Device, method, system and application for evaluating prognosis of squamous cell carcinoma and adenocarcinoma of cervical cancer

Abstract

The present invention provides an apparatus, a method, a system and their applications for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma, belonging to the technical field of medical evaluation. Through bioinformatics analysis methods such as multivariate statistical analysis, differential screening, and COX regression analysis, the present invention obtains two biomarkers, namely adenylate kinase 4 and cerebral cavernous malformation 2 protein, for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma. Based on the biomarkers of the present invention, an evaluation apparatus, a computer-executed method, a computer system, etc. for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma are constructed, which are used to accurately judge the prognosis of cervical cancer patients.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological detection, and particularly relates to devices, methods, systems and their applications for diagnosing or evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma. Background Art

[0002] Cervical cancer is one of the common malignant tumors among women globally, mainly including cervical squamous cell carcinoma (CSCC) and cervical adenocarcinoma (CA). Among them, cervical squamous cell carcinoma accounts for 70%-90% of cervical cancer cases, while the incidence of cervical adenocarcinoma has been gradually increasing in recent years, especially in young women, with an obvious growth trend. Although cervical cancer screening and vaccination have significantly reduced its incidence and mortality, the prognosis of patients with advanced or recurrent cervical cancer is still poor, and there is an urgent need for improved prognostic assessment methods.

[0003] Currently, the prognosis assessment of cervical cancer mainly relies on traditional clinicopathological features, such as tumor stage, tumor size, lymph node metastasis and vascular invasion, etc. Although these indicators can provide certain predictive information, their accuracy and comprehensiveness are limited and cannot fully consider the individual differences of patients. With the rapid development of molecular biology technology, prognosis assessment based on biomarkers has become possible. Specific proteins, gene expression patterns, non-coding RNAs (such as miRNAs, lncRNAs) and immune factors in the tumor microenvironment, etc., are all considered to be closely related to the invasiveness and metastatic potential of cervical cancer. However, the current prognosis assessment methods for cervical squamous cell carcinoma and adenocarcinoma are not perfect and it is difficult to achieve dynamic monitoring and accurate prediction of the patient's condition.

[0004] In addition, the continuous progress of artificial intelligence (AI), machine learning (ML) and big data analysis technologies has made prognosis prediction models based on multi-modal data integration a research hotspot. By combining clinical features, histological data and molecular biomarker information, a more accurate prognosis assessment system can be established. However, there is currently a lack of an assessment platform specifically for cervical squamous cell carcinoma and adenocarcinoma, which cannot effectively integrate these multi-dimensional data and provide real-time and quantitative clinical decision support.

[0005] Therefore, there is an urgent need to develop a new technical platform that can effectively evaluate the prognosis of patients with cervical squamous cell carcinoma and adenocarcinoma. This platform should have the following characteristics: (1) integrating multiple biomarkers and clinical data for detection; (2) dynamically predicting the survival rate, recurrence risk and treatment response of patients through algorithm models; (3) providing simple, efficient and personalized diagnosis and treatment suggestions for clinicians.

[0006] Based on this need, the present invention proposes a device, method, system and its application for the prognosis assessment of cervical squamous cell carcinoma and adenocarcinoma, aiming to improve the accuracy of prognosis assessment, provide a scientific basis for clinical treatment decisions, and ultimately improve the survival outcome of patients. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a device, method, system and its application for assessing the prognosis of cervical squamous cell carcinoma and adenocarcinoma.

[0008] The present invention relates to a device, method and computer system for the prognosis assessment of cervical squamous cell carcinoma and / or cervical adenocarcinoma, aiming to provide a precise prognosis assessment tool for clinical use. The assessment device includes a data acquisition device and a data processing device. The data acquisition device is used to collect the biomarker level data from cervical cancer tissue samples, and these biomarkers are closely related to the prognosis of cervical squamous cell carcinoma and adenocarcinoma. For example, the biomarker for cervical squamous cell carcinoma is Adenylate Kinase 4 (AMPK4), and the biomarker for cervical adenocarcinoma is Cerebellar malformation 2 protein. The data processing device evaluates whether the level value of the obtained biomarker is within a preset risk range based on these detection data, and provides a prognosis prompt for doctors according to this result. If the level value of the biomarker falls within the risk range, the device will give an assessment result of poor prognosis, otherwise it will prompt a good prognosis. In addition, the device can be equipped with a detection device that can directly detect the level value of the biomarker, or read the data from an external storage device through a communication device for processing. The device can be integrated into an automated biomarker determination device to directly output the assessment result after the determination, realizing an integrated detection and assessment function.

[0009] The present invention also provides a computer-executed method for implementing the above assessment process. The method includes reading the detection data of the assessment object, evaluating whether the level value of the biomarker is within the risk range based on the detection data, and outputting a prognosis assessment prompt according to the assessment result. Through this method, the survival expectancy, recurrence risk and treatment response of patients with cervical squamous cell carcinoma and adenocarcinoma can be dynamically and accurately predicted, thus providing a scientific basis for clinical treatment decisions.

