ESCC Tumor Cell Differential Expression Detection Methods and Systems

Abstract

The present invention relates to a method and system for detecting differential expression of ESCC tumor cells. Based on H3K27ac chip, ChIP-seq sequencing technology is used to obtain H3K27ac signals of transcriptomes in various tissues respectively, and the signals are reported to time series analysis and prediction software. The time series analysis and prediction software finds differential enhancers with H3K27ac enrichment signals from the heat map according to the H3K27ac signals, and further identifies corresponding super enhancers (SEs) from enhancers with H3K27ac enrichment signals in Nor, EC and LNC. It is possible to predict and find super enhancers (SEs) that inhibit differential expression of ESCC tumor cell responses. The process of inhibiting ESCC tumor cell proliferation can be further described through SEs description, providing basic research and clinical cancer treatment value for ESCC tumor cell treatment.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to a method and system for detecting differential expression of ESCC tumor cells and electronic equipment. Background Art

[0002] An aberrant and reprogrammed transcriptome is a universal hallmark of human cancer, associated with deregulated cell proliferation, invasion, and metastasis.

[0003] The main causes of abnormal transcriptional patterns are genetic alterations and deregulated signaling pathways and epigenomic traits, including DNA methylation, histone modifications, and other cis-regulatory elements. Multiple studies have highlighted the importance of histone modifications in establishing and maintaining disease and cancer states, and super-enhancers (SEs), composed of clustered enhancers, are considered key controllers of different cell types and disease conditions, and are associated with disease-specific genetic variants and mutations.

[0004] In recent years, epigenomic analysis of tumor-specific promoters, enhancers and super-enhancer landscapes during carcinogenesis has been widely used to elucidate the molecular mechanisms of local and regional oncogenes in several cancers, such as colon cancer, gastric cancer, renal cell carcinoma (RCC) and ependymoma. Although global histone modifications have been implicated in the evolution of distant metastasis during pancreatic and prostate cancer progression, the genome-wide epigenomic landscape of ESCC tumorigenesis and metastasis is still unclear.

[0005] Therefore, to elucidate the role of enhancer dynamics in ESCC progression and lymph node metastasis, it is necessary to predict and characterize enhancers and super enhancers (SEs) in primary ESCC and adjacent tissues such as lymph node cancer (LNC) and normal esophageal epithelial cells. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a method and system for detecting differential expression of ESCC tumor cells and an electronic device.

[0007] In one aspect of the present invention, a method for detecting differential expression of ESCC tumor cells is provided, comprising the following steps:

[0008] Several groups of adjacent normal tissue (Nor), esophageal squamous cell carcinoma (EC) tissue, and lymph node cancer (LNC) tissue samples were preset;

[0009] Based on the H3K27ac chip, ChIP-seq sequencing technology was used to obtain the H3K27ac signals of the transcriptome in each tissue and reported to the time series analysis and prediction software;

[0010] The time series analysis prediction software found the number of promoters (Pro) and enhancers (Enh) in Nor, EC, and LNC, respectively, based on the H3K27ac signal;

[0011] Generate a heat map of the enhancers according to the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map;

[0012] PCA analysis was performed on Nor, EC, and LNC samples, and the corresponding super enhancers (SEs) were identified from enhancers with H3K27ac enrichment signals in Nor, EC, and LNC using PCI gene scores.

[0013] As an optional embodiment of the present invention, optionally, before the time series analysis prediction software is based on the H3K27ac signal, it also includes:

[0014] Generate the corresponding ChIP-seq data set based on the H3K27ac signals of the transcriptomes in each tissue;

[0015] The ChIP-seq data set was subjected to quantile normalization using a preset preprocessing method.

[0016] As an optional embodiment of the present invention, optionally, before the time series analysis prediction software is based on the H3K27ac signal, it also includes:

[0017] Perform Q30 evaluation on the sequencing reads of the ChIP-seq data set of each tissue to determine whether the Q30 of the sequencing reads exceeds the preset value:

[0018] If it exceeds, proceed to the next step;

[0019] Instead, H3K27ac signals were resampled and a new ChIP-seq dataset was generated.

