Abstract
Objectives
This study aimed to explore the expression of eukaryotic translation initiation factor 3 subunit C-like in serous ovarian cancer samples (both paraffin-embedded and fresh samples) and evaluate its clinical value in patients with serous ovarian cancer.
Methods
Twenty-five fresh serous ovarian cancer tissues and their paired paratumor tissues were subjected to reverse transcription–quantitative polymerase chain reaction assay to detect eukaryotic translation initiation factor 3 subunit C-like messenger RNA expression. In addition, 135 paraffin-embedded serous ovarian cancer samples and 36 paratumor samples were assessed for eukaryotic translation initiation factor 3 subunit C-like protein expression using immunohistochemistry.
Results
Both protein and messenger RNA expression levels of eukaryotic translation initiation factor 3 subunit C-like were higher in serous ovarian cancer samples than in paratumor samples, and its high expression was associated with poor overall survival in patients with serous ovarian cancer. In addition, multivariate Cox regression analysis showed that high expression of eukaryotic translation initiation factor 3 subunit C-like was an independent poor prognostic factor for patients with serous ovarian cancer.
Conclusions
Eukaryotic translation initiation factor 3 subunit C-like is upregulated in serous ovarian cancer samples, and it may be recommended as a useful poor prognostic biomarker in patients with serous ovarian cancer.
Keywords: Eukaryotic translation initiation factor 3 subunit C-like, serous ovarian cancer, reverse transcription–quantitative polymerase chain reaction, immunohistochemistry, prognosis, biomarker
Introduction
The mortality rate of ovarian cancer (OC) remains the highest among all gynecologic cancers, 1 and OC is the second-most common cause of gynecologic cancer-related deaths in females worldwide. 2 There are still no effective tools for general population screening; this is evident from the research on the development of economic and cost-effective strategies for early detection and prevention of OC over the past decade. The cost of treatment per patient with OC remains the highest among all cancer types. As an example, the average initial cost in the first year can amount to approximately US$80,000, whereas the final year cost may increase to US$100,000. 3 The most common pathological type is serous ovarian cancer (SOC). 4 The 5-year survival of advanced SOC is very poor, owing to the high recurrence and metastasis rates after operation or chemotherapy. 5 Germline mutations in breast cancer gene (BRCA) 1/2 are the strongest known genetic risk factors for epithelial OC and are found in 6%–15% of women diagnosed with this disease. BRCA1/2 carriers with epithelial OC respond better to platinum-based chemotherapy than noncarriers. This leads to enhanced survival, even though the disease is generally diagnosed at a later stage and higher grade. 6 Therefore, it is very important to discover effective prognostic predictors for improved treatment outcomes.7,8 Although several cancer biomarkers have been used for monitoring the progression and prognosis of SOC,9–14 they are not very sensitive or specific. Currently, CA125 and HE4 are the only approved biomarkers for use in epithelial OC; however, they are not efficient for early detection. Multivariate index assays have been developed to mitigate the limitations of single serum biomarkers in epithelial OC, especially during the presurgical evaluation of adnexal masses. The risk of malignancy algorithm integrates menopausal status, CA125, and HE4 concentrations to diagnose women with a pelvic mass. microRNAs (miRNAs) may have remarkable potential in various aspects of epithelial OC prediction. However, further research is needed regarding their characterization as a biomarker. In particular, before miRNAs can be utilized as a reliable biomarker for clinical use, the steps involved in processing samples need to be standardized and the platforms for detecting miRNA in tumors and blood samples need to be refined. 15 Therefore, it is very important to identify novel biomarkers and predictors for improving the prognosis of patients with SOC.
