Method for establishing a pancreatic cancer screening model based on mass spectrometry technology

Abstract

The present invention discloses a method for establishing a pancreatic cancer screening model based on mass spectrometry flow cytometry, relating to the medical field. The method comprises the following steps: treating a peripheral blood sample with Ficoll separation to obtain PBMCs, and performing mass spectrometry flow cytometry analysis on the PBMCs to obtain a flow cytometric analysis data set; processing the flow cytometric analysis data set using a random forest algorithm, and performing feature screening using the negative proportion of cell surface markers and subpopulation proportions as modeling features to obtain a pancreatic cancer screening model. The present invention can be used for screening and auxiliary diagnosis of pancreatic cancer.

Description

Technical Field

[0001] The present invention relates to the medical field, and more particularly to a method for establishing a liver cancer screening model based on mass spectrometry technology. Background Art

[0002] Pancreatic cancer has an insidious onset and rapidly progresses, with a significant proportion of patients missing the opportunity for radical surgical resection at initial diagnosis, resulting in a dismal prognosis. Reports suggest that patients with tumors confined to the ductal epithelium and less than 1 cm in size have a 100% 5-year survival rate after surgery. However, only 15-20% of patients have the opportunity to undergo curative surgery at initial diagnosis. The carbohydrate antigen CA19-9 is currently the biomarker used in the clinical diagnosis of pancreatic cancer. However, people with Lewis antigen-negative tumors rarely or do not secrete CA19-9. Furthermore, some patients with gastrointestinal tumors, such as those with biliary obstruction, inflammation, and pancreatitis, also have elevated CA19-9 levels, leading to false-positive results. Therefore, there is an urgent need to develop new and effective biomarkers for pancreatic cancer screening.

[0003] Emerging liquid biopsies primarily involve the detection of circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), cell-free RNAs (such as mRNAs and microRNAs), circulating tumor cells (CTCs), and exosomes. These strategies have demonstrated advantages and potential in the field of tumor detection. However, there is currently no widely accepted liquid biopsy method for tumor detection.

[0004] The immune system is closely linked to the health of the human body. The level of an individual's immune system can affect the occurrence, development, and prognosis of tumors. In recent years, emerging studies have shown that systemic changes occur in the individual immune system during tumor progression, some of which may be reflected in the peripheral blood. Previous studies have shown that the hematopoietic function of tumor-burdened hosts is widely impaired, and immature neutrophils and monocytes in the peripheral blood expand abnormally, some of which are transferred to the tumor microenvironment, leading to local immunosuppression. Therefore, comprehensive and detailed monitoring of the human peripheral immune status may provide great help for the prevention, diagnosis, and treatment of diseases. In recent years, mass spectrometry flow cytometry technology has been gradually improved, which can analyze the basic immune status of the human body in a high-dimensional manner and obtain a full range of immune cell composition, phenotype, and functional information. Summary of the Invention

[0005] In view of this, the present invention provides a method for establishing a liver cancer screening model based on mass spectrometry flow cytometry technology, which can be used for the screening and auxiliary diagnosis of pancreatic cancer.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for establishing a pancreatic cancer screening model based on mass spectrometry flow cytometry technology comprises the following steps:

[0008] Peripheral blood samples were treated with Ficoll separation to obtain PBMCs, which were then subjected to mass spectrometry flow cytometry analysis to obtain a flow cytometry data set.

[0009] The flow cytometry data set was processed using the random forest algorithm, and the negative proportion of cell surface markers and the proportion of subpopulations were used as modeling features for feature screening to obtain a pancreatic cancer screening model.

[0010] Optionally, it also includes screening out 1 cell surface marker CD33 and 18 immune cell subsets including CD14-CD33-CD3+CD8+CD27-, CD14-CD33-CD3+CD8+CD85j+, CD14-CD33-CD3-CD19-CD20-CD56+CD94-, CD3-CD19-CD56-HLA-DR-, CD14-CD33-CD3+CD4+, CD14-CD33-CD3+CD4+HLA-DR-CD38+, CD14-CD33-CD3+CD8+, CD14-CD33-CD3+CD8+, CD14-CD33-CD3+CD8+HLA-DR+, CD14-CD33-CD3+CD8+CCR7+CD45RA+, CD14-CD33-CD3+CD8+HLA-DR-CD38+ , CD14-CD33-CD3+CD8+HLA-DR+CD38+, CD14-CD33-CD3+HLA-DR+, CD14-CD33-CD3+CD56+, CD14-CD33-CD19+IgD+CD27+, CD14-CD33-CD3-CD56+CD16+HLA-DR+, CD3-CD19-CD14+, CD14-CD16+HLA-DR+, CD3-CD19-CD56-HLA-DR-CD33+CD11b+.

[0011] Optionally, the model is validated using internal validation sets and external validation sets.

[0012] Optionally, it also includes combining the subject's serum CA19-9 to construct an integrated pancreatic cancer screening model.

