Methods and reagents for distinguishing between uterine tumors

Abstract

The present invention relates to methods for distinguishing between leiomyosarcoma and leiomyoma, as well as to a method for identifying a subject suspected of having leiomyosarcoma. The invention also relates to kits and devices for carrying out said methods of the invention.

Description

[0001] METHODS AND REAGENTS FOR DISTINGUISHING BETWEEN UTERINE TUMORS

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to the field of gynecological cancer diagnostics and, more in particular, to methods for distinguishing between uterine leiomyosarcoma and uterine leiomyoma in a subject, as well as to a method for identifying a subject suspected of having uterine leiomyosarcoma, and to kits and devices for carrying out said methods.

[0004] BACKGROUND OF THE INVENTION

[0005] Uterine leiomyomas (LM) are benign tumors arising in the smooth muscle cells of the uterine wall. They are the most common pelvic tumors in women, with a prevalence of >80% for African American and -70% for Caucasian women before 50 years of age. Although LM are non-malignant tumors, the risk of hidden undiagnosed malignancy, such as uterine leiomyosarcoma (LMS), occurs in one among 498 uterine tumors.

[0006] Laparoscopic myomectomy with morcellation of the tumor is the gold standard therapeutic option for uterine tumors. Unfortunately, clinical symptoms as well as morphological features between LM and LMS are indistinguishable prior to surgery introducing the risk of potential spread of undiagnosed LMS causing early metastasis, poor prognosis and high recurrence rates with limited therapeutic efficacy when the treatment of LMS is by surgery. For this reason, the FDA issued a press release in 2014 discouraging the use of power morcellators to treat myometrial tumors, substituting laparoscopic myomectomy for laparotomy-based procedures and thus increasing morbidity, mortality, and the cost for the patient and healthcare system. Therefore, the development of accurate and non-invasive diagnostic methods is a priority in the field of gynecology and oncology, especially in the health improvement of patients with surgical indication for hysterectomy, laparoscopic or laparotomic myomectomy for diagnosis of uterine tumors. Recently, the concept of "liquid biopsy" has emerged as a minimally invasive alternative to surgical biopsies for solid tumors with highly recurrent mutations, avoiding the sampling of tumor tissue before and after treatment.

[0007] Additionally, the absence of standardized preoperative biomarkers to differentiate uterine LMS versus LM represents an important diagnostic challenge. Thus, the search for standardized molecular criteria to differentiate uterine LMS and LM before surgery to prevent dissemination of hidden malignancies during morcellation represents an important current diagnostic challenge. In fact, available “omics” profiling for LMS has been limited due to the rare incidence of this malignancy (1 in 498 patients undergoing hysterectomy or myomectomy for presumed LM).

[0008] Given these challenges, there is an urgent need to develop more effective strategies for preoperative differential molecular diagnosis of uterine tumors.

[0009] WO2023061914 discloses methods for the differential diagnosis of LMS and LM, as well as methods for the prognosis of patients suffering from LMS which are based on transcriptomic analysis, genomic coverage analysis, genomic mutational analysis (determination of SNV (single nucleotide variant) biomarkers), and the determination of CNVs (copy number variants) in a biopsy sample.

[0010] SUMMARY OF THE INVENTION

[0011] The authors of the present invention, using whole-exome and RNA sequencing (RNAseq), have identified the differential molecular footprint of LMS versus LM. In particular, the authors of the invention have found different gene expression levels between LMS and LM based on the specific transcriptomic profile detected at the RNA level. Leveraging this data, differential gene expression analysis was performed to assess the effect of the differentially expressed genes on gene expression across the study population.

[0012] Therefore, a first aspect of the invention pertains to an in vitro method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions, the method comprising:

[0013] (i) measuring the level of expression of at least one gene selected from POC1 A gene, PTPRT gene, PTCHD1 gene, MUC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene in a biological sample obtained from the subject, and

[0014] (ii) comparing said level of expression with a reference value, wherein a deviation in the level of expression of said at least one gene with respect to said reference value is indicative that the subject is affected by uterine leiomyosarcoma.

[0015] In a second aspect, the invention pertains to an in vitro method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions, the method comprising:

[0016] (i) measuring the level of expression of at least one gene selected from the list shown in Table 2, the list shown in Table 3, the list shown in Table 4, the list shown in Table 6 or the list shown in Table 7 in a biological sample obtained from the subject, and

[0017] (ii) comparing said level of expression with a reference value, wherein a deviation in the level of expression of said at least one gene with respect to said reference value is indicative that the subject is affected by uterine leiomyosarcoma.

[0018] In a third aspect, the invention pertains to an in vitro method for identifying a subject suspected of having uterine leiomyosarcoma as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma, the method comprising:

[0019] (i) determining whether the subject is affected by uterine leiomyosarcoma following any of the methods according to the first or the second aspects of the invention; and

[0020] (ii) designating said subject as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma if the subject is diagnosed as having uterine leiomyosarcoma.

[0021] In another aspect, the invention pertains to a method for treating uterine leiomyosarcoma in a subject in need thereof, comprising the administration of a therapy suitable for the treatment of uterine leiomyosarcoma, wherein the subject to be treated has been identified using any of the methods according to the first or the second aspects of the invention.

[0022] In yet another aspect, the invention pertains to a chemotherapeutic agent, hormonal agent and / or targeted agent for use in the treatment of uterine leiomyosarcoma, wherein the subject to be treated has been identified through any of the methods according to the first or the second aspects of the invention.

[0023] In another aspect, the invention relates to a kit, package and / or device comprising reagents adequate for implementing any of the methods according to the first, the second or the third aspects of the invention.

[0024] In a further aspect, the invention pertains to a computer-implemented method, wherein the method is any of the methods according to the first, the second or the third aspects of the invention.

[0025] In a yet further aspect, the invention pertains to a computer comprising instructions for carrying out any of the methods according to the first, the second or the third aspects of the invention.

[0026] BRIEF DESCRIPTION OF THE FIGURES Figure 1 : Venn diagram displaying differentially expressed genes (DEGs) obtained using 5 selected comparisons between Leiomyosarcoma (LMS) and Leiomyoma (LM) liquid biopsy samples. “LB all” refers to the number of DEGs (37) obtained in the analysis using all samples; “LB S8” refers to the number of DEGs (55) obtained in the analysis using samples filtered by a quality threshold; “LB <6M” refers to the number of DEGs (378) obtained in the analysis using samples which have been frozen for less than 6 months; “LB >41” refers to the number of DEGs (13) obtained in the analysis using samples which derive from subjects over 41 years of age; and “LB <6M & >41” refers to the number of DEGs (234) obtained in the analysis using samples which have been frozen for less than 6 months and which derive from subjects over 41 years of age.

[0027] Figure 2: DEGs in common between results from Solid (SB) and Liquid (LB) Biopsies of Leiomyosarcoma (LMS) and Leiomyoma (LM) samples.

[0028] Figure 3: Volcano plot showing DEGs (diamond-shaped dots) between LMS and LM obtained from the analysis including all samples from the study. Genes with a |log2FC| is > 2 are shown in cross-shaped dots. Genes with a non-significant result are shown in circle-shaped dots (NS).

[0029] Figure 4: Volcano plot showing DEGs (diamond-shaped dots) between LMS and LM obtained from the analysis of the samples filtered by a quality threshold. Genes with a | log2FC| is > 2 are shown in cross-shaped dots. Genes with a non-significant result are shown in circle-shaped dots (NS).

[0030] Figure 5: Volcano plot showing DEGs (diamond-shaped dots) between LMS and LM obtained from the analysis of the samples which have been frozen for less than 6 months. Genes with a |log2FC| is > 2 are shown in cross-shaped dots. Genes with a nonsignificant result are shown in circle-shaped dots (NS).

[0031] Figure 6: Volcano plot showing DEGs (diamond-shaped dots) between LMS and LM obtained from the analysis of the samples which derive from subjects over 41 years of age. Genes with a |log2FC| is > 2 are shown in cross-shaped dots. Genes with a nonsignificant result are shown in circle-shaped dots (NS).

[0032] Figure 7: Volcano plot showing DEGs (diamond-shaped dots) between LMS and LM obtained from the analysis of the samples which have been frozen for less than 6 months and which derive from subjects over 41 years of age. Genes with a |log2FC| is > 2 are shown in cross-shaped dots. Genes with a non-significant result are shown in circleshaped dots (NS).

[0033] DETAILED DESCRIPTION OF THE INVENTION

[0034] The authors of the present invention have found that LM and LMS have specific transcriptomic profiles and have carried out a comparative transcriptomic analysis between histologically confirmed LM (n = 72) and LMS (n = 23) tumors. The results have revealed 13 genes that were differentially expressed obtained from 5 selected comparisons between LMS and LM liquid biopsy samples. Selected group comparisons differed in demographic and technical characteristics, such as the age of the subject, or the freezing time of the sample prior to the RNA extraction and data quality. This difference in the transcriptomic profiles allows distinguishing between one disease or the other in a subject by analyzing the RNA composition in a sample from the subject and classifying the subject affected by LMS or LM using a transcriptomic analysis-based method. Accordingly, in a first aspect, the invention pertains to an in vitro method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions (hereinafter referred to as “the first method of the invention ”) the method comprising:

[0035] (i) measuring the level of expression of at least one gene selected from POC1A gene, PTPRT gene, PTCHD1 gene, MUC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene in a biological sample obtained from the subject, and

[0036] (ii) comparing said level of expression with a reference value, wherein a deviation in the level of expression of said at least one gene with respect to said reference value is indicative that the subject is affected by uterine leiomyosarcoma.

[0037] The expression “distinguishing between”, as used herein, refers to the process of identifying which disease or condition is most likely causing a subject's symptoms when multiple diseases or conditions with similar symptoms are possible, or distinguishing of a particular disease or condition from others that present similar clinical features based on an analysis of the clinical data. This determination, as it is understood by a person skilled in the art, does not claim to be correct in 100% of the analyzed samples. However, it requires that a statistically significant amount of the analyzed samples is classified correctly. The amount that is statistically significant can be established by a person skilled in the art by means of using different statistical methods. Illustrative, non-limiting examples of said statistical methods include determining confidence intervals, determining the p-value, the Student’s t-test or Fisher’s discriminant functions, etc. (see, for example, Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983). The confidence intervals are preferably at least 90%, at least 95%, at least 97%, at least 98% or at least 99%. The p-value is preferably less than 0.1 , less than 0.05, less than 0.01 , less than 0.005 or less than 0.0001. The teachings of the present invention preferably allow correctly diagnosing in at least 60%, in at least 70%, in at least 80%, or in at least 90% of the subjects of a determined group or population analyzed.

[0038] The term “uterine leiomyoma”, also known as “uterine fibroid”, as used herein, refers to a benign tumor that appears in the smooth muscular layer of the uterus.

[0039] The term “uterine leiomyosarcoma”, as used herein, refers to a malignant tumor which originates in the smooth muscular layer of the uterus. The term includes both primary tumors as well as metastasis.

[0040] In a first step, the first method of the invention comprises the determination of the level of expression of at least one gene selected from POC1A gene, PTPRT gene, PTCHD1 gene, MLIC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene in a biological sample obtained from the subject whose diagnosis is to be determined. In some embodiments, the first step of the first method of the invention comprises the determination of the expression levels of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11 , at least 12 or the 13 genes selected from POC1A gene, PTPRT gene, PTCHD1 gene, MUC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene.

[0041] The term "expression level”, as used herein, refers to the measurable quantity of a gene product produced by the gene in a sample of the subject, wherein the gene product can be a transcriptional product or a translational product. As understood by the person skilled in the art, the gene expression level can be quantified by measuring the messenger RNA (mRNA) levels of said gene or of the protein encoded by said gene. In the context of the present invention, the expression level of the genes used in the methods according to the invention can be determined by measuring the levels of mRNA encoded by said gene, or by measuring the levels of the protein encoded by said gene, i.e. the protein or variants thereof. Variants of the proteins encoded by the genes which are measured according to the methods of the invention include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained. The term “sample” or “biological sample”, as used herein, refers to biological material isolated from a subject. The biological sample contains any biological material suitable for detecting DNA, RNA or protein levels. In a particular embodiment, the sample comprises genetic material, e.g., DNA, genomic DNA (gDNA), complementary DNA (cDNA), RNA, heterogeneous nuclear RNA (hnRNA), mRNA, etc., from the subject under study. The sample can be isolated from any suitable tissue or biological fluid such as, for example blood, saliva, plasma, serum, urine, cerebrospinal liquid (CSF), feces, a surgical specimen, a specimen obtained from a biopsy, and a tissue sample embedded in paraffin. Methods for isolating samples are well known to those skilled in the art. In particular, methods for obtaining a sample from a biopsy include gross apportioning of a mass, or micro-dissection or other art-known cell-separation methods. In order to simplify conservation and handling of the samples, these can be formalin-fixed and paraffin- embedded or first frozen and then embedded in a cryosolidifiable medium, such as OCT- compound, through immersion in a highly cryogenic medium that allows rapid freeze.

[0042] Alternatively, the sample from the subject according to the methods of the present invention is a biological fluid sample. The term “biological fluid” and “biofluid” are used interchangeably herein and refer to aqueous fluids of biological origin.

[0043] The biofluid may be obtained from any location (such as blood, plasma, serum, urine, bile, cerebrospinal fluid, aqueous or vitreous humor, or any bodily secretion), an exudate (such as fluid obtained from an abscess or any other site of infection or inflammation), or fluid obtained from a joint (such as a normal joint or a joint affected by disease such as rheumatoid arthritis).

[0044] In a particular embodiment, the sample from the subject according to the methods of the present invention is selected from the group consisting of blood, plasma and serum, and a tissue sample; more preferably from the group consisting of plasma and a tissue sample.

[0045] The term “subject” or “patient” refers herein to a person in need of the analysis described herein. In some embodiments, the subject is a patient. In some embodiments, the subject is a human. In some embodiments, the subject is a female human (a woman). In some embodiments the subject is a female presenting with pathology and / or history consistent with uterine fibroids believed to be a benign neoplasm. In some embodiments the subject is a female presenting with pathology and / or history consistent with uterine fibroids believed to be leiomyoma (LM). In some embodiments the subject is a female presenting with pathology and / or history consistent with uterine fibroids believed to be leiomyoma and desiring surgical intervention. In some embodiments the subject is a female presenting with pathology and / or history consistent with uterine fibroids believed to be leiomyoma, desiring surgical intervention, and requiring an evaluation of the neoplasm to evaluate the risk that the neoplasm is malignant in order to guide the selection of therapy. In some embodiments the subject is a female presenting with pathology and / or history consistent with uterine fibroids, desiring surgical intervention and requiring an evaluation of the neoplasm to evaluate the risk that the neoplasm is a leiomyosarcoma in order to guide the selection of therapy.

[0046] In some embodiments, the sample wherein the expression level of the POC1A, PTPRT, PTCHD1 , MUC4, CENPM, KLHL41 , FLG2, GPR52, MB21 D1, MT-ND1 , MT- CO2, FAM21 B or GSTM1 is determined can be any sample containing cells from the potential tumor. In a particular embodiment, the sample containing cells from the potential tumor is a potential tumor tissue or a portion thereof. In a more particular embodiment, said potential tumor tissue sample is a uterine tissue sample from a patient in which the method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having uterine leiomyosarcoma or uterine leiomyoma is to be carried out. Said sample can be obtained by conventional methods, e.g., biopsy, surgical excision, or aspiration, by using methods well known to those of ordinary skill in the related medical arts. Methods for obtaining the sample from the biopsy include gross apportioning of a mass, or microdissection or other art-known cellseparation methods including partial tumorectomy. Tumor cells can additionally be obtained from fine needle aspiration cytology. In some embodiments, the sample has been obtained by hysterectomy or laparoscopic / laparotomic myomectomy.

[0047] In order to simplify conservation and handling of the samples, these can be formalin-fixed and paraffin-embedded or first frozen and then embedded in a cryosolidifiable medium, such as OCT-compound, through immersion in a highly cryogenic medium that allows for rapid freeze.

[0048] In a particular embodiment of the first method of the invention, the sample wherein the expression levels of the POC1A gene, PTPRT gene, PTCHD1 gene, MUC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene are determined, is a tumor sample obtained by hysterectomy or laparoscopic / laparotomic myomectomy.

[0049] In another particular embodiment, the sample containing the cells from the potential tumor is a biofluid. In a more particular embodiment, said biofluid is selected from the group consisting of blood, plasma and serum from a patient in which the method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having uterine leiomyosarcoma or uterine leiomyoma is to be carried out. Said biofluid sample can be obtained by conventional methods well known to those of ordinary skill in the related arts.

[0050] In yet another particular embodiment of the first method of the invention, the sample wherein the expression levels of the POC1A gene, PTPRT gene, PTCHD1 gene, MLIC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene are determined, is a biofluid obtained by any art-appropriate means.

[0051] Gene expression levels can be quantified by measuring the messenger RNA (mRNA) levels of the gene or of the protein encoded by said gene, i.e. the POC1A protein, PTPRT protein, PTCHD1 protein, MUC4 protein, CENPM protein, KLHL41 protein, FLG2 protein, GPR52 protein, MB21 D1 protein, MT-ND1 protein, MT-CO2 protein, FAM21 B protein or GSTM1 protein or of variants thereof. The POC1A protein, PTPRT protein, PTCHD1 protein, MLIC4 protein, CENPM protein, KLHL41 protein, FLG2 protein, GPR52 protein, MB21 D1 protein, MT-ND1 protein, MT-CO2 protein, FAM21 B protein or GSTM1 protein variants include all the physiologically relevant post- translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained. Said term encompasses the POC1A protein, PTPRT protein, PTCHD1 protein, MLIC4 protein, CENPM protein, KLHL41 protein, FLG2 protein, GPR52 protein, MB21 D1 protein, MT-ND1 protein, MT-CO2 protein, FAM21 B protein or GSTMl protein of any mammal species, including but not being limited to domestic and farm animals (cows, horses, pigs, sheep, goats, dogs, cats or rodents), primates and humans. Preferably, the POC1A protein, PTPRT protein, PTCHD1 protein, MLIC4 protein, CENPM protein, KLHL41 protein, FLG2 protein, GPR52 protein, MB21 D1 protein, MT- ND1 protein, MT-CO2 protein, FAM21 B protein or GSTMl protein is a human protein.

