Method for detecting esophageal cancer sugar metabolism marker based on vav2 related molecular characteristics

Abstract

This application relates to the field of tumor metabolic biomarker detection technology, and discloses a method for detecting glucose metabolism biomarkers in esophageal cancer based on VAV2-related molecular characteristics. The method includes: performing immunohistochemical staining on esophageal cancer tissue samples to obtain VAV2, EIF3F, MTA1, and HIF-1α protein expression sections; scoring the immunoreaction of each section; calculating a comprehensive glucose metabolism phenotype score based on the molecular hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway; and classifying the samples into high-glycolytic, moderate-glycolytic, and low-glycolytic types according to the comprehensive score. This application improves the accuracy and stability of glucose metabolism phenotype discrimination in esophageal cancer.

Description

Technical Field

[0001] This application relates to the field of tumor metabolic biomarker detection technology, and in particular to a method for detecting glucose metabolism biomarkers in esophageal cancer based on VAV2-related molecular characteristics. Background Technology

[0002] Esophageal cancer, a malignant tumor of the digestive system, is characterized by high incidence and mortality, with esophageal squamous cell carcinoma being the most prevalent pathological type. Metabolic reprogramming of tumor cells is a key characteristic of cancer, and abnormal glucose metabolism, particularly the overactivation of the glycolysis pathway, plays a crucial role in the development and progression of esophageal cancer. Current techniques for detecting glucose metabolism in esophageal cancer primarily rely on the detection of single glucose metabolism biomarkers. Common markers include terminal effector molecules of the glycolysis pathway such as glucose transporter GLUT1, hexokinase HK2, and pyruvate kinase PKM2, as well as transcriptional regulators such as hypoxia-inducible factor HIF-1α. The expression levels of these molecules in tumor tissue are detected using methods such as immunohistochemical staining or Western blot, and the glucose metabolism activity status is determined based on the expression levels.

[0003] However, existing single-molecule detection methods have significant limitations. First, most of the detected molecules are located at the downstream effector level of the glucose metabolism regulatory pathway, reflecting only the activity level of the glycolysis process itself and failing to reveal the activation status of upstream regulatory mechanisms. This results in a lack of systematicity and predictability in the identification of glucose metabolism phenotypes. Second, esophageal cancer exhibits high tumor heterogeneity, with significant differences in the expression levels of single molecules in different regions or cell subpopulations. Detection results relying on a single biomarker are unstable and easily affected by the sample collection location, failing to accurately reflect the overall glucose metabolism status of the tumor. Furthermore, existing methods do not consider the differences in the biological importance of molecules at different levels in the glucose metabolism regulatory pathway, using equal weights to evaluate all detection indicators. This ignores the decisive role of upstream initiation signaling molecules in the overall pathway activity, reducing the accuracy of glucose metabolism phenotype identification. Summary of the Invention

[0004] This application provides a method for detecting glucose metabolism biomarkers in esophageal cancer based on VAV2-related molecular characteristics. By establishing a multi-level joint detection method based on the complete glucose metabolism regulatory pathway of VAV2-EIF3F-MTA1 / HIF-1α, it solves the technical problems in the prior art that single molecular detection cannot systematically reflect the glucose metabolism regulatory mechanism and that the detection results are easily interfered with by tumor heterogeneity, thereby improving the accuracy and stability of glucose metabolism phenotype identification in esophageal cancer.

[0005] This application provides a method for detecting glucose metabolism biomarkers in esophageal cancer based on VAV2-related molecular characteristics. The method includes: Step S1: Immunohistochemical staining was performed on esophageal cancer tissue samples to obtain VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections and HIF-1α protein expression sections; Step S2: Immunoreactivity scores were performed on the VAV2 protein expression slice, the EIF3F protein expression slice, the MTA1 protein expression slice, and the HIF-1α protein expression slice to obtain the immunoreactivity score values ​​for each molecule. Step S3: Based on the molecular hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, the immune response scores of each molecule are weighted and calculated to obtain a comprehensive glucose metabolism phenotype score. Step S4: Classify the glucose metabolism phenotype of esophageal cancer samples according to the comprehensive score of the glucose metabolism phenotype, which includes high glucose glycolysis, moderate glucose glycolysis and low glucose glycolysis.

