Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma

Lirui Dai; Yongjie Han; Zhuo Yang; Yuling Zeng; Wulong Liang; Zimin Shi; Yiran Tao; Xianyin Liang; Wanqing Liu; Shaolong Zhou; Zhe Xing; Weihua Hu; Xinjun Wang

doi:10.1111/jcmm.17807

. 2023 Jun 14;27(15):2194–2214. doi: 10.1111/jcmm.17807

Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma

Lirui Dai ^1,^2,³, Yongjie Han ^1,³, Zhuo Yang ^1,³, Yuling Zeng ⁴, Wulong Liang ^1,³, Zimin Shi ^1,^2,³, Yiran Tao ^1,^2,³, Xianyin Liang ^1,^2,³, Wanqing Liu ^1,^2,³, Shaolong Zhou ^1,³, Zhe Xing ^1,^2,³, Weihua Hu ^1,³, Xinjun Wang ^1,^2,^3,^✉

PMCID: PMC10399539 PMID: 37315184

Abstract

Suppressor of cytokine signalling (SOCS) 1/2/3/4 are involved in the occurrence and progression of multiple malignancies; however, their prognostic and developmental value in patients with glioblastoma (GBM) remains unclear. The present study used TCGA, ONCOMINE, SangerBox3.0, UALCAN, TIMER2.0, GENEMANIA, TISDB, The Human Protein Atlas (HPA) and other databases to analyse the expression profile, clinical value and prognosis of SOCS1/2/3/4 in GBM, and to explore the potential development mechanism of action of SOCS1/2/3/4 in GBM. The majority of analyses showed that SOCS1/2/3/4 transcription and translation levels in GBM tissues were significantly higher than those in normal tissues. qRT‐PCR, western blotting (WB) and immunohistochemical staining were used to verify that SOCS3 was expressed at higher mRNA and protein levels in GBM than in normal tissues or cells. High SOCS1/2/3/4 mRNA expression was associated with poor prognosis in patients with GBM, especially SOCS3. SOCS1/2/3/4 were highly contraindicated, which had few mutations, and were not associated with clinical prognosis. Furthermore, SOCS1/2/3/4 were associated with the infiltration of specific immune cell types. In addition, SOCS3 may affect the prognosis of patients with GBM through JAK/STAT signalling pathway. Analysis of the GBM‐specific protein interaction (PPI) network showed that SOCS1/2/3/4 were involved in multiple potential carcinogenic mechanisms of GBM. In addition, colony formation, Transwell, wound healing and western blotting assays revealed that inhibition of SOCS3 decreased the proliferation, migration and invasion of GBM cells. In conclusion, the present study elucidated the expression profile and prognostic value of SOCS1/2/3/4 in GBM, which may provide potential prognostic biomarkers and therapeutic targets for GBM, especially SOCS3.

Keywords: biomarkers, gene expression profiling, glioblastoma, immune infiltration, JAK/STAT3, SOCS1/2/3/4

1. INTRODUCTION

Glioblastoma (GBM) is the most common, aggressive primary malignant brain cancer type in humans worldwide. ¹ GBM, whose cause is unknown, is common among Caucasians and Asians, with the worst survival and highest morbidity among Caucasians. ² GBM can occur at any age, and the incidence increases steadily with age. ³ Currently, the overall survival (OS) time of patients with GBM can be extended via radiotherapy, chemotherapy, surgical treatment and immunotherapy, but it is not possible to reduce its high recurrence rate after treatment. Previous clinical trials have shown that the 5‐year survival rate of GBM is 4%–5%, and the 2‐year survival rate is 26%–33%. ⁴ Hence, it is necessary to further explore the carcinogenic mechanism of GBM and provide better prognostic assessment strategies.

Tumour RNA sequencing (RNA‐seq) data are increasingly being used to identify gene signatures and clinical prognostic factors, including gender, age, GBM tumour grade and Karnofsky score. ⁵ Although previous studies have explored the prognostic factors of glioma, the prognosis of patients with glioma remains poor. Current high‐throughput sequencing technology provides rapid analysis of biomarkers and in‐depth exploration of potential mechanisms of disease. ⁶ It is important to find biomarkers that affect the survival and prognosis of glioma patients as soon as possible. The present study explored the influence of four genes in the suppressor of cytokine signalling (SOCS) family on GBM and experimentally evaluated on the expression and functional influence of SOCS3 in the SOCS family in GBM.

The SOCS family consists of eight members, whose dominant role is to inhibit cytokine signal transduction. ⁷ SOCS1 and SOCS3 can inhibit the signal transduction of various cytokines such as interleukin‐6 (IL6), leukaemia inhibitor factor (LIF), OSM, INF gamma and growth hormone (GH), and play regulatory roles in the activation of various immune responses and the pathogenesis of tumours in vivo. ⁸ SOCS2 is induced by a variety of cytokines that activate STAT5, including GH, IL‐6 and LIF, and it is involved in the ubiquitination of target proteins, including GHR and a variety of signalling proteins. ⁹ SOCS4 is an important regulator of antiviral immunity. ¹⁰ As one of the negative feedback loops of the JAK/STAT signalling pathway, SOCS4 can decrease the STAT3 signalling of EGFR by increasing receptor degradation. ¹¹ SOCS5, SOCS6, SOCS7 and CIS play protumour or antitumour roles in a variety of cancers, and may affect the progression of tumours via different mechanisms. For example, SOCS5 can inhibit the migration and invasion of hepatocellular carcinoma cells in vitro by activating PI3K/Akt/mTOR‐mediated autophagy. ¹² It is worth mentioning that the expression alterations of SOCS1/2/3/4 negatively regulate the JAK/STAT signalling pathway in many tumour cells, so SOCS1/2/3/4 is the main research content of this article. In the current study, SOCS1/2/3/4 were regarded as the key genes affecting the survival and prognosis of patients with GBM, while SOCS3, as a negative regulator, could regulate the JAK/STAT3 signalling pathway and inhibit tumour cell proliferation and tumour development. ¹³ Therefore, SOCS3 was included in the current study for further experimental verification.

2. MATERIALS AND METHODS

2.1. Oncomine analysis

Oncomine (https://www.oncomine.org/) gene expression microarray database was used to analyse genome‐wide expression relevant research. ¹⁴ , ¹⁵ Oncomine was employed to explore SOCS1/2/3/4 RNA levels in brain and central nervous system (CNS) cancer. After retrieving and analysing the Oncomine database, correlation analysis results on SOCS1/2/3/4 genes were included. Among them, SOCS1/2/3/4 genes with significant differences were selected for further analysis. The screening criteria were as follows: (1) gene: SOCS1/2/3/4; (2) cancer type: Brain and CNS Cancer; (3) data type: all; (4) analysis type and target: brain and CNS cancer vs. normal tissue; (5) p < 0.0001, fold‐change>2, gene rank = top 10%. Significant difference was set at p < 0.05.

2.2. Timer2.0 analysis

‘Immune module’ of the TIMER2.0 (http://timer.cistrome.org/) database ¹⁶ allows us to analyse immune infiltration estimations for SOCS1/2/3/4 expression by TIMER and CIBERSORT algorithms. Six immune cell types were performed using TIMER algorithms, including lymphocytes, macrophages, NK cells and neutrophils, etc.

2.3. Analysis of UALCAN

TCGA gene expression data were analysed by the University of ALabama at Birmingham CANcer (UALCAN) (http://ualcan.path.uab.edu/) ¹⁷ database to observe SOCS1/2/3/4 and their hub genes' expression in GBM. In addition, the association between SOCS1/2/3/4 and GBM clinical and pathological features (such as race, IDH status or OS, etc.) and their potential prognostic significance were observed.