[0052] In order to measure the levels of the mRNA encoded by a given gene, the biological sample may be treated to physically, mechanically or chemically disrupt tissue or cell structure, to release intracellular components into an aqueous or organic solution to prepare nucleic acids for further analysis. The nucleic acids are extracted from the sample by procedures known to the skilled person and commercially available. RNA is then extracted from frozen or fresh samples by any of the methods typical in the art, for example, Sambrook, J., et al., 2001. Molecular cloning: A Laboratory Manual, 3rded., Cold Spring Harbor Laboratory Press, N.Y., Vol. 1-3. In some embodiments, the RNA is extracted from formalin-fixed, paraffin embedded tissues. An exemplary deparaffinization method involves washing the paraffinized sample with an organic solvent, such as xylene, for example. Deparaffinized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example include, methanol, ethanol, propanols, and butanols. Deparaffinized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example. Alternatively, the sample is simultaneously deparaffinised and rehydrated. The sample is then lysed and RNA is extracted from the sample. Commercially available kits may be used for RNA extraction from paraffin samples, such as PureLink™ FFPE Total RNA Isolation Kit (Thermofisher Scientific Inc., US). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (1987) Lab Invest. 56:A67, and De Andres et al., BioTechniques 18:42044 (1995). Preferably, care is taken to avoid degradation of the RNA during the extraction process.

[0053] Various technologies are well-known in the art for deducing and / or measuring and / or detecting the levels of one or more transcripts in a cell. Such methods include hybridization-or sequence-based approaches. Hybridization-based approaches typically involve incubating fluorescently labelled cDNA with custom-made microarrays or commercial high-density oligo microarrays. Specialized microarrays have also been designed; for example, arrays with probes spanning exon junctions can be used to detect and quantify distinct spliced isoforms. Genomic tiling microarrays that represent the genome at high density have been constructed and allow the mapping of transcribed regions to a very high resolution, from several base pairs to -100 bp. Hybridization-based approaches are high throughput and relatively inexpensive, except for high-resolution tiling arrays that interrogate large genomes. However, these methods have several limitations, which include: reliance upon existing knowledge about genome sequence; high background levels owing to cross-hybridization; and a limited dynamic range of detection owing to both background and saturation of signals. Moreover, comparing expression levels across different experiments is often difficult and can require complicated normalization methods.

[0054] In contrast to microarray methods, sequence-based approaches directly determine the cDNA sequence. Initially, Sanger sequencing of cDNA or EST libraries was used, but this approach is relatively low throughput, expensive and generally not quantitative. Tag-based methods were developed to overcome these limitations, including serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequencing (MPSS). These tag-based sequencing approaches are high throughput and can provide precise, digital gene expression levels. However, most are based on Sanger sequencing technology, and a significant portion of the short tags cannot be uniquely mapped to the reference genome. Moreover, only a portion of the transcript is analyzed, and isoforms are generally indistinguishable from each other. These disadvantages limit the use of traditional sequencing technology in measuring or detection mRNA levels.

[0055] The present methods can also involve a larger-scale analysis of mRNA levels, e.g., the detection of a plurality of biomarkers (e.g., 2-10, or 5-50, or 10-100, or 50-500 or more at one time). In addition, the methods described here can also involve the step of conducting a transcriptomic analysis (i.e., the analysis of the complete set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition). Understanding the transcriptome can be important for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and disease and how the biomarkers disclosed herein are indicative or predictive of a particular condition (e.g., LM or LMS). The key aims of transcriptomics are: to catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs; to determine the transcriptional structure of genes, in terms of their start sites, 5' and 3' ends, splicing patterns and other post- transcriptional modifications; and to quantify the changing expression levels of each transcript during development and under different conditions.

[0056] Recently, the development of novel high-throughput DNA sequencing methods has provided a new method for both mapping and quantifying transcriptomes. This method, termed RNAseq (RNA sequencing), has advantages over existing approaches for determining transcriptomes. Accordingly, in one embodiment, the expression level of the gene or genes used in the first method of the invention are determined by RNAseq.

[0057] As used herein "RNAseq" or "RNA-seq" refers to a transcriptomic approach where the total complement of RNAs from a given sample is isolated and sequenced using high-throughput next generation sequencing (NGS) technologies (e.g., SOLiD, 454, Illumina, or ION Torrent).

[0058] RNAseq uses deep-sequencing technologies. In general, a population of RNA (total or fractionated, such as poly(A)+) is converted to a library of cDNA fragments with adaptors attached to one or both ends. Each molecule, with or without amplification, is then sequenced in a high-throughput manner to obtain short sequences from one end (single-end sequencing) or both ends (pair-end sequencing). The reads are typically 30- 400 bp, depending on the DNA-sequencing technology used. In principle, any high- throughput sequencing technology can be used for RNA-Seq, e.g., the Illumina IG18, Applied Biosystems SOUD22 and Roche 454 Life Science systems have already been applied for this purpose. The Helicos Biosciences tSMS system is also appropriate and has the added advantage of avoiding amplification of target cDNA. Following sequencing, the resulting reads are either aligned to a reference genome or reference transcripts, or assembled de novo without the genomic sequence to produce a genomescale transcription map that consists of both the transcriptional structure and / or level of expression for each gene.

[0059] Transcriptome analysis by next-generation sequencing (RNA-seq) allows investigation of a transcriptome at unsurpassed resolution. One major benefit is that RNA-seq is independent of a priori knowledge on the sequence under investigation.

[0060] The transcriptome can be profiled by high throughput techniques including SAGE, microarray, and sequencing of clones from cDNA libraries. For more than a decade, oligo nucleotide microarrays have been the method of choice providing high throughput and affordable costs. However, microarray technology suffers from well- known limitations including insufficient sensitivity for quantifying lower abundant transcripts, narrow dynamic range and biases arising from non-specific hybridizations. Additionally, microarrays are limited to only measuring known / annotated transcripts and often suffer from inaccurate annotations. Sequencing -based methods such as SAGE rely upon cloning and sequencing cDNA fragments. This approach allows quantification of mRNA abundance by counting the number of times cDNA fragments from a corresponding transcript are represented in a given sample, assuming that cDNA fragments sequenced contain sufficient information to identify a transcript. Sequencingbased approaches have a number of significant technical advantages over hybridizationbased microarray methods. The output from sequence-based protocols is digital, rather than analog, obviating the need for complex algorithms for data normalization and summarization while allowing for more precise quantification and greater ease of comparison between results obtained from different samples. Consequently, the dynamic range is essentially infinite, if one accumulates enough sequence tags. Sequence-based approaches do not require prior knowledge of the transcriptome and are therefore useful for discovery and annotation of novel transcripts as well as for analysis of poorly annotated genomes. However, until recently the application of sequencing technology in transcriptome profiling has been limited by high cost, by the need to amplify DNA through bacterial cloning, and by the traditional Sanger approach of sequencing by chain termination. The next-generation sequencing (NGS) technology eliminates some of these barriers, enabling massive parallel sequencing at a high but reasonable cost for small studies. The technology essentially reduces the transcriptome to a series of randomly fragmented segments of a few hundred nucleotides in length. These molecules are amplified by a process that retains spatial clustering of the PGR products, and individual clusters are sequenced in parallel by one of several technologies. Current NGS platforms include the Roche 454 Genome Sequencer, Illumina's Genome Analyzer, and Applied Biosystems' SOLiD. These platforms can analyze tens to hundreds of millions of DNA fragments simultaneously, generate giga-bases of sequence information from a single run, and have revolutionized SAGE and cDNA sequencing technology. For example, the 3' tag Digital Gene Expression (DGE) uses oligo-dT priming for first strand cDNA synthesis, generates libraries that are enriched in the 3' untranslated regions of polyadenylated mRNAs, and produces base cDNA tags.

[0061] In various embodiments the use of such sequencing technologies does not require the preparation of sequencing libraries. However, in certain embodiments the sequencing methods contemplated herein requires the preparation of sequencing libraries.

[0062] Any method for making high-throughput sequencing libraries can be used. An example of sequencing library preparation is described in U.S. Patent Application Publication No. US 2013 / 0203606, which is incorporated by reference in its entirety. In some embodiments, this preparation may take the coagulated portion of the sample from the droplet actuator as an assay input. The library preparation process is a ligation-based process, which includes four main operations: (a) blunt-ending, (b) phosphorylating, (c) A-tailing, and (d) ligating adaptors. DNA fragments in a droplet are provided to process the sequencing library. In the blunt-ending operation (a), nucleic acid fragments with 5'- and / or 3 '-overhangs are blunt-ended using T4 DNA polymerase that has both a 3 '-5' exonuclease activity and a 5'-3' polymerase activity, removing overhangs and yielding complementary bases at both ends on DNA fragments. In some embodiments, the T4 DNA polymerase may be provided as a droplet. In the phosphorylation operation (b), T4 polynucleotide kinase may be used to attach a phosphate to the 5'-hydroxyl terminus of the blunt-ended nucleic acid. In some embodiments, the T4 polynucleotide kinase may be provided as a droplet. In the A-tailing operation (c), the 3' hydroxyl end of a dATP is attached to the phosphate on the 5 '-hydroxyl terminus of a blunt-ended fragment catalyzed by exo-Klenow polymerase. In the ligating operation (d), sequencing adaptors are ligated to the A-tail. T4 DNA ligase is used to catalyze the formation of a phosphate bond between the A-tail and the adaptor sequence. In some embodiments involving cfDNA, end-repairing (including blunt-ending and phosphorylation) may be skipped because the cfDNA are naturally fragmented, but the overall process upstream and downstream of end repair is otherwise comparable to processes involving longer strands of DNA.

[0063] In another example, sequencing library preparation can involve the production of a random collection of adapter-modified DNA fragments (e.g., polynucleotides) that are ready to be sequenced. Sequencing libraries of polynucleotides can be prepared from DNA or RNA, including equivalents, analogs of either DNA or cDNA, for example, DNA or cDNA that is complementary or copy DNA produced from an RNA template, by the action of reverse transcriptase. The polynucleotides may originate in double-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA, PCR amplification products, and the like) or, in certain embodiments, the polynucleotides may originated in singlestranded form (e.g., ssDNA, RNA, etc.) and have been converted to dsDNA form.

[0064] By way of illustration, in certain embodiments, single stranded mRNA molecules may be copied into double-stranded cDNAs suitable for use in preparing a sequencing library. The precise sequence of the primary polynucleotide molecules is generally not material to the method of library preparation, and may be known or unknown. In one embodiment, the polynucleotide molecules are DNA molecules. More particularly, in certain embodiments, the polynucleotide molecules represent the entire genetic complement of an organism or substantially the entire genetic complement of an organism, and are genomic DNA molecules (e.g., cellular DNA, cell free DNA (cfDNA), etc.), that typically include both intron sequence and exon sequence (coding sequence), as well as non-coding regulatory sequences such as promoter and enhancer sequences. In certain embodiments, the primary polynucleotide molecules comprise human genomic DNA molecules, e.g., cfDNA molecules present in peripheral blood of a subject.

[0065] Preparation of sequencing libraries for some NGS sequencing platforms is facilitated by the use of polynucleotides comprising a specific range of fragment sizes. Preparation of such libraries typically involves the fragmentation of large polynucleotides (e.g. cellular genomic DNA) to obtain polynucleotides in the desired size range.

[0066] The expression level can be determined using mRNA obtained from a formalin- fixed, paraffin-embedded tissue sample. mRNA may be isolated from an archival pathological sample or biopsy sample which is first deparaffinized. An exemplary deparaffinization method involves washing the paraffinized sample with an organic solvent, such as xylene. Deparaffinized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example, include methanol, ethanol, propanols and butanols. Deparaffinized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example. Alternatively, the sample is simultaneously deparaffinized and rehydrated. The sample is then lysed and RNA is extracted from the sample. Samples can be also obtained from fresh tumor tissue such as a resected tumor. In a particular embodiment samples can be obtained from fresh tumor tissue or from OCT embedded frozen tissue. In another preferred embodiment samples can be obtained by laparoscopic myomectomy and then paraffin-embedded.

[0067] In order to normalize the values of mRNA expression among the different samples, it is possible to compare the expression levels of the mRNA of interest in the test samples with the expression of a control RNA. A “control RNA” as used herein, relates to RNA whose expression levels do not change or change only in limited amounts in tumor cells with respect to non-tumorigenic cells. Preferably, the control RNA is mRNA derived from housekeeping genes and which code for proteins which are constitutively expressed and carry out essential cellular functions. Preferred housekeeping genes for use in the present invention include p-2-microglobulin, ubiquitin, 18-S ribosomal protein, cyclophilin, IPO8, HPRT, GAPDH, PSMB4, tubulin and p-actin.

[0068] In one embodiment, the relative gene expression quantification is calculated according to the comparative threshold cycle (Ct) method using GAPDH, IPO8, HPRT, P-actin or PSMB4 as an endogenous control and commercial RNA controls as calibrators. Final results are determined according to the formula 2_(ACt samP|e-ACtcalibrator), where ACT values of the calibrator and sample are determined by subtracting the Ct value of the target gene from the value of the control gene.

[0069] Suitable methods to determine gene expression levels at the mRNA level include, without limitation, standard assays for determining mRNA expression levels such as qPCR, RT-PCR, RNA protection analysis, Northern blot, RNA dot blot, in situ hybridization, microarray technology, tag based methods such as serial analysis of gene expression (SAGE) including variants such as LongSAGE and SuperSAGE, microarrays, fluorescence in situ hybridization (FISH), including variants such as Flow- FISH, qFiSH and double fusion FISH (D-FISH), and the like.

[0070] In some embodiments, the determination of the expression levels of the gene or genes is carried out by exome-wide gene expression from RNAseq.

[0071] In some embodiments, the first method of the invention is based on a proteomic analysis. The term “proteomic analysis” is used to refer to the analysis of the expression level of one or more proteins in a biological sample. Such analysis can, for example, be accomplished using mass spectrometry, two-dimensional gel electrophoresis, immunoassays, or by any other means for quantifying the level of protein expression in a sample.

[0072] In some embodiments, the determination of the expression levels of the gene or genes is carried out by measuring the expression levels of the protein or proteins or any functionally equivalent variant thereof encoded by said gene or genes.

[0073] In some embodiments, the first method of the invention comprises the determination of the expression levels of the protein encoded by the POC1A gene, PTPRT gene, PTCHD1 gene, MLIC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene, i.e. the POC1A protein, PTPRT protein, PTCHD1 protein, MUC4 protein, CENPM protein, KLHL41 protein, FLG2 protein, GPR52 protein, MB21 D1 protein, MT-ND1 protein, MT-CO2 protein, FAM21 B protein or GSTM1 protein or of variants thereof.

[0074] In some embodiments, the biological sample is a sample containing myometrial cells or RNA derived from myometrial cells. In yet another embodiment, the sample containing myometrial cells or RNA derived from myometrial cells is a myometrial biopsy.

[0075] In yet another embodiment, the sample containing myometrial cells is a biofluid. In a particular embodiment, the biofluid is selected from the group consisting of blood, plasma and serum.

[0076] In the second step, the first method of the invention comprises comparing said level of expression with a reference value.

[0077] The term “reference value”, as used herein, refers to a laboratory value used as a reference for values / data obtained by laboratory examinations of subjects or samples collected from subjects. The reference value or reference level can be an absolute value; a relative value; a value that has an upper and / or lower limit; a range of values; an average value; a median value, a mean value, or a value as compared to a particular control or baseline value. A reference value can be based on an individual sample value, such as for example, a value obtained from a sample from the subject being tested, but at an earlier point in time or from a non-cancerous tissue, or a value obtained from a sample from a different subject to the subject being tested. The reference value can be based on a large number of samples, such as from population of subjects of the chronological age matched group coinciding with that of the subject, or based on a pool of samples including or excluding the sample to be tested. Various considerations are taken into account when determining the reference value of the biomarker. Among such considerations are the age, weight, sex, general physical condition of the patient and the like. For example, equal amounts of a group of at least 2, at least 10, at least 100 to preferably more than 1000 subjects, preferably classified according to the foregoing considerations, for example according to various age categories, are taken as the reference group. In another embodiment, the quantity of the biomarker in a sample from a tested subject may be determined directly relative to the reference value (e.g., in terms of increase or decrease, or fold-increase or fold-decrease). Advantageously, this may allow to compare the quantity of the biomarker in the sample from the subject with the reference value (in other words to measure the relative quantity of any one or more biomarkers in the sample from the subject vis-a-vis the reference value) without the need to first determine the respective absolute quantities of said biomarker.

[0078] As used herein, the term “biomarker” refers to the expression level of a gene, a gene product, or of the protein encoded by said gene, as defined above.

[0079] Typically, reference values are the expression level of the gene being compared in a reference sample. The term “reference sample”, as used herein, means at least a sample obtained from at least a subject (e.g., from an individual subject or from a pool of subjects) affected by uterine leiomyoma. Thus, in an embodiment, the reference value is the mean level of expression of the same gene or genes determined in a group of samples from a group of subjects with uterine leiomyoma. It will be understood that the “reference sample” defined as “a group of samples from a group of subjects” means a pool of samples obtained from a pool of subjects with uterine leiomyoma. In an embodiment, the pool of samples from leiomyoma patients are obtained from a myometrial biopsy from subjects affected by uterine leiomyoma. In a preferred embodiment, the pool of samples from leiomyoma patients are obtained from a biofluid sample from subjects affected by uterine leiomyoma.

[0080] The expression profile of the genes in the reference sample can preferably, be generated from a population of two or more individuals. The population, for example, can comprise 3, 4, 5, 10, 15, 20, 30, 40, 50 or more individuals with uterine leiomyoma. Furthermore, the expression profile of the gene or genes in the reference sample and in the sample of the individual that is going to be diagnosed according to the methods of the present invention can be generated from one individual, provided that said individual is affected by uterine leiomyoma.

