Prognostic value of microRNAs in lung cancer: A systematic review and meta‑analysis

Xiao, Wendi; Zhong, Yucheng; Wu, Lili; Yang, Dawei; Ye, Songqing; Zhang, Min

doi:10.3892/mco.2018.1763

Cited by 19 publications

(18 citation statements)

References 54 publications

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“…Increasing evidence has suggested that miRNAs act as key regulators in the development and progression of human cancers (14)(15)(16). Recently, alterations in the expression of miRNAs have been wildly reported in nearly all human cancers, including NScLc (17)(18)(19). miRNAs can serve as tumor-suppressing or tumor-promoting miRNAs, depending on the functional characterization of their target genes (20).…”

Section: Introductionmentioning

confidence: 99%

MicroRNA-625 inhibits the progression of non‑small cell lung cancer by directly targeting HOXB5 and deactivating the Wnt/β-catenin pathway

Tan

Jiang

et al. 2019

Int J Mol Med

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Numerous microRNAs (miRs) are dysregulated in non-small cell lung cancer (NScLc), serving pivotal roles in its formation and progression. miR-625 is dysregulated in several types of human cancer, but its involvement in the formation and development of NScLc remains poorly understood. In the present study, we aimed to investigate miR-625 expression in NScLc and its role in regulating NScLc cell behavior. miR-625 expression in NScLc tissues and cell lines was detected using reverse transcription-quantitative polymerase chain reaction. The effects of miR-625 overexpression on NScLc cell proliferation, apoptosis, migration and invasion in vitro were assessed using an MTT assay, flow cytometry, and cell migration and invasion assays, respectively. The effects of miR-625 upregulation on NScLc growth were evaluated in an in vivo xenograft model. The molecular mechanisms underlying the tumor-suppressing roles of miR-625 in NScLc were explored in detail. miR-625 expression was determined to be downregulated in NScLc tissues and cell lines. This decreased expression was associated with advanced clinical features and poor overall survival of patients with NScLc. Exogenous miR-625 expression suppressed NScLc cell proliferation, migration and invasion, and induced apoptosis in vitro. miR-625 upregulation hindered NScLc tumor growth in vivo. Homeobox B5 (HOXB5) was proposed to be the direct target gene of miR-625 in NScLc cells. The tumor-suppressing effects of HOXB5 silencing were similar to those of miR-625 overexpression in NScLc cells. In rescue experiments, HOXB5 overexpression partially reversed the inhibitory effects of miR-625 in NScLc cells. miR-625 upregulation directly targeted HOXB5 to deactivate the Wnt/β-catenin signaling pathway in NScLc cells in vitro and in vivo. miR-625 was determined to be associated with HOXB5 suppression and Wnt/β-catenin pathway deactivation, which in turn inhibited the aggressive behavior of NScLc cells in vitro and in vivo.

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Section: Introductionmentioning

confidence: 99%

MicroRNA-625 inhibits the progression of non‑small cell lung cancer by directly targeting HOXB5 and deactivating the Wnt/β-catenin pathway

Tan

Jiang

et al. 2019

Int J Mol Med

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“…It is important to diagnose lung neoplasms as early as possible because of the low 5-year survival. MiRNAs have become a promising tool in the diagnosis and treatment of lung neoplasms [ 39 ]. For example, increased miR-211 levels have been associated with increased mortality in patients with none the top 25 related miRNAs, and the third column contains the top 26–50.…”

