
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00215-8
10.1016/j.tranon.2024.102088
102088
Original Research
SUGT1 regulates the progression of ovarian cancer through the AKT/PI3K/mTOR signaling pathway
Ke Miao a1
Xu Jie b1
Ouyang Ye c1
Chen Junyu d
Yuan Donglan yuandonglan4334@163.com
e⁎
Guo Ting wangjunguoting2008@163.com
b⁎
a Department of Gynecology and Obstetrics, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
b Institute of Clinical Medicine, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, China
c Graduate Management Department, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China
d School of Nursing, Nanjing University of Chinese Medicine, Nanjing, China
e Department of Gynecology and Obstetrics, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, China
⁎ Corresponding authors. yuandonglan4334@163.comwangjunguoting2008@163.com
1 Miao Ke, Jie Xu and Ye Ouyang contributed equally to this work.

20 8 2024
11 2024
20 8 2024
49 1020886 6 2024
1 8 2024
11 8 2024
© 2024 Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• Macroscopic analysis of SUGT1 and cancer prognosis.

• ELF1 stimulates the transcription of SUGT1 through the PI3K/AKT pathway.

• SUGT1 promote the glycolysis process in ovarian cancer.

• SUGT1 was significantly linked to the prognosis of cancer and immune infiltration.

This study investigates the expression and functional roles of SUGT1 in ovarian cancer, utilizing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects. Our analyses reveal that SUGT1 is significantly upregulated in ovarian cancer tissues compared to normal controls. We further explore the prognostic value of SUGT1, where elevated expression correlates with poorer patient outcomes, particularly in ovarian cancer. The functional implications of SUGT1 in cancer biology were assessed through in vitro and in vivo experiments. Gene Set Enrichment Analysis (GSEA) indicates a significant association between high SUGT1 expression and the activation of glycolytic pathways, suggesting a potential role in metabolic reprogramming. Inhibition of SUGT1 via siRNA in ovarian cancer cell lines results in decreased proliferation and increased apoptosis, along with reduced migration and invasion capabilities. Additionally, our study identifies the transcription factor ELF1 as a significant regulator of SUGT1 expression. Through promoter analysis and chromatin immunoprecipitation, we demonstrate that ELF1 directly binds to the SUGT1 promoter, enhancing its transcription. This regulatory mechanism underscores the importance of transcriptional control in cancer metabolism, providing insights into potential therapeutic targets. Our findings establish SUGT1 as a crucial player in the oncogenic processes of ovarian cancer, influencing both metabolic pathways and transcriptional regulation. This highlights its potential as a biomarker and therapeutic target in managing ovarian cancer.

Keywords

SUGT1
Ovarian cancer
Bioinformatics
Animal
Glycolytic
==== Body
pmcIntroduction

SUGT1, an MIS12 kinetochore complex assembly cochaperone, is responsible for encoding a highly conserved protein involved in the centromere assembly process [1]. SUGT1 acts as a chaperone for the Hsp90 protein and ensures efficient microtubule binding site formation by recruiting the MIS12 complex to the kinetochore [2]. Current studies have shown that SUGT1-involved mitomere assembly is involved in a variety of pathophysiological processes in mammals and humans. SUGT1 is thought to be a protective factor for HIV-1 infection since it increases HIV susceptibility by stabilizing microtubule plus-ends and controls the vulnerability of lymphocyte and macrophage infiltration by controlling the nuclear import of the viral DNA [3]. In addition, there are several studies demonstrating the specific role of SUGT1 in tumors. SUGT1 is a significant positive regulator of MHC-I and MHC-II cell surface expression and plays a crucial role in controlling the immune system's surveillance activity against lymphoma, according to Devin Dersh et al. [4]. In addition, significant upregulation of this gene was also observed in some specific cancers, such as colorectal and gastric tumors [5,6,7,8].

The role of SUGT1 in tumor progression is of special significance. However, at present, more basic research is focused on this gene rather than clinical studies. To investigate the role of SUGT1 in tumor diagnosis and treatment, we initiated a pan-cancer research and analysis using SUGT1. We concentrated on the relationship between this gene and the prognosis of 33 different cancers. Finally, we conducted a series of experiments at both the clinical and cellular levels to elucidate the role of SUGT1 in ovarian cancer.

Materials and methods

SUGT1 expression in tumor tissues, normal tissues, and cell lines

The "ggplot2″ package was used to display the expression of SUGT1 in tumor and normal tissues. The Cancer Genome Atlas (TCGA) and GTEx's RNAseq data were retrieved from UCSC XENA (https://xena.ucsc.edu/). The expression of SUGT1 in cancer cells from the Cancer Cell Line Encyclopaedia (CCLE) was also examined using R software. The version of R software is R 4.3.1.

Investigation of the diagnostic and prognostic potential of SUGT1

Clinical data on different cancers were downloaded from the TGCA database. Cox regression and Kaplan-Meier analysis were done for SUGT1, and median values were used to determine the high and low SUGT1 expression groups. In addition to generating hazard ratios (HR) with 95 % confidence intervals and P-values, the following survival metrics were calculated: overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and progression-free survival (PFS). A P-value of < 0.05 was deemed statistically significant. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic effectiveness of SUGT1, and the R packages pROC and ggplot2 were used to generate and show the area under the curve (AUC) and ROC curve. AUC has a larger diagnostic value as it approaches 1, and when it falls below 0.5, it has no diagnostic value.

