Biopsy muscular fascia sample analysis method and system

Abstract

The invention discloses a biopsy muscular fascia sample analysis method and system, and relates to the field of muscular fascia sample analysis. The biopsy myofascia sample analysis method comprises the following steps: acquiring pre-stored RNA data of a plurality of biopsy myofascia sample groups; carrying out RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascia sample group to obtain transcriptome data; performing inter-group difference analysis on the transcriptome data according to a preset comparison group, and extracting a differential expression gene data set; and carrying out BDNF layered enrichment analysis on the differential expression gene data set to obtain a BDNF enrichment significant set of each contrast biopsy myofascia sample group, and carrying out cross validation processing based on the BDNF enrichment significant set of each contrast biopsy myofascia sample group. Therefore, it is effectively ensured that the screened BDNF pathway target spot is highly matched with the pathological characteristics of myofascia lesion and is clearly associated with intervention response, and the potential curative effect of acupuncture intervention is accurately evaluated.

Description

Technical Field

[0001] This invention relates to the field of myofascial sample analysis technology, specifically to a method and system for analyzing biopsy myofascial samples. Background Technology

[0002] Myofascial pain syndrome is one of the most common types of chronic pain in clinical practice. Its core pathological feature is the formation of myofascial trigger points. Myofascial pain syndrome has a high incidence worldwide and is often accompanied by motor dysfunction, anxiety, depression, and other problems, which seriously affect patients' quality of life. At present, the main clinical treatments for myofascial pain syndrome include drug analgesia, physical therapy, local injection, and acupuncture. Although these can partially relieve symptoms, the efficacy is unstable and the recurrence rate is high. The fundamental reason is that the molecular mechanism of the formation and maintenance of myofascial trigger points is still unclear, and there is a lack of precise treatment targets and efficacy prediction basis.

[0003] In recent years, pain research has gradually delved into the molecular and signaling pathway levels, with brain-derived neurotrophic factor (BDNF) playing a crucial role in processes such as neural plasticity and central sensitization of chronic pain. Existing studies have shown that BDNF is abnormally expressed in models of neuropathic pain and inflammatory pain, and participates in the transmission and regulation of pain signals.

[0004] Current technologies only focus on changes in mRNA levels, failing to combine lncRNA analysis and lacking a comparative verification system for dynamic changes before and after clinical intervention. Therefore, current technologies are unable to fully reveal the molecular regulatory network of myofascial pain, nor can they effectively distinguish between disease-related genes and treatment-response genes, resulting in insufficient specificity of potential targets and limited clinical translational value.

[0005] The limitations of existing technologies include at least the following issues: Existing technologies struggle to perform targeted stratification and BDNF pathway enrichment of differentially expressed mRNAs and lncRNAs in biopsy myofascial samples, resulting in a lack of specificity in the association analysis between differentially expressed genes and the BDNF pathway, making it difficult to accurately capture core molecular targets related to myofascial lesions. Furthermore, the results of differentially expressed gene pathway enrichment in myofascial samples require dual verification combining pathological characteristics and intervention responses. However, existing technologies neglect the cross-validation of the pathological adaptability and intervention effectiveness of BDNF enrichment results. This can easily lead to target misjudgment and poor intervention outcomes in scenarios such as the auxiliary diagnosis and intervention plan development of myofascial lesions, potentially reducing the diagnostic accuracy and intervention success rate of myofascial-related diseases. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for analyzing biopsy myofascial samples, which solves the problem that existing technologies lack differential gene stratification and double verification, thus reducing the accuracy of myofascial disease diagnosis and treatment.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing biopsy myofascial samples, comprising the following steps: acquiring RNA data from several pre-stored biopsy myofascial sample groups; performing RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascial sample group to obtain transcriptome data for each biopsy myofascial sample group; performing inter-group differential analysis on the transcriptome data of each biopsy myofascial sample group according to a preset comparison group to extract differentially expressed gene datasets for each comparison biopsy myofascial sample group; performing BDNF hierarchical enrichment analysis on the differentially expressed gene datasets of each comparison biopsy myofascial sample group to obtain a BDNF enriched significant set for each comparison biopsy myofascial sample group; and performing cross-validation based on the BDNF enriched significant set for each comparison biopsy myofascial sample group.

[0008] Furthermore, the specific steps for obtaining transcriptome data for each biopsy myofascial sample group are as follows: based on the RNA data of each biopsy myofascial sample group, a sequencing library for each biopsy myofascial sample group is constructed; the sequencing library of each biopsy myofascial sample group is subjected to quality testing; and paired-end sequencing is performed on the sequencing library of each biopsy myofascial sample group after quality testing to extract transcriptome data for each biopsy myofascial sample group.

[0009] Further, the specific steps for extracting the differentially expressed gene dataset for each comparative biopsy myofascial sample group are as follows: The transcriptome data of each biopsy myofascial sample group are compared with a pre-defined reference genome to obtain the raw gene expression count set for each biopsy myofascial sample group; the raw gene expression count set for each biopsy myofascial sample group is normalized using the FPKM method to obtain the FPKM expression set for each biopsy myofascial sample group; based on the pre-defined comparison group, inter-group statistical tests are performed on the FPKM expression set for each biopsy myofascial sample group to obtain the differentially expressed gene dataset for each comparative biopsy myofascial sample group.

[0010] Furthermore, the specific steps of the intergroup statistical test are as follows: the FPKM expression set of each biopsy myofascial sample group is differentially processed in the preset comparison group to obtain the differential expression dataset of each comparison biopsy myofascial sample group, including the differential expression fold value of each gene and the statistical test p value; the differential expression dataset of each comparison biopsy myofascial sample group is subjected to double screening to extract the differential expression gene dataset of each comparison biopsy myofascial sample group, including the differential expression fold value of each differential gene and the statistical test p value.

[0011] Further, the specific steps for obtaining the BDNF enrichment significant set for each comparative biopsy myofascial sample group are as follows: The differentially expressed gene dataset for each comparative biopsy myofascial sample group is processed for type identification; if the differentially expressed gene in each comparative biopsy myofascial sample group is a differentially expressed mRNA, it is subjected to KEGG pathway and BDNF screening to obtain the differentially expressed mRNA-mapped BDNF enrichment significant set for each comparative biopsy myofascial sample group; if the differentially expressed gene in each comparative biopsy myofascial sample group is a differentially expressed lncRNA, it is subjected to target gene prediction to obtain the target genes of the corresponding differentially expressed lncRNA, and BDNF signaling pathway targeted enrichment is performed to obtain the differentially expressed lncRNA-mapped BDNF enrichment significant set for each comparative biopsy myofascial sample group; based on the differentially expressed mRNA-mapped BDNF enrichment significant set and the differentially expressed lncRNA-mapped BDNF enrichment significant set for each comparative biopsy myofascial sample group, the BDNF enrichment significant set for each comparative biopsy myofascial sample group is extracted.

