Skip to main content
Download PDF

High-expressed ACAT2 predicted the poor prognosis of platinum-resistant epithelial ovarian cancer

Diagnostic Pathology volume 19, Article number: 7 (2024) Cite this article

Abstract

Background

Acetyl-CoA acetyltransferase 2 (ACAT2) is a lipid metabolism enzyme and rarely was researched in epithelial ovarian cancer (EOC).

Methods

ACAT2 expressions were confirmed in two pairs of cell lines (A2780 and A2780/DDP, OVCAR8 and OVCAR8/DDP) from Gene Expression Omnibus database by bioinformatics analysis, and in A2780 and A2780/DDP cell lines by quantitative real-time polymerase chain reaction and western blotting. Tissue samples were stained by immunohistochemistry and scored for ACAT2 expression. The relationships between ACAT2 expression and clinicopathological characteristics were analyzed by χ2 test. The prognosis of ACAT2 was analyzed by the log-rank tests and Cox regression models.

Results

ACAT2 was remarkably upregulated in the above drug-resistant cell lines by mRNA (all P < 0.05) and protein expression (P = 0.026) than those in sensitive ones. Patients were classified as ACAT2-high (n = 51) and ACAT2-low (n = 26) according to immunohistochemical score. ACAT2 expression had a significantly inverse correlation with FIGO stage (P = 0.030) and chemo-response (P = 0.041). A marginal statistical significance existed in ACAT2 expression and ascites volume (P = 0.092). Univariate analysis suggested that high-expressed ACAT2 was associated with decreased platinum-free interval (PFI) (8.57 vs. 14.13 months, P = 0.044), progression-free survival (PFS) (14.12 vs. 19.79 months, P = 0.039) and overall survival (OS) (36.89 vs. 52.40 months, P = 0.044). Multivariate analysis demonstrated that ACAT2 expression (hazard ratio = 2.18, 95% confidence interval: 1.15–4.11, P = 0.017) affected OS independently, rather than PFI and PFS.

Conclusion

The expression of ACAT2 in A2780/DDP and OVCAR8/DDP was higher than the corresponding A2780 and OVCAR8. High-expressed ACAT2 was associated with advanced FIGO stage, chemo-resistance, and decreased PFI, PFS and OS. It was an independent prognostic factor of OS in EOC.

Introduction

Ovarian cancer (OC) is the most lethal gynecologic malignancy, with a 5-year survival rate of 39–47% [1, 2]. Its poor survival rate comes from unclear pathogenesis, delayed diagnosis and primary or progressive chemo-resistance [3]. Platinum-based chemotherapy is the backbone of OC treatment, and benefiting 70% of patients [4]. While 70–85% among them will relapse within 3 years [5] and develop chemo-resistance over time leading to death [6]. Some researchers have reported that immunotherapy [7] and adoptive cell therapy [8] are able to reverse platinum-resistance. However, their response rates in OC are low, ranging from 8–15% [9, 10]. Similar results are observed in combination with immunotherapy [11] and in the application of bevacizumab [12] and poly (ADP-ribose) polymerase inhibitors for first-line maintenance treatment. This may be attributed to the heterogeneity of ovarian cancer and lack of the most representative marker to predict chemo-response [13], which makes clinical treatment challenging.

Recently, some researchers have constructed chemo-response models of epithelial ovarian cancer (EOC) from different perspectives, such as sequencing data from tissue [1, 14, 15] and fecal [16], clinical data including image information [17, 18], and the combination of both data mentioned above [19, 20]. All the models were characterized by small sample sizes and limited false positive rates. There is still an unmet need of new biomarkers to predict individual disease course and chemoresistance. Platinum-resistance contains complex mechanisms involving multiple genes, steps and pathways due to high genomic instability [15]. Studies have reported that microenvironment [21], epigenetics [22], cancer stem cells [23] and related markers are involved in platinum-resistance of OC. So far, no reliable marker has been found to predict chemo-resistance. Thus, it is crucial to identify the most dominant resistance mechanisms by a such biomarker before treatment.

Our previous study constructed a chemo-response model involving three genes: acetyl-CoA acetyltransferase 2 (ACAT2), anterior gradient 2 (AGR2) and heat shock-related 70-kDa protein 2 (HSPA2) [1]. Downregulation of AGR2 was reported to relate to chemo-resistance of EOC [24], which was consistent with ours [1]. Rare studies of HSPA2 and ACAT2 were reported in EOC and chemo-resistance.