[0020] As an optional embodiment of the present invention, optionally, the time series analysis prediction software finds the number of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC respectively according to the H3K27ac signal, including:

[0021] The ChIP-seq data sets of Nor, EC, and LNC were imported into time series analysis prediction software for data collation and statistical analysis of the distribution characteristics of H3K27ac signals in Nor, EC, and LNC;

[0022] According to the distribution characteristics, the enhancers with different distributions in Nor, EC and LNC tissues were identified, and the number of regional changes in Nor, EC and LNC was calculated based on the enhancers with different distributions.

[0023] Combined with the comparison among Nor, EC and LNC, the numbers of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC were found respectively.

[0024] As an optional embodiment of the present invention, optionally, before identifying the enhancers with differences among Nor, EC and LNC tissues, the method further comprises:

[0025] Define enhancers and promoters:

[0026] Significant H3K27ac peaks that were more than 0.5 kb away from the nearest TSSs were defined as enhancers;

[0027] Regions with significant H3K27ac occupancy within ±0.5 kb of TSSs were defined as promoters.

[0028] As an optional embodiment of the present invention, optionally, a heat map of the enhancers is generated according to the enhancers in Nor, EC and LNC, and differential enhancers with H3K27ac enrichment signals are found from the heat map, including:

[0029] According to the types of Nor, EC, and LNC, enhancers in Nor, EC, and LNC were divided into different types of enhancers;

[0030] Generate a heat map of the enhancers according to the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map;

[0031] The differential enhancers were divided into several groups according to the magnitude of gain and loss of the H3K27ac enrichment signal.

[0032] As an optional embodiment of the present invention, optionally, PCA analysis is performed on Nor, EC and LNC samples, and the corresponding super enhancers (SEs) are identified from enhancers with H3K27ac enrichment signals in Nor, EC and LNC using PCI gene scores, including:

[0033] Inputting several groups of the differential enhancers into a cluster analysis model;

[0034] Performing unsupervised hierarchical cluster analysis on several groups of differential enhancers, and using a cluster analysis algorithm to separate the differential enhancers of Nor, EC and LNC into specific traits of different groups;

[0035] Principal component analysis was performed on different population-specific traits. All differentially distributed promoter elements: promoters and enhancers were used to perform PCA on differential promoter elements and enhancer-related genes, and the gene scores of positive PC1 were calculated. The corresponding super enhancers (SEs) in Nor, EC and LNC were determined using the positive PC1 gene scores.

[0036] In another aspect, the present invention provides a system for detecting differential expression of ESCC tumor cells, comprising:

[0037] H3K27ac chip, used to obtain H3K27ac signals of transcriptomes in Nor, EC and LNC sample tissues respectively using ChIP-seq sequencing technology, and reported to time series analysis and prediction software;

[0038] The time series analysis prediction software is used to find the number of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC respectively according to the H3K27ac signal; generate a heat map of the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map; perform PCA analysis on Nor, EC and LNC samples, and use PCI gene scores to identify the corresponding super enhancers (SEs) from the enhancers with H3K27ac enrichment signals in Nor, EC and LNC.

[0039] In another aspect, the present invention further provides an electronic device, comprising:

[0040] processor;

[0041] a memory for storing processor-executable instructions;

[0042] Wherein, the processor is configured to implement the ESCC tumor cell differential expression detection method when executing the executable instructions.

[0043] Technical effects of the present invention:

[0044] The present invention uses ChIP-seq sequencing technology based on H3K27 exchange chip to obtain H3K27ac signals of transcriptomes in various tissues respectively, and reports them to time series analysis and prediction software; the time series analysis and prediction software finds the number of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC respectively according to the H3K27ac signal; generates a heat map of the enhancers according to the enhancers in Nor, EC and LNC, and finds differential enhancers with H3K27ac enrichment signals from the heat map; PCA analysis is performed on Nor, EC and LNC samples, and PCI gene scores are used to identify corresponding super enhancers (SEs) from enhancers with H3K27ac enrichment signals in Nor, EC and LNC. It can predict and find super enhancers (SEs) that inhibit differential expression of ESCC tumor cell response, and the process of inhibiting ESCC tumor cell proliferation can be further described through SEs description, providing basic research and clinical cancer treatment value for ESCC tumor cell treatment.

