Use of a marker or a reagent for detecting a marker in the manufacture of a product for the assisted diagnosis of fulminant type 1 diabetes and a product for the identification of the type

Abstract

The application belongs to the field of medical molecular diagnosis, and particularly relates to application of a marker or a reagent for detecting the marker in preparation of an auxiliary diagnosis product and a typing identification product for fulminant type 1 diabetes. The marker is a long-chain non-coding RNA, namely lncRNA, with the name of ENST00000565978, and the nucleic acid sequence is shown as SEQ ID NO. 1. The reagent includes a primer pair shown as SEQ ID NO. 2 and SEQ ID NO. 3. The application provides a lncRNA marker related to fulminant type 1 diabetes (FT1D), and provides a new detection object for molecular typing of FT1D. The extended queue verification of the application shows that the molecule is stably up-regulated in FT1D, and is still significantly related to FT1D after correction of age; the marker can improve the differential diagnosis efficiency of FT1D and classic T1D in combination with fasting C peptide and glycosylated hemoglobin.

Description

Technical Field

[0001] This invention belongs to the field of medical molecular diagnostics, specifically relating to the application of a biomarker or reagent for detecting biomarkers in the preparation of auxiliary diagnostic products and classification identification products for fulminant type 1 diabetes. Background Technology

[0002] Fulminant type 1 diabetes mellitus (FT1D) is a special type of type 1 diabetes, belonging to the idiopathic category, characterized by acute onset and rapid loss of pancreatic function. Clinical manifestations include a sharp increase in blood glucose (≥16 mmol / L), ketoacidosis, and low glycated hemoglobin (HbA1c), often accompanied by elevated pancreatic enzymes and multiple organ damage. Due to its short course and rapid β-cell destruction, timely identification of FT1D is crucial for clinical management and prognosis.

[0003] Current clinical diagnosis of FT1D mainly relies on indicators such as onset time, blood glucose, HbA1c, fasting or postprandial C-peptide, urine ketone / blood ketone ratio, arterial blood gas, and pancreatic autoantibodies. These indicators are irreplaceable in clinical diagnosis, but they reflect the metabolic state after the disease has already developed and are difficult to directly reflect FT1D-related peripheral immune transcriptional abnormalities.

[0004] In terms of immunological testing, pancreatic autoantibodies such as GADA, IA-2A, and ZnT8A can help determine the autoimmune status related to T1D. However, the antibody performance of FT1D patients is heterogeneous. Some patients may be negative, while others may show evidence of autoimmunity. Therefore, a single antibody indicator is difficult to cover all the clinical manifestations of FT1D.

[0005] With the development of transcriptomics and nucleic acid detection technologies, techniques such as RNA sequencing, microarrays, RT-qPCR, digital PCR, and nucleic acid hybridization have been used for screening molecular biomarkers for diseases.

[0006] The inventors of this application have previously studied invention patent CN202210814048.0, which relates to the application of hsa_circRNA_100632 as a diagnostic biomarker for fulminant type 1 diabetes mellitus (FT1D). The level of hsa_circRNA_100632 in peripheral blood mononuclear cells of FT1D patients is significantly increased, showing a significant difference compared to type 1 diabetes patients and normal controls. Clinically, it can be used as a diagnostic biomarker for the diagnosis and differential diagnosis of FT1D patients. Furthermore, upregulating hsa_circRNA_100632 levels can construct a model of FT1D. However, the detection sensitivity of this patent in distinguishing FT1D from classic type 1 diabetes (T1D) is only 43.6%. Therefore, there is a need in the art to develop other biomarkers for the diagnosis or auxiliary diagnosis of fulminant type 1 diabetes mellitus.

[0007] lncRNAs are believed to participate in processes such as immune cell differentiation, inflammatory signal regulation, cell death, and metabolic homeostasis, and have potential biomarker and regulatory target value in diabetes and its complications. However, in the field of FT1D, current technologies still lack a complete technical solution that uses peripheral blood mononuclear cells as a sample source, focuses on long non-coding RNAs (lncRNAs), and simultaneously covers expression profile screening, clinical expansion validation, diagnostic efficacy evaluation, and in vitro functional validation.

[0008] In summary, existing technologies for the diagnosis or auxiliary diagnosis of fulminant type 1 diabetes mellitus have the following main shortcomings: 1. Existing clinical indicators cannot fully provide molecular subtyping information for FT1D; indicators such as blood glucose, HbA1c, and C-peptide can indicate the disease state, but they are difficult to reflect whether the patient's peripheral immune cell transcriptional regulatory network has undergone remodeling. 2. Existing antibody detection has limitations in differentiating FT1D from classic T1D. The positivity rate and antibody profile of FT1D autoantibodies are heterogeneous; a negative antibody result does not completely rule out immune involvement, and a positive antibody result does not necessarily accurately distinguish between FT1D and classic T1D. 3. Existing technologies rarely propose feasible product forms; even when differential expression of certain RNA molecules is found, corresponding primers, sample processing, expression level calculation, joint interpretation models, and kit configuration schemes are often lacking.

[0009] Therefore, there is a need in the field to develop a new biomarker or reagent for detecting biomarkers for use in the preparation of auxiliary diagnostic products and classification identification products for fulminant type 1 diabetes. Summary of the Invention

[0010] Therefore, the present invention first provides an application of a biomarker in the preparation of auxiliary diagnostic products and typing identification products for fulminant type 1 diabetes. The biomarker is a long non-coding RNA, namely lncRNA, the name of which is ENST00000565978, and its nucleic acid sequence is shown in SEQ ID NO.1.

[0011] In one specific implementation, the biomarker is used in combination with clinical indicators fasting C-peptide and glycated hemoglobin.

[0012] In one specific implementation, the biomarker is detected using total RNA from peripheral blood mononuclear cells as a sample and reverse transcription-real-time quantitative PCR (RT-qPCR) as the detection method.

[0013] In one specific implementation, the classification identification is the identification of fulminant type 1 diabetes and classic type 1 diabetes.

[0014] The present invention also provides the application of a reagent for detecting a biomarker in the preparation of auxiliary diagnostic products and typing identification products for fulminant type 1 diabetes mellitus. The biomarker is a long non-coding RNA, namely lncRNA, named ENST00000565978, whose nucleic acid sequence is shown in SEQ ID NO.1. The reagent includes primer pairs shown in SEQ ID NO.2 and SEQ ID NO.3.