ACAT2 is a subtype of Acetyl-coenzyme A acetyltransferase (ACAT), which is a membrane-bound enzyme and plays important roles in lipid metabolism [25, 26]. Recent years, many studies have found that aberrantly expressed ACAT2 is associated with carcinogenesis and progression, such as hepatocarcinoma [26], clear cell renal cell carcinoma [27], colorectal cancer [28], breast cancer [29], and so on. ACAT1 is another subtype of ACAT [26]. Ayyagari’s study suggested that ACAT1 inhibition had anti-tumor effects and was associated with cisplatin (DDP) sensitivity in EOC [30]. However, rare researches have been done between ACAT2 expression and drug-resistance in EOC. So, ACAT2 was used as the target for this study.

Our previous study suggested that ACAT2 was upregulated in platinum-resistant EOC and related to shorter survival [1]. But it lacked validation both in vitro and vivo. In this study, we utilized EOC patients’ tissue, a pair of ovarian endometroid adenocarcinoma cell lines (A2780 and A2780/DDP) and Gene Expression Omnibus (GEO) database to confirm the above conclusions. We analyzed the correlations between ACAT2 expression and various clinicopathological characteristics related to prognosis.

Materials and methods

Tissue samples

From January 2016 to December 2020, 77 patients with primary EOC treated at the First Affiliated Hospital of Xi’an Jiaotong University were enrolled. There were 72 serous carcinomas with 62 high-grade and 10 low-grade, 2 high-grade serous with endometrioid cancer, 2 high-grade serous with mucinous carcinoma and 1 high-grade clear cell carcinoma. All patients underwent tumor cytoreductive surgery and platinum-based chemotherapy after surgery. Clinicopathological and follow-up data were completed; Patients were excluded with other tumor histories or preoperative radio-chemotherapy. Tissues were formalin-fixed and paraffin-embedded for histopathologic diagnosis and immunohistochemical study. The ethics committee of the First Affiliated Hospital of Xi’an Jiaotong University approved this study (No. XJTU1AF2022LSK-182). Informed consent was obtained from each patient.

Cell lines

The human OC cell lines A2780 and A2780/DDP were donated by Prof. Le Zhao of Center for Translational Medicine, the First Affiliated hospital of Xi’an Jiaotong University (Xi’an, Shannxi). A2780 and A2780/DDP cell lines were cultured in RPMI 1640 medium (Gibco), containing 10% fetal bovine serum (BI) and 1% penicillin-streptomycin (Cytiva) in a humidified incubator at 37 °C with 5% CO2. The medium of A2780/DDP contained 1ug/mL cisplatin (Selleck) to maintain tolerance.

ACAT2 microarray data information

Microarray data information of cisplatin-sensitivity and cisplatin-resistance OC cell lines were obtained from NCBI-GEO (https://www.ncbi.nlm.nih.gov/geo/) database. When “cisplatin-resistance” was used as a keyword to perform query, we selected the original ovarian cancer studies of RNA assay in Homo sapiens for analysis. The expression microarray datasets GSE45553, GSE15709 and GSE33482 were downloaded.

Cell viability

Cells (5 × 104/mL) were seeded into 96 well plates with 100uL per well. After 24 h, different concentrations (0, 0.25, 0.5, 1, 2, 4, 8, 16, 32 ug/mL) of cisplatin were added and incubated for 48 h. Cell viability was measured by cell counting kit-8 (CCK8, Targetmol). A complete medium containing 10% CCK8 was added into each well of plate, which was placed in a dark environment at 37℃ for 2–4 h. Then, a microplate reader (KHB ST-360, shanghai) was used to detect the absorbance (OD value) at 450 nm. Cells incubated with 10% CCK8 complete medium were set as control. The wells only containing 10% CCK8 complete medium were used as blank. Cell viability = (OD experiment - OD blank) / (OD control - OD blank).

ACAT2 expression detected by qRT-PCR

Total RNA was extracted from A2780 and A2780/DDP cell lines using TRIzol reagent (TIANGEN, China) and treated with RNase-free. Reverse transcription was conducted to obtain cDNA from 1ug RNA utilizing cDNA Synthesis Kit (Novozan) and propagated using ChamQ Universal SYBR qPCR Master Mix (Novozan) in real-time PCR (QuantStudio Dx, Life Technologies). The primers were synthesized by Tsingke Biotechnology Co., Ltd. (China). The sequences of primers used are as follows: 5’-GCCTTCCATTATGGGAATAGGA-3’ and 5’-GACCTTCTCTGGGTTTAATCCA-3’ for ACAT2; 5’-GGAGTCCACTGGCGTCTTCA-3’ and 5’-GTCATGAGTCCTTCCACGATACC-3’ for GAPDH. The comparative threshold cycle (2−ΔΔCt) equation was applied to calculate the relative ACAT2 mRNA expression as well as compare the expression of GAPDH as a loading control.