2.4. SangerBox analysis

SangerBox3.0 (http://sangerbox.com/home.html) ¹⁸ was used for prognostic analysis of gene expression, immune cell analysis, immunomodulatory gene analysis, immune checkpoint gene analysis and mutational landscape analysis. Data source: TCGA and GTEx databases. Data transformation: log₂(x + 0.001). Survival data included overall survival (OS), disease‐specific survival (DSS) and progression‐free interval (PFI). Correlation coefficient: Pearson.

2.5. Analysis of biological function

Gene expression data of GBM in HTSeq‐FPKM were acquired from TCGA data set to explore 543 patients with GBM. Pearson's correlation coefficient (|r| > 0.4 and p < 0.001) to screen the coexpressed genes of SOCS1/2/3/4. Moreover, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genome (KEGG) analysis ¹⁹ of coexpressed genes were evaluated by employing the R ‘cluster profiler’ software package to search the possible biological functions of SOCS1/2/3/4. Coexpressed genes of the SOCS1/2/3/4 protein–protein interaction (PPI) network was built by employing the GeneMANIA (http://genemania.org/) database. ²⁰ The PPI network was put into Cytoscape 3.6.1, ²¹ and the cytoHubba plug‐in was used as the screening criteria for hub genes. In total, 10 genes with the highest correlation were considered to be hub genes, and relevant analysis was subsequently conducted.

2.6. Human Protein Atlas (HPA) analysis

The subcellular localization of SOCS1/2/3/4, as well as their expression in GBM were analysed by immunohistochemistry employing the HPA database (https://www.proteinatlas.org/). ²²

2.7. Linked Omics database analysis

Linked Omics Database (http://www.linkedomics.org/login.php) ²³ contains multiomics data and clinical data for 32 cancer types, as well as data for a total of 11,158 patients from the TCGA project. It is also a multiomics database that integrates proteomics data from mass spectrometry (MS) for selected TCGA tumour sample. The differentially expressed genes (DEGs) related to SOCS3 were screened from TCGA‐GBM cohort by the LinkFinder module, and the Pearson correlation coefficient was employed to obtain the results. The results were shown as volcano plots and heat maps.

2.8. TISDB analysis

The association between the immune system and GBM was explored via the TISIDB database (http://cis.hku.hk/TISIDB/), ²⁴ and the correlation between SOCS1/2/3/4 expression in GBM and immune infiltration was evaluated by Spearman's correlation analysis.

2.9. Genetic alterations of SOCS1/2/3/4 in GBM

The cBioPortal database (https://www.cbioportal.org/) ²⁵ , ²⁶ was employed to explore the genetic alterations, gene alteration frequencies and copy number alterations of SOCS1/2/3/4 in GBM.

2.10. Clinical tissue specimens

The tissues of 23 patients with low‐grade glioma (LGG) and GBM (including 12 patients with LGG and 11 patients with GBM) used for reverse transcription‐quantitative PCR (RT‐qPCR) and immunohistochemical staining were obtained from the Fifth Affiliated Hospital of Zhengzhou University (Zhengzhou, China). The studies involving patients were reviewed and approved by the Ethics Review Committee of the Fifth Affiliated Hospital of Zhengzhou University (approval no. KY2021004).

2.11. Cell cultures and transfections

The human GBM cell lines U87‐MG, T89G, U251, U118, LN229, A172 and LN18 were cultured in DMEM medium with 10% foetal bovine serum (FBS) (Biological Industries; Sartorius AG) at 37°C in the presence of 5% CO₂. The small interfering RNAs (siRNAs), including three different SOCS3 siRNA sequences and a control siRNA, were purchased from Guangzhou RiboBio Co., Ltd., and were transfected into cells with Lipofectamine® 3000. SOCS3 plasmid was purchased from Guangzhou Dahong Biotechnology Co., LTD.

2.12. RT‐qPCR assay

Total RNA was extracted from patients' tissue employing TRIzol® (Takara Bio, Inc.) and other reagents such as chloroform, isopropyl alcohol and 75% ethanol. PrimeScript™ RT reagent Kit with gDNA Eraser and TB Green Premix Ex Taq™ II (Takara Bio, Inc.) were used to reversely transcribed RNA into cDNA and for RT‐qPCR. The primers used were as follows: SOCS3, forward, 5′‐CACCTGGACTCCTATGAGAAAGTCA‐3′, reverse, 5′‐ GGGGCATCGTACTGGTCCAGGAA ‐3′, GAPDH, forward, 5′‐ CAGGAGGCATTGCTGATGAT −3′, reverse, 5′‐ GAAGGCTGGGGCTCATTT −3′.

2.13. Western blotting

Cells were washed in PBS, detached with a cell scraper and centrifuged for 10 min at 12,000 × g at 4°C. Cell lysates were boiled for 15 min at 100°C. Total protein (15–20 μg) was electrophoresed by SDS‐PAGE (cat. no. P0012; Epizyme Biotech) and transferred to PVDF membranes (Millipore Sigma) for 100 min, followed by overnight incubation at 4°C with primary antibody against SOCS3 (rabbit monoclonal antibody [mAb]; cat. no. #52113; 1:1000; Cell Signaling Technology, Inc.), (cat. no. #AB16030; 1:100; Abcam), proliferating cell nuclear antigen (PCNA; rabbit mAb; cat. no. #13110; 1:1000; Cell Signaling Technology, Inc.), β‐actin (cat. no. #66009‐1‐lg; 1:5000; Protein Tech Group, Inc.), β‐tubulin (cat. no. #M20005M; 1:5000; Abmart Pharmaceutical Technology Co., Ltd.). The PVDF membrane was then washed with TBST‐Tween 20 (cat. no. #T1087; Beijing Solarbio Science & Technology Co., Ltd.) three times for 15, 10 and 5 min, respectively, and incubated for 2 h at room temperature with a goat antirabbit IgG secondary antibody (Affinity HRP; 1:5000). Finally, the PVDF membrane was washed for 15, 10 and 5 min, and then visualized with enhanced chemiluminescence using the Superstar ECL Plus Ready‐to‐use (cat. no. AR1171, lot no. 16H31C71; Boster Biological Technology).

2.14. Colony formation assay

Transfected cells were digested with trypsin and counted with cell technology plates. Next, 2 mL medium containing 10% FBS was placed into a 6‐well plate, and 500 cells were added to the 6‐well plate. The cells were collected after 10 days of culture, fixed in paraformaldehyde for 30 min, stained with crystal violet for 30 min, washed with PBS, observed and counted.

2.15. Transwell assay

The Transwell chamber and the Matrigel were purchased from Corning Inc and Biozellen, respectively. For migration experiments, the upper layer of each chamber was inoculated with 30,000 cells, and 100 μL serum‐free medium and 800 μL 10% FBS medium were added to the lower layer of the chamber. For the invasion assay, Matrigel was added to the upper layer of the chamber, and the subsequent steps were performed as aforementioned.

2.16. Wound healing assay

A total of 15, 000 cells were planted into 6‐well plates. When transfected cells reached 80% confluence, scratches were produced in the cell monolayer in the middle of each well using a 200 μl pipette tip. The cells were washed using PBS and placed under an inverted microscope (magnification, ×40; Nikon Corporation) for observation and photography. Next, 2 mL medium containing 10% FBS was added to each well. After 48 h, the 6‐well plate was removed, washed with PBS and photographed under an inverted microscope. The migration distance was calculated as follows: Migration distance = Scratch width observed at 0 h—scratch width observed at 48 h.

