Biomarker-based retroperitoneal liposarcoma molecular typing method, system and equipment

Abstract

The invention provides a retroperitoneal liposarcoma molecular typing method, system and equipment based on biomarkers, the method can divide retroperitoneal liposarcoma into three subtypes based on three genes of KLF6, ECM2 and LMNB2, and a foundation can be laid for molecular typing, prognosis and targeted drug design of retroperitoneal liposarcoma.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and in particular, relates to a method, system and device for molecular typing of retroperitoneal liposarcoma based on biomarkers. Background Art

[0002] Retroperitoneal liposarcoma (RPLS) is a soft tissue sarcoma (STS) originating from the retroperitoneum. It has an insidious onset and faces great challenges in diagnosis and treatment. Traditional surgical resection has always been regarded as the main treatment strategy for RPLS, but due to the complex anatomical structure and biological characteristics of the sarcoma, it is extremely difficult to achieve microscopic margin-free resection, resulting in a high postoperative recurrence rate in patients. In the past decade, scientists have tried to improve the postoperative survival rate of patients through personalized surgical resection and neoadjuvant / adjuvant therapy, but the results have not been ideal. It is worth noting that precision medicine, especially the mining of tumor molecular characteristics and marker molecules, has greatly enriched cancer treatment methods and prolonged the median survival of many tumors. However, the limited exploration of the molecular map of RPLS has hindered its development towards precision treatment.

[0003] In the past two decades, the rapid development of next-generation sequencing technology has brought the diagnosis and treatment of tumor diseases into the era of precision medicine. A large number of studies have proposed molecular classification strategies for different tumors, including bladder cancer, prostate cancer, lung cancer, breast cancer, and osteosarcoma. The molecular characteristics of tumors not only provide useful information for prognosis and prediction of different treatments, but also guide the critical clinical decision-making process throughout the treatment process. However, little is known about the molecular characteristics of retroperitoneal liposarcoma, which greatly limits the development of treatment methods for retroperitoneal liposarcoma. Summary of the invention

[0004] In order to make up for the deficiencies of the prior art, the object of the present invention is to provide a molecular typing method for retroperitoneal liposarcoma based on biomarkers.

[0005] To achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a method for molecular typing of retroperitoneal liposarcoma.

[0006] Further, the method comprises: (1) Obtain RNA sequencing data and clinical follow-up information data of samples from patients with retroperitoneal liposarcoma; (2) A survival analysis algorithm was used to identify prognostic genes associated with overall survival and disease-free survival. Specifically, a univariate Cox proportional hazard regression model was constructed, and regression analysis was performed on each gene to screen out prognostic genes with p < 0.05 and statistical significance. (3) The weighted gene co-expression network analysis method was used to functionally cluster the above-mentioned prognostic genes to form multiple modules, and these modules were functionally annotated to identify the main enriched signaling pathways; (4) The top 20 core genes were selected from each module formed as above as the basis, and consensus unsupervised clustering was performed using a non-negative matrix factorization algorithm. Specifically, the expression matrix A of the main signaling pathways and prognostic genes identified above was used, and the non-negative matrix factorization algorithm was applied to the expression matrix A to decompose it into two non-negative matrices W and H. The matrix A was repeatedly factorized, and its output was aggregated to obtain a consistent clustering of retroperitoneal liposarcoma samples, thereby dividing retroperitoneal liposarcoma into three heterogeneous clusters with different biological and clinical characteristics, namely three molecular subtypes, named retroperitoneal liposarcoma S1 subtype, retroperitoneal liposarcoma S2 subtype and retroperitoneal liposarcoma S3 subtype.

[0007] Furthermore, the three molecular typings are determined based on the Cophenetic coefficient, the Dispersion coefficient, and the Silhouette coefficient.

[0008] Furthermore, sources of the RNA sequencing data and clinical information data of retroperitoneal liposarcoma patients include the RESAR database and actual clinical samples.

[0009] Furthermore, the prognosis genes include KLF6, ECM2 and LMNB2.

[0010] In some embodiments, the weighted gene co-expression network analysis (WGCNA) is a systems biology method that uses gene expression data to construct a dimensionless network. First, a gene expression similarity matrix is ​​constructed, that is, the absolute value of the Pearson correlation coefficient between two genes is calculated, and then the gene expression matrix is ​​converted into an adjacency matrix to strengthen the correlation and split the weak correlation from the exponential level. Next, the adjacency matrix is ​​converted into a topological matrix. Based on the topological overlap matrix (TOM), all genes are hierarchically clustered using a dynamic shear dendrogram method, and highly co-expressed genes are grouped into one module. In order to observe the functions of each co-expression module, these modules are further functionally annotated to identify the main biological pathways and functional complexes enriched therein. Finally, the identified key modules and their representative markers were validated with additional data sets, including repeated experiments in independent cohorts, to ensure the consistency and reliability of the findings.

[0011] In some embodiments, the non-negative matrix factorization algorithm (NMF) is a matrix decomposition method under the constraint that all elements in the matrix are non-negative numbers; its basic idea can be simply described as: for any given non-negative matrix A, the NMF algorithm can find a non-negative matrix W and a non-negative matrix H, thereby decomposing a non-negative matrix into the product of two left and right non-negative matrices.

[0012] In some embodiments, after NMF clustering, the corresponding Cophenetic, Dispersion and Silhouette values ​​are calculated, and then the optimal number of subtypes is selected according to the above indicators to ensure the best classification effect. The Cophenetic value measures the consistency between the clustering result and the original distance. The higher the value, the better the clustering effect. The Dispersion value measures the compactness within the cluster and the dispersion between clusters. The lower the value, the better the classification effect. The Silhouette value comprehensively considers the similarity and difference between samples. The larger the value, the higher the classification clarity.

