MicroRNA-146a affects the chemotherapeutic sensitivity and prognosis of advanced gastric cancer through the regulation of LIN52
- Authors:
- Published online on: December 27, 2016 https://doi.org/10.3892/ol.2016.5536
- Pages: 1386-1392
-
Copyright: © Luo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Gastric cancer is a common malignancy affecting the gastrointestinal system, which accounts for 8% of all types of malignant cancer. Although the incidence of gastric cancer has been declining worldwide, it remains high in several countries, particularly in China (1). The development and progression of gastric cancer are associated with multiple genes and numerous transforming steps. Investigating genetic and other risk factors associated with the occurrence and development of gastric cancer, and their correlation with prognosis, is critical to provide theoretical support for improving the diagnosis, prognosis and treatment of gastric cancer (2). MicroRNAs (miRs) are small non-coding RNA molecules of ~22 nucleotides, which regulate gene expression at the post-transcriptional level. The regulatory capacity of miRs includes almost all major cellular activities, including cell proliferation, differentiation and apoptosis. In previous years, a number of studies have shown that microRNAs can have oncogene and tumor suppressor functions, thereby being involved in tumor development and progression (3–5). A previous study showed that miR-146a was associated with gastric cancer metastasis through modulating Wiskott-Aldrich syndrome protein family member 2 (6). Other studies have shown that the expression of miR-146a is associated with tumor cell proliferation and apoptosis in gastric cancer (7,8). miR-146a has direct effects on tumor metastasis and prognosis by affecting epidermal growth factor receptor (EGFR) (8). The expression of LIN52 in gastrointestinal tumors affects drug sensitivity and enhances the effect of imatinib-induced apoptosis in tumor cells (9). In addition, according to the predictions of TargetScan, miR-146a is a target gene regulated by LIN52 (10). The present study further analyzed the effect of miR-146a on the treatment of gastric cancer. The expression of miR-146a was analyzed in 93 clinical cases of advanced gastric cancer using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, and its expression was correlated with the clinical characteristics of advanced gastric cancer and the prognosis of patients.
Materials and methods
Patients and sampling
A total of 93 patients with advanced gastric cancer (57 men and 36 women; median age, 61 years), who underwent surgical treatment between June 2009 and January 2011 in Henan Provincial People's Hospital (Henan, China) were enrolled in the present study. All patients received an oxaliplatin-based chemotherapeutic regimen following surgical treatment, and the efficacy was evaluated in accordance with the Response Evaluation Criteria in Solid Tumors (RESIST) (11) The histopathological analysis of resected tissues was based on the tumor-node-metastasis (TNM) staging criteria of the World Health Organization (11). From each patient, non-tumor tissues were collected 7 cm adjacent to the tumor lesion as an internal control. The histological sections of each resected specimen were classified by pathologists. All patients signed written informed consent prior to their involvement in the study. The experimental protocol of the present study was approved by the Ethics Committee of Henan Provincial People's Hospital.
Reagents
Mouse anti-human LIN52 monoclonal antibody was purchased from Santa Cruz Biotechnology, Inc. (Dallas, TX, USA). The streptavidin-peroxidase (SP) immunohistochemical staining kit and 3,3′-diaminobenzidine (DAB) peroxidase substrate kit were purchased from Fuzhou Maxim Biotech, Inc. (Fujian, China). The experimental procedures for the immunohistochemistry were according to the protocol of the SP staining kit manufacturer. Phosphate-buffered saline (PBS) with Tween detergent was used to replace primary antibody in the negative control for the immunohistochemistry assays. TRIzol reagent was purchased from Thermo Fisher Scientific, Inc. (Waltham, MA, USA). The reverse transcription kit (K1622 MBI) was purchased from Fermentas; Thermo Fisher Scientific, Inc. SYBR® Green PCR Master Mix was purchased from Takara Bio, Inc. (Otsu, Japan). The Stratagene Mx3005P RT-qPCR instrument was purchased from Agilent Technologies, Inc. (Santa Clara, CA, USA). Other conventional reagents for molecular biology were purchased from Sigma-Aldrich (Merck Millipore, Darmstadt, Germany).
