SIN1 facilitates glioma progression and is associated with the KRAS/ERK pathway

Abstract

Purpose

Glioma is the most common primary brain and spinal cord tumor, with effective treatments still lacking. Stress-activated protein kinase-interacting protein 1 (SIN1) has been reported to be upregulated in various tumor types, contributing to tumorigenesis. However, its specific role in glioma remains unclear. This study aimed to investigate SIN1’s expression, clinical significance, biological functions, and underlying molecular mechanisms in glioma.

Methods

SIN1 expression and its association with clinicopathological features and prognosis were analyzed using data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). SIN1 levels were quantified in glioma tissues and cell lines. The functional roles of SIN1 were evaluated by silencing or overexpressing it in glioma cells. RNA sequencing was used to identify downstream pathways, which were further validated through in vitro experiments. Additionally, the relationship between SIN1 and immune cell infiltration was investigated.

Results

SIN1 is aberrantly upregulated in glioma, significantly correlating with adverse clinicopathological features and poor patient prognosis. Functional studies reveal that SIN1 upregulation enhances glioma cell proliferation and migration while suppressing apoptosis. Mechanistically, SIN1 exerts its oncogenic effects might be through the KRAS4A/ERK pathway. Furthermore, SIN1 expression is associated with altered immune cell infiltration within the tumor microenvironment.

Conclusion

This study identifies SIN1 as a critical oncoprotein in glioma, upregulated in tumors and associated with aggressive features and poor survival. It drives tumor progression by enhancing proliferation/migration and suppressing apoptosis might via the KRAS4A/ERK pathway, while potentially modulating immune infiltration. These findings highlight SIN1’s promise as a novel therapeutic target.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11060-025-05166-y.

Introduction

Glioma is the most common primary tumor of the brain and spinal cord, accounting for 40-50% of all brain tumors [1]. Despite its relatively low incidence rate (6.57 per 100,000 individuals in the United States) [2], glioma exhibits a significantly high mortality rate. Generally, Gliomas can be divided into low-grade glioma (LGG) and high-grade glioma (HGG). Glioblastoma (GBM), categorized as HGG, is the most aggressive primary brain tumor. Approximately 57% of newly diagnosed gliomas and 48% of primary central nervous system (CNS) malignant tumors are GBMs, which are among the most lethal and prone-to-recurrence malignant solid tumors [3]. GBM has a very poor prognosis, with a 5-year survival rates of only 5.6%, and a median survival time from initial diagnosis of merely 15 months [2]. Although various therapeutic strategies have been developed—ranging from conventional methods such as chemotherapy, radiotherapy, and surgery to innovative approaches including tumor-treating fields, gene therapy, immunotherapy, and phototherapy [4]—treating glioma remains a significant challenge.

Stress-activated protein kinase interacting protein 1 (SIN1), also known as Mitogen-activated protein kinase-associated protein 1 (MAPKAP1), is a key component of Mammalian Target of Rapamycin Complex 2 (mTORC2). SIN1 is essential for maintaining the integrity and activity of mTORC2 complex [5]. It plays a crucial role in the mTOR pathway, which regulates various biological processes, including cell growth, metabolism, and immunity [6]. The activity of mTORC2 is regulated by Phosphatidylinositol 3-kinase (PI3K) and growth factor receptors [7]. SIN1 phosphorylates and activates Ak strain Transforming (AKT), a downstream effector, thereby regulating diverse cellular functions [8].

Previous research has demonstrated that SIN1 is aberrantly overexpressed and drives tumorigenesis in various types of tumors, exhibiting significant pathological features [8]. SIN1 has been shown to promote proliferation and metastasis in many type cancers, including papillary thyroid carcinoma [9], non-small cell lung cancer [10], hepatocellular carcinoma [11], osteosarcoma [12], prostate cancer [13], cervical squamous cell carcinoma [14], breast cancer [15], and medulloblastoma [16]. Limited data suggest that enhanced mTORC2 activity can stimulate proliferation, migration, and invasion in GBM [17], and that the phosphorylation of Yes-Associated Protein (YAP) by mTORC2 promotes the growth and invasion of glioblastoma [18]. However, the specific role and potential molecular mechanisms of SIN1 in glioma development remain unclear.

Here, we demonstrate that SIN1 is significantly upregulated in both LGG and GBM according to the TCGA dataset. This upregulation was further validated in glioma tissues and cell lines. Notably, SIN1 overexpression correlates closely with poor clinicopathological features and negatively impacts patient prognosis, suggesting its potential as a prognostic indicator and diagnostic marker for glioma. Functional studies reveal that SIN1 knockdown inhibits glioma cell proliferation and migration while promoting apoptosis. Conversely, SIN1 overexpression enhances these processes and suppresses apoptosis. Additionally, SIN1 knockdown suppresses the growth of primary glioma cells. Mechanistically, we demonstrate that SIN1 might promote glioma progression via the Kirsten Rat Sarcoma Viral Oncogene Homolog isoform 4 A (KRAS4A)/ extracellular regulated protein kinase (ERK) signaling pathway. Finally, single-cell sequencing analysis suggests that SIN1 may play a role in modulating immune infiltration.