[0081] Once this reference value is established, the level of the biomarker expressed in the tumor tissue or biofluid from subjects can be compared with the reference value, and thus be assigned a level of deviation with respect to a reference value. The “deviation” can be either an increase or a decrease in the expression level of the biomarker with respect to the reference value. For example, an increase in the expression level of a gene above the reference value of at least 1.1 -fold, 1.5-fold, 2-fold, 5-fold, 10-fold, 20- fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the reference value is considered as “increased” expression level, or an upregulation in the expression level of a gene in a sample with respect to the reference value. Similarly, the expression level of a gene is considered increased in a sample of the subject under study when its expression levels increase with respect to the reference sample by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more. On the other hand, a decrease in the expression level of a gene below the reference value of at least 0.9-fold, 0.75-fold, 0.2-fold, 0.1 -fold, 0.05-fold, 0.025-fold, 0.02-fold, 0.01 -fold, 0.005-fold or even less compared with reference value is considered as “decreased” expression level, or a downregulation in the expression level of a gene in a sample with respect to the reference value. Similarly, the expression level of a gene is considered decreased in a sample of the subject under study when its expression levels decrease with respect to the reference sample by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100% (i.e., absent). The comparison of the expression levels of the gene or genes of interest with the reference value allows distinguishing between uterine leiomyosarcoma or uterine leiomyoma.

[0082] In some embodiments, the patient is detected as having leiomyosarcoma if the expression level of the gene or genes under examination is / are increased with respect to the expression level of said gene or genes found in leiomyoma samples, which are the POC1A gene, the PTPRT gene, the MLIC4 gene, the FLG2 gene, the GPR52 gene, the MT-ND1 gene or the MT-CO2 gene having a log2FoldChange higher than 0 (i.e., if the log2FoldChange value is a positive value).

[0083] In some embodiments, the patient is detected as having leiomyosarcoma if the expression level of the gene or genes under examination is / are decreased with respect to the expression level of said gene or genes found in leiomyoma samples, which is are the PTCHD1 gene, the CENPM gene, the KLHL41 gene, the MB21 D1 gene, the FAM21 B gene or the GSTM1 gene having a log2FoldChange lower than 0 (i.e., if the log2FoldChange value is a negative value).

[0084] In a preferred embodiment, the deviation of the expression level with respect to the reference value in the POC1A gene, the PTPRT gene, the MLIC4 gene, the FLG2 gene, the GPR52 gene, the MT-ND1 gene or the MT-CO2 gene is an increase and wherein the deviation of the expression level with respect to the reference value in the PTCHD1 gene, the CENPM gene, the KLHL41 gene, the MB21 D1 gene, the FAM21 B gene or the GSTM1 gene is a decrease.

[0085] In another embodiment, the first method according to the invention comprises measuring the expression level of the POC1A, PTPRT, PTCHD1 , MLIC4, CENPM, KLHL41 , FLG2, GPR52, MB21 D1 , MT-ND1 , MT-CO2, FAM21 B and GSTM1 genes.

[0086] In some embodiments, the first method according to the invention allows the diagnosis of uterine leiomyosarcoma when the deviation in the level of expression of the gene or genes is / are of at least two fold with respect to the reference value or values for said gene or genes, said reference value being the expression level of the same gene or genes determined in at least a sample from at least a subject with uterine leiomyoma.

[0087] In some embodiments, the first method according to the invention is carried out in a patient that has been previously identified as having a uterine myometrial tumor, being either uterine leiomyosarcoma or uterine leiomyoma by imaging examination, preferably by ultrasonography.

[0088] Second method of the invention (method based on transcriptomic analysis)

[0089] The authors of the present invention have carried out a comparative transcriptomic analysis between histologically confirmed LM (n = 72) and LMS (n = 23) tumors. The results have revealed 440 genes that were differentially expressed obtained from 5 selected comparisons between LMS and LM liquid biopsy samples. Selected group comparisons differed in demographic and technical characteristics, such as the age of the subject, or the freezing time of the sample prior to the RNA extraction and data quality. This difference in the transcriptomic profiles allows distinguishing between one disease or the other in a subject by analyzing the RNA composition in a sample from the subject and classifying the subject affected by LMS or LM using a transcriptomic analysis-based method. Thus, in a second aspect, the invention relates to an in vitro method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions (hereinafter referred to as “the second method of the invention”'), the method comprising:

[0090] (i) measuring the level of expression of at least one gene selected from the list shown in Table 2, the list shown in Table 3, the list shown in Table 4, the list shown in Table 6 or the list shown in Table 7 in a biological sample obtained from the subject, and

[0091] (ii) comparing said level of expression with a reference value, and wherein a deviation in the level of expression of said at least one gene with respect to said reference value is indicative that the subject is affected by from uterine leiomyosarcoma.

[0092] The terms and expressions “distinguishing between”, “uterine leiomyoma”, “uterine leiomyosarcoma”, “expression level”, “biological sample” and “subject” have been defined in the context of the first method of the invention and apply equally to the second method of the invention.

[0093] In a first step, the second method of the invention comprises the determination of the level of expression of at least one gene selected from the list shown in Table 2, the list shown in Table 3, the list shown in Table 4, the list shown in Table 6 or the list shown in Table 7 in a biological sample obtained from the subject whose diagnosis is to be determined.

[0094] In some embodiments, the first step of the second method of the invention comprises the determination of the expression levels of at least 3, at least 15, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35 or the 37 genes listed in Table 2.

[0095] In some embodiments, the first step of the second method of the invention comprises the determination of the expression levels of at least 3, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or the 55 genes listed in Table 3.

[0096] In some embodiments, the first step of the second method of the invention comprises the determination of the expression levels of at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 110, at least 120, at least 130, at least 140, at least 150, at least 160, at least 170, at least 180, at least 190, at least 200, at least 210, at least 220, at least 230, at least 240, at least 250, at least 260, at least 270, at least 280, at least 290, at least 300, at least 310, at least 320, at least 330, at least 340, at least 350, at least 360, at least 370 or the 378 genes listed in Table 4.

[0097] In some embodiments, the first step of the second method of the invention comprises the determination of the expression levels of at least 3, at least 5, at least 10 or the 13 genes listed in Table 6.

[0098] In some embodiments, the first step of the second method of the invention comprises the determination of the expression levels of at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 110, at least 120, at least 130, at least 140, at least 150, at least 160, at least 170, at least 180, at least 190, at least 200, at least 210, at least 220, at least 230 or the 234 genes listed in Table 7.

[0099] In a particular embodiment, the sample from the subject according to the methods of the present invention is selected from the group consisting of blood, plasma and serum, and a tissue sample; more preferably from the group consisting of plasma and a tissue sample.

[0100] In some embodiments, the sample wherein the expression level of the genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 is determined can be any sample containing cells from the potential tumor. In a particular embodiment, the sample containing cells from the potential tumor is a potential tumor tissue or a portion thereof. In a more particular embodiment, said potential tumor tissue sample is a uterine tissue sample from a patient in which the method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having uterine leiomyosarcoma or uterine leiomyoma is to be carried out. Said sample can be obtained by conventional methods, e.g., biopsy, surgical excision, or aspiration, by using methods well known to those of ordinary skill in the related medical arts. Methods for obtaining the sample from the biopsy have been explained in the context of the first method according to the invention and apply equally to the second method of the invention. In some embodiments, the sample has been obtained by hysterectomy or laparoscopic / laparotomic myomectomy.

[0101] In a particular embodiment of the second method of the invention, the sample wherein the expression levels of the genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 are determined, is a tumor sample obtained by hysterectomy or laparoscopic / laparotomic myomectomy.

[0102] In another particular embodiment, the sample containing the cells from the potential tumor is a biofluid. In a more particular embodiment, said biofluid is selected from the group consisting of blood, plasma and serum from a patient in which the method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having uterine leiomyosarcoma or uterine leiomyoma is to be carried out. Said biofluid sample can be obtained by conventional methods well known to those of ordinary skill in the related arts.

[0103] In yet another particular embodiment of the second method of the invention, the sample wherein the expression levels of the genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 are determined, is a biofluid obtained by any art-appropriate means.

[0104] Gene expression levels can be quantified by measuring the messenger RNA (mRNA) levels of the gene or of the protein encoded by said gene, i.e. the protein or proteins encoded by the gene or genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 or of variants thereof. The protein variants of the protein or proteins encoded by the gene or genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained. Said term encompasses the protein or proteins encoded by the gene or genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 of any mammal species, including but not being limited to domestic and farm animals (cows, horses, pigs, sheep, goats, dogs, cats or rodents), primates and humans. Preferably, said protein or proteins encoded by the gene or genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 is a human protein.

[0105] The measurement of the levels of the mRNA encoded by a given gene has been defined in the context of the first method according to the invention and apply equally to the second method of the invention. Accordingly, in one embodiment, the expression level of the gene or genes used in the second method of the invention are determined by RNAseq.

[0106] The term "RNAseq", the transcriptome analysis by next-generation sequencing, next-generation sequencing, sequencing libraries, the determination of the expression level using mRNA, the normalization of the mRNA expression values, the relative gene expression quantification and suitable methods to determine gene expression levels at the mRNA level have been defined in the first method according to the invention and apply equally to the second method of the invention.

[0107] In some embodiments, the determination of the expression levels of the gene or genes used in the second method of the invention is carried out by exome-wide gene expression from RNAseq. In some embodiments, the second method of the invention is based on a proteomic analysis.

[0108] The term “proteomic analysis” has been defined in the first method according to the invention and apply equally to the second method of the invention.

[0109] In some embodiments, the determination of the expression levels of the gene or genes is carried out by measuring the expression levels of the protein or proteins or any functionally equivalent variant thereof encoded by said gene or genes.

[0110] In some embodiments, the second method of the invention comprises the determination of the expression levels of the protein or proteins encoded by the gene or genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7, i.e. the protein or proteins encoded by the gene or genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7 or of variants thereof.

[0111] In some embodiments, the biological sample is a sample containing myometrial cells or RNA derived from myometrial cells. In yet another embodiment, the sample containing myometrial cells or RNA derived from myometrial cells is a myometrial biopsy.

[0112] In yet another embodiment, the sample containing myometrial cells is a biofluid. In a particular embodiment, the biofluid is selected from the group consisting of blood, plasma and serum.

[0113] In the second step, the second method of the invention comprises comparing said level of expression with a reference value. The term "reference value" has been defined in the first method according to the invention and apply equally to the second method of the invention.

[0114] Thus, in another embodiment of the second method of the invention, the reference value is the mean level of expression of the same gene or genes determined in a group of samples from a group of subjects with uterine leiomyoma. The expression “a group of samples from a group of subjects” has been defined in the first method according to the invention and apply equally to the second method of the invention.

[0115] In an embodiment, the pool of samples from leiomyoma patients are obtained from a myometrial biopsy from subjects affected by uterine leiomyoma. In a preferred embodiment, the pool of samples from leiomyoma patients are obtained from a biofluid sample from subjects affected by uterine leiomyoma.

[0116] The expression profile of the genes in the reference sample can preferably, be generated from a population of two or more individuals. The population, for example, can comprise 3, 4, 5, 10, 15, 20, 30, 40, 50 or more individuals with uterine leiomyoma. Furthermore, the expression profile of the gene or genes in the reference sample and in the sample of the individual that is going to be diagnosed according to the methods of the present invention can be generated from one individual, provided that said individual is affected by uterine leiomyoma.

[0117] Once this reference value is established, the level of the biomarker expressed in the tumor tissue or biofluid from subjects can be compared with the reference value, and thus be assigned a level of deviation with respect to a reference value.

[0118] The terms “reference sample”, “biomarker” and “deviation” have been defined in the first method according to the invention and apply equally to the second method of the invention.

[0119] In some embodiments, the patient is detected as having leiomyosarcoma if the expression level of the gene or genes under examination is / are increased with respect to the expression level of said gene or genes found in leiomyoma samples, which are defined in Table 2, in Table 3, in Table 4, in Table 6 or in Table 7 as genes having a log2FoldChange higher than 0 (i.e., if the log2FoldChange value is a positive value).

[0120] In some embodiments, the patient is detected as having leiomyosarcoma if the expression level of the gene or genes under examination is / are decreased with respect to the expression level of said gene or genes found in leiomyoma samples, which is are defined in Table 2, in Table 3, in Table 4, in Table 6 or in Table 7 as genes having a log2FoldChange lower than 0 (i.e., if the log2FoldChange value is a negative value).

[0121] In a preferred embodiment, the deviation in the level of expression of the gene or genes is a downregulation in the sample with respect to the reference value if the log2FoldChange value for said at least one gene as indicated in the Table is a negative value, or the deviation in the level of expression of the gene or genes is an upregulation in the sample with respect to the reference value if the log2FoldChange value for said at least one gene as indicated in the Table is a positive value.

[0122] In another embodiment, the second method according to the invention comprises measuring the expression level of all the genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7.

[0123] In some embodiments, the second method according to the invention allows the diagnosis of uterine leiomyosarcoma when the deviation in the level of expression of the gene or genes is / are of at least two fold with respect to the reference value or values for said gene or genes, said reference value being the expression level of the same gene or genes determined in at least a sample from at least a subject with uterine leiomyoma. In some embodiments, the second method according to the invention is carried out in a patient that has been previously identified as having a uterine myometrial tumor, being either uterine leiomyosarcoma or uterine leiomyoma by imaging examination, preferably by ultrasonography.

[0124] In some embodiments, the biological sample of the second method of the invention, in which the determination of the levels of expression of the gene or genes is to be carried out, can be sorted in groups differing in specific characteristics, such as, but not limited to, groups with specific demographic characteristics or specific technical characteristics. Illustrative, non-limitative examples of specific demographic or technical characteristics include the age of the subject, or the freezing time of the sample prior to the RNA extraction and data quality.

[0125] Accordingly, in some embodiments according to the second method of the invention:

[0126] (i) if the biological sample has been frozen for less than 6 months, then step (i) of the second method of the invention comprises measuring the level of expression of at least one gene selected from the list shown in Table 4,

[0127] (ii) if the biological sample derives from a subject over 41 years of age, then step (i) of the second method of the invention comprises measuring the level of expression of at least one gene selected from the list shown in Table 6, or

[0128] (iii) if the biological sample has been frozen for less than 6 months and it derives from a subject over 41 years of age, then step (i) of the second method of the invention comprises measuring the level of expression of at least one gene selected from the list shown in Table 7.

[0129] Methods for identifying a subject to receive a suitable therapy based on the first method of the invention or the second method of the invention

[0130] The invention also provides a method for identifying a subject suspected of having uterine leiomyosarcoma as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma.

[0131] Thus, in a third aspect, the invention relates to an in vitro method for identifying a subject suspected of having uterine leiomyosarcoma as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma (hereinafter referred to as “the third method of the invention ”) the method comprising: (i) determining whether the subject is affected by uterine leiomyosarcoma following any of the first method of the invention or the second method of the invention; and

[0132] (ii) designating said subject as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma if the subject is diagnosed as having uterine leiomyosarcoma.

[0133] In a first step, the in vitro method for identifying a subject suspected of having uterine leiomyosarcoma as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma comprises determining whether the subject is affected by uterine leiomyosarcoma by using any of the first method of the invention or the second method of the invention.

[0134] In a second step, the method comprises designating a subject as a candidate to be treated with a therapy suitable for the treatment of uterine leiomyosarcoma if the patient is diagnosed as having uterine leiomyosarcoma.

[0135] In some embodiments, when the subject is being diagnosed with uterine leiomyosarcoma, the subject is designated to be treated with a therapy selected from the group consisting of surgery, radiation therapy, chemotherapy, hormonal therapy and targeted therapy.

[0136] In some embodiments, the surgery is a simple hysterectomy, radical hysterectomy or bilateral salpingo-oophorectomy.

[0137] In some embodiments, the therapy is radiation therapy. In the present context, the terms “radiation therapy” or "radiotherapy" refer to the treatment of cancer cells with ionizing radiation in order to control or kill malignant cells. It is meant to include, for example, fractionated radiation therapy, non-fractionated radiation therapy and superfractionated radiation therapy, as well as a combination of radiation and chemotherapy. The type of radiation may further include ionizing (y) radiation, particle radiation, low energy transfer (LET), high energy transfer (HET), X-ray radiation, UV radiation, infrared radiation, visible light, photosensitizing radiation, etc.

[0138] In some embodiments, the chemotherapy includes one or more drugs selected from the group consisting of dacarbazine (DTIC), docetaxel, doxorubicin, epirubicin, gemcitabine, ifosfamide, paclitaxel, temozolomide, trabectedin and vinorelbine.

[0139] In some embodiments, the therapy is an hormonal therapy. The term “hormonal therapy”, as used herein, comprises: progestin, an agonist of the gonadotropin-releasing hormone such as goserelin or leuprolide, leuprorelin acetate, leuprorelin acetate sustained release depot (ATRIGEL), triptorelin pamoate, buserelin, naferelin, histrelin, goserelin, deslorelin, degarelix, ozarelix, ABT-620 (elagolix), TAK-385 (relugolix), EP- 100, KLH-2109 or triptorelinand goserelin acetate, or an aromatase inhibitor, which is defined as a compound which inhibits estrogen production, for instance, the conversion of the substrates androstenedione and testosterone to estrone and estradiol, respectively, and that includes, but is not limited to steroids, especially atamestane, exemestane and formestane and, in particular, non-steroids, especially aminoglutethimide, roglethimide, pyridoglutethimide, trilostane, testolactone, ketokonazole, vorozole, fadrozole, anastrozole and letrozole

[0140] In some embodiments, the therapy is a targeted therapy. The term “targeted as used herein, refers to drugs which attack specific genetic mutations within cancer cells, such as leiomyosarcoma while minimizing harm to healthy cells. In some embodiments, the targeted therapy comprises the use of pazopanib. of the invention

[0141] The invention also provides methods for the treatment of subjects which have been identified as having leiomyosarcomas by any of the first method of the invention or the second method of the invention, wherein if the subject has been diagnosed as having a leiomyosarcoma, the subject is treated with a therapy suitable for the treatment of leiomyosarcoma.

[0142] Thus, in another aspect, the invention relates to a method for treating uterine leiomyosarcoma in a subject in need thereof comprising the administration of a therapy suitable for the treatment of uterine leiomyosarcoma, wherein the subject to be treated has been identified using any of the first method of the invention or the second method of the invention.

[0143] In some embodiments, the therapy is selected from the group consisting of surgery, radiation therapy, chemotherapy, hormonal therapy and targeted therapy.

