LncRNA marker as well as screening method and application thereof

Abstract

The invention relates to the field of molecular diagnosis, and discloses an LncRNA (long non-coding ribonucleic acid) marker as well as a screening method and application thereof, the LncRNA marker comprises one or more of ENST00000517961.2, ENST0000058579.1, ENST00000418393.1, ENST00000533082.1, ENST00000460164.1, ENST00000356672.3, ENST00000558449.1 and ENST00000574212.1, and the LncRNA marker comprises one or more of ENST000005849.1, ENST000005849.1 and ENST00000574212.1. By screening the lncRNA which is obviously differentially expressed in patients with diabetic nephropathy and combining clinical indexes to construct the combined diagnosis model, the kidney injury state can be reflected from the molecular level, and the pathological state of the diabetic nephropathy can be effectively reflected, so that the diagnosis accuracy is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of molecular diagnosis, in particular to a LncRNA marker and a screening method and application thereof. BACKGROUND

[0002] Molecular diagnosis technology is an important means to realize early screening, diagnosis, prognosis evaluation and treatment guidance of diseases by detecting the expression level or structural change of nucleic acids, proteins and other biomolecules in the body. Long non-coding RNA (lncRNA) as a kind of non-coding RNA molecule with a length of more than 200 nucleotides, although it does not directly participate in protein synthesis, it participates in key physiological processes such as cell proliferation, differentiation and apoptosis by regulating gene expression, and it often presents specific expression changes in the development of diseases, and has become a potential biomarker in the field of disease diagnosis, and has shown important application prospects in the molecular diagnosis of tumors, cardiovascular diseases, metabolic diseases and other diseases.

[0003] Diabetic kidney disease (DKD) is one of the most serious microvascular complications of diabetes and the main cause of end-stage renal disease. The early clinical manifestations of DKD are not typical, and the current clinical diagnosis mainly relies on UACR and estimated glomerular filtration rate (eGFR), but both have obvious limitations: UACR is not sensitive to early albuminuria-free DKD, and eGFR is easily disturbed by factors such as age and muscle mass, affecting the accuracy of diagnosis. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a LncRNA marker and a screening method and application thereof, which solves the problem of insufficient accuracy of existing DKD diagnosis.

[0005] To achieve the above purpose, the present application is implemented by the following technical scheme: a LncRNA marker, characterized in that it comprises one or more of ENST00000517961.2, ENST00000585759.1, ENST00000418393.1, ENST00000533082.1, ENST00000460164.1, ENST00000356672.3, ENST00000558449.1 and ENST00000574212.1.

[0006] By adopting the above technical scheme, by screening lncRNAs that are significantly differentially expressed in patients with diabetic kidney disease, and combining with clinical indicators to construct a joint diagnosis model, the kidney damage state can be reflected from the molecular level, and combined with the commonly used clinical indicators, the pathological state of diabetic kidney disease can be effectively reflected, thereby improving the accuracy of diagnosis and solving the problem of insufficient accuracy of existing diabetic kidney disease diagnosis.

[0007] A method for screening LncRNA biomarkers, applied to one of the aforementioned LncRNA biomarkers, includes the following steps:

[0008] Sample Acquisition: Kidney tissue samples from patients with diabetic nephropathy, kidney tissue samples from normal controls, plasma samples from patients with diabetic nephropathy, and plasma samples from healthy controls were obtained.

[0009] Sequencing screening: lncRNAs were sequenced from kidney tissue samples of patients with diabetic nephropathy and kidney tissue samples of normal controls, and differentially expressed lncRNAs were screened from the sequencing results;

[0010] Quantitative validation: The expression levels of differentially expressed lncRNAs were validated in plasma samples using quantitative PCR.

[0011] Preferably, in the step of obtaining samples, the kidney tissue sample of the diabetic nephropathy patient is obtained from a kidney biopsy specimen, and the kidney tissue sample of the normal control is obtained from normal kidney tissue adjacent to the cancer.

[0012] Preferably, the sequencing screening specifically includes the following steps:

[0013] RNA was extracted from kidney tissue samples, and genomic DNA was digested with DNase I. The integrity, purity, and total amount of RNA were then measured to obtain total RNA.

[0014] After removing rRNA from total RNA, the RNA was fragmented, then double-stranded cDNA was synthesized and a sequencing library was constructed. After passing quality control, high-throughput sequencing was performed to obtain sequencing data.

