Application of combined biomarkers IGFALS, HGFA and SVEP1 in liver cancer diagnosis

Abstract

The invention discloses application of combined biomarkers IGFALS, HGFA and SVEP1 in liver cancer diagnosis, and provides IGFALS + HGFA or IGFALS + SVEP1 as a combined biomarker for liver cancer diagnosis for the first time, so that the blank of insufficient diagnosis efficiency of an existing single marker is filled, and a new strategy is provided for early and accurate diagnosis of liver cancer. The combined marker provided by the invention is expected to be applied to early screening, auxiliary diagnosis, prognosis evaluation and treatment effect monitoring of liver cancer, and has huge clinical transformation value and market potential.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to the application of combined biomarkers IGFALS, HGFA, and SVEP1 in the diagnosis of liver cancer. Background Technology

[0002] Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, with consistently high incidence and mortality rates. Early diagnosis and accurate prognostic assessment are crucial for improving the survival rate and treatment outcomes of HCC patients. Currently used clinical diagnostic biomarkers for HCC, such as alpha-fetoprotein (AFP), have significant limitations in terms of sensitivity and specificity. For example, AFP has low sensitivity in the early diagnosis of HCC and can also be elevated in some non-hepatocellular carcinoma diseases, leading to missed diagnoses and misdiagnoses. This results in many HCC patients being diagnosed at an advanced stage, missing the optimal treatment window.

[0003] To overcome the limitations of single biomarkers, researchers have been exploring new and more effective diagnostic strategies. In recent years, combined detection of multiple biomarkers has attracted widespread attention due to its potential synergistic effects and higher diagnostic accuracy. By combining biomarkers from different biological pathways or expression patterns, it is hoped that the sensitivity and specificity of diagnosis can be improved, thereby achieving early and accurate diagnosis of liver cancer.

[0004] The proteins involved in this invention, such as IGFALS (insulin-like growth factor binding protein acid unstable subunit), HGFA (hepatocyte growth factor activator), and SVEP1 (cell surface glycoprotein SVEP1), play important roles in the development and progression of liver cancer, and related studies have reported their potential as single biomarkers for liver cancer. However, these single biomarkers still face challenges in practical clinical applications, and their diagnostic efficacy has not yet reached an ideal level. Therefore, this invention aims to construct a superior combined biomarker by optimizing the combination of these proteins with potential diagnostic value, in order to provide a new solution for the early diagnosis and prognostic assessment of liver cancer. Summary of the Invention

[0005] In view of this, one objective of the present invention is to provide a combined biomarker for the diagnosis of liver cancer; a second objective of the present invention is to provide a liver cancer diagnostic kit; a third objective of the present invention is to provide a liver cancer diagnostic method; and a fourth objective of the present invention is to provide the application of the combined biomarker in the preparation of products for liver cancer diagnosis, prognostic assessment, or treatment efficacy monitoring.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A combined biomarker for the diagnosis of liver cancer, said combined biomarker comprising any one of the following groups of genes or a combination of proteins expressed from the above genes: (1) IGFALS (P35858) and HGFA (Q04756); (2) IGFALS (P35858) and SVEP1 (Q4LDE5).

[0007] In some embodiments of the present invention, the combined biomarkers are used to distinguish liver cancer patients from healthy individuals by detecting the expression levels of IGFALS, HGFA and / or SVEP1 in serum.

[0008] A liver cancer diagnostic kit comprising reagents for detecting the combined biomarkers of claim 1, wherein the reagents comprise antibodies or aptamers that specifically bind to IGFALS, HGFA, and / or SVEP1.

[0009] A method for diagnosing liver cancer includes the following steps: (1) Obtain serum samples from the subjects; (2) Detect the expression levels of IGFALS, HGFA and / or SVEP1 in the sample; (3) Compare the detection results with the expression levels of the healthy control group, and determine whether the subject has liver cancer based on the combined biomarkers described in claim 1.

[0010] In some embodiments of the present invention, mass spectrometry or immunoassay is used to detect the expression level in step (2).

[0011] Application of the combined biomarkers in the preparation of products for liver cancer diagnosis, prognosis assessment, or treatment efficacy monitoring.

[0012] In some embodiments of the present invention, the product is an in vitro diagnostic reagent, a reagent kit, or a testing device.

[0013] The beneficial effects of this invention are as follows: Discovery of novel combined biomarkers: This invention proposes for the first time IGFALS+HGFA and IGFALS+SVEP1 as combined biomarkers for the diagnosis of liver cancer, filling the gap of insufficient diagnostic efficacy of existing single biomarkers and providing a new strategy for the early and accurate diagnosis of liver cancer.