[0045] Further features and aspects of the present invention will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the invention and, together with the description, serve to explain the principles of the invention.

[0047] Figure 1 FIG. 1 is a schematic diagram showing the implementation process of the method for detecting differential expression of ESCC tumor cells of the present invention;

[0048] Figure 2 Shown is a sampling schematic diagram of the present invention;

[0049] Figure 3 Shown is a schematic diagram of enhancers differentially distributed in tissues of the present invention;

[0050] Figure 4 Shown is a schematic diagram of the definition of the enhancer of the present invention;

[0051] Figure 5 Shown is a schematic diagram of the ESCC tumor cell differential expression detection system of the present invention;

[0052] Figure 6 It is a schematic diagram showing the application of the electronic device of the present invention. DETAILED DESCRIPTION

[0053] Various exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0054] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0055] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can also be implemented without certain specific details. In some examples, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0056] Example 1

[0057] like Figure 1 As shown, in one aspect of the present invention, a method for detecting differential expression of ESCC tumor cells is provided, comprising the following steps:

[0058] S1, preset groups of adjacent normal tissue (Nor), esophageal squamous cell carcinoma (EC) tissue and lymph node cancer (LNC) tissue samples;

[0059] S2. Based on the H3K27ac chip, ChIP-seq sequencing technology was used to obtain the H3K27ac signal of the transcriptome in each tissue, and reported to the time series analysis and prediction software;

[0060] S3, Time series analysis prediction software finds the number of promoters (Pro) and enhancers (Enh) in Nor, EC, and LNC based on H3K27ac signals;

[0061] S4. Generate a heat map of the enhancers according to the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map;

[0062] S5. PCA analysis was performed on Nor, EC, and LNC samples, and the corresponding super enhancers (SEs) were identified from enhancers with H3K27ac enrichment signals in Nor, EC, and LNC using PCI gene scores.

[0063] This protocol uses histone H3K27ac to predict super enhancers (SEs), using histone---H3K27ac as a marker to predict differential promoters and transcription start sites (TSSs) in ESCC tumor cells, and promotes carcinogenesis and metastasis through sequential prediction of positioning prediction.

[0064] A set of commonly altered enhancers and LNC-specific enhancers associated with regional gene expression were identified, which can be used for precise subclassification. Several key transcription factors regulating proliferation and migration of esophageal cancer cells were discovered by analyzing the trans-elements in the altered enhancers. Several promising tumor targets responsive to drug inhibition were identified by comprehensive analysis of SE-related gene-drug interaction databases.

[0065] like Figure 2 The figure shows a sampling diagram for promoting transcriptome and epigenomic analysis of adjacent normal tissue (Nor), esophageal squamous cell carcinoma (EC) tissue, and lymph node cancer (LNC) tissue. After the H3K27ac chip collected the signal, it was sent to the time series analysis and prediction software (SPSS18.0 software) for data collation, statistical analysis of the distribution characteristics of H3K27ac signals in Nor, EC, and LNC, and then entered the next step of processing.

[0066] First, H3K27ac profiles defined active regulatory elements in esophageal carcinogenesis and metastasis. To characterize active cis-regulatory elements in esophageal carcinogenesis and metastasis, 28 active histone mark (H3K27ac) profiles were generated from ten pairs of ESCC patients by chromatin immunoprecipitation and sequencing (ChIP-seq) with fresh autopsy samples of adjacent normal esophageal (Nor) tissues, mainly ESCC (further abbreviated EC) and LNC tissues. In addition, a total of 50 transcriptomes from 18 pairs of ESCC patients were examined by RNA sequencing (RNA-seq), and 4 LNC samples with less tumor cells found in clinical pathological examination were excluded.

[0067] The H3K27 AC chip and the time series analysis and prediction software of this embodiment are used together, and the connection and debugging are performed by the test personnel.

[0068] During initialization, a round of H3K27ac signals can be sampled and reported to the time series analysis and prediction software, which will determine whether the H3K27ac signals of Nor, EC, and LNC are complete and meet the test requirements.