2.17. Immunohistochemical staining analysis

Tissue samples of 12 patients with LGG and 11 patients with GBM were fixed with formaldehyde, paraffin embedded, sectioned and incubated with anti‐SOCS3 antibodies overnight at 4°C, followed by incubation with a sheep antirabbit secondary antibody for 20 min at room temperature. Sample was then washed with PBS and incubated with a streptomycin working solution at room temperature for 15 min. Next, DAB chromogenic solution was used, and areas with a strong immune response were selected and observed at ×400 magnification.

2.18. Statistical analysis

GraphPad Prism 9 (GraphPad Software, Inc.) was employed for creating graphs, while SPSS 26.0 (IBM Corp) was employed for statistical analysis. Normally distributed data were expressed as the mean ± standard deviation. Student's t‐test was employed to compare SOCS1/2/3/4 expression in cancer and normal tissues, as well as the proliferation, invasion and migration of GBM cells before and after knocking down SOCS3. Kaplan–Meier curves were employed to analyse patients' survival according to their SOCS1/2/3/4 expression levels. Univariate Cox regression analysis was employed to calculate hazard ratio (HR) and 95% confidence interval (CI) in survival analysis. All R packages used were R software version V4.0.3.

3. RESULTS

3.1. SOCS1/2/3/4 expression is increased in GBM

The Oncomine database was employed to compare SOCS1/2/3/4 mRNA expression between brain and CNS cancer and normal tissues. The results revealed that SOCS1, SOCS2, SOCS3 and SOCS4 mRNA levels were upregulated in patients with brain and CNS cancer (Figure 1A). In‐depth analysis revealed that SOCS1 was overexpressed in patients with GBM, with a fold‐change of 2.133 and a p = 5.37 × 10⁻⁹. In the Sun database, the SOCS1 expression level was high in GBM with a fold‐change of 2.025 and a p = 8.83 × 10⁻¹². In the Murat data set, SOCS2 mRNA expression was increased in patients with GBM, with a fold‐change of 3.344, and p = 1.36 × 10⁻¹⁸. In the TCGA and Lee databases, the mRNA levels of SOCS2 were upregulated in GBM, with a fold‐change of 3.164 and 3.139, and p‐values of 2.01 × 10⁻¹⁴ and 5.22 × 10⁻⁵, respectively. In the Berdel database, the mRNA levels of SOCS3 were increased in GBM, with a fold‐change of 5.701 and p = 3.96 × 10⁻⁷. Moreover, SOCS4 was upregulated in anaplastic astrocytoma and oligodendroglioma in the Sun database, with fold‐changes of 3.062 and 2.357, and p‐values of 4.01 × 10⁻⁷ and 1.61 × 10⁻⁵ (Table 1). In addition, TCGA‐GTEx database and GSE16011 data set in GEO database ²⁷ were used to observe SOCS1/2/3/4 expression differences between GBM and normal tissues. All the results showed that mRNA levels of SOCS1/2/3/4 were significantly higher in GBM than in normal tissues (Figure 1B–F ). The receiver operating characteristic curves were employed to analyse the efficacy of SOCS1/2/3/4 to distinguish between GBM and normal brain samples. The results were 0.852, 0.962, 0.864 and 0.914, respectively, these suggests that SOCS1/2/3/4 genes may possess the potential to identify GBM samples (Figure 2A–D). In addition, a nomogram model that contained multiples important predictors in the Cox analysis was conducted to predict the 1‐, 3‐ and 5‐ year survival rate of patients with GBM. For example, a male patient (8 points) from Asia (0 points), >60 years old (10 points), IDH status: WT (100 points); Karnofsky performance score:<80 (8 points), SOCS1: 5 (0 points), SOCS2: 5 (0 points), SOCS3: 2 (10 points) and SOCS4: 3.4 (0 points). Thus, the total score is 136 points; the patient's 1‐year survival rate is about 78%, the 3‐year survival rate is about 32% and the patient's 5‐year survival rate is 0. The results also revealed that IDH status and SOCS3 contributed the most to the total points and survival probability relative to the other factors in the multivariate regression model (Figure 2E).

(A). *SOCS1/2/3/4 mRNA* expression levels in the Oncomine database in tumours. Red represents overexpressed genes, while blue represents underexpressed genes. The threshold parameters were as follows: p = 1 × 10⁻⁴ and fold‐change =1.5. (B). TCGA‐GTEx databases showed that *SOCS1/2/3/4* expression in normal and GBM tissues. (C–F). GSE16011 data set was used to analyse *SOCS1/2/3/4* expression in normal and tumour tissues. The horizontal line in the middle of each box plot represents the median. When the median in the red box plot is higher than the median in the blue box plot, it means that the gene expression level in the cancer tissue is higher than that in the normal tissue, otherwise it is the opposite. (*p < 0.05; **p < 0.01; ***p < 0.001). *SOCS*, suppressor of cytokine signalling.

TABLE 1.

Expression of SOCS1/2/3/4 in glioma in the Oncomine database.

Name	Types of Glioma vs. Normal	Fold‐change	t‐test	p value	Reference
SOCS1	Glioblastoma vs. Normal	2.133	12.532	5.37E−9	TCGA
SOCS1	Glioblastoma vs. Normal	2.025	8.210	8.83E−12	Sun
SOCS2	Glioblastoma vs. Normal	3.344	11.908	1.36E−18	Mura
SOCS2	Glioblastoma vs. Normal	3.164	15.962	2.01E−14	TCGA
SOCS2	Glioblastoma vs. Normal	3.139	4.714	5.22E−5	Lee
SOCS3	Glioblastoma vs. Normal	5.701	8.703	3.96E−7	Bredel
SOCS4	Anaplastic Astrocytoma vs. Normal	3.062	5.864	4.01E−7	Sun
SOCS4	Oligodendroglioma vs. Normal	2.357	4.628	1.61E−5	Sun

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Abbreviations: GBM, glioblastoma; SOCS, suppressor of cytokine signalling.

The ROC curve to test the value of *SOCS1/2/3/4* to identify GBM tissues was constructed. (A) *SOCS1*; (B) *SOCS2*; (C) *SOCS3*; (D) *SOCS4*. The results were 0.852, 0.962, 0.864 and 0.914, respectively, which suggests that *SOCS1/2/3/4* genes may have the potential to identify GBM samples. (E) Nomogram model predicting the 1‐, 3‐ and 5‐year overall survival in patients with GBM. The nomogram is employed by summing all points identified on the scale for each variable, including gender, race, age, IDH status, Karnofsky performance score and *SOCS1/2/3/4* expression in GBM. The total points projected on the bottom scales reveal the probabilities of 1‐, 3‐and 5‐year overall survival.

3.2. High SOCS1/2/3/4 expression is closely associated with WHO status, histological types, 1p/19q codeletion, primary therapeutic effects and prognosis of patients with GBM

Analysis of the UALCAN database revealed that high SOCS1/2/3/4 expression was associated with the clinicopathological characteristics and prognosis of patients with glioma. High SOCS1 expression was associated with World Health Organization (WHO) status, histological type, IDH status, 1p/19q co‐deletion, age, gender and overall survival (OS). High SOCS2 expression was significantly associated with WHO status, histological type, IDH status, 1p/19q codeletion, age and OS. High SOCS3 expression was positively correlated with WHO status, histological type, IDH status, 1p/19q codeletion, primary therapeutic effects and age and negatively correlated with OS. High SOCS4 expression was associated with WHO status, 1p/19q co‐deletion and primary therapeutic effects (p < 0.05; Table 2) (Figure 3A,B). Furthermore, we analysed the prognostic value of SOCS1/2/3/4 expression in GBM using sangerbox3.0 database, the results revealed that high SOCS3 expression possessed a poorer patient OS, DSS and PFI than that of low SOCS3 expression (p < 0.05; Figure 4), whereas high expression of SOCS1 and SOCS4 in GBM was associated with poor OS and DSS, and high expression of SOCS2 was only associated with poor DSS (Figure 4). These results suggested that SOCS1/2/3/4 were closely associated with clinicopathological features and may be the oncogenes in GBM. In particular, SOCS3 has the greatest prognostic value for GBM patients, which also prepares us for further experimental research on SOCS3.