[0014] Significantly improved diagnostic performance: Through quantitative analysis of multiple performance indicators (AUC, sensitivity, specificity), this invention demonstrates that the combined biomarker has a significant performance improvement in liver cancer diagnosis compared to the single biomarker, especially in terms of AUC value and sensitivity, which is of great significance for improving the accuracy of clinical diagnosis.

[0015] High-reliability mass spectrometry validation: The core proteins in the combined biomarkers have all been validated using high-throughput mass spectrometry technology, ensuring the reliability and credibility of the detection results and laying a solid foundation for subsequent clinical translation and application.

[0016] Broad clinical application prospects: The combined biomarkers proposed in this invention are expected to be applied to early screening, auxiliary diagnosis, prognostic assessment and treatment effect monitoring of liver cancer, with great clinical translational value and market potential. Attached Figure Description

[0017] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 The AUC, sensitivity, and specificity of combination 1 and combination 2 and their respective individual markers; Figure 2 ROC curve analysis for combination 1 (P35858+Q04756) and its individual markers; Figure 3 ROC curve analysis for combination 2 (P35858+Q4LDE5) and its individual markers; Figure 4 The combined biomarker represents the improvement in AUC, sensitivity, and specificity relative to the individual biomarkers it comprises. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0019] The experimental data involved in this invention are all derived from strictly controlled clinical studies and have been approved by the Ethics Committee of the Third People's Hospital of Mianyang City. The research sample covers 80 human serum samples, including 40 healthy controls and 40 liver cancer patients. All samples were collected, processed, and stored under standardized operating procedures to ensure sample quality and the reliability of experimental results.

[0020] Proteomics analysis was performed using a high-resolution mass spectrometry platform. Based on the raw data obtained from mass spectrometry, we first constructed a sample-specific protein database according to the sample source and then used the Pulsar search engine embedded in Spectronaut (v18) to search the database using default parameters. The search database was Homo_sapiens_9606_SP_20231220.fasta (20429 sequences), and a reverse database was added to calculate the false positive rate (FDR) caused by random matching. The search parameters were set as follows: 2 missed cleavage sites; cysteine ​​alkylation (Carbamidomethyl (C)) was a fixed modification, while methionine oxidation and protein N-terminal acetylation were variable modifications; the FDR for protein, peptide, and PSM identification was set to 1%.

[0021] The search results underwent rigorous data filtering and quality control evaluation, including peptide length distribution, peptide quantity distribution, protein coverage distribution, and protein molecular weight distribution, to ensure that the results met the standards. The intensity value of each protein in different samples was processed using Normalized Intensity (I) and then centered to obtain a relative quantitative value (R), calculated as: Rij = Iij / Mean(Ij), where i represents the sample and j represents the protein.

[0022] Based on the quantitative analysis results above, we performed rigorous data filtering and applied machine learning methods to screen and evaluate features in order to find valuable biomarker combination panels. The data analysis process included: calculating the fold change (FC) and T-test significance p-value between the two groups based on the quantitative results, filtering for differences according to set thresholds, and plotting statistical graphs related to the difference analysis. If the sample had three or more groups, we used one-way ANOVA to calculate the significance p-values ​​for multiple groups and used the ANOVA p-values ​​to screen differentially expressed proteins between the groups for subsequent correlation analysis. In addition, we performed functional classification statistical analysis on differentially expressed proteins between the two groups (including GO secondary classification, subcellular localization classification, COG / KOG classification, and KEGG pathway classification statistics), and used Fisher's exact test for enrichment analysis (involving GO, KEGG, Protein domain, Reactome, and WikiPathways). When a project involves multiple experimental groups, enrichment clustering analysis is used to compare the functional relationships of differentially expressed proteins under different experimental conditions, and protein-protein interaction (PPI) network analysis is used to screen for key regulatory proteins under specific experimental conditions. These screening results provide a reference for further in-depth analysis and confirmation of the biological functions and mechanisms of action of candidate biomarkers.

[0023] This study strictly adhered to the ethical norms and data management principles of biomedical research.

[0024] Example 1. Discovery and Validation of Combined Biomarkers This invention provides a novel combination biomarker for liver cancer discovered based on proteomics technology and its application in the diagnosis and prognosis of liver cancer. Specifically, this invention focuses on the following two combinations: Combination 1: IGFALS (P35858) + HGFA (Q04756) Combination 2: IGFALS (P35858) + SVEP1 (Q4LDE5) The discovery and verification of these combined biomarkers mainly involve the following steps: Sample collection and mass spectrometry: Serum samples were collected from healthy individuals and liver cancer patients. Data-Independent Acquisition (DIA) mass spectrometry was used to perform proteomics analysis on the serum samples in order to comprehensively obtain protein information from the samples.

