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An ensemble method to predict target genes and pathways in uveal melanoma

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Published/Copyright: April 10, 2018
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From the journal Open Life Sciences Volume 13 Issue 1

Abstract

Objective

This work proposes to predict target genes and pathways for uveal melanoma (UM) based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC), a causal inference method (IDA) and a regression method (Lasso) utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.

Keywords: uveal melanoma; miRNA; mRNA; target; gene; pathway

1 Introduction

Uveal melanoma (UM) is the most frequent and aggressive ocular primary tumor that arises from neural crest-derived melanocytes of the uveal tract of the eye in adults [1], with an incidence rate of up to 8 per 1,000,000 person years in Europe [2, 3]. The fatality rate of UM is high, since patients are at risk of developing metastases up to 20 years after the initial diagnosis, and 80% of metastatic patients die within one year and 92% within 2 years of the diagnosis of metastases [4, 5]. However, no effective adjuvant therapy is available to prevent metastases, neither is there any effective treatment once metastases have developed at present [3]. With the development of gene expression related analyses, target treatments could provide new insights for effective therapy to large extent and potentially improve patient survival [6]. Besides, understanding the molecular characteristics and mechanisms of UM is critical for the creation of a treatment for this tumor.

It has been demonstrated that intratumoral discordance in gene expression profile is associated with intratumoral heterogeneity based upon histopathologic features in UM [7]. Furthermore, several gene signatures underlying UM have been uncovered, such as Gαq stimulatory subunit GNAQ and BAP1 [8, 9]. However, mutated genes do not play roles individually and similar genes often work together to complete certain biological functions. What’s more, those correlated genes might be regulated by one microRNA (miRNA) whose signatures may be promising biomarkers for the classification or outcome prediction of large number of human cancers [10]. Therefore, investigating miRNAs offers an excellent way to elucidate the complex pathological mechanisms underlying malignant tumors, and gives a hand to the design of drugs for treatments.

In the present study, we proposed to predict targets of miRNAs in UM based on an ensemble method produced by Le et al. [11]. It could solve the inconsistent results problem resulting from individual methods by including complementary results [12]. Specifically, it merged a correlation method (Pearson correlation coefficient, PCC), a causal inference method (IDA) and a regression method (Lasso) utilizing the Borda count election method. Subsequently, the predicted miRNA targets were validated by matching them with the known confirmed databases. Ultimately, pathway enrichment analysis was conducted on target genes to identify target pathways for UM patients. The target genes and pathways might light a new lamp for revealing molecular mechanism of UM and give a hand for target treatments and preventions of this malignant tumor.

2 Materials and methods

2.1 Preparation of miRNA and mRNA data

MiRNA and mRNA expression data for UM patients were downloaded from the Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/), respectively. Only 80 samples which were existed in both miRNA and mRNA expression data were reserved for the following analysis. Subsequently, the miRNAs or mRNAs with expression values = 0 were removed. Then the residual expression values were converted into log2 forms and normalized using a Global Variance Stabilizing Normalization (VSN) method [13]. Consequently, 793 miRNAs and 19,511 mRNAs were obtained in the expression data. For purpose of making the data more confident and reliable, the PCC method was utilized to compute the correlations between miRNA and mRNA. If the absolute PCC value of a pair of miRNA and mRNA was more than 0.7, it would be remained. Finally, a total of 107 miRNAs and 904 mRNAs were obtained for subsequent analyses.

Ethical approval

The conducted research is not related to either human or animals use.

2.2 Prediction of miRNA targets

Using the miRNA and mRNA data, the ensemble method which integrated three methods (PCC, IDA and Lasso) based on Borda count election method, was applied to predict miRNA targets for UM. This process was comprised of three steps:

Firstly, the PCC, IDA and Lasso method was used to predict miRNA targets on the basis of miRNA and mRNA data, and then these miRNA targets were ranked, respectively. Only the top k (k = 100) ranked targets were left to perform the followed analysis. Secondly, Borda rank election method was employed to integrate top k ranks of each miRNA from PCC, IDA and Lasso method, and to produce a single ranking list of elected mRNAs with respect to the miRNA. Here, Borda rank election is a good approach to merge orderly appraising results from several separated methods [14]. A z-score was assigned to the candidate across all voters through the average points. The higher the z-score was, the more significant the prediction results were. At last, we ranked the predicted miRNA targets according to their z-scores and obtain the top k ranked genes from the merged list as the final output, i.e. the potential target genes for the given miRNA of UM.

