A plasma tsRNA molecular marker for predicting postoperative delirium in elderly patients and its application

Abstract

The present invention discloses a plasma tsRNA molecular marker for predicting postoperative delirium in elderly patients and its application. The plasma tsRNA molecular marker is tRF-Gly-14 and / or tRF-Arg-39. The nucleotide sequence of tRF-Gly-14 is AATGGTAGAATTCTCGCC, and the nucleotide sequence of tRF-Arg-39 is TCAGAAGATTGAGGGTTCGAATCCCTTCGTGGTTG. The present invention also provides the application of the above plasma tsRNA molecular marker in the preparation of a kit for predicting postoperative delirium in elderly patients. The kit includes forward and reverse primers for fluorescence quantitative PCR amplification of tRF-Gly-14 and / or tRF-Arg-39. The advantages are high accuracy, high sensitivity and strong specificity in POD prediction, which helps to timely identify high-risk patients.

Description

Technical Field

[0001] The present invention relates to the technical field of prediction of postoperative delirium, and in particular to a plasma tsRNA molecular biomarker for predicting postoperative delirium in elderly patients and its application. Background Art

[0002] Postoperative delirium (abbreviated as POD) is one of the most common complications in elderly patients after surgery, mainly manifested as acute fluctuating changes in postoperative consciousness, attention and behavior. The symptoms usually appear within 1 - 3 days after surgery and are severe. POD not only affects the early prognosis but is also associated with long-term adverse outcomes. Studies have shown that delirious patients have an increased risk of developing complications, perioperative mortality and prolonged hospital stay after surgery, resulting in an increase in medical costs during hospitalization. Long-term follow-up studies have shown that delirious patients have an increased incidence of postoperative cognitive dysfunction, a decline in quality of life and an increased long-term mortality. Therefore, early detection, diagnosis and intervention in the occurrence of POD are of great significance for improving the long-term prognosis and quality of life of postoperative patients. However, the pathogenesis of POD is not yet clear and may be closely related to central nervous system inflammatory response, oxidative stress, central nervous cholinergic system disorder and sleep disorder, etc. At present, the pathogenesis of POD has not been fully elucidated and may be related to multiple factors such as central nervous system inflammatory response, oxidative stress, central nervous cholinergic system disorder and sleep disorder. Clinically, there is still a lack of reliable prediction indicators. The existing assessment methods mainly rely on preoperative risk factor assessment and postoperative symptom observation. This method not only lacks objective quantitative criteria but also cannot timely warn high-risk patients. Therefore, finding objective biomarkers that can predict the risk of POD occurrence before surgery is of great value for guiding clinical prevention and early intervention.

[0003] In recent years, studies have shown that non-coding RNAs play an important role in the regulation of nervous system diseases. Among them, tRNA-derived small RNAs (tsRNAs) are a newly discovered class of regulatory molecules and play an important role in neurodevelopment and neurodegenerative diseases. Studies have shown that abnormal tsRNA signal networks are related to the development, function and disease state of the central nervous system. Studying the mechanism of tsRNA regulating cell processes is expected to discover new diagnostic biomarkers and therapeutic targets for nervous system diseases. Although tsRNAs show potential as biomarkers and therapeutic targets in other nervous system diseases, their role in POD has not been explored yet.

[0004] Although cerebrospinal fluid most directly reflects the changes in the central nervous system, its collection is invasive and has poor clinical practicability. Studies have shown that inflammatory reactions and oxidative stress in the brains of delirious patients produce a large number of inflammatory mediators, which damage the blood-brain barrier and cause related chemicals to diffuse into peripheral blood. Therefore, peripheral blood, as a relatively easily accessible body fluid sample, may also carry biological information reflecting the pathological changes of central nervous system damage, which is beneficial to the study of the related mechanisms of POD. Existing studies have found that the enrichment abundance of tsRNA is relatively high in the circulatory system. At the same time, compared with other non-coding RNAs such as long non-coding RNA (lncRNA) and microRNA (miRNA), tsRNA can exist more stably in human plasma. Moreover, tsRNA has higher stability and abundance in plasma than other non-coding RNAs (such as lncRNA and miRNA), making it a potential circulating biomarker. Therefore, developing postoperative delirium prediction markers based on plasma tsRNA has important clinical significance. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a plasma tsRNA molecular marker for predicting postoperative delirium in elderly patients with high prediction accuracy, high sensitivity and strong specificity, and its application.