Materials and methods

Human tissues and clinical data source

96 glioma tissues that all be identified by pathologists according to 2016 World Health Organization (WHO) classification criteria from the Department of Pathology of the Affiliated Hospital of Xuzhou Medical University between 2016 and 2017 were collected. The ethical review and approval were obtained from the institutional ethics committee of Affiliated Hospital of Xuzhou Medical University (ethical review no. XYFY2018-KL056-01). RNA sequencing (RNA-seq) data of GBM and low-grade glioma (LGG) tissues (n = 689) from The Cancer Genome Atlas (TCGA) database and normal tissues (n = 1157) from Genotype Tissue Expression (GTEx) database were collected. RNA-seq data of glioma tissues after deletion of incomplete data (batch I: n = 413; batch II: n = 273) from Chinese Glioma Genomes Atlas (CGGA) were also used for the clinical analysis.

Cell culture

The human GBM cell lines (U118, U87, U251, T98G and LN229), human normal brain glial cells (HEB) were originally obtained from the American Type Culture Collection (ATCC). The cells were cultured in Dulbecco’s modification of Eagle’s medium (DMEM, Keygen Biotech, China) supplemented with 10% fetal bovine serum (FBS, Takara, Japan) at 37 ℃ in a humid atmosphere containing 5% CO₂.

Stable knockdown and overexpressed cell lines construction

The shRNAs targeting SIN1 (shSIN1-#1: GCCCATTCATAAGTTTGGCT; shSIN1-#2: GCTCATCTGCTGGCAGTAT) and the negative scrambled control shRNA (shCTRL: CCTAAGGTTAAGTCGCCCTCG) were constructed into pSLenti-U6-shRNA-CMV-EGFP-F2A-Puro-WPRE vector (OBiO, China). Glioma cell lines U251 and U118 were transduced with these lentiviruses and subsequently selected with puromycin (Sigma, USA) for 2 weeks to achieve stable SIN1 knockdown. The efficiency of the knockdown was assessed using immunoblotting and quantitative real-time PCR.

The coding sequence (CDS) of SIN1 (NM_001006617) was cloned into a PiggyBac Transposon Vector. Glioma cell lines U251 and U118 were transduced with the vector by HighGene transfection reagent (ABclonal, China) and subsequently selected with puromycin (Sigma, USA) for 2 weeks to achieve SIN1 overexpression. The efficiency of SIN1 overexpression was assessed using immunoblotting and quantitative real-time PCR.

Immunohistochemistry (IHC)

Glioma tissues were fixed in 4% paraformaldehyde, embedded in paraffin and cut into 4 μm sections. The sections were deparaffinized in xylene and rehydrated in a series of graded ethanol. For antigen retrieval, sections were autoclaved in sodium citrate buffer in a pressure cooker for 3 min. Before blocking, the sections were treated with fresh 3% hydrogen peroxide for 10 min. Then the sections were blocked with 10% normal goat serum for 15 min at room temperature (RT). Next, sections were incubated with specific diluted primary antibody against SIN1 (Proteintech, 15463-1-AP, China) overnight at 4℃, followed by incubation with secondary antibodies for 1 h at room temperature. Thereafter, sections were incubated for 2–5 min at room temperature with freshly prepared diaminobenzidine (DAB) and stained with hematoxylin. Slide images were captured using the Olympus microscopy and independently scored by two experienced pathologists blinded to patients’ characteristics.

Scores were calculated on intensity and percentage of positive staining tumor cell nuclei or cytoplasm in the whole tissue stains, which were evaluated according to Fromowitz Standard. Intensity score was defined as follows: 0, negative; 1, weak; 2, moderate; 3, strong. The percentage of positive cells was evaluated using the following scoring system: 0 for 0% staining; 1 for 1–24% staining; 2 for 25–49% staining; 3 for 50–74% staining; and 4 for 75–100% staining.

Hematoxylin-eosin (H&E) staing

The H&E staining was performed using H&E staining kit (Beyotime, China) according to the manufacturer’s instruction. Briefly, tissue sections were first deparaffinized in xylene for 10 min, followed by rehydration through a series of graded alcohols (100%, 90%, 80% and 70%) and rinsed in distilled water. The sections were then stained with hematoxylin for 8 min, followed by a rinse in tap water. To differentiate the staining, the slides were briefly dipped in acid alcohol slow differentiation slolution (Beyotime, China) for 10 s and rinsed again in tap water for 10 min. After thorough rinsing in tap water, eosin staining was carried out for 1 min. Finally, the sections were dehydrated through graded alcohols (70%, 80%, 90% and 100%), cleared in xylene, and mounted with a coverslip using a resinous mounting medium. All stained sections were observed under a light microscope for histopathological analysis.

Quantitative real-time PCR

Total RNA was extracted from cells with TRIzol (Invitrogen) according to the manufacturer’s instruction. cDNA was synthesized from RNA by reverse transcription using HiScript III RT SuperMix for qPCR (+ gDNA wiper) purchased from Vazyme. Quantitative real-time PCR was carried out on an ABI-7500 using TB Green™ Premix Ex Taq™ (Tli RNaseH Plus) (TAKARA). For qPCR, the designed primers are: SIN1-F, 5’-CCTCTGCAGCTGAATAACCC-3’, SIN1-R, 5’-GAGTGCAGAGGGAGGTAGAC-3’; β-Actin-F, 5’-CTGGAACGGTGAAGGTGACA-3’, β-Actin-R, 5’- AAGGGACTTCCTGTAACAACG.

CA -3’. β-Actin was used as an endogenous control and relative gene expression was calculated using the 2^−ΔΔCt method.