[0015] Sequencing data were filtered and compared, unannotated transcripts were screened after transcript assembly, lncRNAs were confirmed by coding potential verification, and differentially expressed lncRNAs were analyzed and screened.

[0016] Preferably, the high-throughput sequencing is performed using the Illumina NovaSeq 6000 platform for paired-end 150bp sequencing.

[0017] Preferably, the differentially expressed lncRNAs are analyzed and screened using DESeq2 analysis, with the screening criteria being |log2FC|>2 and p<0.01.

[0018] Preferably, in the quantitative verification step, the primer sequences used in the quantitative PCR include SEQ ID NO.1-18.

[0019] The application of an lncRNA biomarker in the preparation of a kit for diagnosing diabetic nephropathy, the kit comprising reagents for detecting the lncRNA biomarker, the reagents including reagents for RNA extraction, reverse transcription reagents, quantitative PCR reagents, and primers for quantitative PCR.

[0020] This invention provides a lncRNA biomarker, its screening method, and its application. It offers the following advantages:

[0021] 1. This invention screens lncRNAs that are significantly differentially expressed in patients with diabetic nephropathy and constructs a combined diagnostic model by combining them with clinical indicators. This model can reflect the state of kidney damage at the molecular level and, combined with commonly used clinical indicators, can effectively reflect the pathological state of diabetic nephropathy, thereby improving the accuracy of diagnosis and solving the problem of insufficient accuracy in the current diagnosis of diabetic nephropathy.

[0022] 2. This invention uses plasma as the test sample, which makes the sample collection process simple and safe, without causing trauma to the patient. It also facilitates multiple collections to track disease progression, better meeting the clinical needs for continuous disease management, improving patient compliance, realizing non-invasive detection of diabetic nephropathy, and enhancing safety and convenience.

[0023] 3. This invention utilizes quantitative PCR technology for detection. This technology is mature and stable, with high equipment availability, low reagent costs, and a simple and standardized operating procedure. It can meet the needs of medical institutions at different levels and is conducive to its widespread clinical application, enabling more diabetic patients to benefit from accurate kidney disease screening and diagnosis.

[0024] 4. This invention utilizes lncRNA markers to show specific expression changes in the early stages of the disease. Through combined detection, it can effectively identify kidney damage before it progresses to the stage of obvious clinical symptoms, providing a molecular basis for early intervention, helping to delay disease progression, reduce the risk of end-stage renal disease, and thus helping to achieve early diagnosis of diabetic nephropathy, buying time for disease intervention. Attached Figure Description

[0025] Figure 1 The analysis results for ENST00000517961.2 in Embodiment 3 of the present invention;

[0026] Figure 2 The analysis results of ENST00000585759.1 in Embodiment 3 of the present invention;

[0027] Figure 3 The analysis results of ENST00000418393.1 in Embodiment 3 of the present invention;

[0028] Figure 4The analysis results of ENST00000533082.1 in Embodiment 3 of the present invention;

[0029] Figure 5 The analysis results of ENST00000460164.1 in Embodiment 3 of the present invention;

[0030] Figure 6 The analysis results for ENST00000356672.3 in Embodiment 3 of the present invention;

[0031] Figure 7 The analysis results of ENST00000558449.1 in Embodiment 3 of the present invention;

[0032] Figure 8 The analysis results of ENST00000574212.1 in Embodiment 3 of the present invention;

[0033] Figure 9 The analysis results are those of the combined diagnostic model in Embodiment 3 of the present invention;

[0034] Figure 10 This is a flowchart of a method for screening LncRNA biomarkers proposed in this invention. Detailed Implementation

[0035] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] This invention provides an LncRNA biomarker, including one or more of ENST00000517961.2, ENST00000585759.1, ENST00000418393.1, ENST00000533082.1, ENST00000460164.1, ENST00000356672.3, ENST00000558449.1, and ENST00000574212.1.

[0037] Specifically, the aforementioned LncRNA biomarkers were obtained through systematic screening and validation of tissue and plasma samples from patients with diabetic nephropathy and normal controls. They exhibited significant differential expression in patients with diabetic nephropathy and can serve as specific molecular indicators for the diagnosis of diabetic nephropathy. Generally, by jointly detecting the expression levels of these biomarkers and combining them with commonly used clinical indicators, the pathological state of diabetic nephropathy can be effectively reflected, thereby improving diagnostic accuracy and addressing the current problem of insufficient accuracy in the diagnosis of diabetic nephropathy.

