Unveiling the Role of SLC27A2 in Acute Lymphoblastic Leukaemia: Insights into Immune Evasion and Prognosis

doi:10.21203/rs.3.rs-3723493/v1

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Research Article

Unveiling the Role of SLC27A2 in Acute Lymphoblastic Leukaemia: Insights into Immune Evasion and Prognosis

https://doi.org/10.21203/rs.3.rs-3723493/v1

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Purpose

Solute carrier family 27 member 2 (SLC27A2) is involved in fatty acid metabolism in tumours and represents a prospective target for cancer therapy. However, the role and mechanism of action of SLC27A2 in acute lymphoblastic leukaemia (ALL) remains unclear. Here, we aimed to explore the intrinsic associations between SLC27A2 and ALL and evaluate the prognostic significance, biological functions, and correlation with immune infiltration.

Methods

We used the transcriptome and clinical data from the TARGET dataset. Differentially expressed genes (DEGs) in the SLC27A2 low- and high-expression groups were analysed for prognostic implications and functional enrichment. Furthermore, we analysed the relationship between SLC27A2 gene expression and immune cell infiltration using the ESTIMATE method, which was evaluated using the TIGER platform. Finally, we knocked down SLC27A2 in the Jurkat ALL cell line and conducted cell proliferation, western blotting, flow cytometry, and CCK-8 assays to elucidate the biological function of SLC27A2 in ALL.

Results

Patients with ALL who have higher expression levels of SLC27A2 have poorer overall survival and event-free survival. According to the gene set enrichment analysis, DEGs were primarily enriched with immune system processes and the PI3K-Akt signalling pathway. There was an inverse relationship between SLC27A2 expression and immune cell invasion, suggesting its involvement in tumour immune evasion. In vitro experiments showed that knockdown of SLC27A2 inhibited cell proliferation and protein expression and altered the Akt pathway, with a reduced proportion of B cells.

Conclusion

SLC27A2 plays a vital role in the development of ALL.

Acute lymphoblastic leukaemia

SLC27A2

immune evasion

prognosis

Acute lymphoblastic leukaemia (ALL) is one of the most common haematological cancers; it includes the uncontrolled proliferation immature B or T cells, lymphoblasts, and other lymphoid precursors (Ferrando et al. 2002). ALL, which is the most typical tumour in children, demonstrates an initial remission rate of ≥ 95% in children and 70–90% in adults (Siegel et al. 2023). Over the past 50 years, there has been a remarkable advancement in the outcomes of ALL; however, it remains concerning that some patients with ALL still experience relapse, even with the use of modern and intensive chemotherapy treatments (Pui et al. 2015). Immunotherapy and molecular targeted therapy can improve the survival of patients with ALL (Zhang et al. 2022; Pullarkat et al. 2021). Despite advancements in therapeutic approaches, the prognosis and survival of patients with ALL remain a challenge, necessitating the urgent identification of new therapeutic targets. To improve the early detection rate of ALL and prolong overall survival, it is crucial to identify valuable biomarkers.

Tumour growth relies heavily on the availability of fatty acids (FAs) (Koundouros and Poulogiannis 2020). Studies on various solid tumours, such as liver, lung, and breast cancers, have shown that tumour cells that are resistant to chemotherapy may undergo reprogramming of FA metabolism (Nagarajan et al. 2021; Butler et al. 2020). FAs are transported into cells through CD36 FA translocase and proteins that transport FA (SLC27A1–6 encodes FATP1–6) and subsequently move intracellularly with the assistance of FA-binding proteins (FABP1–12) (Kazantzis and Stahl 2012). The solute carrier family 27 (SLC27) of proteins has six members that are known as FATPs: SLC27A1 to SLC27A6 (FATP1–6) (Kazantzis and Stahl 2012). The significant role played by SLC27A2 in various diseases, including type 2 diabetes, renal fibrosis, and tumour progression, has been acknowledged in previous studies (Qiu et al. 2020; Chen et al. 2020). According to a study by Veglia et al., SLC27A2 exhibits immunosuppressive impacts on neutrophils and facilitates tumour growth (Veglia et al. 2019). However, thus far, there has been no evidence for the involvement of SLC27A2 in ALL. Our research team established the Adriamycin-resistant B-ALL cell line Nalm-6/ADR (NAR) and the T-ALL cell line Jurkat/ADR at an early stage. We compared the differential expression of mRNA and proteins between Nalm-6 and NAR using RNA sequencing and proteomic mass spectrometry. We identified SLC27A2 as a key gene responsible for the observed differences.

In this study, we aimed to investigate the potential involvement of SLC27A2 in ALL. We also explored the potential role of SLC27A2 in ALL prognosis and immune regulation and its effects on ALL cells in vitro. Our study underscores the potential oncogenic role of SLC27A2 in ALL, suggesting that targeting SLC27A2 may be a promising prognostic and chemotherapeutic strategy.

