Non-small cell lung cancer auxiliary diagnosis composition and application and kit thereof

Abstract

The invention provides a non-small cell lung cancer auxiliary diagnosis composition and application and a kit thereof, and relates to the technical field of biological detection.The non-small cell lung cancer auxiliary diagnosis composition comprises a circular RNA ALG8 primer and a circular RNA CAMTA1 primer, circALG8 and circCAMTA1 can be used as markers of early non-small cell lung cancer liquid biopsy, and the non-small cell lung cancer auxiliary diagnosis composition can be applied to diagnosis of non-small cell lung cancer. According to the present invention, with the application of the circALG8 primer and the circCAMTA1 primer, the plasma circALG8 and circCAMTA1 combined diagnosis model can be successfully constructed, the NSCLC patient and the healthy control can be effectively distinguished, the area under the curve is up to 0.8987 when the circALG8 primer and the circCAMTA1 primer are combined to identify the efficiency of the early NSCLC, the identification result has extremely high accuracy, the diagnosis accuracy is significantly improved, the sensitivity reaches 91.78%, and the specificity reaches 75.53%.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technologies, and more particularly, to a non-small cell lung cancer auxiliary diagnosis composition, its application, and a kit. Background Art

[0002] According to cancer mortality statistics in recent years, lung cancer, liver cancer, gastric cancer, breast cancer, and colorectal cancer are the five main causes of cancer-related deaths, among which the number of lung cancer deaths ranks first. Non-small cell lung cancer (NSCLC), as a highly heterogeneous malignant tumor, accounts for approximately 85% of newly diagnosed lung cancer cases. The key factor leading to the high mortality rate of NSCLC is that it is often diagnosed at an advanced stage.

[0003] Although innovative diagnostic screening technologies such as low-dose computed tomography (LDCT) have reduced the lung cancer mortality rate by approximately 20%, such technologies still have a false positive rate of over 96% and may cause various complications. Traditional clinical diagnosis mainly relies on tissue biopsy to determine the histological characteristics and staging of tumors, but this method has many limitations and inherent risks. In contrast, liquid biopsy has the advantages of minimal invasiveness and repeatability, and can isolate various biomarkers from peripheral blood. Among them, plasma is an ideal source for identifying NSCLC-related diagnostic, prognostic, and predictive biomarkers. Although serum biomarkers such as carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), and neuron-specific enolase (NSE) have been widely used in lung cancer diagnosis, due to factors such as infection, benign lesions, and pregnancy, false positive results are still relatively common. Summary of the Invention

[0004] The problem solved by the present invention is how to solve the problem of common false positive results in NSCLC identification.

[0005] To solve the above problems, the present invention provides a non-small cell lung cancer auxiliary diagnosis composition, its application, and a kit.

[0006] In the first aspect, the present invention provides a non-small cell lung cancer auxiliary diagnosis composition, including primers for circular RNA ALG8 and primers for circular RNA CAMTA1, and the specific sequences are as follows:

[0007] circALG8-F: 5'-TCAAGAGGGGCCTCTGTCAT-3' (SEQ ID NO: 1);

[0008] circALG8-R: 5’-ACCCGAAGTTCCACAGAAGT-3’ (SEQ ID NO: 2);

[0009] circCAMTA1-F: 5’-TTTTGGGTGAGTCGGGTGTT-3’ (SEQ ID NO: 3);

[0010] circCAMTA1-R: 5’-GGGGAAACCGTTCTTTGCAC-3’ (SEQ ID NO: 4).

[0011] Optionally, it further includes serum marker neuron-specific enolase, serum marker carcinoembryonic antigen, and / or serum marker cytokeratin 19 fragment.

[0012] In a second aspect, the present invention provides an adjuvant diagnostic composition for non-small cell lung cancer, including primers for circular RNA CAMTA1, and the specific sequences are:

[0013] circCAMTA1-F: 5’-TTTTGGGTGAGTCGGGTGTT-3’ (SEQ ID NO: 3);

[0014] circCAMTA1-R: 5’-GGGGAAACCGTTCTTTGCAC-3’ (SEQ ID NO: 4).

