Biomarker for prognosis of retroperitoneal liposarcoma and application thereof

Abstract

The invention provides a retroperitoneal liposarcoma prognosis biomarker and application thereof. The expression level of the biomarkers ECM2 and KLF6 in retroperitoneal liposarcoma is directly related to prognosis of patients for the first time, and compared with patients with high expression of ECM2 and KLF6, the survival time of patients with low expression of ECM2 and KLF6 is remarkably shortened. By detecting the condition of the ECM2 or KLF6 expression level, the prognosis condition of the patient with the retroperitoneal liposarcoma can be estimated, so that a product which contains a reagent for detecting the ECM2 or KLF6 expression level and is used for predicting the prognosis of the patient with the retroperitoneal liposarcoma is prepared, and the prognosis of the patient can be more accurately and objectively judged.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and in particular relates to a prognostic biomarker for retroperitoneal liposarcoma and an application thereof. Background Art

[0002] Retroperitoneal liposarcoma (RPLS) is a rare tumor with an incidence of approximately 1 case per 100,000 person-years, accounting for 15-20% of all soft tissue sarcomas. Currently, complete surgical resection is the only potentially curative method. However, approximately 40% of primary retroperitoneal liposarcoma originate from the retroperitoneal adipose tissue. Due to the complexity of the anatomical structure, it is difficult to achieve negative resection margins, resulting in a high recurrence rate of retroperitoneal liposarcoma after surgery. According to the degree of differentiation, most retroperitoneal liposarcoma can be divided into well-differentiated liposarcoma (WDLS) and dedifferentiated liposarcoma (DDLS). The 5-year recurrence rate of WDLS and DDLS is 40-60%, while the overall survival rate of WDLS is 80%, which is significantly higher than that of DDLS (44-53%). Therefore, the different biological characteristics of residual retroperitoneal liposarcoma cells after surgery determine the prognosis of patients, and personalized surgical resection strategies can significantly improve the overall survival rate and quality of life of patients with retroperitoneal liposarcoma.

[0003] At present, although a few molecules such as CDK4, MDM2, AURK4 and CCNDBP1 have been reported as diagnostic and prognostic markers for RPLS, these markers are poorly representative and lack validation. Therefore, exploring and discovering new biomarkers to accurately predict and evaluate the prognosis of retroperitoneal liposarcoma, so as to adjust the treatment plan in time, is an important aspect of individualized precision treatment. Summary of the invention

[0004] In order to make up for the deficiencies of the prior art, the purpose of the present invention is to provide a novel prognostic biomarker for retroperitoneal liposarcoma and application thereof.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: The first aspect of the present invention provides the use of a reagent for detecting the expression amount of a biomarker in the preparation of a product for predicting the prognosis of a patient with retroperitoneal liposarcoma.

[0006] Furthermore, the biomarkers include ECM2, KLF6 or LMNB2.

[0007] The term "biomarker" refers to a biological molecule present in an individual at different concentrations that can be used to predict the cancer status of the individual. Biomarkers may include, but are not limited to, nucleic acids, proteins, and variants and fragments thereof. A biomarker may be a DNA comprising all or part of a nucleic acid sequence encoding the biomarker or the complement of such a sequence. Biomarker nucleic acids useful in the present invention are considered to include DNA and RNA comprising all or part of any nucleic acid sequence of interest. In a specific embodiment of the present invention, the biomarkers include ECM2, KLF6 or LMNB2.

[0008] In the present invention, biomarkers such as ECM2, KLF6 or LMNB2 include genes and proteins encoded therein and homologs, mutations, and isoforms thereof. The term encompasses full-length, unprocessed biomarkers, as well as any form of biomarkers derived from processing in cells. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of biomarkers.

[0009] The biomarkers described herein can be used in diagnostic tests to assess the status of a subject's retroperitoneal liposarcoma. The status of a retroperitoneal liposarcoma includes the presence or absence of a retroperitoneal liposarcoma. The status of a retroperitoneal liposarcoma can also include monitoring the course of a retroperitoneal liposarcoma, for example, monitoring disease progression. Based on the status of a subject's retroperitoneal liposarcoma, additional procedures can be indicated, including, for example, additional diagnostic tests or treatment procedures.

[0010] Furthermore, the ECM2 and KLF6 are lowly expressed in patients with retroperitoneal liposarcoma with poor prognosis; and the LMNB2 is highly expressed in patients with retroperitoneal liposarcoma with poor prognosis.