[0013] Furthermore, in the present invention, the retroperitoneal liposarcoma S1 subtype, retroperitoneal liposarcoma S2 subtype and retroperitoneal liposarcoma S3 subtype have the following differences in clinical characteristics: The overall survival time of the three subtypes was significantly different; among them, the S2 subtype had the longest overall survival time and the best prognosis, while the S3 subtype had the shortest overall survival time and the worst prognosis; The tumor microenvironment scores of the three subtypes were significantly different; among them, the tumor microenvironment score of the S2 subtype was the highest, and the tumor microenvironment score of the S3 subtype was the lowest; The tumor sizes of the three subtypes differed significantly; among them, the S3 subtype had the smallest tumor volume; The MDM2 expression levels, Ki67 index and FNCLCC scores of the three subtypes were significantly different; among them, the S3 subtype had the highest MDM2 expression level, Ki67 index and FNCLCC score; The number of surgeries and the proportion of pathological subtypes (DDLS / WDLS) among the three subtypes were significantly different; among them, the S3 subtype had the most surgeries and the most significant proportion of pathological subtypes.

[0014] Furthermore, in the present invention, the retroperitoneal liposarcoma S1 subtype, retroperitoneal liposarcoma S2 subtype and retroperitoneal liposarcoma S3 subtype have the following differences in biological characteristics: Compared with the healthy group samples, the differentially expressed genes of the S1 subtype of retroperitoneal liposarcoma were mainly enriched in immune-related pathways, such as "TNFα", "IL2-STAT5" and "IFNα and IFNβ response"; Compared with the healthy group samples, the differentially expressed genes in the S2 subtype of retroperitoneal liposarcoma were mainly enriched in metabolism-related pathways, such as "lipogenesis" and "bile acid metabolism"; Compared with the healthy group samples, the differentially expressed genes in the S3 subtype of retroperitoneal liposarcoma were mainly enriched in proliferation-related pathways, such as "mitotic spindle" and "G2M checkpoint".

[0015] The second aspect of the present invention provides a method for molecular typing of retroperitoneal liposarcoma based on biomarkers.

[0016] Further, the method comprises the following steps: (1) Obtaining data: Obtaining the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (2) Data processing: Compare the expression values ​​of the three genes KLF6, ECM2, and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (3) Output result: If the KLF6 expression value is the highest in the sample of the patient with retroperitoneal liposarcoma to be tested, the patient is output as the S1 subtype of retroperitoneal liposarcoma; if the ECM2 expression value is the highest in the sample of the patient with retroperitoneal liposarcoma to be tested, the patient is output as the S2 subtype of retroperitoneal liposarcoma; if the LMNB2 expression value is the highest in the sample of the patient with retroperitoneal liposarcoma to be tested, the patient is output as the S3 subtype of retroperitoneal liposarcoma.

[0017] Further, the patient includes humans and / or mammals; Furthermore, the sample includes tissue and body fluid.

[0018] The third aspect of the present invention provides a biomarker-based molecular classification system for retroperitoneal liposarcoma.

[0019] Further, the system comprises: (1) Data acquisition module: obtaining the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (2) Data analysis module: compare the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (3) Result output module: If the KLF6 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S1 subtype of retroperitoneal liposarcoma; if the ECM2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S2 subtype of retroperitoneal liposarcoma; if the LMNB2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S3 subtype of retroperitoneal liposarcoma.

[0020] Furthermore, the patient includes humans and / or mammals.

[0021] Furthermore, the sample includes tissue and body fluid.

[0022] A fourth aspect of the present invention provides a computer device. Further, the device comprises: A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method described in the first aspect of the present invention or the method for molecular typing of retroperitoneal liposarcoma based on biomarkers described in the second aspect of the present invention is implemented.

[0023] A fifth aspect of the present invention provides a computer-readable storage medium.

[0024] Furthermore, the computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method described in the first aspect of the present invention or the method for molecular classification of retroperitoneal liposarcoma based on biomarkers described in the second aspect of the present invention.

[0025] A sixth aspect of the present invention provides the use of a biomarker in the preparation of a product for molecular typing of retroperitoneal liposarcoma.

[0026] Furthermore, the molecular classification of retroperitoneal liposarcoma includes retroperitoneal liposarcoma S1 subtype, retroperitoneal liposarcoma S2 subtype and retroperitoneal liposarcoma S3 subtype.

[0027] Furthermore, the biomarker of the retroperitoneal liposarcoma S1 subtype is KLF6, the biomarker of the retroperitoneal liposarcoma S2 subtype is ECM2, and the biomarker of the retroperitoneal liposarcoma S3 subtype is LMNB2.

[0028] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a new molecular typing method for retroperitoneal liposarcoma, provides a biomarker for molecular typing and its application, and lays a foundation for the classification of retroperitoneal liposarcoma and the design of targeted drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 A schematic diagram of a flow chart of a biomarker-based molecular classification method for retroperitoneal liposarcoma provided in an embodiment of the present invention; Figure 2 A schematic diagram of a system for molecular classification of retroperitoneal liposarcoma based on biomarkers provided in an embodiment of the present invention; Figure 3 A schematic diagram of a device for molecular typing of retroperitoneal liposarcoma based on biomarkers provided in an embodiment of the present invention; Figure 4 Figure 1 is a graph showing the results of RPLS prognostic gene identification; A is a graph showing the conceptual model of the present invention; B is a graph showing the results of prognostic genes significantly associated with OS and DFS; C is a graph showing the results of the top 10 genes with HR>1 and HR<1; and DF is a graph showing the functional annotation results of prognostic genes. Figure 5 The figure is the result of RPLS molecular typing; A is the research and investigation of the clustering effect of NMF method; B is the heat map result display after clustering by NMF method; Figure 6 Figure 1 is a characteristic diagram of different RPLS subtypes; AB is a prognostic result diagram of three RPLS subtypes; C is a metabolic pathway diagram of different subtypes; DJ is a comparative result diagram of the three subtypes in terms of immune infiltration and clinical characteristics; K is a representative marker identification result diagram of the three subtypes; L, N, and P are the relationship results between the expression levels of KLF6, ECM2, and LMNB2 and the OS of RPLS patients; M, O, and Q are the relationship results between the expression levels of KLF6, ECM2, and LMNB2 and the DFS of RPLS patients; Figure 7 Figures 1 and 2 are representative marker validation results; A is the immunohistochemical staining of KLF6, ECM2, and LMNB2 at different expression levels; BC are the relationship results between KLF6 expression level and OS and DFS of RPLS patients; DE are the relationship results between LMNB2 expression level and OS and DFS of RPLS patients; FG are the relationship results between ECM2 expression level and OS and DFS of RPLS patients; HL is the difference in clinical characteristics of the subtypes represented by the three markers. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0032] In some of the processes described in the specification and claims of the present invention and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0034] Example 1 1. Experimental Methods 1. RNA sequencing, preliminary data processing and analysis ① RNA extraction: Total RNA was extracted from samples of training cohort 1 (N=8) and training cohort 2 (N=80) using TRIzol reagent (Invitrogen).