Immunohistochemistry
Following conventional tissue processing, paraffin-embedding and sectioning, each tissue section was dewaxed and hydrated in water, followed by incubation with 3% hydrogen peroxide at room temperature for 15 min and high pressure antigen retrieval for 2 min. Following cooling of the sections to room temperature, each section was blocked in normal goat serum (Fuzhou Maxim Biotech, Inc.) at room temperature for 10 min, and incubated with LIN52 antibody (dilution, 1:300) at 4°C overnight. Each tissue section was then incubated with biotinylated rabbit anti-mouse secondary antibody (Santa Cruz Biotechnology, Inc.) at 37°C for 10 min. Following washing of the tissue sections with PBS, DAB solution was used for color development, followed by repeated washing under tap water and hematoxylin counterstaining. Excess dye from the tissue sections was removed using hydrochloric acid. All tissue sections were dehydrated in graded ethanol solutions and cleared in xylene solution prior to mounting on glass slides using neutral gum. A light microscope was used to visualize staining.
RT-qPCR analysis
Following collection of the resected tissue and snap freezing in liquid nitrogen, each tissue specimen was crushed into a powder, followed by total RNA extraction. TRIizol reagent (1 ml) was added to the tissue powder to lyse the cells for 10 min, followed by transfer of the supernatant to a microcentrifuge tube. Chloroform solution (400 µl) was added to the supernatant, vortexed and subjected to 12,000 g centrifugation at 4°C for 15 min. The supernatant (200 µl) was then transferred to a new RNase-free microcentrifuge tube and mixed with equal quantities of isopropanol by inverting the tube, followed by incubation for 10 min and 12,000 g centrifugation at 4°C for 10 min. The supernatant was discarded, followed by the addition of 1 ml 70% ethanol to the pellet and mixing by gentle inversion of the microcentrifuge tube. The mixture was centrifuged at 12,000 g at 4°C for 10 min, following which the ethanol solution was discarded and the pellet was air-dried. The pellet was then dissolved in diethyl pyrocarbonate-treated distilled water. The total RNA concentration of each sample was determined using an e-Spect ultra-small spectrophotometer (Malcom Co., Ltd., Tokyo, Japan). The RNA quality of each sample was determined based on the optical density (OD) 260/280 ratio, ranging between 1.8 and 2.0. For cDNA synthesis, 20 µl reverse transcriptase solution from the kit was added to 0.1 µg RNA template, miR-146a reverse primer and U6 primer for reverse transcription into cDNA. U6 was the internal reference for analysis of the expression of miR-146a. Details of the primers are listed in Table I. The primers and RNA template were incubated at 65°C for 5 min, followed by cooling on ice, incubation with the reverse transctiption reaction mixture and dNTPs at 42°C for 60 min, and termination of the reaction at 70°C for 5 min. The total volume of master mix for the qPCR analysis was 20 µl, containing 1 µl of cDNA (final concentration 5 ng), 12.5 µl of 2X SYBR Green I Master mix, 0.5 µmol/l miR-146a/U6 specific forward primers and 0.5 µmol/l miR-146a/U6 reverse primers. Each sample had three replicates. The qPCR conditions were as follows: 95°C for 7 min, followed by 40 cycles of denaturation at 95°C for 15 sec, annealing at 60°C for 25 sec and elongation at 72°C for 25 sec. The quantification cycle (Cq) value of the U6 reaction obtained from the qPCR analysis was used to calculate the Cq value representing the relative expression of miR-146a. qPCR was activated at 95°C for 5 min, followed by 45 cycles of denaturation at 95°C for 15 sec, annealing at 62°C for 30 sec and elongation at 72°C for 20 sec. The Cq value, standard and melting curves were automatically generated by the RT-qPCR instrument.
 | Table I.miR-146a and U6 primer sequences for reverse transcription-quantitative polymerase chain reaction analysis. |
Statistical analysis
SPSS 13.0 software was used for the non-parametric rank sum test. Relative expression ≥2.0 was defined as high expression and relative expression <2.0 was defined as low expression following Kaplan-Meier survival analysis. The Cox regression model was based on the forward method of conditional parameter estimates. P<0.05 was considered to indicate a statistically significant difference.
