Use of igsf1 protein as a marker of heart function recovery

Abstract

The present application relates to the field of biological detection, and particularly relates to application of IGSF1 protein as a marker for heart function recovery. The present application provides application of a protein as a marker in preparation of a product for detecting and / or diagnosing heart failure with improved ejection fraction; the protein comprises one or more of IGSF1, LIMS1, MPP1, PTX3, SLC2A3, FERMT3, ARPC1B, IGHG1, PTGS1, ACTN1 and RTN4. The present application provides a novel serum marker, expression level of which can accurately predict a heart failure patient with reduced ejection fraction and potential for heart function recovery, and change earlier than that of ejection fraction. The present application establishes a non-invasive standardized detection process based on serum samples, which is suitable for medical institutions at all levels, and realizes early and non-invasive prediction of heart function recovery potential of heart failure patients.

Description

Technical Field

[0001] This invention relates to the field of biological detection, and in particular to the application of IGSF1 protein as a biomarker for cardiac function recovery. Background Technology

[0002] Heart failure with reduced ejection fraction (HFrEF) is the end stage of cardiovascular disease. With medication or device therapy, approximately 10% to 60% of HFrEF patients can achieve recovery of cardiac function and improvement in ejection fraction, a condition known as heart failure with improved ejection fraction (HFimpEF). However, current methods for predicting cardiac function recovery still have certain limitations.

[0003] 1) Insufficient predictive accuracy: Traditional serum markers such as brain natriuretic peptide have low discriminative power and cannot accurately predict the recovery of cardiac function; 2) Poor accessibility of detection: Imaging methods such as cardiac magnetic resonance imaging equipment are expensive, difficult to detect and analyze, have many examination requirements, and have many relative contraindications, making them difficult to popularize at the grassroots level; 3) Lack of early predictive ability: Existing protocols cannot identify patients with recovery potential before a detectable improvement in left ventricular ejection fraction (LVEF) occurs.

[0004] Therefore, a non-invasive testing method is needed to enable early identification and prediction of heart failure patients with the potential for cardiac function recovery. Summary of the Invention

[0005] In view of this, the present invention provides the application of IGSF1 protein as a biomarker for cardiac function recovery. The present invention provides a novel serum biomarker whose expression level can accurately predict HFrEF patients with potential for cardiac function recovery, and whose changes precede changes in ejection fraction. The present invention establishes a non-invasive standardized detection procedure based on serum samples, adaptable to medical institutions at all levels, enabling early, non-invasive prediction of the potential for cardiac function recovery in HFrEF patients.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] This invention provides the use of proteins as biomarkers in the preparation of products for detecting and / or diagnosing heart failure with improved ejection fraction;

[0008] The proteins include one or more of the following: IGSF1, LIMS1, MPP1, PTX3, SLC2A3, FERMT3, ARPC1B, IGHG1, PTGS1, ACTN1, and RTN4.

[0009] In some embodiments of the present invention, the protein described above is IGSF1.

[0010] This invention also provides the application of IGSF1 as a biomarker in the preparation of products for detecting, predicting, and / or diagnosing the ability to restore cardiac function.

[0011] In some embodiments of the present invention, in the above applications, when the concentration of IGSF1 is less than 15.3 ng / mL or the log2 expression level is ≤4.23, the cardiac function recovery ability is strong; when the concentration of IGSF1 is greater than 15.3 ng / mL or the log2 expression level is ≥4.23, the cardiac function recovery ability is weak.

[0012] This invention also provides the application of IGSF1 as a biomarker in the preparation of models for detecting, predicting and / or diagnosing the ability to recover cardiac function.

[0013] The present invention also provides a prediction model, including:

[0014] Data collection module: Obtains serum proteins from the sample;

[0015] Feature filtering module: Obtains the expression level of the markers mentioned in the above applications;

[0016] Calculation module: Calculates the sample's ability to recover cardiac function using the expressed level.

[0017] In some embodiments of the present invention, the feature screening module in the above prediction model employs ELISA and / or mass spectrometry analysis.