[0035] ②Quality monitoring: RNA degradation and contamination were monitored by 1% agarose gel. RNA purity was checked using a NanoPhotometer spectrophotometer; RNA concentration was measured by Qubit RNA Assay Kit using a Qubit 2.0 fluorometer; RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system.

[0036] ③Library construction: 3-5 μg of total RNA was used as input material for each sample. Sequencing libraries were generated using the NEBNext® Multiplex Small RNA Library Prep Set for Illumina® according to the manufacturer's recommendations, and index codes were added to mark the sequences of each sample.

[0037] ④ Cluster generation: Cluster the samples with index encoding using TruSeq SR ClusterKit v3-cBot-HS (Illumia) on the cBot Cluster Generation System.

[0038] ⑤ Sequencing: After cluster generation, strand-specific cDNA sequencing was performed on the Illumina NovaSeq 6000 platform to generate single-end read data.

[0039] ⑥Data analysis: Cuffdiff (v2.1.1) was used to calculate the FPKM values ​​of mRNAs and noncoding RNAs in each sample. The FPKM value was calculated based on the fragment length and the read count mapped to the fragment. These sequencing data have been deposited in the open archive miscellaneous database OMIX of the China National Center for Bioinformation (CNCB) with the accession number OMIX002786.

[0040] 2. Identification of prognostic genes ① Data preparation: Use the aforementioned RNA sequencing data and clinical follow-up information to ensure that all samples pass quality control. Integrate the data of all samples into a unified data matrix and analyze them in combination with survival time and outcomes (such as overall survival (OS) and disease-free survival (DFS)).

[0041] ② Preprocessing step: Standardize the gene expression data to eliminate systematic errors and technical noise, so that different samples are comparable. Ensure the integrity of the survival data, including confirming the follow-up time, event status and other information of each patient.

[0042] ③ Selection of univariate Cox regression analysis tools: Univariate Cox regression analysis was performed using the “survival” package in R language. The “survival” package is a powerful tool for survival analysis that can effectively evaluate the relationship between gene expression and patient prognosis.

[0043] ④ Construction of Cox regression model: A univariate Cox proportional hazard regression model was constructed, and regression analysis was performed on each gene. The response variables were set as the patient's survival time and event status, and the independent variables were the expression levels of each gene in different samples.

[0044] ⑤Significance screening: Calculate the hazard ratio (HR) and its 95% confidence interval for each gene, and calculate the p value to evaluate statistical significance. Compare the p value with the significance threshold of 0.05 to screen out those prognosis-related genes with p<0.05 and statistical significance. These genes are considered to have potential value in predicting the prognosis of patients with retroperitoneal liposarcoma (RPLS).

[0045] ⑥Result output and annotation: Output the list of significantly prognostic-related genes screened out, and record the corresponding HR, 95% confidence interval and p value. Functionally annotate these prognostic-related genes and explore the biological processes and signaling pathways they may be involved in to further understand their role in the progression of RPLS.

[0046] ⑦ Visualization: Use visualization methods such as forest plot and Kaplan-Meier curve to show the impact of major prognosis-related genes on patient survival, as well as the expression patterns of each important prognostic gene in different groups.

[0047] 3. Gene set enrichment analysis (GSEA) and immune infiltration analysis ① Data preparation: Use the aforementioned RNA sequencing data to ensure that all samples pass quality control. Integrate the data of the tumor group and the normal group into a unified data matrix for subsequent gene set enrichment analysis and immune infiltration analysis.

[0048] ② Selection of gene set enrichment analysis tools: Gene set enrichment analysis was performed using the "GSEA" package in the R language. GSEA (Gene Set Enrichment Analysis) is a statistical method used to determine whether a predefined set of genes exhibits significant differential expression under different biological states.

[0049] ③ Pathway annotation file download: Download the latest version of the pathway annotation file from the msigdb platform (www.gsea-msigdb.org), including the HALLMARK and REACTOME databases. These databases contain widely recognized important biological signaling pathway information.

[0050] ④GSEA analysis process: The tumor group and the normal group samples were compared, and the "GSEA" package was used to perform enrichment analysis on each gene set. The enrichment score (ES) of each predefined gene set between the two groups was calculated to evaluate its significance. The normalized enrichment score (NES) and the corresponding p-value and FDR value were obtained by random permutation test.

[0051] ⑤Significance threshold setting: Determine the screening criteria for significant pathways: FDR < 0.25 is considered to be a statistically significant enrichment result.

[0052] ⑥Result output and annotation: Output the list of significantly enriched signal pathways screened out, and record the corresponding NES, p value and FDR value.

[0053] ⑦Immune infiltration analysis tool selection: Use the ESTIMATE algorithm to measure the immune cell infiltration in tumor tissue, including immune score and matrix score. This algorithm can estimate the immune cell components and non-tumor cell components in malignant tumor tissue based on expression data.

[0054] ⑧Immune infiltration quantification step: The samples were processed using the "estimate" package in the R language, and the immune score and matrix score of each sample were calculated using the ESTIMATE algorithm. The immune microenvironment characteristics between different samples and between different subtypes were evaluated based on the obtained scores to reveal potential immune escape mechanisms or therapeutic targets.