[0010] The present invention also relates to a computer system that executes corresponding steps to provide a convenient and accurate solution for the prognosis assessment of patients with cervical squamous cell carcinoma and adenocarcinoma through data processing and risk judgment, helping doctors make more precise treatment decisions. Specifically, it exemplarily executes the following steps:

[0011] a) Read the detection data of the evaluation object, where the detection data is the level value of the biomarker in the prognostic biomarkers of cervical squamous cell carcinoma and adenocarcinoma detected from the human cervical cancer tissue of the prognostic evaluation object of cervical squamous cell carcinoma and adenocarcinoma. The biomarker for cervical squamous cell carcinoma is adenylate kinase 4, and the biomarker for cervical adenocarcinoma is cerebral cavernous malformation 2 protein;

[0012] b) Determine whether the level value of the biomarker is within the preset risk range;

[0013] c) Output the judgment result of step b), and optionally give a risk prompt. If the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object falls within the risk range, give a risk prompt or risk assessment result of poor prognosis; if the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object does not fall within the risk range, give a risk prompt or risk assessment result of good prognosis; if the level value of cerebral cavernous malformation 2 protein of the cervical adenocarcinoma evaluation object falls within the risk range, give a risk prompt or risk assessment result of poor prognosis; if the level value of cerebral cavernous malformation 2 protein of the cervical adenocarcinoma evaluation object does not fall within the risk range, give a risk prompt or risk assessment result of poor prognosis.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] The present invention provides a novel molecular biomarker that can be used alone to discriminate the prognosis of cervical squamous cell carcinoma and adenocarcinoma, and can be used to construct a device for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma, wherein: the prognostic biomarker for cervical squamous cell carcinoma is adenylate kinase 4, and the prognostic biomarker for cervical adenocarcinoma is cerebral cavernous malformation 2 protein. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is the OPLS-DA analysis of the proteomic results in the embodiment.

[0018] Figure 2 It is the comparison of the protein abundances of AK4 (adenylate kinase 4) in four subtypes in the example.

[0019] Figure 3 It is the comparison of the protein abundances of CCM2 (cerebral cavernous malformation 2 protein) in four subtypes in the example.

[0020] Figure 4 It is the survival curve with AK4 as the stratification condition for squamous cell carcinoma patients in the example.

[0021] Figure 5 This is the survival curve with CCM2 as the stratification condition for adenocarcinoma patients in the example.

[0022] Figure 6 This is the block diagram of an integrated evaluation device for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma in an embodiment of the present invention.

[0023] Figure 7 This is the flowchart of a method for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma executed by a computer in an embodiment of the present invention. Detailed implementation manners

[0024] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the accompanying drawings. Many specific details are set forth in the following description to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined correspondingly without conflict.

[0025] Cervical squamous cell carcinoma and adenocarcinoma are common types of cervical cancer. The present invention provides corresponding biomarkers for the prognosis of cervical squamous cell carcinoma and adenocarcinoma, called biomarkers for the prognosis of cervical squamous cell carcinoma and adenocarcinoma.

[0026] Among them, the biomarkers for the prognosis of cervical squamous cell carcinoma and adenocarcinoma are adenylate kinase 4 and cerebral cavernous malformation 2 protein, respectively.

[0027] It should be noted that both adenylate kinase 4 and cerebral cavernous malformation 2 protein in the present invention are known proteins in the human body. Their protein IDs in the public protein database uniprot are shown in Table 1.

[0028] Adenylate Kinase 4 (AK4) has an accession number of P27144 in the protein database uniprot, belongs to the adenylate kinase family, and plays a key role in cellular energy metabolism and maintaining the adenine nucleotide balance in various subcellular regions. Specifically, adenylate kinase promotes the reversible transfer of phosphate groups from ATP to AMP, thus forming two molecules of ADP. Research shows that AK4 interacts with the ADP / ATP transporter and indirectly regulates the permeability of the mitochondrial membrane, indicating its potential role in regulating mitochondrial function. There is also evidence that an increase in AK4 expression is closely related to tumor metastasis and drug resistance, mainly through mitochondrial activity and oxidative stress. However, there are few reports on its association with the prognosis of cervical squamous cell carcinoma or its use alone as a prognostic biomarker for cervical squamous cell carcinoma.

