CN114863993A - Marker for prognosis prediction of colon cancer, model construction method and system - Google Patents
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Abstract
The invention relates to the technical field of biological medicines, in particular to a marker for prognosis prediction of colon cancer, a model construction method and a system. The invention provides a TLS related quantitative index for prognosis prediction of a colon cancer patient, and the TLS related quantitative index is used as a marker for prognosis prediction of the colon cancer, so that the immune state of the patient can be accurately and objectively reflected, and the accuracy of prognosis prediction is higher. The invention also provides a colon cancer prognosis risk model and a construction method thereof, the model can be independent of the TNM staging to carry out prognosis prediction, can better carry out prognosis risk stratification on colon cancer patients compared with the TNM staging, guides the clinical establishment of more targeted treatment schemes for patients with different prognoses, and is favorable for realizing accurate medical treatment.
Description
Technical Field
The invention relates to the technical field of biological medicines, in particular to a marker for prognosis prediction of colon cancer, a model construction method and a system.
Background
Colon cancer is one of the most common malignancies worldwide, with its incidence and mortality rates ranking the first three in all malignancies. The overall survival rate of colon cancer patients depends on the TNM stage of the tumor, which is a staged form of the tumor in oncology, and is mainly determined by the tumor infiltration, the number of lymph nodes and whether metastasis occurs to other sites, etc., with higher stage generally meaning higher degree of tumor progression and worse prognosis. The 5-year survival rates reported by the United states Joint Committee for cancer are 93.2%, 82.5%, 59.5% and 8.1% for stage I, II, III and IV colon cancer patients, respectively. However, clinically significant differences in prognosis for colon cancer patients with identical TNM stages are often encountered, and as such, TNM stages are not the only factor in determining prognosis for tumor patients. Therefore, finding a more accurate biomarker index and providing a model construction method for predicting the prognosis risk of a colon cancer patient are problems to be solved urgently in the field.
The intestinal tract is used as an important organ for the enrichment of systemic immune cells, and the change of immune microenvironment plays an important role in the occurrence and development of colorectal cancer. The Tertiary Lymphoid Structure (TLS) is an aggregate structure of immune cells present in tissues of colorectal cancer and colorectal cancer, and the immune cell components thereof are diverse. The formation of TLS indicates the enhancement of the infiltration of immune cells and the killing effect on tumors, and is a positive factor influencing the survival and prognosis of patients. Currently, few reports are made on the evaluation of the risk of colon cancer patients by using the quantitative indicators of TLS.
Disclosure of Invention
The invention aims to provide a marker, a model construction method and a system for prognosis prediction of colon cancer.
The invention develops markers, models and systems for prognosis prediction of colon cancer by taking TLS of colon cancer pathological tissues as pathological markers. Through a large number of researches and practices, TLS related quantitative indexes which are obviously related to the prognosis of the colon cancer and can be used for prognosis prediction of the colon cancer are found, and the indexes can realize more accurate risk assessment of prognosis of the colon cancer. By utilizing the TLS related quantitative indexes, the invention constructs a colon cancer prognosis risk model to obtain a colon cancer prognosis risk model with better predictive performance.
Specifically, the invention provides the following technical scheme:
in a first aspect, the present invention provides a marker for prognosis of colon cancer, the marker comprising one or more of the following TLS-related indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions;
the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area;
wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region;
the normalized index is the ratio of the index to the area value of its corresponding region.
The above-mentioned calculation formula of the normalized index is specifically as follows:
TLS normalized mean area = TLS area mean/area value;
TLS normalized median area = TLS area median/area value;
TLS normalized maximum area = maximum TLS area value/zone area value;
TLS normalized total area = TLS area and/or zone area value;
TLS density = TLS total number/zone area value.
In the above calculation formulas, the TLS area average number, the TLS area median, and the maximum TLS area value are calculated for all TLSs in the region corresponding to the index, the area value of the region with the largest area among all TLSs in the region is the maximum TLS area value, the ratio of the sum of the areas of all TLSs to the number of TLSs in the region is the average number of TLS areas, and the median of the area values of all TLSs in the region is the TLS area median. TLS area sum refers to the sum of the areas of all TLS within the corresponding zone. The total number of TLSs is the number of TLSs contained in the corresponding area.
In the above calculation formulas, the area value of a region refers to the area of the region corresponding to the index. Taking the TLS-related index of the normal mucosal area as an example, the calculation formula is as follows:
normal mucosal area TLS normalized mean area = normal mucosal area TLS area mean/normal mucosal area value;
normal mucosal region TLS normalized median area = normal mucosal region TLS median area/normal mucosal region area value;
normal mucosal area TLS normalized maximum area = normal mucosal area maximum TLS area value/normal mucosal area value;
normal mucosal area TLS normalized total area = normal mucosal area TLS area and/normal mucosal area value;
normal mucosal area TLS density = total number of normal mucosal areas TLS/normal mucosal area value.
