Differential diagnosis of mycosis fungoides, method and system

Abstract

The present invention relates to a method for diagnosing mycosis fungoides (MF) or eczema and / or distinguishing MF from eczema or psoriasis, the method comprising: determining an expression of at least one biomarker in a sample, differentiating between MF and eczema and / or ecema or psoriasis, based on the expression of the at least one biomarker in the sample, and generating a differential diagnosis finding based on the expression of the at least one biomarker in the sample The present invention also relates to a system a system for diagnosing mycosis fungoides (MF) or eczema and / or distinguishing MF from eczema or psoriasis, the system comprising: a processing component configured to output at least one dataset, and an analyzing component configured to analyze the at least one dataset, wherein the analyzing component comprises: a determining module configured to determine an expression of at least one biomarker in a sample, a differentiating module configured to differentiate between MF and eczema and / or eczema or psoriasis, based on the expression of the at least one biomarker in the sample, and a finding generating module configured to generate a differential diagnosis finding based on the expression of the at least one biomarker in the sample. Furthermore, the invention relates to a kit for use in a method for diagnosing eczema or mycosis fungoides, and / or distinguishing MF from eczema or psoriasis, the kit comprising at least one mean for quantifying an expression of at least one biomarker in at least one sample.

Description

[0001] Differential diagnosis of Mycosis Fungoides, method and system Field The invention lies in the field of medical diagnosis and particularly in the field of dermatological diagnosis. The goal of the present invention is to provide a method for diagnosis of mycosis fungoides. More particularly, a system to perform the method and corresponding use of the system. Introduction Mycosis Fungoides (MF) is a challenging and debilitating condition characterized by the infiltration of malignant T-cells in the skin. As a subtype of cutaneous T-cell lymphoma (CTCL), MF is a rare malignant tumor disease of the skin with a wide spectrum of clinical manifestations, ranging from erythematous patches and plaques to advanced tumor stage disease. Current treatment options, though available, often yield limited success and are associated with significant side effects, underscoring the need for novel therapeutic strategies. Moreover, using conventional methods such as clinical view and histopathology, these lymphomas can only rarely be differentiated from eczema and / or psoriasis in the initial stage. Dobos et al and Vermeer et al disclose that primary cutaneous lymphomas (CLs) are a heterogenous group of diseases within the group of non-Hodgkin lymphomas. In contrast to their systemic counterparts, primary cutaneous T-cell lymphomas (CTCL) are more frequent than primary cutaneous B-cell lymphomas and make up more than 80% of primary cutaneous lymphomas. CTCL is a rare disease but important for public health planning, healthcare insurers, and pharmaceutical manufacturers. The incidence of CTCL is about approximately 1 per 100,000 per year and it is estimated that CTCLs are underdiagnosed and thereby their incidence is underestimated. Patients with CTCL suffer visible and itching and painful skin lesions pain which influences the physical functioning, family life, social interactions and relationships of patients. The diagnosis is frequently made with significant delay which worsens the psychosocial burden and leads to higher healthcare expenses. Approximately one of four patients diagnosed with an early stage CTCL may progress to advanced forms, that are more difficult to treat. Assaf et al. discloses that primary cutaneous lymphomas are a heterogenous group of lymphatic neoplasias, primarily affecting the skin. The German Central Registry for Cutaneous Lymphomas (ZRKL) of the German Society of Dermatology (DDG) evaluates the epidemiology of cutaneous lymphomas in Germany, revealing a clear predominance of cutaneous T-cell lymphomas (85%) and cutaneous B-cell lymphomas (14%). Mycosis fungoides is the most common representative of CTCL, accounting for 62% of cases. Walia et al. relates to new molecular discoveries that have advanced CTCLs, revealing deeper understanding of tumor biology and immune response, and introducing targeted therapeutic strategies. Experts suggest NGS for T cell clonality detection, which helps identify primary clonal sequences and manage minimal residual analysis. TCR gamma-PCR is the standard, but has variable sensitivity and lower specificity. Lim et al relates to a study of 246 patients with mycosis fungoides (MF) and Sézary syndrome (SS) and found that 63% were male and the median age at diagnosis was 49 years. The majority had early disease, with 78.2% responding to treatment and 10.0% experiencing progression. The mean overall survival was 12.7 years, with death occurring in 2.5% of patients. Prognostic factors associated with recurrence-free survival included male gender, early disease stage, and absence of maintenance treatment after remission. Tsang et al. relates to a study of 1981 MF-CTCL patients found that severe patients incurred higher healthcare costs compared to mild-to-moderate patients. About 51% of patients did not receive MF-CTCL-specific treatment within 60 days of diagnosis. Severe patients had a greater burden of illness, healthcare costs, and utilization compared to mild-to-moderate patients. Low adherence and high discontinuation rates to drug therapy could be a reflection of disease remission after treatment, but further investigation is needed. Zhang et al. relates to a study comparing early mycosis fungoides (eMF) lesions with healthy skin and benign inflammatory dermatitis found 349 genes differentially expressed in eMF lesions. Most genes showed upregulation in chronic dermatitis, making them nonideal markers for eMF. Two genes, TOX and PDCD1, demonstrated high discrimination power between eMF lesions and biopsies from benign dermatitis. These genes, particularly TOX, could serve as molecular markers for histological diagnosis of eMF, a major diagnostic challenge. McGirt et al. relates to a study comparing TOX staining across a spectrum of CTCL, benign inflammatory dermatoses such as psoriasis and spongiotic dermatitis (BID) and normal skin (NS). Positive TOX expression was detected in 73.6% of MF cases and in 31.6% of BID / NS. TOX expression also decreased during CTCL therapeutics and thus might be a useful marker in MF. Pileri et al. relates to a study that investigated whether TOX may be a diagnostic or prognostic marker in MF / SS patients. Results showed an increase in TOX expression from early to advanced phases, but not as a prognostic marker. The study suggests that TOX should be considered more as a prognostic marker than a diagnostic marker. Nielsen et al. relates to a diagnostic classifier using TOX and TRAF1, which was able to differentiate early-stage melanoma (MF) from dermatitis with 85% accuracy in the discovery cohort and 80% in the independent validation cohort. TOX and TRAF1 protein levels were significantly elevated in early-stage MF compared to the dermatitis group. TOX and TRAF1 were also significantly increased in the progression from early-stage MF to tumor stage MF. The protein expression levels of TOX and TRAF1 confirmed the difference between early-stage MF and dermatitis, making it useful for diagnosing MF. Litvinov et al. (2017) relates to cutaneous T-Cell Lymphomas (CTCL), which are rare but potentially devastating malignancies with poorly understood pathogenesis. Early diagnosis takes 6 years, and the disease often masquerades as psoriasis or chronic eczema. A study using TruSeq targeted RNA gene expression on 181 skin samples from CTCL patients and those affected by benign inflammatory dermatoses revealed significant molecular heterogeneity. Differential expression of genes like TOX, FYB, LEF1, CCR4, ITK, EED, POU2AF, IL26, STAT5, BLK, GTSF1, and CCR4 may be useful in prognosticating CTCL. Litvinov et al. (2015) relates to a study aiming to predict the progression and stability of mycosis fungoides patients with stage I-IV cutaneous T-cell lymphoma (CTCL). By analyzing gene expression in biopsy specimens from 60 patients, the researchers identified three distinct clusters based on transcription profiles. The study found that 52 of the 240 genes can be classified into cluster 1-3 expression patterns, consistent with their suggested biologic roles. Additionally, 17 genes identified patients at risk of progression and distinguished mycosis fungoides / Sezary syndrome from benign mimickers such as atopic dermatitis, unspecified dermatosis and psoriasis. This research lays the foundation for developing a personalized molecular approach for diagnosis and treatment of mycosis fungoides. Ralfkiaer et al. relates to a study that analyzed miRNA expression levels in 198 patients with CTCL, peripheral T-cell lymphoma, and benign skin diseases including psoriasis, atopic dermatitis, contact dermatitis and unspecified dermatitis. Results showed that induced and repressed miRNAs differentiate CTCL from benign skin diseases with 90% accuracy. AqRT- PCR analysis confirmed the differential expression of four miRNAs, and miRNA classifiers with high specificity and sensitivity showed high diagnostic potential in CTCL. Soerensen at al. relates to a study that aimed at examining disease-specific miRNA expression in early-stage mycosis fungoides patch and plaque lesion in comparison with psoriasis. They found that early-stage mycosis fungoides exhibited miRNA features that overlapped with those of psoriasis. However, 39 miRNAs, including miR-142-3p, miR-150 and miR-146b, were specific to mycosis fungoides. Nikolaou et al. relates to a study that investigated the relation between psoriasis and myocosis fungoides by retrospectively reviewing all MF cases diagnosed and followed in a 16-year period who carried both MF and psoriasis diagnoses. They found that 7.8% (n= 25 patients) of patients met the inclusion criteria. Twenty patients had psoriatic lesions at the time of MF diagnosis and in eight patients, typical histological findings of both diseases were detected in the same biopsy specimen underlining the need for precise differential diagnostics. As reported by Vaudreuil et al. mycosis fungoides not only presents as psoriasis or other diseases, but psoriasis can also present as mycosis fungoides as they show in their case report. Fahmy et al. relate to a case of a patient who was diagnosed with mycosis fungoides (MF) after treatment with risankizumab, an IL-23 inhibitor, for psoriasis. As there is an established link between tumor necrosis factor-α inhibitors and cutaneous T-cell lymphoma (CTCL), and several reports describe worsening CTCL after exposure to other cytokine blockers, precise diagnostics are essential to differentiate newly developed MF in patients with inflammatory skin diseases from therapy-resistant lesions of benign skin diseases. Summary In light of the above, it is therefore an object of the present invention to overcome or at least to alleviate the shortcomings and disadvantages of the prior art. More particularly, it is an object of the present invention to provide a method for diagnosing eczema and mycosis fungoides, and a corresponding system to carry out the method. It is a further object of the present invention to provide a method for distinguishing between eczema or psoriasis, and mycosis fungoides, and a corresponding system to carry out the method. These objects are met by the present invention. In a first aspect, the invention relates to a method for diagnosing mycosis fungoides (MF) or eczema, the method comprising: determining an expression of at least one biomarker in a sample, differentiating between MF and eczema, and / or differentiating between MF and eczema or psoriasis, based on the expression of the at least one biomarker in the sample, and generating a differential diagnosis finding based on the expression of the at least one biomarker in the sample. The method may comprise differentiating between MF and eczema based on the expression of at least two biomarkers in the sample. The method may also comprise differentiating between MF and eczema or psoriasis based on the expression of at least two biomarkers in the sample. In one embodiment, the method may comprise differentiating between MF and eczema based on the expression of at least three biomarkers in the sample, preferably between at least four biomarkers in the sample, more preferably between at least five biomarkers in the sample. In one embodiment, at least one of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 Moreover, at least two of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 These markers are surprisingly advantageous, as these provide to the approach of the present invention a high discriminative ability, wherein surprisingly HOMER1 has the highest discriminative ability even when alone, exhibiting an improved discriminative ability by further adding RNF213, and subsequently NLRC5. In one embodiment, the at least one biomarker further optionally may comprise at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 Symbol Entrez gene id Symbol Entrez gene id ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 In another embodiment, the at least one biomarker may comprise at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 In a further embodiment, the method may be for diagnosing early stage of MF or eczema, wherein the method may comprise discriminating between MF and eczema in early stage. Moreover, the sample may comprise a sample taken from an individual. The gene names used in the definition of the first aspect of the present invention are standard art-established gene names. Table 1 below provides the full names and functional annotations of these genes. Table 1. Genes distinguishing and / or separating eczema from MF Entrez Symbol Full name Function gene ID RNF213 57674 Ring Finger Protein 213 Ubiquitin ligase, involved in lipid metabolism, angiogenesis and immune processes NLRC5 84166 NLR Family CARD Domain Antiviral immunity Containing 5 GBP4 115361 Guanylate Binding Protein 4 Innate immunity IKZF3 22806 IKAROS Family Zinc Finger 3 Regulation of lymphocyte development HOXC10 3226 Homeobox C10 Morphogenesis DSTN 11034 Destrin Actin metabolism ELOVL5 60481 ELOVL Fatty Acid Elongase 5 Fatty acid metabolism ROBO1 6091 Roundabout Guidance Receptor 1 Axon guidance, tumorigenesis MAPK6 5597 Mitogen-Activated Protein Kinase 6 T-cell development LCK 3932 Proto-Oncogene Tyrosine-Protein T -cell development Kinase LCK TMC8 147138 Transmembrane Channel Like 8 T-cell regulation KRT17 3872 Keratin 17 Keratinization CXCL9 4283 C-X-C Motif Chemokine Ligand 9 T cell trafficking MT-ND3 4537 Mitochondrially Encoded NADH: Mitochondrial electron Ubiquinone Oxidoreductase Core transport Subunit 3 VOPP1 81552 VOPP1 WW Domain Binding Protein NFKB1 activity FLRT3 23767 Fibronectin Leucine Rich Cell-cell adhesion Transmembrane Protein 3 TNFRSF1 8795 TNF Receptor Superfamily Member Apoptosis 0B 10b Entrez

[0002] Symbol Full name Function gene ID PNLIPRP 119548 Pancreatic Lipase Related Protein 3 Lipid metabolism 3 MMP1 4312 Matrix Metallopeptidase 1 Extracellular matrix HOMER1 9456 Homer Scaffold Protein 1 Scaffold protein of postsynaptic density RCSD1 92241 RCSD Domain Containing 1 Actin filament activity ENAH 55740 ENAH Actin Regulator Actin-based motility MUC16 94025 Mucin 16 Barrier formation LRATD1 151354 LRAT Domain Containing 1 Cell motility and morphogenesis H1-1 3024 H1.1 Linker Histone Chromatin formation SH2D1A 4068 SH2 Domain Containing 1A Stimulation of T and B cells PRXL2A 84293 Periredoxin Like 2A Antioxidant activity DISC1 27185 Disrupted In Schizophrenia 1 Neurite outgrowth Protein ALOX15 247 Arachidonate 15-Lipoxygenase Lipid metabolism B Type B CALCRL 10203 Calcitonin Receptor Like Receptor Adrenomedullin receptor activity S100A11 6282 S100 Calcium Binding Protein A11 Cell cycle progression and differentiation CSRP2 1466 Cysteine And Glycine Rich Protein 2 Cellular differentiation CDK1 983 Cyclin Dependent Kinase 1 Cell cycle control USP20 10868 Ubiquitin Specific Peptidase 20 Increased expression of HIF1A targeted genes CES1 1066 Carboxylesterase 1 Metabolism pf exogenous substances GRAP2 9402 GRB2 Related Adaptor Protein 2 Leukocyte signaling Furthermore, the method may comprise: generating a skin condition status hypothesis based on the differential diagnosis finding, predicting a skin condition status of the individual based on the skin condition status hypothesis, and generating a skin condition status prediction. Moreover, the evaluating step may be based on the differential diagnosis finding. Additionally or alternatively, the evaluating step may be based on the skin condition status prediction. In one embodiment, the method may comprise assessing whether the expression of the at least one biomarker represents that the individual may be afflicted with MF or eczema. In another embodiment, the method may comprise: generating a skin condition threshold, and assigning the skin condition status to the individual based on the skin condition threshold, wherein when the expression of the at least one biomarker in the sample of the individual is below the skin condition threshold, the method concludes that the individual may be afflicted with eczema, and above the skin condition threshold, the method concludes that the individual may be afflicted with MF.