[0018] In some embodiments of the present invention, in the above prediction model, when the ELISA result is less than 15.3 ng / mL, the cardiac function recovery ability is strong; when the ELISA result is greater than 15.3 ng / mL, the cardiac function recovery ability is weak.

[0019] In some embodiments of the present invention, in the above prediction model, when the log2 expression level in the mass spectrometry analysis result is ≤4.23, the cardiac function recovery ability is strong; when the log2 expression level in the mass spectrometry analysis result is ≥4.23, the cardiac function recovery ability is weak.

[0020] This invention also provides a method for constructing the above-mentioned prediction model, comprising the following steps:

[0021] S1: Obtain serum protein from the sample;

[0022] S2: Quantify the expression level of the biomarker;

[0023] S3: Calculate the sample’s ability to recover cardiac function using the expressed level.

[0024] Compared to currently used clinical feature models, traditional serum biomarkers, and imaging techniques (such as cardiac magnetic resonance imaging), this invention offers three major advantages:

[0025] 1) Significantly improved predictive accuracy: The AUC of serum IGSF1 as a single biomarker for predicting cardiac function recovery reached 0.795 (95% CI: 0.710~0.880), which is more than 50% higher than that of traditional biomarkers and significantly better than the combination of clinical features. This has been independently validated by an external cohort using ELISA (P=0.009). When the serum IGSF1 concentration was 15.3 ng / mL, the sensitivity for predicting cardiac function recovery reached 78.3% and the specificity was 58.7%, as measured by ELISA.

[0026] 2) Greater clinical applicability: External validation has confirmed the stability of IGSF1 as an independent predictor. Its detection only requires routine serum samples (ELISA test), completely eliminating the dependence on expensive imaging equipment (such as cardiac MRI) and professional analysis technology, making it widely applicable in primary hospitals.

[0027] 3) Early warning value: The downregulation of IGSF1 expression occurred before the improvement of LVEF (median follow-up of 10.9 months), which is the first time that early prediction at the molecular level has been achieved, providing the possibility of moving the intervention time window forward. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0029] Figure 1 The volcano plot shows the differential protein expression levels between patients with HFimpEF and persistent HFrEF; where: each point in the figure represents a protein, red marks are significantly upregulated proteins, blue marks are significantly downregulated proteins, and gray marks are proteins with no significant difference.

[0030] Figure 2 The results of Boruta feature screening and classification are shown; among them, the box plot shows the importance analysis of Boruta features, including three categories of proteins: significantly important proteins: green box (11 types); non-important proteins: red box (22 types); and undetermined proteins: gray box (2 types).

[0031] Figure 3ROC curve analysis of IGSF1 predicting HFimpEF;

[0032] Figure 4 The concentration of IGSF1 in the serum of HFrEF and HFimpEF patients in an external validation cohort, as determined by ELISA.

[0033] Figure 5 ROC curve analysis of HFimpEF in the external validation cohort for IGSF1 prediction by ELISA. Detailed Implementation

[0034] This invention discloses the application of IGSF1 protein as a biomarker for cardiac function recovery.

[0035] It should be understood that the expression “one or more of…” individually includes each of the objects described after the expression, as well as various different combinations of two or more of the described objects, unless otherwise understood from the context and usage. The expression “and / or” combined with three or more described objects should be understood to have the same meaning, unless otherwise understood from the context.

[0036] The terms “including,” “having,” or “containing,” including the use of their grammatical synonyms, should generally be understood as open-ended and non-restrictive, for example, not excluding other unstated elements or steps, unless otherwise specifically stated or understood from the context.

[0037] It should be understood that the order of the steps or the order in which certain actions are performed is not important as long as the invention remains operational. Furthermore, two or more steps or actions can be performed simultaneously.

[0038] The use of any and all instances or exemplary language such as “e.g.” or “including” in this document is merely intended to better illustrate the invention and is not intended to limit the scope of the invention unless the claims are made. No language in this specification should be construed as indicating that any unclaimed element is essential to the practice of the invention.

[0039] Furthermore, the numerical ranges and parameters used to define the present invention are approximate values, and the relevant values ​​in the specific embodiments have been presented as precisely as possible. However, any value inevitably contains standard deviations due to individual test methods. Therefore, unless explicitly stated otherwise, it should be understood that all ranges, quantities, values, and percentages used in this disclosure are modified with the word "approximately". Here, "approximately" generally means that the actual value is within plus or minus 10%, 5%, 1%, or 0.5% of a specific value or range.

