Non-small cell lung cancer diagnostic markers and uses thereof

Abstract

This application relates to a diagnostic biomarker for non-small cell lung cancer (NSCLC) and its application, belonging to the field of medical biodetection technology. This application investigates the differential expression of serum exosomal snoRNAs in NSCLC patients and healthy individuals, and evaluates the efficacy of differentially expressed exosomal snoRNAs in the diagnosis of NSCLC. The results of this application show that AC007956.1, AC093523.1, AL358875.1, and SNORA51 in tumor-derived serum exosomals can serve as novel biomarkers for NSCLC, significantly improving the AUC values ​​of traditional tumor markers, thereby compensating for the shortcomings of existing technologies in the early diagnosis of NSCLC. This is of great significance for the early diagnosis of NSCLC. The application of AC007956.1, AC093523.1, and AL358875.1 as diagnostic biomarkers for non-small cell lung cancer is reported for the first time in this application.

Description

Technical Field

[0001] This application relates to a diagnostic biomarker for non-small cell lung cancer and its application, belonging to the field of medical biodetection technology. Background Technology

[0002] Lung cancer is a leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is pathologically divided into lung adenocarcinoma and squamous cell carcinoma. Most patients are diagnosed at an advanced stage, a key factor contributing to this delay in disease detection due to insufficient early screening. Low-dose computed tomography (CT) is currently the primary screening method for NSCLC. However, it has a high false-positive rate and limited sensitivity for early lesions, potentially leading to unnecessary invasive procedures or underdetection of lesions with low tumor burden. Although tissue biopsy has been extensively studied and proven to have excellent diagnostic efficacy, it remains an invasive procedure. Liquid biopsy, by analyzing tumor biomarkers in blood and other body fluids, such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomes (EXOs), offers non-invasive detection, avoiding the risks associated with traditional tissue biopsy. However, the sensitivity and specificity of current liquid biopsy technologies are still not ideal, especially for early-stage lung cancer. Therefore, the search for more promising circulating tumor biomarkers remains a pressing priority.

[0003] Exosomes are nanoscale extracellular vesicles (40-150 nm) secreted by cells, carrying bioactive molecules such as proteins and nucleic acids (e.g., miRNA, lncRNA, nucleolar small RNA), which can reflect the pathological state of their source cells. Serum exosomes (S-EXOs) have become a core tool in liquid biopsy due to their non-invasiveness, stability, and high specificity, playing an important role in the early diagnosis of tumors. Numerous studies have confirmed that serum exosome biomarkers can effectively distinguish cancer patients from healthy individuals.

[0004] Nucleolar small RNAs (snoRNAs) are non-coding RNAs located in the nucleolus, primarily involved in the cleavage and chemical modification of ribosomal RNA (rRNA). Based on conserved sequence motifs and functional characteristics, snoRNAs are mainly divided into two categories: C / D-box snoRNAs and H / ACA-box snoRNAs. Their unique terminal modification mechanisms and conserved secondary structure features endow them with resistance to nuclease degradation, thereby enhancing their structural stability. snoRNAs exhibit aberrant expression in various cancers and show cancer-type specificity, influencing tumor progression through multiple mechanisms such as regulating RNA modification, modulating the immune microenvironment, and metabolic reprogramming. snoRNAs are enriched in exosomes; benefiting from the membrane protection of exosomes and the structural stability of snoRNAs themselves, exosomal snoRNAs have greater potential as circulating tumor biomarkers.

[0005] To date, most studies have focused on exosomal miRNAs, lncRNAs, circRNAs, and tRNA-derived fragments (tRFs), while research on exosomal snoRNAs, especially their use as co-diagnostic biomarkers, remains relatively limited.

[0006] Low-dose CT has low sensitivity, and existing liquid biopsy biomarkers perform poorly. Therefore, there is an urgent need for better non-invasive diagnostic methods for non-small cell lung cancer (NSCLC). Serum exosomes contain nucleolar small RNAs, which are stable molecules closely related to tumors and are highly promising biomarkers. Chinese patent application 202511640707.3 discloses the application of SNORD54 and SNORD41 as diagnostic biomarkers for NSCLC, which can significantly improve the AUC value of traditional tumor markers and is of great significance for the early diagnosis of NSCLC. On the one hand, their diagnostic accuracy still needs to be improved; on the other hand, new biomarkers are still needed to provide diagnostic information not included in traditional biomarkers, thus providing information dimensions different from traditional protein biomarkers. For example, the diagnosis of non-small cell lung cancer, especially early-stage non-small cell lung cancer, often requires more detailed differentiation. This includes not only distinguishing early-stage non-small cell lung cancer from a healthy state, but also from the state of benign lung diseases, identifying histological types, the degree of tumor invasion, lymph node metastasis, distant metastasis, and TNM staging, etc. All of these require the discovery of new non-small cell lung cancer biomarkers that can provide more information dimensions.

[0007] The high mortality rate of NSCLC is largely attributed to the low rate of early diagnosis; most patients are diagnosed at an advanced stage, thus losing the opportunity for radical treatment. Therefore, finding novel and reliable liquid biopsy biomarkers remains an urgent clinical challenge. Summary of the Invention

[0008] To address the aforementioned issues, this application provides a diagnostic biomarker for non-small cell lung cancer (NSCLC) and its application. Based on snoRNA gene chip data, differentially expressed AC007956.1, AC093523.1, AL358875.1, and SNORA51 were screened. Diagnostic performance tests and results demonstrate that AC007956.1, AC093523.1, AL358875.1, and SNORA51 from tumor-derived serum exosomes can serve as novel biomarkers for NSCLC. These biomarkers significantly improve the diagnostic efficacy of traditional tumor markers CEA and CYFRA21-1, and are particularly effective for early-stage NSCLC. They also demonstrate diagnostic value in differentiating histological types, tumor invasion levels, lymph node metastasis, distant metastasis, and TNM staging, providing more diagnostic information related to NSCLC compared to other biomarkers.