[0055] 4. Functional Annotation ① Data preparation: Use the previously identified prognostic gene list to ensure that these genes have significant differential expression between different groups and are closely related to patient prognosis.

[0056] ② Functional enrichment analysis tool selection: Functional enrichment analysis was performed using the “clusterProfiler” package in R language. This package provides a wealth of bioinformatics tools for analyzing the biological significance of high-throughput gene data.

[0057] ③Gene Ontology (GO) analysis: GO analysis was performed on the prognostic genes to reveal the biological processes (BP), cellular components (CC), and molecular functions (MF) that they may be involved in. Use the enrichGO function in the “clusterProfiler” package to input the prognostic gene list and set appropriate parameters for enrichment analysis. Calculate the significant p-value of each GO entry, and use the Benjamini-Hochberg method to correct for multiple hypothesis testing to obtain the adjusted FDR value.

[0058] ④Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis: KEGG pathway enrichment analysis was performed on the prognostic genes to explore the important signaling pathways and metabolic pathways that they may be involved in. Use the enrichKEGG function in the "clusterProfiler" package, input the prognostic gene list and set appropriate parameters for enrichment analysis. Calculate the significant p value of each KEGG pathway, and use the Benjamini-Hochberg method to correct for multiple hypothesis testing to obtain the adjusted FDR value.

[0059] ⑤Significance threshold setting: Determine the screening criteria for significant enrichment results: FDR < 0.05 is considered to be a statistically significant enrichment result. This means that we only focus on those biological processes and signaling pathways that still show significant changes in multiple comparisons and have large changes.

[0060] ⑥Result output and annotation: Output the screened significantly enriched GO terms and KEGG pathway lists, and record the corresponding p-values ​​and FDR values. Detailed annotations are made for these significant terms and pathways, including their specific names, the number of genes involved, and corresponding functional descriptions.

[0061] ⑦ Visualization: Use visualization methods such as bar charts and bubble charts to display the enrichment of major GO entries and KEGG pathways, as well as the key genes contained in each important entry or pathway.

[0062] 5. Construction of weighted gene co-expression module (WGCNA) To further explore the interactions between genes, we used weighted gene co-expression network analysis (WGCNA). WGCNA is a systems biology tool that can convert the co-expression relationships between genes into connection weights or topological overlap metrics to identify functionally related gene modules. The genes in these modules usually play a role in the same pathway or functional complex and show similar expression patterns.

[0063] ①Data preparation: First, we collected and sorted the gene expression data of patients with retroperitoneal liposarcoma. Through survival analysis, we screened out prognostic genes that were significantly associated with overall survival (OS) and disease-free survival (DFS).

[0064] ②Construction of weighted co-expression network: The screened prognostic genes were input into the “WGCNA” R package to construct a weighted co-expression network. The algorithm first calculated the Pearson correlation coefficient between each pair of genes and then converted it into a connection weight to reflect the possibility that the two genes are jointly involved in the regulatory process.

[0065] ③ Determine the soft threshold parameter: To ensure that the network structure conforms to the scale-free property, we select an appropriate soft threshold parameter to increase the difference between strong and weak edges. This step helps to identify gene modules with important biological significance.

[0066] ④ Dynamic shear dendrogram: Based on the topological overlap matrix (TOM), we used the dynamic shear dendrogram method to hierarchically cluster all prognostic genes and grouped highly co-expressed genes into one module.

[0067] ⑤Module identification and annotation: We further annotated these modules and identified important biological pathways and functional complexes that were enriched in them. For example, through GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, we determined the important roles of each module in cell cycle, TGFβ signaling pathway, angiogenesis, etc.

[0068] ⑥ Set statistical significance threshold: In order to ensure that the results are statistically significant, the co-expression module sets p < 0.05 as the threshold, which means that only those modules that are statistically significantly associated and have a high degree of credibility will be retained for further research.

[0069] ⑦ Validation and Application: Finally, the identified key modules and their representative markers were validated through additional data sets, including repeated experiments in independent cohorts, to ensure the consistency and reliability of the findings.

[0070] 6. Consensus clustering based on non-negative matrix factorization (NMF) ① Data preparation: Use the expression matrix A of the major signaling pathways and prognostic genes identified above to ensure that all sample and gene data are quality controlled and normalized. Matrix A contains the expression levels of each sample in different gene sets, which correspond to the major biological signaling pathways and prognosis-related genes.

[0071] ② Non-negative matrix factorization (NMF) tool selection: Use the "NMF" package in R language to perform non-negative matrix factorization. NMF is a method that decomposes the original matrix into two low-dimensional non-negative matrices, which is suitable for extracting potential feature patterns from high-dimensional data.

[0072] ③Perform NMF decomposition: Apply the NMF algorithm to the expression matrix A and decompose it into two non-negative matrices W and H. Specifically, A≈W*H, where W represents the weight of the basic pattern and H represents the weight of these patterns in each sample. To ensure the stability of the results, the matrix A is repeatedly decomposed and the output results of each iteration are summarized to obtain consistent clustering results.

[0073] ④ Consensus clustering construction: Aggregate the results obtained from multiple repeated decompositions and evaluate the consistency relationship between samples by calculating the consistency matrix. The consistency matrix reflects the frequency with which any two samples are classified into the same cluster during multiple clustering processes, thus helping us determine a more robust and reliable clustering structure.

[0074] ⑤ Selection of the optimal number of subtypes: Use indicators such as Cophenetic coefficient, Dispersion coefficient and Silhouette coefficient to evaluate the consistency effect under different numbers of subtypes. Cophenetic coefficient: measures the consistency between the clustering result and the original distance. The higher the value, the better the clustering effect. Dispersion coefficient: measures the compactness within the cluster and the dispersion between clusters. The lower the value, the better the classification effect. Silhouette coefficient: comprehensively considers the similarity and difference between samples. The larger the value, the higher the classification clarity. Select the optimal number of subtypes based on the above indicators to ensure the best classification effect.