Cell culture

The ES-2 cell lines and OVCAR-3 cell lines were purchased from Procell Life Science&Technology Co., Ltd. (Wuhan, China). ES-2 cells were cultured in the DMEM (KeyGen, China) containing 10 % FBS (Gibico, Thermofisher, USA) and 1 % penicillin/streptomycin (100 U/ml). OVCAR-3 cells were cultured in RPMI-1640 Medium (KeyGen, China) containing 20 % FBS and 1 % penicillin/streptomycin (100 U/ml). All cells were maintained in standard culture conditions of 5 % CO2 at 37 °C.

Cell counting kit- 8 (CCK- 8)

ES-2 cells and OVCAR-3 cells were seeded into 96-well plates (5 × l04 cells/well) and treated with si-NC and si-SUGT1 for 0 h, 24 h, 48 h and 72 h, respectively. After appropriate treatment, the cells were treated with 10 μL of CCK-8 (Beyotime, China) at 37 °C for 2 h. The absorbance values were measured with a microplate reader (Bio-Rad, USA) at 450 nm.

Ethynyldeoxyuridine (EdU) assay

The present study assessed the cell proliferation status, denoting the proportion of cells undergoing DNA replication, through the utilization of an EdU detection kit (RiboBio, Guangzhou, China). The incorporation rate of EdU was ascertained by computing the ratio of cells incorporating EdU to those stained with Hoechst 33,342. A minimum of 500 cells were enumerated for each experimental group.

Transwell assay

The upper chamber was precoated with Matrigel (BD, Biosciences) for the invasion assay. Cells in the serum-free medium were seeded in the upper chamber. The complete medium was then added to the lower chamber. After 24 h of culture, cotton swabs were used to remove non-migrating or non-invading cells from the upper chamber. Cells in the lower chamber were fixed with 4 % paraformaldehyde and stained with 0.1 % crystal violet. The migrated or invaded cells were counted under a microscope.

RNA extraction and quantitative real-time polymerase chain reaction (qRT-PCR)

Using TRIzol reagent produced by ThermoFisher (Waltham, Massachusetts, USA), total RNA was extracted. The integrity and purity of RNA were examined by denaturing agarose gel electrophoresis. The reverse transcription experiment utilized HiScript II Q RT SuperMix from Vazyme (Nanjing, Jiangsu, China), subsequently obtaining cDNA. The qRT-PCR was performed using 2×ChamQ Universal SYBR qPCR Master Mix from Vazyme. The qRT-PCR reactions were carried out on an ABI 7500 system manufactured by Applied Biosystems (Foster City, California, USA). Each 20μL reaction mixture included 2μL of DNA extract, 1μL of forward and reverse primers for each gene, 10μL of SYBR, and 6μL of deionized water. Thermal cycling conditions comprised an initial denaturation step at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at 57 °C for 25 s, and extension at 72 °C for 20 s. β-actin was used as the internal reference gene, and data were analyzed using the 2−ΔΔCT method. The primer sequences (5′ - 3′) are as follows:mSP4-F: CGGCGATGGCTACAGAAGG

mSP4-R: CCAGGAGTCCCTATTTTGCTG

mSUGT1-F: ATGCCCTAATCGACGAGGAC

mSUGT1-R: GCATCATCTGGTTTCTGTTCCA

mELF1-F: TGTCCAACAGAACGACCTAGT

mELF1-R: GGCAGGAAAAATAGCTGGATCAC

mGAPDH-F: GGAGCGAGATCCCTCCAAAAT

mGAPDH-R: GGCTGTTGTCATACTTCTCATGG

Western blot

Total protein was extracted from the cells using RIPA buffer (Beyotime, China), and the protein concentration was quantified by BCA protein assay kit (Beyotime, China). Protein samples (30 μg) were separated by 10 % SDS- PAGE and transferred onto PVDF membranes, thereby being blocked with 5 % defatted milk in TBST for 2 h in room temperature. The membranes were incubated with primary antibodies at 4 °C overnight. Thereafter, the membranes were incubated with HRP-conjugated secondary antibody for 2 h at room temperature. Subsequently, the bands were visualized using an ECL kit (Beyotime, China) and quantified using ImageJ software. The following antibodies (proteintech, China) were used for the western blot analysis: anti-SUGT1, anti-GAPDH and anti-rabbit IgG.

Dual-luciferase assay

The wild type (WT) and mutant SUGT1 were cloned into the pGL3 plasmid. Transient transfection was performed using Lipofectamine 3000 from Thermo Fisher (Carlsbad, CA, USA) following the manufacturer's instructions. One to two days later, luciferase activity was measured using the Dual-Glo Luciferase Assay System from Promega (Madison, WI, USA), and Renilla luciferase activity was used to normalize the firefly luciferase activity. The dual luciferase reporter assay was conducted using a Dual Luciferase Assay Kit from Promega.