Western blot analysis

Cells were lysed in cold RIPA extraction reagent (50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 0.5% sodium deoxycholate, 1% NP-40, and 0.1% SDS) supplemented with protease inhibitor (Roche, German). Total protein concentration was measured with Enhanced BCA Protein Assay kit (Beyotime, China) following the manufacture’s instruction. Protein lysates were separated using 10–12.5% polyacrylamide gel electrophoresis and transferred to PVDF membrane (Millipore, USA). The membrane was blocked with 5% BSA in TBST for 1 h at room temperature, then incubated with a primary antibody at 4℃ overnight. Then the membrane was washed three times using 0.1% TBST buffer for 30 min and incubated with HRP-conjugated secondary antibodies for 1 h at room temperature, followed by three times TBST buffer washing for 30 min. The HRP-conjugated secondary antibody was detected and visualized by ECL reagent (GE Healthcare, USA). The following antibodies were used in western blotting: anti SIN1 (Proteintech, 15463-1-AP, China), anti ERK (Proteintech, 11257-1-AP, China), anti P-ERK (Proteintech, 28733-1-AP, China), anti β-tubulin (Proteintech, 66240-1-lg, China) and anti GAPDH (HUABIO, ET1601-4, China).

Immunofluorescence staining

First, slides were treated with 0.05 mg/mL poly-L-lysine for 2 h, and then cells were seeded. Next, slides with grown-on cells in culture plates were rinsed three times with 1 mL of room-temperature PBS, with a 5 min incubation each time. Then, cells were fixed with 500 µL of 4% paraformaldehyde for 20 min. After that, slides were washed three times with 1 mL PBS, 5 min per wash. Next, 500 µL of 0.1% Triton X-100 was added for permeabilization at 4 °C for 5 min. Then, slides were washed three times with 1 mL PBS again, 5 min each. After removing PBS, 500 µL of 3% BSA was added and incubated at room temperature for 30 min to block nonspecific binding. The blocking solution was then aspirated without subsequent washing. Next, 50 µL of primary antibody diluted in 1% BSA was added, and the slides were placed in a humidified chamber at 4 °C overnight. Then, slides were washed three times with PBS, 5 min each. After removing the liquid on the slides, the diluted fluorescent secondary antibody (1:1000 in 1% BSA) was added and incubated at 37 °C for 1 h in a humidified chamber. After another three 5 min PBS washes, the slides were rinsed briefly in 50 mL water. The liquid was then removed, and the slides were mounted with antifade mounting medium containing DAPI. After mounting, slides were left at room temperature in the dark for over 2 h. Once the mounting medium dried, slides were stored at 4 °C in the dark. Finally, after mounting was complete, images were acquired promptly using a fluorescence microscope. The following primary antibodies were used: SIN1 (Proteintech, 15463-1-AP, China), KRAS4A (Proteintech, 12063-1-AP, China).

Co-immunoprecipitation (Co-IP)

Cells were harvested and washed with cold phosphate-buffered saline (PBS) before being lysed in a buffer containing protease inhibitors. The lysate was centrifuged at 14,000 rpm for 10 min at 4 °C, and the supernatant was collected. To reduce non-specific binding, the supernatant was pre-cleared with protein A/G agarose (MCE, HY-K0230, USA) beads for 30 min at 4 °C. Subsequently, a specific antibody against the target protein was added, and the mixture was incubated overnight with gentle rotation at 4 °C. Afterward, protein A/G agarose beads were added to capture the antibody-protein complexes, followed by 2 h incubation. The beads were washed three times with cold lysis buffer to eliminate non-specific interactions, and the bound proteins were eluted using an SDS-containing elution buffer. The eluted proteins were then analyzed by SDS-PAGE and Western blotting to confirm the presence of the target protein and its interacting partners. The following antibodies were used in western blotting: anti SIN1 (Proteintech, 15463-1-AP, China), anti KRAS4A (Proteintech, 12063-1-AP, China).

CCK-8 assay

Cell viability was measured using Cell Counting Kit-8 (APExBIO, USA) according to the manufacturer’s instruction. Cells were plated at a density of 7 × 10³ cells per well in 96-well plates with six replicates. Then, one hundred microliters of serum-free cell culture medium containing 10 µL WST-8 reagent was added into each well at desired time points and the plates were incubated in cell culture incubator for 3 h. Optical absorbance of each well at 450 nm was measured with a microplate reader. At least three independent experiments were performed for quantification.

Transwell assay

Transwell assay was performed using the 8 μm transwell chambers (BD Biosciences, San Jose, CA) according to the manufacturer’s instructions as previously described [19]. Briefly, U251 and U118 cells (1 × 10⁵ cells/well) were suspended in DMEM without FBS being plated into the upper chamber, and 10% serum-containing DMEM was added to the lower chamber. The chambers were then incubated at 25℃ for 24 h. After incubation, the unmigrated cells on the upper side of the membrane were wiped with a cotton swap, while the migrated cells on the bottom side of the membrane were fixed by 4% PFA and stained with crystal violet (Beyotime, China). The stained cells were imaged (3 images/well) and manually counted from the images.

Flow cytometry

For cell cycle analysis, cell cycle detection kit (Keygen Biotech, China) was used according to the manufacturer’s protocol. In brief, the collected suspended cells (1 × 10⁶ cells/mL) were fixed in 70% ice-cold ethanol overnight. Then the cells were washed and incubated with 500 µL staining buffer (RnaseA: PI = 9: 1) in dark for 30 min at room temperature. Samples were analyzed by the BD FACS Canto II Cytometer (Germany) immediately. At least three independent experiments were performed for quantification.

