Prognostic analysis of SYTL4 in acute myeloid leukemia

Introduction

Acute myeloid leukemia (AML) has a high mortality rate (1), accounting for over 60% of acute leukemia cases, and its incidence increases gradually with age (2). Despite the achievement of complete remission in most patients after treatment with the “3+7” regimen, the 5-year survival rate of patients with AML remains low. With the development of cellular genetics and molecular biology, the application of small-molecule targeted drugs, such as BCL2, IDH1 and IDH2, FLT3 inhibitors, has improved the remission rate and survival of patients with AML. However, AML patients have limited overall remission time and the disease cannot be cured. Survival was shorter in patients with multiple relapses without transplant conditions and in older patients who could not tolerate high-dose chemotherapy. Therefore, exploring the aberrantly expressed molecules in patients with AML and the mechanisms of drug resistance in AML are current research hotspots that drive the development of new strategies for AML treatment, ultimately improving the prognosis and survival of patients with AML.

Exosomes are extracellular vesicles (EVs) of approximately 30–100 nm, containing membrane proteins, cytoplasmic proteins, nuclear proteins, extracellular matrix proteins, metabolites, and nucleic acids including messenger ribonucleic acid (mRNA), non-coding RNA, and deoxyribonucleic acid (DNA) (3-6). Exosomes play a role in the occurrence, development, metastasis, and drug resistance of cancer cells by transporting proteins and nucleic acids, thereby establishing connections with the tumor microenvironment (7,8). In AML, the interactions between leukemic stem cells (LSCs), stromal cells, and leukemia progenitor cells are crucial for development, drug resistance, and recurrence of leukemia (9,10). Exosomes are important carriers of information and participate in cellular communication (11). Their release by leukemic cells maintains and promotes the activity of LSCs through autocrine pathways (12). Moreover, exosomes are involved in resistance to apoptosis, drug resistance, angiogenesis, immune suppression, and inhibition of normal hematopoiesis in leukemia cells (10). Therefore, some researchers have proposed that exosomes themselves, or those designed as carriers, can deliver various treatments, including chemotherapy drugs and immunomodulators to target cells for therapeutic purposes.

The synaptotagmin-like 4 (SYTL4) gene, also known as synaptotagmin-like protein 4 (SLP4) or synaptotagmin-like protein, encodes a member of the synaptotagmin-like protein family. This encoded protein binds to a specific small GTPase, Ras-related protein (Rab), which is involved in intracellular membrane transport. Rab GTPases participate in the formation and biological processes of exosomes (13). Rab proteins control various steps in vesicle transport, including budding, movement, docking, and fusion with the receptor membrane (14). Recent study has found an association between SYTL4 and drug resistance in triple-negative breast cancer and its high expression is correlated with poor prognosis (15); because SYTL4 is correlated with poor prognosis in breast cancer, we hypothesized that this gene may also be correlated with prognosis in AML. This study aimed to analyze the impact of SYTL4 gene on the prognosis of AML and to explore its role within existing prognosis groups based on cytogenetics. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-758/rc).

Methods

Data collection and compilation

We obtained clinical information and RNA sequencing (RNA-seq) data of patients with AML from The Cancer Genome Atlas (TCGA) database (http://xena.ucsc.edu/). The TCGA-Acute Myeloid Leukemia (TCGA-LAML) dataset included 151 AML samples. We used the AnnoProbe package to annotate the TCGA data. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Differentiation and survival analyses

We extracted the expression levels of the SYTL4 gene and performed differential analysis using the Limma package. We compared the expression levels of the gene across different cytogenetic groups. Based on the expression levels of the SYTL4 gene, we divided the samples into high- and low-expression groups, and plotted survival curves using the Kaplan-Meier “survival” package.

Prediction analysis

We constructed receiver operating characteristic (ROC) curves based on the expression levels of the SYTL4 gene to predict the 1-, 3-, and 5-year survival of patients with AML. We performed single- and multi-factor COX analyses to assess the independent prognostic value of SYTL4 expression.

Co-expression analysis of SYTL4 and enrichment analysis of differentially expressed genes in SYTL4 high- and low-expression groups

Using the “Limma” package, a correlation filter standard of corFilter =0.6 and a P value filter of pvalueFilter =0.001 were set. We conducted co-expression analysis to explore positive and negative regulators of SYTL4. We divided the samples into high- and low-expression groups based on SYTL4 expression, and explored the differential genes in the high- and low-expression groups. We used the “pheatmap” package to generate heatmaps of differential genes, and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on these differential genes.

SYTL4 and the tumor immune microenvironment

Immune cell infiltration was analyzed using the CIBERSORTx algorithm. We used the CIBERSORT analysis package in R software and the leukocyte gene signature matrix (LM22) reference gene set to quantitatively analyze 22 immune cells in each TCGA-LAML sample. The immune cell infiltration results for each sample were obtained, and samples with 22 cell proportions equal to 1. According to P value <0.05, differential analysis of immune cells in the SYTL4 high- and low- expression groups was conducted, and correlation analysis between SYTL4 and immune cells and immune checkpoints was performed.

