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Abstract

BACKGROUND:

Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor with a high mortality rate. Aberrant activation of signal transducers and activators of transcription (STAT) signaling results in tumor pathogenesis and progression by regulating cell cycle, cell survival and immune response.

METHODS:

Therapeutic targets and prognostic biomarkers within the STAT family in GBM were explored using web applications and databases.

RESULTS:

High levels of STAT1/3/5A/5B/6 and low levels of STAT4 were observed in GBM patients. GBM patients expressing high STAT1/2/3/5A/6 and low STAT4/5B levels had the worse overall survival. Among the STAT family, STAT4 and STAT6 were the most frequently mutated genes. A low to moderate correlation among members of the STAT family was observed. Additionally, the STATs were involved in activation or inhibition of cancer related pathways. Analysis of immune infiltrates showed STAT5A levels to be significantly correlated with abundance of immune cells and levels of immune gene biomarkers. Gene ontology (GO) functions and KEGG pathway analysis indicated that STAT5A is involved in immune response-regulating signaling pathway, neutrophil and lymphocyte mediated immunity, single-stranded DNA binding, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, NF-kappa B signaling pathway and TNF signaling pathway. Moreover, several kinase and transcription factor targets of STAT5A in GBM were identified.

CONCLUSION:

We report here therapeutic targets, prognostic biomarkers and regulation network of STAT family in GBM. These findings lay a foundation for further studies on the role of STAT family in therapy and prognosis of GBM. Further studies are required to verify our results.

Keywords

Glioblastoma STAT family STAT5A immunotherapy prognostic biomarker

1. Background

Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor, accounting for about 45.6% of all brain malignancies [1]. In 2020, the estimated number of new cases of brain malignancies in America alone is 23,890 [2]. In the past two decades, no significant progress has been made in the standard of care for GBM patients [3]. Moreover, prognosis of GBM patients is poor, with a median overall survival less than 20 months and a 5-year-survival of 5% [4, 5]. Therefore, identification and use of novel biomarkers for prognosis and therapy, and a combination of current treatments are the most promising strategies in management of GBM.

Signal transducers and activators of transcription (STAT) signaling pathways are crucial in the function, proliferation and apoptosis of immune cells such as CD8+ cells and dendritic cells [6]. Aberrant activation of STAT signaling results in tumor pathogenesis and progression by regulating cell cycle, cell survival and immune response [7, 8]. The STAT family comprises of STAT1-4, STAT5A/5B and STAT6. Previous studies have shown STAT1 to function as an anti-oncogene in GBM under hypoxia and inhibited angiogenesis [9]. Interaction between STAT3 and Forkhead box protein M1 (FOXM1) confers radio-resistance in GBM cells [10]. These findings demonstrate a vital function played by STATs in occurrence and progression of GBM. However, potential applications and mechanisms of the STAT family in GBM therapy and prognosis are still unclear.

This study explored expression levels and prognostic value of the STAT family in GBM. The study evaluated single nucleotide variation (SNV), correlation, cancer-related pathways, drug sensitivity, immune infiltrates and functional analysis of the STAT family in GBM. Our findings highlight potential application value and mechanisms of the STAT family in therapy and prognosis of GBM.

2. Materials and methods

2.1 Oncomine

Oncomine (www.oncomine.org/) is a web application offering translational bioinformatics services [11]. It contains over 700 independent datasets, enabling researchers to interrogate gene expression profiles and biological interactions. Using Oncomine, mRNA levels of STATs in GBM were determined at $p<$ 0.05 and a fold-change $=$ 2.

2.2 GEPIA

Gene Expression Profiling Interactive Analysis (GEPIA – www.gepia.cancer-pku.cn/index.html) is web application developed by Zefang et al. at Peking University. Using GEPIA, customizable functions including tumor/normal differential expression analysis and correlation analysis of STAT family in GBM were explored [12]. Pearson correlation coefficient was utilized to performed correlation analysis using The Cancer Genome Atlas (TCGA), GBM dataset ( $N=$ 163) with a $p$ -value of 0.05. Survival curves were drawn using Kaplan-Meier method.