ACAT2 expression detected by western blotting

Western blotting was performed in the above two cell lines. Cells were lysed on ice for 30 min by RIPA lysis buffer containing 1 μm phenylmethanesulfonyl fluoride (Solarbio). The lysates were centrifuged at 13,000 rpm for 30 min to obtain total protein. Its concentration was quantified by a bicinchoninic acid assay (Solarbio). Protein samples were separated by 10% sodium dodecyl sulfate–polyacrylamide gel and transferred to polyvinylidene fluoride membrane (0.45 μm, Merck Millipore). After being blocked with 5% defatted milk powder (BD) for 1 h at room temperature, the primary antibodies (anti-rabbit ACAT2, 1:5000, ab131215, Abcam; anti-mouse GAPDH, 1:50000, 60004-1-Ig, Proteintech) were incubated with membrane overnight at 4℃. After being washed for 3 times, membranes were incubated with horseradish peroxidase-conjugated secondary antibodies (anti-mouse, 1:5000, SA0001-1, Proteintech; anti-rabbit, 1:3000, GB23303, Servicebio) for 1 h at room temperature and visualized with a high ECL detection reagent (AR) using ImageQuant 800 (Amersham).

Immunohistochemistry (IHC)

Paraffin-embedded tissues were serially sectioned at 4 μm thickness. They were baked at 65℃ for 2 h, deparaffinized in xylene, and rehydrated through graded alcohol. Subsequently, slices were placed in 3% hydrogen peroxide, heated in 1x sodium citrate antigen retrieval buffer (pH 6.0) by pressure cooker and blocked with 5% bovine serum albumin (Solarbio). The slices were incubated with rabbit anti-ACAT2 monoclonal antibody overnight at 4℃ (1:300; ab131215, Abcam) and then with the MaxVision-HRP rabbit/mouse antibody (Maixin) at room temperature for 1 h. Diaminobenzidine (Servicebio) was used as the final chromogen. Hematoxylin (Servicebio) was applied to counterstain.

All slices were evaluated by two experienced pathologists in a blinded manner. For the assessment of ACAT2, five high-power fields in each specimen were selected randomly, and cytoplasm staining was examined. Immune score equaled to the percentage of positive cells (0, ≤ 5%; 1, 6 − 25%; 2, 26 − 50%; 3, 51 − 75%; 4, 76 − 100%) multiplied by the staining intensity (0, negative; 1, weak; 2, moderate; 3, strong). Immune scores of 0–4 are defined as low expression and 5–12 as high expression [31].

Outcome measurement

Platinum-free interval (PFI) was measured from the date of last platinum-based chemotherapy to that of disease progression. PFI > 6 months is defined as platinum-sensitivity and < 6 months as platinum-resistance. Survival information was acquired by telephone and medical records. PFS is defined as the time of surgery to the first disease progression or death due to any causes or the date of last follow-up. Overall survival (OS) is defined as the time from surgery to death due to any causes or the date of last follow-up. The follow-up ended on March 14, 2023. Survival time was calculated in months.

Statistical analysis

Categorical variables were presented as frequencies. Continuous variables were expressed as means ± standard deviation. The associations between ACAT2 and clinicopathological parameters were evaluated by χ2 test. Prognostic factors for PFI, PFS and OS were performed by the log-rank test for univariate analysis and Cox-proportional hazards regression model for multivariate analysis. Survival curves were plotted using Kaplan-Meier method. Differences between curves were analyzed by the log-rank test. All experiments were repeated for three times. Paired data was compared by t-test. P < 0.05 was considered as statistically significant. Statistical analyses were performed using SPSS version 22.0 (IBM Corp., Armonk, NY, USA), GraphPad Primer 8.0 and R 3.6.1.