Statistical analysis

We used R version 4.2 for statistical analysis. Statistical significance was set at P<0.05.

Results

Differential analysis

According to the cytogenetic information in the TCGA-LAML clinical data, the samples were grouped into good, poor, and intermediate prognoses groups. The SYTL4 expression level was highest in the poor prognosis group and lowest in the good prognosis group. The good prognosis group was compared with the poor prognosis group (P<0.001), the good prognosis group was compared with the intermediate prognosis group (P<0.001), the intermediate prognosis group was compared with the poor prognosis group (P=0.003) (Figure 1).

Figure 1 Differential analysis of SYTL4 in cytogenetic prognostic groups. SYTL4, synaptotagmin-like 4.

Survival analysis

We divided samples into high- and low-expression groups based on the expression levels of the SYTL4 gene. We plotted Kaplan-Meier survival curves and used the log-rank test to assess the differences in survival between the high- and low-expression groups. The low-expression group had significantly better survival than the high- expression group (P<0.001) (Figure 2).

Figure 2 Survival analysis of SYTL4 high- and low-expression groups. SYTL4, synaptotagmin-like 4.

Prediction analysis

The expression levels of SYTL4 were used to predict the 1-, 3-, and 5-year survival rates of patients with AML. The areas under the ROC curve for TCGA-LAML at 1, 3, and 5 years were 0.725, 0.683, and 0.787, respectively (Figure 3A). Single- and multi-factor COX regression analyses performed for SYTL4 expression, clinical stage, white blood cell count, platelet count, and other clinical indicators showed that the P value for SYTL4 was <0.001, indicating a good predictive performance (Figure 3B,3C).

Figure 3 Prediction analysis. (A) ROC curve of SYTL4; (B) univariate analysis; (C) multivariate analysis. AUC, area under the curve; SYTL4, synaptotagmin-like 4; CI, confidence interval; hb, hemoglobin; ROC, receiver operating characteristic.

Co-expression analysis of SYTL4 and enrichment analysis

Using the “Limma” package with a correlation filter standard of corFilter =0.6 and a P value filter of pvalueFilter =0.001, co-expression analysis revealed that sushi repeat protein X-linked 2 (SRPX2), caveolae associated protein 2 (CAVIN2), calponin epithelial 8 (CPNE8), CAVIN2-AS1, HOXA10 antisense RNA (HOXA10-AS), and CPNE8-AS1 were positive regulators, while lactoperoxidase (LPO), diacylglycerol lipase beta (DAGLB), hepatocyte growth factor (HGF), accession number (AC) 135507.2, and AC113414.1 were negative regulators (Figure 4A). Upregulated genes are shown in red and downregulated genes are shown in blue in the high-expression group (Figure 4B). In addition, differential genes were subjected to GO and KEGG enrichment analyses, revealing enrichment in pathways such as the embryonic skeletal system, platelet alpha granules, collagen-containing extracellular matrix, DNA-binding transcriptional activator activity, and RNA polymerase II (Figure 4C-4E).

Figure 4 Co-expression analysis of SYTL4 and enrichment analysis. (A) SYTL4 coexpression analysis; (B) heat map of different genes in the high- and low-expression groups of SYTL4; (C) GO circle diagram; (D) KEGG bar diagram; (E) KEGG bubble diagram. BP, biological process; CC, cellular component; MF, molecular function; SYTL4, synaptotagmin-like 4; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

SYTL4 and the tumor immune microenvironment

Differential analysis of immune cells in the SYTL4 high- and low-expression groups revealed statistically significant differences in follicular helper T cells (P=0.01), Tregs (P=0.001), monocytes (P=0.045), M2 macrophages (P<0.001), activated dendritic cells (P=0.001), and resting mast cells (P=0.01) (Figure 5A). Correlation analysis between SYTL4 and immune cells showed that when P<0.05, SYTL4 was correlated with immune cells. A correlation coefficient greater than 0 indicates a positive correlation whereas that less than 0 indicates a negative correlation. Tregs, plasma cells, activated dendritic cells, and monocytes were positively correlated with SYTL4 expression whereas resting dendritic cells, follicular helper T cells, resting mast cells, memory B cells, and M2 macrophages were negatively correlated with SYTL4 expression (Figure 5B). Lastly, correlation analysis between SYTL4 and immune checkpoints revealed negative correlations with CD44 and transmembrane emp24 domain trafficking protein 2 (TMIGD2) and positive correlations with tumor necrosis factor (ligand) superfamily member 18 (TNFSF18), CD80, CD274, LAG3, CD276, and TNFSF14 (Figure 5C).