[0038] Please see the appendix Figure 10 A method for screening LncRNA biomarkers, applied to one of the aforementioned LncRNA biomarkers, includes the following steps:

[0039] Sample Acquisition: Kidney tissue samples from patients with diabetic nephropathy, kidney tissue samples from normal controls, plasma samples from patients with diabetic nephropathy, and plasma samples from healthy controls were obtained.

[0040] Furthermore, in the sample acquisition process, kidney tissue samples from patients with diabetic nephropathy were obtained from kidney biopsy specimens, while kidney tissue samples from normal controls were obtained from adjacent normal kidney tissue.

[0041] Specifically, in some embodiments, kidney tissue samples from patients with diabetic nephropathy are obtained from renal biopsy specimens, while kidney tissue samples from normal controls are obtained from adjacent normal kidney tissue after nephrectomy for tumors. Generally, sample collection requires informed consent from the patient or their guardian and approval from the ethics committee to comply with medical ethics guidelines. As an option, three kidney tissue samples from patients with diabetic nephropathy and three from normal controls can be collected for initial differential expression screening; 80 plasma samples from patients with diabetic nephropathy and 80 plasma samples from healthy controls can be collected for subsequent expression level validation. Specifically, after plasma sample collection, it needs to be anticoagulated with EDTA and centrifuged at 3000g for 15 minutes within 2 hours of collection, aliquoted, and stored at -80℃ to avoid RNA degradation affecting subsequent detection.

[0042] Sequencing screening: lncRNAs were sequenced from kidney tissue samples of patients with diabetic nephropathy and kidney tissue samples of normal controls, and differentially expressed lncRNAs were screened from the sequencing results;

[0043] Furthermore, the sequencing screening process specifically includes the following steps:

[0044] RNA was extracted from kidney tissue samples, and genomic DNA was digested with DNase I. The integrity, purity, and total amount of RNA were then measured to obtain total RNA.

[0045] After removing rRNA from total RNA, the RNA was fragmented, then double-stranded cDNA was synthesized and a sequencing library was constructed. After passing quality control, high-throughput sequencing was performed to obtain sequencing data.

[0046] Sequencing data were filtered and compared, unannotated transcripts were screened after transcript assembly, lncRNAs were confirmed by coding potential verification, and differentially expressed lncRNAs were analyzed and screened.

[0047] Furthermore, high-throughput sequencing was performed using the Illumina NovaSeq 6000 platform for paired-end 150bp sequencing.

[0048] Furthermore, differentially expressed lncRNAs were analyzed and screened using DESeq2 analysis, with the screening criteria being |log2FC|>2 and p<0.01.

[0049] Specifically, total RNA was extracted using Trizol reagent and treated with DNase I to remove genomic DNA contamination. Generally, RNA integrity was assessed using an Agilent 2200 TapeStation, requiring a RIN ≥ 7.0; purity was assessed using Nanodrop, requiring an A260 / A280 ratio of 1.8-2.0 and an A260 / A230 ratio ≥ 2.0; and total RNA was quantified using Qubit to confirm a total amount ≥ 1 μg, ensuring the reliability of subsequent experiments.

[0050] Alternatively, the Ribo-Zero kit can be used to remove rRNA to enrich non-coding RNAs such as lncRNA. Specifically, RNA is fragmented to 200±20 nt using a Covaris S220, and then double-stranded cDNA is synthesized using the NEBNext UltraII kit. End repair, A-tail addition, adapter ligation, and 8 cycles of PCR amplification are then performed to construct a sequencing library. Generally, the library must pass Agilent 2100 quality control before high-throughput sequencing. In some embodiments, high-throughput sequencing is performed using the Illumina NovaSeq 6000 platform for paired-end 150bp sequencing, producing ≥20Gb of raw data per sample to ensure sufficient sequencing depth for analysis.

[0051] Raw data was filtered using Trimmomatic v0.39, retaining clean data with Q30 ≥ 85%. Sequences were aligned to the hg38 reference genome using HISAT2v2.2.1, converted to BAM files using samtools, and transcripts were assembled using StringTiev2.2.1. Generally, unannotated transcripts with a length > 200 nt and ≥ 2 exons were selected using gffcompare to meet the basic characteristics of lncRNAs.

