Marker, kit, use for early diagnosis of idiopathic pulmonary fibrosis

Abstract

This application discloses a biomarker, kit, and application for the early diagnosis of idiopathic pulmonary fibrosis, relating to the field of biotechnology. The biomarker for the early diagnosis of idiopathic pulmonary fibrosis includes at least one of the following: the expression product of the NSUN2 gene; the expression product of the COL1A1 gene; and the 5-methylcytosine modification level of COL1A1 gene mRNA. It plays an important role in the early and accurate diagnosis and monitoring of idiopathic pulmonary fibrosis.

Description

Technical Field

[0001] This application relates to the field of biotechnology, and in particular to biomarkers, reagent kits, and applications for the early diagnosis of idiopathic pulmonary fibrosis. Background Technology

[0002] Pulmonary fibrosis is a common, irreversible interstitial lung disease, including idiopathic pulmonary fibrosis (IPF) of unknown cause, and secondary idiopathic pulmonary fibrosis caused by other diseases such as inflammation, infection, or tumors, including bacterial infections, viral infections, lung cancer, chronic bronchitis, chronic pneumonia, emphysema, chronic obstructive pulmonary disease, and allergic diseases such as asthma. The pathophysiological mechanism of idiopathic pulmonary fibrosis is that during the repair and remodeling process after lung tissue damage, the lung parenchyma is replaced by scar tissue, leading to impaired alveolar expansion and contraction and a reduced oxygen exchange rate. Patients clinically present with symptoms such as shortness of breath or even dyspnea, seriously endangering human health.

[0003] Currently, the diagnosis of IPF mainly relies on HRCT (High-Resolution Computed Tomography) and lung biopsy. HRCT has insufficient sensitivity in the early stages of the disease, with an average delay of 2.2 years from symptom onset to definitive diagnosis, and its interpretation is highly subjective. Existing serum biomarkers in lung biopsies, such as KL-6 and SP-D, lack IPF specificity and are difficult to differentiate from other interstitial lung diseases. Therefore, there is an urgent need for a biomarker for the accurate early diagnosis and monitoring of idiopathic pulmonary fibrosis. Summary of the Invention

[0004] The main objective of this application is to provide a biomarker, reagent kit, and application for the early diagnosis of idiopathic pulmonary fibrosis, aiming to provide a biomarker for the accurate early diagnosis and monitoring of idiopathic pulmonary fibrosis.

[0005] This application provides a biomarker for the early diagnosis of idiopathic pulmonary fibrosis, wherein the biomarker for the early diagnosis of idiopathic pulmonary fibrosis includes at least one of the following: The expression product of the NSUN2 gene; The expression product of the COL1A1 gene; 5-methylcytosine modification level of COL1A1 gene mRNA.

[0006] This application provides a kit for the early diagnosis of idiopathic pulmonary fibrosis, the kit comprising at least one of the following reagents for detecting biomarkers for the early diagnosis of idiopathic pulmonary fibrosis as described above: The first reagent for detecting the expression product of the NSUN2 gene; A second reagent for detecting the expression product of the COL1A1 gene; A third reagent for detecting the level of 5-methylcytosine modification in COL1A1 gene mRNA.

[0007] In one feasible embodiment, the first reagent comprises: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 3 and SEQ ID No. 4; And / or, NSUN2 protein-specific binders.

[0008] In one feasible embodiment, the second reagent comprises: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 5 and SEQ ID No. 6; And / or, a COL1A1 protein-specific binder.

[0009] In one feasible embodiment, the third reagent comprises: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 5 and SEQ ID No. 6 and specific antibodies against 5-methylcytosine.

[0010] In a feasible embodiment, the second reagent and the third reagent comprise primer pairs for amplifying an internal reference gene, wherein the internal reference gene is GAPDH, and the nucleotide sequences of the primer pairs for amplifying the internal reference gene are shown in SEQ ID No. 1 and SEQ ID No. 2.

[0011] In one feasible embodiment, the third reagent further includes: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 7 and SEQ ID No. 8, bisulfite, and methylation-specific probes for detecting specific methylation sites.

[0012] This application also provides an application of the biomarker for early diagnosis of idiopathic pulmonary fibrosis as described above in a disease monitoring program for idiopathic pulmonary fibrosis.

[0013] In one feasible embodiment, the idiopathic pulmonary fibrosis course monitoring protocol includes: The study detected at least one of the following in biological samples obtained from the subjects: the expression product level of the NSUN2 gene, the expression product level of the COL1A1 gene, and the 5-methylcytosine modification level of the COL1A1 gene mRNA. The detection results of the biological sample are input into a preset disease monitoring model and compared with a preset diagnostic threshold in the disease monitoring model. Based on the comparison results, the course information of idiopathic pulmonary fibrosis of the subjects is output.

[0014] In one feasible embodiment, the biological sample includes at least one of peripheral blood, serum, plasma, and bronchoalveolar lavage fluid.

[0015] The one or more technical solutions proposed in this application have at least the following technical effects: This application provides a biomarker for the early diagnosis of idiopathic pulmonary fibrosis (IPF), including at least one of the NSUN2 gene expression product, the COL1A1 gene expression product, and the 5-methylcytosine modification level of COL1A1 gene mRNA. First, the biomarker, based on the molecular regulatory mechanism of NSUN2-m5C-COL1A1, is directly related to the core pathological feature of IPF, namely excessive deposition of type I collagen, and can effectively distinguish IPF from other interstitial lung diseases. Second, 5-methylcytosine modification of COL1A1 gene mRNA, as a molecular event at the epitranscriptome level, shows abnormal changes earlier than changes in COL1A1 gene protein expression and tissue morphology, enabling early diagnosis of IPF. Therefore, this application provides a biomarker for the accurate early diagnosis and monitoring of idiopathic pulmonary fibrosis. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 The expression levels of m5C transferases NSUN2, NSUN6, and NSUN7 provided in the embodiments of this application in normal human lung fibroblasts (WI-38) and a fibrotic lung fibroblast model (TGF-β1+WI-38); Figure 1 In Figure A, the mRNA expression levels of m5C transferases NSUN2, NSUN6, and NSUN7 are found in normal human lung fibroblasts (WI-38) and a fibrotic lung fibroblast model (TGF-β1+WI-38). Figure 1 In the middle B, the protein expression levels of m5C transferases NSUN2, NSUN6, and NSUN7 were found in normal human lung fibroblasts (WI-38) and a fibrotic lung fibroblast model (TGF-β1+WI-38). Figure 2The changes in m5C modification levels of NSUN2 mRNA, COL1A1 mRNA, and COL1A1 mRNA after TGF-β1 stimulation are provided in the embodiments of this application. Figure 2 In Figure A, the change in NSUN2 mRNA expression level after TGF-β1 stimulation is shown. Figure 2 In Figure B, the change in COL1A1 mRNA expression level after TGF-β1 stimulation is shown. Figure 2 C represents the change in m5C modification level of COL1A1 mRNA after TGF-β1 stimulation; Figure 3 The efficiency of NSUN2 knockdown and the changes in the m5C modification levels of COL1A1 mRNA and COL1A1 mRNA in the pulmonary fibrosis model cells (TGF-β1+WI-38) provided in the embodiments of this application were studied. Figure 3 In Figure A, the efficiency of NSUN2 knockdown in a lung fibrosis model cell (TGF-β1+WI-38) is verified. Figure 3 B represents the change in COL1A1 mRNA expression level after NSUN2 knockdown; Figure 3 C represents the change in m5C modification level of COL1A1 mRNA after NSUN2 knockdown; Figure 4 Differences in m5C modification levels of NSUN2 mRNA, COL1A1 mRNA, and COL1A1 mRNA in different groups provided in the embodiments of this application; Figure 4 In Figure A, the differences in NSUN2 mRNA expression levels were observed among the healthy control group, other ILD groups, and the IPF group. Figure 4 B represents the differences in COL1A1 mRNA expression levels; Figure 4 C represents the differences in m5C modification levels of COL1A1 mRNA.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] Currently, the diagnosis of IPF mainly relies on HRCT and lung biopsy. HRCT has insufficient sensitivity in the early stages of the disease, with an average delay of 2.2 years from symptom onset to definitive diagnosis, and its interpretation is highly subjective. Existing serum biomarkers in lung biopsies, such as KL-6 and SP-D, lack IPF specificity and are difficult to differentiate from other interstitial lung diseases. Therefore, there is an urgent need for a biomarker for the accurate early diagnosis and monitoring of idiopathic pulmonary fibrosis.