[0144] The term “treatment”, as used herein, comprises any type of therapy, which aims at terminating, ameliorating, delaying and / or reducing the severity of a clinical condition as described herein. Thus, “treatment,” “treating,” and the like, as used herein, refer to obtaining a desired pharmacologic and / or physiologic effect, covering any treatment of a pathological condition or disorder in a mammal, including a human. The effect may be therapeutic in terms of a partial or complete cure for a disorder and / or adverse effect attributable to the disorder. That is, “treatment” includes (1) preventing the disorder from occurring or recurring in a subject, (2) inhibiting the disorder, such as arresting its development, (3) stopping or terminating the disorder or at least symptoms associated therewith, so that the host no longer suffers from the disorder or its symptoms, such as causing regression of the disorder or its symptoms, for example, by restoring or repairing a lost, missing or defective function, or stimulating an inefficient process, or (4) relieving, alleviating, or ameliorating the disorder, or symptoms associated therewith, where ameliorating is used in a broad sense to refer to at least a reduction in the magnitude of a parameter, such as inflammation, pain, and / or immune deficiency.

[0145] The therapeutic method according to the invention is applied to patients which have been diagnosed as having leiomyosarcoma by using any of the first method of the invention or the second method of the invention. In some embodiments, the method comprises a first step in which any of the first method of the invention or the second method of the invention is applied to the subject, a second step in which subjects diagnosed as having leiomyosarcoma are identified, and a third step in which the subjects are treated with a therapy suitable for the treatment of leiomyosarcoma.

[0146] In some embodiments, when the subject is being diagnosed with uterine leiomyosarcoma, the subject is designated to be treated with a therapy selected from the group consisting of surgery, radiation therapy, chemotherapy, hormonal therapy and targeted therapy.

[0147] Suitable surgical therapies, radiation therapies, chemotherapies, hormonal therapies or targeted therapies have been described in the context of the third method of the invention and are equally applicable to the therapeutic methods according to the invention.

[0148] In another aspect, the invention relates to a chemotherapeutic agent, hormonal agent and / or targeted agent for use in the treatment of uterine leiomyosarcoma, wherein the subject to be treated has been identified by any of the first method of the invention or the second method of the invention.

[0149] The term “chemotherapeutic agent”, as used herein, refers to standard chemotherapy drugs, which generally attack any quickly dividing cell, targeted therapy agents and immunomodulatory agents. Illustrative non-limitative examples of chemotherapeutic agents include dacarbazine (DTIC), docetaxel, doxorubicin, epirubicin, gemcitabine, ifosfamide, paclitaxel, temozolomide, trabectedin or vinorelbine. The term “hormonal agent”, as used herein, comprises: progestin, an agonist of the gonadotropin-releasing hormone such as goserelin or leuprolide, leuprorelin acetate, leuprorelin acetate sustained release depot (ATRIGEL), triptorelin pamoate, buserelin, naferelin, histrelin, goserelin, deslorelin, degarelix, ozarelix, ABT-620 (elagolix), TAK-385 (relugolix), EP- 100, KLH-2109 or triptorelinand goserelin acetate, or an aromatase inhibitor, which is defined as a compound which inhibits estrogen production, for instance, the conversion of the substrates androstenedione and testosterone to estrone and estradiol, respectively, and that includes, but is not limited to steroids, especially atamestane, exemestane and formestane and, in particular, non-steroids, especially aminoglutethimide, roglethimide, pyridoglutethimide, trilostane, testolactone, ketokonazole, vorozole, fadrozole, anastrozole and letrozole

[0150] The term “targeted agent”, as used herein, refers to drugs which attack specific genetic mutations within cancer cells, such as leiomyosarcoma while minimizing harm to healthy cells. In some embodiments, the targeted therapy comprises the use of pazopanib.

[0151] Kit of the invention and uses thereof

[0152] In another aspect, the invention relates to a kit, package and / or device that comprises reagents adeguate for implementing any of the first method of the invention, the second method of the invention or the third method of the invention. It will be understood that, depending on the nature of the method, the reagents adeguate for its implementation will vary.

[0153] In the context of the present invention, “kit” is understood as a product containing the different reagents reguired for carrying out the methods of the invention packaged such that it allows being transported and stored. The materials suitable for the packaging of the components of the kit include glass, plastic (polyethylene, polypropylene, polycarbonate, and the like), bottles, vials, paper, sachets, and the like. Where there are more than one component in a kit they may be packaged together if suitable or the kit will generally contain a second, third or other additional container into which the additional components may be separately placed. However, in some embodiments, certain combinations of components may be packaged together comprised in one container means. A kit can also include a means for containing any reagent containers in close confinement for commercial sale. Such containers may include injection or blow- molded plastic containers into which the desired vials are retained. One or more compositions of a kit can be lyophilized. In some embodiments, all compositions of a kit of the disclosure will be lyophilized. In some embodiments, a kit of the disclosure with one or more lyophilized agents will be supplied with a re-constitution buffer. Reagents and components of kits may be comprised in one or more suitable container means. A container means may generally comprise at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted.

[0154] Furthermore, kits according to the invention can also comprise one or more reagents for preparing crude cell lysates and / or reagents for extracting, isolating and / or purification of nucleic acids from a sample. Additional components can comprise particles with affinity for nucleic acids and / or solid supports with affinity for nucleic acids, one or more wash buffers, binding enhancers, binding solutions, polar solvents, alcohols, elution buffers, filter membranes and / or columns for isolation of DNA / RNA. A kit may further comprise reagents for downstream processing of an isolated nucleic acid and may include without limitation at least one RNase inhibitor; at least one cDNA construction reagents (such as reverse transcriptase); one or more reagents for amplification of RNA, one or more reagents for amplification of DNA including primers, reagents for purification of DNA, probes for detection of specific nucleic acids. Furthermore, the kits of the invention can contain instructions for the simultaneous, sequential, or separate use of the different components that are in the kit. Said instructions can be in the form of printed material or in the form of an electronic medium capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes, and the like), optical media (CD-ROM, DVD), and the like. The media may additionally or alternatively contain Internet addresses providing said instructions.

[0155] In some embodiments, the kit comprises primers or probes adequate for the detection of the expression levels of one or more of the genes, the expression levels of which are determined in the first method of the invention or in the second method of the invention. In some embodiments, when any of the first method of the invention or the second method of the invention is based on the determination of the expression levels of one or more genes, the kits comprise primers or probes adequate for the detection of the expression levels of said one or more genes.

[0156] The term "primer" as used herein refers to oligonucleotides that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the primer within a sequence of the target polynucleotide sequence. A primer can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides. For proper hybridization to the target sequence, a primer can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to the hybridized portion of the target polynucleotide sequence. Oligonucleotides useful as primers may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts. (1981) 22: 1859-1862, using an automated synthesizer, as described in Needham-Van Devanter et al, Nucleic Acids Res. (1984) 12: 6159-6168. Primers are useful in nucleic acid amplification reactions in which the primer is extended to produce a new strand of the polynucleotide. Primers can be readily designed by a skilled artisan using common knowledge known in the art, such that they can specifically anneal to the nucleotide sequence of the target nucleotide sequence of the at least one biomarker provided herein. Usually, the 3' nucleotide of the primer is designed to be complementary to the target sequence at the corresponding nucleotide position, to provide optimal primer extension by a polymerase.

[0157] The term "probe" as used herein refers to oligonucleotides or analogs thereof that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the probe within a sequence of the target polynucleotide sequence. Exemplary probes can be, for example DNA probes, RNA probes, or protein nucleic acid (PNA) probes. A probe can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides. For proper hybridization to the target sequence, a probe can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to hybridized portion of the target polynucleotide sequence. Probes can also be chemically synthesized according to the solid phase phosphoramidite triester method as described above. Methods for preparation of DNA and RNA probes, and the conditions for hybridization thereof to target nucleotide sequences, are described in Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition. Cold Spring Harbor Laboratory Press, 1989, Chapters 10 and 11.

[0158] In a preferred embodiment, the reagents adequate for the determination of the expression levels of one or more genes comprise at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% of the total amount of reagents adequate for the determination of the expression levels of genes forming the kit.

[0159] In another embodiment, the invention relates to the use of the kit of the invention for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions, or for identifying a subject as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma.

[0160] Computer Systems and Devices suitable for carrying out the methods of the invention

[0161] The methods of the invention can be performed using software, hardware, firmware, hardwiring, or combinations of any of these.

[0162] Accordingly, in another aspect, the invention relates to a computer-implemented method, wherein the method is any of the first method of the invention, the second method of the invention or the third method of the invention. In another aspect, the invention relates to a computer comprising instructions for carrying out any of the first method of the invention, the second method of the invention or the third method of the invention.

[0163] The invention is described below by way of the following examples, which are merely illustrative and do not limit the scope of the invention.

[0164] EXAMPLES

[0165] Materials and Methods

[0166] Clinical trial and study patient characteristics

[0167] A prospective, observational and multicenter case-control study was carried out to evaluate the diagnostic precision (sensitivity, specificity, negative predictive value and positive predictive value) in the detection of molecular differences by liquid biopsy in patients with a surgical indication for hysterectomy or myomectomy due to the diagnosis of myometrial tumors (leiomyoma I leiomyosarcoma) according to standard clinical practice.

[0168] Epidemiological, histopathological, and clinical outcomes of the 95 participant women between 18 and 80 years old and with a body mass index (BMI) of 18.5-40.0 kg / m2, with suspected myometrial tumor (leiomyoma, LM / leiomyosarcoma, LMS) based on clinical symptoms and imaging diagnosis (LM, n = 72 and LMS, n = 23) were selected to participate in the present study. Women with the same age and BMI but without a uterine tumor were used as control subjects.

[0169] The objective of the study was to determine the molecular diagnosis of uterine tumors by liquid biopsy as a preoperative diagnosis, using pathological diagnosis as a "gold standard" to validate the molecular diagnosis.

[0170] Patients diagnosed with LM had a median age of 48 years (range: 30 - 71 years), while patients with LMS had a median age of 59 years (range: 43 - 82 years).

[0171] The use of human blood samples was previously approved by the Institutional Review Board of the 15 national hospitals involved in the study.

[0172] Patients signed and provided written informed consent. Those diagnosed with bacterial, fungal, or viral infections were excluded. This study was conducted in accordance with the Declaration of Helsinki. All specimens were anonymized after collection and histologically evaluated by expert pathologists to confirm the diagnosis according to World Health Organization criteria.

[0173] This study was registered on ClinicalTrials.gov with ID NCT04935333, and data were monitored by a clinical research associate.

[0174] Clinical sample collection

[0175] After obtaining informed consent, 10-ml whole blood samples were collected from 97 patients with clinical diagnosis of myometrial tumor, and 25 control patients. All samples were processed in randomized batches. Briefly, one tube of blood was collected per patient in Cell-Free DNA BCTs (Streck Inc.) and processed into plasma. For this purpose, all blood was centrifuged at 1600g for 15 min, and plasma was transferred to a new tube which underwent a second centrifuge step. The plasma supernatant was aliquoted and stored at -80°C until use.

[0176] Nucleic acid isolation, quality control and quantification Ct-RNA was purified from plasma with the Serum / Plasma Advanced Kit according to the manufacturer’s instructions with minor modifications. Quality control and quantification analysis were performed using 2100 Bioanalyzer system (Agilent, Santa Clara, CA, USA). Samples with 100 ng total RNA were selected for further experiments.

[0177] Ct-RNA library preparation and sequencing

[0178] Ct-RNA sequencing libraries were prepared using the Illumina RNA Prep (Illumina, California, USA), according to the manufacturer’s instructions. In brief, biotinylated oligos targeting sequence-specific coding regions of the transcriptome were hybridized to sequencing libraries and pulled down by magnetic streptavidin beads to enrich libraries for RNA sequencing analysis. The final enriched library was then reamplified by PCR (manufacturer instructions, step 6th) to provide sufficient yield for the sequencing assay.

[0179] Control points for quantification of the intermediate and final c-DNA libraries were performed with DNA D1000 Analysis chips on the 2100 Bioanalyzer system (Agilent). Final enrichment libraries were quantified and normalized to a final library concentration of 10 nM through quantitative PCR assay (qPCR) using KAPA library Quant kit (Roche, Basilea, Switzerland) in a QuantStudio5 (Applied Biosystems, ThermoFisher Scientific, Waltham, Massachusetts). Lastly, each quantified library was pooled into a single pool with final concentration of ~2pM and loaded in a NextSeq 550.

[0180] Bioinformatics analysis, data quality and filtering

[0181] Raw data from pair-ended Illumina sequencing was downloaded from Illumina BaseSpace and demultiplexed to generate FASTQ files using bcl2fastq conversion software (version 2.16.0.10)

[0182] (https: / / emea.support.illumina.com / sequencing / sequencing_software / bcl2fastq- conversion-software.html). Then, each sample was aligned to the reference genome (GRCh37) using STAR alignment software (version 2.7.2). Uniquely mapped reads were filtered using samtools (version 1 .9) and duplicates were removed with PICARD software (version 2.18.15) (https: / / broadinstitute.github.io / picard). Then, reads with mapping quality <10 were filtered using samtools. Finally, gene transcript abundance was estimated using the HTseq-count python package (version 0.11.2). In addition, duplicated reads rates were assessed using dupRadar R package. Its slope parameter indicates how fast duplicated reads increase in a sample. Differential gene expression analysis

[0183] Batch effect was corrected using Combat method included in SVA package (https: / / bioconductor.org / packages / sva). Differential gene expression analysis between LMS and LM samples was performed using the DEseq2 package from the Bioconductor repository and represented with ggplot2 (https: / / ggplot2.tidyverse.org) and EnhancedVolcano (https: / / bioconductor.org / packages / EnhancedVolcano) libraries. Data was normalized and features with a minimum of 10 counts in at least a number of samples were kept. The number of samples to apply the 10 counts threshold was calculated by the arithmetic mean of included samples divided by 2. Only genes with a |log2FC| is > 2 and a p-Adjusted value < 0.05 were considered differentially expressed genes (DEGs).

[0184] Example 1

[0185] A total of 440 genes were obtained from 5 selected group comparisons between leiomyosarcoma (LMS) and leiomyoma (LM) liquid biopsy samples (Figure 1).

[0186] Selected group comparisons differed in demographic and technical characteristics, such as the age of the subject, or the freezing time of the sample prior to the RNA extraction and data quality.

[0187] The 5 selected group comparisons were the following:

[0188] (a) Comparison including all samples from the study

[0189] (b) Comparison including samples filtered by a quality threshold

[0190] (c) Comparison including samples which have been frozen for less than 6 months

[0191] (d) Comparison including samples which derive from subjects over 41 years of age

[0192] (e) Comparison including samples which have been frozen for less than 6 months and which derive from subjects over 41 years of age

[0193] The complete list of the differentially expressed genes (DEGs) from the selected comparisons using liquid biopsy samples include 6 genes in common with the previous DEGs results of solid biopsy (WO2023061914) (Figure 2 and Table 1).

[0194] Table 1 : Common genes between solid and liquid biopsies results.

[0195] (a) Comparison including all samples from the study To identify DEGs in the comparison including all samples from the study, RNAseq analysis of 72 LM and 23 LMS tumor samples was performed. From this analysis, a total of 37 genes were found to be significantly differentially expressed between both groups of samples (LM and LMS), of which 30 genes were upregulated and 7 genes downregulated in LMS samples with respect to the reference value (Table 2 and Figure 3).

[0196] Table 2: DEGs between LMS and LM from all samples comparison.

[0197] Gene baseMean log2FoldChange IfcSE stat pvalue padj FLG 1458.11569 0.17675 0.03122 5.66178 0 0.00028 OR1 I1 108.20656 0.21001 0.03844 5.4638 0 0.00031 OR10K2 122.80325 0.22903 0.04203 5.44901 0 0.00031 HCLS1 285.78447 0.14157 0.02815 5.02942 0 0.00205 FLG2 1086.3725 0.17776 0.0355 5.00772 0 0.00205 EFNA4 83.1639 0.24018 0.05074 4.73337 0 0.00685 PKLR 391 .54662 0.16916 0.03789 4.4642 1 E-05 0.02136 ADAR 747.30069 0.09892 0.02288 4.32278 2E-05 0.02205 RPTN 342.53078 0.15408 0.03516 4.38271 1 E-05 0.02205 OR9G4 122.52472 0.16789 0.03872 4.33658 1 E-05 0.02205 OR10T2 94.54094 0.1886 0.0433 4.3561 1 E-05 0.02205 PVRL4 293.48144 0.19026 0.0432 4.40444 1 E-05 0.02205 KRTAP6-1 34.12688 0.3237 0.07443 4.34885 1 E-05 0.02205 ARID5B 407.01384 -0.0937 0.02176 -4.30613 2E-05 0.02208 OR6K2 129.24063 0.17656 0.04126 4.27878 2E-05 0.02331 MB21 D1 119.79638 -0.22914 0.05385 -4.2551 2E-05 0.0243 IGSF11 181.46701 0.12954 0.0308 4.20587 3E-05 0.02846 OR1 N1 84.21573 0.16927 0.04065 4.16373 3E-05 0.03236 OR10J3 121.68788 0.17343 0.04184 4.1446 3E-05 0.03333 RPS6 120.2214 -0.16791 0.04102 -4.09357 4E-05 0.03939 EPHB1 411.94434 0.10026 0.02456 4.08258 4E-05 0.03939 TNN 500.62113 0.12546 0.03081 4.07217 5E-05 0.03939 C7orf73 39.97076 -0.24711 0.06105 -4.04741 5E-05 0.04102 KLF6 403.47339 -0.11748 0.02907 -4.04069 5E-05 0.04102 NEUROD4 131.72981 0.15626 0.03875 4.0328 6E-05 0.04102 SLX4IP 190.38716 -0.15683 0.03916 -4.00514 6E-05 0.04271 COA7 80.04748 0.16853 0.04207 4.00587 6E-05 0.04271 ZNF333 336.82984 0.09638 0.0243 3.96596 7E-05 0.04447 SMG5 467.07943 0.10928 0.02749 3.97477 7E-05 0.04447 PLEK2 160.98117 0.13697 0.03456 3.96275 7E-05 0.04447 BBS1 37.47083 0.26249 0.06619 3.96575 7E-05 0.04447 HNRNPDL 113.97844 -0.164 0.0416 -3.94259 8E-05 0.04686 MUC1 179.57564 0.16248 0.04138 3.92612 9E-05 0.04867

[0198] TTC21A 595.56231 0.09918 0.02542 3.9016 0.0001 0.04906

[0199] DNM2 699.94025 0.12592 0.0322 3.91121 9E-05 0.04906

[0200] HRH1 143.954 0.15576 0.03997 3.89657 0.0001 0.04906

[0201] GPR52 98.11404 0.207 0.05309 3.8987 0.0001 0.04906

[0202] (b) Comparison including samples filtered by a quality threshold To identify DEGs in the comparison including samples filtered by a quality threshold, RNAseq analysis of 70 LM and 23 LMS tumor samples was performed. In this comparison, 2 LM samples, which had been frozen for more than 12 months, were filtered out due to having a dupRadar slope parameter < 8. This threshold allowed to maintain samples with a controlled level of duplicated reads while having a balanced number of samples and obtained results. From this analysis, a total of 55 genes were found to be significantly differentially expressed between both groups of samples, of which 44 genes were upregulated and 11 genes downregulated in LMS samples with respect to the reference value (Table 3 and Figure 4). Table 3: DEGs between LMS and LM from quality threshold comparison.