[0052] Alternatively, coding potential verification can be performed using CNCIv2, CPC2 v0.1, and PfamScan v1.6, where CNCIv2 score < 0, CPC2 v0.1 non-coding score > 0.5 and ORF < 300aa, and PfamScan v1.6 E-value > 1e-5, to confirm that the transcript is a lncRNA.

[0053] In some embodiments, DESeq2 analysis was used to screen differentially expressed lncRNAs, with screening criteria of |log2FC|>2 and p<0.01, in order to obtain significantly differentially expressed lncRNAs, namely the above-mentioned lncRNA markers.

[0054] Quantitative validation: The expression levels of differentially expressed lncRNAs were validated in plasma samples using quantitative PCR.

[0055] Furthermore, in the quantitative verification step, the primer sequences used for quantitative PCR include SEQ ID NO.1-18.

[0056] Specifically, quantitative validation includes steps such as plasma RNA extraction, RNA quality testing, reverse transcription, and quantitative PCR.

[0057] In one possible implementation, the steps for plasma RNA extraction are as follows: Prepare washing buffer A and washing buffer B, wherein washing buffer A is 21 mL of stock solution mixed with 9 mL of anhydrous ethanol, and washing buffer B is 9 mL of stock solution mixed with 21 mL of anhydrous ethanol; take 200 μL of plasma sample, add 4 μL of RNA carrier, 300 μL of lysis buffer and 20 μL of digestion solution, and vortex to mix; incubate the mixture in a 56°C water bath for 10 minutes to induce lysis; add 1 mL of anhydrous ethanol and mix by inverting; take 760 μL of the above mixture and add it to the adsorption column, let it stand for 2 minutes, then centrifuge at 12000 rpm at 4°C for 2 minutes, and discard the waste liquid; repeat the operation to transfer the remaining mixture to the adsorption column; add 500 μL of washing buffer A to the adsorption column, centrifuge at 12000 rpm at 4°C for 2 minutes, and discard the waste liquid; add 500 μL of washing buffer B, centrifuge under the same conditions, and discard the waste liquid; empty column centrifuge at 12000 rpm. Centrifuge at 4°C for 2 minutes to remove residual waste liquid; place the adsorption column into a new RNase-free EP tube, add about 40 μL of elution buffer, let stand for 3 minutes, then centrifuge at 12000 rpm at 4°C for 2 minutes to collect the RNA solution.

[0058] Generally, the concentration and purity of RNA are detected using a NanoDrop-2000 micro spectrophotometer, requiring an A260 / 280 ratio between 1.8 and 2.2 to ensure that the RNA quality meets the requirements of subsequent experiments.

[0059] In some embodiments, reverse transcription was performed using the Evo M-MLV reverse transcription kit, converting 1 μg of total RNA into cDNA. Specifically, the reverse transcription reaction system was 20 μL, including 2 μL gDNA Clean Reaction Mix Ver.2, 4 μL 5×Evo M-MLV RT Reaction Mix Ver.2, an appropriate amount of total RNA, and RNase-free water to make up the volume; the reaction conditions were incubation at 42°C for 15 minutes, followed by heating at 85°C for 5 seconds to terminate the reaction. The cDNA could be used directly for quantitative PCR or stored at -20°C.

[0060] In quantitative PCR, the primer sequences used include SEQ ID NO. 1-18, where SEQ ID NO. 1-2 corresponds to ENST00000517961.2, SEQ ID NO. 3-4 corresponds to ENST00000585759.1, and SEQ ID NO. 5-6 corresponds to ENST00000418393.1.

[0061] SEQ ID NO.7-8 correspond to ENST00000533082.1, SEQ ID NO.9-10 correspond to ENST00000460164.1, SEQ ID NO.11-12 correspond to ENST00000356672.3, SEQ ID NO.13-14 correspond to ENST00000558449.1, SEQ ID NO.15-16 correspond to ENST00000574212.1, and SEQ ID NO.17-18 correspond to the internal reference gene β-actin.

[0062] As an alternative, quantitative PCR was performed using the SYBR Green Pro Taq HS premix kit. The reaction volume was 20 μL, including 10 μL 2×SYBR Green Pro Taq HS Premix, 2 μL cDNA, 0.4 μL forward primer (10 μM), 0.4 μL reverse primer (10 μM), and 7.2 μL RNase-free water. The reaction conditions were 95℃ pre-denaturation for 30 seconds, 95℃ denaturation for 5 seconds, and 60℃ annealing for 30 seconds, for a total of 40 cycles. Melting curve analysis was performed at the end. Generally, gene expression levels were normalized using β-actin as an internal control. -ΔΔCt The method calculates relative expression levels to verify the expression changes of differentially expressed lncRNAs in plasma samples.