Results
Positive protein expression of LIN52
As shown in Fig. 1A, positive staining of the LIN52 protein, identified as brownish yellow granules, was distributed predominantly in the cytoplasm of the advanced gastric cancer tissues. In the normal tissues adjacent to the tumor, the expression of LIN52 was low (Fig. 1B).
Correlation between the expression of miR-146a in advanced gastric cancer tissues and clinicopathological parameters
The expression of miR-146a in the advanced gastric cancer tissue was significantly correlated with the clinical TNM staging of the patients (P<0.05). The expression of miR-146a in stage III gastric cancer tissues was significantly higher, compared with that in stage IV gastric cancer tissues (P<0.05). Patients with lymph node metastasis had lower expression levels of miR-146a, compared with patients without lymph node metastasis. However, no significant correlation was found between the expression of miR-146a in advanced gastric cancer tissue and other clinicopathological factors, including gender, age and tumor differentiation (P>0.05; Table II).
| Table II.Expression of miR-146a and LIN52 in 93 patients with gastric cancer and their correlation with clinicopathological parameters. |
Correlation between the expression of miR-146a and patient survival rates
The results of the Kaplan-Meier survival analysis showed that the expression of miR-146a had a significant effect on the survival rates of patients with advanced gastric cancer. Patients with high expression levels of miR-146a had significantly higher survival rates, compared with patients with low expression levels of miR-146a (P<0.05; Fig. 2).
Correlation between the expression levels of miR146a and LIN52 in advanced gastric cancer
The present study further analyzed the correlation between the expression levels of miR146a and LIN52. Among the 44 specimens with a high expression of miR146a, only 8 (18.2%) showed LIN52 immunoreactivity (P<0.001; Table III).
Cox regression analysis of prognostic factors in patients with gastric cancer
The clinical prognostic factors of patients with advanced gastric cancer were used as dependent variables and their relevant effects were used as independent variables for Cox regression analysis. As shown in Table IV, TNM staging, lymph node metastasis and the expression of miR-146a were independent risk factors for the prognosis of patients with advanced gastric cancer.
Expression of miR-146a and chemotherapeutic sensitivity
The present study also assessed the association between the expression of miR-146a and chemotherapeutic sensitivity in patients with advanced gastric cancer. The analysis showed that patients with advanced gastric cancer tissues expressing a low level of miR-146a had a poor prognosis (Fig. 3). The present study evaluated the efficacy of chemotherapy in patients with advanced gastric cancer according to the RESIST standard and divided the patients into three groups: Complete remission (CR), partial remission (PR) and disease progression (PD). The characteristics of the expression of miR-146a were analyzed in the three groups. The expression of miR-146a in the CR group was the highest, with significant differences between the PR and PD groups, and the PR and PD groups (P<0.05), but not between the PR and CR groups (P>0.05).
Evaluation of diagnostic specificity and sensitivity of miR-146a in patients with advanced gastric cancer using ROC curve regression analysis
The area under the ROC curve of miR-146a in the advanced gastric cancer tissues and corresponding adjacent non-tumor tissue was 0.760 [5% confidence interval (CI)=0.589–0.942; P=0.016], suggesting that miR-146a was significant for distinguishing between advanced gastric cancer tissue and normal tissue (P<0.05). Lower expression levels of miR-146a increased the likelihood of advanced gastric cancer. These results indicated the suitability of using miR-146a as an adjuvant diagnostic marker for advanced gastric cancer. In the present study, the relative expression of miR-146a was 19.75, which was used as the evaluation threshold for the diagnosis of advanced gastric cancer, with 94.1% sensitivity and 61.5% specificity (Fig. 4).