[0023] The one or more technical solutions proposed in this application have at least the following technical effects: This application provides a biomarker for the early diagnosis of idiopathic pulmonary fibrosis (IPF), including the NSUN2 gene expression product, the COL1A1 gene expression product, and the 5-methylcytosine modification level of the COL1A1 gene mRNA. First, the biomarker, based on the molecular regulatory mechanism of NSUN2-m5C-COL1A1, is directly related to the core pathological feature of IPF, namely excessive deposition of type I collagen, and can effectively distinguish IPF from other interstitial lung diseases. Second, 5-methylcytosine modification, as a molecular event at the epitranscriptome level, shows abnormal changes earlier than protein expression changes and tissue morphological changes, enabling early diagnosis of IPF. Furthermore, the biomarker in this application can be detected in non-invasive biological samples such as peripheral blood and serum. Patients only need routine blood collection to complete the test, avoiding the surgical risks of lung biopsy and radiation exposure from HRCT. Patient compliance is high, and the test can be repeated, making it suitable for dynamic disease monitoring and treatment efficacy evaluation. Therefore, embodiments of this application provide a highly efficient biomarker for the early diagnosis of idiopathic pulmonary fibrosis.

[0024] It is important to note that NSUN2 (NOP2 / Sun RNA methyltransferase family member 2) is a core member of the RNA m5C (5-methylcytosine) methyltransferase family. Its main function is to catalyze the methylation of the 5th carbon atom of cytosine in various RNAs, including tRNA, mRNA, and lncRNA, forming m5C modification. NSUN2 recognizes RNA substrates and transfers methyl groups (the methyl donor is S-adenosylmethionine, SAM) through its conserved catalytic domains (C271 and C321 sites). m5C modification affects RNA stability, translation efficiency, and RNA-protein interactions. NSUN2 is overexpressed in various cancers, promoting cell proliferation and migration; NSUN2 mutations may also lead to neurodevelopmental disorders.

[0025] COL1A1 (Collagen type I alpha 1 chain, encoding the α1 chain of type I collagen) corresponds to type I collagen, which is the most abundant collagen protein in the human body. The core pathological feature of IPF is the overactivation of fibroblasts, which synthesize and secrete large amounts of type I and type III collagen, leading to pulmonary fibrosis and structural damage.

[0026] This application provides a biomarker for the early diagnosis of idiopathic pulmonary fibrosis, which includes at least one of the following: The expression product of the NSUN2 gene; The expression product of the COL1A1 gene; 5-methylcytosine modification level of COL1A1 gene mRNA.

[0027] The biomarkers in this embodiment are designed based on the core pathological mechanism of IPF: the overproduction and deposition of type I collagen and its upstream epigenetic regulatory mechanisms. COL1A1 is a gene encoding the α1 chain of type I collagen, and its overexpression directly leads to abnormal deposition of type I collagen in lung tissue. This is a key pathological feature that distinguishes IPF from other types of interstitial lung disease. The mechanism by which NSUN2 regulates the m5C modification of COL1A1 has a direct causal relationship with the pathophysiological process of IPF, and therefore should theoretically have higher disease specificity than existing empirical biomarkers. Furthermore, this embodiment provides multidimensional detection capabilities, simultaneously detecting NSUN2 expression or activity, the 5-methylcytosine modification level of COL1A1 gene mRNA, and the expression level of COL1A1. This combined detection of upstream regulators and downstream targets, through multiple validations, improves the accuracy and reliability of early diagnosis.

[0028] The biomarker provided in this embodiment is firstly based on the molecular regulatory mechanism of NSUN2-m5C-COL1A1, which is directly related to the core pathological feature of IPF, namely excessive deposition of type I collagen, and can effectively distinguish IPF from other interstitial lung diseases. Secondly, 5-methylcytosine modification, as a molecular event at the epitranscriptome level, shows abnormal changes earlier than changes in protein expression and tissue morphology, enabling early diagnosis of IPF. Furthermore, the biomarker in this embodiment can be detected in non-invasive biological samples such as peripheral blood and serum. Patients only need routine blood collection to complete the test, avoiding the surgical risks of lung biopsy and radiation exposure of HRCT. Patient compliance is high and the test can be repeated, making it suitable for dynamic disease monitoring and treatment efficacy evaluation. Therefore, this embodiment provides a highly efficient biomarker for the early diagnosis of idiopathic pulmonary fibrosis.

[0029] This application also provides an application of a biomarker for early diagnosis of idiopathic pulmonary fibrosis in a disease monitoring program for idiopathic pulmonary fibrosis, the application including: The study detected at least one of the following in biological samples obtained from the subjects: the expression product level of the NSUN2 gene, the expression product level of the COL1A1 gene, and the 5-methylcytosine modification level of the COL1A1 gene mRNA. In one feasible embodiment, by detecting the levels of biomarkers in the subject's biological samples and combining them with a pre-set assessment model, the disease progression information of idiopathic pulmonary fibrosis (IPF) (such as whether the disease is in the early / progressive / stable stage, prognostic risk level, etc.) is analyzed and output. Biomarkers for early diagnosis of IPF include at least one of the following: expression product of the NSUN2 gene; expression product of the COL1A1 gene; and 5-methylcytosine modification level of COL1A1 gene mRNA. This achieves full automation of molecular detection, data analysis, and result output, providing objective and quantitative evidence for disease progression monitoring.

[0030] In one feasible implementation, the biological sample includes at least one of peripheral blood, serum, plasma, and bronchoalveolar lavage fluid.