[0006] The technical solution provided in this application obtains protein expression sections of four key node molecules—VAV2, EIF3F, MTA1, and HIF-1α—through systematic immunohistochemical staining of esophageal cancer tissue samples. This establishes a multi-dimensional detection system covering the entire glucose metabolism regulatory pathway, from initiation signals to transcriptional effects. Compared to existing methods that only detect single glycolytic enzymes or transcription factors, this system comprehensively reflects the activation state of glucose metabolism regulatory mechanisms, rather than merely focusing on phenotypic observations of the metabolic process itself. This fundamentally improves the systematic understanding of glucose metabolism activity in esophageal cancer. The immunoreaction score semi-quantitatively assesses the expression levels of the four molecules by integrating staining intensity and the proportion of positive cells. This transforms subjective colorimetric observation into objective numerical scoring, eliminating detection bias caused by interpretation differences between different observers. This ensures the reproducibility and comparability of the detection results, providing a standardized data foundation for subsequent quantitative comprehensive scoring calculations and avoiding the limitations of existing technologies where qualitative descriptions are difficult to quantify and statistically compare.

[0007] The weighted calculation method based on the molecular hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway embodies the core technical contribution of this invention. This method sets differentiated weight coefficients according to the biological importance of each molecule in the pathway, so that the upstream initiating molecule VAV2 and the intermediate regulatory molecule EIF3F contribute more to the overall score than the downstream transcriptional effector molecules MTA1 and HIF-1α. This hierarchical weight allocation strategy accurately reflects the unidirectional transmission characteristics of glucose metabolism regulatory signals from upstream decision-making to downstream execution, enabling the overall score to more sensitively capture early signal changes in the activation of the regulatory pathway. Compared with the existing technology that uses equal weighting for all detection indicators, it has higher phenotypic discrimination accuracy and predictive ability. The comprehensive glucose metabolism phenotype score integrates the expression information of multiple molecules into a unified quantitative index. Through weighted summation, it realizes the transformation from discrete multidimensional data to a continuous single score value. This comprehensive score can offset the fluctuations in the expression of individual molecules caused by tumor heterogeneity, reduce the impact of sample collection location and testing batch on the results, and make the identification of glucose metabolism phenotype in esophageal cancer more stable and reliable. It provides an operable molecular diagnostic basis for clinical selection of metabolic targeted therapy strategies. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of an embodiment of the method for detecting glucose metabolism biomarkers in esophageal cancer based on VAV2-related molecular characteristics in this application. Figure 2 This is a schematic diagram illustrating the classification of esophageal cancer samples into glucose metabolism phenotypes based on a comprehensive score of glucose metabolism phenotype in an embodiment of this application. Detailed Implementation

[0010] This application provides a method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0011] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics in this application includes: Step S1: Immunohistochemical staining was performed on esophageal cancer tissue samples to obtain VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections and HIF-1α protein expression sections; Specifically, through systematic immunohistochemical staining of esophageal cancer tissue samples, protein expression was detected targeting four key molecules in the VAV2-EIF3F-MTA1 / HIF-1α complete glucose metabolism regulatory pathway. In the procedure, tissue sections after dewaxing, hydration, and antigen retrieval were incubated with four specific antibodies. Enzyme-linked colorimetric reactions resulted in a brown-yellow signal for the target proteins. The detection of each molecule was performed independently on sequential sections to ensure consistency of detection conditions and comparability of results.

[0012] Step S2: Immunoreactivity scores were performed on VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections, and HIF-1α protein expression sections to obtain the immunoreactivity score values ​​for each molecule. Specifically, the immunoreactivity score was used to perform a semi-quantitative analysis of the staining results using an immunoreactivity scoring system. This scoring system comprehensively considers two dimensions: staining intensity and the proportion of positive cells. Staining intensity was divided into four levels based on the depth of staining, from negative to strongly positive, with scores ranging from 0 to 3 points. The proportion of positive cells was divided into four intervals based on the percentage of stained cells, with scores ranging from 1 to 4 points. Finally, the scores from both dimensions were multiplied to obtain the immunoreactivity score for a single molecule, ranging from 0 to 12 points. This score directly reflects the expression abundance of the molecule in the tissue.

[0013] Step S3: Based on the molecular hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, the immune response scores of each molecule are weighted and calculated to obtain a comprehensive glucose metabolism phenotype score. Specifically, the weighted calculation process sets differential weight coefficients based on the biological hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway. VAV2, as the pathway initiator molecule, is located at the upstream of signal transduction, and its expression changes occur earliest and have a decisive impact on downstream pathways; EIF3F, as an intermediate regulatory molecule, receives the VAV2 signal and regulates downstream transcription factors; MTA1 and HIF-1α, as transcriptional effector molecules, directly regulate the expression of glycolysis-related genes. The immune response scores of the four molecules are multiplied by their corresponding weight coefficients and then summed to obtain a comprehensive glucose metabolism phenotype score, which integrates complete pathway activity information from signal initiation to transcription execution.