Eukaryotic translation initiation factor 3 subunit C-like (EIF3CL) gene is located at 16p12.1, with an exon count of 23. EIF3CL is a subunit of the eukaryotic translation initiation factor 3 complex, which plays a crucial role in protein synthesis. Although its exact function in cancer remains unclear, Chen et al. 16 reported that EIF3CL expression was higher in diffuse intrinsic pontine glioma (DIPG) samples than in the paired normal brain tissues. They further demonstrated that EIF3CL knockdown by RNA interference effectively repressed DIPG cell proliferation and anchorage-independent growth and induced tumor cell apoptosis. In addition, they found that EIF3CL silencing downregulated the phosphorylation of adenosine monophosphate-activated protein kinase-α and nuclear factor kappa B p65 proteins, and these signaling pathways have been reported in relation to cancer cell proliferation. Their results showed that EIF3CL plays an oncogenic role in DIPG. Recently, Saad et al. reported that EIF3CL was associated with melanoma pathogenesis. 17 In contrast, Sharma et al. reported that EIF3CL served as a tumor suppressor gene and played an anti-cancer role in breast cancer cells. 18 However, to the best of our knowledge, there have been no reports on the relationship between EIF3CL expression and SOC.
Therefore, this study aimed to explore the expression of EIF3CL in SOC and determine the relationship of EIF3CL expression with clinicopathological factors and prognosis in patients with SOC.
Materials and methods
Fresh samples
Between August 2013 and March 2020, 25 fresh SOC tissues and their paired paratumor tissues were diagnosed at the Integrated Hospital of Traditional Chinese Medicine, Southern Medical University. The inclusion criterion was tumor tissues from patients diagnosed with SOC postoperatively. The exclusion criterion was tissues pathologically classified as not having SOC. All fresh tissues were immediately preserved in liquid nitrogen. This study was obtained approval from the Ethics Committee of the Integrated Hospital of Traditional Chinese Medicine, Southern Medical University (NFZXYEC-201810sb-K1). All patients with SOC signed a consent form prior to operation. All procedures performed in this study were in accordance with the Helsinki Declaration of 1975, as revised in 2024.
Paraffin-embedded samples
From December 2009 to August 2020, a total of 135 paraffin-embedded SOC samples and 36 paired paratumor samples were collected for this study; these samples were pathologically diagnosed at the Memorial Hospital of Sun Yat-sen University and the Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, respectively. The inclusion and exclusion criteria were identical to those for fresh samples, as described above. The survival duration was retrospectively calculated from the operation time until 1 March 2020 (final follow-up). Approval of this study was obtained from the Ethics Committee of the Memorial Hospital of Sun Yat-sen University. All patients with SOC provided written informed consent prior to cytoreduction surgery. All procedures performed in this study were in accordance with the Helsinki Declaration of 1975, as revised in 2024.
Reverse transcription–quantitative polymerase chain reaction (RT–qPCR)
RT–qPCR was performed as described in a previous study. 19 The expression levels of EIF3CL in all paratumor tissues were set as 1.00 ± 0.00 (control group). The thermocycling conditions were 95°C for 10 min to activate DNA polymerase, followed by 45 cycles of 95°C for 15 s, 60°C for 15 s, and 72°C for 10 s. Independent experiments were performed more than three times. The PCR primers used were as follows: EIF3CL forward, 5′-CAAGTTCAATATCATCGCCTCT-3′ and reverse, 5′-CATCCATTCGTTCCACCA-3′; GAPDH forward, 5′-CCATCTTCCAGGAGCGAGAT-3′ and reverse, 5′-TGCTGATGATCTTGAGGCTG-3′.
Immunohistochemistry (IHC)
EIF3CL protein expression in SOC and paratumor tissues was detected using IHC. This assay was performed as described in a previous study. 20 The tissue sections were incubated with anti-rabbit EIF3CL polyclonal antibodies (1:100; ab237757; Abcam, UK) overnight at 4°C.
Statistical analysis
Overall, 56 patients had died by the time of the final follow-up, and 26 patients were lost to follow-up. All data analyses were performed using Statistical Package for the Social Sciences 21.0 (IBM Corp.) and GraphPad Prism 7 (GraphPad Software, Inc.). The statistical methods were performed as described in a previous study. 21 All P-values <0.05 were considered to indicate statistical significance.