[0031] In one feasible embodiment, peripheral blood, serum, and plasma are suitable for routine, non-invasive, and repeatable clinical testing; bronchoalveolar lavage fluid is suitable for in-depth studies that require precise reflection of local lung lesions.

[0032] Alternatively, peripheral blood mononuclear cells (including lymphocytes and monocytes) can be used as biological samples to detect the expression of NSUN2 and COL1A1 in circulating immune cells.

[0033] The test results of biological samples are input into a preset disease monitoring model and compared with the preset diagnostic thresholds in the disease monitoring model. In one feasible embodiment, a preset disease monitoring model includes preset diagnostic thresholds for each biomarker. The test results are compared with these preset diagnostic thresholds to determine positive / negative results and the disease stage. If the biomarker test value is greater than or equal to the diagnostic threshold, the result is considered positive; if the biomarker test value is less than the diagnostic threshold, the result is considered negative. For a disease monitoring model with multiple biomarkers, the test values ​​of each biomarker are substituted into a risk scoring formula to calculate a comprehensive score. This comprehensive score is then compared with the diagnostic thresholds of the disease monitoring model to provide a determination result.

[0034] Optionally, the method for constructing a pre-defined disease monitoring model includes the following steps: We collected IPF patient samples with known disease stages as the training set; detected the expression levels of NSUN2 and COL1A1 and the m5C modification level; and labeled them in conjunction with clinical gold standards (such as HRCT score, lung function, survival time, etc.); and established a mapping relationship model between biomarker combination and disease course through machine learning. After verifying the accuracy of the model, it is then embedded into the system.

[0035] In one feasible embodiment, the disease monitoring protocol can also input multiple test results of the same subject at different time points into the disease monitoring model to generate a disease progression trend graph. The trend graph is used to assess the rate of disease progression (such as the slope of the change in biomarker levels over time) and treatment response (such as the percentage decrease in biomarker levels from baseline after treatment).

[0036] Based on the comparison results, the course information of idiopathic pulmonary fibrosis in the subjects is output.

[0037] In one feasible implementation, the output disease course information includes at least one of the following: a textual diagnostic report (containing the subject's biomarker test values, comparison results with diagnostic thresholds, and disease course judgment conclusions), a visualization chart (containing a bar chart of biomarker expression levels, a ROC curve (Receiver Operating Characteristic curve) chart, and a disease course trend chart), a risk score (containing risk level and confidence interval), and treatment recommendations (clinical management recommendations based on the disease course judgment results, such as recommendations for regular monitoring, recommendations for strengthening anti-fibrotic treatment, and recommendations for further imaging examinations).

[0038] The idiopathic pulmonary fibrosis (IPF) course monitoring solution provided in this embodiment automates the entire process from biological sample detection to course assessment and result output. It provides clinicians with objective and quantitative evidence for course monitoring, which helps to achieve accurate staging, individualized treatment, and dynamic follow-up management of IPF patients.

[0039] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0040] Example 1 Sample Collection and Preparation 1. Sample Collection This study included 110 participants, divided into three groups: IPF patient group (n = 40), healthy control group (n = 40), and other ILD (Interstitial Lung Disease) control group (n = 30). The inclusion criteria for the IPF patient group were as follows: all IPF patients met the 2022 ATS / ERS / JRS / ALAT guidelines for diagnosis, had UIP (Usual Interstitial Pneumonia) imaging pattern confirmed by HRCT, and were excluded from ILD caused by known etiologies such as connective tissue disease, CHP (Hypersensitivity Pneumonitis), occupational dust exposure, etc., and from complications such as severe liver, kidney, heart failure, and malignant tumors. The healthy control group consisted of healthy individuals without lung disease or other serious systemic diseases. The other ILD control group consisted of 30 patients, including 20 with NSIP (Nonspecific Interstitial Pneumonia) and 10 with CHP. Biological samples were collected from the subjects, including peripheral blood, serum, plasma, bronchoalveolar lavage fluid, or other body fluid samples.

[0041] 2. Sample Preparation 2.1 RNA extraction RNA extraction was performed using the TRIzol reagent method, based on the principle of guanidine isothiocyanate-phenol-chloroform liquid chromatography extraction. Samples were lysed with TRIzol, vortexed with chloroform, and centrifuged. The upper aqueous phase (containing RNA) was then collected, and RNA was precipitated with isopropanol, washed with 75% ethanol, dried, and dissolved in RNase-free water. The RNA concentration must meet the following requirements: not less than 50 ng / μL; its A260 / A280 ratio should be between 1.8 and 2.0, its A260 / A230 ratio should be greater than 1.8, and it should be free of genomic DNA contamination.

[0042] 2.2 Protein Extraction Protein extraction was performed using a standard protein lysis buffer. The lysis buffer contained 50 mM Tris-HCl (Tris(hydroxymethyl)aminomethane hydrochloride) buffer (pH 7.4–7.6), 150 mM NaCl, 1% Triton X-100 or 1% NP-40 (Nonidet P-40), 0.5% sodium deoxycholate, 0.1% SDS (Sodium dodecyl sulfate), 1 mM EDTA (Ethylenediaminetetraacetic acid), and freshly added protease inhibitors including 1 mM PMSF (Phenylmethylsulfonyl fluoride) and a mixture of protease inhibitors.

[0043] Add an appropriate amount of lysis buffer to cell or tissue samples, lyse on ice or at 4°C for 30 min with intermittent vortexing, centrifuge at 12000-14000 rpm (approximately 15000 g) at 4°C for 10-15 min, and collect the supernatant as the total protein extract. Perform protein quantification immediately after extraction or freeze at -80°C for later use.

[0044] Alternatively, use the BCA Protein Assay Kit (Pierce / Thermo or other companies) to allow the protein to release Cu under alkaline conditions. 2+ Reduced to Cu + Cu + It forms a purple complex with BCA (bicinchoninic acid). The protein concentration of the samples was calculated based on the standard curve, and the protein samples were stored at -80 °C.

[0045] Example 2: Detection of NSUN2 expression levels 1. RT-qPCR (Reverse Transcription Quantitative Polymerase Chain Reaction) detection of NSUN2 mRNA levels 1.1 RNA reverse transcription to synthesize cDNA The extracted total RNA was reverse transcribed using reverse transcriptase to synthesize first-strand cDNA. The total volume of the reverse transcription reaction system was 20 μL, containing the following components: RNA template, reverse transcriptase (M-MLV (Moloney Murine Leukemia Virus) reverse transcriptase can be used), a mixture of dNTPs (Deoxyribonucleoside Triphosphate) (2.5 mM final concentration of each of the four deoxyribonucleosides), reverse transcription primers (500 ng random primers can be used to reverse transcribe all RNA, or 500 ng Oligo-dT (Oligodeoxythymidylic acid) primers can be used to specifically reverse transcribe mRNA with polyA tails, or a combination of both can be used to accommodate different RNA types), RNase (Ribonuclease) inhibitor, and reverse transcription buffer. The reaction conditions were set as follows: incubation at 37 ℃ or 42 ℃ for 30–60 min for reverse transcription extension, followed by heating at 70 ℃ for 15 min to inactivate the reverse transcriptase and terminate the reaction. The reverse transcription product, cDNA, can be used immediately for subsequent qPCR detection or frozen at -20 ℃ for later use.