[0014] Step S4: Classify the glucose metabolism phenotype of esophageal cancer samples according to the comprehensive score of glucose metabolism phenotype. The glucose metabolism phenotype includes high glucose glycolysis, moderate glucose glycolysis and low glucose glycolysis.

[0015] Specifically, esophageal cancer samples were classified into three glucose metabolism phenotypes based on the numerical range of a comprehensive glucose metabolism phenotype score. A score greater than 9 indicates high expression of molecules at all pathway nodes, with significantly enhanced glycolytic activity, and is classified as a high-glycolytic type. A score between 4.5 and 9 indicates elevated expression of some node molecules but moderate overall pathway activity, and is classified as a moderate-glycolytic type. A score less than 4.5 indicates generally low expression of pathway node molecules, with weak glycolytic activity, and is classified as a low-glycolytic type. This classification method combines upstream regulatory signals with downstream metabolic effects, and has higher phenotypic accuracy compared to traditional methods that only detect single glycolytic enzymes.

[0016] In one specific embodiment, step S1 includes: Dewaxing and hydration treatment was performed on paraffin-embedded esophageal cancer tissue samples to obtain hydrated tissue sections; The hydrated tissue sections were subjected to heat-induced antigen retrieval treatment to obtain antigen-exposed tissue sections. The antigen-exposed tissue sections were sequentially subjected to peroxidase inactivation treatment and non-specific blocking treatment to obtain blocked tissue sections. After blocking, the tissue sections were incubated with VAV2 antibody, EIF3F antibody, MTA1 antibody and HIF-1α antibody respectively and then colored to obtain VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections and HIF-1α protein expression sections.

[0017] Specifically, after dissolving paraffin in xylene, the tissue is sequentially hydrated with 100%, 95%, 85%, and 75% ethanol, and finally rinsed with double-distilled water. This process transforms the tissue from a hydrophobic to a hydrophilic state, allowing the subsequent antibody solution to fully penetrate the tissue and bind to the target protein. Heat-induced antigen retrieval treatment breaks the protein cross-links formed by formalin fixation in the tissue through high-temperature heating, re-exposing the masked antigenic epitopes. Specifically, the hydrated tissue sections are immersed in citrate buffer or EDTA buffer and heated under high pressure for 15 minutes. The pH differences of different buffers are optimized for the antigenic epitope characteristics of different proteins.

[0018] Peroxidase inactivation was performed by incubating tissue sections with 3% hydrogen peroxide solution to completely inhibit endogenous peroxidase activity in the tissue, avoiding non-specific background signals that could interfere with the true antigen-antibody reaction results during subsequent HRP-labeled secondary antibody staining. Non-specific blocking involved covering the tissue section surface with phosphate buffer containing 5% bovine serum albumin to block non-specific protein binding sites and prevent false positive signals caused by antibody binding to non-target proteins via electrostatic adsorption or hydrophobic interactions. After blocking, specific primary antibodies for VAV2, EIF3F, MTA1, and HIF-1α were added and incubated overnight at 4°C. Subsequently, HRP-labeled secondary antibody was added and incubated at room temperature. Finally, DAB staining was performed, resulting in a brownish-yellow precipitate at the location of the antigen-antibody complex. After counterstaining the cell nuclei with hematoxylin, the sections were mounted, and the protein expression localization and intensity of different molecules were observed under a microscope.

[0019] In one specific embodiment, step S2 includes: The staining intensity and positive cell ratio of VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections and HIF-1α protein expression sections were scored respectively to obtain the staining intensity score and positive cell ratio score of each molecule. The staining intensity score and the positive cell proportion score are multiplied to obtain the molecular immune response score, which ranges from 0 to 12.

[0020] Specifically, the staining intensity score is determined by observing the depth of DAB staining in sections expressing VAV2, EIF3F, MTA1, and HIF-1α under a microscope. Negative staining (no brownish-yellow signal) is assigned 0 points; weak positive staining (light brownish-yellow signal) is assigned 1 point; moderate positive staining (clear brownish-yellow signal) is assigned 2 points; and strong positive staining (dark brownish-yellow signal) is assigned 3 points. The positive cell percentage score is assigned in segments based on the percentage of cells showing positive staining signals relative to the total number of cells in the field of view: 0-10% is assigned 1 point, 11-50% is assigned 2 points, 51-80% is assigned 3 points, and greater than 80% is assigned 4 points. This scoring dimension reflects the distribution breadth of the target protein in the tissue.