TABLE 2.

Association between SOCS1/2/3/4 expression and clinicopathological characteristics of patients with glioma in the UALCAN database. SOCS1/2/3/4, suppressor of cytokine signalling 1/2/3/4; G2, G3: low‐grade glioma; G4: GBM, glioblastoma.

Clinicopathologic features		SOCS1 (p value)	SOCS2 (p value)	SOCS3 (p value)	SOCS4 (p value)
WHO status	G2 vs. G3	2.30E−3	4.89E−5	6.05E−8	1.23E−4
	G2 vs. G4	5.86E−52	4.91E−46	1.39E−54	7.03E−3
	G3 vs. G4	8.26E−35	1.15E−25	2.51E−26	1.00
Histological type	Astrocytoma vs. Glioblastoma	5.38E−4	3.53E−5	2.46E−7	0.703
	Astrocytoma vs. Oligoastrocytoma	0.476	0.156	4.52E−2	3.70E−2
	Astrocytoma vs. Oligodendroglioma	1.52E−2	3.68E−7	1.47E−3	5.14E−3
	Glioblastoma vs. Oligoastrocytoma	4.26E−5	4.67E−9	5.38E−11	0.365
	Glioblastoma vs. Oligodendroglioma	2.86E−4	2.48E−12	2.31E−7	0.147
	Oligoastrocytoma vs. Oligodendroglioma	0.583	0.442	0.810	0.990
IDH	WT vs. Mut	1.53E−76	4.81E−59	3.11E−62	0.759
1p/19q. codeletion	Codel vs. noncodel	3.15E−14	3.73E−29	1.94E−19	8.01E−5
Primary therapy outcome	CR vs. PD	7.01E−2	1.70E−4	6.00E−6	1.00
	CR vs. SD	1.00	1.00	0.0659	0.908
	CR vs. PR	0.540	1.00	1.00	1.000
	PD vs. SD	0.120	9.61E−3	6.02E−2	0.627
	PD vs. PR	1.00	0.0796	1.11E−4	0.336
	SD vs. PR	0.750	1.00	6.01E−6	1.00
Gender	Female vs. Male	0.038	0.106	0.606	0.355
Age	<=60 vs. >60	4.92E−20	4.81E−13	1.83E−14	0.149
OS	Alive vs. Dead	2.44E−26	4.9E−25	2.43E−31	0.110
DFS	Alive vs. Dead	0.030	1.90E−3	9.30E−3	0.420

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Abbreviations: CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease.

Expression and clinicopathological parameters of *SOCS1/2/3/4* in patients with glioma in the UALCAN database. A (a–d) Association between expression of *SOCS1/2/3/4* and WHO grade; A (e–h) Association between expression of *SOCS1/2/3/4* and histological grade; A (i–l) Association between expression of *SOCS1/2/3/4* and 1p/19q co‐deletion; B (a–d) Association between expression of *SOCS1/2/3/4* and primary therapy outcome; B (e–h) Association between expression of *SOCS1/2/3/4* and IDH status, gender, age and OS, respectively. G2, G3: low‐grade glioma; G4: GBM, glioblastoma. CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease.

Survival curve analysis of the prognostic significance of high and low SOCS1/2/3/4 expression in glioma using Sangerbox3.0 database. Overall survival: OS, disease‐specific survival: DSS and progression‐free interval: PFI.

3.3. Coexpressed genes of SOCS1/2/3/4 and gene and protein networks

Analysis of TCGA database revealed 224 positively correlated and 230 negatively correlated genes for SOCS1; 380 positively correlated and 66 negatively correlated genes for SOCS2; 876 positively correlated and 470 negatively correlated genes for SOCS3; and 3988 positively correlated and 189 negatively correlated genes for SOCS4. The top 10 coexpressed genes that were positively and negatively correlated with SOCS1/2/3/4 are shown in a heat map (Figure 5A–D). Furthermore, a Venn diagram showed seven intersections of SOCS1/3/4 coexpressed genes (Figure 5E). The PPI network, which was generated by the GeneMANIA website, revealed that 20 potential target genes interacted with SOCS1/2/3/4 (Figure 5F). Next, correlation analysis of SOCS1/2/3/4 was performed using the cBioPortal database, ²⁸ and the results revealed that SOCS1 and SOCS3 were negatively correlated with SOCS4, while other genes were positively correlated (Figure 5G). As SOCS3 has the greatest prognostic value for GBM, we explore the coexpressed genes of SOCS3 in GBM, the Linked Omics database was used to analyse the mRNA sequencing analysis of 153 cases of patients with GBM in TCGA. The results showed that there were more differentially expressed genes positively correlated with SOCS3 than negatively correlated (Figure 5H–J). showed 50 genes that were positively or negatively correlated, respectively, with SOCS3. Notably, SOCS3 was most strongly correlated with BCL3, THBD and TREM1, with Pearson correlation coefficients of 0.6854, 0.6526 and 0.6415, respectively. For example, as a proto‐oncogene, BCL3 is closely associated with the NF‐κB signalling pathway and is a member of the IκB family. Therefore, SOCS3 may be involved in the progression of GBM through the NF‐κB signalling pathway. PPI network analysis showed that there were 20 genes associated with SOCS1/2/3/4, and 10 representatively hub genes were selected via correlation analysis, including CUL5, IL6R, IL6ST, IFNGR1, IFNGR2, IFNAR1, AREL1, MET, EGFR and CISH (Table S1). GEPIA ²⁹ analysis of these 10 genes showed increased expression of IL6ST, IFNGR1, IFNGR2, IFNAR1, EGFR and CISH in GBM (p < 0.05) (Figure S1). In addition, CUL5, IL6R, IFNGR1, IFNGR2, IFNAR1, AREL1, MET, EGFR and CISH were associated with the OS (months) of patients with GBM (Figure S2A). GBM patients with high transcriptional levels of IL6R, IFNGR1, IFNGR2, IFNAR1, MET, EGFR and CISH were significantly associated with short OS, while GBM patients with high transcriptional levels of CUL5 and AREL1 were related to longer OS. On the contrary, IFNGR1, IFNGR2, IFNAR1, AREL1, MET and CISH were associated with the disease‐free progression (DFS) (months) of patients with GBM (Figure S2B). GBM patients with high transcriptional levels of IL6ST, IFNGR1, IFNGR2, IFNAR1, MET and CISH were significantly associated with short DFS, while GBM patients with high transcriptional levels of AREL1 were related to longer DFS.

(A–D) The heat map showed the upregulated and downregulated top 10 differentially expressed genes (DEGs). Positive and negative DEGs of *SOCS1* (A), *SOCS2* (B), *SOCS3* (C), *SOCS4* (D) according to heat map and Venn diagram. (E) Intersection representing DEGs of *SOCS1/2/3/4*. |r| > 0.4 and p < 0.001. (F) Gene network associated with *SOCS1/2/3/4* and interactions between proteins encoded by genes of *SOCS1/2/3/4* using GeneMANIA. (G) Correlation analysis for *SOCS1/2/3/4*. (H) The total significantly associated genes with *SOCS3* distinguished by Pearson test in the TCGA‐GBM cohort. (I,J) Top 50 positively and negatively associated with *SOCS3* in TCGA‐GBM were shown, respectively, via heat maps. Red, positively linked genes; blue, negatively linked genes.