[0009] This application provides a detection reagent for serum exosome snoRNA markers in the preparation of diagnostic reagents for serum samples of non-small cell lung cancer. The detection reagent for serum exosome snoRNA markers includes a detection reagent for specifically detecting the content of exosomes AC007956.1, AC093523.1, AL358875.1 and SNORA51 markers in serum.

[0010] Optionally, AC007956.1 is the snoRNA with RNAcentral ID URS0000A95EA4, and its corresponding Ensemble Transcript ID is ENST00000410672.2; and / or, The AC093523.1 is the snoRNA with RNAcentral ID URS000071FF42, and its corresponding EnsembleTranscript ID is ENST00000390947.2; and / or, The AL358875.1 is the snoRNA with RNAcentral ID URS00006B09CE, and its corresponding EnsembleTranscript ID is ENST00000391236.2.

[0011] Optionally, the nucleotide sequence of AC007956.1 is as shown in SEQ ID No. 1; and / or, The nucleotide sequence of AC093523.1 is shown in SEQ ID No. 2; and / or, The nucleotide sequence of AL358875.1 is shown in SEQ ID No. 3.

[0012] Optionally, the diagnostic reagent for serum samples of non-small cell lung cancer may further include a detection reagent for specifically detecting the content of CEA and / or CYFRA21-1 protein in serum.

[0013] This application provides a detection reagent for serum exosome snoRNA markers in the preparation of a diagnostic kit for serum samples of non-small cell lung cancer. The detection reagent for serum exosome snoRNA markers includes a reagent specifically for detecting the levels of exosomes AC007956.1, AC093523.1, AL358875.1, and SNORA51 markers in serum.

[0014] Optionally, AC007956.1 is the snoRNA with RNAcentral ID URS0000A95EA4, and its corresponding Ensemble Transcript ID is ENST00000410672.2; and / or, The AC093523.1 is the snoRNA with RNAcentral ID URS000071FF42, and its corresponding EnsembleTranscript ID is ENST00000390947.2; and / or, The AL358875.1 is the snoRNA with RNAcentral ID URS00006B09CE, and its corresponding EnsembleTranscript ID is ENST00000391236.2.

[0015] Optionally, the nucleotide sequence of AC007956.1 is as shown in SEQ ID No. 1; and / or, The nucleotide sequence of AC093523.1 is shown in SEQ ID No. 2; and / or, The nucleotide sequence of AL358875.1 is shown in SEQ ID No. 3.

[0016] Optionally, the serum sample diagnostic kit for non-small cell lung cancer may also include a detection reagent for specifically detecting the levels of CEA and / or CYFRA21-1 proteins in serum.

[0017] The beneficial effects of this application include, but are not limited to: 1. Based on the diagnostic biomarkers for non-small cell lung cancer and their applications in this application, the study subjects AC007956.1, AC093523.1, AL358875.1, and SNORA51 were identified through snoRNA gene chip data screening. It was demonstrated that these four snoRNAs were significantly overexpressed in tumor tissues and serum exosomes of tumor patients compared to healthy volunteers, indicating their potential role in the development and progression of NSCLC.

[0018] 2. According to the non-small cell lung cancer diagnostic biomarkers and their applications in this application, these four snoRNAs can be stably enriched in exosomes, which is the structural basis for their use as circulating tumor biomarkers. This has been confirmed by snoRNA enrichment experiments and RNase degradation experiments in the specific scheme of this application.

[0019] 3. Based on the diagnostic biomarkers for non-small cell lung cancer (NSCLC) and their applications in this application, ROC curve analysis confirmed that each of them has significantly high diagnostic efficacy: 1) When four serum exosomal snoRNAs are used as diagnostic biomarkers for NSCLC, the overall diagnostic efficacy (AUC) of the four biomarkers combined is 0.9276, with a sensitivity of 88.8% and a specificity of 86.7%; 2) When serum exosomal snoRNAs are used as diagnostic biomarkers for early NSCLC, the AUC of the four biomarkers combined increases to 0.9345, with a sensitivity of 88.8% and a specificity of 86.6%; 3) When exosomal snoRNAs are used to differentiate NSCLC from benign lung diseases, the combined analysis of the four biomarkers shows better diagnostic efficacy, with an AUC of 0.9124, a sensitivity of 82.92%, and a specificity of 93.75%; 4) They also demonstrate diagnostic value in differentiating histological types, tumor invasion levels, lymph node metastasis, distant metastasis, and TNM staging, and can provide more diagnostic information related to non-small cell lung cancer compared to other biomarkers.

[0020] 4. According to the diagnostic biomarkers for non-small cell lung cancer and their applications in this application, the biomarkers provided by the scheme of this application can achieve very considerable diagnostic efficacy and significantly improve the AUC value of traditional tumor markers, thereby making up for their shortcomings in the early diagnosis of NSCLC. The biomarkers provided by the scheme of this application are of great significance for the early diagnosis of NSCLC, have high clinical application value, and are also highly competitive in the market for the preparation of detection reagents. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1AThis is a transmission electron microscope image of the exosomes involved in Example 4 of this application.

[0022] Figure 1B This is the result of nanoparticle tracking analysis involved in Example 4 of this application.

[0023] Figure 1C The results of Western blot analysis of exosome markers CD81, CD63 and CD9 in the vesicle and cell samples involved in Example 4 of this application are shown.

[0024] Figure 2A This is a clustering heatmap of serum exosome snoRNA microarray analysis of NSCLC patients and healthy individuals involved in Example 2 of this application.

[0025] Figure 2B Volcano plots of serum exosome snoRNA microarrays from NSCLC patients and healthy individuals involved in Example 2 of this application.