2.3 PrognoScan

PrognoScan (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html) is a public database for performing meta-analysis of prognostic value of genes [13]. By searching large, publicly available cancer microarray datasets, it enables evaluation of relationships between gene expression and prognosis. PrognoScan was used to explore the significance of STAT family in prognosis of GBM patients.

2.4 TIMER

Tumor IMmune Estimation Response (TIMER, www.cistrome.shinyapps.io/timer/) is web application developed by Li et al. for analyzing immune infiltrates [14]. The web server was used to determine immune cell infiltrates and immune gene biomarker correlation of STAT family in GBM. Table 3 shows immune gene biomarkers and their corresponding immune cells, which have been identified in previous studies [15, 16, 17]. The analysis was conducted using Spearman correlation coefficient based on TCGA GBM dataset ( $N=$ 163).

2.5 GSCALite

Gene Set Cancer Analysis (GSCALite, www.bioinfo. life.hust.edu.cn/web/GSCALite/) is a web-based platform developed by Liu et al. for analyzing genes related to cancer [18]. Customizable functions including SNV, cancer pathway activity and drug analysis of STAT family in GBM were explored using GSCALite. By analyzing cancer pathway activity, STAT family members were grouped into two (High and Low) by median expression. The difference in pathway activity score (PAS) between the groups was defined using Student’s $t$ -test and P-value adjusted by FDR. FDR $<$ 0.05 was considered significant. When PAS (Gene A group High) $>$ PAS (Gene A group Low), we considered gene A to have an activating effect on a pathway, otherwise it would have an inhibiting effect. Spearman correlation test was used to analyze correlation between STAT family members and 265 small molecules from Genomics of Drug Sensitivity in Cancer (GDSC). All the analyses were conducted using TCGA GBM dataset ( $N=$ 163).

Figure 1.

Transcriptional expression of STAT family in 20 different types of cancer diseases from Oncomine database. Difference in transcriptional expression compared by Student’s $t$ -test. Cut-off of $p$ -value and fold change: $p$ -value $=$ 0.02, Fold Change $=$ 2, gene rank $=$ 10%, data type: mRNA.

2.6 LinkedOmics

LinkedOmics (www.linkedomics.org) is a publicly available portal containing multi-omics data and clinical data of 11158 patients from the TCGA project, which could performed translational bioinformatics services [19]. Various customizable functions including correlated gene analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, kinase and transcription factor-target analysis were explored using LinkedOmics and the TCGA GBM sample ( $N=$ 163). Pearson Correlation and Gene Set Enrichment Analysis (GSEA) were conducted at $p$ -value $<$ 0.05.

2.7 GeneMANIA

GeneMANIA (www.genemania.org) is a prediction server for gene prioritization and predicting gene function [20]. Customizable functions including kinase LCK and transcription factor ELF1 network were explored with GeneMANIA by constructing a protein-protein interaction (PPI) network.

3. Results

3.1 Expression and prognosis analysis of STAT family in GBM

To assess the potential involvement of STAT family in GBM, their differential expression at mRNA and protein level was analyzed using multiple datasets hosted on Oncomine and GEPIA. Analysis of STAT family mRNA expression in 20 cancer types (Oncomine) showed a significant upregulation and downregulation in several cancers including GBM (Fig. 1). Particularly, STAT1 was significantly upregulated in tumor tissues, with a fold change of 3.136 and a $p$ -value of 1.58E-10 (Fig. 1, Table 1) [21]. This finding is consistent with a previous study that reported upregulation of STAT1 in tumor tissues with a fold change of 2.701, and a $p$ -value of 1.69E-5 [22]. Two datasets showed an increase of STAT3 in GBM with a fold change of 2.076 and 2.270 [23, 24]. Data obtained from Lixin et al. showed STAT5A and STAT5B to be increased in GBM (STAT5A: fold change $=$ 2.255; STAT5B fold change $=$ 2.953) [24]. Expression of STAT6 was significantly upregulated GBM, with a fold change 2.103 ( $p=$ 0.004). Differential expression analysis using GEPIA showed high levels of STAT1, STAT3, and STAT5A, and a low level of STAT4 in GBM tissues compared with brain tissues (Fig. 2A).