[0029] The accession number of Cerebral cavernous malformations 2 protein (CCM2) in the protein database Uniprot is Q9BSQ5. It is a scaffold protein that plays a role in the stress-activated p38 mitogen-activated protein kinase (MAPK) signaling cascade. This protein interacts with SMAD specific E3 ubiquitin protein ligase 1 (also known as SMURF1) through a phosphotyrosine-binding domain, promoting the degradation of RhoA. The normal cytoskeletal structure, cell-cell interaction, and lumen formation of endothelial cells all require this protein. Mutations in this gene can lead to cerebral cavernous malformations. However, there are few reports on its association with the prognosis of cervical adenocarcinoma or its use alone as a prognostic biomarker for cervical adenocarcinoma.

[0030] Table 1 Protein IDs and Chinese and English Names

[0031]

[0032] It should be noted that among the above-mentioned prognostic markers for cervical squamous cell carcinoma and adenocarcinoma, the corresponding markers can be used alone as the basis for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma.

[0033] Single markers have many advantages in clinical applications. Their detection process is simple, the operation is convenient, and they are suitable for rapid implementation, especially in resource-limited medical settings, where they are more economical. In addition, the detection results of single markers are usually intuitive and clear, facilitating analysis and interpretation, and are also easy to standardize, thus ensuring the consistency and comparability of detection results. More importantly, the technologies related to single markers are convenient for conversion into commercial diagnostic reagents or devices, which can be widely promoted in primary medical institutions. Therefore, although single markers may have certain limitations, their unique advantages still have important application values in clinical detection and technology transformation.

[0034] As a preferred embodiment of the present invention, the prognostic markers for cervical squamous cell carcinoma and adenocarcinoma used to evaluate the prognosis of cervical squamous cell carcinoma and adenocarcinoma are adenylate kinase 4 and cerebral cavernous malformations 2 protein, respectively.

[0035] In another embodiment of the present invention, an evaluation device for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma is provided, including:

[0036] A data acquisition device for acquiring the detection data of the evaluation object. The detection data is the level value of the above-mentioned prognostic markers for cervical squamous cell carcinoma and adenocarcinoma (single marker or a combination of 2 markers) detected from the human cervical cancer tissue of the evaluation object for the prognosis of cervical squamous cell carcinoma and adenocarcinoma;

[0037] A data processing device for determining whether an evaluation object is a low-expression patient or a high-expression patient based on detection data; and

[0038] An output device for giving a prognosis evaluation according to the judgment result of the data processing device.

[0039] Optionally, the device further includes a detection device for detecting the level value of the biomarker.

[0040] The prognostic biomarker for cervical squamous cell carcinoma is adenylate kinase 4, and the prognostic biomarker for cervical adenocarcinoma is cerebral cavernous malformation 2 protein.

[0041] It should be noted that the high or low expression of each biomarker in the present invention needs to be obtained through statistical analysis based on the level values of the biomarkers of different populations in a large amount of experimental data. As a preference of the present invention, exemplarily, the low or high expression of the biomarker is a comparison between the level value of the biomarker in the human cervical cancer tissue of the evaluation object and the statistical level value in the human cervical cancer tissue with cervical squamous cell carcinoma or adenocarcinoma.

[0042] In the above data processing device, when it is determined that the prognostic biomarkers for cervical squamous cell carcinoma and adenocarcinoma are low-expressed or high-expressed, corresponding evaluation result prompts for the prognosis of cervical squamous cell carcinoma and adenocarcinoma can be given. If it is impossible to determine whether the prognostic biomarkers for cervical squamous cell carcinoma and adenocarcinoma are low-expressed or high-expressed, result prompts such as "uncertain" or "to be determined" can be given, or further combined with other evaluation systems or criteria to make a further judgment and give an evaluation result prompt.

[0043] It should be noted that the data acquisition device is an input device for inputting data or a communication device for reading data from an external data storage device through an interface. When using the input device, the data detected by the external device can be input into the evaluation device, and then an evaluation result can be given. When using the communication device, the corresponding external data storage device can be the data memory on the device for automatically measuring the foregoing biomarkers. The evaluation device of the present invention can be integrated in the device for automatically measuring the biomarkers, so as to directly output the evaluation result after the measurement is completed, realizing an integrated detection and evaluation function.

[0044] The present invention also relates to a computer execution method for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma, and the method includes the following steps:

[0045] a) Reading the detection data of cervical cancer patients, where the detection data is the level values of adenylate kinase 4 and cerebral cavernous malformation 2 protein, which are the prognostic biomarkers for cervical squamous cell carcinoma and adenocarcinoma, detected from the human cervical cancer tissue of the evaluation object for the prognosis of cervical squamous cell carcinoma and adenocarcinoma;

[0046] b) Data processing to calculate whether the level value of the biomarker is high or low based on the level value in step a), so as to determine whether the evaluation object is a low-expression patient or a high-expression patient; and

[0047] c) Output the evaluation result. Exemplarily, if it is determined that the evaluation object is a low-expression patient or a high-expression patient, a corresponding evaluation result prompt for the prognosis of cervical squamous cell carcinoma and adenocarcinoma will be given.