[0025] Data analysis and differential protein screening: In-depth analysis of mass spectrometry data, including library search and identification of peptides and proteins. Dimensionality reduction methods such as principal component analysis (PCA) were used to observe the distribution differences between healthy and liver cancer samples. Further differential expression analysis was performed to screen for proteins that were significantly upregulated or downregulated in liver cancer patients; these proteins are candidates for constructing combinatorial biomarkers.

[0026] Pathway enrichment analysis: Gene Ontology (GO) enrichment and pathway analysis were performed on the screened differentially expressed proteins to understand their biological functions and potential mechanisms in the development and progression of liver cancer, providing a theoretical basis for the rationality of combined biomarkers.

[0027] Machine learning model building and biomarker selection: Samples are randomly divided into training and test sets. Machine learning algorithms, such as support vector machines (SVM) and random forests, are used to build and optimize models for candidate proteins, and single protein biomarkers with high diagnostic efficacy, such as IGFALS, HGFA, and SVEP1, are selected from them.

[0028] Construction and validation of combined biomarkers: Based on the diagnostic performance and biological relevance of individual biomarkers, combinations such as IGFALS+HGFA and IGFALS+SVEP1 were constructed. The diagnostic performance of these combinations was validated using independent sample sets, evaluating their performance in terms of AUC, sensitivity, and specificity.

[0029] Example 2. Application of combined biomarkers in the diagnosis of liver cancer The following experimental data demonstrate the superior performance of the combined biomarkers proposed in this invention in the diagnosis of liver cancer. We comprehensively evaluated combination 1 (IGFALS+HGFA) and combination 2 (IGFALS+SVEP1) and their respective individual biomarkers in terms of AUC, sensitivity, and specificity.

[0030] 1. Summary of Performance Indicators Figure 1 The average performance of combination 1 and combination 2, and their respective individual biomarkers, in terms of AUC, sensitivity, and specificity is presented. The data clearly demonstrate that the combined biomarkers exhibit superior performance across all key metrics. Specifically: Combination 1 (P35858+Q04756): Its AUC value was as high as 0.981, significantly higher than that of individual biomarkers Q04756 (0.920) and P35858 (0.875). This indicates that Combination 1 has extremely high accuracy in distinguishing between liver cancer patients and healthy controls. In terms of sensitivity, Combination 1 reached 0.925, higher than Q04756 (0.875) and P35858 (0.825), meaning that this combination can more effectively identify true liver cancer patients. In terms of specificity, Combination 1 was 0.900, also better than P35858 (0.775), and slightly improved compared to Q04756 (0.875), indicating that it performed well in excluding non-liver cancer patients.

[0031] Combination 2 (P35858+Q4LDE5): Its AUC value was 0.966, significantly higher than that of the individual biomarkers Q4LDE5 (0.920) and P35858 (0.875), demonstrating excellent diagnostic accuracy. In terms of sensitivity, Combination 2 achieved a sensitivity of 1.000, meaning it can identify 100% of liver cancer patients, possessing extremely high clinical application value. Regarding specificity, Combination 2 was 0.800, slightly lower than Q4LDE5 (0.925), but still higher than P35858 (0.775), and its high sensitivity is of great significance in early screening.

[0032] 2. ROC Curve Analysis Figures 2-3 The ROC curve visually demonstrates the superior ability of the combined biomarker compared to a single biomarker in distinguishing liver cancer patients from healthy individuals. The combined curve, being closer to the upper left corner, indicates higher diagnostic accuracy.

[0033] ROC curve analysis was performed on combination 1 (P35858+Q04756) and combination 2 (P35858+Q4LDE5), as well as the individual markers contained within each combination. Figure 2 The ROC curve plot can be used to visually observe: ROC curve of combination 1 (P35858+Q04756): The ROC curve of this combination (AUC = 0.98) is significantly above the curves of individual biomarkers P35858 (AUC = 0.88) and Q04756 (AUC = 0.92), and closer to the upper left corner. This indicates that combination 1 has significantly higher diagnostic accuracy in distinguishing liver cancer patients from healthy controls. This curve representation visually demonstrates the synergistic effect of the combined biomarkers in improving diagnostic efficacy.