Identification of TLS and determination of the area thereof in colon cancer pathological tissues can be performed according to TLS identification and determination criteria and methods which are conventional in the art. Specifically, the criterion for TLS in each tumor-associated region is as follows: t lymphocytes are wrapped around B lymphocytes in a quasi-circular, elliptical, teardrop or other shaped structure with or without germinal centers, and with clearly identifiable High Endothelial Venules (HEVs).
When the distal region contains adipose tissue outside the serosa of the intestinal wall, TLS at this adipose tissue should be identified with lymph nodes, i.e. lack of envelope around TLS, and lymph nodes with intact envelope.
The invention further utilizes the means of single factor analysis, multi-factor analysis and the like to screen the TLS related indexes in the markers in the training queue, and determines the indexes and the combination thereof which can realize more accurate prognosis prediction of colon cancer.
Preferably, the markers comprise one or more of the following TLS-related indicators: the normalized average area of the normal mucosa region TLS, the normalized maximum area of the normal mucosa region TLS, the normalized median area of the normal mucosa region TLS, the normalized total area of the normal mucosa region TLS, the normal mucosa region TLS density, the normalized average area of the distal region TLS, the normalized maximum area of the distal region TLS, the normalized median area of the distal region TLS, the normalized total area of the distal region TLS, and the distal region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is any one of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of any 2-15 of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is any one of the following TLS-related indicators: the normalized average area of the normal mucosal region TLS, the normalized maximum area of the normal mucosal region TLS, the normalized total area of the normal mucosal regions TLS, the density of the normal mucosal regions TLS, the normalized average area of the distal region TLS, the normalized maximum area of the distal region TLS, the normalized median area of the distal region TLS, the normalized average area of the infiltrated edge region TLS, and the normalized median area of the infiltrated edge region TLS.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of any 2-9 of the following TLS-related indicators: the normalized average area of the normal mucosal region TLS, the normalized maximum area of the normal mucosal region TLS, the normalized total area of the normal mucosal regions TLS, the density of the normal mucosal regions TLS, the normalized average area of the distal region TLS, the normalized maximum area of the distal region TLS, the normalized median area of the distal region TLS, the normalized average area of the infiltrated edge region TLS, and the normalized median area of the infiltrated edge region TLS.
In some embodiments of the invention, the marker for prognosis of colon cancer is the normal mucosal region TLS normalized maximum area.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of the distal region TLS normalized maximum area, the distal region TLS normalized mean area and the normal mucosal region TLS normalized total area.
The tumor area is an area where tumor cells grow and infiltrate, and does not include an infiltration border area.
The infiltration border area is an area within a width range of 400 μm from both sides of the boundary line by taking the tumor cell infiltration line as the boundary line.
The normal mucosal area described above is normal mucosal tissue adjacent to the infiltrated border area.
In the present invention, the colon cancer pathological tissue sample is a tissue including tumor, infiltration margin and adjacent normal mucosa and distal region, longitudinally covering mucosa, submucosa, muscle layer, serosa and fat tissue around intestinal wall.
The various TLS-related indicators described above were derived from colon cancer pathological tissue sections of colon cancer patients. The various TLS related indexes can be obtained by scanning colon cancer pathological tissue sections of colon cancer patients, dividing the sections, identifying TLS in each section and detecting the area of each section.
In the present invention, the preparation and scanning of pathological tissue section, the identification of TLS and the area detection method are not particularly limited, and may be performed by methods conventional in the art.
The general method is exemplified as follows: before scanning the colon cancer pathological tissue section, the method also comprises the step of staining the pathological tissue section, wherein the staining comprises HE staining and immunohistochemical marker staining.
Wherein HE staining is aimed at identifying the respective tumor-associated region and the TLS structure within the respective region; the purpose of immunohistochemical labeling and staining is to label components such as T lymphocytes, B lymphocytes, follicular dendritic cells, and High Endothelial Venules (HEVs) in TLS to aid TLS identification, and these indices may be individually labeled in serial sections of tissues or jointly labeled in the same tissue section.
The selectable markers for immunohistochemical marker staining include, but are not limited to, CD3, CD8, CD4, CD20, CD23, CD21, MECA-79, and the like, and the types of markers are not particularly limited, and only the above-mentioned purpose of immunohistochemical marker staining can be achieved.
For the scanning of pathological tissue section, a digital pathology Whole Section (WSI) scanning mode can be adopted, including various scene modes of obtaining HE staining and immunohistochemical marker staining, which include not only bright field scanning but also fluorescence scanning mode. By adopting the WSI scanning technology, the characteristics of the tumor immune microenvironment of the colon cancer patient can be evaluated more comprehensively and objectively, and TLS index data related to the prognosis of the tumor patient and contained in a pathological image can be comprehensively obtained.
A tumor area, a normal mucosa area, an infiltration edge area and a distal area are selected in the WSI scanning image, and the area or the range of each area is measured; TLS structures are identified and selected through HE morphological characteristics and immunohistochemical markers in WSI scanning images, the area of the TLS is measured, and the TLS structures are counted in different areas.