[0015] In one specific embodiment, the product uses the β-actin gene as an internal control, and its upstream and downstream primers are shown in SEQ ID NO.4 and SEQ ID NO.5, respectively.

[0016] The beneficial effects of this invention include: this invention increases the expression of the molecule ENST00000565978 in peripheral blood PBMCs (peripheral blood mononuclear cells); the expanded cohort shows that this molecule is upregulated in FT1D patients, and higher than in classic T1D patients; after age adjustment, it is still significantly associated with FT1D, which can provide a new nucleic acid detection target for FT1D auxiliary diagnosis and subtyping identification. Attached Figure Description

[0017] Figure 1 Volcano plot of differentially expressed lncRNAs in PBMCs of FT1D patients.

[0018] Figure 2 Principal component analysis diagram of differentially expressed lncRNAs.

[0019] Figure 3 A ranking diagram of the infiltration levels of 24 types of immune cells in all subject samples.

[0020] Figure 4 The image shows a heatmap of immune infiltration between the FT1D group and the healthy control group.

[0021] Figure 5 This is a graph showing the differences in immune cell subsets between FT1D and the healthy control group.

[0022] Figure 6 This is a network diagram of lncRNA-miRNA-mRNA ceRNA.

[0023] Figure 7 This is a screening diagram for immune-related candidate lncRNAs.

[0024] Figure 8 This is a graph showing the expression differences of ENST00000565978 in the three groups of people.

[0025] Figure 9 This is a single-index ROC (Receiving Operator Characteristic) plot for ENST00000565978.

[0026] Figure 10 This is the ROC plot of ENST00000565978 combined with clinical indicators.

[0027] Figure 11 White light image of Jurkat T cells in the knockdown control group.

[0028] Figure 12 Fluorescence image of Jurkat T cells in the knockdown control group.

[0029] Figure 13 White light image of Jurkat T cells from the sh-lncRNA-KD1 group.

[0030] Figure 14 Fluorescence pattern of Jurkat T cells in the sh-lncRNA-KD1 group.

[0031] Figure 15 White light image of Jurkat T cells from the sh-lncRNA-KD2 group.

[0032] Figure 16 Fluorescence pattern of Jurkat T cells in the sh-lncRNA-KD2 group.

[0033] Figure 17 The image shows the knockdown efficiency of ENST00000565978 obtained by RT-qPCR.

[0034] Figure 18 White light image of Jurkat T cells in the overexpression control group.

[0035] Figure 19 Fluorescence pattern of Jurkat T cells in the overexpression control group.

[0036] Figure 20 White light image of Jurkat T cells in the ENST00000565978 overexpression group.

[0037] Figure 21 Fluorescence image of Jurkat T cells overexpressing ENST00000565978.

[0038] Figure 22 The graph shows the overexpression efficiency of ENST00000565978 obtained by RT-qPCR.

[0039] Figure 23 The growth curve of CCK-8 (cell counting kit-8) after knocking down ENST00000565978.

[0040] Figure 24CCK-8 growth curve after overexpression of ENST00000565978.

[0041] Figure 25 A scatter plot of apoptosis detected by flow cytometry for the knockdown model. Figure 25 In the table, A represents the knockdown control group (sh-lncRNA-Ctrl), B represents the KD1 group (sh-lncRNA-KD1), and C represents the KD2 group (sh-lncRNA-KD2).

[0042] Figure 26 for Figure 25 The graph shows the apoptosis rate statistics of the knockdown model.

[0043] Figure 27 A scatter plot of apoptosis detected by flow cytometry for the overexpression model. Figure 27 In the diagram, A represents the empty vector control group (lncRNA-OE-Ctrl), and B represents the overexpression vector group (lncRNA-OE).

[0044] Figure 28 for Figure 27 The graph shows the apoptosis rate statistics of the overexpression model.

[0045] Figure 29 This is a diagram showing the expression of transcription factors after knocking down ENST00000565978.

[0046] Figure 30 This is a diagram showing the expression of transcription factors after overexpression of ENST00000565978. Detailed Implementation

[0047] The biomarker described in this invention is lncRNA ENST00000565978, also known as AC017100.1. Genomic location: chr18 29265618–29266786:+, transcript length approximately 1169 nt. This invention provides a FT1D-assisted diagnostic and subtyping identification scheme based on lncRNA ENST00000565978. This scheme can determine whether a subject possesses FT1D-related molecular expression characteristics by detecting the expression level of ENST00000565978 in their samples; it can also combine this expression level with FCP, HbA1c, islet autoantibodies, onset time, or ketoacidosis-related indicators to construct an auxiliary differentiation model between FT1D and classic T1D.

[0048] The preferred specific testing procedure includes: collecting peripheral blood from the subject; isolating peripheral blood mononuclear cells (PBMCs); extracting total RNA; detecting RNA concentration, purity, and integrity; obtaining cDNA through reverse transcription; performing RT-qPCR amplification of ENST00000565978 using the aforementioned primers; correcting with an internal reference gene; and using 2-ΔΔCt The relative or absolute expression level can be calculated using the standard curve method; the detection results can be compared with those of healthy controls, classic T1D controls, training cohort thresholds, or joint model outputs.

[0049] When the expression level of ENST00000565978 is higher than the reference level for healthy controls or the classic T1D reference level, it suggests that the subject has FT1D-related molecular expression characteristics. This judgment does not replace clinical diagnosis, but serves as an auxiliary diagnostic or subtype identification indicator for FT1D.

[0050] The combined model of the biomarker with fasting C-peptide (FCP) and glycated hemoglobin (HbA1c) can be established using logistic regression, linear discriminant analysis, random forest, support vector machine, or other classification methods. The preferred model form is: logit(P) = β0 + β1 × ENST00000565978 expression level + β2 × FCP + β3 × HbA1c, where P is the FT1D genotyping probability; variables such as autoantibodies, onset time, and ketoacidosis-related indicators can also be included.