For apoptosis analysis, Annexin-V-Alexa Fluor 647/PI apoptosis detection kit (Fcmacs, China) was used according to the protocol of manufacturer. In brief, cells were harvested using trypsin without EDTA to produce the single cell suspension. Then cells were resuspended in 100 µL 1 × binding buffer at a concentration of 1 × 10⁶ cells/mL, followed by staining with 5 µL Annexin-V-Alexa Fluor 647 and 10 µL 20 µg/mL Propidium Iodide in dark for 15 min at room temperature. Flow cytometry analyzed the samples immediately (FACS Canto II, Germany). At least three independent experiments were performed for quantification. For SIN1-overexpressing U251 and U118 cells treated with 120 µM and 400 µM Temozolomide (TMZ), respectively, for three days, apoptosis levels were assessed.

Glioma xenografts and in vivo imaging system

Lentivirus carrying luciferase, sourced from OBiO, China, was used to infect both SIN1 stably knockdown and negative control U118 cells. Cell lines with stable GFP-Luc expression were systematically isolated. The BALB/c Nude mice were randomly divided into two groups, each group consisting of eight mice. Well-growing luciferase labeled SIN1 stable knockdown and control U118 glioma cells were prepared. Each mouse was inoculated with 1 × 10⁶ cells in 5 µL PBS. All mice were anesthetized with 1.5% pentobarbital sodium (6 µL/g). 1 × 10⁶ cells in 5 µL ice-cold PBS were subsequently injected into the right cerebral cortex (2 mm to the right of the anterior fontanel, 1 mm anterior to the coronal suture, and 3 mm beneath the skull). The growth of the tumor was monitored using Night OWL II LB 983 in vivo imaging system bioluminescent Imaging (Berthold Technologies, Germany). After ether anesthesia, the mice were euthanized by cervical dislocation, and brain tissue was collected for H&E staining.

Bioinformatic analysis

The expression analyses were conducted via R package “ggplot2”, “stats” and “car”. Univariate and multivariate Cox regression analyses were conducted via R package “survival”. The ROC curve was plotted via R package “ggplot2” and “pROC”.

Survival analysis

The survival analysis of glioma between SIN1 high-expression and low expression groups was performed by Kaplan–Meier method and Cox regression. The Kaplan–Meier survival curves were plotted via R package “ggplot2”, “survival” and “survminer”.

Protein-protein interaction (PPI) network analysis

The PPI network analysis was acquired using the STRING website (https://string-db.org/). To conduct the analysis, the protein name “SIN1” and the organism “Homo sapiens” were searched. The following basic settings were applied: network type was set to “full STRING network,” the meaning of network edges was defined as “evidence,” active interaction sources were selected as “experiments,” the minimum required interaction score was set to “medium confidence (0.400),” and the maximum number of interactors displayed was limited to “no more than 20 interactors in the first shell.”

Transcriptome sequencing analysis

Total RNA of SIN1 silenced U251 was extracted by Trizol reagent (Invitrogen) and sequenced by using Illumina HiSeq2500 (Gene Denovo Biotechnology, Guangzhou, China). RNA sequencing data were deposited in the Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra), the accession number was PRJNA1206313. The volcano plot of differentially expressed genes was generated using R package “EnhancedVolcano”. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on differentially expressed genes (DEGs) using the R package “clusterProfiler” to elucidate the potential biological functions and signaling pathways influenced by SIN1. The GO analysis included assessments of biological processes (BP), cellular components (CC), and molecular functions (MF). Additionally, Gene Set Enrichment Analysis (GSEA) was conducted to explore pathways associated with SIN1, utilizing the MSigDB Collection (c2.cp.v7.2.symbols.gmt) within the clusterProfiler R package.

Single-cell RNA sequencing analysis

The single-cell data of glioma was obtained from GEO database (GSE131928) [20]. The single-cell gene expression was further analyzed using the open-source single-cell sequencing database: Single Cell Portal.

Immune infiltration analysis

The RNA-seq expression data and clinical data were obtained from TCGA database, the values were further log-transformed (log2 (value + 1)). The immune infiltration levels corresponding to glioma were calculated based on Single-Sample GSEA (ssGSEA) algorithm provided in the R package “GSVA” and markers of immune cell types [21]. The lollipop and scatter plots were generated using the R package “ggplot2”. The correlation analysis was performed by chi-square (χ²) test, Pearson’s correlation, or Spearman’s correlation analysis.

Statistical analysis

Statistical analyses were performed with GraphPad Prism software (version 9.5.0, Graphpad Software, Inc.). The statistical test for two groups used student’s t-test, for more than two groups used one-way ANOVA or Kruskal-Wallis test. Statistical acquired from TCGA were merged and conducted by R (4.2.1).