[0003] ,sle & d o n omis 1 3 tcit s R n E 1 % % % % %esier M 2 r g g F O N8.3 5 2 89.8. . .%efofiL e 3 6 1 8 r H R 2 8 7 9 8 9 7dh htiltie w nwre n d n o M Voikr r ait tS% % % % %c M a w celV e relh4 3 5 9 8eti .% 8 2.9.7.9.3 S nilo e F S w 4 8 7 8 8 8 7Ser ts ut o a n o e ts d bFo l raoit g d o ex% % % %r b d w c %a g o reolh eti%84.86.38.39.40.8wr X m F S w 4 8 6 8 7 9 6oF cig tninsisonu ci in g s dos roiol is) t s e a tc se % % %%2(ig rg wrelh r2%2 2 3% etig.9 7. . .d o L e r o 3 5 1 0 F S w e n r 3 8 7 9 8 9 8a.s n e n – riofoogoiesis n s acit s r s n e hsieila e p % % % % %r rylgcg g S sa n m8).39.87.9.8.%e ec o L e r F o o 2 6 1 9rn F B ( c n 5 8 7 9 8 9 7os a s m h a rtL oifg r wnMie nfispVo vueSir s c s o g ) hta o e htnir %% % %%1(e s r w6ns g s etl .6.%33 3 4.1.1.s oiL a L e oif5 2 8 4t r p 2 9 8 9 9 9 9phe d c n c nao o n h g d n tct ecioisoisonirnnit s ss p ee asier o e tli% %% % % %r tefb g s r o g s g htfif 1.4.3.8.8.9.ioL e e R a L e r wfi3 2 3 5 8 7 2 v 2 9 8 9 8 9 9dr o watse n e n n ht ecioit sois sfgoysie o se % % % % %n n g r s r a o g5.6.9 1 4oie s g 9 1. . .% L t R a L e 3 5 7 7 1 r 2 9 8am9 8 9 9ulwofC Caho U Uv g r y y A A E e.n2ik yr r d y etitir r e e e c rvicia lelp e a v d m o b s c a o c m e n rtifalu cs sin c e e C r erb ooisiu n a c - e o oa mo M B d e c 1 p O c R c T c N g B A F S S R S P S 5 Additionally or alternatively, the method further may comprise automatically generating at least one skin condition suggestion, wherein when the expression of the at least one biomarker in the sample of the individual reads below a detection limit of the skin condition threshold, the at least one skin condition suggestion may comprise prompting an evaluation by a medical professional. In one embodiment, the assessing step may be based on the differential diagnosis finding. In another embodiment, the assessing step may be based on the skin condition status prediction. Moreover, the sample taken from an individual may comprise at least one individual with a skin lesion suspected of at least one of: eczema and cutaneous lymphoma. In one embodiment, the method may comprise identifying at least one MF-Eczema differentiating gene significantly differentially expressed in the sample among at least two different expressed genes. Furthermore, the skin condition threshold may comprise at least one MF-Eczema distinguishing parameter and a MF-Eczema distinguishing reference parameter. In a further embodiment, the method may comprise measuring a fold change of the expression of at least one MF-Eczema differentiating gene. Moreover, the measuring of the fold change may comprise measuring change in a magnitude of log2FoldChange of at least one MF-Eczema differentiating gene. Further, the method may comprise processing at least one set of samples. Additionally or alternatively, processing the at least one set of samples may comprise: resampling the set of samples at least 50 times, preferably at least 100 times, more preferably at least 150 times, such as 200 times; and automatically generating a resampled dataset. Moreover, the processing the at least one set of samples may comprise performing sample cross validation. In one embodiment, the method may comprise executing a differential gene expression analysis between at least one of: eczema and non-lesional condition, cutaneous lymphoma and non-lesional condition, and eczema and cutaneous lymphoma. In a further embodiment, the method may comprise: identifying the at least one MF- Eczema differentiating gene, and differentiating the at least one MF-Eczema differentiating gene into at least one of the at least one MF-Eczema differentiating gene associated to eczema, and at least one of the at least one MF-Eczema differentiating gene associated to cutaneous lymphoma, wherein differentiating of the at least one MF-Eczema differentiating gene may be based on the skin condition threshold. For instance, the approach of the present invention may allow taking genes which have an absolute value of log2FoldChange > 1 and a padj < 0.05, and intersecting the genes with absolute value with genes from genes which distinguish healthy from disease and eczema from cutaneous lymphoma. In one embodiment, the method may comprise training at least one machine learning module using any data of any of the method steps. Moreover, the training may comprise using a training data set comprising a normalization approach and a standard scaling. Additionally or alternatively, the normalization approach may comprise executing a trimmed mean of M-values (TMM) normalization. Moreover, the method may comprise identifying at least one sample dataset for the predicting step. Furthermore, for identifying the at least one sample data set for the predicting step, the method may comprise: using the at least one MF-Eczema differentiating gene, training a recursive forward feature selection (RFFS), and generating an optimized list of MF-Eczema differentiating genes based on the at least one sample dataset. In one embodiment, the method may comprise optimizing by means of the RFFS at least one of: the skin condition threshold, and the at least one MF-Eczema distinguishing parameter. In another embodiment, the method may comprise storing the list of MF-Eczema differentiating genes in at least one of: random access memory (RAM), server, cloud, and database. In a further embodiment, the method may comprise: applying robust rank aggregation on the list of MF-Eczema differentiating genes to find at least one MF-Eczema differentiating gene consistently contained in the list, applying Benjamin-Hochberg correction for multiple testing for number of iterations and for number of MF-Eczema differentiating genes, and selecting at least one MF-Eczema differentiating gene from the list of MF-Eczema differentiating genes based on outcome of the robust rank aggregation and the Benjamin- Hochberg correction. In one embodiment, for generating the differential diagnosis finding, the method may comprise: analyzing the expression of the at least one biomarker in the sample, and calculating an expression value. Moreover, the method may comprise analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering eczema. In one embodiment, the method may comprise analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. Further, calculating the expression value may be based on analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering eczema and the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. In a further embodiment, the analyzing and calculating steps may be performed by the at least one machine learning module. In an even further embodiment, the eczema may comprise at least one of: atopic eczema, contact dermatitis, dyshidrotic eczema, nummular eczema, allergic contact dermatitis, irritant dermatitis, seborrheic dermatitis, and stasis dermatitis. Furthermore, the method may comprise: identifying at least one stage of the mycosis fungoides, and distinguishing among the least one stage, wherein the at least one stage may comprise at least one of: patch stage, plaque stage, tumor stage, and erythrodermic stage. In one embodiment, the expression of the at least one biomarker may comprise at least one expression level comprising: an expression at a mRNA level, and an expression at a protein level. Moreover, the at least one expression at the mRNA level may be determined by at least one nucleic acid amplification method. Additionally or alternatively, the at least one nucleic acid amplification method may comprise at least one of: polymerase chain reaction (PCR), and isothermal amplification method. Moreover, the PCR may comprise at least one of: conventional PCR, real-time PCR (qPCR), reverse transcription PCR (RT-PCR), Nested PCR, Multiplex PCR, Digital PCR (dPCR), Hot start PCR, and droplet PCR. In one embodiment, the isothermal amplification method may comprise at least one of: loop-mediated isothermal amplification method (LAMP), recombinase polymerase amplification (RPA), helicase-dependent amplification method (HDA), and nicking enzyme amplification reaction (NEAR). Furthermore, the loop-mediated isothermal amplification method (LAMP) may comprise at least one of: reverse transcription LAMP (RT-LAMP), multiple primer LAMP (MPL), real-time LAMP, lateral flow LAMP, turbidimetric LAMP, and LAMP with clustered regularly interspaced short palindromic repeats [CRISPR] (CRISPR-LAMP). The at least one expression at the protein level may comprise at least antibody comprising at least one of: labeled antibody, and bound antibody. Moreover, the labeled antibody may comprise a label comprising at least one of: fluorescent label, luminescent label, and radioactive label. In one embodiment, the method may comprise determining the at least one antibody by means of at least one secondary labeled antibody. Detection of antibodies may be carried using immunofluorescence analytical techniques and / or enzyme-linked immunosorbent assays (ELISA), or similar analytical techniques known in the art. Furthermore, the method may be a computer-implemented method. The invention further relates to a method for distinguishing mycosis fungoides (MF) from eczema or psoriasis, the method comprising: determining an expression of at least one biomarker in a sample, differentiating between MF and eczema, and / or differentiating between MF and eczema or psoriasis. In one embodiment, the method may comprise differentiating between MF and, eczema or psoriasis, based on the expression of at least two and / or at least three biomarkers in the sample, preferably between at least four biomarkers in the sample, more preferably between at least five biomarkers in the sample. In one embodiment, at least one of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 In another embodiment, at least one of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 Unless stated otherwise, the above description describing the method differentiating between MF and eczema is also applied to describe the method differentiating between MF and eczema or psoriasis by making use of the cited biomarkers. In other words, the method may distinguish if the data analyzed by the method may be categorized as MF or, may be categorized as eczema or psoriasis, wherein eczema or psoriasis refers to a single category encompassing the possibility of at least one of eczema or psoriasis. Any reference to “eczema” in the preceding method description differentiating between MF and eczema may be understood as a reference to “eczema or psoriasis” as well. The gene names used in the definition of the currently being described method for differentiating MF from eczema or psoriasis, of the present invention are standard art- established gene names. Table 3 below provides the full names and functional annotations of these genes. Table 3. Genes distinguishing and / or separating MF from eczema or psoriasis Entrez Symbol gene Full name Function ID RNF213 57674 Ring Finger Protein 213 Ubiquitin ligase, involved in lipid metabolism, angiogenesis and immune processes NLRC5 84166 NLR Family CARD Domain Antiviral immunity Containing 5 GBP4 115361 Guanylate Binding Protein 4 Innate immunity Entrez Symbol gene Full name Function ID IKZF3 22806 IKAROS Family Zinc Finger 3 Regulation of lymphocyte development HOXC10 3226 Homeobox C10 Morphogenesis DSTN 11034 Destrin Actin metabolism ELOVL5 60481 ELOVL Fatty Acid Elongase 5 Fatty acid metabolism ROBO1 6091 Roundabout Guidance Receptor 1 Axon guidance, tumorigenesis MAPK6 5597 Mitogen-Activated Protein Kinase 6 T-cell development LCK 3932 Proto-Oncogene Tyrosine-Protein T -cell development Kinase LCK TMC8 147138 Transmembrane Channel Like 8 T-cell regulation KRT17 3872 Keratin 17 Keratinization CXCL9 4283 C-X-C Motif Chemokine Ligand 9 T cell trafficking MT-ND3 4537 Mitochondrially Encoded NADH: Mitochondrial electron Ubiquinone Oxidoreductase Core transport Subunit 3 VOPP1 81552 VOPP1 WW Domain Binding Protein NFKB1 activity FLRT3 23767 Fibronectin Leucine Rich Cell-cell adhesion Transmembrane Protein 3 TNFRSF10 8795 TNF Receptor Superfamily Member Apoptosis B 10b PNLIPRP3 119548 Pancreatic Lipase Related Protein 3 Lipid metabolism MMP1 4312 Matrix Metallopeptidase 1 Extracellular matrix HOMER1 9456 Homer Scaffold Protein 1 Scaffold protein of postsynaptic density RCSD1 92241 RCSD Domain Containing 1 Actin filament activity ENAH 55740 ENAH Actin Regulator Actin-based motility MUC16 94025 Mucin 16 Barrier formation LRATD1 151354 LRAT Domain Containing 1 Cell motility and morphogenesis H1-1 3024 H1.1 Linker Histone Chromatin formation SH2D1A 4068 SH2 Domain Containing 1A Stimulation of T and B cells PRXL2A 84293 Periredoxin Like 2A Antioxidant activity Entrez Symbol gene Full name Function ID DISC1 27185 Disrupted In Schizophrenia 1 Neurite outgrowth Protein ALOX15B 247 Arachidonate 15-Lipoxygenase Lipid metabolism Type B CALCRL 10203 Calcitonin Receptor Like Receptor Adrenomedullin receptor activity S100A11 6282 S100 Calcium Binding Protein A11 Cell cycle progression and differentiation CSRP2 1466 Cysteine And Glycine Rich Protein 2 Cellular differentiation CDK1 983 Cyclin Dependent Kinase 1 Cell cycle control USP20 10868 Ubiquitin Specific Peptidase 20 Increased expression of HIF1A targeted genes CES1 1066 Carboxylesterase 1 Metabolism pf exogenous substances GRAP2 9402 GRB2 Related Adaptor Protein 2 Leukocyte signaling ZC3H12D 340152 Zinc finger CCCH-type containing Protein coding, cell growth 12D In a further embodiment, the psoriasis may consist of at least one of: Psoriais vulgaris (Plaque-type psoriasis), inverse psoriasis, guttate psoriasis, palmoplantar psoriais, generalized pustular psoriasis, palmoplantar pustulosis, erythrodermic psoriasis, psoriasis capitis. The approach of the present invention is particularly advantageous, as it allows working with rare diseases using a small sample size, imbalanced data and classification of minority class is more important than majority class. Moreover, the approach of the present invention is notably advantageous, it permits working with high-dimensional feature space, working with highly correlated structures within high-dimensional feature space, identifying small clinically translatable signatures, working directly on gene space enabling interpretability and clinical application, and filtering for artifacts such as lowly expressed genes. In a second aspect, the invention relates to a system for diagnosing mycosis fungoides (MF) or eczema, the system comprising: a processing component configured to output at least one dataset, and an analyzing component configured to analyze the at least one dataset, wherein the analyzing component comprises: a determining module configured to determine an expression of at least one biomarker in a sample, a differentiating module configured to differentiate between MF and eczema based on the expression of the at least one biomarker in the sample, and a finding generating module configured to generate a differential diagnosis finding based on the expression of the at least one biomarker in the sample. In one embodiment, the differentiating module may be configured to differentiate between MF and eczema based on the expression of at least two biomarkers in the sample. In another embodiment, the differentiating module may be configured to differentiate between MF and eczema based on the expression of at least three biomarkers in the sample, preferably between at least four biomarkers in the sample, more preferably between at least five biomarkers in the sample. At least one of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 At least two of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 The at least one biomarker further optionally may comprise at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 Symbol Entrez gene id Symbol Entrez gene id TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 In another embodiment, the at least one biomarker may comprise at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 In one embodiment, the system may be configured to diagnose early stage of MF or eczema, wherein the system may be configured to discriminate between MF and eczema in early stage. The sample may comprise a sample taken from an individual. In another embodiment, the system may be configured to: generate a skin condition status hypothesis based on the differential diagnosis finding, predict a skin condition status of the individual based on the skin condition status hypothesis, and generate a skin condition status prediction. Moreover, the system may comprise a prediction module configured to generate the skin condition status hypothesis based on the differential diagnosis finding. In one embodiment, the prediction module may be configured to predict the skin condition status of the individual based on the skin condition status hypothesis, and generate the skin condition status prediction. In another embodiment, the prediction module may be integrated into the analyzing component. Moreover, the system may be configured to evaluate the skin status based on the differential diagnosis finding. Further, the system may be configured to evaluate the skin status based on the skin condition status prediction. Additionally or alternatively, the system may be configured to assess whether the expression of the at least one biomarker represents that the individual may be afflicted with MF or eczema. In one embodiment, the system may be configured to generate a skin condition threshold, and assign the skin condition status to the individual based on the skin condition threshold, wherein when the expression of the at least one biomarker in the sample of the individual is below the skin condition threshold, the system may be configured to output that the individual may be afflicted with eczema, and above the skin condition threshold, the system may be configured to output that the individual may be afflicted with MF. Furthermore, the system may be configured to automatically output at least one skin condition suggestion, wherein when the expression of the at least one biomarker in the sample of the individual reads below a detection limit of the skin condition threshold, the at least one skin condition suggestion comprises prompting an evaluation by a medical professional. In one embodiment, the sample taken from an individual may comprise at least one individual with a skin lesion suspected of at least one of: eczema and cutaneous lymphoma. Moreover, the system may be configured to identify at least one least one MF-Eczema differentiating gene significantly differentially expressed in the sample among at least two different expressed genes. In one embodiment, the skin condition threshold may comprise at least one MF-Eczema distinguishing parameter and a MF-Eczema distinguishing reference parameter. Further, the system may be configured to measure a fold change of the expression of at least one MF-Eczema differentiating gene. Moreover, the measure of the fold change may comprise a measure of a change in a magnitude of log2FoldChange of at least one MF-Eczema differentiating gene. In one embodiment, the system may be configured to process at least one set of samples. In another embodiment, the system may be configured to resample the at least one set of samples at least 50 times, preferably at least 100 times, more preferably at least 150 times, such as 200 times, and automatically generate a resampled dataset. In a further embodiment, the system may be configured to perform a sample cross validation to process the at least one set of samples. In another embodiment, the system may comprise executing a differential gene expression analysis between at least one of: eczema and non-lesional condition, cutaneous lymphoma and non-lesional condition, and eczema and cutaneous lymphoma. Moreover, the system may be configured to resample the at