For the preparation of pathological tissue sections, the following methods can be used: the method comprises the steps of obtaining a colon cancer pathological tissue specimen of a patient through an operation, fixing, dehydrating, transparentizing, waxing and embedding the colon cancer pathological tissue specimen, and preparing a tissue section.
In a second aspect, the invention provides the use of a test product as described above in the manufacture of a product for prognosis of colon cancer.
Preferably, when the product is used for prognosis prediction of colon cancer, survival prognosis analysis is firstly carried out according to the TLS related index of a colon cancer patient with known prognosis, an optimal prognosis model is selected, and a statistically optimal threshold value method is utilized, such as: determining the threshold value at the minimum value of the P value as an optimal threshold value by using a surv _ cutpoint function in an R packet survminer; or using a quantile function in the R packet stats, dividing continuous variables into Score High and Score Low groups according to the quantile values of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90%, respectively, performing survival analysis, taking the quantile value with the obvious P value and the maximum HR value as an optimal threshold value, comparing the TLS related index of the patient to be prognosticated and predicted with the risk threshold value, and predicting the prognosis condition of the patient to be prognosticated and predicted according to the comparison result.
More preferably, the detection product of the marker comprises one or more selected from the group consisting of a tissue section preparation reagent, a tissue section staining reagent, a tissue section scanning device, and image analysis software.
The invention also provides application of the marker in constructing a colon cancer prognosis prediction model.
In a third aspect, the invention provides a construction method of a colon cancer prognosis prediction model based on TLS quantitative analysis and the TLS related indexes.
Specifically, the invention provides a method for constructing a colon cancer prognosis risk model, which comprises the following steps:
(1) obtaining TLS related indexes of colon cancer pathological tissue samples of colon cancer patients;
(2) performing linear regression analysis by taking the TLS related indexes as risk factors to construct a prognosis risk model;
the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions;
the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area;
wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region;
the normalized index is the ratio of the index to the area value of its corresponding region.
The invention further utilizes the means of single factor analysis, multi-factor analysis and the like to screen the related TLS indexes in the training queue, and determines the indexes and the combination thereof which can realize more accurate prognosis prediction of colon cancer.
Preferably, the markers comprise one or more of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is any one of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of any 2-15 of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is the normal mucosal region TLS normalized maximum area.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of the distal region TLS normalized maximum area, the distal region TLS normalized mean area and the normal mucosal region TLS normalized total area.
Preferably, the tumor region is a region where tumor cells grow and infiltrate, and does not include an infiltration margin region.
The infiltration edge area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line.
The normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
The TLS-related index is derived from colon cancer pathological tissue sections of colon cancer patients.
In the model construction method, the colon cancer patient is preferably clinically confirmed colon cancer patient in stage II, III or IV, and the patient has detailed clinical risk factors, survival state and follow-up time information. Collecting a large number of colon cancer patients to construct a training queue, and obtaining TLS related indexes of colon cancer pathological tissue samples of each colon cancer patient in the training queue.
In the model construction method, the colon cancer pathological tissue sample is a tissue comprising tumor, infiltration margin and adjacent normal mucosa, and longitudinally covers mucosa, submucosa, muscle layer, serous membrane and fat tissue around intestinal wall.
The model construction method further comprises the step of verifying the effectiveness of the prognosis risk model for prognosis prediction of colon cancer patients in a verification queue.
In a fourth aspect, the invention provides a colon cancer prognosis risk model constructed by the method for constructing a colon cancer prognosis risk model.
As can be understood by those skilled in the art, the colon cancer prognosis risk models constructed by using different combinations of TLS-related indicators provided by the invention are different by adopting the model construction method.
By way of example, in some embodiments of the invention, the colon cancer prognostic risk model is:
the risk value =1.37530162+0.06566891 TLS _ F _ Maxarea _ norm +0.40739196 TLS _ F _ meanearea _ norm +1.06475078 TLS _ N _ area _ norm, where TLS _ F _ Maxarea _ norm is the normalized maximum area of the distal region TLS, TLS _ F _ meanearea _ norm is the normalized average area of the distal region TLS, and TLS _ N _ area _ norm is the normalized total area of the normal mucosal region TLS.
In a fifth aspect, the present invention provides a system for prognosis prediction of colon cancer, the system comprising:
(1) the data acquisition module is used for acquiring TLS related indexes in a colon cancer pathological tissue sample of a colon cancer patient to be prognosticated;
wherein the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions;
the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area;
wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region;
the standardized index is the ratio of the index to the area value of the corresponding region;
(2) a prediction module for providing the TLS-related index data obtained by the data acquisition module as input data to a prognostic risk model to obtain a risk value,
the prognosis risk model is constructed by adopting the model construction method or is the prognosis risk model;
(3) and the prediction result output module is used for acquiring the output result of the prognosis risk model in the prediction module and outputting the prediction result.
The invention further utilizes the means of single factor analysis, multi-factor analysis and the like to screen the related TLS indexes in the training queue, and determines the indexes and the combination thereof which can realize more accurate prognosis prediction of colon cancer.