[0051] The beneficial effects of this invention are as follows: First, it proposes the FT1D-related lncRNA marker ENST00000565978, providing a new detection target for FT1D molecular typing. This invention uses the long non-coding RNA ENST00000565978 as the core molecular marker for FT1D auxiliary diagnosis and typing identification. This marker differs from hsa_circRNA_100632 in the background technology, as they are different in molecular type, nucleotide sequence, transcript annotation, and potential mechanism of action. In other words, this invention proposes a new detection target at the level of peripheral immune transcriptional regulation of FT1D. Second, the detection material is derived from PBMCs, facilitating implementation through conventional detection platforms such as RT-qPCR. Third, extended cohort validation shows that this molecule is stably upregulated in FT1D and remains significantly associated with FT1D after age correction. Fourth, combining it with FCP and HbA1c can improve the diagnostic efficacy of differentiating FT1D from classic T1D. Fifth, Jurkat T cell knockdown and overexpression experiments provide in vitro functional evidence of this molecule's relationship with cell viability, apoptosis, and some immune-related indicators.

[0052] Compared to approaches that rely solely on clinical metabolic indicators or autoantibodies, this invention adds molecular information at the peripheral immune transcription level; compared to simple expression profile screening approaches, this invention further provides extended sample validation, ROC evaluation, and in vitro functional experiments, resulting in a more complete chain of evidence and facilitating the subsequent development of executable detection products.

[0053] In addition to RT-qPCR, this invention may also employ digital PCR, RNA sequencing, microarray detection, nucleic acid hybridization, in situ hybridization, fluorescent probe method, isothermal amplification, CRISPR nucleic acid detection, or other techniques capable of quantifying the expression level of ENST00000565978.

[0054] In addition to FCP and HbA1c, ENST00000565978 can also be used in combination with postprandial C-peptide, blood glucose, GADA, IA-2A, ZnT8A, DKA (diabetic ketoacidosis) related indicators, HDL-C (high-density lipoprotein cholesterol), ENST00000587099, or other lncRNA / mRNA / miRNA biomarkers. Besides single-item test kits or combined test kits, the products described in this invention can also be chips, frozen-intervention mixed systems, test strips / plates, automated nucleic acid detection cartridges, data analysis software, clinical auxiliary interpretation systems, or companion diagnostic research tools.

[0055] Sample source: PBMC is preferred, but whole blood, plasma, serum, isolated immune cells, exosomes, or nucleic acids extracted from the above samples are also acceptable.

[0056] In this invention, the sequences SEQ ID NO.1 to SEQ ID NO.5 are as follows.

[0057] SEQ ID NO.1:

[0058] SEQ ID NO. 2: TCAGCCCCATCTTGCTTCAG.

[0059] SEQ ID NO. 3: CCTGCGCACACACATTTGAA.

[0060] SEQ ID NO. 4: GTGGCCGAGGACTTTGATTG.

[0061] SEQ ID NO. 5: CCTGTAACAACGCATCTCATATT.

[0062] Example 1

[0063] This example is a screening of differential expression profiles of PBMCs in FT1D patients.

[0064] Peripheral blood was collected from 6 age- and sex-matched healthy controls from 6 FT1D patients. PBMCs were isolated and total RNA was extracted. The expression profiles of lncRNA and mRNA were detected using the Arraystar Human LncRNA Microarray V5.0 chip.

[0065] Figure 1 Volcano plot of differentially expressed lncRNAs in PBMCs of FT1D patients. Figure 1 In the graph, the horizontal axis represents the logarithmic transformation value of the difference fold, and the vertical axis represents statistical significance. The up-regulation and down-regulation points are located on either side of zero, directly indicating that the number of up-regulated and down-regulated molecules is relatively large. Figure 1 This indicates that the FT1D group had a relatively large number of differentially expressed lncRNAs with clearly defined directions compared to the healthy control group (Control). Based on the criteria of a fold change of at least 2 and P < 0.05, a total of 1465 differentially expressed lncRNAs were identified, of which 1016 were upregulated and 449 were downregulated; simultaneously, 1025 differentially expressed mRNAs were obtained, of which 533 were upregulated and 492 were downregulated.

[0066] Figure 2 Principal component analysis diagram of differentially expressed lncRNAs. Figure 2 Each point in the graph represents a subject sample, and the distance between points reflects the similarity of the overall lncRNA expression profile. The FT1D group and the healthy control group (HC) samples showed separation in principal component space, indicating a systematic difference in the overall lncRNA expression patterns of the two PBMCs. These results establish the basis for screening candidate lncRNA biomarkers from the PBMCs of FT1D patients.

[0067] Example 2

[0068] This embodiment focuses on functional enrichment, immune infiltration, and candidate molecule screening.

[0069] LncRNA-mRNA co-expression relationships were constructed based on differential lncRNA and differential mRNA expression matrices. Further enrichment analyses were performed on relevant mRNAs using GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genetics and Genomes). GO biological process enrichment analysis showed that differentially expressed lncRNA-related mRNAs were concentrated in processes related to immune response, inflammation regulation, and cellular activity. In the GO molecular function enrichment analysis, each point corresponds to a significantly enriched molecular function item; larger points indicate more genes involved, and darker colors indicate stronger statistical evidence. GO molecular function enrichment analysis showed that differentially expressed lncRNA-related mRNAs are involved in receptor binding, enzyme regulation, and immune-related molecular functions. In the GO cellular component enrichment analysis, each point corresponds to a significantly enriched cellular component item; GO cellular component enrichment analysis showed that relevant mRNAs were distributed in various cellular structures such as the cell membrane, organelles, and protein complexes, providing a localization basis for subsequent pathway and immune network analysis.

[0070] The size and length of the dots or bars in the KEGG pathway enrichment diagram reflect the number of genes or the degree of enrichment, while the color reflects significance. The KEGG pathway enrichment results are concentrated in pathways such as type 1 diabetes, IL-17 signaling, cytokine-cytokine receptor interaction, and apoptosis, indicating that the differentially related lncRNA network is associated with FT1D immune inflammation and cell death-related processes.

[0071] The relative enrichment levels of 24 types of immune cells were calculated using ssGSEA (single-sample gene set enrichment analysis).

[0072] Figure 3 A ranking diagram of the infiltration levels of 24 types of immune cells in all subject samples. Figure 3 In the study, the bars or color blocks for each sample represent the relative enrichment level of ssGSEA for 24 types of immune cells. The composition patterns of different samples differ, indicating that the peripheral immune cell characteristics of FT1D and healthy controls are not completely consistent. This result reflects relative enrichment rather than absolute cell count.

[0073] Figure 4 This is a heatmap of immune infiltration between the FT1D group and the healthy control group. It is used to determine whether there are overall differences in the immune infiltration profiles between the FT1D group and the healthy control group (HC) based on heatmap distribution and clustering patterns. Figure 4 In the heatmap, the color represents the relative enrichment level after standardization; the FT1D group and the healthy control group formed different color distribution patterns on multiple types of immune cells, indicating that there are overall differences in the immune infiltration spectra between the two groups.