[0006] The technical solution adopted by the present invention to solve the above technical problems is as follows: A plasma tsRNA molecular marker for predicting postoperative delirium in elderly patients, wherein the plasma tsRNA molecular marker is tRF-Gly-14 and / or tRF-Arg-39, and the nucleotide sequence of tRF-Gly-14 is shown as SEQ ID NO.1: 5'-AATGGTAGAATTCTCGCC-3', and the nucleotide sequence of tRF-Arg-39 is shown as SEQ ID NO.2: 5'-TCAGAAGATTGAGGGTTCGAATCCCTTCGTGGTTG-3'.

[0007] The present invention also provides the application of the above plasma tsRNA molecular marker in the preparation of a kit for predicting postoperative delirium in elderly patients.

[0008] Furthermore, the kit includes forward and reverse primers for fluorescence quantitative PCR amplification of tRF-Gly-14 and / or tRF-Arg-39. The nucleotide sequence of the forward primer for fluorescence quantitative PCR amplification of tRF-Gly-14 is shown as SEQ ID NO.3: 5'-TGGCGGACGAATGGTAGAAT-3', and the nucleotide sequence of the reverse primer for fluorescence quantitative PCR amplification of tRF-Gly-14 is shown as SEQ ID NO.4: 5'-TATCCTTGTTCACGACTCCTTCAC-3'; the nucleotide sequence of the forward primer for fluorescence quantitative PCR amplification of tRF-Arg-39 is shown as SEQ ID NO.5: 5'-TCAGAAGATTGAGGGTTCGAATC-3', and the nucleotide sequence of the reverse primer for fluorescence quantitative PCR amplification of tRF-Arg-39 is shown as SEQ ID NO.6: 5'-GTGCAGGGTCCGAGGT-3'.

[0009] Compared with the prior art, the advantages of the present invention are as follows: The present invention discloses for the first time a plasma tsRNA molecular marker for predicting postoperative delirium in elderly patients and its application. The preoperative plasma tsRNA molecular markers tRF-Gly-14 and / or tRF-Arg-39 in POD patients are highly expressed, and they can be detected by peripheral blood, with less trauma and easy access. Compared with other non-coding RNAs, plasma tsRNA has higher stability and richer content. By using the collected peripheral blood specimens to detect the preoperative plasma tsRNA molecular markers tRF-Gly-14 and / or tRF-Arg-39, it is possible to conveniently, quickly and efficiently diagnose and predict POD patients at the molecular level, with strong pertinence, high sensitivity, high prediction accuracy, improved POD detection rate, and can be used as a supplementary tool for preoperative risk assessment. The present invention provides a new molecular marker for the early warning of postoperative delirium, which helps to timely identify high-risk patients, guide clinical intervention and improve the surgical prognosis. Description of the Drawings

[0010] Figure 1 It is a clustering heat map of the preoperative plasma tsRNA expression profiles of POD patients and normal postoperative patients in the screening set samples;

[0011] Figure 2 It is a volcano plot of the difference in the expression levels of tsRNA between two groups of POD patients and normal postoperative patients in the screening set samples, where the X-axis usually represents the fold difference in gene expression, and the Y-axis represents the statistical significance of the difference;

[0012] Figure 3Venn diagram showing the overlap between differentially expressed tsRNAs in POD patients and normal patients after surgery in the screening set samples;

[0013] Figure 4 Results of the significance analysis of the expression levels of plasma tsRNA molecular markers in POD patients and non-POD patients on the day before surgery in the validation set samples, where A is tRF-Gly-14 and B is tRF-Arg-39;