Chromatin immunoprecipitation (ChIP) assay

Samples were fixed with 1 % formaldehyde in PBS and sonicated to obtain DNA fragments ranging from 200 to 500 bp. The sonicated samples were then immunoprecipitated using 5 μg of anti-SUGT1 or anti-IgG antibodies. After the elution of the immune complexes and reversal of crosslinks, a qRT-PCR analysis was performed. ChIP assays were conducted using a ChIP assay kit from Merck Millipore (Darmstadt, Germany).

Immunohistochemical staining

We collected pathological tissues from 86 OV patients and its adjacent normal tissue slices in Taizhou People's Hospital Affiliated to Nanjing Medical University. All patients signed informed consent, and all experimental procedures for this study have been approved by the Ethics Committee of Taizhou People's Hospital Affiliated to Nanjing Medical University (Approval Number: KY 2022–178–01). The samples were fixed with 4 % paraformaldehyde, embedded in paraffin, and sectioned into 5 μM thick sections. After routine deparaffinization and rehydration, antigen retrieval was performed using citrate sodium atna high temperature. Furthermore, the sections were incubated with anti-SUGT1 (proteintech, China) at 4 °C overnight. After incubation with the secondary antibody, the sections were stained with diaminobenzidine and counterstained with hematoxylin. Images were observed under a microscope.

Analysis of cell apoptosis

The remaining cells from each group were incubated and digested with trypsin for 24 h. Then, 100 μl of 1x binding buffer was added to the flow cytometry tube, followed by Annexin-V and PI (5 μl) in each sample. The samples were then cultured at room temperature in the dark for 15 min. Next, 400 μl of binding buffer was added to each sample, and cell apoptosis was detected using a flow cytometer (BD USA).

Animal experiment

According to the ethical approval from Taizhou People's Hospital (Reference Number DW2023–001–01), we obtained 4-week-old female BALB/c nude mice for our animal experiments. Initially, we digested and centrifuged the stably transfected A2780 cells during their exponential growth phase. Subsequently, these cells were resuspended in a mixture, with 100μL per tube, and were inoculated into the armpits of the mice (1 cm). We observed and measured the tumor volume and growth curve. At the end of the experiment, the mice were anesthetized using 1 % chloral hydrate, and the excised tumors were ranked in descending order and photographed. Finally, euthanasia was performed on the mice through cervical dislocation, followed by the removal of the skin covering the tumors using ophthalmic scissors. These are the key steps and highlights of the experiment.

Measurement of extracellular acidification rate (ECAR)

Cells were seeded at a density of 4 × 10^4 cells per well in a medium supplemented with 2 mmol/L glutamine for ECAR analysis, or with 2 mmol/L glutamine, 10 mmol/L glucose, and 1 mmol/L pyruvate for OCR analysis. The extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) were analyzed using the XFe96 Extracellular Flux Analyzer (Agilent Tech, Santa Clara, CA, USA).

Statistical analysis

We used t-test for paired comparisons and ANOVA for multiple group comparisons. Kaplan-Meier method was used to construct survival curves, and log-rank test was used to estimate the difference in survival between different groups. All experiments were repeated at least three times, and results are reported as mean ± standard deviation (SD). Data analysis was performed using GraphPad Prism 9 and R software with statistical significance set at P < 0.05.

Results

Expression of SUGT1 in human cancer tissues

The present study employed the TCGA database to validate the expression of SUGT1 in diverse malignancies, with the aim of exploring its role in cancer. The findings revealed that SUGT1 was significantly upregulated in BLCA, BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, READ, STAD, and UCEC, while its expression was comparatively low in KICH, KIRP, and THCA(Fig. 1A). The present study utilized a combination of TCGA and GTEx data to confirm the expression of SUGT1 in cancer. The findings revealed a significant upregulation of SUGT1 in various cancer types, including BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KIRC, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, SKCM, STAD, THCA, THYM, UCEC, and USC. Conversely, KICH, LAML, and TGCT exhibited low expression levels of SUGT1(Fig. 1B). Tumor pairing was additionally employed to identify elevated expression in BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, PRAD, STAD, and UCEC, while diminished expression was observed in KICH and THCA(Fig. 1C). The present study utilized cBioPortal to investigate the frequency and nature of genetic alterations in SUGT1 across various malignancies. Results indicated that deep deletion was the predominant genetic change observed in Prostate Cancer, Bladder Cancer, Hepatobiliary Cancer, Cervical Cancer, Ovarian Epithelial Tumor, Sarcoma, Mature B-cell Neoplasms, Breast Cancer, Glioma, and non-small Cell Lung Cancer. Conversely, mutation was the most frequently observed genetic alteration in Endometrial Cancer, Seminoma, Melanoma, Adrenocortical Carcinoma, and Pancreatic Cancer(Fig. 1D). The mutation sites are shown below(Fig. 1E). Copy number variation datasets were gathered for various tumor types and subsequently merged with gene expression data. Our analysis revealed variations in SUGT1 across 19 tumor types, including GBM, LGG, CESC, LUAD, and ESCA (Fig. 1F). This study has revealed noteworthy genetic variations and expression of SUGT1 across diverse cancer types, thereby establishing a novel theoretical foundation for future SUGT1 research. Additionally, this study offers valuable insights into the development of innovative and efficacious cancer therapies that target SUGT1. Given the elevated expression of SUGT1 in multiple cancer types, it may be regarded as a promising candidate for cancer treatment.Fig. 1 Differential expression of SUGT1. (A) SUGT1 expression in TCGA. (B) SUGT1 expression in TCGA and GTEx. (C) SUGT1 Pairwise Expression in TCGA. (D) Bar chart of SUGT1 change frequency and type of different cancer types. (E) SUGT1 mutation location, type, and number. (F)Expression of SUGT1 in CNV of different tumors. Ns, non-significant, * P < 0.05; * * P < 0.01; *** P < 0.001; **** P < 0.0001.