[0034] ROC curve of combination 2 (P355858+Q4LDE5): Similarly, the ROC curve of this combination (AUC = 0.97) is significantly better than that of individual biomarkers P35858 (AUC = 0.88) and Q4LDE5 (AUC = 0.92), and is closer to the upper left corner. This again verifies the effectiveness of the combination strategy in improving diagnostic accuracy. Although the specificity of combination 2 is slightly lower than that of Q4LDE5, its significant improvement in sensitivity (as shown in the aforementioned performance metric summary of 1.000) makes its overall diagnostic performance still very good, especially suitable for scenarios requiring high-sensitivity screening.

[0035] In summary, the ROC curve analysis results and the performance index summary data corroborate each other, jointly emphasizing the excellent diagnostic performance and clinical application potential of the combined biomarkers proposed in this invention in the diagnosis of liver cancer.

[0036] Overall, these data strongly demonstrate that a combination strategy can significantly improve the comprehensive performance of biomarkers in liver cancer diagnosis, providing solid data support for the innovation and practicality of this invention.

[0037] 3. Quantitative Analysis of Performance Improvement To more clearly demonstrate the improved detection performance brought about by combined biomarkers, we quantified the improvements in AUC, sensitivity, and specificity of the combined biomarker relative to its individual constituent biomarkers. These data strongly demonstrate the synergistic effect of the combination strategy.

[0038] The quantitative improvement data and charts above clearly show the improvement of combination 1 (P35858+Q04756): Compared to P35858: Combination 1 improved AUC by 0.106, sensitivity by 0.100, and specificity by 0.125. This indicates that combining Q04756 with P35858 significantly enhances the overall diagnostic accuracy, the ability to identify true positives, and the ability to exclude true negatives.

[0039] Compared to Q04756: Combination 1 showed an improvement of 0.061 in AUC, 0.050 in sensitivity, and 0.025 in specificity. This further demonstrates that the addition of P35858 positively complements and enhances the diagnostic performance of Q04756, enabling the combination to outperform any single biomarker.

[0040] Improvements to Combination 2 (P35858+Q4LDE5): Compared to P35858: Combination 2 improved AUC by 0.091, sensitivity by 0.175, and specificity by 0.025. Of particular note is the significant improvement in sensitivity of 0.175, which is of paramount clinical importance for the early screening and diagnosis of liver cancer, and can substantially reduce the rate of missed diagnoses.

[0041] Compared to Q4LDE5: Combination 2 improved AUC by 0.046 and sensitivity by 0.175. Although specificity decreased slightly (-0.125), considering the significant improvement in sensitivity and the overall increase in AUC, this combination still demonstrates a significant advantage in overall diagnostic efficacy. In some clinical scenarios, high sensitivity may be more critical than high specificity, such as in the initial screening phase.

[0042] These quantitative analysis results clearly demonstrate that the two combined biomarkers proposed in this invention are not simply the superposition of individual biomarkers, but rather achieve a diagnostic effect of "1+1>2" through synergistic action. This significant performance improvement is the core technical highlight of this invention and the key to its patentability and clinical application value.

[0043] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A combined biomarker for the diagnosis of liver cancer, characterized in that, The combined biomarkers include any one of the following groups of genes or a combination of proteins expressed from the above genes: (1) IGFALS (P35858) and HGFA (Q04756); (2) IGFALS (P35858) and SVEP1 (Q4LDE5).

2. The combined biomarker according to claim 1, characterized in that, The combined biomarkers are used to distinguish liver cancer patients from healthy individuals by detecting the expression levels of IGFALS, HGFA, and / or SVEP1 in serum.

3. A liver cancer diagnostic kit, characterized in that, The reagent comprises a reagent for detecting the combined biomarkers of claim 1, the reagent comprising an antibody or aptamer that specifically binds to IGFALS, HGFA and / or SVEP1.

4. A method for diagnosing liver cancer, characterized in that, Includes the following steps: (1) Obtain serum samples from the subjects; (2) Detect the expression levels of IGFALS, HGFA and / or SVEP1 in the sample; (3) Compare the detection results with the expression levels of the healthy control group, and determine whether the subject has liver cancer based on the combined biomarkers described in claim 1.

5. The diagnostic method according to claim 4, characterized in that, In step (2), mass spectrometry or immunoassay techniques are used to detect the expression level.

6. The use of the combined biomarker of claim 1 in the preparation of products for liver cancer diagnosis, prognosis assessment or treatment efficacy monitoring.

7. The application according to claim 6, characterized in that, The product is an in vitro diagnostic reagent, kit, or testing device.

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