Discussion
Gastric cancer is one of the most common types of cancer in China, with a high incidence rate and insidious early symptoms (12). At the time of diagnosis, patients with gastric cancer are often at moderate and/or advanced stages of the disease, with poor prognosis (11). Therefore, the degree of malignancy and the mortality rates of patients with gastric cancer are relatively high. At present, there is no method for the effective early diagnosis of gastric cancer and, if diagnosed with an advanced stage of disease, radical surgery is not an option for patients. Advances in molecular targeted therapy and chemotherapies have not significantly improved the median survival rates of patients with advanced gastric cancer (13). Although α-fetoprotein, carcinoembryonic antigen, CA125 and CA199 enhance the early diagnostic sensitivity of gastric cancer, their diagnostic specificities as markers of gastric cancer are low (14). It is necessary to investigate novel diagnostic markers for gastric cancer to provide guidance for cancer diagnosis and treatment (15). To address this problem, studies are focusing on the use of miRs in gastric cancer as potential biomarkers (16). Evaluation of the expression of miR in tumor tissue and patient serum is important in the early diagnosis and prognosis of gastric cancer (17). In the present study, the expression of miR-146a in gastric cancer tissue was correlated with the early diagnosis of gastric cancer and the prognosis of patients, and predictive analysis on these parameters was performed.
Through binding with the mRNA 3′-untranslated region (3′UTR), a single miR may have regulatory effects against various mRNAs. Typically, there is differential expression of miRs in tumor tissues, compared with normal tissues. miRs usually have specific and stable expression in tissues, therefore, miRs offer potential as prognostic markers (18,19). Binding between miRs and the 3′UTR of mRNA suppresses mRNA transcription and affects the expression of oncogenes and tumor suppressor genes, thereby promoting or inhibiting tumor occurrence, development and metastasis. A previous study showed that miRs can be used as potential targets in anticancer therapy (20). Another prevopis study showed that miRs are associated with tumor metastasis, tolerance of anticancer therapy and tumor progression (21,22). In addition, miRs have only ~20 nucleotide bases, which facilitates its stable expression in tissues and its detection (23). Therefore, miRs have received increasing attention in tumor diagnosis, treatment evaluation and determining the prognosis of cancer (24,25).
Although the biological functions of miRs remain to be fully elucidated, the expression of miR in normal tissue, compared with tumor tissue, is often apparent. To date, increasing applications of miRs as tissue-specific markers have become available for the analysis of primary tumor metastasis (26,27). The present study showed that the relative expression of miR-146a in gastric cancer tissue was significantly lower, compared with that in the corresponding adjacent normal gastric mucosa, suggesting that miR-146a acted as a tumor suppressor gene. Early detection of the expression of miR-146a of gastric cancer may assist in the early diagnosis of the disease. In the present study, correlation analysis between the expression of miR-146a and relevant clinicopathological factors in gastric cancer showed that a low expression of miR-146a was significantly associated with lymph node metastasis in the patients. The relative expression of miR-146a in patients with advanced gastric cancer and lymph node metastasis was lower, compared with that in patients with advanced gastric cancer without lymph node metastasis. These results also suggested that miR-146a may act as a tumor suppressor, which increased the degree of malignancy and led to early metastasis of the tumor. As tumors progressed between early stages (stage I and II) and late stages (stage III and IV), the expression of miR-146a decreased, although no statistical significance was found. Previous studies also showed that the expression of miRs in patient tumor tissue had no significant correlation with the clinical staging of advanced gastric cancer (28,29), which was also observed in tissue samples. Further studies are required to investigate the reasons behind this discrepancy. In the present study, the expression of miR-146a was correlated with patient survival rates, which was consistent with the findings of a previous study on gastric cancer (8). Compared with previous findings, the present study showed that the expression of miR-146a was associated with early diagnosis of gastric cancer, and also had specificity in the diagnosis of gastric cancer. The present study also found that the expression of miR-146a was associated with the efficacy of anticancer treatment. Patients with high expression levels of miR-146a in advanced gastric cancer tissues demonstrated a significantly higher treatment efficacy.
