Application of SOX4 inhibitor in preparation of medicine for relieving or treating malignant T cell related diseases

Abstract

The invention relates to application of an SOX4 inhibitor in preparation of a medicine for relieving or treating malignant T cell related diseases. Specifically, the invention proves that malignant T cells promote the formation of CAFs through an epithelial cell-mesenchymal transformation pathway related to SOX4. And the CAFs improve the invasiveness and the metastasis of the malignant T cells through an IL-6 / JAK2 / STAT3 / SOX4 or an IL-6 / HIIF-1 alpha / SOX4 pathway. SOX4 may be a key regulatory gene in the positive feedback loop, and targeting SOX4 can destroy interaction between tumor cells and TME, so that immunosuppressive TME is reversed, and malignant T cell related diseases are treated.

Description

Technical Field

[0001] The present invention relates to the field of medicine, and in particular to malignant T-cell related diseases, especially cutaneous T-cell lymphoma, and particularly to the field of drug development for the treatment of mycosis fungoides. Background Art

[0002] Mycosis fungoides (MF) is a low-grade malignant cutaneous T-cell lymphoma (CTCL) characterized by neoplastic proliferation of T lymphocytes, accounting for about 50% of all primary cutaneous T-cell lymphomas. Most MFs have an indolent progressive biological behavior, but there are still a small number of unpredictable treatment resistance and malignant transformation. Therefore, exploring the pathogenesis of MF and early intervention in the malignant evolution and treatment resistance of MF are the key to improving the quality of life and prognosis of MF patients.

[0003] In recent years, more and more studies have focused on the role of the tumor microenvironment (TME) in the occurrence and development of MF. Rindler et al. explored the TME immune landscape of patients with advanced MF through single-cell V(D)J sequencing analysis. In addition, some studies have reported molecular subtyping schemes of malignant T cells, revealing the complex crosstalk between each subtype of immune cells in the TME. Other studies have further revealed the immunosuppressive TME pattern mediated by the interaction between malignant T cells and myeloid cells. In the tumor microenvironment, fibroblasts play a variety of key roles through multiple mechanisms, including promoting tumor growth, enhancing chemotherapy resistance, inhibiting immune responses, and supporting angiogenesis. These effects have a profound impact on tumor growth, metastasis, immune escape, and treatment response. However, the role of fibroblasts in MF is not yet fully understood.

[0004] Therefore, in view of the above-mentioned problems existing in the existing technology, further clarifying the relevant mechanisms in the occurrence and development of MF can provide new ideas for the development of therapeutic drugs for mycosis fungoides. Summary of the invention

[0005] The purpose of the present invention is to overcome the above technical problems and provide the use of SOX4 inhibitors in the preparation of drugs for alleviating or treating malignant T cell-related diseases.

[0006] In some embodiments, the alleviation or treatment of malignant T cell-related diseases specifically involves inhibiting the proliferation of malignant T cells and / or promoting the apoptosis of malignant T cells.

[0007] In some embodiments, the malignant T cell-related disease is selected from T cell lymphoma and / or T cell leukemia.

[0008] Preferably, the T-cell lymphoma includes one or more of peripheral T-cell lymphoma (PTCL), cutaneous T-cell lymphoma (CTCL), anaplastic large cell lymphoma (ALCL), enteropathy-associated T-cell lymphoma (EATL), and hepatosplenic T-cell lymphoma (HSTCL).

[0009] Preferably, the T cell leukemia includes one or more of acute T cell lymphocytic leukemia, T cell prolymphocytic leukemia, adult T cell leukemia, and T cell large granular lymphocytic leukemia.

[0010] Furthermore, the T-cell lymphoma is preferably cutaneous T-cell lymphoma, more preferably primary cutaneous T-cell lymphoma, and most preferably mycosis fungoides.

[0011] In some embodiments, the malignant T cells are Hut-78 cells or Myla cells.

[0012] In some embodiments, the SOX4 inhibitor is selected from one or more of small molecule compounds, protein drugs, and gene drugs.

[0013] Preferably, the SOX4 inhibitor is selected from SOX4 antibodies.

[0014] Preferably, the SOX4 inhibitor is a gene drug.

[0015] More preferably, the gene drug is selected from small interfering RNA (siRNA) or short hairpin RNA (shRNA), more preferably small interfering RNA.

[0016] More preferably, the gene drug is siRNA.

[0017] In some embodiments, the SOX4 inhibitor inhibits the invasiveness and metastasis of malignant T cells through the IL-6 / JAK2 / STAT3 / SOX4 or IL-6 / HIF-1α / SOX4 pathway.