[0046] 1.2 Primer Design Specific primers were designed for the mRNA sequence of the NSUN2 gene (GenBank accession number NM_017755) using Primer3 or NCBI's Primer-BLAST software.

[0047] The NSUN2 primer sequence used in this embodiment is as follows: SEQ ID NO:3 (forward primer): GTTTGACTGTGCTTTCCGGC; SEQ ID NO:4 (reverse primer): CTTCAGCACGATGCTTCCCT.

[0048] The experimental design simultaneously detects the internal reference gene GAPDH, and its primer sequences are as follows: SEQ ID NO:1 (forward primer): CAAATTCCATGGCACCGTCA; SEQ ID NO:2 (reverse primer): GACTCCACGACGTACTCAGC; Internal reference genes are used to correct for differences in RNA extraction amount, RNA quality and reverse transcription efficiency among different samples, so that the expression level of NSUN2 is comparable.

[0049] 1.3 qPCR amplification reaction A real-time quantitative PCR reaction system was prepared with a total volume of 20 μL, containing the following components: 2 μL cDNA template (using a 1:10 dilution of reverse transcription product), 0.4 μL each of forward and reverse primers (final concentration 200 nM each), 10 μL SYBR Green fluorescent dye Master Mix, and finally, RNase-free and DNase-free ultrapure water to a final volume of 20 μL. All components were prepared on ice, mixed thoroughly, briefly centrifuged, and aliquoted into 96-well qPCR plates and sealed for later use. The amplification temperature cycling program was set to a two-step method: first, a pre-denaturation at 95 °C for 10 min to activate hot-start Taq (Thermus aquaticus) DNA polymerase and completely denature the cDNA template; then, 40 cycles of amplification were performed, each cycle including 95 °C denaturation for 15 s and 60 °C annealing extension for 1 min; fluorescence signals were acquired before the end of the 60 °C annealing extension step in each cycle. After amplification, a melting curve analysis program was added, with the temperature slowly and continuously increased from 60 ℃ to 95 ℃ (approximately 0.5 ℃ per second), while continuously monitoring changes in fluorescence intensity to confirm the specificity of the PCR product.

[0050] 1.4 Results The relative expression level of NSUN2 mRNA was calculated using the 2^(-ΔΔCt) method. When the relative expression level of NSUN2 mRNA exceeded the diagnostic threshold determined by ROC curve analysis, the sample was considered NSUN2 positive, suggesting possible IPF. The diagnostic threshold for NSUN2 mRNA expression level was determined by collecting samples from IPF patients, healthy controls, and other ILD control groups, performing ROC curve analysis, and selecting the threshold with the highest Youden index as the optimal diagnostic threshold.

[0051] 2. ELISA detection of NSUN2 protein levels 2.1 Antibody Coating Anti-NSUN2 monoclonal antibody (purchased from Cell Signaling Technology) was diluted to the working concentration (2~10 μg / mL) with carbonate-bicarbonate coating buffer (pH 9.6), and 100 μL was added to each well of a 96-well ELISA plate. The plate was incubated overnight at 4 °C for antibody coating.

[0052] 2.2 Enclosure Discard the coating solution and wash the plate three times with PBST wash buffer (PBS containing 0.05% Tween-20), 300 μL each time. Add 200 μL of blocking buffer (5% BSA dissolved in PBS) to each well and incubate at room temperature for 1–2 h to block non-specific protein binding sites. After blocking, wash three times with PBST.

[0053] 2.3 Incubation of samples or standards Add the sample to be tested or a known concentration of NSUN2 recombinant standard protein. Serum samples are typically diluted 10–100 times. Set up a series of concentration gradients for the standard protein (0, 0.5, 1, 2, 5, 10 ng / mL, etc.), with each concentration in duplicate wells. Add 100 μL of the diluted sample or standard to each well and incubate at room temperature for 1–2 h. After incubation, wash five times with PBST.

[0054] 2.4 Detection of antibody incubation Add 100 μL of HRP-labeled anti-NSUN2 detection antibody (diluted 1:1000 to 1:5000 with antibody dilution buffer) to each well and incubate at room temperature for 1 h to form a sandwich complex of "capture antibody-NSUN2-detection antibody". After incubation, wash 5 times with PBST.

[0055] 2.5 Colorimetric Reaction Add 100 μL of TMB substrate solution to each well and incubate at room temperature in the dark for 10–30 min. After sufficient color development, add 50 μL of stop solution (2M sulfuric acid) to each well to terminate the reaction and change the color from blue to yellow.

[0056] 2.6 Absorbance Measurement The absorbance of each well was measured using a microplate reader at a main wavelength of 450 nm and a reference wavelength of 650 nm. The final OD (Optical Density) value was calculated: OD = OD 450 -OD 650 .

[0057] 2.7 Results A four-parameter logistic regression standard curve (R²) was plotted with the logarithm of the standard protein concentration on the x-axis and the OD value on the y-axis. 2 (≥0.99). Substitute the OD value of the sample to be tested into the standard curve equation to calculate the NSUN2 protein concentration. For diluted samples, multiply by the dilution factor. When the NSUN2 protein concentration is higher than the diagnostic threshold for NSUN2 protein level determined by ROC curve analysis, it is judged as NSUN2 protein expression positive.

[0058] See results Figure 1 , Figure 1A represents the mRNA expression levels of m5C transferases NSUN2, NSUN6, and NSUN7 in normal human lung fibroblasts (WI-38 (Wistar Institute-38, human lung fibroblasts)) and a fibrotic lung fibroblast model (TGF-β1 (Transforming Growth Factor-beta 1) + WI-38). Figure 1 B represents the m5C transferase protein expression levels of NSUN2, NSUN6, and NSUN7 in normal human lung fibroblasts (WI-38) and a fibrotic lung fibroblast model (TGF-β1+WI-38). ** indicates P < 0.01, and *** indicates P < 0.001. The results showed that only the mRNA and protein levels of NSUN2 differed significantly between the two groups, and were significantly elevated in the fibrotic cell model (P = 0.005 and P < 0.001). There were no significant differences in NSUN6 and NSUN7 (P > 0.05 for both). This indicates that NSUN2 is a key methyltransferase regulating mRNA m5C modification in IPF.

[0059] Example 3: Detection of m5C modification level of COL1A1 mRNA 1. m5C-RIP-qPCR method 1.1 RNA Sample Preparation Total RNA was extracted from biological samples according to the method in Example 1. The RNA was dissolved in RNase-free water or TE buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA) to prepare a concentration of 0.5–1 μg / μL.

[0060] Optionally, an RNA fragmentation step is performed: the RNA sample is added to a container containing Mg²⁺. + In Tris-HCl fragmentation buffer, the RNA is heated at 94 °C for 5–10 min to randomly break it into fragments of 100–300 nucleotides in length, thereby exposing the m5C modification sites and improving the efficiency of immunoprecipitation.