Fig. 1

Relationship between SUGT1 expression and tumor prognosis

The present study conducted an in-depth investigation into the prognostic significance of SUGT1 across various cancer types. The results of the prognosis analysis heatmap revealed that SUGT1 serves as a risk factor for patients diagnosed with ACC, BRCA, HNSC, and OV, while it exhibits a protective effect for those with KIRC and LGG. Specifically, the analysis focused on the impact of SUGT1 on overall survival outcomes. Given that non-cancer death events are incorporated into the overall survival (OS) outcome, a DSS analysis was performed, which produced congruent findings with the OS analysis. The DSS analysis further demonstrated that SUGT1 is a risk factor for the aforementioned cancers. The outcomes of DFI and PFI analyses also substantiated that SUGT1 is a risk factor for these cancers and exhibited a significant association with tumor prognosis, particularly among patients with ACC, BRCA, HNSC, and OV(Fig. 2A). In addition to this, results from OS, DSS, PFI and DFI all indicated that SUGT1 is a hazard factor for ovarian cancer (OV), suggesting its potential as an important prognostic biomarker in OV patient outcomes. To further understand how PDIA3 affects patient prognosis, univariate Cox regression analysis was used to analyze the prognosis of 32 types of TCGA cancers. The results shown in the forest plot indicate that in ACC (HR = 2.282 [95 % CI], P = 0.0355), BRCA (HR = 1.407 [95 % CI], pP = 0.036), HNSC (HR = 1.521 [95 % CI], P = 0.0022), OV (HR = 1.596 [95 % CI], P = 0 0.0004), LGG (HR=0.623 [95 % CI], P = 0 0.0094), KIRC (HR=0.568[95 % CI], P = 0.0003). These results are consistent with those shown in the heatmap (Fig. 2B). We also used KM curves to show OS prognosis analysis for different cancers, and the results were consistent with those mentioned above as well (Fig. 2C). The research has determined that SUGT1 represents a noteworthy prognostic biomarker for diverse cancer types. In general, SUGT1 functions as a hazard factor for ovarian cancer and a risk factor for ACC, BRCA, HNSC, and other malignancies.Fig. 2 The correlation between SUGT1 expression and different cancer prognosis. (A) Based on univariate Cox regression and Kaplan-Meier models, the correlation between SUGT1 expression and overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI) and progression-free interval (PFI) was summarized. Red indicates that SUGT1 is a risk factor affecting the prognosis of cancer patients, while green represents a protective factor. Only results with P-values <0.05 are displayed. (B) The forest plot demonstrates the prognostic role of SUGT1 in cancer using a univariate Cox regression method. The pink color representing the type of cancer indicates that SUGT1 is a statistically significant risk factor. (C) Kaplan-Meier overall survival curves of SUGT1 in ACC, BRCA, HNSC, KIRC, LGG and OV.

Fig. 2

Additionally, the diagnostic utility of SUGT1 as a distinct biomarker of pan-cancer was investigated. Its diagnostic sensitivity and specificity were assessed using the ROC curve. The ROC curve showed that the tumors with AUC greater than 0.9 were as follows: CHOL (AUC = 1, CI: 1–1), KICH (AUC = 0.972, CI: 0.938–1.000) and PAAD (AUC = 0.971, CI: 0.953−0.988). Tumors with AUC between 0.7–0.9 included COAD (AUC = 0.870, CI: 0.840–0.899), DLBC (AUC = 0.832, CI: 0.794–0.870), GBM (AUC = 0.808, CI: 0.777–0.839), LGG (AUC = 0.855, CI: 0.837–0.873), LIHC (AUC = 0.807, CI: 0.759–0.856), READ (AUC = 0.805, CI: 0.740−0.870), STAD (AUC = 0.822, CI: 0.734−0.909), UCS (AUC = 0.849, CI: 0.773−0.924), CESC (AUC = 0.757, CI: 0.678−0.836), ESCA (AUC = 0.742, CI: 0.569−0.916), HNSC (AUC = 0.704, CI: 0.648−0.780), TGCT (AUC = 0.761, CI: 0.708−0.815) THYM (AUC = 0.798, CI: 0.763−0.833), UCEC (AUC = 0.772, CI: 0.717−0.828), the ROC and diagnostic value of remaining cancers was low(Fig. 3). In summary, the ROC curve analysis reveals that SUGT1 exhibits elevated diagnostic sensitivity and specificity for select cancer types, thereby establishing its potential as a dependable prognostic and diagnostic indicator for said cancers.Fig. 3 SUGT1 expression and ROC curves of each tumor as diagnostic biomarkers. AUC: area under the curve.