[0018] In some embodiments, the SOX4 inhibitor disrupts the interaction between tumor cells and the tumor microenvironment by targeting SOX4.

[0019] The beneficial effects achieved by the present invention are as follows:

[0020] The present invention confirms that malignant T cells promote the formation of CAFs through the epithelial-mesenchymal transition pathway associated with SOX4. CAFs in turn increase the invasiveness and metastasis of malignant T cells through the IL-6 / JAK2 / STAT3 / SOX4 or IL-6 / HIIF-1α / SOX4 pathways. SOX4 may be a key regulatory gene in this positive feedback loop. Targeting SOX4 can disrupt the interaction between tumor cells and TME, thereby reversing the immunosuppressive TME and achieving the treatment of malignant T cell-related diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 : Clinical and histopathological images of two patients with MF. A. Clinical photo of MF lesions at the biopsy site. Photographs of MF1 show the lesions at the same location before and after treatment. B. Histopathological images of MF lesions in two patients (for the initial diagnosis of MF).

[0022] Figure 2 : Single-cell transcriptome profiles of MF patients and HC. A. MF single-cell transcriptome sequencing analysis process. B. UMAP plots obtained by clustering analysis of 42,480 single cells from two MF patients and one HC. C. UMAP plots of 9 cell types in each sample. D. Heat map showing the top 10 SDE (significantly differentially expressed) genes in each cell type. E. Analysis of the cell proportion of different cell types in each sample. F. Violin plots showing the CNV levels of 9 cell types.

[0023] Figure 3 : Transcriptome characteristics of T cell subsets in MF patients and HCs. A. UMAP shows that T cells were clustered individually to obtain 16 cell subsets. B. Heat map of the expression levels of the top 10 SDE genes in each T cell subset. C. UMAP plot shows the distribution pattern of T cells from different sample sources. Different colors represent different sample sources. Heat map of CNV analysis of DT cell subsets. Red represents high CNV levels, and blue represents low CNV levels. Expression of specific markers of ET cell subtypes on the UMAP plot. F. Violin plots show the CNV levels of 16 T cell subsets. G. Violin plots show the expression of specific markers in T cell subtypes (C01-C06, C08, C10, C11, C13, C16).

[0024] Figure 4: Gene regulatory network in MF. A. Volcano plot of DEGs in malignant T cells, comparing MF1_b1 (plaque stage) and MF1_c1 (tumor stage) (log2foldchange>1.5, P<0.05). B. Volcano plot of DEGs in malignant T cells, comparing MF2_a1 (plaque stage) and MF2_c1 (tumor stage) (log2foldchange>1.5, P<0.05). C. Venn plot showing the genes that are downregulated in common between DEGs in MF1 (plaque stage and tumor stage) and DEGs in MF2 (plaque stage and tumor stage). D, E. IHC staining of pathological sections of MF2 patch stage, tumor stage and LP tissues, labeling CD103 (ITGAE) (D) and NR4A1 (E) positive cells, ×20. Scale bar: 100 μm. F, H. CSI association clustering heatmap between CD4+ T cell (F) and CD8+ T cell (H) subset regulons. Color changes from blue to yellow indicate that the CSI association value changes from low to high. G, I. Activity heatmaps of CSI association modules in each CD4+T cell (G) and CD8+T cell (I) subset. Rows represent CSI modules manually divided according to the CSI clustering heatmap, and columns represent different cell populations. Color changes from blue to yellow indicate that the activity of CSI modules changes from low to high. J. GSVA was used to perform a differential test analysis on the pathway activity scores in each CD4+T cell subset. Color changes from blue to red indicate that the pathway is more enriched in this group.

[0025] Figure 5 : Transcriptome characteristics of fibroblast subsets in MF patients and HCs. A. UMAP plots of 7 fibroblast subsets obtained by cluster analysis. B. Heat map of expression levels of the top 10 SDE genes in fibroblast subtypes. C. UMAP plot of CAFs marker gene expression. D. Pseudo-time series analysis of each fibroblast subset was performed using Monocle2, with each dot representing a cell. E. Pseudo-time trajectory plot, with colors from dark to light, indicating the time of differentiation from early to late. F. Pseudo-time gene expression spectrum heat map. Each row represents a gene, the horizontal axis from left to right represents the time from early to late, and the color from blue to red represents the expression of the gene from low to high. G. Pseudo-time gene expression trend plot of fibroblasts. The horizontal axis from left to right represents the time from early to late, and the vertical axis is the expression level of the gene. Points of different colors represent different fibroblast subsets, showing the changes in gene expression trends in different subpopulations over time. H. GSVA was used to perform differential test analysis on the pathway activity scores in fibroblast subpopulations. The color changes from blue to red, indicating that the pathway is more enriched in the subpopulation.