[0074] Figure 5This is a graph showing the differences in immune cell subsets between FT1D and the healthy control group (Control). Figure 4 ordinate and Figure 5 The x-axis numbers 1-24 correspond to the following cell types: 1: Type 17 helper T cells, 2: Type 1 regulatory T cells, 3: Type 1 helper T cells, 4: Type 2 helper T cells, 5: B cells, 6: CD4-positive T cells, 7: CD8-positive T cells, 8: γδ T cells, 9: Naïve CD4-positive T cells, 10: Naïve CD8-positive T cells, 11: Monocytes, 12: Exhausted T cells, 13: Macrophages, 14: Follicular helper T cells, 15: Mucosa-associated homeostatic T cells, 16: Dendritic cells, 17: Innate regulatory T cells, 18: Cytotoxic T cells, 19: Effector memory T cells, 20: Inducible regulatory T cells, 21: Neutrophils, 22: Central memory T cells, 23: Natural killer T cells, 24: Natural killer cells. Figure 5 Further display of CD4 + T cells, CD8 + The relative enrichment levels of T cells, central memory T cells, dendritic cells, effector memory T cells, and exhausted T cells were decreased in the FT1D group (P<0.05).

[0075] The ssGSEA results of this invention are used to compare relative enrichment features and are not equivalent to the absolute cell counts obtained by flow cytometry. Figures 3 to 5 The results provide a clear analytical target for establishing the correlation between lncRNA and immune cells, especially T cell subsets, in subsequent studies.

[0076] by The standard was FDR < 0.05, where FDR stands for false discovery rate. 3753 significant lncRNA-immune cell correlations were obtained, involving 1242 lncRNAs and 18 types of immune cells. UpSet plots of lncRNA-immune cell class correlations and lncRNA-T cell subset correlations were generated. In the UpSet plots, the connection points represent the cell classes involved in the intersection, and the bar height represents the number of lncRNAs in the corresponding intersection, used to locate candidate molecules that simultaneously correlate with multiple immune cell classes or T cell subsets.

[0077] A ceRNA (competitive endogenous RNA) network was constructed by combining immune pathway-related mRNAs, T cell infiltration-related lncRNAs, subcellular localization, and database prediction. Figure 6 This is a network diagram of lncRNA-miRNA-mRNA ceRNA. Figure 6 In the diagram, nodes of different shapes or colors represent lncRNA, miRNA, and mRNA, respectively, and the connecting lines represent the regulatory relationships predicted by the database and retained after screening. Figure 6 The network shown contains 10 differentially expressed lncRNAs, 30 miRNAs, and 45 mRNAs, indicating that candidate lncRNAs may participate in immune-related regulation through ceRNA relationships; however, this network is a predictive association and cannot prove direct regulation or causal relationships on its own.

[0078] Figure 7 This is a screening diagram for immune-related candidate lncRNAs. Figure 7 This approach integrates criteria such as immune pathway relevance, T cell infiltration relevance, and subcellular localization, gradually narrowing down candidate molecules from a broad set of relevant factors to those suitable for clinical testing and in vitro functional validation. It summarizes previous screening evidence and forms a crucial link in selecting ENST00000565978 for subsequent clinical validation and cell experiments. Figure 7 In the diagram, the blue area corresponding to number 299 represents lncRNA gene symbols involved in the core KEGG pathway related to FT1D; the pink area corresponding to number 270 represents lncRNA gene symbols significantly associated with cell infiltration; and the purple area corresponding to number 666 represents candidate lncRNAs that may be involved in both T cell function and disease mechanisms. Further screening using subcellular localization yielded 27 cytoplasmic lncRNAs for identifying candidate molecules for clinical and functional validation. In this invention, co-expression, correlation, and ceRNA networks are used for candidate molecule screening and do not constitute direct regulatory or causal evidence on their own.

[0079] Example 3

[0080] This example demonstrates RT-qPCR validation for the ENST00000565978 extended cohort.

[0081] The extended validation cohort included 51 patients with FT1D, 64 patients with classic T1D, and 112 healthy controls. Total RNA from PBMCs was reverse transcribed and then subjected to RT-qPCR. The preferred primers were ENST00000565978, with the forward primer 5'-TCAGCCCCATCTTGCTTCAG-3' and the reverse primer 5'-CCTGCGCACACACATTTGAA-3'. Internal reference genes were used for correction, and relative expression levels were calculated using equivalent quantification methods.

[0082] Figure 8 This is a graph showing the expression differences of ENST00000565978 in the three groups of people. Figure 8 The three distributions in the data correspond to healthy controls, FT1D, and classic T1D, respectively. Figure 8The results showed that ENST00000565978 was significantly upregulated in the FT1D group, and significantly higher than in the classic T1D group (P<0.01). After age adjustment in multivariate analysis, ENST00000565978 remained significantly positively correlated with FT1D (β=0.263, P=0.003), indicating that it can provide information on the expression of FT1D-related molecules. Therefore, this molecule can provide FT1D-related PBMC transcriptional information that is distinct from conventional metabolic indicators; its use is limited to auxiliary diagnosis or subtyping identification, and it does not replace clinical diagnostic criteria.

[0083] Example 4

[0084] This embodiment demonstrates ROC evaluation using both single and combined indicators.

[0085] Figure 9 This is a single-index ROC plot for ENST00000565978. Figure 9 In the ROC curve, the closer the curve is to the upper left corner, the stronger the discrimination ability; the diagonal line represents random discrimination. The AUC (area under the ROC curve) of ENST00000565978 in distinguishing between FT1D and healthy controls was 0.674 (95% CI: 0.578–0.770, P<0.001), and the AUC in distinguishing between classic T1D and healthy controls was 0.536 (95% CI: 0.440–0.632, P>0.05). These results indicate that ENST00000565978, when used alone, has a certain but limited auxiliary discrimination ability, and in particular, its AUC of 0.536 cannot be used to claim that it can effectively distinguish between classic T1D and healthy controls.