[0021] The immunoreactivity score is calculated by multiplying the staining intensity score by the positive cell proportion score. This calculation integrates both the intensity and distribution information of protein expression. Since the staining intensity score ranges from 0 to 3 points and the positive cell proportion score ranges from 1 to 4 points, the resulting immunoreactivity score ranges from 0 to 12 points. A higher score indicates a higher expression level of the molecule in the tissue. When a molecule shows negative staining, its immunoreactivity score is 0 points regardless of the positive cell proportion. When a molecule shows strong positive staining and the positive cell proportion is greater than 80%, its immunoreactivity score reaches the maximum of 12 points. This score, as the basis for subsequent weighted calculations, directly reflects the expression status of molecules at various nodes in the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway.

[0022] In one specific embodiment, step S3 involves weighted calculation of the immune response scores of each molecule based on the molecular hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, including: Based on the regulatory hierarchy of each molecule in the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, weight coefficients were assigned to the immune response scores of each molecule to obtain the weight coefficients of VAV2, EIF3F, MTA1, and HIF-1α. The comprehensive score of glucose metabolism phenotype is obtained by weighting and summing the scores of each molecular immune response with their corresponding weight coefficients.

[0023] Specifically, the weighting coefficients were assigned differentially based on the biological hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway. In this pathway, VAV2, located at the upstream of signal transduction as the initiating regulator, exhibits the earliest expression changes and determines the activation state of the entire pathway, thus receiving a high weight. EIF3F, as a direct downstream target of VAV2, receives upstream signals and transmits regulatory instructions downstream, also receiving a high weight. MTA1 and HIF-1α, located at the transcriptional effector level of the pathway, act as transcriptional regulators directly regulating the expression of glycolysis-related genes; their expression levels are influenced by upstream molecules, therefore receiving relatively low weights. This hierarchical weighting reflects the relative importance of molecules at each node in the complete regulatory chain from signal initiation and intermediate transmission to effector execution.

[0024] The weighted summation operation multiplies the individual immune response scores of VAV2, EIF3F, MTA1, and HIF-1α by their respective weighting coefficients and then sums them. Specifically, the VAV2 immune response score is multiplied by its weighting coefficient, the EIF3F immune response score by its weighting coefficient, the MTA1 immune response score by its weighting coefficient, and the HIF-1α immune response score by its weighting coefficient. These four products are then summed to obtain the comprehensive glucose metabolism phenotype score. This comprehensive score integrates the expression information of molecules at all levels of the glucose metabolism regulatory pathway from upstream to downstream. By differentiating the weighting coefficients, the expression changes of upstream regulatory molecules contribute more significantly to the comprehensive score, thereby improving the accuracy and stability of identifying the glucose metabolism activity status in esophageal cancer.

[0025] In one specific embodiment, weighting coefficients are assigned to the immune response scores of each molecule based on its regulatory level within the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, including: VAV2 was used as the starting molecular level in the glucose metabolism regulatory pathway, and the weighting coefficient of VAV2 was set to 0.25. EIF3F was used as an intermediate regulatory level in the glucose metabolism regulation pathway, and the weighting coefficient of EIF3F was set to 0.25. MTA1 and HIF-1α were used as transcriptional effector levels in the glucose metabolism regulatory pathway, with the weighting coefficients for MTA1 and HIF-1α set to 0.15 and 0.15 respectively.

[0026] Specifically, VAV2 was assigned a weight of 0.25 as the initiating molecular level of the glucose metabolism regulatory pathway. This weighting is based on VAV2's core initiation role in the pathway. Published studies have shown that VAV2 is highly expressed in esophageal cancer tissues and is closely related to tumor progression. Changes in its expression level directly determine the recruitment and activation status of downstream EIF3F, thereby initiating the entire glucose metabolism reprogramming process. EIF3F, as an intermediate regulatory level, was also assigned a weight of 0.25. As the eukaryotic translation initiation factor 3F subunit, EIF3F mediates the expression regulation of MTA1 and HIF-1α after receiving VAV2 signals. According to the protein-protein interaction data shown in the uploaded materials, VAV2 and EIF3F showed a significant positive correlation in the esophageal cancer transcriptome, indicating that they have equally important synergistic effects in glucose metabolism regulation.