least one set of samples and automatically generate the resampled dataset via the analyzing component. Furthermore, the system may be configured to identify the at least one MF-Eczema differentiating gene, and differentiate the at least one MF-Eczema differentiating gene into at least one of the at least one MF-Eczema differentiating gene associated to eczema, and at least one of the at least one MF-Eczema differentiating gene associated to cutaneous lymphoma, wherein the system may be configured to differentiate the at least one MF- Eczema differentiating gene based on the skin condition threshold. In one embodiment, the system may comprise at least one machine learning module. In another embodiment, the system may comprise a training module configured to train the at least one machine learning module using any data of any of the method steps. Moreover, the training module may be configured to train the at least one machine learning module using a training data set comprising a normalization approach and a standard scaling. The normalization approach may comprise executing a trimmed mean of M-values (TMM) normalization. In a further embodiment, the system may be configured to identify at least one sample data set to predict the skin condition status. Furthermore, system may comprise may be configured to use the at least one MF-Eczema differentiating gene, train a recursive forward feature selection (RFFS), and generate an optimized list of MF-Eczema differentiating genes based on the at least one sample data set. Moreover, the system may be configured to optimize by means of the RFFS at least one of: the skin condition threshold, and the at least one MF-Eczema distinguishing parameter. Additionally or alternatively, the system may be configured to store the list of MF-Eczema differentiating genes in at least one of: random access memory (RAM), server, cloud, and database. In one embodiment, the system may be configured to apply robust rank aggregation on the list of genes to find at least one MF-Eczema differentiating gene consistently contained in the list, apply Benjamin-Hochberg correction for multiple testing for number of iterations and for number of MF-Eczema differentiating genes, and select at least one MF-Eczema differentiating gene from the list of MF-Eczema differentiating genes based on outcome of the robust rank aggregation and the Benjamin-Hochberg correction. Additionally or alternatively, for generating the differential diagnosis finding, the system may be configured to analyze the expression of the at least one biomarker in the sample, and calculate an expression value. Moreover, system may be configured to analyze the expression of the at least one biomarker in samples taken from at least one individual suffering eczema. In one embodiment system may be configured to analyze the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. In another embodiment, the system may be configured to calculate the expression value based on analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering eczema and the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. Moreover, the system may be configured to analyze and calculate the expression via the at least one machine learning module. The eczema may comprise at least one of: atopic eczema, contact dermatitis, dyshidrotic eczema, nummular eczema, allergic contact dermatitis, irritant dermatitis, seborrheic dermatitis, and stasis dermatitis. Furthermore, system may be configured to identify at least one stage of the mycosis fungoides, and distinguish among the least one stage, wherein the at least one stage may comprise at least one of: patch stage, plaque stage, tumor stage, and erythrodermic stage. In one embodiment, the expression of the at least one biomarker may comprise at least one expression level comprising: an expression at a mRNA level, and an expression at a protein level. Moreover, the at least one expression at the mRNA level may be determined by at least one nucleic acid amplification method. The at least one nucleic acid amplification method may comprise at least one of: polymerase chain reaction (PCR), and isothermal amplification system. The PCR may comprise at least one of: conventional PCR, real-time PCR (qPCR), reverse transcription PCR (RT-PCR), Nested PCR, Multiplex PCR, Digital PCR (dPCR), Hot start PCR, and droplet PCR. Further, the isothermal amplification method may comprise at least one of: loop-mediated isothermal amplification system (LAMP), recombinase polymerase amplification (RPA), helicase-dependent amplification system (HDA), and nicking enzyme amplification reaction (NEAR). The loop-mediated isothermal amplification method (LAMP) may comprise at least one of: reverse transcription LAMP (RT-LAMP), multiple primer LAMP (MPL), real-time LAMP, lateral flow LAMP, turbidimetric LAMP, and LAMP with clustered regularly interspaced short palindromic repeats [CRISPR] (CRISPR-LAMP). In one embodiment, the at least one expression at the protein level may comprise at least antibody comprising at least one of: labeled antibody, and bound antibody. The labeled antibody may comprise a label comprising at least one of: fluorescent label, luminescent label, and radioactive label. In a further embodiment, the system may be configured to determine the at least one antibody by means of at least one secondary labeled antibody. In one embodiment, the system may be configured to carry out any of the method as recited herein. In another embodiment, the system may comprise one computer-assisted component configured to carry out any of the method as recited herein. Furthermore, method may comprise prompting the system as recited herein to perform any of the steps of the method as recited herein. Additionally or alternatively, the system may be an automated system. The invention further relates to a system for distinguishing mycosis fungoides (MF) from eczema or psoriasis, the system comprising: a processing component configured to output at least one dataset, and an analyzing component configured to analyze the at least one dataset, wherein the analyzing component comprises: a determining module configured to determine an expression of at least one biomarker in a sample, a differentiating module configured to differentiate between MF and eczema based on the expression of the at least one biomarker in the sample, and a finding generating module configured to generate a differential diagnosis finding based on the expression of the at least one biomarker in the sample. In one embodiment, the system may be configured to differentiate between MF and, eczema or psoriasis, based on the expression of at least two and / or three biomarkers in the sample, preferably between at least four biomarkers in the sample, more preferably between at least five biomarkers in the sample. In one embodiment, at least one of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 In another embodiment, at least one of the at least one biomarker may be selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 Unless stated otherwise, the above description describing the system differentiating between MF and eczema is also applied to describe the method differentiating between MF and eczema or psoriasis by making use of the cited biomarkers. In other words, the system may distinguish if the data analyzed by the system may be categorized as MF or, may be categorized as eczema or psoriasis, wherein eczema or psoriasis refers to a single category encompassing the possibility of at least one of eczema or psoriasis. Any reference to “eczema” in the preceding system description configured to differentiated between MF and eczema may be understood as a reference to “eczema or psoriasis” as well. In a further embodiment, the psoriasis may consist of at least one of: Psoriasis vulgaris (Plaque-type psoriasis), inverse psoriasis, guttate psoriasis, palmoplantar psoriasis, generalized pustular psoriasis, palmoplantar pustulosis, erythrodermic psoriasis, psoriasis capitis. In a third aspect, the invention relates to a kit for use in a method for diagnosing eczema or mycosis fungoides, the kit comprising at least one means for quantifying an expression of at least one biomarker in at least one sample. The method may be according to method as recited herein. Additionally or alternatively, the method may be carried out by the system as recited herein. In one embodiment, the at least one means comprising at least one of: primer, and antibody. The at least one primer may comprise a forward primer and a reserve primer comprising at least one of: Entrez Symbol forward primer sequence reverse primer sequence gene id RNF213 57674 ACCGTCCAAGAAGGAGCTA TGGCATTAGCAGCTTCCCA HOMER1 9456 TCCGTAGCATAAGAGCTGAAAC TGAAGATAGGTTGTTCCCCCA NLRC5 84166 GCAGATGCTGGGGTTAGCA TTTCACCAGGTGGCTGATGC GBP4 115361 GAAAAGAAACAGGTTGAGTGGGA GGAGGACCTCGTTTGCCTTA Entrez Symbol forward primer sequence reverse primer sequence gene id IKZF3 22806 TCCTGCATCAGCTTCAATGTC GAATGGGCGTTCACCAGTA HOXC10 3226 AAAAGGAGAGGGCCAAAGCTG CTGCCTTTATCTCCTCTTTCGCT DSTN 11034 GCAAGCTCCAAGGATGCAATTA CTTGACATTCATGTTTTATGCCTTGELOVL5 60481TTCTTCTGTCAGGGCACACG CCAGAGGACACGGATAATCTTCAROBO1 6091 TCAAGTTGGGTCTGAACCTC AGTCCGTCCCAAAGCAACA MAPK6 5597 TCGGAGAAGTCCCGTTGTAT TCACAATCAAAGCTACCGTCCA LCK 3932 CGATGTGTGTGAGAACTGCC CATTTCGGATGAGCAGCGTG TMC8 147138 ACCCTGAACTTGACCCTCCA TGGAACCTCAAAACGCCAGG KRT17 3872 CAGTACAAGAAAGAACCGGTGAC CTTGCCATCCTGGACCTCTT CXCL9 4283 GGGCATCATCTTGCTGGTTCT TTCTCACTACTGGGGTTCCTTG VOPP1 81552 GAGGCTGTGGTACTTCTGGTTC CAGCAGAAAAGCACGCCCAT FLRT3 23767 TTCTCGTCTTCCTGGGTTCTG TCATGGTCAGCAGTGTTGAGGT TNFRSF10B 8795 TTACCTGAAAGGCATCTGCTCA AGGTCGTTGTGAGCTTCTGT PNLIPRP3 119548 TGGCACATCAAGAGGAAAAGA ACCATCTTTGAAACACCCTAACCT MMP1 4312 TTGTCAGGGGAGATCATCGG TCCTCCAGGTCCATCAAAAGG RCSD1 92241 TGAAGGACATGGAGGAAAGACC ACGCCGAGTTGTCCACATTG ENAH 55740 CGGCACCATGAGTGAACAG GCACCCACTTCTTATTGGCA MUC16 94025 GGCCTCTCGTATCTGTGAGC AGGGGTCCAGTACCGACG LRATD1 151354 GGAAATCACGGATCCCCGC GTGGGTGATGCGGTCCAGTT H1-1 3024 AGGCAACGGGTGCATCTAAA TTCGGAGTCTTGACGCTCTT SH2D1A 4068 TGTACTGCCTATGTGTGCTGTAT GTCTGGGACACTCGGTATGT PRXL2A 84293 CTTGGGGGAGTTTTCGTGGT GCTCAAGAAGAATGCCCTGCT DISC1 27185 CCCAACTACCAGCCTTGCTT CGTCCAGAAATGGTTTCCTGAT ALOX15B 247 GTCAGTGCAGGGCAGTTTGA TTGGGCATCCAAGCACAGG CALCRL 10203 ATGCAAGACCCCATTCAACAA CCATCCATCCCAGGTTCTGTT S100A11 6282 CTCAGCTCCAACATGGCAAAA TGGAAGACAGCAATCAGGGAC CSRP2 1466 TCCAGATGTGGGGATTCTGT AGTTTTTGTGCCAGGGCTTTC CDK1 983 TCGTCATCCAAATATAGTCAGTCTT CTGGAATCCTGCATAAGCACA USP20 10868 GGCATAAGAAACGGCCAAGC TCGGAACATTGGGTTGACCA CES1 1066 CCTCGTACCCTCCTATGTGC CTCTGAGAGTAACTGCCCCG GRAP2 9402 ATTTCCACCAGGAACGCCG TTCATTTCACTGCCCAAGCC Furthermore, the kit may comprise at least one means for obtaining a skin sample. Moreover, the kit may comprise at least one means for isolating RNA from the skin sample. The at least one means for isolating RNA from the skin sample may comprise at least one of: organic extraction, spin column extraction, and magnetic particle extraction. Further, the kit may comprise at least one means for enriching RNA from the skin sample. The at least one means for enriching RNA from the skin sample may comprise at least one of: poly (A) enrichment, enzymatic removal, and probe-based depletion. In one embodiment, the kit may comprise at least one skin analysis approach. The analysis of the skin may comprise performing PCR. In another embodiment, the kit may comprise at least one means for preparing tissue sections comprising at least one of: cryotome; microtome; non-invasive or minimally- invasive sampling tools such as curettes, tapes, stickers; biopsy punch; and microneedles. Further, the kit may comprise a machine-readable set of instructions, which when executed provides to an authorized user instructions for prompting the authorized user to at least one of: prompt the system according to any of the preceding system embodiments to carry out the method according to any of the preceding method embodiments, prompt the method according to any of the preceding method embodiments, and use the kit according to any of the preceding kit embodiments. Further, the at least one means for obtaining the skin sample may comprise a processing device for tape strips, skin scrapings, punch biopsies, micro- or minibiopsies, microtome sections from Formalin-Fixed Paraffin-Embedded (FFPE) tissue. Moreover, the invention relates to a kit for use in a method for distinguishing mycosis fungoides from eczema or psoriasis, the kit comprising at least one means for quantifying an expression of at least one biomarker in at least one sample. The method may be according to method as recited herein. Additionally or alternatively, the method may be carried out by the system as recited herein. In one embodiment, the at least one means comprising at least one of: primer, and antibody. The at least one primer may comprise a forward primer and a reserve primer comprising at least one of: Entrez Symbol forward primer sequence reverse primer sequence gene id RNF213 57674 ACCGTCCAAGAAGGAGCTA TGGCATTAGCAGCTTCCCA HOMER1 9456 TCCGTAGCATAAGAGCTGAAAC TGAAGATAGGTTGTTCCCCCA NLRC5 84166 GCAGATGCTGGGGTTAGCA TTTCACCAGGTGGCTGATGC GBP4 115361 GAAAAGAAACAGGTTGAGTGGGA GGAGGACCTCGTTTGCCTTA IKZF3 22806 TCCTGCATCAGCTTCAATGTC GAATGGGCGTTCACCAGTA Entrez Symbol forward primer sequence reverse primer sequence gene id HOXC10 3226 AAAAGGAGAGGGCCAAAGCTG CTGCCTTTATCTCCTCTTTCGCT DSTN 11034 GCAAGCTCCAAGGATGCAATTA CTTGACATTCATGTTTTATGCCTTGELOVL5 60481TTCTTCTGTCAGGGCACACG CCAGAGGACACGGATAATCTTCAROBO1 6091 TCAAGTTGGGTCTGAACCTC AGTCCGTCCCAAAGCAACA MAPK6 5597 TCGGAGAAGTCCCGTTGTAT TCACAATCAAAGCTACCGTCCA LCK 3932 CGATGTGTGTGAGAACTGCC CATTTCGGATGAGCAGCGTG TMC8 147138 ACCCTGAACTTGACCCTCCA TGGAACCTCAAAACGCCAGG KRT17 3872 CAGTACAAGAAAGAACCGGTGAC CTTGCCATCCTGGACCTCTT CXCL9 4283 GGGCATCATCTTGCTGGTTCT TTCTCACTACTGGGGTTCCTTG VOPP1 81552 GAGGCTGTGGTACTTCTGGTTC CAGCAGAAAAGCACGCCCAT FLRT3 23767 TTCTCGTCTTCCTGGGTTCTG TCATGGTCAGCAGTGTTGAGGT TNFRSF10B 8795 TTACCTGAAAGGCATCTGCTCA AGGTCGTTGTGAGCTTCTGT PNLIPRP3 119548 TGGCACATCAAGAGGAAAAGA ACCATCTTTGAAACACCCTAACCT MMP1 4312 TTGTCAGGGGAGATCATCGG TCCTCCAGGTCCATCAAAAGG RCSD1 92241 TGAAGGACATGGAGGAAAGACC ACGCCGAGTTGTCCACATTG ENAH 55740 CGGCACCATGAGTGAACAG GCACCCACTTCTTATTGGCA MUC16 94025 GGCCTCTCGTATCTGTGAGC AGGGGTCCAGTACCGACG LRATD1 151354 GGAAATCACGGATCCCCGC GTGGGTGATGCGGTCCAGTT H1-1 3024 AGGCAACGGGTGCATCTAAA TTCGGAGTCTTGACGCTCTT SH2D1A 4068 TGTACTGCCTATGTGTGCTGTAT GTCTGGGACACTCGGTATGT PRXL2A 84293 CTTGGGGGAGTTTTCGTGGT GCTCAAGAAGAATGCCCTGCT DISC1 27185 CCCAACTACCAGCCTTGCTT CGTCCAGAAATGGTTTCCTGAT ALOX15B 247 GTCAGTGCAGGGCAGTTTGA TTGGGCATCCAAGCACAGG CALCRL 10203 ATGCAAGACCCCATTCAACAA CCATCCATCCCAGGTTCTGTT S100A11 6282 CTCAGCTCCAACATGGCAAAA TGGAAGACAGCAATCAGGGAC CSRP2 1466 TCCAGATGTGGGGATTCTGT AGTTTTTGTGCCAGGGCTTTC CDK1 983 TCGTCATCCAAATATAGTCAGTCTT CTGGAATCCTGCATAAGCACA USP20 10868 GGCATAAGAAACGGCCAAGC TCGGAACATTGGGTTGACCA CES1 1066 CCTCGTACCCTCCTATGTGC CTCTGAGAGTAACTGCCCCG GRAP2 9402 ATTTCCACCAGGAACGCCG TTCATTTCACTGCCCAAGCC ZC3H12D 340152 TTCTCTGCGACCCATAGTGA TCTTTATTTCCATGGCTCATCGC Unless stated otherwise, the above description describing the kit differentiating between MF and eczema is also applied to describe the kit differentiating between MF and eczema or psoriasis by making use of the biomarkers used by the corresponding method. In other words, the kit may distinguish if the data analyzed by the method may be categorized as MF or, may be categorized as eczema or psoriasis, wherein eczema or psoriasis refers to a single category encompassing the possibility of at least one of eczema or psoriasis. Any reference to “eczema” in the preceding kit description configured to differentiated between MF and eczema may be understood as a reference to “eczema or psoriasis” as well. In a fourth aspect, the invention relates to the use of the system as recited herein for carrying out the method according as recited herein. Moreover, the use of the kit as recited herein in the method as recited herein. Furthermore, the use of the at least one biomarker for diagnosing eczema or MF in a sample taken from an individual, the at least one biomarker comprising at least one of: Symbol Entrez gene id Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 RCSD1 92241 GBP4 115361 ENAH 55740 IKZF3 22806 MUC16 94025 HOXC10 3226 LRATD1 151354 DSTN 11034 H1-1 3024 ELOVL5 60481 SH2D1A 4068 ROBO1 6091 MAPK6 5597 LCK 3932 PRXL2As 84293 TMC8 147138 DISC1 27185 KRT17 3872 ALOX15B 247 CXCL9 4283 CALCRL 10203 MT-ND3 4537 S100A11 6282 VOPP1 81552 CSRP2 1466 FLRT3 23767 CDK1 983 TNFRSF10B 8795 USP20 10868 PNLIPRP3 119548 CES1 1066 MMP1 4312 GRAP2 9402 :sh h t t g g n n elelt 9931 0080705708221 0701g 8 7 56 7 57 6 65 5 7 75 5 58 6 7 75 5 64 6 77 5nic u w d ollor ofp eh htt e g 91 00935392000202041 0080001 291 1 1 1sin 1 222 1 222 1 222 22 2 2 22 2 22 1 222 2 21 222 2rpelmo GcTA T TTC y a A CCT A T C T CCA GCT C G C CGGTT G CG TA T CA TTGTA T AGGA TTTCG TA GTCmACGTACGCATTGTCAAGTATCGGTGGTGG TAr r CCTTTTTTCGGACCCGTCAT CTTCGTGTC e e CCACGTAAA CCTTCTC AAGCATACCACGCGmimiec C GCATTAACGC TC CTCTTC CGACCTCTCTAG TTGCCTTCCA GGTCAATGC GCTATC A r r n TT TCTTA A GCGCTCACCTCTGGGTC TGAp pC e C TACTGGTCCAGA GCTCACGCCTG GAGTe e GGTCCGGACGAGGCGAAATTAGATA GGAAAv s u GTGGTTTCAGAAGG A AC TTCGCAGAACCCr r q ATTCTCAAAAGATTGCGACGCG T TAC CGes e e CGGT TC CATACCAGTGTTT GTAGACCGGTe v s GGre AAGCG CATCCAACC AAG GTTATCCAACCA C TTAC GTTAACTTT GCCATAAATTCTC e r TTACGh TACAGT GTC C TAGGTGCATATT GAACGGGGGAAAGT CCG GTAGCTTAGAACCGTAtA d CGCGGCC AACACACAAGCCTCCCGG ACG ATA AA CGTC GCATA CCGTACCCGGGGTCC CTATG n TAA G TCT GT T T G G T GAaGGTGAG A TG GC AGG CTT AGT TTTC A T GCAGTCG TAA CG CGCCCCGTCTTCGGTC G GGT r T T T G G C C C A T C T C T C T A A T A G A G T G G C T C T A C emih r tpg 92931 1 20000031 21 21 02909030001 1 05 n 1 21 22 2 22 2 22 2 22 2 2 22 2 21 21 222 22 2 22 2draelwr A TofC T C G A A C C T C e A G GT GTT AA C AT AA ThtA GCT TCTGCTCATCTGCTGGA C A TTAAATG gnr Aie TAAT A CGCGGGACTACCGTGTCAGGGGCAGTGTGCAGA TGATACACTG CGTCGTGC ACAAAGA GCTAATC TCGTTGATAC CTCT T CCTsirme GCAGAAGCATC C CTTAAAAGCCGGGTA TGpir c A TTAATA TAC GTGCGCGATCTT TGCGTA GGTTCCACAGACATTGT TG G AGCTATGAT m p n AG TC GG CACCGAGAAGTTCTCTTCCTT Ao d e GGGGTGGGTCGAGGATCA CAGGTTC AAGTcr u AA GC G CCAGATTCGGGGGT TTTGGCCGAr a q AAGAGGAG ATCGTAT GAAGAAG A AGCAGA e w e s GTGC AATAAG AA CCGTTTACGC AGTGTGGTGCGCAG GGGCGTGTACGATGCGCACAC TAmir ACCACTGCT ATAAATTAT CACAG G CAC Cr o C GA ATGGAGA CGCAAGACTCCCGATG CTGCpf CGTGAGCTTGT ATTTGCGGCCTAG G T G T C T e TATAAC GCTAGGC TAACGTAA ATAAT C A A A A noG GAGGATGGTCTCGCCCCGCCACCG GAGGC G TA TA TC TA A AACAAT t CCAA A A CACACCAG CGGG G AGCAGTG G AGACA G GGG TCC T TGCCG s C AC T G GCA T A GT TC TC C T C TTGTG GG C TTCC a A C G G T T T T T C G G A T C C G A C T Teltaz di4 1 8 8 4e er e 76666641 1 72323275421 0554835 326 56302389931 7856951 4423 9870 3h t 641 582040597825779 270 2621 42868 t,n n e 7941 2 31 0 3 1 00 6 4 1 3 1 2545 47 2024 9t E g 5 81 2 1 6 53 1 348 2 81 495 9 1 348 2 1 61n emid l B o o 0 b b 31 1 3 B 1 R 0 1 51 F P 1 AA 5 L1mem 1 y 2 E5 3 C L 6 O MC4 F XNV K 8791 3 S RP 1 1 6 1 D 1 21 1 RA21 BP 1 LP TRI P DHCT1 DLCXC0 Pe S F RP T C BZ O AK TCP RF LMS AUA- 2 XS OL0 RKn NOL OS O RXO C 1 I LA S D R H N K L CM LNN N R HR 1oG I H D E R M L T K C V F T P M R E M L H S P D A C S C Cn I t hat e g h nte,ll a t 769 u c 75 7diu dvid or nip n a ht m g 000 o n 22 2 rfelnekateld piAG e r CCC ma n e C ACC CGse Agmie GC r c TGAaz 4 1 0 5pn TTC n Ciert 6 5 3 5 3 8 5 42 47 2 3 4 8 2 6 6 2 0 1 2 6 92 81 7 02 8 6 6e e G u CsiGA s n 4 5 3 8 6 0 C E 9 29 55 4 0 0 9 1 3 4 48 7 2 4 0 4 2 42 01 6 1 89 0s Ga1 1 9r q GATie e TT r v s TGC os e AAA r C CpAGT r AA GTToGT GCA a B 1 CTC m l 1 T CT e o R T z 1 6 1 A 1 A 51 L 1 c b E D H 1 D D 2 2 2 L 1 X R A P 1 0 2 1 P e m M S A C TA 1- 2 X C C 0 S O L 0 R K P S A h m y O C U 1 I A S D S E R S H R N E R H R L 1 t M L H S P D A C S C C U g 9 o C G 00 n 22 1 rfelF d Mig e