3.4. GSEA analysis

Since SOCS3 has the greatest prognostic value in GBM, we also further performed enrichment analysis for SOCS3, GSEA (gene set enrichment analysis) was performed to identify the functional enrichment of high SOCS3 expression and low SOCS3 expression (Figure 6A). Notably, we found that SOCS3 were associated with IL‐6/JAK/STAT3 signalling pathway in GBM. Our further enrichment analysis of SOCS3 found that high‐risk group was significantly associated with IL‐6/JAK/STAT3 (Figure 6B). In order to confirm this idea, we used siRNA and plasmid to knock down and overexpress SOCS3 in LN229 cells, respectively. Cells were collected to extract proteins, and STAT3 and p‐STAT3 antibodies were incubated with Western blot to observe the protein expression in different treatment groups. The results showed that as a suppressor of cytokine signalling 3, knockdown of SOCS3 significantly increased the expression of p‐STAT3 (Figure 6C), while overexpression of SOCS3 decreased the expression of p‐STAT3 (Figure 6D). The results showed that SOCS3 may regulate the progression of GBM via JAK/STAT3 signalling pathway.

(A) GSEA analysis was employed to explore cancer hallmarks that enriched high and low *SOCS3* expression. (B) GSEA showed that IL‐6/JAK/STAT3 signalling pathway was differentially enriched in *SOCS3*. (C,D) Western blot revealed the relationship between *SOCS3* and JAK/STAT3 signalling pathway.

3.5. Correlation of SOCS1/2/3/4 expression with immune characteristics

Previous studies have shown that tumour infiltrating lymphocytes (TILs) are independent predictors in tumours. ³⁰ The present study analysed the association between SOCS1/2/3/4 gene level and tumour‐infiltrating immune cells by using the XianTao academic (https://www.xiantao.love) and TIMER2.0 databases, and found that SOCS1/2/3/4 affects tumour‐infiltrating immune cells in GBM (Figure 7). As shown in Figure 7, SOCS1/2/3/4 are associated with a wide variety of immune cells. For instance, SOCS1 expression was positively correlated with immune cells, including a DC, neutrophils and cytotoxic cells, but negatively correlated with Tgd cells. SOCS2 expression was negatively correlated with several immune cell types, including Mast cells, Th17 cells and B cells, and was positively correlated with a DC cells. SOCS3 expression was positively correlated with macrophages and neutrophils, but negatively correlated with Tgd cells. Furthermore, SOCS4 expression was negatively correlated with Treg, iDC, macrophage and Th17 cells, while it was positively correlated with T helper cells and Tgd cells. Next, the association between SOCS1/2/3/4 expression and abundance of 28 TILs was analysed via the TISIDB database, and Figures [Link], [Link] show the association between SOCS1/2/3/4 expression and TILs in different types of tumours. In GBM, SOCS1/2/3/4 expression was significantly associated with multiple TILs.

Correlation of *SOCS1/2/3/4* expression with immune infiltration in GBM using XianTao academic and TIMER2.0. (A–D) Correlation between the expression of *SOCS1/2/3/4* and multiples tumour‐infiltrating lymphocytes (TILs) in GBM.

3.6. Analysis of SOCS3 and immunoregulation‐associated genes and immune cell infiltration

The above analysis revealed that SOCS1/2/3/4 expression was significantly correlated with multiple TILs. In order to further analyse the relationship between SOCS3 and immune regulatory genes, immune checkpoint genes and immune cell infiltration, we searched Sangerbox3.0 website based on TCGA and GTEx databases. Since immune checkpoint genes provide important targets for tumour immunotherapy, we specifically analysed the correlation between immunomodulatory genes and immune checkpoint genes and glioma. We found that SOCS3 was positively correlated with most of the related genes in glioma, including immune activation genes, immune checkpoint genes, chemokine genes and chemokine receptor genes (Figure 9A,B). We downloaded the agreed standardized pan‐cancer data set from the UCSC database (http://xenabrowser.net/), ³¹ extracted the expression data of SOCS3 in each sample, further screened the samples, applied log₂ (x + 0.001) transformation to each expression value, extracted the glioma expression profile and mapped the expression profile to GeneSymbol. Stromal, immune and ESTIMATE scores of glioma patients were calculated based on gene expression using the R package ESTIMATE (version 1.0.12, http://bioinformatics.mdanderson.org/public‐software/estimate/). Finally, the immune infiltration scores of glioma samples were obtained, and the corr.tes function of R package psych (version 2.1.6) was used to calculate Pearson's correlation coefficient between SOCS3 and immune infiltration scores in glioma. Finally, SOCS3 was found to be significantly positively correlated with immune infiltration in gliomas, including GBM (Figure 9C–E). Based on the Timer and xCELL databases, we further analysed the correlation between SOCS3 and immune cells in GBM, and the results showed that SOCS3 was positively correlated with macrophages, neutrophils and DC cells, but negatively correlated with B cells (Figure 9F–H), which was consistent with the results in Figure 7C. However, Figure 9G reveals that SOCS3 in GBM is positively correlated with major histocompatibility complex (MHC) and effector cells (EC), while negatively correlated with suppressor cells (SC) and others. Collectively, these results reveal the potential role of SOCS3 and immune‐related genes in GBM and provide a reference for further research by subsequent researchers.

The relationship between *SOCS3* and immunoregulation‐associated genes and immune cell infiltration. (A,B) Analysis of immunomodulatory gene and immune checkpoint gene. (C,D) Analysis of immune cell infiltration. (E–H) Analysis of immune cell via Timer and xCELL.

3.7. SOCS3 expression and epigenetic modification and mutation landscape

To explore the role of SOCS3 in epigenetic modification, SOCS3 was analysed at the epigenetic genome level. We used the cBioPortal database to explore the categories and frequencies of SOCS3 gene alterations in different types of cancer. The results showed that SOCS3 gene amplification and mutation are the main types of genetic alterations, especially hepatocellular carcinoma, breast cancer and melanoma. SOCS3 gene amplification was the main type of genetic alteration in glioma (Figure S7A). The main types of SOCS3 mutations are missense and truncation mutation (Figure S7B). Copy number values were significantly associated with diploid, gain and shallow deletion alterations of SOCS3 (Figure S7C). RNA methylation is a common epigenetic modification and includes N1‐methyladenosine (m1A), cytosine‐5‐methylation (m5C) and N6‐methyladenosine (m6A). We analysed their correlation with SOCS3 expression levels. As shown in Figure 8A, there was a significant positive correlation between SOCS3 and 44 RNA modifications (m1A, ³² m5C ³² and m6A ³² ) in the majority of human gliomas with statistical significance. SOCS3 mutation frequencies were analysed in high and low expression groups in Figure 8B, and 50 genes with the highest mutation frequencies were identified. Mutation frequency reaches more than 10% of genes including TP53 (34.0%), PTEN (31.2%), EGFR (29.8%), TTN (27.0%), MUC16 (15.6%), SPTA1 (12.1%) and NF1 (12.1%), The most common type of mutation is missense mutation. It can be seen that the mutation frequency of TP53, PTEN, EGFR, TTN, MUC16, SPTA1 and NF1 genes is significantly increased, suggesting that SOCS3 may promote the occurrence and development of GBM.

Analysis of epigenetic modification and mutation landscape. (A) Analysis of SOCS3 and epigenetic modification. (B) Analysis of SOCS3 and mutation landscape.

