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SQLE—a promising prognostic biomarker in cervical cancer: implications for tumor malignant behavior, cholesterol synthesis, epithelial-mesenchymal transition, and immune infiltration

BMC Cancer volume 24, Article number: 1133 (2024) Cite this article

Abstract

Background

Cervical cancer, encompassing squamous cell carcinoma and endocervical adenocarcinoma (CESC), presents a considerable risk to the well-being of women. Recent studies have reported that squalene epoxidase (SQLE) is overexpressed in several cancers, which contributes to cancer development.

Methods

RNA sequencing data for SQLE were obtained from The Cancer Genome Atlas. In vitro experiments, including colorimetry, colony formation, Transwell, RT-qPCR, and Western blotting were performed. Furthermore, a transplanted CESC nude mouse model was constructed to validate the tumorigenic activity of SQLE in vivo. Associations among the SQLE expression profiles, differentially expressed genes (DEGs), immune infiltration, and chemosensitivity were examined. The prognostic value of genetic changes and DNA methylation in SQLE were also assessed.

Results

SQLE mRNA expression was significantly increased in CESC. ROC analysis revealed the strong diagnostic ability of SQLE toward CESC. Patients with high SQLE expression experienced shorter overall survival. The promotional effects of SQLE on cancer cell proliferation, metastasis, cholesterol synthesis, and EMT were emphasized. DEGs functional enrichment analysis revealed the signaling pathways and biological processes. Notably, a connection existed between the SQLE expression and the presence of immune cells as well as the activation of immune checkpoints. Increased SQLE expressions exhibited increased chemotherapeutic responses. SQLE methylation status was significantly associated with CESC prognosis.

Conclusion

SQLE significantly affects CESC prognosis, malignant behavior, cholesterol synthesis, EMT, and immune infiltration; thereby offering diagnostic and indicator roles in CESC. Thus, SQLE can be a novel therapeutic target in CESC treatment.

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Introduction

Cervical cancer, comprising squamous cell carcinoma and endocervical adenocarcinoma (CESC), constitutes a substantial portion of the global mortality attributed to cancers affecting women. In the year 2020, it registered 604,127 new cases and 341,831 fatalities on a global scale [1]. Although various screening methods, including, ThinPrep cytology, pap smear, and human papillomavirus tests, have helped in partially decreasing the CESC incidence rate, they have limited specificity and sensitivity along with significant laboratory needs and dependability on the clinician’s expertise [2]. Furthermore, once CESC progresses to the metastatic or recurrent stage, it becomes incurable, with an overall survival time (OS) of approximately 12 months [3]. Over 85% of CESC cases occur in low- and middle-income countries, and the mortality rate is six times higher relative to that in developed countries [4]. Therefore, novel molecular markers and advanced approaches are urgently warranted to understand the complicated molecular mechanisms underlying CESC.

Squalene epoxidase (SQLE) serves as a crucial enzyme downstream in the regulation of cholesterol synthesis. Positioned within the endoplasmic reticulum, it acts as an oxidizing agent, converting squalene into 2,3-epoxysqualene, a pivotal intermediate in the biosynthesis of sterols, cholesterol, and various essential molecules [5]. The SQLE expression is extremely low in most non-cholesterol tissues, but most abundant in the liver, followed by that in the intestine, skin, and nerve tissues. SREBP2 and cholesterol directly regulate the SQLE expression. In a high-cholesterol state, cholesterol degrades SQLE via E3 ubiquitin ligase membrane-associated circular CH protein 6. On the other hand, in a low-cholesterol state, SREBP2 can promote SQLE synthesis [6, 7]. Hence, SQLE performs a crucial function in the regulation of cholesterol equilibrium within the body.

Numerous recent investigations have indicated a strong association between SQLE and the initiation and progression of malignant tumors. Tumors often exhibit aberrant SQLE expression, accompanied by disruptions in metabolism and hormonal imbalances. SQLE is implicated in the key processes such as the cell cycle, epithelial-mesenchymal transition (EMT), cellular proliferation, migration, invasion, and other malignant traits associated with tumors. Acting as a carcinogenic factor, SQLE contributes to the initiation, advancement, and metastasis of tumors [8]. SQLE is abnormally expressed in cancers, including the lung, liver, colon, bladder, and prostate cancers, possibly leading to poor patient prognosis [9]. However, although some studies suggest that the SQLE expression is related to the prognosis of CESC to an extent [10, 11], no studies have yet reported the significance of SQLE in the development and tumor microenvironment (TME) of CESC; furthermore, its biological function in CESC remains unelucidated.

In this current research, we investigated the prognostic and predictive importance of SQLE mRNA expression in CESC by analyzing The Cancer Genome Atlas (TCGA) datasets. In addition, we explored the involvement of SQLE in the progression, cholesterol biosynthesis, and EMT of CESC through both in vitro and in vivo investigations. Furthermore, we scrutinized the connection among the SQLE mRNA expression and immune cell infiltration within the CESC tissues, tracked alterations in immune checkpoints, and delved into chemosensitivity and epigenetic factors. Our study findings offer novel insights into the pathogenic role of SQLE in CESC, which can be beneficial for developing accurate diagnostic and therapeutic approaches for this disease.

Methods

Acquiring expression data of SQLE in carcinoma samples

The expression analysis of SQLE in pan-cancer was conducted using the Timer 2.0 database. Information on patients with CESC was gathered using data from the TCGA database, encompassing SQLE mRNA levels and clinicopathological features, including age, TNM, and clinical staging. Based on the level of SQLE expression, we assigned the samples to either the high or low expression groups.

Acquisition of clinical samples

In 2022, we obtained eight sets of primary CESC tissues along with their matching neighboring normal tissues from patients who had surgery at the Department of Obstetrics and Gynecology in our institution. The following criteria were required for inclusiveness: a verified diagnosis of CESC and no prior history of radiotherapy or chemotherapy before the surgery.