[0048] The present invention also provides a computer system, which performs the following steps:

[0049] a) Calculate the difference or ratio between the level value of the biomarker in the cervical cancer tissue of the evaluation object for the prognosis of cervical squamous cell carcinoma and adenocarcinoma and the statistical level value in the human cervical cancer tissue with cervical squamous cell carcinoma or adenocarcinoma. Among them, the prognostic biomarkers for cervical squamous cell carcinoma and adenocarcinoma are adenylate kinase 4 and cerebral cavernous malformation 2 protein respectively;

[0050] b) Determine whether the calculated difference or ratio is high or low to determine whether the evaluation object is a low-expression patient or a high-expression patient; and

[0051] c) Output the judgment result in step b) and optionally give a risk prompt. Exemplarily, if it is determined that the evaluation object is a low-expression patient or a high-expression patient, a corresponding evaluation result prompt for the prognosis of cervical squamous cell carcinoma and adenocarcinoma will be given.

[0052] Next, the present invention further demonstrates the selection principle, process and effect of the above-mentioned biomarker through a specific embodiment, so as to facilitate those skilled in the art to understand the essence of the present invention.

[0053] Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art. The reagents used in the present invention are all of analytical pure or above specifications. Among them, the chromatographic column used has the model: Acclaim Pep Map100 C18 column, and the manufacturer is Thermo; the liquid chromatograph has the model: EASY-nLC 1200, and the manufacturer is Thermo.

[0054] Example 1: Selection of Biomarker

[0055] Sample inclusion criteria: 1. Patients pathologically (including histology and cytology) diagnosed with cervical cancer. 2. Cervical tissue samples have been collected from the patients before treatment. 3. Age > 18 years old.

[0056] Sample exclusion criteria: Complicated with other primary malignant tumors.

[0057] Based on the above inclusion and exclusion criteria, tissue samples from 404 participants who visited Zhejiang Cancer Hospital from 2015 to 2020 were obtained. These 404 participants included 298 cases of squamous cell carcinoma of the cervix, 74 cases of adenocarcinoma of the cervix, 22 cases of adenosquamous carcinoma of the cervix, and 10 cases of neuroendocrine carcinoma of the cervix. The freshly collected cervical cancer tissues from the subjects were directly placed into cryotubes, and the cryotubes were then stored in an -80°C refrigerator and transported with dry ice.

[0058] Reagents used in the present invention: urea, thiourea, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS), tributylphosphine, formic acid, methanol, ammonium formate, ammonium bicarbonate, acetonitrile, dithiothreitol (DTT), iodoacetamide (IAM).

[0059] I. Sample processing and loading onto the machine

[0060] Sample processing: The samples were homogenized in lysis buffer (7 M urea, 2 M thiourea, 5% CHAPS, 2 mM tributylphosphine) supplemented with protease inhibitor. After standing for 20 minutes, the mixture was centrifuged at 14000 g for 10 minutes to remove tissue debris. The supernatant was collected, and the protein concentration was determined using the Bradford protein assay. After that, protein extraction and digestion were carried out. 100 μg of protein was purified by methanol / chloroform precipitation, and the resulting protein precipitate was dried. The dried precipitate was resuspended in 50 mM ammonium bicarbonate, reduced at 56°C for 30 min in 0.25 M dithiothreitol, and then alkylated at 37°C in the dark for 30 min in 0.3 M iodoacetamide. After dilution of the protein sample with 100 mM ammonium bicarbonate, trypsin digestion was performed (enzyme-substrate ratio 1:50, 37°C, 16 hours). The trypsin digestion was stopped by adding 20 μL of 0.1% aqueous formic acid solution. Then, desalting of the peptides was carried out using ZipTip Pipette Tips (Merch Millipore, MA) according to the standard procedure.

[0061] The polypeptides of each sample were resuspended in mobile phase A (0.1% FA) and mixed with 0.8 μL of 2.5×iRT standard (Biognosys, Zurich, Switzerland).

[0062] Peptide separation was performed using an EASY-nLC 1200 (Thermo Fisher Scientific, San Jose, USA) equipped with an Acclaim Pep Map 100 C18 column (250 mm × 75 μm, 2 μm, Thermo Scientific Inc.). The mobile phase for separation consisted of solvent A (0.1% formic acid aqueous solution) and solvent B (80% acetonitrile aqueous solution, 0.1% formic acid aqueous solution). For each acquisition, peptide separation was carried out with a 65-minute gradient (0 - 1 minute, 3 - 8% of buffer B; 1 - 52 min, 8% - 35% of buffer B; 52 - 56 min, 35% - 95% of buffer B; 56 - 65 min, 95% of buffer B) at a flow rate of 300 μl / min. Peptides eluted from the analytical column were ionized into an Orbitrap Exploris 480 (Thermo Fisher Scientific, San Jose, USA) at a potential of +2.2 kV. The target column loading was 500 ng of total peptides per injection (measured by NanoDrop before loading), and the pressure was 550 bar. The temperature of the ion transfer tube was maintained at 320 °C. For MS1 full scan, the Orbitrap mass analyzer acquired ions in the m / z range of 350 - 1500 with a high resolution of 120,000. The maximum ion injection time was 50 ms. MS2 acquisition was performed in the highest speed mode with a duty cycle time of 3 s. Precursor ions were selected and fragmented by high-energy collision dissociation (HCD) with a normalized collision energy of 32%. Each acquisition cycle included a survey scan with a resolution of 30,000 (normalized automatic gain control target (AGC) of 2000%, injection time (IT) of 54 ms), and 30 DIA cycles with an isolation window of 5.6.