Results

The expression of ACAT2 in ovarian cancer cell lines

A2780 and A2780/DDP were treated with different concentrations of cisplatin for 48 h and the values of half maximal inhibitory concentration (IC50) were measured by CCK8. The IC50 of A2780 and A2780/DDP were 2.46 and 14.26 ug/mL (Fig. 1a); The semi-quantitative analyses of half inhibition rate in A2780 and A2780/DDP cell lines were shown in Fig. 1b. The expression of ACAT2 confirmed in A2780/DDP and OVCAR8/DDP was significantly higher at mRNA level by bioinformatics analysis (P < 0.05, Fig. 1c–e) and qRT-PCR (P < 0.01, Fig. 1f) than the corresponding A2780 and OVCAR8. The protein level of A2780/DDP was higher than A2780 (P = 0.026, Fig. 1g-h).

Fig. 1
figure 1

ACAT2 expression level was detected by bioinformatics analysis, qRT-PCR and western blotting in ovarian cancer cell lines and stained by immunohistochemistry with anti-ACAT2 antibody in ovarian cancer tissues. (a) The cisplatin dose-response curve in A2780 and A2780/DDP cells. They were exposed to DDP with different concentrations (0, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0 ug/mL) for 48 h. Cell viability was measured by CCK8. (b) Semi-quantitative analyses of the half inhibition rate of A2780 and A2780/DDP. (c-e) The relative ACAT2 mRNA expression level in A2780 and A2780/DDP from GSE 15709 and GSE 33482 (data normalization processing), and in OVCAR8 and OVCAR8/DDP from GSE 45553. (f) The relative expression level of ACAT2 mRNA in A2780 and A2780/DDP. It was indicated as a normalization of GAPDH in each sample to the control. (g) The western blotting of ACAT2 in A2780 and A2780/DDP cells. GAPDH was used as the endogenous reference. (h) The relative expression level of ACAT2 protein was indicated as a normalization of GAPDH in each sample to the control. (i) ACAT2-low expression in platinum-sensitive (x100); (j) ACAT2-low expression in platinum-sensitive (x400); (k) ACAT2-high expression in platinum-sensitive (x100); (l) ACAT2-high expression in platinum-sensitive (x400); (m) ACAT2-low expression in platinum-resistant (x100); (n) ACAT2-low expression in platinum-resistant (x400); (o) ACAT2-high expression in platinum-resistant (x100); (p) ACAT2-high expression in platinum-resistant (x400). (q) Immunohistochemical semi-quantitative scores of ACAT2 expression in OC tissues

Full size image

ACAT2 expression in relation to patients’ clinicopathological characteristics

ACAT2 is mainly expressed in the cytoplasm and occasionally in the nucleus. ACAT2 expression was shown in Fig. 1i-p. The IHC score of 4 is used as a cutoff value for ACAT2-low (n = 26) and ACAT2-high (n = 51) expression. The IHC semi-quantitative scores of ACAT2 expression in resistant OC tissues were higher than that in sensitive those (6.58 ± 2.88vs 5.31 ± 2.59, P = 0.049), which were shown in Fig. 1q. The correlations between ACAT2 expression and 77 EOC patients’ clinicopathological characteristics were presented in Table 1. Results showed that ACAT2 expression was significantly associated with International Federation of Gynecology and Obstetrics (FIGO) stage (P = 0.030) and chemo-response (P = 0.041). There was marginal statistical significance between ACAT2 expression and ascites volume (P = 0.092), but no relationship to other clinicopathological characteristics (all P > 0.05).

Table 1 Correlations between ACAT2 expression and the clinicopathological characteristics
Full size table

Univariate analysis of prognosis for PFI, PFS and OS

Table 2 showed the comparisons between survival outcomes and various clinicopathologic parameters by Kaplan-Meier method.

Table 2 Prognostic factors for platinum-free interval (PFI), progression-free survival (PFS) and overall survival (OS) selected by kaplan-meier analysis
Full size table

FIGO stage (P = 0.012), chemo-response (P < 0.001), ascites volume (P = 0.048), surgical satisfaction (P = 0.002) and ACAT2 expression (P = 0.044) were significantly related to PFI.

FIGO stage (P = 0.001), chemo-response (P < 0.001), surgical satisfaction (P = 0.005) and ACAT2 expression (P = 0.039) were significantly associated with PFS, while ascites volume had a significantly marginal effect on PFS (P = 0.074).

Pathological type (P = 0.004), chemo-response (P < 0.001), HE4 (P = 0.022), ascites volume (P = 0.013), surgical satisfaction (P = 0.025) and ACAT2 expression (P = 0.044) had significantly negative effects on OS, while FIGO stage produced a slight influence on OS (P = 0.079).