Immunohistochemistry (IHC)

The specimens were treated with the primary antibodies against SQLE (1:200, Proteintech, USA) and Ki-67 (1:2000, Proteintech) at 4°C for overnight. Subsequently, they were treated with secondary antibodies (1:1000, ab288151, Abcam, UK) for 120 min at room temperature (RT) of 25°C. Next, 3,3’-diaminobenzidine chromogenic solution (Absin, China) was utilized for color development. Hematoxylin (Beyotime, China) was used to counterstain the nuclei for 3 min at RT. Thereafter, all histological samples were dehydrated, preserved in Histo-Clear, and sealed with neutral balsam, followed by analyses by light microscopy (Eclipse Ni, Nikon, Japan).

Western blotting

The cells and tissues underwent treatment with RIPA lysis buffer (Beyotime) to extract their total proteins contents (20 µg/sample). Next, 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis was performed for protein separation, followed by gel transfer onto a PVDF membrane (Bio-Rad, USA). The membrane was blocked in 5% skimmed milk for 1 h at RT. Next, the membrane was incubated overnight at 4 °C with rabbit polyclonal primary antibodies against SQLE (1:1000, Proteintech), N-cadherin (1:3000, Proteintech), E-cadherin (1:5000, Proteintech), or vimentin (1:1000, Proteintech). GAPDH (1:20000, Proteintech) was used as the internal protein standard. Subsequently, the membrane was exposed to the goat anti-rabbit secondary antibody (1:5000, Proteintech) for 2 h at RT. In a darkroom, prepared ECL luminescent liquid (Meilunbio, China) was added to the PVDF membranes, and X-ray films (Kodak, USA) were placed on top. The X-ray film was exposed for 30 s using a press box and then successively immersed in developing and fixing solutions for 1–3 min. Finally, the X-ray films were scanned to visualize the immunoblots.

Quantitative real-time polymerase chain reaction (RT-qPCR)

The transfected cells and xenograft tumors were treated with RNA extraction kit (Tiangen, China) for total RNA extraction. Then, reverse transcription was performed by utilizing the SweScript RT I First-Strand cDNA Synthesis Kit (Servicebio, China). The synthesized cDNA was mixed with SYBR Green qPCR Master Mix (Universal) (MCE, USA) and specific primers were used for RT-qPCR, which was subsequently amplified on the RT-qPCR system (CFX384, Bio-Rad). Using the 2−ΔΔCT method, the relative expression of SQLE was ascertained, with GAPDH serving as a standardized internal control. The following primers of SQLE were used in the study: forward: 5’-TGACAATTCTCATCTGAGGTCCA-3’ and reverse: 5’ -CCTGAGCAAGGATATTCACGACA-3’.

SQLE interference using short hairpin RNA (shRNA) in cell culture

Human HeLa and SiHa cells (Procell, China) were cultivated in minimum essential medium (HyClone, USA) complemented with 10% fetal bovine serum (FBS) (HyClone) and maintained in a controlled environment at 37°C under a 5% CO2 atmosphere. Lentiviral vectors (Hanbio, China) were used to create stably transfected HeLa and SiHa cell lines with decreased SQLE expression. These vectors either contained SQLE-specific shRNA (sh-SQLE) or a negative control (sh-NC). The sequence that targeted SQLE was as follows: 5’-GCACCACAGTTTAAAGCAAAT-3’. Transfection reagent polybrene (Hanbio) was used to perform the cell transfection. After the transfection period, the stably transfected cells were extracted in puromycin as per the manufacturer’s recommendation. Western blotting and RT-qPCR were performed to assess the transfection effectiveness, and the harvested cells were saved for subsequent research.

Cell counting Kit-8 (CCK-8) assay

At the end of the transfection period, 0.8 × 105 cells/mL of HeLa and SiHa cells were cultured in a 96-well plate and incubated at 37 °C. For the evaluation of the effect of cholesterol on the cell growth, the same cell culture conditions were maintained. After cell adhesion and proliferation, the cells were treated with a pre-mixed solution of cholesterol (Sigma-Aldrich, USA) and minimum essential medium (HyClone), with cholesterol concentrations of 0–20 µM. The CCK-8 kit (Abcam) was used exactly as directed by the manufacturer. In each well, 10% of the CCK-8 reagent was added, and this process was replicated five times across separate wells. After 4 h of continuous incubation at 37 °C, the optical density was calculated at 450 nm with a microplate reader (Cytation 5, BioTek, USA). The absorbance measurements acquired were used to assess the cell viability.

Colony formation assay

Initially, we plated 700 cells per well in a 6-well plate. After incubating for 10 days, we refreshed the medium and rinsed the cells with phosphate-buffered saline (PBS). Subsequently, we fixed the colonies by treating the cells with 4% paraformaldehyde (Biosharp, China) for 30 min. Next, we stained the cells with crystal violet (Beyotime) for 10 min. We photographed the colonies and conducted analysis using an optical microscope (Nikon, Japan). Colonies that were visible and contained a minimum of 50 cells were identified and counted.

Scratch assay

We conducted a scratch assay to evaluate the migratory potential of HeLa and SiHa cells. Transfected cells were cultured in a 6-well plate until near confluency. After 48 h, longitudinal wounds were created on the cell monolayers using sterilized 20-µL pipette tips. Next, the detached cells were rinsed in PBS. Photographs of scratch dimensions were taken at 0 and 24 h post-culturing with an optical microscope. The migratory index was subsequently quantified utilizing ImageJ software (NIH) with the application of the following formula: Cell migration index = (0 h scratch area – 24 h scratch area) / 0 h scratch area.

Analyses of the migration characteristics of HeLa and SiHa cells

Transfected HeLa and SiHa cells were maintained in a serum-free medium and then placed in the upper compartments of Transwell inserts coated with Matrigel (BD Biosciences, USA) that had been pre-treated. In the lower chambers, we added minimal essential medium containing 10% FBS. After a 24 h incubation period, the cells were allowed to migrate through the Matrigel-coated membrane. Next, the cells were fixed using 4% paraformaldehyde (Biosharp) and stained with Giemsa (Leagene, China). The number of invading cells in randomly selected areas was subsequently determined using a light microscope at 100× magnification.