[0075] ⑥ Consensus clustering result output and annotation: Output the subtype label of each RPLS (retroperitoneal liposarcoma) sample, and record the corresponding consistency score and various evaluation index data. Detailed annotations are made for each subtype, including its characteristic description, important signaling pathways involved, and predictive prognostic information.

[0076] ⑦ Visualization: Use visualization methods such as heat maps and bar charts to show the consistency between different subtypes and within samples. Make detailed charts, such as heat maps with color coding and label annotations, to intuitively present important differences between samples of different categories and reveal potential biological mechanisms or clinical significance.

[0077] 7. Immunohistochemistry (IHC) ① Antibody preparation: KLF6, LMNB2 and ECM2 antibodies for immunohistochemistry were purchased from Bioss.

[0078] ② Sample treatment: The samples were dewaxed for 15 minutes each time, for a total of 3 times, using xylene. After routine hydration treatment, the samples were immersed in phosphate buffered saline (PBS) for 10 minutes.

[0079] ③ Antigen retrieval: Use a pressure cooker for antigen retrieval, using Tris-EDTA buffer (PH=9.0) and high pressure time for 2.5 minutes.

[0080] ④ Endogenous peroxidase blocking: Treat the sample with 3% endogenous peroxidase blocker (ZSBIO, PV-6000) for 10 minutes to reduce nonspecific staining.

[0081] ⑤ Block nonspecific reactions: Incubate the sample in goat serum (ZSBIO, ZLI-9022) to block nonspecific binding sites.

[0082] ⑥ Primary antibody incubation: Add primary antibody (KLF6, EMC2 and LMNB2 at a dilution ratio of 1:500) and incubate overnight at 4°C.

[0083] ⑦ Secondary antibody incubation: The next day, wash the samples with PBS, then add goat anti-rabbit secondary antibody (ZSBIO, PV-9000) and incubate at room temperature for 1 hour.

[0084] ⑧DAB color development and contrast staining: After washing, the DAB kit (ZSBIO, ZLI-9018) was used for color development, followed by hematoxylin staining, 1% hydrochloric acid alcohol differentiation, ammonia blueing, and neutral gum sealing.

[0085] ⑨Result evaluation: The IHC results were evaluated by pathologists. The staining range score was 0-100%, and the intensity score was defined as follows: negative (0 points), low expression (1 point), moderate expression (2 points), and high expression (3 points). The final score calculation formula was: IHC score = staining range score × staining intensity score.

[0086] 2. Experimental Results 1. Identification of prognostic genes: In the training cohort (N = 80), we first identified prognostic genes associated with overall survival (OS) and disease-free survival (DFS) by survival analysis. A total of 3550 genes were found to be significantly associated with OS and DFS (p < 0.05; see Figure 4 B), the top 10 genes with HR>1 and HR<1 are as follows Figure 4 C. Functional annotation showed that these genes were mainly enriched in important pathways such as cell cycle, TGFβ signaling pathway, angiogenin and cell senescence (see Figure 4 DF).

[0087] 2. Functional clustering and subtype division: The weighted gene co-expression network analysis (WGCNA) method was used to perform functional clustering of these 3550 prognostic genes to form multiple modules. We selected the top 20 core genes from each module as the basis and used non-negative matrix factorization (NMF) for classification. Finally, three RPLS subtypes were significantly distinguished (see Figure 5 AB). Log-rank analysis results showed that compared with subtype 1 (S1) and subtype 3 (S3), subtype 2 (S2) had the best prognosis, including OS (P<0.0046) and DFS (P<0.0001), while S3 had the worst prognosis (see Figure 6 AB).

[0088] 3. Biological characteristics and functional annotations: Functional annotation of characteristic genes showed that “obesity”, “overnutrition” and “PPAR signaling pathway” were the main enriched terms in S2, while the other two subtypes had different characteristic enriched pathways. The cancer hallmark gene set represents tumor-specific biological processes and states. To verify these differences, we scored each subtype patient by single sample gene set enrichment analysis (ssGSEA) and found that metabolism-related pathways, such as “lipogenesis” and “bile acid metabolism”, were most correlated with S2; proliferation-related pathways, such as “mitotic spindle” and “G2M checkpoint”, were important biological processes in S3, while immune-related pathways, such as “TNFα”, “IL2-STAT5” and “IFNα and IFNβ response”, were mainly enriched in S1 (see Figure 6 C).

[0089] 4. Comparison of clinical characteristics: Comparison of immune infiltration and clinical features among the three subtypes showed that, although S3 had the lowest tumor microenvironment score and the smallest tumor size, it had the highest MDM2 expression level, the highest Ki67 index and FNCLCC score. In addition, it had the most number of surgeries and the most significant pathological subtype ratio (DDLS / WDLS) (see Figure 6 DJ). These results suggest that different subtypes display unique and interrelated biological features, thus linking the different clinical, pathological, and prognostic characteristics of RPLS patients.

[0090] 5. Identification and verification of representative biomarkers: In the NMF classification process, we identified KLF6, ECM2, and LMNB2 as representative markers corresponding to subtypes S1, S2, and S3, respectively (see Figure 6K). Log-rank analysis was performed based on the expression levels of these three markers in RPL patients. The results showed that RPL patients with high expression of KLF6 and ECM had better OS (KLF6: P=0.034; ECM: P=0.035; see Figures 6L, 6N) and DFS (KLF6: P=0.0096; ECM: P<0.001; see Figure 6 M, 6O). However, patients with high LMNB expression had worse OS (P = 0.013, Figure 6P) and DFS (P < 0.001, Figure 6Q).