Multivariate analysis of prognosis for PFI, PFS and OS

The independent prognostic factors for survival outcomes were analyzed by Cox-regression and displayed in Table 3. The survival plots were presented in Fig. 2. Results showed that chemo-response (hazard ratio (HR) = 535.86, P = 0.001) was an independent prognostic factor of PFI (Table 3 and Fig. 2a). Pathological type (HR = 2.80, P = 0.005), chemo-response (HR = 9.41, P < 0.001) and HE4 (HR = 1.41, P = 0.043) had independent effects on PFS (Table 3 and Fig. 2b-d). Pathological type (HR = 4.08, P = 0.001), chemo-response (HR = 2.46, P = 0.003), HE4 (HR = 2.13, P = 0.001), surgical satisfaction (HR = 1.69, P = 0.049) and ACAT2 expression (HR = 2.18, P = 0.017) also affected OS independently (Table 3 and Fig. 2e-i).

Fig. 2
figure 2

Kaplan-Meier survival curves for platinum-free interval (PFI), progression-free survival (PFS) and overall survival (OS) stratified by clinicopathological parameters in EOC; (a) was stratified by chemo-response for PFI; (b-d) were stratified by pathological type, chemo-response and HE4 for PFS; (e-i) were stratified by pathological type, chemo-response, HE4, surgical satisfaction and ACAT2 expression for OS

Full size image
Table 3 Prognostic factors for platinum-free interval (PFI), progression-free survival (PFS) and overall survival (OS) selected by cox regression
Full size table

Discussion

Platinum-resistance is a major factor leading to shorter survival of OC [3]. Many researches [3,4,5] have concentrated on exploring the mechanism of platinum-resistance in OC, but rare remarkable progression has been made. ACAT2 is a new biomarker related to cancer [26,27,28,29]. Scarce studies of ACAT2 have been reported in OC. In this study, we verified high-expressed ACAT2 was significantly associated with chemo-resistance, advanced FIGO stage and decreased PFI, PFS and OS (all P < 0.05). Until now, no research has focused on this point.

Recent years, studies have shown that ACAT2 is aberrantly expressed in tumor, such as hepatocarcinoma [26], clear cell renal cell carcinoma [27], colorectal cancer [28] and breast cancer [29]. Weng’s study proved that high-expressed ACAT2 was related to advanced clinical stage and poor OS [32]. While in Zhao’s research [33], high-expressed ACAT2 was related to early tumor stage and longer survival, which was contradicted with ours. The heterogeneity of tumor may cause the above differences. Rare studies have reported the relationship between ACAT2 expression and chemo-resistance or ovarian cancer. However, Chemo-response was an independent prognosis factor of PFI, PFS and OS, which was consistent with the results of Hsiao-Yun Lu’s [14], Danielle Ikoma’s [34] and Jesus Gonzalez Bosquet’s [35]. Multivariate analysis showed that ACAT2 expression was an independent risk factor for OS of EOC, but not for PFI and PFS. A study about clear cell renal cell carcinoma suggested that ACAT2 expression was not an independent prognostic factor of survival [33]. This conclusion still requires more large-scale studies to confirm.

However, it is essential to explore the mechanism of chemo-resistance induced by ACAT2 in OC. At present, studies about ACAT2 expression mainly concentrate on the proliferation, migration and invasion of tumors [29, 32], lipid metabolism [25, 26] and radiation resistance [36]. A study has reported ACAT2 is a target for treatment of coronary heart disease related to hypercholesterolemia [37]. Souchek’s study proved that high-expressed ACAT2 was related to pancreatic cancer radiation resistance, which could be used as a novel target for radiotherapy sensitization [36]. No researchers have focused on the mechanism of chemo-resistance caused by aberrant ACAT2 expression. A study about Parkinson’s disease revealed that member 9 of the 70 kDa Heat Shock Protein (HSPA9) might be a potential interactant of ACAT2 by tandem affinity purification/mass spectra [38]. Related researches demonstrated HSPA9 was overexpressed in platinum-resistant OC [39] and breast cancer [40], and participated in resistance through P53 signaling pathway. Thus, we predicted the direct interaction between ACAT2 and HSPA9 existed, and further confirmed our hypothesis through online bioinformatics websites (https://cn.string-db.org/) (see supplement Fig. 1.). A published study revealed the upregulation of ACAT2 in chemo-resistant OC tissue mainly depended on an epigenetic approach of DNA hypomethylation [1]. Thus, we speculate that methylated ACAT2 participates in ovarian cancer chemo-resistance by acting on the HSPA9/P53 signaling pathway, which needs to be further explored.