[0063] The application of an lncRNA biomarker in the preparation of a kit for diagnosing diabetic nephropathy, the kit comprising reagents for detecting the lncRNA biomarker, including reagents for RNA extraction, reverse transcription reagents, quantitative PCR reagents, and primers for quantitative PCR.

[0064] Specifically, reagents used for RNA extraction may include lysis buffer, digestion solution, RNA carrier, washing solution A, washing solution B, elution buffer, etc.; reverse transcription reagents may include gDNA Clean Reaction Mix, reverse transcriptase, reaction buffer, etc.; quantitative PCR reagents may include SYBR Green premix, RNase free water, etc.; the primers used for quantitative PCR are the sequences corresponding to SEQ ID NO.1-18 above.

[0065] In some embodiments, this kit can assist in the diagnosis of diabetic nephropathy by detecting the expression levels of the aforementioned LncRNA markers in plasma samples, providing molecular-level evidence for clinical diagnosis. Generally, the kit should be used in accordance with the instruction manual to ensure the accuracy and reliability of the test results.

[0066] The following is a further description with reference to specific embodiments:

[0067] Example 1:

[0068] This embodiment 1 provides a method for screening incRNA biomarkers, including the following steps:

[0069] Step 1: Collect kidney tissue samples

[0070] Three kidney tissue specimens from patients with DKD were collected from kidney tissue obtained through puncture biopsies performed at the Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, between May 2021 and May 2024. Three normal control samples were collected from adjacent normal kidney tissue after nephrectomy for tumors. Informed consent was obtained from all patients or their guardians before sample collection, and all samples were signed. All human specimen collection studies were approved by the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University.

[0071] Step 2: RNA extraction and sequencing

[0072] Total RNA was extracted using Trizol reagent. Genomic DNA was digested with DNase I. RNA integrity was verified using an Agilent 2200 TapeStation (RIN ≥ 7.0), and purity was determined using Nanodrop (A260 / A280 = 1.8–2.0, A260 / A230 ≥ 2.0). Quantitative confirmation of total amount ≥1μg; subsequently Ribo-ZeroTM The kit removes rRNA, and the Covaris S220 fragments the RNA to 200±20 nt. Ultra TM The II kit synthesized double-stranded cDNA, which underwent end repair, A-tail addition, adapter ligation, and 8 cycles of PCR amplification to construct a library. After passing Agilent 2100 quality control, paired-end 150bp sequencing was performed on the Illumina NovaSeq 6000 platform, yielding ≥20Gb of raw data per sample.

[0073] Step 3: Data Analysis

[0074] The raw data were filtered using Trimmomatic v0.39 to obtain Clean Data with Q30 ≥ 85%; sequences were aligned to the hg38 reference genome using HISAT2 v2.2.1, converted to BAM files using samtools, and then transcripts were assembled using StringTie v2.2.1; unannotated transcripts were screened using gffcompare, retaining transcripts with a length > 200 nt and ≥ 2 exons, and then validated using triple coding potential by CNCIv2 (score < 0), CPC2 v0.1 (non-coding score > 0.5 and ORF < 300aa), and PfamScan v1.6 (E-value > 1e-5); finally, differential analysis was performed using DESeq2 v1.34.0 (|log2FC| > 2 and p < 0.01).

[0075] Step 4: Filter Results

[0076] Eight significantly upregulated lncRNAs were identified: ENST00000517961.2, ENST00000585759.1, ENST00000418393.1, ENST00000533082.1, ENST00000460164.1, ENST00000356672.3, ENST00000558449.1, and ENST00000574212.1.

[0077] Example 2:

[0078] Example 2 verifies the expression levels of the eight lncRNAs screened in Example 1 in the plasma of 80 healthy controls and 80 DKD patients using qPCR.