[0086] Figure 10 This is the ROC plot of ENST00000565978 combined with clinical indicators. Figure 10 First, it is shown that the AUC of ENST00000565978 (Gene7) in distinguishing between FT1D and classic T1D is 0.622 (95% CI: 0.520–0.724, P < 0.001). Figure 10 In the comparison between FT1D and classic T1D, the AUC of fasting C-peptide (FCP) alone was 0.789 (95% CI: 0.691-0.887, P<0.0001), the AUC of ENST00000565978 (Gene7) combined with FCP (fasting C-peptide) was 0.797 (95% CI: 0.701-0.893, P<0.0001), and the AUC of ENST00000565978 (Gene7) combined with FCP (fasting C-peptide) was 0.817 (95% CI: 0.728-0.906, P<0.0001). Figure 10The addition of ENST00000565978 shows that the overall discrimination ability is improved, and the three-index joint model has the highest overall discrimination efficiency in this invention, supporting the use of ENST00000565978 as a supplementary molecular index for FCP and HbA1c.

[0087] Example 5

[0088] This example illustrates the effect of ENST00000565978 on Jurkat T cell function.

[0089] Stable Jurkat T cell lines with ENST00000565978 knockdown and overexpression were constructed using lentivirus. Figures 11 to 16 White light and fluorescence images of the knockdown control, KD1, and KD2.

[0090] Figure 11 White light image of Jurkat T cells in the knockdown control group. Figure 11 Used to record the basic morphology, density, and distribution of Jurkat T cells in the empty vector control group of the knockdown experiment, serving as a unified reference for white light observation in the KD1 and KD2 treatment groups; it is the starting point for microscopic quality control of the knockdown model, used to determine whether subsequent inter-group phenomena may be affected by significant culture conditions or abnormal cell morphology.

[0091] fluorescence Figure 11-16 and Figure 18-21 The scale length is 200μm.

[0092] Figure 12 Fluorescence image of Jurkat T cells in the knockdown control group. Figure 12 The green fluorescence reflects the reporting signal after the lentiviral vector enters the cell, and is used to determine the infection coverage. Figure 12 This graph is used to display the green fluorescence reporter signal and viral infection coverage of the knockdown empty vector control group, and to establish a batch-specific quality control baseline for the fluorescence images of the KD1 and KD2 groups. This graph only indicates whether the vector infection conditions are comparable and is not used to prove that the target RNA has been knocked down. Whether the model is valid must be confirmed by subsequent RT-qPCR results.

[0093] Figure 13 White light image of Jurkat T cells from the sh-lncRNA-KD1 group. Figure 13 This was used to observe the morphology, density, and distribution of Jurkat T cells after treatment with the first knockdown sequence KD1, and to compare with... Figure 11 The knockdown control group was compared accordingly; Figure 13 Provides raw white light state records for the KD1 group, and Figure 14 Infection coverage quality control and Figure 17 The quantitative expression levels of these substances together constitute the evidence for establishing the KD1 model.

[0094] Figure 14 Fluorescence pattern of Jurkat T cells in the sh-lncRNA-KD1 group. Figure 14 This confirms that the KD1 lentiviral vector has entered a significant proportion of cells, indicating that the group possesses the technical prerequisites for conducting target RNA knockdown quantification and functional detection. Figure 14 and Figure 13 The states of the KD1 group were recorded from both fluorescence and white light perspectives, but this did not replace [the original state]. Figure 17 Quantitative confirmation of the actual knockdown magnitude.

[0095] Figure 15 White light image of Jurkat T cells from the sh-lncRNA-KD2 group. Figure 15 Used to record the morphology, density, and distribution of cells after treatment with the second independent knockdown sequence KD2, and compared with... Figure 11 control group and Figure 13 The comparison with the KD1 group; the purpose of setting the second sequence is to reduce the impact of the randomness of the single intervention sequence on the conclusion. This figure provides the corresponding white light quality control record for the subsequent efficiency and functional results of the KD2 group.

[0096] Figure 16 Fluorescence pattern of Jurkat T cells in the sh-lncRNA-KD2 group. Figure 16 Used to confirm the infection coverage of the KD2 lentiviral vector and to compare with Figure 12 and Figure 14 Together, they proved that all knockdown-related groups had completed infection quality control; Figure 16 The KD2 group is supported for RT-qPCR quantification and phenotypic detection, but whether the actual knockdown is sufficient depends on... Figure 17 As the standard. Figure 16 The green fluorescence indicates successful infection with the KD2 lentiviral vector. This figure only reflects the reporting signal and infection coverage; the actual knockdown extent can be seen in [the figure below]. Figure 17 .

[0097] Figure 17 The image shows the knockdown efficiency of ENST00000565978 obtained by RT-qPCR. Figure 17 This quantitative test, using KD1 and KD2 to truly reduce ENST00000565978 expression, is crucial evidence for determining the success of the knockdown model. It undertakes... Figures 11 to 16 This study aimed to improve the morphology and infection control of cells and establish necessary prerequisites for attributing changes in cell viability, apoptosis, and transcription factors in subsequent research. Figure 17 In the figure, the vertical axis represents the normalized relative gene expression level compared to the control group, with the control group set to 1. Figure 17The study included a knockdown control group (sh-lncRNA-Ctrl), a KD1 group (sh-lncRNA-KD1), and a KD2 group (sh-lncRNA-KD2).

[0098] Figures 11 to 16 As can be seen, round or near-round Jurkat T cells and cell clusters were visible in the white light field of the KD1, KD2, and knockdown control groups (sh-lncRNA-Ctrl), exhibiting the common aggregation distribution of suspended cells. Extensive green fluorescence signals were visible in the fluorescence field of all three groups, indicating that good lentiviral infection coverage was achieved in each group. Microscopic images are used for morphological and infection quality control; cell viability cannot be judged solely by the number of cell clusters, nor can knockdown efficiency be directly compared using the fluorescence intensity of different images. Figure 17 In the RT-qPCR results shown, the relative expression level of the sh-lncRNA-Ctrl group was approximately 1.0 after normalization, while that of the sh-lncRNA-KD1 and sh-lncRNA-KD2 groups was approximately 0.46 and 0.45, respectively, which is approximately 46% and 45% of the control group, equivalent to a reduction of approximately 54% and 55%. The differences between the sh-lncRNA-KD1 and sh-lncRNA-KD2 groups and the knockdown control group were both P<0.001. Figure 17 The results showed that both knockdown sequences could significantly reduce the expression of ENST00000565978, and the knockdown cell model was successfully constructed.