[0015] In a third aspect, the present invention provides an adjuvant diagnostic composition for non-small cell lung cancer, including primers for circular RNA ALG8, and the specific sequences are:

[0016] circALG8-F: 5’-TCAAGAGGGGCCTCTGTCAT-3’ (SEQ ID NO: 1);

[0017] circALG8-R: 5’-ACCCGAAGTTCCACAGAAGT-3’ (SEQ ID NO: 2).

[0018] In a fourth aspect, the present invention provides an adjuvant diagnostic kit for non-small cell lung cancer, including the adjuvant diagnostic composition for non-small cell lung cancer as described above.

[0019] Optionally, it further includes primers for the internal reference gene GAPDH.

[0020] Optionally, the primers for the internal reference gene GAPDH, and the specific sequences are:

[0021] GAPDH-F: 5'-GAAGGTGAAGGTCGGAGTC-3' (SEQ ID NO: 5);

[0022] GAPDH-R: 5'-GAAGATGGTGATGGGATTTC-3' (SEQ ID NO: 6).

[0023] Optionally, it further includes reverse transcription quantitative polymerase chain reaction reagents.

[0024] In a fifth aspect, the present invention provides an application of the non-small cell lung cancer auxiliary diagnosis composition as described above in the field of preparing non-small cell lung cancer diagnostic reagents.

[0025] In a sixth aspect, the present invention provides an application of the non-small cell lung cancer auxiliary diagnosis composition as described above in the field of preparing early diagnosis non-small cell lung cancer reagents.

[0026] The beneficial effects of the non-small cell lung cancer auxiliary diagnosis composition, its application and the kit of the present invention are as follows: circALG8 and circCAMTA1 can serve as markers for liquid biopsy of early non-small cell lung cancer (NSCLC), and a combined diagnosis model of plasma circALG8 and circCAMTA1 is successfully constructed, which can effectively distinguish NSCLC patients (including early cases) from healthy controls. The area under the curve (AUC) of the efficiency curve for identifying early NSCLC by combining the primers of circALG8 and the primers of circCAMTA1 is as high as 0.8987, and the identification result has extremely high accuracy, significantly improving the diagnostic accuracy, with a sensitivity of 91.78% and a specificity of 75.53%. Primers are designed for circular RNA genes as biomarkers to achieve specific diagnosis of NSCLC, and the false positive rate can be controlled to less than 1% or even close to 0%. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the relative expression level of circALG8 in tumor tissues and adjacent tissues of Example 1;

[0028] Figure 2 Schematic diagram showing the down-regulation of the relative expression level of circALG8 in tumor tissues compared with adjacent tissues in Example 1;

[0029] Figure 3 Schematic diagram of the relative expression level of circCAMTA1 in tumor tissues and adjacent tissues of Example 1;

[0030] Figure 4 Schematic diagram showing the down-regulation of the relative expression level of circCAMTA1 in tumor tissues compared with adjacent tissues in Example 1;

[0031] Figure 5Schematic diagram of the relative expression levels of circALG8 in the healthy control group and the cancer patient group in the plasma training cohort of Example 2;

[0032] Figure 6 Schematic diagram of the relative expression levels of circCAMTA1 in the healthy control group and the cancer patient group in the plasma training cohort of Example 2;

[0033] Figure 7 Schematic diagram of the relative expression levels of circALG8 in the healthy control group and the cancer patient group in the plasma validation cohort of Example 2;

[0034] Figure 8 Schematic diagram of the relative expression levels of circCAMTA1 in the healthy control group and the cancer patient group in the plasma validation cohort of Example 2;

[0035] Figure 9 Schematic diagram of the relative expression levels of circALG8 in the healthy control group and the TNM stage I patient group in Example 2;

[0036] Figure 10 Schematic diagram of the relative expression levels of circALG8 in the healthy control group and the TNM stage II-IV patient group in Example 2;

[0037] Figure 11 Schematic diagram of the relative expression levels of circCAMTA1 in the healthy control group and the TNM stage I patient group in Example 2;