Preferably, the markers comprise one or more of the following TLS-related indicators: a normal mucosa region TLS normalized average area, a normal mucosa region TLS normalized maximum area, a normal mucosa region TLS normalized median area, a normal mucosa region TLS normalized total area, a normal mucosa region TLS density, a distal region TLS normalized average area, a distal region TLS normalized maximum area, a distal region TLS normalized median area, a distal region TLS normalized total area, a distal region TLS density, an infiltrated edge region TLS normalized average area, an infiltrated edge region TLS normalized maximum area, an infiltrated edge region TLS normalized median area, an infiltrated edge region TLS normalized total area, an infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is any one of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of any 2-15 of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is the normal mucosal region TLS normalized maximum area.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of the distal region TLS normalized maximum area, the distal region TLS normalized mean area and the normal mucosal region TLS normalized total area.
Preferably, the tumor region is a region where tumor cells grow and infiltrate, and does not include an infiltration margin region.
The infiltration edge area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line.
The normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
The TLS-related index is derived from colon cancer pathological tissue sections of colon cancer patients.
In the above system for prognosis of colon cancer, the prediction module preferably further compares the risk value output by the prognosis risk model with a threshold, where a low risk is determined when the risk value is greater than the threshold, and a high risk is determined when the risk value is less than or equal to the threshold.
In a sixth aspect, the present invention provides a computer device comprising a memory for storing a program and a processor that implements the following method when executing the program:
obtaining TLS related indexes in colon cancer pathological tissue samples of colon cancer patients to be prognosticated and predicted; wherein the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions; the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area; wherein the distal region is a region of the colon cancer tissue sample except for a tumor region, a normal mucosa region and an infiltration border region; the standardized index is the ratio of the index to the area value of the corresponding region;
providing the TLS related index data serving as input data to a prognosis risk model to obtain a risk value, wherein the prognosis risk model is constructed by adopting the model method or is the prognosis risk model;
and obtaining an output result of the prognosis risk model and outputting a prediction result.
The invention further utilizes the means of single factor analysis, multi-factor analysis and the like to screen the related TLS indexes in the training queue, and determines the indexes and the combination thereof which can realize more accurate prognosis prediction of colon cancer.
Preferably, the markers comprise one or more of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is any one of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of any 2-15 of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is the normal mucosal region TLS normalized maximum area.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of the distal region TLS normalized maximum area, the distal region TLS normalized mean area and the normal mucosal region TLS normalized total area.
Preferably, the tumor region is a region where tumor cells grow and infiltrate, and does not include an infiltration margin region.
The infiltration edge area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line.
The normal mucosa region is normal mucosa tissue adjacent to the infiltrated border region.
The TLS-related index is derived from colon cancer pathological tissue sections of colon cancer patients.
In a seventh aspect, the present invention provides a computer readable storage medium having a program stored thereon, the program when executed implementing the method of:
obtaining TLS related indexes in colon cancer pathological tissue samples of colon cancer patients to be prognosticated and predicted; wherein the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions; the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area; wherein the distal region is a region of the colon cancer tissue sample except for a tumor region, a normal mucosa region and an infiltration border region; the standardized index is the ratio of the index to the area value of the corresponding region;
providing the TLS related index data serving as input data to a prognosis risk model to obtain a risk value, wherein the prognosis risk model is constructed by adopting the model method or is the prognosis risk model;
obtaining an output result of the prognosis risk model and outputting a prediction result;
the invention further utilizes the means of single factor analysis, multi-factor analysis and the like to screen the related TLS indexes in the training queue, and determines the indexes and the combination thereof which can realize more accurate prognosis prediction of colon cancer.
Preferably, the markers comprise one or more of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is any one of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of any 2-15 of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
In some embodiments of the invention, the marker for prognosis of colon cancer is the normal mucosal region TLS normalized maximum area.
In some embodiments of the invention, the marker for prognosis of colon cancer is a combination of the distal region TLS normalized maximum area, the distal region TLS normalized mean area and the normal mucosal region TLS normalized total area.
Preferably, the tumor region is a region where tumor cells grow and infiltrate, and does not include an infiltration margin region.
The infiltration edge area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line.
The normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
The TLS-related index is derived from colon cancer pathological tissue sections of colon cancer patients.
The beneficial effects of the invention at least comprise the following points:
1. compared with the detection of the number or density of genes, proteins and single types of lymphocytes related to the prognosis of the colon cancer, the TLS related quantitative index provided by the invention is used as a marker for prognosis prediction of the colon cancer, can accurately and objectively reflect the immune state of a patient, has higher prognosis prediction accuracy, and provides a new index and a method for evaluating the prognosis of a patient with the colon cancer.
2. The colon cancer prognosis risk model provided by the invention can be independent of TNM staging to predict colon cancer prognosis, can better stratify prognosis risk of colon cancer patients compared with TNM staging, guides the clinic to respectively make more targeted treatment schemes for patients with different prognoses, and is beneficial to realizing accurate medical treatment.