[0099] Figures 18 to 21 White light and fluorescence images are shown for the overexpression control group and the overexpression group. White light images are used to compare cell morphology, density, and distribution, while green fluorescence is used to determine viral infection coverage. Fluorescence signals cannot replace target RNA quantification; intervention efficiency must be confirmed by RT-qPCR results.

[0100] Figure 18 White light image of Jurkat T cells in the overexpression control group. Figure 18 It is used to record the basic morphology, density, and distribution of Jurkat T cells in the control group overexpressing the empty vector, serving as a microscopic morphological reference for white light observation of the target RNA overexpression group; it initiates a model validation chain in the opposite direction to the knockdown experiment, ensuring that subsequent overexpression phenotypes are compared against the background of the corresponding empty vector.

[0101] Figure 19 Fluorescence pattern of Jurkat T cells in the overexpression control group. Figure 19 This figure is used to confirm the green fluorescence reporter signal and viral infection coverage in the control group overexpressing the empty vector, establishing a consistent quality control baseline for subsequent fluorescence observations in the overexpression group. This figure only demonstrates the infection condition and does not prove increased target RNA expression; the specific overexpression fold is determined by… Figure 22 confirm. Figure 19In the study, green fluorescence reflects the infection coverage of the control group overexpressing the empty vector.

[0102] Figure 20 White light image of Jurkat T cells in the ENST00000565978 overexpression group. Figure 20 This was used to observe the morphology, density, and distribution of cells overexpressing ENST00000565978, and to compare with... Figure 18 Compared with the empty vector control group. It provides a white light pristine state record of the overexpression group, compared with... Figure 21 Infection coverage results and Figure 22 The quantitative expression of these factors together constitute the evidence for establishing the overexpression model.

[0103] Figure 21 Fluorescence image of Jurkat T cells overexpressing ENST00000565978. Figure 21 This was used to confirm that the target RNA overexpression vector had entered a significant proportion of Jurkat T cells, demonstrating that the group possessed the technical capabilities for quantifying expression levels and detecting function; it is related to Figure 19 Used to compare infection coverage between two groups, but whether the target RNA was successfully overexpressed must be determined by [the relevant authority / organization]. Figure 22 The RT-qPCR results were used for determination.

[0104] Figure 22 The graph shows the overexpression efficiency of ENST00000565978 obtained by RT-qPCR. Figure 22 The study included an empty vector control group (lncRNA-OE-Ctrl) and an overexpression vector group (lncRNA-OE).

[0105] Figures 18 to 21 In the diagram, white light images are used to compare cell morphology, aggregation state, density and distribution within the field of view between the two groups, while green fluorescence images are used to confirm that both the overexpression vector group (lncRNA-OE) and the empty vector control group (lncRNA-OE-Ctrl) have completed lentiviral infection. Figure 22 The RT-qPCR plot shown is used to quantitatively determine whether ENST00000565978 is truly elevated in the overexpression group, which is a necessary prerequisite for interpreting subsequent results on cell viability, apoptosis, transcription factors, and cytokines under overexpression conditions. Figures 18 to 21As can be seen, round or near-round Jurkat T cells and cell clusters were visible in the white light field of both the lncRNA-OE and lncRNA-OE-Ctrl groups, exhibiting a suspended, aggregated growth state. Green fluorescence signals were visible in the fluorescence field of both groups, indicating that both the overexpression vector group and the empty vector control group achieved lentiviral infection coverage. Green fluorescence only reflects vector infection and reporter gene expression; fluorescence intensity can also be affected by exposure, gain, and the degree of cell aggregation. It cannot replace target RNA quantification, nor can the overexpression fold be determined solely by fluorescence intensity. Figure 22 In the RT-qPCR results, the relative expression level of lncRNA-OE-Ctrl in the empty vector control group was approximately 1.0, while the column height of the lncRNA-OE overexpression vector group was approximately 49 to 50. The difference between the overexpression group and the overexpression control group was P < 0.001. This result indicates that ENST00000565978 was significantly increased in the overexpression group, and the overexpression cell model was successfully constructed.

[0106] CCK-8 tests were performed at 0, 24, 48, 72, and 96 hours, with 5 technical duplicates set up for each group and repeated independently 3 times.

[0107] Figure 23 CCK-8 growth curve after knocking down ENST00000565978. Figure 23 This includes the knockdown control group, the KD1 group (P25K1747, i.e., sh-lncRNA-KD1), and the KD2 group (P25K1748, i.e., sh-lncRNA-KD2). Figure 23 This study was used to examine whether reduced ENST00000565978 expression affected the viability or proliferative capacity of Jurkat T cells over time. It was conducted using... Figure 17 Based on the confirmed valid KD1 and KD2 models, the functional phenotype in the knockdown direction is displayed and compared with... Figure 24 The overexpression results were used to jointly determine whether the molecule had a directional relationship with the cell growth state. Figure 23 In the figure, the horizontal axis represents 0, 24, 48, 72, and 96 hours, and the vertical axis represents cell viability as reflected by the CCK-8 absorbance, i.e., the OD value at 450 nm. The curves of the KD1 (P25K1747) and KD2 (P25K1748) groups were lower than those of the knockdown control group, with an overall difference of P<0.001, indicating that the viability or proliferation capacity of Jurkat T cells decreased after knockdown of ENST00000565978.

[0108] Figure 24 CCK-8 growth curve after overexpression of ENST00000565978. Figure 24This was used to examine whether increasing ENST00000565978 expression produced the opposite change in cell viability to knockdown. Together with the previous figure, it forms two-way evidence of "decreased expression—decreased viability, increased expression—enhanced viability," thereby strengthening the functional support of ENST00000565978 in relation to T cell survival or proliferation. Figure 24 The results showed that the overexpression group (lncRNA-OE) curve was higher than that of the overexpression control group (lncRNA-OE-Ctrl), with an overall difference of P<0.001, indicating that Jurkat T cell viability or proliferation was enhanced after increasing ENST00000565978 expression. Figure 23 and Figure 24 The results showed that decreased ENST00000565978 expression was associated with decreased Jurkat T cell viability, while increased expression was associated with enhanced cell viability.

