Small intestinal cancer prognosis prediction method based on GPNMB positive macrophages

Abstract

The invention provides a small intestinal cancer prognosis prediction method based on GPNMB positive macrophages, which comprises the following steps: S1, data acquisition: carrying out proteomics detection on formalin-fixed paraffin embedded sections of small intestinal cancer patients, and meanwhile, carrying out follow-up visit on survival information of the patients; s2, feature extraction: extracting GPNMB positive macrophage feature genes from small intestinal cancer single cell sequencing data; s3, analysis and evaluation: evaluating GPNMB positive macrophage infiltration scores in a small intestinal cancer proteome queue; and S4, prognosis prediction: determining the optimal cutoff value of the GPNMB positive macrophages for predicting the small intestinal cancer prognosis. The method can provide an accurate prognosis prediction means for the small intestinal cancer patient, and is beneficial to early intervention of the small intestinal cancer patient.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and in particular relates to a method for predicting the prognosis of small intestinal cancer based on GPNMB-positive macrophages. Background Art

[0002] Small intestinal cancer is highly malignant and has an insidious onset. Early diagnosis and treatment are difficult, and two-thirds of patients are often diagnosed in the advanced stage with a poor prognosis. The 5-year survival rate is less than 40%, of which the survival rate of stage 4 patients is only about 20%, and the prognosis of each stage is lower than that of colorectal cancer. Currently, TMN staging and pathological grading are the main prognostic prediction methods for small intestinal cancer, but the prognosis prediction of patients based on clinical staging and grading is often unsatisfactory. Some early patients still have survival events in the early stage, which suggests that clinical staging and grading often only include phenotypic information of the patient's tumor components, but fail to indicate the comprehensive status of the tumor microenvironment of patients with small intestinal cancer.

[0003] With the development of high-throughput technology, the use of omics technology to convert tumor microenvironment components into important methods for clinical prognosis prediction has gradually matured and received widespread attention. As previous studies have reported, small intestinal cancer may have a significant immunosuppressive microenvironment, which is a key factor in tumor progression. Therefore, using the core immunosuppressive cell subsets of small intestinal cancer patients to construct a small intestinal cancer prognosis prediction method may have significant clinical value. Patent CN114863993B discloses a marker, model construction method and system for colon cancer prognosis prediction. The marker includes TLS-related quantitative indicators as markers for colon cancer prognosis prediction. At present, there is no invention for predicting the prognosis of small intestinal cancer based on the key immunosuppressive cell subsets of small intestinal cancer. However, as the incidence of small intestinal cancer in my country increases year by year, there is an urgent need to develop a prognosis prediction method for small intestinal cancer patients. Summary of the invention

[0004] The present invention provides a small intestinal cancer prognosis prediction method based on GPNMB-positive macrophages, which can accurately identify small intestinal cancer patients with poor prognosis, so as to facilitate early intervention for patients.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a method for predicting the prognosis of small intestinal cancer based on GPNMB-positive macrophages, characterized in that it comprises the following steps:

[0006] S1: Data acquisition: Protein profiles were detected on formalin-fixed paraffin-embedded sections of patients with small intestinal cancer, and the survival information of the patients was followed up;

[0007] S2: Feature extraction: Extract macrophage subsets from the single-cell data of small intestine cancer and annotate them to obtain GPNMB-positive macrophages. Then compare the differential genes of GPNMB-positive macrophages with those of other macrophage subsets as the characteristic genes of GPNMB-positive macrophages.

[0008] S3: Analysis and evaluation: In the small intestinal cancer proteome cohort, the GPNMB-positive macrophage infiltration score was evaluated in small intestinal cancer patients using the signature genes of GPNMB-positive macrophages;

[0009] S4: Prognosis prediction: Determine the optimal cutoff value for high and low grouping of GPNMB-positive macrophage survival analysis in patients with small intestinal cancer so that it has the best performance in predicting prognosis.

[0010] Furthermore, the method used to extract macrophage subpopulations in S2 is to perform dimensionality reduction clustering using the Seurat package, and the FindAllMarker function is used to compare the differential genes of GPNMB-positive macrophages relative to other macrophage subpopulations.

[0011] Furthermore, the S3 small intestine cancer proteome cohort was obtained from the small intestine cancer pathology library of Huashan Hospital Affiliated to Fudan University.

[0012] Furthermore, the GPNMB-positive macrophage infiltration score evaluation method in S3 is the ssGSEA method.