[0038] Figure 12 Schematic diagram of the relative expression levels of circCAMTA1 in the healthy control group and the TNM stage II-IV patient group in Example 2;

[0039] Figure 13 Schematic diagram of the relative expression levels of circALG8 in the TNM stage I patient group and the TNM stage II-IV patient group in Example 2;

[0040] Figure 14 Schematic diagram of the relative expression levels of circCAMTA1 in the TNM stage I patient group and the TNM stage II-IV patient group in Example 2;

[0041] Figure 15 Schematic diagram of the ROC curves of circALG8, circCAMTA1, and the combination of circALG8 and circCAMTA1 in the healthy control group and the cancer patient group in Example 3;

[0042] Figure 16Schematic diagram of ROC curves of circALG8, circCAMTA1, and the combination of circALG8 and circCAMTA1 for the healthy control group and the TNM stage I patient group in Example 3;

[0043] Figure 17 Schematic diagram of ROC curves of circALG8, circCAMTA1, and the combination of circALG8 and circCAMTA1 for the healthy control group and the TNM stage II-IV patient group in Example 3;

[0044] Figure 18 Schematic diagram of ROC curves of circALG8, circCAMTA1, and the combination of circALG8 and circCAMTA1 for the TNM stage I patient group and the TNM stage II-IV patient group in Example 3;

[0045] Figure 19 Schematic diagram of ROC curves of tumor markers NSE, CEA, CYFRA21-1, and their combinations in Example 4;

[0046] Figure 20 Schematic diagram of ROC curves of tumor marker NSE and its combinations with circALG8, circCAMTA1, and circCOMBO respectively in Example 4;

[0047] Figure 21 Schematic diagram of ROC curves of tumor marker CEA and its combinations with circALG8, circCAMTA1, and circCOMBO respectively in Example 4;

[0048] Figure 22 Schematic diagram of ROC curves of tumor marker CYFRA21-1 and its combinations with circALG8, circCAMTA1, and circCOMBO respectively in Example 4;

[0049] Figure 23 Schematic diagram of ROC curves of the combination of tumor markers NSE, CEA, CYFRA21-1 and their combinations with circALG8, circCAMTA1, and circCOMBO respectively in Example 4. Detailed implementation methods

[0050] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following detailed description of specific embodiments of the present invention will be provided in conjunction with the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0051] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention;

[0052] The term "including" and its variations used herein are open-ended, that is, "including but not limited to"; the term "based on" is "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiment". The relevant definitions of other terms will be given in the following description.

[0053] In the related art, circular RNA (circRNA) is generally considered a class of endogenous non-coding RNA. Thanks to its high stability, circRNA is gradually becoming a potential biomarker for various human diseases. A large number of studies have shown that circRNA shows significant expression differences in patients with esophageal cancer, gastric cancer, colorectal cancer, and lung cancer. More importantly, circRNA plays an important role in the process of tumorigenesis and development. The detection of circRNA in blood samples confirms its potential as a diagnostic biomarker at all stages of tumors.

[0054] In view of the problems existing in the above related art, the embodiments of the present invention provide a non-small cell lung cancer auxiliary diagnosis composition, its application, and a kit.

[0055] A non-small cell lung cancer auxiliary diagnosis composition provided by an embodiment of the present invention includes primers for circular RNA ALG8 and primers for circular RNA CAMTA1, and the specific sequences are as follows:

[0056] circALG8-F: 5'-TCAAGAGGGGCCTCTGTCAT-3' (SEQ ID NO: 1);

[0057] circALG8 - R: 5’-ACCCGAAGTTCCACAGAAGT-3’ (SEQ ID NO: 2);

[0058] circCAMTA1 - F: 5’-TTTTGGGTGAGTCGGGTGTT-3’ (SEQ ID NO: 3);

[0059] circCAMTA1 - R: 5’-GGGGAAACCGTTCTTTGCAC-3’ (SEQ ID NO: 4).