[0109] Figure 25 A scatter plot of apoptosis detected by flow cytometry for the knockdown model. Figure 25 In the table, A represents the knockdown control group (sh-lncRNA-Ctrl), B represents the KD1 group (sh-lncRNA-KD1), and C represents the KD2 group (sh-lncRNA-KD2). Figure 25 This method is used to visualize the representative original quadrant distribution of Annexin V-APC / 7-AAD double staining flow cytometry in the knockdown control, KD1, and KD2 groups, allowing for a direct visual representation of the composition of surviving, early apoptotic, and late apoptotic or dead cells in each group. It is... Figure 26 The original graphical representation of the statistical results from repeated experiments is used; a single representative graph is not used to replace the determination of statistical significance. Annexin V-APC / 7-AAD double-stained scatter plots distinguish between surviving, early-apoptotic, late-apoptotic, or dead cells according to quadrants. From... Figure 25 It is evident that, compared to the knockdown control group, the proportion of apoptosis-related quadrants increased in the KD1 group; the change was less pronounced in the KD2 group, requiring further investigation. Figure 26 Interpretation of statistical results from repeated experiments.

[0110] Figure 26 for Figure 25 The graph shows the apoptosis rate statistics of the knockdown model. The graph includes the knockdown control group (sh-lncRNA-Ctrl), the KD1 group (sh-lncRNA-KD1), and the KD2 group (sh-lncRNA-KD2). This graph is used to summarize and statistically test the apoptosis rate from multiple independent experiments of the knockdown model. Figure 25 The representative phenomena are transformed into expressible statistical conclusions. Figure 26The study clearly showed significant evidence of increased apoptosis in KD1, while KD2 only exhibited a non-significant increasing trend. Specifically, the proportion of apoptosis was significantly increased in the KD1 group (P<0.01), while the KD2 group only showed an increasing trend and did not reach statistical significance (P>0.05). Therefore, the significance of the knockdown experiment is limited to KD1.

[0111] It should be noted that, Figure 25 The apoptosis rate in the KD1 group was 5.5% in Q2 and 12.8% in Q3, totaling 18.3%. Figure 25 The image from the KD1 group is one of three parallel datasets. The apoptosis rates in the other two parallel datasets for the KD1 group were 37.38% and 33.96%, respectively, resulting in a mean of 29.88% for the three parallel datasets. This is consistent with... Figure 26 The apoptosis rate in the KD1 group was 29.88%.

[0112] Figure 27 A scatter plot of apoptosis detected by flow cytometry for the overexpression model. Figure 27 In the figure, A represents the empty vector control group (lncRNA-OE-Ctrl), and B represents the overexpression vector group (lncRNA-OE). This figure is used to show the representative original quadrant distribution of flow cytometry apoptosis detection in the overexpression control group and the ENST00000565978 overexpression group, and to visually observe the changes in apoptosis-related quadrants (i.e., Q2 and Q3 quadrants) along the overexpression direction. It is related to... Figure 25 The original streaming cytometry evidence constitutes the forward and reverse models, but whether it is significant needs to be determined by... Figure 28 The statistical data from repeated experiments shall be used as the standard. Figure 27 It is evident that the quadrant distribution of Annexin V-APC / 7-AAD differs between the overexpression control group and the overexpression group, with a decreased proportion of apoptosis-related quadrants in the overexpression group; specific differences are indicated by... Figure 28 The statistical results of repeated experiments shall prevail.

[0113] It should be noted that, Figure 27 The apoptosis rate in the control group was 1.92% in Q2 and 7.8% in Q3, with the sum being 9.75%. Figure 27 The apoptosis rate in the overexpression group was 1.62% in Q2 and 6.78% in Q3, with a combined rate of 8.4%. Figure 27 The images for the control group and the overexpression group are from one of three parallel datasets. The apoptosis rates in the other two parallel datasets were 12.38% and 12.36% in the control group, and 8.29% and 10.05% in the overexpression group, respectively. Therefore, the average of the three parallel datasets for the control group was 11.50% ± 1.52%, and the average of the three parallel datasets for the overexpression group was 8.91% ± 0.99%. This is consistent with... Figure 28 The apoptosis rates of 11.50% and 8.91% as statistically observed in the study correspond to these values.

[0114] Figure 25 and Figure 27 In the diagram, the X-axis (Annexin V-APC) is used to reflect whether "the cell has begun apoptosis" and the Y-axis (7-AAD) is used to reflect whether "the cell membrane is still intact and whether the cell has died".

[0115] Figure 28 for Figure 27 The graph shows the apoptosis rate statistics of the overexpression model. The graph includes the empty vector control group (lncRNA-OE-Ctrl) and the overexpression vector group (lncRNA-OE). Figure 28 This was used to summarize and statistically test the apoptosis rate in the overexpression model to confirm... Figure 27 The observed reduction in apoptosis is statistically supported. It is consistent with... Figure 24 The results of enhanced cell viability corroborate each other and are consistent with Figure 23 and Figure 26 The knockdown direction results collectively support the association of ENST00000565978 with pro-survival and anti-apoptotic states in the Jurkat T cell model. Figure 28 The results showed that the apoptosis rate in the overexpression group was lower than that in the overexpression control group (P=0.07), but there was no statistical difference between the two. It can still be seen that ENST00000565978 is associated with increased cell survival and decreased apoptosis in the Jurkat T cell model.

[0116] Figure 29 This is a transcription factor expression diagram after knocking down ENST00000565978. In the diagram, Control represents the healthy control group; P25K1747 represents the KD1 group, i.e., sh-lncRNA-KD1; and P25K1748 represents the KD2 group, i.e., sh-lncRNA-KD2. Figure 29 This study investigated whether T cell-related transcription factors changed after knocking down ENST00000565978, aiming to determine whether specific transcriptional state alterations accompanied cell viability and apoptosis phenotype. The results extended functional evidence to T-bet and RORC-related areas; however, since no consistent and significant changes were observed in GATA3 and FOXP3, this figure only supports related transcriptional state alterations. Figure 29 The knockdown showed a significant decrease in T-bet and RORC expression (P<0.01 or P<0.001), while no consistent significant changes were observed in GATA3 and FOXP3. The results support the association between ENST00000565978 and T-bet / RORC-related transcriptional status, but cannot be used to directly conclude that T cell subsets have differentiated.

