Wnt/catenin β1/microRNA 183 predicts recurrence and prognosis of patients with colorectal cancer

Chen, Yuzhuo; Wu, Song

doi:10.3892/ol.2018.7886

Cited by 18 publications

(16 citation statements)

References 28 publications

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“…Among the deregulated genes involved in the EMT signaling, CTNNB1, as well as JAK1 play key roles in colon cancer progression: CTNNB1 through the activation of Wnt/β-catenin signaling, and JAK/STAT pathway by regulating cell survival and proliferation, differentiation and migration 72 . Our findings are in line with previous studies showing that CTNNB1 and JAK family members are colon cancer recurrence-specific genes and prognostic biomarkers 62,[72][73][74] . Notably, we also identified NLRC4 gene, a member of NOD-like receptors in the "Inflammasome Pathway", as a novel gene related to colon cancer recurrence.…”

Section: Characterization Of Genes and Pathways Involved In Patients'supporting

confidence: 93%

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Gliozzo

Perlasca

Mesiti

et al. 2020

Sci Rep

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Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification. Phenotype and outcome prediction using sets of selected biomarkers are well-established prediction tasks in the context of computational biology, including different prediction problems ranging from the response to a specific drug 1,2 , diagnosis and prognosis 3-5 , classification of cancer subtypes 6 , outcome and recurrence prediction 7-9 and other related prediction problems 10. State-of-the-art methods for these problems are largely based on inductive supervised models that use sets of selected biomarkers, usually represented as vectors, to predict the phenotype or outcome of interest (see, e.g. 11-13), without taking into account the relationships between individuals. Several works proposed "network-based" methods by constructing graphs of patients, in order to discover the underlying structure of the data (e.g. discovery of subtypes of diseases, clinical stratification of patients) 14-17. These methods mainly used unsupervised approaches and hence have been not specifically designed and are not appropriate for phenotype/outcome prediction problems. Recently a few works proposed semi-supervised "network-based" approaches for the prediction of the phenotype/outcome of patients, on the basis of their bio-molecular profiles (e.g. gene expression of genotypic profiles) 18,19 , including also methods able to integrate multiple sources of omics data 20 , and methods based on Supervised Random Walks 21 , specifically modified for the classification of tumors 22. In this work, we introduce a novel network-based method for modeling in the "patient space". In this context the nodes of the network represent patients through an n-dimensional set of biomarker values (e.g. a set of gene expression values), and edges represent similarities between the biomarkers of a pair of patients. Hence, this "patient-space" differs from the classical "biomarker-space", where nodes represent biomarkers and edges similarities between biomarkers and not between patients 23,24...

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Section: Characterization Of Genes and Pathways Involved In Patients'supporting

confidence: 93%

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Gliozzo

Perlasca

Mesiti

et al. 2020

Sci Rep

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“…EMT process is driven by various pathways, containing TGF-b1/Smad, Wnt/b-catenin, Notch, Hedgehog and other pathways [32]. Considering Wnt/b-catenin signalling was suggested as a downstream signalling of miR-183 [33,34,35], we further studied whether circ_0026344 could impact this signalling. Data presented in this study demonstrated that circ_0026344 was capable of repressing Wnt/b-catenin signalling via a miR-183-dependent fashion.…”

Section: Discussionmentioning

confidence: 99%

Circ_0026344 restrains metastasis of human colorectal cancer cells via miR-183

Shen

Cheng

Liu

et al. 2019

Artificial Cells, Nanomedicine, and Biotechnology

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Background: CircRNA circ_0026344 was previously revealed as a tumour-suppressive gene in colorectal cancer (CRC) progression. The purpose of this research was to investigate the role of circ_0026344 in CRC cells metastasis induced by chemokines. Methods: Two human CRC cell lines SW480 and Caco-2 were treated by CCL20 and CXCL8. Cell proliferation, migration/invasion, expression of epithelial-mesenchymal transition (EMT) inducers and the expression of circ_0026344 were measured using sulforhodamine B assay, Transwell chamber, western blot and qRT-PCR, respectively. The effects of circ_0026344 on CRC cells migration/invasion and the expression of EMT inducers were evaluated. Moreover, the downstream miRNA and signalling pathways of circ_0026344 were studied. Results: CCL20 and CXCL8 synergized to facilitate the proliferation, migration and invasion of CRC cells. At the meantime, E-cadherin was downregulated, whereas N-cadherin, Vimentin and Snail were upregulated by CCL20 and CXCL8 co-stimulation, which was accompanied by the mobilization of PI3K/ AKT/ERK signalling. More interestingly, the expression of circ_0026344 was down-regulated by CCL20 and CXCL8 co-stimulation. Silence of circ_0026344 increased the migratory and invasive capacities of CRC cells and increased EMT process as well. Overexpression of circ_0026344 led to a contrary impact. miR-183 was negatively regulated by circ_0026344, and the inhibitory effects of circ_0026344 overexpression on Wnt/b-catenin pathway were reversed when miR-183 was overexpressed. Conclusion: Overexpression of circ_0026344 restrained CRC metastasis and EMT induced by CCL20 and CXCL8 synergistical treatment. miR-183 was a downstream effector of circ_0026344, and the antitumour function of circ_0026344 might be involved in the repressed Wnt/b-catenin signalling. HIGHLIGHTS 1. CCL20 and CXCL8 synergize to decrease the expression of circ_0026344; 2. Silence of circ_0026344 promotes CRC cells migration, invasion and EMT process; 3. miR-183 is a downstream effector of circ_0026344.