[0069] If the test conditions are not met, for example, the Q30 score of the sequencing reads of ChIP-seq and RNA-seq data is lower than 90%, or the mapping ratio to the human genome does not exceed 75%, the H3K27ac signal needs to be re-collected, and the time series analysis and prediction software will issue an alarm signal to remind the tester of the alarm reason why the test conditions have not been met.

[0070] At the same time, it is necessary to determine whether the signal strength of each H3K27ac signal meets the standard based on the sampled H3K27ac signal, such as whether the signal value of the H3K27ac signal exceeds k (determined by the tester). If it does not meet the standard, a tissue sample needs to be resampled (according to the type of the current H3K27ac signal, the type is automatically determined and the sample of this type is automatically resampled to the H3K27 communication chip).

[0071] After the tester readjusts the sample and system parameters, the collection is retested and the next step is not taken until the standard is met.

[0072] As an optional embodiment of the present invention, optionally, before the time series analysis prediction software is based on the H3K27ac signal, it also includes:

[0073] Generate the corresponding ChIP-seq data set based on the H3K27ac signals of the transcriptomes in each tissue;

[0074] The ChIP-seq data set was subjected to quantile normalization using a preset preprocessing method.

[0075] The quantile normalization process can be completed by the tester in the background.

[0076] As an optional embodiment of the present invention, optionally, before the time series analysis prediction software is based on the H3K27ac signal, it also includes:

[0077] Perform Q30 evaluation on the sequencing reads of the ChIP-seq data set of each tissue to determine whether the Q30 of the sequencing reads exceeds the preset value:

[0078] If it exceeds, proceed to the next step;

[0079] Instead, H3K27ac signals were resampled and a new ChIP-seq dataset was generated.

[0080] In order to meet the calculation and statistics of the sampling signal, quality control analysis showed that the Q30 score of the sequencing reads of ChIP-seq and RNA-seq data was 90%, and the mapping ratio to the human genome was more than 75%.

[0081] As an optional embodiment of the present invention, optionally, the time series analysis prediction software finds the number of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC respectively according to the H3K27ac signal, including:

[0082] The ChIP-seq data sets of Nor, EC, and LNC were imported into time series analysis prediction software for data collation and statistical analysis of the distribution characteristics of H3K27ac signals in Nor, EC, and LNC;

[0083] According to the distribution characteristics, the enhancers with different distributions in Nor, EC and LNC tissues were identified, and the number of regional changes in Nor, EC and LNC was calculated based on the enhancers with different distributions.

[0084] Combined with the comparison among Nor, EC and LNC, the numbers of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC were found respectively.

[0085] After the H3K27ac chip collects the signal, it is sent to the time series analysis and prediction software (such as SPSS software) for data sorting, statistical analysis of the distribution characteristics of the H3K27ac signal in Nor, EC and LNC, and then proceed to the next step of processing.

[0086] like Figure 3 As shown, enhancers that were differentially distributed in Nor (n=10), EC (n=10) and LNC (n=8) tissues were identified. The number of altered (gained or lost) promoters (Pro) or enhancers (Enh) is given by comparing Nor to EC or LNC or EC to LNC.

[0087] As an optional embodiment of the present invention, optionally, before identifying the enhancers with differences among Nor, EC and LNC tissues, the method further comprises:

[0088] Define enhancers and promoters:

[0089] Significant H3K27ac peaks that were more than 0.5 kb away from the nearest TSSs were defined as enhancers;

[0090] Regions with significant H3K27ac occupancy within ±0.5 kb of TSSs were defined as promoters.

[0091] Prior to elemental analysis, H3K27ac signals were quantile normalized in 28 ChIP-seq datasets.

[0092] like Figure 4As shown, in the present invention, enhancers are defined as significant H3K27ac peaks (enhancers) that are more than 2 kb away from the nearest TSSs (TSSs horizontal line where the arrow line is located in the figure), and within TSSs of ± 2 kb (TSSs ± 2 kb), regions with significant H3K27ac occupancy are considered promoters. The numbers of promoters and enhancers reached saturation after 24 and 19 samples, respectively, indicating that most cis-regulatory elements can be obtained from 28 samples of 10 ESCC patients.

[0093] Next, we will use the types of Nor, EC, and LNC to construct a heat map based on the distribution characteristics of the H3K27ac enrichment signals of enhancers, and distribute and display the H3K27ac enrichment signals of enhancers of Nor (n=10), EC (n=10), and LNC (n=8).