[0014] Figure 5 ROC curve showing the predictive value of the relative expression of plasma tsRNA molecular markers before surgery in the validation set samples for POD in elderly patients;

[0015] Figure 6 ROC curve showing the predictive value of the relative expression of plasma tsRNA molecular markers before surgery in the validation set samples after propensity matching score for POD in elderly patients;

[0016] Figure 7 Analysis of the correlation between the relative expression level of plasma tRF-Gly-14 in POD patients and the scores of CAM and DRS-98 scales in the validation set samples, where A is the score of the CAM scale and B is the score of the DRS-98 scale;

[0017] Figure 8 Analysis of the correlation between the relative expression level of plasma tRF-Arg-39 in POD patients and the scores of CAM and DRS-98 scales in the validation set samples, where A is the score of the CAM scale and B is the score of the DRS-98 scale. Detailed implementation method

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0019] Specific Example 1: Screening preoperative plasma differentially expressed tsRNA molecular markers for predicting POD patients.

[0020] 1. Screening sample collection: 20 elderly patients (≥60 years old) who underwent orthopedic surgery in the First Affiliated Hospital of Ningbo University from August 2022 to December 2023 were collected. All patients signed informed consent forms before surgery, and the study was approved by the hospital ethics committee.

[0021] The inclusion criteria were as follows: (1) Elderly patients scheduled for elective lower limb orthopedic surgery under subarachnoid block or tracheal intubation general anesthesia; (2) Age ≥60 years old; (3) American Society of Anesthesiologist’ physical status (ASA) classification of I-Ⅲ; (4) No history of drug abuse.

[0022] The exclusion criteria are as follows: (1) The total score of the Activity of Daily Living Scale (ADL) ≤ 23 points; (2) Patients with severe respiratory, circulatory or other system dysfunctions; (3) Other neurological diseases (including stroke, Parkinson's disease, Alzheimer's disease, etc.) and cognitive decline caused by vascular or traumatic factors; (4) Patients with low compliance, such as those with severe hearing or visual impairments, reading or comprehension disorders, who are unable to cooperate in completing the scale assessment.

[0023] 2. Plasma sample collection: During the clinical sample collection process of this study, fasting venous blood of the patients was collected on the morning before the surgery. The elbow vein was punctured to collect 5 mL of venous blood. After collection, the blood sample was left to stand at 4°C for 20 minutes to promote the natural sedimentation of blood components, and then placed in a high-speed centrifuge (centrifugation conditions: 3500 rpm / min, 5 minutes). After centrifugation, a non-enzymatic disposable pipette was used to carefully extract the upper layer of milky yellow transparent plasma into a non-enzymatic centrifuge tube and stored in a -80°C refrigerator to ensure its quality and stability. Given the easy degradation characteristics of RNA, the extraction of total plasma RNA was usually carried out on the same day to maximize the preservation of RNA integrity and activity. At the same time, the patient's name and hospital number were recorded. After the CAM scale assessment after the surgery, according to the CAM score results, the patient samples were divided into the POD patient group (case group) or the postoperative normal patient CON group (non-POD patient group, control group).

[0024] 3. Postoperative delirium assessment: Follow-up was conducted twice a day (8:00 - 10:00 a.m. and 3:00 - 4:00 p.m.) on the 1st - 3rd days after the surgery (if the patient developed POD, the follow-up continued until the symptoms disappeared). The Confusion Assessment Scale (CAM) was used to assess POD, and the Delirium Rating Scale - Revised 98 (DRS-R-98) was used to assess the severity of POD. At the same time, the Visual Analogue Scale Score (VAS) was used to grade the postoperative pain of the patients. The diagnostic criteria for POD are as follows: (1) Acute onset and fluctuating changes; (2) Attention disorder; (3) Thinking confusion; (4) Change in the level of consciousness. If (1) + (2) are met, and (3) or (4) is met, then POD can be diagnosed. The perioperative scale assessment was completed by an anesthesiologist who had received specialized training, and this anesthesiologist did not participate in the intraoperative management of the patient.

