Identification of STAT4 as a biomarker associated with tumor immunity and prognosis in acute myeloid leukemia

doi:10.21203/rs.3.rs-2253291/v1

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Identification of STAT4 as a biomarker associated with tumor immunity and prognosis in acute myeloid leukemia

https://doi.org/10.21203/rs.3.rs-2253291/v1

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Acute myelogenous leukemia (AML) is a common malignancy and is supposed to have the ability to escape host immune surveillance. This study aimed to identify key genes in AML that may affect tumor immunity and to provide prognosis biomarkers of AML. The Cancer Genome Atlas (TCGA)dataset was screened for transcription factors (TFs) involved in immunity and influencing survival, combining Gene Expression Omnibus (GEO) data to validate the impact on patient survival. A prognostic signature was established using four transcription factors, and these genes play an important role in the immune system, with higher regulatory T cell (Treg) scores in high-risk patients compared to the low-risk group. Analysis of individual genes showed that STAT4 and Treg are closely related, which may be due to STAT4 transcribing related genes that affect immunity. STAT4 expression was positively correlated with the proportion of abnormal cells and promoted AML recurrence as verified by AML clinical patient samples. In conclusion, these findings suggest that STAT4 may be a potential biomarker for AML prognosis. As a key gene affecting the prognosis of AML patients, STAT4 may be a therapeutic target.

AML

STAT4

prognostic biomarker

Treg

transcription factor

Acute myeloid leukemia (AML) is a malignant clonal disease caused by myeloid or hematopoietic progenitor cells. It is characterized by rapid progression, a short natural course, and a poor prognosis(Shalliset al., 2019). AML progresses rapidly and most patients eventually become resistant to treatment or relapse and develop relapsed or refractory AML, with a 5-year survival rate of only 29.5% and no significant improvement in 5-year overall survival in patients older than 65 years (Bewersdorf and Abdel-Wahab 2022).

Immunotherapy is one of the most promising approaches for the treatment of tumors(Yang 2015), and the interaction of the immune system with AML cells offers the possibility of immunotherapy for AML(Barrett 2020). Immunotherapeutic approaches to AML include antibody-mediated therapies, checkpoint inhibitors and T-cell retargeting therapies, but the development of immunotherapy is still in its early stages due to the lack of unique targets and immune-mediated adverse events observed in studies(Liu 2021). Given the importance of immunotherapy in the overall treatment of AML, especially for drug-resistant patients (Vadakekolathuet al., 2020), the discovery of new immunotherapeutic markers is urgent.

Regulatory T cells (Treg) are a subpopulation of T cells with immunosuppressive regulatory immune functions that antagonize effector T cells and maintain immune homeostasis(Tanaka and Sakaguchi 2017;Togashiet al., 2019). In recent years, numerous studies have shown that Treg cells can impair immune surveillance of tumors by the immune system, suppress the responses of anti-tumor immune, and promote tumor growth(Liet al., 2020༛Nishikawa and Koyama 2021). Therefore, Treg cells are considered to be an important target for tumor immunotherapy. Exploring the role of Treg cells in AML and gaining insight into the relationship between Treg cells and leukemic immune response as well as prognosis is essential to improve the therapeutic effect of AML.

In this study, we obtained gene expression profiles of AML patients from the TCGA database and then constructed a prognostic model based on transcription factors. Immune scores were calculated based on the prognostic model, the genes that exert an influence on tumor immunity were subsequently identified. The role of the gene was validated by analyzing AML clinical patients.

Data collection and identification of Differentially expressed genes (DEGs)

A schematic representation of our study outline is shown in Fig. 1. Gene expression profiling interactive analysis (GEPIA2) web server (http://gepia2.cancer-pku.cn/#analysis) was used to obtain expression difference between AML and normal tissues of the GTEx database. The genes which meet the following criteria are defined as DEGs: the cut-off value |log 2‐fold‐change [FC]| > 1 and p value < 0.05. The AML RNA-seq transcriptome profiles and related clinical information were downloaded from The Cancer Genome Atlas (TCGA). The immune-related genes (IRGs) were identified through the ImmPort database (https://www.immport.org/shared/) and the transcription factor (TF) set was downloaded from Cistrome Cancer (http://cistrome.org/CistromeCancer/).

Identification And Validation Of Tf-irg Regulatory Network

Univariate cox regressions were used to evaluate the relationships between the TFs and the patient's overall survival (OS). TFs with p < 0.05 and Hazard Ratio (HR) > 1 were screened for further analysis. After that, TFs and IRGs were carried out for further protein-protein interaction (PPI) network construction. PPI network was constructed by the STRING website and Cytoscape to elucidate potential interactions between TFs and IRGs.

