: ΔCq = mean Cq (internal reference) - mean Cq (target mRNA, circRNA, and miRNA), and the corresponding values for the relative levels of mRNA, circRNA, and miRNA were 2^(ΔCq).

[0062] 6. Statistical analysis

[0063] Statistical analysis was performed using the SPSS 22.0 software package (SPSS Inc., Chicago, USA) and GraphPad Prism 8.0 (GraphPad Software, USA). After transforming the relative levels of circRNAs, mRNAs, and miRNAs into a normal distribution, ANOVA and Tukey’s HSD tests were used to analyze the statistical differences in the levels of circRNAs, mRNAs, and miRNAs among the healthy, pneumonia, benign lung tumor, and non-small cell lung cancer groups. To compare the levels of circRNAs, miRNAs, mRNAs, and traditional tumor markers between two subgroups (LUSC and LUAC, N0 and N1-3, M0 and M1, stages I-II and III-IV, and tumor size < 5 cm and ≥ 5 cm), we used the nonparametric Mann-Whitney U test. The diagnostic abilities of circRNAs, miRNAs, mRNAs, and traditional tumor markers were detected by receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated. Logistic regression models were selected to combine miRNAs or circular RNAs, and the combined probabilities were obtained. P < 0.05 was considered statistically significant.

[0064] 7. Research results

[0065] We detected the expression levels of the above-mentioned 3 miRNAs, 1 circRNA and 16 target mRNAs in the sera of randomly selected 20 healthy individuals and 20 NSCLC patients. Based on the significant downregulation of miRNAs and the reverse upregulation of circRNAs and mRNAs in lung cancer cell lines, hsa-miR-3180-5p, hsa_circ_0061235 and PMM1L were selected for further study.

[0066] To investigate the diagnostic potential of the above-selected circRNA-miRNA-mRNA network in NSCLC, we quantified the expression levels of serum hsa-miR-3180-5p, hsa_circ_0061235 and PMM1L in four sample groups: healthy individuals, pneumonia, benign lung tumors and non-small cell lung cancer. Then, the expression levels of hsa-miR-3180-5p, hsa_circ_0061235 and PMM1L were compared among different cohorts. Correlation analysis with clinicopathological characteristics (histological subtype, TNM stage, lymph node metastasis and distant metastasis) was performed by Mann-Whitney U test. Meanwhile, ROC analysis was conducted to evaluate the sensitivity and specificity of multiple combinations of miRNAs, circRNAs and traditional lung tumor markers (CEA, NSE and CYFRA21-1).

[0067] The results showed that for differentiating healthy individuals from those with non-small cell lung cancer, among different combinations of hsa-miR-3180-5p, hsa_circ_0061235, and PMM1L, the AUC values of hsa-miR-3180-5p + hsa_circ_0061235, hsa_circ_0061235 + PMM1L, hsa-miR-4482-3p + PMM1L, and hsa-miR-3180-5p + hsa_circ_0061235 + PMM1L were 0.907, 0.939, 0.963, and 0.968 respectively (Figure 3). For differentiating benign lung tumors from non-small cell lung cancer, among different combinations of hsa-miR-3180-5p, hsa_circ_0061235, and PMM1L, the AUC values of hsa-miR-3180-5p + hsa_circ_0061235, hsa_circ_0061235 + PMM1L, hsa-miR-4482-3p + PMM1L, and hsa-miR-3180-5p + hsa_circ_0061235 + PMM1L were 0.846, 0.907, 0.852, and 0.919 respectively (Figure 4). The AUC values of the traditional tumor markers (CEA, NSE, and CYFRA21-1) for differentiating benign lung tumors and NSCLC were 0.708, 0.621, and 0.663 respectively ( Figure 5)。The AUC areas of the gene expression levels of hsa_circ_0061235, hsa-miR-3180-5p alone or in combination for distinguishing non-small cell lung cancer with or without lymph node metastasis were 0.696, 0.759, and 0.791, respectively (Figure 6). The AUC areas of the gene expression levels of hsa_circ_0061235, hsa-miR-3180-5p alone or in combination for distinguishing non-small cell lung cancer with or without distant metastasis were 0.622, 0.681, and 0.695, respectively (Figure 7). The AUC areas of the gene expression levels of hsa_circ_0061235, hsa-miR-3180-5p, and PMM1L alone for distinguishing stages I-II and III-IV were 0.752, 0.830, and 0.612, respectively (Figure 8). Among various combinations, the AUC areas of hsa-miR-3180-5p + hsa_circ_0061235, hsa_circ_0061235 + PMM1L, hsa-miR-4482-3p + PMM1L, and hsa-miR-3180-5p + hsa_circ_0061235 + PMM1L were 0.866, 0.785, 0.865, and 0.891, respectively (Figure 9). In comparison, the AUC values of traditional tumor markers (CEA, NSE, and CYFRA21-1) for distinguishing stages I-II and III-IV were only 0.519, 0.516, and 0.455, respectively (Figure 10).