[0026] Figure 2C This is the result of the analysis of differentially expressed snoRNAs in NSCLC patients involved in Example 2 of this application.

[0027] Figure 3A This is the result of significant enrichment of snoRNAs involved in Example 2 of this application in purified exosomes.

[0028] Figure 3B The RNase A digestion treatment involved in Example 2 of this application does not affect the expression level results.

[0029] Figure 3C The results from Example 2 of this application show that the content of AC007956.1 remained stable after being left at room temperature for 24 hours.

[0030] Figure 3D The results from Example 2 of this application show that the content of AC093523.1 remained stable after being left at room temperature for 24 hours.

[0031] Figure 3E The results from Example 2 of this application show that the AL358875.1 content remained stable after being left at room temperature for 24 hours.

[0032] Figure 3F The result of Example 2 of this application shows that the SNORA51 content remained stable after being left at room temperature for 24 hours.

[0033] Figure 4A The results of Example 8 of this application show a significant upregulation of AC007956.1 in NSCLC patients compared to healthy volunteers.

[0034] Figure 4BThe results of Example 8 of this application show a significant upregulation of AC093523.1 in NSCLC patients compared to healthy volunteers.

[0035] Figure 4C The results of Example 8 of this application show a significant upregulation of AL358875.1 in NSCLC patients compared to healthy volunteers.

[0036] Figure 4D The results of Example 8 of this application show a significant upregulation of SNORA51 in NSCLC patients compared to healthy volunteers.

[0037] Figure 4E This is the ROC curve diagram for distinguishing NSCLC patients from healthy volunteers in AC007956.1, which is involved in Example 8 of this application.

[0038] Figure 4F This is the ROC curve diagram for distinguishing NSCLC patients from healthy volunteers in AC093523.1, which is involved in Example 8 of this application.

[0039] Figure 4G This is the ROC curve of AL358875.1 used in Example 8 of this application to distinguish between NSCLC patients and healthy volunteers.

[0040] Figure 4H ROC curves for distinguishing NSCLC patients from healthy volunteers using SNORA51, as described in Example 8 of this application.

[0041] Figure 4I ROC curves for distinguishing NSCLC patients from healthy volunteers using AC007956.1, AC093523.1, AL358875.1, and SNORA51 in Example 8 of this application.

[0042] Figure 5A The results of Example 9 of this application show a significant upregulation of AC007956.1 in early-stage NSCLC patients compared to healthy volunteers.

[0043] Figure 5B The results of Example 9 of this application show a significant upregulation of AC093523.1 in early-stage NSCLC patients compared to healthy volunteers.

[0044] Figure 5C The results of Example 9 of this application show a significant upregulation of AL358875.1 in early-stage NSCLC patients compared to healthy volunteers.

[0045] Figure 5D The results of Example 9 of this application show a significant upregulation of SNORA51 in early-stage NSCLC patients compared to healthy volunteers.

[0046] Figure 5E This is an ROC curve diagram for distinguishing between early-stage NSCLC patients and healthy volunteers in AC007956.1, which is involved in Example 9 of this application.

[0047] Figure 5F ROC curve for distinguishing between early-stage NSCLC patients and healthy volunteers in AC093523.1 as described in Example 9 of this application.

[0048] Figure 5G ROC curve for distinguishing early-stage NSCLC patients from healthy volunteers using AL358875.1 as described in Example 9 of this application.

[0049] Figure 5H ROC curves for distinguishing early-stage NSCLC patients from healthy volunteers using SNORA51, as described in Example 9 of this application.

[0050] Figure 5I ROC curves for distinguishing between early-stage NSCLC patients and healthy volunteers using AC007956.1, AC093523.1, AL358875.1, and SNORA51 in Example 9 of this application.

[0051] Figure 6A This is the ROC curve of AC007956.1 and CEA used in Example 10 of this application to distinguish between NSCLC patients and healthy volunteers.

[0052] Figure 6B This is an ROC curve diagram showing the combined use of AC093523.1 and CEA to distinguish NSCLC patients from healthy volunteers in Example 10 of this application.

[0053] Figure 6C This is the ROC curve of AL358875.1 combined with CEA used in Example 10 of this application to distinguish between NSCLC patients and healthy volunteers.

[0054] Figure 6D This is the ROC curve of SNORA51 and CEA combined to distinguish NSCLC patients from healthy volunteers in Example 10 of this application.

[0055] Figure 6E This is an ROC curve plot showing the combined use of AC007956.1, AC093523.1, AL358875.1, SNORA51, and CEA to distinguish between NSCLC patients and healthy volunteers in Example 10 of this application.

[0056] Figure 6F This is the ROC curve of AC007956.1 and CYFRA21-1 used in Example 10 of this application to distinguish between NSCLC patients and healthy volunteers.

[0057] Figure 6G This is an ROC curve plot showing the combined use of AC093523.1 and CYFRA21-1 to distinguish between NSCLC patients and healthy volunteers in Example 10 of this application.

[0058] Figure 6H This is an ROC curve diagram showing the combined use of AL358875.1 and CYFRA21-1 to distinguish between NSCLC patients and healthy volunteers in Example 10 of this application.

[0059] Figure 6I This is an ROC curve diagram of the combination of SNORA51 and CYFRA21-1 in Example 10 of this application to distinguish between NSCLC patients and healthy volunteers.

[0060] Figure 6J ROC curves for distinguishing NSCLC patients from healthy volunteers using AC007956.1, AC093523.1, AL358875.1, SNORA51, and CYFRA21-1 in Example 10 of this application.

[0061] Figure 6K ROC curves for distinguishing NSCLC patients from healthy volunteers using AC007956.1, AC093523.1, AL358875.1, SNORA51, CEA, and CYFRA21-1 in combination, as described in Example 10 of this application.

