This article has Open Peer Review reports available.
MiR-107 and miR-99a-3p predict chemotherapy response in patients with advanced colorectal cancer
- Sonia Molina-Pinelo1,
- Amancio Carnero1,
- Fernando Rivera2,
- Purificacion Estevez-Garcia1,
- Juan Manuel Bozada3,
- Maria Luisa Limon4,
- Marta Benavent1,
- Javier Gomez5,
- Maria Dolores Pastor1,
- Manuel Chaves4,
- Rocio Suarez1,
- Luis Paz-Ares1, 4,
- Fernando de la Portilla6,
- Andres Carranza-Carranza1,
- Isabel Sevilla7,
- Luis Vicioso8 and
- Rocio Garcia-Carbonero1, 4Email author
© Molina-Pinelo et al.; licensee BioMed Central Ltd. 2014
Received: 3 February 2014
Accepted: 20 August 2014
Published: 7 September 2014
MicroRNAs (miRNAs) are involved in numerous biological and pathological processes including colorectal cancer (CRC). The aim of our study was to evaluate the ability of miRNA expression patterns to predict chemotherapy response in a cohort of 78 patients with metastatic CRC (mCRC).
We examined expression levels of 667 miRNAs in the training cohort and evaluated their potential association with relevant clinical endpoints. We identified a miRNA profile that was analysed by RT-qPCR in an independent cohort. For a set of selected miRNAs, bioinformatic target predictions and pathway analysis were also performed.
Eight miRNAs (let-7 g*, miR-107, miR-299-5p, miR-337-5p, miR-370, miR-505*, miR-889 and miR-99a-3p) were significant predictors of response to chemotherapy in the training cohort. In addition, overexpression of miR-107, miR-337-5p and miR-99a-3p, and underexpression of miR-889, were also significantly associated with improved progression-free and/or overall survival. MicroRNA-107 and miR-99a-3p were further validated in an independent cohort as predictive markers for chemotherapy response. In addition, an inverse correlation was confirmed in our study population between miR-107 levels and mRNA expression of several potential target genes (CCND1, DICER1, DROSHA and NFKB1).
MiR-107 and miR-99a-3p were validated as predictors of response to standard fluoropyrimidine-based chemotherapy in patients with mCRC.
KeywordsMicroRNAs Advanced colorectal cancer Chemotherapy response Prediction
Colorectal cancer (CRC) is one of the most common malignant tumors worldwide . Despite advances in early detection, about one third of patients present metastatic disease at diagnosis, and ~40% of those with early-stage tumors eventually relapse at some point over the course of the disease . Systemic therapy is the mainstay of care for patients with metastatic CRC (mCRC) . Several combination regimens including fluoropyrimidines and oxaliplatin and/or irinotecan, with or without monoclonal antibodies targeting VEGF or EGFR, have been successfully developed and are associated with response rates of 40-60% and a median survival of 20–24 months [4–9]. Despite the undeniable progress achieved, still a considerable proportion of patients do not respond to therapy and reliable tools to prospectively identify which patients are more likely to benefit are needed.
Several driver mutations have been identified to be relevant in CRC carcinogenesis [10, 11]. The most commonly involved pathways include the Wnt/β-catenin, TGF-β/BMP, TP53, receptor tyrosine kinase, KRAS and PI3K signaling pathways . Many of these proteins are altered and seem to be affected by microRNA regulation. In this sense, the miR-135 family may play an important role in early CRC development as it down-regulates APC, leading to activation of the Wnt/β-catenin pathway . On the other hand, the lethal-7 (let-7) family of miRNAs has been found to display tumor suppressor functions by repressing translation of KRAS. Interestingly, patients with KRAS-mutated CRC and high let-7 levels seem to benefit from EGFR-targeted agents, suggesting that let-7 expression could potentially counteract resistance mediated by RAS activating mutations . KRAS has been also described to be a direct target of other miRNAs such as miR-143, miR-146b-3p, miR-18a, and miR-486-5p [14–17] and miR-126 has been implicated in PI3K signalling . Other miRNAs known to be involved in CRC pathogenesis affect epithelial differentiation (miR-141 and miR-200c), WNT signaling (miR-145, miR-135a and miR-135b), and migration and invasion (miR-21, miR-373 and miR-520c) [19–22].
