Biomarkers used for the diagnosis and treatment of gastric cancer

Abstract

This invention discloses biomarkers for the diagnosis and treatment of gastric cancer. This application proposes a novel non-invasive method for the early detection of peritoneal metastases in gastric cancer patients. The RSA model combines 4-mRNA genomes (BUB1, SPC25, CT83, and MMP3) with clinical characteristics. Furthermore, it provides the application of 4-mRNAs in the treatment of gastric cancer, offering new directions for improving risk stratification, treatment planning, and monitoring of gastric cancer patients, as well as for effective treatment of gastric cancer, and has broad application prospects.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological medicine, and particularly relates to a marker for diagnosing and treating gastric cancer. BACKGROUND

[0002] Gastric cancer is still one of the main causes of cancer-related deaths worldwide, and is particularly common in East Asian countries. Despite the progress in diagnosis and treatment strategies, the prognosis of patients with locally advanced gastric cancer (LAGC) is still poor due to the high incidence of metastasis and recurrence. Among them, peritoneal metastasis is particularly occult, which is often occult and cannot be detected by conventional imaging techniques. Early and accurate detection of occult peritoneal metastasis is crucial for optimizing treatment plans and improving the overall survival rate of LAGC patients. SUMMARY

[0003] To make up for the deficiencies of the prior art, the present application provides a marker for diagnosing and treating gastric cancer.

[0004] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0005] The first aspect of the present application provides the use of a reagent for detecting the expression level of a biomarker in a sample in the preparation of a product for diagnosing gastric cancer / diagnosing gastric cancer metastasis / predicting the prognosis of gastric cancer, wherein the biomarker comprises any one or more of BUB1, SPC25, CT83 and MMP3.

[0006] Further, the gastric cancer metastasis is peritoneal metastasis of gastric cancer.

[0007] Further, the prediction of the prognosis of gastric cancer includes predicting the survival period of gastric cancer, predicting the treatment efficacy of gastric cancer, predicting the recurrence of gastric cancer, and predicting the cachexia of gastric cancer.

[0008] Further, the survival period includes overall survival (OS) and disease-free survival (DFS).

[0009] Further, the treatment efficacy of gastric cancer also includes the success rate of conversion therapy.

[0010] Further, the recurrence of gastric cancer is peritoneal recurrence of gastric cancer.

[0011] Further, the reagent includes an oligonucleotide probe specifically recognizing the biomarker gene, a primer specifically amplifying the biomarker gene, or a binding agent specifically binding to the protein encoded by the biomarker gene.

[0012] Further, the primer sequence specifically amplifying BUB1 is shown in SEQ ID NO: 1-2.

[0013] Further, the primer sequence specifically amplifying SPC25 is shown in SEQ ID NO: 3-4.

[0014] Further, the primer sequence for specifically amplifying CT83 is shown as SEQ ID NO: 5-6.

[0015] Further, the primer sequence for specifically amplifying MMP3 is shown as SEQ ID NO: 7-8.

[0016] Further, the sample comprises tissue, blood.

[0017] Further, the blood comprises peripheral blood, serum.

[0018] Further, the gastric cancer comprises locally advanced gastric cancer (LAGC).

[0019] The second aspect of the present application provides a product for diagnosing gastric cancer / diagnosing gastric cancer metastasis / predicting prognosis of gastric cancer, the product comprising a reagent for detecting the expression level of a biomarker in a sample, the biomarker comprising any one or more of BUB1, SPC25, CT83 and MMP3.

[0020] Further, the reagent further comprises a detectable substance.

[0021] Further, the product comprises a kit, a test paper.

[0022] The third aspect of the present application provides the use of a biomarker in constructing a model for diagnosing gastric cancer metastasis, the biomarker comprising any one or more of BUB1, SPC25, CT83 and MMP3.

[0023] Further, the index of the model further comprises T stage, N stage, pathological type.

[0024] Further, the gastric cancer metastasis is peritoneal metastasis of gastric cancer.

[0025] The fourth aspect of the present application provides a model for diagnosing gastric cancer metastasis, the index of the model comprising the level of a biomarker, the biomarker comprising any one or more of BUB1, SPC25, CT83 and MMP3.

[0026] Further, the index of the model further comprises T stage, N stage, pathological type.

[0027] Further, the model is a nomogram model.

[0028] Further, the calculation formula of the model comprises:

[0029] The probability of gastric cancer metastasis = [(4.855 x 4-mRNA) + (4.139 x T stage) + (4.140 x N stage) + (3.147 x pathological type) + (-9.961)], or

[0030] The probability of gastric cancer metastasis = [(6.558×4-mRNA)+(8.467×T stage)+(8.054×N stage)+(8.164×pathological type)-14.876].

[0031] Furthermore, the gastric cancer metastasis mentioned refers to peritoneal metastasis of gastric cancer.

[0032] A fifth aspect of the invention provides the use of inhibitors of biomarkers in the preparation of pharmaceutical compositions for treating gastric cancer, said biomarkers including any one or more of BUB1, SPC25, CT83, and MMP3.

[0033] Furthermore, the inhibitors include nucleic acid inhibitors and protein inhibitors.

[0034] Furthermore, the nucleic acid inhibitors include siRNA, shRNA, and ribozymes.

[0035] Furthermore, the nucleic acid inhibitor is selected from siRNA.

[0036] Furthermore, the siRNA sequence of BUB1 is shown in SEQ ID NO:9 or SEQ ID NO:10.

[0037] Furthermore, the siRNA sequence of SPC25 is shown in SEQ ID NO:11 or SEQ ID NO:12.

[0038] Furthermore, the siRNA sequence of CT83 is shown in SEQ ID NO:13 or SEQ ID NO:14.

[0039] Furthermore, the siRNA sequence of MMP3 is shown in SEQ ID NO:15 or SEQ ID NO:16.

[0040] Furthermore, the treatment of gastric cancer includes inhibiting gastric cancer metastasis.

[0041] Furthermore, the inhibition of gastric cancer metastasis includes the inhibition of peritoneal metastasis of gastric cancer.

[0042] A sixth aspect of the present invention provides a pharmaceutical composition comprising an inhibitor of a biomarker, said biomarker being any one or more of BUB1, SPC25, CT83, and MMP3.

[0043] Furthermore, the pharmaceutical composition also includes a pharmaceutically acceptable carrier.

[0044] A seventh aspect of the invention provides the use of biomarkers as targets in screening candidate drugs for the treatment of gastric cancer, said biomarkers including any one or more of BUB1, SPC25, CT83 and MMP3.

[0045] Furthermore, the method for screening candidate drugs for treating gastric cancer includes: testing the effect of the test substance on the expression level of biomarkers in the sample, wherein a decrease in biomarker levels after using the test substance indicates that the test substance is a candidate drug for treating gastric cancer.

[0046] The eighth aspect of the invention provides the use of inhibitors of biomarkers in the preparation of products that regulate gene / protein levels, wherein the biomarkers include one or more of BUB1, SPC25, CT83 and MMP3, and the genes / proteins include one or more of MMP9, N-cadherin, vimentin and E-cadherin.

[0047] Furthermore, the inhibitors include nucleic acid inhibitors and protein inhibitors.

[0048] Furthermore, the nucleic acid inhibitors include siRNA, shRNA, and ribozymes.

[0049] Furthermore, the nucleic acid inhibitor is selected from siRNA.

[0050] The ninth aspect of the present invention provides any of the following methods:

[0051] (1) A method for screening candidate drugs for treating gastric cancer, the method comprising: testing the effect of a test substance on the expression level of a biomarker in a sample, wherein, after using the test substance, a decrease in the biomarker level indicates that the test substance is a candidate drug for treating gastric cancer, the biomarker including any one or more of BUB1, SPC25, CT83 and MMP3.

[0052] (2) A method for inhibiting gastric cancer cell metastasis in vitro, the method comprising administering an inhibitor of a biomarker, said biomarker including any one or more of BUB1, SPC25, CT83 and MMP3;

[0053] (3) A method for regulating gene / protein expression in vitro, the method comprising administering an inhibitor of a biomarker, the biomarker comprising any one or more of BUB1, SPC25, CT83 and MMP3, and the gene / protein comprising any one or more of MMP9, N-cadherin, vimentin and E-cadherin.

