HEK293T cells and T-ALL cell lines; MOLT-4, and JURKAT cells, were purchased from the American Type Culture Collection, (ATCC, Manassas, VA). HEK293T cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS, Biological Industries, Israel) and 1% penicillin/streptomycin (Beyotime Biotechnology, China). MOLT-4 and JURKAT cells were cultured in RPMI-1460 media (Biological Industries, Israel) supplemented with 10% fetal bovine serum (FBS, Biological Industries, Israel), 1% L-glutamine (Hyclone, United States), and 1% penicillin/streptomycin (Beyotime Biotechnology, China). The cells in the culture were maintained at 37°C in a humidified atmosphere with 5% CO2.

The plasmid constructs for the overexpression of CCR9, knockdown of CCR9, and a control plasmid were acquired from the Public Protein/Plasmid Library Company. Stable cell line construction was performed following the procedure described in our published study (Zeng et al., 2023). The stable transgenic cell lines obtained after the screening were utilized for subsequent experiments. The efficiency of silencing was verified by RT-qPCR, flow cytometry, and Western blot.

The cells were starved in RPMI media with only 1% FBS for 12 h, followed by seeding of 1×10⁶ cells per well in a 12-well plate. To assess the effect of the CCR9 ligand on the total cholesterol (TC) level, the cells were treated with 100 ng.mL^-1 of CCL25. After incubation of the cells for 0 h, 24 h, and 48 h, culture medium was collected at the respective time points. The TC in the culture medium was determined according to the total cholesterol assay kit (Abbkine, catalog number KTB2220, China). First, TC working solution was preheated at 37 °C for ∼ 30 min, from which 150 μL was added to each well in a 96-well plate, followed by the addition of 50 μL of measuring standard solution, or RIPA blank solution. After mixing, the reaction was incubated at room temperature for 15 min, and the absorbance was measured at 500 nm wavelength using a microplate reader (TECAN, Switzerland). Finally, a standard curve was generated, and the corresponding total cholesterol concentration of each well was calculated according to the standard curve.

The total cellular RNA was isolated using the TRIzol reagent (Thermo Fisher Scientific, United States) following the manufacturer’s instructions. The mRNA was reverse transcribed into cDNA using the RT-kit following the manufacturer’s guidelines (R323-01; Vazyme, China). The cDNA was amplified with ChamQ SYBR qPCR Master Mix (Q311-02; Vazyme, China) using the Quant Studio 6 Flex Real-Time PCR System (Life Technologies, United States). The expression values of the target gene were normalized according to the reference genes GAPDH and ACTB. The gene expression quantification was evaluated using the 2^−ΔΔCT method. The sequence information of the primers used in this study is listed in Table 1.

Isolated cells were lysed with RIPA lysis buffer supplemented with the protease inhibitor PMSF (Beyotime Biotechnology, China) after washing the cells with PBS. The protein content in the cellular lysate was quantified using the BCA kit (Beyotime Biotechnology, China). The lysate samples were separated on SDS-PAGE after boiling at 100°C for 5 min. The proteins on the gel were cold transferred to a polyvinylidene difluoride membrane (Millipore, United States) for 2 h. After transfer, membrane blocking was done with 5% milk dissolved in PBST, followed by incubation with the target primary antibody for overnight at 4°C with gentle shaking. The membrane was washed and probed with secondary antibodies (Proteintech, China) for 1 h. The protein signal in the membrane was detected using an ultra-high-sensitivity ECL kit (MedChemExpress, United States). Antibodies: CCR9 (Abcam, ab32556), HMGCS1 (Proteintech, 17643-1-AP), MSMO1 (Immunoway, YT7443), HMGCR (HUABIO ET1702-41), MVD (Proteintech, 15331-1-AP), ACTIN (HUABIO, ET1702-52), GAPDH (ABclonal, AC002), HRP-conjugated anti-mouse IgG secondary antibodies (Proteintech, SA00001-1), HRP-conjugated anti-rabbit IgG secondary antibodies (Proteintech, SA00001-2).