2.1 Data Download and Data Preprocessing

A TARGET-ALL expression profile and clinical information were retrieved from the Therapeutically Applicable Research to Generate Effective Treatments website (http://ocg.cancer.gov/), and we collected three datasets (GSE154109, GSE132509, GSE153697) based on the GEO database (http://www.ncbi.nlm.nih.gov/geo) for the subsequent single-cell analysis. After performing background correction, normalization, and expression calculation as part of the pre-processing, the probes were tagged with the official gene symbols listed in the platform-specific annotation file. In subsequent information processing, inappropriate cases, duplicates, and missing data were eliminated.

2.2 Survival and Prognostic Analyses

The TARGET dataset was utilised for our analysis, and patients were stratified into high- and low-expression groups according to their median levels of SLC27A2 mRNA expression. To evaluate the prognostic variation between different SLC27A2 expression groups, we utilised the Survfit function from the survival package in R software. We employed the log-rank test to determine the statistical significance of the prognostic disparity between the groups. The Area Under the Curve (AUC) was calculated using pROC software (version 1.17.0.1). Finally, we included a cohort of 272 patients expressing SLC27A2 with available clinical prognostic data for single and multifactor regression analyses, as shown in Table S1. In this process analysis, we would like to thank Sangerbox 3.0 (version 1.1.3) and Yongxin Liu (Shenzhen, China) for their contributions (Shen et al. 2022).

2.3 Nomogram Construction based on SLC27A2 Expression and Clinical Features

Evaluation of the prognostic associations of SLC27A2 expression in patients with ALL using single-and multifactor Cox regression analyses. We utilised a prognostic nomogram-based survival risk assessment model that incorporated clinical variables and SLC27A2 expression to estimate the chances of survival at 1, 3, and 5 years in individuals diagnosed with ALL, and a calibration curve was used to assess the predictive power of the columnar graph.

2.4 SLC27A2 differential expression analysis

The 'limma' package was used to detect genes that displayed differential expression between the groups characterized by high and low expression. Additionally, we utilised the 'pheatmap' package to generate a heat map visualizing these differentially expressed genes (DEGs). A heat map was generated by applying filter conditions with a logFC greater than one and a p-value less than 0.05. Multiple testing was adjusted using the False Discovery Rate, and the top 50 DEGs were selected for visualization in a heat map. A protein–protein interaction (PPI) network of the DEGs was constructed using the STRING online database (Szklarczyk et al. 2015). A confidence score threshold of > 0.4 was set to determine significant interactions. Next, we analysed and visualized the interactive relationships among candidate proteins using Cytoscape software (Shannon et al. 2003). An innovative Cytoscape plugin called cytoNCA (Tang et al. 2015) was used to rank the nodes in the network based on their features. After applying the betweenness (BC) algorithm, the key genes were identified. This approach allowed us to identify the pivotal genes that play significant roles in the network based on their centrality and influence within the network.

2.5 Functional Enrichment Analyses

To analyse functional enrichment in gene sets, we conducted Gene Ontology (GO) enrichment analyses for both high- and low-expression groups using the R package org.Hs.eg.db (version 3.1.0) to create GO gene annotation. Gene set enrichment results were obtained after the genes were mapped to the background set using ClusterProfiler (version 3.14.3) in R. Gene sets included biochemical processes (BP), molecular functions (MF), and cell components (CC). Using the methods described above, we analysed the pathways from the Kyoto Encyclopedia of Genomes (KEGG). To assess the functional and pathway enrichment differences between the high expression of SLC27A2 and low expression, we used the 'Limma' package and the 'cluster Profiler' package in R (version 4.1.3).

2.6 Immunological Analysis

The online database Tumor Immune Single Cell Hub (TISCH) (Sun et al. 2021) is available at http://tisch.compgenomics.org and was used for the study we conducted. We found that SLC27A2 was predominantly expressed in malignant cells.

For all samples (TARGET dataset, 272 patients), the 'estimateScore' algorithm was used to calculate immune scores, the ESTIMATE score, and stromal scores, and these scores were compared between high and low SLC27A2 expression groups. Subsequently, the website TIMER 2.0 was used (Li et al. 2020) (http://timer.cistrome.org/) to calculate the immune estimation, and by comparing high and low SLC27A2 expression groups with the XCELL algorithms, the degree of immune cell infiltration was evaluated. qRT-PCR was used to validate the association between SLC27A2 expression and genes related to immune checkpoints.

The TIGER (Chen et al. 2023) (http://tiger.canceromics.org/) database was used to identify immunotherapies linked to SLC27A2 and to assess the response of patients who received various immunotherapeutic treatments. We also examined the correlation between different tumours and distinct immune checkpoints.

2.7 Drug Sensitivity by oncoPredict

We used the "oncoPredict" package in R to investigate differences in drug susceptibility between groups with high and low SLC27A2 expression. Drug susceptibility was accurately predicted by analysing the half-maximal inhibitory concentration (IC50). Based on the predicted results, uprosertib was selected and validated using the MTT assay.