Prognostic Signature Construction

The least absolute shrinkage and selection operator (LASSO) analysis by using R software was performed to select optimal prognostic TFs. The multivariate Cox regression analysis was used to perform the risk model. The Risk score (RS)formula is: Risk score (RS) = Σ gene expression ×coefficient, based on the median value, the RS values were divided into high-risk and low-risk groups. The patients were divided into high-risk and low-risk groups based on the median risk score as the cutoff value. To assess the performance of our signatures, the “survival receiver operating characteristic (ROC)” package was used to generate ROC curves at 1, 3 and 5 years, and the corresponding time-dependent area under the curves (AUCs) was calculated simultaneously. The performance of TFs-based signature was assessed using Kaplan-Meier analysis.

Enrichment Analysis Of Degs Between High-risk And Low-risk Groups

Expression profiles were compared between high-risk and low-risk groups to identify DEGs by using the R software limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using the “ClusterProfiler” R package.

Validation Of The Key Genes Based On Tcga And Geo

GEPIA was used to verify the survival and expression patterns. Three external GEO AML cohorts-GSE5122 and GSE12417 were used as validation sets by using the PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/).

Validation And Analysis Of Target Gene

The ChEA3 (https://maayanlab.cloud/chea3/) was used to estimate the target of TF. The OS and methylation status of the gene were analyzed by Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/) which is a tumor genome analysis platform that integrates genomic data from the TCGA library and normal tissue data from GTEX; For genomic analysis in a unified data analysis process.

Validation Of The Stat4 In Clinical Tissue Samples By Real-time Pcr

A total of 22 AML patient samples were collected from the First Affiliated Hospital of Dalian Medical University (Liaoning, China). The use of blood samples in the study has been approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University. The STAT4 gene mRNA expression was assayed by real-time PCR. Total RNA was extracted with TRizol reagent (Thermo Fisher, Cat # 15596026), followed by reverse transcription of cDNA. Real-time PCR was performed using the SYBR Green PCR Master Mix (Thermo Fisher, Cat # 4309155). The qPCR primer sequences used were listed as follows: STAT4 forward: 5’- TGTTGGCCCAATGGATTGAAA’ and reverse: 5’- GGAAACACGACCTAACTGTTCAT’, GAPDH forward: 5’- ACAACTTTGGTATCGTGGAAGG-3’ and reverse: 5’- GCCATCACGCCACAGTTTC-3’. The proportion of abnormal cells were detect by flow cytometry(Beckman,navios),WT1/ABLwas assayed by real-time PCR(Roche,480) with the Kit(Yuanqi,Shanghai).

Statistical Analyses

Results are recorded as means ± standard error of the mean for at least three independent experiments and analyzed by R software (Version 4.1.2). The Wilcoxon rank-sum test and the Kruskal-Wallis test were used for the comparison of continuous variables. Pearson analysis was used for the correlation analyses. data were considered statistically significant as follows: *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001.

Identification of IRGs and TFs in TCGA cohort

We obtained gene expression profiles from TCGA, and after analysis, a total of 10375 DEGs were identified (Fig. 2A). Cancer is a multi-step process and requires the activation of transcription factors (TFs) for growth and survival. Many of the TFs reported so far are critical for carcinogenesis(Vishnoiet al., 2020). Transcription factors have been reported to play an important role in regulating different aspects of cancer immune function by regulating the expression of immune-related genes(Palazonet al., 2014;Yuet al., 2009༛Sun 2017). Therefore, we identified transcription factors and immune-related genes in the differential genes. 607 IRGs were selected by matching IRGs to DEGs (Fig. 2B). After that, 129 TFs were identified as significantly different by matching TFs to DEGs. Among these, 83 TFs were upregulated (Fig. 2C). To discover the potential prognostic significance of each upregulated TFs, we performed a univariate Cox hazard regression analysis. The expressions of 15 TFs were found to be significantly associated with AML patient survival (Fig. 2D). The DEGs constructed a PPI network based on the STRING database using Cytoscape software to explore the underlying mechanism between the TFs and IRGs (Fig. 2E).

Development Of Prognostic Signature

Then, to avoid potential overfitting, LASSO Cox regression analysis was used to select the key OS-related TFs for modeling (Fig. 3A). As a result, a total of four TFs (BATF, PRDM1, RFX5, STAT4) were identified and selected to develop a prognostic signature. According to the median risk score, patients were divided into high-risk and low-risk groups (Fig. 3B). As the risk score of patients increased, the number of deaths increased (Fig. 3B). The heatmap of gene expression between high-risk and low-risk groups in the TCGA cohort was shown (Fig. 3B). The AUC of our signature for 1-year, and 3-year and 5-year OS were 0.79, 0.81 and 0.95, respectively, indicating the high predictive capacity of the signature (Fig. 3C). The Kaplan-Meier analysis showed that patients in the high-risk group had significantly shorter OS than patients in the low-risk group (Fig. 3D).