According to biological prediction, miR-146a binds to the 3′UTR of LIN52 to regulate the expression of LIN52 and affect LIN52 function. The immunohistochemical staining showed that the protein expression of LIN52 in advanced gastric cancer tissue was negatively correlated with the expression of miR-146a, suggesting that LIN52 was involved as an oncogene, compared with miR-146a. A previous study showed that inhibition of the expression of LIN52 in gastrointestinal stromal tumors promoted imatinib-induced apoptosis and was involved in tumor suppression (9). The present study found that the inhibition of LIN52 by miR-146a resulted in improved survival rates of the patients. Several drugs used for chemotherapy have poor efficacy against tumor cells at the G0 phase of the cell cycle. However, numerous tumor cells are at the G0 phase, which is also a major reason for the resistance to drug chemotherapy (30). The activation of LIN52 directly affects the cell cycle, allowing the majority of cells to remain at the G0 resting phase (31) and preventing the cells from entering the DNA synthesis phase (S phase) (32). miR-146a inhibits the expression of LIN52 and reduces the number of cancer cells remaining in the G0 phase, thereby increasing the ratio of cancer cells at the S phase and improving the efficacy of anticancer therapy. A previous study showed that miRs affect the cell cycle of cancer cells, which may be one of the reasons they can affect the resistance of tumors to drugs (33). Although the present study showed that the expression of miR-146a was significantly correlated with the expression of LIN52 and with chemotherapeutic sensitivity, it is unclear whether miR-146a affects and reverses the effects of chemotherapy through LIN52 to regulate cancer cell cycle. Further investigations are required to verify these mechanisms.
Although miRs are promising markers in the diagnosis and prognosis of cancer, and dozens of miRs can be used for the diagnosis of gastric cancer (34,35), there are several restrictions on methods and technical limitations in the clinical detection of miRs. Currently, several detection methods and software packages are available for miR detection in different types of cancer. However, standardized approaches are different, leading to inconsistent findings among studies (36). For this reason, it is necessary to develop standardized methods of assessment and introduce housekeeping miRs with stable expression, including miR-16 and RUN6B. For the detection of gastric cancer, serum miR-93 is recommended as a marker gene to identify healthy controls (37). Others have also suggested the use of miRs with low expression levels in humans but high expression in lower organisms, including Caenorhabditis elegans, as internal controls (38). With advancements in the future, microRNAs are likely to be of increased clinical use and offer more accurate guidance in cancer investigations.
Acknowledgements
This study was supported by the Natural Science Foundation of China (grant no. U1504820).