From a clinical perspective, several studies have identified groups of miRNAs with potential utility for early diagnosis or prognostic stratification of CRC patients. However, there are no robust studies to evaluate the potential ability of miRNA to predict response to selected chemotherapy regimens. Based on these premises, the purpose of this study was to evaluate the ability of miRNA expression patterns to predict chemotherapy response in patients with mCRC treated with fluoropyrimidine-based standard chemotherapy regimens.
Patients and tumor samples
Patients that met the following inclusion criteria were selected for the present study: (1) histologically confirmed diagnosis of primary CRC; (2) TNM stage IV; (3) fluoropyrimidine-based first-line chemotherapy for advanced disease; (4) measurable disease per RECIST criteria; (5) adequate clinical data recorded in medical charts; (6) adequate tissue specimen available (snap-frozen at -80°C with a proportion of tumor cells > 50%). This study was approved by the ethics committees of Hospital Universitario Virgen del Rocio (Sevilla), Hospital Marques de Valdecilla (Santander) and Hospital Virgen de la Victoria (Malaga), and all patients provided written informed consent prior to study entry.
Characteristics of study population
Training cohort (N = 39)
Validation cohort (N = 39)
Age, years – median [range]
Gender - N(%)
Histology of primary tumor - N(%)
Chemotherapy regimen - N(%)
Response to chemotherapy - N(%)
Objective Response (CR, PR)
No Response (SD, PD)
Survival, months – median [range]
Clinical outcome variables and statistical analysis
Descriptive statistics were used to characterize the most relevant clinical parameters. The association of categorical variables was explored by the chi-squared test or Fisher’s exact test. To assess distribution of continuous variables among study groups parametric (t-test) or non-parametric tests (Kruskal-Wallis or Mann–Whitney tests) were employed when appropriate.
Tumor response was evaluated by conventional methods according to the standard RECIST 1.0 criteria: a complete response (CR) was defined as the disappearance of all measurable and evaluable evidence of disease; a partial response (PR) was defined as a ≥ 30% decrease in the sum of the longest diameters of target lesions; stable disease (SD) was considered if the tumor burden decreased less than 30% or increased less than 20%; and progressive disease (PD) was indicated by a >20% increase in the sum of the longest diameters of target lesions or the appearance of any new lesion. Patients were classified according to best response to chemotherapy in two groups: those that achieved an objective response (Responders [R]: CR + PR) and those that did not (Non-responders [NR]: SD + PD). Progression Free Survival (PFS) was defined as the time elapsed from the date of initiation of first-line chemotherapy to the date of the first documented evidence of disease progression. Overall survival (OS) was calculated from the start of therapy for advanced disease to the date of death from any cause. The Kaplan-Meier product limit method was used to estimate time-dependent variables (PFS and OS), and differences observed among patient subgroups were assessed by the log rank test. Multivariate analyses were performed using the Cox proportional hazards model. P < 0.05 was considered significant. All analyses were performed using the Statistical Package for the Social Sciences software (SPSS 17.0 for Windows; SPSS Inc, Chicago, IL).
RNA isolation and miRNA qRT-PCR assay
Total RNA, containing small RNA, was extracted from tumor tissue samples by mirVana miRNA isolation kit (Ambion, Austin, TX, USA) according to the manufacturer’s instructions. Mature human miRNA expression was detected and quantified using the TaqMan® Low Density Arrays (TLDA) based on Applied Biosystems’ 7900 HT Micro Fluidic Cards (Applied Biosystems, CA, USA) following instructions provided by the manufacturer. The Human MicroRNA Card Set v2.0 array is a two card set containing a total of 384 TaqMan® MicroRNA Assays per card to enable accurate quantification of 667 human microRNAs, all catalogued in the miRBase database. TLDAs were performed in a two-step process, as previously described .