Our study had some limitations. First, the small sample size of non-serous carcinoma and FIGO I-II in our study biased the research results; Second, rare studies between ACAT2 expression and chemo-response further confirmed our conclusions; Third, we failed to thoroughly explore the mechanism of ACAT2 overexpression leading to chemo-resistance, which would be our next research purpose; Last, more large-scale studies are needed to validate the relationship between ACAT2 expression and chemo-response in the future.

Conclusion

The expression of ACAT2 in A2780/DDP and OVCAR8/DDP was higher than the corresponding A2780 and OVCAR8. High-expressed ACAT2 was associated with advanced FIGO stage, chemo-resistance, and decreased PFI, PFS and OS. Multivariate analysis demonstrated that ACAT2 expression was an independent prognosis factor of OS in EOC.

Data availability

The data in this article are available in the text and supplementary.

Abbreviations

ACAT2:

Acetyl-CoA acetyltransferase 2

EOC:

epithelial ovarian cancer

PFI:

platinum-free interval

PFS:

progression-free survival

OS:

overall survival

OC:

ovarian cancer

IC50:

half inhibitory concentration

CCK8:

cell counting kit-8

FIGO:

International Federation of Gynecology and Obstetrics

CA125:

carbohydrate antigen 125

HE4:

human epididymal protein 4

HR:

hazard ratio

CI:

confidence interval

References

  1. Zhao L, et al. A polygenic methylation prediction model associated with response to chemotherapy in epithelial Ovarian cancer. Mol Ther Oncolytics. 2021;20:545–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Moufarrij S, et al. Epigenetic therapy for Ovarian cancer: promise and progress. Clin Epigenetics. 2019;11(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Tavsan Z, Kayali HA. EpCAM-claudin-tetraspanin-modulated Ovarian cancer progression and drug resistance. Cell Adh Migr. 2020;14(1):57–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Liu Y, et al. TTK is a potential therapeutic target for cisplatin-resistant Ovarian cancer. J Ovarian Res. 2021;14(1):128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nowak M, Klink M. The role of Tumor-Associated macrophages in the Progression and Chemoresistance of Ovarian Cancer. Cells, 2020. 9(5).

  6. Chuang YT, Chang CL. Extending platinum-free interval in partially platinum-sensitive recurrent Ovarian cancer by a non-platinum regimen: its possible clinical significance. Taiwan J Obstet Gynecol. 2012;51(3):336–41.

    Article  PubMed  Google Scholar 

  7. Le Saux O, Ray-Coquard I, Labidi-Galy SI. Challenges for immunotherapy for the treatment of platinum resistant Ovarian cancer. Semin Cancer Biol. 2021;77:127–43.

    Article  PubMed  Google Scholar 

  8. Rosenberg SA, et al. Adoptive cell transfer: a clinical path to effective cancer immunotherapy. Nat Rev Cancer. 2008;8(4):299–308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Matulonis UA, et al. Antitumor activity and safety of pembrolizumab in patients with advanced recurrent Ovarian cancer: results from the phase II KEYNOTE-100 study. Ann Oncol. 2019;30(7):1080–7.

    Article  CAS  PubMed  Google Scholar 

  10. Hamanishi J, et al. Safety and Antitumor activity of Anti-PD-1 antibody, Nivolumab, in patients with platinum-resistant Ovarian Cancer. J Clin Oncol. 2015;33(34):4015–22.

    Article  CAS  PubMed  Google Scholar 

  11. Zamarin D, et al. Randomized phase II trial of Nivolumab Versus Nivolumab and Ipilimumab for recurrent or persistent Ovarian Cancer: an NRG Oncology Study. J Clin Oncol. 2020;38(16):1814–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pujade-Lauraine E, et al. Bevacizumab combined with chemotherapy for platinum-resistant recurrent Ovarian cancer: the AURELIA open-label randomized phase III trial. J Clin Oncol. 2014;32(13):1302–8.

    Article  CAS  PubMed  Google Scholar 

  13. Cancer Genome Atlas Research. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609–15.

    Article  Google Scholar 

  14. Lu HY, et al. Ovarian cancer risk score predicts chemo-response and outcome in epithelial ovarian carcinoma patients. J Gynecol Oncol. 2021;32(2):e18.