[0012] Furthermore, the specific steps for obtaining the BDNF enrichment significant set of differentially expressed mRNAs for each comparative biopsy myofascial sample group are as follows: KEGG signaling pathway enrichment analysis was performed on the differential expression fold values ​​and statistical p-values ​​of each differentially expressed mRNA in each comparative biopsy myofascial sample group to obtain a KEGG pathway significant enrichment table for each comparative biopsy myofascial sample group; BDNF pathway screening was then performed on the KEGG pathway significant enrichment table for each comparative biopsy myofascial sample group to obtain the BDNF enrichment significant set of differentially expressed mRNAs for each comparative biopsy myofascial sample group.

[0013] Further, the specific steps for obtaining the target gene set of each differentially expressed LncRNA in each comparative biopsy myofascial sample group are as follows: For each differentially expressed LncRNA in each comparative biopsy myofascial sample group, cis-acting target gene prediction processing is performed to obtain the candidate cis-acting target gene set of its corresponding differentially expressed LncRNA; for each differentially expressed LncRNA in each comparative biopsy myofascial sample group, trans-acting target gene prediction processing is performed to obtain the candidate trans-acting target gene set of its corresponding differentially expressed LncRNA; based on the candidate cis-acting target gene set and the candidate trans-acting target gene set of each differentially expressed LncRNA in each comparative biopsy myofascial sample group, the target gene set of its corresponding differentially expressed LncRNA is extracted.

[0014] Furthermore, the specific steps for cross-validation based on the BDNF enriched significant set of each comparative biopsy myofascial sample group are as follows: screen and validate the BDNF enriched significant set of each comparative biopsy myofascial sample group; generate a BDNF intervention assessment report based on the screening and validation results.

[0015] Further, the specific steps of the screening and validation process are as follows: Pathological pathway screening and validation processing is performed on the BDNF enrichment significant set of each comparative biopsy myofascial sample group to obtain the pathological validation identifier of each comparative biopsy myofascial sample group; intervention response pathway validation processing is performed on the BDNF enrichment significant set of each comparative biopsy myofascial sample group to obtain the intervention validation identifier of each comparative biopsy myofascial sample group.

[0016] A biopsy myofascial sample analysis system includes: a data acquisition module for acquiring RNA data from several pre-stored biopsy myofascial sample groups; a sequencing and transcription analysis module for performing RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascial sample group to obtain transcriptome data for each biopsy myofascial sample group; a differential expression analysis module for performing inter-group differential analysis on the transcriptome data of each biopsy myofascial sample group according to preset comparison groups to extract differentially expressed gene datasets for each comparison biopsy myofascial sample group; a BDNF enrichment analysis module for performing BDNF hierarchical enrichment analysis on the differentially expressed gene datasets of each comparison biopsy myofascial sample group to obtain a BDNF enrichment significance set for each comparison biopsy myofascial sample group; and a BDNF validation feedback module for performing cross-validation based on the BDNF enrichment significance set of each comparison biopsy myofascial sample group.

[0017] The present invention has the following beneficial effects:

[0018] (1) This biopsy myofascial sample analysis method accurately identifies the differentially expressed genes in the biopsy myofascial samples and implements a targeted hierarchical enrichment strategy for differentially expressed mRNAs and lncRNAs: KEGG and BDNF pathways are directly screened for differentially expressed mRNAs to ensure their direct association with the BDNF pathway; cis and trans target genes are predicted for differentially expressed lncRNAs, and then the BDNF signaling pathway is targeted and enriched through the target gene-mediated enrichment, fully reflecting its indirect regulatory characteristics. Finally, the BDNF enrichment significance sets of the two types of molecules are integrated to form a comprehensive target set. On this basis, through cross-validation processing of pathological pathway screening and intervention response pathway validation, a BDNF intervention evaluation report containing pathological validation markers and intervention validation markers is generated, which effectively ensures that the screened BDNF pathway targets are highly compatible with the pathological characteristics of myofascial lesions and have a clear association with the intervention response, significantly improving the accuracy of the targets.

[0019] (2) The biopsy myofascial sample analysis method has constructed a complete data processing system from RNA data to the final enriched significant set, which ensures the reliability of the analysis results. In the transcriptome data acquisition stage, the standardized operation of sequencing library construction, quality detection and paired-end sequencing ensures the integrity and high quality of the original data. In the differential gene extraction process, the data bias is eliminated by reference genome alignment and FPKM normalization. Then, through intergroup statistical tests and double screening, the gene dataset with real differential expression characteristics is accurately captured, effectively eliminating the interference of experimental errors and false differential signals, so that the final BDNF enriched significant set can truly reflect the association status between differential genes and BDNF pathway in myofascial samples.

[0020] (3) This biopsy myofascial sample analysis method integrates the BDNF pathway association information of differential mRNA and differential LncRNA to form a comprehensive BDNF enrichment saliency set. At the same time, it generates a targeted BDNF intervention assessment report based on cross-validation, which has outstanding translational value. The direct enrichment results of differential mRNA and the indirect regulatory results of differential LncRNA complement each other, and fully present the molecular regulatory network of the BDNF signaling pathway in myofascial lesions. The dual verification markers of pathology and intervention response provide clear guidance for intervention, which can help to accurately identify the core regulatory targets of myofascial lesions and objectively evaluate the potential efficacy of acupuncture intervention, so as to improve the accuracy of diagnosis and treatment of myofascial pain syndrome.

[0021] (4) This biopsy myofascial sample analysis system, through modular decomposition and collaborative design, constructs a fully automated analysis system from RNA data input to validation report output to reflect high efficiency. The data acquisition module realizes stable acquisition and integration of RNA data from multiple biopsy myofascial samples; the sequencing and transcription analysis module automatically completes transcriptome data extraction through standardized library construction, quality detection and paired-end sequencing processes, shortening the data processing cycle; the differential expression analysis module automatically completes genome alignment, normalization and inter-group statistical tests based on preset algorithms, accurately screening differential gene datasets without manual intervention; the BDNF enrichment analysis module automatically executes hierarchical enrichment strategies for differential mRNA and differential LncRNA, realizing efficient integration of the association information between the two types of molecules and the BDNF pathway; the BDNF validation feedback module automatically completes dual validation of pathology and intervention response, and generates standardized evaluation reports, thereby quickly adapting to the analysis needs of different preset comparison groups and improving the analysis efficiency of batch samples.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1This is a flowchart of a biopsy myofascial sample analysis method according to the present invention.

[0024] Figure 2 This is a flowchart illustrating the specific steps involved in obtaining transcriptomic data for each biopsy myofascial sample group in the biopsy myofascial sample analysis method of the present invention.