[0054] Advantages and beneficial effects of the present invention:

[0055] This application proposes a novel non-invasive method for the early detection of peritoneal metastasis in gastric cancer patients. The RSA model combines 4-mRNA genomes (BUB1, SPC25, CT83, and MMP3) with clinical characteristics. Furthermore, it provides the application of 4-mRNA in the treatment of gastric cancer, offering new directions for improving risk stratification, treatment planning, and monitoring of gastric cancer patients, as well as for effective treatment of gastric cancer, and has broad application prospects. Attached Figure Description

[0056] Figure 1 This is a flowchart of the research design for discovering and validating 4-mRNA combinations to predict occult peritoneal metastasis in LAGC patients;

[0057] Figure 2 This is a diagram showing the identification and preliminary validation of biomarkers for peritoneal metastasis in LAGC using public databases and transcriptome data. Specifically, 2A is a Venn diagram of differentially expressed genes from TCGA, GSE62254, and paired mRNA sequencing data; 2B is a volcano diagram of differentially expressed genes from GSE62254 data; 2C is a volcano diagram of differentially expressed genes from TCGA data; 2D is a volcano diagram of differentially expressed genes from paired mRNA sequencing data; 2E is a diagram of 4-mRNA expression in 42 matched cancer tissues from patients with and without peritoneal metastasis; 2F is a diagram of 4-mRNA expression in peripheral blood from 33 matched patients; 2G is a Western blot map of proteins in cancer tissues from patients with and without peritoneal metastasis; and 2H is... Immunohistochemical analysis of 4-mRNA in matched cancer tissue samples: 2I is the quantitative immunohistochemical result of 4-mRNA; 2J-2M is the correlation between five-year DFS and high mRNA expression and low mRNA expression; 2N is the correlation between 4-mRNA expression level and 41 clinicopathological features; 2O is the overall survival analysis of 4-mRNA; 2P is the protein-protein interaction (PPI) network diagram of 4-mRNA; 2Q-T is the Western blot quantitative analysis result of 4-mRNA in primary cancer tissues of patients with and without peritoneal metastasis; 2U-X is the overall survival curve of 4-mRNA gene in formalin-fixed specimens.

[0058] Figure 3The model was trained and validated using 4-mRNA in fresh frozen tissue samples to detect peritoneal metastasis in patients with laminar dysplasia of the kidney (LAGC). 3A is a multivariate logistic regression analysis plot of factors influencing peritoneal metastasis in LAGC patients; 3B is a nomogram of 4-mRNA features and clinical characteristics predicting peritoneal metastasis in LAGC patients; 3C is the ROC curve of various predictor variables in the training set; 3D is the ROC curve of various predictor variables in the validation set; 3E is the confusion matrix of various prediction models in the training and validation sets; 3F is the calibration curve of the RSA model in the training set; 3G is the calibration curve of the RSA model in the validation set; and 3H is the performance index of different prediction models in the training set. Radar charts: 3I is a performance indicator radar chart of different prediction models in the validation set; 3J is a clinical double-layer concentric circle chart of various prediction models in the training and validation sets; 3K is a log-rank test survival curve chart of patients in the training set; 3L is a log-rank test survival curve chart of patients in the validation set; 3M is an AUC comparison chart of different prediction models in stratified analysis based on the expression of molecular markers HER2 and PDL1; 3N is a specificity comparison chart of different prediction models in stratified analysis based on TNM staging and the expression of molecular markers HER2 and PDL1; 3O is a sensitivity comparison chart of different prediction models in stratified analysis based on TNM staging and the expression of molecular markers HER2 and PDL1.

[0059] Figure 4 This section presents the conversion therapy outcomes and cachexia maps for patients with peritoneal metastatic gastric cancer predicted using the 4-mRNA model and fresh frozen tissue specimens. 4A is a conversion therapy protocol map; 4B is a waterfall chart of CT assessment of efficacy after conversion therapy; 4C is a cachexia grading map defined by the Asian Cachexia Working Group consensus; 4D is a map comparing the pathological regression grades of the primary lesion after conversion therapy in high-risk and low-risk patients according to the RSA model; 4E is a CT assessment map comparing changes in the primary lesion before and after conversion therapy in high-risk and low-risk groups according to the RSA model; 4F is a map showing changes in peritoneal metastases assessed by laparoscopic exploration before and after conversion therapy in high-risk and low-risk groups according to the RSA model; 4G... 4H is a comparative chart of clinical efficacy and cachexia incidence among different models; 4I is a comparative analysis chart of conversion therapy efficacy between high-risk and low-risk patients in the RSA model; 4J is a comparative chart of CT assessment efficacy among different models; 4K is a comparative analysis chart of pathological regression grading among different models; 4L is a chart of ROC curves comparing the effectiveness of different models in predicting CT assessment results; 4M is a chart of ROC curves assessing the predictive accuracy of different models in predicting the success rate of conversion therapy; 4N is a chart of ROC curves predicting the occurrence of cachexia among different models; and 4O is a three-year OS curve for patients receiving conversion therapy.

[0060] Figure 5This study validates a 4-mRNA-based model using fresh frozen specimens and detects free peritoneal cancer cells in patients with lacunar infarct-associated leukemia (LAGC). Figure 5A shows a representative image of free cancer cells in peritoneal lavage fluid; Figure 5B shows ROC curves for various predictive variables; Figure 5C shows the calibration curve for the RSA model; Figure 5D shows the confusion matrix for various predictive models; Figure 5E shows radar charts of performance indicators for different predictive models; Figure 5F shows a double-layered concentric circle plot of the clinical advantage of various predictive models; Figure 5G shows the log-rank test survival curve; and Figure 5H shows a prospective clinical study (ChiCTR1800014817). The charts show the treatment outcomes of patients divided into high-risk and low-risk groups according to the RSA model. 5I is a comparison chart of the incidence of cachexia in the high-risk and low-risk groups in the prospective clinical study (ChiCTR1800014817) using the RSA model. 5J is a waterfall chart of the treatment outcomes before and after conversion therapy for high-risk and low-risk patients in the RSA model in the prospective clinical study 44 (NCT03718624). 5K is an analysis chart of the incidence of cachexia in high-risk and low-risk patients in the RSA model in the prospective clinical study (NCT03718624).

[0061] Figure 6 This chart shows the non-invasive prediction and detection of peritoneal metastasis in patients with laminar cystic angina (LAGC) using a model based on peripheral blood 4-mRNA. Specifically, 6A shows the changes in the A260 / 280 ratio of peripheral blood samples at different time points; 6B is a nomogram showing the prediction of peritoneal metastasis in LAGC patients by combining 4-mRNA profiling with clinical characteristics; 6C shows the ROC curves of various prediction models in the training set; 6D shows the ROC curves of various prediction models in the validation set; 6E shows the confusion matrix of different prediction models in the training and validation sets; and 6F shows the calibration curve of the RSA model in the training set. 6G is the calibration curve of the RSA model in the validation set; 6H is the radar chart of the performance indicators of different prediction models in the training set; 6I is the radar chart of the performance indicators of different prediction models in the validation set; 6J is the double-layer concentric circle diagram of the clinical benefits of various prediction models in the training and validation sets; 6K is the log-rank test survival curve of patients in the training set; 6L is the log-rank test survival curve of patients in the validation set; 6N is the comparison chart of CT assessment effects among different models; 6O is the comparison analysis chart of pathological regression grading among different models; 6P is the comparison chart of cachexia incidence among different prediction models.