Eight miRNAs (let-7 g*, miR-107, miR-299-5p, miR-337-5p, miR-370, miR-505*, miR-889 and miR-99a-3p), which were selected because their expression in the Taqman Low Density Array card assays was significantly associated with response to chemotherapy and clinical outcome, were further analyzed in an independent validation cohort by qPCR. For this, RNA was reverse transcribed to cDNA using TaqMan® MicroRNA Assays (Applied Biosystems, CA, USA). Ten ng of total RNA were reverse transcribed using the TaqMan miRNA reverse transcription kit in a total volume of 15 μl, according to the manufacturer's protocol. The reactions were incubated for 30 min at 16°C, 30 min at 42°C, and 5 min at 85°C, and then kept at 4°C. Thereafter, 1.33 μL of cDNA was used for TaqMan MicroRNA Assays. The reactions were incubated at 95°C for 10 min, followed by 40 cycles of 15 sec at 95°C and 1 min at 60°C. All experiments were performed in triplicate.
Analysis of miRNA expression profiles
Expression of target miRNAs was normalized to the expression of MammU6, the most widely-used endogenous miRNA control for RT-qPCR in the literature. One non-human miRNA was used in each experiment as a negative control. Finally, the cards were processed and analyzed on an ABIPrism 7900 HT Sequence Detection System. Cycle threshold (Ct) values were calculated with the SDS software v.2.3 using automatic baseline settings and a threshold of 0.2. Relative quantification of miRNA expression was calculated by the 2-ΔΔCt method (Applied Biosystems user bulletin no.2 (P/N 4303859)). MicroRNAs expression was computed using Real-Time Statminer© software v.4.2 (Integromics, Inc). This software performs a moderate t-test between the groups (R versus NR) and corrects them using the Benjamini-Hochberg algorithm with the False Discovery Rate (FDR) set at a value of 5%. For undetected miRNAs with Ct values beyond the maximum Ct 36, the StatMiner software imputed a value set to the maximum Ct. For the purpose of this study, significant miRNA expression was considered only when miRNAs were detected in at least 50% of samples in each group being compared. The raw and normalized TaqMan array data have been deposited in the Gene Expression Omnibus under the accession number GSE48664.
Experimentally verified mRNA by previous research were determined using the web-accessible information resource miRWalk . We then validated 9 potential target genes according to expression levels of mir-107 by Taqman real-time RT-PCR assay (Applied Biosystems, CA, USA). Expression of miR-107 was normalized to the expression of MammU6. Pearson's correlation coefficient was used to assess the linear association of miRNA and target mRNA expression (SPSS 17.0 for Windows; SPSS Inc, Chicago, IL).
3′-UTR reporter assay for miR target validation
Confirmation of miR-107-binding to the 3′-UTR of CCDN1. HEK 293 cells at 80% confluency were co-transfected with luciferase reporter plasmids harboring the complete 3′-UTR of the desired gene (SwitchGear Genomics) along with 100nM of miR107-mimic or miRNA control (Sigma). DharmaFECT Duo (Thermo Scientific) was used as the transfection reagent in Opti-MEM (Life Technologies). Luminescence was assayed 24 hours later using LightSwitch Assay Reagents (SwitchGear Genomics) according to the manufacturer's instructions. Knockdown was assessed by calculating luciferase signal ratios for specific miRNA/non-targeting control, using empty reporter vector as control for non-specific effects. Each experiment was performed in triplicate
MicroRNA profile development
MicroRNA expression patterns according to objective response to chemotherapy
Differently expressed miRNAs by objective response to chemotherapy (Training Cohort)
R vs NR (-ΔΔCt)
Impact of selected miRNAs expression on progression free and overall survival
Univariate and multivariate analysis of predictive miRNA for PFS and OS in metastatic colorectal cancer patients (Training Cohort)
HR (95% CI)
HR (95% CI)
HR (95% CI)
HR (95% CI)
MicroRNA target prediction
In this study, we have evaluated global miRNA expression patterns in mCRC patients treated with fluoropyrimidine-based standard chemotherapy regimens. We identified eight miRNAs (let-7 g*, miR-107, miR-299-5p, miR-337-5p, miR-370, miR-505*, miR-889 and miR-99a-3p), the expression of which was significantly associated with response to chemotherapy. In addition, overexpression of miR-107, miR-337-5p and miR-99a-3p, and underexpression of miR-889, were also significantly associated with improved progression-free and/or overall survival. Moreover, miR-107 and miR-99a-3p were further validated in an independent cohort as predictive markers for chemotherapy response. This is to our knowledge the first study to assess the predictive role of miRNA expression profiles in patients with advanced CRC treated with fluoropyrimidines in combination with either oxaliplatin (77%) or irinotecan (18%), the most commonly used chemotherapy regimens in the treatment of this disease.