Figure 2.

Expression and prognosis analysis of STAT family in GBM (GEPIA). (A) Box plots showing gene expression using data derived from GEPIA comparing expression of specific STAT family members in GBM and normal tissues. Difference in STAT family expression compared by Student’s $t$ -test using TCGA GBM dataset ( $N=$ 163). (B) Survival curves derived from survival data in TCGA GBM dataset ( $N=$ 163) using Kaplan-Meier method. * $p<$ 0.05.

Table 1

mRNA levels of STAT family in glioblastoma (ONCOMINE)

STATs	Fold Change	$P$ value	$t$ -test	Reference
STAT1	3.136 2.701	1.58E-10 1.69E-5	9.074 6.363	PMCID: PMC556127 PMID: 12894235
STAT2	NA	NA	NA	NA
STAT3	2.076 2.270	2.81E-7 2.30E-10	11.595 8.047	PMID: 16204036 PMID: 16616334
STAT4	NA	NA	NA	NA
STAT5A	2.255	7.46E-4	7.909	PMID: 16616334
STAT5B	2.953	4.61E-9	7.559	PMID: 16616334
STAT6	2.103	0.004	4.798	PMID: 16697959

Difference in expression of members of the STAT family determined using Student’s $t$ -test. Cut-off of $p$ -value and fold change; $p$ -value $=$ 0.02, Fold Change $=$ 2, gene rank $=$ 10%, data type; mRNA.

Figure 3.

Single Nucleotide Variation (SNV), co-expression and cancer-related pathway analysis of STAT family in GBM (GSCALite). (A) SNV analysis of STAT family in GBM. (B) Correlation heat map of STAT family in GBM; Pearson correlation coefficient, $p<$ 0.05 was considered significant. (C) Heatmap showing STAT family members with inhibiting or activating functions in cancer related pathways. The STATs were divided into two groups (High and Low) by median expression. Difference of pathway activity score (PAS) between groups was defined by Student’s $t$ -test and $p$ -value adjusted by FDR. FDR $<$ 0.05 was considered significant. When PAS (Gene A group High) $>$ PAS (Gene A group Low), gene A was considered to have an activating effect on a pathway, otherwise it would have an inhibiting effect on the same pathway. Analyses conducted using TCGA GBM dataset ( $N=$ 163).

The results of prognosis analysis of STAT family in GBM using PrognoScan are shown in Table 2. Notably, GBM patients with high expression of STAT1 (HR [95% CI]: 1.55 [1.11–2.16], $p=$ 0.010313), and STAT2 (HR [95% CI]: 1.55 [1.11–2.16], $p=$ 0.018909) had the worse overall survival. Two datasets showed GBM patients with high expression of STAT4 (HR [95% CI]: 0.25 [0.09–0.70], $p=$ 0.007913), and STAT5B (HR [95% CI]: 0.74 [0.46–1.19], $p=$ 0.012642) to have a better overall survival. Data obtained from TGGA were used to verify these results, indicating that GBM patients with high expression of STAT1 ( $p=$ 0.00035), STAT2 ( $p=$ 0.00032), STAT3 ( $p=$ 0.016), STAT5A ( $p<$ 0.0001) and STAT6 ( $p=$ 0.0086) had the worse overall survival (Fig. 2B).

Table 2

Overall Survival of STAT family in glioblastoma (PrognoScan)

STATs	Dataset	N	Cox $p$ -value	HR [95% CI]
STAT1	GSE4412-GPL96	74	0.010313	1.55 [1.11–2.16]
STAT2	MGH-glioblastoma	50	0.017809	7.49 [1.42–39.59]
STAT3	NA	NA	NA	NA
STAT4	GSE7696	70	0.007913	0.25 [0.09–0.70]
STAT5A	NA	NA	NA	NA
STAT5B	MGH-glioblastoma	50	0.012642	0.74 [0.46–1.19]
STAT6	NA	NA	NA	NA

Analysis; Kaplan-Meier. N; numbers of patients.

Figure 4.