Results

SIN1 was aberrantly upregulated in glioma and closely related to the poor clinicopathologic features of glioma

To investigate the expression and distribution of SIN1 in glioma, we analyzed data from the GEPIA database, which provides gene expression profiles for a range of tumor samples and their paired normal tissues. Our comparative analysis demonstrated that SIN1 is aberrantly overexpressed in both GBM and LGG (Fig. 1A). Additionally, immunohistochemical staining of glioma tissue samples revealed markedly higher SIN1 expression in glioma tissues compared to adjacent non-cancerous tissues (Fig. 1B, C). Notably, SIN1 expression was significantly elevated in high-grade gliomas compared to low-grade gliomas (Fig. 1D), suggesting an association with clinical progression. We further quantified SIN1 protein levels in normal brain glial cells (HEB) and five glioma cell lines (U87, U118, LN229, T98G, and U251) using Western blot analysis (Fig. 1E). Our results showed elevated SIN1 protein levels in these glioma cell lines compared to normal brain glial cells. Collectively, these findings indicate that SIN1 is abnormally overexpressed in glioma.

Fig. 1.

SIN1 is aberrantly overexpressed in glioma (A) The expression of SIN1 mRNA was analyzed across diverse normal human tissues and tumor tissues obtained from the TCGA and GTEx projects. (B) Representative images of SIN1 immunohistochemical staining in grade I glioma tissue, grade II glioma tissue, grade III glioma tissue, grade IV glioma tissue and para-tumor tissue. Scale bar: 50 μm. (C) SIN1 protein levels were increased in glioma tissues compared to para-tumor tissues (D) SIN1 protein levels in high-grade glioma tissues were significantly higher than low-grade glioma tissue. (E) The protein levels of SIN1 were examined in five glioma cell lines and HEB by western blotting analysis. GAPDH was used as loading control. The band intensity was determined by densitometry by using ImageJ and normalized to β-Tubulin. HEB, human normal brain glial cells. ns, no significance, *p < 0.05, **p < 0.01, and ***p < 0.001

The positive correlation observed between SIN1 levels and the WHO grade in glioma tissues suggests that SIN1 may play a crucial role in determining the clinicopathological features of glioma patients. To investigate the clinical implication of SIN1 expression, we categorized patients with GBM and LGG from the TCGA database into high and low SIN1 expression groups using the median value as the cutoff. As shown in Table S1, there was a significant association between SIN1 overexpression and several clinicopathological features, including WHO grade, isocitrate dehydrogenase (IDH) status, 1p/19q codeletion, and patient age. Furthermore, we evaluated the relationship between SIN1 expression and overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) events in GBM and LGG patients. Our results revealed that SIN1 expression was significantly higher in dead patients compared to alive patients (Fig. S1A). Additionally, we analyzed the SIN1 expression in human GBM and LGG samples from the TCGA database, finding SIN1 levels were markedly elevated in patients with poor clinicopathological features, such as higher WHO grades (G2 vs. G3 vs. G4), IDH wild-type status, and absence of 1p/19q co-deletion (Fig. S1B). These findings were further corroborated using data from the CGGA database (Fig. S1C, D). Collectively, these results demonstrate that SIN1 expression is strongly associated with poor clinicopathological features in glioma patients.

To investigate the prognostic value of SIN1 in glioma, we divided glioma samples from TCGA database into high-risk and low-risk groups using the median risk score as the threshold. The results demonstrated that patients in the high-risk group exhibited worse prognosis and higher mortality rates (Fig. S2A). Furthermore, glioma sample data from TCGA and CGGA databases were categorized into SIN1 high-expression and low-expression groups based on the median expression level of SIN1. Kaplan-Meier survival analysis revealed that the OS, DSS, and PFI survival rates in the TCGA database were significantly negatively correlated with SIN1 expression levels (Fig. S2B). Similarly, in two independent datasets from the CGGA database, OS was markedly lower in the SIN1 high-expression group compared to the low-expression group (Fig. S2C, D). ROC curve analysis confirmed that SIN1 has a high diagnostic value in gliomas, with an area under the curve (AUC) of 0.937 (Fig. S2E). In conclusion, these findings indicate that SIN1 may serve as a valuable prognostic biomarker for gliomas.

SIN1 promotes proliferation and migration of glioma cells

To study the pathological function of SIN1 in glioma, we employed shRNA to suppress SIN1 expression in U118 and U251 glioma cell lines, both of which exhibit high endogenous levels of SIN1 (Fig. 1E). SIN1 expression was significantly reduced in both cell lines (Fig. 2A, B). We observed that SIN1 knockdown markedly inhibited cell proliferation in both U118 and U251 cells (Fig. 2C). Flow cytometry analysis revealed that SIN1 knockdown induced cell cycle arrest in the G2/M phase, thereby suppressing proliferation in these cells (Fig. 2D). Furthermore, SIN1 suppression attenuated migration (Fig. 2E) and enhanced apoptosis in U118 and U251 cells (Fig. 2F).

Fig. 2.

SIN1 knockdown inhibits the proliferation and migration of glioma cells (A) qRT-PCR was used to evaluate the knockdown efficiency of SIN1 shRNA in U251 and U118 cells. β-actin was selected as reference gene. (B) Western blot was used to evaluate the protein levels of SIN1 in SIN1 knockdown U251 and U118 cells. GAPDH was used as the loading control. The band intensity was determined by densitometry by using ImageJ and normalized to GAPDH. (C) The cell viability of SIN1 knockdown U251 and U118 cells at 24, 48 and 72 h were assessed using the CCK8 assay. (D) Cell cycle distribution of SIN1 knockdown U251 and U118 cells was assessed by flow cytometry. (E) The migration ability of SIN1 knockdown U251 and U118 cells were assessed by transwell assay. Scale bar: 50 μm. (F) Apoptosis of SIN1 knockdown U251 and U118 cells were measured by Flow Cytometry. ns, no significance, *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001