[0091] 6. Further verification: We validated the above representative markers in the Retroperitoneal Sarcoma Registry (RESAR) cohort (N = 174, NCT03838718). IHC staining revealed that RPL patients with high expression of KLF6 and ECM2 had higher OS (Figure 7B, D) and DFS (Figure 7C, E) than those with low expression. However, patients with high expression of LMNB2 had lower OS and DFS (Figure 7F-G), which is consistent with previous studies in the training cohort. In order to further validate the new molecular typing strategy based on these three genes, we divided RPL patients into three subgroups for predictive analysis according to which marker had the highest expression level. The results showed that the ECM2 subgroup (i.e., S2 subtype) had the best OS and DFS, while the LMNB2 subgroup (i.e., S3 subtype) had the worst OS and DFS. In addition, the highest pathological type ratio (DD / W), Ki67 level, and the most number of surgeries were observed in the LMNB2 subgroup, which were consistent with the disease progression and the results of the training group ( Figure 7 HL).

[0092] Example 2 like Figure 1 As shown, Example 2 of the present invention provides a method for molecular typing of retroperitoneal liposarcoma based on biomarkers. Specifically, the method comprises the following steps: 101: Obtain the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; In some embodiments, the patient can be human or non-human, and can include, for example, animal strains or breeds used as "model systems" for research purposes. Likewise, patients can include adults or adolescents (e.g., children). In addition, the patient is preferably a mammal (e.g., human or non-human). Examples of mammals include, but are not limited to, any member of the class of mammals: humans, non-human primates (e.g., chimpanzees), and other apes and monkeys; livestock, such as cattle, horses, sheep, goats, pigs; domestic animals, such as rabbits, dogs, and cats; experimental animals include rodents, such as rats, mice, and guinea pigs, etc. Examples of non-mammals include, but are not limited to, birds, fish, etc. In one embodiment provided herein, the patient is human.

[0093] In some embodiments, a sample refers to a composition obtained or derived from a patient / subject that contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical and / or physiological characteristics. For example, a sample refers to any sample derived from a patient / subject that is expected or known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell cultures, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymphatic fluid, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tissue culture fluid, tissue extracts, homogenized tissues, cell extracts, and combinations thereof.

[0094] In a specific embodiment of the present invention, the sources of patients and samples are as follows: ① Patient selection: Patients enrolled in the study were required to be diagnosed with surgically resectable retroperitoneal liposarcoma (RPLS). According to WHO criteria, the histological type of RPLS was confirmed by biopsy or surgical specimens by a pathologist specializing in sarcoma. Exclusion criteria included age less than 18 years, severe mental illness affecting informed consent or compliance, and inability to ensure adequate follow-up. ② Tissue specimen collection: Tumor specimens from 88 RPLS patients and another cohort of 174 RPLS patients were collected from our local hospital. These cohorts were derived from the Retroperitoneal Sarcoma Registry (RESAR, NCT03838718). ③ Surgery time range: All patients underwent radical resection surgery between January 2015 and May 2019. ④ Sample processing: RPLS tissue specimens were quickly frozen in liquid nitrogen within 1 hour and then stored in a -80℃ freezer for future use. ⑤ Clinical information collection: Clinical information was obtained from medical records, and all patients had not received chemotherapy or radiotherapy. Overall survival (OS) was defined as the time interval between the latest surgery and death from tumor, or for patients who were still alive, the time interval between the latest surgery and the last observation. Disease-free survival (DFS) was defined as the time interval between the latest surgery and the diagnosis of recurrence or death. ⑥ Informed consent: Informed consent was obtained from each patient for all surgical procedures and specimen collection. This study was reported in accordance with the REMARK criteria (McShane et al., 2005).

[0095] In some embodiments, the expression values ​​of the three genes KLF6, ECM2 and LMNB2 can be obtained by measuring the amount of RNA, mRNA or any other RNA species using methods well known in the art to obtain expression at the nucleic acid level, including digital PCR and real-time (RT) quantitative or semi-quantitative PCR, fluorescence activated cell sorting (FACS) and in situ hybridization.

[0096] The term "RT-PCR" is also called "reverse transcription polymerase chain reaction" or "reverse transcription polymerase chain reaction", which is a technology that combines reverse transcription (RT) of RNA and polymerase chain amplification (PCR) of cDNA. First, cDNA is synthesized from RNA by the action of reverse transcriptase, and then the cDNA is used as a template to amplify and synthesize the target fragment under the action of DNA polymerase. RT-PCR technology is sensitive and widely used. It can be used to detect gene expression levels in cells, the content of RNA viruses in cells, and directly clone the cDNA sequence of specific genes.

[0097] The term "in situ hybridization" refers to the process of using a specifically labeled nucleic acid of known sequence as a probe to hybridize with nucleic acids in cells or tissue sections to accurately and quantitatively locate a specific nucleic acid sequence.

[0098] 102: Compare the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; In one example, we first identified prognostic genes associated with overall survival (OS) and disease-free survival (DFS) by survival analysis, and found that 3550 genes were significantly associated with OS and DFS (p < 0.05; see Figure 4 B), the top 10 genes with HR>1 and HR<1 are as follows Figure 4 C. Functional annotation showed that these genes were mainly enriched in important pathways such as cell cycle, TGFβ signaling pathway, angiogenin and cell senescence (see Figure 4 DF).

[0099] In one embodiment, we used weighted gene co-expression network analysis (WGCNA) to perform functional clustering on these 3550 prognostic genes to form multiple modules. We selected the top 20 core genes from each module as the basis and used non-negative matrix factorization (NMF) for classification. Finally, three RPLS subtypes were significantly distinguished (see Figure 5 AB).

[0100] In one example, we found that the three subtypes of retroperitoneal liposarcoma had significant differences in biological characteristics. Log-rank analysis results showed that compared with subtype 1 (S1) and subtype 3 (S3), subtype 2 (S2) had the best prognosis, including OS (P<0.0046) and DFS (P<0.0001), while S3 had the worst prognosis (see Figure 6 AB). Functional annotation of characteristic genes showed that “obesity”, “overnutrition” and “PPAR signaling pathway” were the main enriched terms in the S2 subtype, while the other two subtypes had different characteristic enriched pathways. The cancer hallmark gene set represents tumor-specific biological processes and states. To verify these differences, we scored each subtype of patients by single sample gene set enrichment analysis (ssGSEA) and found that metabolism-related pathways, such as “lipogenesis” and “bile acid metabolism”, were most relevant to the S2 subtype; proliferation-related pathways, such as “mitotic spindle” and “G2M checkpoint”, were important biological processes in S3, while immune-related pathways, such as “TNFα”, “IL2-STAT5” and “IFNα and IFNβ response”, were mainly enriched in S1 (see Figure 6 C). Although immune response activation can significantly inhibit tumor progression, the concomitant upregulation of PI3K-Akt-mTOR and KRAS signaling pathways reduces the prognosis of S1 patients.