[0013] The above technical solution demonstrates that, compared to existing technologies, the present invention provides a method for establishing a liver cancer screening model based on mass spectrometry technology. Application of the model in patients with resectable pancreatic cancer and negative CA19-9 results: PBIScore and iPBIScore maintain good detection efficacy in patients with resectable pancreatic cancer, with AUCs of 0.88 and 0.95, respectively. In patients with negative CA19-9, PBIScore and iPBIScore can still screen for pancreatic cancer, with AUCs of 0.88 and 0.86, respectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0015] Figure 1 PBIScore and iPBIScore for pancreatic cancer patients and non-pancreatic cancer populations;

[0016] Figure 2a-2c AUC curve and model prediction performance summary table. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] The present invention discloses a method for establishing a liver cancer screening model based on mass spectrometry technology, which is as follows:

[0019] Peripheral blood samples (5 ml / case) were collected and sent to the laboratory for further processing within 12 hours at room temperature or within 48 hours at 4°C. The subjects of the collection should meet the following requirements: clear diagnosis of the disease (pathology is confirmed by puncture, and patients undergoing surgical treatment are further confirmed by histopathology); signed informed consent. The collection exclusion criteria include: history of cancer-related treatment; acute infection period; blood transfusion therapy within 6 months; use of drugs that affect peripheral blood components in the past 2 weeks; local recurrence of tumors; organ decompensated dysfunction; immunodeficiency syndrome; blood precancerous diseases; receiving immunosuppressive therapy; and coagulation dysfunction. A total of 1,104 peripheral blood samples were collected from an affiliated hospital and a branch hospital of Zhejiang University School of Medicine from 2019 to 2021 for this project. After the collected peripheral blood samples were transferred to the laboratory, they were processed as follows:

[0020] (1) After strict sample quality control, PBMCs are obtained by Ficoll separation of peripheral blood samples. The number of PBMC cells obtained should be greater than 3×10 6 , the activity rate was higher than 85%, and further mass spectrometry flow cytometry analysis was performed to obtain flow cytometry related data.

[0021] (2) The dataset is divided into three parts: training set, internal validation set, and external validation set. The training set and internal validation set data are processed by random forest algorithm to perform feature screening. Features were selected based on the highest feature score, and finally one cell surface marker CD33 and 18 immune cell subsets were screened out, including CD14-CD33-CD3+CD8+CD27-, CD14-CD33-CD3+CD8+CD85j+, CD14-CD33-CD3-CD19-CD20-CD56+CD94-, CD3-CD19-CD56-HLA-DR-, CD14-CD33-CD3+CD4+, CD14-CD33-CD3+CD4+HLA-DR-CD38+, CD14-CD33-CD3+CD8+, CD14-CD33-CD3+CD8+, CD14-CD33-CD3+CD8+HLA-DR+, CD14-CD33-CD3+CD8+CCR7+CD45RA+, CD14-CD33-CD3+CD8+HLA-DR-CD38+ Based on the selected features, the training set was standardized and a random forest model was constructed again. A 10-fold cross-validation method was used to ensure that every sample participated in both modeling and testing, thereby eliminating the impact of sample differences on the model and finding the optimal hyperparameters.

[0022] (3) The established model was applied in internal and external validation sets to verify the screening efficacy of the model. The model was further applied in the population with resectable pancreatic cancer and the population with negative CA19-9 to evaluate the model's early screening ability and its superiority over CA19-9.

[0023] Combine Figure 1 as well as Figure 2a-2c , we can know that:

[0024] 1. PBIScore and iPBIScore of pancreatic cancer patients and non-pancreatic cancer populations: The PBIScore and iPBIScore of pancreatic cancer patients were significantly higher than those of non-pancreatic cancer populations in the training set, internal validation set, and external validation set.

[0025] 2. AUC curves and model predictive efficacy summary table: PBIScore and iPBIScore demonstrated excellent screening efficacy in both internal and external validation sets. The iPBIScore model demonstrated the best detection capability, further improving screening efficacy over the PBIScore model and outperforming CA19-9.

[0026] 3. Application of the model in patients with resectable pancreatic cancer and negative CA19-9: PBIScore and iPBIScore maintained good detection performance in patients with resectable pancreatic cancer, with AUCs of 0.88 and 0.95, respectively. In patients with negative CA19-9, PBIScore and iPBIScore were still able to screen for pancreatic cancer, with AUCs of 0.88 and 0.86, respectively.

[0027] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0028] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for establishing a pancreatic cancer screening model based on mass spectrometry technology, characterized in that: The following steps are involved: Peripheral blood samples were treated with Ficoll separation to obtain PBMCs, which were then subjected to mass spectrometry flow cytometry analysis to obtain a flow cytometry data set. The flow cytometry dataset was processed using a random forest algorithm, and the negative proportion of cell surface markers and subpopulation proportions were used as modeling features for feature screening to obtain a pancreatic cancer screening model. It also includes the screening of 1 cell surface marker CD33 and 18 immune cell subsets including CD14-CD33-CD3+CD8+CD27-, CD14-CD33-CD3+CD8+CD85j+, CD14-CD33-CD3-CD19-CD20-CD56+CD94-, CD3-CD19-CD56-HLA-DR-, CD14-CD33-CD3+CD4+, CD14-CD33-CD3+CD4+HLA-DR-CD38+, CD14-CD33-CD3+CD8+, CD14-CD33-CD3+CD8+, CD14-CD33-CD3+CD8+HLA-DR+, and CD14-CD33-CD3+CD3+CD8+. +CD8+CCR7+CD45RA+, CD14-CD33-CD3+CD8+HLA-DR-CD38+, CD14-CD33-CD3+CD8+HLA-DR+CD38+, CD14-CD33-CD3+HLA-DR+, CD14-CD33-CD3+CD56+, CD14-CD33-CD19+IgD+CD27+, CD14-CD33-CD3-CD56+CD16+HLA-DR+, CD3-CD19-CD14+, CD14-CD16+HLA-DR+, CD3-CD19-CD56-HLA-DR-CD33+CD11b+.

2. The method for establishing a pancreatic cancer screening model based on mass spectrometry technology according to claim 1, characterized in that: It also includes using internal validation sets and external validation sets to validate the model.

3. The method for establishing a pancreatic cancer screening model based on mass spectrometry technology according to claim 1, characterized in that: It also includes combining the subjects' serum CA19-9 to construct an integrated pancreatic cancer screening model.

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