Drug sensitivity of STAT family in GBM (GSCALite). Negative correlation indicate that low expression of gene is resistant to the drug and vice versa. Spearman’s correlation analysis conducted with TCGA GBM dataset ( $N=$ 163).

3.2 Single nucleotide variation, correlation, cancer-related pathways and drug sensitivity analysis of STAT family in GBM

Missense mutation was the most common variant classification and C $>$ T was the most common SNV class (Fig. 3A). Among the members of the STAT family, STAT4 and STAT6 were the most frequently mutated genes (Fig. 3A). There was a low to moderate correlation among members of the STAT family (Fig. 3B). Members of the STAT family play significant roles in cancer related pathways (Fig. 3C). In GBM, STATs were demonstrated to function in activating apoptosis pathway, EMT pathway, hormone ER pathway, RAS/MAPK pathway and RTK pathway, while inhibiting cell cycle pathway, DNA damage response pathway, hormone AR pathway and P13K/AKT pathway (Fig. 3C). GBM patients with high STAT5A expression leads to the sensitivity of 59 drugs or small molecules. Furthermore, GBM patients with high STAT5A expression leads to the sensitivity of 42 drugs or small molecules (Fig. 4).

Figure 5.

Immune cell infiltration of STAT5A in GBM (TIMER). STAT5A expression is significantly positively associated with infiltrating levels of B cells, CD8+ T cells, CD4 $+$ T cells, macrophages, neutrophils, and dendritic cells in GBM. Analysis; Spearman’s correlation coefficient, dataset; TCGA GBM ( $N=$ 163).

3.3 Immune infiltrates analysis of STAT5A in GBM.

Expression of STAT5A was high in GBM, making it a potential prognostic biomarker. Moreover, STAT5A was involved in activation and inhibition of key cancer related pathways and drug resistance. This implies that STAT5A plays significant roles in GBM pathogenesis and progression. Under this backdrop, STAT5A was selected for further analysis. Correlation between STAT5A and immune infiltrates in GBM was analyzed owing to the important roles the STAT family play in immune response [25, 26]. The levels of STAT5A were significantly correlated with abundance of B cell ( $p=$ 1.91e-02), CD4 $+$ T cells ( $p=$ 9.55e-03), Macrophage ( $p=$ 1.64e-04), Neutrophils ( $p=$ 6.63e-04) and Dendritic cells ( $p=$ 1.60e-16) (Fig. 5).

Expression of STAT5A was also strongly correlated with expression of gene biomarkers in GBM presented in Table 3. In reference to gene biomarkers of CD8+ T cell, data obtained from TIMER showed CD8B expression to be positively associated with STAT5A expression while data from CGGA showed the expression of both CD8A and CD8B to be positively associated with expression of STAT5A. Data obtained from both TIMER and CGGA indicated a strong correlation between expression of STAT5A and the expression of T cell (CD3D, CD3E, CD2), Monocyte (CD86 and CD115), TAM (CCL2, CD68, IL10) and M2 Macrophage (CD163, VSIG4, MS4A4A) gene biomarkers. From the analysis of gene biomarkers of Natural killer cell in TIMER, the levels of KIR2DL3, KIR2DL4, and KIR3DL1 were positively correlated with STAT5A expression. In both TIMER and CGGA, eight gene biomarkers of Dendritic cell were strongly associated with STAT5A expression. Similarly, the levels of STAT4, STAT1, TNF, GATA3, STAT6 and IL13 were positively correlatively with STAT5A expression. Two gene biomarkers (FOXP3 and TGFB1) of Treg and three gene biomarkers (PD-1, CTLA4 and GZMB) of exhibited a strong correlation with STAT5A expression. Therefore, STAT5A presents a suitable target for immunotherapy in GBM.