[0061] 1.2 m5C Immunoprecipitation Reaction Add the prepared RNA sample to the immunoprecipitation reaction buffer, which consists of: 150 mM NaCl, 10 mM Tris-HCl (pH 7.4), 0.1% NP-40 or Triton X-100, 1 mM PMSF, and 40–80 units of RNasin or RiboLock. Add 2–5 μg of m5C-specific antibody (preferably Active Motif rabbit polyclonal antibody 61255 or MBL mouse monoclonal antibody RN017M) to the reaction system. Mix the RNA, buffer, and antibody to a total reaction volume of approximately 500 μL, and incubate overnight at 4 °C for 12–16 h to form a stable antibody-m5C-modified RNA immune complex.

[0062] 1.3 Capture and washing of immune complexes Add 50 μL of Protein A magnetic beads (for rabbit antibodies) or Protein G magnetic beads (for mouse antibodies) to the immunoprecipitation reaction system. The magnetic beads should be washed 2-3 times with immunoprecipitation buffer beforehand. Continue incubation at 4°C for 2-4 hours. Then place the reaction tube on a magnetic separator and let it stand for 1-2 minutes, carefully aspirating the supernatant.

[0063] First, wash the magnetic beads three times with low-salt washing buffer (150 mM NaCl), 500 μL each time; then wash three times with high-salt washing buffer (300-500 mM NaCl); finally, wash once with immunoprecipitation buffer (150 mM NaCl).

[0064] 1.4 Elution of m5C-modified RNA Add elution buffer to the cleaned magnetic beads to elute the RNA. After elution, place the reaction tube back on the magnetic rack and carefully collect the supernatant (containing m5C modified RNA).

[0065] The elution method may include one of the following: (1) Competitive elution: Use an elution buffer containing an excess of free m5C nucleoside; (2) Proteinase K digestion method: Add 100 μL of 0.5-1 mg / mL proteinase K solution and incubate at 50 °C for 1 h; (3) Heating elution: Suspend the magnetic beads in 50~100 μL TE (Tris-EDTA) buffer or water and heat at 95 °C for 5 min.

[0066] 1.5 Purification and Reverse Transcription of Enriched RNA RNA in the elution buffer was purified using the Qiagen RNeasy MinElute Cleanup Kit, following the kit instructions. The RNA was then eluted with 20–30 μL of elution buffer. The purified m5C-enriched RNA was used as a template for reverse transcription to synthesize cDNA, following the same reverse transcription method as in step 1.1 of Example 2.

[0067] 1.6 qPCR quantification of the target gene COL1A1 The cDNA was amplified by qPCR using a specific primer pair (SEQ ID NO:5 and SEQ ID NO:6) designed for the COL1A1 gene. The qPCR reaction system and amplification conditions were the same as steps 1.3 and 1.4 in Example 2. Two parallel samples were set up: an IP sample (enriched by m5C antibody immunoprecipitation) and an Input control sample (10% of the total RNA was retained as a control before immunoprecipitation). Reverse transcription and qPCR were performed in parallel, and the Ct values ​​were obtained for each sample.

[0068] The COL1A1 primer sequence used in this embodiment is as follows: SEQ ID NO:5 (forward primer): GCAGACCCCTCTCTGCAC; SEQ ID NO:6 (reverse primer): AAGCCTTTGCTGTCCACCAG.

[0069] 1.7 Results The enrichment fold of m5C-RIP was calculated by comparing the Ct values ​​of the COL1A1 gene in biological samples and control samples, using the following formula: Enrichment factor = 2^[Ct(Input) - Ct(IP)] × 10 If the enrichment fold value is equal to 1, it means that there is no m5C modification or the modification level is very low on COL1A1 mRNA; if the enrichment fold value is greater than 1, it means that there is m5C modification on COL1A1 mRNA; the larger the enrichment fold value, the higher the level of m5C modification.

[0070] When the enrichment fold of COL1A1 mRNA m5C in a test sample is higher than the diagnostic threshold determined by ROC curve analysis, it is considered a positive result for elevated m5C modification level. The diagnostic threshold for COL1A1 mRNA m5C modification level was determined by collecting samples from IPF patient group, healthy control group, and other ILD control group, performing m5C-RIP-qPCR detection to obtain enrichment fold data, plotting ROC curves, and selecting the enrichment fold with the largest Youden index as the diagnostic threshold for COL1A1 mRNA m5C level.

[0071] 2. Bisulfite-PCR sequencing method 2.1 RNA reverse transcription Following the method in step 1.1 of Example 2, the extracted total RNA was reverse transcribed into cDNA.

[0072] 2.2 Chemical transformation treatment cDNA was chemically treated with sodium bisulfite using the EZ DNA Methylation Kit from Zymo Research. 0.1–2 μg of cDNA was mixed with the bisulfite conversion reagent and subjected to temperature cycling in a thermal cycler (70°C denaturation, 50–60°C conversion) for several hours to ensure complete transformation. Desulfonation buffer was then added, and the DNA was finally purified using a purification column to obtain the bisulfite-treated DNA. After treatment, PCR and sequencing were performed using the completely unmethylated region of the internal control gene as a template to check the C-to-T conversion rate, which should be ≥99%.

[0073] 2.3 PCR amplification of specific regions of the COL1A1 gene Design PCR primers to amplify specific regions in the COL1A1 gene that may contain m5C modifications. Primer design must consider the sequence changes after bisulfite treatment: unmodified C is converted to T, while m5C remains C. Amplify the target region using conventional PCR or touchdown PCR programs; the amplified product size is several hundred bp.

[0074] The primer sequences used in this embodiment are as follows: SEQ ID NO:7 (forward primer): AATTAATCTCAACAAACC; SEQ ID NO:8 (reverse primer): ATTTTTTGTGGTTGGGGAG.

[0075] 2.4 Sequencing analysis and calculation of m5C modification ratio DNA sequencing was performed on the PCR amplification products. Sanger sequencing was used for known candidate sites; high-throughput sequencing was used for multi-site or large-region analysis. The sequenced sequences were compared with the untreated COL1A1 mRNA reference sequence. A methylation-specific TaqMan probe (the probe sequence assumes the position is C, indicating methylation; only methylated templates can perfectly pair with the probe to generate a signal) was designed for quantitative PCR detection. If the C in the reference sequence remains C in the sequenced sequence, it indicates that the site is modified by m5C; if it changes to T, it indicates that it is not modified.

[0076] For mixed cell populations, a single base position may exhibit both C and T peaks. The formula for calculating the m5C modification ratio is: m5C modification ratio (%) = C peak signal intensity / (C peak signal intensity + T peak signal intensity) × 100%.

[0077] When the m5C modification ratio at a specific site is higher than the diagnostic threshold for the m5C modification level of the COL1A1 gene mRNA, it is determined to be positive for m5C modification at that site.