[0040] This study protocol was approved by the Ethics Committee of Ruijin Hospital in Shanghai, and all participants provided written informed consent.

[0041] This invention is the first to discover and externally validate that IGSF1 is a specific biomarker for predicting cardiac function recovery (progression to HFimpEF) in patients with HFrEF. Downregulation of IGSF1 expression levels (HFimpEF group vs. HFrEF group: 8.40 ng / mL vs. 15.37 ng / mL, P=0.009) is strongly correlated with myocardial recovery. When the serum IGSF1 concentration is 15.3 ng / mL, the sensitivity for predicting cardiac function recovery reaches 78.3%, and the specificity is 58.7%.

[0042] Biomarker combination containing IGSF1

[0043] 1) The 11 protein combinations (IGSF1, LIMS1, MPP1, PTX3, SLC2A3, FERMT3, ARPC1B, IGHG1, PTGS1, ACTN1 and RTN4) selected by the Boruta machine learning algorithm can be used as the core feature set of the cardiac function recovery prediction model.

[0044] 2) Alternative: Free combination of IGSF1 with other differentially expressed proteins (33 upregulated proteins with FC>1.5 and 1 downregulated protein with FC<0.67) (e.g., combinations of 2-4 proteins).

[0045] Detection threshold and judgment criteria based on IGS F1

[0046] 1) Detection of serum IGSF1 concentration by ELISA: When the serum IGSF1 concentration was 15.3 ng / mL, the sensitivity for predicting cardiac function recovery reached 78.3% and the specificity was 58.7%.

[0047] 2) Alternative: In mass spectrometry detection, the expression level of log2 ≤ 4.23 can also be used as the discrimination threshold.

[0048] Develop a reagent kit for predicting cardiac function recovery

[0049] 1) Core component: Anti-IGSF1 antibody (such as AssayGenie HUEB0538).

[0050] 2) Detection method: ELISA kit (or chemiluminescent immunoassay chip, mass spectrometry detection reagent kit).

[0051] 3) The kit contains a multi-antibody array for the detection of 11 characteristic protein combinations (IGSF1, LIMS1, MPP1, PTX3, SLC2A3, FERMT3, ARPC1B, IGHG1, PTGS1, ACTN1 and RTN4) screened using the Boruta algorithm.

[0052] Implementation process of prediction methods

[0053] 1) Step 1: Obtain serum samples from HFrEF patients;

[0054] 2) Step 2: Quantify IGSF1 expression levels (by ELISA / mass spectrometry);

[0055] 3) Step 3: Determine the risk of cardiac function recovery based on the threshold (≤15.3 ng / mL, high probability of recovery);

[0056] In the embodiments and effect examples of this invention, all raw materials and reagents used can be purchased from the market.

[0057] The present invention will be further illustrated below with reference to the embodiments:

[0058] Example Method

[0059] 1. Study population

[0060] This study consecutively included 278 patients diagnosed with heart failure with ventriculoperitoneal ejection fraction (HFrEF) (LVEF ≤ 40%) during hospitalization at Ruijin Hospital, Shanghai, between January 2022 and July 2023, who underwent transthoracic echocardiography (ETC) follow-up within 3-12 months. Exclusion criteria included: previous heart transplantation (n=6); congenital heart disease or severe valvular disease (n=58); malignant tumors (n=15); severe liver dysfunction (n=24); stage 4-5 chronic kidney disease (n=12); and lack of serum samples (n=75). A total of 88 HFrEF patients were ultimately included. Based on the follow-up ETC results, they were divided into two groups:

[0061] ① Heart function recovery (HFimpEF) group: meeting the following criteria: (1) absolute increase of LVEF ≥10% and (2) LVEF >40% at follow-up.

[0062] ② Group with unrecoverable cardiac function (persistent HFrEF): did not meet the above criteria.

[0063] If the patient undergoes multiple follow-up echocardiogram examinations, the last result within 12 months should be used.