Drawings
Fig. 1 is a schematic diagram of dividing four different regions, namely a normal mucosal region, a tumor region, an infiltration boundary line (two sides are infiltration edge regions), and a distal region in a pathological tissue section WSI of a colon cancer patient according to embodiment 1 of the present invention, and the TLS structure in the diagram is selected and labeled.
FIG. 2 is a high magnification of the TLS structure in WSI of colon cancer patients in example 1 of the present invention, and the follicular center is clearly shown.
FIG. 3 shows the result of single-factor analysis of the effect of TLS markers associated with different regions on prognosis of colon cancer patients in example 1, wherein the multiple TLS markers associated with the TLS-N, TLS-F and TLS-IM regions are factors (P < 0.05) having significant effect on predicting prognosis of colon cancer patients.
FIG. 4 is a result of survival analysis of TLS checker in colon cancer patients associated with prognosis of colon cancer patients in example 1 of the present invention, wherein the Progression Free Survival (PFS) of TLS checker high group patients is higher than that of TLS checker low group patients (P = 0.0082).
FIG. 5 is a forest chart of the results of the multifactorial analysis including clinical risk parameters in example 1 of the present invention, where TLS checker is an independent and significant prognostic factor (P < 0.05) for colon cancer patients and is superior to other clinical factor parameters, compared to other clinical indicators.
FIG. 6 shows the analysis results of training cohorts in LASSO regression survival analysis results in example 2 of the present invention.
Fig. 7 is an analysis result of the validation queue in the LASSO regression survival analysis result in embodiment 2 of the present invention.
Fig. 8 is a forest chart of the results of the multifactorial analysis including clinical risk parameters in example 2 of the present invention, and compared with other clinical indicators, the model predicted risk value is an independently significant prognostic factor (P = 0.007) for colon cancer patients.
FIG. 9 is a pie chart of the results of the multi-factor analysis including clinical risk parameters in example 2 of the present invention, wherein the model predicted risk value has the greatest weight (34.37%) among all relevant factors of the multi-factor analysis compared with other clinical indicators and is better than other clinical factors.
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The main reagents and instruments used in the following examples are shown in table 1.
TABLE 1 Main reagents and Equipment
Example 1 TLS-associated index acquisition and screening
1. Case selection
In this example, the pathological tissue samples of 103 patients with colon cancer were collected, and the detailed clinical pathological information of these patients was collected, such as: the prognosis of TNM staging, preoperative CEA and CA19.9 levels, tumor location, neuro-infiltration, lymphatic vascular infiltration, and tumor recurrence or metastasis, are shown in Table 2.
TABLE 2103 clinical pathological information of colon cancer patients
2. Preparation of tissue sections
(1) Fresh colon cancer tissue specimens were taken, including tumors, infiltrated margins and tissues adjacent to normal tissues, with the pieces longitudinally covering the mucosa, submucosa, muscle layer, serosa and periintestinal adipose tissue. Soaking in 10% neutral buffered formalin for 24-48 hr for fixation, and placing into an embedding box along longitudinal section.
(2) The embedding cassette was placed in a dehydrator and dehydrated with different concentrations of ethanol and xylene and waxed.
(3) Placing the tissue in an embedding machine, adding melted wax for embedding and marking, cooling and solidifying at-20 ℃, taking out the wax block after complete solidification, trimming and slicing.
(4) And (3) placing the trimmed wax block in a slicing machine, setting the slicing thickness to be 4 microns, moving the cut tissue to a sheet spreading machine, lightly fishing out the flattened tissue slice by using a positive charge anti-falling glass slide, moderately baking the slice by using the sheet baking machine, and storing the marked slice in a slice box.
(5) HE staining of tissue sections was performed and histopathological changes were clarified by microscopic observation.
3. Immunohistochemical staining
The 4 μm paraffin sections prepared in (4) above were subjected to CD3 immunohistochemical staining. The staining is carried out in the automatic immunohistochemistry, and the staining is finished after the section is subjected to dewaxing, hydration, anti-CD 3 antibody incubation, secondary antibody incubation, DAB color development, hematoxylin counterstaining cell nucleus, mounting and other series of operations.
4. Full scan tissue image
And (3) loading the dyed slices into a chinaman Nanozomer XR automatic digital scanner, circling the ROI area of each slice after previewing, automatically scanning all circled slices by the scanner, and finally obtaining complete WSI.