[0062] Figure 7This chart presents data on the prediction of GC-CY1 in peripheral blood samples using a 4-mRNA-based model and its validation of peritoneal recurrence in patients with negative tumor markers after radical surgery. Specifically, 7A shows the ROC curves of different prediction models in the GC-CY1 patient validation set; 7B shows the calibration curve of the RSA model used for GC-CY1 validation; 7C is a radar chart comparing the performance indicators of various prediction models for GC-CY1 patients; 7D is a log-rank test survival curve for GC-CY1 patients; 7E is a double-layer concentric circle plot showing the clinical benefit of using clinical characteristics and 4-mRNA data to predict GC-CY1; 7F is a confusion matrix plot showing the combined prediction of GC-CY1 using clinical characteristics and 4-mRNA data; 7G is a prediction chart of peritoneal recurrence after radical surgery in patients with LAGC using the RSA model; 7H shows the ROC curves of different models predicting peritoneal metastasis in patients with negative tumor markers; 7I shows the ROC curves of different models predicting peritoneal recurrence after radical surgery in patients with LAGC; and 7J compares the performance of different prediction models in... Radar charts of performance metrics in patients with negative tumor markers; 7K is a radar chart comparing the performance metrics of the model in patients with peritoneal recurrence and metastasis after radical resection; 7L is a double-layer concentric circle plot of the clinical benefit of the RSA model in predicting postoperative peritoneal recurrence in 46 patients with negative tumor markers; 7M is a confusion matrix plot of the RSA model in predicting postoperative peritoneal recurrence in patients with negative tumor markers; 7N is a comparison plot of the consistency between the RSA model and laparoscopic exploration in predicting peritoneal metastasis in the prospective observational study (NCT06478394); 7O is a ROC curve plot of various prediction models for peritoneal metastasis in the prospective observational study (NCT06478394); 7P is an overall survival curve plot of patients with peritoneal recurrence after radical resection of LAGC in the validation group; 7Q is an overall survival curve plot of patients with negative tumor markers in the validation group; 7R is a calibration curve plot of the prediction of peritoneal recurrence after radical resection of LAGC in the validation group; 7S is a calibration curve plot of the prediction of prognosis in patients with negative tumor markers in the validation group.

[0063] Figure 8This diagram illustrates the effect of 4-mRNA on HGC-27 cell metastasis in vitro and in vivo. Figures 8A-D show the migration ability of HGC-27 cells after knockdown of BUB1, SPC25, CT83, and MMP3, respectively. Figures 8E-H show the invasion and metastatic potential of HGC-27 cells after knockdown of BUB1, SPC25, CT83, and MMP3. Figure 8I represents the peritoneal metastatic tumors of HGC-27 cells after intraperitoneal injection of gene-specific knockdown in mice. Figure 8J represents the peritoneal metastatic tumors of HGC-27 cells after intraperitoneal injection of knockdown of BUB1, SPC25, CT83, and MMP3 in mice. The quantitative images are as follows: 8K-L is the immunohistochemical (IHC) image of peritoneal metastatic tumors in mice injected with BUB1 and SPC25 to knock down HGC-27 cells (left), and the IHC staining quantitative images of MMP9, N-cadherin, E-cadherin and vimentin in each group (right); 8M-8P is the immunohistochemical (IHC) image of peritoneal metastatic tumors in mice injected with CT83 (8M) and MMP3 (8O) to knock down HGC-27 cells, and the IHC staining quantitative images of MMP9, N-cadherin, E-cadherin and vimentin in each group (8N, 8P). Detailed Implementation

[0064] The following provides definitions for some of the terms used in this specification. Unless otherwise stated, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0065] This invention provides the application of reagents for detecting the expression level of biomarkers in samples in the preparation of products for diagnosing gastric cancer / diagnosing gastric cancer metastasis / predicting gastric cancer prognosis, wherein the biomarkers include any one or more of BUB1, SPC25, CT83 and MMP3.

[0066] In some embodiments, BUB1, SPC25, CT83, or MMP3 includes wild-type, mutant, or fragments thereof. The term encompasses full-length, unprocessed BUB1, SPC25, CT83, or MMP3, as well as any form of BUB1, SPC25, CT83, or MMP3 derived from cells and processed. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of BUB1, SPC25, CT83, or MMP3. The term encompasses BUB1, SPC25, CT83, or MMP3 from, for example, human and any other vertebrate sources, including mammals such as primates and rodents (e.g., mice and rats), BUB1 gene ID: 699, SPC25 gene ID: 57405, CT83 gene ID: 203413, and MMP3 gene ID: 4314.

[0067] The reagent also includes detectable substances.

[0068] In some embodiments, a detectable substance refers to any substance that can be detected by fluorescence, spectroscopy, photochemistry, biochemistry, immunology, electrical, optical, or chemical means. Preferably, such substances are suitable for immunological detection (e.g., enzyme-linked immunosorbent assay, radioimmunoassay, fluorescence immunoassay, chemiluminescence immunoassay, etc.). Such substances are well known in the art and include, but are not limited to, enzymes (e.g., horseradish peroxidase, alkaline phosphatase, β-galactosidase, urease, glucose oxidase, etc.) and radionuclides (e.g.,...). 3 H, 125I , 35 S, 14 C or 32 P), fluorescent dyes (e.g., fluorescein isothiocyanate (FITC), fluorescein, tetramethylrhodamine isothiocyanate (TRITC), phycoerythrin (PE), Texas red, rhodamine, quantum dots or cyanine dye derivatives (e.g., Cy7, Alexa 750)), acridine esters, magnetic beads, calorimetric markers such as colloidal gold or colored glass or plastic (e.g., polystyrene, polypropylene, latex, etc.) beads, and biotin for binding avidin (e.g., streptavidin) modified with the above markers.

[0069] This invention provides a model for diagnosing gastric cancer metastasis, wherein the model's indicators include the levels of biomarkers, and the biomarkers include any one or more of BUB1, SPC25, CT83, and MMP3.

[0070] The model's indicators also include T stage, N stage, and pathological type.

[0071] The model is a nodal graph model.

[0072] In some implementations, the nomogram model, also known as the nomogram model, is a clinical prediction model that integrates multiple predictive indicators and then uses scaled line segments to draw them on the same plane at a certain scale. This is used to express the relationships between the variables in the prediction model and to assess disease risk.

[0073] In some implementations, a logistic regression formula is used to construct the model. The sample is a fresh frozen sample. The model calculation formula is: Probability of gastric cancer metastasis = [(4.855×4-mRNA)+(4.139×T stage)+(4.140×N stage)+(3.147×pathological type)+(-9.961)]. The model is visualized in the form of a nomogram. The nomogram model includes 7 straight lines arranged from top to bottom and parallel to each other. Each straight line represents a scale with markings.

[0074] The first row contains a fraction scale (Points), with values ​​ranging from 0 to 100.

[0075] The second row is the pathology scale. When the pathology type is High / Median, the score is 0, and when the pathology type is Low / None, the score is 74.

[0076] The third row is the T-stage scale (T.stage). When the T-stage is T2 / T3, the score is 0, and when the T-stage is T4, the score is 92.

[0077] The fourth row is the N-stage scale (N.stage). When the N-stage is N0, the score is 0, and when the N-stage is N+, the score is 92.

[0078] The fifth row is the 4-mRNA scale (X4.panel). When the 4-mRNA expression level is low, the score is 0, and when the 4-mRNA expression level is high, the score is 100.

[0079] The sixth row is the total points scale, with a value range of 0-400.

[0080] The seventh line is the risk of metastasis scale, with a value range of 0-0.95.

[0081] In some implementations, a model is constructed using a logistic regression formula with peripheral blood samples as the sample. The model calculation formula is: Probability of gastric cancer metastasis = [(6.558 × 4 - mRNA) + (8.467 × T stage) + (8.054 × N stage) + (8.164 × pathological type) - 14.876]. This model is visualized in the form of a nomogram, which consists of 7 straight lines arranged from top to bottom and parallel to each other. Each straight line represents a scale with markings.

[0082] The first row contains a fraction scale (Points), with values ​​ranging from 0 to 100.

[0083] The second row is the pathology scale. When the pathology type is High / Median, the score is 0, and when the pathology type is Low / None, the score is 97.

[0084] The third row is the T-stage scale (T.stage). When the T-stage is T2 / T3, the score is 0, and when the T-stage is T4, the score is 100.