[0101] In one example, we found that the three retroperitoneal liposarcoma subtypes had significant differences in immune infiltration and clinical characteristics. The comparison results showed that the S3 subtype had the lowest tumor immune microenvironment score and the smallest tumor size, but it had the highest MDM2 expression level, as well as the highest Ki67 index and FNCLCC score. In addition, it had the most number of surgeries and the most significant pathological subtype ratio (DDLS / WDLS) (see Figure 6 DJ). These results suggest that different subtypes display unique and interrelated biological features, thus linking the different clinical, pathological, and prognostic characteristics of RPLS patients.

[0102] In some embodiments, the tumor immune microenvironment score is an indicator for evaluating the immune status of the tumor. It comprehensively evaluates the immune response of the tumor by evaluating immune cells, cytokines and other related factors in the tumor microenvironment, such as T cell infiltration, PD-L1 expression, CD8+ T cells and CD4+ T cell infiltration. This scoring system can predict the development and prognosis of the tumor and provide an important reference for doctors. A higher score usually means that the tumor has a stronger immune response and may be more sensitive to immunotherapy; a lower score may indicate a weaker immune response and the need for other treatments.

[0103] In some embodiments, the expression level of MDM2, the Ki index, and the FNCLCC score are different indicators used in the medical field to evaluate tumor characteristics and predict tumor behavior. MDM2 is an oncogene whose expression level is associated with the occurrence and development of a variety of tumors. For example, in non-small cell lung cancer (NSCLC), the expression level of MDM2 is closely related to the degree of differentiation of the tumor, the TNM stage, and whether there is lymph node metastasis. In addition, a study showed that a new MDM2 inhibitor (APG-115) showed anti-tumor activity against salivary gland cancer, especially adenoid cystic carcinoma, further proving the importance of MDM2 in tumor treatment. MDM2 accelerates tumor progression by promoting tumor cell proliferation and inhibiting apoptosis. The Ki index is generally used to evaluate the proliferation activity of tumor cells, that is, the growth rate of tumor cells. It is an indicator that reflects the activity of cell proliferation. The higher the value, the more active the proliferation of tumor cells. The FNCLCC score is a histological grading system used to assess the malignancy of soft tissue sarcomas. The scoring system is based on multiple indicators such as tumor tissue differentiation, tumor necrosis, and nuclear division to judge the malignancy of the tumor. The FNCLCC score directly reflects the malignancy of the tumor and the patient's prognosis. A high score usually means a higher risk of recurrence and a worse prognosis. In one example, we identified three representative markers for RPLS subtypes. During the NMF classification process, we identified KLF6, ECM2, and LMNB2 as representative markers corresponding to subtypes S1, S2, and S3, respectively (see Figure 6 K). In the S1 subtype, KLF6 had the highest expression level; in the S2 subtype, ECM2 had the highest expression level; and in the S3 subtype, LMNB2 had the highest expression level.

[0104] 103: Output results.

[0105] If the KLF6 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the output patient is retroperitoneal liposarcoma S1 subtype; if the ECM2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the output patient is retroperitoneal liposarcoma S2 subtype; if the LMNB2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the output patient is retroperitoneal liposarcoma S3 subtype.

[0106] Example 3 like Figure 2 As shown, Example 3 of the present invention provides a system for molecular classification of retroperitoneal liposarcoma based on biomarkers.

[0107] The system is programmed or otherwise configured to include a data acquisition module 201, a data analysis module 202 and a result output module 203. Specifically, the system includes: Data acquisition module 201: obtaining the expression values ​​of three genes, KLF6, ECM2 and LMNB2, in the sample of the patient with retroperitoneal liposarcoma to be tested; Data analysis module 202: comparing the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of the patients with retroperitoneal liposarcoma to be tested; The result output module 203 is used to output the classification result.

[0108] Specifically, if the KLF6 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S1 subtype of retroperitoneal liposarcoma; if the ECM2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S2 subtype of retroperitoneal liposarcoma; if the LMNB2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S3 subtype of retroperitoneal liposarcoma.

[0109] The system may be the user's electronic device or a computer system located remotely from the electronic device.

[0110] Example 4 like Figure 3 As shown, Embodiment 4 of the present invention provides a computer device and a computer-readable storage medium.

[0111] The computer device 300 includes a processor 301 and a memory 302 coupled to the processor 301. The memory 302 stores program instructions. When the program instructions are executed by the processor 301, the processor 301 executes the method described in the above embodiment 1 or the method for molecular classification of retroperitoneal liposarcoma based on biomarkers described in the above embodiment 2.

[0112] The processor 301 may also be referred to as a CPU (Central Processing Unit). The processor 301 may be an integrated circuit chip having signal processing capabilities. The processor 301 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0113] The memory 302 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0114] The computer device 300 may be a mobile electronic device.

[0115] The storage medium of the embodiment of the present invention stores program instructions that can implement the method described in the above embodiment 1 or the method for molecular classification of retroperitoneal liposarcoma based on biomarkers described in the above embodiment 2, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer device such as a computer, a server, a mobile phone, or a tablet.

[0116] It should be understood that the systems, devices and methods described in the present invention can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0117] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solution of the present embodiment and are not intended to limit it. Although the present embodiment is described in detail with reference to the given examples, a person of ordinary skill in the art may modify or replace the technical solution of the present embodiment as required without departing from the spirit and scope of the technical solution of the present embodiment.