[0013] Furthermore, the method for determining the optimal cutoff value for high and low grouping of GPNMB-positive macrophage survival analysis in patients with small intestinal cancer in S4 is the surv_cutpoint() function of the survminer package.

[0014] Furthermore, the optimal cutoff value is 0.472.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The specific method of the present invention is to perform protein spectrum detection on formalin-fixed paraffin-embedded sections of patients with small intestinal cancer, and follow up the survival information of patients at the same time; extract GPNMB-positive macrophage characteristic genes from small intestinal cancer single-cell sequencing data, use the characteristic genes of GPNMB-positive macrophages to evaluate the infiltration score of small intestinal cancer patients, determine the optimal cutoff value for high and low grouping of GPNMB-positive macrophage survival analysis of small intestinal cancer patients, and perform survival analysis. + The prognosis prediction of small intestinal cancer based on the expression of proteins in macrophages can accurately and quickly identify patients with small intestinal cancer with poor prognosis, so as to facilitate precise drug selection and economical treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the drawings without paying creative work.

[0018] Figure 1 is a flow chart of the present invention;

[0019] Figure 2 This is a schematic diagram of single-cell dimensionality reduction clustering of GPNMB-positive macrophages in small intestinal cancer, where GPNMB+Mac represents GPNMB-positive macrophages;

[0020] Figure 3 This is a schematic diagram of the functional comparison of GPNMB-positive macrophages in small intestinal cancer, in which GPNMB+Mac represents GPNMB-positive macrophages;

[0021] Figure 4 This is a schematic diagram of the spatial localization of GPNMB-positive macrophages in small intestinal cancer. In the figure, GPNMB+Mac represents GPNMB-positive macrophages;

[0022] Figure 5 Schematic diagram of survival analysis of the abundance of GPNMB-positive macrophage infiltration and patient prognosis in patients with small intestinal cancer. DETAILED DESCRIPTION

[0023] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0024] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0025] Example 1

[0026] A method for predicting the prognosis of small intestinal cancer based on GPNMB-positive macrophages, the process is as follows Figure 1 As shown, the specific steps are as follows:

[0027] S1: Data acquisition: Protein profile detection was performed on formalin-fixed paraffin-embedded sections of patients with small intestinal cancer, and the survival information of the patients was followed up;

[0028] S2: Feature extraction: Seurat package was used to perform dimensionality reduction clustering on the single-cell data of small intestinal cancer, and macrophage subpopulations were extracted and annotated to obtain GPNMB-positive macrophages. The FindAllMarker function was used to compare the differential genes of GPNMB-positive macrophages relative to other macrophage subpopulations as the characteristic genes of GPNMB-positive macrophages;

[0029] S3: Analysis and evaluation: In the small intestinal cancer proteome cohort, the GPNMB-positive macrophage infiltration score was evaluated in small intestinal cancer patients using the signature genes of GPNMB-positive macrophages;

[0030] S4: Prognosis prediction: The optimal cutoff value for high and low grouping of GPNMB-positive macrophage survival analysis in patients with small intestinal cancer was determined to enable it to have the best performance in predicting prognosis. The prognostic ability was demonstrated by Kaplan-Meier survival analysis. All statistical analyses were performed in R language (version 4.1.3).

[0031] Example 2

[0032] Example 2 is further described based on Example 1:

[0033] A method for predicting the prognosis of small intestinal cancer based on GPNMB-positive macrophages, the process is as follows Figure 1 As shown, the specific steps are as follows:

[0034] S1: Data acquisition: Protein profile detection was performed on formalin-fixed paraffin-embedded sections of patients with small intestinal cancer, and the survival information of the patients was followed up;

[0035] Specifically, after proteomic mass spectrometry detection, the proteomic data were filtered to retain only proteins with expression greater than 0 in more than 75% of the samples, and the follow-up period for patient survival information was 5 years of overall survival.

[0036] S2: Feature extraction: Seurat package was used to perform dimensionality reduction clustering on the single-cell data of small intestinal cancer, and macrophage subpopulations were extracted and annotated to obtain GPNMB-positive macrophages. The FindAllMarker function was used to compare the differential genes of GPNMB-positive macrophages relative to other macrophage subpopulations as the characteristic genes of GPNMB-positive macrophages;