Transwell migration/chemotaxis and invasion assay were carried out in 24-well transwell chambers (Corning) using 5 μm pore size polycarbonate insets (Corning, United States). For transwell migration assay, culture with 1×10⁵ suspended in cells in 1% FBS medium was seeded in the upper chamber. 600 μL culture medium with 10% FBS was added into the lower chamber. For chemotaxis assay, 100 ng/mL CCL25 in RPMI 1640 with 1% FBS was placed in the lower well, and cell suspension containing 1 × 10⁵ in 1% FBS was added to the upper well of the chamber. For the invasion assay, the transwell chamber was coated with 100 μL of 1:7 diluted Matrigel (BD Biosciences, United States). Cells (1–2 × 10⁵) suspended in 2% FBS medium were seeded onto the upper chambers followed by addition of either 10% FBS medium or 2% FBS medium with CCL25 to the bottom chambers. The cells were allowed to migration for 24 h at 37°C. The metastatic cells were counted using an inverted microscope with a hemacytometer. The results of migration/invasion assays are expressed as fold values.

For the chemotaxis assay after simvastatin treatment, cells were treated with different concentrations of simvastatin 3 μM, 6 μM, 12 μM and 24 μM, (Sigma, S6196) for 24 h. Subsequently, 1×10⁵ cells in 1% FBS medium were seeded in the upper chamber, and 600 μL medium with 10% FBS was added to the lower chambers. After 12 h, the cells in the lower well were recovered and counted using a Neubauer chamber.

Cells were washed and resuspended at a concentration of 1×10⁶ cells per 100 μL in PBS. Cells were stained with the corresponding monoclonal antibodies as per the manufacturer’s instructions, then washed and analyzed with FACS Aria III flow cytometer (BD, United States). The FACS data were analyzed with FlowJo (BD Bio, United States). CCR9 (BD Pharmingen, United States), Hu CD45 APC HI30 100Tst (BD Pharmingen, United States), and DAPI (BD Pharmingen, United States).

MOLT4 cells, JURKAT cells, JURKAT cells overexpressing CCR9 (Oe-CCR9-JURKAT) cells, and the GFP control were used for the RNA-sequencing. We performed all the RNA-seq in biological replicates. The total RNA of the corresponding cells was extracted following the aforementioned protocol. The integrity of RNA was assessed by 1% agarose gel electrophoresis, and integrity was validated by running the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, United States). A total of 1 μg of RNA per sample was used as input material to generate the RNA-sequencing library using the Illumina NEBNext^® UltraTM RNA Library Prep Kit following the manufacturer’s protocol. The library was sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. After pretreatment of the raw reads (filtering and QC), clean reads were aligned to the human reference genome using Hisat2 v2.0.5. After mapping to the reference genome, the read numbers mapped to each gene were initially counted with FeatureCounts (v 1.5.0-p3), followed by the calculation of FPKM values for each gene based on the length of the gene and the read counts mapping to it. The read counts were adjusted by the edgeR program package through one scaling normalized factor. Next, the edgeR R package (version 3.22.5) was used to perform differential expression analysis between the conditions. Adjacent p-values were calculated following Benjamini and Hochberg’s approach. The corrected p-values of <0.05 and log2FC > 1 were set as the threshold for defining a significant differential gene expression.

The enrichment of the DEGs in the Gene Ontology (GO), KEGG pathway, Reactome pathway, DisGeNET pathway, and disease ontology (DO) pathway was implemented by the clusterProfiler R package (3.8.1) in R-studio. Pathways associated with the DEGs with p-values ≤ 0.05 were regarded as significant. STRING database was used to construct the network of genes and was visualized with Cytoscape (v.3.9.1).

GSE48558 and GSE26713 datasets used in this study were retrieved from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo). GSE48558 dataset contains gene expression data obtained from normal and malignant hematopoietic cells. GSE26713 dataset contains the gene expression data of 117 human T-ALL bone marrow samples and 7 normal individual bone marrow samples. Additionally, GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) was used to compare the expression of the genes between leukemia and normal conditions. UALCAN database (http://ualcan.path.uab.edu/index.html), containing the cancer genome atlas (TCGA) gene expression data, was utilized to determine the expression of genes in various cancers.

NTG mice (female, 4–6 weeks old, Institute of Model Animals, Wuhan University, China) were reared in a sterile animal facility on a 12-h light and dark cycle. The mice were treated in accordance with European Union guidelines and with the approval of the Medical Ethics Committee of Wuhan University School of Medicine (Permit Number: WP20220022). MOLT-4 and JURKAT cells constitutively expressing the luciferase gene under a ubiquitous promoter were tail injected into NTG immunocompromised mice (1×10⁷ cells per mouse). Simultaneously, PBS was used as a control. Next, JURKAT cell line overexpressing CCR9 (OeCCR9-JURKAT) was injected into the NTG. For comparison GFP expressing–JURKAT cells (GFP-JURKAT) was used. The imaging system Xtreme BI (Bruker, United States) was used to image bioluminescence in vivo. Mice were intraperitoneally injected with 150 mg/kg D-Luciferin and Potassium Salt (Yeason, China). Engraftment and disease progression were analyzed by the assessment of body weight, peripheral blood smear, flow cytometry, IHC staining, bioluminescence imaging, and mouse organ examination.