[0079] Primer sequences for target gene and internal reference gene:

[0080] ENST00000517961.2-F 5'-ATAGTTCGGGAGACCCACA-3',

[0081] SEQ ID NO.1;

[0082] ENST00000517961.2-R 5’-TACAACCTCCTCCAACGGCA-3’,

[0083] SEQ ID NO.2;

[0084] ENST00000585759.1-F 5’-TCATCACCTCAAATCGCCTGT-3’,

[0085] SEQ ID NO.3;

[0086] ENST00000585759.1-R 5’-CTGAATTCTGGGAAGATGGGAT-3’,SEQ ID NO.4;

[0087] ENST00000418393.1-F 5’-CAAAGGCGTCAGCATTGGGA-3’,

[0088] SEQ ID NO.5;

[0089] ENST00000418393.1-R 5’-TGGCAATCCAGCCTTCTGAT-3’,

[0090] SEQ ID NO.6;

[0091] ENST00000533082.1-F 5’-GAGCTCCTGGCACACTTACA-3’,

[0092] SEQ ID NO.7;

[0093] ENST00000533082.1-R 5’-AGCCTTTCCATAGGGCACAA-3’,

[0094] SEQ ID NO.8;

[0095] ENST00000460164.1-F 5’-GTCGTGGAACTCAGGCGCT-3’,SEQ ID NO.9;

[0096] ENST00000460164.1-R 5’-GTAGGTCTGGGTGCCGAAGT-3’,

[0097] SEQ ID NO.10;

[0098] ENST00000356672.3-F 5'-AGCCGATATACTCCCCTCACT-3',

[0099] SEQ ID NO.11;

[0100] ENST00000356672.3-R 5'-AAGAGAGTGAGCCCTTGGAGA-3', SEQ ID NO.12;

[0101] ENST00000558449.1-F 5'-ACAACACACCCCACATCCG-3', SEQ ID NO.13;

[0102] ENST00000558449.1-R 5'-CTTCCAGTCCAGTGCTGTTG-3',

[0103] SEQ ID NO.14;

[0104] ENST00000574212.1-F 5'-ACAGTGAATGGCAGTTGGGT-3',

[0105] SEQ ID NO.15;

[0106] ENST00000574212.1-R 5'-TTGCCAGTCATTACAGATGTCCT-3', SEQ ID NO.16;

[0107] β-Actin-F 5'-CATGTACGTTGCTATCCAGGC-3', SEQ ID NO. 17;

[0108] β-Actin-R 5'-CTCCTTAATGTCACGCACGAT-3', SEQ ID NO. 18.

[0109] Step 1: Sample Preparation

[0110] UACR data and plasma samples were collected from 80 healthy controls without kidney disease and 80 patients with DKD at Shandong Provincial Hospital Affiliated to Shandong First Medical University between May 2021 and May 2024. Plasma samples were anticoagulated with EDTA and centrifuged (3,000g × 15min) within 2 hours, then aliquoted and stored at -80℃. Informed consent was obtained from all patients or their guardians before sample collection. All human specimen collection studies were approved by the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University.

[0111] Step 2: RNA extraction and qPCR

[0112] 2.1 Plasma RNA Extraction

[0113] (1) Prepare the washing solution according to the instructions. The specific steps are as follows:

[0114] Washing solution A: Add 9 mL of anhydrous ethanol to 21 mL of stock solution and mix thoroughly;

[0115] Washing solution B: Add 21 mL of anhydrous ethanol to 9 mL of stock solution and mix thoroughly.

[0116] (2) Take a 1.5 mL EP tube without RNase, mix 200 μL of plasma sample with 4 μL of RNA carrier, add 300 μL of lysis buffer and 20 μL of digestion solution, and vortex to mix.

[0117] (3) Place the well-mixed 1.5mL EP tube in a 56℃ water bath and incubate for 10min to allow it to lyse.

[0118] (4) Add 1 mL of anhydrous ethanol and mix by inverting the container.

[0119] (5) Take 760 μL of the above mixed liquid into the adsorption column, let it stand for 2 min, centrifuge at 12000 rpm and 4℃ for 2 min, and discard the waste liquid.

[0120] (6) Transfer the remaining 760 μL back into the adsorption column and repeat step (5).

[0121] (7) Add 500 μL of washing solution A to the adsorption column, centrifuge at 12000 rpm and 4℃ for 2 min, and discard the waste liquid.

[0122] (8) Add 500 μL of washing solution B to the adsorption column, centrifuge at 12000 rpm and 4℃ for 2 min, and discard the waste liquid.

[0123] (9) Centrifuge at 12000 rpm at 4℃ for 2 min to wash away residual waste liquid.