2.8 Cell Culture and Transfection

The Jurkat cell line of ALL and the human B lymphoblast cell line HMy2.CIR (preserved at the Institute of Hematology at Fujian Medical University's Union Hospital with STR certification and no mycoplasma contamination) were used in this experiment. Jurkat cells were grown in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% foetal bovine serum (FBS) (Cegrogen Biotech GmbH A0500-3010) and 1% penicillin-streptomycin solution 100X (PS) (Tagene, ABK0005C, China). HMy2.CIR cells were maintained in Iscove's Modified Dulbecco's Medium (IMDM) with 1% PS and 10% FBS. These cells were incubated in a humidified incubator at 37℃ with 5% CO₂. GeneChem (Shanghai, China) was used to generate the lentiviral shRNAs. The targeting sequences of sh-SLC27A2 were as follows: 5´-CCATACTTCTTCCAGGACATA-3´ and 5´-GCTGATTACCTACCTAGTTAT-3´; the sequence of CON313 was 5´-TTCTCCGAACGTGTCACGT-3´. These sequences corresponded to Jurkat_sh1, Jurkat_sh2, and Jurkat_nc, respectively. According to the manufacturer's instructions, the Jurkat cell line was cultivated with the lentivirus shRNAs in a 12-well culture plate at 37°C using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) to help transfection. Negative control and SLC27A2 shRNAs were transfected into Jurkat cells using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. After successful transfection, the Jurkat cell was cultured for 3 days in RPMI-1640 mix with 2 µg/ml of puromycin, and transfection efficiency was evaluated in subsequent experiments. Finally, the control stable transfer strain Jurkat_nc and experimental stable transfer strains Jurkat_sh1 and Jurkat_sh2 were successfully established.

2.9 Western blot

After collecting cells (Jurkat_nc, Jurkat_sh1, and Jurkat_sh2), the cells were lysed for 30 min on ice with protein and phosphatase inhibitors in RIPA buffer and fragmented using an ultrasonic disruptor followed by centrifugal separation at 4°C for 15 min at 13,000 rpm. The supernatant was added to an EP tube after the debris was removed from the cells with the appropriate proportion of 5 x loading buffer, and the sample was heated at 100°C for 10 min. To measure protein concentration, we used a Thermo Scientific BCA Protein Assay Kit (Waltham, MA, USA). After separation by 10% SDS-PAGE, the protein samples were transferred onto nitrocellulose (NC) membranes at low temperatures. Skimmed milk powder (5%) was used as a blocking solution for two hours, which was followed by overnight incubation at 4℃ with the primary antibodies (SLC27A2: 1:1000, Santa, USA; GAPDH: 1:10000, CST, USA). The following day, the membranes were incubated for one hour with a horseradish peroxidase-labelled secondary antibody (1:5000). Protein expression was visualized using an ECL kit (Thermo Fisher Scientific).

2.10 qRT − PCR

Total RNA was isolated using TRIZOL Reagent (Life Technologies, Carlsbad, CA, USA), and we synthesized cDNA with AMV reverse transcriptase (Promega, Wisconsin, USA). Based on the manufacturer's instructions, cDNA was amplified using the SYBR qPCR Master Mix (Q331-02, Nanjing Vazyme Biotech, China) and a 7500 Fast Real-Time PCR System (Applied Biosystems, MD, USA). As an internal reference, we used the 18S gene. The primer sequences are listed in Table S2.

2.11 Flow cytometry

Jurkat_nc, Jurkat_sh1, and Jurkat_sh2 cells (1 × 10⁵ cells/well) along with HMy2.CIR cells (1 × 10⁶ cells/well) were collected and co-cultivated in RPMI-1640 medium containing 10% FBS and 1% PS. In an incubator with 5% CO₂, the co-culture cells were incubated for 24 h at 37°C. Subsequently, the cells were harvested and washed with FACS buffer consisting of PBS and 2% FBS. The cells were then incubated with a dead dye (BV510) for 15 min, followed by washing. Next, the cells were stained with antibodies against CD19 (BD Biosciences, Franklin Lakes, NJ, USA) and CD3 (BD Biosciences) surface markers for 30 min. Thereafter, the cells were washed and analysed using a flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA).

