-
To investigatethe expression of miR-378a-3p in CC cells, we performed RT-qPCR analysis to determine the relative miR-378a-3p levels in the serum of 48 pairs of patients with CC and healthy controls. As shown in Figure 1A, serum miR-378a-3p expression was decreased in CC patients compared to that in normal subjects. Similarly, the analysis of the expression of miR-378a-3p in tissues from CC patients and their adjacent normal tissues revealed that miR-378a-3p expression was markedly decreased in the CC group (Figure 1B).
Figure 1. MiR-378a-3p is downregulated in cervical cancer (CC) serum and tissues. (A) Real-time quantitative polymerase chain reaction analysis was performed to evaluate the relative expression levels of miR-378a-3p in 48 tissue sample pairs of patients with CC and healthy control subjects. (B) The expression levels of miR-378a-3p in CC tissues and adjacent normal tissues were measured. *P < 0.05 and **P < 0.01 versus the control group indicate that the difference was significant.
-
The relationship between CC clinicopathological features and serummiR-378a-3p expression shown in Table 1 revealed that miR-378a-3p expression was closely related to tumor size, tumor-node-metastasis (TNM) stage, and lymph node metastasis. For the CC patient samples, patients with low miR-378a-3p expression had larger tumor sizes; more tumors in stages II, III, and IV; and lymph node metastasis. However, miR-378a-3p expression was not associated with age or pathological grading.
Table 1. Relevance between serum miR-378a-3p expression and clinical pathologic characteristics of cervical cancer
Variable No. of patients (n = 48) miR-378a-3p expression χ2 P High (n = 19) Low (n = 29) Age (years) ≤ 50 13 5 8 0.009 0.923 > 50 35 14 21 Tumor size (cm) ≤ 4 20 12 8 5.976 0.015 > 4 28 7 21 TNM stage I–II 18 11 7 5.581 0.018 III–IV 30 8 22 Pathological grading Well (I) 3 3 0 5.141 0.077 Moderate (II–III) 34 14 20 Poor (IV) 11 3 8 Lymph node metastasis Negative 23 13 10 5.298 0.022 Positive 25 6 19 -
The relationship between serum miR-378a-3p expression and the overall survival (OS) of patients with CC was analyzed using the Kaplan-Meier method. Patients with CC were separated into high and low miR-378a-3p expression groups. The results showed that low miR-378a-3p expression was related to poor OS in CC patients (Figure 2A). Moreover, the survival analysis of patients, based on miR-378a-3p expression using Kaplan-Meier Plotter visualization software[23] and The Cancer Genome Atlas database, showed that the mortality rates of patients with low miR-378a-3p expression were significantly higher compared to those with high expression (Figure 2B).
Figure 2. Analysis on the overall survival (OS) of cervical cancer (CC) patients with different miR-378a-3p expression levels. (A) The relationship between serum miR-378a-3p expression and the OS of 48 CC patients was analyzed by the Kaplan-Meier method. (B) OS analysis of miR-378a-3p was performed using Kaplan-Meier Plotter visualization software based on The Cancer Genome Atlas database (N = 306). The figure was downloaded from Kaplan-Meier Plotter.
-
After transfection with miR-378a-3p mimics, CCK-8, cell colony formation, and scratch wound healing assays were used to investigate the effect of miR-378a-3p expression on CC cell proliferation. Compared to the NC group, the viability of SiHa and HeLa cells in the miR-378a-3p mimic group was significantly decreased at 48 h (Figure 3A). The results of the cell colony formation assay revealed that the number and size of the colonies of the transfected SiHa and HeLa cells in the mimic group were suppressed (Figure 3B). In addition, the migration ability was dramatically inhibited after transfection with miR-378a-3p mimics (Figure 3C).
Figure 3. Overexpression of miR-378a-3p inhibits cervical cancer (CC) cell proliferation. (A) The optical density values in SiHa and HeLa cells in the miR-378a-3p mimic and NC groupswere determined by the Cell Counting Kit-8 assay. (B) The cell colony formation assay was performed to assess the proliferation of SiHa and HeLa cells. (C) The wound healing assay was performed to evaluate the migration of SiHa and HeLa cells. *P < 0.05 versus the control group indicate that the difference was significant.