2.3 Validations of predicted miRNA targets

To validate the feasibility and confidence of the predicted miRNA targets in UM patients, we compared our results with the union of four popular databases, miRTarbase v4.5 [15], Tarbase v6.0 [16], miRecords v2013 [17] and miRWalk v2.0 [18]. Briefly, miRTarbase provides the most current and comprehensive information of experimentally validated miRNA-mRNA target interactions [19]. While TarBase is the first resource to provide experimentally verified miRNA target interactions by surveying pertinent literature [20]. As for miRecords, it accumulates experimentally validated miRNA targets and computationally predictes miRNA targets [17]. Last but not least, miRWalk is an available comprehensive resource that hosts the predicted as well as experimentally validated miRNA target interaction pairs [18]. After removing the duplicated interactions, we could obtain a union of known interactions and referred them to confirmed interactions in the paper. If a miRNA target interaction was involved in confirmed interactions, we thought that the predicted miRNA target was validated.

2.4 Pathway enrichment analysis

In order to investigate biological functions of miRNA targets enriched in the top k miRNA-mRNA interactions, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was carried out based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.ncifcrf.gov/) tool [21].Here, the KEGG database (http://www.genome.jp/kegg/) is a collection of manually drawn pathway maps for metabolism, genetic information processing, environmental information processing [22]. Besides, the Fisher’s exact test was employed to identify significant pathways between UM patients and normal controls [23]. The threshold of significance was defined as P < 0.01 which were adjusted by false discovery rate (FDR) based on Benjamini & Hochberg method [24].

3 Results

3.1 Predicted miRNA targets

In the current study, a total of 107 miRNAs and 904 mRNAs of UM were prepared from the TCGA database for the subsequent analyses. Based on these expression data, miRNA targets were predicted by PCC, IDA and Lasso method respectively, and the top 100 targets from the three individual methods were integrated by the Borda rank election method. For each miRNA, only its top 100 targets were computed. During this process, a z-score was calculated for each miRNA-mRNA interaction. All interactions were ordered in descending order of z-scores, and the top 50 interactions were regarded as seed miRNA-mRNA interactions for UM patients, as displayed in Table 1.

Table 1

Seed miRNA-mRNA interactions for UM patients

IDmiRNAmRNAz-scoreIDmiRNAmRNAz-score
1hsa-mir-203ASPG320426hsa-mir-3166LMAN1873
2hsa-mir-195BSDC1317927hsa-mir-3612MC2R851
3hsa-mir-3915C4BPA300728hsa-mir-335MEST822
4hsa-mir-30aC6orf155297229hsa-mir-155MIR155HG809
5hsa-mir-1253C6orf191274830hsa-mir-186MKNK1774
6hsa-mir-511-2CD209253031hsa-mir-92bMMP11748
7hsa-mir-150CD96248432hsa-mir-501NEDD9729
8hsa-mir-3927DEFB109P1B221833hsa-mir-142NLRP1713
9hsa-mir-1247DIO3210434hsa-mir-708ODZ4710
10hsa-mir-221EXTL1200735hsa-mir-935OGG1705
11hsa-mir-887FBXL7198636hsa-mir-143OR51E1703
12hsa-mir-504FGF13186337hsa-mir-3200OSBP2701
13hsa-mir-105-1GABRA3185338hsa-mir-139PDE2A700
14hsa-mir-1185-2GPX5179439hsa-let-7bSEC22C697
15hsa-mir-1185-1HECW1176640hsa-mir-383SGCZ693
16hsa-mir-196bHOXA10173541hsa-mir-584SH3TC2689
17hsa-mir-196a-1HOXC10168442hsa-mir-134SLIT3682
18hsa-mir-196a-2HOXC11150743hsa-mir-181a-1SORBS2680
19hsa-mir-10bHOXD8143644hsa-mir-513bTBC1D22B679
20hsa-mir-3614ISG15133245hsa-mir-199a-1TGFBI679
21hsa-mir-874KLHL3110546hsa-mir-140NFATC4678
22hsa-mir-2861KRT39108247hsa-mir-24-2PAIP2B672
23hsa-mir-511-1LILRB597348hsa-mir-532PCBP4670
24hsa-mir-618LIN7A92749hsa-mir-216bPDC669
25hsa-mir-873LINGO290450hsa-mir-151PYCRL668