[0085] The fourth row is the N-stage scale (N.stage). When the N-stage is N0, the score is 0, and when the N-stage is N+, the score is 98.

[0086] The fifth row is the 4-mRNA scale (X4.panel). When the 4-mRNA expression level is low, the score is 0, and when the 4-mRNA expression level is high, the score is 90.

[0087] The sixth row is the total points scale, with a value range of 0-450.

[0088] The seventh line is the risk of metastasis scale, with a value range of 0.1-0.45.

[0089] The present invention provides a pharmaceutical composition comprising an inhibitor of a biomarker, the biomarker being any one or more of BUB1, SPC25, CT83 and MMP3.

[0090] The pharmaceutical composition also includes other drugs.

[0091] In some implementations, other drugs include other drugs for treating gastric cancer, including but not limited to chemotherapy drugs and targeted therapies. Chemotherapy drugs include oxaliplatin and fluorouracil preparations, such as 5-fluorouracil, or capecitabine, and tegafur. Additionally, targeted therapies for treating gastric cancer include apatinib.

[0092] The pharmaceutical composition also includes a pharmaceutically acceptable carrier.

[0093] In some embodiments, a pharmaceutically acceptable carrier refers to a non-toxic material that does not interact with the active component of the pharmaceutical composition. The pharmaceutically acceptable carrier refers to a natural or synthetic, organic or inorganic component that, when used in combination with the active component, facilitates application. In some embodiments, a pharmaceutically acceptable carrier comprises one or more compatible solid or liquid fillers, diluents, or encapsulating substances suitable for administration to a patient. The components of the pharmaceutical compositions of this application generally do not exhibit interactions that significantly affect the desired therapeutic effect.

[0094] The invention is further illustrated below with reference to specific embodiments. It should be understood that the specific embodiments described herein are by way of example and are not intended to limit the invention. The main features of the invention can be used in various embodiments without departing from the scope of the invention.

[0095] Example

[0096] 1. Materials and Methods

[0097] Screening mRNA biomarkers

[0098] Figure 1 This paper outlines the process of biomarker discovery and validation for peritoneal metastasis in gastric cancer (LAGC) conducted in this study. First, several datasets were analyzed for biomarker identification, including the GSE62254 cohort from the Gene Expression Integration (GEO) database, comprising 300 gastric cancer patients (121 with peritoneal metastasis and 179 without). Additionally, data from the Cancer Genome Atlas (TCGA) cohort, including 412 gastric cancer tissue samples and 36 normal tissue samples, were utilized. Furthermore, transcriptome sequencing data were collected from 17 LAGC patients (5 with peritoneal metastasis and 12 with primary lesions but no peritoneal metastasis) at the Fourth Hospital of Hebei Medical University (FHHMU).

[0099] To further validate the clinical applicability of these biomarkers as predictors of peritoneal metastasis in gastric cancer, various specimens (fresh frozen gastroscopy biopsies and peripheral blood samples) from multiple large clinical centers in China were utilized. Forty-two pairs of fresh frozen cancer tissue specimens and 33 peripheral blood samples were collected from patients with and without peritoneal metastasis at the Fourth Hospital of Hebei Medical University (FHHMU) using a 1:1 propensity score matching method. The expression of selected mRNAs was assessed using qRT-PCR. In addition, 36 pairs of formalin-fixed specimens, also matched 1:1 propensity score, were analyzed to further elucidate the expression of 4-mRNA by immunohistochemistry. These samples were collected at different time points at FHHMU.

[0100] Validating mRNA biomarkers

[0101] First, a cohort of 313 gastric cancer patients from three treatment centers (FHHMU, Shijiazhuang People's Hospital (SJZPH), and Hengshui People's Hospital (HSCPH)) was used as the training set for predicting peritoneal metastasis. An external validation cohort included 131 patients from three additional centers: Baoding Central Hospital (BDCH), Nanjing University Jinling Hospital (NJJLH), and Wuhan University People's Hospital (WHPH). All patients underwent laparoscopic exploration, and peritoneal metastasis was confirmed by biopsy. In addition, data were collected from 43 gastric cancer patients from the aforementioned six centers who received conversion therapy, including hyperthermic intraperitoneal chemotherapy (HIPEC) combined with neoadjuvant intraperitoneal and systemic (NIPS) paclitaxel. This was done to evaluate whether the predictive model could identify patients who might benefit from this conversion therapy.

[0102] To evaluate the ability of nomograms to predict peritoneal exfoliative cytology (GC-CY1) gastric cancer patients, data from 159 patients from two centers, FHHMU and SJZPH, were analyzed. All patients underwent laparoscopic exploration and peritoneal exfoliative cytology, and GC-CY1 cases were confirmed by the presence of free cancer cells in the peritoneal cavity.

[0103] To facilitate the transition of mRNA sequencing from tissue specimens to non-invasive liquid biopsy, a retrospective analysis of serum samples from LAGC patients at six centers was conducted. The training cohort comprised 215 LAGC patients from three treatment centers (FHHMU, SJZPH, and HSCPH), while the validation cohort included serum samples from 127 patients from the remaining three centers (BDCH, NJJLH, and WHPH). Additionally, data from 41 patients at the six centers who received HIPEC in combination with NIPS paclitaxel were collected to assess the predictive accuracy of treatment outcomes.

[0104] A prospective clinical observational study (NCT06478394) was also registered to evaluate the effectiveness of mRNA prognostics in predicting peritoneal metastasis. This study began recruiting patients from January to June 2024. Peripheral blood samples were collected from all participants for qRT-PCR analysis prior to laparoscopic exploration. Calibrated bar charts were used to predict the likelihood of peritoneal metastasis, which were then compared with the results of laparoscopic exploration.

[0105] All patients were monitored for recurrence or progression according to gastric cancer treatment guidelines, using laboratory tests, endoscopy, and abdominal and pelvic CT scans. Tissue specimens from all patient groups were frozen in liquid nitrogen and stored at -80°C. Surgical specimens were processed according to the Chinese Society of Clinical Oncology guidelines, and tumor and lymph node staging was performed according to AJCC 8th edition. All procedures were conducted in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants, with approval from the institutional review committees of the participating institutions.

[0106] Treatment response assessment

[0107] In this study, all newly diagnosed gastric cancer patients underwent abdominal CT scans to assess tumor size. Except for those with pyloric obstruction, patients were instructed to drink 800-1000 ml of water 30 minutes before the scan. Intramuscular injection of scopolamine bromide was administered to reduce gastrointestinal motility, ensure adequate gastric distension, and prevent gastric wall thickening, thereby improving lesion localization and observation. This method allows for accurate identification of target lesions on CT scans, enabling precise measurement of tumor diameter and thickness to assess changes after neoadjuvant therapy. For each patient, the axial image showing the largest lesion was selected to measure the longest tumor diameter, and perigastric lymph node lesions with a short diameter >1.5 cm were recorded. All measurements were performed by experienced radiologists. The sum of the diameters of all target lesions at baseline (longest diameter of non-lymph node lesions, shortest axis of lymph node lesions) was used as a reference for subsequent assessments. The final measurement was the average of three measurements, used to evaluate the treatment effect before and after conversion therapy.

[0108] The evaluation criteria for tumor response are based on the RECIST version 1.1, which categorizes response into complete response (CR), partial response (PR), stable disease (SD), and disease progression (PD). CR is defined as the disappearance of all target lesions; PR is defined as a reduction of at least 30% in the total diameter of target lesions from baseline; PD is defined as an increase of at least 20% in the total diameter of target lesions from baseline; and SD is the change that did not meet the criteria for PR or PD. The objective response rate (ORR) is the proportion of patients achieving CR and PR, while the disease control rate (DCR) includes patients achieving CR, PR, or SD.

[0109] Pathological sections from patients who underwent radical surgical resection were reviewed by two pathologists, and tumor regression was graded using the Tumor Regression Grade (TRG) scale according to the AJCC / CAP guidelines. TRG 0 was defined as the absence of residual tumor cells in multiple consecutive sections under a microscope; TRG 1 was defined as a few discrete clusters of tumor cells under the plasma membrane; TRG 2 was defined as the presence of fibrosis and residual tumor cell debris within the lesion; and TRG 3 was defined as the presence of a stable, non-fibrotic cell population within the lesion.