[0094] As an optional embodiment of the present invention, optionally, a heat map of the enhancers is generated according to the enhancers in Nor, EC and LNC, and differential enhancers with H3K27ac enrichment signals are found from the heat map, including:

[0095] According to the types of Nor, EC, and LNC, enhancers in Nor, EC, and LNC were divided into different types of enhancers;

[0096] Generate a heat map of the enhancers according to the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map;

[0097] The differential enhancers were divided into several groups according to the magnitude of gain and loss of the H3K27ac enrichment signal.

[0098] According to the presence or absence of H3K27ac enrichment signals, enhancer elements with a specified number in Nor, EC, and LNC can be found from enhancers. This is because elements that are suppressed due to cellular expression show significant gains or losses in Nor, EC, and LNC. Therefore, in a representative patient, these differential enhancers can be divided into several groups of differential enhancers according to the presence or absence of H3K27ac enrichment signals, such as G1 (ec-specific gain; n = 985), G2 (LNC-specific gain: n = 837), G3 (common gain: n = 1974), G4 (ec-specific loss: n = 871), G5 (LNC-specific loss: n = 548), and G6 (common loss: n = 2715).

[0099] As an optional embodiment of the present invention, optionally, PCA analysis is performed on Nor, EC and LNC samples, and the corresponding super enhancers (SEs) are identified from enhancers with H3K27ac enrichment signals in Nor, EC and LNC using PCI gene scores, including:

[0100] Inputting several groups of the differential enhancers into a cluster analysis model;

[0101] Performing unsupervised hierarchical cluster analysis on several groups of differential enhancers, and using a cluster analysis algorithm to separate the differential enhancers of Nor, EC and LNC into specific traits of different groups;

[0102] Principal component analysis was performed on different population-specific traits. All differentially distributed promoter elements: promoters and enhancers were used to perform PCA on differential promoter elements and enhancer-related genes, and the gene scores of positive PC1 were calculated. The corresponding super enhancers (SEs) in Nor, EC and LNC were determined using the positive PC1 gene scores.

[0103] Unsupervised hierarchical clustering analysis of differential enhancers in 28 Nor, EC, and LNC samples. Unsupervised hierarchical clustering analysis of these differential enhancers showed that the three types of enhancer profiles were clearly separated as population-specific traits. You can refer to the following steps:

[0104] 1. Data collection and preprocessing

[0105] Data collection: (refer to the data samples of differential enhancers of each Nor, EC and LNC sample collected above in the present invention).

[0106] Data preprocessing: The raw sequencing data is quality controlled, aligned to the reference genome, repetitive sequences are removed, peaks are detected, etc. to obtain the enhancer regions and their signal strengths in each sample.

[0107] 2. Feature extraction

[0108] Enhancer signal quantification: For each sample, the signal intensity of each enhancer region (such as read counts or normalized signal values) is calculated as a feature for subsequent clustering.

[0109] 3. Unsupervised Hierarchical Cluster Analysis

[0110] Distance calculation: Select an appropriate distance metric (such as Euclidean distance, Manhattan distance, or cosine distance) to calculate the similarity between different enhancer regions.

[0111] Hierarchical clustering: Use a hierarchical clustering algorithm (such as agglomerative clustering or divisive clustering) to cluster enhancer regions. In agglomerative clustering, each enhancer region is initially considered as an independent cluster, and then the most similar clusters are gradually merged until a predetermined number of clusters is reached or a termination condition is met. In divisive clustering, all enhancer regions are initially considered as one cluster and then gradually split into smaller clusters.

[0112] Metric selection: Choose an appropriate metric to determine the distance between clusters, such as minimum distance (single linkage), maximum distance (full linkage), average distance (average linkage), or mean distance.

[0113] 4. Result analysis and verification

[0114] Cluster analysis: Analyze clustering results to identify enhancer groups with population-specific traits. This usually involves comparing clustering structures between different populations and looking for enhancer clusters that are only enriched in specific populations.

[0115] Bioinformatics analysis: The identified population-specific enhancers were further subjected to bioinformatics analysis, such as Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, to understand their potential functions and regulatory mechanisms.