[0203] Gene baseMean log2FoldChange IfcSE stat pvalue padj FLG 1486.45103 0.18081 0.0313 5.77745 0 0.00014 OR10K2 125.39435 0.23102 0.04203 5.4969 0 0.00035 OR1 I1 110.97664 0.20411 0.03868 5.27697 0 0.0008 HCLS1 292.22579 0.14143 0.02847 4.96823 0 0.00308 EFNA4 84.5096 0.25062 0.05119 4.89641 0 0.00356 FLG2 1109.18896 0.17427 0.03588 4.85721 0 0.00362 KRTAP6-1 34.90909 0.33044 0.07482 4.41649 1 E-05 0.02088 PVRL4 299.48744 0.19245 0.04386 4.38789 1 E-05 0.02088 OR6K2 132.0266 0.17736 0.04034 4.39702 1 E-05 0.02088 RPTN 350.12285 0.15463 0.03484 4.43829 1 E-05 0.02088 SMG5 475.53875 0.11795 0.02715 4.34431 1 E-05 0.02317 OR10T2 96.75889 0.18667 0.0434 4.30132 2E-05 0.02383 PKLR 400.55186 0.16478 0.0382 4.31312 2E-05 0.02383 OR1 N1 85.88375 0.17228 0.04077 4.22594 2E-05 0.02554 ARID5B 415.46125 -0.09341 0.02209 -4.22857 2E-05 0.02554 KLF6 413.3715 -0.1231 0.02906 -4.23566 2E-05 0.02554 MB21 D1 122.00429 -0.229 0.05404 -4.23773 2E-05 0.02554 BBS1 37.64447 0.27572 0.06561 4.20243 3E-05 0.02677 OR9G4 125.65474 0.16356 0.03907 4.18601 3E-05 0.02726 ADAR 764.70354 0.09534 0.02291 4.16102 3E-05 0.0289 OR10J3 124.76643 0.17159 0.04196 4.08915 4E-05 0.0351 EPHB1 420.8083 0.10116 0.02477 4.08409 4E-05 0.0351 HNRNPDL 117.08058 -0.172 0.04193 -4.10213 4E-05 0.0351 IGSF11 185.32499 0.12804 0.03146 4.0697 5E-05 0.03579 RPS6 122.80457 -0.1705 0.04208 -4.0521 5E-05 0.03705 COA7 81.73413 0.17006 0.04224 4.02573 6E-05 0.03848 TNN 511.18487 0.12548 0.03117 4.02512 6E-05 0.03848 FCRL5 515.62365 0.09775 0.02437 4.01059 6E-05 0.03947 ZNF333 343.24612 0.09875 0.02474 3.99169 7E-05 0.04128 PAX6 452.45009 -0.09455 0.02379 -3.97401 7E-05 0.04298 B4GALT3 165.79708 0.15452 0.03896 3.96605 7E-05 0.04301 GPR52 100.61509 0.20367 0.05318 3.83017 0.00013 0.04776 ACER1 98.71037 0.16668 0.04335 3.84543 0.00012 0.04776 FAM189B 181.19829 0.16586 0.04325 3.83509 0.00013 0.04776 MUC1 183.41554 0.16307 0.04203 3.87956 0.0001 0.04776 SLC25A44 96.56203 0.16217 0.04229 3.83488 0.00013 0.04776 OR5AR1 88.9408 0.15853 0.04134 3.83533 0.00013 0.04776 DCST2 317.78392 0.15281 0.03995 3.82521 0.00013 0.04776 DENND4B 506.62558 0.15008 0.0384 3.90815 9E-05 0.04776 NEUROD4 135.5637 0.1486 0.03836 3.87398 0.00011 0.04776 OR5A2 124.06508 0.14697 0.03817 3.8507 0.00012 0.04776 POC1A 195.35978 0.14094 0.03685 3.82477 0.00013 0.04776 PLEK2 164.19125 0.13606 0.03499 3.88912 0.0001 0.04776 ATP8B2 512.12134 0.1299 0.03334 3.89668 0.0001 0.04776 DNM2 715.45034 0.12734 0.03278 3.88465 0.0001 0.04776 NCSTN 684.53344 0.105 0.02739 3.83332 0.00013 0.04776 LRRFIP1 549.51892 -0.11605 0.03 -3.86788 0.00011 0.04776 SLX4IP 194.16807 -0.15342 0.03954 -3.88024 0.0001 0.04776 C7orf73 40.55219 -0.235 0.06107 -3.84802 0.00012 0.04776 C4orf6 33.76979 -0.26744 0.06931 -3.85845 0.00011 0.04776 FLVCR2 397.48692 0.11467 0.03006 3.81502 0.00014 0.04872 ACKR2 110.62679 0.16615 0.0438 3.79337 0.00015 0.0493 HHATL 164.0402 0.15574 0.04095 3.80286 0.00014 0.0493 PTPRT 650.11231 0.0977 0.02573 3.79668 0.00015 0.0493 NUP50 182.27577 -0.13595 0.03577 -3.80026 0.00014 0.0493

[0204] (c) Comparison including samples which have been frozen for less than 6 months To identify DEGs in this comparison, RNAseq analysis of 19 LM and 7 LMS tumor samples which have been frozen for less than 6 months was performed. From this analysis, a total of 378 genes were found to be significantly differentially expressed between both groups of samples, of which 100 genes were upregulated and 278 genes downregulated in LMS samples with respect to the reference value (Tables 4 and 5 and Figure 5).

[0205] Table 4: DEGs between LMS and LM samples which have been frozen for less than 6 months (columns are separated by commas).