In vivo study

Female BALB/cA-nu nude mice (age: 7 weeks, weight: 19–21 g) (Changsheng Biotechnology, China) were cared for in a specific pathogen-free environment. Stably transfected HeLa cells were harvested and resuspended in PBS solution at a concentration of 1 × 108 cells/mL. Inoculate 0.1 mL of the cell suspension subcutaneously into the dorsal region under the armpit of nude mice. Each group, including the control group and SQLE knockdown group, consists of six mice. Following 6 days of implantation, noticeable nodules emerged beneath the skin, displaying gradual growth over time. The tumor measurements, including the length and width, were regularly gauged with calipers. On day 21, humane euthanasia was performed with overdose pentobarbital injection, and the transplanted tumors were excised, photographed, weighed, and measured. Tumor volume was calculated using the formula:

$$\:\text{T}\text{u}\text{m}\text{o}\text{r}\:\text{v}\text{o}\text{l}\text{u}\text{m}\text{e}=\frac{\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\times\:\:{\text{W}\text{i}\text{d}\text{t}\text{h}}^{2}}{2}$$

The excised tumor specimens were bifurcated: one segment was promptly frozen at -196 °C and the other was immersed in a 4% paraformaldehyde (Biosharp).

Quantification of total cholesterol levels

Transfected HeLa and SiHa cells (1 × 106), xenograft tumor tissues (10 mg), and the patient’s tissue (10 mg) samples were rinsed in cold PBS. The samples were then resuspended in a 200 µL mixture of chloroform: isopropanol: NP-40 (7:11:0.1) using a microhomogenizer to extract the lipids. After centrifuging the samples at 15,000 ×g for 8 min using a centrifuge (Microfuge 22R, Beckman Coulter, USA), the organic phase was moved into a fresh tube without the pellet. The extract was then dried in the air at 50 °C to eliminate traces of chloroform and vacuumed for 30 min to eliminate any remaining organic solvent traces. Subsequently, the extract was dissolved by vortexing it with 200 µL of Assay Buffer II/Assay Buffer. Based on the manufacturer’s recommendation, the reaction mixture for each sample was generated using the Cholesterol/Cholesteryl ester quantitative kit (Abcam). The colorimetric assay method was employed to measure the absorbance at 570 nm with a microplate reader (Cytation 5, BioTek), which was then analyzed to determine the total cholesterol content. Triplicate measurements were performed for each sample to ensure accuracy.

Exploration and profiling of differentially expressed genes (DEGs)

To identify the genes with specifically expressed patterns and their functional relevance in CESC, bioinformatics analysis was conducted using publicly available datasets [12, 13]. Particularly, the LinkedOmics database was utilized to explore the DEGs between the high and low expression groups of SQLE in the CESC data obtained from TCGA [14]. The data were analyzed by constructing volcano plots and applying the following criteria for significance: FDR-optimized P < 0.05, fold-shift > 1.5. From these analyses, the 50 DEGs with the maximum up- and down-regulation were identified and presented as a heatmap. For additional functional analysis, data retrieved from Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for biological annotation and pathway mapping, respectively.

Correlation analysis between SQLE and TME

A single-sample GSEA examination was conducted using the R package GSVA to determine immune infiltration in CESC. We then quantified the infiltration rates of 24 immune cell types to comprehend the immune milieu in CESC. Eight immune checkpoint-relevant transcripts, namely LAG3, CTLA4, TIGIT, SIGLEC15, HAVCR2, CD274, PDCD1, and PDCD1LG2, were selected and their expression data were collected. Finally, we compared the correlation between SQLE expression and immune checkpoints.

Predictive assessment of chemotherapy responses

The R package “pRRophetic” was used to predict the chemosensitivity of each CESC sample; this software is based on the largest pharmacogenomics database (Genomics of Drug Sensitivity in Cancer, https://www.cancerrxgene.org/). Moreover, the half-maximal inhibitory concentrations (IC50) for several chemotherapy agents were assessed by applying a ridge-regression model analysis with predetermined variables set at their default levels.

Gene alteration and methylation patterns analyses

We analyzed CESC patients’ data from two integrated databases, TCGA Firehouse Legacy and TCGA PanCancer Atlas, using cBioPortal (www.cBioPortal.org). The analysis focused on SQLE gene alterations, encompassing both gene alteration frequency and the impact of gene alteration on patients’ survival. We utilized Kaplan–Meier / log-rank testing to evaluate the variation between the survival curves. To investigate the significance of CpG methylation in SQLE, we examined the predictive values of methylation-related data, as obtained from MethSurv (biit.cs.ut.ee/methsurv/). Finally, we computed the survival outcome based on the collected outcomes.

Statistical analyses

All the experiments in this study were repeated thrice. For statistical analyses, R software (version 4.3.2) was used. Through the Wilcoxon test, we compared two cohorts and used the statistical tools (https://www.home-for-researchers.com/) to construct the ROC curves to assess the analytic significance. The medical parameters were correlated with the transcription of SQLE via Chi-square tests. Using online tools, the cohorts were compared for OS through the Kaplan–Meier analysis. The prognostic variables for CESC were determined via Cox regression analysis by using both the univariate and multivariate models. The IC50 analyses assessed the cytotoxicity of the tested chemotherapeutic agents. P < 0.05 was deemed as indicative of statistical significance.

Results

Expression analysis of SQLE in cancer

The SQLE expression was determined in different tumor models present in the Timer 2.0 pan-cancer database. Eleven tumors exhibited SQLE overexpression: BLCA, BRCA, CESC, COAD, ESCA, HNSC, LIHC, LUSC, READ, STAD, and UCEC; on the other hand, four tumors exhibited SQLE downregulation: KIRC, KIRP, PRAD, and THCA (Fig. 1A). By analyzing CESC data, we observed that SQLE was expressed at considerably higher levels in cancer samples than in healthy ones (P < 0.001, Fig. 1B). Furthermore, subgroup analysis was conducted to explore the correlation between SQLE expression and the clinical characteristics of the patients. Figure 1C-J illustrated that SQLE expression was positively associated with T stage (P < 0.01), N stage (P < 0.05), clinical stage (P < 0.05), and negatively associated with the patient’s weight (P < 0.05). SQLE expression was also associated with tumor histological types (P < 0.001). However, it was not correlated to the patient’s age, M stage, and tumor histological grade (P > 0.05). In addition, according to the protein expression analysis, SQLE had elevated levels in cancer specimens relative to their relevant controls in most clinical samples (Fig. 1K-L), consistent with the data retrieved from TCGA.