Altered miR-107 expression has been involved in several cancer types, including head and neck squamous cell carcinoma (HNSCC), ovarian, gastric or breast cancer, among others [25–27]. Our results have demonstrated that expression of this miRNA significantly influences sensitivity to fluoropyrimidine-based chemotherapy in patients with advanced colorectal cancer. miR-107 transcription is induced by p53 and it seems to function as a tumor suppressor gene in HNSCC cell lines through downregulation of protein kinase Cϵ (PKCϵ) . PKCϵ is elevated in HNSCC and has been associated with a more aggressive phenotype . Consistent with this, other groups have reported a tumor suppressor function for miR-107 in other cancer models including bladder, colon and pancreatic cancer. With regard to human colon cancer, miR-107 has been shown to regulate tumor angiogenesis by targeting hypoxia inducible factor-1β (HIF-1β) . Indeed, overexpression of miR-107 in HCT116 colon cancer cells suppressed angiogenesis, tumor growth and tumor VEGF expression in mice. Decreased tumor angiogenesis induced by miR-107 may make tumor cells more vulnerable to a variety of cellular insults including genotoxic stress induced by DNA-damaging agents (i.e. conventional cytotoxic chemotherapy). In fact, antiangiogenic drugs such as the VEGF-targeting agents bevacizumab or aflibercept have demonstrated to be synergistic in combination with fluoropyrimidine-based chemotherapy in patients with advanced colorectal cancer. Moreover, other authors have shown that, compared with wild type tumors, tumors that lack HIF-1α are poorly vascularized but are faster growing, perhaps because of a loss of dependency upon neovascularization. These findings would be consistent with the increased response rate and improved prognosis observed in our series for patients over-expressing miR-107 [30, 31]. In addition, overexpression of miR-107 has been recently shown in gastric cancers in comparison with normal tissue, and up-regulation of these miRNA increased the proliferation of gastric cancer cells . In colon cancer models some authors have reported that miR-103/107 may promote metastasis by targeting the metastasis suppressors DAPK and KLF4 . They also found that, in the clinical setting, the signature of a miR-103/107 high, DPAK and KLF4 low expression profile correlated with the extent of lymph node and distant metastasis. However, no information was provided this study regarding relevant characteristics of the patient population such as stage of disease or therapeutic interventions. The discrepancies observed related to miR-103/107 function could be attributed to tissue- or context–specific effects, or may simply reflect the great complexity governing intra- and inter-cellular signaling networks. On the other hand, the precise role in cancer of the other validated miRNA in our series, miR-99a-3p, remain greatly unknown to date.
To explore the potential biological function of miR-107, we then identified validated targets using the computational prediction algorithm from miRWalk . AKT1, CCND1, DICER1, DROSHA, FASN, FBXW7, NFKB1 and TP53 are involved in several key pathways relevant to cancer such as the PI3K/Akt pathway and the miRNA-processing machinery [34–39]. As expected, we confirmed in individual tumor samples of our patients an inverse correlation of these target mRNA and miR-107 expression levels, being this correlation significant for CCND1, DICER1, DROSHA and NFKB1. These results may be considered a further validation of the functional role of miR-107 in the transcriptional regulation of these key genes in cancer.
Our study has identified that miR-107 and miR-99a-3p may be used to predict response to therapy with standard fluoropyrimidine-based chemotherapy regimens in patients with mCRC. These results underline the great potential of miRNAs as novel biomarkers for personalized treatment strategies and also as potential therapeutic targets. Moreover, given the fact that CRC cells may release aberrantly expressed miRNAs into peripheral blood, miRNA profiling could also have a great potential as a minimally-invasive tool for prediction or monitoring of therapeutic outcome.