Table 3
Correlation between STAT5A and gene biomarkers of immune cells in GBM (TIMER)

Description	Gene biomarkers	TIMER		CCGA
		Cor	$P$ -value	Cor	$P$ -value
CD8+ T cell	CD8A	0.159	0.0501	0.40	***
	CD8B	0.275	***	0.46	***
T cell (general)	CD3D	0.355	***	0.54	***
	CD3E	0.492	***	0.59	***
	CD2	0.393	***	0.60	***
B cell	CD19	0.242	**	0.58	***
	CD79A	0.079	0.332	0.5	***
Monocyte	CD86	0.473	***	0.62	***
	CD115 (CSF1R)	0.563	***	0.57	***
TAM	CCL2	0.494	***	0.48	***
	CD68	0.554	***	0.70	***
	IL10	0.666	***	0.38	***
M1 Macrophage	INOS (NOS2)	$-$ 0.077	0.343	0.24	***
	IRF5	0.487	***	0.64	***
	COX2 (PTGS2)	0.584	***	0.18	***
M2 Macrophage	CD163	0.47	***	0.55	***
	VSIG4	0.401	***	0.56	***
	MS4A4A	0.436	***	0.58	***
Neutrophils	CD66b (CEACAM8)	$-$ 0.076	0.348	–	–
	CD11b (ITGAM)	0.627	***	0.66	***
	CCR7	0.269	***	0.39	***
Natural killer cell	KIR2DL1	0.113	0.166	–	–
	KIR2DL3	0.171	*	–	–
	KIR2DL4	0.356	***	–	–
	KIR3DL1	0.167	*	–	–
	KIR3DL2	0.099	0.222	–	–
	KIR3DL3	0.114	0.162	–	–
	KIR2DS4	0.158	0.0507	–	–
Dendritic cell	HLA-DPB1	0.509	***	0.55	***
	HLA-DQB1	0.348	***	0.23	***
	HLA-DRA	0.459	***	0.55	***
	HLA-DPA1	0.440	***	0.47	***
	BDCA-1 (CD1C)	0.207	*	0.29	***
	BDCA-4 (NRP1)	0.405	***	0.38	***
	CD11c (ITGAX)	0.277	***	0.64	***
Th1	T-bet (TBX21)	0.006	0.939	0.46	***
	STAT4	0.294	***	0.23	***
	STAT1	0.259	**	0.42	***
	IFN-g (IFNG)	0.068	0.402	–	–
	TNF-a (TNF)	0.237	**	0.39	***
Th2	GATA3	0.399	***	0.55	***
	STAT6	0.553	***	0.69	***
	STAT5A	–	–	–	–
	IL13	$-$ 0.188	*	0.35	***
Tfh	BCL6	0.124	0.126	0.27	***
	IL21	$-$ 0.052	0.525	–	–
Th17	STAT3	0.348	***	0.39	***
	IL17A	$-$ 0.028	0.733	–	–
Treg	FOXP3	0.395	***	0.44	***
	CCR8	0.297	***	–	–
	STAT5B	0.051	0.531	0.17	***
	TGFb (TGFB1)	0.481	***	0.69	***
T cell exhaustion	PD-1 (PDCD1)	0.186	*	0.43	***
	CTLA4	0.396	***	0.47	***
	LAG3	0.001	0.992	0.47	***
	TIM-3 (HAVCR2)	0.411	***	–	–
	GZMB	0.316	***	0.46	***

* $p<$ 0.05, ** $p<$ 0.01, *** $p<$ 0.001. Analysis; Spearman’s correlation test. Dataset; TCGA GBM ( $N=$ 163).

3.4 Enrichment analysis of STAT5A in GBM

Figure 6A showed the genes significantly correlated with STAT5A in GBM. Among these genes, the top 50 most significant genes positively (Fig. 6B) and negatively (Fig. 6C) associated with STAT5A were also extracted. Moreover, Supplementary Fig. 1 showed the top three genes positively associated with STAT5A, which were TMEM199 (cor $=$ 0.6939, $p=$ 2.721e-23), FES (cor $=$ 06883, $p=$ 8.324e-23), and SLC24A6 (cor $=$ 0.6829, $p=$ 2.414e-22). Enrichment analyses of STAT5A, including GO function and KEGG pathways, were performed. The data of GO function analysis in Fig. 6D–F suggested that the functions of STAT5A in GBM mainly enriched in immune response-regulating signaling pathway, neutrophil and lymphocyte mediated immunity, regulation of immune effector process, immunological synapse, cytokine receptor binding, single-stranded DNA binding, and immunoglobulin binding. Furthermore, the result of KEGG pathway analysis suggested that the functions of STAT5A in GBM mainly enriched in cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, and Th17 cell differentiation (Fig. 6G and H).