[0124] (10) Place the adsorption column into a new RNase-free EP tube, add approximately 40 μL of elution buffer, let stand for 3 min, and centrifuge at 12000 rpm at 4℃ for 2 min. The remaining liquid in the tube is the RNA solution. This RNA can be used directly for the next experiment or stored at -80℃.

[0125] 2.2 Measurement of RNA concentration and purity using a NanoDrop-2000 micro spectrophotometer

[0126] (1) Turn on the instrument and select "nucleic acid" - "RNA".

[0127] (2) Lift the robotic arm of the instrument and wipe the base with clean, lint-free paper. Add 1 μL of sterile, enzyme-free water to the base and click Blank.

[0128] (3) Wipe the base with clean, dust-free paper, then add 1 μL of sterile, enzyme-free water to the base. Click the “Mearsure” button. If the measurement result is ≤1 ng / μL, you can start measuring the concentration and purity of the sample. If the result is ≥1 ng / μL, you need to repeatedly add sterile, enzyme-free water to clean until the measurement result is ≤1 ng / μL.

[0129] (4) Wipe the base with clean, dust-free paper, then add 1 μL of RNA sample to the base. Click the “Mearsure” button to record the concentration and purity of the RNA. If 260 / 280 is between 1.8 and 2.2, it indicates that the RNA sample is of acceptable purity and can be used for subsequent experiments.

[0130] (5) After all samples have been tested, add sterile, enzyme-free liquid 2-3 times to clean the base, then wipe it dry with lint-free paper and turn off the instrument.

[0131] 2.3 Reverse transcription

[0132] 1 μg of total RNA was reverse transcribed into cDNA using the Evo M-MLV Reverse Transcription Kit (Aikerui Biotechnology, China) according to the manufacturer's instructions. The cDNA sample can be used directly for real-time PCR detection or stored at -20°C.

[0133] The reverse transcription reaction system is shown in Table 1;

[0134] Table 1

[0135]

[0136] 2.4. Quantitative Real-Time PCR Analysis

[0137] The primers used in real-time PCR were synthesized by Boshan Biotechnology. Before use, the primer lyophilized powder needs to be centrifuged at 4°C and 12,000 rpm for 2 minutes, and then an appropriate amount of DEPC water is added to completely dissolve the lyophilized powder, so that its final concentration is 10 μM.

[0138] The PCR reaction system was prepared on ice according to the manufacturer's instructions using the SYBR Green Pro Taq HS premixed qPCR kit (Aikerui Biotechnology, China). The reaction mixture was added to 96-well plates and sealed. The plates were centrifuged at 3000 rpm for 2 min at room temperature. The quantitative expression level of the target gene was detected by qPCR using a Roche 480 PCR instrument. Gene expression levels were normalized to β-actin and analyzed using the 2-ΔΔCt method.

[0139] The qPCR reaction system is shown in Table 2;

[0140] Table 2

[0141]

[0142] Example 3:

[0143] Please see the appendix Figure 1 -Appendix Figure 9 Example 3 presents a systematic analysis of the eight lncRNAs selected in Example 1. The analysis method is univariate receiver operating characteristic analysis (univariate ROC analysis), and the study investigates whether the combined use of multiple variables can improve predictive ability.

[0144] The specific steps are as follows: Univariate analysis of the expression levels of the lncRNAs obtained in Example 2 was performed using IBM SPSS Statistics, and receiver operating characteristic (ROC) curves were plotted. To investigate whether the combined use of multiple variables could improve predictive ability, this example also used Logistic regression to construct a joint diagnostic model of the above eight lncRNAs and UACR using IBM SPSS Statistics. The AUC value was 0.995, and at a cutoff value of 0.95, the sensitivity was 98.8% and the specificity was 96.3%.

[0145] Example 4:

[0146] A diagnostic kit for diabetic nephropathy, comprising a detection reagent for detecting one or more lncRNAs selected from ENST00000517961.2, ENST00000585759.1, ENST00000418393.1, ENST00000533082.1, ENST00000460164.1, ENST00000356672.3, ENST00000558449.1, and ENST00000574212.1.