[0011] In some embodiments, the prognostic indicators include objective response rate (ORR), overall survival rate (OS), progression-free survival (PFS), time to progression (TTP), disease-free survival (DFS), time to treatment failure (TTF), response rate (RR), complete response (CR), partial response (PR). In a specific embodiment of the present invention, the prognostic indicators include overall survival and / or disease-free survival.

[0012] The term "expression" generally refers to the process by which the information encoded by a gene is converted into structures present and operating in a cell. Therefore, according to the present invention, the "expression" of a gene may refer to transcription into a polynucleotide, translation into a protein, or even post-translational modification of a protein. Fragments of transcribed polynucleotides, translated proteins, or post-translationally modified proteins should also be considered expressed, whether they are derived from transcripts generated by alternative splicing or degraded transcripts, or from post-translational processing of proteins (e.g., by proteolysis).

[0013] An "elevated" or "higher" amount or level of a biomarker refers to an amount that is equal to or greater than the level of expression of the biomarker in a healthy control population, and the biomarker is overexpressed relative to the control expression level by at least 1.1 times, such as at least 1.1 times, 1.2 times, 1.3 times, 1.4 times, 1.5 times, 1.6 times, 1.7 times, 1.8 times, 1.9 times, 2.0 times, 2.1 times, 2.2 times, 2.3 times, 2.4 times, 2.5 times, 2.6 times, 2.7 times, 2.8 times, 2.9 times, 3.0 times, 3.1 times, 3.2 times, 3.3 times, 3.4 times, or 3.5 times or more.

[0014] A "reduced" or "lower" amount or level of a biomarker means that the amount is less than the median amount of the biomarker in a healthy control population, and the biomarker is underexpressed relative to the control expression level by at least 1.1 times, such as at least 1.1 times, 1.2 times, 1.3 times, 1.4 times, 1.5 times, 1.6 times, 1.7 times, 1.8 times, 1.9 times, 2.0 times, 2.1 times, 2.2 times, 2.3 times, 2.4 times, 2.5 times, 2.6 times, 2.7 times, 2.8 times, 2.9 times, 3.0 times, 3.1 times, 3.2 times, 3.3 times, 3.4 times, or 3.5 times, or more.

[0015] In some embodiments, in order to define the high and low expression of biomarker molecules, all patients in the cohort are sorted according to the sequencing expression values ​​of each marker, and the high and low expression of each marker molecule is defined by the median expression value. In a specific embodiment of the present invention, the median expression value of KLF6 is 1.2; the median expression value of LMNB2 is 1.8; and the median expression value of ECM2 is 1.1.

[0016] Furthermore, the reagent includes a reagent for detecting the expression amount of biomarker mRNA and / or the expression amount of biomarker protein.

[0017] Furthermore, the reagents for detecting the mRNA expression of biomarkers include reagents used in the following methods: PCR-based detection methods, Southern hybridization, Northern hybridization, dot hybridization, fluorescence in situ hybridization, DNA microarray, ASO method, and high-throughput sequencing platform.

[0018] Furthermore, the reagent for detecting the expression level of biomarker mRNA includes specific primers and / or probes.

[0019] Furthermore, the reagents for detecting the expression of biomarker proteins include reagents used in the following methods: enzyme-linked immunosorbent assay, radioimmunoassay, sandwich assay, protein blotting, flow cytometry, fluorescence-assisted cell sorting, enzyme substrate colorimetric assay and antigen-antibody aggregation, mass spectrometry, immunohistochemical staining, immunoprecipitation analysis, complement fixation analysis, flow cytometry fluorescence separation technology and protein chip.

[0020] In some embodiments, the quantitative detection method based on PCR includes the step of reverse transcribing mRNA into cDNA and / or the step of measuring the content of cDNA. Preferably, the method for measuring the content of cDNA includes but is not limited to PCR, NASBA, RPA, SDA, LAMP, HAD, NEAR, MDA, RCA, LCR, RAM.

[0021] The term "primer" refers to an oligonucleotide that hybridizes to a sequence in a target nucleic acid (a "primer binding site") and is capable of serving as a point of initiation of synthesis along a complementary strand of nucleic acid under conditions suitable for such synthesis.