[0025] Figure 3 This is a block diagram of a biopsy myofascial sample analysis system according to the present invention. Detailed Implementation

[0026] Please see Figure 1 This invention provides a technical solution: a method for analyzing biopsy myofascial samples, comprising the following steps: acquiring RNA data (which is the total RNA extracted from each independent biopsy sample from the biopsy myofascial samples, and its quality meets the RNA-seq library construction standards, such as an RNA integrity index (RIN) value of not less than 7.0 and a concentration of not less than 50 ng / μL) from several biopsy myofascial sample groups (including normal biopsy myofascial sample groups, interventional biopsy myofascial sample groups, and model biopsy myofascial sample groups, and each biopsy myofascial sample group may include multiple independent biopsy samples) pre-stored in a database. RNA data from each biopsy myofascial sample group were subjected to high-throughput RNA-seq sequencing to obtain transcriptome data for each group. Inter-group differential analysis was performed on the transcriptome data of each biopsy myofascial sample group according to pre-defined comparison groups to extract differentially expressed gene datasets for each comparison biopsy myofascial sample group. BDNF hierarchical enrichment analysis was performed on the differentially expressed gene datasets of each comparison biopsy myofascial sample group to obtain BDNF enriched significant sets for each comparison biopsy myofascial sample group. Cross-validation was then performed based on the BDNF enriched significant sets of each comparison biopsy myofascial sample group.

[0027] It should be noted that the normal biopsy myofascial sample group consists of upper trapezius muscle tissue samples from individuals who do not meet the diagnostic criteria for myofascial pain syndrome and who do not have tight muscle bands / nodules in the upper trapezius muscle and do not experience pain upon pressure. Specifically, the myofascial tissue is located at the midpoint between the seventh cervical vertebra and the acromion in the upper trapezius muscle. After the samples were collected by minimally invasive biopsy, they were immediately placed in isopentane cooled by liquid nitrogen for rapid freezing and then transferred to a -80°C freezer for storage. The RNA data was obtained from the samples after liquid nitrogen grinding, extraction with a kit, and quality control.

[0028] The intervention biopsy myofascial sample group consisted of myofascial trigger points (MTrPs) tissue samples from individuals exhibiting characteristics related to myofascial pain syndrome (MPS). Individuals corresponding to this type of sample underwent standardized acupuncture intervention (5 times a week for 2 consecutive weeks, for a total of 10 times), and MTRP nodules in the upper trapezius muscle of the neck were collected. After the samples underwent the same cryopreservation, RNA extraction, and quality control procedures as described above, the corresponding RNA data were obtained.

[0029] The model biopsy myofascial sample group consisted of myofascial trigger points (MTrPs) tissue samples from individuals exhibiting characteristics related to myofascial pain syndrome (MPS). The individuals corresponding to this type of sample underwent no intervention treatment, and the MTRP nodule tissue of the upper trapezius muscle in the neck was directly collected. After the samples underwent the same cryopreservation, RNA extraction, and quality control procedures as described above, the corresponding RNA data were obtained.

[0030] The specific steps for cross-validation based on the BDNF enrichment significance set of each comparative biopsy myofascial sample group are as follows: The BDNF enrichment significance set of each comparative biopsy myofascial sample group is screened and validated separately; a BDNF intervention assessment report is generated based on the screening and validation results, specifically: integrating the "pathological validation passed" and "intervention validation passed" indicators; a structured diagnostic conclusion is generated only when, for the same BDNF signaling pathway, both the pathological validation indicator for the comparison between the "model group and the normal group" and the intervention validation indicator for the comparison between the "intervention group and the model group" are "passed," such as: Analysis shows that the pathological state of myofascial pain is related to abnormal activation of the BDN signaling pathway, and the received acupuncture intervention can effectively restore the activity of this pathway, thus suggesting that acupuncture treatment targeting the BDNF pathway has a positive prognostic response.

[0031] The specific steps of the screening and verification process are as follows: For each comparative biopsy myofascial sample group, the BDNF enrichment significance set is subjected to pathological pathway screening and verification to obtain the pathological verification identifier for each comparative biopsy myofascial sample group. Specifically, the comparative biopsy myofascial sample group consists of a normal biopsy myofascial sample group and a model biopsy myofascial sample group. If the p-value of the mRNA enrichment statistical test is less than the preset pathological significance threshold (which can be set to 0.05), and the sign of the mRNA pathway enrichment value conforms to the expected activation direction of the pathway in the pathological state of myofascial pain (i.e., a positive value), then a "pathological verification passed" identifier is generated; otherwise, a "pathological verification failed" identifier is generated.

[0032] For each comparative biopsy myofascial sample group, the BDNF enrichment significance set was processed for intervention response pathway validation to obtain an intervention validation label for each comparative biopsy myofascial sample group. Specifically, the comparative biopsy myofascial sample group consists of the intervention biopsy myofascial sample group and the model biopsy myofascial sample group. The p-values ​​of both the mRNA enrichment statistical test and the lncRNA enrichment statistical test are less than the preset treatment response significance threshold (which can be set to 0.05), and the mRNA pathway enrichment value and lncRNA pathway enrichment value are both less than 0. If both are true, an "Intervention Validation Passed" label is generated; otherwise, an "Intervention Validation Failed" label is generated.

[0033] Specifically, such as Figure 2As shown, the specific steps for obtaining transcriptomic data for each biopsy myofascial sample group are as follows: Based on the RNA data of each biopsy myofascial sample group, a sequencing library for each biopsy myofascial sample group was constructed. Specifically, the mRNA molecules were specifically captured and purified from the total RNA of each sample using magnetic beads linked by polyd(T) oligomers. After purification, the mRNA was heated at 94°C for 8 minutes in a buffer system containing divalent cations to randomly break it into small fragments. Using these fragmented mRNAs as a model, the first cDNA strand was synthesized using random primers and reverse transcriptase. The first cDNA strand was then synthesized using DNA polymerase I and ribonucleic acid. Enzyme H synthesizes a second cDNA strand, forming a double-stranded cDNA product. The double-stranded cDNA fragment is end-repaired, and an "A" base is added to its 3' end. The cDNA fragment is ligated using sequencing adapters with different sequence tags (i.e., unique index sequences) provided in the kit. The product with the adapter is purified and specifically amplified and enriched using PCR technology to obtain the final cDNA library that can be sequenced. For each independent biopsy sample in the group, a different index adapter is used to repeat the above independent process to generate a sequencing library with a unique index tag that uniquely corresponds to the sample.

[0034] The sequencing libraries of each biopsy myofascial sample group were subjected to quality testing. Specifically, the Qubit® 3.0 fluorometer was used to accurately quantify the purified sequencing library of each sample to obtain its concentration (in nanomoles per liter, nM). At the same time, the Agilent 2100 bioanalyzer was used to test the library to verify the effective size range of the library fragments and accurately calculate its molar concentration to ensure that the sequencing library of each sample meets the preset standards in terms of concentration, purity and fragment size distribution.