[0078] Example 4: Detection of COL1A1 expression level 1. RT-qPCR method for detecting COL1A1 mRNA expression The expression level of COL1A1 mRNA was detected using RT-qPCR technology. The methodological principles and technical operation steps were the same as those for detecting NSUN2 mRNA in Example 2.

[0079] The COL1A1 primer sequence used in this embodiment is as follows: SEQ ID NO:5 (forward primer): GCAGACCCCTCTCTGCAC; SEQ ID NO:6 (reverse primer): AAGCCTTTGCTGTCCACCAG.

[0080] The internal reference gene used is GAPDH, and its primer sequence is as follows: SEQ ID NO:1 (GAPDH forward primer): CAAATTCCATGGCACCGTCA; SEQ ID NO:2 (GAPDH reverse primer): GACTCCACGACGTACTCAGC.

[0081] The qPCR reaction system configuration, amplification reaction conditions, and data acquisition were the same as in steps 1.3 and 1.4 of Example 2. The relative expression level of COL1A1 mRNA was calculated using the 2^(-ΔΔCt) method. When the calculated relative expression level of COL1A1 mRNA was higher than the diagnostic threshold determined by ROC curve analysis, it was considered COL1A1 expression positive, suggesting possible IPF. The diagnostic threshold for COL1A1 mRNA expression was determined as follows: samples were collected from the IPF patient group, healthy control group, and other ILD control groups; ROC curve analysis was performed; and the largest fold change in Youden's index was selected as the diagnostic threshold for COL1A1 mRNA expression level.

[0082] 2. ELISA detection of COL1A1 protein levels 2.1 Sample Preparation The sample type is serum or plasma. Serum preparation method: Whole blood samples without anticoagulant are allowed to stand at room temperature for 30 minutes or at 4 °C for 1–2 hours to allow for complete coagulation. Centrifuge at 3000 rpm for 10–15 minutes and carefully aspirate the supernatant pale yellow, transparent serum. Plasma preparation method: Whole blood is collected using EDTA anticoagulant tubes. Immediately after collection, centrifuge at 3000 rpm for 10–15 minutes to separate the plasma. Serum or plasma samples should be stored frozen at -80 °C, avoiding repeated freeze-thaw cycles.

[0083] 2.2 Testing Procedures The COL1A1 protein level was detected using a double-antibody sandwich ELISA method. The specific operating steps were the same as those for the NSUN2 protein ELISA detection method in Example 2, including antibody coating, blocking, sample incubation, detection antibody incubation, colorimetric reaction, and absorbance measurement. A 96-well plate was coated with a COL1A1-specific capture antibody. The sample to be tested or COL1A1 standard protein was added and incubated to allow the capture antibody to specifically bind to the COL1A1 protein. An HRP-labeled detection antibody was then added to form a sandwich complex. After TMB color development, the absorbance was measured at 450 nm.

[0084] 2.3 Standard Curve Plotting and Concentration Calculation A series of concentration gradients (e.g., 0, 0.5, 1, 2, 5, 10 ng / mL) were prepared using COL1A1 recombinant standard protein, with each concentration set up in duplicate wells to establish a standard curve. Four-parameter logistic regression (4-PL) was used to fit the standard curves, and the correlation coefficient R0 was [value missing]. 2 ≥0.99. Substitute the OD value of the sample to be tested into the standard curve equation to calculate the COL1A1 protein concentration. For diluted samples, multiply by the dilution factor.

[0085] 2.4 Results When the calculated COL1A1 protein concentration in a sample is higher than the diagnostic threshold determined by clinical validation data, it is considered that COL1A1 protein expression is positive.

[0086] 3. Detection of collagen metabolism markers 3.1 Detection System The collagen metabolism biomarker detection system developed by Nordic Bioscience based on novel epitope antibody technology was used, and the standard operating procedures provided by the kit were strictly followed to ensure the reliability of the results and comparability between different laboratories.

[0087] 3.2 Quantitative Methods A series of concentration gradients (typically 5-7 concentration points covering the expected sample concentration range, such as 0, 5, 10, 25, 50, and 100 ng / mL) were prepared using the standard peptides provided in the kit (with known concentrations accurately determined by mass spectrometry or amino acid analysis). Each concentration was replicated in pairs. A standard curve was constructed with the logarithm of the standard peptide concentrations on the x-axis and the OD value on the y-axis. A four-parameter logistic regression (4-PL) was used for fitting, and the correlation coefficient R was calculated. 2 ≥0.99. Substitute the absorbance values ​​measured for the sample into the standard curve equation to calculate the concentration of PRO-C1 (N-terminal propeptide of type I collagen) or C1M (MMP-generated type I collagen fragment). Results are reported in ng / mL or μg / L.

[0088] 3.3 Results When the concentration of PRO-C1 or C1M in a sample is higher than the diagnostic threshold established based on clinical validation data, it is determined to be a type I collagen metabolism disorder, indicating increased fibrotic activity, and is therefore considered positive.

[0089] Example 5: Changes in NSUN2 and COL1A1 expression in a TGF-β1-induced pulmonary fibrosis cell model To verify the role of the NSUN2-m5C-COL1A1 regulatory axis in the development of pulmonary fibrosis, this embodiment first collected and prepared WI-38 human lung fibroblast samples according to the method in Example 1. Then, the expression level of NSUN2 mRNA was detected by RT-qPCR according to Example 2, the expression level of COL1A1 mRNA was detected by RT-qPCR according to Example 4, and the m5C modification level of COL1A1 mRNA was detected by m5C-RIP-qPCR according to Example 3, so as to verify the expression change pattern of the three in an in vitro pulmonary fibrosis model.

[0090] Specifically, WI-38 cells were used as the research object. An in vitro pulmonary fibrosis model was established by treating the cells with TGF-β1 (5 ng / mL) for 24 h. WI-38 cells treated with an equal volume of culture medium served as the control group. After cell treatment, total RNA was extracted using the TRIzol method in step 2.1 of Example 1. After passing quality control, cDNA was synthesized by reverse transcription according to step 1.1 of Example 2. The mRNA expression levels of NSUN2 and COL1A1 were detected using the NSUN2-specific primers (SEQ ID NO:3 and SEQ ID NO:4) designed in step 1.2 of Example 2 and the COL1A1-specific primers (SEQ ID NO:5 and SEQ ID NO:6) designed in step 1 of Example 4, following the qPCR reaction system and amplification conditions in step 1.3 of Example 2. Meanwhile, following the m5C-RIP-qPCR method in steps 1.2 to 1.6 of Example 3, m5C-specific antibodies were used to enrich RNA containing m5C modification, and then qPCR quantification was performed using COL1A1 primers (SEQ ID NO:5 and SEQ ID NO:6) to detect the m5C modification level of COL1A1 mRNA.

[0091] Reference Figure 2 , Figure 2 A represents the change in NSUN2 mRNA expression level after TGF-β1 stimulation (RT-qPCR); Figure 2 B represents the change in COL1A1 mRNA expression level after TGF-β1 stimulation (RT-qPCR); Figure 2 C represents the change in m5C modification level of COL1A1 mRNA after TGF-β1 stimulation (m5C-RIP-qPCR). ** indicates P < 0.01, and *** indicates P < 0.001.