The slides of bone marrow (BM) and peripheral blood (PB) from mice were stained with Wright’s Giemsa stain solution (Solarbio, G1020) according to the manufacturer’s instructions and were then observed or photographed under the microscope (Nikon, Japan).

The tissue samples from the brain, liver, and spleen of mice were fixed with 4% paraformaldehyde and then embedded in paraffin. The paraffinized tissues were sliced into sections. The preserved tissue sections were deparaffinized, rehydrated, and finally stained with hematoxylin and eosin using the HE Staining Kit (Servicebio, G1003). All the procedures were performed according to the manufacturer’s instructions. All the finished sections were observed and photographed under the microscope (Nikon, Japan).

The IHC staining assay was performed as described in the previous study (Lei et al., 2022). Briefly, the microarray tissue samples were dewaxed with xylene twice for 15 min and rehydrated with an increased concentration of ethanol for 5 min each. Sodium citrate buffer was used for antigen retrieval, and the samples were heated at 100°C followed by inhibition of the endogenous peroxidase activity with 3% H2O2. Then the sections were blotted with the primary antibodies against CD45 (Servicebio, GB113885) and SREBF2 (Proteintech 14508-1-AP). After washing with PBS, the tissue sections were incubated with a secondary antibody (Servicebio, G1215). The histochemistry score (H-score) was calculated to assess the IHC results. H-score = (percentage of cells of weak intensity × 1) + (percentage of cells of moderate intensity × 2) + (percentage of cells of strong intensity × 3).

The statistical analysis and data visualization was performed with GraphPad Prism (v. 8.0). Unpaired t-test and one-way ANOVA followed by Dunnett’s test were employed. A p-value < 0.05 was considered statistically significant.

To characterize the tumorigenic potentials of the two T-ALL cell lines, an in vivo investigation with the NTG mice model was performed following the workflow outlined in Figure 1A. To this end, JURKAT and MOLT4 cells expressing the luciferase gene and PBS were injected intravenously (i.v.) into the immunodeficient NTG mice. The mice were maintained for 26 days, imaged, and sacrificed for subsequent pathogenic examinations. A significant body weight reduction was observed in mice bearing MOLT4 cells (MOLT4-NTG), represented by the red line, as compared to the group of mice injected with JURKAT cells (JURKAT-NTG) and the PBS control (PBS-NTG) (Figure 1B). Likewise, the bioluminescence imaging presented a significantly increased expansion of the cells in MOLT4-NTG, as shown by the enhanced luciferase signal, compared to JURKAT-NTG and PBS-NTG mice (Figure 1C). Next, we determined the organ weight of the mice in three groups and found a robust increase in liver and spleen weight of MOLT4-NTG mice as compared to JURKAT-NTG and PBS-NTG mice (Supplementary Figures S1A, B), showing a robust increased tumor formation ability of MOLT4 cells. We used flow cytometry to detect the dissemination of JURKAT and MOLT4 cells in multiple organs, respectively, by gating on human CD45. Consistently, a profound increase in the percentage of CD45⁺ cells was observed not only in BM and PB but also in the liver and spleen of MOLT4-NTG mice, showing a remarkably increased organ infiltration of MOLT4 cells as compared to JURKAT cells (Figures 1D–H). Moreover, HE and WG staining of the tumor sections displayed an increased tumor infiltration of the MOLT4 cells to the liver, spleen, brain, bone marrow, and peripheral blood as compared to the JURKAT cells (Supplementary Figure S1C). To corroborate these findings, we performed IHC of the brain, liver, and spleen tissue sections acquired from NTG mice of the two groups and found an elevated number of CD45⁺ cells in the liver of MOLT4-NTG mice as compared to liver obtained from JURKAT-NTG and PBS-NTG mice (Figures 2A, B). We observed a similar trend in the spleen (Figures 2A, C). However, no striking difference in the number of CD45⁺ cells in brain of the different mice groups was observed (Figures 2A, D). Taken together, these results support the notion that MOLT4 cells display a relatively higher tissue infiltration ability as compared to JURKAT cells.