[0035] Paired-end sequencing was performed on the sequencing libraries of each biopsy myofascial sample group after quality testing to extract transcriptome data for each biopsy myofascial sample group. Specifically, the sequencing libraries of each sample that passed quality testing were mixed in equimolar amounts and amplified by bridge PCR on the IlluminacBot cluster generation system to generate the DNA clusters required for sequencing. The flow cell carrying the DNA clusters was placed on the Illumina NovaSeq 6000 sequencing platform for paired-end sequencing. The sequencing process was controlled and monitored in real time by the instrument's built-in DataCollection Software. After sequencing, the software output a file containing the original sequences (Sequenced Reads) and their corresponding quality values. Using the unique index sequence information pre-embedded in the sequencing library, the mixed data generated by sequencing was split into each original sample by the data processing software, thereby obtaining the original transcriptome data corresponding one-to-one with each biopsy myofascial sample in each group, that is, the sequence file containing all gene expression information of the sample.

[0036] In this implementation scheme, high-throughput, high-parallel mixed sequencing is achieved by using unique index sequences (barcodes) for library construction. A large number of samples can be processed simultaneously in a single run, greatly improving sequencing efficiency and significantly reducing the cost per sample. Secondly, the standardized and streamlined library construction and quality control scheme (such as TruSeq™ kits, Qubit quantification, and Agilent 2100 fragment analysis) ensures the consistency and high quality of library construction. In particular, strict quality control of fragment size and concentration directly affects the yield and quality of sequencing data, effectively avoiding sequencing failures or data deviations caused by library issues. Finally, paired-end sequencing based on the Illumina NovaSeq 6000 platform can obtain high-depth and high-precision transcriptome coverage. Its paired-end read length design is particularly beneficial for the accurate splicing and quantification of transcript structures, and can quickly generate standardized transcriptome data files corresponding to each independent biopsy sample, which is the fundamental guarantee of the reliability of the entire analysis process.

[0037] Specifically, the steps for extracting the differentially expressed gene dataset for each comparative biopsy myofascial sample group are as follows: The transcriptome data of each biopsy myofascial sample group are compared with a preset reference genome to obtain the original gene expression count set for each biopsy myofascial sample group. Specifically, using sequence alignment software (such as HISAT2 or STAR), the transcriptome data (i.e., FASTQ format sequencing sequence file) of each independent biopsy sample in each biopsy myofascial sample group is used as input and compared with a preset reference genome (such as the human reference genome GRCh38) to determine the best matching position of each sequencing fragment (read) on the reference genome. After the alignment is completed, the sequence alignment result file of each sample is output. Its standard format is SAM (SequenceAlignment / MAP) file or its binary compressed format BAM file.

[0038] The gene quantification tool (such as featureCounts or HTSeq-count) is used as input, along with the generated BAM file and a preset gene annotation file (such as GTF or GFF3 format, which defines the coordinate range of all genes on the reference genome). The tool iterates through each aligned fragment in the BAM file, classifies and counts them into the specific gene region they cover based on their alignment position. The key counting rule is: only those fragments that are successfully and uniquely aligned to the gene region are counted, and ambiguous fragments that can be aligned to multiple genes or multiple positions in the genome are excluded to ensure the accuracy of quantification.

[0039] For each independent biopsy sample, a row (or column) of raw gene expression count matrix is ​​generated. This data is a list or vector, where each entry corresponds to a gene, and the value of the entry is the number of valid sequencing fragments aligned to that gene region. This value is the raw expression count of that gene in that sample. The count data of all independent biopsy samples are summarized to form a complete raw gene expression count set, which is the set of raw expression fragment counts corresponding to each gene in each independent biopsy sample in each biopsy myofascial sample group. This count set is presented in matrix form, where the matrix rows / columns correspond to independent biopsy samples, and the columns / rows correspond to genes. The values ​​in the matrix are the valid sequencing fragment counts of the corresponding genes in the corresponding samples.

[0040] It should be noted that the statistical objects of the gene raw expression count set are limited to known genes in the preset reference genome that have been clearly marked by gene annotation files (GTF / GFF3 format), covering coding genes corresponding to known mRNAs and non-coding genes corresponding to known lncRNAs, and do not include unknown transcripts not recorded in the annotation files.

[0041] For unknown transcripts in the transcriptome data, novel gene and novel lncRNA predictions were performed simultaneously: StringTie software was used to splice transcripts from the transcriptome data of each independent biopsy sample to obtain spliced ​​transcript sequences; then, gffcompare software was used to align the spliced ​​transcript sequences to a preset reference genome, screening out unaligned transcript sequences (i.e., unknown transcripts not recorded in the reference genome annotation file); novel lncRNA predictions were performed on the unaligned transcript sequences, specifically including: using Cuffmerge software to combine spliced ​​transcripts from each sample, screening candidate lncRNA sequences according to the criteria of exon number > 2, length > 200 bp, and FPKM > 0.5 (calculated using Cuffquant software); subsequently, Cuffcompare software was used to add data to the exon regions of the candidate lncRNA sequences. Library annotation was performed to eliminate overlapping sequences with known coding genes and transcripts with potential coding capabilities, ultimately identifying candidate novel lncRNA sequences. (It should be noted that after the candidate novel lncRNAs are identified, their expression authenticity must be verified using RT-PCR technology: specific primers are designed for each candidate novel lncRNA, i.e., crossing the exon linker region, and PCR amplification is performed using the cDNA of the corresponding sample as a template. The target band is detected by agarose gel electrophoresis, and its size is consistent with the predicted transcript. Furthermore, qRT-PCR verifies that its expression trend is consistent with the RNA-seq results, i.e., R²≥0.8, before it can be included in the complete gene raw expression count set.) The raw expression counts of candidate novel lncRNAs in each independent biopsy sample are counted, and they are supplemented into the count set according to the matrix format of the above gene raw expression count set to form a complete gene raw expression count set containing known genes and candidate novel lncRNAs.

[0042] The original gene expression counts for each biopsy myofascial sample group were normalized using the FPKM method to obtain the FPKM expression set for each biopsy myofascial sample group, as follows:

[0043] Based on the effective sequencing fragment count of each gene in each independent biopsy sample within each biopsy myofascial sample group, combined with the total exon length of each gene recorded in the pre-defined gene annotation file, and the total effective sequencing fragment count obtained after sequence alignment of each independent biopsy sample in each biopsy myofascial sample group (i.e., the total number of sequencing fragments successfully and uniquely aligned to the reference genome), the calculation is normalized according to the FPKM standard formula. For each gene, the effective sequencing fragment count corresponding to that gene is used as the numerator, and the product of the total effective sequencing fragment count of that sample and the total exon length of that gene is used as the denominator, then multiplied by 10. 9Scale conversion was performed to obtain the FPKM value of each gene in the corresponding independent biopsy sample, so as to generate the FPKM expression set, that is, the FPKM value of each gene in each biopsy myofascial sample group in the corresponding independent biopsy sample.