[0063] MS / MS spectra were searched against the SwissProt human database on the UniProt website (www.UniProt.org) using Spectronaut v.18 (Biognosys, Zurich, Switzerland). The data filtering was set to “Qvalue”. Default settings were used unless otherwise specified. The false discovery rate (FDR) was set to 1% at both the protein and peptide precursor levels.

[0064] II. Biomarker discovery in bioanalysis

[0065] Figure 1The OPLS-DA results of the proteome in this example are shown. In the figure, SCC represents squamous cell carcinoma, AC represents adenocarcinoma, ASC represents adenosquamous carcinoma, and NEC represents neuroendocrine carcinoma. The OPLS-DA analysis diagram reflects the overall distribution of each group of samples.

[0066] Biological prognostic markers are mainly obtained through bioinformatics analysis methods such as differential screening, univariate COX regression analysis, and multivariate COX regression analysis. The main criterion for screening differences is a P value < 0.05, and the criterion for COX regression analysis is a P value < 0.05.

[0067] Screening of squamous cell carcinoma biomarkers:

[0068] First, compared with the other three subtypes respectively, the differential proteins of squamous cell carcinoma were screened out. Then, univariate COX regression analysis was performed on each subtype to screen out the proteins that have significant prognostic significance only for squamous cell carcinoma. Subsequently, the intersection of the above two groups of proteins was taken to obtain proteins that are both differentially expressed and have prognostic significance. Then, the above potential prognostic biomarkers were combined with age and stage for multivariate COX regression analysis to obtain the final prognostic biomarker adenylate kinase 4 (AK4). The hazard ratio (HR) of the biomarker is shown in Table 2.

[0069] Figure 2 It is the comparison of the protein abundances of adenylate kinase 4 (AK4) for the four subtypes. Squamous cell carcinoma is significantly upregulated compared with the other three subtypes. In the figure, SCC represents the cervical squamous cell carcinoma group, AC represents the cervical adenocarcinoma group, ASC represents the cervical adenosquamous carcinoma group, and NEC represents the cervical neuroendocrine carcinoma group.

[0070] Figure 4 It is to find the optimal cut-off point of adenylate kinase 4 (AK4) in patients with cervical squamous cell carcinoma using the surv_cutpoint function. The patients were divided into two groups according to the protein expression level. The figure shows the survival curves of the two groups. The P value is < 0.0001, indicating that the group with high adenylate kinase 4 expression has a worse prognosis.

[0071] Table 2 Hazard Ratio of Biomarkers

[0072]

[0073] Screening of adenocarcinoma biomarkers:

[0074] First, compared with the other three subtypes respectively, differential proteins in adenocarcinoma were screened out. Then, univariate COX regression analysis was performed on each subtype to screen out the proteins that had significant prognostic significance only for adenocarcinoma. Subsequently, the intersection of the above two groups of proteins was taken to obtain the proteins that were both differentially expressed and had prognostic significance. Then, the above potential prognostic biomarkers were analyzed together with age and stage by multivariate COX regression analysis to obtain the final prognostic biomarker, cerebral cavernous malformation 2 protein (CCM2). The box plot of the protein abundance of cerebral cavernous malformation 2 protein in the four subtypes is as Figure 3 shown, and the stratified survival curve in adenocarcinoma is as Figure 5 shown, and the hazard ratio (HR) of the biomarker is shown in Table 2.

[0075] Figure 3 is the comparison of the protein abundance of cerebral cavernous malformation 2 protein (CCM2) in the four subtypes. Compared with the other three subtypes, adenocarcinoma was significantly downregulated. In the figure, SCC represents the cervical squamous cell carcinoma group, AC represents the cervical adenocarcinoma group, ASC represents the cervical adenosquamous carcinoma group, and NEC represents the cervical neuroendocrine carcinoma group.

[0076] Figure 5 is to find the optimal cut-off point for cerebral cavernous malformation 2 protein (CCM2) in patients with cervical adenocarcinoma using the surv_cutpoint function, and the patients were divided into two groups according to the protein expression level. The figure shows the survival curves of the two groups. The P value is 0.00015, indicating that the group with high expression of cerebral cavernous malformation 2 protein has a worse prognosis.