2.12 Pan-Cancer Analysis

We used Sangerbox 3.0, an open online site, for pan-cancer analysis (Shen et al. 2022). We downloaded the uniform pan-cancer datasets TARGET, TCGA, and GTEx (PANCAN, N = 19131, G = 60499) from the UCSC xenabrowser.net dataset. Briefly, we extracted the expression data for SLC27A2 in each sample and applied a log2(x + 0.001) transformation to each expression value. Subsequently, we excluded cancer types that had fewer than three samples within a single cancer type, and finally, we obtained data on the gene expression of thirty-four different types of cancer. In addition, based on a previous TCGA prognostic study (Liu et al. 2018), we obtained high-quality TCGA prognostic datasets based on follow-up data from the UCSC Cancer Browser (https://xenabrowser.net/datapages/) for TARGET and excluded samples with less than 30 days of follow-up. Furthermore, we transformed each expression value with log2(x + 0.001), excluded cancer types with fewer than 10 samples each, and acquired the expression data of 44 cancer types and their overall survival rates. We analysed the expression data of the SLC27A2 gene and 60 immune checkpoint pathway genes in each sample (Thorsson et al. 2019), which were filtered to include primary tumours, primary solid tumours, primary hematogenous cancers of the bone marrow and peripheral blood, and all normal samples. Based on the Pearson correlation between SLC27A2 and the five immune pathway marker genes, we calculated the correlation coefficient. Finally, we calculated the Pearson correlation coefficient between SLC27A2 and the five classes of immune pathway marker genes. In addition to SLC27A2, we collected the expression data for 150 genes representing five types of immune pathways (chemokines, receptors, major histocompatibility complexes (MHCs), immunostimulators, and immunoinhibitors) in each sample. Subsequently, Pearson correlation was calculated between SLC27A2, and the markers associated with these five immune pathways.

3.1 SLC27A2 was Up-regulated in Pan-Cancer and had Prognostic Value in ALL

To investigate SLC27A2's potential role in oncogenesis, we initially examined the genetic expression profile of SLC27A2 across thirty-four human cancers from TCGA and TARGET datasets using the Sangerbox 3.0 online platform. As depicted in Fig. 1a, SLC27A2 exhibited significant overexpression in eighteen types of cancer compared to normal samples. Next, we performed a prognostic analysis of SLC27A2 expression using the SANGERBOX 3.0 online platform. Using statistical tests, including the log-rank test, we assessed the prognostic significance of SLC27A2 expression in various tumour types. Remarkably, we observed that elevated SLC27A2 expression was associated with worse prognosis in five cancer types, including thymoma (THYM), ocular melanoma (UVM), ALL, pheochromocytoma & paraganglioma (PCPG), and acute lymphoblastic leukaemia-relapse (ALL-R), with ALL demonstrating the most significant correlation (Fig. 1b). To further explore whether SLC27A2 is associated with immunity, we performed an immune-related pan-cancer analysis. Consequently, we analysed immunomodulatory genes (Fig. 2a) and immune checkpoints (Fig. 2b) associated with SLC27A2 in various cancer types. Our findings revealed that SLC27A2 has been implicated in multiple immunomodulatory pathways, including chemokine receptors, immunoinhibitors, immunostimulators, and MHCs. Furthermore, we performed calculations to determine the Pearson correlation between SLC27A2 and marker genes within these five immune pathway categories. We discovered that SLC27A2 has implications not only in the prognosis of various tumours but also in the modulation of immune responses.

3.2 The Prognostic Significance of SLC27A2 in ALL

The clinical significance of SLC27A2 was substantiated by survival analysis results obtained from the TARGET-ALL dataset. Here, we applied the median expression cutoff and conducted analyses on all patients, dividing them into SLC27A2 high- and low-expression groups. Analysis of the Kaplan–Meier survival data illustrated in Figs. 3a and 3b showed a significant correlation between SLC27A2 expression and poor overall survival (p = 4.7e-4) and event-free survival (EFS) (p = 1.1e-4). Figure 3c shows that the AUC at 1-year, 3-year, and 5-year time points were 0.68, 0.73, and 0.70, respectively.

Building on these findings, additional studies were conducted to investigate the relationship between SLC27A2 expression and clinical features including age, sex, minimal residual disease, bone marrow (BM) relapse, central nervous system (CNS) relapse, and other relevant clinical characteristics. Cox regression models revealed that SLC27A2 expression, CNS relapse, and BM relapse were independent predictors of overall survival (OS), with all variables showing significant associations (all p < 0.05; Fig. 3e, f). Prognostic heat map analysis visually represented the correlation between different risk scores and six clinical characteristics of the patients. Our expectations were confirmed by the fact that the survival time of the patients significantly decreased as the risk score increased. Specifically, SLC27A2 expression and CNS and BM recurrence were identified as risk factors for ALL. Moreover, a higher risk score indicated a greater prognostic risk for patients (Fig. 3d). Finally, based on the prognostic characteristics of 272 patients, the Cox method was used to construct a nomogram (Fig. 3g). Overall, the model had a C-index of 0.88 (95% CI:0.775–0.987, p-value = 2.15e-12), indicating a high predictive accuracy. A calibration curve was generated to validate the model (Fig. 3h).

3.3 Functional Enrichment Analysis

Next, we conducted differential gene analysis between groups with high and low SLC27A2 expression, resulting in the identification of 3018 DEGs. Subsequently, heat maps were generated to display the expression patterns of the 50 most significantly downregulated and upregulated genes (logFC < -1 or logFC > 1, p < 0.05) (Fig S1).