[0037] Specifically, the Seurat package was used to perform dimensionality reduction clustering on the single-cell data of small intestinal cancer to obtain various cell subpopulations, and the macrophage subpopulation was further annotated by dimensionality reduction clustering to define the macrophage subpopulation ( Figure 2), and observed that the proportion of GPNMB-positive macrophages in tumor tissues was significantly increased compared with normal tissues. Functional analysis showed that the antigen presentation ability of GPNMB-positive macrophages was significantly decreased compared with DNASE1L3-positive macrophages resident in the normal small intestine ( Figure 3 ). Spatial transcriptome deconvolution analysis confirmed that GPNMB-positive macrophages have a close spatial relationship with T cells, thus confirming that GPNMB-positive macrophages are the core immunosuppressive cells in the small intestinal cancer microenvironment ( Figure 4 ), suggesting that it may be a factor for poor prognosis. The characteristic genes of GPNMB-positive macrophages compared with other macrophages (Log Foldchange>0.5) were compared by the FindAllMarker function for analysis and evaluation.

[0038] S3: Analysis and evaluation: In the small intestinal cancer proteome cohort, the GPNMB-positive macrophage infiltration score was evaluated in small intestinal cancer patients using the signature genes of GPNMB-positive macrophages;

[0039] Specifically, the cell infiltration score evaluation method is the gsva function in the GSVA package, with the parameter setting method as ssgsea and kcdf as Gaussian.

[0040] S4: Prognosis prediction: Determine the optimal cutoff value for high and low grouping of GPNMB-positive macrophage survival analysis in patients with small intestinal cancer, so that it has the best performance in predicting prognosis, and demonstrate the prognostic ability through Kaplan-Meier survival analysis. All statistical analyses were performed in R language (version 4.1.3).

[0041] Specifically, the optimal cutoff value for survival analysis high and low grouping is the surv_cutpoint() function of the survminer package, and the optimal cutoff value is 0.472. Patients with an infiltration score higher than 0.472 are considered to have a poor prognosis. Kaplan-Meier survival analysis showed that patients with non-poor prognosis had no death events within 5 years, while the 5-year survival rate of patients with poor prognosis was less than 25%, which was statistically significant (P = 0.024) ( Figure 5 ).

[0042] As can be seen from the above, there is a lack of effective prognostic targets for patients with small intestinal cancer, and the development of targeted prognostic prediction methods has important clinical value. Therefore, we established a prognostic prediction model based on GPNMB-positive macrophages, the core immunosuppressive cells of small intestinal cancer, and found that patients with high GPNMB-positive macrophage infiltration have poor prognosis, with a 5-year overall survival rate of less than 25%, which can achieve very early intervention for patients with poor prognosis of small intestinal cancer.

[0043] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0044] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the prognosis of small intestinal cancer based on GPNMB-positive macrophages, characterized in that: The steps include: S1: Data acquisition: Protein profiles were detected on formalin-fixed paraffin-embedded sections of patients with small intestinal cancer, and the survival information of the patients was followed up; S2: Feature extraction: Extract macrophage subsets from the single-cell data of small intestine cancer and annotate them to obtain GPNMB-positive macrophages. Then compare the differential genes of GPNMB-positive macrophages with those of other macrophage subsets as the characteristic genes of GPNMB-positive macrophages. S3: Analysis and evaluation: In the small intestinal cancer proteome cohort, the GPNMB-positive macrophage infiltration score was evaluated in small intestinal cancer patients using the signature genes of GPNMB-positive macrophages; S4: Prognosis prediction: Determine the optimal cutoff value for high and low grouping of GPNMB-positive macrophage survival analysis in patients with small intestinal cancer so that it has the best performance in predicting prognosis.

2. The method for predicting the prognosis of small intestinal cancer according to claim 1, characterized in that: The method used for extracting macrophage subpopulations in S2 is to perform dimensionality reduction clustering using the Seurat package, and to compare the differential genes of GPNMB-positive macrophages with those of other macrophage subpopulations using the FindAllMarker function.

3. The method for predicting the prognosis of small intestinal cancer according to claim 1, characterized in that: The S3 small intestine cancer proteome cohort was obtained from the small intestine cancer pathology library of Huashan Hospital Affiliated to Fudan University.

4. The method for predicting the prognosis of small intestinal cancer according to claim 1, characterized in that: The method for evaluating the GPNMB-positive macrophage infiltration score in S3 is the ssGSEA method.

5. The method for predicting the prognosis of small intestinal cancer according to claim 1, characterized in that: The method for determining the optimal cutoff value for high and low grouping of GPNMB-positive macrophage survival analysis in patients with small intestinal cancer in S4 is the surv_cutpoint() function of the survminer package.

6. The method for predicting the prognosis of small intestinal cancer according to claim 5, characterized in that: The optimal cutoff value is 0.472.

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