Long Noncoding RNA PCAT18 Upregulates SPRR3 to Promote Colorectal Cancer Progression by Binding to miR-759

Discovery of novel microRNA mimic repressors of ribosome biogenesis

While microRNAs and other non-coding RNAs are the next frontier of novel regulators of mammalian ribosome biogenesis (RB), a systematic exploration of microRNA-mediated RB regulation has not yet been undertaken. We carried out a high-content screen in MCF10A cells for changes in nucleolar number using a library of 2,603 mature human microRNA mimics. Following a secondary screen for nucleolar rRNA biogenesis inhibition, we identified 72 novel microRNA negative regulators of RB after stringent hit calling. Hits included 27 well-conserved microRNAs present in MirGeneDB, and were enriched for mRNA targets encoding proteins with nucleolar localization or functions in cell cycle regulation. Rigorous selection and validation of a subset of 15 microRNA hits unexpectedly revealed that most of them caused dysregulated pre-rRNA processing, elucidating a novel role for microRNAs in RB regulation. Almost all hits impaired global protein synthesis and upregulated CDKN1A ( p21 ) levels, while causing diverse effects on RNA Polymerase 1 (RNAP1) transcription and TP53 protein levels. We discovered that the MIR-28 siblings, hsa-miR-28-5p and hsa-miR-708-5p, directly and potently target the ribosomal protein mRNA RPS28 via tandem primate-specific 3' UTR binding sites, causing a severe pre-18S pre-rRNA processing defect. Our work illuminates novel microRNA attenuators of RB, forging a promising new path for microRNA mimic chemotherapeutics.

An integrative transcriptome analysis reveals potential predictive, prognostic biomarkers and therapeutic targets in colorectal cancer

BACKGROUND

A deep understanding of potential molecular biomarkers and therapeutic targets related to the progression of colorectal cancer (CRC) from early stages to metastasis remain mostly undone. Moreover, the regulation and crosstalk among different cancer-driving molecules including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs) in the transition from stage I to stage IV remain to be clarified, which is the aim of this study.

METHODS

We carried out two separate differential expression analyses for two different sets of samples (stage-specific samples and tumor/normal samples). Then, by the means of robust dataset analysis we identified distinct lists of differently expressed genes (DEGs) for Robust Rank Aggregation (RRA) and weighted gene co-expression network analysis (WGCNA). Then, comprehensive computational systems biology analyses including mRNA-miRNA-lncRNA regulatory network, survival analysis and machine learning algorithms were also employed to achieve the aim of this study. Finally, we used clinical samples to carry out validation of a potential and novel target in CRC.

RESULTS

We have identified the most significant stage-specific DEGs by combining distinct results from RRA and WGCNA. After finding stage-specific DEGs, a total number of 37 DEGs were identified to be conserved across all stages of CRC (conserved DEGs). We also found DE-miRNAs and DE-lncRNAs highly associated to these conserved DEGs. Our systems biology approach led to the identification of several potential therapeutic targets, predictive and prognostic biomarkers, of which lncRNA LINC00974 shown as an important and novel biomarker.

CONCLUSIONS

Findings of the present study provide new insight into CRC pathogenesis across all stages, and suggests future assessment of the functional role of lncRNA LINC00974 in the development of CRC.

lncRNA prostate cancer-associated transcript 18 upregulates activating transcription factor 7 to prevent metastasis of triple-negative breast cancer via sponging miR-103a-3p

Long non-coding RNA (lncRNA) prostate cancer-associated transcript 18 (PCAT18) is a potential diagnostic target for adenocarcinoma. However, its role in triple-negative breast cancer (TNBC) remains largely unknown. Based on data from an online database, a significant decline in lncRNA PCAT18 was observed in patients with TNBC subtype compared to a population with normal breast tissue. Patients with TNBC with high PCAT18 levels presented good outcomes. Patients with TNBC with high PCAT18 had a lower rate of lymph node-positive metastasis than those with low PCAT18. PCAT18-upregulation inhibited, while PCAT18-downregulation promoted, migration and expression of matrix metalloproteinases 9/2 (MMP9/MMP2) and uridylyl phosphate adenosine (uPA) in TNBC cells. Activating transcription factor 7 (ATF7) was positively associated with PCAT18, and ATF7-inhibition abrogated the anti-migration effects of PCAT18 on TNBC cells. Mechanistically, miR-103a-3p directly targeted and inhibited ATF7 expression. PCAT18 competitively sponges miR-103a-3p, promoting the expression of ATF7. Exogenous PCAT18 was associated with lower incidence of lung metastasis followed by the upregulation of ATF7, which was prevented by the treatment of miR-103a-3p mimics. Collectively, PCAT18 was expressed at low levels in TNBC, and PCAT18 could sponge miR-103a-3p and promote ATF7 expression, resulting in prevention of TNBC metastasis. Thus, PCAT18 can serve as a predictive factor for patients with metastatic TNBC.