[0206] Gene baseMean log2FoldChange IfcSE stat pvalue padj MT-ND1 31.15932 2.27773 0.33108 6.87971 0 0 NHSL2 337.76049 -0.40681 0.06588 -6.17514 0 0 KCND1 211.34632 -0.44795 0.07135 -6.27848 0 0 MT-CO2 20.2197 2.32409 0.3821 6.08243 0 1 E-05 USP17L18 32.45514 1.03721 0.1745 5.94386 0 1 E-05 L1CAM 792.67814 -0.37129 0.06282 -5.90989 0 1 E-05 GATA1 187.41502 -0.45192 0.07694 -5.87371 0 1 E-05 FLNA 1170.72723 -0.46802 0.07866 -5.94963 0 1 E-05 NDUFA1 90.11203 -0.59598 0.09886 -6.02849 0 1 E-05 SAT1 81.5517 -0.61186 0.10488 -5.83369 0 1 E-05 CCDC22 242.97481 -0.38898 0.06839 -5.68773 0 2E-05 PIM2 104.4135 -0.5004 0.08828 -5.66829 0 2E-05 TBC1 D3B 70.99607 -1.04502 0.18586 -5.62258 0 3E-05 ATP2B3 468.8959 -0.42226 0.07705 -5.4802 0 5E-05 WDR13 127.62145 -0.5348 0.09771 -5.47318 0 5E-05 STARD8 384.48379 -0.36572 0.06762 -5.40855 0 7E-05 FAM155B 105.20249 -0.50006 0.09239 -5.41253 0 7E-05 GRIPAP1 409.66654 -0.37359 0.0696 -5.36783 0 8E-05 MBD3L1 53.37601 0.51579 0.0974 5.29531 0 0.00011 TCEAL1 31.12892 -0.78846 0.14841 -5.31278 0 0.00011 BCORL1 548.42461 -0.35042 0.06703 -5.22815 0 0.00015 OR2J1 85.61589 0.42436 0.08139 5.21383 0 0.00016 IQSEC2 557.66463 -0.33105 0.06388 -5.18265 0 0.00016 PRPS2 150.19095 -0.37751 0.07263 -5.19782 0 0.00016 TSPAN7 99.39778 -0.49277 0.0951 -5.18158 0 0.00016 KRTAP10-6 57.12107 0.54312 0.10531 5.15738 0 0.00017 TEX11 544.69999 -0.37859 0.07347 -5.15308 0 0.00017 CCL3L1 15.84417 -1.46076 0.2829 -5.16349 0 0.00017 SLC9B1 P1 8.33748 1.51273 0.29539 5.12112 0 0.00018 HBG1 25.73016 0.8114 0.15853 5.11828 0 0.00018 C16orf52 134.12027 0.39724 0.07759 5.1198 0 0.00018 FAM21 B 110.70743 -3.65654 0.73151 -4.9986 0 0.00034 PTCHD1 355.14199 -0.34612 0.06946 -4.98284 0 0.00035 PRRG3 93.03466 -0.52191 0.1053 -4.95653 0 0.00039 AVPR2 134.58496 -0.41524 0.08463 -4.9067 0 0.00048 CXorf64 93.48364 -0.47963 0.09771 -4.90843 0 0.00048 PSG3 186.84283 0.32492 0.06637 4.89534 0 0.00049 CT55 80.64772 -0.49538 0.10142 -4.88452 0 0.0005 TSC22D3 88.63042 -0.58805 0.1204 -4.88405 0 0.0005 SRPX 181.96947 -0.35681 0.07328 -4.86921 0 0.00052 WDR44 454.02164 -0.33615 0.06943 -4.84148 0 0.00058 SUV39H1 76.46488 -0.46329 0.09707 -4.77256 0 0.0008 RNF128 205.15648 -0.37191 0.0786 -4.73186 0 0.00093 CFP 212.33056 -0.38891 0.08213 -4.73511 0 0.00093 AWAT2 162.88806 -0.43005 0.09095 -4.72843 0 0.00093 LUZP4 175.00844 -0.35024 0.07424 -4.71754 0 0.00096 MAOA 567.72199 -0.31212 0.06623 -4.7128 0 0.00097 FTSJ1 282.07029 -0.36743 0.07811 -4.70413 0 0.00099 PRAMEF2 186.03488 -0.52581 0.112 -4.69455 0 0.00101 PRKX 148.47189 -0.34474 0.07353 -4.68814 0 0.00102 PSG2 188.71953 0.30875 0.06592 4.68355 0 0.00103 RHOXF1 49.04325 -0.51474 0.11006 -4.67696 0 0.00104 DOCK11 1049.67276 -0.33619 0.072 -4.66921 0 0.00106 TRIM65 116.05266 0.34264 0.07381 4.64199 0 0.00118 FGD1 516.45246 -0.33235 0.07164 -4.6394 0 0.00118 TAZ 293.00777 -0.33903 0.07318 -4.63289 0 0.0012 HAUS1 141.28082 -0.3649 0.07893 -4.62293 0 0.00121 OTC 385.31681 -0.37223 0.08046 -4.62643 0 0.00121 FAM211A 78.53169 0.39232 0.08506 4.61216 0 0.00125 RNASE13 23.66783 0.64827 0.14185 4.57023 0 0.00151 FRMPD3 581.40043 -0.30458 0.06713 -4.53716 1 E-05 0.00174 FUNDC1 84.09754 -0.42788 0.09475 -4.51573 1 E-05 0.00189 SMS 369.13328 -0.36753 0.08177 -4.49476 1 E-05 0.00205 OPA3 63.25833 0.51418 0.11459 4.48715 1 E-05 0.0021 PSG5 182.68956 0.35216 0.07933 4.439 1 E-05 0.00256 GPKOW 185.45024 -0.38642 0.08707 -4.43803 1 E-05 0.00256 RBMX2 114.65648 -0.38566 0.08719 -4.42325 1 E-05 0.00266 RPL10 156.42212 -0.39627 0.0896 -4.42259 1 E-05 0.00266 NDUFB11 44.03605 -0.58175 0.13194 -4.40926 1 E-05 0.00279 GPR52 100.35347 0.34859 0.07929 4.39663 1 E-05 0.00284 FLG2 1137.30557 0.19984 0.0454 4.40154 1 E-05 0.00284 CXorf40B 69.98453 -0.51601 0.11734 -4.39755 1 E-05 0.00284 TKTL1 286.57103 -0.29864 0.06803 -4.39001 1 E-05 0.00288 FAM90A1 367.24366 -0.3263 0.07454 -4.37743 1 E-05 0.00301 GJB1 77.27936 -0.49687 0.11432 -4.3462 1 E-05 0.00343 ZFX 429.90822 -0.28213 0.06517 -4.32885 1 E-05 0.00362 PIR 209.34383 -0.34033 0.07859 -4.33023 1 E-05 0.00362 SRPK3 171.10616 -0.31367 0.07252 -4.32528 2E-05 0.00363 MB21 D1 119.56685 -0.34487 0.07987 -4.3178 2E-05 0.00371 HSDL2 182.44055 -0.2595 0.06036 -4.29922 2E-05 0.00379 ARRDC4 174.86941 -0.28429 0.06612 -4.29963 2E-05 0.00379 FOXO4 168.48099 -0.31798 0.07386 -4.30509 2E-05 0.00379 HMGN5 119.86672 -0.37914 0.08816 -4.30086 2E-05 0.00379 FCGR3B 49.10344 -0.97265 0.22615 -4.30096 2E-05 0.00379 CD40LG 216.34862 -0.35608 0.08289 -4.29589 2E-05 0.0038 BEST4 94.35534 -0.38034 0.08907 -4.27031 2E-05 0.00422 MCF2 602.5511 -0.29979 0.07065 -4.24358 2E-05 0.0047 SCAND1 10.64838 0.96235 0.2271 4.23751 2E-05 0.00472 FAM122B 178.09847 -0.32263 0.07614 -4.23734 2E-05 0.00472 CHDC2 256.37368 -0.35604 0.08423 -4.22671 2E-05 0.00487 OTUD6A 62.70451 -0.45371 0.10741 -4.22404 2E-05 0.00487 NPIPB6 102.92334 -0.4796 0.11357 -4.22296 2E-05 0.00487 ACE2 392.32564 -0.29491 0.06993 -4.21727 2E-05 0.00494 NONO 209.60103 -0.28368 0.0674 -4.20886 3E-05 0.00507 HLA-A 59.40584 0.56031 0.13346 4.19846 3E-05 0.00526 ELF4 351.75627 -0.27377 0.0654 -4.18595 3E-05 0.0055 JOSD2 43.59233 0.51669 0.12392 4.16962 3E-05 0.00584 CXorf22 380.58417 -0.30403 0.07317 -4.15524 3E-05 0.0061 PNMA3 170.83486 -0.33728 0.08114 -4.15688 3E-05 0.0061 PCDHA4 152.25338 0.27559 0.06649 4.14507 3E-05 0.00631 UBQLN2 197.07479 -0.31621 0.07635 -4.14183 3E-05 0.00634 CHRNA10 102.73351 0.33223 0.08044 4.13042 4E-05 0.00635 TLR8 298.54575 -0.26022 0.06299 -4.13086 4E-05 0.00635 POLA1 798.70115 -0.27622 0.06685 -4.13187 4E-05 0.00635 TBC1 D8B 508.78564 -0.34436 0.08332 -4.13272 4E-05 0.00635 TIMM8A 78.9981 -0.4317 0.10436 -4.1366 4E-05 0.00635 IDH3G 189.60997 -0.31252 0.07623 -4.09961 4E-05 0.00719 VSIG1 190.39869 -0.29688 0.07262 -4.08823 4E-05 0.00748 APOO 159.79247 -0.35381 0.08668 -4.0817 4E-05 0.00755 FAM3A 161.58332 -0.35877 0.08785 -4.08372 4E-05 0.00755 DMD 3102.43735 -0.26545 0.06514 -4.07479 5E-05 0.00764 BOLL 185.27323 -0.27445 0.06734 -4.07555 5E-05 0.00764 TAGLN2 92.58855 0.33704 0.08301 4.06008 5E-05 0.00804 NAP1 L3 135.56791 -0.34169 0.08418 -4.05878 5E-05 0.00804 HS6ST2 150.68337 -0.3094 0.07627 -4.0564 5E-05 0.00805 TXLNG 204.7201 -0.27142 0.06704 -4.04897 5E-05 0.00824 BRCC3 187.90731 -0.30953 0.07653 -4.04444 5E-05 0.00833 DNASE1 L1 48.57917 -0.54118 0.13389 -4.0419 5E-05 0.00835 FAM120C 451.23236 -0.25111 0.06224 -4.03436 5E-05 0.0085 TMEM187 76.30269 -0.40937 0.10149 -4.03375 5E-05 0.0085 ZNF280C 367.0461 -0.33148 0.08248 -4.019 6E-05 0.00897 ZBTB33 193.67958 -0.29047 0.07236 -4.01417 6E-05 0.00901 FAM47A 192.42404 -0.30922 0.077 -4.01605 6E-05 0.00901 PASD1 276.20554 -0.32567 0.08117 -4.01233 6E-05 0.00901 ZNF547 149.01798 0.27431 0.06845 4.00742 6E-05 0.00912 HLA-DOB 34.99197 0.48115 0.12026 4.00104 6E-05 0.0093 LRP5L 153.06081 0.33529 0.08393 3.99506 6E-05 0.00939 OCRL 852.74224 -0.2463 0.06163 -3.99649 6E-05 0.00939 DCAF12L2 83.68357 -0.375 0.09438 -3.97346 7E-05 0.0102 B3GNT6 29.20879 0.57423 0.14468 3.96894 7E-05 0.01027 OR8H2 87.63052 0.35233 0.08879 3.96808 7E-05 0.01027 HCCS 234.45876 -0.29554 0.07459 -3.9623 7E-05 0.01037 MAGEC2 96.64483 -0.35032 0.08839 -3.96349 7E-05 0.01037 PLS3 376.70162 -0.34338 0.08678 -3.95701 8E-05 0.01052 ARSH 229.72064 -0.29113 0.07361 -3.95498 8E-05 0.01053 POTEC 308.25265 -0.26454 0.06694 -3.95204 8E-05 0.01058 TANGO2 127.81251 0.33184 0.08405 3.94823 8E-05 0.0106 HDAC6 520.37296 -0.24657 0.06245 -3.94809 8E-05 0.0106 FAM47C 286.29155 -0.29443 0.0746 -3.94654 8E-05 0.0106 CYBB 548.05348 -0.28951 0.0734 -3.94439 8E-05 0.01062 DUSP9 63.02302 -0.46454 0.11789 -3.94065 8E-05 0.01071 KRTAP5-10 38.37645 0.52294 0.133 3.93186 8E-05 0.01078 SLIT1 33.6224 0.48969 0.12459 3.93043 8E-05 0.01078 MUC4 891.52779 0.246 0.06261 3.92903 9E-05 0.01078 DCTN3 140.5943 -0.28341 0.07206 -3.93292 8E-05 0.01078 IRAKI 207.60107 -0.33158 0.08446 -3.92569 9E-05 0.01078 ATOH1 35.92665 -0.53155 0.13537 -3.92655 9E-05 0.01078 PRR23D1 25.22845 -0.82631 0.21012 -3.93253 8E-05 0.01078 C3orf80 10.22808 -1.08434 0.27583 -3.9312 8E-05 0.01078 C20orf202 28.62355 0.55634 0.14178 3.92391 9E-05 0.01079 RPS3A 56.49118 -0.40387 0.10298 -3.92177 9E-05 0.01081 TBC1 D28 85.75607 0.37734 0.09628 3.91932 9E-05 0.01085 SLC16A8 54.10367 0.45662 0.11659 3.91655 9E-05 0.01091 GAB3 221.98657 -0.29076 0.07437 -3.90954 9E-05 0.01116 CXorf67 86.87845 -0.38892 0.09959 -3.90517 9E-05 0.01129 SLC9A6 544.08375 -0.29315 0.07525 -3.89546 0.0001 0.01165 TFF2 64.0809 -0.38613 0.09915 -3.89435 0.0001 0.01165 CTPS2 377.91465 -0.26521 0.0682 -3.88867 0.0001 0.01185 LZIC 120.42089 0.2943 0.07572 3.88674 0.0001 0.01187 PHKA1 1251.69142 -0.28134 0.07248 -3.88157 0.0001 0.01205 CNKSR2 545.66536 -0.26936 0.06945 -3.87833 0.00011 0.01214 C4orf48 12.60875 0.76577 0.1976 3.87527 0.00011 0.01218 OR10H3 90.29974 0.29124 0.07517 3.87444 0.00011 0.01218 HCN2 107.68994 -0.30764 0.07957 -3.86629 0.00011 0.01252 FAM230A 47.02168 0.5067 0.13117 3.86298 0.00011 0.01254 ATP11C 633.41774 -0.27744 0.07182 -3.86314 0.00011 0.01254 FKBP1A 50.33631 0.39117 0.10149 3.85414 0.00012 0.01292 CXorf57 324.28556 -0.27903 0.07248 -3.84989 0.00012 0.01299 PPP3R2 54.0519 -0.39895 0.10359 -3.85141 0.00012 0.01299 HTATSF1 268.37687 -0.26885 0.06988 -3.84756 0.00012 0.01304 GDI1 328.07843 -0.29312 0.07622 -3.84571 0.00012 0.01306 TMSB15A 47.10065 -0.47115 0.12288 -3.83429 0.00013 0.0136 KLF8 167.08752 -0.27271 0.07117 -3.83181 0.00013 0.01366 ITIH6 432.94886 -0.25355 0.06651 -3.81193 0.00014 0.01464 PRDX4 170.45656 -0.29997 0.07867 -3.8132 0.00014 0.01464 UBE2NL 58.22869 -0.38303 0.1007 -3.80384 0.00014 0.01495 HIST1 H2AB 30.16036 -0.57432 0.15095 -3.80465 0.00014 0.01495 IL17D 8.90737 0.8337 0.21945 3.79913 0.00015 0.01507 FMR1 NB 118.06902 -0.3119 0.08208 -3.79973 0.00014 0.01507 FAM212B 104.83328 0.31441 0.08289 3.79325 0.00015 0.01534 RGAG4 119.86271 -0.3434 0.09056 -3.79196 0.00015 0.01534 PORCN 414.24019 -0.26703 0.07055 -3.78481 0.00015 0.01565 CBLN1 68.87156 -0.35455 0.09369 -3.78427 0.00015 0.01565 ZNF671 144.13076 0.24467 0.0647 3.7814 0.00016 0.01574 NUDT11 37.06305 -0.47562 0.12608 -3.7723 0.00016 0.01624 KDM6A 1042.13898 -0.25846 0.06857 -3.76905 0.00016 0.01637 ARHGAP4 226.96162 -0.30479 0.08097 -3.76439 0.00017 0.01641 CYLC1 203.56831 -0.35275 0.09367 -3.76573 0.00017 0.01641 OCLM 26.92075 -0.58781 0.15615 -3.76442 0.00017 0.01641 TFDP3 124.59399 -0.2918 0.07757 -3.76165 0.00017 0.0165 SDHAF1 8.98845 0.84486 0.2249 3.75661 0.00017 0.01675 ZNF81 367.53342 -0.27654 0.07367 -3.75378 0.00017 0.01685 GLA 323.08751 -0.25584 0.06818 -3.75225 0.00018 0.01687 C7orf71 76.51124 0.31415 0.08381 3.74822 0.00018 0.01705 GUK1 95.77112 0.3443 0.09193 3.74521 0.00018 0.01717 FAM217B 107.90028 0.27462 0.07358 3.73228 0.00019 0.01799 ZNF645 130.87937 -0.31345 0.08415 -3.72483 0.0002 0.01834 TMEM27 119.25303 -0.3194 0.08574 -3.72512 0.0002 0.01834 C19orf35 20.03834 0.70818 0.19054 3.71679 0.0002 0.01884 LACTBL1 67.9708 0.37251 0.10041 3.70974 0.00021 0.01927 OR52B6 93.94985 0.31259 0.08439 3.70417 0.00021 0.0196 KCNJ4 42.70677 0.43827 0.11847 3.69944 0.00022 0.01977 CXorf23 321.15698 -0.28325 0.07654 -3.70051 0.00022 0.01977 GK 602.80958 -0.30494 0.08249 -3.69687 0.00022 0.01988 LONRF2 263.19324 -0.1885 0.05106 -3.69213 0.00022 0.02015 PAK3 555.40271 -0.27034 0.07326 -3.6902 0.00022 0.02021 UXT 69.8148 -0.3954 0.10721 -3.68819 0.00023 0.02027 HDAC8 358.75452 -0.22329 0.06066 -3.68082 0.00023 0.02071 LRCH2 318.12773 -0.30158 0.08195 -3.68021 0.00023 0.02071 ADIPOR1 145.26862 -0.28067 0.0763 -3.67837 0.00023 0.02076 RNF185 122.86874 0.24629 0.06698 3.67714 0.00024 0.02077 AMER1 371.98414 -0.2572 0.07008 -3.67032 0.00024 0.02113 LHFPL1 91.01003 -0.35718 0.09729 -3.67134 0.00024 0.02113 EMD 105.23142 -0.37158 0.1015 -3.66102 0.00025 0.02181 HIST1 H3H 23.94268 0.61392 0.16786 3.65733 0.00025 0.02182 CACNA1 F 1361.07181 -0.2496 0.06824 -3.65759 0.00025 0.02182 CENPM 99.80098 -0.3039 0.08309 -3.65742 0.00025 0.02182 CACNG5 123.90538 -0.28573 0.07824 -3.65217 0.00026 0.02216 FAM209A 37.30314 0.4348 0.11927 3.64554 0.00027 0.02233 ZBED6 105.82195 0.26891 0.07374 3.64697 0.00027 0.02233 OFD1 796.43072 -0.28002 0.07676 -3.64789 0.00026 0.02233 CTAGE6 95.24419 -0.63045 0.1729 -3.64633 0.00027 0.02233 KRTAP4-4 20.28861 0.71923 0.19755 3.64077 0.00027 0.02249 OR7D4 130.33322 0.25333 0.06956 3.64169 0.00027 0.02249 P2RY4 88.48628 -0.36282 0.09967 -3.64024 0.00027 0.02249 LONRF3 305.9904 -0.21714 0.0597 -3.63698 0.00028 0.02262 LAS1 L 299.76639 -0.28069 0.07719 -3.63652 0.00028 0.02262 RFC5 222.47678 -0.20872 0.05742 -3.63509 0.00028 0.02264 F8 1411.05276 -0.25239 0.06948 -3.63249 0.00028 0.02268 FGL1 129.42962 -0.28142 0.07747 -3.6324 0.00028 0.02268 FAM104B 89.0675 -0.33092 0.09114 -3.63092 0.00028 0.02271 FAM133A 76.19086 -0.34783 0.09588 -3.62764 0.00029 0.0229 MRPL2 109.53556 -0.29336 0.08108 -3.61803 0.0003 0.02367 OPN1MW 29.36525 -1.29013 0.35674 -3.61642 0.0003 0.02371 CTAG2 54.95942 -0.53765 0.14875 -3.61437 0.0003 0.0238 CAPN6 290.01204 -0.26928 0.07462 -3.60848 0.00031 0.02414 SPAG11 B 100.79713 -0.48594 0.13465 -3.60885 0.00031 0.02414 C1 orf210 38.84506 0.42079 0.11673 3.60475 0.00031 0.02439 CENPI 399.50202 -0.293 0.08134 -3.60234 0.00032 0.02441 PPP1 R14A 42.52071 -0.43019 0.11939 -3.60317 0.00031 0.02441 EDA 285.36926 -0.2808 0.07799 -3.6003 0.00032 0.0245 NAA10 78.89488 -0.32551 0.09046 -3.59856 0.00032 0.02456 C10orf82 107.27524 -0.27174 0.07557 -3.59603 0.00032 0.0247 G6PD 261.74516 -0.29438 0.08206 -3.58729 0.00033 0.02544 CYSLTR1 112.09995 -0.32729 0.09128 -3.58551 0.00034 0.02551 TLR7 355.00292 -0.24431 0.06818 -3.58323 0.00034 0.02552 ZNF275 130.08923 -0.39891 0.11129 -3.58428 0.00034 0.02552 GREM2 37.68735 0.48373 0.13505 3.5818 0.00034 0.02556 SSB 198.49983 -0.24917 0.06965 -3.57744 0.00035 0.02571 IL1 RAPL2 291.20518 -0.26481 0.07402 -3.5777 0.00035 0.02571 FAM199X 150.53219 -0.26929 0.07528 -3.5771 0.00035 0.02571 TCEAL4 132.47625 -0.26565 0.07433 -3.57396 0.00035 0.02573 CUL4B 676.23351 -0.28488 0.07971 -3.57383 0.00035 0.02573 GBX1 83.41772 -0.32222 0.09014 -3.57482 0.00035 0.02573 PHF6 283.56016 -0.2935 0.08217 -3.57186 0.00035 0.02582 BRWD3 1347.59232 -0.26713 0.07484 -3.56961 0.00036 0.02594 PTDSS2 179.08695 0.24543 0.06878 3.56852 0.00036 0.02595 SLITRK4 265.14823 -0.25845 0.07245 -3.56726 0.00036 0.02597 RAB42 20.66735 0.53975 0.15152 3.56213 0.00037 0.02617 EDA2R 136.99279 -0.27315 0.07668 -3.56197 0.00037 0.02617 RPS6KA6 395.85404 -0.30337 0.08516 -3.56233 0.00037 0.02617 MAGEB6 97.44217 -0.32787 0.09207 -3.56129 0.00037 0.02617 LRRC27 311 .44364 -0.19232 0.05403 -3.55979 0.00037 0.02622 SEC16B 20.70249 -0.59535 0.1673 -3.55854 0.00037 0.02624 POF1 B 311.51465 -0.30092 0.08475 -3.5506 0.00038 0.02694 SCD 170.12531 -0.22907 0.06459 -3.54669 0.00039 0.02724 ZNF75D 246.57283 -0.26213 0.07393 -3.54565 0.00039 0.02725 CNNM1 254.32335 -0.19753 0.05573 -3.54426 0.00039 0.02729 OR4M2 82.26879 -0.78918 0.22357 -3.52988 0.00042 0.02871 OR1J4 94.69592 0.26954 0.07639 3.52869 0.00042 0.02873 AMOT 338.23579 -0.25583 0.07253 -3.52743 0.00042 0.02876 RPS6KA3 687.09273 -0.28461 0.08077 -3.52367 0.00043 0.02907 PSMA3 187.18292 0.26049 0.07408 3.51642 0.00044 0.02919 ILF3 412.3347 0.16395 0.04661 3.51725 0.00044 0.02919 GPC4 224.37032 -0.24924 0.07089 -3.51584 0.00044 0.02919 DDX26B 377.11094 -0.26276 0.07462 -3.52129 0.00043 0.02919 HTR2C 175.83852 -0.27004 0.07673 -3.51921 0.00043 0.02919 MAGEB2 104.54339 -0.33599 0.09551 -3.51767 0.00044 0.02919 POTEG 308.88517 -0.45205 0.12844 -3.5196 0.00043 0.02919 CLCN6 419.12924 0.1878 0.05346 3.5128 0.00044 0.02942 ARMCX1 117.38886 -0.29103 0.08302 -3.50549 0.00046 0.03013 RAB9A 74.3725 -0.32522 0.09288 -3.50152 0.00046 0.03048 SMIM8 15.18068 0.64205 0.18368 3.49544 0.00047 0.03091 GPR119 113.61673 -0.27185 0.07778 -3.49489 0.00047 0.03091 THOC2 755.50097 -0.27726 0.07932 -3.4956 0.00047 0.03091 FUCA2 97.99063 -0.27296 0.07813 -3.49375 0.00048 0.03094 PPEF1 329.35403 -0.23429 0.06713 -3.48994 0.00048 0.03127 COLGALT2 313.41636 0.17973 0.05154 3.4875 0.00049 0.03145 KRT74 364.47172 0.19814 0.05687 3.48421 0.00049 0.03173 SPDYE3 141.84732 0.24642 0.07081 3.4802 0.0005 0.0321 ARL17B 24.351 0.52993 0.15232 3.47901 0.0005 0.03213 PLXNA3 446.50617 -0.27107 0.07794 -3.47801 0.00051 0.03214 DOK3 77.82812 0.45173 0.13035 3.46539 0.00053 0.03328 TMEM150B 68.87117 0.34222 0.09871 3.46681 0.00053 0.03328 TTC8 539.4497 0.24666 0.07119 3.46497 0.00053 0.03328 CA5B 169.33132 -0.24753 0.07143 -3.46508 0.00053 0.03328 SCRN2 131.3187 0.23948 0.06929 3.45616 0.00055 0.03413 C6orf89 175.73012 -0.20933 0.06058 -3.45531 0.00055 0.03413 SRPX2 430.80785 -0.23122 0.06694 -3.45414 0.00055 0.03413 MSRA 168.88571 -0.23627 0.06841 -3.45367 0.00055 0.03413 ABCB7 627.91181 -0.27491 0.07957 -3.4551 0.00055 0.03413 FAM169A 263.40099 -0.22012 0.06377 -3.45194 0.00056 0.03424 OR4D9 72.59531 0.29854 0.08655 3.44921 0.00056 0.0344 ARMCX5 178.47575 -0.26608 0.07715 -3.44891 0.00056 0.0344 HDHD1 111 .77432 -0.26375 0.07655 -3.4456 0.00057 0.03471 XCR1 96.54798 0.28308 0.08221 3.44354 0.00057 0.03486 AMMECR1 120.30791 -0.27406 0.07963 -3.4418 0.00058 0.03497 SYAP1 172.81036 -0.24889 0.0724 -3.43757 0.00059 0.03535 ZDHHC15 255.09146 -0.26158 0.07612 -3.43624 0.00059 0.03535 GABRQ 270.00494 -0.26672 0.07761 -3.43657 0.00059 0.03535 PCDHGB7 163.4294 -0.22755 0.06627 -3.43375 0.0006 0.03556 NBPF11 46.17654 0.37068 0.10829 3.42303 0.00062 0.03664 RBM10 636.28496 -0.23381 0.0683 -3.42356 0.00062 0.03664 APLN 60.26346 -0.38157 0.11142 -3.42474 0.00062 0.03664 GSPT2 182.4416 -0.24539 0.07174 -3.42068 0.00062 0.03678 IL1 RAPL1 443.48564 -0.29688 0.0868 -3.42022 0.00063 0.03678 MORC4 438.66365 -0.22071 0.06456 -3.41867 0.00063 0.03688 NRK 752.15504 -0.23343 0.06832 -3.41702 0.00063 0.03699 TCEAL2 64.30961 -0.35331 0.10367 -3.40791 0.00065 0.03812 ELK1 147.04865 -0.33329 0.0979 -3.4042 0.00066 0.0384 GSTM1 45.70759 -3.64304 1.06995 -3.40487 0.00066 0.0384 WAS 323.8294 -0.27495 0.0808 -3.40301 0.00067 0.03845 LILRB1 270.53951 0.23219 0.06826 3.4015 0.00067 0.03854 HS3ST3A1 27.73456 0.50599 0.14883 3.39968 0.00067 0.03865 OR5J2 89.98413 0.26451 0.07782 3.39908 0.00068 0.03865 ANKRD49 88.39446 -0.28699 0.08446 -3.39806 0.00068 0.03867 UGT2B10 268.2129 0.26158 0.077 3.39708 0.00068 0.03869 ORAI2 78.7767 0.35289 0.1041 3.38976 0.0007 0.03962 EMC2 181.79041 -0.26359 0.07779 -3.38868 0.0007 0.03966 OR4D1 108.6855 0.24985 0.07377 3.38691 0.00071 0.03972 GSTT2 29.86661 -0.61289 0.18097 -3.3866 0.00071 0.03972 AWAT1 183.10344 -0.22896 0.06767 -3.38369 0.00072 0.04002 DKC1 513.8922 -0.23284 0.06892 -3.37823 0.00073 0.0407 KLHL41 125.49435 -0.24796 0.07347 -3.37519 0.00074 0.04103 GOLGA8F 50.0643 0.42657 0.12642 3.37432 0.00074 0.04104 DDX3X 321.32117 -0.23103 0.0685 -3.37285 0.00074 0.04113 FAM86C1 94.23876 0.29669 0.08801 3.37123 0.00075 0.04125 ZNF398 218.37476 -0.19903 0.05907 -3.36954 0.00075 0.04138 USP9X 1222.34039 -0.2448 0.07275 -3.36499 0.00077 0.04195 ZNF576 25.36955 0.51694 0.15367 3.36392 0.00077 0.04199 MNT 64.90443 0.31825 0.09467 3.36167 0.00077 0.04221 PJA1 205.32441 -0.24266 0.07221 -3.36023 0.00078 0.0423 HSPA2 144.94785 -0.24267 0.07223 -3.35947 0.00078 0.0423 RPGR 838.67785 -0.23037 0.06872 -3.35214 0.0008 0.0433 HIST1 H2BB 22.38595 0.4881 0.14566 3.351 0.00081 0.04336 ABHD17A 43.55561 0.39261 0.1172 3.34984 0.00081 0.04341 C17orf78 143.98894 -0.24624 0.07355 -3.34798 0.00081 0.04358 FAAH2 283.65056 -0.21345 0.06397 -3.33676 0.00085 0.04504 LACC1 134.95529 -0.24986 0.07489 -3.33644 0.00085 0.04504 ARX 89.17148 -0.33528 0.10049 -3.33655 0.00085 0.04504 H1 FNT 49.05727 -0.38045 0.1141 -3.33427 0.00086 0.04526 FAM58A 153.8843 -0.24835 0.07453 -3.33243 0.00086 0.04544 ZC3H12A 135.51691 0.24989 0.07505 3.32973 0.00087 0.04575 SLC25A34 73.64229 0.3515 0.10566 3.32661 0.00088 0.04579 CSTF2 336.85251 -0.23 0.06914 -3.32664 0.00088 0.04579 BGN 133.90353 -0.26913 0.08088 -3.3275 0.00088 0.04579 PLP2 129.34936 -0.27476 0.0826 -3.32632 0.00088 0.04579 GOLGA8A 71.08974 0.35976 0.10839 3.31924 0.0009 0.04645 HYAL1 131.23334 0.26806 0.08073 3.32066 0.0009 0.04645 PLD3 161.75084 0.23312 0.0702 3.32075 0.0009 0.04645 MIS18A 81.12601 -0.30383 0.09154 -3.31922 0.0009 0.04645 CTSD 42.95032 0.39274 0.11855 3.31273 0.00092 0.0468 TRIM25 217.52198 -0.19883 0.05999 -3.31418 0.00092 0.0468 FAM122C 246.23059 -0.22288 0.06721 -3.31625 0.00091 0.0468 ATP6AP2 316.88173 -0.24303 0.0733 -3.31555 0.00091 0.0468 CXorf65 107.62189 -0.27139 0.08193 -3.3125 0.00092 0.0468 MMGT1 101.20806 -0.28922 0.08731 -3.31273 0.00092 0.0468 PSRC1 92.11508 0.28022 0.08464 3.31085 0.00093 0.04683 MORF4L2 72.17488 -0.37345 0.1128 -3.31089 0.00093 0.04683 GLTPD1 55.34123 0.33212 0.10042 3.30729 0.00094 0.04692 VOPP1 130.14989 0.22106 0.06684 3.3073 0.00094 0.04692 MRS2 213.7063 -0.21361 0.06457 -3.30803 0.00094 0.04692 TOMM22 42.55904 -0.3899 0.11784 -3.30867 0.00094 0.04692 CASK 925.60973 -0.23192 0.07023 -3.30256 0.00096 0.04759 HCFC1 438.09755 -0.23146 0.07017 -3.29871 0.00097 0.04812 CPLX1 32.9287 0.4219 0.12795 3.29745 0.00098 0.0482 PPM1 L 163.07628 -0.21052 0.06391 -3.29423 0.00099 0.04863 DIAPH2 609.29797 -0.24175 0.07357 -3.28591 0.00102 0.04996