Conversely, overexpression of SIN1 in U118 and U251 cells (Fig. 3A, B) significantly promoted cell proliferation, as indicated by increased cell viability (Fig. 3C). Flow cytometry analysis demonstrated a notable increase in the proportion of cells in S-phase compared to controls (Fig. 3D). Additionally, SIN1 overexpression substantially facilitated migration (Fig. 3E) and decreased apoptosis in these glioma cells (Fig. 3F). Notably, even in the presence of TMZ, SIN1 overexpression continued to suppress apoptosis relative to the control group (Fig. S3). Collectively, our findings suggest that SIN1 plays a critical role in promoting proliferation and migration while inhibiting apoptosis, thus contributing to glioma progression.

Fig. 3.

Overexpression of SIN1 promotes the proliferation and migration of glioma cells (A) qRT-PCR was used to evaluate the overexpress efficiency of SIN1 in U251 and U118 cells. β-actin was selected as reference gene. (B) Western blot was used to evaluate the protein levels of SIN1 in SIN1 overexpressed U251 and U118 cells. β-tubulin was used as the loading control. The band intensity was determined by densitometry using ImageJ and normalized to β-tubulin. (C) The cell viability of SIN1 overexpressed U251 and U118 cells at 24, 48 and 72 h was assessed using the CCK8 assay. (D) Cell cycle distribution of SIN1 overexpressed U251 and U118 cells were assessed by flow cytometry. (E) The migration ability of SIN1 overexpressed U251 and U118 cells were assessed by transwell assay. Scale bar: 50 μm. (F) Apoptosis of SIN1 overexpressed U251 and U118 cells were measured by Flow Cytometry. ns, no significance, *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001

Silencing SIN1 inhibits the growth of primary glioma

To investigate the effect of SIN1 on the in vivo growth of intracranial primary gliomas, we established an intracranial glioma model using luciferase-labeled U118 cells with SIN1 knockdown. In vivo imaging analysis revealed that SIN1 silencing significantly suppressed the growth of orthotopic gliomas in this model (Fig. 4A, B). Furthermore, histological examination of brain sections from tumor xenografts, stained with H&E, confirmed that SIN1 knockdown substantially inhibited the growth of orthotopic gliomas (Fig. 4C).

Fig. 4.

SIN1 knockdown suppressed the growth of primary glioma (A) Luciferase-labeled SIN1 knockdown U118 cells were used to establish the intracranial glioma model, with tumor growth monitored in vivo through bioluminescent imaging. Representative bioluminescent images of the U118 tumor-bearing mice are shown. (B) Quantification of tumor growth in U118 tumor-bearing mice. The dashed box indicates the tumor tissue. (C) Tumors were collected for further H&E staining after U118 tumor-bearing mice were sacrificed. ns, no significance, **p < 0.01

KEGG pathway and GO term enrichment analysis of SIN1 in glioma

We aimed to characterize the functional and physical interaction partners of SIN1. To this end, we first analyzed the protein–protein interaction network involving SIN1 using STRING, as shown in Fig. 5A, which visually presents potential interacting proteins such as MTOR, AKT1, PRKDC, KRAS and others. This network provides a broader biological context for SIN1’s role and highlights its potential involvement in key signaling pathways relevant to glioma, including the mTOR, RAS pathway. These interactions suggest that SIN1 may contribute to glioma progression through these pathways, offering a foundation for further investigation into its specific mechanisms in glioma. To validate the potential functional role of SIN1, RNA-Seq analysis was performed on U251 cells transfected with either a negative control or SIN1-specific knockdown constructs. A volcano plot depicting differentially expressed genes (DEGs) is presented in Fig. 5B. Based on these DEGs, GO and KEGG enrichment analyses were performed to elucidate the biological role of SIN1 in glioma.

Fig. 5.

Function and pathway enrichment analysis of SIN1 in glioma (A) The protein–protein interaction network of SIN1 in Homo sapiens was generated by STRING tool. (B) Volcano plot of DEGs between shCTRL and shSIN1 U251 cells. Red dots represent upregulated genes, yellow dots indicate downregulated genes, and blue dots indicate genes with no differential expression. (C) GO BP terms enrichment analysis of DEGs in SIN1 knockdown U251 cells. (D) GO MF terms enrichment analysis of DEGs in SIN1 knockdown U251 cells. (E) GO CC terms enrichment analysis of DEGs in SIN1 knockdown U251 cells. (F) KEGG enrichment analysis of DEGs in SIN1 knockdown U251 cells

GO enrichment analysis revealed significant enrichment of the terms “developmental process”, “system development”, “anatomical structure development”, and “tissue development” within the biological process (BP) category (Fig. 5C). In addition, the molecular function (MF) category showed significant enrichment for the term “growth factor binding” (Fig. 5D), suggesting SIN1’s involvement in regulating cell proliferation in glioma. Moreover, significant enrichment was observed for the BP terms “regulation of cell migration” and “regulation of cell motility” (Fig. 5C). Several molecular functions related to the extracellular matrix (ECM) were also enriched, including “extracellular matrix structural constituent”, “extracellular matrix structural constituent conferring tensile strength”, “extracellular matrix binding”, “heparin binding”, “ephrin receptor activity”, and “glycosaminoglycan binding” (Fig. 5D). In the cellular component (CC) category, enrichment was noted for the terms “collagen-containing extracellular matrix” and “cell junction” (Fig. 5E). Furthermore, KEGG pathway enrichment analysis identified significant enrichment in the pathways “ECM-receptor interaction”, “focal adhesion”, and “cell adhesion molecules” (Fig. 5F). Together, these findings indicate that SIN1 plays a crucial role in both the regulation of cell proliferation and migration in glioma.