[0116] Experimental verification: If possible, verify the accuracy of the clustering results and the function of the enhancer by experimental methods (such as qRT-PCR, fluorescence in situ hybridization, etc.).

[0117] 5. Visualization and reporting

[0118] Hierarchical clustering tree visualization: Use a dendrogram to display the results of hierarchical clustering to help understand the similarity relationship and clustering process between enhancer regions.

[0119] Enhancer spectrum heat map: Draw a heat map of the enhancer spectrum to show the difference in signal intensity between different populations and enhancer regions, and assist in identifying population-specific enhancers.

[0120] Result report: Organize the analysis process and results and write a detailed report, including data collection, preprocessing, cluster analysis, result analysis and verification, etc.

[0121] When performing unsupervised hierarchical clustering analysis, unsupervised machine learning algorithms can be combined to perform unsupervised hierarchical clustering analysis on differential enhancers. The clustering structure can be displayed in a dendrogram to facilitate analysis of the data type of the differential enhancers. Euclidean distance can be further used for auxiliary classification, including:

[0122] The specific steps of using Euclidean distance for auxiliary classification can be summarized as follows:

[0123] 1. Data preparation

[0124] Training sample set and test sample points: First, you need to prepare a training sample set containing multiple known categories and one or more test sample points to be classified.

[0125] 2. Feature selection and standardization

[0126] Feature extraction: Extract the same features from the training sample set and the test sample points.

[0127] Data standardization: Since Euclidean distance is sensitive to the scale of the data, the data needs to be standardized to ensure that each feature has the same weight. This usually involves transforming the data into a standard normal distribution with a mean of 0 and a variance of 1, or using other standardization methods.

[0128] 3. Calculate Euclidean distance

[0129] Distance calculation: For each feature in the test sample point, calculate the difference with the corresponding feature of each sample point in the training sample set. Then, add the squares of these differences and take the square root to get the Euclidean distance between the test sample point and each sample point in the training sample set.

[0130] 4. Classification

[0131] Nearest neighbor principle: According to the calculated Euclidean distance, the test sample point is classified into the category of the nearest training sample point. If there are multiple test sample points, repeat the above steps for each test sample point.

[0132] 5. Evaluation and Verification

[0133] Classification effect evaluation: Use appropriate evaluation indicators (such as accuracy, recall, F1 score, etc.) to evaluate the classification effect.

[0134] Cross-validation: To improve the reliability of classification results, you can consider using cross-validation methods, such as K-fold cross-validation, to evaluate the performance of the classification model.

[0135] To perform unsupervised hierarchical clustering analysis on differential enhancers and use the Euclidean distance algorithm to further assist in classifying their data types, you can follow the following steps:

[0136] First, it is necessary to prepare the data type feature data of differential enhancers. These data should contain feature vectors of different differential enhancers for subsequent clustering analysis.

[0137] Next, preprocess the data. Since Euclidean distance is sensitive to the data scale, it may be necessary to standardize the feature data to ensure that each feature has the same weight. This usually involves converting the data to a standard normal distribution with a mean of 0 and a variance of 1, or using other standardization methods.

[0138] Then, select a hierarchical clustering method. Depending on the specific implementation method, hierarchical clustering can be divided into agglomerative clustering (bottom-up) and divisive clustering (top-down). When selecting a clustering method, you can determine it based on the characteristics of the data and the clustering requirements.

[0139] Next, calculate the Euclidean distance. In the hierarchical clustering process, it is necessary to calculate the Euclidean distance between the feature vectors of different differential enhancers. The Euclidean distance formula is the square root of the sum of the squares of the differences between two vectors, which represents the straight-line distance between the two vectors in multidimensional space. By calculating the Euclidean distance, the similarities and differences between different differential enhancers can be evaluated.

[0140] After that, hierarchical clustering was performed based on the Euclidean distance. In agglomerative clustering, each differential enhancer was initially considered as an independent cluster, and then the most similar clusters were gradually merged until the predetermined number of clusters was reached or the termination condition was met. In divisive clustering, all differential enhancers were initially considered as one cluster and then gradually split into smaller clusters. In the clustering process, the Euclidean distance was used as a metric to evaluate the similarity between clusters.