[0117] Figure 30This is a graph showing transcription factor expression after overexpression of ENST00000565978. The graph includes the empty vector control group (lncRNA-OE-Ctrl) and the overexpression vector group (lncRNA-OE). This graph was used to examine whether overexpression of ENST00000565978 could produce a significant effect opposite to knockdown on T-bet, GATA3, FOXP3, and RORC, and to compare it with... Figure 29 This allows for a two-way comparison. Figure 30 The results showed that T-bet, GATA3, FOXP3, and RORC did not show statistically significant changes under overexpression conditions (P>0.05); this negative result indicates that overexpression did not directly drive changes in the expression of the above transcription factors under the experimental conditions. Figure 30 In the study, no statistically significant changes were observed in any of the four transcription factors. This result was used to objectively limit the mechanistic conclusion: that the existing evidence supports the association of ENST00000565978 with some T cell functions and transcriptional states, but is insufficient to assert that overexpression can directly drive the differentiation of the above transcription factors or T cell subsets.

[0118] The concentrations of cytokines IL-10, IL-4, IL-2, TNF-α, IFN-γ, and TGF-β1 in cell culture supernatant were detected using ELISA (enzyme-linked immunosorbent assay). Knockdown of any cytokine showed no statistically significant difference. Overexpression of ENST00000565978 increased IL-10, decreased IL-2 and TNF-α (both P < 0.01), while IL-4, IFN-γ, and TGF-β1 showed no significant changes. These results indicate that ENST00000565978 overexpression is associated with selective changes in the cytokine secretion profile, rather than synchronous changes in all cytokines.

[0119] Example 6

[0120] This example describes the ENST00000565978 nucleic acid detection kit and its detection process.

[0121] One implementation includes sample collection or preservation consumables, RNA extraction reagents, reverse transcription reagents, ENST00000565978 specific primers, internal reference gene primers, qPCR premix, positive control, negative control, blank control, calibrators, and a result calculation module. Peripheral blood PBMCs are preferred as samples, but peripheral blood nucleic acid samples that have been methodologically validated to stably detect this transcript can also be used.

[0122] Peripheral blood was collected and PBMCs were isolated for testing. Total RNA was extracted and quality controlled. After reverse transcription, RT-qPCR was performed. ΔCt was calculated as Ct(ENST00000565978) - Ct (internal reference), or the relative or absolute expression level was obtained from a standard curve. Pre-established and independently validated reference ranges or cutoff values ​​should be used for the same testing platform and sample type; values ​​obtained from different platforms should not be directly mixed.

[0123] When used for FT1D-assisted identification, the ENST00000565978 expression value is compared with a validated reference threshold; elevated expression indicates that the subject possesses FT1D-related molecular expression characteristics. When used for FT1D and classic T1D subtyping, ENST00000565978, FCP, and HbA1c are input into a fixed joint model, and the output FT1D subtyping probability is compared with a preset threshold. The output results are auxiliary interpretation information and should be interpreted in conjunction with clinical data such as onset time, blood glucose, C-peptide, HbA1c, ketoacidosis, and pancreatic autoantibodies.

[0124] Results Interpretation Boundaries: 1. Elevated expression and ROC results support ENST00000565978 as an auxiliary diagnostic or subtyping indicator for FT1D, but do not constitute a sole basis for diagnosis; clinical conclusions still need to be combined with FT1D diagnostic criteria and complete patient clinical data. 2. Correlation, enrichment analysis, and ceRNA network analysis are used for candidate molecule screening and can illustrate statistical associations or predictive relationships, but cannot be described as proven direct molecular regulation or pathogenic causation. 3. Fluorescence microscopy is used to determine lentiviral infection coverage; whether target RNA knockdown or overexpression is successful is confirmed by RT-qPCR quantification results.

[0125] This invention adds the molecular information of ENST00000565978 expression in peripheral blood PBMCs. An expanded cohort study showed that this molecule is upregulated in FT1D patients, and higher than in classic T1D patients; it remains significantly associated with FT1D after age adjustment, providing a new nucleic acid detection target for FT1D-assisted diagnosis and subtyping.

[0126] ENST00000565978 can be detected by RT-qPCR and can be combined with FCP and HbA1c to form a joint discriminant model. The AUC of the three-indicator joint model in distinguishing FT1D from classical T1D is 0.817, which is higher than that of the ENST00000565978 single-indicator and FCP single-indicator models, forming a technical solution that can be converted into a joint detection kit or result calculation module.

[0127] In vitro knockdown and overexpression experiments showed that ENST00000565978 was associated with changes in Jurkat T cell viability, apoptosis, T-bet / RORC expression, and secretion of some cytokines, providing functional evidence for its use in evaluating the function and mechanism of FT1D-related T cells.

[0128] In summary, this invention relates to the application of a biomarker or reagent for detecting a biomarker in the preparation of auxiliary diagnostic products and classification identification products for fulminant type 1 diabetes mellitus (FT1D). The biomarker is a long non-coding RNA, i.e., a lncRNA, named ENST00000565978, whose nucleic acid sequence is shown in SEQ ID NO.1. The reagent comprises primer pairs shown in SEQ ID NO.2 and SEQ ID NO.3. This invention proposes a lncRNA biomarker related to FT1D, providing a new detection target for FT1D molecular classification; extended cohort validation of this invention shows that this molecule is stably upregulated in FT1D and remains significantly associated with FT1D after age correction; the biomarker, combined with FCP and HbA1c, can improve the diagnostic efficacy for differentiating FT1D from classic T1D.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. The application of a biomarker in the preparation of auxiliary diagnostic products and classification identification products for fulminant type 1 diabetes mellitus, characterized in that, The marker is a long non-coding RNA, namely lncRNA, named ENST00000565978, and its nucleic acid sequence is shown in SEQ ID NO.

1.

2. The application according to claim 1, characterized in that, The biomarker was used in combination with clinical indicators fasting C-peptide and glycated hemoglobin.

3. The application according to claim 1, characterized in that, The biomarkers were detected using total RNA from peripheral blood mononuclear cells as a sample and by reverse transcription real-time quantitative PCR.

4. The application according to claim 1, characterized in that, The classification identification refers to the differentiation between fulminant type 1 diabetes and classic type 1 diabetes.

5. The application of a reagent for detecting biomarkers in the preparation of auxiliary diagnostic products and classification identification products for fulminant type 1 diabetes mellitus, characterized in that, The marker is a long non-coding RNA, namely lncRNA, named ENST00000565978, whose nucleic acid sequence is shown in SEQ ID NO.

1. The reagent includes primer pairs shown in SEQ ID NO.2 and SEQ ID NO.

3.

6. The application according to claim 5, characterized in that, The product uses the β-actin gene as an internal control, and its upstream and downstream primers are shown in SEQ ID NO.4 and SEQ ID NO.5, respectively.

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