We performed RNA-sequencing of MOLT4 and JURKAT cell lines to characterize the differences in the gene expression profiles and the associated pathways that cause the differences in the tumorigenic potential of the two cell types. We identified 4414 DEGs, including 2076 upregulated genes using the criteria DESeq2, padj = < 0.05, and Log2 FC > 1 (Supplementary Table S1) and 2338 downregulated genes using the criteria DESeq2, padj = < 0.05, and log2 FC > −1 in the R program (Supplementary Table S2). The differential expression of genes between the two cell lines is represented as a heatmap (Figure 3A), and the distribution of the DEGs between the cells is illustrated in the volcano plot (Figure 3B). KEGG pathway enrichment analysis displayed the involvement of the DEGs in the PI3K-Akt signaling pathway, cell adhesion molecules, and hematopoietic cell lineage (Supplementary Table S3, Figure 3C). The Reactome database integrates the biological and molecular pathways of model organisms, including humans (Croft et al., 2010). The Reactome pathway enrichment analysis with a padj > 0.05 as a threshold displayed the association of the DEGs with various vital cell cycle-related pathways, including mitotic G1-G1/S phases, G1/S transition, M/G1 transition, and tumor suppressor pathways, including PTEN regulation and signaling by the TGF-beta receptor complex (Figure 3D). The DisGeNET database is a collection of resources providing information about the gene and its variants in human diseases (Piñero et al., 2020). The DisGeNET gene ontology (GO) analysis showed the abundance of genes in leukemic pathways such as precursor cell lymphoblastic leukemia, leukemia-T cell, and adult T-cell lymphoma/leukemia (Supplementary Figure S2A). The gene ontology function of the DEGs revealed the enrichment of these genes in various important biological pathways such as T-cell activation, actin cytoskeleton, transcription factor activity, DNA binding, and axon development (Supplementary Table S4 and Supplementary Figure S2B). Moreover, the biological function (BF) category of the GO analysis revealed the enrichment of the DEGs in T-cell activation, axon development, and embryonic organ morphogenesis. Regarding the cellular component, DEGs were enriched in the actin cytoskeleton, receptor complex, and extracellular matrix component. Whereas the DEGs were associated with various enzymatic activities, including protein tyrosine kinase, transcriptional activation, enzyme inhibition, and signal transduction, in the molecular function (MF) category of the GO analysis (Supplementary Figure S2C).

These findings highlight the distinct gene expression profiles and high signaling variability across the two cell lines. Moreover, enrichment in various important biological pathways, such as T-cell development and activation, may lead to the distinct developmental fates and subsequent tumorigenic potentials of the two cell lines.

In order to get crucial candidate genes in T-ALL, we set a stringent criterion (log2FC > 5 and padj value = <0.05) to get significantly upregulated genes. We found C-C chemokine receptor 9 (CCR9) with a log2FC = 7.941006 and padj = 2.58E-253 as one of the crucial genes with clinical relevance in T-ALL (Supplementary Table S5). To explore the role of CCR9 in T-ALL, we analyzed published T-ALL GSE datasets (GSE33315, GSE48558, and GSE13159) and observed an elevated expression of CCR9 in clinical samples and T-ALL cell lines (Supplementary Figure S3). Next, we determined the expression of CCR9 in both cells and noted that MOLT4 cells displayed an increased expression of CCR9 (Figures 4A, B). We then investigated the role of CCR9 in T-ALL by upregulating CCR9 in JURKAT cells (OeCCR9-JURKAT) (Figures 4C, D). Next, to assess whether the upregulation of CCR9 influences the migration and invasion, and chemotaxis abilities of the T-ALL cells, in vitro transwell migration/chemotaxis and invasion assays were performed. A pictorial representation of the mechanism of these assays is given in Supplementary Figure S4. We found that migration of the JURKAT cells through the chamber increased when CCR9 was upregulated as compared to the GFP control (Figure 4E). C-C chemokine ligand 25 (CCL25) a ligand for CCR9, orchestrates the trafficking of lymphocytes and induces migration of CCR9^high T-ALL cells, polarization, and microvilli absorption, enhancing T-ALL cell infiltration (Tu et al., 2016). To check whether CCR9 ligand impact cell migration and invasion, CCL25 was added to the cell culture media in the bottom chambers to stimulate the cells. We observed a synergistic effect of CCL25 addition on the migration of CCR9-overexpressing JURKAT cells (Figure 4E). Next, the CCR9 overexpressing JURKAT cells were checked for their invasive properties by assessing their migration through a Matrigel-coated transwell chamber. Notably, the invasive capacity of the JURKAT cell was increased with the overexpression of CCR9 as compared to GFP. (Figure 4F), whereas no remarkable difference in the invasion of OeCCR9-JURKAT cells was observed with the addition of CCL25 (Figure 4F). Since MOLT4 cells constitutively express CCR9, we aimed to ascertain its role in MOLT4 cells by repressing its expression. To this end, we stably silenced CCR9 by using two different short hairpin RNAs (shRNAs) (Table 1) (Figures 4G, H) and analyzed their influence on the migration and invasion of MOLT4 cells. Of note, reduced migration and invasion of MOLT4 cells upon the silencing of CCR9 with shRNA-2 were observed (Figures 4I, J). We observed that the addition of CCL25 facilitated the migration and invasion of the MOLT4 cells in the control (vector) group, suggesting the dependence of CCL25 on its cognate receptor, CCR9. These findings highlight the functional role of CCR9 in the aggressiveness of the leukemic cells and support the notion that the aberrant expression of CCR9 in turn could potentially contribute to distinct T-ALL outcomes.