[0110] Diagnostic criteria for cancer cachexia

[0111] The diagnostic criteria for cancer cachexia are as follows: (1) unintentional weight loss of more than 5% within 6 months; (2) Body mass index (BMI) <20 kg / m² in Europe and America. 2 Chinese body mass index <18.5 kg / m² 2 (2) Weight loss of more than 2% in any one of the following within 6 months; (3) Limb skeletal muscle index indicating sarcopenia (male <7.26kg / m 2 Women <5.45kg / m 2(4) There is a decrease in food intake and / or systemic inflammation.

[0112] RNA extraction and gene expression analysis

[0113] Total RNA was extracted from fresh frozen surgical tissue using TRIzol reagent (Invitrogen, Frederick, Massachusetts, USA) according to the manufacturer's instructions. For serum samples, total RNA was isolated using the PAXgene Blood RNA Kit (Qiagen, Hilden, Germany). The extracted RNA was then reverse transcribed into cDNA using the GoScript Reverse Transcription System Kit (Promega) according to the manufacturer's protocol. Quantitative reverse transcription PCR (qRT-PCR) was subsequently performed. Using 2^ -ΔΔCT The method calculates the relative expression level of the target gene and normalizes it using GAPDH as an internal reference, where ΔCT represents the difference between the CT values ​​of the target gene and GAPDH.

[0114] Protein-protein interaction (PPI) network analysis

[0115] A human PPI network was constructed using the STRING database (https: / / string-db.org). Enrichr (https: / / maayanlab.cloud / Enrichr / ) was used to perform gene set and pathway analysis on the four-gene candidate list.

[0116] Wound healing test

[0117] Scrape a monolayer of HGC-27 cells transfected with siRNA using a 10 μL pipette tip. Measure the distance of the scratch healing migration under a microscope 24-48 hours later, using an image taken at 0 hours as a control. Standardize the data and calculate the relative migration rate.

[0118] Transwell migration and invasion detection

[0119] Prepare the Transwell insert according to the manufacturer's instructions, either uncoated (for migration assays) or Matrigel-coated (for invasion assays). Transfect HGC-27 cells (4 × 10⁻⁶) 4 Inoculate into the upper chamber and incubate for 24–48 hours. Count the cells that have migrated or invaded the lower surface in five randomly selected microscope fields.

[0120] Western Blotting

[0121] After transfection, HGC-27 cells were lysed with RIPA buffer containing 1% PMSF, total protein was extracted, denatured proteins were separated by 10% SDS-PAGE, transferred to PVDF membrane (Seven, Beijing), and detected with BUB1, SPC25, CT83, MMP3, and GAPDH antibodies. Immunoreactivity bands were detected with ECL Plus reagent (Solarbio).

[0122] Immunohistochemistry (IHC)

[0123] Tissue sections were dewaxed at 60°C for 2 hours and treated with xylene. Antigen retrieval was performed using EDTA, and endogenous peroxidase activity was quenched with 3% hydrogen peroxide. Sections were then blocked with 5% bovine serum albumin (BSA) and incubated overnight with primary antibody at 4°C. The next day, secondary antibody was applied for antigen detection using diaminobenzidine (DAB), followed by nuclear counterstaining with hematoxylin. The percentage of positive cells was calculated using ImageJ software. Scoring criteria were based on the percentage of positive cells: 1 for ≤25%, 2 for 26%–50%, 3 for 51%–75%, and 4 for >75%. Staining intensity was also scored: 1 for no staining, 2 for light brown, 3 for medium brown, and 4 for dark brown. The final immunohistochemical (IHC) score was obtained by multiplying the coverage percentage by the staining intensity score.

[0124] Animal experiments

[0125] In vivo experiments were approved by the Experimental Animal Management Committee (IACUC) of the Fourth Affiliated Hospital of Hebei Medical University (IACUC Approval No.: 20240255). All BALB / c mice were obtained from Beijing Spiford Biotechnology Co., Ltd. To evaluate the effect of gene knockdown on peritoneal metastasis, HGC-27 cells transfected with lentivirus encoding siRNA targeting a specific gene were intraperitoneally injected into mice. Mice were sacrificed when a significant difference was observed between the experimental and control groups, and the results were statistically analyzed. Each mouse received only one treatment.

[0126] Statistical analysis

[0127] Statistical analyses were performed using IBM SPSS (v23), R (v3.6.3), and GraphPad Prism (v8.0). Important clinicopathological variables and mRNA classifiers were identified through univariate and multivariate logistic regression analyses. Variables showing importance in univariate analyses were included in multivariate regressions. In the discovery phase, the Wilcoxon rank-sum test with Bonferroni correction was used to examine differential gene expression between peritoneal and non-peritoneal metastasis groups. In the clinical validation phase, a gene-based risk score was developed using logistic regression with backward elimination. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values, calculated via the pROC package in R. ROC curve comparisons were performed using the DeLong test. The sensitivity, specificity, positive and negative predictive values, precision, and accuracy of the 4-mRNA biomarker groups across all cohorts were determined using the reportROC package, with results presented as a confusion matrix. The optimal ROC curve cutoff was determined using the Youden index. Individuals were grouped into high-risk or low-risk groups using the Youden index and median risk score to predict peritoneal metastasis. Disease-free survival (DFS) was analyzed using the Kaplan-Meier method, defined as the time from radical surgery to disease recurrence or death due to disease progression. Patients who survived without recurrence were re-evaluated at the 5-year milestone, while patients lost to follow-up without evidence of recurrence within 5 years were evaluated at their last visit. Statistical significance was defined as P < 0.05.

[0128] 2. Experimental Results

[0129] Identification of LAGC peritoneal metastasis-related mRNAs

[0130] Analysis identified four differentially expressed genes—BUB1, SPC25, CT83, and MMP3. Figure 2 A). These genes are upregulated in cancer tissue with peritoneal metastasis compared to cancer tissue without peritoneal metastasis. Figure 2 BD).

[0131] To this end, a pilot cohort was established at FHHMU to further validate the expression of 4-mRNAs (BUB1, SPC25, CT83, and MMP3) in tissue and peripheral blood samples. To minimize the influence of clinical variables, a 1:1 propensity score matching method was used, selecting 42 pairs of primary cancer tissue samples with and without peritoneal metastases. qRT-PCR results confirmed that the four candidate mRNAs were highly expressed in cancer tissues with peritoneal metastases. Figure 2 E), and their expression levels were significantly correlated with the patient's clinicopathological features. Figure 2 N). Similar results were observed in 33 matched peripheral blood samples.Figure 2 F). Furthermore, Western blot analysis showed that these four genes were significantly upregulated in cancer tissues with peritoneal metastases. Figure 2 G, Figure 2 QT).

[0132] Furthermore, analysis of IHC staining in 36 pairs of patients (1:1 propensity score matched) showed that mRNA expression levels were significantly higher in cancer tissues with peritoneal metastases compared to those without peritoneal metastasis (P<0.05). Figure 2 HI). Follow-up data further indicated that patients with high IHC expression had worse 5-year overall survival (OS) and disease-free survival (DFS) compared to patients with low IHC expression. Figure 2 JM, Figure 2 UX). Kaplan-Meier survival analysis (https: / / kmplot.com / analysis / ) confirmed these results. Figure 2 Furthermore, a protein-protein interaction network was constructed using the STRING database (https: / / www.string-db.org / ) and visualized using Cytoscape 3.9.1, elucidating the potential roles of these mRNAs in gastric cancer. Figure 3 P).