nin h e sigC u z 1 8 4 6r GC GG g e 6 rt 3 6 4 1 3 2 7 76 61 5 0 6 1 7 2 1 2 3 7 5 8 2 30 84 9 9 3 7 7 8 3 55 6 e A ATCnimC tie CGGsin 1 2 7 9 E 75 48 1 22 3 1 0 5 9 4 8 2 5 7 1 06 6 5 3 1 3 4 4 18 32 8r c CTdn GAC p r e GTA A o dr u CC CGfr:fa q ATG eoe AC k e Bwr s ACA r C a n 01o GC f A Col t 3 0 5 3 F AAA m o ois b 1 5 3 1 C L 1 6 7 9 D 1 3 S TTC a 2 C 4P F X N O K 8 T V B P 1 L N P T R AGCbelm F R C L B Z S O A K T C -T P R F CCTT e y N O n t S R N G K L O C M R X O L N GCTaI H D E R M L T K C M V F T T G C Aot g sanid el siz r ei8 rt e 662 t p 86004am n n 0 e o Ee 1 g 1 9 htcf r o ek e r s a l oum b eoim 0 2 h tby 21 P,e S P S S A r n ER eoU C G ht t r s u a Fel :sh h t t g g n n elel501 03583021 9931 00807057082 g tc 876 76 7 56 7 5 76 6 55 7 7 55 5 86 7 75 5 niu w d ollor ofp eh h t t g 91 00935392000202041 1 sin 1 22 21 222 1 222 2 22 2 2 22 2 21 222 2 rpelmo G T di cTA TTC C y CCT A G T C Te a A CCA GCT C CGGTTTG GTAG GG TTTC n emACGTACGCATTGTT CAAGA A AT T C TGG r r CCTTTTTTC GAC CGTTAT GGCTTge e CCACGTAAAGCCTCCTCC AGCATACz ermimiec C GCATTAACGC TCTGCCTTCCA CTTC GTTCA TCACGAC ATGCCTC GC t 2 r r n 13p pn TTTCT e CGGTCTTA A GCGCG TACTGGTCCAG CG ATCAC T CTCACTG CGCGG GACGAGGCGGAACTAGACT E 4 ev e u GTG CTT AGAAGG AAACTTT GTA r sr q ATTGT ACAAGAT GCGACGC CTCA e e e CGGCTC ACATACTAGT TTTGGTAG s s e v GG TGTCTCC ACCCAAGGGTTATCCA r e AAGCCATTACA TTAACTT GCCATAA e r TTACG h TACAGT GTCGC TA ATATTTGAACGGGG CGGT AGCCCG GTAGCTTAGA t Al d CGCGGCAAC CACAAGCCTCCCGGGGA AAGTC GCA CTTA CCGTTACTCCGG TC o n GACAAGGTATATGGTG GC GAGGGCT b 1aG TGT TTGATTC T G A TCAA C GC CCGTCTC T G G C C C AC T CG T CT T CC T A AC T P r T A G A G T G Gm y M e S Mmih r t p g 92931 1 20000031 21 21 02909030 n 1 21 222 22 2 2 22 2 22 2 2 22 2 1 21 222didraele n wr e og fA G C A C T z 8 2 e C G GA G C AA C Ae 4 5 h A A GCTTT TC TTT CTGCGGA C ATT rt 5 1t9 0 g r ATAA CTTCAGC GGGACTACAG T C GTTA GG CAGG GT CCAGAAGGAT n 1 4nie CGG TACAC GC TGTGACA A TCG E 1 3siTAAAA CCGTTCGTTT TACGCCGT rmie GC GAAGAATCCCGCGCAA ATC TG p r c AGTTCCTCATACATT TGCGAGCTGC mpn G o e GATTT A GGGTCGGGGACACTGAGTGGTCATT GTCGAGGCGCAAATCTC GT c dr u A q AAGGCGG CCA ATATGGGGGTAG AGA TT GGAGTCGGTATTC AAGAAGT T r a e GTGC GAAGTTTACGCG GTGTGGAG 3 e w s AATAAACC GGC TGTAA C ATGCGTA lo P D R 2mir C r o AC ACG ATACTGA TGGAT G AAATTA CGCATG GCA ACCAGCG T CC b P 1 f CGGT A CTT GAATTTGAG C C G TAG I HpCAAGCG CTGTGCACGTCAGACTA TGm L 3 e TT AGGGTGAGTAC CCACG A CC CG y S N C n GGGATAA P ZoTAGTCT GTCCTA ACAG t CCAACAA s C ACCA T G GCACCAGACGGG G AGCAGTT T A GT TC TCGCAGAC T C A C G GTA TT TGG TT GGGG T TG T C G G AG a T C elt a z di4661 8 8 4 66641 27 1 05 3 e er e 7 6 0 381 72323565424425489 h t 651 382049931 7857951 2703262 t , n n e 74 t E g 5945 81 22 1 2311 0059782 6 6 5 34 1 34183793 2 81 1 4295541 00 95 1 3448 n e mid l B o o 03 b b 31 R 0 1 P A 5 1 51 6 F R 1 61 1 A m e m 1 y 2 EC43 L 791 3 S F CNVOK 81 LP TRP D 2 I 1P DH1 T1 DL e S F MRP ZXTOBP AKCTCP RF L S ACA- 2 X n NOLB OS O RXO M U 1 R K L CM LNN CN R HR o H N G I H D E R M L T K C V F T P M R E M L H S P n I ht g n elt 21 0701 7690 c 64 67 7 57 5 7 6 u d or p ht g 291 1 1 1 0003 n 21 222 22 2 2 2 elT C A TC G ACA G r TGTGTCAG C ACCCT e CGGGTT C CA CATG CACGCmie TCCTACG Cr c TATCGAGCA AT Cpn TCGTGATG GTTTCG e e GG A AG s u CCG AGAACGACT r q GTAA CCC GA e e GCGGATTCv s A AC CC CAGTT TCTGC AC e ATTCGCAA T r GAAA TCGCT ACCGTAAATT CGCATTAAGTA CGTATGGTTT T AGT GG GGCATT TCGGTCTC C T C T A C T CT TC T ht g 001 1 050090 n 22 22 2 22 2 1 2 elTT C AA T TAAATGC A r TGCAGAGCG CTAATCAGCG e T T T Tmie G ACC A C TTC TGCG G r c TG G ATGTACTGA AT ACTpn d e C T CCAT AGAGTAA TGCAC r u q GGCCG CC AGC GAATGC a e GCA AACGCw s CACAGAA AAr CCACTC CCGo f ATGGCTGTCG ACCC CTACAT A AGA AA AG TTCT AACGGC CC AATAGCC CCTT CTGCCG T TT T A CC TCGCTC C G T G C AT T z d ei5 3 2 r 26 8625 t e 87 1 4028 36 1 2648860 n n 720 90040 Ee 2 1 61 1 1 94 g 3 l o b B D 5 1 2 m 1 1 L R1 A2 0 21 y CXC0 P 1 21 P H S SI OL LA0 RKP S A3 1 S DS ERC D A C S C C U C G Z The present technology is also described by the following numbered embodiments. Below, method embodiments will be discussed. These embodiments are abbreviated by the letter “M” followed by a number. When reference is herein made to a method embodiment, those embodiments are meant. M1. A method for diagnosing mycosis fungoides (MF) or eczema, the method comprising determining an expression of at least one biomarker in a sample, differentiating between MF and eczema based on the expression of the at least one biomarker in the sample, and generating a differential diagnosis finding based on the expression of the at least one biomarker in the sample. M2. The method according to the preceding embodiment, wherein the method comprises differentiating between MF and eczema based on the expression of at least two biomarkers in the sample. M3. The method according to any of the two preceding embodiments, wherein the method comprises differentiating between MF and eczema based on the expression of at least three biomarkers in the sample, preferably between at least four biomarkers in the sample, more preferably between at least five biomarkers in the sample. M4. The method according to any of the preceding embodiments, wherein at least one of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 M5. The method according to the preceding embodiment, wherein at least two of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 M6. The method according to any of two preceding embodiments, wherein the at least one biomarker further optionally comprises at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 M7. The method according to any of three preceding embodiments, wherein the at least one biomarker comprises at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 M8. The method according to any of the preceding embodiments, wherein the method is for diagnosing early stage of MF or eczema, wherein the method comprises discriminating between MF and eczema in early stage. M9. The method according to the preceding embodiment, wherein the sample comprises a sample taken from an individual. M10. The method according to any of the preceding embodiments, wherein the method comprises generating a skin condition status hypothesis based on the differential diagnosis finding, predicting a skin condition status of the individual based on the skin condition status hypothesis, and generating a skin condition status prediction. M11. The method according to the preceding embodiment, wherein the evaluating step is based on the differential diagnosis finding. M12. The method according to any of the two preceding embodiments, wherein the evaluating step is based on the skin condition status prediction. M13. The method according to any of the preceding embodiments, wherein the method comprises assessing whether the expression of the at least one biomarker represents that the individual is afflicted with MF or eczema. M14. The method according to any of the preceding embodiments, wherein the method comprises generating a skin condition threshold, and assigning the skin condition status to the individual based on the skin condition threshold, wherein when the expression of the at least one biomarker in the sample of the individual is below the skin condition threshold, the method concludes that the individual is afflicted with eczema, and above the skin condition threshold, the method concludes that the individual is afflicted with MF. M15. The method according to any of the preceding two embodiments, wherein the method further comprises automatically generating at least one skin condition suggestion, wherein when the expression of the at least one biomarker in the sample of the individual reads below a detection limit of the skin condition threshold, the at least one skin condition suggestion comprises prompting an evaluation by a medical professional. M16. The method according to any of the two preceding embodiments, wherein the assessing step is based on the differential diagnosis finding. M17. The method according to any of the three preceding embodiments and with the features of embodiment M10, wherein the assessing step is based on the skin condition status prediction. M18. The method according to the preceding embodiment and with the features of embodiment M9, wherein the sample taken from an individual comprises at least one individual with a skin lesion suspected of at least one of: eczema and cutaneous lymphoma. M19. The method according to any of the preceding embodiments, wherein the method comprises identifying at least one MF-Eczema differentiating gene significantly differentially expressed in the sample among at least two different expressed genes. M20. The method according to any of the preceding embodiments and with the features of embodiment M14, wherein the skin condition threshold comprises at least one MF- Eczema distinguishing parameter and a MF-Eczema distinguishing reference parameter. M21. The method according to the two preceding embodiments, wherein the method comprises measuring a fold change of the expression of at least one MF-Eczema differentiating gene. M22. The method according to the preceding embodiment, wherein the measuring of the fold change comprises measuring change in a magnitude of log2FoldChange of at least one MF-Eczema differentiating gene. M23. The method according to any of the preceding embodiments, wherein the method comprises processing at least one set of samples. M24. The method according to the preceding embodiment, wherein processing the at least one set of sample comprises resampling the set of samples at least 50 times, preferably at least 100 times, more preferably at least 150 times, such as 200 times; and automatically generating a resampled dataset. M25. The method according to any of the two preceding embodiments, wherein the processing the at least one set of sample comprises performing sample cross validation. M26. The method according to any of the preceding embodiments and with the features of any of embodiments M19 to M22, wherein the method comprises executing a differential gene expression analysis between at least one of: eczema and non-lesional condition, cutaneous lymphoma and non-lesional condition, and eczema and cutaneous lymphoma. M27. The method according to any of the preceding embodiments and with the features of embodiment M20, wherein the method comprises identifying the at least one MF-Eczema differentiating gene, and differentiating the at least one MF-Eczema differentiating gene into at least one of the at least one MF-Eczema differentiating gene associated to eczema, and at least one of the at least one MF-Eczema differentiating gene associated to cutaneous lymphoma, wherein differentiating of the at least one MF-Eczema differentiating gene is based on the skin condition threshold. M28. The method according to any of the preceding embodiments, wherein the method comprises training at least one machine learning module using any data of any of the method steps. M29. The method according to the preceding embodiment, wherein the training comprises using a training data set comprising a normalization approach and a standard scaling. M30. The method according to the preceding embodiment, wherein the normalization approach comprises executing a trimmed mean of M-values (TMM) normalization. M31. The method according to any of the three preceding embodiments and with the features of embodiment M10, wherein the method comprises identifying at least one sample dataset for the predicting step. M32. The method according to the preceding embodiment and with the features of embodiment M19, wherein for identifying the at least one sample data set for the predicting step, the method comprises using the at least one MF-Eczema differentiating gene, training a recursive forward feature selection (RFFS), and generating an optimized list of MF-Eczema differentiating genes based on the at least one sample dataset. M33. The method according to the preceding embodiment and embodiments M14 and / or M20, wherein the method comprises optimizing by means of the RFFS at least one of: the skin condition threshold, and the at least one MF-Eczema distinguishing parameter. M34. The method according to any of the two preceding embodiments, wherein the method comprises storing the list of MF-Eczema differentiating genes in at least one of: random access memory (RAM), server, cloud, and database. M35. The method according to any of the preceding embodiments and with feature of the preceding embodiment, wherein the method comprises applying robust rank aggregation on the list of MF-Eczema differentiating genes to find at least one MF-Eczema differentiating gene consistently contained in the list; applying Benjamin-Hochberg correction for multiple testing for number of iterations and for number of MF-Eczema differentiating genes, and selecting at least one MF-Eczema differentiating gene from the list of MF-Eczema differentiating genes based on outcome of the robust rank aggregation and the Benjamin- Hochberg correction. M36. The method according to any of the preceding embodiments, wherein for generating the differential diagnosis finding, the method comprises analyzing the expression of the at least one biomarker in the sample, and calculating an expression value. M37. The method according to the preceding embodiment, wherein the method comprises analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering eczema. M38. The method according to any of the two preceding embodiments, wherein the method comprises analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. M39. The method according to the three preceding embodiments, wherein calculating the expression value is based on analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering eczema and the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. M40. The method according to any of the four preceding embodiments and with the features of embodiment M28, wherein the analyzing and calculating steps is performed by the at least one machine learning module. M41. The method according to any of the preceding embodiments, wherein the eczema comprises at least one of: atopic eczema, contact dermatitis, dyshidrotic eczema, nummular eczema, allergic contact dermatitis, irritant dermatitis, seborrheic dermatitis, and stasis dermatitis. M42. The method according to any of the preceding embodiments, wherein the method comprises identifying at least one stage of the mycosis fungoides, and distinguishing among the least one stage, wherein the at least one stage comprises at least one of: patch stage, plaque stage, tumor stage, and erythrodermic stage. M43. The method according to any of the preceding embodiments, wherein the expression of the at least one biomarker comprises at least one expression level comprising an expression at a mRNA level, and an expression at a protein level. M44. The method according to the preceding embodiment, wherein the at least one expression at the mRNA level is determined by at least one nucleic acid amplification method. M45. The method according to the preceding embodiment, wherein the at least one nucleic acid amplification method comprises at least one of: polymerase chain reaction (PCR), and isothermal amplification method. M46. The method according to the preceding embodiment, wherein the PCR comprises at least one of: conventional PCR, real-time PCR (qPCR), reverse transcription PCR (RT-PCR), Nested PCR, Multiplex PCR, Digital PCR (dPCR), Hot start PCR, and droplet PCR. M47. The method according to any of the two preceding embodiments, wherein the isothermal amplification method comprises at least one of: loop-mediated isothermal amplification method (LAMP), recombinase polymerase amplification (RPA), helicase- dependent amplification method (HDA), and nicking enzyme amplification reaction (NEAR). M48. The method according to the preceding embodiment, wherein the loop-mediated isothermal amplification method (LAMP) comprises at least one of: reverse transcription LAMP (RT-LAMP), multiple primer LAMP (MPL), real-time LAMP, lateral flow LAMP, turbidimetric LAMP, and LAMP with clustered regularly interspaced short palindromic repeats [CRISPR] (CRISPR-LAMP). M49. The method according to any of the three preceding embodiments, wherein the at least one expression at the protein level comprises at least antibody comprising at least one of: labeled antibody, and bound antibody. M50. The method according to the preceding embodiment, wherein the labeled antibody comprises a label comprising at least one of: fluorescent label, luminescent label, and radioactive label. M51. The method according to any of the two preceding embodiments, wherein the method comprises determining the at least one antibody by means of at least one secondary labeled antibody. M52. The method according to any of the preceding embodiments, wherein the method is a computer-implemented method. M53. The method according to any of the preceding embodiments, wherein the method is a method for distinguishing MF and, eczema or psoriasis, wherein any reference to “eczema” in any of the preceding method embodiments is a reference to “eczema or psoriasis”. M54. The method according to the preceding method embodiment, with the features of M1, M2 and / or M3, wherein at least one of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 M55. The method according to the preceding method embodiment, wherein at least two of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 M56. The method according to any of the preceding embodiments, wherein the psoriasis comprises at least one of: Psoriasis vulgaris (Plaque-type psoriasis), inverse psoriasis, guttate psoriasis, palmoplantar psoriasis, generalized pustular psoriasis, palmoplantar pustulosis, erythrodermic psoriasis, psoriasis capitis. Below, system embodiments will be discussed. These embodiments are abbreviated by the letter “S” followed by a number. When reference is herein made to a system embodiment, those embodiments are meant. S1. A system for diagnosing mycosis fungoides (MF) or eczema, the system comprising a processing component configured to output at least one dataset, and an analyzing component configured to analyze the at least one dataset, wherein the analyzing component comprises a determining module configured to determine an expression of at least one biomarker in a sample, a differentiating module configured to differentiate between MF and eczema based on the expression of the at least one biomarker in the sample, and a finding generating module configured to generate a differential diagnosis finding based on the expression of the at least one biomarker in the sample. S2. The system according to the preceding embodiment, wherein the differentiating module is configured to differentiate between MF and eczema based on the expression of at least two biomarkers in the sample. S3. The system according to any of the two preceding embodiments, wherein the differentiating module is configured to differentiate between MF and eczema based on the expression of at least three biomarkers in the sample, preferably between at least four biomarkers in the sample, more preferably between at least five biomarkers in the sample. S4. The system according to any of the preceding system embodiments, wherein at least one of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 S5. The system according to the preceding system embodiment, wherein at least two of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 S6. The system according to any of two preceding embodiments, wherein the at least one biomarker further optionally comprises at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 S7. The system according to any of three preceding embodiments, wherein the at least one biomarker comprises at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 S8. The system according to