To corroborate the in vitro findings of the oncogenic function of CCR9, we tested its role in vivo by xenotransplanting the OeCCR9-JURKAT into NTG mice. To this end, 5-week-old NTG mice were subjected to i.v. injection of OeCCR9-JURKAT cells (OeCCR9-JURKAT-NTG) as compared to JURKAT cells expressing GFP (GFP-JURKAT-NTG). The mice were reared for 25 days before tumor imaging for luciferase signal intensity and disease progression analysis (Figure 5A). Consequently, a robust decline in body weight was observed in OeCCR9-JURKAT-NTG mice as compared to GFP-JURKAT-NTG mice (Figure 5B). We observed an enhanced luciferase signal in mice harboring OeCCR9-JURKAT, showing the abundance of CCR9-overexpressing JURKAT cells compared to the GFP-JURKAT cells (Figure 5C). Moreover, analysis of the organ weight showed a remarkable increase in spleen (Supplementary Figure S5A) and liver weight (Supplementary Figure S5B), marking increased organ infiltration of OeCCR9-JURKAT cells. To assess the increased infiltration of OeCCR9-JURKAT cells that could lead to enhanced disease progression, the absolute number of CD45⁺ cells in BM and PB was quantified with flow cytometry. Consistently, we observed a noticeable increase in the number of CD45⁺ cells in the BM and PB of OeCCR9-JURKAT-NTG mice as compared to the GFP-JURKAT-NTG group (Figures 5D–F). HE and WG staining also portrayed elevated T-ALL formation in the PB, BM, spleen, and liver (Supplementary Figure S5C). Next, we performed IHC of CD45⁺ cells in the liver, spleen, and brain tissue sections obtained from mice in the two groups. We observed an elevated number of CD45⁺ cells in the liver and spleen of OeCCR9-JURKAT-NTG mice as compared to the liver tissue section obtained from GFP-JURKAT-NTG mice (Figures 6A–C). However, no striking difference in the number of CD45⁺ cells infiltrating the brain was observed (Figures 6A, D). Based on these findings from our in vitro and in vivo investigations, CCR9 promotes the infiltration of T-ALL cells into the liver and spleen.

To gain insights into the molecular mechanism governing the aggressive T-ALL phenotype conferred as a result of the overexpression of CCR9, we performed RNA-sequencing of the OeCCR9-JURKAT and GFP-JURKAT cells. Using the criteria DESeq2, padj = < 0.05, and log2 FC > 1, we obtained a total of 18 DEGs, including 17 upregulated genes and 1 downregulated gene (Table 2) (Figure 7A). A 4-fold overexpression of CCR9 is shown in the volcano plot, thus confirming the reliability of our RNA sequencing data (Figure 7B). We performed the DisGeNET GO enrichment analysis to get the disease phenotype associated with CCR9 overexpression and found its enrichment in leukemic pathways including precursor cell lymphoblastic leukemia, acute promyelocytic leukemia, and acute lymphocytic leukemia (Supplementary Figure S6A). The KEGG function revealed the association of DEGs with metabolic pathways including carbon metabolism, biosynthesis of amino acids, and biosynthesis of steroids (Supplementary Figure S6B). Moreover, when investigated in GO function, DEGs were found supplemented in the lipid biosynthesis pathways, including the steroid biosynthetic process, cholesterol biosynthetic process, steroid metabolic process, and regulation of the steroid metabolic process (Supplementary Table S6; Figures 7C, D).