[0133] 4-mRNA predicts peritoneal metastasis in fresh frozen samples from LAGC patients

[0134] First, qRT-PCR was used to quantitatively assess the expression of 4-mRNAs (BUB1, SPC25, CT83, and MMP3) in fresh frozen samples from 313 patients from three centers (FHHMU, SJZPH, and HSCPH). These samples constituted the training set (4-mRNA primer sequences are shown in Table 1). Multivariate logistic regression analysis showed that these four mRNAs were independent risk factors for peritoneal metastasis in patients with LAGC (all P < 0.05). Further analysis was conducted to examine the effects of other clinical characteristics and the 4-mRNA group on the risk of peritoneal metastasis in LAGC patients. The results showed that the expression of 4-mRNA combination (OR = 4.855, 95% CI: 2.508–9.396, P < 0.001), T stage (OR = 4.139, 95% CI: 1.389–12.329, P = 0.011), and N stage (OR = 4.140, 95% CI: 4.85 ...

[0135] 1.193-14.365, P=0.025) and pathological type (OR=3.147, 95%CI: 1.021-9.696, P=0.046) were independent risk factors for peritoneal metastasis in LAGC. Figure 3A). The probability of peritoneal metastasis was calculated using logistic regression: [(4.855×4-mRNA)+(4.139×T stage)+(4.140×N stage)+(3.147×pathological type)+(-9.961)], with a threshold of 0.1373112. This model was visualized as a nomogram, providing an intuitive method for predicting the risk of peritoneal metastasis in patients with laminar angina pectoris (LAGC). Name B).

[0136] Table 1 4-mRNA primer sequences

[0137] Sequence BUB1_F TGTCCTTCAATACATACAGTGGGT (SEQ ID NO: 1) BUB1_R GGAACTCTCTGGGTTCAGCC (SEQ ID NO: 2) SPC25_F AGCGAATGCAGAGAGGTTGA (SEQ ID NO: 3) SPC25_R CCTCAAGATGAGGGGCACTA (SEQ ID NO: 4) CT83_F ACTCCTAGCGAGCAGCATTC (SEQ ID NO: 5) CT83_R CCCGAGAGAGGTCGTAGACT (SEQ ID NO: 6) MMP3_F TGAGGACACCAGCATGAACC (SEQ ID NO: 7) MMP3_R ACTTCGGGATGCCAGGAAAG (SEQ ID NO: 8) Figure 3

[0138] By combining the 4-mRNA genome with clinical variables, a predictive model called the risk stratification assessment (RSA) model was developed. This model showed strong predictive ability for peritoneal metastasis, with an AUC of 0.836 (95% CI: 0.789-0.884, P<0.001). Figure 3 C, 3E (above), 3H). The DeLong test showed that the AUC of the RSA model was significantly higher than that of the clinical model in the training set (0.836 vs. 0.711; P = 0.001). Furthermore, the calibration curve of this model further emphasizes its predictive accuracy. Figure 3 F). Patients were divided into low-risk and high-risk groups based on the threshold determined by the Youden Index. The 3-year overall survival (OS) of high-risk patients was significantly worse than that of low-risk patients (38.6% vs. 57.2%, P<0.001). Figure 3 K).

[0139] These findings were validated in a separate validation set of 131 LAGC patients from three additional centers, using the same statistical parameters as the training set. The RSA model based on 4-mRNA and clinical characteristics performed better in predicting peritoneal metastasis than either 4-mRNA or clinical characteristics alone (RSA model: AUC = 0.882, 95% CI: 0.817–0.948, P < 0.001; 4-mRNA: AUC = 0.811, 95% CI: 0.714–0.907, P < 0.001; clinical characteristics: AUC = 0.788, 95% CI: 0.702–0.873, P < 0.001), consistent with the training set results. Figure 3 D, 3E (below) Figure 3 I).

[0140] Calibration curve analysis further confirmed the model's predictive accuracy on the validation set. Notably, the RSA model effectively distinguished the 3-year overall survival (OS) between the high-risk and low-risk groups (41.4% vs. 60.0%, P < 0.001), supporting the results on the training set. Figure 3Furthermore, stratified analysis based on HER2 and PDL1 expression levels in all patients across the training and validation sets showed that the RSA model consistently outperformed models based solely on clinical characteristics or the 4-mRNA group in predicting peritoneal metastasis. Figure 3 MO).

[0141] To more clearly illustrate the clinical significance of the RSA model in predicting peritoneal metastasis, the results are presented using a double-layered concentric circle plot. In the training set, the plot shows that 50.8% of patients with laparoscopic laparoscopic gastrostomy (LAGC) had a high risk of peritoneal metastasis, while 49.2% had a low risk. Following laparoscopic exploration, peritoneal metastasis was found in 15.3% (48 out of 313) of high-risk patients and 3.5% (11 out of 313) of low-risk patients. Notably, using the RSA model, peritoneal metastasis was found in only 2.2% (7 out of 313) of low-risk patients, compared to 16.6% (52 out of 313) in high-risk patients. Figure 3 The proportion of low-risk patients identified by the clinical characteristic model was higher (55.3% vs. 49.2%) than that identified by the clinical characteristic model. Consistent results were observed in the external validation set. Figure 4 (J below) This further confirms the effectiveness of the RSA model in predicting peritoneal metastasis in LAGC patients and its clinical relevance.

[0142] 4-mRNA predicts the efficacy of conversion therapy and cachexia in patients with peritoneal metastases from gastric cancer.

[0143] This study systematically analyzed the treatment strategies for all patients with peritoneal metastases in both the training and validation sets. Specifically, it investigated 43 patients with peritoneal metastatic gastric cancer who received HIPEC combined with neoadjuvant peritoneal infusion and systemic chemotherapy (NIPS). The detailed treatment process is as follows: Figure 4 As shown in Figure A. Of the 43 patients, 16 (37.2%) were classified as low-risk by the RSA model, and 27 (62.8%) were classified as high-risk. The waterfall plot shows that the objective response rate (ORR) in the high-risk group was 3.7% (1 / 27), and the disease control rate (DCR) was 48.1% (13 / 27). In contrast, the ORR in the low-risk group was 62.5% (10 / 16), and the DCR was 87.5% (14 / 16), a statistically significant difference (ORR: P < 0.001, DCR: P = 0.006). Figure 4 B, 4G, 4I). It is noteworthy that the success rate of conversion therapy was higher in the low-risk group than in the high-risk group (B, 4G, 4I). Figure 4 H), the proportion of patients in the low-risk group reaching TRG grade 1 was higher ( Figure 4 K). Figure 4DF summarized the comparison of pathological regression grades, changes in local lesions on abdominal CT, and changes in peritoneal metastases before and after conversion therapy in two risk groups based on the RSA model. ROC curve analysis further confirmed that the RSA model was effective in predicting the short-term efficacy of conversion therapy (AUC = 0.866). Figure 4 L) and the success rate of conversion therapy (AUC = 0.770, Figure 4 The M) group was superior to the clinical characteristics and 4-mRNA group.

[0144] Multiple studies have shown that patients with advanced gastric cancer and distant metastases are prone to cachexia during treatment, which significantly impacts clinical efficacy. Therefore, identifying peritoneal metastatic gastric cancer patients at risk of cachexia during conversion therapy is crucial. To this end, we used existing consensus guidelines for cachexia classification to measure skeletal muscle function in 43 patients with peritoneal metastatic gastric cancer undergoing conversion therapy. Figure 4 C). The RSA model revealed that the proportion of cachexia in high-risk patients was 63.0% (17 / 27), while the proportion in low-risk patients was 31.3% (5 / 16), a statistically significant difference (P = 0.044). Figure 4 GH, 4J). Further ROC curve analysis supported these findings, showing that the RSA model outperformed clinical characteristics (AUC = 0.680) and the 4-mRNA group (AUC = 0.714) in predicting cachexia (AUC = 0.837). Figure 5 N). Furthermore, in the RSA model, the 3-year overall survival (OS) of high-risk patients was significantly worse than that of low-risk patients (14.8% vs. 37.5%, P = 0.021); Figure 5 O).