Results
EIF3CL mRNA expression is higher in fresh SOC samples than in paratumor samples
RT–qPCR was performed to detect EIF3CL mRNA expression in 25 fresh SOC samples and the paired paratumor samples (Figure 1(a)). The expression levels in all paired paratumor samples was set at 1.00 ± 0.00. The results showed a significant difference in EIF3CL mRNA expression between fresh SOC samples and paratumor samples (P < 0.0001; Figure 1(b)).
Figure 1.
Comparison of EIF3CL mRNA expression levels between fresh SOC tissues and paired paratumor tissues using RT–qPCR. (a) EIF3CL mRNA expression levels in each cancer tissue were compared with those in matched paratumor tissues. (b) The mean expression levels of EIF3CL were compared between SOC tissues and paratumor tissues (t = 5.648, P < 0.0001). EIF3CL: eukaryotic translation initiation factor 3 subunit C-like; mRNA: messenger RNA; SOC: serous ovarian cancer; RT–qPCR: reverse transcription–quantitative polymerase chain reaction.
Protein expression of EIF3CL in paraffin-embedded SOC and paratumor samples
To detect EIF3CL protein expression in SOC, 135 SOC samples and 36 matched paratumor samples were assessed using IHC assay. The results showed that 52/135 (38.52%) samples had low/no staining (EIF3CL−) and 83/135 (61.48%) samples had moderate/strong staining (EIF3CL+). Of the 36 paratumor samples, 26 (72.22%) were EIF3CL− and 10 (27.78%) were EIF3CL+. There was a significant difference between the tumor and paratumor samples (P = 0.0149; Figure 2(a)). Furthermore, IHC analysis showed that EIF3CL protein staining was localized at the cytoplasm (Figure 2(b) and (c)).
Figure 2.
High expression of EIF3CL in SOC tissues based on immunohistochemistry. (a) EIF3CL protein expression in SOC and paratumor tissues (χ2 = 13.37, P = 0.0003). (b) Comparison of SOC and paratumor tissues, which were immunohistochemically stained for EIF3CL (400× magnification) and (c) varied staining of EIF3CL in SOC tissues (400× magnification). EIF3CL: eukaryotic translation initiation factor 3 subunit C-like; SOC: serous ovarian cancer; IHC: immunohistochemistry.
EIF3CL is correlated with overall survival (OS) in patients with SOC
In this study, patients with SOC and high expression of EIF3CL showed a median OS time of only 22 months (median recurrence-free survival (RFS): 10.5 months), while those with low expression of EIF3CL showed a median OS time of 33 (median RFS: 16.5) months. In addition, Kaplan–Meier survival analysis showed that the difference in OS was significant between patients with EIF3CL+ and EIF3CL− expression (Figure 3(a) and (b)); however, such a difference was not observed for RFS. Survival analyses showed that the cumulative OS and RFS rates of patients with SOC increased with rising EIF3CL expression levels (Figure 3).
Figure 3.
EIF3CL overexpression is associated with OS and RFS of patients with SOC based on Kaplan–Meier analysis. (a) High EIF3CL expression was associated with the (a) OS (P = 0.0106) and (b) RFS (P = 0.0463) of patients with SOC. EIF3CL: eukaryotic translation initiation factor 3 subunit C-like; OS: overall survival; RFS: recurrence-free survival; SOC: serous ovarian cancer.
EIF3CL protein expression is associated with clinicopathological parameters of SOC. Furthermore, χ2 or Fisher’s exact test was preformed to analyze the association between EIF3CL expression and clinicopathological parameters. The results showed significant associations between EIF3CL expression and clinicopathological parameters such as International Federation of Gynecology and Obstetrics (FIGO) stage, intraperitoneal metastasis, intestinal metastasis, and intraperitoneal recurrence (Table 1).
Table 1.