[0026] Figure 6: Intercellular communication between T cells and fibroblasts. A. Cell communication network diagram shows the number of interaction relationships between T cells and fibroblast subsets. The thicker the line, the greater the number of ligand-receptor interaction pairs. B. Bubble chart shows the receptor-ligand pairs that are significantly correlated between malignant T cells and CAFs in all samples. The size of the circle indicates the significance level, and the redder the circle, the greater the expression value of the interacting cell gene. C. Receptor-ligand pairs with significant correlation between malignant T cells (C01, C10, C11) and CAF subtypes. D. IHC staining of SOX4, FAP, IL-6, TWIST1, and MMP2 in pathological sections of MF2 patch stage, tumor stage, and LP lesion tissue, ×20. Scale bar: 100μm. E, F. Kaplan-Meier survival analysis was used to stratify 49 RNA-seq samples in the MF cohort published in the GEO database according to SOX4 expression levels, and the relationship between SOX4 expression levels and overall survival (OS) (E) and progression-free survival (PFS) (F) of MF patients was obtained. G. IF staining of FAP (red), SOX4 (green), and IL-6 (magenta) on paraffin-embedded tissue sections of tumor stage lesions in MF2 patients. DAPI (blue) was used to observe cell nuclei. Scale bar: 50 μm.

[0027] Figure 7 : Histogram of the expression abundance of the target RNA in the sample (ΔCt = target Ct value - internal reference Ct value, the sample with a relatively larger ΔCt has a relatively lower expression abundance of the target RNA; when the ΔCt value is ≤12, the expression abundance of the target RNA in the sample is high; when 12<ΔCt value<16, the expression abundance of the target RNA in the sample is medium; when the ΔCt value is ≥16, the expression abundance of the target RNA in the sample is low).

[0028] Figure 8 :WB was used to detect the expression of SOX4 in cells.

[0029] Fig. 9 :Effect of SOX4 gene silencing on the proliferation ability of cutaneous T-cell lymphoma cells (Hut-78).

[0030] Fig.10 :Effect of SOX4 gene silencing on the proliferation ability of cutaneous T-cell lymphoma cells (Myla).

[0031] Fig.11 :Effect of SOX4 gene silencing on the cell cycle of cutaneous T-cell lymphoma cells (Hut-78).

[0032] Fig.12 :Effect of SOX4 gene silencing on the cell cycle of cutaneous T-cell lymphoma cells (Myla).

[0033] Fig.13 :Effect of SOX4 gene silencing on apoptosis of cutaneous T-cell lymphoma cells (Hut-78).

[0034] Fig.14 :Effect of SOX4 gene silencing on apoptosis of cutaneous T-cell lymphoma cells (Myla).

[0035] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The experimental methods in the following examples are conventional methods unless otherwise specified; the experimental materials used in the following examples are purchased from commercial channels unless otherwise specified.

[0038] Patient recruitment and sample processing

[0039] Two patients with advanced MF were recruited from the Cutaneous Lymphoma Clinic of Hangzhou Third People's Hospital (Table 1) (one MF patient, MF1, and one MF with large cell transformation patient, MF2). All patients had not received any topical or systemic treatment within 6 months before the collection of biospecimens. All diagnoses were verified by at least two dermatopathologists according to the WHO-EORTC classification criteria. Biopsies were taken from flat and palpable lesions of the two MF patients (e.g. Figure 1 In addition, the corresponding adjacent non-lesional skin and PBMCs were collected in MF1. At the same time, one normal skin tissue (derived from HC of a patient who underwent surgery for non-skin diseases) was collected as a control study. All samples were obtained with informed consent and approval from the Medical Ethics Committee of Hangzhou Third Hospital.

[0040] Table 1: Information of enrolled patients

[0041]

[0042] Main cells and reagents

[0043] Human skin T-cell lymphoma cell lines: MyLa, Hut-78 (purchased from Shanghai GeneCare Gene Medicine Technology Co., Ltd.)