[0145] 4-mRNA predicts peritoneal free cancer cells in fresh frozen specimens from LAGC patients

[0146] Gastric cancer with positive peritoneal lavage fluid cytology (GC-CY1) is a unique type of peritoneal metastasis, currently classified as stage IV in the UICC / AJCC TNM staging system 8th edition and the Japanese classification of gastric cancer 15th edition. GC-CY1 is characterized by the absence of visible peritoneal metastasis within the abdominal cavity, but the detection of cancer cells in the peritoneal lavage fluid. Figure 5 A). It was hypothesized that the RSA model could also predict GC-CY1 cases. To test this, 159 patients with LAGC who underwent laparoscopic exploration and abdominal exfoliative cytology were recruited. The RSA model significantly outperformed clinical characteristics (AUC = 0.761, 95% CI: 0.684–0.838) and 4-mRNA (AUC = 0.830, 95% CI: 0.751–0.909) in predicting GC-CY1 outcomes (AUC = 0.888, 95% CI: 0.834–0.941).Figure 5 B).

[0147] Confusion matrices and radar plots further demonstrate that, compared to clinical characteristics and the 4-mRNA genome, the RSA model exhibits superior discriminative ability, sensitivity, and specificity in predicting GC-CY1. Figure 5 DE). Calibration curve analysis also verified the model's predictive accuracy on the validation set. Figure 5 C). The double-layer concentric circle plot shows that, compared with the clinical model, the RSA model identified more patients in the low-risk group (61.6% vs. 53.3%) and a higher proportion of GC-CY1 patients in the high-risk group (18.9% vs. 17.4%). Figure 5 F). Consistent with previous cohort follow-up results, stratification into high-risk and low-risk groups was performed based on the nomogram. The results showed that the 3-year overall survival (OS) in the high-risk group was significantly lower than that in the low-risk group (41.0% vs. 62.2%, P = 0.0019). Figure 5 G).

[0148] To verify that the RSA model can also predict the efficacy of conversion therapy in GC-CY1 patients, two prospective neoadjuvant therapy cohorts conducted by FHHMU were included (NCT03718624 (n=36) and ChiCTR1800014817 (n=38)). In both cohorts, results showed that patients classified as high-risk by the RSA model had significantly worse prognoses in terms of ORR, DCR, and conversion success rate compared to low-risk patients (ChiCTR1800014817: ORR 23.5% vs. 76.2%, P = 0.004; DCR 76.5% vs. 100.0%, P = 0.032; conversion success rate 11.6% vs. 66.7%, P = 0.001; NCT03718624: ORR 57.1% vs. 95.5%, P = 0.008; DCR 85.7% vs. 100.0%, P = 0.144; conversion success rate 50.0% vs. 95.5%, P = 0.003). Figure 5 H, 5J). Furthermore, this difference persisted in later follow-up, with the incidence of cachexia in high-risk patients being higher than in low-risk patients in both groups (ChiCTR1800014817: 70.6% vs. 33.3%, P = 0.022). Figure 6 I; NCT03718624: 78.6% vs. 27.3%, P = 0.003, Figure 6 K).

[0149] 4-mRNA predicts peritoneal metastasis in peripheral blood samples from LAGC patients

[0150] First, 42 peripheral blood samples from LAGGC were randomly selected for quality control analysis to confirm that the A260 / 280 and A260 / 230 ratios were within the normal range at different time points, making them suitable for qRT-PCR analysis. Figure 6 A). Using a training set of 215 samples from FHHMU, SJZPH, and HSCPH, a nomogram for predicting peritoneal metastasis in patients with LAGC was constructed by applying multivariate logistic regression analysis based on 4-mRNA and clinical characteristics. Figure 6 B). The probability of peritoneal metastasis risk was calculated using the logistic regression coefficient formula: [(6.558×4-mRNA group)+(8.467×T stage)+(8.054×N stage)+(8.164×pathological type)-14.876], with a threshold of 0.1838748.

[0151] ROC curve analysis of the training set showed that the AUC for predicting peritoneal metastasis based on clinical characteristics was 0.746 (95% CI: 0.677-0.816), and the AUC for predicting peritoneal metastasis based on 4-mRNA was 0.778 (95% CI: 0.694-0.863), both lower than the AUC of 0.857 (95% CI: 0.803-0.911) for the RSA model. Figure 6 C). Confusion matrices and radar charts further demonstrate that the RSA model outperforms clinical and 4-mRNA models in terms of sensitivity, specificity, and accuracy in predicting peritoneal metastasis. Figure 6 E, 6H). Calibration curve analysis confirmed the good fit of the RSA model (E, 6H). Figure 6 F). Two-layer concentric circle analysis showed that the RSA model had greater clinical benefit than the single model, with the detection rate of no peritoneal metastasis decreasing from 36.8% to 21.9% in the high-risk group and increasing from 45.1% to 60.0% in the low-risk group. Figure 6 (J). When patients were divided into low-risk and high-risk groups according to the Youden index, the 3-year overall survival (OS) of patients in the high-risk group was significantly lower than that in the low-risk group (29.6% vs. 68.7%, P<0.001). Figure 6 K).

[0152] These findings were further validated in external validation sets from three additional diagnostic and treatment centers. ROC curve analysis showed that the RSA model had the highest AUC among the three models (AUC = 0.883, 95% CI: 0.824–0.941, P < 0.001). Figure 6 D). Radar chart and confusion matrix analysis confirmed that the RSA model was superior to the 4-mRNA and clinical feature models in terms of sensitivity, specificity, and accuracy in predicting peritoneal metastasis. Figure 6 E, 6I). Calibration curve analysis also demonstrates the superior predictive performance of the RSA model (E, 6I).Figure 6 These results demonstrate that 4-mRNA, initially developed for tissue analysis, can be effectively applied to liquid biopsy detection of peripheral blood samples. The RSA model, combining serum 4-mRNA data with clinical characteristics, can effectively predict peritoneal metastasis in patients with LAGC. The double-layer concentric circle plot further confirms the potential of the RSA model in enhancing clinical decision-making and optimizing patient management. Figure 6 Furthermore, follow-up data showed that the 3-year overall survival (OS) rate in the high-risk group was significantly lower than that in the low-risk group (34.0% vs. 62.2%, P = 0.0028). Figure 7 L).

[0153] To further evaluate the potential of the RSA model as a tool for predicting the efficacy of conversion therapy in patients with peritoneal metastases from gastric cancer, an analysis was conducted on 41 patients who received HIPEC combined with NIPS. Detailed treatment regimens are as follows: Figure 7 As shown in M. It is noteworthy that models based solely on clinical characteristics or 4-mRNA are ineffective in identifying patients who may benefit from translational therapy. In contrast, the RSA model successfully distinguished between high-risk and low-risk groups and identified the risk of cachexia (…). Figure 7 NP).

[0154] 4-mRNA predicts GC-CY1 and peritoneal recurrence in peripheral blood samples from patients with LAGC.

[0155] Seventy-six patients from FHHMU were analyzed. Of these, 15 patients (19.7%) were diagnosed with GC-CY1 following laparoscopic exploration and peritoneal lavage fluid examination. Nomograms developed from the training set validated that the RSA model (AUC = 0.817, 95% CI: 0.714–0.921) outperformed the clinical characteristic model (AUC = 0.686, 95% CI: 0.548–0.825) and 4-mRNA (AUC = 0.789, 95% CI: 0.667–0.911) in predicting GC-CY1. Figure 7 A). Calibration curve ( Figure 7 B) and confusion matrix ( Figure 7 (C, 7F) further confirmed that the RSA model had higher sensitivity, specificity, and accuracy compared to the clinical characteristic model and the 4-mRNA group. Furthermore, the RSA model was more effective than other models in accurately identifying high-risk patients. Figure 7 E). Furthermore, the Log-rank test showed that the 3-year overall survival (OS) in the high-risk group was significantly lower than that in the low-risk group (36.4% vs. 55.8%, P = 0.023). Figure 7 D).

[0156] To validate that the RSA model can also predict postoperative peritoneal recurrence in patients with LAGC, data were collected from 148 patients across six diagnostic and treatment centers. All patients underwent abdominal CT or PET-CT scans postoperatively. Figure 7 As shown in G, longitudinal dynamic peripheral blood analysis of a single patient demonstrated that the RSA model could indicate a high risk of peritoneal recurrence up to 16 months in advance. ROC curve analysis showed that the AUC value of the RSA model in predicting peritoneal recurrence (AUC = 0.827, 95% CI: 0.757–0.897) was superior to the clinical characteristic model (AUC = 0.758, 95% CI: 0.653–0.862) and the 4-mRNA combination (AUC = 0.721, 95% CI: 0.633–0.810). Figure 7 I). The calibration curve also shows a good model fit ( Figure 7 R).