any of the preceding system embodiments, wherein the system is configured to diagnose early stage of MF or eczema, wherein the system is configured to discriminate between MF and eczema in early stage. S9. The system according to the preceding embodiment, wherein the sample comprises a sample taken from an individual. S10. The system according to any of the preceding embodiments, wherein the system is configured to generate a skin condition status hypothesis based on the differential diagnosis finding, predict a skin condition status of the individual based on the skin condition status hypothesis, and generate a skin condition status prediction. S11. The system according to any of the preceding system embodiments and with the feature of the preceding embodiment, wherein the system comprises a prediction module configured to generate the skin condition status hypothesis based on the differential diagnosis finding. S12. The system according to any of the two preceding embodiment and with the features of the preceding embodiment, wherein the prediction module is configured to predict the skin condition status of the individual based on the skin condition status hypothesis, and generate the skin condition status prediction. S13. The system according to any of the preceding two embodiment, wherein the prediction module is integrated into the analyzing component. S14. The system according to the preceding embodiment, wherein the system is configured to evaluate the skin status based on the differential diagnosis finding. S15. The system according to any of the two preceding embodiments, wherein the system is configured to evaluate the skin status based on the skin condition status prediction. S16. The system according to any of the preceding system embodiments, wherein the system is configured to assess whether the expression of the at least one biomarker represents that the individual is afflicted with MF or eczema. S17. The system according to any of the preceding system embodiments, wherein the system is configured to generate a skin condition threshold, and assign the skin condition status to the individual based on the skin condition threshold, wherein when the expression of the at least one biomarker in the sample of the individual is below the skin condition threshold, the system is configured to output that the individual is afflicted with eczema, and above the skin condition threshold, the system is configured to output that the individual is afflicted with MF. S18. The system according to any of the two preceding embodiments, wherein the system is configured to automatically output at least one skin condition suggestion, wherein when the expression of the at least one biomarker in the sample of the individual reads below a detection limit of the skin condition threshold, the at least one skin condition suggestion comprises prompting an evaluation by a medical professional. S19. The system according to the preceding embodiment and with the features of embodiment S9, wherein the sample taken from an individual comprises at least one individual with a skin lesion suspected of at least one of: eczema and cutaneous lymphoma. S20. The system according to any of the preceding embodiments, wherein the system is configured to identify at least one least one MF-Eczema differentiating gene significantly differentially expressed in the sample among at least two different expressed genes. S21. The system according to any of the preceding embodiments and with the feature of embodiment S17, wherein the skin condition threshold comprises at least one MF-Eczema distinguishing parameter and a MF-Eczema distinguishing reference parameter. S22. The system according to the two preceding embodiments, wherein the system is configured to measure a fold change of the expression of at least one MF-Eczema differentiating gene. S23. The system according to the preceding embodiment, wherein the measure of the fold change comprises a measure of a change in a magnitude of log2FoldChange of at least one MF-Eczema differentiating gene. S24. The system according to any of the preceding system embodiments, wherein the system is configured to process at least one set of samples. S25. The system according to any of the preceding system embodiments, wherein the system is configured to resample the at least one set of samples at least 50 times, preferably at least 100 times, more preferably at least 150 times, such as 200 times, and automatically generate a resampled dataset. S26. The system according to any of the two preceding embodiments, wherein the system is configured to perform a sample cross validation to process the at least one set of samples. S27. The system according to any of the preceding embodiments and with the features of any of embodiments S20 to S22, wherein the system comprises executing a differential gene expression analysis between at least one of: eczema and non-lesional condition, cutaneous lymphoma and non-lesional condition, and eczema and cutaneous lymphoma. S28. The system according to any of the two preceding embodiments, wherein the system is configured to resample the at least one set of samples and automatically generate the resampled dataset via the analyzing component. S29. The system according to any of the preceding system embodiments and with the features of embodiment S21, wherein the system is configured to identify the at least one MF-Eczema differentiating gene, and differentiate the at least one MF-Eczema differentiating gene into at least one of the at least one MF-Eczema differentiating gene associated to eczema, and at least one of the at least one MF-Eczema differentiating gene associated to cutaneous lymphoma, wherein the system is configured to differentiate the at least one MF-Eczema differentiating gene based on the skin condition threshold. S30. The system according to any of the preceding system embodiments, wherein the system comprises at least one machine learning module. S31. The system according to the preceding system embodiment, wherein the system comprises a training module configured to train the at least one machine learning module using any data of any of the method steps. S32. The system according to the preceding embodiment, wherein the training module is configured to train the at least one machine learning module using a training data set comprising a normalization approach and a standard scaling. S33. The system according to the preceding embodiment, wherein the normalization approach comprises executing a trimmed mean of M-values (TMM) normalization. S34. The system according to any of the four preceding embodiments and with the features of embodiment S10, wherein the system is configured to identify at least one sample data set to predict the skin condition status. S35. The system according to the preceding embodiment and with the features of embodiment S29, the system comprises is configured to use the at least one MF-Eczema differentiating gene, train a recursive forward feature selection (RFFS), and generate an optimized list of MF-Eczema differentiating genes based on the at least one sample data set. S36. The system according to the preceding embodiment and embodiment S21, wherein the system is configured to optimize by means of the RFFS at least one of: the skin condition threshold, and the at least one MF-Eczema distinguishing parameter. S37. The system according to any of the two preceding embodiments, wherein the system is configured to store the list of MF-Eczema differentiating genes in at least one of: random access memory (RAM), server, cloud, and database. S38. The system according to any of the preceding system embodiments and with the features of the preceding embodiment, wherein the system is configured to apply robust rank aggregation on the list of genes to find at least one MF-Eczema differentiating gene consistently contained in the list; apply Benjamin-Hochberg correction for multiple testing for number of iterations and for number of MF-Eczema differentiating genes, and select at least one MF-Eczema differentiating gene from the list of MF-Eczema differentiating genes based on outcome of the robust rank aggregation and the Benjamin- Hochberg correction. S39. The system according to any of the preceding system embodiments, wherein for generating the differential diagnosis finding, the system is configured to analyze the expression of the at least one biomarker in the sample, and calculate an expression value. S40. The system according to the preceding embodiment, wherein the system is configured to analyze the expression of the at least one biomarker in samples taken from at least one individual suffering eczema. S41. The system according to any of the two preceding embodiments, wherein the system is configured to analyze the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. S42. The system according to the three preceding embodiments, wherein the system is configured to calculate the expression value based on analyzing the expression of the at least one biomarker in samples taken from at least one individual suffering eczema and the expression of the at least one biomarker in samples taken from at least one individual suffering mycosis fungoides. S43. The system according to any of the four preceding embodiments and with the features of embodiment S30, wherein the system is configured to analyze and calculate the expression via the at least one machine learning module. S44. The system according to any of the preceding system embodiments, wherein the eczema comprises at least one of: atopic eczema, contact dermatitis, dyshidrotic eczema, nummular eczema, allergic contact dermatitis, irritant dermatitis, seborrheic dermatitis, and stasis dermatitis. S45. The system according to any of the preceding system embodiments, wherein the system is configured to identify at least one stage of the mycosis fungoides, and distinguish among the least one stage, wherein the at least one stage comprises at least one of: patch stage, plaque stage, tumor stage, and erythrodermic stage. S46. The system according to any of the preceding system embodiments, wherein the expression of the at least one biomarker comprises at least one expression level comprising an expression at a mRNA level, and an expression at a protein level. S47. The system according to the preceding embodiment, wherein the at least one expression at the mRNA level is determined by at least one nucleic acid amplification method. S48. The system according to the preceding embodiment, wherein the at least one nucleic acid amplification method comprises at least one of: polymerase chain reaction (PCR), and isothermal amplification system. S49. The system according to the preceding embodiment, wherein the PCR comprises at least one of: conventional PCR, real-time PCR (qPCR), reverse transcription PCR (RT-PCR), Nested PCR, Multiplex PCR, Digital PCR (dPCR), Hot start PCR, and droplet PCR. S50. The system according to any of the two preceding embodiments, wherein the isothermal amplification method comprises at least one of: loop-mediated isothermal amplification system (LAMP), recombinase polymerase amplification (RPA), helicase- dependent amplification system (HDA), and nicking enzyme amplification reaction (NEAR). S51. The system according to the preceding embodiment, wherein the loop-mediated isothermal amplification method (LAMP) comprises at least one of: reverse transcription LAMP (RT-LAMP), multiple primer LAMP (MPL), real-time LAMP, lateral flow LAMP, turbidimetric LAMP, and LAMP with clustered regularly interspaced short palindromic repeats [CRISPR] (CRISPR-LAMP). S52. The system according to any of the three preceding embodiments, wherein the at least one expression at the protein level comprises at least antibody comprising at least one of: labeled antibody, and bound antibody. S53. The system according to the preceding embodiment, wherein the labeled antibody comprises a label comprising at least one of: fluorescent label, luminescent label, and radioactive label. S54. The system according to any of the two preceding embodiments, wherein the system is configured to determine the at least one antibody by means of at least one secondary labeled antibody. S55. The system according to any of the preceding system embodiments, wherein the system is configured to carry out any of the method steps according to any of the preceding method embodiments. S56. The system according to any of the preceding system embodiments, wherein the system comprises one computer-assisted component configured to carry out any of the method steps according to any of the preceding embodiments. M53. The method according to any of the preceding method embodiments, wherein the method comprises prompting the system according to any of the preceding system embodiments to perform any of the steps of the method according to any of the preceding method embodiments. S57. The system according to any of the preceding system embodiments, wherein the system is an automated system. S58. The system according to any of the preceding system embodiments, wherein the system is a system for distinguishing MF and, eczema or psoriasis, wherein any reference to “eczema” in any of the preceding system embodiments is a reference to “eczema or psoriasis”. S59. The system according to the preceding system embodiment, with the features of S1, S2 and / or S3, wherein at least one of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 S60. The system according to the preceding system embodiment, wherein at least two of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 S61. The system according to any of the preceding system embodiments, wherein the psoriasis comprises at least one of: Psoriasis vulgaris (Plaque-type psoriasis), inverse psoriasis, guttate psoriasis, palmoplantar psoriasis, generalized pustular psoriasis, palmoplantar pustulosis, erythrodermic psoriasis, psoriasis capitis. Below, kit embodiments will be discussed. These embodiments are abbreviated by the letter “K” followed by a number. When reference is herein made to a kit embodiment, those embodiments are meant. K1. A kit for use in a method for diagnosing eczema or mycosis fungoides, the kit comprising at least one means for quantifying an expression of at least one biomarker in at least one sample. K2. The kit according to the preceding embodiment, wherein the method is according to any of the preceding method embodiments. K3. The kit according to any of the two preceding embodiments, wherein the method is carried out by the system according to any of the preceding system embodiments. K4. The kit according to any of the preceding kit embodiments, wherein the at least one means comprising at least one of: primer, and antibody. K5. The kit according to the preceding embodiment, wherein the at least one primer comprises a forward primer and a reserve primer comprising at least one of: Entrez Symbol forward primer sequence reverse primer sequence gene id RNF213 57674 ACCGTCCAAGAAGGAGCTA TGGCATTAGCAGCTTCCCA HOMER1 9456 TCCGTAGCATAAGAGCTGAAAC TGAAGATAGGTTGTTCCCCCA NLRC5 84166 GCAGATGCTGGGGTTAGCA TTTCACCAGGTGGCTGATGC GBP4 115361 GAAAAGAAACAGGTTGAGTGGGA GGAGGACCTCGTTTGCCTTA IKZF3 22806 TCCTGCATCAGCTTCAATGTC GAATGGGCGTTCACCAGTA HOXC10 3226 AAAAGGAGAGGGCCAAAGCTG CTGCCTTTATCTCCTCTTTCGCT DSTN 11034 GCAAGCTCCAAGGATGCAATTA CTTGACATTCATGTTTTATGCCTTGELOVL5 60481TTCTTCTGTCAGGGCACACG CCAGAGGACACGGATAATCTTCAROBO1 6091 TCAAGTTGGGTCTGAACCTC AGTCCGTCCCAAAGCAACA MAPK6 5597 TCGGAGAAGTCCCGTTGTAT TCACAATCAAAGCTACCGTCCA LCK 3932 CGATGTGTGTGAGAACTGCC CATTTCGGATGAGCAGCGTG TMC8 147138 ACCCTGAACTTGACCCTCCA TGGAACCTCAAAACGCCAGG KRT17 3872 CAGTACAAGAAAGAACCGGTGAC CTTGCCATCCTGGACCTCTT CXCL9 4283 GGGCATCATCTTGCTGGTTCT TTCTCACTACTGGGGTTCCTTG VOPP1 81552 GAGGCTGTGGTACTTCTGGTTC CAGCAGAAAAGCACGCCCAT FLRT3 23767 TTCTCGTCTTCCTGGGTTCTG TCATGGTCAGCAGTGTTGAGGT TNFRSF10B 8795 TTACCTGAAAGGCATCTGCTCA AGGTCGTTGTGAGCTTCTGT PNLIPRP3 119548 TGGCACATCAAGAGGAAAAGA ACCATCTTTGAAACACCCTAACCT MMP1 4312 TTGTCAGGGGAGATCATCGG TCCTCCAGGTCCATCAAAAGG RCSD1 92241 TGAAGGACATGGAGGAAAGACC ACGCCGAGTTGTCCACATTG ENAH 55740 CGGCACCATGAGTGAACAG GCACCCACTTCTTATTGGCA MUC16 94025 GGCCTCTCGTATCTGTGAGC AGGGGTCCAGTACCGACG LRATD1 151354 GGAAATCACGGATCCCCGC GTGGGTGATGCGGTCCAGTT H1-1 3024 AGGCAACGGGTGCATCTAAA TTCGGAGTCTTGACGCTCTT SH2D1A 4068 TGTACTGCCTATGTGTGCTGTAT GTCTGGGACACTCGGTATGT PRXL2A 84293 CTTGGGGGAGTTTTCGTGGT GCTCAAGAAGAATGCCCTGCT DISC1 27185 CCCAACTACCAGCCTTGCTT CGTCCAGAAATGGTTTCCTGAT ALOX15B 247 GTCAGTGCAGGGCAGTTTGA TTGGGCATCCAAGCACAGG CALCRL 10203 ATGCAAGACCCCATTCAACAA CCATCCATCCCAGGTTCTGTT S100A11 6282 CTCAGCTCCAACATGGCAAAA TGGAAGACAGCAATCAGGGAC Entrez Symbol forward primer sequence reverse primer sequence gene id CSRP2 1466 TCCAGATGTGGGGATTCTGT AGTTTTTGTGCCAGGGCTTTC CDK1 983 TCGTCATCCAAATATAGTCAGTCTT CTGGAATCCTGCATAAGCACA USP20 10868 GGCATAAGAAACGGCCAAGC TCGGAACATTGGGTTGACCA CES1 1066 CCTCGTACCCTCCTATGTGC CTCTGAGAGTAACTGCCCCG GRAP2 9402 ATTTCCACCAGGAACGCCG TTCATTTCACTGCCCAAGCC K6. The kit according to any of the preceding kit embodiments, wherein the kit comprises at least one means for obtaining a skin sample. K7. The kit according to any of the preceding kit embodiments, wherein the kit comprises at least one means for isolating RNA from the skin sample. K8. The kit according to the preceding embodiment, wherein the at least one means for isolating RNA from the skin sample comprises at least one of: organic extraction, spin column extraction, and magnetic particle extraction. K9. The kit according to any of the preceding kit embodiments, wherein the kit comprises at least one means for enriching RNA from the skin sample. K10. The kit according to the preceding embodiment, wherein the at least one means for enriching RNA from the skin sample comprises at least one of: poly (A) enrichment, enzymatic removal, and probe-based depletion. K11. The kit according to any of the preceding kit embodiments, wherein the kit comprises at least one skin analysis approach. K12. The kit according to the preceding embodiment, wherein the analysis of the skin comprises performing PCR. K13. The kit according to any of the preceding kit embodiments, wherein the kit comprises at least one means for preparing tissue sections comprising at least one of: cryotome; microtome; non-invasive or minimally-invasive sampling tools such as curettes, tapes, stickers; biopsy punch; and microneedles. K14. The kit according to any of the preceding kit embodiments, wherein the kit comprises a machine-readable set of instructions, which when executed provides to an authorized user instructions for prompting the authorized user to at least one of prompt the system according to any of the preceding system embodiments to carry out the method according to any of the preceding method embodiments, prompt the method according to any of the preceding method embodiments, and use the kit according to any of the preceding kit embodiments. K15. The kit according to any of the preceding kit embodiments and with the features of embodiment K5, wherein the at least one means for obtaining the skin sample comprises a processing device for tape strips, skin scrapings, punch biopsies, micro- or minibiopsies, microtome sections from Formalin-Fixed Paraffin-Embedded (FFPE) tissue. K16. The kit according to any of the preceding kit embodiments, wherein the kit is a kit for use in a method for distinguishing eczema or psoriasis, and mycosis fungoides, wherein any reference to “eczema” in any of the preceding kit embodiments is a reference to “eczema or psoriasis” K17. The kit according to the preceding kit embodiment, with the features of K4, wherein the at least one primer comprises a forward primer and a reserve primer comprising at least one of: Entrez Symbol forward primer sequence reverse primer sequence gene id RNF213 57674 ACCGTCCAAGAAGGAGCTA TGGCATTAGCAGCTTCCCA