[0027] MTA1 and HIF-1α, as transcriptional effectors, were each assigned a weight of 0.15. This lower weight compared to upstream molecules reflects that transcription factors are at the effector execution position rather than the signaling decision position in the pathway. MTA1, as a methyltransferase-related protein, participates in chromatin remodeling and regulates the transcription of glycolytic genes, while HIF-1α, as a hypoxia-inducible factor, directly binds to the promoter regions of glycolytic genes to promote gene expression. The expression levels of both are influenced by upstream VAV2 and EIF3F. The sum of the four weights is 0.80. This value provides a standardized weighting framework for the subsequent calculation of the comprehensive glucose metabolism phenotype score, ensuring that the score truly reflects the overall activity status of the VAV2-EIF3F-MTA1 / HIF-1α complete pathway.

[0028] In one specific embodiment, the weighted summation of each molecular immune response score and its corresponding weight coefficient is performed to obtain a comprehensive glucose metabolism phenotype score, specifically: The weighted scores for each molecule were obtained by multiplying the VAV2 immune response score by a weighting factor of 0.25, the EIF3F immune response score by a weighting factor of 0.25, the MTA1 immune response score by a weighting factor of 0.15, and the HIF-1α immune response score by a weighting factor of 0.15. The weighted scores of each molecule are summed to obtain a comprehensive score for the glucose metabolism phenotype.

[0029] Specifically, the weighted score for each molecule is calculated by multiplying the immune response score of each of the four molecules by their corresponding weighting coefficients. The VAV2 immune response score is multiplied by 0.25 to obtain the VAV2 weighted score, the EIF3F immune response score by 0.25 to obtain the EIF3F weighted score, the MTA1 immune response score by 0.15 to obtain the MTA1 weighted score, and the HIF-1α immune response score by 0.15 to obtain the HIF-1α weighted score. Since the immune response score of a single molecule ranges from 0 to 12, after the weighting coefficient multiplication, the weighted scores for VAV2 and EIF3F range from 0 to 3, and the weighted scores for MTA1 and HIF-1α range from 0 to 1.8. This weighting process ensures that the upstream regulatory molecules contribute more to the final overall score than the downstream effector molecules.

[0030] The comprehensive glucose metabolism phenotypic score is obtained by summing the weighted scores of four molecules: VAV2, EIF3F, MTA1, and HIF-1α. The theoretical range of the comprehensive score is 0 to 9.6. This comprehensive score integrates complete hierarchical information from signal initiation to transcriptional effect in the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway. Compared to traditional methods that only detect single glycolytic enzymes such as HIF-1α or GLUT1, this comprehensive scoring system reduces the interference of tumor heterogeneity on the results of single biomarker detection through multi-level molecular joint detection, making the identification of glucose metabolism activity status in esophageal cancer more accurate and stable.

[0031] In one specific embodiment, step S4, which classifies the glucose metabolism phenotype of esophageal cancer samples based on a comprehensive glucose metabolism phenotype score, includes: When the comprehensive score of glucose metabolism phenotype is greater than 9, the esophageal cancer sample is determined to be of the high glucose glycolysis type; When the comprehensive score of glucose metabolism phenotype is between 4.5 and 9, the esophageal cancer sample is identified as having a moderate glycolytic type. When the comprehensive score of glucose metabolism phenotype is less than 4.5, the esophageal cancer sample is judged to be of the low glucose glycolysis type.

[0032] Specifically, the criteria for identifying the high-glycolytic phenotype are a comprehensive glucose metabolism phenotype score greater than 9. This threshold indicates that all four molecules—VAV2, EIF3F, MTA1, and HIF-1α—are highly expressed in esophageal cancer tissues, and the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway is strongly activated. The immune response scores of molecules at each level of the pathway are close to or reach the maximum value of 12 points, and the weighted comprehensive score exceeds 9 points. Esophageal cancer cells with this phenotype exhibit significantly enhanced glycolytic activity, with glucose uptake and lactate production rates significantly higher than normal cells, and high sensitivity to glycolysis inhibitors such as 2-deoxyglucose and other metabolically targeted drugs.

[0033] The thresholds for intermediate and low glycolytic phenotypes are based on the overall expression levels of pathway molecules. An overall score of 4.5 to 9 for intermediate glycolytic phenotype indicates that some molecules in the pathway are highly expressed while others are expressed at moderate to low levels, or that all molecules at all nodes are expressed at moderate levels, indicating moderate glycolytic activity. An overall score less than 4.5 for low glycolytic phenotype indicates that molecules at all nodes in the pathway are generally underexpressed, and the overall activity of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway is weak. Esophageal cancer cells with this phenotype primarily rely on oxidative phosphorylation rather than glycolysis for energy and have low sensitivity to glycolysis inhibitors. This three-category system precisely stratifies esophageal cancer samples according to their glucose metabolism activity status, providing molecular diagnostic evidence for clinical selection of metabolic targeted therapy strategies.