Notably, the significant enrichment of ECM-related terms in the GO analysis, along with enriched KEGG pathways such as “cytokine-cytokine receptor interaction” suggests that SIN1 may also be involved in tumor immune regulation. Previous studies have reported that SIN1 plays a critical role in regulating the development, metabolism, signal transduction, and immune responses of immune cells [6]. Single-cell sequencing analysis of gliomas confirmed that SIN1 is expressed not only in malignant cells but also in immune-related cells such as macrophages, T cells, and oligodendrocytes (Fig. S4A). We further explored the relationship between SIN1 expression and immune cell infiltration in gliomas. Our results indicate that SIN1 expression was positively correlated with the infiltration of Th2 cells, macrophages, T helper cells, eosinophils, activated dendritic cells (aDCs), and neutrophils, while negatively correlated with the infiltration of NK cd56bright cells and plasmacytoid dendritic cells (pDCs) (Fig. S4B, C). These results suggest that SIN1 expression might be associated with immune infiltration in the glioma microenvironment.

SIN1 May promote proliferation and migration of glioma via activate KRAS4A/ERK pathway

A previous study demonstrated that SIN1 directly interacts with the KRAS4A isoform via an atypical RAS-binding domain in cells [22, 23]. This interaction was confirmed in glioma cells was confirmed through Co-IP analysis (Fig. 6A). Immunofluorescence staining further corroborated the colocalization of SIN1 with KRAS4A (Fig. 6B). As a pivotal signaling molecule, KRAS regulates numerous biological processes primarily by activating downstream ERK pathways; notably, KRAS4A exhibits enhanced efficiency in activating the ERK signaling pathway [24]. KEGG pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA) revealed a significant correlation between ERK pathway activity and SIN1 expression (Figs. 5F and 6C). Furthermore, Western Blot results showed that p-ERK levels were significantly reduced in SIN1-knockdown U251 and U118 cells (Fig. 6D), whereas these levels increased markedly in SIN1-overexpressing U251 and U118 cells (Fig. 6E). Collectively, these results suggest that SIN1 is associated with the KRAS/ERK pathway in glioma, may promote glioma proliferation and migration by activating it.

Fig. 6.

SIN1 is associated with the KRAS/ERK pathway in glioma. (A) Co-Immunoprecipitation analysis of SIN1 and KRAS4A in U251 and U118 cells. (B) Representative immunofluorescence images of SIN1 and KRAS4A in U251 and U118 cells. SIN1 is labeled in green, KRAS4A in red, and nuclei are stained with DAPI in blue. Scale bar: 10 μm. (C) Enrichment plots from GSEA. SIN1 was closely related to “ERK signaling pathway”. (D) Immunoblot analysis of p-ERK and ERK protein levels in SIN1 knockdown U251 and U118 cell lines. β-Tubulin was used as the loading control. The band intensity was determined by densitometry by using ImageJ and normalized to β-Tubulin. (E) Immunoblot analysis of p-ERK and ERK protein levels in SIN1 overexpressed U251 and U118 cell lines. β-Tubulin was used as the loading control. The band intensity was determined by densitometry by using ImageJ and normalized to β-Tubulin. ns, no significance, *p < 0.05, **p < 0.01

Discussion

In this study, we conducted a comprehensive analysis of the role of SIN1 in glioma progression and its association with the KRAS/ERK signaling pathway. Our results demonstrate that SIN1 is aberrantly overexpressed in glioma, consistent with previous studies suggesting its tumorigenic role [8]. Further analysis revealed that higher SIN1 expression in gliomas correlates with poorer clinicopathological features, including higher WHO grades, IDH-wildtype status, and lack of 1p/19q codeletion. These findings underscore the pivotal role of SIN1 in glioma progression. To further elucidate the functional significance of SIN1 in glioma cells, we performed experiments involving either knockdown or overexpression of SIN1. Our results indicate that SIN1 promotes cell proliferation and migration while inhibiting apoptosis in glioma cells. Additionally, SIN1 knockdown suppresses the growth of primary glioma cells. Mechanistically, SIN1 exerts its oncogenic effects via activation of the KRAS4A/ERK signaling pathway. These novel findings enhance our understanding of the molecular mechanisms driving glioma development and progression. Taken together, our data highlights the oncogenic potential of SIN1 in glioma, supporting its role as a key driver of malignant progression.

The association between SIN1 and the KRAS/ERK signaling pathway represents a meaningful contribution to the field. This pathway is well established for its critical roles in regulating cell growth, proliferation, and survival, and its dysregulation is frequently implicated in cancer development [25]. By identifying SIN1 as a key regulator within this pathway in glioma, our study highlights a novel therapeutic target. The potential of SIN1 as a candidate for targeted therapy offers a promising direction for future research and may lead to the development of more effective treatment strategies for glioma patients.