HOMER1 9456 TCCGTAGCATAAGAGCTGAAAC TGAAGATAGGTTGTTCCCCCA NLRC5 84166 GCAGATGCTGGGGTTAGCA TTTCACCAGGTGGCTGATGC GBP4 115361 GAAAAGAAACAGGTTGAGTGGGA GGAGGACCTCGTTTGCCTTA IKZF3 22806 TCCTGCATCAGCTTCAATGTC GAATGGGCGTTCACCAGTA HOXC10 3226 AAAAGGAGAGGGCCAAAGCTG CTGCCTTTATCTCCTCTTTCGCT DSTN 11034 GCAAGCTCCAAGGATGCAATTA CTTGACATTCATGTTTTATGCCTTGELOVL5 60481TTCTTCTGTCAGGGCACACG CCAGAGGACACGGATAATCTTCAROBO1 6091 TCAAGTTGGGTCTGAACCTC AGTCCGTCCCAAAGCAACA MAPK6 5597 TCGGAGAAGTCCCGTTGTAT TCACAATCAAAGCTACCGTCCA LCK 3932 CGATGTGTGTGAGAACTGCC CATTTCGGATGAGCAGCGTG TMC8 147138 ACCCTGAACTTGACCCTCCA TGGAACCTCAAAACGCCAGG KRT17 3872 CAGTACAAGAAAGAACCGGTGAC CTTGCCATCCTGGACCTCTT CXCL9 4283 GGGCATCATCTTGCTGGTTCT TTCTCACTACTGGGGTTCCTTG VOPP1 81552 GAGGCTGTGGTACTTCTGGTTC CAGCAGAAAAGCACGCCCAT FLRT3 23767 TTCTCGTCTTCCTGGGTTCTG TCATGGTCAGCAGTGTTGAGGT TNFRSF10B 8795 TTACCTGAAAGGCATCTGCTCA AGGTCGTTGTGAGCTTCTGT PNLIPRP3 119548 TGGCACATCAAGAGGAAAAGA ACCATCTTTGAAACACCCTAACCT Entrez Symbol forward primer sequence reverse primer sequence gene id MMP1 4312 TTGTCAGGGGAGATCATCGG TCCTCCAGGTCCATCAAAAGG RCSD1 92241 TGAAGGACATGGAGGAAAGACC ACGCCGAGTTGTCCACATTG ENAH 55740 CGGCACCATGAGTGAACAG GCACCCACTTCTTATTGGCA MUC16 94025 GGCCTCTCGTATCTGTGAGC AGGGGTCCAGTACCGACG LRATD1 151354 GGAAATCACGGATCCCCGC GTGGGTGATGCGGTCCAGTT H1-1 3024 AGGCAACGGGTGCATCTAAA TTCGGAGTCTTGACGCTCTT SH2D1A 4068 TGTACTGCCTATGTGTGCTGTAT GTCTGGGACACTCGGTATGT PRXL2A 84293 CTTGGGGGAGTTTTCGTGGT GCTCAAGAAGAATGCCCTGCT DISC1 27185 CCCAACTACCAGCCTTGCTT CGTCCAGAAATGGTTTCCTGAT ALOX15B 247 GTCAGTGCAGGGCAGTTTGA TTGGGCATCCAAGCACAGG CALCRL 10203 ATGCAAGACCCCATTCAACAA CCATCCATCCCAGGTTCTGTT S100A11 6282 CTCAGCTCCAACATGGCAAAA TGGAAGACAGCAATCAGGGAC CSRP2 1466 TCCAGATGTGGGGATTCTGT AGTTTTTGTGCCAGGGCTTTC CDK1 983 TCGTCATCCAAATATAGTCAGTCTT CTGGAATCCTGCATAAGCACA USP20 10868 GGCATAAGAAACGGCCAAGC TCGGAACATTGGGTTGACCA CES1 1066 CCTCGTACCCTCCTATGTGC CTCTGAGAGTAACTGCCCCG GRAP2 9402 ATTTCCACCAGGAACGCCG TTCATTTCACTGCCCAAGCC ZC3H12D 340152 TTCTCTGCGACCCATAGTGA TCTTTATTTCCATGGCTCATCGC Below, use embodiments will be discussed. These embodiments are abbreviated by the letter “U” followed by a number. When reference is herein made to a use embodiment, those embodiments are meant. U1. Use of the system according to any of the preceding system embodiments for carrying out the method according to any of the preceding method embodiments. U2. Use of the kit according to any of the preceding kit embodiments in the method according to any of the preceding method embodiments. U3. Use of the at least one biomarker for diagnosing eczema or MF in a sample taken from an individual, the at least one biomarker comprising at least one of: Symbol Entrez gene id Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 RCSD1 92241 GBP4 115361 ENAH 55740 IKZF3 22806 MUC16 94025 Symbol Entrez gene id Symbol Entrez gene id HOXC10 3226 LRATD1 151354 DSTN 11034 H1-1 3024 ELOVL5 60481 SH2D1A 4068 ROBO1 6091 PRXL2A 84293 MAPK6 5597 DISC1 27185 LCK 3932 ALOX15B 247 TMC8 147138 CALCRL 10203 KRT17 3872 S100A11 6282 CXCL9 4283 CSRP2 1466 MT-ND3 4537 CDK1 983 VOPP1 81552 USP20 10868 FLRT3 23767 CES1 1066 TNFRSF10B 8795 GRAP2 9402 PNLIPRP3 119548 MMP1 4312 U4. Use of the at least one biomarker for distinguishing MF from eczema or psoriasis in a sample taken from an individual, the at least one biomarker comprising at least one of: Symbol Entrez gene id Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 RCSD1 92241 GBP4 115361 ENAH 55740 IKZF3 22806 MUC16 94025 HOXC10 3226 LRATD1 151354 DSTN 11034 H1-1 3024 ELOVL5 60481 SH2D1A 4068 ROBO1 6091 PRXL2A 84293 MAPK6 5597 DISC1 27185 LCK 3932 ALOX15B 247 TMC8 147138 CALCRL 10203 KRT17 3872 S100A11 6282 CXCL9 4283 CSRP2 1466 MT-ND3 4537 CDK1 983 VOPP1 81552 USP20 10868 FLRT3 23767 CES1 1066 TNFRSF10B 8795 GRAP2 9402 PNLIPRP3 119548 MMP1 4312 ZC3H12D 340152 The present invention will now be described with reference to the accompanying drawings which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention. Fig. 1 schematically depicts a system for diagnosing MF or eczema and / or differentiating MF from eczema or psoriasis according to embodiments of the present invention; Fig. 2 schematically depicts a differential gene expression analysis for feature selection according to embodiments of the present invention; Fig. 3 schematically depicts use Lasso regression instead of forward selection according to the embodiments of the present invention; Fig. 4 depicts a graphical representation of boxplots of significant genes over disease for diagnosis of MF or eczema according to embodiments of the present invention; Fig. 5 conceptually depicts a correlation of the lasso logistic regression and FFS biomarkers according to embodiment of the present invention; Fig. 6 schematically depicts genes used for the method according to embodiments of the present invention; Fig. 7 schematically depicts detection of the biomarkers according to embodiments of the present invention; Fig. 8 depicts a graphical representation of boxplots of the performance of measured genes HOMER1, RNF213, GBP4, and LCK according to embodiments of the present invention; Fig. 9 depicts a graphical representation of boxplots of the performance of measured genes HOMER1, RNF213, NLRC5, GBP4, ZC3H12D, and LCK according to embodiments of the present invention; Fig. 10 depicts a graphical representation of boxplots of the performance of measured genes HOMER1, RNF213, NLRC5, GBP4, ZC3H12D, LCK, FLRT5, and PNLIPRP3 according to embodiments of the present invention; Fig. 11 schematically depicts a computing device suitable for carrying out the method according to embodiments of the present invention. It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings. Fig. 1 schematically depicts a system 100 for diagnosing MF or eczema (and / or eczema or psoriasis) according to embodiments of the present invention. In simple terms, the system 100 is for diagnosing mycosis fungoides (MF) or eczema (and / or eczema or psoriasis) and comprises a processing component 110 configured to output at least one dataset, and an analyzing component 120 configured to analyze the at least one dataset. The analyzing component 120 may further comprise a plurality of modules such as a determining module 122 configured to determine an expression of at least one biomarker in a sample, a differentiating module 124 configured to differentiate between MF and eczema (and / or eczema or psoriasis) based on the expression of the at least one biomarker in the sample, and a finding generating module 126 configured to generate a differential diagnosis finding based on the expression of the at least one biomarker in the sample. In one embodiment, the system 100 may also comprise a server (not depicted), which may be a local server or a remote server, or partially local and partially remote server. In a further embodiment, the server may also be allocated in a cloud. Moreover, the differentiating module 124 may be configured to differentiate between MF and eczema(and / or eczema or psoriasis) based on the expression of at least two biomarkers in the sample. Moreover, the system 100 may comprise a prediction module (not depicted) configured to generate the skin condition status hypothesis based on the differential diagnosis finding. In one embodiment, the prediction module may be configured to predict the skin condition status of the individual based on the skin condition status hypothesis, and generate the skin condition status prediction. In another embodiment, the prediction module may be integrated into the analyzing component 120. Fig. 2 schematically depicts a differential gene expression analysis according to embodiments of the present invention. In simple terms. Fig. 2 depicts a training dataset use by the method of the present invention, wherein the method may comprise processing at least one set of samples. In one embodiment of the present invention, processing of the at least one set of samples may comprises conducting a plurality of steps, in particular, a plurality of steps of a computer implemented method. For instance, this may comprise resampling the set of samples at least 50 times, preferably at least 100 times, more preferably at least 150 times, such as 200 times; and automatically generating a resampled dataset. Furthermore, processing of the at least one set of samples may comprise performing sample cross validation. Further, the method as depicted in Fig.2 may comprise executing of the differential gene expression analysis between at least one of eczema and non-lesional condition, cutaneous lymphoma and non-lesional condition, and eczema and cutaneous lymphoma. For this purpose, the method may comprise: identifying the at least one MF-Eczema differentiating gene, and differentiating the at least one MF-Eczema differentiating gene into: at least one of the at least one MF-Eczema differentiating gene associated to eczema, and at least one of the at least one MF-Eczema differentiating gene associated to cutaneous lymphoma, wherein differentiating of the at least one MF-Eczema differentiating gene is based on the skin condition threshold. Fig. 2 further depicts the training, which comprises using a training data set comprising a normalization approach and a standard scaling, wherein the normalization approach may comprise executing a trimmed mean of M-values (TMM) normalization. For identifying the at least one sample data set for the predicting step, the method may comprise using the at least one MF- Eczema differentiating gene, training a recursive forward feature selection (RFFS), and generating an optimized list of MF-Eczema differentiating genes based on the at least one sample dataset. This approach allows feature selection, which may then allow to forward sequential feature selection with logistic regression and / or sort features according to time when added. Moreover, the method as depicted in Fig. 2 may comprise: applying robust rank aggregation on the list of MF-Eczema differentiating genes to find at least one MF- Eczema differentiating gene consistently contained in the list, applying Benjamin-Hochberg correction for multiple testing for number of iterations and for number of MF-Eczema differentiating genes, and selecting at least one MF-Eczema differentiating gene from the list of MF-Eczema differentiating genes based on outcome of the robust rank aggregation and the Benjamin-Hochberg correction. In order words, Fig. 2 schematically discloses a plurality of steps, comprising applying differential gene expression analysis between eczema / cutaneous lymphoma patients and all non lesional patients; filtering both results for the genes which have an absolute value of log2FoldChange >=1 and a padj < 0.01 in eczema or cutaneous lymphoma comparison (or both), which are the genes distinguish healthy from non-healthy; starting of bootstrapping, which may be repeated for instance 200 times; sampling 6 patients such as 3 eczema, 3 cutaneous lymphoma; executing differential gene expression analysis between eczema and cutaneous lymphoma; taking genes which have an absolute value of log2FoldChange > 1 and a padj < 0.05; intersecting the genes having absolute value with genes from the filtering step, i.e., genes which distinguish healthy from disease and eczema from cutaneous lymphoma; using the other samples of the training set and apply TMM normalization and standard scaling; using only the genes of intersecting and training recursive forward feature selection optimizing the F1-score and storing the genes in a list. Furthermore, Fig.2 depicts applying Robust Rank Aggregation on the 200 gene lists to find the genes which are consistently contained in the lists; applying Benjamin-Hochberg correction for multiple testing for number of iterations and for number of genes; and selecting the genes which have a padj value below 0.01 Moreover, Fig.2 depicts a forward sequential feature selection. This may be performed by using F1-score as performance metric; using tolerance of 0.5 %; using a logistic regression model (with weights to balance imbalanced data sets and L2 regularization); using stratified cross-validation with 4 splits (3 folds for training, 1 fold for evaluation); starting with an empty set of features and a max_performance (in our case F1-score) of 0; adding one gene to the feature set and train the model on training fold; evaluating the performance on left out fold of cross-validation (F1-score); storing the mean performance over all folds; iterating over all genes and compare the performances; adding the gene which achieves the best performance to gene set if the performance is better than max_performance – tolerance; setting max_performance to this performance; and repeating all the described steps until max_performance does not increase anymore. The method as depicted in Fig. 2 may also be performed using a plurality of different models – such as SVM, logistic regression, xgboost – for applying forward feature selection, however, results of the present method are consistent across the different methods. Fig. 3 schematically depicts use Lasso regression instead of forward selection according to the embodiments of the present invention. In simple terms, Fig. 3 depicts the same concepts as shown in Fig. 2, but instead of forward selection, lasso logistic regression is used. In general, there exist two approaches how to find the biomarkers: (1) finding the minimal markers for getting a good predictive performance, and (2) giving more genes as output and ore reflecting the entire disease signature, which allows a better predictive performance due to higher number of genes. Thus, the different between the Fig. 2 and Fig. 3 is the use of lasso logistic. In simple words, Fig. 3 schematically depicts the method comprising the following steps: applying differential gene expression (DGE) analysis between eczema / cutaneous lymphoma patients and all non_lesional patients; filtering both results for the genes which have an absolute value of log2FoldChange >=1 and a padj < 0.01 in eczema or cutaneous lymphoma comparison (or both) which are then genes which distinguish healthy from non- healthy; starting of bootstrapping repeated a plurality of times, such as 200 times; sampling 6 patients (3 eczema, 3 cutaneous lymphoma); executing DGE analysis between eczema and cutaneous lymphoma; taking genes which have an absolute value of log2FoldChange > 1 and a padj < 0.05; intersecting the genes with absolute values with genes from the filtering step, i.e., the genes which distinguish healthy from disease and eczema from cutaneous lymphoma; using the other samples of the training set and apply TMM normalization and standard scaling; training logistic regression model with L1- regularization and lambda=1; sorting the genes by the absolute value of their coefficient Drop genes that have a coefficient of 0; storing the genes in a list; applying Robust Rank Aggregation on the 200 gene lists to find the genes which are consistently contained in the lists; applying Benjamin-Hochberg correction for multiple testing for number of iterations and for number of genes; selecting the genes which have a padj value below 0.01; removing the correlated genes with Variance Inflation Factor > 5. For this the following may be used: which allows computing VIF for feature j. Example 1: Evaluation The present invention comprises evaluating data to diagnose MF or eczema. For this purpose, label “0” may be used to encode cutaneous t-cell lymphoma and label “1” for eczema, and thus, the genes may be evaluated in nested stratified cross-validation fashion, where hyperparameter may be applied for tuning in an inner loop and evaluate the fitted model in an outer loop. For this, an f2-score may be used and pos_label=0 to minimize the cutaneous lymphoma patients which are classified as eczema patients. Moreover, the invention allows to concatenate predictions and the ground truth data for every fold to get predictions and ground truth data for the entire training set and compute evaluation metrics on the entire training set. This may be done to get more meaningful results, since every fold contains only a small number of MF samples. In order to get robust estimates for the mentioned metrics, it is possible to repeat the explained procedure a plurality of times such as 100 times with different splits (different seeds) and report mean values and standard deviations. For example, a logistic regression model or a linear support vector machine (SVM) may be used. Below are metrics used in the present invention for evaluation: Predicted as cutaneous Predicted as eczema lymphoma Cutaneous lymphoma patient True negatives False positives (Label 0) Eczema patient False negatives True positives (Label 1) The above metrics may be used to evaluate a logistic regression model trained in a nested cross-validated fashion on previously selected three genes as described above. Mean over 100 Standard deviation over 100 Metric repetitions repetitions Balanced accuracy 89.2% 1.5% F1-score 77% 2.6% Sensitivity 93.2% 1.3% Specificity 85.3% 3% ROC AUC score 91.3% 2.2% PR AUC Score 80% 4.4% Additionally, it is also possible to evaluate on an external test set, which may comprise a reliability dependent of the size of the test set. An example of such an external evaluation may be seen as follows: Training Set Test Set Sensitivity 95.2% 95.2% Specificity 87.5% 66.7% F1-score 82.4% 66.7% AUC of PR curve 79% 91.7% Roc AUC 92.9% 98.4% Training set Predicted as cutaneous lymphoma Predicted as eczema Cutaneous lymphoma 14 2 samples Eczema samples 4 80 Test set Predicted as cutaneous Predicted as eczema lymphoma Cutaneous lymphoma samples 2 1 Eczema samples 1 20 Example 2: Found biomarkers and evaluation of Lasso regression Metric Mean over 100 repetitions Standard deviation over 100 repetitions Balanced accuracy 92.1% 1.6% F1-score 83.4% 3.3% Sensitivity 95.3% 1.5% Specificity 88.8% 3.2% ROC AUC score 97.8% 1.1% PR AUC Score 92.9% 2.1% The above table show the results of the evaluation of the method as depicted in Fig. 3. Fig. 4 depicts a graphical representation of boxplots of significant genes over disease for diagnosis of MF or eczema. In particular, Fig. 4 depicts a two-sided Wilcoxon rank sum test showing significant expression difference of the discovered biomarkers between eczema and cutaneous T-cell lymphoma. The reference “****” corresponds to a p-adjusted value below 0.0001. Example 3: Applying FFS with logistic regression The following tables 4.1 and 4.2 show results of the method as described in Fig. 2, when all 36 genes are protected. Table 4.1 shows that is possible to detect a minimal signature of three genes which perform well. Table 4.2. shows that it is possible to find a signature of four genes instead which work much worse in comparison to Table 4.1. Especially, the ability to classify mf (F1-score) drops significantly. Table 4.1: HOMER1, NLRC5, RNF213 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 89.2% 1.5% F1-score (MF) 77 % 2.6% Sensitivity 93.2% 1.3% Specificity 85.2% 3 % ROC AUC Score 91.3 % 2.2% PR AUC Score 80 % 4.4% Table 4.2: SLFN12L, BTN3A1, SLMAF8, ZC3H12D Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 80 % 2.3% F1-score (MF) 56.5% 3 % Sensitivity 80.4% 3.2% Specificity 79.8% 5.3% ROC AUC Score 89.3% 2.2% PR AUC Score 63.3% 4.9% Example 4: Applying lasso logistic regression and vif The following tables 4.1 and 4.2 show results of the method as described in Fig. 3. In particular, here the method of present invention is applied as depicted in Fig. 3 to a data set when all 36 genes are protected. The approach of the present invention allows to find a much smaller signature, i.e., 9 biomarkers in Table 4.2 vs 23 biomarkers in Table 4.1. However, such a perform appears to be worse than when making use of the 23 identified genes. Remarkably, it performs even worse with 9 genes than