[0062] Figure 7A This is an ROC curve diagram showing the combination of AC007956.1 and CEA in Example 10 of this application to distinguish between early-stage NSCLC patients and healthy volunteers.

[0063] Figure 7B This is an ROC curve plot showing the combination of AC093523.1 and CEA in Example 10 of this application to distinguish between early-stage NSCLC patients and healthy volunteers.

[0064] Figure 7C This is an ROC curve diagram showing the combination of AL358875.1 and CEA in Example 10 of this application to distinguish between early-stage NSCLC patients and healthy volunteers.

[0065] Figure 7D This is the ROC curve of SNORA51 and CEA combined to distinguish between early-stage NSCLC patients and healthy volunteers in Example 10 of this application.

[0066] Figure 7EROC curves for distinguishing early-stage NSCLC patients from healthy volunteers using AC007956.1, AC093523.1, AL358875.1, SNORA51, and CEA in Example 10 of this application.

[0067] Figure 7F This is an ROC curve plot showing the combined use of AC007956.1 and CYFRA21-1 in Example 10 of this application to distinguish between early-stage NSCLC patients and healthy volunteers.

[0068] Figure 7G This is an ROC curve plot showing the combined use of AC093523.1 and CYFRA21-1 in Example 10 of this application to distinguish between early-stage NSCLC patients and healthy volunteers.

[0069] Figure 7H This is an ROC curve plot showing the combined use of AL358875.1 and CYFRA21-1 to distinguish between early-stage NSCLC patients and healthy volunteers in Example 10 of this application.

[0070] Figure 7I ROC curves for distinguishing between early-stage NSCLC patients and healthy volunteers using the combined use of SNORA51 and CYFRA21-1 in Example 10 of this application.

[0071] Figure 7J ROC curves for distinguishing between early-stage NSCLC patients and healthy volunteers using AC007956.1, AC093523.1, AL358875.1, SNORA51, and CYFRA21-1 in Example 10 of this application.

[0072] Figure 7K ROC curves for distinguishing early-stage NSCLC patients from healthy volunteers using AC007956.1, AC093523.1, AL358875.1, SNORA51, CEA, and CYFRA21-1 in combination, as described in Example 10 of this application.

[0073] Figure 8A The results of AC007956.1 were significantly upregulated in T1-4 stage patients compared to Tis stage NSCLC patients in Example 11 of this application.

[0074] Figure 8B This is an ROC curve diagram for distinguishing between patients with T1-4 stage NSCLC and patients with Tis stage NSCLC, as described in Example 11 of this application (AC007956.1).

[0075] Figure 8C The results of Example 11 of this application show a significant upregulation of AL358875.1 in T1-4 stage patients compared to Tis stage NSCLC patients.

[0076] Figure 8D ROC curves for distinguishing between T1-4 stage patients and Tis stage NSCLC patients using AL358875.1 as described in Example 11 of this application.

[0077] Figure 8E This is an ROC curve diagram showing the combined differentiation between T1-4 stage patients and Tis stage NSCLC patients using AC007956.1 and AL358875.1 in Example 11 of this application.

[0078] Figure 9A The results of AC007956.1 were significantly upregulated in NSCLC patients compared to patients with benign lung diseases, as described in Example 12 of this application.

[0079] Figure 9B The results of Example 12 of this application show a significant upregulation of AC093523.1 in NSCLC patients compared to patients with benign lung diseases.

[0080] Figure 9C The results of Example 12 of this application show a significant upregulation of AL358875.1 in NSCLC patients compared to patients with benign lung diseases.

[0081] Figure 9D The results of SNORA51 upregulation in NSCLC patients compared to patients with benign lung diseases are shown in Example 12 of this application.

[0082] Figure 9E This is an ROC curve diagram for distinguishing between NSCLC patients and patients with benign lung diseases, based on AC007956.1, which relates to Embodiment 12 of this application.

[0083] Figure 9F This is an ROC curve diagram for distinguishing between NSCLC patients and patients with benign lung diseases, based on AC093523.1 in Embodiment 12 of this application.

[0084] Figure 9G This is an ROC curve diagram for distinguishing between NSCLC patients and patients with benign lung diseases using AL358875.1 as described in Example 12 of this application.

[0085] Figure 9H This is an ROC curve diagram of SNORA51 used in Example 12 of this application to distinguish between NSCLC patients and patients with benign lung diseases.

[0086] Figure 9I This is an ROC curve diagram showing the combined differentiation between NSCLC patients and patients with benign lung diseases using AC007956.1, AC093523.1, AL358875.1, and SNORA51 in Example 12 of this application. Detailed Implementation

[0087] The present application is described in detail below with reference to the embodiments, but the present application is not limited to these embodiments. Unless otherwise specified, the raw materials and reagents in the embodiments of the present application are all purchased through commercial channels.

[0088] Based on the pressing technical challenges, this application aims to evaluate the diagnostic efficacy of serum exosomal snoRNAs (S-EXO-snoRNAs) for NSCLC (including early-stage NSCLC). Serum exosomes were separated from NSCLC patients and healthy donors using ultracentrifugation and identified by qNano nanoparticle tracking analysis, transmission electron microscopy (TEM), and Western blot. Differentially expressed snoRNAs were screened using gene chips and qRT-PCR. The diagnostic efficacy of serum exosomal snoRNAs (S-EXO-snoRNAs) was validated by ROC analysis in 240 NSCLC patients, 205 healthy subjects, and 48 patients with benign lung diseases.

[0089] First, snoRNA microarray analysis was performed on serum exosomes from lung cancer patients and healthy individuals to identify four differentially expressed snoRNAs: AC007956.1, AC093523.1, AL358875.1, and SNORA51. Subsequently, PCR was used to detect the expression levels of snoRNAs in serum exosomes from 240 lung cancer patients, 205 healthy subjects, and 48 patients with benign lung diseases.