[0141] Finally, the clustering results are analyzed and interpreted. Through hierarchical clustering analysis, the clustering structure of different differential enhancers can be obtained. These clustering structures can be further analyzed to identify differential enhancer groups with similar characteristics and explore their potential functions and regulatory mechanisms. At the same time, the clustering results can also be visualized, such as drawing a hierarchical clustering tree or heat map, so as to understand the clustering results more intuitively.

[0142] Through the above steps, the Euclidean distance algorithm can be used to perform unsupervised hierarchical clustering analysis on differential enhancers and further assist in classifying their data types.

[0143] Finally, principal component analysis of 28 Nor (n=10), EC (n=10), and LNC (n=8) samples was performed using all differentially distributed promoter elements: promoters and enhancers. Principal component analysis (PCA) of differential promoter element and enhancer-associated genes successfully separated normal and cancer samples along PC1: In addition, EC and LNC samples had the top 100 altered element-associated genes (50 gain and 50 loss), genes with positive PC1 scores (such as GFI1MSI2RUNX3CD72 and EX01) in promoter analysis and genes with negative PC1 scores (such as CD72RUNX3TNFSF8NCF1C and CTNNB1) in enhancer analysis that were considered potential oncogenes promoting carcinogenesis.

[0144] This enables the prediction of abnormal expression of enhancers.

[0145] The number of samples of the present invention may be unlimited.

[0146] The time series analysis and prediction software of this embodiment may be SPSS.

[0147] Obviously, those skilled in the art should understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0148] Example 2

[0149] like Figure 5 As shown, based on the implementation principle of Example 1, another aspect of the present invention provides a system for detecting differential expression of ESCC tumor cells, comprising:

[0150] H3K27ac chip, used to obtain H3K27ac signals of transcriptomes in Nor, EC and LNC sample tissues respectively using ChIP-seq sequencing technology, and reported to time series analysis and prediction software;

[0151] The time series analysis prediction software is used to find the number of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC respectively according to the H3K27ac signal; generate a heat map of the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map; perform PCA analysis on Nor, EC and LNC samples, and use PCI gene scores to identify the corresponding super enhancers (SEs) from the enhancers with H3K27ac enrichment signals in Nor, EC and LNC.

[0152] The functions of the above-mentioned H3K27 communication chip and time series analysis and prediction software are described in Example 1, and will not be repeated in this example.

[0153] The modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0154] Example 3

[0155] like Figure 6 As shown, further, in another aspect, the present invention also provides an electronic device, comprising:

[0156] processor;

[0157] a memory for storing processor-executable instructions;

[0158] Wherein, the processor is configured to implement the ESCC tumor cell differential expression detection method when executing the executable instructions.

[0159] The electronic device according to the embodiment of the present invention includes a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to implement the above-mentioned ESCC tumor cell differential expression detection method when executing the executable instructions.

[0160] Here, it should be noted that the number of processors may be one or more. At the same time, the electronic device in the embodiment of the present invention may also include an input device and an output device. The processor, memory, input device, and output device may be connected via a bus or in other ways, which are not specifically limited here.

[0161] The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs and various modules, such as the program or module corresponding to the ESCC tumor cell differential expression detection method in the embodiment of the present invention. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0162] The input device can be used to receive input numbers or signals. The signal can be a key signal related to user settings and function control of the device / terminal / server. The output device can include a display device such as a display screen.

[0163] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for detecting differential expression of ESCC tumor cells, characterized in that: The steps include: Several groups of adjacent normal tissue (Nor), esophageal squamous cell carcinoma (EC) tissue, and lymph node cancer (LNC) tissue samples were preset; Based on the H3K27ac chip, ChIP-seq sequencing technology was used to obtain the H3K27ac signals of the transcriptome in each tissue and reported to the time series analysis and prediction software; The time series analysis prediction software found the number of promoters (Pro) and enhancers (Enh) in Nor, EC, and LNC, respectively, based on the H3K27ac signal; Generate a heat map of the enhancers according to the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map; PCA analysis was performed on Nor, EC, and LNC samples, and the corresponding super enhancers (SEs) were identified from enhancers with H3K27ac enrichment signals in Nor, EC, and LNC using PCI gene scores.