[0025] 4. Matching and grouping of patients' clinical data: Record the patients' clinical data, including gender, age, history of underlying diseases, education level, surgical-related conditions (surgical type, operation duration, anesthesia method, intraoperative fluid infusion volume, and intraoperative blood loss), and the results of routine blood biochemical tests, etc. The cases and controls were strictly matched in terms of gender, age, ASA classification, surgical type, years of education, and related comorbidities. According to the results of postoperative follow-up with the CAM scale, 4 POD patients and 4 postoperative normal patients with matched clinical data were selected as the initial screening cohort. The preoperative plasma samples of 4 POD patients (case group) and 4 postoperative normal patients (control group) in the first screening stage were sent to Shanghai Kangcheng Company for tsRNA high-throughput sequencing. The comparison of the clinical data of the POD cases and the control group used for screening differential tsRNA in the first batch is shown in Table 1 below.

[0026] Table 1 Comparison of the clinical data of the POD case group and the control group in the first screening stage

[0027]

[0028] 5. Differential expression analysis of plasma tsRNA in POD patients: Use R language to analyze the differential expression of tRF and tiRNA. Use the P value and fold change to screen for tsRNA molecules with significant differential expression between the case group and the control group samples. We defined that tsRNAs with an absolute fold change > 1.5 and a P value < 0.05 had significant differential expression. Use a volcano plot and a heat map to display the differentially expressed tsRNAs. Use a Venn diagram to compare the numbers of commonly expressed and specifically expressed tsRNAs in the two groups.

[0029] Perform unsupervised hierarchical tsRNA clustering heat map analysis on the tsRNA expression profile of delirium patients. As Figure 1 shown, the expression levels on the heat map are represented by different colors, ranging from blue (below the average level) to red (above the average level). Use K-means clustering to classify and separate the samples. Genes with similar expression patterns will be clustered together. According to the clustering analysis heat map, it can be intuitively reflected that there are obvious differences in the expression patterns between POD patients and postoperative normal patients.

[0030] The expression levels of tsRNAs between two groups were compared by volcano plots. The volcano plots of differentially expressed tsRNAs can directly display the fold change (FC) of tsRNA expression and the significance of these differences, and can intuitively obtain information on upregulated and downregulated tsRNAs. Statistical analysis was performed according to the conditions of FC > 1.5 and P < 0.05, and 90 significantly differentially expressed tsRNAs were screened out. As Figure 2 shown, 34 upregulated tsRNAs are shown in red, 56 downregulated tsRNAs are shown in green, and tsRNAs with no significant differential expression between the two groups are shown in gray.

[0031] Venn diagrams were used to describe the overlap between differentially expressed tsRNAs in POD patients and normal patients after surgery. The diagrams intuitively show the number of tsRNAs co-expressed in the two groups and also show the number of tsRNAs specifically expressed in each group. As Figure 3 shown, 4467 tsRNAs were co-expressed in both groups, 2882 tsRNAs were specifically expressed in POD patients, and 2270 tsRNAs were specifically expressed in normal patients after surgery.

[0032] 6. Determination of differentially expressed tsRNAs: This study mainly focused on the genes with the most significant changes in expression levels between the case group and the control group, as these genes are most likely to directly affect the observed phenotypic results. TsRNAs with FC > 2 or FC < -2 and P value < 0.05 were selected from the 2882 tsRNAs specifically expressed in POD patients as candidate target genes, and then the two genes with the smallest P value, tRF-Gly-14 and tRF-Arg-39, were selected as target genes, which were used as plasma tsRNA molecular markers for predicting delirium patients. The nucleotide sequence of tRF-Gly-14 is shown in SEQ ID NO.1: 5'-AATGGTAGAATTCTCGCC-3', the fold change is 812.9835508, the P value is 0.002416916, and the expression is upregulated. The nucleotide sequence of tRF-Arg-39 is shown in SEQ ID NO.2: 5'- TCAGAAGATTGAGGGTTCGAATCCCTTCGTGGTTG -3', the fold change is 516.0845998, the P value is 0.011743269, and the expression is upregulated.