Figure 5 SYTL4 and the tumor immune microenvironment. (A) Analysis of immune cell differences in the SYTL4 high- and low-expression groups, “**” indicates that the immune cells were statistically significant between the SYTL4 high- and low-expression groups; (B) correlation analysis between SYTL4 and immune cells; (C) correlation analysis between SYTL4 and immune checkpoints. NK, natural killer; SYTL4, synaptotagmin-like 4.

Discussion

Acute myeloid leukemia is a highly heterogeneous malignant disease of the hematopoietic system. Although the combination of small-molecule targeted therapies has improved the remission rates of AML compared to traditional chemotherapy alone, relapse and drug resistance remain significant challenges. In recent study, exosomes have been shown to play a role in the cellular communication between leukemia cells and the immune microenvironment by promoting leukemia cell growth and suppressing normal hematopoiesis (16). In this study, we obtained clinical information and RNA-seq data of patients with AML from the TCGA database, and extracted SYTL4 expression level. Differential analysis revealed that higher expression of SYTL4 was significantly associated with a poorer prognosis, as evidenced by higher expression levels in the cytogenetically poorer groups and lower survival rates in the high-expression group. This suggests that high expression of SYTL4 may be a high-risk factor for poor prognosis in AML. Ostrowski et al. showed that knockout of Rab27a in human HeLa cells inhibits exosome secretion, and cells with reduced expression of SYTL4/SLP4 also secrete fewer exosomes (17). It was further discovered that SYTL4/SLP4 may mediate the function of Rab27a, suggesting a correlation between SYTL4 and exosome secretion. Leukemia-associated exosomes have been implicated in resistance to apoptosis and drug resistance in leukemia. Although the development of leukemia involves multiple cytogenetic and molecular abnormalities, analysis of timROC curves, single-factor COX, and multi-factor COX revealed that the SYTL4 gene has good predictive performance. This indicates that SYTL4 could serve as a prognostic indicator in patients with AML.

Through co-expression analysis, we discovered that SRPX2, CAVIN2, CPNE8, CAVIN2-AS1, HOXA10-AS, and CPNE8-AS1 are positive regulators of SYLT4, suggesting that they are associated with a poor prognosis in AML. SRPX2 is highly expressed in various cancers, and study have shown that it promotes osteosarcoma progression by activating yars1 tyrosyl-tRNA synthetase 1 (YAR1) (18). In addition, SRPX2 promotes tumor proliferation and migration in thyroid papillary carcinoma through the FAK pathway (19). High CPNE8 expression is correlated with poor prognosis in AML (20), and CPNE8 promotes metastasis in gastric cancer (21). HOXA10-AS is a long non-coding RNA (lncRNA), proven to be an oncogenic gene in kmt2a-rearranged AML, and its high expression is associated with poor prognosis in AML (22). Differential analysis of the SYTL4 high- and low-expression groups revealed that these differential genes were mainly enriched in pathways such as the embryonic skeletal system, platelet alpha granules, collagen-containing extracellular matrix, DNA-binding transcriptional activator activity, and RNA polymerase II.

The activation or inhibition of signaling networks in the tumor immune microenvironment, and the interactions among immune cells, are relevant for the development of AML. We observed differences in Tregs between the SYTL4 high- and low-expression groups and a positive correlation between Tregs and SYTL4. Recent research suggests that Tregs can inhibit the differentiation of normal hematopoietic cells, aid immune evasion of AML cells, and enhance the stemness of AML cells (23). High Treg expression is also considered an adverse prognostic factor in AML. Furthermore, we found a positive correlation between SYTL4 and immune checkpoints such as programmed death-ligand 1 (PD-L1; CD274) and CD276. CD274 encodes PD-L1, and high expression of PD-L1 in patients with AML contributes to the immune evasion of AML cells and promotes leukemia development (24). CD276, also known as B7-H3, is highly expressed in cancer cells and facilitates immune evasion and surveillance of cancer cells (25).

There are some limitations in this study. First, the dataset was limited. Second, there was a lack of clinical experimental validation. Further studies are required to confirm these hypotheses.

Conclusions

In summary, by analyzing clinical information from patients with AML in TCGA database, we hypothesized that the expression of SYTL4 is negatively correlated with the prognosis of patients with AML. Specifically, higher expression levels of SYTL4 are associated with a poorer prognosis and shorter survival times in patients with AML. Combined with previous research findings, we speculate that SYTL4 may be related to the secretion of exosomes in AML. Due to the limitations in this study, further studies are required to confirm these hypotheses

Acknowledgments

The authors express their sincere gratitude to Professor Wang from the Hematologic Oncology Department at Sun Yat-sen University Cancer Center for her valuable guidance and contributions to this research.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-758/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-758/prf

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-758/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study is a bioinformatics study based on an existing database and does not require medical ethical review.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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