[0044] SOX-4 antibody: Mouse (purchased from Santa Cruz, sc-518016) 47 kDa

[0045] GAPDH antibody: Mouse (purchased from Santa Cruz, sc-32233) 37 kDa

[0046] Trypan blue: Sigma

[0047] Cell Counting Kit-8: DOJINDO

[0048] Annexin V-FITC / PI apoptosis kit: Lianke Biotechnology

[0049] Target lentivirus: Vector Vector name: GV493

[0050] Element sequence: hU6-MCS-CBh-gcGFP-IRES-puromycin

[0051] Reference number: CON313

[0052] Control insert sequence: TTCTCCGAACGTGTCACGT

[0053] For more information about the vector map, please refer to: http: / / www.genechem.com.cn / service / index.php?ac=gene&at=vector_search&keyword=GV493

[0054] Single-cell RNA-seq data analysis

[0055] (1) Sequencing data quality control and gene quantification

[0056] The raw data (raw reads) generated in high-throughput sequencing are fastq format sequences. The 10x Genomics official software CellRanger (v5.0.0) is used to perform data quality statistics on the raw data and compare it with the reference genome. The software quantifies high-throughput single-cell transcriptome data by identifying the Barcode markers that distinguish cells in the sequence and the UMI markers of different mRNA molecules in each cell, and obtains high-quality quality control statistics such as cell number, gene median value, and sequencing saturation.

[0057] (2) Gene quantification quality control and data preprocessing

[0058] Based on the preliminary quality control of Cellranger, the Seurat (v4.0.0) software package was further used to perform quality control on the data. In theory, the number of genes expressed by most cells, the UMI counts, and the expression proportion of mitochondrial transcripts should show a specific concentrated distribution pattern. Therefore, in the quality control process, low-quality cells were screened and filtered based on the distribution of indicators such as nUMI, nGene, and percent.mito. The specific quality control plan is: retain cells with more than 200 genes, more than 1000 UMIs, log10GenesPerUMI greater than 0.7, less than 20% mitochondrial UMI, and less than 5% red blood cell genes as high-quality cells, and use DoubletFinder (v2.0.2) software to effectively remove double cells.

[0059] (3) Dimensionality reduction and cluster analysis

[0060] In view of the batch effect between different samples in this study, the MNN (mutual nearest neighbors) dimensionality reduction algorithm was first used to eliminate the batch effect. Subsequently, based on the dimensionality reduction results of MNN, the UMAP (Uniform Manifold Approximation and Projection) algorithm was used to visualize the clustering of single cell groups. In the clustering process, the SNN algorithm was used to obtain the optimal cell clustering results.

[0061] (4) Marker gene identification

[0062] The FindAllMarkers function in the Seurat package was used to identify marker genes. This step aims to identify genes that are differentially upregulated in each cell classification relative to other cell populations. These genes are potential marker genes for each cell classification. In order to more intuitively display the expression patterns of these marker genes, VlnPlot and FeaturePlot functions were further used for visualization.

[0063] (5) Cell type identification

[0064] Cell type identification was performed using the SingleR (v1.4.1) package. The SingleR package is based on a single-cell reference expression quantitative public dataset, calculates the correlation between the expression profile of the cell to be identified and the reference dataset, and assigns the cell type with the highest correlation in the reference dataset to the cell to be identified, effectively reducing the interference of human subjective factors and improving the accuracy of identification. Specifically, the identification principle is to calculate the Spearman correlation between the expression profile of each cell in the sample and the expression profile of each annotated cell in the reference dataset, and select the cell type with the greatest expression correlation with the sample cell in the dataset as the final cell type to be identified.

[0065] (6) Separate cluster analysis of T cells and fibroblasts

[0066] Seurat's "FindMarkers" function was used to identify cell subset-specific marker genes. The specific method is as follows: by using the Wilcoxon rank sum test, the cells of a specific cell subset were compared with all other cells to determine the differentially expressed genes (DEGs) of the cell subset. The Bonferroni-corrected p value of less than 0.05 was used as the threshold to identify statistically significant DEGs. Genes whose average expression in a specific cell subset was more than 2 times higher than the average expression in other cell subsets were selected as landmark genes. Cell type-related landmark genes and top-ranked differentially expressed genes that have been reported in the literature were used to annotate each cell subset of T cells and fibroblasts.

[0067] (7) CNVs analysis

[0068] According to the gene expression in the single-cell transcriptome data, the CNV value of each chromosome region was evaluated using the inferCNV (v1.0.4) software package (cutoff set to 0.1). In this study, T cells were regarded as malignant cells, and other cells were regarded as normal cells. First, the genes were sorted according to their chromosomal positions, and 101 genes were used as the sliding window size to calculate the average gene expression. Then, the gene expression of normal cells was used as a control, and the final CNV result file was generated after denoising.