RGC is funded by Fondo de Investigación Sanitaria (PI10/02164), Servicio Andaluz de Salud (PI-0259/2007) and RTICC (R12/0036/0028). SM-P is funded by Fondo de Investigación Sanitaria (CD1100153) and Fundación Científica de la Asociación Española Contra el Cáncer. MDP is funded by Fondo de Investigación Sanitaria (CD0900148). AC lab was supported by grants to from the Spanish Ministry of Economy and Competitivity, ISCIII (Fis: PI12/00137, RTICC: RD12/0036/0028), Consejeria de Ciencia e Innovacion (CTS-6844) and Consejeria de Salud of the Junta de Andalucia (PI-0135-2010 and PI-0306-2012). The authors thank the donors and the Andalusian Public Health System Biobank Network (ISCIII-Red de Biobancos RD09/0076/00085) for the human tumor specimens provided for this study.
- Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D: Global cancer statistics. CA Cancer J Clin. 2011, 61 (2): 69-90. 10.3322/caac.20107.View ArticlePubMedGoogle Scholar
- Parkin DM: International variation. Oncogene. 2004, 23 (38): 6329-6340. 10.1038/sj.onc.1207726.View ArticlePubMedGoogle Scholar
- Andre T, Boni C, Navarro M, Tabernero J, Hickish T, Topham C, Bonetti A, Clingan P, Bridgewater J, Rivera F, de Gramont A: Improved overall survival with oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial. J Clin Oncol. 2009, 27 (19): 3109-3116. 10.1200/JCO.2008.20.6771.View ArticlePubMedGoogle Scholar
- de Gramont A, Figer A, Seymour M, Homerin M, Hmissi A, Cassidy J, Boni C, Cortes-Funes H, Cervantes A, Freyer G, Papamichael D, Le Bail N, Louvet C, Hendler D, de Braud F, Wilson C, Morvan F, Bonetti A: Leucovorin and fluorouracil with or without oxaliplatin as first-line treatment in advanced colorectal cancer. J Clin Oncol. 2000, 18 (16): 2938-2947.PubMedGoogle Scholar
- Cunningham D, Sirohi B, Pluzanska A, Utracka-Hutka B, Zaluski J, Glynne-Jones R, Koralewski P, Bridgewater J, Mainwaring P, Wasan H, Wang JY, Szczylik C, Clingan P, Chan RT, Tabah-Fisch I, Cassidy J: Two different first-line 5-fluorouracil regimens with or without oxaliplatin in patients with metastatic colorectal cancer. Ann Oncol. 2009, 20 (2): 244-250.View ArticlePubMedGoogle Scholar
- Douillard JY, Cunningham D, Roth AD, Navarro M, James RD, Karasek P, Jandik P, Iveson T, Carmichael J, Alakl M, Gruia G, Awad L, Rougier P: Irinotecan combined with fluorouracil compared with fluorouracil alone as first-line treatment for metastatic colorectal cancer: a multicentre randomised trial. Lancet. 2000, 355 (9209): 1041-1047. 10.1016/S0140-6736(00)02034-1.View ArticlePubMedGoogle Scholar
- Saltz LB, Cox JV, Blanke C, Rosen LS, Fehrenbacher L, Moore MJ, Maroun JA, Ackland SP, Locker PK, Pirotta N, Elfring GL, Miller LL: Irinotecan plus fluorouracil and leucovorin for metastatic colorectal cancer. Irinotecan Study Group. N Engl J Med. 2000, 343 (13): 905-914. 10.1056/NEJM200009283431302.View ArticlePubMedGoogle Scholar
- Garcia-Carbonero R, Gomez Espana MA, Casado Saenz E, Alonso Orduna V, Cervantes Ruiperez A, Gallego Plazas J, Garcia Alfonso P, Juez Martel I, Gonzalez Flores E, Lomas Garrido M, Isla Casado D: SEOM clinical guidelines for the treatment of advanced colorectal cancer. Clin Transl Oncol. 2010, 12 (11): 729-734. 10.1007/s12094-010-0587-4.View ArticlePubMedGoogle Scholar