-
Mice were subcutaneously injected with HeLa cells transfected with lentivirus-packaged miR-378a-3p mimics, and the tumor volume was measured every 7 days for 28 days. The results showed that the tumor volumein the miR-378a-3p mimic group was lower than that in the NC group (Figure 4A). We also measured the tumor weight in the two groups, and the results revealed that the tumor weight was dramatically reduced with overexpression of miR-378a-3p (Figure 4B). In addition, after injection of miR-378a-3p mimics, the isolated tumor tissues had a higher level of miR-378a-3p expression (Figure 4C). Moreover, immunohistochemical analysis of Ki-67 expression in isolated tumor tissues showed that Ki-67 expression was significantly decreased in the miR-378a-3p group (Figure 4D).
Figure 4. MiR-378a-3p inhibits tumor growth in vivo. (A) Tumor volume was measured every 7 days. (B) Tumors were isolated after 28 days of HeLa cell injection, and images for representative mice and tumors were captured (left). The tumor weight was also calculated (right). (C) The expressions levels of miR-378a-3p in isolated tumors were determined. (D) The Ki-67 expression level was determined by immunohistochemical analysis. *P < 0.05 and **P < 0.01 versus the small interfering RNA negative control group indicate that the difference was significant.
doi: 10.3967/bes2021.026
MicroRNA-378a-3p Downregulation as a Novel Biomarker with Poor Clinical Outcomes in Cervical Cancer
-
Abstract:
Objective Cervical cancer (CC) is one of the most common malignant tumors in gynecology. This study aimed to investigate the prognostic significance of serum microRNA (miR)-378a-3p in CC and the effect of miR-378a-3p on tumor growth. Methods Real-time quantitative polymerase chain reaction analysis was used to measure the expression of miR-378a-3p in serum from patients with CC and healthy control subjects as well as from CC tissues and adjacent normal tissues. The association between serum miR-378a-3p levels and clinicopathological factors was analyzed. The correlation between miR-378a-3p levels and overall survival (OS) of CC patients was determined by Kaplan-Meier analysis. The CC cell proliferation and migration abilities after transfection of miR-378a-3p mimics were detected by Cell Counting Kit-8 and scratch wound healing assays, respectively. Tumor volume and weight in mice treated with miR-378a-3p were measured using a caliper and an electronic balance. Results MiR-378a-3p expression was downregulated in the serum and tissues of CC patients compared to that in healthy control subjects and normal tissues, respectively. Low expression of miR-378a-3p was positively correlated with large tumor size, advanced tumor stage, and lymph node metastasis. The OS of patients with low expression of miR-378a-3p was significantly lower than that of patients with high expression. Overexpression of miR-378a-3p suppressed the proliferation and migration of CC cells. In vivo studies indicated that overexpression of miR-378a-3p was associated with decreased tumor volume and weight in mice. Conclusion MiR-378a-3p downregulation is associated with the development and prognosis of CC, suggesting that it may be a potential biomarker for CC. -
Key words:
- Cervical cancer /
- miR-378a-3p /
- Biomarker /
- Prognosis /
- Tumor growth
-
Figure 1. MiR-378a-3p is downregulated in cervical cancer (CC) serum and tissues. (A) Real-time quantitative polymerase chain reaction analysis was performed to evaluate the relative expression levels of miR-378a-3p in 48 tissue sample pairs of patients with CC and healthy control subjects. (B) The expression levels of miR-378a-3p in CC tissues and adjacent normal tissues were measured. *P < 0.05 and **P < 0.01 versus the control group indicate that the difference was significant.
Figure 2. Analysis on the overall survival (OS) of cervical cancer (CC) patients with different miR-378a-3p expression levels. (A) The relationship between serum miR-378a-3p expression and the OS of 48 CC patients was analyzed by the Kaplan-Meier method. (B) OS analysis of miR-378a-3p was performed using Kaplan-Meier Plotter visualization software based on The Cancer Genome Atlas database (N = 306). The figure was downloaded from Kaplan-Meier Plotter.
Figure 3. Overexpression of miR-378a-3p inhibits cervical cancer (CC) cell proliferation. (A) The optical density values in SiHa and HeLa cells in the miR-378a-3p mimic and NC groupswere determined by the Cell Counting Kit-8 assay. (B) The cell colony formation assay was performed to assess the proliferation of SiHa and HeLa cells. (C) The wound healing assay was performed to evaluate the migration of SiHa and HeLa cells. *P < 0.05 versus the control group indicate that the difference was significant.