Fig. 3

The expression and prognostic role of SUGT1 in ovarian cancer

The present study conducted an analysis of ovarian cancer data obtained from TCGA and GTEx, with a particular focus on the expression of SUGT1 in ovarian cancer. The results of the analysis revealed a significant upregulation of SUGT1 in cancerous tissues (Fig. 4A). In addition, immunohistochemistry staining was employed to identify the expression of SUGT1 in 86 ovarian cancer tissues and their corresponding normal tissues. The findings substantiated that the expression of SUGT1 was elevated in ovarian cancer tissues compared to their normal counterparts, and that SUGT1 was expressed in both the cytoplasm and nucleus (Fig. 4B). Furthermore, the results obtained from immunofluorescence analysis demonstrate a higher level of SUGT1 expression in the cytoplasmic region as opposed to the nuclear region (Fig. 4C). The study findings indicate that SUGT1 serves as a reliable prognostic marker in ovarian cancer. Consequently, we proceeded to investigate the functional significance of SUGT1 in ovarian cancer. Employing the median value, SUGT1 was categorized into high and low groups, and the baseline data table of ovarian cancer patients is presented herewith (Supplement Table 1). Utilizing the TCGA database, we performed univariate and multivariate Cox analyses on SUGT1 OS data among patients with ovarian cancer (Table 1). The results showed that according to univariate analysis, high SUGT1 (P = 0.001), stage IV (P = 0.037) and age >60 years old (P = 0.021) were prognostic factors for OV OS. Multivariate analysis showed that in OV, high SUGT1 (P = 0.001), stage IV (P = 0.019) and age >60 years old (P = 0.007) were prognostic factors for OS. In OV, single SUGT1, stage IV and age >60 years old are independent predictors of OS prognosis. Then, the independent prognostic factors of the above-mentioned OS were incorporated to construct a nomogram based on SUGT1 to predict the 1-, 3-, and 5-year survival probabilities in TCGA-OV cohort. The nomogram can be used by providers to evaluate the prognosis of OV patients. The calibration curve also demonstrated satisfactory consistency between predicted and actual 1-, 3-, and 5-year survival probabilities(Fig. 4D).Fig. 4 The expression and prognostic role of SUGT1 in ovarian cancer. (A) The IHC showed that SUGT1 protein is highly expressed in tumor tissues, statistical results of IHC score of SUGT1. *P < 0.05. (B) Distribution of SUGT1 in tumor tissues, n = 3, *P < 0.05. (C) Immunofluorescence analysis of SUGT1 expression. (D) Nomogram prognostic score after multivariate analysis. Calibration maps of the 1-, 3-, and 5-year OS prediction nomogram.

Fig. 4

Table 1 Univariate and multivariate Cox regression analysis.

Table 1Characteristics	Total(N)	Univariate analysis	Multivariate analysis	
Hazard ratio (95 % CI)	P value	Hazard ratio (95 % CI)	P value	
SUGT1	379		< 0.001			
Low	189	Reference		Reference		
High	190	1.551 (1.195 - 2.011)	< 0.001	1.543 (1.162 - 2.048)	0.003	
Clinical stage	376		0.054			
Stage I&Stage II	24	Reference		Reference		
Stage III	294	2.058 (0.911 - 4.649)	0.083	1.489 (0.609 - 3.641)	0.383	
Stage IV	58	2.556 (1.085 - 6.025)	0.032	1.670 (0.655 - 4.262)	0.283	
Tumor status	337		< 0.001			
Tumor free	72	Reference		Reference		
With tumor	265	9.598 (4.487 - 20.532)	< 0.001	9.094 (4.243 - 19.490)	< 0.001	
Age	379		0.022			
≤ 60	207	Reference		Reference		
> 60	172	1.352 (1.045 - 1.749)	0.022	1.330 (1.009 - 1.752)	0.043	

SUGT1 promotes glycolysis in ovarian cancer cells

During gene set enrichment analysis (GSEA), we found that gene sets associated with SUGT1 were significantly enriched in ovarian cancer samples (TCGA-OV database). Notably, the expression of SUGT1 was closely linked to various pathways involved in cell cycle and energy metabolism. The enrichment of HALLMARK_MYC_TARGETS_V1 and HALLMARK_MYC_TARGETS_V2 gene sets indicated significant activation of MYC target genes in samples expressing SUGT1. MYC, a known proliferation driver, regulates networks involving cell growth and metabolic processes, including glycolysis (HALLMARK_GLYCOLYSIS). Additionally, the enrichment of HALLMARK_GLYCOLYSIS further confirmed the enhanced activity of the glycolytic pathway in samples with high SUGT1 expression, consistent with the metabolic reprogramming of cancer cells. These findings reveal potential mechanistic links between SUGT1 expression, cell proliferation, and energy metabolism, particularly through the regulation of MYC and glycolysis pathways, offering potential molecular targets for future therapeutic strategies (Fig. 5A and B). Moreover, we assessed the levels of glycolysis and mitochondrial oxidative phosphorylation activity through ECAR and OCR measurements. si-SUGT1 significantly inhibited glycolysis levels and functionality in leukemia cells and increased both basal and maximal OCR in these cells. To elucidate the impact of si-SUGT1 on the glycolytic metabolic levels in leukemia cells, we measured the production of glycolytic/gluconeogenic products (Fig. 5C). As shown, si-SUGT1 notably suppressed the production of pyruvate, lactate, citrate, and malate in leukemia cells. In summary, CSRP1 promotes glycolysis in leukemia cells (Fig. 5D).Fig. 5 SUGT1 promotes glycolysis in ovarian cancer cells. (A) GSEA enrichment analysis of SUGT1 in ovarian cancer. (B) SUGT1 is enriched in the glycolysis pathway. (C) OCR and ECAR of AML cells in varying groups after transfected as detected with Seahorse XP-96. (D) Pyruvic acid, lactate, citrate, and malate contents in AML cells after transfected were detected; n = 3, *P < 0.05, **P < 0.01, ***P < 0.001.