Prognostic and predictive markers are crucial in the WHO classification of CNS tumors and glioma clinical practice, aiding in assessing patient outcomes and selecting appropriate therapies [26]. Key biomarkers such as IDH mutant status, 1p/19q codeletion, H3F3A alterations, ATRX mutation, MGMT promoter methylation, CDKN2A deletion, EGFR amplification and mutation, Chromosome 7 Gain and Chromosome 10 Loss, and TERT promoter mutation have been used for accurate classification, predicting prognosis, and guiding personalized treatment strategies in gliomas [27]. Although existing biomarkers play a critical role in diagnosing and treating gliomas, challenges remain, including limitations in detection technologies, individual variability, and treatment resistance. Thus, it is imperative to identify novel molecular markers to improve diagnostic accuracy and therapeutic outcomes. Our analysis demonstrated that SIN1 expression negatively correlates with glioma patient prognosis, as evidenced by Kaplan-Meier survival analysis. Additionally, ROC curve analysis indicated an AUC of 0.937 (≥ 0.9), suggesting that SIN1 may serve as a promising prognostic biomarker for glioma. However, further prospective studies are needed to validate SIN1 as a reliable biomarker in glioma management. Combining SIN1 with existing biomarkers could improve the accuracy of prognostic models and facilitate more tailored treatment approaches.

GO and KEGG enrichment analyses revealed that SIN1 is closely associated with ECM. The ECM plays a pivotal role in cancer progression and metastasis by modulating tumor biomechanics, facilitating cell invasion, and orchestrating the tumor microenvironment [28]. Our results confirm that SIN1 promotes glioma cell proliferation and migration, findings that align with previous studies in non-small cell lung cancer [10], hepatocellular carcinoma [11], prostate cancer [13], and breast cancer [15]. These analyses suggest that SIN1 may influence tumor cell motility and its adaptation to the microenvironment through interactions with the ECM. This discovery highlights new research directions for exploring SIN1 as a critical regulator in tumor progression and its role in the dynamic tumor microenvironment. Investigating how SIN1 interacts with ECM molecules, such as integrins and various matrix metalloproteinases, could provide deeper insights into the molecular mechanisms underlying tumor invasion and metastasis. Furthermore, targeting SIN1 or its downstream signaling pathways might offer novel therapeutic strategies for glioma treatment.

SIN1 plays a crucial role in regulating various stages of T-cell functional maturation, including cytokine production and immune niche regulation, particularly in the formation and exhaustion of CD8⁺ T cell memory [29]. SIN1 specifically regulates B cell growth, metabolism, and immunity by coordinating the activation of mTORC1 and c-Myc [30]. Despite these insights, the precise mechanisms by which SIN1 modulates immune infiltration and the tumor microenvironment remain largely unexplored. Our analyses demonstrated SIN1 was associated with cytokine-cytokine receptor interaction and ECM receptor interaction, suggesting that SIN1 could influence the tumor immune microenvironment by mediating communication between immune cells and ECM. Consistent with this, single-cell RNA sequencing analysis revealed SIN1 expression in macrophages and T cells, indicating its involvement in the regulation of immune cell function within the glioma microenvironment. Moreover, SIN1 correlated with the infiltration of immune cells in the glioma microenvironment, such as Th2 cells, macrophage, and pDC, etc. These results suggest that SIN1 might play an important role in modulating immune cell infiltration and immune escape within glioma tumors. SIN1 might affect immune escape and response to immunotherapies by influencing ECM and immune cell infiltration. A limitation of our study is that the role of SIN1 in immune regulation in glioma was explored only through bioinformatics analysis, lacking experimental validation. Further research is needed to clarify the precise mechanisms by which SIN1 regulates immune cell infiltration and ECM. Targeting SIN1 may enhance anti-tumor immunity and improve the efficacy of immunotherapies.

While our findings demonstrate that SIN1 promotes glioma progression primarily through the KRAS4A/ERK pathway, its role within the mTORC2 complex deserves further exploration. Although SIN1’s functions extend beyond mTORC2 (as exemplified by its interaction with KRAS4A (Fig. 6A, B) [22, 23]), isolating mTORC2-specific contributions remains challenging. Our current experimental approaches, which focus on manipulating SIN1 and analyzing KRAS4A/ERK outcomes, may not fully capture mTORC2 activity or its specific downstream effectors in glioma cells. Additionally, the observed association between SIN1 and the tumor immune microenvironment suggests potential crosstalk that could influence mTORC2 signaling. Future studies should specifically examine mTORC2 integrity, substrate phosphorylation, and its interplay with KRAS/ERK signaling and microenvironmental factors to comprehensively define SIN1’s multifaceted roles in glioma progression and its therapeutic potential.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

The study was conceptualized by H.C. Experimental were performed by H.C., M.L., Z.Y., J.W., Y.C., and J.S. Data analysis was conducted by H.C., J.S., and J.R. The initial manuscript draft was authored by H.C., with critical revisions provided by D.Y. All authors reviewed and approved the final version of the manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China (NSFC Grant No. 82171348 to D.Y.) and the Fundamental Research Program of Xuzhou Medical University (Grant No. 53681921).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethical approval

The ethical review and approval were obtained from the institutional ethics committee of Affiliated Hospital of Xuzhou Medical University (ethical review no. XYFY2018-KL056-01). All animal work was approved by the Laboratory Animal Ethics Committee of Xuzhou Medical University (No. 2022085126). Informed Consent: N/A. Registry and the Registration No. of the study/trail: N/A.

Competing interests

The authors declare no competing interests.