Fig. 1
figure 1

The expression of SQLE in pan-cancer and CESC. (A) The mRNA levels of SQLE in cancerous and noncancerous tissues were assessed from the pan-cancer data. (B) The relative SQLE mRNA expressions in CESC and noncancerous tissues. (C–J) Correlation between the SQLE expressions and clinicopathological parameters of CESC patients, including age, TNM stage, clinical stage, histological type, and histological grade. The SQLE expressions in CESC tissues and adjacent healthy tissues were assessed by (K) IHC staining and (L) Western blotting. Statistical significance, *P < 0.05, **P < 0.01, ***P < 0.001. ns denotes not significant

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Analyzing the relationship between SQLE expression and clinical data of CESC patients

We divided the sample datasets in TCGA into cohorts with high and low SQLE expression. The clinical characteristics and demographics, including age, weight, TNM classification, histological type, and primary therapy outcome, were compared between these two groups (Table 1). We observed that the SQLE mRNA expression was strongly linked to patient’s weight (P = 0.019), histological type (P < 0.001), and primary therapy outcome (P = 0.003). This correlation between the SQLE expression and specific clinical features underscores its potential impact on the clinical management of CESC, particularly in stratifying patients based on tumor progression.

Table 1 Association between the SQLE expression and clinical data of CESC patients
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SQLE is a strong diagnostic indicator of CESC development

We constructed the receiver operating characteristic (ROC) curves using the obtained SQLE expression data (Fig. 2A) and obtained an area under the curve (AUC) of 0.847, suggesting excellent diagnostic efficiency. Furthermore, we conducted an AUC analysis of the different CESC tumor stages and observed outstanding correspondence, with AUC scores of 0.836, 0.864, 0.851, and 0.895 for stages I, II, III, and IV, respectively (Fig. 2B-E). Building on these diagnostic capabilities, SQLE’s utility extends to various differentiating stages of CESC, which further highlights its robustness as a clinical diagnostic tool.

Fig. 2
figure 2

ROC curves to evaluate the diagnostic value of SQLE in CESC cohorts. The curves represent the sensitivity and specificity of SQLE in distinguishing healthy samples (A) from CESC samples at different stages, including (B) stage I, (C) stage II, (D) stage III, and (E) stage IV tumors

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SQLE is a prognostic indicator of CESC

Kaplan–Meier methods were used to further analyze the predictive significance of SQLE expression. We confirmed that SQLE overexpression was strongly related to the decreased survival rates of all patients (P = 0.043) (Fig. 3A). CESC subgroup analysis revealed that the SQLE expression was related to the survival outcomes in T3/T4 (P = 0.01) and III/IV (P = 0.021) stages but not with that in other subgroups (P > 0.05) (Fig. 3B-M). Table 1 presents the number of patients included in each subgroup. Furthermore, univariate analysis illustrated that low OS was strongly related to T (P < 0.001), N (P = 0.002), and M stages (P = 0.023); clinical phases (P < 0.001); and SQLE expression (P = 0.019). Moreover, multivariate analysis using Cox regression models revealed T staging and SQLE expression as independent risk variables for CESC prognosis (hazard ratio [HR] = 8.921, P = 0.009 and HR = 1.789, P = 0.039, respectively) (Table 2). The association of SQLE with poorer survival outcomes further positions it as a critical prognostic biomarker, potentially guiding therapeutic strategies for high-risk CESC patients.

Fig. 3
figure 3

Survival analysis for CESC patients based on the SQLE expression. (A) Kaplan–Meier plot of OS rates for all CESC patients. (B–M) Subgroup analysis based on age, TNM stage, clinical stage, and histological grade

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Table 2 Univariate and multivariate analyses of the OS in CESC patients
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Involvement of SQLE in CESC development via in vitro studies

To elucidate the function of SQLE in CESC development, we used shRNA to knockdown SQLE in both HeLa and SiHa cells. We next performed RT-qPCR and Western blotting to determine the knockdown efficiency in these cell lines (Fig. 4A-B). The CCK-8 assay showed a significant reduction in cell proliferation in both HeLa (P < 0.01) and SiHa (P < 0.001) cells following SQLE knockdown compared to the control (Fig. 4C-D). Similarly, the colony formation assay demonstrated a marked decrease in colony numbers in both HeLa (P < 0.001) and SiHa (P < 0.001) cells transfected with sh-SQLE (Fig. 4E-F). Furthermore, the scratch assay revealed that SQLE knockdown significantly inhibited cell migration in both HeLa (P < 0.001) and SiHa (P < 0.001) cells compared to sh-NC cells (Fig. 4G-H). The Transwell invasion assay further confirmed that the invasive capabilities of HeLa (P < 0.001) and SiHa (P < 0.0001) cells were significantly reduced after SQLE knockdown (Fig. 4I-J). These in vitro results provide compelling evidence for SQLE’s role in promoting cancer cell proliferation, migration, and invasion, thereby laying the groundwork for further mechanistic studies.