Fig. 5

SUGT1 expression and function in ovarian cancer

To examine the involvement of SUGT1 in the development of ovarian cancer, we utilized three distinct siRNA variants to suppress SUGT1 expression in ES-2 and OVCAR-3 ovarian cancer cell lines. The efficacy of SUGT1 knockdown was validated through Western blot analysis, demonstrating that the application of siRNA-2 led to a decrease of >50 % in SUGT1 expression in both cellular populations (Fig. 6A). In order to evaluate the influence of SUGT1 on cellular proliferation, we conducted CCK-8 and EDU experiments. Our findings indicate that a decrease in SUGT1 expression resulted in a reduction of proliferation activity in both ES-2 and OVCAR-3 cells, as demonstrated by the CCK-8 assay (Fig. 6B). Additionally, Annexin-V/PI double staining analysis revealed that the suppression of SUGT1 expression promoted cell apoptosis (Fig. 6C).The findings from wound healing experiments and Transwell experiments indicate that the downregulation of SUGT1 considerably impedes the migration and invasion ability of ovarian cancer cells (Fig. 6D and E). Therefore, it can be inferred that the suppression of SUGT1 expression curtails the survival, proliferation, migration, and invasion capacity of these cells.Fig. 6 SUGT1′s role in the occurrence and development of ovarian cancer. (A) Verification of interference efficiency of SUGT1 in ES-2 and OVCAR-3 cells, respectively. (B) CCK-8 assays showed that down regulation of SUGT1 expression inhibited the proliferation of ES-2 and OVCAR-3 cells. (C) Apoptosis analysis by Annexin-V/PI double staining. (D) Wound healing assay showing representative images. (E) Cell invasion and migration ability were detected by transwell assay. The right figures are the corresponding invaded cells number. n = 3, *P < 0.05, ** P < 0.01.

Fig. 6

SUGT1 influence on ovarian cancer via the PI3K/AKT/mTOR signaling pathway

In order to delve deeper into the mechanistic role of SUGT1 in the occurrence and development of ovarian cancer, we conducted Gene Set Enrichment Analysis (GSEA) and found that SUGT1 is enriched in the PI3K/AKT/mTOR signaling pathway (Fig. 7A). Through Western blot experiments, we observed that knocking down SUGT1 significantly inhibits the activity of the PI3K/AKT/mTOR signaling pathway (Fig. 7B). These results further support the pivotal role of SUGT1 in the pathophysiology of ovarian cancer.Fig. 7 SUGT1 affects osteosarcoma by regulating the PI3K/AKT/mTOR signaling pathway. (A) SUGT1 Enrichment in the PI3K/AKT/mTOR Signaling Pathway. (B) Western Blot Analysis of PI3K/AKT/mTOR Pathway Changes after SUGT1 Silencing.

Fig. 7

SUGT1 promotes the in vivo proliferation of ovarian cancer cells

To validate the effect of SUGT1 on the in vivo proliferation of ES-2 ovarian cancer cells, we conducted xenograft experiments using ES-2 cells with SUGT1 gene knockout (si-SUGT1) and untreated ES-2 cells in nude mice. Subsequently, we euthanized these mice and measured the volume of the transplanted tumors to assess the impact of SUGT1 on tumor growth. The results showed that the knockout of SUGT1 in ES-2 cells led to smaller tumor volumes and slower tumor growth, indicating the significant role of SUGT1 in the proliferation of ES-2 ovarian cancer cells (Fig. 8A-C).Fig. 8 SUGT1 induces the growth of osteosarcoma cells in vivo. (A) Images of subcutaneous xenograft tumors. (B) Subcutaneous xenograft tumor volume. (C) Subcutaneous xenograft tumor weight. (D) Multiple immunofluorescence staining of bax and bcl-2 in nude mouse tumor tissues. n = 3, ***P < 0.001.

Fig. 8

Furthermore, through multi-immunofluorescence analysis, we observed significant changes in the expression levels of key proteins in the shSUGT1 group compared to the control group. Specifically, the level of BCL-2 was significantly reduced, while the level of BAX protein was significantly increased (Fig. 8D). These findings suggest that SUGT1 may play a crucial role in the proliferation and survival of ovarian cancer cells, and its knockout may inhibit cell proliferation and promote apoptosis. These discoveries provide valuable clues for further research into the function of SUGT1 and potential therapeutic strategies.