[0069] (8) Differential gene and enrichment analysis

[0070] The FindMarkers function in the Seurat package was used to screen differentially expressed genes. The screening conditions were set as follows: p-value less than 0.05 and multiple differences greater than 1.5. The hypergeometric distribution test was used to perform GO and KEGG enrichment analysis on the significantly differentially expressed genes screened out.

[0071] (9) Cell trajectory and pseudo-time analysis

[0072] The Monocle2 (v2.9.0) package was used to infer the cell differentiation trajectory. The specific steps are as follows: First, the importCDS function of the Monocle2 package was used to convert the Seurat object into a CellDataSet object. The differentialGeneTest function was used to screen out the genes used for cell sorting (ordering gene, P < 0.01). Then the reduceDimension function was used for dimensionality reduction clustering. Finally, the orderCells function was used to infer the cell differentiation trajectory.

[0073] (10) Gene set variation analysis

[0074] First, the GSEABase package (v1.44.0) was used to download and organize the required background gene set files from the KEGG database (https: / / www.kegg.jp / ). Then, the GSVA package (v1.30.0) was used to score the pathway activity values ​​of individual cells. Finally, the LIMMA software package (v3.38.3) was used to calculate the differences in signal pathway activity between different groups.

[0075] (11) SCENIC analysis

[0076] SCENIC analysis used the motifs database of RcisTarget and GRNboost (SCENIC v1.1.2.2, RcisTarget v1.2.1 and AUCell v1.4.1), and the default parameters were used during operation. The specific process is as follows: First, the potential target genes of each transcription factor (TFs) were identified based on co-expression. Then, the RcisTarget package was used to perform motif analysis on these potential target genes to screen out the real transcription factors and their corresponding target genes. Finally, the activity of each regulator in each cell was scored with the help of the AUCell package. In order to further explore the cell type specificity of the regulator, the scFunctions package was used to calculate the specificity score based on the Jensen-Shannon divergence, and the connection specificity index (CSI) between the regulator and the target gene was analyzed.

[0077] (12) CellPhoneDB Cell Communication Analysis

[0078] CellPhoneDB (v2.0) was used to identify ligand-receptor interaction pairs in single-cell transcriptome data. The specific steps are as follows: First, genes expressed in at least 10% of cells in a specific cell type were selected for further analysis. Then the cell type markers of all cells were randomly arranged (the default random arrangement was 1000 times) to form a new cell population. By calculating the average expression level of the ligand in the cell population after random arrangement and the average expression level of the receptor in the cell type that interacted with it, a null distribution was generated for each ligand-receptor pair in each pairwise comparison between the two cell types, and the corresponding P value was further calculated based on the normal distribution curve generated by the interaction score of the arranged ligand-receptor pair. Finally, the two R packages Igraph and Circlize were used to draw the cell communication network diagram.

[0079] Survival analysis

[0080] GEO gene expression and survival data were downloaded from NCBI's Gene Expression Database and can be accessed through GEO series accession number GSE168508 (https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE168508). The samples were divided into high and low groups according to the optimal cut point determined by the R function surv_cutpoint. Kaplan-Meier survival curves were drawn by the survminer software package.

[0081] Other experiments

[0082] Other experiments (such as cell extraction and culture, immunohistochemistry, immunofluorescence, lentiviral infection, qRT-PCR, Western Blot) were performed using conventional experimental procedures in the art.

[0083] Statistical analysis

[0084] The data of this study were subjected to more than 3 independent experiments and statistically processed using the statistical analysis software SPSS. The comparison between the two groups of data in the experiment used the completely randomized t-test (independent sample T-test), while the comparison between multiple groups of data used one-way ANOVA. When the P value was less than 0.05, the experimental results were considered to be statistically significant (* represents P<0.05, ** represents P<0.01, *** represents P<0.001, **** represents P<0.0001).

[0085] Example 1 Single-cell RNA sequencing reveals cellular characteristics of MF

[0086] Through single cell suspension preparation, library construction and sequencing analysis ( Figure 2) and quality control of the obtained data, a total of 42,480 cell transcriptome data were obtained. After unsupervised cluster analysis of these 42,480 cells, 17 cell subpopulations were obtained. Since there were batches between different samples in this project, the MNN dimensionality reduction algorithm was first used to eliminate the batch effect, and then the UMAP algorithm was used to visualize the single cell clustering based on the MNN dimensionality reduction results, and finally the optimal cell clustering was obtained.