Claims

1. A molecular typing method for retroperitoneal liposarcoma, characterized in that: The method comprises: (1) Obtain RNA sequencing data and clinical follow-up information data of samples from patients with retroperitoneal liposarcoma; (2) A survival analysis algorithm was used to identify prognostic genes associated with overall survival and disease-free survival. Specifically, a univariate Cox proportional hazard regression model was constructed, and regression analysis was performed on each gene to screen out prognostic genes with p < 0.05 and statistical significance. (3) The weighted gene co-expression network analysis method was used to functionally cluster the above-mentioned prognostic genes to form multiple modules, and these modules were functionally annotated to identify the main enriched signaling pathways; (4) The top 20 core genes were selected from each module formed above as the basis, and consensus unsupervised clustering was performed using a non-negative matrix factorization algorithm. Specifically, the expression matrix A of the main signaling pathways and prognostic genes identified above was used, and the non-negative matrix factorization algorithm was applied to the expression matrix A to decompose it into two non-negative matrices W and H. The matrix A was repeatedly factorized, and its output was aggregated to obtain a consistent clustering of retroperitoneal liposarcoma samples, thereby dividing retroperitoneal liposarcoma into three heterogeneous clusters with different biological and clinical characteristics, namely three molecular subtypes, named retroperitoneal liposarcoma S1 subtype, retroperitoneal liposarcoma S2 subtype, and retroperitoneal liposarcoma S3 subtype. The three molecular typings are determined based on the Cophenetic coefficient, the Dispersion coefficient, and the Silhouette coefficient.

2. The method according to claim 1, characterized in that In step (1), the sources of the RNA sequencing data and clinical information data of patients with retroperitoneal liposarcoma include the RESAR database and actual clinical samples; Preferably, the prognosis genes include KLF6, ECM2 and LMNB2.

3. A method for molecular typing of retroperitoneal liposarcoma based on biomarkers, characterized in that: The method comprises the following steps: (1) Obtaining data: Obtaining the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (2) Data processing: Compare the expression values ​​of the three genes KLF6, ECM2, and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (3) Output result: If the KLF6 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, then the patient is output as retroperitoneal liposarcoma S1 subtype; If the ECM2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the output patient is retroperitoneal liposarcoma S2 subtype; if the LMNB2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the output patient is retroperitoneal liposarcoma S3 subtype.

4. A molecular classification system for retroperitoneal liposarcoma based on biomarkers, characterized in that: The system comprises: (1) Data acquisition module: obtaining the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (2) Data analysis module: compare the expression values ​​of the three genes KLF6, ECM2 and LMNB2 in the samples of patients with retroperitoneal liposarcoma to be tested; (3) Result output module: If the KLF6 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S1 subtype of retroperitoneal liposarcoma; if the ECM2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S2 subtype of retroperitoneal liposarcoma; if the LMNB2 expression value in the sample of the patient with retroperitoneal liposarcoma to be tested is the highest, the patient is output as the S3 subtype of retroperitoneal liposarcoma.

5. The method according to claim 3 or the system according to claim 4, characterized in that: The patient includes humans and / or mammals; Preferably, the sample includes tissue or body fluid.

6. The method according to claim 1 or the method for molecular classification of retroperitoneal liposarcoma based on biomarkers according to claim 3 or the system according to claim 4, characterized in that: The S1, S2 and S3 subtypes of retroperitoneal liposarcoma have the following differences in clinical characteristics: The overall survival time of the three subtypes was significantly different; among them, the S2 subtype had the longest overall survival time and the best prognosis, while the S3 subtype had the shortest overall survival time and the worst prognosis; The tumor microenvironment scores of the three subtypes were significantly different; among them, the tumor microenvironment score of the S2 subtype was the highest, and the tumor microenvironment score of the S3 subtype was the lowest; The tumor sizes of the three subtypes differed significantly; among them, the S3 subtype had the smallest tumor volume; The MDM2 expression levels, Ki67 index and FNCLCC scores of the three subtypes were significantly different; among them, the S3 subtype had the highest MDM2 expression level, Ki67 index and FNCLCC score; The number of surgeries and the proportion of pathological subtypes (DDLS / WDLS) among the three subtypes were significantly different; among them, the S3 subtype had the most surgeries and the most significant proportion of pathological subtypes.

7. The method according to claim 1 or the method for molecular typing of retroperitoneal liposarcoma based on biomarkers according to claim 3 or the system according to claim 4, characterized in that: The S1 subtype, S2 subtype and S3 subtype of retroperitoneal liposarcoma have the following differences in biological characteristics: Compared with the healthy group samples, the differentially expressed genes of the S1 subtype of retroperitoneal liposarcoma were mainly enriched in immune-related pathways, such as "TNFα", "IL2-STAT5" and "IFNα and IFNβ response"; Compared with the healthy group samples, the differentially expressed genes in the S2 subtype of retroperitoneal liposarcoma were mainly enriched in metabolism-related pathways, such as "lipogenesis" and "bile acid metabolism"; Compared with the healthy group samples, the differentially expressed genes in the S3 subtype of retroperitoneal liposarcoma were mainly enriched in proliferation-related pathways, such as "mitotic spindle" and "G2M checkpoint".

8. A computer device, characterized in that: The device comprises: A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method of claim 1 or the method of molecular typing of retroperitoneal liposarcoma based on biomarkers of claim 3 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method of claim 1 or the method of molecular typing of retroperitoneal liposarcoma based on biomarkers according to claim 3 is implemented.

10. Use of a biomarker in the preparation of a product for molecular typing of retroperitoneal liposarcoma, characterized in that: The molecular classification of retroperitoneal liposarcoma includes retroperitoneal liposarcoma S1 subtype, retroperitoneal liposarcoma S2 subtype and retroperitoneal liposarcoma S3 subtype; The biomarker of the retroperitoneal liposarcoma S1 subtype is KLF6, the biomarker of the retroperitoneal liposarcoma S2 subtype is ECM2, and the biomarker of the retroperitoneal liposarcoma S3 subtype is LMNB2.

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