with the three genes HOMER1, NLRC5 and RNF213 of the method as described in Fig. 2. Table 5.1: CALCRL, GRAP2, TNFRSF10B, PRXL2A, H1-1, FLRT3, USP20, ENAH, VOPP1, GBP4, DISC1, LRATD1, S100A11, CDK1, CSRP2, ALOX15B, MUC16, SH2D1A, MMP1, RCSD1, MT-ND3, CES1, PNLIPRP3 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 92.1% 1.6% F1-score(MF) 83.4% 3.3% Sensitivity 95.3% 1.5% Specificity 88.8% 3.2% ROC AUC Score 97.8% 1.1% PR AUC Score 92.9% 2.1% Table 5.2: TRAF1, KRT6B, FCGR2B, MFNG, FADS2, ZAP70, SLOC2B1, PRC1, BTN3A1 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 81.3% 2.8% F1-score (MF) 61.5% 4.8% Sensitivity 85.9 % 4.3% Specificity 76.6% 6 % ROC AUC Score 91 % 2.5% PR AUC Score 73.2% 5.7% Fig. 5 conceptually depicts a correlation of the lasso logistic regression and FFS biomarkers according to embodiment of the present invention. In simple terms, Fig. 5 depicts a high correlation between NLRC5 and RNF213. In particular, Fig. 5 depicts pairwise correlations of the genes detected via the method of the present invention, in particular, the approaches described in Figs. 2 and 3, respectively. Fig. 5 depicts that a notably high correlation between RNF213 and NLRC5. Example 5: Applying FFS with logistic regression The following tables 6.1 and 6.2 show results of the method as described in Fig. 2. In Particular, table 6.1 shows performance of the model of the present invention with the three genes and how the results change if the method as described in Fig. 3 is applied with protecting these three genes. It is possible to see that the method still finds a small signature of only four genes. However, these cannot compete performance wise with the previously detected genes. Especially, the f1-score regarding mf drops quite a lot. Table 6.2 shows in particular GBP4 including in all 36 genes and SLFN12L and MLLT6 also chosen when all 36 genes were removed. Table 6.1: HOMER1, NLRC5, RNF213 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 89.2% 1.5% F1-score (MF) 77 % 2.6% Sensitivity 93.2% 1.3% Specificity 85.2% 3 % ROC AUC Score 91.3 % 2.2% PR AUC Score 80 % 4.4% Table 6.2: BTN3A1, GBP4, SLFN12L MLLT6 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 82.3 % 2.3% F1-score (MF) 60.6% 4 % Sensitivity 83.3% 3.2% Specificity 81.2% 3.5% ROC AUC Score 90 % 2.4% PR AUC Score 65.8% 4.7% Fig. 6 schematically depicts biomarkers used for the method according to embodiments of the present invention. In simple terms, Fig. 6 depicts from which framework all the 36 protected genes stem. FFS_Logistic, FFS_SVM and FFS_Xgboost result from the implementation of the steps of the method of the present invention, wherein different models are used such as Logistic regression, SVM & Xgboost, within the multivariate module. Lasso contains all 29 genes detected through the method of the present invention as described in Fig. 3, before the variance inflation factor is used, and lasso_vif after the variance inflation factor is applied. Notably, genes GBP4, CXCL9, IKZF3 and NLRC5 are genes that are detected across both of the frameworks of the present invention. Example 6: Applying lasso logistic regression and vif The following tables 7.1 and 7.2 show results of the method as described in Fig. 3. In particular, it is possible to observe that when the method as described in Fig. 3 is applied, which is always extracting a bigger disease signature, it is possible to see that it is not affected by only protecting the three genes HOMER1, NLRC5 and RNF213, because these were not picked up before either. Table 7.1: CALCRL, GRAP2, TNFRSF10B, PRXL2A, H1-1, FLRT3, USP20, ENAH, VOPP1, GBP4, DISC1, LRATD1, S100A11, CDK1, CSRP2, ALOX15B, MUC16, SH2D1A, MMP1, RCSD1, MT-ND3, CES1, PNLIPRP3 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 92.1% 1.6% F1-score 83.4% 3.3% Sensitivity 95.3% 1.5% Specificity 88.8% 3.2% ROC AUC Score 97.8% 1.1% PR AUC Score 92.9% 2.1% Table 7.2: CALCRL, GRAP2, TNFRSF10B, PRXL2A, H1-1, FLRT3, USP20, ENAH, VOPP1, GBP4, DISC1, LRATD1, S100A11, CDK1, CSRP2, ALOX15B, MUC16, SH2D1A, MMP1, RCSD1, MT-ND3, CES1, PNLIPRP3 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 92.1% 1.6% F1-score 83.4% 3.3% Sensitivity 95.3% 1.5% Specificity 88.8% 3.2% ROC AUC Score 97.8% 1.1% PR AUC Score 92.9% 2.1% Fig. 7 schematically depicts detection of the biomarkers according to embodiments of the present invention. In particular, Fig. 7 depicts detection of the biomarkers according to the method of the present invention, wherein the detected genes vary depending on which model, e.g., Logistic Regression, SVM and Xgboost, is used within the multivariate module of the framework of the present invention, in particular, as described in Fig. 2. Moreover, Fig. 7 depicts that HOMER1 is consistently selected independent of the chosen model. Besides, Fig. 7 shows that also RNF213 and NLRC5 are selected by the SVM and the logistic regression model. Example 7: Permutations of HOMER1, RNF213 and NLRC5 Tables 8.1, 8.2, and 8.3 shows results of single permutations for HOMER1, RNF213 and NLRC5, respectively. It is possible to observe that the performance of single genes alone, which notably shows that HOMER1 is the gene which when used alone performs best. Table 8.1: HOMER1 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 85.3% 1.2% F1-score (MF) 68.3% 2.3% Sensitivity 89% 1.5% Specificity 81.6% 1.9% ROC AUC Score 87.5% 1.9% PR AUC Score 71.9% 5% Table 8.2: RNF213 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 84.2% 2.4% F1-score (MF) 62.9% 2.4% Sensitivity 84% 1.2% Specificity 84.3% 5.4% ROC AUC Score 91.3% 2% PR AUC Score 69.3% 5.9% Table 8.3: NLRC5 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 84.2% 2.4% F1-score (MF) 62.9% 2.4% Sensitivity 84% 1.2% Specificity 84.3% 5.4% ROC AUC Score 91.3% 2% PR AUC Score 69.3% 5.9% Tables 8.4, 8.5 and 8.6 show results of two gene permutations for HOMER1, RNF213 and NLRC5. In particular, it is possible to see that HOMER1 and RNF213 perform best of all these permutations and might be able to distinguish mf from eczema alone (without NLRC5). Table 8.4: HOMER1 and RNF213 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 89.8% 2% F1-score (MF) 78.2% 3.8% Sensitivity 93.5% 1.7% Specificity 86.1% 3.6% ROC AUC Score 91.9% 1.8% PR AUC Score 78.1% 4.3% Table 8.5: HOMER1 and NLRC5 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 89.8% 2% F1-score (MF) 78.2% 3.8% Sensitivity 93.5% 1.7% Specificity 86.1% 3.6% ROC AUC Score 91.9% 1.8% PR AUC Score 78.1% 4.3% Table 8.6: RNF213 and NLRC5 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 89.8% 2% F1-score (MF) 78.2% 3.8% Sensitivity 93.5% 1.7% Specificity 86.1% 3.6% ROC AUC Score 91.9% 1.8% PR AUC Score 78.1% 4.3% Moreover, the approach of the present invention may also allow ranking the performance of the biomarkers. As shown previously, HOMER1 is the most important gene performance- wise. This is also reflected in the output of the robust rank aggregation of the method as described in Fig. 2, where HOMER1 has the smallest p-value. Using the p-values of the robust rank aggregation the ranking after HOMER1 would be NLRC5 and then RNF213. However, as shown above, when performing two genes permutation the performance - boost of combining HOMER1 with RNF213 is bigger than of the combination of HOMER1 with NLRC5. In particular, based on the results of table 7.1, it is possible to rank the genes based on the p-values as: (1) HOMER1, (2) NLRC5 and (3) RNF213. Based on the results of table 7.2, it is possible to rank the genes based on the p-values as: (1) HOMER1, (2) RNF213 and (3) NLRC5, wherein NLRC5 may be optional. Table 8.7: Using HOMER1, NLRC5 and RNF213 Metric Robust Evaluation F-2 Standard deviation score Balanced Accuracy 89.2% 1.5% F1-score (MF) 77 % 2.6% Sensitivity 93.1% 1.3% Specificity 85.1% 3 % ROC AUC Score 91 % 2.2% PR AUC Score 80 % 4.3% Table 8.8: Using HOMER1 & RNF213 Metric Robust Evaluation F-2 score Standard deviation Balanced Accuracy 89.8% 2% F1-score (MF) 78.2% 3.8% Sensitivity 93.5% 1.7% Specificity 86.1% 3.6% ROC AUC Score 91.9% 1.8% PR AUC Score 78.1% 4.3% The present invention also comprises evaluating data to differentiate MF from eczema or psoriasis. For this purpose, label “0” may be used to encode cutaneous t-cell lymphoma and label “1” for eczema or psoriasis, and thus, the genes may be evaluated in nested stratified cross-validation fashion, where hyperparameter may be applied for tuning in an inner loop and evaluate the fitted model in an outer loop. For this, an f2-score may be used and pos_label=0 to minimize the cutaneous lymphoma patients which are classified as eczema or psoriasis patients. Moreover, the invention allows to concatenate predictions and the ground truth data for every fold to get predictions and ground truth data for the entire training set and compute evaluation metrics on the entire training set. This may be done to get more meaningful results, since every fold contains only a small number of MF samples. In order to get robust estimates for the mentioned metrics, it is possible to repeat the explained procedure a plurality of times such as 100 times with different splits (different seeds) and report mean values and standard deviations. For example, a logistic regression model or a linear support vector machine (SVM) may be used. Below are metrics used in the present invention for evaluation: Predicted as cutaneous Predicted as eczema lymphoma or psoriasis Cutaneous lymphoma patient True negatives False positives (Label 0) Eczema or psoriasis patient False negatives True positives (Label 1) The above metrics may be used to evaluate multiple models trained in a nested cross- validated fashion on selected genes as described above. It is worth noting that Fig. 8, Fig. 9 and Fig. 10 are the result of evaluations performed on qPCR data, showcasing the real-time application of the invention. The evaluation has been made according to two reference genes: TBP and SDHAF. Fig.8 depicts a graphical representation of boxplots of the performance of measured genes HOMER1, RNF213, GBP4, and LCK using a logistic regression model and linear support vector machine. The performance of the model making use of the above-mentioned genes is measured according to the type of model, the reference gene selected, the F1-score, the sensitivity, the specificity, the Roc AUC and the balanced accuracy. Fig.9 depicts a graphical representation of boxplots of the performance of measured genes HOMER1, RNF213, NLRC5, GBP4, ZC3H12D, and LCK using a logistic regression model and linear support vector machine. The performance of the model making use of the above- mentioned genes is measured according to the type of model, the reference gene selected, the F1-score, the sensitivity, the specificity, the Roc AUC and the balanced accuracy. Fig. 10 depicts a graphical representation of boxplots of the performance of measured genes HOMER1, RNF213, NLRC5, GBP4, ZC3H12D, LCK, FLRT5, and PNLIPRP3 using a logistic regression model and linear support vector machine. The performance of the model making use of the above-mentioned genes is measured according to the type of model, the reference gene selected, the F1-score, the sensitivity, the specificity, the Roc AUC and the balanced accuracy. Fig. 11 provides a schematic of a computing device 200. The computing device 200 may comprise a computing unit 35, a first data storage unit 30A, a second data storage unit 30B and a third data storage unit 30C. The computing device 200 can be a single computing device or an assembly of computing devices. The computing device 200 can be locally arranged or remotely, such as a cloud solution. On the different data storage units 30 the different data can be stored. Additional data storages can be also provided and / or the ones mentioned before can be combined at least in part. The computing unit 35 can access the first data storage unit 30A, the second data storage unit 30B and the third data storage unit 30C through the internal communication channel 260, which can comprise a bus connection 260. The computing unit 30 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programable gate array). The first data storage unit 30A may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The second data storage unit 30B may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The third data storage unit 30C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). It should be understood that generally, the first data storage unit 30A (also referred to as encryption key storage unit 30A), the second data storage unit 30B (also referred to as data share storage unit 30B), and the third data storage unit 30C (also referred to as decryption key storage unit 30C) can also be part of the same memory. That is, only one general data storage unit 30 per device may be provided, which may be configured to store the respective encryption key (such that the section of the data storage unit 30 storing the encryption key may be the encryption key storage unit 30A), the respective data element share (such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B), and the respective decryption key (such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A). In some embodiments, the third data storage unit 30C can be a secure memory device 30C, such as, a self-encrypted memory, hardware-based full disk encryption memory and the like which can automatically encrypt all of the stored data. The data can be decrypted from the memory component only upon successful authentication of the party requiring to access the third data storage unit 30C, wherein the party can be a user, computing device, processing unit and the like. In some embodiments, the third data storage unit 30C can only be connected to the computing unit 35 and the computing unit 35 can be configured to never output the data received from the third data storage unit 30C. This can ensure a secure storing and handling of the encryption key (i.e., a private key) stored in the third data storage unit 30C. In some embodiments, the second data storage unit 30B may not be provided but instead the computing device 200 can be configured to receive a corresponding encrypted share from the database 60. In some embodiments, the computing device 200 may comprise the second data storage unit 30B and can be configured to receive a corresponding encrypted share from the database 60. The computing device 200 may comprise a further memory component 240 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The memory component 240 may also be connected with the other components of the computing device 200 (such as the computing component 35) through the internal communication channel 260. Further the computing device 200 may comprise an external communication component 230. The external communication component 230 can be configured to facilitate sending and / or receiving data to / from an external device (e.g., a backup device, a recovery device, a database). The external communication component 230 may comprise an antenna (e.g., Wi-Fi antenna, NFC antenna, 2G / 3G / 4G / 5G antenna and the like), USB port / plug, LAN port / plug, contact pads offering electrical connectivity and the like. The external communication component 230 can send and / or receive data based on a communication protocol which can comprise instructions for sending and / or receiving data. Said instructions can be stored in the memory component 240 and can be executed by the computing unit 35 and / or external communication component 230. The external communication component 230 can be connected to the internal communication component 260. Thus, data received by the external communication component 230 can be provided to the memory component 240, computing unit 35, first data storage unit 30A and / or second data storage unit 30B and / or third data storage unit 30C. Similarly, data stored on the memory component 240, first data storage unit 30A and / or second data storage unit 30B and / or third data storage unit 30C and / or data generated by the computing unit 35 can be provided to the external communication component 230 for being transmitted to an external device. In addition, the computing device 200 may comprise an input user interface 210 which can allow the user of the computing device 200 to provide at least one input (e.g., instruction) to the computing device 200. For example, the input user interface 210 may comprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like. Additionally, still, the computing device 200 may comprise an output user interface 220 which can allow the computing device 200 to provide indications to the user. For example, the output user interface 210 may be a LED, a display, a speaker and the like. The output and the input user interface 200 may also be connected through the internal communication component 260 with the internal component of the device 200. The processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP, APU, or FPGA. The memory may be singular or plural, and may be, but not limited to, being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM. The data processing device can comprise means of data processing, such as, processor units, hardware accelerators and / or microcontrollers. The data processing device 20 can comprise memory components, such as, main memory (e.g., RAM), cache memory (e.g., SRAM) and / or secondary memory (e.g., HDD, SDD). The data processing device can comprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the memory components and the processing components. The data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet. The data processing device can comprise user interfaces, such as: (1) output user interface, such as: - screens or monitors configured to display visual data (e.g., displaying graphical user interfaces of the questionnaire to the user), - speakers configured to communicate audio data (e.g., playing audio data to the user), (2) input user interface, such as: - camera configured to capture visual data (e.g., capturing images and / or videos of the user), - microphone configured to capture audio data (e.g., recording audio from the user), - keyboard configured to allow the insertion of text and / or other keyboard commands (e.g., allowing the user to enter text data and / or other keyboard commands by having the user type on the keyboard) and / or trackpad, mouse, touchscreen, joystick – configured to facilitate the navigation through different graphical user interfaces of the questionnaire. The data processing device can be a processing unit configured to carry out instructions of a program. The data processing device can be a system-on-chip comprising processing units, memory components and busses. The data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer. The data processing device can be a server, either local and / or remote. The data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and / or user interface (such as the upper-mentioned user interfaces). It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Reference numbers and letters appearing between parentheses in the claims, identifying features described in the embodiments and illustrated in the accompanying drawings, are provided as an aid to the reader as an exemplification of the matter claimed. The inclusion of such reference numbers and letters is not to be interpreted as placing any limitations on the scope of the claims. While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims. Whenever a relative term, such as “about”, “substantially” or “approximately” is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., “substantially straight” should be construed to also include “(exactly) straight”. Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), …, followed by step (Z). Corresponding considerations apply when terms like “after” or “before” are used.