- Aranda E, Abad A, Carrato A, Cervantes A, Garcia-Foncillas J, Garcia Alfonso P, Garcia Carbonero R, Gomez Espana A, Tabernero JM, Diaz-Rubio E: Treatment recommendations for metastatic colorectal cancer. Clin Transl Oncol. 2011, 13 (3): 162-178. 10.1007/s12094-011-0636-7.View ArticlePubMedGoogle Scholar
- Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, Mandelker D, Leary RJ, Ptak J, Silliman N, Szabo S, Buckhaults P, Farrell C, Meeh P, Markowitz SD, Willis J, Dawson D, Willson JK, Gazdar AF, Hartigan J, Wu L, Liu C, Parmigiani G, Park BH, Bachman KE, Papadopoulos N, Vogelstein B, Kinzler KW, Velculescu VE: The consensus coding sequences of human breast and colorectal cancers. Science. 2006, 314 (5797): 268-274. 10.1126/science.1133427.View ArticlePubMedGoogle Scholar
- Wood LD, Parsons DW, Jones S, Lin J, Sjoblom T, Leary RJ, Shen D, Boca SM, Barber T, Ptak J, Silliman N, Szabo S, Dezso Z, Ustyanksky V, Nikolskaya T, Nikolsky Y, Karchin R, Wilson PA, Kaminker JS, Zhang Z, Croshaw R, Willis J, Dawson D, Shipitsin M, Willson JK, Sukumar S, Polyak K, Park BH, Pethiyagoda CL, Pant PV, et al: The genomic landscapes of human breast and colorectal cancers. Science. 2007, 318 (5853): 1108-1113. 10.1126/science.1145720.View ArticlePubMedGoogle Scholar
- Nagel R, le Sage C, Diosdado B, van der Waal M, Oude Vrielink JA, Bolijn A, Meijer GA, Agami R: Regulation of the adenomatous polyposis coli gene by the miR-135 family in colorectal cancer. Cancer Res. 2008, 68 (14): 5795-5802. 10.1158/0008-5472.CAN-08-0951.View ArticlePubMedGoogle Scholar
- Ruzzo A, Graziano F, Vincenzi B, Canestrari E, Perrone G, Galluccio N, Catalano V, Loupakis F, Rabitti C, Santini D, Tonini G, Fiorentini G, Rossi D, Falcone A, Magnani M: High Let-7a MicroRNA levels in KRAS-mutated colorectal carcinomas May rescue anti-EGFR therapy effects in patients with chemotherapy-refractory metastatic disease. Oncologist. 2012, 17 (6): 823-829. 10.1634/theoncologist.2012-0081.View ArticlePubMedPubMed CentralGoogle Scholar
- Ragusa M, Majorana A, Statello L, Maugeri M, Salito L, Barbagallo D, Guglielmino MR, Duro LR, Angelica R, Caltabiano R, Biondi A, Di Vita M, Privitera G, Scalia M, Cappellani A, Vasquez E, Lanzafame S, Basile F, Di Pietro C, Purrello M: Specific alterations of microRNA transcriptome and global network structure in colorectal carcinoma after cetuximab treatment. Mol Cancer Ther. 2010, 9 (12): 3396-3409. 10.1158/1535-7163.MCT-10-0137.View ArticlePubMedGoogle Scholar
- Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, Cheng A, Labourier E, Reinert KL, Brown D, Slack FJ: RAS is regulated by the let-7 microRNA family. Cell. 2005, 120 (5): 635-647. 10.1016/j.cell.2005.01.014.View ArticlePubMedGoogle Scholar
- Chen X, Guo X, Zhang H, Xiang Y, Chen J, Yin Y, Cai X, Wang K, Wang G, Ba Y, Zhu L, Wang J, Yang R, Zhang Y, Ren Z, Zen K, Zhang J, Zhang CY: Role of miR-143 targeting KRAS in colorectal tumorigenesis. Oncogene. 2009, 28 (10): 1385-1392. 10.1038/onc.2008.474.View ArticlePubMedGoogle Scholar