[0157] Radar charts and confusion matrices confirmed that the RSA model was significantly superior to the clinical characteristic model and 4-mRNA in predicting peritoneal recurrence after radical surgery. Figure 7 K, 7M). The double-layer concentric circle diagram further emphasizes the potential of the RSA model in enhancing clinical decision-making and optimizing patient management. Figure 7 L). Furthermore, follow-up data showed that the 3-year overall survival (OS) rate in the high-risk group identified by the RSA model was significantly lower than that in the low-risk group (34.6% vs. 54.2%, P = 0.0029). Figure 7 P).

[0158] The performance of the RSA model in predicting peritoneal metastasis in patients with negative peripheral blood tumor markers (CA19-9, CA72-4, CEA) was also evaluated. Data from all tumor marker-negative patients from six diagnostic and treatment centers, totaling 92 patients with LAGC, were pooled. Compared with single models, the RSA model showed the highest AUC value in predicting peritoneal metastasis (AUC = 0.782, 95% CI: 0.683–0.881). Figure 7 Further analysis showed that the RSA model was superior to the clinical features and 4-mRNA model in terms of sensitivity, specificity, and accuracy. Figure 8 J, 7M (on). The calibration curves also confirmed the superior predictive performance of the RSA model ( Figure 8 Clinical benefit analysis showed that, compared with the clinical characteristic model, the RSA model increased the detection rate of peritoneal metastasis in the high-risk group from 15.2% to 18.4%, and decreased the detection rate of peritoneal metastasis in the low-risk group from 3.3% to 0.0%. Figure 8 (L below). Furthermore, long-term follow-up showed that the 3-year overall survival (OS) of high-risk patients identified by the RSA model was significantly lower than that of low-risk patients (29.6% vs. 56.2%, P = 0.0022).Figure 8 Q).

[0159] In addition, an observational prospective clinical study (NCT06478394) was initiated to further validate the performance of the 4-mRNA group in predicting peritoneal metastasis. This study included 120 LAGC patients enrolled between January and June 2024. Peripheral blood samples were collected from all patients for 4-mRNA testing prior to laparoscopic exploration. Of these, 20 patients (16.7%) were classified as high-risk by the RSA model, and 100 patients (83.3%) were classified as low-risk. Laparoscopic exploration subsequently confirmed peritoneal metastasis in 28 patients (23.3%). In the high-risk group, only 3 patients (15.0%) did not have peritoneal metastasis, while in the low-risk group, 11 patients (11.0%) were found to have peritoneal metastasis. ​ Further ROC curve analysis showed that in this prospective study, the RSA model had a higher AUC for predicting peritoneal metastasis than the single model (N). ​ O).

[0160] Inhibiting 4-mRNA inhibits gastric cancer metastasis

[0161] Based on these findings, the clinical potential of the 4-mRNA genome in predicting peritoneal metastasis in patients with LAGC was explored, and the biological roles of the 4-mRNAs (BUB1, SPC25, CT83, and MMP3) comprising this genome were analyzed in detail. Specific siRNA sequences targeting these genes were utilized (Table 2). Wound healing and transwell assays showed that silencing BUB1, SPC25, CT83, and MMP3 significantly reduced the migration and invasion abilities of HGC-27 cells. ​ AH).

[0162] Table 2. siRNA sequences of 4-mRNA

[0163]

[0164] In addition, knockdown experiments targeting BUB1, SPC25, CT83, and MMP3 were performed in BALB / c nude mice to examine the effects of these genes on peritoneal metastasis of HGC-27 cells. ​ I). Compared with the control group, the siRNA treatment group showed a significant reduction in peritoneal metastasis ( ​ J). Furthermore, IHC staining showed that silencing these genes led to decreased expression of metastatic markers (MMP9) and mesenchymal markers (N-cadherin, vimentin), and increased expression of epithelial markers (E-cadherin). ​ KP).

[0165] Overall, these in vitro and in vivo results suggest that four genes in the 4-mRNA genome promote the migration, invasion, and intraperitoneal metastasis of gastric cancer cells.

[0166] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. Use of reagents for detecting expression levels of biomarkers consisting of BUB1, SPC25, CT83 and MMP3 in preparing a product for diagnosing peritoneal metastasis of gastric cancer, wherein the peritoneal metastasis of gastric cancer is LAGC peritoneal metastasis.

2. Use according to claim 1, characterized in that, The reagents comprise oligonucleotide probes specifically recognizing the biomarkers, primers specifically amplifying the biomarkers.

3. Use according to claim 2, characterized in that, The primer sequences for specifically amplifying BUB1 are shown in SEQ ID NO: 1-2.

4. Use according to claim 2, characterized in that, The primer sequences for specifically amplifying SPC25 are shown in SEQ ID NO: 3-4.

5. Use according to claim 2, characterized in that, The primer sequences for specifically amplifying CT83 are shown in SEQ ID NO: 5-6.

6. Use according to claim 2, characterized in that, The primer sequences for specifically amplifying MMP3 are shown in SEQ ID NO: 7-8.

7. Use according to claim 1, characterized in that, The sample comprises tissue, blood.

8. Use according to claim 7, characterized in that, The blood comprises peripheral blood, serum.

9. The use according to claim 1, characterized in that, The reagents further comprise detectable substances.

10. The use according to claim 1, characterized in that, The product comprises a kit, a test paper.

11. Use of biomarkers consisting of BUB1, SPC25, CT83 and MMP3 in constructing a model for diagnosing peritoneal metastasis of gastric cancer, wherein the peritoneal metastasis of gastric cancer is LAGC peritoneal metastasis.

12. A model for diagnosing peritoneal metastasis of gastric cancer, characterized by, The model comprises the levels of biomarkers consisting of 4-mRNAs consisting of BUB1, SPC25, CT83 and MMP3, wherein the peritoneal metastasis of gastric cancer is LAGC peritoneal metastasis.

13. The model of claim 12, wherein, The model is a nomogram model.

14. Use of inhibitors of biomarkers consisting of BUB1, SPC25, CT83 and MMP3 in preparing a pharmaceutical composition for treating gastric cancer. The inhibitors are selected from siRNAs. The siRNA sequence of BUB1 is shown in SEQ ID NO: 9 or SEQ ID NO:

10. The siRNA sequence of SPC25 is shown in SEQ ID NO: 11 or SEQ ID NO:

12. The siRNA sequence of CT83 is shown in SEQ ID NO: 13 or SEQ ID NO:

14. The siRNA sequence of MMP3 is shown in SEQ ID NO: 15 or SEQ ID NO:

16.

15. Use according to claim 14, characterized in that, The treatment of gastric cancer comprises inhibiting metastasis of gastric cancer.

16. Use according to claim 15, characterized in that, The inhibition of metastasis of gastric cancer comprises inhibiting peritoneal metastasis of gastric cancer.

17. A pharmaceutical composition for treating gastric cancer, comprising, as an active ingredient, a compound or a salt thereof according to claim 1. The pharmaceutical composition comprises inhibitors of biomarkers consisting of BUB1, SPC25, CT83 and MMP3. The inhibitors are selected from siRNAs. The siRNA sequence of BUB1 is shown in SEQ ID NO: 9 or SEQ ID NO:

10. The siRNA sequence of SPC25 is shown in SEQ ID NO: 11 or SEQ ID NO:

12. The siRNA sequence of CT83 is shown in SEQ ID NO: 13 or SEQ ID NO:

14. The siRNA sequence of MMP3 is shown in SEQ ID NO: 15 or SEQ ID NO:

16.

18. The pharmaceutical composition of claim 17, wherein, The pharmaceutical composition further comprises a pharmaceutically acceptable carrier.

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