[0034] Figure 2 This is a schematic diagram illustrating the classification of esophageal cancer samples into glucose metabolism phenotypes based on a comprehensive glucose metabolism phenotype score in an embodiment of this application. Figure 2 As shown, esophageal cancer samples were classified into three glucose metabolism phenotypes based on a comprehensive glucose metabolism phenotype score. The box plot illustrates the score distribution characteristics of each phenotype. The horizontal axis represents the three glucose metabolism phenotypes and their corresponding score intervals, while the vertical axis represents the comprehensive glucose metabolism phenotype score range from 0 to 12 points. The red dashed line marks the high glucose glycolysis threshold (score of 9 points), and the blue dashed line marks the low glucose glycolysis threshold (score of 4.5 points). The comprehensive scores of the high glucose glycolysis type samples are concentrated between 8.8 and 10.2, with a median of approximately 9.5 points, indicating that this type of sample... The VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway in this study was strongly activated. The comprehensive scores of the moderate glycolytic phenotype samples ranged from 5.0 to 8.0, with a median of approximately 6.5, indicating that the pathway was at a moderate activation level. The comprehensive scores of the low glycolytic phenotype samples ranged from 2.0 to 4.2, with a median of approximately 3.2, indicating that the overall activity of the pathway was weak. The score distribution boundaries among the three phenotypes were clear and there was no overlap, verifying the accuracy and reliability of phenotype classification based on the comprehensive score threshold.

[0035] In one specific embodiment, staining intensity scores were performed on VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections, and HIF-1α protein expression sections, including: The staining intensity of VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections, and HIF-1α protein expression sections was determined to obtain the staining intensity level. The staining intensity is scored and assigned according to the staining intensity level, with negative staining assigned 0 points, weak positive staining assigned 1 point, moderate positive staining assigned 2 points, and strong positive staining assigned 3 points, thus obtaining the staining intensity score.

[0036] The determination of staining intensity was assessed by observing the intensity of the brownish-yellow precipitate signal produced by the DAB staining reaction in VAV2, EIF3F, MTA1, and HIF-1α protein expression sections under an optical microscope. Negative staining indicated no brownish-yellow signal or only very weak background staining in the section; weak positive staining indicated a light brownish-yellow signal but a pale color; moderate positive staining indicated a clear brownish-yellow signal with a moderate color; and strong positive staining indicated a deep brownish-yellow or brownish-red signal with a concentrated color. This determination process required identical microscope illumination and magnification. The staining intensity level of the test section was determined by comparing the staining depth of known positive and negative control sections.

[0037] The scoring system converts staining intensity levels into quantitative values. A score of 0 for negative staining indicates no expression of the molecule or expression levels below the detection threshold in the tissue; 1 point for weak positive staining indicates low expression; 2 points for moderate positive staining indicates moderate expression; and 3 points for strong positive staining indicates high expression. This scoring system transforms subjective observation of staining intensity into objective numerical scores. The staining intensity score directly reflects the abundance of the target protein in the tissue; a higher score indicates a higher protein expression level. This score, along with the positive cell proportion score, forms a complete protein expression assessment system as an important dimension in calculating the immune response score.

[0038] In one specific embodiment, the proportion of positive cells was scored for VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections, and HIF-1α protein expression sections, including: The percentage of positive cells in VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections, and HIF-1α protein expression sections was statistically analyzed to obtain the percentage of positive cells. The positive cell percentage is scored in segments: 0-10% positive cells are assigned 1 point, 11-50% positive cells are assigned 2 points, 51-80% positive cells are assigned 3 points, and positive cells greater than 80% are assigned 4 points, thus obtaining the positive cell percentage score.

[0039] The counting of positively stained cells involves counting cells in multiple fields of view within the tumor region of a tissue section under a microscope. Cells exhibiting a brownish-yellow DAB staining signal are identified as positively stained cells. The total number of cells, including both positively stained and unstained cells, is determined as the total cell count. The percentage of positive cells is calculated by dividing the number of positively stained cells by the total cell count and multiplying by 100%. This statistical process requires randomly selecting at least five fields of view under high magnification, counting at least 200 cells in each field. The average percentage of positive cells in each field is then taken as the percentage of positive cells for that section. This percentage reflects the distribution of the target protein in the tumor tissue.