We found that among the 50 interactions, 10 of them had z-score > 2,000, especially 3 ones with z-score > 3,000, while the z-score of 12 interactions ranged from 1,000 to 2,000. In details, the pair of hsa-mir-203-ASPG obtained the highest z-score of 3,204. The other two interactions with z-score > 3,000 were hsa-mir-195-BSDC1 (z-score = 3,179), and hsa-mir-3915-C4BPA (z-score = 3,007). The followed two miRNA-mRNA interactions were hsa-mir-30a-C6orf155 (z-score = 2972), and hsa-mir-1253-C6orf191 (z-score = 2748). Interestingly, HOXA10 was regulated by two miRNAs (hsa-mir-196b and hsa-mir-196a-1) at the same time.

3.2 Validations of predicted miRNA targets

With an attempt to validate miRNA targets predicted by the ensemble method, we took a comparison of our results with confirmed miRTarBase, Tarbase, miRecords and miRWalk database. In short, miRTarbasev4.5 contains 37,372 miRNA-mRNA interactions (covering 576 miRNAs). There were 20,095 interactions with 228 miRNAs in Tarbase v6.0. A total of 21,590 interactions representing 195 miRNAs were found in miRecords v2013. And miRWalk v2.0 covers 1,710 miRNA-mRNA interactions involved 226 miRNAs. By removing the duplicated interactions, we obtained total 62,858 confirmed interactions for validations.When comparing our predicted miRNA-mRNA interactions with confirmed interactions, 38 interactions were matched, which further indicated that our method was an available and valuable method for predicting miRNA targets.

3.3 Pathway enrichment analysis

After prediction and validation for miRNA targets obtained from the ensemble method, we aimed to identify significant functional gene sets of miRNA targets. Due to the too large scale of miRNA targets, we selected genes enriched in the top 1, 000 ranked interactions which might be more important than the others for UM as study objects. Thus, KEGG pathway enrichment analysis was conducted on 601 targets in the top 1,000 miRNA-mRNA interaction based on the DAVID tool. When setting the cut-off as p-value < 0.05 (adjusted by Benjamini–Hochberg (BH) method), a total of 12 target pathways were detected (Table 2). The top five significant pathways were Phototransduction (P = 1.85E-06), Chemokine signaling pathway (P = 4.36E-05), Ribosome (P = 7.13E-04), Phenylalanine metabolism (P = 2.25E-03), and Cytokine-cytokine receptor interaction (P = 5.02E-03). Particularly, Phototransduction was comprised of 9 targets including CNGB1, GNAT1, GNAT2, GNGT1, GUCA1A, GUCY2F, RCVRN, RHO and GUCA1C. Meanwhile, the Chemokine signaling pathway consisted of 21 targets (ADCY1, GNB3, GNGT1, HCK, ITK, PRKCD, CCL4, CCL5, CXCL11, VAV2, CXCL14, CXCR6, GNG13, RPL10A, RPL3, RPL11, RPL22, RPL35A, RPS8, RPS23 and RPS27A).