[0022] The term "probe" refers to a molecule that can bind to a specific sequence or subsequence or other part of another molecule. Unless otherwise indicated, the term "probe" generally refers to a polynucleotide probe that can bind to another polynucleotide (often referred to as a "target polynucleotide") through complementary base pairing. Depending on the stringency of the hybridization conditions, the probe can bind to a target polynucleotide that lacks complete sequence complementarity with the probe. The probe can be labeled directly or indirectly. Hybridization methods include, but are not limited to: solution phase, solid phase, mixed phase or in situ hybridization assays.

[0023] In some embodiments, the reagent for detecting the expression of biomarker proteins includes an antibody or fragment thereof targeting a biomarker, and the antibody or fragment thereof of the biomarker can specifically bind to a biomarker protein (e.g., ECM2, KLF6, or LMNB2). Preferably, the reagent for detecting the expression of biomarker proteins also includes a secondary antibody, which can bind to the antibody or fragment thereof of the aforementioned biomarker and develop color. Preferably, the color developing agent includes fluorescein, enzyme, metal ion, isotope. Preferably, the antibody or fragment thereof of the biomarker can also be combined with a marker, and the detection of the marker indicates that the biomarker protein is expressed. Preferably, the marker includes a fluorescent molecule and a chemiluminescent marker. Preferably, the fluorescent molecule includes FAM, FITC, VIC, JOE, TET, CY3, CY5, ROX, TexasRed, or LCRED460. Preferably, the chemiluminescent marker comprises peroxidase, alkaline phosphatase, luciferase, aequorin, functionalized iron-porphyrin derivatives, luminal, luminol, isoluminol, acridinium ester, sulfonamide.

[0024] Furthermore, the products include test kits, chips, test strips, and high-throughput sequencing platforms.

[0025] Preferably, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry analysis kit, an immunohistochemistry detection kit, an ELISA kit and an electrochemiluminescence detection kit.

[0026] Preferably, the chip includes a gene chip or a protein chip.

[0027] Preferably, the test paper comprises a test paper carrier and an oligonucleotide fixed on the test paper carrier, and the oligonucleotide is capable of detecting the transcription level of the biomarker.

[0028] Furthermore, the kit includes one or more substances of the following group: a container, instructions for use, a positive control substance, a negative control substance, a buffer, an auxiliary agent or a solvent.

[0029] In some embodiments, the kit also includes a buffer, a preservative; the kit may also include necessary components for detecting detectable agents; the kit may also contain a control sample or a series of control samples, which can be measured and compared with the test samples contained therein; further, other components of the kit include but are not limited to: tools for collecting biological samples, tools for labeling detection agents (binders).

[0030] In certain embodiments, the components of the test kit can be packaged in the form of an aqueous medium or in a lyophilized form. Suitable containers in the test kit generally include at least a vial, test tube, flask, PET bottle, syringe or other container, in which a component can be placed, and preferably, can be appropriately divided. When there is more than one component in the test kit, the test kit will generally also include a second, third or other additional container, in which additional components are placed separately. However, components of different combinations can be contained in a vial. The test kit of the present invention will generally also include a container for accommodating reactants, sealed for commercial sale. Such a container may include a plastic container of injection molding or blow molding, in which the required vial can be retained.

[0031] In some embodiments, the chip includes a gene chip and a protein chip; the gene chip includes a solid phase carrier and an oligonucleotide probe fixed on the solid phase carrier, and the oligonucleotide probe includes an oligonucleotide probe for ECM2, KLF6 or LMNB2 gene for detecting the transcription level of the biomarker gene described in the first aspect of the present invention; the protein chip includes a solid phase carrier and a specific antibody for ECM2, KLF6 or LMNB2 protein fixed on the solid phase carrier; the gene chip can be used to detect the expression level of multiple genes including ECM2, KLF6 or LMNB2 gene (for example, multiple genes related to retroperitoneal liposarcoma). The protein chip can be used to detect the expression level of multiple proteins including ECM2, KLF6 or LMNB2 protein (for example, multiple proteins related to retroperitoneal liposarcoma). By simultaneously detecting multiple markers related to retroperitoneal liposarcoma, the accuracy of diagnosis or prediction of prognosis of retroperitoneal liposarcoma can be greatly improved.