Association between EIF3CL expression and clinicopathological parameters using the χ2 or Fisher’s Exact test.
| Parameters | Total |
EIF3CL
|
χ2 test P value | Fisher’s Exact test pP value | ||
|---|---|---|---|---|---|---|
| Absent or no expression | Moderate or strong expression | |||||
| Age (years) | ≤50 | 53 | 23 | 30 | 0.3702 | |
| >50 | 82 | 29 | 53 | |||
| FIGO stage | I | 10 | 8 | 2 | 0.0079 | |
| II | 16 | 9 | 7 | |||
| III | 82 | 28 | 54 | |||
| IV | 27 | 7 | 20 | |||
| Lymph node metastasis | No | 28 | 13 | 15 | 0.9220 | |
| Yes | 20 | 9 | 11 | |||
| Intraperitoneal metastasis | No | 36 | 23 | 13 | 0.0003 | |
| Yes | 99 | 29 | 70 | |||
| Intestinal metastasis | No | 62 | 34 | 28 | 0.0004 | |
| Yes | 73 | 18 | 55 | |||
| Vital status | Alive | 53 | 25 | 28 | 0.2479 | |
| Dead | 56 | 20 | 36 | |||
| Intraperitoneal recurrence | No | 89 | 40 | 49 | 0.0493 | |
| Yes | 41 | 11 | 30 | |||
| Distant recurrence | No | 109 | 44 | 65 | 0.5455 | |
| Yes | 21 | 7 | 14 | |||
| Differentiation grade | G1/G2 | 45 | 19 | 26 | 0.4381 | |
| G3 | 85 | 30 | 55 | |||
| Platinum resistance | No | 131 | 51 | 80 | >0.9999 | |
| Yes | 3 | 1 | 2 | |||
| Ascites with tumor cells (+) | No | 24 | 10 | 14 | 0.1592 | |
| Yes | 26 | 6 | 20 | |||
| CA125 (U/mL) | ≤35 | 14 | 7 | 7 | 0.3511 | |
| >35 | 121 | 45 | 76 | |||
EIF3CL: eukaryotic translation initiation factor 3 subunit C-like; FIGO: International Federation of Gynecology and Obstetrics.
EIF3CL is an independent prognostic factor for SOC
In addition, univariate and multivariate Cox regression analysis were performed to explore the clinical prognostic factors in patients with SOC. Univariate Cox regression analysis showed that high EIF3CL expression, chemotherapy after operation, and hyperthermic intraperitoneal chemotherapy (HIPEC) were significant prognostic factors for SOC (Table 2). Furthermore, multivariate Cox regression analysis showed that high EIF3CL expression and HIPEC were independent prognostic factors for SOC (Table 2); however, intestinal metastasis was not a significant factor.
Table 2.
Univariate and multivariate Cox regression analyses of prognostic factors in patients with SOC.
| Variable | Univariate analysis |
Multivariate analysis |
||||
|---|---|---|---|---|---|---|
| Number of patients | p | Regression coefficient (SE) | P | Exp (B) | 95% confidence interval | |
| EIF3CL expression | 0.012 | 0.305 | 0.010 | 2.225 | 1.206–4.104 | |
| Low expression | 55 | |||||
| High expression | 80 | |||||
| Chemotherapy after operation | 0.030 | 0.537 | 0.065 | 0.362 | 0.123–1.063 | |
| No | 5 | |||||
| Yes | 129 | |||||
| HIPEC | 0.006 | 0.508 | 0.002 | 4.932 | 1.800–13.515 | |
| No | 123 | |||||
| Yes | 12 | |||||
SOC: serous ovarian cancer; SE: standard error; HIPEC: hyperthermic intraperitoneal chemotherapy.