Table 2

Target pathways in top 1000 miRNA-mRNA interactions

IDPathwaymiRNA targetsP value
1PhototransductionCNGB1;GNAT1;GNAT2;GNGT1;GUCA1A;GUCY2F;RCVRN;RHO;GUCA1C1.85E-06
2Chemokine signaling pathwayADCY1;GNB3;GNGT1;HCK;ITK;PRKCD;CCL4;CCL5;CXCL11;VAV2;CXCL14;CXCR6;GNG13;4.36E-05
3RibosomeRPL10A;RPL3;RPL11;RPL22;RPL35A;RPS8;RPS23;RPS27A7.13E-04
4Phenylalanine metabolismDDC;HPD;MAOB2.25E-03
5Cytokine-cytokine receptor interactionTNFRSF8;CSF2RB;CTF1;IL2RB;IL12RB1;LTB;NGFR;CCL4;CCL5;CXCL11;TNFRSF1B;CXCL14;CXCR6;TNFRSF19;RELT5.02E-03
6Long-term depressionGRIA1;GRIA3;GRID2;GRM5;IGF1;RYR12.33E-02
7Primary immunodeficiencyLCK;PTPRC;TAP1;ZAP703.74E-02
8Cell adhesion molecules (CAMs)HLA-F;PECAM1;PTPRC;SDC2;SIGLEC1;CNTNAP1;CADM1;CNTNAP2;CADM33.85E-02
9Amyotrophic lateral sclerosis (ALS)DAXX;GRIA1;MAPK12;TNFRSF1B;DERL13.91E-02
10Tyrosine metabolismDDC;HPD;MAOB;HEMK14.77E-02
11Glycosaminoglycan biosynthesis - heparan sulfate / heparinEXT1;EXTL1;NDST44.79E-02
12Neuroactive ligand-receptor interactionCHRNA3;CHRNA4;CHRNB3;EDNRB;GABRA1;GABRA3;GABRG2;GRIA1;GRIA3;GRID2;GRIK1;GRM5;HTR2B;MC2R4.91E-02

  1. The p-values have been corrected based on Benjamini & Hochberg method. P<0.01 was considered as the threshold of significance.

4 Discussion

MiRNAs, a family of small non-coding RNA molecules, regulate expressions of genes by promoting mRNA degradation and repressing translation [25]. Their roles and functions in tumors have attracted more and more attentions from researchers, and the possible inferences are that miRNA participate in cancer-related processes, including proliferation, metabolism, differentiation, apoptosis and even cancer development and progression [26]. But there have been few studies to uncover miRNA targets in UM systemically. Hence, in this paper, we predicted target genes and pathways for UM patients based on the ensemble method that was an integration of PCC, IDA and Lasso methods.

Briefly, PCC is the commonly used correlation method for the strength between a pair of variables [27]. But it often leads to negative rank of miRNA-mRNA correlations due to down-regulation of miRNAs for mRNAs [11]. In addition, the PCC would not be greatly reduced if the data were in the non-linear distribution [28]. Meanwhile, IDA is a causal inference method that counts the causal effects between two variables [29, 30]. And the miRNA-mRNA correlations predicted by the IDA method have parts of overlap with outcomes of the follow-up gene knockdown experiments [31]. As for the Lasso, it minimizes the usual sum of squared errors, with a bound on the sum of the absolute values of the coefficients [32]. Like the limitation of PCC method, the miRNA-mRNA pairs identified by Lasso have negative effects are ranked at the top of the ranking list to favor the down regulation. Moreover, the ensemble method captured confirmed interactions in the incomplete ground truth that existing individual methods fail to discover, although there is no complete ground truth of miRNA target prediction [11].

Therefore, we employed Borda count election method to integrate the above three methods together, and obtained the ensemble method. Generally speaking, great challenges have been occurred on validating our predicted results, because the amount of experimentally confirmed miRNA targets is still limited and there is no complete authority for accessing and comparing different computational methods [33]. Hence the feasibility of our predicted results has been validated by comparing them with confirmed interactions. Results of the ensemble method showed that hsa-mir-203-ASPG, hsa-mir-195-BSDC1 and hsa-mir-3915-C4BPA were the most important miRNA-mRNA interactions, and consequently ASPG, BSDC1 and C4BPA were more critical target genes for UM than the others predicted. However, there have still been no studies to investigate the regulatory mechanisms of hsa-mir-203-ASPG, hsa-mir-195-BSDC1, and hsa-mir-3915-C4BPA. miR-203 has been reported to be overexpressed in pancreatic adenocarcinoma cells [34], while it also has been suggested as a tumor-inhibitory miRNA in hepatocellular carcinoma [35]. The abnormal of miR-195 in many cancers has also been reported by many researchers. It increased in breast cancer and chronic lymphocytic leukemia while decreased in gastric cancer, hepatocellular carcinoma, colorectal carcinoma and bladder cancer[36]. So far, study on miR-3915 was still limited. ASPG (asparaginase, also known as 60-kDa lysophospholipase) catalyzes the hydrolysis of L-asparagine to L-aspartate and ammonia [37]. It is used for remission induction and intensification treatment in all pediatric regimens and in the majority of adult treatment protocols [38]. C4BPA (complement component 4 binding protein alpha) a member of a super-family of proteins composed predominantly of tandemly arrayed short consensus repeats of approximately 60 amino acids [39]. It had been reported that the C4BPA locus was a new susceptibility locus for venous thrombosis visa protein S regulation, opening a new research area focusing on C4BP regulatory pathway [40]. It is the first time to uncover the relations between the target genes and UM, and further experimental validations would be finished as soon as possible.