3.8. Verification of SOCS1/2/3/4 mRNA expression in glioma tissue samples

The mRNA expression of SOCS1/2/3/4 in 174 cases of GBM and noncancer tissues was analysed by TCGA database, and the association between SOCS1/2/3/4 mRNA expression and clinicopathological features is shown in Table 3. It was found that age and IDH status were the common influencing factors of SOCS1/2/3/4 mRNA levels. In addition, the SOCS1 mRNA levels were associated with OS and DFS, while the SOCS2 mRNA levels were associated with gender, race, Karnofsky performance score, OS and DFS. The mRNA level of SOCS3 was associated with Karnofsky performance score, OS and DFS, while the mRNA level of SOCS4 was markedly associated with Karnofsky performance score.

TABLE 3.

Association between SOCS1/2/3/4 and clinicopathological parameters in patients with GBM.

Clinicopathological parameters		N	SOCS1 expression (2 − ΔCq)		SOCS2 expression (2 − ΔCq)		SOCS3 expression (2 − ΔCq)		SOCS4 expression (2 − ΔCq)
Clinicopathological parameters		N	Mean ± SD	p value	Mean ± SD	p value	Mean ± SD	p value	Mean ± SD	p value
Gender	Male	109	3.439 ± 0.981	0.221	2.839 ± 1.192	0.007	4.735 ± 1.371	0.172	2.487 ± 0.374	0.285
Gender	Female	59	3.521 ± 1.079	0.366	2.776 ± 1.412	0.003	4.946 ± 1.316	0.152	2.496 ± 0.366	0.457
Age (years)	≤60	87	1.939 ± 0.828	0.077	2.777 ± 1.239	0.019	6.393 ± 1.561	0.034	4.044 ± 0.490	0.010
Age (years)	>60	81	2.197 ± 0.842	0.001	2.859 ± 1.308	0.001	6.567 ± 1.303	0.243	3.995 ± 0.473	0.861
Race	Asian	5	1.847 ± 0.868	0.207	3.016 ± 1.885	0.711	3.922 ± 1.324	0.901	2.867 ± 0.137	0.266
	Black or African American	11	1.982 ± 0.661	0.711	2.163 ± 1.280	0.044	4.480 ± 1.418	0.315	2.323 ± 0.501	0.498
	White	150	2.081 ± 0.858	0.002	2.876 ± 1.239	0.000	4.855 ± 1.351	0.013	2.487 ± 0.359	0.471
Karnofsky performance score	<80	36	2.148 ± 0.755	0.063	2.891 ± 1.264	0.094	4.782 ± 1.209	0.885	2.568 ± 0.345	0.408
Karnofsky performance score	≥80	92	2.009 ± 0.880	0.106	2.906 ± 1.288	0.016	4.893 ± 1.415	0.024	2.493 ± 0.359	0.009
IDH	WT	149	2.141 ± 0.826	0.000	2.899 ± 1.275	0.000	4.942 ± 1.283	0.115	2.468 ± 0.368	0.488
IDH	Mut	12	1.231 ± 0.748	0.022	2.029 ± 0.972	0.159	3.206 ± 1.561	0.023	2.672 ± 0.284	0.023
OS	Alive	32	1.804 ± 0.790	0.314	2.832 ± 1.401	0.018	4.777 ± 1.067	0.642	4.217 ± 0.453	0.178
OS	Dead	136	2.124 ± 0.845	0.001	2.813 ± 1.242	0.002	4.816 ± 1.414	0.025	3.974 ± 0.477	0.231
DFS	Alive	34	1.798 ± 0.788	0.129	2.798 ± 1.366	0.010	4.821 ± 1.048	0.635	2.629 ± 0.382	0.050
DFS	Dead	121	2.090 ± 0.822	0.002	2.790 ± 1.229	0.004	4.813 ± 1.441	0.019	2.456 ± 0.362	0.169

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3.9. Analysis of SOCS1/2/3/4 protein expression level by IHC staining

The HPA database for immunohistochemical staining was employed to explore the protein levels of SOCS1/2/3/4 in glioma samples (low grade and high grade) and normal brain tissue. The results revealed that SOCS1/2/3/4 protein expression in high‐grade glioma tissues were higher than those in LGG and normal tissues. The expression level of SOCS1/2/3/4 in normal tissues was the lowest (Figure 10). These findings suggested that upregulation of SOCS1/2/3/4 may predict advanced malignancies such as GBM.

The immunohistochemistry (IHC) results from the Human Protein Atlas (HPA) was used to explore the protein level of *SOCS1/2/3/4* genes in normal and glioma tissues. The figure revealed the alteration of *SOCS1/2/3/4* protein expression levels in glioma (low grade and high grade) and normal brain tissue.

3.10. Experimental verification of the fact that SOCS3 is highly expressed in GBM cell lines and patients' tissues

To further verify the aforementioned bioinformatics analysis results, a series of experiments were carried out. RNA was extracted from 14 tissue specimens for the PT‐qPCR analysis, and it was found that patients with GBM had higher SOCS3 mRNA expression than those found in normal tissues (Figure 11A). Western blotting was performed on seven human GBM cell lines, and it was found that A172, U87, U118, LN229 and U251 cells had higher SOCS3 expression levels (Figure 11B). Therefore, A172 and LN229 cells were transfected with siRNAs and the knockdown efficiency was statistically significant, particularly for LN229 (Figure 11C,D). IHC staining revealed that SOCS3 was highly expressed in nine of 11 tissues derived from patients with GBM, while SOCS3 expression was low in all 12 patients with LGG (Figure 11E,F). In conclusion, the present study experimentally confirmed that SOCS3 exhibited high mRNA and protein levels in GBM cell lines and tissues derived from patients with GBM.

*SOCS3 mRNA* and protein expression in GBM cell line and human tissue. (A) qRT‐PCR was employed to explore the *mRNA* expression of *SOCS3* in GBM and normal tissues, (B) Western blotting was employed to evaluate the protein expression of *SOCS3* in GBM cell lines, (C,D) A172 and LN229 cells were transfected with siRNA, and the knockdown efficiency was evaluated by Western blotting. The results of each interference experiment were repeated more than three times. (E,F) IHC was used to explore *SOCS3* expression in LGG and GBM human tissue specimens. **p < 0.01.

3.11. Knockdown of SOCS3 attenuates GBM cells proliferation, migration and invasiveness

To identify the functional role of SOCS3 in GBM, LN229 cells with higher transfection efficiency were selected for further experiments. As aforementioned above, siRNA was used for transfection and Western blotting was performed to verify the knockdown efficiency. Numerous studies have revealed that SOCS3 expression is closely associated with the proliferation of tumour cells. ³³ , ³⁴ The current study used SOCS3 as a marker of cell proliferation for knockdown experiments, and detected PCNA expression by Western blotting, which revealed that the expression level of PCNA was reduced (Figure 12A). In addition, transfected cells were plated into 6‐well plates for colony formation assay, and the cell proliferation of groups subjected to different treatments was observed 10 days later. It was found that the cell proliferation of SOCS3 knockdown group was significantly reduced (Figure 12B). Wound healing and Transwell assays revealed that the migration and invasion abilities of LN229 cells with SOCS3 knockdown were markedly reduced (Figure 12C–E). In conclusion, inhibition of SOCS3 expression could reduce the proliferation, migration and invasion of GBM cell lines.

(A,B) Western blotting and colony formation assay were performed to explore the influence of *SOCS3* on GBM cell proliferation. (C–E) Wound healing assay and Transwell assays were employed to explore the influence of *SOCS3* on GBM cell migration and invasion. **p < 0.01, *p < 0.05.