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Claims

Claims 1. A method for diagnosing mycosis fungoides (MF) or eczema, the method comprising determining an expression of at least one biomarker in a sample, differentiating between MF and eczema based on the expression of the at least one biomarker in the sample, and generating a differential diagnosis finding based on the expression of the at least one biomarker in the sample, wherein at least one of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 2. The method according to the preceding claim, wherein the at least one biomarker comprises at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 3. A method for differentiating mycosis fungoides (MF) from eczema or psoriasis, wherein any reference to “eczema” in any of the preceding method claims and / or any method claim with the features of claim 1, is a reference to “eczema or psoriasis”, whereinat least one of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 340152 4. The method according to any of the preceding claims, wherein the method comprises generating a skin condition status hypothesis based on the differential diagnosis finding, predicting a skin condition status of the individual based on the skin condition status hypothesis, and generating a skin condition status prediction.

5. The method according to any of the preceding claims, wherein the method comprises generating a skin condition threshold, and assigning the skin condition status to an individual based on the skin condition threshold, wherein when the expression of the at least one biomarker in the sample of the individual is below the skin condition threshold, the method concludes that the individual is afflicted with eczema, and above the skin condition threshold, the method concludes that the individual is afflicted with MF, wherein the method further comprises automatically generating at least one skin condition suggestion, wherein when the expression of the at least one biomarker in the sample of the individual reads below a detection limit of the skin condition threshold, the at least one skin condition suggestion comprises prompting an evaluation by a medical professional.

6. The method according to any of the preceding claims, wherein the method comprises identifying at least one MF-Eczema differentiating gene significantly differentially expressed in the sample among at least two different expressed genes, wherein the skin condition threshold comprises at least one MF-Eczema distinguishing parameter and a MF-Eczema distinguishing reference parameter, wherein the method comprises measuring a fold change of the expression of at least one MF-Eczemadifferentiating gene, wherein the measuring of the fold change comprises measuring change in a magnitude of log2FoldChange of at least one MF-Eczema differentiating gene, wherein the method comprises identifying the at least one MF-Eczema differentiating gene, and differentiating the at least one MF-Eczema differentiating gene into at least one of the at least one MF-Eczema differentiating gene associated to eczema, and at least one of the at least one MF-Eczema differentiating gene associated to cutaneous lymphoma, wherein differentiating of the at least one MF-Eczema differentiating gene is based on the skin condition threshold.

7. The method according to any of the preceding claims, wherein the method comprises training at least one machine learning module using any data of any of the method steps, wherein the training comprises using a training data set comprising a normalization approach and a standard scaling, wherein the normalization approach comprises executing a trimmed mean of M-values (TMM) normalization, wherein the method comprises identifying at least one sample dataset for the predicting step.

8. The method according to any of the preceding claims, wherein the method comprises identifying at least one stage of the mycosis fungoides, and distinguishing among the least one stage, wherein the at least one stage comprises at least one of: patch stage, plaque stage, tumor stage, and erythrodermic stage.

9. A system for diagnosing mycosis fungoides (MF) or eczema, the system comprising a processing component configured to output at least one dataset, and an analyzing component configured to analyze the at least one dataset, wherein the analyzing component comprises a determining module configured to determine an expression of at least one biomarker in a sample, a differentiating module configured to differentiate between MF and eczema based on the expression of the at least one biomarker in the sample, and a finding generating module configured to generate a differential diagnosis finding based on the expression of the at least one biomarker in the sample, wherein at least one of the at least one biomarker is selected from a group comprising at least one of:Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 10. The system according to the preceding claim, wherein the at least one biomarker comprises at least one of: Symbol Entrez gene id Symbol Entrez gene id GBP4 115361 RCSD1 92241 IKZF3 22806 ENAH 55740 HOXC10 3226 MUC16 94025 DSTN 11034 LRATD1 151354 ELOVL5 60481 H1-1 3024 ROBO1 6091 SH2D1A 4068 MAPK6 5597 PRXL2A 84293 LCK 3932 DISC1 27185 TMC8 147138 ALOX15B 247 KRT17 3872 CALCRL 10203 CXCL9 4283 S100A11 6282 MT-ND3 4537 CSRP2 1466 VOPP1 81552 CDK1 983 FLRT3 23767 USP20 10868 TNFRSF10B 8795 CES1 1066 PNLIPRP3 119548 GRAP2 9402 MMP1 4312 11. A system for differentiating mycosis fungoides (MF) from eczema or psoriasis, wherein any reference to “eczema” in any of the preceding system claims and / or any system claim with the features of claim 9, is a reference to “eczema or psoriasis”, wherein at least one of the at least one biomarker is selected from a group comprising at least one of: Symbol Entrez gene id RNF213 57674 HOMER1 9456 NLRC5 84166 ZC3H12D 34015212. The system according to any of the two the preceding claims, wherein the system is configured to generate a skin condition status hypothesis based on the differential diagnosis finding, predict a skin condition status of the individual based on the skin condition status hypothesis, and generate a skin condition status prediction.

13. The system according to any of claims 9 to 12, wherein the system is configured to generate a skin condition threshold, and assign the skin condition status to individual based on the skin condition threshold, wherein when the expression of the at least one biomarker in the sample of the individual is below the skin condition threshold, the system is configured to output that the individual is afflicted with eczema, and above the skin condition threshold, the system is configured to output that the individual is afflicted with MF, wherein the system is configured to automatically output at least one skin condition suggestion, wherein when the expression of the at least one biomarker in the sample of the individual reads below a detection limit of the skin condition threshold, the at least one skin condition suggestion comprises prompting an evaluation by a medical professional.

14. The system according to any of claims 9 to 13, wherein the system is configured to identify at least one least one MF-Eczema differentiating gene significantly differentially expressed in the sample among at least two different expressed genes,wherein the skin condition threshold comprises at least one MF-Eczema distinguishing parameter and a MF- Eczema distinguishing reference parameter, wherein the system is configured to measure a fold change of the expression of at least one MF-Eczema differentiating gene, wherein the measure of the fold change comprises a measure of a change in a magnitude of log2FoldChange of at least one MF-Eczema differentiating gene, wherein the system is configured to identify the at least one MF-Eczema differentiating gene, and differentiate the at least one MF-Eczema differentiating gene into at least one of the at least one MF-Eczema differentiating gene associated to eczema, and at least one of the at least one MF-Eczema differentiating gene associated to cutaneous lymphoma, wherein the system is configured to differentiate the at least one MF-Eczema differentiating gene based on the skin condition threshold.

15. The system according to any of claims 9 to 14, wherein the system comprises at least one machine learning module, wherein the system comprises a training module configured to train the at least one machine learning module using any data of any of the method steps, wherein the training is configured to train the at least one machine learning module according to any step of any of the preceding method claims, wherein the normalization approach comprises executing a trimmed mean of M-values (TMM) normalization, wherein the system is configured to identify at least one sample data set to predict the skin condition status.

16. The system according to any of claims 9 to 15, wherein the system is configured to identify at least one stage of the mycosis fungoides, and distinguish among the least one stage, wherein the at least one stage comprises at least one of: patch stage, plaque stage, tumor stage, and erythrodermic stage.

17. A kit for use in a method for diagnosing eczema or mycosis fungoides, the kit comprising at least one mean for quantifying an expression of at least one biomarker in at least one sample, wherein the method is according to any of the preceding method claims.

18. A kit for use in a method for differentiating mycosis fungoides from eczema or psoriasis, the kit comprising at least one mean for quantifying an expression of at least one biomarker in at least one sample, wherein the method is according to any of the preceding method claims.

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