- Tsang WP, Kwok TT: The miR-18a* microRNA functions as a potential tumor suppressor by targeting on K-Ras. Carcinogenesis. 2009, 30 (6): 953-959. 10.1093/carcin/bgp094.View ArticlePubMedGoogle Scholar
- Guo C, Sah JF, Beard L, Willson JK, Markowitz SD, Guda K: The noncoding RNA, miR-126, suppresses the growth of neoplastic cells by targeting phosphatidylinositol 3-kinase signaling and is frequently lost in colon cancers. Genes Chromosomes Cancer. 2008, 47 (11): 939-946. 10.1002/gcc.20596.View ArticlePubMedPubMed CentralGoogle Scholar
- Burk U, Schubert J, Wellner U, Schmalhofer O, Vincan E, Spaderna S, Brabletz T: A reciprocal repression between ZEB1 and members of the miR-200 family promotes EMT and invasion in cancer cells. EMBO Rep. 2008, 9 (6): 582-589. 10.1038/embor.2008.74.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang Q, Gumireddy K, Schrier M, le Sage C, Nagel R, Nair S, Egan DA, Li A, Huang G, Klein-Szanto AJ, Gimotty PA, Katsaros D, Coukos G, Zhang L, Puré E, Agami R: The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat Cell Biol. 2008, 10 (2): 202-210. 10.1038/ncb1681.View ArticlePubMedGoogle Scholar
- Slaby O, Svoboda M, Michalek J, Vyzula R: MicroRNAs in colorectal cancer: translation of molecular biology into clinical application. Mol Cancer. 2009, 8: 102-10.1186/1476-4598-8-102.View ArticlePubMedPubMed CentralGoogle Scholar
- Slaby O, Svoboda M, Fabian P, Smerdova T, Knoflickova D, Bednarikova M, Nenutil R, Vyzula R: Altered expression of miR-21, miR-31, miR-143 and miR-145 is related to clinicopathologic features of colorectal cancer. Oncology. 2007, 72 (5–6): 397-402.PubMedGoogle Scholar
- Molina-Pinelo S, Suarez R, Pastor MD, Nogal A, Marquez-Martin E, Martin-Juan J, Carnero A, Paz-Ares L: Association between the miRNA signatures in plasma and bronchoalveolar fluid in respiratory pathologies. Dis Markers. 2012, 32 (4): 221-230. 10.1155/2012/873749.View ArticlePubMedPubMed CentralGoogle Scholar
- Dweep H, Sticht C, Pandey P, Gretz N: miRWalk–database: prediction of possible miRNA binding sites by "walking" the genes of three genomes. J Biomed Inform. 2011, 44 (5): 839-847. 10.1016/j.jbi.2011.05.002.View ArticlePubMedGoogle Scholar
- Datta J, Smith A, Lang JC, Islam M, Dutt D, Teknos TN, Pan Q: microRNA-107 functions as a candidate tumor-suppressor gene in head and neck squamous cell carcinoma by downregulation of protein kinase Cvarepsilon. Oncogene. 2011, 31 (36): 4045-4053.View ArticlePubMedPubMed CentralGoogle Scholar
- Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM: A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A. 2006, 103 (7): 2257-2261. 10.1073/pnas.0510565103.View ArticlePubMedPubMed CentralGoogle Scholar
- Van der Auwera I, Limame R, van Dam P, Vermeulen PB, Dirix LY, Van Laere SJ: Integrated miRNA and mRNA expression profiling of the inflammatory breast cancer subtype. Br J Cancer. 2010, 103 (4): 532-541. 10.1038/sj.bjc.6605787.View ArticlePubMedPubMed CentralGoogle Scholar