[0087] By identifying the expression of cell type marker genes, 9 cell types were annotated, including B cells (653 cells), endothelial cells (4101 cells), epithelial cells (4761 cells), fibroblasts (7087 cells), mast cells (786 cells), myeloid cells (1290 cells), neutrophils (262 cells), smooth muscle cells (100 cells), and T / NK cells (19981 cells). To further confirm the annotation of clustered cells, the specific expression genes of each cell type were identified. At the same time, the results showed that these 9 cell types were present in each sample, but the infiltration ratio of each cell type was different. Comparison of the differences in cell type enrichment between MF patients and HCs revealed that MF patients had obvious inter-tumor heterogeneity, which was most obvious in the distribution of T cells. Large-scale chromosomal CNVs analysis showed that the CNV level of T cell subsets was significantly increased compared with other cells.

[0088] Example 2 Heterogeneity among malignant T cell transcriptomes

[0089] T cells were extracted individually and re-clustered to obtain 16 cell clusters. The malignancy of the 16 T cell clusters was determined by inferring large-scale CNVs of cells through the obtained ScRNA-Seq data. T cell clusters C01, C02, C03, C06, C10, C11, C13 and C16 showed high levels of CNVs. These cell clusters were defined as malignant T cells: C01_CD4-Malignant, C02_CD8-Malignant, C03_CD4-Malignant, C06_CD8-Malignant, C10_CD8-Malignant, C11_CD4-Malignant, C13_CD4-Malignant, C16_CD8-Malignant. The remaining reactive T cells were defined by genes related to typical T cell functions, such as C04_CD4-CCR7C05_CD4-IL7R and C08_CD8-GZMK( Figure 3 ).

[0090] Tumor heterogeneity in MF is reflected not only in differences in cell distribution between samples, but also in differences in gene expression of each T cell subtype. Increased expression of tumor-related genes, including KIR3DL2, TOX, TWIST1, GTSF1, and CD40LG, was found in malignant T cell subsets. High expression of KIR3DL2, a recognized diagnostic and prognostic marker for CTCI, is associated with a shortened survival time of patients, which has been confirmed in circulating tumor cells of SS patients. Abnormal expression of TWIST1 and TOX is common in MF and SS, predicting a poor prognosis for patients. Upregulation of GTSF1 may be one of the reasons for the resistance of malignant T cells to apoptosis. Downregulation of CD7 and DPP4 is a characteristic of malignant T cells in MF skin and peripheral blood, and can be used as alternative markers to distinguish between malignant and benign T cells.

[0091] Example 3: Transcription factor activation promotes MF progression

[0092] Transcription factors TFs and their downstream regulatory genes play an important role in the occurrence and development of tumors. SCENIC software can be used to identify the regulons co-expressed between TFs and potential target genes in CD4+ and CD8+ T cells, and the CSI of the regulon is used to represent the correlation between different regulons. At the same time, regulons with higher CSI may co-regulate downstream genes and be jointly responsible for cell functions. The analysis results of CD4+ T cells showed ( Figure 4 ), 41 regulons were divided into 4 regulatory modules. In most malignant CD4+ T cells, TCF7 showed a higher regulon activity score. In addition, FOS, JN, JNB, JND, IRF1, STAT3, MYC, and ETS1 in modules 2 and 4 also showed high regulatory activity.

[0093] In addition, GSVA revealed activated signaling pathways in each T cell subtype, including Wnt, JAK-STAT, Notch, PPAR, and IL-17 signaling pathways, which are often reported to be involved in the pathogenesis of MF. Many of the above TFs are ultimately activated by these pathways and regulate the differentiation and activation of T cells, promoting tumor progression and immune escape. In CD8+ T cells, module 3, including EZH2, SOX4, ETV3, NRID1, NR3C1, FOXO1, and NFKB1, showed higher activity levels in C10_CD8-Malignant than in other CD8+ T cell subtypes.

[0094] According to the above results, SOX4 was found to be highly active in malignant T cells. SOX4 is a member of the Sry-related high-mobility group box family of TFs, which is characterized by the presence of a conserved DNA binding domain, the high-mobility group box. SOX4 expression was significantly increased in the following three malignant T cell subsets: C01_CD4-Malignant, C11_CD4-Malignant, and C10_CD8-Malignant. The DEGs results also reflected the increasing expression trend of SOX4 during disease progression.

[0095] Example 4 Detection of transcriptional expression patterns of tumor-associated fibroblasts (CAFs) in MF

[0096] To investigate the biological characteristics of fibroblasts isolated from MF and HC, they were re-clustered into 10 fibroblast subpopulations. ACTA2 (α-SMA), FAP, S100A4, and PDGFRβ can be used as a set of markers to identify CAFs. Based on these CAF markers and other fibroblast-characteristic genes, fibroblasts were annotated into 7 subtypes: CAF-SLPI, CAF-CD70, CAF-COL4A4, CAF-WNT2, NFAPOE, NF-COCH, and NF-COMP ( Figure 5 ).