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.

LncRNA PCAT18 Promotes Non-Small Cell Lung Cancer Progression by Sponging miR-4319

Introduction

NSCLC (non-small cell lung cancer), the most common type of human cancer, is a main cause of cancer-associated mortality. Accumulating evidence has confirmed that long non-coding RNAs serve crucial roles in NSCLC development.

Methods

The PCAT18 expression in NSCLC tissues and cell lines were evaluated by reverse transcription-quantitative PCR. Cell Counting Kit-8 assays, colony formation study, wound healing assays and transwell invasion assays, and tumor xenograft experiments were performed to investigate the biological functions of PCAT18 in NSCLC. Luciferase reporter, RNA-binding protein immunoprecipitation (RIP) and RNA pull-down assays were further used to explore the association between PCAT18 and miR-4319.

Results

PCAT18 expression was up-regulated in NSCLC tissues and cell lines. Furthermore, PCAT18 silencing inhibited NSCLC cell proliferation, migration and invasion, while co-transfection with a miR-4319 inhibitor reversed these biological effects, and miR-4319 inhibited NSCLC growth in vivo. Additionally, PCAT18 silencing promoted NSCLC cell apoptosis and induced G1 stage arrest. Moreover, luciferase reporter assays illustrated that PCAT18 regulated miR-4319 directly, and a RIP assay and RNA pull-down analysis further demonstrated that miR-4319 inhibited PCAT18 in a RNA-induced silencing complex-dependent manner. Finally, PCAT18 silencing impaired the growth of NSCLC in vivo.

Conclusion

In conclusion, these findings demonstrated that PCAT18 promoted NSCLC development by sponging miR-4319. PCAT18 may serve as a crucial biomarker for the diagnosis and targeted therapy of NSCLC.

miR-140-5p Aggravates Insulin Resistance via Directly Targeting GYS1 and PPP1CC in Insulin-Resistant HepG2 Cells

BACKGROUND

Much attention has been paid to the regulatory role of microRNA (miRNA) in insulin resistance. Nevertheless, how miR-140-5p regulates insulin resistance remains unclear. In this research, we aim to investigate the roles of miR-140-5p in insulin resistance.

METHODS

qRT-PCR is used to analyze the expression level of miR-140-5p in insulin-resistant HepG2 cells. Glucose consumption and glucose uptake are detected to study the effect of miR-140-5p knockdown in insulin-resistant HepG2 cells and miR-140-5p overexpression in HepG2 cells. Bioinformatic analysis, luciferase reporter assay and confirmatory experiments are applied to identify the target gene bound with miR-140-5p and study the effect of miR-140-5p on the downstream substrates of target genes. Rescue experiments have verified the roles of miR-140-5p and target gene in glucose metabolism.

RESULTS

The expression level of miR-140-5p was upregulated in insulin-resistant HepG2 cells and was significantly correlated with cellular glucose metabolism. Functionally, miR-140-5p overexpression induced impairment of glucose consumption and glucose uptake. Besides, bioinformatics analysis indicated that glycogen synthetase (GYS1) and protein phosphatase 1 catalytic subunit gamma (PPP1CC) were the target genes of miR-140-5p. Western blotting and qRT-PCR results revealed a negative correlation between GYS1, PPP1CC and miR-140-5p. The glycogen detection results showed that miR140-5p inhibited the production of the downstream substrates of the target gene. Rescue experiments showed that inhibition of GYS1 or PPP1CC partially enhanced the insulin-resistant effects of miR-140-5p knockdown in insulin-resistant HepG2 cells.

CONCLUSION

miR-140-5p overexpression augments the development of insulin resistance and miR-140-5p may be served as a therapeutic target of metabolic diseases.