Functional enrichment analyses, including KEGG and GO analyses, were conducted to gain insights into the potential functions of the DEGs. These analyses aimed to identify enriched biological processes, cellular components, molecular functions, and pathways associated with the identified gene set. BP analyses revealed that 3018 DEGs were enriched in various biological processes, including immune system processes, immune responses, cell surface receptor signalling pathways, responses to external stimuli, cell activation, immune effector processes, myeloid leukocyte activation, leukocyte-mediated immunity, leukocyte migration, and humoral immune responses (Fig. 4a). CC analysis revealed that DEGs were mainly enriched in cellular components, including the extracellular region, vesicles, extracellular regions, extracellular spaces, extracellular organelles, extracellular vesicles, extracellular exosomes, cytoplasmic vesicles, secretory vesicles, and secretory granules (Fig. 4b). In the MF analysis, 3018 DEGs were mainly involved in signalling receptor binding, identical protein binding, receptor regulator activity, receptor-ligand activity, G protein-coupled receptor binding, antigen binding, carbohydrate binding, protein homodimerization activity, cytokine activity, and chemokine receptor binding (Fig. 4c). KEGG pathway analysis indicated that these genes are implicated in various important pathways such as the PI3K-Akt signalling pathway, cytokine-cytokine receptor interaction, chemokine signalling pathway, phagosome, viral protein interaction with cytokines and cytokine receptors, fluid shear stress, atherosclerosis, rheumatoid arthritis, Staphylococcus aureus infection, malaria, and African trypanosomiasis (Fig. 4d).

Furthermore, we performed functional enrichment and pathway analyses of the DEGs using Gene Set Enrichment Analysis (GSEA). Further, GO functional enrichment analysis revealed that the SLC27A2 high-expression group tended to be heavily enriched in various biological processes, including humoral immune response, sensory perception of smell, immunoglobulin complex, and olfactory receptor activity (Fig. 4e). KEGG pathway analysis revealed that several biological pathways were significantly enriched in the group with high SLC27A2 expression, including ascorbate and aldarate metabolism, cytokine receptor interaction, drug metabolism, cytochrome P450, and olfactory transduction (Fig. 4f).

3.4 SLC27A2 Differential Gene Protein Interaction Network

We conducted a protein interaction analysis of the DEGs to identify the core genes. After screening the 369 differential genes with a logFC greater than 2 or less than − 2, we subjected them to analysis on the String online website. By setting a confidence level threshold of 0.4 and hiding disconnected nodes, we identified 211 genes that remained in the analysis. These genes represented a subset of the initial differential genes that showed significant interactions and functional associations, providing a focused set of candidates for further investigation. Using Cytoscape, we imported the PPI data generated by the online string analysis. To determine which genes were hubs in the network, the betweenness (BC) algorithm implemented in the Cytoscape cytoNCA plugin was applied. This algorithm ranks nodes in the network based on their network properties, allowing us to identify the most central and influential genes in the interaction network. Finally, fifty genes were ranked based on their BC scores, and the results are shown in Fig S2A. In this graph, the degree of colour shades represent the BC scores, with darker shades indicating higher scores. The top twelve genes screened were THY1, CXCL12, PVALB, FCGR3A, SDC1, NKX2-5, VCAM1, GNAZ, LPL, SNCB, CACNA1B, PDGFRA. Using qRT-PCR, we demonstrated that SLC27A2 was positively correlated with CXCL12, PVALB, FCGR3A, NKX2-5, LPL VCAM1 and CACNA1B (Fig S2B).

3.5 Relationships between SLC27A2 and Immunity

By analysing the TARGET data, we investigated the relationship between the tumour immune microenvironment and SLC27A2 expression. As shown in Fig. 5a, a significant correlation was found between SLC27A2 expression and various immune-related factors, including immune score, myeloid dendritic cells, monocytes, macrophage M1, class-switched memory B cells, microenvironment score, eosinophils, naïve B cells, granulocyte-monocyte progenitors, and B cells (ALL, p < 0.05), as determined by XCELL analysis. Furthermore, the high expression group exhibited higher estimated, stromal, and immune scores (Fig. 5b) (ALL p < 0.05), indicating that SLC27A2 significantly influences the immune status of the tumour microenvironment.

Furthermore, we analysed the correlation between SLC27A2 expression and the number of infiltrating cells in ALL. The results shown in Fig. 6 reveal significant associations between SLC27A2 and various immune cell populations. Specifically, SLC27A2 positively correlated with B cells (r = 0.38), naïve B cells (r = 0.34), endothelial cells (r = 0.21), immune score (r = 0.35), granulocyte-monocyte progenitors (r = 0.17), microenvironment score (r = 0.36), CD4 + Th1 T cells (r = 0.19), and M1 macrophages (r = 0.2). Conversely, SLC27A2 exhibited negative correlations with T cell CD4 + memory (r=-0.29) and T cell CD4 + naïve (r=-0.22). All associations were statistically significant (p < 0.05).