[0032] In some embodiments, "chip", also referred to as "array", refers to a solid support comprising connected nucleic acid or peptide probes. The array typically comprises a plurality of different nucleic acid or peptide probes attached to the surface of a substrate at different known positions. These arrays, also referred to as "microarrays", can typically be produced using mechanical synthesis methods or light-guided synthesis methods that incorporate a combination of photolithography and solid phase synthesis methods. The array may comprise a flat surface, or may be a nucleic acid or peptide on a bead, gel, polymer surface, fiber such as an optical fiber, glass, or any other suitable substrate. The array may be packaged in a manner that allows for diagnosis of a fully functional device or other manipulations.

[0033] "Microarray" is an orderly arrangement of hybridization array elements such as polynucleotide probes (e.g., oligonucleotides) or binding agents (e.g., antibodies) on a substrate. The substrate can be a solid substrate, for example, a glass or silica slide, a bead, a fiber optic binder, or a semi-solid substrate, such as a nitrocellulose membrane. The nucleotide sequence can be DNA, RNA, or any arrangement thereof.

[0034] The second aspect of the present invention provides the use of a biomarker or a promoter thereof in the preparation of a product for preventing and / or treating retroperitoneal liposarcoma.

[0035] Furthermore, the biomarkers include ECM2 and / or KLF6.

[0036] Preferably, the promoter promotes the expression of ECM2 and / or KLF6.

[0037] Preferably, the promoter comprises a vector that specifically promotes overexpression of ECM2 and / or KLF6.

[0038] Preferably, said treatment comprises improving prognosis.

[0039] In the present invention, the promoter refers to any substance that can increase the activity of ECM2 and / or KLF6 protein, improve the stability of ECM2 and / or KLF6 gene or protein, upregulate the expression of ECM2 and / or KLF6 protein, increase the effective action time of ECM2 and / or KLF6 protein, or promote the transcription and translation of ECM2 and / or KLF6 gene, and these substances can be used in the present invention. For example, the promoter includes a vector that promotes the overexpression of ECM2 and / or KLF6.

[0040] The third aspect of the present invention provides the use of an inhibitor of LMNB2 in the preparation of a product for preventing and / or treating patients with retroperitoneal liposarcoma.

[0041] Furthermore, the inhibitor can knock out the LMNB2 gene or reduce the expression level of LMNB2.

[0042] Preferably, the inhibitor includes reagents used in siRNA interference, CRISPR / cas9 method, homologous recombination, gene knockout, gene replacement, gene silencing, site-directed mutagenesis, and chemical drug methods.

[0043] Preferably, said treatment comprises improving prognosis.

[0044] In some embodiments, the treatment includes but is not limited to reducing the rate of disease progression, stopping disease progression, improving symptoms, curing symptoms, improving prognosis, increasing radiosensitivity (radiotherapy sensitivity), preventing recurrence, and prolonging survival.

[0045] A fourth aspect of the present invention provides a system or device for diagnosing whether a subject suffers from retroperitoneal liposarcoma, predicting whether a subject has a risk of suffering from retroperitoneal liposarcoma, or predicting the prognosis of a patient with retroperitoneal liposarcoma.

[0046] Furthermore, the system or device comprises: an analysis unit adapted to measure the amount of a biomarker in a sample from a subject; and An evaluation unit comprising a stored reference and a data processor having implemented an algorithm for comparing the amount of the biomarker measured by the analysis unit with the stored reference, thereby diagnosing whether the subject suffers from retroperitoneal liposarcoma or predicting whether the subject has a risk of suffering from retroperitoneal liposarcoma or predicting the prognosis of a patient with retroperitoneal liposarcoma.

[0047] Furthermore, the biomarkers include ECM2, KLF6 or LMNB2.

[0048] A fifth aspect of the present invention provides a computer-readable storage medium.

[0049] Furthermore, the computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the system or device described in the fourth aspect of the present invention is implemented.

[0050] The term "and / or" as used herein in phrases such as "A and / or B" is intended to include both A and B; A or B; A (alone); and B (alone). BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 : is the result diagram of RPLS prognostic gene identification; A is the model diagram of the present invention; B is the result diagram of prognostic genes significantly associated with OS and DFS; C is the result diagram of the top 10 genes with HR>1 and HR<1; DF is the result diagram of the functional annotation of prognostic genes; Figure 2 It is the result diagram 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 3 are characteristic diagrams of different RPLS subtypes; AB are prognostic results of patients with three RPLS subtypes; C is a metabolic pathway diagram of patients with 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 4Figure 1 is a representative marker validation result diagram; A is the immunohistochemical staining diagram of different expression levels of KLF6, ECM2 and LMNB2; 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 are the differences in OS, DFS, the highest pathological type ratio (DD / W), Ki67 level and the maximum number of surgeries among the three marker expression levels. DETAILED DESCRIPTION

[0052] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. The following examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. Simple improvements to the present invention made according to the essence of the present invention all fall within the scope of protection claimed in the present invention.