Figure 6.

Enrichment of STAT5A in GBM (LinkedOmics). (A) Genes associated with STAT5A in GBM based on TCGA GBM dataset ( $N=$ 163). (B) The 50 most significant genes with a positive correlation with STAT5A in GBM. (C) The 50 most significant genes with a negative correlation with STAT5A in GBM. (D–F) GO analysis. (G) KEGG pathway analysis. (H) KEGG pathway annotations of cytokine-cytokine receptor interaction. Genes associated with STAT5A obtained using Spearman’s correlation test. GO analysis and KEGG pathway analysis conducted using GSEA.

3.5 Kinase and transcription factor target network analysis of STAT5A in GBM

Kinase and transcription factor target networks of STAT5A in GBM were also explored (Table 4). Here, kinases LCK, SYK, LYN, HCK, and TTK were identified as the five most significant kinase network targets. The PPI network showed kinase LCK target networks to be involved in regulation of immune response, antigen receptor-mediated signaling pathway, T cell activation and receptor signaling pathway (Fig. 7A). The five most significant transcription factor targets identified were V$ELF1_Q6, V$IRF_Q6, V$PEA3_Q6, V$PU1_Q6, and V$NERF_Q2. PPI network indicated that transcription factor ELF1 networks are involved in regulation of immune response, lymphocyte, leukocyte and B cell activation (Fig. 7B).

Figure 7.

PPI networks of kinase LCK and transcription factor ELF1 networks (GeneMANIA). (A) The applied bioinformatics methods and the biological functions of gene sets of kinase LCK networks. (B) The applied bioinformatics methods and the biological functions of gene sets of transcription factor ELF1 networks.

Table 4

Kinase and transcription factor targets of STAT5A in GBM (LinkedOmics)

Enriched category	Geneset	LeadingEdgeNum	FDR
Kinase target	Kinase_LCK	27	0
	Kinase_SYK	18	0
	Kinase_LYN	24	0
	Kinase_HCK	11	0
	Kinase_TTK	5	0
Transcription factor target	V$ELF1_Q6	86	0
Transcription factor target	V$IRF_Q6	76	0
	V$PEA3_Q6	91	0
	V$PU1_Q6	56	0.00014
	V$NERF_Q2	64	0.00015

Enrichment analyses performed using GSEA.

4. Discussion

The STAT family mediates various biological processes including cellular immunity, invasion, differentiation and apoptosis [27]. Activated by diverse cytokines, STAT signaling play a central role in immunity, cell death, cancer pathogenesis and progression [8]. Studies have shown that the STAT family exert important functions in cancers such as hepatocellular carcinoma. However, the role of the STAT family in GBM has not been studied.

From the expression analysis, the levels of STAT1/3/ 5A/5B/6 were high in GBM, while the levels of STAT4 were significantly low. GBM patients with high expressions of STAT1/2/3/5A/6 and low expressions of STAT4/5B had a worse overall survival. These findings are consistent with results from previous studies. For instance, Balaram et al reported STAT1 to be upregulated in GBM and correlated with a worse prognosis [28]. Another study fronted STAT3 as a potential biomarker for prognosis of GBM [29]. These findings corroborate our recommendation of the STAT family as potential prognostic biomarkers in GBM.

Cancer-related pathway analysis showed that the STAT family play significant roles in GBM mainly by activating; the apoptosis pathway, EMT pathway, hormone ER pathway, RAS/MAPK pathway, RTK pathway, and inhibiting; the cell cycle pathway, DNA damage response pathway, hormone AR pathway and P13K/AKT pathway. In an earlier study, Adetola et al reported that STAT3 inhibition induced apoptosis in cancer cells [30]. In hepatocellular carcinoma, STAT1 has been reported as a tumor-suppressor, inducing G0/G1 cell cycle arrest and apoptosis of tumor cells [31]. Therefore, the STAT family may regulate pathogenesis and progression of GBM via these pathways.