Discussion
EIF3CL plays a dual role in different cancer types; it has been reported to play an oncogenic role in DIPG 16 and melanoma 17 and an anti-cancer role in breast cancer. 18 However, To the best of our knowledge, no studies have reported EIF3CL expression in SOC. In the present study, our results showed that EIF3CL expression was considerably higher in SOC samples than in paratumor samples based on RT–qPCR and IHC analyses. Thus, our study is the first to demonstrate EIF3CL upregulation in SOC samples. These results are similar to those of a previous study, which demonstrated EIF3CL upregulation in DIPG 16 and melanoma. 17 Our results showed that EIF3CL may play a candidate oncogenic role in SOC.
Furthermore, EIF3CL protein expression was associated with the FIGO stage, intraperitoneal metastasis, intestinal metastasis, and intraperitoneal recurrence. Moreover, high EIF3CL expression was associated with intraperitoneal metastasis, intestinal metastasis, and intraperitoneal recurrence, which are factors representing the progression and recurrence of SOC. Thus, this study demonstrated that EIF3CL may correlate with the invasion, metastasis, and recurrence of SOC, similar to its role in DIPG. 16 Thus, our results showed that EIF3CL may play a vital role in the pathogenesis, progression, and recurrence of SOC. However, further research, including more in vitro and in vivo experiments, is required to confirm these findings.
In addition, high EIF3CL expression, chemotherapy after operation, and HIPEC were significant prognostic factors for SOC based on univariate Cox regression analysis. Chemotherapy after operation and HIPEC are known to be effective treatment methods for SOC, and they are closely associated with SOC prognosis. Furthermore, multivariate regression analysis revealed that EIF3CL protein expression and HIPEC were indeed independent prognostic factors for SOC; however, chemotherapy after operation was no longer a significant factor. These results suggested that EIF3CL expression can be recommended as a meaningful SOC prognostic factor.
The results of the present study suggested that EIF3CL was upregulated in SOC samples compared with that in paratumor samples, and high EIF3CL expression showed poor OS prognosis in patients with SOC. In the future, an increasing number of in vitro and in vivo experiments are needed to demonstrate the exact role of EIF3CL and its detailed molecular mechanism in the carcinogenesis and progression of SOC.
Conclusion
The present study suggested that EIF3CL was upregulated in cancer tissues compared with that in paratumor tissues, and high EIF3CL expression indicates poor prognosis in patients with SOC.
Acknowledgements
We thank Professor Zhongqiu Lin for supplying the clinical samples. We would also like to thank all participants who contributed to this work.
Authors’ contributions: Conception: Zhaoyang Zeng and Jilong Yao
Interpretation or analysis of data: Tingting Li and Jianhuan Yuan
Preparation of the manuscript: Tingting Li, Qudi Qiao, and Zhongqiu Lin
Revision for important intellectual content: Tingting Li and Longyang Liu
Supervision: Jilong Yao
The authors declare no conflict of interest.
Funding: This study was supported by the President Funds of Nanfang Hospital, Southern Medical University (No. 2024A033), Guangdong Basic and Applied Basic Research Foundation (grant nos. 2020A1515110030 and 2022A1515220208), and the Guangzhou science and Technology Program (grant no. 202102080060).
ORCID iD: Longyang Liu https://orcid.org/0000-0002-1014-3625
Consent for publication
Not applicable.
Data availability statement
The datasets generated and analyzed in this study are not publicly available to protect the patients’ privacy but are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
The ethics committees of the Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, as well as the Memorial Hospital of Sun Yat-sen University authorized the experimental and research protocols of this study. All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All controls and patients (or relatives of patients who already died) provided written informed consent.