[0060] In this example, circALG8 and circCAMTA1 can be used as markers for liquid biopsy of early non - small cell lung cancer (NSCLC). A combined diagnostic model of plasma circALG8 and circCAMTA1 was successfully constructed, which can effectively distinguish NSCLC patients (including early cases) from healthy controls. When the primers of circALG8 and circCAMTA1 were used in combination to identify early NSCLC, the area under the curve (AUC) of the efficiency curve was as high as 0.8987. The identification result had extremely high accuracy, significantly improving the diagnostic accuracy. The sensitivity reached 91.78% and the specificity reached 75.53%. Primers were designed for circular RNA genes as biomarkers to achieve specific diagnosis of NSCLC, and the false positive rate could be controlled to less than 1% or even close to 0%.

[0061] Specifically, the primers for circular RNA ALG8 include the forward primer circALG8 - F and the reverse primer circALG8 - R; the primers for circular RNA CAMTA1 include the forward primer circCAMTA1 - F and the reverse primer circCAMTA1 - R.

[0062] Optionally, it further includes at least one of the serum markers neuron - specific enolase, carcinoembryonic antigen, and cytokeratin 19 fragment.

[0063] In this alternative example, the combination of circular RNA ALG8 and circular RNA CAMTA1 used in conjunction with traditional serum markers such as NSE, CEA, and CYFR21 - 1 can significantly improve the diagnostic accuracy of NSCLC. When identifying early NSCLC, the area under the curve (AUC) of the efficiency curve is as high as 0.925!

[0064] Another embodiment of the present invention provides an adjuvant diagnostic composition for non - small cell lung cancer, including primers for circular RNA CAMTA1, and the specific sequence is:

[0065] circCAMTA1-F: 5’-TTTTGGGTGAGTCGGGTGTT-3’ (SEQ ID NO: 3);

[0066] circCAMTA1-R: 5’-GGGGAAACCGTTCTTTGCAC-3’ (SEQ ID NO: 4).

[0067] In this example, circCAMTA1 can serve as a biomarker for liquid biopsy of early non-small cell lung cancer (NSCLC), and can effectively distinguish NSCLC patients (including early cases) from healthy controls.

[0068] Another example of the present invention provides a non-small cell lung cancer auxiliary diagnosis composition, including primers for circular RNA ALG8, and the specific sequences are:

[0069] circALG8-F: 5’-TCAAGAGGGGCCTCTGTCAT-3’ (SEQ ID NO: 1);

[0070] circALG8-R: 5’-ACCCGAAGTTCCACAGAAGT-3’ (SEQ ID NO: 2).

[0071] In this example, circALG8 can serve as a biomarker for liquid biopsy of early non-small cell lung cancer (NSCLC), and can effectively distinguish NSCLC patients (including early cases) from healthy controls.

[0072] Another example of the present invention provides a non-small cell lung cancer auxiliary diagnosis kit, including the non-small cell lung cancer auxiliary diagnosis composition as described above.

[0073] Optionally, it further includes primers for the internal reference gene GAPDH.

[0074] In this optional example, the expression level of the internal reference gene GAPDH is relatively high, which is convenient for detection, can be compatible with multiplex detection, and is not likely to compete for PCR reagents when co-amplified with the target gene.

[0075] Optionally, the primers for the internal reference gene GAPDH, and the specific sequences are:

[0076] GAPDH-F: 5'-GAAGGTGAAGGTCGGAGTC-3' (SEQ ID NO: 5);

[0077] GAPDH-R: 5'-GAAGATGGTGATGGGATTTC-3' (SEQ ID NO: 6).

[0078] Optionally, it further includes reverse transcription quantitative polymerase chain reaction reagents.

[0079] In this optional embodiment, reverse transcription quantitative polymerase chain reaction (RT-qPCR) has high sensitivity, high specificity, and quantification ability.

[0080] Use of a non-small cell lung cancer auxiliary diagnostic composition as described above in the field of preparing reagents for diagnosing non-small cell lung cancer provided by another embodiment of the present invention.

[0081] Use of a non-small cell lung cancer auxiliary diagnostic composition as described above in the field of preparing reagents for early diagnosing non-small cell lung cancer provided by another embodiment of the present invention.