[0044] Based on the pre-defined comparison groups (the normal biopsy myofascial sample group and the model biopsy myofascial sample group are one comparison group, and the intervention biopsy myofascial sample group and the model biopsy myofascial sample group are another comparison group), the FPKM expression set of each biopsy myofascial sample group is subjected to inter-group statistical tests to obtain the differentially expressed gene dataset of each comparison biopsy myofascial sample group.

[0045] The specific steps for the intergroup statistical test are as follows: The FPKM expression set of each biopsy myofascial sample group is differentially processed in a pre-defined comparison group to obtain the differential expression dataset for each comparison biopsy myofascial sample group, including the fold change in expression for each gene and the p-value for the statistical test. Specifically:

[0046] For each gene (such as multiple mRNAs, LncRNAs, and novel LncRNAs), the FPKM values ​​of all independent biopsy samples in both groups (taking the comparison group of normal biopsy myofascial sample group and model biopsy myofascial sample group as an example) are extracted. The statistical test P value of the expression difference between the groups is calculated by using a pre-set negative binomial distribution statistical model to measure the statistical significance of the gene expression difference between the two groups. In the specific calculation, the dispersion of gene expression (i.e., the degree of expression fluctuation between biological replicates within the group) is estimated based on the FPKM values ​​of all independent biopsy samples in both groups. Then, a statistical model is constructed by combining the dispersion data. The confidence of the conclusion that "there is a difference in gene expression levels between the two groups" is determined by hypothesis testing. The smaller the statistical test P value, the more significant the gene expression difference between the two groups. At the same time, the arithmetic mean of the FPKM values ​​of the gene in all independent biopsy samples in both groups is calculated to obtain the average gene expression level in both groups. Then, the average FPKM value of the model group is divided by the average FPKM value of the normal group to obtain the differential expression fold value, which characterizes the magnitude and direction of the gene expression difference.

[0047] The specific statistical model for the negative binomial distribution is as follows:

[0048] Let the target gene be Myofascial samples are ( (Covering all independent biopsy samples from both normal and model biopsy myofascial sample groups), converting the FPKM value of this gene into an effective expression count. (Standardized counts after eliminating interference from sequencing depth and gene length), model assumptions It follows a negative binomial distribution, that is... in, Represents a negative binomial distribution; For genes In the sample The expected mean of expression in For genes The dispersion parameter;

[0049] To eliminate the interference of sequencing depth differences on expression levels, The derivation is made through a linear correction model, namely: ,in, For the sample Effective sequencing depth (i.e., the total number of sequencing fragments that are successfully and uniquely matched to the reference genome). Assign a grouping variable to the samples (0 for normal biopsy myofascial samples and 1 for model biopsy myofascial samples). For genes The group effect coefficient, the magnitude and sign of which directly reflect the magnitude and direction of the difference in gene expression between the two groups;

[0050] Discreteness parameter Used for quantifying genes The degree of expression variability among biological replicates within the group was estimated using an empirical Bayesian method by integrating effective gene expression count data from all genes. ;in, to For genes Within the same group Effective expression count in individual myofascial samples The representative empirical Bayesian estimation function, The larger the value, the more significant the expression fluctuation of the gene in the replicate samples within the group;

[0051] Based on the above model, the statistical significance of the difference in gene expression between the two groups is determined by the likelihood ratio test, namely: ,in, Let X be the likelihood ratio statistic, which follows a function with 1 degree of freedom. 2 distributed; The log-likelihood value is given under the alternative hypothesis (that there is a difference in expression between the two groups). for The maximum likelihood estimate; The null hypothesis (no difference in expression between the two groups, i.e.) remains unchanged. The log-likelihood value; through X stored in the database 2 Distributed table lookup Convert the results to a statistical test p-value to determine the significance of the difference in gene expression between the two groups;

[0052] The differential expression datasets of each comparative biopsy myofascial sample group were subjected to double screening to extract the differentially expressed gene datasets of each comparative biopsy myofascial sample group, including the differential expression fold value and statistical test p value of each differentially expressed gene. Specifically, the differential expression fold value and statistical test p value of each gene in each comparative biopsy myofascial sample group were compared with the preset differential expression fold value threshold (e.g., 2 or 0.5) and statistical test p threshold (e.g., 0.05).

[0053] When the differential expression fold threshold is 2, if there is a differential expression fold value greater than or equal to the differential expression fold threshold and the statistical test p value is less than the statistical test p threshold, then the gene is a differentially expressed gene (if it is mRNA, the differentially expressed gene is a differential mRNA; if it is lncRNA, the differentially expressed gene is a differential lncRNA; if it is a new lncRNA, the differentially expressed gene is a differential new lncRNA).

[0054] When the differential expression fold threshold is 0.5, if there is a differential expression fold value less than or equal to the differential expression fold threshold and the statistical test p-value is less than the statistical test p-threshold, then the gene is a differentially expressed gene.

[0055] This implementation scheme employs a strict counting rule with unique alignment and the FPKM standardization method, effectively eliminating quantitative noise caused by multiple mapping sequences and the impact of differences in sequencing depth and gene length on expression levels. This fundamentally ensures the accuracy and reproducibility of the analysis results. Secondly, it not only covers known mRNAs and lncRNAs but also innovatively integrates the prediction and quantification of new lncRNAs, greatly expanding the boundaries of molecular discovery and avoiding the risk of missing key regulatory molecules due to incomplete annotation information. This lays a solid foundation for a comprehensive analysis of the pain regulatory network. Finally, based on statistical tests combined with dual threshold screening of fold change and p-value, it takes into account the statistical significance of differences, thereby effectively controlling false positives and ensuring that the final differentially expressed gene dataset is highly reliable. This significantly improves the directionality and success rate of subsequent enrichment analysis and target validation.

[0056] Specifically, the steps to obtain the BDNF enriched significant set for each comparative biopsy myofascial sample group are as follows: perform type identification processing on the differentially expressed gene dataset of each comparative biopsy myofascial sample group; if the differentially expressed gene in each comparative biopsy myofascial sample group is a differentially expressed mRNA, perform KEGG pathway and BDNF screening processing on it to obtain the differentially expressed mRNA-mapped BDNF enriched significant set for each comparative biopsy myofascial sample group.