[0077] Example 2: Further confirmation of the biomarker

[0078] Randomly select 100 cervical cancer tissue samples from patients who visited Zhejiang Cancer Hospital from 2020 to 2023. These 100 patients include patients with cervical squamous cell carcinoma and patients with cervical adenocarcinoma. The freshly collected cervical cancer tissues of the subjects were directly placed into cryotubes, and the cryotubes were placed in a -80°C refrigerator for storage and transported with dry ice.

[0079] The abundance of adenylate kinase 4 (AK4) in patients with cervical squamous cell carcinoma and the abundance of cerebral cavernous malformation 2 protein (CCM2) in patients with cervical adenocarcinoma were detected respectively, and the surv_cutpoint function was used to find the optimal cut-off point, and the patients were divided into two groups according to the protein expression level.

[0080] Survival curve analysis was performed on the two biomarkers respectively. The Log-rank test was performed on the survival times of the two groups with high and low expression of adenylate kinase 4 protein in patients with squamous cell carcinoma. The result showed that P was less than <0.001, and the group with high expression had a worse prognosis. The Log-rank test was performed on the survival curves of the two groups with high and low expression of cerebral cavernous malformation 2 protein in patients with adenocarcinoma. The result showed that P < 0.001, and the group with high expression had a worse prognosis.

[0081] The above results show that adenylate kinase 4 can be used to evaluate the prognosis of cervical squamous cell carcinoma, and the group with high adenylate kinase 4 expression has a worse prognosis; brain cavernous malformation 2 protein can be used to evaluate the prognosis of cervical adenocarcinoma, and the group with high brain cavernous malformation 2 protein expression has a worse prognosis.

[0082] Example 3: Integrated evaluation device for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma

[0083] This embodiment provides an integrated assessment device for the prognosis of cervical squamous cell carcinoma and / or cervical adenocarcinoma. This device combines data acquisition, processing, evaluation, and output functions to comprehensively and accurately assess the prognosis of patients with cervical squamous cell carcinoma and adenocarcinoma. Through automated biomarker detection and data analysis, the device provides real-time prognostic assessment results, providing a scientific basis for clinicians' treatment decisions.

[0084] The evaluation device mainly consists of the following modules (see Figure 6 ):

[0085] Data acquisition module: used to collect biomarker data from patients' cervical cancer tissue samples.

[0086] Data processing module: used to process the acquired marker data, evaluate whether it falls into the risk range, and calculate the prognostic risk.

[0087] Risk assessment output module: outputs prognostic assessment results based on data processing results and provides risk warnings for patients.

[0088] Communication interface module: used for data interaction with external devices and data storage systems, supporting data upload, download and remote diagnosis functions.

[0089] The data acquisition module utilizes automated biomarker detection technology, using integrated sensors and measurement equipment to detect specific biomarkers in cervical tissue samples. For cervical squamous cell carcinoma, the marker is adenylate kinase 4 (AMPK4), while for cervical adenocarcinoma, the marker is cerebral cavernous malformation 2 protein. The device converts the marker levels into digital signals and transmits them through the built-in data acquisition module, providing the basis for subsequent data processing.

[0090] The data processing module is the core component of the device, responsible for receiving the detection data from the data acquisition module and performing data analysis and evaluation. The specific steps are as follows:

[0091] Marker data analysis: First, the data processing module preprocesses the detected marker data, including denoising, standardization, etc. Then, according to the preset risk thresholds, it determines whether the level values of each biomarker fall within the corresponding risk ranges.

[0092] Risk assessment: The algorithm model embedded in the device determines whether each marker belongs to the high-risk area according to the preset risk range rules. For example, if the level value of adenylate kinase 4 (AMPK4) in cervical squamous cell carcinoma falls within the high-risk range, the assessment result indicates "poor prognosis"; if its level value is lower than this range, it indicates "good prognosis". Similarly, the level value of cerebral cavernous malformation 2 protein in cervical adenocarcinoma will also be evaluated in a similar way.

[0093] After the data processing module completes the analysis and evaluation of the marker data, the risk assessment output module generates a prognostic evaluation report for the patient according to the evaluation results. The report includes the following parts:

[0094] Prognostic risk level: According to the evaluation results, the device automatically generates prognostic risk assessment prompts, such as "low risk", "medium risk" or "high risk".

[0095] Personalized treatment recommendations: According to the results of the risk assessment, the device can also provide preliminary personalized treatment recommendations. For example, if the evaluation result shows high risk, it is recommended to carry out more aggressive treatment; if it is low risk, regular follow-up and monitoring can be considered.

[0096] Result visualization: The evaluation results are presented in the form of charts and text, which is convenient for doctors to quickly understand and make decisions. The charts may include trend charts of risk factors, prediction charts of patient survival expectations, etc.