[0082] The present invention will be further described below in conjunction with specific embodiments.

[0083] Example 1

[0084] 1. Plasma transcriptome sequencing (RNA-seq) analysis was performed on total RNA obtained from plasma samples of 3 patients with lung adenocarcinoma, 3 patients with lung squamous cell carcinoma, and 3 cancer-free patients, and significantly downregulated circRNAs (>2-fold change, P<0.05) in the plasma of non-small cell lung cancer (NSCLC) patients were screened out. The most significantly different has_circ_0008737 (named circCAMTA1 according to the parental gene) and has_circ_0003615 (named circALG8) were selected for verification.

[0085] 2. RNA was extracted from tumor tissues and paired adjacent normal tissues of lung cancer patients. According to the manufacturer's instructions, total RNA was isolated from the tissues using TRIzol reagent (Invitrogen, Carlsbad, California, USA), and the quality and concentration of the total RNA were measured using a DeNovix DS-11 spectrophotometer (DeNovix, DE, USA). Whether its expression in tumor tissues and paired adjacent normal tissues was consistent with the prediction was verified by RT-qPCR. The results are as Figure 1 and Figure 2 shown. The expression of circALG8 in NSCLC cancer tissues was significantly lower than that in adjacent tissues (P = 0.0044), while as Figure 3 and Figure 4 shown, the downregulation of circCAMTA1 was more significant (P<0.0001).

[0086] 3. Tumor tissues and paired adjacent normal tissues of 74 NSCLC patients diagnosed in the First Affiliated Hospital of Ningbo University were collected in this study, and plasma samples of 94 healthy controls and 120 lung cancer patients were collected during the same period. It should be specifically noted that all NSCLC patients had not received chemotherapy, radiotherapy, targeted therapy, or immunotherapy before sampling. All biological samples were stored in an ultra-low temperature environment of -80 °C until RNA extraction. This study was approved by the Medical Research Ethics Committee of Ningbo University (Ethical batch number: NBU-2023-142), and all subjects signed informed consent forms.

[0087] 4. Analyze the correlation between the expression of circALG8 and circCAMTA1 and clinicopathology.

[0088] The expression of circALG8 and circCAMTA1 was detected by RT-qPCR in tumor tissues and adjacent tissues of 74 lung cancer patients, and the correlation between their expression and clinicopathological features was statistically analyzed. As shown in Table 1 and Table 2, the expression levels of circALG8 (P = 0.001) and circCAMTA1 (P = 0.03) were significantly correlated with the clinical stage of malignant tumors (TNM staging). It is worth noting that although the expression of circALG8 was not significantly associated with clinicopathological features such as patient gender, age, and smoking history, the p-value of the correlation between circCAMTA1 expression and age in Table 1 was 0.209, without statistical correlation; the p-value with age in Table 2 was 0.047, showing a potential correlation (P = 0.047).

[0089] Table 1 Correlation analysis of the expression level of circALG8 and clinicopathological features of NSCLC patients

[0090]

[0091] ***P<0.001

[0092] Table 2 Correlation analysis of the expression level of circCAMTA1 and clinicopathological features of NSCLC patients

[0093]

[0094] *P<0.05

[0095] Example 2

[0096] 120 NSCLC patients and 94 healthy controls were randomly and evenly divided into a training set and a validation set, and the expression of circRNAs in plasma was analyzed by RT-qPCR.

[0097] 1. Detection of the expression of circALG8 and circCAMTA1 in NSCLC plasma by RT-qPCR reaction. The specific experimental steps are as follows:

[0098] (1) Total RNA extraction from plasma: ① Take 300 μL of plasma sample, add 900 μL of TRIpure LS lysis buffer (Invitrogen, USA), pipette and mix well, then vortex for proper time until completely lysed, and place on a 4°C water bath for 10 min; ② Add 200 μL of chloroform in the dark, shake vigorously for 15 s, and then let stand on ice for 3 min; ③ Centrifuge at 4°C, 12,000 g for 15 minutes, aspirate the upper aqueous layer into a new RNase-free EP tube, add an equal volume of isopropanol, and let stand at room temperature for 10 minutes; ④ Centrifuge at 4°C, 12,000 g for 10 min, discard the supernatant, and retain the RNA pellet; ⑤ Add 1 ml of 75% ethanol to wash the RNA pellet, centrifuge at 4°C, 8,000 g for 5 minutes, and discard the supernatant; ⑥ Carefully aspirate the ethanol, repeat the operation in ⑤ once or more times; ⑦ Open the lid and let stand until the residual ethanol completely evaporates, add RNase-free water to dissolve the RNA pellet to obtain total RNA, and place it in a -80°C refrigerator.

[0099] (2) Reverse transcription into cDNA: Use Plus One-Step First-Strand cDNA Synthesis SuperMix (gDNA Purge) kit (Novoprotein, China), and operate according to the instructions: ① Add 0.1 ng - 1 μg of total RNA, 1 μL of gDNA Purge (genomic DNA removal), add ddH2O to 10 μL, and then add Plus 1st Strand cDNA Synthesis SuperMix 10 μL; ② Pipette the mixture evenly and centrifuge briefly; ③ The reverse transcription program is: 50°C for 15 min, 85°C for 5 s to obtain cDNA.

[0100] (3) Detection of the expression of circALG8 and circCAMTA1 by RT-qPCR: Using GAPDH as an internal reference, use SYBR qPCR SuperMix Plus kit: ① The reaction system of PCR is as follows: 1 μL of cDNA, 5 μL of SYBR qPCR SuperMix, 0.4 μL each of 10 μM upstream and downstream primers. The upstream and downstream primers are:

[0101] circALG8-F: 5’-TCAAGAGGGGCCTCTGTCAT-3’ (SEQ ID NO: 1);

[0102] circALG8-R: 5’-ACCCGAAGTTCCACAGAAGT-3’ (SEQ ID NO: 2);

[0103] circCAMTA1-F: 5’-TTTTGGGTGAGTCGGGTGTT-3’ (SEQ ID NO: 3);

[0104] circCAMTA1-R: 5’-GGGGAAACCGTTCTTTGCAC-3’ (SEQ ID NO: 4);

[0105] Primers for the internal reference gene GAPDH, the specific sequences are:

[0106] GAPDH-F: 5'-GAAGGTGAAGGTCGGAGTC-3' (SEQ ID NO: 5);

[0107] GAPDH-R: 5'-GAAGATGGTGATGGGATTTC-3' (SEQ ID NO: 6).

[0108] Add ddH2O to make the total volume 10 μL. ② On the 480 System (Roche, USA), the reaction program is: 95°C for 1 min, 95°C for 20 s, 60°C for 20 s, 72°C for 30 s, perform 40 cycles, 95°C for 1 min, 55°C for 30 s, 95°C for 30 s, record the Ct value. ③ According to the obtained Ct value, normalize the expression levels of circALG8 and circCAMTA1 through the internal reference GAPDH, ΔCt = Ct circALG8 / circCAMTA1 -Ct GAPDH and Ct circALG8 / circCAMTA1 is Ct circALG8 or Ct circCAMTA1 Use 2 -ΔCT to calculate the relative expression level of circRNA.

[0109] Adopt random grouping and randomly allocate the subjects to the training set and the validation set. As Figure 5 and Figure 6 shown, in the plasma training cohort of the training set, compared with the healthy control group, the expression levels of circALG8 (P < 0.0001) and circCAMTA1 (P < 0.0001) in the plasma of NSCLC patients were both significantly downregulated. As Figure 7 and Figure 8As shown, the results were repeatedly verified in the plasma validation cohort of the validation set (P<0.0001). To further evaluate the correlation between the expression levels of circRNAs and disease stages, we analyzed plasma samples from patients with different TNM stages. As Figure 9 shown, the expression of circALG8 in stage I patients (n = 73) was significantly lower than that in healthy controls (n = 94) (P<0.0001). As Figure 10 shown, a downward trend was also observed in patients with stage II-IV (n = 13) (P = 0.0075). As Figure 11 and Figure 12 shown, circCAMTA1 was significantly downregulated in both stage I (P<0.0001) and stage II-IV (P = 0.0001) patients. As Figure 13 and Figure 14 shown, there were no significant differences in the expression levels of circALG8 (P = 0.7174) and circCAMTA1 (P = 0.5942) between stage I and stage II-IV patients. From the above results, it can be seen that circALG8 and circCAMTA1 were generally downregulated in the plasma of NSCLC patients, and this feature was not related to the disease stage.