4. DISCUSSION

GBM is a malignant brain tumour with a high mortality rate and a high degree of malignancy. In recent years, the number of studies on molecular biomarkers and their effects on tumour treatment, survival and prognosis has increased. Davis et al revealed the molecules and pathways associated with the pathogenesis of GBM through gene mapping, which may help to develop new treatment methods for GBM. ³⁵

Using TCGA and GEO data sets as well as various databases, we found that SOCS1/2/3/4 were high expressed in brain and CNS cancer and lymphoma, while low expressed in bladder cancer, gastric cancer, liver cancer, lung cancer, ovarian cancer and prostate cancer. To further explore the expression of SOCS protein in brain tumours, TCGA‐GTEx database was used to analyse the mRNA expression levels of SOCS1/2/3/4, respectively, the results demonstrated that SOCS1/2/3/4 expression level was higher in GBM than in normal samples. High mRNA expression was associated with WHO grade, histological type, 1p/19q co‐deletion, therapeutic effect and prognosis. The higher the WHO grade of glioma, the higher the expression of SOCS1/2/3/4 and the worse the survival and prognosis of patients, which was statistically significant in SOCS1/2/3. TCGA database was used to explore SOCS1/2/3/4 coexpressed genes, construct PPI networks and analyse the effects of coexpressed genes on the OS and DFS of patients with GBM. The present study further performed GSEA enrichment analyses of SOCS1/2/3/4 coexpression genes and found that SOCS1/2/3/4 coexpression was mainly involved in JAK/STAT signalling pathway.

The survival and prognosis of glioma patients are closely associated with a variety of clinicopathological parameters. Our study found that the expression of SOCS1/2/3 increased with the increase of WHO grade of glioma, especially the abnormal expression of SOCS3 in high‐grade glioma. The expression of SOCS1/2/3 in GBM was significantly higher in gliomas of different histological classifications than in other histological types, while the expression of SOCS4 was not significantly different in gliomas of different histological classifications. In addition, SOCS1/2/3/4 expression is closely associated with 1p/19q co‐deletion, and SOCS1/3/4 expression is associated with IDH status, age and overall survival of glioma patients. In conclusion, SOCS1/2/3/4 is associated with a variety of clinicopathological parameters in glioma, especially SOCS3, which provides guidance for us to improve the prognosis of glioma patients.

Genetic and epigenetic alterations are known to affect gene expression and may also be associated with adverse clinical outcomes. ²² We mainly analysed the types and frequencies of SOCS3 gene alterations in cancers, and found that gene mutation and amplification were the main types of alterations, and SOCS3 mutations were mainly missense mutations and truncation mutations. In glioma, SOCS3 is positively correlated with 44 RNA modifications (m1A, m5C and m6A), and the most common type of mutation is missense mutation, and the mutation frequency of TP53, PTEN, EGFR, TTN, MUC16, SPTA1 and NF1 genes significantly increased. Overall, SOCS3 may contribute to the occurrence and development of GBM.

The tumour immune microenvironment is associated with the therapeutic and prognostic response of tumours. ³⁶ , ³⁷ The current study revealed that SOCS1/2/3/4 expression was associated with immune cell infiltration in GBM. Specifically, SOCS1 was positively correlated with CD4⁺ T cells, neutrophils, myeloid dendritic cells and other immune cells, but negatively correlated with tumour purity. SOCS2 expression was negatively correlated with CD4⁺ T cells, neutrophils and B cells, and positively correlated with tumour purity. SOCS3 expression was positively correlated with macrophages and neutrophils, but negatively correlated with B cells and tumour purity. SOCS4 expression was negatively correlated with macrophages and NK cells, but positively correlated with CD4+ T cells and tumour purity. The present study found that SOCS1 and SOCS3 expression levels were associated with GBM tumour‐promoting immune cells, while SOCS2 and SOCS4 were not. These results provide knowledge about SOCS3 expression, tumour immunotherapy and prognosis in GBM.

SOCS proteins are an intracellular cytokine‐inducible proteins that inhibit the JAK/STAT signalling pathway. ³⁸ The SOCS protein family consists of eight members, namely SOCS1–SOCS7 and CIS. Each protein member contains an SH2 domain and a specific SOCS box. Notably, SOCS1 and SOCS3 possess a kinase inhibitory region (KIR), which is the main functional domain. ³⁹ The present study revealed that the mRNA and protein expressions levels of SOCS3 in GBM cell lines and patients with GBM were higher than those in normal tissues and LGG. Moreover, high SOCS3 expression was associated with poor overall survival, disease‐specific survival and progression‐free survival in GBM patients. These results suggest that the SOCS family, particularly SOCS3, may be a potential diagnostic and prognostic tool that promotes carcinogenesis in patients with GBM.

At present, SOCS proteins play important roles in regulating tumour progression in a variety of cancers. Zhou et al. found that exosome miR‐155 secreted by melanoma cells promotes the expression of receptor fibroblast proangiogenic factors via the SOCS1/JAK2/STAT3 signalling pathway, thereby inducing the preangiogenic switch of CAFs. Therefore, exosome miR‐155 may be a potential target to control the angiogenesis of melanoma. ⁴⁰ As a METTL3‐mediated m6A modification target, m6A modification of SOCS2 mRNA disappears with METTL3 knockdown, thus promoting the increase of SOCS2 mRNA expression and ultimately inhibiting the progression of hepatocellular carcinoma. ⁴¹ Studies have found that IL‐23 causes the 3'‐UTR binding of miR‐25 and SOCS4 by upregulation of miR‐25 expression, resulting in the inhibition of SOCS4, and ultimately promoting the migration and invasion of thyroid cancer cells. ⁴² Zhang et al. found that SOCS3 promoted cell proliferation and invasion in non‐small cell lung cancer by regulating the expression of Pyk2. ⁴³ It has also been found that EYA2 is downregulated in hepatocellular carcinoma (HCC), and EYA2 can regulate SOCS3 expression in combination with DACH1 transcription, thereby inhibiting HCC progression via SOCS3‐mediated blockade of the JAK/STAT signalling pathway. ⁴⁴ Current studies have found that the high expression of SOCS3 can promote the proliferation of GBM cells. ³⁴ Therefore, we want to further study the other functions of SOCS3, including migration and invasion, and explore the potential mechanisms of SOCS3's possible regulatory role by bioinformatics analysis, so as to prepare for further exploration of SOCS3 mechanisms in GBM in the future.

The current study found that GBM with high SOCS1/2/3/4 expression had a poor prognosis. Our group previously found a potential correlation between SOCS3 expression and GBM. ⁴⁵ The current study verified the presence of high SOCS3 expression in a variety of cell lines through cell experiments, and the RT‐qPCR and IHC analyses of human tissue specimens demonstrated the high expression of SOCS3 in GBM. To further explore the potential role of SOCS3 as a marker for poor prognosis of GBM, SOCS3 was knocked down by siRNA, and it was found that cell lines with reduced SOCS3 expression had lower proliferation levels, and reduced migration and invasion abilities. In conclusion, SOCS3 may be a potential prognostic marker for patients with GBM.

In conclusion, SOCS3 was identified as a predictor of survival in patients with GBM by analysing RNA‐based gene expression profiles from TCGA data sets and online databases. Moreover, the present findings verified high SOCS3 expression in GBM cell lines and human tissues through in vitro experiments, and demonstrated that GBM cell lines with high SOCS3 expression exhibited higher proliferation, migration and invasion abilities.