[0097] To achieve remote diagnosis and data sharing, the device is equipped with a communication interface module, which can be connected to external data storage devices or medical information systems. This module supports data exchange with external devices through standardized protocols, and users can remotely access the evaluation results through the network interface. This function enables doctors in different regions to share data and conduct joint diagnoses.

[0098] In addition, the communication interface module also supports real-time data updates to ensure that the device can be updated and optimized according to the latest clinical research and risk assessment criteria during actual use.

[0099] The system architecture of this evaluation device mainly includes the following parts:

[0100] Hardware layer: It includes a data acquisition module (biomarker detection sensor, sample processing unit), a data processing module (computer processing unit, storage device), an output module (display, printer, audio output, etc.) and a communication module (network interface, USB interface, etc.).

[0101] Software layer: Based on the hardware architecture of the device, it provides a graphical interface, data input and output, algorithm calculation, and risk assessment functions. The software layer ensures the stable operation of the device and real-time data processing through an embedded operating system or a real-time operating system.

[0102] The process of using this device is as follows:

[0103] Sample collection: The clinician takes a cervical tissue sample from the patient's body and places it in the sample processing unit of the device.

[0104] Marker detection: The data acquisition module detects the cervical squamous cell carcinoma and adenocarcinoma markers in the sample through a biomarker detection sensor and obtains the level data of the markers.

[0105] Data input and processing: The biomarker data is input into the data processing module, and the module analyzes the data according to the preset algorithm rules to evaluate whether the level value of the biomarker falls within the risk range.

[0106] Evaluation output: The device generates a prognostic evaluation report, and the doctor makes corresponding treatment decisions based on the evaluation results.

[0107] Result feedback: The evaluation results are uploaded to the hospital information system through the communication interface, and a remote access function is provided to facilitate other experts to participate in the joint diagnosis.

[0108] The evaluation device of this embodiment has the following several significant advantages:

[0109] High efficiency: The device can quickly obtain biomarker data and perform real-time processing, reducing the time consumption of traditional pathological examinations.

[0110] Accuracy: Through the preset biomarker risk range, the device can provide accurate prognostic evaluations, helping to reduce the misdiagnosis rate.

[0111] Personalization: The device provides a personalized prognostic evaluation report based on the specific biomarker data of the patient, helping the doctor make precise treatment decisions.

[0112] Integration: This device integrates biomarker detection, data processing, and risk assessment functions.

[0113] Embodiment 4: A method for prognostic evaluation of cervical squamous cell carcinoma and adenocarcinoma performed by a computer

[0114] Step 1: Read the detection data of the evaluation object

[0115] In this embodiment, biomarker detection data is first obtained from a patient's cervical cancer tissue sample. The data includes the level values of biomarkers related to cervical squamous cell carcinoma and cervical adenocarcinoma. Among them, the biomarker for cervical squamous cell carcinoma is Adenylate Kinase 4 (AMPK4), and the biomarker for cervical adenocarcinoma is Cerebellar malformation 2 protein. The detection data of these biomarkers can be obtained through an automated biomarker detection device or read from an external data storage device through an interface.

[0116] Step 2: Data processing and risk range determination

[0117] After reading the biomarker data, the system will perform data processing according to preset criteria or databases. First, the system will perform data preprocessing, including denoising, normalization, and calibration, etc., to ensure the accuracy of the data. After processing, the system will evaluate whether the level value of the biomarker falls within the preset risk range.

[0118] For cervical squamous cell carcinoma, the system will determine whether the level of Adenylate Kinase 4 (AMPK4) falls within the high-risk or low-risk range. If the detection data of AMPK4 falls within the high-risk range, the system considers that the patient has a relatively high risk of poor prognosis.

[0119] For cervical adenocarcinoma, the system will determine whether the level of Cerebellar malformation 2 protein falls within the high-risk range. If the detection data of this biomarker exceeds the preset normal range, the system will prompt that the patient has a relatively high risk of prognosis.

[0120] Step 3: Risk assessment and prompt

[0121] According to the data processing results in Step 2, the system will evaluate the prognosis of the patient and generate a corresponding prognosis risk prompt. The prognosis evaluation results include:

[0122] High-risk prompt: If the level value of AMPK4 in a cervical squamous cell carcinoma patient falls within the high-risk range, or the level value of Cerebellar malformation 2 protein in a cervical adenocarcinoma patient falls within the high-risk range, the system will prompt "poor prognosis" and recommend further clinical examinations and treatment interventions.

[0123] Low-risk prompt: If the biomarker level value does not fall within the risk range, the system will prompt "good prognosis" and recommend routine monitoring.

[0124] Step 4: Output the prognosis evaluation result

[0125] The system will generate a detailed prognosis evaluation report, which includes the following content:

[0126] Basic information of the evaluation object (such as patient name, age, gender, etc.).