[0110] Example 3

[0111] Construct a combined diagnostic model based on plasma circALG8 and circCAMTA1.

[0112] We analyzed and evaluated the clinical value of plasma-derived circALG8 and circCAMTA1 and their combination in the diagnosis of NSCLC by binary logistic regression and ROC curve analysis methods in SPSS version 13.0 (SPSS Inc., USA) using the data obtained from the analysis in Example 2.

[0113] The results showed that, as Figure 15 shown, the AUC of circALG8 alone was 0.7791 (specificity 54.17%, sensitivity 91.49%), and the AUC of circCAMTA1 was 0.8286 (sensitivity 75.00%, specificity 85.11%). The diagnostic efficiency was significantly improved when the two were combined (AUC = 0.8810, sensitivity 75.83%, specificity 87.23%), and it could effectively distinguish NSCLC patients from healthy controls. As Figure 16As shown, in the diagnosis of early NSCLC (TNM stage I), the AUC of circALG8 was 0.7840 (sensitivity = 54.79%, specificity = 91.49%), the AUC of circCAMTA1 was 0.8413 (sensitivity = 73.97%, specificity = 85.11%), and the AUC of the combined detection increased to 0.8987 (sensitivity = 91.78%, specificity = 75.53%), significantly distinguishing TNM stage I patients from healthy controls. As Figure 17 shown, this combined model also showed enhanced diagnostic efficacy in advanced patients (TNM stage II-IV). Notably, as shown in Table 3, the combined detection significantly improved the diagnostic performance of early and advanced NSCLC, but as Figure 18 shown, there was no significant difference in the AUC values between stage I and stage II-IV patients. These findings indicate that the plasma circRNA combination has potential application value as a diagnostic tool for early NSCLC.

[0114] Table 3 Analysis of the diagnostic value of circALG8 and circCAMTA1 in NSCLC patients

[0115]

[0116] Example 4

[0117] The plasma circRNA combination significantly enhances the diagnostic efficacy of traditional serum markers.

[0118] The diagnostic efficacy of the circRNA combination was compared with that of traditional serum markers (NSE, CEA, and CYFR21-1). We combined the data obtained from the analysis in Example 2 with the data collected on traditional tumor markers (NSE, CEA, CYFRA21-1) and their combinations in the sera of NSCLC patients and performed ROC curve analysis using binary logistic regression in SPSS version 13.0 (SPSS Inc., USA) and GraphPad Prism 9 (GraphPad Software, USA).

[0119] ROC curve analysis of traditional tumor markers (NSE, CEA, CYFRA21-1) and their combinations in the sera of 80 NSCLC patients, the results are as Figure 19 and Table 4 shown. By combining plasma circALG8 and circCAMTA1 with traditional markers, the results are as Figure 20 and Table 4 shown, there was a significant improvement in diagnostic efficacy: the AUC of the combination of circCAMTA1 and NSE reached 0.9010 (P < 0.0001, sensitivity = 83.12%, specificity = 95.83%); as Figure 21As shown in Table 4, the AUC of circRNA combined with CEA was 0.9022 (P < 0.0001, sensitivity = 71.95%, specificity = 98.41%); as Figure 22 shown in Table 4, the AUC of circCAMTA1 combined with CYFR21-1 reached 0.9099 (P < 0.0001, sensitivity = 79.95%, specificity = 96.15%); as Figure 23 shown in Table 4, the AUC of circCAMTA1 combined with three traditional serum markers was further increased to 0.9255 (P < 0.0001, sensitivity = 85.33%, specificity = 95.45%).