References
|
Yang T, Zeng H, Chen W, Zheng R, Zhang Y, Li Z, Qi J, Wang M, Chen T, Lou J, et al: Helicobacter pylori infection, H19 and LINC00152 expression in serum and risk of gastric cancer in a Chinese population. Cancer Epidemiol. 44:147–153. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Colquhoun A, Arnold M, Ferlay J, Goodman KJ, Forman D and Soerjomataram I: Global patterns of cardia and non-cardia gastric cancer incidence in 2012. Gut. 64:1881–1882. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Sempere LF: Integrating contextual miRNA and protein signatures for diagnostic and treatment decisions in cancer. Expert Rev Mol Diagn. 11:813–827. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Imam JS, Buddavarapu K, LeeChang JS, Ganapathy S, Camosy C, Chen Y and Rao MK: MicroRNA-185 suppresses tumor growth and progression by targeting the Six1 oncogene in human cancers. Oncogene. 29:4971–4979. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Jin ZW, Jiang W and Wang L: Biomarkers for gastric cancer: Progression in early diagnosis and prognosis (Review). Oncol Lett. 9:1502–1508. 2015.PubMed/NCBI | |
|
Yao Q, Cao Z, Tu C, Zhao Y, Liu H and Zhang S: MicroRNA-146a acts as a metastasis suppressor in gastric cancer by targeting WASF2. Cancer Lett. 335:219–224. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Hou Z, Xie L, Yu L, Qian X and Liu B: MicroRNA-146a is down-regulated in gastric cancer and regulates cell proliferation and apoptosis. Med Oncol. 29:886–892. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Kogo R, Mimori K, Tanaka F, Komune S and Mori M: Clinical significance of miR-146a in gastric cancer cases. Clin Cancer Res. 17:4277–4284. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Boichuk S, Parry JA, Makielski KR, Litovchick L, Baron JL, Zewe JP, Wozniak A, Mehalek KR, Korzeniewski N, Seneviratne DS, et al: The DREAM complex mediates GIST cell quiescence and is a novel therapeutic target to enhance imatinib-induced apoptosis. Cancer Res. 73:5120–5129. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Grimson A, Farh KK, Johnston WK, GarrettEngele P, Lim LP and Bartel DP: MicroRNA targeting specificity in mammals: Determinants beyond seed pairing. Mol Cell. 27:91–105. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Jensen EH and Tuttle TM: Preoperative staging and postoperative surveillance for gastric cancer. Surg Oncol Clin N Am. 16:329–342. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Duan X, Cao W, Wang L, Liu S, Liu Z, Zhang B, Yang H, Feng T, Zhang J, Zhang X, et al: Genetic variants in TERT are associated with risk of gastric cancer in a Chinese Han population. Oncotarget. Nov 4–2016.(Epub ahead of print). View Article : Google Scholar | |
|
Takahashi TY, Saikawa Y and Kitagawa Y: Gastric cancer: Current status of diagnosis and treatment. Cancers (Basel). 5:48–63. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Matsumoto K, Ueyama H, Matsumoto K, Akazawa Y, Komori H, Takeda T, Murakami T, Asaoka D, Hojo M, Tomita N, et al: Clinicopathological features of alpha-fetoprotein producing early gastric cancer with enteroblastic differentiation. World J Gastroenterol. 22:8203–8210. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
He CZ, Zhang KH, Li Q, Liu XH, Hong Y and Lv NH: Combined use of AFP, CEA, CA125 and CAl9-9 improves the sensitivity for the diagnosis of gastric cancer. BMC Gastroenterol. 13:872013. View Article : Google Scholar : PubMed/NCBI | |
|
Wu WY, Xue XY, Chen ZJ, Han SL, Huang YP, Zhang LF, Zhu GB and Shen X: Potentially predictive microRNAs of gastric cancer with metastasis to lymph node. World J Gastroenterol. 17:3645–3651. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Liu HS and Xiao HS: MicroRNAs as potential biomarkers for gastric cancer. World J Gastroenterol. 20:12007–12017. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Cui L, Zhang X, Ye G, Zheng T, Song H, Deng H, Xiao B, Xia T, Yu X, Le Y and Guo J: Gastric juice MicroRNAs as potential biomarkers for the screening of gastric cancer. Cancer. 119:1618–1626. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Liu JL, Gao W, Kang QM, Zhang XJ and Yang SG: Prognostic value of survivin in patients with gastric cancer: A systematic review with meta-analysis. PLoS One. 8:e719302013. View Article : Google Scholar : PubMed/NCBI | |