- Pan Q, Bao LW, Teknos TN, Merajver SD: Targeted disruption of protein kinase C epsilon reduces cell invasion and motility through inactivation of RhoA and RhoC GTPases in head and neck squamous cell carcinoma. Cancer Res. 2006, 66 (19): 9379-9384. 10.1158/0008-5472.CAN-06-2646.View ArticlePubMedPubMed CentralGoogle Scholar
- Yamakuchi M, Lotterman CD, Bao C, Hruban RH, Karim B, Mendell JT, Huso D, Lowenstein CJ: P53-induced microRNA-107 inhibits HIF-1 and tumor angiogenesis. Proc Natl Acad Sci U S A. 2010, 107 (14): 6334-6339. 10.1073/pnas.0911082107.View ArticlePubMedPubMed CentralGoogle Scholar
- Carmeliet P, Dor Y, Herbert JM, Fukumura D, Brusselmans K, Dewerchin M, Neeman M, Bono F, Abramovitch R, Maxwell P, Koch CJ, Ratcliffe P, Moons L, Jain RK, Collen D, Keshert E: Role of HIF-1alpha in hypoxia-mediated apoptosis, cell proliferation and tumour angiogenesis. Nature. 1998, 394 (6692): 485-490. 10.1038/28867.View ArticlePubMedGoogle Scholar
- Yu JL, Rak JW, Carmeliet P, Nagy A, Kerbel RS, Coomber BL: Heterogeneous vascular dependence of tumor cell populations. Am J Pathol. 2001, 158 (4): 1325-1334. 10.1016/S0002-9440(10)64083-7.View ArticlePubMedPubMed CentralGoogle Scholar
- Li F, Liu B, Gao Y, Liu Y, Xu Y, Tong W, Zhang A: Upregulation of MicroRNA-107 induces proliferation in human gastric cancer cells by targeting the transcription factor FOXO1. FEBS Lett. 2014, 588 (4): 538-544. 10.1016/j.febslet.2013.12.009.View ArticlePubMedGoogle Scholar
- Chen RH, Chen HY, Lin YM, Chung HC, Lang YD, Lin CJ, Huang J, Wang WC, Lin FM, Chen Z, Huang HD, Shyy JY, Liang JT, Chen RH: miR-103/107 promote metastasis of colorectal cancer by targeting the metastasis suppressors DAPK and KLF4. Cancer Res. 2012, 72 (14): 3631-3641. 10.1158/0008-5472.CAN-12-0667.View ArticlePubMedGoogle Scholar
- Shimizu T, Tolcher AW, Papadopoulos KP, Beeram M, Rasco DW, Smith LS, Gunn S, Smetzer L, Mays TA, Kaiser B, Wick MJ, Alvarez C, Cavazos A, Mangold GL, Patnaik A: The clinical effect of the dual-targeting strategy involving PI3K/AKT/mTOR and RAS/MEK/ERK pathways in patients with advanced cancer. Clin Cancer Res. 2012, 18 (8): 2316-2325. 10.1158/1078-0432.CCR-11-2381.View ArticlePubMedGoogle Scholar
- Leong S, Messersmith WA, Tan AC, Eckhardt SG: Novel agents in the treatment of metastatic colorectal cancer. Cancer J. 2010, 16 (3): 273-282. 10.1097/PPO.0b013e3181e076c5.View ArticlePubMedGoogle Scholar
- Carnero A: Novel inhibitors of the PI3K family. Expert Opin Investig Drugs. 2009, 18 (9): 1265-1277. 10.1517/13543780903066798.View ArticlePubMedGoogle Scholar
- Carnero A: The PKB/AKT pathway in cancer. Curr Pharm Des. 2010, 16 (1): 34-44. 10.2174/138161210789941865.View ArticlePubMedGoogle Scholar
- Vivanco I, Sawyers CL: The phosphatidylinositol 3-Kinase AKT pathway in human cancer. Nat Rev Cancer. 2002, 2 (7): 489-501. 10.1038/nrc839.View ArticlePubMedGoogle Scholar
- Paz-Ares L, Blanco-Aparicio C, Garcia-Carbonero R, Carnero A: Inhibiting PI3K as a therapeutic strategy against cancer. Clin Transl Oncol. 2009, 11 (9): 572-579. 10.1007/s12094-009-0407-x.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/14/656/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.