As mentioned above, KEGG pathway enrichment analysis for 601 target genes in top 1,000 miRNA-mRNA interactions were performed, and 12 target pathways with P < 0.05 were identified. Importantly, Phototransduction and Chemokine signaling pathway were the most ones for UM compared with normal controls. The definition for Phototransduction in KEGG pathway database is a biochemical process by which the photoreceptor cells generate electrical signals in response to captured photons. Aguila et al revealed that heat shock protein 90 inhibition on visual function are likely to relate to essential its client proteins in the phototransduction pathway in the retina and potentially elsewhere in the eye [41]. Hence target pathway Phototransduction was related to UM tightly.

In conclusion, we have successfully predicted miRNA target genes and pathways for UM patients based on the ensemble method. The findings in this study might shed new light on uncovering the molecular mechanism underlying UM, and provide potential target signatures for prevention and treatment of this tumor. Moreover, whether the predicted miRNA targets are indeed involved in the development of UM, need to be confirmed by experiments urgently.

Acknowledgement

This work was supported by Youth Fund of the 2nd Hospital of Shandong University (Y2014010037); Grants from the National Natural Science Foundation of China (31500699).

  1. Conflict of interest: Authors state no conflict of interest.

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Received: 2017-11-30
Accepted: 2018-01-16
Published Online: 2018-04-10

© 2018 Chao Wei et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Articles in the same Issue