Section: Resultsmentioning

confidence: 99%

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

et al. 2020

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Background Many studies prove that miRNAs have significant roles in diagnosing and treating complex human diseases. However, conventional biological experiments are too costly and time-consuming to identify unconfirmed miRNA-disease associations. Thus, computational models predicting unidentified miRNA-disease pairs in an efficient way are becoming promising research topics. Although existing methods have performed well to reveal unidentified miRNA-disease associations, more work is still needed to improve prediction performance. Results In this work, we present a novel multiple meta-paths fusion graph embedding model to predict unidentified miRNA-disease associations (M2GMDA). Our method takes full advantage of the complex structure and rich semantic information of miRNA-disease interactions in a self-learning way. First, a miRNA-disease heterogeneous network was derived from verified miRNA-disease pairs, miRNA similarity and disease similarity. All meta-path instances connecting miRNAs with diseases were extracted to describe intrinsic information about miRNA-disease interactions. Then, we developed a graph embedding model to predict miRNA-disease associations. The model is composed of linear transformations of miRNAs and diseases, the means encoder of a single meta-path instance, the attention-aware encoder of meta-path type and attention-aware multiple meta-path fusion. We innovatively integrated meta-path instances, meta-path based neighbours, intermediate nodes in meta-paths and more information to strengthen the prediction in our model. In particular, distinct contributions of different meta-path instances and meta-path types were combined with attention mechanisms. The data sets and source code that support the findings of this study are available at https://github.com/dangdangzhang/M2GMDA. Conclusions M2GMDA achieved AUCs of 0.9323 and 0.9182 in global leave-one-out cross validation and fivefold cross validation with HDMM V2.0. The results showed that our method outperforms other prediction methods. Three kinds of case studies with lung neoplasms, breast neoplasms, prostate neoplasms, pancreatic neoplasms, lymphoma and colorectal neoplasms demonstrated that 47, 50, 49, 48, 50 and 50 out of the top 50 candidate miRNAs predicted by M2GMDA were validated by biological experiments. Therefore, it further confirms the prediction performance of our method.

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“…A powerful and useful method to investigate the relationship between SNPs and miRNA expression is to analyze the genome-wide expression quantitative trait loci (miR-eQTL) [24]. Some miR-eQTLs have been associated with disease pathogenesis, such as the pathogenesis of autoimmune disease [25], cancers [26], and neurological disorders [27], indicating that miR-eQTLs can be effective biomarkers for predicting disease prognosis.…”

Section: Introductionmentioning

confidence: 99%

Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data

et al. 2020

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Background Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required. Methods We used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model. We examined the biological significance of our findings with single nucleotide polymorphism-microRNA pairs (miR-eQTLs) by focusing on the target genes of the miRNAs. We investigated functional modules from the target genes with the occurrence of hub genes though a large-scale protein-protein interaction network analysis. We further examined the expression of the genes in 610 blood samples (271 ADs, 248 MCIs, and 91 cognitively normal elderly subjects [CNs]). Results The final prediction model, composed of 24 miR-eQTLs and three clinical factors (age, sex, and APOE4 alleles), successfully classified MCI patients into low and high risk of MCI-to-AD conversion (log-rank test P = 3.44 × 10−4 and achieved a concordance index of 0.702 on an independent test set. Four important hub genes associated with AD pathogenesis (SHC1, FOXO1, GSK3B, and PTEN) were identified in a network-based meta-analysis of miR-eQTL target genes. RNA-seq data from 610 blood samples showed statistically significant differences in PTEN expression between MCI and AD and in SHC1 expression between CN and AD (PTEN, P = 0.023; SHC1, P = 0.049). Conclusions Our proposed model was demonstrated to be effective in MCI-to-AD conversion prediction. A network-based meta-analysis of miR-eQTL target genes identified important hub genes associated with AD pathogenesis. Accurate prediction of MCI-to-AD conversion would enable earlier intervention for MCI patients at high risk, potentially reducing conversion to AD.

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Prognostic value of microRNAs in lung cancer: A systematic review and meta‑analysis

Cited by 19 publications

References 54 publications

MicroRNA-625 inhibits the progression of non‑small cell lung cancer by directly targeting HOXB5 and deactivating the Wnt/β-catenin pathway

MicroRNA-625 inhibits the progression of non‑small cell lung cancer by directly targeting HOXB5 and deactivating the Wnt/β-catenin pathway

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data

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