[0090] The results showed that these snoRNAs were significantly upregulated in lung cancer patients (including early-stage patients) and exhibited good diagnostic efficacy. Furthermore, when these four exosomal snoRNAs were used in combination with traditional tumor markers, they effectively compensated for the low diagnostic efficacy of CEA and CYFRA21-1. The results of this study suggest that exosomal snoRNAs have the potential to serve as diagnostic biomarkers for NSCLC.

[0091] Four S-EXO-snoRNAs (AC007956.1, AC093523.1, AL358875.1, and SNORA51) were significantly upregulated in NSCLC patients. They exhibited high diagnostic accuracy for NSCLC (combined AUC = 0.925, sensitivity = 88.8%, specificity = 86.7%), and also performed well in early-stage NSCLC (ES-NSCLC) (combined AUC = 0.9345, sensitivity = 88.8%, specificity = 86.6%). These four snoRNAs significantly improved the diagnostic efficacy of traditional tumor markers CEA and CYFRA21-1, especially in early-stage NSCLC.

[0092] Furthermore, the four S-EXO-snoRNAs demonstrated diagnostic value in differentiating histological types, tumor invasion levels, lymph node metastases, distant metastases, and TNM staging. Notably, in the cohort included in this study, this combination of biomarkers showed superior ability to distinguish NSCLC patients from those with benign lung diseases (combined AUC = 0.9124, sensitivity = 82.92%, specificity = 93.75%). Therefore, the four S-EXO-snoRNAs, AC007956.1, AC093523.1, AL358875.1, and SNORA51, exhibited good diagnostic efficacy.

[0093] The present application solution will be described below through specific embodiments.

[0094] Example 1 Sample Preparation This application included 240 NSCLC patients, 205 healthy donors, and 48 patients with benign lung diseases. After collecting serum samples, blood cells were first removed by centrifugation at 2000 g for 10 minutes, followed by centrifugation at 12000 g for 10 minutes at 4°C to remove cell debris; the resulting supernatant was then placed... Store at 80℃ for subsequent ultracentrifugation. NSCLC staging was determined according to the 8th edition of the Tumor-Lymph Node-Metastasis (TNM) staging criteria of the International Association for the Study of Lung Cancer (IASLC). Prior to blood collection, all NSCLC patients had no history of anti-tumor treatment. Healthy volunteers and patients with benign lung diseases had no endocrine, immune, or metabolic disorders.

[0095] Example 2: Exosomal snoRNA microarray analysis Total RNA was extracted from exosomes of 3 NSCLC patients and 3 healthy donors using TRIzol reagent (Invitrogen). RNA concentration was detected using a NanoDrop ND-1000 spectrophotometer, and RNA integrity (quality) was assessed using Bioanalyzer 2100 or denaturing gel electrophoresis. Expression profiling was performed using the Arraystar small RNA microarray, which contains over 955 specific probes for quantification of snoRNAs. The experimental procedure included RNA dephosphorylation, end labeling, and microarray hybridization. After scanning slides using an Agilent G2505C microarray scanner, the resulting microarray images were analyzed using Agilent Feature Extraction software (v11.0.1.1). Raw intensity values ​​were log2 transformed and quantile normalized. Differentially expressed snoRNAs between the two groups were screened using a fold change (FC) > 1.3 and P < 0.05 as the threshold, and cluster heatmaps and volcano plots were generated using R software.

[0096] To screen for differentially expressed snoRNAs, we performed snoRNA microarray analysis on serum exosomes from 3 NSCLC patients and 3 healthy individuals. Using P < 0.05 and FC > 1.3 as criteria, the results showed a difference in snoRNA expression between the two groups. Figure 2A (See volcano map) Figure 2B Based on fold changes, we selected the top 25 snoRNAs with the most significant upregulation and further validated them in NSCLC patients and healthy individuals using qRT-PCR. Ultimately, we confirmed that four exosomal snoRNAs (AC007956.1, AC093523.1, AL358875.1, and SNORA51) were differentially expressed in NSCLC patients, and their expression trends were consistent with the microarray data. Figure 2C ) Detailed gene information of these four exosomal snoRNAs is shown in Table 1, where the sequences of AC007956.1, AC093523.1, AL358875.1 and SNORA51 are shown as SEQ ID No.1, SEQ ID No.2, SEQ ID No.3 and SEQ ID No.4, respectively.

[0097] Table 1. Detailed genetic information of four exosomal snoRNAs

[0098] To clarify the distribution characteristics of exosomal snoRNAs, the expression levels of four specific snoRNAs in exosomal decanter (EDS) and purified exosomal samples were examined.

[0099] The results showed that these snoRNAs were significantly enriched in purified exosomes. Figure 3A RNase A digestion does not affect its expression level. Figure 3B This indicates that snoRNAs are encapsulated within exosomes, protecting them from enzymatic degradation. Furthermore, the snoRNA levels remained stable after being stored at room temperature for up to 24 hours. Figure 3C , Figure 3D , Figure 3E , Figure 3F ).

[0100] Example 3: Isolation of serum exosomes Serum exosomes were isolated using an ultracentrifugation-based method. First, serum was centrifuged at 10,000 g for 30 minutes at 4°C to remove larger vesicles. The supernatant was then ultracentrifuged (Beckman Colter, USA; 100,000 g, 2 hours, 4°C) to precipitate exosomes. After washing with phosphate-buffered saline (PBS), the exosome precipitate was recovered by ultracentrifugation again under the same conditions (100,000 g, 2 hours, 4°C). This study strictly followed the official guidelines issued by the International Society for Extracellular Vesicles (ISEV) and employed a widely accepted triple quality control strategy for exosomes to verify sample concentration and purity: 1) Transmission electron microscopy (TEM) revealed a typical cup-shaped morphology of exosomes; 2) Nanoparticle tracking analysis (NTA) showed that the particle size of the isolated vesicles was mainly distributed within the standard range of 30–150 nm, with an exosome concentration of 2.3 × 10¹¹ particles / mL and a particle-to-protein ratio (PtP) of 1.67 × 10¹¹. 8 3) Western blot analysis confirmed high expression of exosome classic positive marker protein.