AUTHOR CONTRIBUTIONS

Lirui Dai: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); resources (lead); validation (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Yongjie Han: Formal analysis (supporting); validation (supporting); writing – review and editing (supporting). Zhuo Yang: Data curation (supporting); formal analysis (supporting); methodology (supporting); resources (supporting). Yuling Zeng: Methodology (equal); validation (equal). Wulong Liang: Conceptualization (supporting); funding acquisition (equal); project administration (supporting); resources (supporting); supervision (supporting); validation (supporting). Zimin Shi: Validation (equal); writing – review and editing (equal). Yiran Tao: Validation (supporting); writing – review and editing (equal). Xianyin Liang: Validation (supporting); writing – review and editing (supporting). Wanqing Liu: Resources (supporting); validation (supporting). Shaolong Zhou: Funding acquisition (equal); resources (supporting). Zhe Xing: Methodology (supporting); validation (supporting). Weihua Hu: Project administration (supporting); supervision (supporting). Xinjun Wang: Conceptualization (lead); funding acquisition (lead); supervision (lead).

FUNDING INFORMATION

The present study was supported by grants from the National Natural Science Foundation of China (grant nos. 81972361 and 81874068) and Medical Science and Technology Project of Henan Province (grant nos. LHGJ20210487 and 222102310039).

CONFLICT OF INTEREST STATEMENT

The authors declare no potential competing interests.

CONSENT

The patients/participants provided their written informed consent to participate in this study.

Supporting information

Figure S1

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Figure S2

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Figure S3

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Figure S4

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Figure S5

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Figure S6

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Figure S7

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Table S1

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Dai L, Han Y, Yang Z, et al. Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma. J Cell Mol Med. 2023;27:2194‐2214. doi: 10.1111/jcmm.17807

DATA AVAILABILITY STATEMENT

All data included in this study are available by contacting the corresponding authors.

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25. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1. doi: 10.1126/scisignal.2004088 [DOI] [PMC free article] [PubMed] [Google Scholar]
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27. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41:D991‐D995. doi: 10.1093/nar/gks1193 [DOI] [PMC free article] [PubMed] [Google Scholar]
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31. Navarro Gonzalez J, Zweig AS, Speir ML, et al. The UCSC genome browser database: 2021 update. Nucleic Acids Res. 2021;49:D1046‐d1057. doi: 10.1093/nar/gkaa1070 [DOI] [PMC free article] [PubMed] [Google Scholar]
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38. Mahony R, Ahmed S, Diskin C, Stevenson NJ. SOCS3 revisited: a broad regulator of disease, now ready for therapeutic use? Cell Mol Life Sci. 2016;73:3323‐3336. doi: 10.1007/s00018-016-2234-x [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Tamiya T, Kashiwagi I, Takahashi R, Yasukawa H, Yoshimura A. Suppressors of cytokine signaling (SOCS) proteins and JAK/STAT pathways: regulation of T‐cell inflammation by SOCS1 and SOCS3. Arterioscler Thromb Vasc Biol. 2011;31:980‐985. doi: 10.1161/atvbaha.110.207464 [DOI] [PubMed] [Google Scholar]
40. Zhou X, Yan T, Huang C, et al. Melanoma cell‐secreted exosomal miR‐155‐5p induce proangiogenic switch of cancer‐associated fibroblasts via SOCS1/JAK2/STAT3 signaling pathway. J Exp Clin Cancer Res. 2018;37:242. doi: 10.1186/s13046-018-0911-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Chen M, Wei L, Law CT, et al. RNA N6‐methyladenosine methyltransferase‐like 3 promotes liver cancer progression through YTHDF2‐dependent posttranscriptional silencing of SOCS2. Hepatology. 2018;67:2254‐2270. doi: 10.1002/hep.29683 [DOI] [PubMed] [Google Scholar]
42. Mei Z, Chen S, Chen C, et al. Interleukin‐23 facilitates thyroid cancer cell migration and invasion by inhibiting SOCS4 expression via MicroRNA‐25. PLoS One. 2015;10:e0139456. doi: 10.1371/journal.pone.0139456 [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Zhang S, Wang W, Wang E, Qiu X. SOCS3 expression is inversely correlated with Pyk2 in non‐small cell lung cancer and exogenous SOCS3 inhibits proliferation and invasion of A549 cells. Pathology. 2012;44:434‐440. doi: 10.1097/PAT.0b013e328354ffdf [DOI] [PubMed] [Google Scholar]
44. Liu ZK, Li C, Zhang RY, et al. EYA2 suppresses the progression of hepatocellular carcinoma via SOCS3‐mediated blockade of JAK/STAT signaling. Mol Cancer. 2021;20:79. doi: 10.1186/s12943-021-01377-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Dai L, Tao Y, Shi Z, et al. SOCS3 acts as an Onco‐immunological biomarker with value in assessing the tumor microenvironment, pathological staging, histological subtypes, therapeutic effect, and prognoses of several types of cancer. Front Oncol. 2022;12:881801. doi: 10.3389/fonc.2022.881801 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1

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Figure S2

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Figure S3

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Figure S4

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Figure S5

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Figure S6

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Figure S7

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Table S1

Click here for additional data file.^{(12.2KB, docx)}

Data Availability Statement

All data included in this study are available by contacting the corresponding authors.

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PERMALINK

Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma

Lirui Dai

Yongjie Han

Zhuo Yang

Yuling Zeng

Wulong Liang

Zimin Shi

Yiran Tao

Xianyin Liang

Wanqing Liu

Shaolong Zhou

Zhe Xing

Weihua Hu

Xinjun Wang

Abstract

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Oncomine analysis

2.2. Timer2.0 analysis

2.3. Analysis of UALCAN

2.4. SangerBox analysis

2.5. Analysis of biological function

2.6. Human Protein Atlas (HPA) analysis

2.7. Linked Omics database analysis

2.8. TISDB analysis

2.9. Genetic alterations of SOCS1/2/3/4 in GBM

2.10. Clinical tissue specimens

2.11. Cell cultures and transfections

2.12. RT‐qPCR assay

2.13. Western blotting

2.14. Colony formation assay

2.15. Transwell assay

2.16. Wound healing assay

2.17. Immunohistochemical staining analysis

2.18. Statistical analysis

3. RESULTS

3.1. SOCS1/2/3/4 expression is increased in GBM

FIGURE 1.

TABLE 1.

FIGURE 2.

3.2. High SOCS1/2/3/4 expression is closely associated with WHO status, histological types, 1p/19q codeletion, primary therapeutic effects and prognosis of patients with GBM

TABLE 2.

FIGURE 3.

FIGURE 4.

3.3. Coexpressed genes of SOCS1/2/3/4 and gene and protein networks

FIGURE 5.

3.4. GSEA analysis

FIGURE 6.

3.5. Correlation of SOCS1/2/3/4 expression with immune characteristics

FIGURE 7.

3.6. Analysis of SOCS3 and immunoregulation‐associated genes and immune cell infiltration

FIGURE 9.

3.7. SOCS3 expression and epigenetic modification and mutation landscape

FIGURE 8.

3.8. Verification of SOCS1/2/3/4 mRNA expression in glioma tissue samples

TABLE 3.

3.9. Analysis of SOCS1/2/3/4 protein expression level by IHC staining

FIGURE 10.

3.10. Experimental verification of the fact that SOCS3 is highly expressed in GBM cell lines and patients' tissues

FIGURE 11.

3.11. Knockdown of SOCS3 attenuates GBM cells proliferation, migration and invasiveness

FIGURE 12.

4. DISCUSSION

AUTHOR CONTRIBUTIONS

FUNDING INFORMATION

CONFLICT OF INTEREST STATEMENT

CONSENT

Supporting information

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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