5. TLS labeling
A pathologist uses digital pathology analysis software NDP to select a normal mucosa area, a tumor area, an infiltration border area (namely a boundary area of a tumor and surrounding tissues) and a far-end area except the normal mucosa area, the tumor area, the infiltration border area and the far-end area in a WSI scanning image, and the area of each area is measured; TLS structures are further identified and selected through HE morphological characteristics and CD3 marks in the WSI scanning images, and the area of the TLS is measured and counted. As shown in FIGS. 1 and 2, TLS can be divided into TLS-T (tumor region), TLS-N (normal mucosa region), TLS-IM (infiltrating border region including 400 μm width range inside and outside the infiltrating border) and TLS-F (distal region except the above region) according to the position of TLS in the tissue section, and the TLS area, the area of each region and the TLS counting result are combined to obtain data values of 20 TLS-related indexes (four regions of normal mucosa region, tumor region, infiltrating border region and distal region, each region including five TLS indexes of TLS standardized average area, TLS standardized maximum area, TLS standardized median area, TLS standardized total area and TLS density). Taking the normal mucosa region as an example, the region includes TLS _ N _ meana _ norm (normal mucosa region TLS normalized average area), TLS _ N _ Maxarea _ norm (normal mucosa region TLS normalized maximum area), TLS _ N _ media _ norm (normal mucosa region TLS normalized Median area), TLS _ N _ area norm (normal mucosa region TLS normalized total area), and TLS _ N _ dense (normal mucosa region TLS density), and so on for the other three regions.
6. Screening for colon cancer prognosis-related TLS markers
Screening TLS-associated markers for prognosis of colon cancer patients by one-way analysis, the method is approximately as follows:
using the 20 TLS-related index data values obtained in the above 5 as risk factors, screening the TLS index related to the colon cancer patient prognosis by using single-factor analysis, finding that TLS-N (the TLS normalized average area, the TLS normalized maximum area, the TLS normalized total area, the TLS density, the TLS-F (the TLS normalized average area, the TLS normalized maximum area, the TLS normalized median area) and the TLS index related to the TLS-IM region (the TLS normalized average area, the TLS normalized median area) can effectively predict the colon cancer patient prognosis, and stratifying the risk of the patient, wherein the results are shown in FIG. 3, and the plurality of TLS indexes related to the TLS-N, TLS-F and the TLS-IM region have a significant influence on the colon cancer prognosis (P < 0.05).
Taking the normalized maximum area of the normal mucosal region TLS as an example, the 103 continuous variables are subjected to a surv _ cutoff function in an R-packet survminer to obtain a threshold at the minimum value of the P value as an optimal threshold (cutoff value, i.e., TLS checker), and the patients are classified into a TLS checker High group (patients with TLS _ N _ Maxarea _ norm values higher than the cutoff value) and a TLS checker Low group (patients with TLS _ N _ Maxarea _ norm values lower than or equal to the cutoff value) based on the threshold. Survival analysis was performed in 103 colon cancer patients using TLS checker, and the Kaplan-Meier survival curve showed that the progression free survival time (PFS) was higher in the TLS checker high group of patients than in the TLS checker low group of patients (P = 0.0082) (as shown in fig. 4).
As shown in fig. 5, TLS checker is an independent and significant prognostic factor (P = 0.016) for colon cancer patients, and the results of multifactorial analysis of clinical risk factors such as TLS checker, TNM stage (TNM stage), preoperative CEA and CA19.9 levels, tumor neuro-infiltration and lymphatic vascular infiltration are shown.
Example 2 construction of a Colon cancer prognostic Risk model based on TLS-related indices
This example includes pathological tissue samples of 171 colon cancer patients who have been surgically removed, and collects detailed clinical pathological information of these patients, such as: prognosis for TNM staging, preoperative CEA and CA19.9 levels, tumor location, neuro-infiltration, lymphatic vascular infiltration, and tumor recurrence or metastasis. The clinical follow-up time of the patients ranged from 10 months to 50.1 months, with an average follow-up time of 32.3 months. Based on TLS-N and TLS-F region related TLS indexes screened and determined in the embodiment 1, a colon cancer prognosis risk model is constructed by adopting multi-factor regression analysis, and the specific method is as follows:
1. construction of colorectal cancer prognosis risk model by Lasso regression analysis
171 patients were randomly divided into a model training cohort and a validation cohort, wherein the model training cohort 113 people, the validation cohort 58 people, and the detailed clinical information of the two groups of patients are shown in tables 3 and 4.
TABLE 3113 model training team for patient clinical pathology information
Table 458 cases of model validation cohort patient clinical pathology information
And (3) performing Linear regression analysis by taking 10 TLS related data values as risk factors, wherein the used regression methods comprise three regression analysis methods of Lasso, Logistic and Linear, and obtaining a plurality of effective colon cancer prognosis risk models.
And adopting 1000-fold cross validation for Lasso analysis, taking AUC as a target parameter which is expected to be optimized when the model is selected by the cross validation, and selecting the optimal lambda value in the cross validation result to obtain a Risk value (Risk score).