[0057] If the differentially expressed gene in each comparative biopsy myofascial sample group is a differentially expressed LncRNA, target gene prediction processing is performed to obtain the target genes of the corresponding differentially expressed LncRNA. Then, BDNF signaling pathway targeting enrichment processing is performed to obtain a significant set of differentially expressed LncRNA-BDNF enriched genes for each comparative biopsy myofascial sample group. The logic of obtaining the significant set of differentially expressed LncRNA-BDNF enriched genes is consistent with that of obtaining the significant set of differentially expressed mRNA-BDNF enriched genes. This set includes, but is not limited to, BDNF signaling pathway number, pathway name, list of differentially expressed LncRNA gene IDs targeting the BDNF pathway, list of BDNF pathway target gene IDs corresponding to the differentially expressed LncRNA (i.e., the corresponding mRNA), target gene source type (cis / trans), enrichment fold of the target gene set, and P-value of the enrichment statistical test of the target gene set, thus forming a significant set of differentially expressed LncRNA-BDNF enriched genes for each comparative biopsy myofascial sample group.

[0058] Based on the differential mRNA mapping BDNF enrichment significant set and differential lncRNA mapping BDNF enrichment significant set for each comparative biopsy myofascial sample group, the BDNF enrichment significant set for each comparative biopsy myofascial sample group is extracted. Specifically, using the standard number of the BDNF signaling pathway as the unique matching identifier, the two mapping sets of the same comparative biopsy myofascial sample group are aligned. For the gene list under this pathway, if a gene is both a differential mRNA and predicted as an mRNA target gene by at least one differential lncRNA, then the gene is marked as a high-confidence associated gene and retained.

[0059] The mean values ​​of pathway enrichment and p-values ​​for enrichment statistics of all differentially expressed mRNAs (corresponding to differentially expressed mRNAs with high confidence associated genes) were calculated to obtain the mRNA pathway enrichment value and p-value. The mean values ​​of LncRNA pathway enrichment and p-values ​​for LncRNA enrichment statistics of all differentially expressed lncRNAs (corresponding to differentially expressed mRNAs with high confidence associated genes) were calculated to construct the BDNF enrichment significance set of this control group.

[0060] The specific steps for obtaining the BDNF enrichment significance set of differentially expressed mRNAs for each comparative biopsy myofascial sample group are as follows: KEGG pathway enrichment analysis was performed on the differential expression fold values ​​and statistical p-values ​​of each differentially expressed mRNA in each comparative biopsy myofascial sample group to obtain a KEGG pathway significant enrichment table for each comparative biopsy myofascial sample group. Specifically, using pathway enrichment statistics, with a pre-defined KEGG pathway database (such as the Human Genome KEGG Pathway Database, whose corresponding background genome is the set of all human genes included in the database) as the reference background, the differentially expressed mRNAs were mapped to the BDNF enrichment significance set. The RNA set is compared with the known gene sets of each pathway in the database. Specifically, the hypergeometric test algorithm is used to calculate the proportion of genes belonging to a certain KEGG pathway in the differentially expressed mRNA set to the total number of differentially expressed mRNAs. This proportion is then compared with the proportion of genes belonging to that pathway in the KEGG pathway database to the total number of genes in the background genome to obtain the enrichment statistical test p-value for that pathway. At the same time, the pathway enrichment fold is calculated as (number of genes belonging to a certain pathway in the differentially expressed mRNA set / total number of differentially expressed mRNAs) / (number of genes belonging to a certain pathway in the background genome / total number of genes in the background genome).

[0061] Set a screening threshold (e.g., enrichment statistical test P value < 0.05 and enrichment fold > 1), screen out pathways that meet the threshold conditions, sort them in ascending order of enrichment statistical test P value, and organize the pathway number, pathway name, number of differentially expressed mRNAs contained in the pathway, enrichment fold, enrichment statistical test P value to obtain a KEGG pathway significant enrichment table for each comparative biopsy myofascial sample group.

[0062] BDNF pathway screening was performed on the KEGG pathway significant enrichment table of each comparative biopsy myofascial sample group to obtain the differential mRNA mapping BDNF enrichment significant set for each comparative biopsy myofascial sample group. Specifically, the screening was based on the preset standard pathway identifier of the BDNF signaling pathway. The BDNF signaling pathway specifically refers to the BDNF signaling pathway, and the identifier is consistent with the pathway naming rules in the KEGG pathway database.

[0063] The targeted screening operation traversed the KEGG pathway significant enrichment table of each comparative biopsy myofascial sample group, and retrieved and precisely matched the unique target entry in the table whose pathway name or pathway number was completely consistent with the standard pathway identifier of the BDNF signaling pathway.

[0064] Data extraction and integration: Extract all information of the target BDNF pathway entries, including but not limited to pathway number, pathway name, list of differentially expressed mRNA gene IDs, enrichment fold, and enrichment statistical test p-value, to form a significant BDNF enrichment set of differentially expressed mRNAs for each comparative biopsy myofascial sample group.

[0065] The specific steps to obtain the target gene set of each differentially expressed LncRNA in each comparative biopsy myofascial sample group are as follows: For each differentially expressed LncRNA in each comparative biopsy myofascial sample group, cis-acting target gene prediction processing is performed to obtain the candidate cis-target gene set of its corresponding differentially expressed LncRNA. Specifically, based on a pre-set reference genome and corresponding gene annotation files, complete genomic localization data of each differentially expressed LncRNA (including known differentially expressed LncRNAs and novel differentially expressed LncRNAs) is extracted. The genomic localization data of novel differentially expressed LncRNAs is obtained based on transcriptome sequencing assembly results and de novo genome alignment analysis, including the start coordinates, end coordinates, and chromosome number (e.g., chr1, chr2, etc.) of the LncRNA on the chromosome. Localization information can be extracted using Bedtools software or the R language GenomicRanges package.

[0066] Based on the molecular mechanism of lncRNA cis-regulation (i.e., regulating the expression of genes that are physically close to each other on the genome through proximity), a 10kb genomic region upstream and downstream of the differential lncRNA is defined as the candidate search range for cis-target genes, centered on the genomic location of the lncRNA. For example, the starting coordinate of the candidate search range is S-10000bp and the ending coordinate is E+10000bp. If S-10000bp is less than 0 (i.e., beyond the chromosome start end), the starting coordinate of the candidate range is adjusted to 0 to ensure that the search range does not exceed the chromosome boundary.

[0067] The intersection analysis of the above-defined candidate search range with the gene annotation file of the reference genome is performed to extract the coding genes corresponding to all annotated mRNAs within the range, which are used as candidate cis-target genes for the differentially expressed lncRNA. The intersection analysis can be performed using the Bedtoolsintersect tool. The input files are the candidate range Bed file of the differentially expressed lncRNA and the gene annotation GTF / GFF3 file of the reference genome. The output file is the gene ID, gene name and corresponding genomic coordinates of all mRNAs within the range.

[0068] The mRNA genes obtained from the above screening are deduplicated to remove duplicate genes caused by overlapping gene annotations or search ranges, forming multiple mRNA cis-target genes (referring to mRNA coding genes located within 10kb upstream and downstream of the LncRNA genomic localization, which play a role through the proximity regulation mechanism) for each differential LncRNA, i.e., the candidate cis-target gene set.