Figure 3. Prognostic analyses for AML patients. (A) LASSO coefficient profiles of the genes. (B) Development of the prognostic signature based on 4-OS-related TFs. (C)The AUC curves of the signature for 1,3 and 5 years. (D) OS of AML patients in high- and low-risk groups.

Degs Between High-risk And Low-risk Groups Were Involved In Regulating Immune Responses

The results above show the difference in OS between patients in the high-risk and low-risk groups. The two groups were further analyzed to obtain DEG by using the limma package, 499 genes were up-regulated and 179 genes were down-regulated (Fig. 4A). The DEGs existed in the KEGG pathway and biological processes related to immunity, such as cytokine-cytokine receptor interaction, leukocyte migration, and positive regulation of cytokine production. These data suggest that these DEGs may play a role in regulating the immune response in the progression of AML.

Figure 4. DEGs between the high-risk and the low-risk groups. (A) Volcano plot of the DEGs. (B) Significantly enriched KEGG pathway of the DEGs. (C) Significantly enriched gene ontology biological process of the DEGs. (D) Significantly enriched gene ontology cellular component of the DEGs. (D) Significantly enriched gene ontology molecular function of the DEGs.

A High Level Of Tregs In The High-risk Group Was Associated With Stat4

To determine the relationship between the TFs and the immune infiltrating cells, we used the CIBERSORT algorithm to estimate the difference between the low-risk and high-risk groups. A barplot shows the proportion of 22 kinds of immune infiltrating cells in AML cases (Fig. 5A). We found that the high-risk group was positively related to Tregs, which are related to immunosuppression, and are negatively related to gamma delta T cells (Fig. 5B), which are related to strong cytotoxic and kill a broad range of tumor cells (Sebestyenet al., 2020). We performed Spearman correlation analyses to explore the relationships between immune infiltrating cells (Fig. 5C) and TFs (Fig. 5D). Significant results are marked with color for the degree of correlation, the results showed a significant correlation between these STAT4 and Tregs (Fig. 5D), and STAT4 may be the main contributor to the level of Tregs.

Validation Of Prognosis-related Genes In External Aml Cohorts

GEPIA was used to validate the expression and impact on survival of these four prognosis-related genes, BATF, STST4, RFX5 and PRDM1 in AML (Fig. 6A, B). The effect of genes on patient prognosis was verified by testing the prognosis of patients' OS in external AML cohorts (GSE5122 and GSE12417). The results confirmed that patients in the gene high expression group had a worse prognosis (Fig. 6C).

Analysis Of The Target Irgs Regulated By Stata4

The correlation test between STAT4 expression and immune cells is presented, and it was found that macrophage and NKT were negatively correlated with STAT4 expression, while inducible Treg (iTreg) was positively correlated with STAT4 expression (Figs. 7A). The above results suggest that STATA4 affects immunity, and the downstream target genes of STATA4 will be identified next. We obtained a set of immune genes regulated by STATA4 through the ChEA3 website, which integrates a collection of gene set libraries generated from multiple sources including TF-gene co-expression and TF-target associations(Keenanet al., 2019), combined with co-expression analysis to identify possible transcriptional target genes of STATA4. The results of the enrichment analysis of these target genes demonstrate their involvement in immune system processes, immune response, defense response, and natural killer cell-mediated cytotoxicity (Fig. 7B, C). In addition, we performed a survival analysis of these genes showed that most of them were unfavorable for patient survival (Fig. 7D). These genes, which are involved in tumor immunity and are unfavorable to patient survival, may be downstream genes of STAT4 affecting patient prognosis.

It is well known that the transcription level of genes is related to the methylation level of promoter regions, to further infer the reason for the high expression of STAT4 in AML the TCGA database was used to analyze the expression of STAT4 and its methylation status of promoter regions. We found that STAT4 methylation expression in the low-risk group was significantly higher than that in the high-risk group (Fig. 7E), and Pearson correlation analysis showed that STAT4 DNA methylation was negatively correlated with mRNA expression (Fig. 7F).

Validation In Clinical Tissue Samples

The STAT4 expression in different FAB classification was showed in Figrue8A. To validate the bioinformatics analysis results, 10 healthy controls and 22 AML patient samples were studied to analyze the clinical relevance of STAT4 expression. The STAT4 expression in AML patients was significantly higher than that in healthy control group (Fig. 8B). And the expression of STAT4 was positively correlated with the proportion of abnormal cells in AML (Fig. 8C) which were similar to the bioinformatics results. WT1 (Wilms tumor 1) /ABL was defined as an indicator of AML recurrence (Polaket al., 2012). We observed that STAT4 expression levels positively correlated with WTI/ABL, suggesting that STAT4 promotes AML recurrence (Fig. 8D).