[0120] It is worth noting that the combination of circALG8 and traditional serum markers also showed a significant improvement in diagnostic efficacy. The combined application of plasma circRNA combinations and traditional serum markers significantly improved the diagnostic efficacy (Table 4). These results indicate that the combined application of circRNA combinations and traditional serum markers can significantly improve the accuracy of early diagnosis of NSCLC.

[0121] Table 4 Analysis table of the combined diagnostic value of circALG8 and circCAMTA1 with traditional markers

[0122]

[0123]

[0124] *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

[0125] Although the present invention is disclosed as above, the scope of protection of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the scope of protection of the present invention.

Claims

1. An adjuvant diagnostic composition for non-small cell lung cancer, characterized in that, Primers for circular RNA ALG8 and primers for circular RNA CAMTA1, with specific sequences as follows: circALG8-F: 5’-TCAAGAGGGGCCTCTGTCAT-3’ (SEQ ID NO: 1); circALG8-R: 5’-ACCCGAAGTTCCACAGAAGT-3’ (SEQ ID NO: 2); circCAMTA1-F: 5’-TTTTGGGTGAGTCGGGTGTT-3’ (SEQ ID NO: 3); circCAMTA1-R: 5’-GGGGAAACCGTTCTTTGCAC-3’ (SEQ ID NO: 4).

2. The adjuvant diagnostic composition for non-small cell lung cancer according to claim 1, wherein It also includes at least one of the serum markers neuron-specific enolase, serum marker carcinoembryonic antigen, and serum marker cytokeratin 19 fragment.

3. An adjuvant diagnostic composition for non-small cell lung cancer, characterized in that, Primers for circular RNA CAMTA1, with specific sequences as follows: circCAMTA1-F: 5’-TTTTGGGTGAGTCGGGTGTT-3’ (SEQ ID NO: 3); circCAMTA1-R: 5’-GGGGAAACCGTTCTTTGCAC-3’ (SEQ ID NO: 4).

4. An adjuvant diagnostic composition for non-small cell lung cancer, characterized in that, Primers for circular RNA ALG8, with specific sequences as follows: circALG8-F: 5’-TCAAGAGGGGCCTCTGTCAT-3’ (SEQ ID NO: 1); circALG8-R: 5’-ACCCGAAGTTCCACAGAAGT-3’ (SEQ ID NO: 2).

5. An auxiliary diagnostic kit for non-small cell lung cancer, characterized in that, It includes the non-small cell lung cancer auxiliary diagnosis composition according to any one of claims 1 to 2, or the non-small cell lung cancer auxiliary diagnosis composition according to claim 3, or the non-small cell lung cancer auxiliary diagnosis composition according to claim 4.

6. The adjuvant diagnostic kit for non-small cell lung cancer according to claim 5, wherein It also includes primers for the internal reference gene GAPDH.

7. The adjuvant diagnostic kit for non-small cell lung cancer according to claim 6, wherein The primers for the internal reference gene GAPDH, with specific sequences as follows: GAPDH-F: 5'-GAAGGTGAAGGTCGGAGTC-3' (SEQ ID NO: 5); GAPDH-R: 5'-GAAGATGGTGATGGGATTTC-3' (SEQ ID NO: 6).

8. The non-small cell lung cancer adjuvant diagnosis kit according to claim 5, wherein, It also includes reverse transcription quantitative polymerase chain reaction reagents.

9. Use of the non-small cell lung cancer auxiliary diagnosis composition according to any one of claims 1 to 2, or the non-small cell lung cancer auxiliary diagnosis composition according to claim 3, or the non-small cell lung cancer auxiliary diagnosis composition according to claim 4 in the field of preparing reagents for diagnosing non-small cell lung cancer.

10. Use of the non-small cell lung cancer auxiliary diagnosis composition according to any one of claims 1 to 2, or the non-small cell lung cancer auxiliary diagnosis composition according to claim 3, or the non-small cell lung cancer auxiliary diagnosis composition according to claim 4 in the field of preparing reagents for early diagnosis of non-small cell lung cancer.

Images