[0053] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0054] In each of the following examples, the sources of patients and tissue specimens used are as follows: ① Patient selection: Patients enrolled in the study must be diagnosed with surgically resectable retroperitoneal liposarcoma (RPLS). According to WHO standards, the histological type of RPLS is confirmed by a pathologist specializing in sarcoma through biopsy or surgical specimens. Exclusion criteria include age less than 18 years, severe mental illness affecting informed consent or compliance, and inability to ensure adequate follow-up.

[0055] 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).

[0056] ③Surgical time range: All patients underwent radical resection surgery between January 2015 and May 2019.

[0057] ④ Sample processing: RPLS tissue specimens were quickly frozen in liquid nitrogen within 1 hour and then stored in a -80℃ refrigerator for future use.

[0058] ⑤Clinical information collection: Clinical information was collected from medical records. 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.

[0059] 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).

[0060] Example 1 Identification of prognostic genes 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).

[0061] 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.

[0062] 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.

[0063] ④Cluster generation: Cluster the samples with index encoding using TruSeq SR ClusterKit v3-cBot-HS (Illumia) on the cBot Cluster Generation System.

[0064] ⑤ Sequencing: After cluster generation, strand-specific cDNA sequencing was performed on the Illumina NovaSeq 6000 platform to generate single-end read data.

[0065] 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.

[0066] (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)).

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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).

[0071] 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.

[0072] 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.

[0073] (3) 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] ⑤Significance threshold setting: Screening criteria for determining 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.

[0078] 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.

[0079] 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.

[0080] 2. Experimental results In training cohort 2 (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 1 B), the top 10 genes with HR>1 and HR<1 are as follows Figure 1 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 1 DF).

[0081] Example 2 Classification of RPLS subtypes 1. Experimental methods (1) Gene set enrichment analysis (GSEA) and immune infiltration analysis ① Data preparation: Use the RNA sequencing data of Example 1 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] ⑤Significance threshold setting: Determine the screening criteria for significant pathways: FDR<0.25 is considered to be a statistically significant enrichment result.

[0086] ⑥Result output and annotation: Output the list of significantly enriched signal pathways screened out, and record the corresponding NES, p value and FDR value.

[0087] 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.

[0088] 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.

[0089] (2) 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] (3) 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 2. Experimental results 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 2 AB).

[0105] 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 3 AB).

[0106] 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 3 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.

[0107] 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 3 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.

[0108] During the NMF classification process, we identified KLF6, ECM2, and LMNB2 as representative markers corresponding to subtypes S1, S2, and S3, respectively (see Figure 3 K). 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 ECM2 had better OS (KLF6: P=0.034; ECM2: P=0.035; see Figure 3L, 3N) and DFS (KLF6: P=0.0096; ECM2: P<0.001; see Figure 3M, N). However, patients with high LMNB2 expression had worse OS (P = 0.013, Figure 3P) and DFS (P < 0.001, Figure 3 O).

[0109] Example 3 Validation of representative markers 1. Experimental methods Immunohistochemistry: ① Antibody preparation: KLF6, LMNB2 and ECM2 antibodies for immunohistochemistry were purchased from Bioss.

[0110] 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.

[0111] 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.

[0112] ④ Endogenous peroxidase blocking: Treat the sample with 3% endogenous peroxidase blocker (ZSBIO, PV-6000) for 10 minutes to reduce nonspecific staining.

[0113] ⑤ Block nonspecific reactions: Incubate the sample in goat serum (ZSBIO, ZLI-9022) to block nonspecific binding sites.