[0207] Table 5: DEGs between LMS and LM samples which have been frozen for less than 6 months with a |log2FC| value > 2. log2FoldC gene baseMean IfcSE stat pvalue padj hange

[0208] MT-ND1 31.15932 2.27773 0.33108 6.87971 0 0

[0209] MT-CO2 20.2197 2.32409 0.3821 6.08243 0 1 E-05

[0210] FAM21 B 110.70743 -3.65654 0.73151 -4.9986 0 0.00034

[0211] GSTM1 45.70759 -3.64304 1.06995 -3.40487 0.00066 0.0384

[0212] (d) Comparison including samples which derive from subjects over 41 years of age

[0213] To identify DEGs in this comparison, RNAseq analysis of 57 LM and 23 LMS tumor samples which derive from subjects over 41 years of age was performed. From this analysis, a total of 13 genes were found to be significantly differentially expressed between both groups of samples, of which 12 genes were upregulated and 1 gene downregulated in LMS samples with respect to the reference value (Table 6 and Figure 6).

[0214] Table 6: DEGs between LMS and LM samples which derive from subjects over 41 years of age. log2Fold gene baseMean IfcSE stat pvalue padj Change

[0215] FLG 1530.66941 0.18309 0.0326 5.61691 0 0.00035

[0216] OR10K2 129.97238 0.22422 0.04211 5.32494 0 0.00068

[0217] OR1 I1 114.10074 0.21231 0.04001 5.30652 0 0.00068

[0218] HCLS1 300.77477 0.14164 0.02922 4.84683 0 0.00572

[0219] FLG2 1138.49528 0.18097 0.03797 4.76604 0 0.00686

[0220] OR1 N1 88.08035 0.19023 0.04205 4.52403 1 E-05 0.01845

[0221] EFNA4 87.19575 0.23136 0.05352 4.3231 2E-05 0.03679

[0222] OR9G4 128.81614 0.17196 0.04034 4.26305 2E-05 0.03679

[0223] RPTN 361 .23496 0.15221 0.03569 4.26449 2E-05 0.03679

[0224] PAX6 464.04178 -0.10252 0.02378 -4.31128 2E-05 0.03679

[0225] OR8U1 105.07897 0.18247 0.04347 4.19776 3E-05 0.04471

[0226] OR6K2 135.77939 0.17734 0.04248 4.17477 3E-05 0.04535

[0227] GPR52 102.87349 0.22309 0.05395 4.13556 4E-05 0.04969

[0228] (e) Comparison including samples which have been frozen for less than 6 months and which derive from subjects over 41 years of age

[0229] To identify DEGs in this comparison, RNAseq analysis of 16 LM and 7 LMS tumor samples which have been frozen for less than 6 months and which derive from subjects over 41 years of age was performed. From this analysis, a total of 234 genes were found to be significantly differentially expressed between both groups of samples, of which 70 genes were upregulated and 164 genes downregulated in LMS samples with respect to the reference value (Tables 7 and 8 and Figure 7).

[0230] Table 7: DEGs between LMS and LM samples which have been frozen for less than 6 months and which derive from subjects over 41 years of age (columns are separated by commas). gene baseMean log2FoldChange IfcSE stat pvalue Padj

[0231] MT-CO2 21.5892 2.67223 0.37343 7.15591 0 0

[0232] MT-ND1 33.977 2.4962 0.34665 7.20083 0 0

[0233] NDUFA1 92.52969 -0.61468 0.09697 -6.33865 0 0

[0234] USP17L18 29.7478 1.01398 0.16739 6.05759 0 1 E-05

[0235] TBC1 D3B 71.49943 -1.07624 0.18239 -5.90066 0 1 E-05

[0236] KCND1 211.83612 -0.42998 0.07529 -5.71069 0 3E-05 gene baseMean log2FoldChange IfcSE stat pvalue padj SAT1 82.89351 -0.60487 0.11068 -5.465 0 0.00012 NHSL2 340.64552 -0.37648 0.06964 -5.40648 0 0.00015 HBG1 25.48289 0.85507 0.16011 5.34038 0 0.00019 CCDC22 248.01565 -0.38974 0.07349 -5.30322 0 0.00019 PIM2 104.4867 -0.48957 0.09197 -5.32298 0 0.00019 L1CAM 807.73309 -0.35164 0.06764 -5.19834 0 0.00029 GATA1 189.97946 -0.43138 0.08288 -5.20518 0 0.00029 KRTAP10-6 58.18167 0.57642 0.11188 5.15235 0 0.00034 C16orf52 136.06701 0.4227 0.08262 5.1164 0 0.00034 FLNA 1189.51578 -0.43732 0.08525 -5.12988 0 0.00034 FAM155B 107.2491 -0.48087 0.09385 -5.12375 0 0.00034 FAM21 B 122.51251 -3.75009 0.7347 -5.10424 0 0.00034 PRAMEF2 188.76739 -0.51838 0.10199 -5.08274 0 0.00036 MBD3L1 55.86779 0.50569 0.09979 5.06772 0 0.00037 BCORL1 556.56737 -0.35518 0.07117 -4.99061 0 0.00053 WDR13 128.12812 -0.5121 0.10356 -4.94495 0 0.00064 TCEAL1 30.77505 -0.74617 0.1515 -4.9253 0 0.00068 TSPAN7 99.7434 -0.48653 0.09996 -4.86742 0 0.00088 CT55 82.13292 -0.50917 0.10521 -4.83932 0 0.00097 PRPS2 153.31321 -0.38251 0.0794 -4.81737 0 0.00104 ATP2B3 474.78369 -0.39905 0.08318 -4.79769 0 0.0011 OR2J1 88.21471 0.41187 0.08598 4.79017 0 0.00111 IQSEC2 566.35305 -0.32383 0.06786 -4.77181 0 0.00117 SUV39H1 78.2061 -0.46947 0.09869 -4.75721 0 0.00122 PSG3 191.18784 0.32973 0.07017 4.69894 0 0.00147 STARD8 390.09364 -0.34197 0.07276 -4.70022 0 0.00147 TEX11 550.37103 -0.36849 0.07828 -4.70747 0 0.00147 RHOXF1 50.2557 -0.52795 0.11314 -4.66636 0 0.00168 GRIPAP1 416.48632 -0.34615 0.07493 -4.6195 0 0.00204 SLC9B1 P1 8.55285 1.37589 0.30106 4.57011 0 0.00223 RNASE13 23.4963 0.6854 0.14916 4.59517 0 0.00223 PSG5 184.16839 0.36564 0.08005 4.56782 0 0.00223 FAM90A1 375.40361 -0.3491 0.0763 -4.57519 0 0.00223 PRRG3 93.74062 -0.50299 0.10966 -4.58677 0 0.00223 TSC22D3 90.9068 -0.57186 0.12497 -4.57615 0 0.00223 AVPR2 136.51613 -0.40028 0.08945 -4.47467 1 E-05 0.00339 PSG2 194.40262 0.31213 0.06987 4.46747 1 E-05 0.00342 LUZP4 179.20644 -0.35078 0.07888 -4.44693 1 E-05 0.00368 JOSD2 45.12215 0.55026 0.12461 4.41602 1 E-05 0.0041 PRKX 149.84852 -0.33716 0.07638 -4.41392 1 E-05 0.0041 NDUFB11 45.77284 -0.58868 0.13355 -4.4078 1 E-05 0.00413 PTCHD1 362.03104 -0.32562 0.07418 -4.38954 1 E-05 0.00428 SRPX 182.02022 -0.34185 0.07816 -4.37402 1 E-05 0.00428 FGD1 525.22522 -0.34348 0.07842 -4.38006 1 E-05 0.00428 HAUS1 141.9969 -0.36376 0.08277 -4.39503 1 E-05 0.00428 AWAT2 166.08372 -0.42732 0.09769 -4.37435 1 E-05 0.00428 CCL3L1 15.58225 -1.55505 0.35507 -4.37949 1 E-05 0.00428 WDR44 460.67254 -0.32659 0.07481 -4.36579 1 E-05 0.00436 FAM211A 80.19514 0.38474 0.08969 4.28959 2E-05 0.00605 gene baseMean log2FoldChange IfcSE stat pvalue padj CXorf64 93.96758 -0.4248 0.09923 -4.28085 2E-05 0.00618 OTUD6A 64.92447 -0.46158 0.10843 -4.25701 2E-05 0.00675 DOCK11 1058.33572 -0.32756 0.07717 -4.24457 2E-05 0.00701 RBMX2 116.14861 -0.39688 0.09359 -4.24084 2E-05 0.00701 RNF128 206.84896 -0.35228 0.08348 -4.22 2E-05 0.00744 SMS 377.85047 -0.36366 0.08615 -4.22132 2E-05 0.00744 TKTL1 291.64397 -0.30697 0.07306 -4.20144 3E-05 0.00795 FUNDC1 85.10681 -0.41822 0.09968 -4.19555 3E-05 0.00803 MAOA 579.89228 -0.30157 0.07206 -4.18525 3E-05 0.00827 GPR52 104.0789 0.34877 0.08381 4.16139 3E-05 0.00904 OPA3 65.05525 0.48531 0.11712 4.14388 3E-05 0.00961 CFP 214.49641 -0.35297 0.08539 -4.13352 4E-05 0.00991 CTAGE6 100.51088 -0.69107 0.16738 -4.12863 4E-05 0.00997 OTC 390.40795 -0.35374 0.08599 -4.11374 4E-05 0.01048 GJB1 78.27462 -0.48499 0.1186 -4.08934 4E-05 0.01148 TRIM65 120.41224 0.31463 0.07702 4.08481 4E-05 0.01154 KRTAP5-10 38.35022 0.5608 0.13763 4.07454 5E-05 0.01185 FOXO4 170.59882 -0.31268 0.07678 -4.07232 5E-05 0.01185 SDHAF1 8.77989 0.95054 0.23438 4.0555 5E-05 0.01256 C4orf48 12.80253 0.82875 0.20495 4.04367 5E-05 0.01287 TAZ 297.75785 -0.32208 0.07965 -4.0437 5E-05 0.01287 KRTAP4-4 20.1293 0.73902 0.183 4.0383 5E-05 0.01297 FKBP1A 50.00419 0.42371 0.10499 4.03572 5E-05 0.01297 MUC4 894.07082 0.25922 0.06431 4.03076 6E-05 0.013 HSDL2 184.41476 -0.25812 0.06411 -4.0264 6E-05 0.013 FTSJ1 288.54178 -0.3415 0.08482 -4.02625 6E-05 0.013 ARRDC4 178.87937 -0.28335 0.07043 -4.02298 6E-05 0.01302 PCDHA4 156.02938 0.28436 0.07078 4.01757 6E-05 0.01316 NPIPB6 103.08118 -0.46351 0.11566 -4.00756 6E-05 0.01357 FLG2 1160.97581 0.20146 0.05039 3.99807 6E-05 0.01386 CHDC2 258.61326 -0.3517 0.08805 -3.99426 6E-05 0.01386 CXorf40B 69.5811 -0.50163 0.12556 -3.995 6E-05 0.01386 OR8H2 89.6892 0.3707 0.0931 3.98188 7E-05 0.01444 OCLM 28.14701 -0.62187 0.15636 -3.97706 7E-05 0.01457 NAP1 L3 139.75768 -0.34055 0.08574 -3.97186 7E-05 0.01472 FAM230A 46.5007 0.52951 0.13369 3.96082 7E-05 0.01525 TAGLN2 95.66278 0.33168 0.08401 3.94818 8E-05 0.0159 MB21 D1 120.36567 -0.33532 0.08516 -3.93735 8E-05 0.01629 PRR23D1 25.43337 -0.7959 0.20201 -3.93987 8E-05 0.01629 TFF2 65.20681 -0.39641 0.101 -3.925 9E-05 0.01696 ZNF547 150.64797 0.28318 0.07225 3.91973 9E-05 0.01698 HMGN5 120.76165 -0.36236 0.0924 -3.92154 9E-05 0.01698 MNT 63.74638 0.37293 0.09549 3.90536 9E-05 0.01748 PNMA3 174.09148 -0.33456 0.08561 -3.90792 9E-05 0.01748 GPKOW 187.7711 -0.35995 0.09211 -3.90763 9E-05 0.01748 ZFX 435.26906 -0.27401 0.07025 -3.90079 0.0001 0.01752 SPAG11 B 103.6872 -0.45387 0.11638 -3.90009 0.0001 0.01752 GOLGA6L2 254.60315 0.31881 0.08196 3.88987 0.0001 0.01809 ARMCX1 121.02686 -0.35622 0.09166 -3.88654 0.0001 0.01817 gene baseMean log2FoldChange IfcSE stat pvalue padj TBC1 D8B 513.01597 -0.34191 0.08804 -3.88335 0.0001 0.01823 APOO 163.42328 -0.35763 0.09222 -3.87797 0.00011 0.01846 RPL10 158.97733 -0.36866 0.09519 -3.87272 0.00011 0.01869 CD40LG 219.68574 -0.34119 0.08817 -3.86958 0.00011 0.01876 ACE2 397.60777 -0.28949 0.07516 -3.85171 0.00012 0.01981 SRPK3 172.61554 -0.30029 0.07796 -3.85174 0.00012 0.01981 FAM122B 180.01257 -0.31221 0.08119 -3.84567 0.00012 0.02012 FRMPD3 586.82843 -0.27857 0.07253 -3.84083 0.00012 0.02034 C7orf71 77.56832 0.33877 0.08832 3.8356 0.00013 0.02048 OR52B6 94.81881 0.33657 0.08777 3.83477 0.00013 0.02048 CHRNA10 106.53948 0.31958 0.08357 3.82392 0.00013 0.02104 TMEM187 78.49548 -0.4063 0.10625 -3.82417 0.00013 0.02104 IL17D 9.04109 0.88026 0.23083 3.8134 0.00014 0.02109 PIR 212.05812 -0.3157 0.08275 -3.81501 0.00014 0.02109 BEST4 93.94452 -0.35238 0.0924 -3.81354 0.00014 0.02109 NUDT11 38.63433 -0.4879 0.12769 -3.82089 0.00013 0.02109 OR4M2 87.10268 -0.88288 0.23155 -3.81292 0.00014 0.02109 ORAI2 78.69884 0.37487 0.0984 3.80943 0.00014 0.02121 HLA-A 59.77343 0.52148 0.1371 3.80363 0.00014 0.02154 SLIT1 34.09173 0.50262 0.13259 3.79076 0.00015 0.02246 NONO 212.12092 -0.27336 0.07214 -3.78921 0.00015 0.02246 B3GNT6 29.99957 0.5736 0.15164 3.78272 0.00016 0.02285 MCF2 607.46546 -0.28852 0.07631 -3.78105 0.00016 0.02285 FAM217B 109.29185 0.29265 0.07764 3.76922 0.00016 0.02377 CACNG5 127.39635 -0.30188 0.08017 -3.76564 0.00017 0.02393 FAM104B 89.65059 -0.36164 0.09623 -3.75803 0.00017 0.02448 SLC16A8 55.04496 0.45658 0.12157 3.75575 0.00017 0.02451 PPP3R2 55.05706 -0.40123 0.10692 -3.75254 0.00018 0.02464 HLA-DOB 35.53966 0.4728 0.12638 3.74104 0.00018 0.0256 BOLL 186.62385 -0.27184 0.0729 -3.72887 0.00019 0.02635 CYLC1 207.1791 -0.35462 0.09512 -3.72816 0.00019 0.02635 FAM3A 164.30503 -0.35505 0.09516 -3.73102 0.00019 0.02635 HIST1 H2AB 30.81724 -0.57182 0.15352 -3.72484 0.0002 0.02651 IRAKI 210.70562 -0.33551 0.09027 -3.71689 0.0002 0.02696 CBLN1 71.00293 -0.36076 0.09706 -3.71693 0.0002 0.02696 KCNJ4 43.38851 0.45443 0.123 3.69464 0.00022 0.02784 TXLNG 204.53635 -0.26063 0.07049 -3.69739 0.00022 0.02784 POTEC 314.30446 -0.26419 0.07151 -3.69461 0.00022 0.02784 POLA1 806.90538 -0.26664 0.07213 -3.69635 0.00022 0.02784 DCTN3 142.04842 -0.2855 0.07706 -3.70476 0.00021 0.02784 ZBTB33 197.51655 -0.28815 0.07798 -3.69528 0.00022 0.02784 DCAF12L2 85.63729 -0.37221 0.10072 -3.69557 0.00022 0.02784 ATOH1 36.27237 -0.51322 0.1388 -3.69759 0.00022 0.02784 LHFPL1 91.86056 -0.36723 0.09961 -3.68675 0.00023 0.02852 UBQLN2 202.9072 -0.29233 0.07937 -3.68284 0.00023 0.02876 TLR8 301.47178 -0.25227 0.06857 -3.67897 0.00023 0.02901 PLS3 381.98536 -0.33765 0.09189 -3.67448 0.00024 0.02914 TIMM8A 78.91066 -0.40094 0.10912 -3.67444 0.00024 0.02914 DMD 3154.41458 -0.25957 0.07091 -3.66081 0.00025 0.02977 gene baseMean log2FoldChange IfcSE stat pvalue padj ZNF280C 369.85679 -0.32039 0.08752 -3.66069 0.00025 0.02977 MAGEH1 68.67607 -0.40004 0.10919 -3.66362 0.00025 0.02977 DUSP9 62.83904 -0.44363 0.12104 -3.66512 0.00025 0.02977 TMSB15A 47.8686 -0.47278 0.12908 -3.66263 0.00025 0.02977 C20orf202 29.056 0.5482 0.15009 3.65247 0.00026 0.02998 LRP5L 154.50798 0.32366 0.08859 3.65356 0.00026 0.02998 CXorf22 383.2765 -0.28656 0.07844 -3.6534 0.00026 0.02998 HS6ST2 152.87331 -0.29351 0.08033 -3.65396 0.00026 0.02998 GBX1 85.32601 -0.34439 0.09437 -3.64933 0.00026 0.03016 DOK3 78.16681 0.48883 0.1341 3.64528 0.00027 0.03026 TANGO2 131.13535 0.3119 0.08553 3.64656 0.00027 0.03026 MAGEC2 97.36354 -0.33522 0.09211 -3.63928 0.00027 0.03079 RFC5 227.23082 -0.21886 0.06021 -3.63477 0.00028 0.03114 HTATSF1 272.93848 -0.27012 0.07438 -3.63135 0.00028 0.03137 PASD1 281.27358 -0.31136 0.08597 -3.62181 0.00029 0.03236 HIST1 H3H 24.30736 0.62746 0.1735 3.61644 0.0003 0.03238 GUK1 97.76177 0.33785 0.09344 3.61555 0.0003 0.03238 BRCC3 187.71149 -0.29599 0.08181 -3.61804 0.0003 0.03238 DNASE1 L1 48.10373 -0.50129 0.13861 -3.61658 0.0003 0.03238 FAM120C 461 .62994 -0.24337 0.06735 -3.61345 0.0003 0.03245 FCGR3B 48.34239 -0.95339 0.26462 -3.60293 0.00031 0.0336 IDH3G 192.6693 -0.2897 0.08063 -3.59308 0.00033 0.0347 OCRL 867.16077 -0.24075 0.06707 -3.58959 0.00033 0.03497 FAM209A 38.3982 0.44884 0.12521 3.5848 0.00034 0.03531 UBE2NL 58.41842 -0.37977 0.10596 -3.58412 0.00034 0.03531 C3orf80 10.16625 -0.98825 0.27653 -3.5738 0.00035 0.03652 VSIG1 192.79673 -0.27325 0.07657 -3.56886 0.00036 0.03701 SCAND1 11.30085 0.84325 0.23659 3.56417 0.00037 0.03737 CTAG2 54.65895 -0.52402 0.14705 -3.56346 0.00037 0.03737 FAM212B 105.97736 0.31501 0.08844 3.56187 0.00037 0.03739 CENPM 101.52001 -0.30744 0.08636 -3.56008 0.00037 0.03744 OR10H3 92.73038 0.28073 0.07898 3.55462 0.00038 0.03786 ELF4 355.74914 -0.25102 0.07062 -3.55427 0.00038 0.03786 C1 orf210 39.98683 0.42825 0.12058 3.55163 0.00038 0.03794 HCN2 108.54973 -0.29786 0.0839 -3.55032 0.00038 0.03794 RPS3A 55.85479 -0.37448 0.1055 -3.54956 0.00039 0.03794 C10orf82 109.5605 -0.27682 0.07804 -3.54687 0.00039 0.03812 ZBED6 106.56348 0.27764 0.07846 3.53859 0.0004 0.03899 PRDX4 172.99424 -0.2923 0.08261 -3.53822 0.0004 0.03899 OR5J2 92.10444 0.2828 0.08013 3.52922 0.00042 0.03971 PHKA1 1273.35907 -0.27532 0.07799 -3.53008 0.00042 0.03971 CXorf57 328.54209 -0.27572 0.07811 -3.52975 0.00042 0.03971 MRPL2 112.65458 -0.29821 0.08459 -3.52553 0.00042 0.04007 LACTBL1 68.09389 0.37084 0.10557 3.51271 0.00044 0.04142 CTPS2 381.73725 -0.25669 0.07304 -3.51441 0.00044 0.04142 CXorf67 87.96678 -0.37295 0.10616 -3.51326 0.00044 0.04142 FMR1 NB 119.13201 -0.31315 0.08931 -3.50652 0.00045 0.04204 PRAMEF10 88.9544 -0.63092 0.17995 -3.50609 0.00045 0.04204 FAM47A 194.76758 -0.27968 0.07984 -3.50299 0.00046 0.04232 gene baseMean log2FoldChange IfcSE stat pvalue Padj ARL17B 24.1744 0.56001 0.16 3.50016 0.00046 0.04256 POTEG 315.31247 -0.44975 0.1287 -3.49454 0.00047 0.04325 LRRC27 318.08078 -0.20142 0.05773 -3.48908 0.00048 0.04393 GOLGA8A 71.64417 0.39538 0.11337 3.48739 0.00049 0.04399 CTSD 43.68661 0.42168 0.121 3.48508 0.00049 0.04412 EMD 107.46 -0.36961 0.10609 -3.48406 0.00049 0.04412 CNKSR2 552.17294 -0.26107 0.07499 -3.48126 0.0005 0.04416 ANKRD49 89.53959 -0.2997 0.08608 -3.48181 0.0005 0.04416 DNAJC14 58.84576 0.42362 0.12191 3.47484 0.00051 0.04442 HDAC8 365.89779 -0.22149 0.06371 -3.47646 0.00051 0.04442 KLF8 168.37557 -0.26003 0.07479 -3.47654 0.00051 0.04442 LACC1 136.30103 -0.26168 0.07531 -3.47459 0.00051 0.04442 ATP1 1 C 639.03762 -0.26821 0.0773 -3.46962 0.00052 0.04504 LILRB1 272.39712 0.24826 0.07164 3.4653 0.00053 0.04535 CYBB 555.10284 -0.27093 0.07816 -3.46613 0.00053 0.04535 HCCS 237.91398 -0.27645 0.07988 -3.46082 0.00054 0.0459 SLC9A6 550.14328 -0.27704 0.08012 -3.45778 0.00054 0.046 HNRNPCL1 84.33405 -0.43858 0.12682 -3.45827 0.00054 0.046 TCEAL4 135.3069 -0.27137 0.07858 -3.45319 0.00055 0.04658 FTH1 25.54703 0.50906 0.14753 3.45066 0.00056 0.04681 OR4D1 110.08929 0.26885 0.07807 3.44376 0.00057 0.04717 GLA 328.87341 -0.24944 0.07236 -3.44711 0.00057 0.04717 GDI1 335.4176 -0.28865 0.08381 -3.44405 0.00057 0.04717 SEC16B 20.42733 -0.59834 0.1737 -3.44465 0.00057 0.04717 CLCN6 425.26454 0.20195 0.05872 3.43932 0.00058 0.04774 FAM47C 288.47746 -0.27165 0.07904 -3.43669 0.00059 0.04799 LRCH2 319.63045 -0.29478 0.08583 -3.43434 0.00059 0.0482 GK 613.23679 -0.29462 0.08587 -3.43086 0.0006 0.04861 ZC3H12A 137.85687 0.26619 0.07772 3.42514 0.00061 0.04943 ZNF671 147.30232 0.23868 0.06971 3.42393 0.00062 0.04943 DMWD 88.24872 0.33025 0.09653 3.42119 0.00062 0.04972 PSMA3 189.47898 0.26857 0.07856 3.41861 0.00063 0.04998