    Article  PubMed  Google Scholar 

  15. Zheng H, et al. Construction and validation of a platinum sensitivity predictive model with multiple genomic variations for epithelial Ovarian Cancer. Front Oncol. 2021;11:725264.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gong TT, et al. Application of machine learning in prediction of Chemotherapy resistant of Ovarian Cancer based on gut microbiota. J Cancer. 2021;12(10):2877–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kozlowska E, et al. Mathematical modeling predicts response to Chemotherapy and Drug combinations in Ovarian Cancer. Cancer Res. 2018;78(14):4036–44.

    Article  CAS  PubMed  Google Scholar 

  18. Mao MM, et al. [Prediction of platinum-based chemotherapy sensitivity for epithelial Ovarian cancer by multi-sequence MRI-based radiomic nomogram]. Zhonghua Yi Xue Za Zhi. 2022;102(3):201–8.

    CAS  PubMed  Google Scholar 

  19. Plotti F et al. Role of BRCA mutation and HE4 in Predicting Chemotherapy Response in Ovarian Cancer: a retrospective pilot study. Biomedicines, 2021. 9(1).

  20. Yi X, et al. Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in Ovarian cancer treatment. Biomed Pharmacother. 2021;133:111013.

    Article  CAS  PubMed  Google Scholar 

  21. Ghoneum A, et al. Exploring the clinical value of Tumor microenvironment in platinum-resistant Ovarian cancer. Semin Cancer Biol. 2021;77:83–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Greville G, et al. Epigenetic regulation of glycosylation and the impact on chemo-resistance in breast and Ovarian cancer. Epigenetics. 2016;11(12):845–57.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wilczynski JR, Wilczynski M, Paradowska E. Cancer Stem cells in Ovarian Cancer-A source of Tumor Success and a Challenging Target for Novel therapies. Int J Mol Sci, 2022. 23(5).

  24. Alves MR, et al. Downregulation of AGR2, p21, and cyclin D and alterations in p53 function were associated with Tumor progression and chemotherapy resistance in epithelial ovarian carcinoma. Cancer Med. 2018;7(7):3188–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Liang JJ, et al. Overexpression of human diacylglycerol acyltransferase 1, acyl-coa:cholesterol acyltransferase 1, or acyl-CoA:cholesterol acyltransferase 2 stimulates secretion of apolipoprotein B-containing lipoproteins in McA-RH7777 cells. J Biol Chem. 2004;279(43):44938–44.

    Article  CAS  PubMed  Google Scholar 

  26. Song BL, et al. Human acyl-CoA:cholesterol acyltransferase 2 gene expression in intestinal Caco-2 cells and in hepatocellular carcinoma. Biochem J. 2006;394(Pt 3):617–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cheng L, et al. Circ_RPL23A acts as a miR-1233 sponge to suppress the progression of clear cell renal cell carcinoma by promoting ACAT2. J Bioenerg Biomembr. 2021;53(4):415–28.

    Article  CAS  PubMed  Google Scholar 

  28. Weng M, et al. ACAT2 promotes cell proliferation and Associates with malignant progression in Colorectal Cancer. Onco Targets Ther. 2020;13:3477–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang Y, et al. Leptin promotes the migration and invasion of Breast cancer cells by upregulating ACAT2. Cell Oncol (Dordr). 2017;40(6):537–47.

    Article  CAS  PubMed  Google Scholar 

  30. Ayyagari VN, et al. Assessment of acyl-CoA cholesterol acyltransferase (ACAT-1) role in Ovarian cancer progression-An in vitro study. PLoS ONE. 2020;15(1):e0228024.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang J, et al. Increased co-expression of MEST and BRCA1 is associated with worse prognosis and immune infiltration in Ovarian cancer. Gynecol Oncol. 2022;164(3):566–76.

    Article  CAS  PubMed  Google Scholar 

  32. Erratum. ACAT2 promotes cell proliferation and Associates with malignant progression in Colorectal Cancer [Corrigendum]. Onco Targets Ther. 2021;14:4641–3.

    Article  Google Scholar 

  33. Zhao Z, et al. Prognostic significance of two lipid metabolism enzymes, HADHA and ACAT2, in clear cell renal cell carcinoma. Tumour Biol. 2016;37(6):8121–30.