[0101] Example 4: Transmission Electron Microscopy Analysis For transmission electron microscopy (TEM), 15 μL of exosome sample was dropped onto a copper grid and fixed with 50 μL of 1% glutaraldehyde for 5 minutes. After fixation, the copper grid was rinsed with double-distilled water for 2 minutes. It was then stained with 50 μL of uranium oxalate (pH 7) for 5 minutes, followed by treatment with 50 μL of methylcellulose-uranium acetate for 10 minutes. After removing residual liquid, the copper grid was air-dried at room temperature for 5–10 minutes. Imaging was performed using a Tecnai G2Spirit TEM (Thermo Fisher, USA).

[0102] Transmission electron microscopy images show spherical vesicles with diameters of 50–150 nm, consistent with the typical size and morphological characteristics of exosomes. Figure 1A Nanoparticle tracking analysis further confirmed the above particle size characteristics. Figure 1B Western blot analysis showed that the isolated vesicles highly expressed exosome markers CD81, CD63, and CD9, while these markers were not detected in cell samples. Figure 1C ).

[0103] Example 5: Nanoparticle Tracking Analysis The particle size distribution and concentration of exosomes were determined using a ZetaView PMX 110 instrument (Particle Metrix, Meerbusch, Germany) and its accompanying software (ZetaView v8.04.02) via nanoparticle tracking analysis (NTA). Prior to analysis, the isolated exosome samples were appropriately diluted with 1×PBS (Biological Industries, Israel) to achieve optimal detection conditions. Data were collected from 11 different locations for each sample. The instrument was calibrated using 110 nm polystyrene reference particles, and all assays were performed under controlled temperature conditions ranging from 23 to 37 °C.

[0104] Example 6 Western blot detection Cellular and exosome protein extracts were obtained by lysis on ice for 30 minutes using radioimmunoprecipitation (RIPA) buffer (Beyotime, China). Protein quantification was performed using a BCA assay kit. Subsequently, exosomes and cellular proteins were separated by SDS-PAGE, transferred to PVDF membranes (Millipore, USA), blocked with 5% skim milk for 4 hours, and incubated overnight at 4°C with primary antibodies targeting CD9, CD63, and CD81 (1:1000; Cell Signaling Technology, USA). The membranes were then incubated with HRP-labeled secondary antibody at room temperature for 1 hour. Protein bands were visualized using ECL reagent (Bio-Rad, USA).

[0105] Example 7 RNA extraction and qRT-PCR Total RNA was extracted from exosomes using 500 μL TRIzol reagent (Thermo Fisher Scientific, USA), and cDNA was synthesized by reverse transcription using the Mix-X miRNA First-Strand Synthesis Kit (Aikerui Biotechnology, China) according to the kit instructions. Exosomal snoRNA expression levels were detected on a LightCycler 480 system (Roche, Switzerland). Each PCR reaction system contained 2 μL cDNA template, 6.4 μL RNase-free water, 0.8 μL each of forward and reverse primers, and 10 μL SYBR-Green premix (Aikerui Biotechnology, China). U6 was used as an internal control gene. The relative expression level was calculated using the ΔCT method, i.e., the CT value of the target snoRNA minus the CT value of U6. The corresponding primer sequences are shown in Table 2 (corresponding to SEQ ID No. 5, SEQ ID No. 6, SEQ ID No. 7, SEQ ID No. 8, SEQ ID No. 9, and SEQ ID No. 10, respectively).

[0106] Table 2

[0107] Example 8: Four serum exosomal snoRNAs as diagnostic biomarkers for NSCLC Statistical analysis was performed using GraphPad Prism 8.0 (GraphPad Software, USA) and SPSS 25.0 (IBM, Germany). The Kolmogorov-Smirnov test was used to determine the normality of the data. Parametric tests were used for normally distributed variables; the Mann-Whitney test was used for non-normally distributed variables. One-way ANOVA or the Kruskal-Wallis test was used for comparisons among multiple groups, depending on the circumstances. Results are expressed as median and interquartile range. ROC curve analysis was used to assess diagnostic value; a two-sided p-value < 0.05 was considered statistically significant.

[0108] Next, the diagnostic value of these four S-EXO-snoRNAs was evaluated in a large cohort including 240 NSCLC patients and 205 healthy volunteers. The results showed that all four snoRNAs were significantly upregulated in NSCLC patients compared to healthy controls, consistent with the microarray results. Figure 4A , Figure 4B , Figure 4C and Figure 4D ).

[0109] Table 3 lists the correlations between snoRNA expression and clinicopathological parameters in NSCLC patients. Exosomal snoRNA AC007956.1 was associated with T stage (P=0.0481); exosomal snoRNA AL358875.1 was associated with histological type (P=0.0313), T stage (P=0.0229), and TNM stage (P=0.0218); exosomal SNORA51 was associated with histological type (P=0.0220) but had no significant correlation with other clinical features.

[0110] Table 3

[0111] Note: The median of ΔCt (interquartile range) is the median of the interquartile range, and the p-value is the test result for comparing the differences in ΔCt expression among the groups: A p-value < 0.05 indicates statistical significance.