[0040] The segmented scoring divides the percentage of positive cells into four intervals based on their distribution range and assigns corresponding scores to each interval. A positive cell percentage of 0-10% indicates that the target protein is expressed only in a very small number of tumor cells and is awarded 1 point; a positive cell percentage of 11-50% indicates that the target protein is expressed in some tumor cells and is awarded 2 points; a positive cell percentage of 51-80% indicates that the target protein is expressed in most tumor cells and is awarded 3 points; and a positive cell percentage greater than 80% indicates that the target protein is expressed in almost all tumor cells and is awarded 4 points. This scoring dimension complements the staining intensity score. Staining intensity reflects the abundance of protein expression in a single cell, while the percentage of positive cells reflects the prevalence of protein expression in the tissue. The combination of the two can comprehensively assess the expression status of VAV2, EIF3F, MTA1, and HIF-1α in esophageal cancer tissue.

[0041] In one specific embodiment, the hydrated tissue sections are subjected to heat-induced antigen retrieval treatment, including: Hydrated tissue sections were immersed in citrate buffer or EDTA buffer with a pH of 6.0 and a pH of 9.0, respectively. Hydrated tissue sections immersed in buffer solution were subjected to high-pressure heating for 15 minutes to obtain antigen-exposed tissue sections.

[0042] The selection of citrate buffer and EDTA buffer was optimized based on the chemical properties of different protein antigenic epitopes. Citrate buffer with a pH of 6.0 is suitable for proteins with better antigen retrieval effects in acidic environments, such as VAV2 and EIF3F, while EDTA buffer with a pH of 9.0 is suitable for proteins with better antigen retrieval effects in alkaline environments, such as MTA1 and HIF-1α. The dewaxed and hydrated tissue sections were completely immersed in the corresponding buffers, ensuring no air bubbles adhered to the section surface. Sufficient buffer volume was maintained to ensure pH stability. The pH difference between different antigen retrieval solutions promotes the breaking of cross-links by altering the ionic environment surrounding protein molecules.

[0043] The high-pressure heating process was carried out in a sealed pressure cooker. Tissue sections immersed in buffer solution were placed inside the pressure cooker and heated to boiling. The pressure was maintained for 15 minutes. The high temperature and pressure caused the protein cross-links formed by formalin fixation to hydrolyze and break, re-exposing the antigenic epitopes that had been masked by the cross-links to the protein surface. The 15-minute heating time was set based on a balance between antigen retrieval efficiency and tissue morphology preservation. Too short a time would result in insufficient antigen retrieval, leading to low antibody binding efficiency; too long a time would severely damage the tissue structure, affecting morphological observation. After heating, the tissue sections were allowed to cool naturally to room temperature to stabilize the tissue structure before proceeding with the subsequent antibody incubation steps.

[0044] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting glucose metabolism biomarkers in esophageal cancer based on VAV2-related molecular characteristics, characterized in that, The method includes: Step S1: Immunohistochemical staining was performed on esophageal cancer tissue samples to obtain VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections and HIF-1α protein expression sections; Step S2: Immunoreactivity scores were performed on the VAV2 protein expression slice, the EIF3F protein expression slice, the MTA1 protein expression slice, and the HIF-1α protein expression slice to obtain the immunoreactivity score values ​​for each molecule. Step S3: Based on the molecular hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, the immune response scores of each molecule are weighted and calculated to obtain a comprehensive glucose metabolism phenotype score. Step S4: Classify the esophageal cancer samples into glucose metabolism phenotypes based on the comprehensive score of the glucose metabolism phenotype, which includes high glucose glycolysis, moderate glucose glycolysis and low glucose glycolysis.

2. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 1, characterized in that, Step S1 includes: Dewaxing and hydration treatment was performed on paraffin-embedded esophageal cancer tissue samples to obtain hydrated tissue sections; The hydrated tissue sections were subjected to heat-induced antigen retrieval treatment to obtain antigen-exposed tissue sections; The antigen-exposed tissue sections were subjected to peroxidase inactivation treatment and non-specific blocking treatment in sequence to obtain blocked tissue sections; The blocked tissue sections were incubated with VAV2 antibody, EIF3F antibody, MTA1 antibody and HIF-1α antibody respectively and then colored to obtain VAV2 protein expression sections, EIF3F protein expression sections, MTA1 protein expression sections and HIF-1α protein expression sections.

3. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 1, characterized in that, Step S2 includes: The staining intensity score and positive cell ratio score of the VAV2 protein expression section, the EIF3F protein expression section, the MTA1 protein expression section and the HIF-1α protein expression section were respectively performed to obtain the staining intensity score and positive cell ratio score of each molecule. The staining intensity score and the positive cell proportion score are multiplied to obtain the molecular immune response score, which ranges from 0 to 12.

4. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 1, characterized in that, In step S3, based on the molecular hierarchy of the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, the immune response scores of each molecule are weighted and calculated, including: Based on the regulatory hierarchy of each molecule in the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway, weight coefficients were assigned to the immune response scores of each molecule to obtain the VAV2 weight coefficient, EIF3F weight coefficient, MTA1 weight coefficient, and HIF-1α weight coefficient. The comprehensive score of the glucose metabolism phenotype is obtained by weighting and summing the immune response scores of each molecule with their corresponding weighting coefficients.

5. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 4, characterized in that, The immune response score of each molecule in the VAV2-EIF3F-MTA1 / HIF-1α glucose metabolism regulatory pathway is assigned a weighting coefficient based on the regulatory hierarchy of each molecule, including: VAV2 was used as the starting molecular level for the glucose metabolism regulatory pathway, and the weighting coefficient of VAV2 was set to 0.

25. EIF3F was used as an intermediate regulatory level in the glucose metabolism regulatory pathway, and the weighting coefficient of EIF3F was set to 0.

25. MTA1 and HIF-1α were used as transcriptional effector levels in the glucose metabolism regulatory pathway, with the weighting coefficients of MTA1 and HIF-1α set to 0.15 and 0.15 respectively.

6. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 5, characterized in that, The step of performing a weighted summation operation on the molecular immune response scores and their corresponding weighting coefficients to obtain the comprehensive score of the glucose metabolism phenotype is as follows: The weighted score for each molecule is obtained by multiplying the VAV2 immune response score by the weighting coefficient 0.25, the EIF3F immune response score by the weighting coefficient 0.25, the MTA1 immune response score by the weighting coefficient 0.15, and the HIF-1α immune response score by the weighting coefficient 0.

15. The weighted scores of each molecule are summed to obtain the comprehensive score of the sugar metabolism phenotype.

7. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 1, characterized in that, Step S4, which classifies the glucose metabolism phenotype of esophageal cancer samples based on the comprehensive score of glucose metabolism phenotype, includes: When the comprehensive score of the glucose metabolism phenotype is greater than 9, the esophageal cancer sample is determined to be of the high glucose glycolysis type. When the comprehensive score of the glucose metabolism phenotype is between 4.5 and 9, the esophageal cancer sample is determined to be of moderate glycolytic type. When the comprehensive score of the glucose metabolism phenotype is less than 4.5, the esophageal cancer sample is determined to be of the low glucose glycolytic type.

8. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 3, characterized in that, The staining intensity scoring of the VAV2 protein expression sections, the EIF3F protein expression sections, the MTA1 protein expression sections, and the HIF-1α protein expression sections includes: The staining intensity of the VAV2 protein expression section, the EIF3F protein expression section, the MTA1 protein expression section, and the HIF-1α protein expression section was determined to obtain the staining intensity level. The staining intensity is scored and assigned according to the staining intensity level, with negative staining assigned 0 points, weak positive staining assigned 1 point, moderate positive staining assigned 2 points, and strong positive staining assigned 3 points, thus obtaining the staining intensity score.

9. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 3, characterized in that, The positive cell ratio scoring of the VAV2 protein expression slices, EIF3F protein expression slices, MTA1 protein expression slices, and HIF-1α protein expression slices includes: The percentage of positive cells in the VAV2 protein expression section, the EIF3F protein expression section, the MTA1 protein expression section, and the HIF-1α protein expression section was statistically analyzed to obtain the percentage of positive cells. The positive cell percentage is scored in segments, with 1 point assigned to positive cells from 0 to 10%, 2 points to positive cells from 11 to 50%, 3 points to positive cells from 51 to 80%, and 4 points to positive cells greater than 80%, thus obtaining the positive cell percentage score.

10. The method for detecting esophageal cancer glucose metabolism biomarkers based on VAV2-related molecular characteristics according to claim 2, characterized in that, The process of performing heat-induced antigen retrieval treatment on the hydrated tissue sections includes: The hydrated tissue sections were immersed in citrate buffer or EDTA buffer, wherein the pH of the citrate buffer was 6.0 and the pH of the EDTA buffer was 9.

0. The hydrated tissue sections immersed in buffer solution were subjected to high-pressure heating for 15 minutes to obtain the antigen-exposed tissue sections.

Images