Fig. 4
figure 4

Knockdown of SQLE suppresses CESC cell proliferation and invasion. (A, B) The knockdown efficiency of sh-SQLE in HeLa (A) and SiHa (B) cells was determined by RT-qPCR and Western blotting. (C, D) CCK-8 assay demonstrating reduced cell growth in HeLa (C) and SiHa (D) cells transfected with sh-SQLE. (E, F) Colony forming assay displaying reduced colony numbers in HeLa (E) and SiHa (F) cells transfected with sh-SQLE. (G, H) Scratch assay demonstrating inhibited cell migration in HeLa (G) and SiHa (H )cells transfected with sh-SQLE compared to that in sh-NC cells. (I, J) Transwell assay demonstrating reduced invasion ability in HeLa (I) and SiHa (J) cells transfected with sh-SQLE. Error bars represent the mean ± SD. Statistical significance, **P < 0.01, ***P < 0.001, ****P < 0.0001

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Involvement of SQLE in CESC development via in vivo studies

Next, we performed in vivo studies to further validate the abovementioned outcomes. SQLE knockdown HeLa cells were implanted into nude mice and the tumor features were analyzed after 21 days. In our transplanted CESC mice model, the tumor weight of sh-NC and sh-SQLE cohorts was 163.82 ± 25.89 and 48.82 ± 13.68 mg, respectively (P < 0.01; Fig. 5A-B); on the other hand, the tumor volume was 146.52 ± 46.89 and 43.73 ± 14.45 mm3, respectively (P < 0.001) (Fig. 5C), at the endpoint date. In addition, RT-qPCR demonstrated that SQLE expression was markedly decreased in the sh-SQLE cohort (P < 0.001) (Fig. 5D). Moreover, tumor tissues from the sh-SQLE cohort exhibited a comparatively decreased level of malignancy in histological grading and decreased expression of the proliferation-associated indicator Ki-67 when compared with those from the sh-NC cohort (Fig. 5E-F). These in vivo findings align with the in vitro results, thereby reinforcing the hypothesis that SQLE is a key driver of tumor growth and aggressiveness in CESC.

Fig. 5
figure 5

SQLE knockdown attenuates tumor growth in CESC models. The assessment of (A) tumor size, (B) weight, and (C) volume in xenograft models. (D) RT-qPCR, (E) histological staining, and (F) IHC analysis of xenograft tumors. Typical Ki-67 positive cells are indicated by red arrows. Error bars indicate the mean ± SD. Statistical significance, **P < 0.01, ***P < 0.001

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SQLE expression facilitates EMT in CESC

By analyzing SQLE protein levels in transfected cells and the xenograft tumors of CESC, we made an essential observation: SQLE downregulation in HeLa cells significantly elevated the levels of the EMT marker E-cadherin, while the protein levels of N-cadherin and vimentin decreased (Fig. 6A). Similar changes were observed in SiHa cells, where SQLE knockdown also led to an increase in E-cadherin levels and a decrease in N-cadherin and vimentin levels (Fig. 6B). Consistently, these findings were also observed in the xenograft tumors. Notably, compared with the subcutaneous tumors generated by the negative control, those generated by sh-SQLE revealed considerably elevated level of E-cadherin but decreased levels of N-cadherin and vimentin proteins (Fig. 6C). Collectively, our findings imply that SQLE plays an essential function in developing EMT—a process closely associated with the migratory and invasive properties of cancer cells. By influencing the expression of EMT markers, SQLE appears to facilitate the transition of epithelial cells to a more mesenchymal phenotype, which is critical for tumor metastasis.

Fig. 6
figure 6

SQLE knockdown significantly diminished EMT in CESC in vitro and in vivo. (A) Western blotting was performed to detect the expression of EMT markers E-cadherin, N-cadherin, and Vimentin in sh-NC- and sh-SQLE-transfected HeLa cells. (B) Western blotting was performed to detect the expression of EMT markers E-cadherin, N-cadherin, and Vimentin in sh-NC- and sh-SQLE-transfected SiHa cells. (C) Western blotting was performed to detect the expression of EMT markers E-cadherin, N-cadherin, and Vimentin in sh-NC and sh-SQLE xenograft tumors

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SQLE expression is closely associated with cholesterol levels in CESC

We measured cholesterol levels in patient tissues, cells, and xenograft tumors to determine the significance of SQLE and cholesterol metabolism in CESC. Interestingly, the cholesterol levels were higher in eight tumor tissue samples than in the matched normal tissue samples (P < 0.001 and P < 0.0001) (Fig. 7A). Importantly, SQLE knockdown significantly decreased the intracellular cholesterol levels relative to that in the control group (P < 0.0001) (Fig. 7B-C). Consistently, in the xenograft mice model, cholesterol levels were higher in the transplanted tumors in the control group. In contrast, the tumors in the sh-SQLE group displayed a noticeable decrease (P < 0.05) (Fig. 7D). In addition, HeLa and SiHa cells were incubated with different concentrations of cholesterol in vitro, and the resultant cell proliferation was detected through the CCK-8 assay. The results, expressed as relative cell growth with 0 µM cholesterol as the control, demonstrated that cholesterol concentrations of 5, 10, 15, and 20 µM could significantly enhance HeLa and SiHa cell proliferation (Fig. 7E-F). This relationship between the SQLE expression and cholesterol levels suggests a broader role for cholesterol metabolism in the pathophysiology of CESC, implying the critical role of cholesterol homeostasis in tumor progression.

Fig. 7
figure 7

SQLE expression was closely related to the cholesterol levels in vitro and in vivo of CESC. (A) Measurement of the cholesterol content in eight collected CESC tumor tissues and their matched noncancerous tissues. (B) The measurement of intracellular cholesterol level in sh-NC- or sh-SQLE-transfected HeLa cells. (C) The measurement of intracellular cholesterol level in sh-NC- or sh-SQLE-transfected SiHa cells. (D) The measurement of intracellular cholesterol in sh-NC and sh-SQLE xenograft tumors. (E) Measurement of the effects of extracellular cholesterol on the proliferation of Hela cells. (F) Measurement of the effects of extracellular cholesterol on the proliferation of SiHa cells. Statistical significance, *P < 0.05, ***P < 0.001, ****P < 0.0001

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Analysis of the DEGs associated with SQLE: identification and functional insights

The LinkedOmics database was used to investigate and annotate the DEGs between the high and low expression groups of SQLE. Figure 8A displays the results of our analysis: 1567 genes (represented using red dots) were positively associated with SQLE, whereas 894 genes (represented using blue dots) were negatively associated. From the positively and negatively correlated gene sets, we chose 50 genes each and constructed a heatmap (Fig. 8B). To determine the potential involvement of these DEGs in specific molecular and biological processes, we performed the KEGG pathway and GO enrichment analyses. The KEGG analysis revealed that the DEGs are primarily involved in proteoglycans in cancer, the PI3K–Akt signaling pathway, human papillomavirus infection, and the hippo signaling pathway. On the other hand, GO analysis revealed the significant role of these SQLE-associated genes in the growth and differentiation of the skin, epidermis, and keratinocytes (Fig. 8C). The identification of these DEGs provides a molecular basis for understanding how SQLE may influence tumorigenesis through key signaling pathways, thereby offering targets for potential therapeutic interventions.