ELF1 promoted SUGT1 transcription

To delve into the molecular mechanisms underlying the upregulation of SUGT1 expression, we utilized hTFtarget, ENCODE, and JASPAR databases to identify transcription factors related to the regulation of SUGT1 expression in ovarian cancer (OV). Through this database analysis, we preliminarily identified 17 potential upstream transcription factors (Fig. 9A). Subsequently, we analyzed the correlation between these transcription factors and SUGT1 expression using the TCGA database, finding significant correlations with ELF1 and SP4 (Fig. 9B). Given the potential importance of ELF1 in regulating SUGT1 expression, we chose it for further experimental validation. siRNA-mediated knockdown of ELF1 resulted in a significant decrease in SUGT1 mRNA levels (Fig. 9C), while knockdown of SP4 showed no significant effect (Supplementary Figure 1B). Additionally, we obtained DNA binding motif information for ELF1 from the JASPAR database (Fig. 9D, Supplementary Figure 1A).To further dissect how ELF1 mediates the transcriptional regulation of SUGT1, we constructed a series of truncated SUGT1 promoter-luciferase reporter constructs. Luciferase reporter assays revealed the presence of an ELF1 response element within the 0 to 800 base pair region of the SUGT1 gene promoter (Fig. 10A). Based on this finding, we conducted detailed mutational analyses of the predicted SUGT1 promoter sequence, particularly between bases 459 to 471, creating wild-type (SUGT1-WT) and mutant (SUGT1-Mut) luciferase reporter vectors. Further experiments showed that overexpression of CREB1 significantly increased the luciferase activity of the SUGT1-WT vector, while having a minimal effect on the SUGT1-Mut vector (Fig. 10B). ChIP assays also confirmed a direct interaction between the SUGT1 promoter region and CREB1 (Fig. 10C).In summary, these data clearly delineate the molecular mechanism by which ELF1 enhances the expression of SUGT1 in ovarian cancer by directly binding to the promoter region of SUGT1. This discovery not only enriches our understanding of the regulatory mechanisms of SUGT1 expression but also provides a scientific basis for potential therapeutic targets in ovarian cancer.Fig. 9 ELF1 promotes SUGT1 transcription. (A) Venn diagram showing transcription factors that bind to the SUGT1 promoter region, identified through the hTFtarget, ENCODE, and JASPAR databases. (B) Analysis of the correlation between transcription factors and SUGT1 in the TCGA database. (C) Impact of ELF1 knockdown on SUGT1 mRNA expression. (D) Schematic of the putative ELF1 binding motif and its relative score determined using JASPAR. Cells were transfected with the full-length SUGT1 promoter or one of three truncation mutants, and luciferase activity was analyzed post-transfection.

Fig. 9

Fig. 10 ELF1 promotes SUGT1 transcription. (A-B) Cells were transfected with SUGT1-WT or SUGT1-MUT promoter constructs, and luciferase activity was analyzed post-transfection. (C) ChIP analysis of ELF1 binding to the SUGT1 promoter. Input and IgG served as positive and negative controls, respectively. n = 3, *P < 0.05, ****P < 0.0001.

Fig. 10

Discussion

SUGT1, also known as S-phase kinase-associated protein 1 (skp1), has been proven to play a vital role in the early cellular response to LIV-1 infection [3]. Previous research has shown that the co-chaperone of heat shock protein 90, SUGT1, participates in the innate response in mammals and that the suppressor of its G2 allele may bind proteins to microtubules [1,9,10]. Further studies identified SUGT1 as an MHC-Ⅰ and MHC-Ⅱ co-regulator that mimics RFX5 function, as well as interact with ribosome elongation factor eEF1A1 that binds defective ribosomal products and stimulates their degradation [4,11,12]. Apart from these, detailed studies have focused on the correlation between the expression of SUGT1 and cancer. In patients with colorectal cancer (CRC), It was shown by Masaaki Iwatsuki et al. that the mean level of SUGT1 mRNA expression in tumor tissue specimens was significantly higher than that in normal tissues, and that the high SUGT1 expression group had a significantly worse prognosis than the low expression group in terms of recurrence frequency [7].To be specific, SUGT1 contributes to cancer development by stabilizing oncoproteins and that SUGT1 is a potential therapeutic target [6]. While the relationship between SUGT1 and other types of cancer remains a mystery. Current research has proved the association between immune infiltration and cancer progression through the tumor microenvironment (TME) and emphasized the vital role immune cells played [13,14]. The role of methylation has progressively come into focus from a micromolecular perspective, and it will take significant work to put the prognostic and therapeutic benefits of DNA and RNA methylations to use in clinical settings [15,16]. However, the crosslink between SUGT1 and immune infiltration, and gene methylation still warrants further research.