The role of TLS-related markers in colon cancer prognosis is demonstrated below, with the model obtained in the Lasso regression analysis as an example, which reached more than 70% AUC in both training and validation sets without overfitting. The model is as follows: y =1.37530162+0.06566891 × TLS _ F _ Maxarea _ norm +0.40739196 × TLS _ F _ means _ norm +1.06475078 × TLS _ N _ area _ norm; where y is the Risk value (Risk Score), TLS _ F _ Maxarea _ norm is the normalized maximum area of the distal region TLS, TLS _ F _ Meanarea _ norm is the normalized mean area of the distal region TLS, and TLS _ N _ area _ norm is the normalized total area of the normal mucosal region TLS;
the Risk values (Risk Score) of the training cohort patients were divided into Score High and Score Low groups, respectively, according to the quantile values 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, using the quantile function in R-package stats, and subjected to survival analysis. When the quantile is located at 60%, the P value is significant and the HR value is minimal, the training team patients used for model construction are divided into a high Risk group (Risk Score is less than or equal to 1.31) and a low Risk group (Risk Score is greater than 1.31) by taking the sextant as the cutoff value, the survival analysis result is shown in figure 6, and the low Risk group in the training team is 41 patients with progression-free survival time (PFS) which is significantly higher than that of 72 patients with colon cancer in the high Risk group (P =0.007, HR = 0.2622).
2. Cohort validation validity of prognostic risk models
The prognosis Risk model constructed in 1 above was used for validation in validation cohort, and the predicted results are shown in fig. 7, and the prognosis of colon cancer patients in high Risk group (Risk Score ≦ 1.31) is actually worse than that in low Risk group (Risk Score > 1.31) (P =0.0427, HR = 0.2868), and the results prove the validity of the model for prognosis prediction of colon cancer.
3. Significance of clinical factors such as Risk value Risk Score and nerve infiltration predicted by multi-factor analysis model on prognosis of colon cancer patients
The clinical Risk factors such as the model predicted Risk value Risk Score, the TNM stage, the preoperative CEA and CA19.9 level, the tumor nerve infiltration and the lymphatic vessel infiltration are subjected to multi-factor analysis, and as a result, as shown in fig. 8 and 9, compared with other clinical indexes, the model Risk Score is an independent and significant prognostic factor (P = 0.007) of a colon cancer patient, and has the largest weight (34.37%) in all relevant factors of the multi-factor analysis and is superior to other clinical factor parameters.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (26)
1. A marker for prognosis of colon cancer, wherein the marker comprises one or more of the following TLS-associated indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions;
the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area;
wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region;
the normalized index is the ratio of the index to the area value of its corresponding region.
2. The marker of claim 1, wherein the marker comprises one or more of the following TLS-related indicators: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
3. The marker of claim 1, wherein the tumor region is a region of tumor cell growth and infiltration, excluding the infiltration border region;
and/or the infiltration border area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line;
and/or, the normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
4. The marker according to any one of claims 1 to 3, wherein the index is derived from a colon cancer pathological tissue section of a colon cancer patient.
5. Use of a product for detecting a marker according to any one of claims 1 to 4 in the manufacture of a product for prognosis of colon cancer.
6. The application of claim 5, wherein when the product is used for prognosis prediction of colon cancer, a judgment threshold value is determined according to the TLS-related index of a colon cancer patient with known prognosis, then the TLS-related index of the patient to be prognosticated is compared with the threshold value, and the prognosis of the patient to be prognosticated is predicted according to the comparison result.
7. The use of claim 6, wherein the test product comprises one or more selected from the group consisting of a tissue section preparation reagent, a tissue section staining reagent, a tissue section scanning device, and image analysis software.
8. Use of a marker according to any one of claims 1 to 4 for the construction of a model for the prognosis of colon cancer.
9. A method for constructing a colon cancer prognosis risk model, which is characterized by comprising the following steps:
(1) obtaining TLS related indexes of colon cancer pathological tissue samples of colon cancer patients;
(2) performing linear regression analysis by taking the TLS related indexes as risk factors to construct a prognosis risk model;
the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions;
the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area;
wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region;
the normalized index is the ratio of the index to the area value of its corresponding region.
10. The method for constructing a colon cancer prognostic risk model according to claim 9, wherein the TLS-related indicators include one or more of the following: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
11. The method of claim 9, wherein the tumor region is a region where tumor cells grow and infiltrate, and does not include an infiltration border region;
and/or the infiltration border area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line;
and/or, the normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
12. The construction method according to any one of claims 9 to 11, wherein the TLS-associated index is derived from a colon cancer pathological tissue section of a colon cancer patient.
13. The colon cancer prognosis risk model constructed by the method for constructing a colon cancer prognosis risk model according to any one of claims 9 to 12.
14. A system for prognosis prediction of colon cancer, the system comprising:
(1) the data acquisition module is used for acquiring TLS related indexes in a colon cancer pathological tissue sample of a colon cancer patient to be prognosticated;
wherein the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions;
the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area;
wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region;
the standardized index is the ratio of the index to the area value of the corresponding region;
(2) a prediction module for providing the TLS-related index data obtained by the data acquisition module as input data to a prognostic risk model to obtain a risk value,
the prognostic risk model is constructed by the method of any one of claims 9 to 12 or the prognostic risk model of claim 13;
(3) and the prediction result output module is used for acquiring the output result of the prognosis risk model in the prediction module and outputting the prediction result.