Fig. 8
figure 8

Identification of DEGs associated with SQLE in CESC. (A) Volcano-plot analysis of DEGs, with red and blue colors indicating the upregulated and downregulated genes, respectively. (B) Heat-map displayed the results of DEGs’ hierarchical clustering analysis. Red and blue colors represent upregulated and downregulated DEGs, respectively. (C) KEGG/GO pathway enrichment analysis of DEGs

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SQLE expression is associated with TME

We determined the correlation between SQLE mRNA expression in CESC samples and immune cell infiltration, as well as the immune checkpoints. Higher SQLE mRNA expression was positively associated with central memory T cells (Tcm) (P < 0.001) and macrophages (P = 0.006) but negatively correlated with CD8+ T cell invasiveness (P = 0.024), cytotoxic cells (P = 0.010), mast cells (P = 0.004), eosinophils (P = 0.001), natural killer (NK) cells (P < 0.001), Th17 cells (P < 0.001), effector memory T Cell (Tem) (P < 0.001), plasmacytoid dendritic cells (pDCs) (P < 0.001), and NKCD56bright cells (P < 0.001) (Fig. 9A-B). Furthermore, we investigated the predictive capacity of SQLE in terms of immunotherapy. SQLE mRNA expression was inversely associated with LAG3, PDCD1, and TIGIT expression (Fig. 9C). SQLE’s impact on immune cell infiltration within the TME underscores its role in modulating immune responses, which could be crucial for the development of immune-based therapies.

Fig. 9
figure 9

Correlation between SQLE expression and immune infiltration in CESC samples. (A) Forest plot depicting the correlation between SQLE expression and infiltration of immune cells. (B) Spearman correlation analysis of the SQLE expression and immune cell infiltration. (C) Comparative analysis of the immune checkpoint expression profiles between SQLE low- and high-expressing groups. Significant differences, *P < 0.05, **P < 0.01, ***P < 0.001

Full size image

Relationship between SQLE mRNA expression and chemotherapeutic responses in CESC

Chemotherapy is a commonly applied treatment modality for individuals with advanced-stage CESC. In clinical settings, the chemotherapeutic drugs recommended to manage CESC include cisplatin, mitomycin C, docetaxel, bleomycin methotrexate, gemcitabine, and paclitaxel. In the present study, the sensitivity of CESC toward chemotherapy was assessed by examining the IC50 values of different medications. The IC50 values of docetaxel (P = 0.0014), methotrexate (P = 0.0079), and paclitaxel (P = 5.8e-06) were decreased in patients with SQLE overexpression. However, the IC50 values of bleomycin (P = 0.3), cisplatin (P = 0.25), gemcitabine (P = 0.64), and mitomycin C (P = 0.17) were not significantly different (Fig. 10A-G). These findings suggest that the SQLE expression may serve as a biomarker for predicting chemotherapy sensitivity, thereby potentially guiding treatment alternatives for CESC patients.

Fig. 10
figure 10

Correlation between the SQLE expression and chemotherapy responses. The assessment of IC50 value for (A) bleomycin, (B) cisplatin, (C) docetaxel, (D) gemcitabine, (E) methotrexate, (F) mitomycin C, and (G) paclitaxel, respectively. ns denotes not significant

Full size image

Altered methylation patterns of SQLE in CESC

As shown in Fig. 11B, by analyzing all CESC patients’ data from TCGA (Firehose Legacy and PanCancer Atlas) database, we observed gene alterations in SQLE in 3% of patients (Fig. 11A). However, Kaplan–Meier/log-rank analysis indicated no statistical difference in the survival outcomes between the altered and unaltered cohorts (P = 0.297) (Fig. 11C). Next, we assessed the DNA methylation profiles of SQLE, particularly focusing on the CpG sites with predictive values. Ten specific CpG methylation sites were identified, with cg23587955 exhibiting the highest degree of DNA methylation (Fig. 11D). Notably, four specific CpG methylation sites, namely, cg09984392 (P = 0.0492), cg14660676 (P = 0.0262), cg16688232 (P = 0.0252), and cg22011731 (P = 0.0176) (Table 3), were linked to prognosis. In particular, patients with high SQLE methylation of cg16688232 (HR = 1.737) exhibited inferior OS, whereas those with high SQLE methylation of cg09984392 (HR = 0.608), cg14660676 (HR = 0.573) and cg22011731 (HR = 0.547) exhibited superior OS. The methylation patterns of SQLE in CESC provide new insights into the epigenetic regulation of this gene, which may have implications for both prognosis and targeted therapy.

Fig. 11
figure 11

Genetic alterations and methylation in SQLE in CESC. (A) OncoPrint visualization-based genetic alteration map for SQLE. (B) Variations in the SQLE expression in CESC were obtained from TCGA Firehose Legacy and TCGA PanCancer Atlas databases. (C) Kaplan–Meier plot analysis comparing OS in patients with and without SQLE genomic alterations. (D) The assessment of SQLE methylation and expressions

Full size image
Table 3 Prognostic impact of the hypermethylation levels in CESC patients
Full size table

Discussion

CESC is frequently characterized by subtle or nonspecific symptoms, resulting in delayed diagnosis and poor prognosis. The natural CESC history involves 10 years of asymptomatic disease progression before presentation as a symptomatic or advanced disease [15,16,17]. Considering this rather long asymptomatic period, early detection is crucial for improving prognosis and patient outcomes. Regular physical examinations are vital for detecting CESC at an early stage; however, many individuals either neglect or delay such examinations. As a result, early and accurate diagnosis is vital because it significantly affects the success of overall disease management. With this context, identifying novel, highly sensitive, and specific biomarkers to facilitate early detection of CESC is not only important but also essential for improving the patient outcomes. Moreover, understanding the role of these biomarkers in the TME and tumor metabolism is expected to provide deeper insights into CESC pathophysiology.