[0069] For each differentially expressed lncRNA in each comparative biopsy myofascial sample group, trans-acting target gene prediction was performed to obtain a candidate trans-acting target gene set for the corresponding differentially expressed lncRNA. Specifically, the nucleotide sequence of each differentially expressed lncRNA was extracted (based on transcriptome splicing and genome alignment results). The nucleotide sequence of the new differentially expressed lncRNA was determined based on the complete transcriptome sequence obtained by transcriptome sequencing splicing. The CDS sequence of all mRNAs was extracted from the GTF / GFF3 annotation file of the pre-defined reference genome to construct an mRNA sequence library.

[0070] Using BLASTN software, differentially matched LncRNA sequences were used as query sequences and mRNA sequence libraries were used as databases for local alignment (parameter settings: mismatch rate ≤15%, shortest matching length ≥15bp, E value ≤1e-5, screening for mRNAs with pair similarity ≥85% and number of mismatches ≤3). mRNAs that met the local alignment conditions were selected as preliminary candidate mRNAs.

[0071] Thermodynamic analysis of RNA-RNA pairing complexes was performed on preliminary candidate mRNAs and their corresponding differentially expressed lncRNAs. The minimum free energy of the pairing complex was calculated using a conventional RNA thermodynamic model. mRNAs with a minimum free energy < -20 kcal / mol were screened. The conventional RNA thermodynamic model can be the nearest-neighbor (NN) thermodynamic model of RNA secondary structure. This model is used to calculate the free energy of RNA-RNA interactions. Its core principle is as follows: This model is based on the nearest-neighbor interaction rule of RNA base pairing. The core assumption is that the total free energy of the RNA-RNA pairing complex consists of the interaction energy (stack energy) of adjacent base pairs, the initiation energy of terminal base pairs, and the cyclization energy of unpaired bases (such as hairpin loops, internal loops, and bulge loops). By quantifying the thermodynamic parameters of different base pairing combinations (AU, UA, GC, CG, GU, UG) and the adjacent base environment, the minimum free energy of the pairing complex is accurately calculated. The smaller the minimum free energy value (the larger the absolute value of the negative value), the more stable the pairing and binding of lncRNA and mRNA.

[0072] The formula for calculating the total minimum free energy of a paired complex is: ,in, Stacking free energy of adjacent base pairs (core contributor), the value of which is determined by the specific base pairing combination and the adjacent base environment (e.g., the stacking energy of adjacent GC and CG is -1.4 kcal / mol, and the stacking energy of adjacent AU and UA is -0.9 kcal / mol. The parameters are derived from internationally recognized RNA thermodynamic parameter libraries, such as the Turner parameter library). The initiation free energy of the paired helix ends is a fixed correction value (e.g., the initiation energy of the double-stranded RNA ends is +0.4 kcal / mol) used to correct for differences in the stability of the terminal base pairs. The cyclization free energy of unpaired bases forming a ring structure is calculated based on the type of ring (hairpin ring, inner ring, etc.) and its length. Short rings (3-6 bases) have higher cyclization energies, while long rings have cyclization energies that are close to a constant.

[0073] Based on the local alignment results of differentially expressed lncRNAs and mRNAs, the specific steps for applying this model are as follows:

[0074] Based on the "LncRNA-mRNA local pairing regions" obtained by blastn screening, a potential RNA-RNA pairing complex secondary structure model was constructed. The thermodynamic parameter library of this model (such as the Turner2004 parameter set) was called, and the stacking energy, initiation energy, and cyclization energy were calculated step by step according to the above formula. The total free energy of the pairing complex was obtained by summing them. mRNAs with a total free energy < -20 kcal / mol were screened and included in the candidate trans target gene set to ensure that the screened target genes and differentially expressed lncRNAs have stable interaction capabilities.

[0075] The screened mRNAs are then compiled, and repetitive sequences are removed (if the same mRNA is matched multiple times, the pairing result with the minimum free energy is retained), forming multiple mRNA trans target genes for each differential LncRNA (referring to mRNA encoding genes that bind to LncRNA through sequence complementary pairing, i.e., minimum free energy < -20 kcal / mol, and function through post-transcriptional regulatory mechanisms), i.e., the candidate trans target gene set;

[0076] Based on the candidate cis-target gene set and candidate trans-target gene set of each differentially expressed LncRNA in each comparative biopsy myofascial sample group, the target gene set of the corresponding differentially expressed LncRNA is extracted. Specifically, if the same mRNA is simultaneously screened as both the mRNA cis-target gene and the mRNA trans-target gene of a certain differentially expressed LncRNA, then the mRNA is retained; otherwise, it is removed, so as to obtain multiple mRNA target genes corresponding to each differentially expressed LncRNA.

[0077] In this implementation plan, by integrating the direct expression association of differentially expressed mRNAs and the indirect regulatory association of differentially expressed lncRNAs, a two-dimensional evidence chain of expression and regulation is formed. Furthermore, by screening high-confidence associated genes, the plan focuses on key pivot genes under dual regulation, greatly enhancing the biological credibility and specificity of the discovered BDNF pathway-related targets. Secondly, the mapping sets of differentially expressed mRNAs and differentially expressed lncRNAs follow a unified data structure (including pathway identifiers, enrichment statistics, gene lists, etc.), ensuring the feasibility and rigor of subsequent integration and comparison. At the same time, the hypergeometric test and screening based on KEGG standard pathways standardizes and reproduces the analysis process, avoiding subjective bias. Finally, using a unique BDNF pathway identifier as the core of screening and introducing "cis / trans" target gene prediction (based on genomic proximity and sequence complementarity), the analysis can accurately identify the core regulatory network directly related to the BDNF pathway from a massive number of differentially expressed genes, revealing the possible upstream and downstream transcriptional regulation, and providing more comprehensive and reliable data for understanding the deep mechanisms of acupuncture intervention.

[0078] Please see Figure 3 This invention provides a technical solution: a biopsy myofascial sample analysis system, comprising: a data acquisition module for acquiring RNA data from several pre-stored biopsy myofascial sample groups; a sequencing and transcription analysis module for performing RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascial sample group to obtain transcriptome data for each biopsy myofascial sample group; a differential expression analysis module for performing inter-group differential analysis on the transcriptome data of each biopsy myofascial sample group according to a preset comparison group, and extracting differentially expressed gene datasets for each comparison biopsy myofascial sample group; a BDNF enrichment analysis module for performing BDNF hierarchical enrichment analysis on the differentially expressed gene datasets of each comparison biopsy myofascial sample group to obtain a BDNF enrichment significant set for each comparison biopsy myofascial sample group; and a BDNF validation feedback module for performing cross-validation processing based on the BDNF enrichment significant set of each comparison biopsy myofascial sample group.