[0237] Table 8: DEGs between LMS and LM samples which have been frozen for less than 6 months and which derive from subjects over 41 years of age with a |log2FC| value > 2. log2FoldC gene baseMean IfcSE stat pvalue padj hange

[0238] MT-CO2 21.5892 2.67223 0.37343 7.15591 0 0

[0239] MT-ND1 33.977 2.4962 0.34665 7.20083 0 0

[0240] FAM21 B 122.51251 -3.75009 0.7347 -5.10424 0 0.00034

[0241] Overall, the results of these analyses have demonstrated that the application of this technology allows the diagnosis of the tumor in a non-invasive and objective way, based on the detection of molecular differences from circulating genetic material in peripheral blood of patients with suspected myometrial tumor (leiomyoma I leiomyosarcoma), as well as the development of biomarkers and effective therapies in the treatment of uterine leiomyosarcomas.

Claims

1. 54CLAIMS1 . An in vitro method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions, the method comprising:(i) measuring the level of expression of at least one gene selected from POC1A gene, PTPRT gene, PTCHD1 gene, MUC4 gene, CENPM gene, KLHL41 gene, FLG2 gene, GPR52 gene, MB21 D1 gene, MT-ND1 gene, MT-CO2 gene, FAM21 B gene or GSTM1 gene in a biological sample obtained from the subject, and(ii) comparing said level of expression with a reference value, wherein a deviation in the level of expression of said at least one gene with respect to said reference value is indicative that the subject is affected by uterine leiomyosarcoma.

2. The method according to claim 1 , wherein the deviation of the expression level with respect to the reference value in the POC1A gene, the PTPRT gene, the MUC4 gene, the FLG2 gene, the GPR52 gene, the MT-ND1 gene or the MT-CO2 gene is an increase and / or wherein the deviation of the expression level with respect to the reference value in the PTCHD1 gene, the CENPM gene, the KLHL41 gene, the MB21 D1 gene, the FAM21 B gene or the GSTM1 gene is a decrease.

3. An in vitro method for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions, the method comprising:(i) measuring the level of expression of at least one gene selected from the list shown in Table 2, the list shown in Table 3, the list shown in Table 4, the list shown in Table 6 or the list shown in Table 7 in a biological sample obtained from the subject, and(ii) comparing said level of expression with a reference value, wherein a deviation in the level of expression of said at least one gene with respect to said reference value is indicative that the subject is affected by uterine leiomyosarcoma.

554. The method according to claim 3, wherein the deviation in the level of expression of the at least one gene is a downregulation in the sample with respect to the reference value if the log2FoldChange value for said at least one gene as indicated in the Table is a negative value, or wherein the deviation in the level of expression of the at least one gene is an upregulation in the sample with respect to the reference value if the log2FoldChange value for said at least one gene as indicated in the Table is a positive value.

5. The method according to claim 1 or 3, wherein the reference value is the mean level of expression of the same gene or genes determined in a group of samples from a group of subjects with uterine leiomyoma.

6. The method according to claim 1 , wherein the method comprises measuring the expression level of the POC1A, PTPRT, PTCHD1 , MUC4, CENPM, KLHL41 , FLG2, GPR52, MB21 D1 , MT-ND1 , MT-CO2, FAM21 B and GSTM1 genes.

7. The method according to claim 3, wherein the method comprises measuring the expression level of all the genes comprised in Table 2, Table 3, Table 4, Table 6 or Table 7.

8. The method according to claim 3, wherein:(i) if the biological sample has been frozen for less than 6 months, then step (i) comprises measuring the level of expression of at least one gene selected from the list shown in Table 4,(ii) if the biological sample derives from a subject over 41 years of age, then step (i) comprises measuring the level of expression of at least one gene selected from the list shown in Table 6, or(iii) if the biological sample has been frozen for less than 6 months and it derives from a subject over 41 years of age, then step (i) comprises measuring the level of expression of at least one gene selected from the list shown in Table 7.

9. The method according to any one of the preceding claims, the method comprising diagnosing uterine leiomyosarcoma when the deviation in the level of expression of the at least one gene is of at least two fold with respect to the reference value,56 said reference value being the expression level of the same gene or genes determined in at least a sample from at least a subject with uterine leiomyoma.

10. The method according to any one of the preceding claims, wherein the subject has been previously identified as having a myometrial tumor by imaging examination, preferably by ultrasonography.11 . The method according to any one of the preceding claims, wherein the determination of the expression levels of one or more genes is carried out by exome-wide gene expression from RNAseq.

12. The method according to any one of the preceding claims, wherein the biological sample is a sample containing myometrial cells or RNA derived from myometrial cells.

13. The method according to claim 12, wherein the sample containing myometrial cells or RNA derived from myometrial cells is a myometrial biopsy or a biofluid.

14. The method according to claim 13, wherein the biofluid is selected from the group consisting of blood, plasma and serum.

15. An in vitro method for identifying a subject suspected of having uterine leiomyosarcoma as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma, the method comprising:(i) determining whether the subject is affected by uterine leiomyosarcoma following the method of any one of claims 1 to 14; and(ii) designating said subject as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma if the subject is diagnosed as having uterine leiomyosarcoma.

16. The method according to claim 15, wherein the therapy suitable for the treatment of uterine leiomyosarcoma is selected from the group consisting of surgery, radiation therapy, chemotherapy, hormonal therapy and targeted therapy.5717. The method according to claim 16, wherein the surgery is simple hysterectomy, radical hysterectomy or bilateral salpingo-oophorectomy, wherein radiation therapy includes ionizing (y) radiation, particle radiation, low energy transfer (LET), high energy transfer (HET), X-ray radiation, UV radiation, infrared radiation, visible light, photosensitizing radiation, wherein the chemotherapy includes one or more drugs selected from the group consisting of dacarbazine (DTIC), docetaxel, doxorubicin, epirubicin, gemcitabine, ifosfamide, paclitaxel, temozolomide, trabectedin and vinorelbine, wherein the hormonal therapy comprises progestin, an agonist of the gonadotropin-releasing hormone or an aromatase inhibitor, and / or wherein the targeted therapy includes pazopanib.

18. A method for treating uterine leiomyosarcoma in a subject in need thereof comprising the administration of a therapy suitable for the treatment of uterine leiomyosarcoma, wherein the subject to be treated has been identified using a method according to any of claims 1 to 14.

19. The method according to claim 18, wherein the therapy suitable for the treatment of uterine leiomyosarcoma is selected from the group consisting of surgery, radiation therapy, chemotherapy, hormonal therapy and targeted therapy.

20. The method according to claim 19, wherein the surgery is simple hysterectomy, radical hysterectomy or bilateral salpingo-oophorectomy, wherein radiation therapy includes ionizing (y) radiation, particle radiation, low energy transfer (LET), high energy transfer (HET), X-ray radiation, UV radiation, infrared radiation, visible light, photosensitizing radiation, wherein the chemotherapy includes one or more drugs selected from the group consisting of dacarbazine (DTIC), docetaxel, doxorubicin, epirubicin, gemcitabine, ifosfamide, paclitaxel, temozolomide, trabectedin and vinorelbine, wherein the hormonal therapy comprises progestin, an agonist of the gonadotropin-releasing hormone or an aromatase inhibitor, and / or wherein the targeted therapy includes pazopanib.21 . A chemotherapeutic agent, hormonal agent and / or targeted agent for use in the treatment of uterine leiomyosarcoma, wherein the subject to be treated has been identified by a method according to any of claims 1 to 14.

22. The chemotherapeutic agent, hormonal agent and / or targeted agent according to claim 21 , wherein the chemotherapeutic agent includes one or more drugs selected from the group consisting of dacarbazine (DTIC), docetaxel, doxorubicin, epirubicin, gemcitabine, ifosfamide, paclitaxel, temozolomide, trabectedin and vinorelbine, wherein the hormonal agent comprises progestin, an agonist of the gonadotropin-releasing hormone or an aromatase inhibitor, and / or wherein the targeted agent includes pazopanib.

23. A kit, package and / or device comprising reagents adequate for implementing the methods according to any one of claims 1 to 20.

24. The kit, package and / or device according to claim 23, wherein the reagents comprise primers or probes adequate for the detection of the expression levels of one or more of the genes, the expression levels of which are determined in the methods according to any of claims 1 to 20.

25. Use of a kit according to claim 23 or 24 for distinguishing between uterine leiomyosarcoma or uterine leiomyoma in a subject suspected of having one of these conditions, or for identifying a subject as a candidate to receive a suitable therapy to treat uterine leiomyosarcoma.

26. A computer-implemented method, wherein the method is as defined in any of claims 1 to 17.

27. A computer comprising instructions for carrying out a method as defined in any of claims 1 to 17.

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