    Article  CAS  PubMed  Google Scholar 

  34. Ikoma D, et al. A nuclear polymorphism at the 8q24 region is associated with improved survival time and chemo-response in high-grade serous Ovarian cancer. Oncol Lett. 2021;22(4):733.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gonzalez Bosquet J, et al. Prediction of chemo-response in serous Ovarian cancer. Mol Cancer. 2016;15(1):66.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Souchek JJ, et al. Unbiased analysis of Pancreatic cancer radiation resistance reveals cholesterol biosynthesis as a novel target for radiosensitisation. Br J Cancer. 2014;111(6):1139–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Rudel LL, Lee RG, Parini P. ACAT2 is a target for treatment of coronary Heart Disease associated with hypercholesterolemia. Arterioscler Thromb Vasc Biol. 2005;25(6):1112–8.

    Article  CAS  PubMed  Google Scholar 

  38. Davison EJ, et al. Proteomic analysis of increased parkin expression and its interactants provides evidence for a role in modulation of mitochondrial function. Proteomics. 2009;9(18):4284–97.

    Article  CAS  PubMed  Google Scholar 

  39. Yang L, et al. Inhibition of mortalin expression reverses cisplatin resistance and attenuates growth of Ovarian cancer cells. Cancer Lett. 2013;336(1):213–21.

    Article  CAS  PubMed  Google Scholar 

  40. Hou S et al. PCDHGB7 Increases Chemosensitivity to Carboplatin by Inhibiting HSPA9 via Inducing Apoptosis in Breast Cancer Dis Markers, 2019. 2019: p. 6131548.

Download references

Acknowledgements

We extend our thanks to the patients and their families and acknowledge the Department of Pathology, First Affiliated Hospital of Xi’an Jiaotong University for providing access to tissue materials. Thanks to Prof. Le Zhao from the Center for Translational Medicine, the First Affiliated hospital of Xi’an Jiaotong University (Xi’an, Shannxi) for presenting A2780 and A2780/DDP cells.

Funding

This work was supported by the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University, China (XJTU1AF-2018-017 and XJTU1AF-CRF-2019-002), the Natural Science Basic Research Program of Shaanxi (2018JM7073 and 2017ZDJC-11), the Key Research and Development Program of Shaanxi (2017ZDXM-SF-068 and 2019QYPY-138), the Innovation Capability Support Program of Shaanxi (2017XT-026 and 2018XT-002), and the Medical Research Project of Xi’an Social Development Guidance Plan (2017117SF/YX011-3). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The opinions, results, and conclusions reported in this article are those of the authors, and are independent from these funding sources.

Author information

Author notes

  1. Jinfeng Wang and Zhe Yang contributed equally to this work.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, First Affiliated Hospital of Xi’an Jiaotong University, 277 Yanta West Road, Xi’an, Shaanxi, 710061, China

    Jinfeng Wang, Lanbo Zhao, Jing Ji, Yadi Bin, Siyi Zhang & Qiling Li

  2. Department of Pathology, First Affiliated Hospital of Xi’an Jiaotong University, 277 Yanta West Road, Xi’an, Shaanxi, 710061, China

    Zhe Yang, Yu Liu & Huilian Hou

  3. The MED-X Institute, The First Affiliated Hospital of Xi’an Jiaotong University, Western China Science and Technology Innovation Harbor, Building 21, Xi’an, 710000, China

    Han Bai

Authors
  1. Jinfeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhe Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Han Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lanbo Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yadi Bin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Siyi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Huilian Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Qiling Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [J. W.], [Z.Y.], [J.J.], [H.B.], [Y.L.] and [H.L.]. The first draft of the manuscript was written by [J.W.]. Writing-review and editing were performed by [J.W.], [L.Z.], [Y.B.], [S.Z.], [J.J.] and [Q.L.]. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Huilian Hou or Qiling Li.

Ethics declarations

Ethics approval

This study was approved by the ethics committee of the First Affiliated Hospital of Xi’an Jiaotong University (No. XJTU1AF2022LSK-182).

Consent to participate

All authors participate in this article.

Consent to publish

All authors agree to publish this article.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Yang, Z., Bai, H. et al. High-expressed ACAT2 predicted the poor prognosis of platinum-resistant epithelial ovarian cancer. Diagn Pathol 19, 7 (2024). https://doi.org/10.1186/s13000-023-01435-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13000-023-01435-4

Keywords

Diagnostic Pathology

ISSN: 1746-1596

Contact us