Cholesterol plays multiple crucial roles in sustaining life activities. As an essential component of the cell membrane, cholesterol is vital for maintaining the integrity and fluidity of the cell membrane. Under normal physiological conditions, the synthesis, uptake, transport, and excretion of cholesterol are strictly regulated [18]. However, when cells undergo malignant transformation, this balance gets disrupted. Our study highlights this disruption as a potential driver of tumor progression, where cholesterol metabolism is leveraged by cancer cells to meet their high energy and structural demands. The imbalance in the cholesterol metabolism can provide energy for rapidly growing tumor cells, enhance the aggregation of signaling molecules in the cell membrane, and activate signal transduction. Furthermore, cholesterol imbalance is associated with the regulation of cell adhesion molecule expression and the alterations in the dynamics of the cytoskeleton [19, 20]. Recently, several studies revealed an imbalance in the cholesterol metabolism in patients with cancer, which is manifested by the upregulation of cholesterol synthesis, increased intake, and the abnormal accumulation of several metabolites. These findings underscore the importance of cholesterol homeostasis in tumor biology, suggesting that disturbances in cholesterol metabolism can enhance the adaptability of tumor cells to the TME, thereby promoting invasion and metastasis [21, 22].

SQLE is a key enzyme in the cholesterol biosynthesis pathway, responsible for converting squalene into squalene epoxide, which is one of the rate-limiting steps in cholesterol synthesis. The overexpression of SQLE not only increases the synthesis of cholesterol, thereby supporting the membrane structure and energy requirements of tumor cells, but also participates in the regulation of the tumor microenvironment through cholesterol metabolism products, affecting the function of immune cells and promoting tumor cells in evading immune surveillance [6, 23, 24]. This dual-role positions SQLE as a central player not only in cholesterol metabolism but also in the complex interplay between tumor cells and their microenvironment. SQLE occupies a pivotal position in various tumors and has recently emerged as an attractive research topic within the realm of cancer [25,26,27]. Yang et al. have reported that the proto-oncogene MYC can induce high SQLE expression, thereby promoting tumor cell proliferation [28]. Furthermore, Jun et al. have reported that decreased SQLE owing to cholesterol accumulation aggravates the progression of colon cancer by activating the β-catenin oncogenic pathway and inactivating the p53 tumor-suppressor pathway [29]. These insights from related studies reinforce the critical role of SQLE in oncogenesis, making it a promising target for further investigation, particularly in CESC.

Exploring SQLE involvement in CESC, including its contribution to tumor growth, prognosis, and TME, is essential. Our database research provides strong evidence of SQLE’s overexpression in CESC when compared to that in healthy tissues. This finding was further validated by using clinical samples. Furthermore, we established that SQLE is a promising diagnostic biomarker for CESC, effectively distinguishing between disease stages I and IV. Therefore, SQLE expression may be a valuable biomarker for clinical application. Moreover, our in vitro and xenograft studies substantiated the pro-oncogenic role of SQLE, demonstrating its contribution to tumor proliferation and survival. The association between SQLE expression and cholesterol levels in CESC tissues and cells further underscores its role in tumor biology, highlighting cholesterol’s contribution to tumor growth.

EMT, the first and crucial step in tumor cell invasion and distant metastasis, is an essential biological process that promotes the invasion and metastasis of epithelial cell-derived cancerous cells. It induces changes in the shape of the epithelial cells and transforms them into spindle-shaped cells with increased mobility. This process involves cell cytoskeleton reorganization and extracellular matrix remodeling, leading to the loss of polarity and adhesion in cancer cells, increasing invasion and distant metastasis [30, 31]. EMT activation is associated with the loss of solid tumor morphology and epithelial-like functional phenotypes, increased chemotherapy resistance, and enhanced cancer stem cell-like properties; together, they increase tumor cell invasion and migration [32, 33]. Given these critical roles, it seems important to understand the factors that drive EMT for developing new therapeutic strategies. Current investigations have indicated that SQLE contributes to EMT in cases of esophageal squamous cell carcinoma, pancreatic cancer, and colorectal cancer [34,35,36]. In our study, consistent with these past findings, the SQLE expression in CESC was found to be positively correlated with the expression of the EMT markers N-cadherin and vimentin, but negatively with that of the EMT marker E-cadherin. This suggests that SQLE promotes EMT in CESC; therefore, it may function as an upstream regulatory factor for EMT in CESC.

To elucidate the mechanism underpinning the participation of SQLE in CESC development, we explored the DEGs associated with SQLE expression. GO analysis revealed the biological mechanisms involved in the growth and differentiation of the skin and the epidermis. These findings suggest that SQLE might influence CESC development through pathways linked to cellular differentiation and structural organization. We observed that these processes may be related to cancer-associated biological mechanisms, such as rapid mesenchymal transition and cellular differentiation. Furthermore, KEGG analysis revealed an increase in several significant signal transduction molecules. Among them, proteoglycan is an important effector molecule in the cell surface and surrounding cell microenvironment; it belongs to the noncollagenous component of the extracellular matrix. It can interact with receptors and ligands that regulate tumorigenesis and angiogenesis and participate in these processes [37]. The loss or overactivation of the Hippo pathway can result in abnormal cell growth and homeostatic imbalance in tissues and organs, ultimately resulting in abnormal tissue and organ development and tumor occurrence [38]. The PI3K–Akt pathway is highly associated with protein synthesis, angiogenesis, and chemotherapy resistance of tumors. In particular, human papillomavirus infection is a direct factor associated with CESC occurrence [39]. Therefore, we speculated that high SQLE expression promotes CESC progression via the abovementioned signaling pathways and biological processes.