[0112] ROC curve analysis showed that all four exosomal snoRNAs (AC007956.1, AC093523.1, AL358875.1, and SNORA51) could effectively distinguish between NSCLC patients and healthy volunteers, with AUCs of 0.5951, 20.83%, and 98.54%, respectively. Figure 4E AC093523.1 has a value of 0.8193, a sensitivity of 77.5%, and a specificity of 74.15%. Figure 4F ); AL358875.1 has a sensitivity of 85.42% and a specificity of 85.85% ( ); Figure 4G The SNORA51 value was 0.7973, with a sensitivity of 85.83% and a specificity of 63.41%. Figure 4H ).

[0113] When the four tests are combined, the overall diagnostic efficacy (AUC) reaches 0.9276, the sensitivity is 88.8%, and the specificity is 86.7%. Figure 4I ).

[0114] Example 9: Serum exosomal snoRNAs as diagnostic biomarkers for early NSCLC In patients with early-stage NSCLC (ES-NSCLC), serum exosomal snoRNAs were significantly more highly expressed in early-stage patients compared to healthy donors. Figure 5A , Figure 5B , Figure 5C and Figure 5D Regarding diagnostic efficacy, ROC curve analysis showed that AC007956.1 had an AUC of 0.6168, a sensitivity of 26.79%, and a specificity of 98.54%. Figure 5E The AUC of AC093523.1 was 0.8170, with a sensitivity of 86.61% and a specificity of 63.9%. Figure 5F The AUC of AL358875.1 was 0.9216, with a sensitivity of 90.18% and a specificity of 80.98%. Figure 5G The AUC of SNORA51 was 0.7756, with a sensitivity of 86.61% and a specificity of 61.46%. Figure 5H ).

[0115] In particular, after the four joint ( Figure 5I The AUC was increased to 0.9345, the sensitivity was 88.8%, and the specificity was 86.6%.

[0116] Example 10: Serum exosomal snoRNAs enhance the diagnostic accuracy of NSCLC tumor markers Carcinoembryonic antigen (CEA) is a pan-tumor prognostic marker and is recommended for the diagnosis of NSCLC. When exosomal snoRNAs AC007956.1, AC093523.1, AL358875.1, or SNORA51 were combined with CEA, the AUC of CEA significantly increased from 0.734 to 0.776, 0.861, 0.918, or 0.840, respectively. Figure 6A , Figure 6B , Figure 6C and Figure 6D ).

[0117] In particular, when all five markers were combined, the AUC increased to 0.934, with sensitivity and specificity reaching 84.5% and 93.7%, respectively. Figure 6E ).

[0118] Cytokeratin 19 fragment CYFRA21-1 is a circulating tumor marker, with an AUC of 0.856 when measured alone. When combined with AC093523.1, AL358875.1, or SNORA51, the AUC increased to 0.900, 0.943, or 0.885, respectively. Figure 6G , Figure 6H , Figure 6I AC007956.1 failed to significantly improve the AUC of CYFRA21-1 (), while AC007956.1 failed to significantly improve the AUC of CYFRA21-1 (). Figure 6F ).

[0119] In particular, the AUC reached 0.951 when all five parameters were combined. Figure 6J ).

[0120] It is worth noting that the combined AUC of all six markers was the highest, reaching 0.954, with corresponding increases in sensitivity and specificity to 89.6% and 90.7%, respectively. Figure 6K ).

[0121] Meanwhile, in the early NSCLC ( Figure 7A , Figure 7B , Figure 7C , Figure 7D , Figure 7E , Figure 7F , Figure 7G , Figure 7H , Figure 7I , Figure 7J , Figure 7K When AC007956.1, AC093523.1, AL358875.1, or SNORA51 were combined with CEA, the AUC increased from 0.588 to 0.654, 0.825, 0.930, or 0.797, respectively. When all four exosomal snoRNAs were combined with CEA, the AUC increased to 0.933, with a sensitivity of 82.8% and a specificity of 92.7%. Similarly, when AC007956.1, AC093523.1, AL358875.1, or SNORA51 were combined with CYFRA21-1, the AUC increased from 0.757 to 0.761, 0.848, 0.937, or 0.828, respectively.

[0122] When all four are combined, the AUC increases to 0.947, the sensitivity is 92.2%, and the specificity is 86.8%.

[0123] However, when snoRNA, CEA and CYFRA21-1 were used together for diagnosis, the AUC value remained at 0.947, without a significant increase.

[0124] Example 11: Association between AC007956.1, AL358875.1 and tumor invasiveness, and subgroup analysis of other clinical parameters of four snoRNAs. As is well known, carcinoma in situ and invasive carcinoma have different biological behaviors. Determining whether a tumor has breached the basement membrane is of great significance for the treatment and prognosis of lung cancer patients.

[0125] like Figure 8A , Figure 8B , Figure 8C and Figure 8D As shown, compared with Tis stage NSCLC patients, T1-4 stage patients had significantly higher serum exosomal snoRNAs (AC007956.1 and AL358875.1), with AUC values ​​of 0.6256 and 0.6441, respectively. Figure 8E The results showed that when the two were combined, the AUC reached 0.6761, the sensitivity was 73.66%, and the specificity was 56.52%.

[0126] These results indicate that these two serum exosomal snoRNAs have moderate discriminative ability in distinguishing invasive NSCLC from carcinoma in situ, suggesting that they may serve as auxiliary biomarkers for assessing tumor invasion status.

[0127] Furthermore, the diagnostic efficacy of four snoRNAs combined in different clinical feature subgroups was evaluated. As summarized in Table 3 above, the ROC curve analysis results are as follows: the AUC for distinguishing adenocarcinoma from squamous cell carcinoma was 0.6273 (sensitivity 72.34%, specificity 55.23%); the AUC for distinguishing carcinoma in situ from invasive carcinoma was 0.6731 (sensitivity 90.24%, specificity 39.13%), slightly lower than the combination of AC007956.1 and AL358875.1. For lymph node metastasis, the AUC was 0.6204 (sensitivity 54.55%, specificity 68.55%); for distant metastasis, the AUC reached 0.6765 (sensitivity 68.75%, specificity 59.41%); and for TNM staging, the AUC was 0.6606 (sensitivity 86.12%, specificity 39.13%).