Figure 4. MiR-378a-3p inhibits tumor growth in vivo. (A) Tumor volume was measured every 7 days. (B) Tumors were isolated after 28 days of HeLa cell injection, and images for representative mice and tumors were captured (left). The tumor weight was also calculated (right). (C) The expressions levels of miR-378a-3p in isolated tumors were determined. (D) The Ki-67 expression level was determined by immunohistochemical analysis. *P < 0.05 and **P < 0.01 versus the small interfering RNA negative control group indicate that the difference was significant.
Table 1. Relevance between serum miR-378a-3p expression and clinical pathologic characteristics of cervical cancer
Variable No. of patients (n = 48) miR-378a-3p expression χ2 P High (n = 19) Low (n = 29) Age (years) ≤ 50 13 5 8 0.009 0.923 > 50 35 14 21 Tumor size (cm) ≤ 4 20 12 8 5.976 0.015 > 4 28 7 21 TNM stage I–II 18 11 7 5.581 0.018 III–IV 30 8 22 Pathological grading Well (I) 3 3 0 5.141 0.077 Moderate (II–III) 34 14 20 Poor (IV) 11 3 8 Lymph node metastasis Negative 23 13 10 5.298 0.022 Positive 25 6 19 -
[1] Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin, 2015; 65, 87−108. doi: 10.3322/caac.21262 [2] Li HR, Wu XH, Cheng X. Advances in diagnosis and treatment of metastatic cervical cancer. J Gynecol Oncol, 2016; 27, e43. doi: 10.3802/jgo.2016.27.e43 [3] Luo H, Xu XH, Yang J, et al. Genome-wide somatic copy number alteration analysis and database construction for cervical cancer. Mol Genet Genomics, 2020; 295, 765−73. doi: 10.1007/s00438-019-01636-x [4] Balasubramaniam SD, Balakrishnan V, Oon CE, et al. Key Molecular Events in Cervical Cancer Development. Medicina (Kaunas), 2019; 55, 384. doi: 10.3390/medicina55070384 [5] Ghasemi F, Shafiee M, Banikazemi Z, et al. Curcumin inhibits NF-kB and Wnt/β-catenin pathways in cervical cancer cells. Pathol Res Pract, 2019; 215, 152556. doi: 10.1016/j.prp.2019.152556 [6] Shafabakhsh R, Reiter RJ, Mirzaei H, et al. Melatonin: a new inhibitor agent for cervical cancer treatment. J Cell Physiol, 2019; 234, 21670−82. doi: 10.1002/jcp.28865 [7] Chen YH, Hou YY, Yang Y, et al. Gene expression changes in cervical squamous cancers following neoadjuvant interventional chemoembolization. Clin Chim Acta, 2019; 493, 79−86. doi: 10.1016/j.cca.2019.02.012 [8] Aguda BD. Modeling microRNA-transcription factor networks in cancer. Adv Exp Med Biol, 2013; 774, 149−67. [9] Granados López AJ, López JA. Multistep model of cervical cancer: participation of miRNAs and coding genes. Int J Mol Sci, 2014; 15, 15700−33. doi: 10.3390/ijms150915700 [10] Tutar Y. miRNA and cancer; computational and experimental approaches. Curr Pharm Biotechnol, 2014; 15, 429. doi: 10.2174/138920101505140828161335 [11] Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov, 2017; 16, 203−22. doi: 10.1038/nrd.2016.246 [12] Mirzaei H, Yazdi F, Salehi R, et al. SiRNA and epigenetic aberrations in ovarian cancer. J Can Res Ther, 2016; 12, 498−508. doi: 10.4103/0973-1482.153661 [13] Di Leva G, Garofalo M, Croce CM. MicroRNAs in cancer. Annu Rev Pathol, 2014; 9, 287−314. doi: 10.1146/annurev-pathol-012513-104715 [14] Aghdam AM, Amiri A, Salarinia R, et al. MicroRNAs as diagnostic, prognostic, and therapeutic biomarkers in prostate cancer. Crit Rev Eukaryot Gene Expr, 2019; 29, 127−39. doi: 10.1615/CritRevEukaryotGeneExpr.2019025273 [15] Lin SB, Gregory RI. MicroRNA biogenesis pathways in cancer. Nat Rev Cancer, 2015; 15, 321−33. doi: 10.1038/nrc3932 [16] Savardashtaki A, Shabaninejad Z, Movahedpour A, et al. miRNAs derived from cancer-associated fibroblasts in colorectal cancer. Epigenomics, 2019; 11, 1627−45. doi: 10.2217/epi-2019-0110 [17] Naeli P, Yousefi F, Ghasemi Y, et al. The role of MicroRNAs in lung cancer: implications for diagnosis and therapy. Curr Mol Med, 2020; 20, 90−101. doi: 10.2174/1566524019666191001113511 [18] Khani P, Nasri F, Khani Chamani F, et al. Genetic and epigenetic contribution to astrocytic gliomas pathogenesis. J Neurochem, 2019; 148, 188−203. doi: 10.1111/jnc.14616 [19] Ikeda K, Horie-Inoue K, Ueno T, et al. miR-378a-3p modulates tamoxifen sensitivity in breast cancer MCF-7 cells through targeting GOLT1A. Sci Rep, 2015; 5, 13170. doi: 10.1038/srep13170 [20] Wei XF, Li H, Zhang BW, et al. miR-378a-3p promotes differentiation and inhibits proliferation of myoblasts by targeting HDAC4 in skeletal muscle development. RNA Biol, 2016; 13, 1300−9. doi: 10.1080/15476286.2016.1239008 [21] Xu ZH, Yao TZ, Liu W. miR-378a-3p sensitizes ovarian cancer cells to cisplatin through targeting MAPK1/GRB2. Biomed Pharmacother, 2018; 107, 1410−7. doi: 10.1016/j.biopha.2018.08.132 [22] Li H, Dai SJ, Zhen TT, et al. Clinical and biological significance of miR-378a-3p and miR-378a-5p in colorectal cancer. Eur J Cancer, 2014; 50, 1207−21. doi: 10.1016/j.ejca.2013.12.010 [23] Nagy Á, Lánczky A, Menyhárt O, et al. Author correction: validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep, 2018; 8, 11515. doi: 10.1038/s41598-018-27521-y [24] Ditto A, Bogani G, Leone Roberti Maggiore U, et al. Oncologic effectiveness of nerve-sparing radical hysterectomy in cervical cancer. J Gynecol Oncol, 2018; 29, e41. doi: 10.3802/jgo.2018.29.e41 [25] Siegel EM, Riggs BM, Delmas AL, et al. Quantitative DNA methylation analysis of candidate genes in cervical cancer. PLoS One, 2015; 10, e0122495. doi: 10.1371/journal.pone.0122495 [26] Sadri Nahand J, Moghoofei M, Salmaninejad A, et al. Pathogenic role of exosomes and microRNAs in HPV-mediated inflammation and cervical cancer: a review. Int J Cancer, 2020; 146, 305−20. doi: 10.1002/ijc.32688 [27] Nahand JS, Taghizadeh-Boroujeni S, Karimzadeh M, et al. microRNAs: new prognostic, diagnostic, and therapeutic biomarkers in cervical cancer. J Cell Physiol, 2019; 234, 17064−99. doi: 10.1002/jcp.28457 [28] Zhang T, Zou P, Wang TJ, et al. Down-regulation of miR-320 associated with cancer progression and cell apoptosis via targeting Mcl-1 in cervical cancer. Tumor Biol, 2016; 37, 8931−40. doi: 10.1007/s13277-015-4771-6 [29] Yu JJ, Wang Y, Dong RF, et al. Circulating microRNA-218 was reduced in cervical cancer and correlated with tumor invasion. J Cancer Res Clin Oncol, 2012; 138, 671−4. doi: 10.1007/s00432-012-1147-9 [30] Zhao S, Yao D, Chen J, et al. MiR-20a promotes cervical cancer proliferation and metastasis in vitro and in vivo. PLoS One, 2015; 10, e0120905. doi: 10.1371/journal.pone.0120905 [31] Sanches JGP, Xu YC, Yabasin IB, et al. miR-501 is upregulated in cervical cancer and promotes cell proliferation, migration and invasion by targeting CYLD. Chem Biol Interact, 2018; 285, 85−95. doi: 10.1016/j.cbi.2018.02.024 [32] Alsina-Sanchís E, Figueras A, Lahiguera A, et al. TGFβ controls ovarian cancer cell proliferation. Int J Mol Sci, 2017; 18, 1658. doi: 10.3390/ijms18081658 [33] Robert J. Biologie de la métastase biology of cancer metastasis. Bull Cancer, 2013; 100, 333−42. doi: 10.1684/bdc.2013.1724