Next, we assessed the role of these DEGs in the cholesterol biosynthesis pathway. The genes belonging to the cholesterol biosynthesis pathway that are upregulated with CCR9 overexpression are represented in a heatmap (Figure 8A). A protein-protein interaction (PPI) network of the DEGs associated with the cholesterol biosynthesis pathway was retrieved from the online resource STRING and visualized with Cytoscape (Figure 8B). The network shows a closer association of these genes with each other. To corroborate the elevated expression of the cholesterol biosynthesis pathway genes in our genome-wide transcriptome data, we checked the expression of the rate limiting enzyme, HMGCR, and three main genes, including MSMO1, HMGCS1, and MVD at mRNA level, which showed a significant upregulation of these genes (Figure 8C). Notably, we also verified the elevated expression of the master regulator of the cholesterol biosynthesis pathway, SREBF2 (Madison, 2016) (Figure 8C). To assess the expression of these genes at protein level, Western blotting was performed. We confirmed the overexpression of these genes with CCR9 upregulation as compared to GFP control (Figure 8D). Furthermore, we screened the expression of these genes in GSE48558 (Cramer-Morales et al., 2013) and found a relatively higher expression of MSMO1, HMGCS1, MVD,SREBF2, and HMGCR in T-ALL cell lines and patients (Supplementary Figure S7A). Moreover, the analysis of GSE26713 (Homminga et al., 2011) also showed an increased expression of MSMO1,HMGCS1, SREBF2, and HMGCR in T-ALL cell lines and patients (Supplementary Figures S7A, B), but MVD showed no significant difference (Supplementary Figure S6B). In addition, the increased expression of MSMO1, HMGCS1, MVD, and SREBF2 across solid tumors vs. normal tissues based on the analysis of TCGA datasets (Supplementary Figures S8A–D) further signifies the crucial role of these metabolic genes in multiple cancers.

To check whether CCR9-induced upregulation of the cholesterol biosynthesis genes has an impact on the total cellular cholesterol level, we detected the total cholesterol level in the OeCCR9-JURKAT and GFP-JURKAT cells. Notably, we found a slight increase in the cholesterol concentration of the OeCCR9-JURKAT cells (46.35 μmol/L) as compared to the GFP-JURKAT cells (43.46 μmol/L) (Figure 8E). Notably, TC levels were increased further (50.71 μmol/L) when CCL25 was added to stimulate CCR9-overexpressing JURKAT cells. These findings support the notion that the upregulation of CCR9 could enhance total cellular cholesterol production in T-ALL cells, and this effect is further potentiated with the addition of CCL25.

To investigate the effect of increased cholesterol biosynthesis on the aggressiveness of the JURKAT cells, we inhibited the total cholesterol biosynthesis by treating the cells with varying concentrations of simvastatin (3 μM, 6 μM, 12 μM, and 24 μM), a hypocholesterolemic drug that blocks the activity of HMGCR receptor (Corsini et al., 1995). We observed that a concentration above 10 μM compromised the viability of the cells leading to cell death (data not shown). Subsequently, we selected a 6 μM concentration of treatment for the chemotaxis assay and observed a notable decrease in the migration of OeCCR9-JURKAT cells with simvastatin as compared to the mock treatment (Figure 8F). Moreover, immunohistochemical analysis of the main transcription factor, SREBF2, in the mice tissues xenografted with OeCCR9-JURKAT cells and GFP-JURKAT cells revealed a relatively increased expression of SREBF2 in the liver tissue of the OeCCR9-JURKAT mice as compared to the GFP counterpart (Supplementary Figures S9A, B). However, no distinction in the expression of SREBF2 was observed in the spleen tissue (Supplementary Figures S9C, D) between the two groups, suggesting enhanced cholesterol production in the liver, the organ primarily responsible for cholesterol production. In addition to these experiments, we used the https://tnmplot.com/analysis/ database, which contains ChIP-seq and RNA-seq data of various tumors vs. normal tissue, to check the correlation between CCR9 and SREBF2. A significant positive correlation (R = 0.43, p value = 0.00) between CCR9 and SREBF2 in ALL was observed (Supplementary Figure S9D).

Taken together, these results suggest that CCR9 overexpression promotes cholesterol biosynthesis in JURKAT cells by upregulating the candidate genes, and blocking the cholesterol biosynthesis pathway with simvastatin reduces the aggressiveness of the CCR9-overexpressing JURKAT cells.