In this study, we used the TCGA AML dataset to screen for genes with significant prognostic value and to identify potential prognostic indicators for AML. TFs can play a key role in influencing tumor progression by regulating the transcription of multiple genes(Lourencoet al., 2021). We identified four TFs which were selected for prognostic modeling. BATF is necessary to maintain normal numbers of mature innate lymphoid cells and NK cells in the bone marrow and peripheral tissues(Liuet al., 2020). RFX5 promotes cell cycle progression and inhibits apoptosis in hepatocellular carcinoma(Chenet al., 2020). Previous research has shown that STAT4 activates different cytokines through the JAK-STAT signaling pathway and plays an important role in a variety of autoimmune and inflammatory diseases(Yanget al., 2020). We found that STAT4 is most closely related to the prognosis and tumor immunity of AML, so we explored the function and mechanism of STAT4 in AML.

Cancer cells need to evade the host's antitumor response to survive and metastasize, solid tumors must increase their cell numbers and adapt to their microenvironment(Hanahan 2022). In these respects, members of the STAT family can play a major regulatory role, and they produce different effects by altering gene transcription in effector cells. Seven STAT genes have been identified in the human genome that play an important role in regulating physiological cellular processes and are used to regulate the epigenetics of immune cells(Verhoevenet al., 2020). Members of the STAT family are thought to be implicated in the development of human cancers and STAT3 and STAT5 are considered to be carcinogens with important implications for cancer biology. STAT4 appears to have a more limited role in tumor biology. STAT4 is critically involved in the metastasis of ovarian(Zhaoet al., 2017) and colon cancers(Chenget al., 2015). However, the exact role of STAT4 in cancer remains unclear, STAT4 plays an opposite role in hepatocellular carcinoma(Wanget al., 2015), breast(Wanget al., 2018) and gastric(Nishiet al., 2017).

We evaluated the prognostic value of STAT4 in TCGA and GEO datasets. The results of the study showed that high expression of STAT4 is accompanied by lower OS. The results suggest that high STAT4 expression in AML can be used as an independent prognostic indicator for patients with AML. Based on a comprehensive analysis of immune infiltrating cells in the prognostic model, we found that higher levels of immune infiltrating cells in the high-risk group were associated with survival. By combining the correlation between immune infiltrating cells, GSEA and OS with STAT4, we found a significant correlation between STAT4 and tumor immunity, especially for Tregs that exert immunosuppressive effects. STAT4 may act as a transcription factor to regulate immune-related downstream genes to mediate immune responses, but its deeper mechanisms need to be further explored.

In conclusion, this study's results suggest that STAT4 is a valid prognostic indicator, and its high expression predicts lower survival and high levels of Tregs, its targeted intervention may be beneficial in treating AML patients.

Conflict of Interest

The authors declare no conflict of interest.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Data availability statement

In this study, publicly available datasets were analyzed by NCBI Gene Expression Omnibus (GSE5122 and GSE12417) and the Cancer Genome Atlas (https://portal.gdc.cancer.gov/).

Ethics statement

The studies involving human subjects were reviewed and approved by the Ethical Committee of First Affiliated Hospital of Dalian Medical University.

Funding

This study was partially funded by the National Natural Science Foundation of China (81871725) and the Foundation of Department of Education of Liaoning Province (LJKZ0862).

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No competing interests reported.

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Version 1

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You are reading this latest preprint version

Identification of STAT4 as a biomarker associated with tumor immunity and prognosis in acute myeloid leukemia

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Data collection and identification of Differentially expressed genes (DEGs)

Identification And Validation Of Tf-irg Regulatory Network

Prognostic Signature Construction

Enrichment Analysis Of Degs Between High-risk And Low-risk Groups

Validation Of The Key Genes Based On Tcga And Geo

Validation And Analysis Of Target Gene

Validation Of The Stat4 In Clinical Tissue Samples By Real-time Pcr

Statistical Analyses

Results

Identification of IRGs and TFs in TCGA cohort

Development Of Prognostic Signature

Degs Between High-risk And Low-risk Groups Were Involved In Regulating Immune Responses

A High Level Of Tregs In The High-risk Group Was Associated With Stat4

Validation Of Prognosis-related Genes In External Aml Cohorts

Analysis Of The Target Irgs Regulated By Stata4

Validation In Clinical Tissue Samples

Discussion

Declarations

References

Additional Declarations

Status:

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