[0064] 2. Serum sample preparation and proteomics analysis

[0065] Study samples were collected during the enrollment hospitalization period after fasting. Blood samples were centrifuged and stored at -80℃ until analysis. Each sample was assigned a unique study number. Serum proteins were enriched using the MagicOmics-DMB kit (Beijing Qinglian Biotechnology Co., Ltd.) and processed automatically according to the instructions using the MagicOmics-AP-96 system (Beijing Qinglian Biotechnology Co., Ltd.). Approximately 130 ng of peptide from each sample was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS / MS) along with the internal standard iRT peptide (Biognosys). Chromatographic separation was performed using a C18 analytical column (IonOpticks AUR2-25075C18A-CSI, 1.6 µm particle size, 250 mm × 75 µm) and a Bruker Daltonics nanoElute system with the column temperature maintained at 50℃. Mobile phase A: 0.1% formic acid aqueous solution; Mobile phase B: 0.1% formic acid acetonitrile solution. Peptides were eluted with a 2-80% mobile phase B gradient for 30 min and then directly electrosprayed into a timsTOF Pro mass spectrometer (Bruker Daltonics) in dia-PASEF mode. The dia-PASEF scanning protocol covered the 1 / K0 range of 0.61-1.53 ​​and the m / z range of 375-1300, with 37 25 Th windows set. All experiments were performed at the National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine.

[0066] 3. Database retrieval

[0067] The raw DIA files were retrieved using the directDIA mode of Spectronaut software (v18.6), referencing the human Swiss-Prot database (January 2023 version, containing 20,389 entries), with default parameters used. Global normalization was employed for cross-batch normalization. The proteomics data has been uploaded to the iProX platform (Project ID: IPX0012358000, access link: [link missing]).

[0068] 4. Proteomics Analysis and Validation

[0069] Data analysis was performed using the R statistical software package v.4.5.1. Protein intensity was log2 transformed, and proteins with missing values ​​>40% were removed. Missing values ​​were then filled using K-nearest neighbor imputation (KNN, DreamAI R package). Differentially expressed proteins between the HFimpEF and HFrEF cohorts were analyzed using unpaired t-tests. Significance was defined as a fold change (FC) >1.5 or <0.67, and a false discovery rate (FDR) adjusted for P <0.05 (Benjamini-Hochberg method). Differences were visualized using volcano plots. Diagnostic efficacy was assessed using receiver operating characteristic (ROC) curves. The area under the curve (AUC) and 95% confidence interval (CI) for univariate / multivariate curves were reported, and 10-fold cross-validation was used.

[0070] The Boruta machine learning algorithm (based on a random forest feature selection method, with 700 iterations) was used to screen key proteins from all differentially expressed proteins.

[0071] 5. External verification queue design

[0072] To validate the general predictive value of the novel serum protein biomarker, this study employed an independent external cohort for validation. We included patients with heart failure-related cardiac metabolic assessment (HFrEF) (LVEF ≤ 40%) hospitalized between January 2019 and December 2023 from the Registry for Cardiac Metabolic Assessment in Heart Failure (CURE-HF, NCT06939985). Inclusion and exclusion criteria were the same as in step 1, ultimately selecting 352 eligible patients. Patients were categorized based on echocardiographic results within 12 months of follow-up:

[0073] 1) Heart function recovery group (HFimpEF): absolute increase in LVEF ≥10% and LVEF >40% at follow-up (n=194);

[0074] 2) Group with unrecovered cardiac function (persistent HFrEF): Did not meet the above criteria (n=158). Baseline serum protein concentrations were measured using enzyme-linked immunosorbent assay (ELISA) (kit: AssayGenie HUEB0538, Ireland), strictly following the instructions. Diagnostic efficacy was assessed using ROC curves.

[0075] 6. Statistical Analysis

[0076] Continuous variables were expressed as median (interquartile range [IQR]) or mean ± standard deviation (SD), while categorical variables were described as frequency (percentage). Normality of continuous variables was assessed using the Shapiro-Wilk test. Differences between groups for non-normally distributed continuous variables were analyzed using the Mann-Whitney U test or the Kruskal-Wallis test. Comparisons between groups were performed using the Student t-test or one-way ANOVA. All statistical analyses were performed using the R statistical software package v.4.5.1 (R Project for Statistical Computing, Vienna, Austria). A two-sided p-value < 0.05 was considered statistically significant.