[0092] Figure 2 The results showed that WI-38 cells were used as the research subject, and an in vitro pulmonary fibrosis model was established by treating them with TGF-β1 (5 ng / mL) for 24 h. WI-38 cells treated with an equal volume of culture medium served as the control group. RT-PCR was used to detect the mRNA expression levels of related genes, and m5C-RIP-qPCR was used to detect the m5C modification level of COL1A1 mRNA. The results showed that the expression levels of NSUN2 and COL1A1 mRNA were significantly increased after TGF-β1 treatment compared with the control group. Figure 2 A, Figure 2 B, P = 0.008 and P < 0.001), while the m5C modification level of COL1A1 mRNA was significantly increased (B, P = 0.008 and P < 0.001). Figure 2C, P<0.001); In the figure, Control represents untreated normal cells, serving as the control group; TGF-β1 represents cells stimulated with the cytokine TGF-β1, serving as the treatment group; IgG represents the control group using non-specific antibodies, used to detect background signals; and anti-m5C represents the experimental group using antibodies that specifically recognize m5C modifications.

[0093] Example 6: Effects of NSUN2 knockdown on COL1A1 mRNA m5C modification and expression in a pulmonary fibrosis model To further verify the regulatory role of NSUN2 in COL1A1 mRNA m5C modification and COL1A1 expression, this example conducted an NSUN2 knockdown experiment in a fibrotic lung fibroblast model. Cell culture, sample preparation, and detection methods followed the steps in Examples 1, 2, 3, and 4, respectively.

[0094] Specifically, siRNA targeting NSUN2 (si-NSUN2) or negative control siRNA (si-NC) was transfected into a TGF-β1 (5 ng / mL)-induced fibrotic lung fibroblast model (TGF-β1+WI-38) and cultured for 48 h. Total RNA and total protein were extracted according to steps 2.1 and 2.3 of Example 1, respectively. The NSUN2 mRNA expression level was detected by RT-qPCR in step 1 of Example 2 to verify the knockdown efficiency, the COL1A1 mRNA expression level was detected by RT-qPCR in step 1 of Example 4, and the m5C modification level of COL1A1 mRNA was detected by m5C-RIP-qPCR in step 1 of Example 3.

[0095] Reference Figure 3 , Figure 3 A is the validation of NSUN2 knockdown efficiency in pulmonary fibrosis model cells (TGF-β1+WI-38) (RT-qPCR); Figure 3 B represents the change in COL1A1 mRNA expression level after NSUN2 knockdown (RT-qPCR); Figure 3 C represents the change in m5C modification level of COL1A1 mRNA after NSUN2 knockdown (m5C-RIP-qPCR). *** indicates P < 0.001.

[0096] Figure 3 The results showed that transfecting siRNA targeting NSUN2 into a fibrotic lung fibroblast model (TGF-β1+WI-38) constructed an NSUN2 knockdown cell model (si-NSUN2). Figure 3 A). RT-qPCR analysis showed that COL1A1 mRNA expression was significantly downregulated after si-NSUN2 (P<0.001). Figure 3 B). The m5C modification level of COL1A1 mRNA was detected by m5C-RIP-qPCR. The results showed that the m5C modification level of COL1A1 mRNA was significantly reduced after si-NSUN2 (P<0.001). Figure 3 C). This demonstrates that NSUN2 upregulates COL1A1 expression by modifying COL1A1 mRNA with m5C methylation. In the figure, si-NC is the negative control group transfected with non-targeting, non-functional small interfering RNA; si-NSUN2 is the experimental group transfected with small interfering RNA specifically targeting the NSUN2 gene; lgG is the group that used a non-specific immunoglobulin G antibody as a negative control in the RNA immunoprecipitation experiment; and anti-m5C is the group that used an antibody specifically recognizing m5C in the RNA immunoprecipitation experiment.

[0097] Example 7: Human Trial To verify the clinical diagnostic value of biomarkers in IPF patients, peripheral blood samples were collected from subjects and total RNA was extracted according to the method in Example 1. The expression levels of NSUN2 mRNA, COL1A1 mRNA m5C modification level, and COL1A1 mRNA expression level were detected according to the methods in Examples 2, 3, and 4, respectively.

[0098] One hundred and ten subjects were selected and divided into three groups: IPF patient group (n=40), healthy control group (n=40), and other ILD control group (n=30). The inclusion criteria for the IPF patient group were as follows: all IPF patients met the 2022 ATS / ERS / JRS / ALAT guidelines for diagnosis, had a UIP imaging pattern confirmed by HRCT, and were excluded from ILD caused by known etiologies such as connective tissue disease, CHP, and occupational dust exposure, as well as from complications such as severe liver, kidney, or heart failure, and malignant tumors. The healthy control group consisted of healthy individuals without lung disease or other serious systemic diseases. The other ILD control group consisted of 30 subjects, including 20 with NSIP and 10 with CHP.

[0099] Reference Figure 4 In human peripheral blood samples, the clinical feasibility, differential expression significance, and diagnostic performance of NSUN2 mRNA expression level, COL1A1 mRNA m5C modification level, and COL1A1 mRNA expression level as early diagnostic biomarkers for IPF were systematically validated, and the optimal diagnostic threshold (cut-off value) for each biomarker was determined.

[0100] Figure 4A represents the differences in NSUN2 mRNA expression levels among the healthy control group, other ILD groups, and IPF group (RT-qPCR). Figure 4 B represents the differences in COL1A1 mRNA expression levels (RT-qPCR); Figure 4 C represents the difference in m5C modification levels of COL1A1 mRNA (m5C-RIP-qPCR), with an IgG control set up to verify specificity. **P<0.01, ***P<0.001.

[0101] Figure 4 The results showed that, compared with the healthy control group, the peripheral blood NSUN2 mRNA and protein expression levels were significantly increased in the IPF patient group (P<0.001), the m5C modification level of COL1A1 mRNA was significantly increased (P<0.001), and the COL1A1 mRNA expression level was significantly increased (P<0.001). Compared with other ILD control groups, the expression levels of the three biomarkers in the IPF patient group were also significantly increased (P<0.05 for all), indicating that the three biomarkers of the present invention are specifically highly expressed in IPF, and not just a non-specific response to ILD.

[0102] Example 8: Association between COL1A1 mRNA m5C modification level and clinically relevant indicators To further evaluate the clinical significance of COL1A1 mRNA m5C modification levels, this example performs a correlation analysis between the detection data and clinical indicators of the IPF patient group (n=40). COL1A1 mRNA m5C modification levels were detected using the m5C-RIP-qPCR method described in step 1 of Example 3.

[0103] The results are shown in Table 1, which presents the correlation analysis between the m5C modification level of COL1A1 mRNA and clinically relevant indicators.