2. The method for detecting differential expression of ESCC tumor cells according to claim 1, characterized in that: Prior to time series analysis prediction software based on H3K27ac signal, also included: Generate the corresponding ChIP-seq data set based on the H3K27ac signals of the transcriptomes in each tissue; The ChIP-seq data set was subjected to quantile normalization using a preset preprocessing method.

3. The method for detecting differential expression of ESCC tumor cells according to claim 2, characterized in that: Prior to time series analysis prediction software based on H3K27ac signal, also included: Perform Q30 evaluation on the sequencing reads of the ChIP-seq data set of each tissue to determine whether the Q30 of the sequencing reads exceeds the preset value: If it exceeds, proceed to the next step; Instead, H3K27ac signals were resampled and a new ChIP-seq dataset was generated.

4. The method for detecting differential expression of ESCC tumor cells according to claim 1, characterized in that: The time series analysis prediction software finds the number of promoters (Pro) and enhancers (Enh) in Nor, EC, and LNC based on H3K27ac signals, including: The ChIP-seq data sets of Nor, EC, and LNC were imported into time series analysis prediction software for data collation and statistical analysis of the distribution characteristics of H3K27ac signals in Nor, EC, and LNC; According to the distribution characteristics, the enhancers with different distributions in Nor, EC and LNC tissues were identified, and the number of regional changes in Nor, EC and LNC was calculated based on the enhancers with different distributions. Combined with the comparison among Nor, EC and LNC, the numbers of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC were found respectively.

5. The method for detecting differential expression of ESCC tumor cells according to claim 4, characterized in that: Prior to identifying the differentially expressed enhancers in the Nor, EC, and LNC tissues, we also included: Define enhancers and promoters: Significant H3K27ac peaks that were more than 0.5 kb away from the nearest TSSs were defined as enhancers; Regions with significant H3K27ac occupancy within ±0.5 kb of TSSs were defined as promoters.

6. The method for detecting differential expression of ESCC tumor cells according to claim 5, characterized in that: A heat map of the enhancers was generated based on the enhancers in Nor, EC and LNC, and differential enhancers with H3K27ac enrichment signals were found from the heat map, including: According to the types of Nor, EC, and LNC, enhancers in Nor, EC, and LNC were divided into different types of enhancers; Generate a heat map of the enhancers according to the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map; The differential enhancers were divided into several groups according to the magnitude of gain and loss of the H3K27ac enrichment signal.

7. A method and system for detecting differential expression of ESCC tumor cells according to claim 6, characterized in that: PCA analysis was performed on Nor, EC, and LNC samples, and the PCI gene scores were used to identify the corresponding super enhancers (SEs) from enhancers with H3K27ac enrichment signals in Nor, EC, and LNC, including: Inputting several groups of the differential enhancers into a cluster analysis model; Performing unsupervised hierarchical cluster analysis on several groups of differential enhancers, and using a cluster analysis algorithm to separate the differential enhancers of Nor, EC and LNC into specific traits of different groups; Principal component analysis was performed on different population-specific traits. All differentially distributed promoter elements: promoters and enhancers were used to perform PCA on differential promoter elements and enhancer-related genes, and the gene scores of positive PC1 were calculated. The corresponding super enhancers (SEs) in Nor, EC and LNC were determined using the positive PC1 gene scores.

8. A ESCC tumor cell differential expression detection system, characterized in that: include: H3K27ac chip, used to obtain H3K27ac signals of transcriptomes in Nor, EC and LNC sample tissues respectively using ChIP-seq sequencing technology, and reported to time series analysis and prediction software; The time series analysis prediction software is used to find the number of promoters (Pro) and enhancers (Enh) in Nor, EC and LNC respectively according to the H3K27ac signal; generate a heat map of the enhancers in Nor, EC and LNC, and find the differential enhancers with H3K27ac enrichment signals from the heat map; perform PCA analysis on Nor, EC and LNC samples, and use PCI gene scores to identify the corresponding super enhancers (SEs) from the enhancers with H3K27ac enrichment signals in Nor, EC and LNC.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the ESCC tumor cell differential expression detection method according to any one of claims 1 to 7 when executing the executable instructions.

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