[0097] Typical CAF markers such as FAP, S100A4, and PDGFRB were highly expressed in the four CAF subsets. THC staining of the CAF marker FAP showed that in the same patient (MF2), FAP-positive cells were significantly increased in the tumor stage lesions compared with the plaque stage lesions. CAFs were detected in all lesions from MF patients, but were particularly enriched in paracancerous tissues. CAF-CD70 and CAF-SLPI were mainly derived from paracancerous tissues of plaque stage MF (MF1_b2), while CAF-WNT2 and CAF-COL4A4 were mainly derived from paracancerous tissues of tumor stage MF (MF1_c2). However, the distribution of normal fibroblasts (NFs) subtypes overlapped in MF and HC samples. Pseudo-temporal analysis of fibroblasts also reflected this heterogeneity. The differentiation trajectory started with NFs and differentiated into two different branches of CAFs (state1 and state2) as the trajectory differentiated. CAFs along both branches showed upregulation of extracellular matrix (ECM) markers, including COL6A3, POSTN, FN1, DCN, IGFBP6, and SLPI, whereas cells differentiated along state2 (mainly from CAF-COL4A4 and CAF-WNT2) showed upregulation of IL-6, IGF1, FBLN1, C3, and C7.

[0098] Example 5 Study on the interaction between CAFs and malignant T cells

[0099] CAFs are involved in tumor progression and increase the invasiveness of malignant T cells through multiple pathways. CAFs can produce a large number of growth factors, cytokines, and chemokines, including TGF-β, IL-6, and CCL2, which contribute to the immune escape of malignant cells. In C01, C10, and C11 malignant T cell subtypes, the expression of IL-6 receptor (IL-6R) and SOX4 is increased ( Figure 3 E, G). HC and immunofluorescence IF verified the expression and localization of SOX4, NR4A1, FAP and IL-6 in MF lesions and LP at various stages ( Figure 6 ). SOX4 and IL-6 are highly expressed in the tumor stage lesions. IF shows the expression and co-localization of FAP (red), SOX4 (green), and IL-6 (magenta). On the other hand, HIF-1α and CASC15 were also upregulated in the malignant clones ( Figure 3 G). This is consistent with previous findings that IL-6 produced by CAFs activates HIF-1α in tumor cells, which in turn transcriptionally activates IncRNA CASC15, thereby promoting SOX4 expression.

[0100] Based on the above results, it can be concluded that SOX4 may play a key role in the transformation of NFs to CAFs in MF through the TGF-β / SMAD and Wnt / β-catenin pathways. In addition, CAFs can promote the development of MF through the IL-6 / JAK2 / STAT3 / SOX4 and IL-6 / HIF-1α / SOX4 pathways. The open MF cohort data were obtained from the GEO database and the relationship between SOX4 expression and the prognosis of MF patients was studied in them. Kaplan-Meier survival analysis showed that in this MF cohort, higher SOX4 expression levels were associated with shorter overall survival (OS) (P<0.001) and progression-free survival (PFS) (P<0.001), confirming that increased SOX4 expression is associated with poor prognosis of patients.

[0101] Next, we studied the interaction between T cells and fibroblasts through CellphoneDB. The results showed that CAFs had multiple receptor-ligand interactions with malignant T cells, such as LRP1-MDK, PDGFRB-PDGFD, CXCR4-CXCL12, TGFBR3-TGFBR1, IGFIR-IGF1, FGFRI-FGF7, DPP4-CXCL12, and EGFR-MIF.

[0102] Example 6 In vitro detection of SOX4 expression in skin T cell lymphoma cells

[0103] Skin T cell lymphoma cells Hu-78 and Myla were cultured in vitro. The expression of SOX4 mRNA in the cells was detected by Q-PCR. The results are as follows Figure 7 As shown, it can be seen that the expression abundance of the gene SOX4 is high in the two cell lines.

[0104] The RNA interference technology was used to design and synthesize a recombinant lentiviral vector (SOX4-siRNA expression vector) that specifically targets the SOX4 gene and transfect it into Hut-78 and Myla cell lines. The experiment was divided into 4 groups, and the parameters of each group are shown in Tables 2 and 3, respectively, which are the SOX4 gene silencing recombinant lentiviral particles (LV-SOX4-RNAi) transfected cells (experimental groups KD1, KD2, and KD3), and the empty vector recombinant lentiviral particles (NC) transfected cell groups; the expression of SOX4 in each group of cells was detected by WB, and the results are shown in Tables 2 and 3. Figure 8 As shown, in both cell lines, the expression of SOX4 was downregulated after SOX4 gene silencing compared with the NC group.