[0127] The test data of each biomarker and its corresponding risk range.

[0128] Prognostic risk level (such as low risk, medium risk, high risk).

[0129] Personalized treatment suggestions or clinical observation suggestions to assist doctors in making decisions based on the prognostic evaluation results.

[0130] Step 5: Data storage and sharing

[0131] The evaluation results will be stored in the hospital's Electronic Health Record (EHR) system, and doctors can query and view the evaluation records of this patient at any time. Through the communication interface of the system, the evaluation results can also be uploaded to the cloud database for further review and diagnostic support by telemedicine experts.

[0132] Step 6: Continuous monitoring and feedback

[0133] During the patient's treatment process, the computer system supports continuous monitoring. If the subsequent biomarker test data of the patient changes, the system can update the risk assessment results in real time and adjust the treatment plan according to the new data. Through the dynamic association with the treatment process, the system will provide regular prognostic evaluations and treatment feedback to help clinicians make timely decisions (see Figure 7 ).

[0134] Through the above method, the prognostic evaluation of cervical squamous cell carcinoma and adenocarcinoma can be carried out more accurately and quickly, and at the same time, it provides an efficient decision-making support tool for clinicians. This method uses an automated evaluation process executed by a computer, reduces the error of manual analysis, and improves the personalized treatment effect of patients. Those of ordinary skill in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a program, and the said program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a disk or an optical disc, etc.

[0135] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An evaluation device for evaluating the prognosis of cervical squamous cell carcinoma, characterized in that, Comprising: A data acquisition device for acquiring detection data of an evaluation object, where the detection data is the level value of a biomarker in the cervical squamous cell carcinoma prognostic markers detected from the human cervical cancer tissue of the cervical squamous cell carcinoma prognosis evaluation object; A data processing device for calculating whether the detection data of the biomarker obtained based on the detection data of the evaluation object is within the risk range of the corresponding biomarker, where the cervical squamous cell carcinoma biomarker is adenylate kinase 4.

2. The evaluation device according to claim 1, characterized in that: a) The device further includes a detection device for detecting the level value of the biomarker; or b) If the detection data of the biomarker in the cervical squamous cell carcinoma prognostic markers falls within the risk range of the cervical squamous cell carcinoma prognosis, a corresponding evaluation result prompt for the cervical squamous cell carcinoma prognosis is given.

3. The evaluation device according to claim 1, characterized in that, In the data processing device, if the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object falls within the risk range, a risk prompt or risk assessment result of poor prognosis is given; if the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object does not fall within the risk range, a risk prompt or risk assessment result of good prognosis is given.

4. The evaluation device according to claim 2, characterized in that, The data acquisition device is an input device for inputting data or a communication device for reading data from an external data storage device or the storage device of the detection device through an interface.

5. The evaluation device according to claim 4, wherein When a communication device is adopted, its corresponding external data storage device is the data memory on the device for measuring the biomarker.

6. The evaluation device according to claim 4, characterized in that, It is integrated in the automated measurement device of the biomarker, so as to directly output the evaluation result after the measurement is completed, realizing an integrated detection and evaluation function.

7. A computer execution method for evaluating the prognosis of cervical squamous cell carcinoma, the method comprising the following steps: a) Reading the detection data of a cervical cancer patient, where the detection data is the level value of the cervical squamous cell carcinoma biomarker adenylate kinase 4 detected from the human cervical cancer tissue of the cervical squamous cell carcinoma prognosis evaluation object; b) Data processing to calculate the level value of the biomarker based on the level value in step a), and determine whether the calculated detection data of the biomarker is within the risk range of the corresponding biomarker; c) Outputting an evaluation result. When the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object falls within the risk range, a risk prompt or risk assessment result of poor prognosis is given; when the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object does not fall within the risk range, a risk prompt or risk assessment result of good prognosis is given.

8. The execution method according to claim 7, wherein in step c), if there is a situation where the level value of the biomarker is within the risk range of the cervical squamous cell carcinoma prognosis, a corresponding evaluation result prompt for the cervical squamous cell carcinoma prognosis is given.

9. A computer system, which executes the following steps: a) Reading the detection data of the evaluation object, where the detection data is the level value of the biomarker in the cervical squamous cell carcinoma prognostic markers detected from the human cervical cancer tissue of the cervical squamous cell carcinoma prognosis evaluation object, and the cervical squamous cell carcinoma biomarker is adenylate kinase 4; b) Judging whether the level value of the biomarker is within a preset risk range; c) Output the judgment result of step b) and give a risk warning. If the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object falls within the risk range, give a risk warning of poor prognosis or a risk assessment result; if the level value of adenylate kinase 4 of the cervical squamous cell carcinoma evaluation object does not fall within the risk range, give a risk warning of good prognosis or a risk assessment result.

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