15. The system of claim 14, wherein the TLS-related metrics comprise one or more of the following: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
16. The system of claim 14, wherein the tumor region is a region of tumor cell growth and infiltration, excluding an infiltration border region;
and/or the infiltration border area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line;
and/or, the normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
17. The system of any one of claims 14 to 16, wherein the TLS-related indicator is derived from a colon cancer pathological tissue section of a colon cancer patient.
18. The system for prognosis of colon cancer according to claim 17, wherein said prediction module further comprises comparing the risk value outputted from said prognostic risk model with a threshold value, wherein a low risk is a risk value greater than the threshold value, and a high risk is a risk value less than or equal to the threshold value.
19. A computer device, characterized in that the computer device comprises a memory for storing a program and a processor which, when executing the program, implements the method of:
obtaining TLS related indexes in colon cancer pathological tissue samples of colon cancer patients to be prognosticated and predicted; wherein the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions; the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area; wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region; the standardized index is the ratio of the index to the area value of the corresponding region;
providing the TLS-related index data as input data to a prognostic risk model to obtain a risk value, wherein the prognostic risk model is constructed by adopting the method of any one of claims 9-12 or the prognostic risk model of claim 13;
and obtaining an output result of the prognosis risk model and outputting a prediction result.
20. The computer device of claim 19, wherein the TLS-related metrics comprise one or more of the following metrics: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
21. The computer device of claim 19, wherein the tumor region is a region of tumor cell growth and infiltration, excluding an infiltration border region;
and/or the infiltration border area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line;
and/or, the normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
22. The computer device of any one of claims 19 to 21, wherein the TLS-related indicator is derived from a colon cancer pathological tissue section of a colon cancer patient.
23. A computer-readable storage medium, having a program stored thereon, which when executed implements a method of:
obtaining TLS related indexes in colon cancer pathological tissue samples of colon cancer patients to be prognosticated and predicted; wherein the TLS-related indicators comprise one or more of the following indicators: TLS normalized mean area, TLS normalized median area, TLS normalized maximum area, TLS normalized total area, TLS density of colon cancer tumor-associated regions; the tumor-related area is one or more selected from tumor area, normal mucosa area, infiltration border area and distal end area; wherein the distal region is the region of the colon cancer pathological tissue sample except for the tumor region, the normal mucosa region and the infiltration border region; the standardized index is the ratio of the index to the area value of the corresponding region;
providing the TLS-related index data as input data to a prognostic risk model to obtain a risk value, wherein the prognostic risk model is constructed by adopting the method of any one of claims 9-12 or the prognostic risk model of claim 13;
and obtaining an output result of the prognosis risk model and outputting a prediction result.
24. The computer-readable storage medium of claim 23, wherein the TLS-related metrics comprise one or more of the following metrics: normal mucosa region TLS normalized mean area, normal mucosa region TLS normalized maximum area, normal mucosa region TLS normalized median area, normal mucosa region TLS normalized total area, normal mucosa region TLS density, distal region TLS normalized mean area, distal region TLS normalized maximum area, distal region TLS normalized median area, distal region TLS normalized total area, distal region TLS density, infiltrated edge region TLS normalized mean area, infiltrated edge region TLS normalized maximum area, infiltrated edge region TLS normalized median area, infiltrated edge region TLS normalized total area, infiltrated edge region TLS density.
25. The computer-readable storage medium of claim 23, wherein the tumor region is a region where tumor cells grow and infiltrate, excluding an infiltration border region;
and/or the infiltration border area is an area which takes a tumor cell infiltration line as a boundary line and is within the width range of 400 mu m from two sides of the boundary line;
and/or, the normal mucosal area is normal mucosal tissue adjacent to the infiltrated border area.
26. The computer-readable storage medium of any one of claims 23 to 25, wherein the TLS-related indicator is derived from a colon cancer pathological tissue section of a colon cancer patient.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110564848A (en) * | 2019-04-15 | 2019-12-13 | 上海市同仁医院 | Application of interleukin-36 in preparation of colorectal cancer prognosis product |
| US20200237255A1 (en) * | 2007-11-26 | 2020-07-30 | C. R. Bard, Inc. | System and Methods for Guiding a Medical Instrument |
| CN112972688A (en) * | 2021-02-08 | 2021-06-18 | 吉林大学第一医院 | Application of PPAR delta inhibitor and immunotherapy drug in preparation of antitumor drugs |
| CN113717942A (en) * | 2020-05-26 | 2021-11-30 | 华东师范大学 | Immunotherapy method combining chimeric antigen receptor and type I interferon and application thereof |
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| CN113717942A (en) * | 2020-05-26 | 2021-11-30 | 华东师范大学 | Immunotherapy method combining chimeric antigen receptor and type I interferon and application thereof |
| CN112972688A (en) * | 2021-02-08 | 2021-06-18 | 吉林大学第一医院 | Application of PPAR delta inhibitor and immunotherapy drug in preparation of antitumor drugs |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117476097A (en) * | 2023-10-25 | 2024-01-30 | 中山大学附属第六医院 | Colorectal cancer prognosis and treatment response prediction model based on tertiary lymphoid structure characteristic genes, and construction method and application thereof |
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