[0128] Example 12: Exosomal snoRNAs used to differentiate NSCLC from benign diseases To verify the ability of this biomarker combination to distinguish between NSCLC and benign lung diseases, this study included 48 patients with benign lung diseases. The results showed that, compared with patients with benign lung diseases, the expression levels of four exosomal snoRNAs were significantly increased in NSCLC patients. Figure 9A , Figure 9B , Figure 9C and Figure 9D ROC curve analysis further confirmed that AC007956.1, AC093523.1, AL358875.1, and SNORA51 can all effectively distinguish NSCLC patients from patients with benign lung lesions. The AUC, sensitivity, and specificity of each biomarker are as follows: AC007956.1 has an AUC of 0.6542, a sensitivity of 36.25%, and a specificity of 93.75%. Figure 9E The AUC of AC093523.1 was 0.8066, with a sensitivity of 72.92% and a specificity of 79.17%. Figure 9F The AUC of AL358875.1 was 0.9051, with a sensitivity of 88.75% and a specificity of 87.50%. Figure 9G The AUC of SNORA51 was 0.7970, the sensitivity was 69.58%, and the specificity was 81.25%. Figure 9H ).

[0129] Notably, the combined analysis of the four biomarkers demonstrated superior diagnostic efficacy, with an AUC of 0.9124, a sensitivity of 82.92%, and a specificity of 93.75%. Figure 9I ).

[0130] This study identified a group of serum exosomal snoRNAs (including AC007956.1, AC093523.1, AL358875.1, and SNORA51) that showed good diagnostic efficacy for NSCLC.

[0131] In this study, the overall AUC of four serum exosomal snoRNA combinations reached 0.925, with a sensitivity of 88.8% and a specificity of 86.7%, demonstrating diagnostic efficacy comparable to several previously reported high-performance exosomal RNA features. Notably, for early-stage NSCLC, this combination achieved an AUC of 0.9345, with a sensitivity of 88.8% and a specificity of 86.6%, outperforming several previously reported exosomal RNA biomarkers, including tRF combinations, single snoRNA markers, and other RNA-based features.

[0132] Furthermore, in this study, the combination of four serum exosomal snoRNAs with CEA and CYFRA21-1 further enhanced the diagnostic efficacy for NSCLC or early-stage NSCLC. Notably, the four snoRNAs identified in this study are structurally conserved, resistant to RNase degradation, and highly enriched in exosomes. Therefore, compared with other exosomal RNAs such as miRNAs and lncRNAs, they exhibit better sample stability and possess unique advantages as diagnostic biomarkers for non-small cell lung cancer.

[0133] This study also found that the association between specific snoRNAs and clinicopathological features suggests broader biological and clinical significance. In particular, the association between exosomal AL358875.1 and histological subtype, T stage, and TNM stage, and the association between AC007956.1 and AL358875.1 and the transformation from carcinoma in situ to invasive carcinoma, indicate that these snoRNAs may play an important role in tumor progression. These findings are consistent with growing evidence that snoRNAs can regulate multiple biological processes, including rRNA modification, ribosome biosynthesis, and cell signaling pathways, thereby promoting tumorigenesis, progression, and other oncogenic phenotypes. Although the exosomal snoRNAs AC007956.1 and AL358875.1 have a moderate ability to distinguish between invasive and non-invasive lesions (AUC=0.6761), this finding provides a new approach to differentiating between carcinoma in situ and invasive carcinoma, offering more diagnostic information not included in traditional biomarkers. This is of great significance for the diagnosis of non-small cell lung cancer, especially early-stage non-small cell lung cancer.

[0134] The above description is merely an embodiment of this application, and the scope of protection of this application is not limited to these specific embodiments, but is determined by the claims of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the technical concept and principles of this application should be included within the scope of protection of this application.

Claims

1. The application of a serum exosome snoRNA marker detection reagent in the preparation of a diagnostic reagent for serum samples of non-small cell lung cancer, characterized in that, The detection reagent for serum exosome snoRNA markers includes a detection reagent for specifically detecting the levels of AC007956.1, AC093523.1, AL358875.1 and SNORA51 markers in serum exosomes; Wherein, AC007956.1 is a snoRNA with the nucleotide sequence shown in SEQ ID No.1; AC093523.1 is a snoRNA with the nucleotide sequence shown in SEQ ID No.2; and AL358875.1 is a snoRNA with the nucleotide sequence shown in SEQ ID No.

3.

2. The application according to claim 1, characterized in that, The serum sample diagnostic reagent for non-small cell lung cancer also includes a detection reagent that specifically detects the content of CEA and / or CYFRA21-1 protein in serum.

3. The application of a serum exosome snoRNA marker detection reagent in the preparation of a diagnostic kit for serum samples of non-small cell lung cancer, characterized in that, The detection reagent for serum exosome snoRNA markers includes a detection reagent for specifically detecting the levels of AC007956.1, AC093523.1, AL358875.1 and SNORA51 markers in serum exosomes; Wherein, AC007956.1 is a snoRNA with the nucleotide sequence shown in SEQ ID No.1; AC093523.1 is a snoRNA with the nucleotide sequence shown in SEQ ID No.2; and AL358875.1 is a snoRNA with the nucleotide sequence shown in SEQ ID No.

3.

4. The application according to claim 3, characterized in that, The serum sample diagnostic kit for non-small cell lung cancer also includes a detection reagent for specifically detecting the levels of CEA and / or CYFRA21-1 proteins in serum.

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