[0033] Specific Example 2: Verify the predictive value of the plasma tsRNA molecular markers screened in Specific Example 1 for POD.

[0034] Verification sample collection: Additionally, 158 elderly patients (≥60 years old) who underwent orthopedic surgery between August 2022 and December 2023 at the First Affiliated Hospital of Ningbo University were collected. All patients signed informed consent forms before surgery, and the study was approved by the hospital ethics committee. Among them, there were 30 patients in the POD group and 128 patients in the Non-POD group. The inclusion criteria and exclusion criteria were the same as those in the above specific Example 1. Fasting venous blood of all patients was collected one day before surgery, and at the same time, the patients' names and hospital numbers were recorded. After the CAM scale assessment was completed after surgery, according to the CAM score results, the patient samples were determined to be divided into a group of 30 POD patients (case group) or a group of 128 normal postoperative patients (Non-POD patient group, control group). Table 2 shows the comparison of the demographics and clinical characteristics between the case group and the control group in the validation set.

[0035] Table 2 Demographics and clinical characteristics of elderly POD patients

[0036]

[0037] 2. Determination of the expression level of tsRNA by RT-qPCR: The total RNA of the preoperative plasma samples of the collected elderly patients was extracted using Trizol LS reagent (Invitrogen, USA), and the plasma total RNA was reverse transcribed into cDNA using a cDNA reverse transcription kit (Genemay Biotech, China). Using the SYBR Green SuperMix kit (TransGen Biotech, China), RT-qPCR was performed on the Roche LightCycler 480 system. The forward and reverse primer sequences for the RT-qPCR quantitative amplification of tRF-Gly-14 and tRF-Arg-39 are as follows:

[0038] The nucleotide sequence of the forward primer for the fluorescence quantitative PCR amplification of tRF-Gly-14 is shown as SEQ ID NO.3: 5'-TGGCGGACGAATGGTAGAAT-3', and the nucleotide sequence of the reverse primer for the fluorescence quantitative PCR amplification of tRF-Gly-14 is shown as SEQ ID NO.4: 5'-TATCCTTGTTCACGACTCCTTCAC-3'.

[0039] The nucleotide sequence of the forward primer for the fluorescence quantitative PCR amplification of tRF-Arg-39 is shown as SEQ ID NO.5: 5'-TCAGAAGATTGAGGGTTCGAATC-3', and the nucleotide sequence of the reverse primer for the fluorescence quantitative PCR amplification of tRF-Arg-39 is shown as SEQ ID NO.6: 5'-GTGCAGGGTCCGAGGT-3'.

[0040] The nucleotide sequence of the forward primer for the fluorescence quantitative PCR amplification of the internal reference gene U6 is shown as SEQ ID NO.7: 5'-ATTGGAACGATACAGAGAAGATT-3', and the nucleotide sequence of the reverse primer for the fluorescence quantitative PCR amplification of the internal reference gene U6 is shown as SEQ ID NO.8: 5'-GGAACGCTTCACGAATTTG-3'. The 2 −△△Ct method was used to analyze the relative expression levels of the target genes.

[0041] The results are as Figure 4 shown in A of Figure 4 and B of

[0042] In the 158-case validation cohort, compared with the control group, the expression levels of the two target tsRNAs, tRF-Gly-14 and tRF-Arg-39, on the day before surgery in the POD group were significantly increased (P<0.001). Figure 5 As shown, the AUC value of the ROC curve of tRF-Gly-14 reached 0.868 (P<0.001), the sensitivity was 81.5%, and the specificity was 86.3%; the AUC value of the ROC curve of tRF-Arg-39 reached 0.936 (P<0.001), the sensitivity was 92.6%, and the specificity was 91.8%, fully meeting the criteria for a good prediction level, and fully demonstrating its value in predicting POD. In summary, both tRF-Gly-14 and tRF-Arg-39 showed extremely strong prediction abilities in the ROC analysis, and their areas under the curve and high sensitivities indicated that they could well predict postoperative delirium patients and non-delirium patients.