[0105] Table 2: Cell infection virus information

[0106] Virus Name Experiment group label Virus titer (TU / ml) Virus dosage (μl) LV-SOX4-RNAi(106386-1) KD1 7E+08 14.29 LV-SOX4-RNAi(106387-11) KD2 7E+08 14.29 LV-SOX4-RNAi(106388-1) KD3 1E+09 10.00 Negative control virus CON313 NC 1E+09 10.00

[0107] Table 3: Synthetic oligo information

[0108]

[0109] Example 7 Effect of SOX4 gene silencing on proliferation ability, cell cycle and apoptosis of cutaneous T-cell lymphoma cells

[0110] CCK-8 method was used to detect the effect of SOX4 gene silencing on the proliferation ability of cutaneous T-cell lymphoma cells. Fig. 9 , 10 As shown, compared with the control group, the cell viability and proliferation of the SOX4 gene silencing group decreased (day 5, P<0.05).

[0111] Flow cytometry was used to detect the cell cycle. Fig.11 , 12 As shown, in Hut78 cells, the number of cells in the G1 phase decreased in the SOX4 gene silencing group (P<0.05), and there was no significant change in the cells in the S phase and G2 / M phase; while in Myla cells, the number of cells in the G1 phase did not change significantly, the number of cells in the S phase decreased (P<0.05), and the number of cells in the G2 / M phase increased significantly (P<0.05) in the SOX4 gene silencing group.

[0112] The apoptosis of cells was further detected. Fig.13 , 14 As shown in the results, SOX4 gene silencing promoted the apoptosis of Hut78 cells (P<0.05), but had no significant effect on the apoptosis of Myla cells.

[0113] In general, inhibiting the expression of SOX4 will have a certain inhibitory effect on the activity of malignant T cells and can be applied to the relief or treatment of MF.

[0114] Unless otherwise defined, all professional and scientific terms used herein have the same meanings as those familiar to those skilled in the art. In addition, any methods and materials similar or equivalent to those described herein may be applied to the present invention. The preferred implementation methods and materials described herein are for demonstration purposes only and are not intended to limit the content of this application.

Claims

1. Application of SOX4 inhibitors in the preparation of drugs for alleviating or treating malignant T cell-related diseases.

2. The use according to claim 1, characterized in that: The alleviation or treatment of malignant T cell-related diseases specifically includes inhibiting the proliferation of malignant T cells and / or promoting the apoptosis of malignant T cells.

3. The use according to claim 1, characterized in that: The malignant T cell-related disease is selected from T cell lymphoma and / or T cell leukemia; preferably, the T cell lymphoma includes one or more of peripheral T cell lymphoma (PTCL), cutaneous T cell lymphoma (CTCL), anaplastic large cell lymphoma (ALCL), enteropathy-associated T cell lymphoma (EATL), and hepatosplenic T cell lymphoma (HSTCL), and the T cell leukemia includes one or more of acute T lymphocytic leukemia, T cell prolymphocytic leukemia, adult T cell leukemia, and T cell large granular lymphocytic leukemia.

4. The use according to claim 3, characterized in that: The T-cell lymphoma is preferably cutaneous T-cell lymphoma, more preferably primary cutaneous T-cell lymphoma, and most preferably mycosis fungoides.

5. The use according to any one of claims 1 to 3, characterized in that: The malignant T cells are Hut-78 cells or Myla cells.

6. The use according to claim 1, characterized in that: The SOX4 inhibitor is selected from one or more of small molecule compounds, protein drugs, and gene drugs, preferably SOX4 antibodies or gene drugs.

7. The use according to claim 6, characterized in that: The gene drug is selected from small interfering RNA (siRNA) or short hairpin RNA (shRNA).

8. The use according to claim 7, characterized in that: The gene drug is preferably small interfering RNA.

9. The use according to any one of claims 1 to 8, characterized in that: The SOX4 inhibitor inhibits the invasiveness and metastasis of malignant T cells through the IL-6 / JAK2 / STAT3 / SOX4 or IL-6 / HIF-1α / SOX4 pathway.

10. The use according to any one of claims 1 to 8, characterized in that: The SOX4 inhibitor disrupts the interaction between tumor cells and the tumor microenvironment by targeting SOX4.

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