[0079] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0080] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for analyzing biopsy myofascial samples, characterized in that, The method comprises the following steps: obtaining RNA data of a plurality of biopsy myofascial sample groups stored in advance; performing RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascial sample group to obtain transcriptome data of each biopsy myofascial sample group; performing group difference analysis on the transcriptome data of each biopsy myofascial sample group according to a preset comparison group to extract a differentially expressed gene dataset of each comparison biopsy myofascial sample group; performing BDNF hierarchical enrichment analysis on the differentially expressed gene dataset of each comparison biopsy myofascial sample group to obtain a BDNF enrichment significant set of each comparison biopsy myofascial sample group; performing cross-validation processing based on the BDNF enrichment significant set of each comparison biopsy myofascial sample group.

2. The biopsy myofascial sample analysis method of claim 1, wherein, The specific steps of obtaining the transcriptome data of each biopsy myofascial sample group are as follows: constructing a sequencing library of each biopsy myofascial sample group based on the RNA data of each biopsy myofascial sample group; performing quality detection processing on the sequencing library of each biopsy myofascial sample group respectively; performing double-end sequencing based on the sequencing library of each biopsy myofascial sample group after quality detection processing to extract the transcriptome data of each biopsy myofascial sample group.

3. The biopsy myofascial sample analysis method of claim 2, wherein, The specific steps of extracting the differentially expressed gene dataset of each comparison biopsy myofascial sample group are as follows: aligning the transcriptome data of each biopsy myofascial sample group with a preset reference genome respectively to obtain a gene raw expression count set of each biopsy myofascial sample group; performing normalization processing on the gene raw expression count set of each biopsy myofascial sample group based on the FPKM method to obtain an FPKM expression set of each biopsy myofascial sample group; performing inter-group statistical test processing on the FPKM expression set of each biopsy myofascial sample group based on a preset comparison group to obtain a differentially expressed gene dataset of each comparison biopsy myofascial sample group.

4. The biopsy myofascial sample analysis method of claim 3, wherein, The specific steps of inter-group statistical test processing are as follows: performing difference processing on the FPKM expression set of each biopsy myofascial sample group in the preset comparison group to obtain a differentially expressed dataset of each comparison biopsy myofascial sample group, including a differential expression fold value and a statistical test P value of each gene; performing double screening processing on the differentially expressed dataset of each comparison biopsy myofascial sample group to extract a differentially expressed gene dataset of each comparison biopsy myofascial sample group, including a differential expression fold value and a statistical test P value of each differentially expressed gene.

5. The biopsy myofascial sample analysis method of claim 1, wherein, The specific steps of obtaining the BDNF enrichment significant set of each comparison biopsy myofascial sample group are as follows: performing type identification processing on the differentially expressed gene dataset of each comparison biopsy myofascial sample group; if the differentially expressed gene of each comparison biopsy myofascial sample group is a differentially expressed mRNA, performing KEGG pathway and BDNF screening processing thereon to obtain a differentially expressed mRNA mapped BDNF enrichment significant set of each comparison biopsy myofascial sample group; If the differential genes of each comparison biopsy fascia sample group are differential LncRNAs, target gene prediction processing is performed on the differential LncRNAs to obtain target genes of the differential LncRNAs, and BDNF signaling pathway target enrichment processing is performed to obtain a differential LncRNA mapping BDNF enrichment significant set of each comparison biopsy fascia sample group; Based on the differential mRNA mapping BDNF enrichment significant set and the differential LncRNA mapping BDNF enrichment significant set of each comparison biopsy fascia sample group, a BDNF enrichment significant set of each comparison biopsy fascia sample group is extracted.

6. The biopsy myofascial sample analysis method of claim 5, wherein, The specific steps for obtaining the differential mRNA mapping BDNF enrichment significant set of each comparison biopsy fascia sample group are as follows: The difference expression fold value and statistical test P value of each differential mRNA of each comparison biopsy fascia sample group are subjected to KEGG signaling pathway enrichment analysis to obtain a KEGG pathway significant enrichment table of each comparison biopsy fascia sample group; The KEGG pathway significant enrichment table of each comparison biopsy fascia sample group is subjected to BDNF pathway screening processing to obtain a differential mRNA mapping BDNF enrichment significant set of each comparison biopsy fascia sample group.

7. The method of claim 5, wherein the biopsy myofascial sample is analyzed for, The specific steps for obtaining the target gene set of each differential LncRNA of each comparison biopsy fascia sample group are as follows: Each differential LncRNA of each comparison biopsy fascia sample group is subjected to cis-acting target gene prediction processing to obtain a candidate cis-target gene set of the differential LncRNA; Each differential LncRNA of each comparison biopsy fascia sample group is subjected to trans-acting target gene prediction processing to obtain a candidate trans-target gene set of the differential LncRNA; Based on the candidate cis-target gene set and the candidate trans-target gene set of each differential LncRNA of each comparison biopsy fascia sample group, a target gene set of the differential LncRNA is extracted.

8. The biopsy myofascial sample analysis method of claim 1, wherein, The specific steps for cross-validation processing based on the BDNF enrichment significant set of each comparison biopsy fascia sample group are as follows: The BDNF enrichment significant set of each comparison biopsy fascia sample group is subjected to screening verification processing, respectively; A BDNF intervention evaluation report is generated based on the screening verification processing result.

9. The biopsy myofascial sample analysis method according to claim 8, characterized in that, The specific steps for screening verification processing are as follows: The BDNF enrichment significant set of each comparison biopsy fascia sample group is subjected to pathological pathway screening verification processing to obtain a pathology verification identifier of each comparison biopsy fascia sample group; The BDNF enrichment significant set of each comparison biopsy fascia sample group is subjected to intervention response pathway verification processing to obtain an intervention verification identifier of each comparison biopsy fascia sample group.

10. A biopsy myofascial sample analysis system applying the biopsy myofascial sample analysis method according to any one of claims 1 to 9, characterized by, The specific steps for generating the BDNF intervention evaluation report based on the screening verification processing result are as follows: The data acquisition module is configured to acquire RNA data of a plurality of biopsy fascia sample groups stored in advance; The sequencing transcription analysis module is configured to perform RNA-seq high-throughput sequencing on the RNA data of each biopsy fascia sample group to obtain transcriptome data of each biopsy fascia sample group; The differential expression analysis module is configured to perform inter-group differential analysis on the transcriptome data of each biopsy fascia sample group according to a preset comparison group to extract a differential expression gene data set of each comparison biopsy fascia sample group; The BDNF enrichment analysis module is configured to perform BDNF hierarchical enrichment analysis on the differentially expressed gene dataset of each comparative biopsy myofascial sample group, to obtain a BDNF enrichment significant set of each comparative biopsy myofascial sample group. The BDNF verification feedback module is configured to perform cross-validation processing based on the BDNF enrichment significant set of each comparative biopsy myofascial sample group.

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