[0114] ⑥ Primary antibody incubation: Add primary antibody (KLF6, ECM2 and LMNB2 at a dilution ratio of 1:500) and incubate overnight at 4°C.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 2. Experimental results We validated the above representative markers (KLF6, ECM2 and LMNB2) in the Retroperitoneal Sarcoma Registry Cohort (RESAR) (N = 174, NCT03838718). IHC staining showed that RPLS patients with high expression of KLF6 and ECM2 had higher OS ( Figure 4B, D ) and DFS ( Figure 4C, E ) than those with low expression. However, patients with high expression of LMNB had lower OS and DFS ( Figure 4F-G ), which is consistent with the previous study in the training cohort.

[0119] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection of the claims of the present invention.

Claims

1. Use of a reagent for detecting the expression of a biomarker in the preparation of a product for predicting the prognosis of a patient with retroperitoneal liposarcoma, characterized in that: The biomarkers include ECM2, KLF6 or LMNB2.

2. The use according to claim 1, characterized in that: The ECM2 and KLF6 are lowly expressed in patients with retroperitoneal liposarcoma with poor prognosis; and the LMNB2 is highly expressed in patients with retroperitoneal liposarcoma with poor prognosis.

3. The use according to claim 1, characterized in that: The reagents include reagents for detecting the expression level of biomarker mRNA and / or the expression level of biomarker protein.

4. The use according to claim 3, characterized in that: The reagents for detecting the expression of biomarker mRNA include reagents used in the following methods: PCR-based detection methods, Southern hybridization, Northern hybridization, dot hybridization, fluorescence in situ hybridization, DNA microarray, ASO method, and high-throughput sequencing platform; Preferably, the reagent for detecting the expression amount of biomarker mRNA includes primers and / or probes; Preferably, the reagents for detecting the expression amount of biomarker proteins include reagents used in the following methods: enzyme-linked immunosorbent assay, radioimmunoassay, sandwich assay, protein blotting, flow cytometry, fluorescence-assisted cell sorting, enzyme substrate colorimetric assay and antigen-antibody aggregation, mass spectrometry, immunohistochemical staining, immunoprecipitation analysis, complement fixation analysis, flow cytometry fluorescence separation technology and protein chip.

5. The use according to claim 1, characterized in that: The products include test kits, chips, test strips, and high-throughput sequencing platforms; Preferably, the kit includes a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry analysis kit, an immunohistochemistry detection kit, an ELISA kit, and an electrochemiluminescence detection kit; Preferably, the chip includes a gene chip and a protein chip; Preferably, the test paper comprises a test paper carrier and an oligonucleotide fixed on the test paper carrier, and the oligonucleotide is capable of detecting the transcription level of the biomarker.

6. The use according to claim 5, characterized in that: The kit includes one or more substances of the following group: a container, instructions for use, a positive control substance, a negative control substance, a buffer, an auxiliary agent or a solvent.

7. Use of a biomarker or a promoter thereof in the preparation of a product for preventing and / or treating retroperitoneal liposarcoma, characterized in that: The biomarkers include ECM2 and / or KLF6; Preferably, the promoter promotes the expression of ECM2 and / or KLF6; Preferably, the promoter comprises a vector that specifically promotes overexpression of ECM2 and / or KLF6; Preferably, said treatment comprises improving prognosis.

8. Use of an inhibitor of LMNB2 in the preparation of a product for preventing and / or treating patients with retroperitoneal liposarcoma, wherein the inhibitor can knock out the LMNB2 gene or reduce the expression of LMNB2; Preferably, the inhibitor includes reagents used in siRNA interference, CRISPR / cas9 method, homologous recombination, gene knockout, gene replacement, gene silencing, site-directed mutagenesis, and chemical drug methods; Preferably, said treatment comprises improving prognosis.

9. A system or device for diagnosing whether a subject suffers from retroperitoneal liposarcoma or predicting whether a subject has a risk of suffering from retroperitoneal liposarcoma or predicting the prognosis of a patient with retroperitoneal liposarcoma, characterized in that: The system or device comprises: an analysis unit adapted to measure the amount of a biomarker in a sample from a subject; and an evaluation unit comprising a stored reference and a data processor having implemented therein an algorithm for comparing the amount of the biomarker measured by the analysis unit with the stored reference, thereby diagnosing whether the subject suffers from retroperitoneal liposarcoma or predicting whether the subject is at risk of suffering from retroperitoneal liposarcoma or predicting the prognosis of a patient suffering from retroperitoneal liposarcoma; The biomarkers include ECM2, KLF6 or LMNB2.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the system or device according to claim 9 is implemented.

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