miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

Hsu, Sheng Da; Tseng, Yu‐Ting; Shrestha, Sirjana; Lin, Yuling; Khaleel, Anas; Chou, Chih‐Hung; Chu, Chao-Fang; Huang, Hsi‐Yuan; Lin, Ching‐Yuan; Ho, Shu-Yi; Jian, Ting-Yan; Lin, Feng‐Mao; Chang, Tzu-Hao; Weng, Shun-Long; Liao, Kuang‐Wen; Liao, I‐En; Liu, Chun-Chi; Huang, Hsien-Da

doi:10.1093/nar/gkt1266

Cited by 720 publications

(541 citation statements)

References 69 publications

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“…Some confirmed miRNA-target interaction for miRNAs reported in the present study (taken from Hsu et al 2014) 32 miRNA ID Confirmed Target Has-miR-494-3p PTEN, CDK6, ARNTL, BcL2L11 Has-miR-1973 None Has-miR-4286 PDCD11 Has-miR-29b-3p HDAC4, CTNNBIP1, CoL4A2, CoL1A1, CoL5A3, ACVR2A, SP1, CDK6, BACE1, SFPQ, DNMT3B, DNMT3A, MCL1, BcL2, DNMT1, S100B, VEGFA, ESR1, NCOA3, TET1 Has-miR-1260a DDX1, ZNF432, TMEM41A, HNRNPAB Has-miR-21-3p SUV420H1, MED25 Has-miR-1280_v18 None Has-miR-1260b TMEM167A, CD276, ACAT1 Lake Placid, NY USA) in PBS/1% BSA was dispersed on each slide followed by incubation in humidified atmosphere for 1 h at room temperature. Slides were washed 3 times for 10 min each in PBS/1%BSA, incubated for 1 h with the secondary antibody (1:600, Alexa Fluor-594 goat anti-mouse, Molecular Probes-Invitrogen, Karlsruhe, Germany) and washed again 4 times in PBS/0.5%Tween 20.…”

supporting

confidence: 90%

Epigenetics and miRNA as predictive markers and targets for lung cancer chemotherapy

El‐Awady

Hersi

Al-Tunaiji

et al. 2015

Cancer Biology & Therapy

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Abbreviations: 5AZA, 5-aza-2 0 -deoxycytidine; BSA, bovine serum albumin; DMSO, dimethyl sulfoxide; DNMT, DNA methyltransferase; HAT, histone acetyl transferase; HDAC, histone deacetylase; NSCLC, non-small cell lung cancer; PBS, phosphate-buffered saline; SCLC, small-cell lung cancer; TSA, trichostatin A; 5mc, 5-methyl cytosine.Lung cancer cells show inherent and acquired resistance to chemotherapy. The lack of good predictive markers/ novel targets and the incomplete understanding of the mechanisms of resistance limit the success of lung cancer response to chemotherapy. In the present study, we used an isogenic pair of lung adenocarcinoma cell lines; A549 (wild-type) and A549DOX11 (doxorubicin resistant) to study the role of epigenetics and miRNA in resistance/response of non-small cell lung cancer (NSCLC) cells to doxorubicin. Our results demonstrate differential expression of epigenetic markers whereby the level of HDACs 1, 2, 3 and4, DNA methyltransferase, acetylated H2B and acetylated H3 were lower in A549DOX11 compared to A549 cells. Fourteen miRNAs were dys-regulated in A549DOX11 cells compared to A549 cells, of these 14 miRNAs, 4 (has-mir-1973, 494, 4286 and 29b-3p) have shown 2.99 -4.44 fold increase in their expression. This was associated with reduced apoptosis and higher resistance of A549DOX11cells to doxorubicin and etoposide. Sequential treatment with the epigenetic modifiers trichostatin A or 5-aza-2'-deoxycytidine followed by doxorubicin resulted in: (i) enhanced sensitivity of both cell lines to doxorubicin especially at low concentrations, (ii) enhanced doxorubicin-induced DNA damage in both cell lines, (iii) dysregulation of some miRNAs in A549 cells. In conclusion, A549DOX11 cells resistant to DNA damaging drugs have epigenetic profile and miRNA expression different from the sensitive cells. Moreover, epigenetic modifiers may reverse the resistance of certain NSCLC cells to DNA damaging agents by enhancing induction of DNA damage. This may open the door for using epigenetic profile/miRNA expression of some cancer cells as resistance markers/targets to improve response of resistant cells to doxorubicin and for the use of combination doxorubicin/epigenetic modifiers to reduce doxorubicin toxicity.