|
Cerne JZ, Gersak K and Novakovic S: The influence of the genetic variant within miRNA-binding site in estrogen receptor alpha gene on the risk of breast cancer in postmenopausal women on hormone replacement therapy. Cancer Biomark. 8:123–128. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Kim BH, Hong SW, Kim A, Choi SH and Yoon SO: Prognostic implications for high expression of oncogenic microRNAs in advanced gastric carcinoma. J Surg Oncol. 107:505–510. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Wang J, Song YX and Wang ZN: Non-coding RNAs in gastric cancer. Gene. 560:1–8. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Zandberga E, Kozirovskis V, Ābols A, Andrējeva D, Purkalne G and Linē A: Cell-free microRNAs as diagnostic, prognostic, and predictive biomarkers for lung cancer. Genes Chromosomes Cancer. 52:356–369. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Verma M, Lam TK, Hebert E and Divi RL: Extracellular vesicles: Potential applications in cancer diagnosis prognosis, and epidemiology. BMC Clin Pathol. 15:62015. View Article : Google Scholar : PubMed/NCBI | |
|
Migliore C and Giordano S: Resistance to targeted therapies: A role for microRNAs? Trends Mol Med. 19:633–642. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, et al: A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA. 103:2257–2261. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Rosenfeld N, Aharonov R, Meiri E, Rosenwald S, Spector Y, Zepeniuk M, Benjamin H, Shabes N, Tabak S, Levy A, et al: MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol. 26:462–429. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Li C, Li JF, Cai Q, Qiu QQ, Yan M, Liu BY and Zhu ZG: MiRNA-199a-3p: A potential circulating diagnostic biomarker for early gastric cancer. J Surg Oncol. 108:89–92. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Cai H, Yuan Y, Hao YF, Guo TK, Wei X and Zhang YM: Plasma microRNAs serve as novel potential biomarkers for early detection of gastric cancer. Med Oncol. 30:4522013. View Article : Google Scholar : PubMed/NCBI | |
|
Shah MA and Schwartz GK: Cell cycle-mediated drug resistance: An emerging concept in cancer therapy. Clin Cancer Res. 7:2168–2181. 2001.PubMed/NCBI | |
|
Litovchick L, Florens LA, Swanson SK, Washburn MP and DeCaprio JA: DYRK1A protein kinase promotes quiescence and senescence through DREAM complex assembly. Genes Dev. 25:801–813. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Sadasivam SS, Duan S and DeCaprio JA: The MuvB complex sequentially recruits B-Myb and FoxM1 to promote mitotic gene expression. Genes Dev. 26:474–489. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Xie L, Jing R, Qi J, Lin Z and Ju S: Drug resistance-related microRNAs in hematological malignancies: Translating basic evidence into therapeutic strategies. Blood Rev. 29:33–44. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Rotkrua P, Shimada S, Mogushi K, Akiyama Y, Tanaka H and Yuasa Y: Circulating microRNAs as biomarkers for early detection of diffuse-type gastric cancer using a mouse model. Br J Cancer. 108:932–940. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Tsujiura M, Komatsu S, Ichikawa D, Shiozaki A, Konishi H, Takeshita H, Moriumura R, Nagata H, Kawaguchi T, Hirajima S, et al: Circulating miR-18a in plasma contributes to cancer detection and monitoring in patients with gastric cancer. Gastric Cancer. 18:271–279. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Tiberio P, Callari M, Angeloni V, Daidone MG and Appierto V: Challenges in using circulating miRNAs as cancer biomarkers. Biomed Res Int. 2015:7314792015. View Article : Google Scholar : PubMed/NCBI | |
|
Song J, Bai Z, Han W, Zhang J, Meng H, Bi J, Ma X, Han S and Zhang Z: Identification of suitable reference genes for qPCR analysis of serum microRNA in gastric cancer patients. Dig Dis Sci. 57:897–904. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Kroh EM, Parkin RK, Mitchell PS and Tewari M: Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods. 50:298–301. 2010. View Article : Google Scholar : PubMed/NCBI |