The role of cancer immunity and metabolism in the TME in antitumor therapy research has attracted much attention recently. The TME not only contains tumor cells but also a complex network of a range of immune cells and extracellular components such as chemokines, cytokines, and extracellular matrix [40, 41]. These cellular components are critical for immune surveillance and antitumor activities. However, in the context of immune dysregulation, these components may contribute to immune evasion and tumor progression. Our current investigation revealed a negative link between SQLE expression and the infiltration of diverse immune cell types, such as CD8 + T cells, cytotoxic cells, mast cells, eosinophils, NK cells, Th17 cells, Tem, pDCs, and NKCD56bright cells. This decrease in immune cell infiltration suggests that high SQLE expression may enable tumors to evade the immune response, facilitating tumor immune escape in the TME of CESC. These findings indicate that SQLE could be a crucial target for enhancing the effectiveness of immunotherapies in CESC.

Immune checkpoint inhibitors refer to a class of molecules found on the surface of immune cells; they perform critical roles in maintaining the stability of the immune system. Immunotherapy applying monoclonal antibodies targeted toward the immune checkpoints has achieved impressive success in managing various advanced cancers [42]. In the present study, patients with SQLE overexpression exhibited decreased expression of typical immunological checkpoint molecules, namely, LAG3, PDCD1, and TIGIT. These discoveries suggest that SQLE exerts cancer-stimulating activity by inhibiting the immune response pathways. Subsequently, we hypothesized that increased SQLE levels indicate a weaker immunotherapy response in cancer treatment. This hypothesis, if further validated, could have significant implications for the development of more effective immunotherapeutic strategies targeting SQLE in CESC.

Presently, there have been monumental advances in the detection technology, leading to a greater interest in the function of cancer epigenetics, particularly variations in the DNA methylation status in cancer biomarkers [43]. The introduction of laboratory and medication approaches depended on DNA methylation variations has led to pivotal advancements in crucial breakthroughs in tumor diagnosis, prognosis assessment, and treatment response anticipation [44, 45]. We investigated the epigenetic mechanisms underlying cancer development and emphasized the importance of methylation in specific CpG islands, which can predict the aggressiveness of CESC. Taken together, these findings offer a promising direction for developing novel therapeutic strategies that target epigenetic alterations for CESC treatment.

Our findings collectively highlight the multifaceted role of SQLE in CESC, from its impact on tumor biology and microenvironment to its potential as a diagnostic, prognostic, and therapeutic biomarker. These findings not only provide new insights into the biology of CESC but also highlight the potential areas for future research, particularly in understanding the mechanistic pathways linking SQLE to tumor progression.

Conclusion

Thus, our findings demonstrated that SQLE expression is a valuable prognostic marker for CESC, particularly in terms of immunotherapy, and is closely associated with EMT and cholesterol metabolism. Our study findings form a basis for further investigating the role of SQLE in the molecular pathways that mediate CESC tumor progression and malignancy, thereby increasing the potential for developing targeted therapies in the future.

Data availability

All data generated or analyzed during this study are included in this published article. SQLE transcription profiles of samples from CESC patients with their clinical and other relevant medical information were obtained from the TCGA platform (https://portal.gdc.cancer.gov/).

Abbreviations

CESC  :

Cervical squamous cell carcinoma and endocervical adenocarcinoma

SQLE:

Squalene epoxidase

DEG:

Differentially expressed gene

OS:

Overall survival

EMT:

Epithelial-mesenchymal transition

TME:

Tumor microenvironment

TCGA:

The Cancer Genome Atlas

IHC:

Immunohistochemistry

RT :

Room temperature

FBS:

Fetal bovine serum

CK-8:

CCell Counting Kit-8

PBS:

Phosphate-buffered saline

GO:

Gene ontology

KEGG :

Kyoto Encyclopedia of Genes and Genomes

IC50 :

Half-maximal inhibitory concentrations

ROC:

Receiver operating characteristic

AUC:

Area under curve

HMGCR:

3-hydroxy-3-methylglutaryl-CoA reductase

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Acknowledgements

The authors would like to thank MJEditor (www.mjeditor.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This study was supported by Jilin Provincial Science and Technology Department (YDZJ202301ZYTS069), and Wu Jie-Ping Medical Foundation (The role of the CNGB1 and its immune relevance in the progression of cervical cancer).

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Authors and Affiliations

  1. Department of Radiation Oncology, The Second Hospital of Jilin University, 218 Zi-qiang Street, Nan-guan District, Changchun, Jilin, 130041, PR China

    Yue-Chen Zhao, Yun-Feng Li, Ling Qiu, Jie Cui & Tie-Jun Wang

  2. NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, Jilin, 130021, PR China

    Yue-Chen Zhao, Shun-Zi Jin, Yan-Nan Shen & Tie-Jun Wang

  3. Department of Hematology and Oncology, The Second Hospital of Jilin University, Changchun, Jilin, 130041, PR China

    Chao-He Zhang

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Contributions

TJ.W. conceived and designed the research, and performed the experiments. YC.Z. analyzed and interpreted the data, and wrote the manuscript. YF.L. and L.Q. made changes, added references to the manuscript. YN.S. and SZ.J. provided design and guidance during the manuscript revision stage. CH.Z and J.C. proofread and reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tie-Jun Wang.

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The investigation adhered to the principles outlined in the Declaration of Helsinki, and the research protocol received approval from the Medical Ethics Committee (Second Hospital of Jilin University). Prior to tissue specimen acquisition, informed consent was obtained from all participants. It is affirmed that animal experimentation was carried out with the endorsement of the Animal Ethics Committee (College of Basic Medicine of Jilin University), and all procedures were documented in alignment with the ARRIVE guidelines for reporting animal experiments.

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Zhao, YC., Li, YF., Qiu, L. et al. SQLE—a promising prognostic biomarker in cervical cancer: implications for tumor malignant behavior, cholesterol synthesis, epithelial-mesenchymal transition, and immune infiltration. BMC Cancer 24, 1133 (2024). https://doi.org/10.1186/s12885-024-12897-0

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