Tumour cells employ various pathways to achieve immune escape and promote further progression. One common strategy is the overexpression of immunosuppressive checkpoint molecules that hinder the antitumor immune response. As part of our study, we examined the relationship between SLC27A2 expression and several immune checkpoints. The findings in Fig. 7a demonstrate a positive correlation between SLC27A2 expression levels and multiple inhibitory checkpoint molecules, such as CD27, CD274, LAG-3, PDCD1, TNFRSF18, TNFRSF9, CTLA4, TIM-1, TIM-3, and TIGIT, in ALL. To validate these findings, we conducted experiments on Jurkat_nc, Jurkat_sh1, and Jurkat_sh2 cell lines, focusing on selected immune checkpoints (Fig. 7b).

Based on single-cell analysis, we examined three ALL datasets. The results revealed distinct expression levels of SLC27A2 in immune, malignant, and stromal cells. (Fig. 8a). In the ALL-GSE132509 dataset, SLC27A2 was expressed in T cells, B cells, erythrocytes, and macrophages (Fig. 8b). In the ALL-GSE 154109 dataset, various levels of SLC27A2 expression were found in T cells, NK cells, erythroid progenitor cells, and malignant cells, and precursor erythrocytes and tumour cells, SLC27A2 is highly expressed (Fig. 8c). In the ALL-GSE153697 dataset, SLC27A2 expression was limited to the plasma cells (Fig. 8d). This observation can be attributed to the dataset being obtained after CD19 immunotherapy, which influenced the expression profile of SLC27A2 in the context of this specific treatment. In conclusion, these findings provide insights into the cell-specific expression of SLC27A2 in ALL and its potential implications in immune responses and the tumour microenvironment.

Using the TIGER database, we conducted an additional assessment of immunotherapy outcomes in patients with tumours expressing SLC27A2. This assessment included treatments, such as anti-PD1, anti-ATLA-4, and combination therapies. We then filtered the data to identify results with significant p-values (Fig. 9a). Notably, we focused on melanoma, as it demonstrated heightened sensitivity to immunotherapy in patients with elevated SLC27A2 expression (Fig. 9b-f). Continuing our analysis of various tumours expressing SLC27A2 (Fig. 9j), we observed that SLC27A2 expression and the immune checkpoint CD274 were positively correlated in DLBC, SKCM, and UCS (Fig. 9g-i). These findings are consistent with the results of the present study.

3.6 Prediction of drug sensitivity and potential small molecule inhibitors

To improve treatment outcomes in patients with ALL, we compared chemotherapeutic and targeted drug sensitivity between groups with and without high or low SLC27A2 protein expression. Among the 133 drugs that showed differential sensitivity between the two groups, 14 were more sensitive in the SLC27A2 low expression group (Fig. 10).

3.7 SLC27A2 expression in patients with ALL

To investigate the expression of SLC27A2 in patients with ALL compared to normal subjects, we conducted qRT-PCR and western blot using PBMCs collected from a cohort of patients with ALL with our clinical data (n = 20) and a group of normal subjects (n = 5). Our results indicated that SLC27A2 expression was significantly elevated in patients with ALL compared to normal patients (Fig. 11a-c).

3.8 Knockdown of SLC27A2 Inhibits ALL Cells Progression In Vitro and the Protein Expression of ALL

To explore the oncogenic role of SLC27A2 in ALL, Jurkat cells were transfected with Jurkat_nc, Jurkat_sh1, and Jurkat_sh2. qRT-PCR (Fig. 11d), western blot protein expression assays (Fig. 11e), and CCK8 cell proliferation assays (Fig. 11f) were performed. As knockdown result of knocking down SLC27A2, ALL cell lines underwent significant proliferative inhibition.

Considering the previous pathway analysis, we selected uprotersib for further validation, as it correlates with the AKT pathway. The experimental results showed that the Jurkat_sh1 and Jurkat_sh2 cell lines with SLC27A2 knockdown were more sensitive to protersib drugs than the control Jurkat_nc (Fig. 11h).

Using flow cytometry, we employed circle gating to analyse Jurkat_nc, Jurkat_sh1, and Jurkat_sh2 cells based on B cell surface markers (CD19). The observed correlation between the proportion of B-cells and the expression of SLC27A2 was in line with our earlier immunoassay findings, providing further consistency to our results (Fig. 11g).

ALL is a cancerous condition that primarily affects the blood and bone marrow. It is characterized by the rapid growth of immature lymphocytes, which are a type of white blood cells. Although ALL is more commonly observed in children, it can also occur in adults and accounts for ~ 20% of adult acute leukaemias (Puckett and Chan 2022). The typical symptoms of ALL include fatigue, pale skin, fever, increased susceptibility to bleeding or bruising, swollen lymph nodes, and bone pain (Puckett and Chan 2022). In recent decades, significant advancements have been made in research on ALL, particularly in genomics, transcriptomics, and epigenomics (DeAngelo et al. 2020). Despite significant progress in understanding and treating ALL, a major challenge is the high relapse rate in patients undergoing chemotherapy (DuVall et al. 2022). Investigating the molecular pathways involved in progression, chemotherapeutic efficacy, and relapse of ALL is essential for advancing treatment approaches. Notably, lipid metabolism-related proteins have been recognized as significant contributors to the pathogenesis of cancer, including ALL. Unravelling the role of lipid metabolism-related proteins in leukaemia can offer valuable insights into the underlying disease mechanisms and potentially open new avenues for the development of targeted therapies. Therefore, understanding the molecular pathways associated with lipid metabolism-related proteins in ALL holds great promise for future research and therapeutic advancements (Zhang et al. 2022).