[0068] Therefore, we reached the conclusion that the circRNA-miRNA-mRNA combination achieved the highest AUC value (0.968). Compared with other combinations, it indicated that the circRNA-miRNA-mRNA network we selected had the highest sensitivity and specificity in distinguishing benign and malignant lung tumors. At the same time, the circRNA-miRNA-mRNA network also had high sensitivity and specificity in distinguishing NSCLC with different degrees of malignancy.

[0069] In summary, this study constructed a circulating NSCLC-related circRNA-miRNA-mRNA network (hsa-miR-3180-5p, hsa_circ_0061235, and PPM1L) based on serum miRNA sequencing. The relative levels of the selected circRNA-miRNA-mRNA network in serum were compared among four study cohorts (healthy, pneumonia, benign lung tumor, and non-small cell lung cancer) by quantifying circRNAs, mRNAs, and miRNAs using qRT-PCR. We found that hsa-miR-3180-5p, hsa_circ_0061235, and PPM1L were abnormally expressed in the serum of non-small cell lung cancer patients and had the ability to distinguish between benign and malignant lung tumors. The overall combination of the three RNAs had a higher AUC value than other combinations and traditional tumor markers.

[0070] The above description is not a limitation of the present invention, nor is the present invention limited to the above examples. Changes, modifications, additions, or substitutions made by those of ordinary skill in the art within the scope of the essence of the present invention shall also fall within the protection scope of the present invention.

Claims

1. A circRNA-miRNA-mRNA composition for the diagnosis of non-small cell lung cancer, characterized in that: in the circRNA-miRNA-mRNA composition, the circRNA gene is hsa_circ_0061235, the miRNA gene is hsa-miR-3180-5p, and the mRNA gene is PPM1L.

2. Use of a reagent for detecting the circRNA-miRNA-mRNA composition for the diagnosis of non-small cell lung cancer according to claim 1 in the preparation of an early diagnosis or detection drug for non-small cell lung cancer.

3. Use of a reagent for detecting the circRNA-miRNA-mRNA composition for the diagnosis of non-small cell lung cancer according to claim 1 in the preparation of an early diagnosis or detection kit for non-small cell lung cancer.

4. Use of the reagent for detecting the circRNA-miRNA-mRNA composition for the diagnosis of non-small cell lung cancer according to claim 3 in the preparation of an early diagnosis or detection kit for non-small cell lung cancer, characterized in that: the kit includes hsa_circ_0061235 fluorescence quantitative PCR specific upstream primer: 5’-TGTTCACGCTAGCCAACCTA-3’; hsa_circ_0061235 fluorescence quantitative PCR specific downstream primer: 5’-TCATCATTCACAGCTTCCCG-3’; hsa-miR-3180-5p specific reverse transcription stem-loop primer: 5’- CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCGACGTGG-3’ hsa-miR-3180-5p fluorescence quantitative PCR specific upstream primer: 5’-TCGGCAGGCTTCCAGACGCTCC-3’; PPM1L fluorescence quantitative PCR specific upstream primer: 5’-GATAGGAAGAATTCATGCAGGTGGA-3’ PPM1L fluorescence quantitative PCR specific downstream primer: 5’-TCAGCTGCAGTTGTTGAAAAAG-3’.

5. Use of the reagent for detecting the circRNA-miRNA-mRNA composition for the diagnosis of non-small cell lung cancer according to claim 4 in the preparation of an early diagnosis or detection kit for non-small cell lung cancer, characterized in that: the expression level of the hsa-miR-3180-5p gene is negatively correlated with the incidence of non-small cell lung cancer, and the expression level of the hsa_circ_0061235 and / or PPM1L gene is positively correlated with the risk of non-small cell lung cancer.

Images