In the present study, we observed the levels of STAT5A to be significantly correlated with abundance of immune cells and the levels of immune gene biomarkers. These immune cells and immune gene biomarkers have previously been reported as therapy targets and play a significant role in various types of cancer. Immune cells, including dendritic cells and CD8+ cytotoxic T lymphocytes are promising therapeutic targets in bone metastasis [32]. In breast cancer, CD8+ T cells and regulatory T cells have been reported as reliable prognostic biomarkers [33]. Similarly, CTLA4 and PD-1 have been proposed as immune checkpoint for cancer immunotherapy [34]. Here, we present STAT5A as a potential therapeutic target for cancer immunotherapy.

Correlation between STAT5A expression and several drugs or small molecules, including I-BET 762 and Nilotinib was evaluated. I-BET 762 has been shown to inhibit tumor cell proliferation, serving as a potential therapy for pancreatic cancer. Another study demonstrated the potential use of I-BET 762 in preventing breast and lung cancer. Nilotinib, a second-generation tyrosine kinase inhibitor, is widely regarded as a therapy target for chronic myeloid leukemia. In GBM, Nilotinib could regulate cell invasion. I-BET 762 and Nilotinib could also serve as therapy targets or play significant roles in GBM progression through STAT5A-associated signing. Further studies are required to explore this possibility.

Gene Ontology (GO) functions and KEGG pathways analysis indicated that STAT5A is involved in immune response-regulating signaling pathway, neutrophil and lymphocyte mediated immunity, single-stranded DNA binding, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, NF-kappa B signaling pathway and TNF signaling pathway. These functions and pathways are associated with immune response, carcinogenesis and progression. Fernando et al. reported NOD-like receptors as important players and targets in the interface between innate immunity and cancer [35]. A previous study has also shown TNF signaling pathway to be involved in regulation of proliferation and invasion in cervical carcinoma [36]. Single-stranded DNA-binding proteins are crucial in maintaining the integrity of a genome [37]. Furthermore, single-stranded DNA-binding proteins are involved in cell-cycle checkpoint activation following DNA damage in cancer [37]. Therefore, STAT5A may affect pathogenesis and progression of GBM through these signaling pathways.

This study had some limitations. First, the sample size was relatively small, and we recommend a larger cohort study to be performed. Moreover, our study was short of clinicopathological features analysis. Functional analysis should be done to validate the role of STAT family in GBM both in vivo and vitro.

5. Conclusion

This study outlines expression and demonstrates the prognostic value of STAT family in GBM. Expression levels of STAT5A are significantly correlated with abundance of immune cells and the level of immune gene biomarkers. Moreover, STAT5A is involved in immune response-regulating signaling pathway, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, and TNF signaling pathway. These results underscore the clinical significance of and regulation networks of STAT family in GBM. The findings lay a foundation for further studies on potential roles of the STAT family in therapy and prognosis of GBM.

6. List of abbreviations

GBM: Glioblastoma, STAT: Signal transducer and activator of transcription, SNV: single nucleotide variation, TCGA: The Cancer Genome Atlas, GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes

Footnotes

Funding

This study was funded by High-level Hospital Construction Research Project of Maoming People’s Hospital.

Supplementary data

Supplementary Fig. 1.

The three most significant genes correlated with STAT5A in GBM (LinkedOmics).

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Mining database for the therapeutic targets and prognostic biomarkers among STAT family in glioblastoma

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Background

2. Materials and methods

2.1 Oncomine

2.2 GEPIA

2.3 PrognoScan

2.4 TIMER

2.5 GSCALite

2.7 GeneMANIA

3. Results

3.1 Expression and prognosis analysis of STAT family in GBM

Table 3 Correlation between STAT5A and gene biomarkers of immune cells in GBM (TIMER)

5. Conclusion

6. List of abbreviations

Footnotes

Funding

Supplementary data

References

Table 3
Correlation between STAT5A and gene biomarkers of immune cells in GBM (TIMER)