Tumours, including leukaemia, prostate cancer, and breast cancer, rely heavily on fatty acid oxidation (FAO) to generate sufficient ATP for their increased energy demands (Shim et al. 2022). Fatty acids (FAs), which serve as the primary energy source for fatty acid oxidation (FAO), are taken up from the extracellular matrix by membrane proteins including FATPs (Khan et al. 2018) and CD36 (Susztak et al. 2005; Baines et al. 2012). SLC27A2, a membrane protein, exhibits tumour-suppressive properties in several cancer types. It acts as a tumour suppressor gene in lung, ovarian, and prostate cancer (Feng et al. 2022) and neuroblastoma (Tao et al. 2022). Studies have also found that SLC27A2 is associated with tumour proliferation and migration in differentiated thyroid cancer, underscoring its importance in this specific cancer context (Feng et al. 2022). SLC27A2 is expressed in immune cells, including oncogenic polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), which suppress immune responses in the tumour microenvironment. Inhibition of SLC27A2 blocks its immunosuppressive activity, resulting in delayed tumour progression (Veglia et al. 2019). Eliminating lipid uptake by targeting SLC27A2 increases melanoma cell sensitivity to therapy in an aging microenvironment (Alicea et al. 2020). The researchers postulated that the presence of SLC27A2 in patients could serve as a potential predictive marker for cancer recurrence.

Furthermore, our investigation into the relationship between SLC27A2 expression and various clinical parameters revealed significant correlations with ALL, including survival outcomes and several clinical features. These findings emphasize the potential clinical significance of SLC27A2 as a prognostic indicator of ALL. In this study, we found that SLC27A2 mRNA is overexpressed in ALL and serves as a potential diagnostic marker for this disease. Elevated SLC27A2 expression is associated with poor prognosis and can independently predict overall survival with ALL. Univariate and multifactorial regression analyses were performed to assess the prognostic significance of SLC27A2 expression in ALL. Our results demonstrated that SLC27A2 can be used independently as a prognostic factor. These findings highlight the importance of SLC27A2 as a prognostic biomarker and potential therapeutic target for ALL.

DEG-enriched GO biological processes were predominantly involved in the immune system. A recent study provided evidence linking SLC27A2 to the acquisition of immunosuppressive activity by PMN-MDSCs and its involvement in regulating immune responses (Veglia et al. 2019). KEGG pathway enrichment analysis revealed significant enrichment of the PI3K-Akt signalling pathway among the DEGs. In this study, SLC27A2 was implicated in the regulation of cellular processes mediated by the PI3K-Akt signalling pathway. Studies shows that the PI3K/AKT pathway acts on different downstream target proteins that contribute to oncogenesis, invasion, tumour proliferation, and metastasis (Rascio et al. 2021).

According to GSEA, SLC27A2 expression was positively correlated with humoral immunity. Therefore, we further analysed the immune microenvironment of SLC27A2 between the high- and low-expression groups and found significant differences in stromal, immune, and estimated scores. Based on the above results, we assessed the level of immune cell infiltration and its correlation with the copy number of SLC27A2. Our findings revealed a notable association between the copy number of SLC27A2 and immune cell infiltration. The expression of SLC27A2 influences immune cell infiltration levels, with positive correlations observed for memory B cells, B cells, endothelial cells, macrophage M1 cells, and CD4 + Th1 class switching. In contrast, negative correlations were observed with T cell CD4 + naive, myeloid dendritic cells, and T cell CD4 + memory. Research has indicated a positive correlation between memory B cells and tumour immunity, emphasizing their role in immune responses against tumours (Palm and Henry 2019). Furthermore, studies have highlighted the involvement of endothelial cells in promoting tumour growth by regulating the tumour microenvironment (Fan et al. 2012).