[0077] Example Results

[0078] 1. Characteristics of the research population

[0079] Ultimately, 55 patients with HFimpEF and 33 patients with persistent HFrEF were included. The median follow-up was 10.9 months (IQR 7.1–12.3). During this period, the LVEF of patients with HFimpEF significantly increased from 34.5% ± 4.5% to 54.3% ± 8.2%, while the LVEF of patients with HFrEF remained at a low level (baseline 33.6% ± 5.1% vs. follow-up 33.6% ± 4.8%).

[0080] Baseline characteristics showed no significant differences between the two groups in terms of sex, age, smoking, body mass index, blood pressure, and comorbidities (hypertension, diabetes, atrial fibrillation, and history of myocardial infarction) (P>0.05). Compared with the HFrEF group, patients with HFimpEF had higher levels of total cholesterol, LDL cholesterol, and uric acid, and a higher rate of β-blocker use. Although there was no difference in the proportion of ischemic causes between the two groups, the HFimpEF group had a lower rate of revascularization. There were no statistically significant differences between the two groups in the proportion of other drug treatments and cardiac resynchronization therapy.

[0081] 2. Differentially expressed proteins between HFimpEF and persistent HFrEF

[0082] Proteomics analysis quantified 2461 serum proteins, of which 1712 were detected in ≥60% of samples. Compared with persistent HFrEF, HFimpEF patients identified 33 upregulated and 2 downregulated differentially expressed proteins. The most significantly differentially expressed protein was immunoglobulin superfamily member 1 (IGSF1); see Figure 1The expression level was significantly decreased in the HFimpEF group, with a mean expression level (log2 transformed) of 3.773, compared to 4.433 in the HFrEF group. The FC of log2 was -0.660 (FDR corrected P < 0.001).

[0083] 3. Biomarker screening based on Boruta algorithm

[0084] The Boruta algorithm was used to automatically screen key protein biomarkers from differentially expressed proteins. Ultimately, 11 proteins, including IGSF1 (IGSF1, LIMS1, MPP1, PTX3, SLC2A3, FERMT3, ARPC1B, IGHG1, PTGS1, ACTN1, and RTN4), were identified as significantly differentiating HFimpEF from persistent HFrEF (see [link to study]). Figure 2 ).

[0085] 4. ROC curve analysis

[0086] ROC curve analysis evaluated the predictive efficacy of IGSF1 for heart function recovery (HFimpEF). Figure 3 The results showed that IGSF1 had high predictive efficacy for HFimpEF, with an AUC of 0.795 (95% CI: 0.710-0.880). The optimal cutoff point was 4.23 (after log2 transformation), at which the sensitivity for predicting HFimpEF recovery was 85.5%, the specificity was 66.7%, and the prediction accuracy reached 78.4%. In contrast, the traditional heart failure biomarker N-terminal pro-B-type natriuretic peptide (NT-proBNP) had no ability to differentiate cardiac function recovery (AUC=0.532, 95% CI: 0.402-0.662).

[0087] 5. External validation results

[0088] In the CURE-HF independent external validation cohort, serum IGSF1 expression levels were verified by ELISA. The results showed that the concentration of IGSF1 in the HFimpEF group was significantly lower than that in the HFrEF group [8.40 (4.37-17.23) vs 15.37 (9.75-33.50) ng / mL, P=0.009]. Figure 4 The results were consistent with the mass spectrometry data from the internal validation. In the external validation, the AUC of IGSF1 measured by ELISA for predicting HFimpEF was 0.705 (95% CI: 0.575–0.834), with an optimal cut point of 15.3 ng / mL. At this point, the sensitivity for predicting cardiac function recovery was 78.3%, the specificity was 58.7%, and the Youden index was 1.37. Figure 5 ).

[0089] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. Use of an agent that detects IGSF1 in the manufacture of a product for predicting the recovery of heart function in a heart failure patient with reduced ejection fraction.

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