  1. Research Article
  2. Purification of Tea saponins and Evaluation of its Effect on Alcohol Dehydrogenase Activity
  3. Runt-related transcription factor 3 promoter hypermethylation and gastric cancer risk: A meta-analysis
  4. Risk Factors for Venous Thromboembolism in Hospitalized Patients in the Chinese Population
  5. Value of Dual-energy Lung Perfusion Imaging Using a Dual-source CT System for the Pulmonary Embolism
  6. A new combination of substrates: biogas production and diversity of the methanogenic microorganisms
  7. mTOR modulates CD8+ T cell differentiation in mice with invasive pulmonary aspergillosis
  8. Direct Effects on Seed Germination of 17 Tree Species under Elevated Temperature and CO2 Conditions
  9. Role of water soluble vitamins in the reduction diet of an amateur sportsman
  10. Aberrant DNA methylation involved in obese women with systemic insulin resistance
  11. 16S ribosomal RNA-based gut microbiome composition analysis in infants with breast milk jaundice
  12. Characterization of Haemophilus parasuis Serovar 2 CL120103, a Moderately Virulent Strain in China
  13. MiRNA-145 induces apoptosis in a gallbladder carcinoma cell line by targeting DFF45
  14. Telmisartan induces osteosarcoma cells growth inhibition and apoptosis via suppressing mTOR pathway
  15. Optimizing the Formulation for Ginkgolide B Solid Dispersion
  16. Determination of the In Vitro Gas Production and Potential Feed Value of Olive, Mulberry and Sour Orange Tree Leaves
  17. Factors Influencing the Successful Isolation and Expansion of Aging Human Mesenchymal Stem Cells
  18. The Value of Diffusion-Weighted Magnetic Resonance Imaging in Predicting the Efficacy of Radiation and Chemotherapy in Cervical Cancer
  19. Chemical profile and antioxidant activity of Trollius europaeus under the influence of feeding aphids
  20. SSR Markers Suitable for Marker Assisted Selection in Sunflower for Downy Mildew Resistance
  21. A Fibroblast Growth Factor Antagonist Peptide Inhibits Breast Cancer in BALB/c Mice
  22. Antihyperglycemic and antihyperlipidemic effects of low-molecular-weight carrageenan in rats
  23. Microbial indicators and environmental relationships in the Umhlangane River, Durban, South Africa
  24. TUFT1 promotes osteosarcoma cell proliferation and predicts poor prognosis in osteosarcoma patients
  25. Long non-coding RNA-2271 promotes osteogenic differentiation in human bone marrow stem cells
  26. The prediction of cardiac events in patients with acute ST segment elevation myocardial infarction: A meta–analysis of serum uric acid
  27. Risk expansion of Cr through amphibious clonal plant from polluted aquatic to terrestrial habitats
  28. Overexpression of Zinc Finger Transcription Factor ZAT6 Enhances Salt Tolerance
  29. Sini decoction intervention on atherosclerosis via PPARγ-LXRα-ABCA1 pathway in rabbits
  30. Soluble myeloid triggering receptor expressed on myeloid cell 1 might have more diagnostic value for bacterial ascites than C-reactive protein
  31. A Preliminary Study on the Newly Isolated High Laccase-producing Fungi: Screening, Strain Characteristics and Induction of Laccase Production
  32. Hydrolytic Enzyme Production by Thermophilic Bacteria Isolated from Saudi Hot Springs
  33. Analysis of physiological parameters of Desulfovibrio strains from individuals with colitis
  34. Emodin promotes apoptosis of human endometrial cancer through regulating the MAPK and PI3K/ AKT pathways
  35. Down-regulation of miR-539 indicates poor prognosis in patients with pancreatic cancer
  36. Inhibitory activities of ethanolic extracts of two macrofungi against eggs and miracidia of Fasciola spp.