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supporting

confidence: 90%

Epigenetics and miRNA as predictive markers and targets for lung cancer chemotherapy

El‐Awady

Hersi

Al-Tunaiji

et al. 2015

Cancer Biology & Therapy

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“…According to the miRNAs described in Table II, we predicted miRNAs targeted mRNAs though miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/) (26). Finally, we selected the intersection mRNAs through the predicted mRNAs and bioinformatics analyzed differentially expressed 489 mRNAs.…”

Section: Go and Pathway Analysis Of Differentially Expressed Genesmentioning

confidence: 99%

Integrated analysis of long non-coding RNA competing interactions reveals the potential role in progression of human gastric cancer

Liang

Yao

et al. 2016

International Journal of Oncology

111

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Abstract. Abnormal expression of long non-coding RNAs (lncRNAs) have been shown to play an important role in tumor biology. The Cancer Genome Atlas (TCGA) platform is a large sample sequencing database of lncRNAs, and further analysis of the associations between these data and patients' clinical related information can provide new approaches to find the functions of lncRNA. In the present study, 361 RNA sequencing profiles of gastric cancer (GC) patients were selected from TCGA. Then, we constructed the lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network of GC. There were 25 GC specific lncRNAs (fold change >2, p<0.05) identified, 19 of them were included in ceRNA network. Subsequently, we selected these 19 key lncRNAs and analyzed the correlations with clinical features and overall survival, 14 of them were discriminatively expressed with tumor size, tumor grade, TNM stage and lymphatic metastasis (p<0.05). In addition, eight lncRNAs (RPLP0P2, FOXD2-AS1, H19, TINCR, SLC26A4-AS1, SMIM10L2A, SMIM10L2B and SNORD116-4) were found to be significantly associated with overall survival (log-rank p<0.05). Finally, two key lncRNAs HOTAIR and UCA1 were selected for validation of their expression levels in 82 newly diagnosed GC patients by qRT-PCR. Results showed that the fold changes between TCGA and qRT-PCR were 100% in agreement. In addition, we also found that HOTAIR was significantly correlated with tumor size and lymphatic metastasis (p<0.05), and UCA1 was significantly correlated with tumor size, TNM stage and lymphatic metastasis (p<0.05).The clinical relevance of the two lncRNAs and the bioinformatics analysis results were almost the same. Overall, our study showed the GC specific lncRNAs expression patterns and a ceRNA network in GC. Clinical features related to GC specific lncRNAs also suggested these lncRNAs are worthwhile for further study as novel candidate biomarkers for the clinical diagnosis of GC and potential indicators for prognosis. IntroductionNoncoding RNAs (ncRNAs) are transcripts that have no ability of coding proteins, which widely exit in high eukaryotics. According to their characteristics, ncRNAs can be divided into several subtypes including transfer RNA, small nucleolar RNA (snoRNA), ribosomal RNA (rRNA), microRNA (miRNA) and long non-coding RNA (lncRNA). The amount of the ncRNAs transcripts is >98% of the whole genome transcripts and have been suggested to represent transcriptional noise (1). However, more and more evidence indicates that transcriptional output of genome is far more complex than predicted, and suggests new paradigms of ncRNA regulation (2).Recent studies suggest that the ncRNAs may play important biological roles in transcriptional regulation, cellular development, formation of chromosome and RNA modification (3). Based on the transcript size, ncRNAs are grouped into small ncRNAs (<200 bp) and long ncRNAs (>200 bp, up to 100 kb). lncRNA is the functional end-product, and the level of lncRNA expression correlates directly with the level of the active molecule. Thus, ...

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“…Positive data samples were collected from validated targets sites published in databases such as miRecords [24] and mirTarbase [25]. Randomly generated negative samples might be giving best results in cross validation, but may fail to repeat the same performance in real test cases.…”

Section: Data Collectionmentioning

confidence: 99%

HTar: Hidden Markov Model Based MicroRNA Binding Site Prediction

Salim¹,

SS²

2017

J Proteomics Bioinform

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miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

Cited by 720 publications

References 69 publications

Epigenetics and miRNA as predictive markers and targets for lung cancer chemotherapy

Epigenetics and miRNA as predictive markers and targets for lung cancer chemotherapy

Integrated analysis of long non-coding RNA competing interactions reveals the potential role in progression of human gastric cancer

HTar: Hidden Markov Model Based MicroRNA Binding Site Prediction

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