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“…MiR-181a 31–33 , miR-16 34–36 and miR-133b 37–39 were individually investigated in three studies. MiR-126 8, 40 , miR-133a 41, 42 , miR-182 43, 44 , miR-183 45, 46 , miR-199b 47, 48 , miR-22 49, 50 , miR-224 51, 52 , miR-24-3p 53, 54 , miR-7 55, 56 , were separately analyzed in two studies. Seven researches studied two miRs concomitantly 30, 34, 38, 39, 57–59 , while the others analyzed a single miR.…”

Section: Resultsmentioning

confidence: 99%

“…Among the evaluated studies, 49 demonstrated the downregulation of miR-199b 47, 48 , miR-6852 106 , miR-302c 67 , miR-466 68 , miR-650 123 , miR-196b-5p 70 , miR-944 71 , miR-30d 72 , miR-296 73 , miR-187 124 , let-7a-5p 98 , miR-132 99 , miR-206 100 , miR-148a 125 , miR-33b 76 , miR-99b-5p 120 , miR-154 78 , miR-217 126 , miR-149 79 , miR-194 80 , miR-7 56 , miR-24-3p 54 , miR-29b 127 , miR-15a 34 , miR-16 34, 36 , miR-204-5p 128 , miR-96-5p 121 , miR-193a-5p 108 , miR-100 81 , miR-133a 41 , miR-139-5p 129 , miR-124-5p 130 , miR-335 82 , miR-378 101 , miR-126 40 , miR-378a-3p 58 , miR-378a-5p 58 , miR-218 83 , miR-138 110 , miR-625 111 , miR-212 112 , miR-124 131 , miR-22 49 , miR-133b 39 , miR-193a-3p 104 , miR-195 113 and miR-30c 103 associated with worse prognosis (lower overall survival) of CRC patients. Fifty-six studies identified the upregulation of miR-181a 31–33 , miR-182 43, 44 , miR-1296 84 , miR-888 64 , miR-28-5p 132 , miR-183 45, 46 , miR-200c 114 , miR-34a 133 , miR-15a-5p 134 , miR-181c 135 , miR-24-3p 53 , miR-16 35 , miR-7 55 , miR-96 86 , miR-1260b 115 , miR-20a-5p 63 , miR-517a 87 , miR-503 136 , miR-106b 88 , miR-211 137 , miR-320e 89 , miR-592 90 , miR-196a 57 , miR-196b 57 , miR-376a …”

Section: Resultsmentioning

confidence: 99%

“…In total, 17 studies demonstrated the downregulation of 18 miRs associated with worse disease free-survival such as miR-196b-5p 70 , miR-944 71 , miR-30d 72 , miR-22 50 , miR-296 73 , miR-199b 47 , miR-187 124 , let-7a-5p 98 , miR-132 99 , miR-29b 127 , miR-15a 34 , miR-16 34 , miR-34a-5p 65 , miR-193a-5p 108 , miR-138 110 , miR-126 8 , miR-212 112 and miR-124 131 . Twenty-one studies associated the upregulation of miR-888 64 , miR-28-5p 132 , miR-183 45 , miR-200c 114 , miR-34a 133 , miR-15a-5p 134 , miR-181c 135 , miR-24-3p 53 , miR-16 35 , miR-96 86 , miR-1260b 115 , miR-20a-5p 63 , miR-517a 87 , miR-503 136 , miR-106b 88 , miR-320e 89 , miR-196a 57 , miR-196b 57 , miR-224 51 , miR-494 61 , miR-181a 32 , miR-21 25, 26, 30 and miR-155 30 with worse disease free-survival.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Tissue micro-RNAs associated with colorectal cancer prognosis: a systematic review

Santos

Penna

Carneiro

et al. 2019

Preprint

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Colorectal cancer (CRC) is a multifactorial disease commonly diagnosed worldwide, with high mortality rates. Several studies demonstrate important associations between differential expression of micro-RNAs (miRs) and the prognosis of CRC. However, only a few systematic reviews emphasize the most relevant miRs able to contribute to the establishment of new prognostic biomarkers in CRC patients. The present study aimed to identify differentially expressed tissue miRs associated with prognostic factors in CRC patients, through a systematic review of the Literature. Using the PubMed database, Cochrane Library and Web of Science, studies published in English evaluating miRs differentially expressed in tumor tissue and significantly associated with the prognostic aspects of CRC were selected. All the included studies used RT-PCR (Taqman or SYBR Green) for miR expression analysis and the period of publication was from 2009 to 2018. A total of 115 articles accomplished the inclusion criteria and were included in the review. The studies investigated the expression of 102 different miRs associated with prognostic aspects in colorectal cancer patients. The most frequent oncogenic miRs investigated were miR-21, miR-181a, miR-182, miR-183, miR-210 and miR-224 and the hyperexpression of these miRs was associated with distant metastasis, lymph node metastasis and worse survival in patients with CRC. The most frequent tumor suppressor miRs were miR-126, miR-199b and miR-22 and the hypoexpression of these miRs was associated with distant metastasis, worse prognosis and a higher risk of disease relapse (worse disease-free survival). Specific tissue miRs are shown to be promising prognostic biomarkers in patients with CRC, given their strong association with the prognostic aspects of these tumors, however, new studies are necessary to establish the sensibility and specificity of the miRs in order to use them in clinical practice.

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Wnt/catenin β1/microRNA 183 predicts recurrence and prognosis of patients with colorectal cancer

Cited by 18 publications

References 28 publications

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Circ_0026344 restrains metastasis of human colorectal cancer cells via miR-183

Tissue micro-RNAs associated with colorectal cancer prognosis: a systematic review

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