To further clarify the role of SLC27A2 in the microenvironment, we evaluated immune checkpoints. We found that SLC27A2 expression was positively related to immune checkpoints, such as CTLA4, PD-L1 (CD274), TNFRSF9, CD27, LAG-3, TNFRSF18, TIM-1, TIM-3, TIGIT, and PD-1 (PDCD1). In a recent study, researchers identified an expanding subset of B cells expressing T-cell immunoglobulin and mucin structural domain 1 (TIM-1, encoding Havcr1). This distinct subgroup of B cells has a specific transcriptional signature featuring various collaborative inhibitory molecules, including PD-1, TIM-3, TIGIT, and LAG-3. Remarkably, the specific deletion of Havcr1 from B cells has demonstrated a dual effect: it not only substantially impedes tumour growth but also enhances the effectiveness of effector T cell responses (Bod et al. 2023). However, PD-1, which is encoded by PDCD1, is expressed on activated T cells, whereas PD-L1, which is encoded by CD274, is often overexpressed on tumour cell surfaces. PD-1 and PD-L1 interact to inhibit T-cell activation and induce cell death, ultimately contributing to immune evasion by tumour cells (Cha et al. 2019). A previous study showed that combination therapy targeting both SLC27A2, and PD-1 improved the prognosis in mouse models of cancer (Veglia et al. 2019). Therefore, we hypothesized that SLC27A2 is involved in tumour immune evasion. In summary, our results indicate a close correlation between SLC27A2 expression and immune infiltration as well as immunosuppression in the ALL microenvironment.

In our in vitro studies, we first verified that SLC27A2 expression was higher than normal in ALL at both the mRNA and protein levels. We successfully knocked down SLC27A2 expression in ALL cell lines, resulting in a significant reduction in both mRNA and protein levels. This SLC27A2 knockdown led to a notable decrease in ALL cell proliferation, as observed in a fluorescent cell counting assay. Moreover, it is well known that AKT is one of the central hubs of the signalling pathway, which regulates the metabolism, survival, and proliferation of the cell and the organism. Our findings further confirm the involvement of the AKT pathway in SLC27A2. Uprotersib, an AKT inhibitor, was selected as the drug of choice. Additionally, we conducted validation experiments on one of the differential genes enriched in drug pathways and the aforementioned immune checkpoints. Our results demonstrated that the sensitivity of ALL cells to the drug was enhanced upon SLC27A2 knockdown, as evidenced by a lower IC50 value. Furthermore, we observed a decrease in the expression level of immune escape-related checkpoint mRNA in the experimental group, flow cytometry results also showed decreased expression of B-cell immunosuppression-related genes in the experimental group. These findings suggest that SLC27A2 knockdown contributes to increased drug sensitivity and reduced immune escape in patients with ALL. There were limitations to this experiment due to the fact that only one cell line of acute leukaemia was used, and the sample size was small. Furthermore, we need to conduct additional in vivo and in vitro experiments to confirm and elucidate SLC27A2's role in ALL.

Funding：This study was supported by National Natural Science Foundation of China (U2005204, 82000142), Natural Science Foundation of Fujian Province (2022J01735), Joint Funds for the innovation of science and Technology, Fujian province(2021Y9086).

Competing Interests：The authors declare that they have no competing interests.

Author Contributions：Conceptualization, LL; methodology, LL and JL; software, LL and JL; validation, YC, YZ, YH, and LL; formal analysis, YC, YH, and LL; writing—original draft preparation, LL; writing—review and editing, JH, YC; project administration, JH, YC; funding acquisition, LL, JH, YC. All authors have read and agreed to the published version of the manuscript.

Data Availability：The data supporting the findings of this study are included in this article. Additional requests can be directed to the corresponding author via email.

Ethics Approval: All the experiments were approved by the review board of the approved by the Ethics Committee and the Institutional Review Board of Fujian Medical University Union Hospital (2023136). All patients gave written informed consent for the use of their clinical specimens for medical research.

Consent to Participate: Informed consent was obtained from all individual participants included in the study.

Consent to Publish: Written informed consent for publication was obtained from the patients. All authors have agreed to publish this manuscript.

Acknowledgments: We would like to thank Editage (www.editage.cn) for the English language editing.

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No competing interests reported.

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Unveiling the Role of SLC27A2 in Acute Lymphoblastic Leukaemia: Insights into Immune Evasion and Prognosis

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Abstract

Purpose

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and Methods

2.1 Data Download and Data Preprocessing

2.2 Survival and Prognostic Analyses

2.3 Nomogram Construction based on SLC27A2 Expression and Clinical Features

2.4 SLC27A2 differential expression analysis

2.5 Functional Enrichment Analyses

2.6 Immunological Analysis

2.7 Drug Sensitivity by oncoPredict

2.8 Cell Culture and Transfection

2.9 Western blot

2.10 qRT − PCR

2.11 Flow cytometry

2.12 Pan-Cancer Analysis

3. Results

3.1 SLC27A2 was Up-regulated in Pan-Cancer and had Prognostic Value in ALL

3.2 The Prognostic Significance of SLC27A2 in ALL

3.3 Functional Enrichment Analysis

3.4 SLC27A2 Differential Gene Protein Interaction Network

3.5 Relationships between SLC27A2 and Immunity

3.6 Prediction of drug sensitivity and potential small molecule inhibitors

3.7 SLC27A2 expression in patients with ALL

3.8 Knockdown of SLC27A2 Inhibits ALL Cells Progression In Vitro and the Protein Expression of ALL

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

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