[0147] sequence:

[0148] SEQ ID NO.1: F 5'-ATAGTTCGGGGGAGACCCACA-3'

[0149] SEQ ID NO.2: R 5'-TACAACCTCCTCCAACGGCA-3'

[0150] SEQ ID NO.3: F 5'-TCATCACCTCAAATCGCCTGT-3'

[0151] SEQ ID NO.4:R 5'-CTGAATTCTGGGAAGATGGGAT-3'

[0152] SEQ ID NO.5:F 5'-CAAAGGCGTCAGCATTGGGA-3'

[0153] SEQ ID NO.6: R 5'-TGGCAATCCAGCCTTCTGAT-3'

[0154] SEQ ID NO.7:F 5'-GAGCTCCTGGCACACTTACA-3'

[0155] SEQ ID NO.8:R 5'-AGCCTTTCCATAGGGCACAA-3'

[0156] SEQ ID NO.9:F 5'-GTCGTGGAACTCAGGCGCT-3'

[0157] SEQ ID NO.10:R 5'-GTAGGTCTGGGTGCCGAAGT-3'

[0158] SEQ ID NO.11:F 5'-AGCCGATACTCCCCTCACT-3'

[0159] SEQ ID NO.12: R 5'-AAGAGAGTGAGCCCTTGGAGA-3'

[0160] SEQ ID NO.13:F 5'-ACAACACACCCACATCCG-3'

[0161] SEQ ID NO.14:R 5'-CTTCCAGTCCAGTGCTGTTTG-3'

[0162] SEQ ID NO.15:F 5'-ACAGTGAATGGCAGTTGGGT-3'

[0163] SEQ ID NO.16:R 5'-TTGCCAGTCATTACAGATGTCCT-3'

[0164] SEQ ID NO.17:F 5'-CATGTACGTTGCTATCCAGGC-3'

[0165] SEQ ID NO.18:R 5'-CTCCTTTAATGTCACGCACGAT-3'

[0166] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A lncRNA biomarker, characterized in that, Including one or more of ENST00000517961.2, ENST00000585759.1, ENST00000418393.1, ENST00000533082.1, ENST00000460164.1, ENST00000356672.3, ENST00000558449.1, and ENST00000574212.

1.

2. A method for screening LncRNA biomarkers, characterized in that: The application of the LncRNA biomarker according to claim 1 includes the following steps: Sample Acquisition: Kidney tissue samples from patients with diabetic nephropathy, kidney tissue samples from normal controls, plasma samples from patients with diabetic nephropathy, and plasma samples from healthy controls were obtained. Sequencing screening: lncRNAs were sequenced from kidney tissue samples of patients with diabetic nephropathy and kidney tissue samples of normal controls, and differentially expressed lncRNAs were screened from the sequencing results; Quantitative validation: The expression levels of differentially expressed lncRNAs were validated in plasma samples using quantitative PCR.

3. The method for screening LncRNA biomarkers according to claim 2, characterized in that: In the sample acquisition process, the kidney tissue sample from the diabetic nephropathy patient was obtained from a kidney biopsy specimen, and the kidney tissue sample from the normal control was obtained from normal kidney tissue adjacent to the cancer.

4. The method for screening LncRNA biomarkers according to claim 2, characterized in that: The sequencing screening specifically includes the following steps: RNA was extracted from kidney tissue samples, and genomic DNA was digested with DNase I. The integrity, purity, and total amount of RNA were then measured to obtain total RNA. After removing rRNA from total RNA, the RNA was fragmented, then double-stranded cDNA was synthesized and a sequencing library was constructed. After passing quality control, high-throughput sequencing was performed to obtain sequencing data. Sequencing data were filtered and compared, unannotated transcripts were screened after transcript assembly, lncRNAs were confirmed by coding potential verification, and differentially expressed lncRNAs were analyzed and screened.

5. The method for screening LncRNA biomarkers according to claim 4, characterized in that: The high-throughput sequencing was performed using the Illumina NovaSeq 6000 platform for paired-end 150bp sequencing.

6. The method for screening LncRNA biomarkers according to claim 4, characterized in that: The differentially expressed lncRNAs were screened using DESeq2 analysis, with the screening criteria being |log2FC|>2 and p<0.

01.

7. The method for screening LncRNA biomarkers according to claim 2, characterized in that: In the quantitative verification step, the primer sequences used in the quantitative PCR include SEQ ID NO.1-18.

8. The application of the LncRNA biomarker according to claim 1 in the preparation of a kit for diagnosing diabetic nephropathy, characterized in that: The kit includes reagents for detecting lncRNA markers, including reagents for RNA extraction, reverse transcription reagents, quantitative PCR reagents, and primers for quantitative PCR.

Images