  37. PAQR6 expression enhancement suggests a worse prognosis in prostate cancer patients
  38. Characterization of a potential ripening regulator, SlNAC3, from Solanum lycopersicum
  39. Expression of Angiopoietin and VEGF in cervical cancer and its clinical significance
  40. Umbilical Cord Tissue Derived Mesenchymal Stem Cells Can Differentiate into Skin Cells
  41. Isolation and Characterization of a Phage to Control Vancomycin Resistant Enterococcus faecium
  42. Glycogen Phosphorylase Isoenzyme Bb, Myoglobin and BNP in ANT-Induced Cardiotoxicity
  43. BAG2 overexpression correlates with growth and poor prognosis of esophageal squamous cell carcinoma
  44. Relationship between climate trends and grassland yield across contrasting European locations
  45. Review Articles
  46. Mechanisms of salt tolerance in halophytes: current understanding and recent advances
  47. Salivary protein roles in oral health and as predictors of caries risk
  48. Nanoparticles as carriers of proteins, peptides and other therapeutic molecules
  49. Survival mechanisms to selective pressures and implications
  50. Up-regulation of key glycolysis proteins in cancer development
  51. Communications
  52. In vitro plant regeneration of Zenia insignis Chun
  53. DNA barcoding of online herbal supplements: crowd-sourcing pharmacovigilance in high school
  54. Case Reports
  55. Management of myasthenia gravis during pregnancy: A report of eight cases
  56. Three Cases of Extranodal Rosai-Dorfman Disease and Literature Review
  57. Letters to the Editor
  58. First report of Chlamydia psittaci seroprevalence in black-headed gulls (Larus ridibundus) at Dianchi Lake, China
  59. Special Issue on Agricultural and Biological Sciences - Part II
  60. Chemical composition of essential oil in Mosla chinensis Maxim cv. Jiangxiangru and its inhibitory effect on Staphylococcus aureus biofilm formation
  61. Secondary metabolites of Antarctic fungi antagonistic to aquatic pathogenic bacteria
  62. Study of Seizure-Manifested Hartnup Disorder Case Induced by Novel Mutations in SLC6A19
  63. Transcriptome analysis of Pinus massoniana Lamb. microstrobili during sexual reversal
  64. Mechanism of oxymatrine-induced human esophageal cancer cell apoptosis by the endoplasmic reticulum stress pathway
  65. Methylation pattern polymorphism of cyp19a in Nile tilapia and hybrids
  66. A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation
  67. A novel TNNI3 gene mutation (c.235C>T/ p.Arg79Cys) found in a thirty-eight-year-old women with hypertrophic cardiomyopathy
  68. Remote Sensing-Based Extraction and Analysis of Temporal and Spatial Variations of Winter Wheat Planting Areas in the Henan Province of China
  69. Topical Issue on Precision Medicine
  70. Serum sTREM-1, PCT, CRP, Lac as biomarkers for death risk within 28 days in patients with severe sepsis
  71. IL-17 gene rs3748067 C>T polymorphism and gastric cancer risk: A meta-analysis
  72. Efficacy of Danhong injection on serum concentration of TNF-α, IL-6 and NF-κB in rats with intracerebral hemorrhage
  73. An ensemble method to predict target genes and pathways in uveal melanoma
  74. Evaluation of the quality of CT images acquired with smart metal artifact reduction software
  75. NPM1A in plasma is a potential prognostic biomarker in acute myeloid leukemia
  76. Arterial infusion of rapamycin in the treatment of rabbit hepatocellular carcinoma to improve the effect of TACE
  77. New progress in understanding the cellular mechanisms of anti-arrhythmic drugs
https://doi.org/10.1515/biol-2018-0013
Keywords for this article
uveal melanoma; miRNA; mRNA; target; gene; pathway
Creative Commons
BY-NC-ND 4.0