From a macroscopic viewpoint, we analysed the relationship between the expression of SUGT1 and cancer prognosis. OS and PFI Kaplan-Meier survival curve showed that the higher expression of SUGT1 was associated with poorer prognosis mainly in HNSC and OV, and on the contrary, the lower expression of SUGT1 was linked to poorer prognosis in KIRC and LGG, underlining that SUGT1 could function as a potential prognostic indicator in cancers. Further ROC analysis further indicated that SUGT1 has a significant diagnostic value in cancer and could act as an independent biomarker.

Through the study of clinical samples, we found that SUGT1 was highly expressed in ovarian cancer tissues compared to normal tissues. We further verified that at the cellular level, SUGT1 is highly expressed in ovarian cancer cells, and knockdown of SUGT1 can inhibit the proliferation and migration of ES-2 and OVCAR-3 cells.In the in vivo experiment, we implanted ovarian cancer cells with knocked down SUGT1 expression into nude mice. The results showed that, compared to the control group without SUGT1 knockdown, the tumors formed by the SUGT1 knockdown cells in the nude mice were significantly smaller in size. This finding further demonstrates the crucial role of SUGT1 in the development of ovarian cancer and provides valuable experimental evidence for the development of new ovarian cancer treatment strategies targeting SUGT1.

Transcriptional regulation occurs at the level of the gene promoter and is tightly controlled by stage-specific transcription factors, including transcriptional activation and repression. Given our discovery of SUGT1 overexpression and its significant carcinogenic effects in hepatocellular carcinoma (HCC), our aim is to identify the positive transcription factor regulating SUGT1 expression. Our findings indicate that ELF1 stimulates the transcription of SUGT1, which is a key transcription factor involved in tumor growth.

ELF1 (E74-Like Factor 1) is a protein belonging to the ETS transcription factor family. Previous studies have found that its role includes cell proliferation, differentiation and development [17]. In cancer research, members of the ETS family have been extensively studied due to their important roles in regulating gene expression and influencing cell behavior [[18], [19], [20]]. A 2019 genome-wide study found that this gene can increase the susceptibility of East Asian women to ovarian cancer [21], and research by P Asiabi et al. further pointed out that this gene plays an extremely critical role in the progression of multiple types of ovarian tumors [22]. Unfortunately, there are no studies that directly point out how this gene promotes the progression of ovarian cancer. Our research shows that this gene can promote the malignant behavior of ovarian cancer through the PI3K/AKT pathway by promoting the expression of SUGT1. To a certain extent, it can be regarded as a supplement to this field.

In addition, our study also pointed out another important mechanism, that is, SUGT1 can promote the glycolysis process in ovarian cancer. Glycolysis is the process by which cells break down glucose to produce energy and occurs in both normal and cancer cells. Cancer cells often exhibit the "Warburg effect", which preferentially produces energy through glycolysis rather than oxidative phosphorylation, even in the presence of sufficient oxygen [23]. Previous studies have shown that ovarian cancer cells can obtain enough energy to support their rapid proliferation and growth in a tumor microenvironment with insufficient oxygen supply by enhancing glycolysis [24], and in a hypoxic environment, the glycolysis produced during glycolysis large amounts of lactic acid can lower the pH of the tumor microenvironment, which helps tumor cells evade immune surveillance and promote local invasion [25,26]. Notably, glycolysis is associated with the activation of multiple signaling pathways that promote cancer cell survival and drug resistance. Previous studies have pointed out that the PI3K/Akt/mTOR pathway is often activated in ovarian cancer, and they can directly or indirectly promote glycolysis [27,28]. This is consistent with the conclusion of our study, and because of the regulatory effect of SUGT1 on this pathway, it further increases the feasibility of using this gene as a therapeutic target. It is worth noting that many studies have been devoted to regulating the glycolysis process in the progression of ovarian cancer. Some key enzymes in the glycolysis process (hexokinase, lactate dehydrogenase) and key proteins (such as Glucose transporter) and other related inhibitors have been proven to have good application prospects, and our research further enriches insights in this field [29,30].

There remain several limitations to our investigation. Firstly, as a bioinformatics analysis, our results still need a cellular or animal experiments to verify, as well as specific pathways from a molecular aspect. Secondly, since our data are mainly from databases that could not take age, sex, and complications into consideration, there might exist some sample bias due to innate research shortages. Thus, greater sample size investigations are warranted.

Conclusions

In conclusion, we explored the correlation between SUGT1 and cancer, especially in OV, and found that the expression of SUGT1 was significantly linked to the prognosis of cancer and immune infiltration, which broadened a new horizon for clinical therapy and could become a potential immunotherapeutic target.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Data are available at the links mentioned throughout the manuscript.

Funding

This work was financially supported by the Social Development Plan of Taizhou, China (SSF20210116 ), and the project of Taizhou People's Hospital (ZL202023 ).

CRediT authorship contribution statement

Miao Ke: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jie Xu: Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Data curation, Conceptualization. Ye Ouyang: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Data curation. Junyu Chen: Writing – review & editing, Supervision, Resources. Donglan Yuan: Writing – review & editing, Supervision, Resources, Investigation. Ting Guo: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Investigation, Funding acquisition.

Declaration of competing interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Appendix Supplementary materials

Image, application 1

Acknowledgement

We thank the mentioned public databases for providing us the data and analytical tools.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.102088.
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