[0104]

[0105] Based on the results in Table 1, this embodiment uses Spearman correlation analysis to evaluate the correlation between COL1A1 mRNA m5C modification level and clinical indicators (lung function, disease comprehensive score, serum markers, clinical functional status and imaging assessment, etc.) as well as the expression levels of NSUN2 mRNA and COL1A1 mRNA detected at the same time. Among them, DLCO% pred (Diffusing Capacity of the Lung for Carbon Monoxide, percent of predicted value), FVC% pred (Forced Vital Capacity, percent of predicted value), CPI (Composite Physiologic Index), GAP score (Gender-Age-Physiology score), KL-6 (Krebsvon den Lungen-6), SP-D (Surfactant Protein D), 6MWD (Six-Minute Walk Distance), mMRC (Modified Medical Research Council Dyspnea Scale), and CT fibrosis score (High-Resolution Computed Tomography Fibrosis Score).

[0106] Table 1 shows the results, which indicate the correlation between COL1A1 mRNA m5C modification level and the percentage of DLCO predicted (r= 0.71, 95% CI = [-0.32, -0.89], P = 0.008) and FVC% as a percentage of predicted value (r = The score was significantly negatively correlated with GAP score (r=0.75, 95%CI=[-0.21,-0.88], P=0.011), significantly positively correlated with KL-6 score (r=0.80, 95%CI=[0.53,0.92], P=0.010), SP-D score (r=0.82, 95%CI=[0.38,0.91], P=0.023), mMRC score (r=0.71, 95%CI=[0.29,0.93], P=0.009), and HRCT fibrosis score (r=0.84, 95%CI=[0.46,0.93], P<0.001), and significantly negatively correlated with 6MWD score (r= The mean value was 0.69 (95% CI = [-0.18, -0.87], P = 0.015). Furthermore, the COL1A1 mRNA m5C modification level was significantly positively correlated with both NSUN2 mRNA (r = 0.79, 95% CI = [0.31, 0.94], P = 0.013) and COL1A1 mRNA expression level (r = 0.83, 95% CI = [0.52, 0.93], P < 0.001). These results indicate that the COL1A1 mRNA m5C modification level can reflect the progression of IPF from multiple dimensions, including lung function, disease severity, classic serum markers, and imaging findings.

[0107] Example 9: Diagnostic efficacy assessment of individual biomarkers and combined models To systematically evaluate the early diagnostic value of various biomarkers and their combined diagnostic models for IPF, this embodiment performed ROC curve analysis on NSUN2 mRNA, COL1A1 mRNA m5C modification level, COL1A1 mRNA, and existing clinical biochemical biomarkers KL-6 and SP-D, and constructed a dual-marker combined early diagnostic model of NSUN2 mRNA and COL1A1 mRNA m5C modification level. The detection methods for each biomarker were the same as those in Example 2 (NSUN2 mRNA), Example 3 (COL1A1 mRNA m5C modification level), Example 4 (COL1A1 mRNA), and Example 1 (ELISA detection methods).

[0108] The ROC curve analysis results are shown in Table 2, where KL-6 (Krebs von den Lungen-6, salicylated sugar chain antigen-6); SP-D (Surfactant Protein D); ROC; AUC (Area Under the Curve).

[0109] Table 2. ROC curve analysis of COL1A1 and clinically relevant indicators for identifying IPF

[0110] As shown in Table 2, among the single biomarkers, the AUC of COL1A1 mRNA m5C modification level for identifying IPF was 0.832, with a sensitivity of 82.8% and a specificity of 85.4%, demonstrating higher diagnostic performance than the clinical biochemical biomarkers KL-6 (AUC = 0.736) and SP-D (AUC = 0.752). The AUC of NSUN2 mRNA expression level for identifying IPF was 0.804. A combined diagnostic model based on NSUN2 mRNA expression level and COL1A1 mRNA m5C modification level was constructed, achieving an AUC of 0.891, a sensitivity of 87.9%, and a specificity of 88.7%, which was superior to each single biomarker (DeLong test, P = 0.013), with a diagnostic threshold of 7.86. These results indicate that the combined detection of NSUN2 mRNA and COL1A1 mRNA m5C modification levels can further improve the diagnostic efficacy of IPF, providing a novel and accurate molecular early diagnostic strategy for clinical IPF.

[0111] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

Claims

1. A marker for early diagnosis of idiopathic pulmonary fibrosis, characterized by, The biomarkers for early diagnosis of idiopathic pulmonary fibrosis include at least one of the following: The expression product of the NSUN2 gene; The expression product of the COL1A1 gene; 5-methylcytosine modification level of COL1A1 gene mRNA.

2. A kit for early diagnosis of idiopathic pulmonary fibrosis, characterized by, The kit includes at least one of the following reagents for detecting biomarkers for early diagnosis of idiopathic pulmonary fibrosis as described in claim 1: The first reagent for detecting the expression product of the NSUN2 gene; A second reagent for detecting the expression product of the COL1A1 gene; A third reagent for detecting the level of 5-methylcytosine modification in COL1A1 gene mRNA.

3. The kit for early diagnosis of idiopathic pulmonary fibrosis according to claim 2, wherein The first reagent includes: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 3 and SEQ ID No. 4; And / or, NSUN2 protein-specific binders.

4. The kit for early diagnosis of idiopathic pulmonary fibrosis according to claim 2, wherein The second reagent includes: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 5 and SEQ ID No. 6; And / or, a COL1A1 protein-specific binder.

5. The kit for early diagnosis of idiopathic pulmonary fibrosis according to claim 2, wherein The third reagent includes: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 5 and SEQ ID No. 6 and specific antibodies against 5-methylcytosine.

6. The kit for early diagnosis of idiopathic pulmonary fibrosis according to claim 4 or 5, wherein The second reagent and the third reagent include primer pairs for amplifying an internal reference gene, wherein the internal reference gene is GAPDH, and the nucleotide sequences of the primer pairs for amplifying the internal reference gene are shown in SEQ ID No. 1 and SEQ ID No.

2.

7. The kit for early diagnosis of idiopathic pulmonary fibrosis according to claim 2, wherein The third reagent also includes: Primer pairs consisting of the nucleotide sequences shown in SEQ ID No. 7 and SEQ ID No. 8, bisulfite, and methylation-specific probes for detecting specific methylation sites.

8. The application of a biomarker for early diagnosis of idiopathic pulmonary fibrosis as described in claim 1 in a disease monitoring program for idiopathic pulmonary fibrosis.

9. Use according to claim 8, wherein the compound is ###0002### The idiopathic pulmonary fibrosis disease monitoring protocol includes: The study detected at least one of the following in biological samples obtained from the subjects: the expression product level of the NSUN2 gene, the expression product level of the COL1A1 gene, and the 5-methylcytosine modification level of the COL1A1 gene mRNA. The detection results of the biological sample are input into a preset disease monitoring model and compared with a preset diagnostic threshold in the disease monitoring model. Based on the comparison results, the course information of idiopathic pulmonary fibrosis of the subjects is output.

10. The application as described in claim 9, characterized in that, The biological samples include at least one of peripheral blood, serum, plasma, and bronchoalveolar lavage fluid.

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