Articles in the same Issue

  1. Research Article
  2. Purification of Tea saponins and Evaluation of its Effect on Alcohol Dehydrogenase Activity
  3. Runt-related transcription factor 3 promoter hypermethylation and gastric cancer risk: A meta-analysis
  4. Risk Factors for Venous Thromboembolism in Hospitalized Patients in the Chinese Population
  5. Value of Dual-energy Lung Perfusion Imaging Using a Dual-source CT System for the Pulmonary Embolism
  6. A new combination of substrates: biogas production and diversity of the methanogenic microorganisms
  7. mTOR modulates CD8+ T cell differentiation in mice with invasive pulmonary aspergillosis
  8. Direct Effects on Seed Germination of 17 Tree Species under Elevated Temperature and CO2 Conditions
  9. Role of water soluble vitamins in the reduction diet of an amateur sportsman
  10. Aberrant DNA methylation involved in obese women with systemic insulin resistance
  11. 16S ribosomal RNA-based gut microbiome composition analysis in infants with breast milk jaundice
  12. Characterization of Haemophilus parasuis Serovar 2 CL120103, a Moderately Virulent Strain in China
  13. MiRNA-145 induces apoptosis in a gallbladder carcinoma cell line by targeting DFF45
  14. Telmisartan induces osteosarcoma cells growth inhibition and apoptosis via suppressing mTOR pathway
  15. Optimizing the Formulation for Ginkgolide B Solid Dispersion
  16. Determination of the In Vitro Gas Production and Potential Feed Value of Olive, Mulberry and Sour Orange Tree Leaves
  17. Factors Influencing the Successful Isolation and Expansion of Aging Human Mesenchymal Stem Cells
  18. The Value of Diffusion-Weighted Magnetic Resonance Imaging in Predicting the Efficacy of Radiation and Chemotherapy in Cervical Cancer
  19. Chemical profile and antioxidant activity of Trollius europaeus under the influence of feeding aphids
  20. SSR Markers Suitable for Marker Assisted Selection in Sunflower for Downy Mildew Resistance
  21. A Fibroblast Growth Factor Antagonist Peptide Inhibits Breast Cancer in BALB/c Mice
  22. Antihyperglycemic and antihyperlipidemic effects of low-molecular-weight carrageenan in rats
  23. Microbial indicators and environmental relationships in the Umhlangane River, Durban, South Africa
  24. TUFT1 promotes osteosarcoma cell proliferation and predicts poor prognosis in osteosarcoma patients
  25. Long non-coding RNA-2271 promotes osteogenic differentiation in human bone marrow stem cells
  26. The prediction of cardiac events in patients with acute ST segment elevation myocardial infarction: A meta–analysis of serum uric acid
  27. Risk expansion of Cr through amphibious clonal plant from polluted aquatic to terrestrial habitats
  28. Overexpression of Zinc Finger Transcription Factor ZAT6 Enhances Salt Tolerance
  29. Sini decoction intervention on atherosclerosis via PPARγ-LXRα-ABCA1 pathway in rabbits
  30. Soluble myeloid triggering receptor expressed on myeloid cell 1 might have more diagnostic value for bacterial ascites than C-reactive protein
  31. A Preliminary Study on the Newly Isolated High Laccase-producing Fungi: Screening, Strain Characteristics and Induction of Laccase Production
  32. Hydrolytic Enzyme Production by Thermophilic Bacteria Isolated from Saudi Hot Springs
  33. Analysis of physiological parameters of Desulfovibrio strains from individuals with colitis
  34. Emodin promotes apoptosis of human endometrial cancer through regulating the MAPK and PI3K/ AKT pathways
  35. Down-regulation of miR-539 indicates poor prognosis in patients with pancreatic cancer
  36. Inhibitory activities of ethanolic extracts of two macrofungi against eggs and miracidia of Fasciola spp.
  37. PAQR6 expression enhancement suggests a worse prognosis in prostate cancer patients
  38. Characterization of a potential ripening regulator, SlNAC3, from Solanum lycopersicum
  39. Expression of Angiopoietin and VEGF in cervical cancer and its clinical significance
  40. Umbilical Cord Tissue Derived Mesenchymal Stem Cells Can Differentiate into Skin Cells
  41. Isolation and Characterization of a Phage to Control Vancomycin Resistant Enterococcus faecium
  42. Glycogen Phosphorylase Isoenzyme Bb, Myoglobin and BNP in ANT-Induced Cardiotoxicity
  43. BAG2 overexpression correlates with growth and poor prognosis of esophageal squamous cell carcinoma
  44. Relationship between climate trends and grassland yield across contrasting European locations
  45. Review Articles
  46. Mechanisms of salt tolerance in halophytes: current understanding and recent advances
  47. Salivary protein roles in oral health and as predictors of caries risk
  48. Nanoparticles as carriers of proteins, peptides and other therapeutic molecules
  49. Survival mechanisms to selective pressures and implications
  50. Up-regulation of key glycolysis proteins in cancer development
  51. Communications
  52. In vitro plant regeneration of Zenia insignis Chun
  53. DNA barcoding of online herbal supplements: crowd-sourcing pharmacovigilance in high school
  54. Case Reports
  55. Management of myasthenia gravis during pregnancy: A report of eight cases
  56. Three Cases of Extranodal Rosai-Dorfman Disease and Literature Review
  57. Letters to the Editor
  58. First report of Chlamydia psittaci seroprevalence in black-headed gulls (Larus ridibundus) at Dianchi Lake, China
  59. Special Issue on Agricultural and Biological Sciences - Part II
  60. Chemical composition of essential oil in Mosla chinensis Maxim cv. Jiangxiangru and its inhibitory effect on Staphylococcus aureus biofilm formation
  61. Secondary metabolites of Antarctic fungi antagonistic to aquatic pathogenic bacteria
  62. Study of Seizure-Manifested Hartnup Disorder Case Induced by Novel Mutations in SLC6A19
  63. Transcriptome analysis of Pinus massoniana Lamb. microstrobili during sexual reversal
  64. Mechanism of oxymatrine-induced human esophageal cancer cell apoptosis by the endoplasmic reticulum stress pathway
  65. Methylation pattern polymorphism of cyp19a in Nile tilapia and hybrids
  66. A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation
  67. A novel TNNI3 gene mutation (c.235C>T/ p.Arg79Cys) found in a thirty-eight-year-old women with hypertrophic cardiomyopathy
  68. Remote Sensing-Based Extraction and Analysis of Temporal and Spatial Variations of Winter Wheat Planting Areas in the Henan Province of China
  69. Topical Issue on Precision Medicine
  70. Serum sTREM-1, PCT, CRP, Lac as biomarkers for death risk within 28 days in patients with severe sepsis
  71. IL-17 gene rs3748067 C>T polymorphism and gastric cancer risk: A meta-analysis
  72. Efficacy of Danhong injection on serum concentration of TNF-α, IL-6 and NF-κB in rats with intracerebral hemorrhage
  73. An ensemble method to predict target genes and pathways in uveal melanoma
  74. Evaluation of the quality of CT images acquired with smart metal artifact reduction software
  75. NPM1A in plasma is a potential prognostic biomarker in acute myeloid leukemia
  76. Arterial infusion of rapamycin in the treatment of rabbit hepatocellular carcinoma to improve the effect of TACE
  77. New progress in understanding the cellular mechanisms of anti-arrhythmic drugs
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