[0043] 4. ROC curve analysis of the predictive value of the relative expression of preoperative plasma tRF-Gly-14 and tRF-Arg-39 for POD in elderly patients after propensity score matching: To control the influence of baseline differences, propensity score matching (PSM) analysis was performed to obtain more reliable comparison results. We used STATA software to match the POD group and the non-POD group, with a caliper value of 0.05. The covariates used for matching included age, ASA classification, operation duration, operation type, and anesthesia method. After 1:1 nearest neighbor matching, a POD group and a non-POD group with good between-group balance were obtained.

[0044] The results are as Figure 6As shown, among the 29 pairs of matched samples, two types of tsRNAs, tRF-Gly-14 and tRF-Arg-39, in preoperative plasma still showed significant predictive ability, with AUC values of 0.931 (P<0.001) and 0.979 (P<0.001), respectively. Therefore, the target tsRNAs still have the value of being used as plasma tsRNA molecular markers for predicting POD after propensity matching scoring.

[0045] 5. Correlation analysis of the expression levels of preoperative plasma tsRNA molecular markers with the scores of the postoperative CAM scale and DRS-98 scale. Currently, the CAM scale is usually used clinically for the preliminary screening of delirium and the DRS-98 scale for the assessment of the severity of delirium, and the DRS-98 score is proportional to the severity of the disease. To explore whether the expression levels of preoperative tRF-Gly-14 and tRF-Arg-39 affect the severity of POD, we performed a correlation analysis of the expression levels of tRF-Gly-14 and tRF-Arg-39 with the CAM and DRS-98 scores. The results are shown in Figure 7. The level of tRF-Gly-14 in the preoperative plasma of POD patients was positively correlated with the scores of the CAM and DRS-98 scales (P < 0.05). Similarly, as Figure 8 shown, the level of preoperative plasma tRF-Arg-39 in POD patients was positively correlated with the scores of the postoperative CAM and DRS-98 scales (P < 0.05). The above results prove that the expression levels of preoperative plasma tRF-Gly-14 and tRF-Arg-39 show an upward trend with the aggravation of the severity of POD.

[0046] The above description is not a limitation of the present invention, nor is the present invention limited to the above examples. Changes, modifications, additions, or substitutions made by those of ordinary skill in the art within the scope of the essence of the present invention shall also fall within the protection scope of the present invention.

Claims

1. Use of a plasma tsRNA molecular marker in the preparation of a kit for predicting postoperative delirium in elderly patients, characterized in that: The plasma tsRNA molecular markers are tRF-Gly-14 and / or tRF-Arg-39, the nucleotide sequence of the tRF-Gly-14 is shown in SEQ ID NO.1: 5'-AATGGTAGAATTCTCGCC-3', and the nucleotide sequence of the tRF-Arg-39 is shown in SEQ ID NO.2: 5'-TCAGAAGATTGAGGGTTCGAATCCCTTCGTGGTTG-3'.

2. The use of the plasma tsRNA molecular marker according to claim 1 in the preparation of a kit for predicting postoperative delirium in elderly patients, characterized in that: The kit comprises forward and reverse primers for tRF-Gly-14 and / or tRF-Arg-39 fluorescence quantitative PCR amplification, wherein the nucleotide sequence of the forward primer for tRF-Gly-14 fluorescence quantitative PCR amplification is as shown in SEQ ID NO.3: 5'-TGGCGGACGAATGGTAGAAT-3', and the nucleotide sequence of the reverse primer for tRF-Gly-14 fluorescence quantitative PCR amplification is as shown in SEQ ID NO.4: 5'-TATCCTTGTTCACGACTCCTTCAC-3'; the nucleotide sequence of the forward primer for tRF-Arg-39 fluorescence quantitative PCR amplification is as shown in SEQ ID NO.5: 5'-TCAGAAGATTGAGGGTTCGAATC-3', and the nucleotide sequence of the reverse primer for tRF-Arg-39 fluorescence quantitative PCR amplification is as shown in SEQ ID NO.6: 5'-GTGCAGGGTCCGAGGT-3'.

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