References
- 1.Taylor KN, Eskander RN. PARP inhibitors in epithelial ovarian cancer. Recent Pat Anticancer Drug Discov 2018; 13: 145–158. [DOI] [PubMed] [Google Scholar]
- 2.Lheureux S, Braunstein M, Oza AM. Epithelial ovarian cancer: evolution of management in the era of precision medicine. CA Cancer J Clin 2019; 69: 280–304. [DOI] [PubMed] [Google Scholar]
- 3.Ghose A, Bolina A, Mahajan I, et al. Hereditary ovarian cancer: towards a cost-effective prevention strategy. Int J Environ Res Public Health 2022; 19: 12057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018. CA Cancer J Clin 2018; 68: 284–296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Smith RA, Andrews KS, Brooks D, et al. Cancer screening in the United States, 2018: a review of current American Cancer Society guidelines and current issues in cancer screening. CA Cancer J Clin 2018; 68: 297–316. [DOI] [PubMed] [Google Scholar]
- 6.Aliyuda F, Moschetta M, Ghose A, et al. Advances in ovarian cancer treatment beyond PARP inhibitors. Curr Cancer Drug Targets 2023; 23: 433–446. [DOI] [PubMed] [Google Scholar]
- 7.Weberpals JI, Koti M, Squire JA. Targeting genetic and epigenetic alterations in the treatment of serous ovarian cancer. Cancer Genet 2011; 204: 525–535. [DOI] [PubMed] [Google Scholar]
- 8.Jayson GC, Kohn EC, Kitchener HC, et al. Ovarian cancer. Lancet 2014; 384: 1376–1388. [DOI] [PubMed] [Google Scholar]
- 9.Liu L, Yi J, Yuan J, et al. FOXO1 overexpression is correlated with poor prognosis in epithelial ovarian cancer. Cancer Biomark 2020; 28: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Zeng Z, Ji N, Yi J, et al. LGR4 overexpression is associated with clinical parameters and poor prognosis of serous ovarian cancer. Cancer Biomark 2020; 28: 65–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Liu L, Yi J, Deng X, et al. MYH9 overexpression correlates with clinicopathological parameters and poor prognosis of epithelial ovarian cancer. Oncol Lett 2019; 18: 1049–1056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Yao Y, Liu L, He W, et al. Low expression of KIF7 indicates poor prognosis in epithelial ovarian cancer. Cancer Biomark 2019; 26: 481–489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Podust V, Zhang Z, Fung ET, et al. Biomarker for ovarian and endometrial cancer: hepcidin. Patent US7510842, USA, 2009.
- 14.Mansfield BC, Yip PF, Amonkar S, et al. Predictive biomarkers for ovarian cancer. Patent US10605811, USA, 2014.
- 15.Ghose A, McCann L, Makker S, et al. Diagnostic biomarkers in ovarian cancer: advances beyond CA125 and HE4. Ther Adv Med Oncol 2024; 16: 17588359241233225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Chen J, Lin Z, Barrett L, et al. Identification of new therapeutic targets and natural compounds against diffuse intrinsic pontine glioma (DIPG). Bioorg Chem 2020; 99: 103847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Saad MN, Hamed M. Transcriptome-wide association study reveals new molecular interactions associated with melanoma pathogenesis. Cancers (Basel) 2024; 16: 2517. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Sharma M, Dey U, Das AS, et al. Anti-tumor potential of high salt in breast cancer cell lines. Mol Biol Rep 2024; 51: 1002. [DOI] [PubMed] [Google Scholar]
- 19.Liu L, Chen C, Liu P, et al. MYH10 combines with MYH9 to recruit USP45 by deubiquitinating snail and promotes serous ovarian cancer carcinogenesis, progression, and cisplatin resistance. Adv Sci (Weinh) 2023; 10: e2203423. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 20.Liu L, Hu K, Zeng Z, et al. Expression and clinical significance of microtubule-actin cross-linking factor 1 in serous ovarian cancer. Recent Pat Anticancer Drug Discov 2021; 16: 66–72. [DOI] [PubMed] [Google Scholar]
- 21.Liu L, Lv J, Lin Z, et al. Co-overexpression of GRK5/ACTC1 correlates with the clinical parameters and poor prognosis of epithelial ovarian cancer. Front Mol Biosci 2021; 8: 785922. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analyzed in this study are not publicly available to protect the patients’ privacy but are available from the corresponding author on reasonable request.