Computational Analysis of lncRNA Function in Cancer.

Long noncoding RNAs (lncRNAs) have been shown to play crucial roles in cancer biology. With the help of computational analysis illustrated here, the joint effects of lncRNAs and clinical variables can be quantified in a Cox model on cancer recurrence. Of importance, the predictive accuracy was then validated with the prognostic scores computed based on the suggested model. Further investigation of these potential lncRNAs would provide useful insights following the study of the mechanisms underlying the differential expression of these lncRNAs in association with and possibly contributing to cancer recurrence. Ultimately, the expanding knowledge of the function of lncRNAs curated by computational analysis will suggest new targets for cancer treatment.

LncRNA AK023948 is a positive regulator of AKT.

Despite the overwhelming number of human long non-coding RNAs (lncRNAs) reported so far, little is known about their physiological functions for the majority of them. The present study uses a CRISPR/Cas9-based synergistic activation mediator (SAM) system to identify potential lncRNAs capable of regulating AKT activity. Among lncRNAs identified from this screen, we demonstrate that AK023948 is a positive regulator for AKT. Knockout of AK023948 suppresses, whereas rescue with AK023948 restores the AKT activity. Mechanistically, AK023948 functionally interacts with DHX9 and p85. Importantly, AK023948 is required for the interaction between DHX9 and p85 to hence the p85 stability and promote AKT activity. Finally, AK023948 is upregulated in breast cancer; interrogation of TCGA data set indicates that upregulation of DHX9 in breast cancer is associated with poor survival. Together, this study demonstrates two previously uncharacterized factors AK023948 and DHX9 as important players in the AKT pathway, and that their upregulation may contribute to breast tumour progression.

Regulation of PCGEM1 by p54/nrb in prostate cancer.

PCGEM1 is a long non-coding RNA (lncRNA) that is often upregulated in prostate cancer. However, little is known how PCGEM1 is regulated. In the present study, we show transcriptional regulation of PCGEM1 in response to androgen deprivation by p54/nrb. While ectopic expression of p54/nrb increases, suppression of p54/nrb by RNAi or knockout (KO) reduces PCGEM1. Moreover, rescue experiments indicate that re-expression of p54/nrb in KO cells restores the ability to induce PCGEM1, leading to upregulation of the androgen receptor splice variant AR3 which has been shown to play a role in castration resistance. Finally, 3,3'-Diindolylmethane (DIM), a known chemoprevention agent, is capable of suppressing PCGEM1 expression by preventing the interaction of p54/nrb with the PCGEM1 promoter. In particular, DIM reduces tumor growth by suppression of PCGEM1 and promoting apoptosis in the castrated xenograft mouse model. Together, these results demonstrate a novel mechanism of p54/nrb-mediated expression of PCGEM1 and AR3, contributing to castration resistance in prostate cancer.

Transient resistance to DNA damaging agents is associated with expression of microRNAs-135b and -196b in human leukemia cell lines.

The acquisition of resistance to anticancer drugs is widely viewed as a key obstacle to successful cancer therapy. However, detailed knowledge of the initial molecular events in the response of cancer cells to these chemotherapeutic and stress responses, and how these lead to the development of chemoresistance, remains incompletely understood. Using microRNA array and washout and rechallenge experiments, we found that short term treatment of leukemia cells with etoposide led a few days later to transient resistance that was associated with a corresponding transient increase in expression of ABCB1 mRNA, as well as microRNA (miR)-135b and miR-196b. This phenomenon was associated with short-term exposure to genotoxic agents, such as etoposide, topotecan, doxorubicin and ionizing radiation, but not agents that do not directly damage DNA. Further, this appeared to be histiotype-specific, and was seen in leukemic cells, but not in cell lines derived from solid tumors. Treatment of leukemic cells with either 5-aza-deoxycytidine or tricostatin A produced similar increased expression of ABCB1, miR-135b, and miR-196b, suggesting a role for epigenetic regulation of this phenomenon. Bioinformatics analyses revealed that CACNA1E, ARHGEF2, PTK2, SIAH1, ARHGAP6, and NME4 may be involved in the initial events in the development of drug resistance following the upregulation of ABCB1, miR-135b and miR-196b. In summary, we report herein that short-term exposure of cells to DNA damaging agents leads to transient drug resistance, which is associated with elevations in ABCB1, miR-135b and miR-196b, and suggests novel components that may be involved in the development of anticancer drug resistance.

Regulation of androgen receptor splice variant AR3 by PCGEM1.

The androgen receptor (AR) is required for prostate development and is also a major driver of prostate cancer pathogenesis. Thus androgen deprivation therapy (ADT) is the mainstay of treatment for advanced prostate cancer. However, castration resistance due to expression of constitutively active AR splice variants is a significant challenge to prostate cancer therapy; little is known why effectiveness of ADT can only last for a relatively short time. In the present study, we show that PCGEM1 interacts with splicing factors heterogeneous nuclear ribonucleoprotein (hnRNP) A1 and U2AF65, as determined by RNA precipitation and Western blot, suggesting a role for PCGEM1 in alternative splicing. In support of this possibility, PCGEM1 is correlated with AR3, a predominant and clinically important form of AR splice variants in prostate cancer. Moreover, androgen deprivation (AD) induces PCGEM1 and causes its accumulation in nuclear speckles. Finally, we show that the AD-induced PCGEM1 regulates the competition between hnRNP A1 and U2AF65 for AR pre-mRNA. AD promotes PCGEM1 to interact with both hnRNP A1 and U2AF65 with different consequences. While the interaction of PCGEM1 with hnRNP A1 suppresses AR3 by exon skipping, its interaction with U2AF65 promotes AR3 by exonization. Together, we demonstrate an AD-mediated AR3 expression involving PCGEM1 and splicing factors.

Linc-RoR promotes c-Myc expression through hnRNP I and AUF1.

Linc-RoR was originally identified to be a regulator for induced pluripotent stem cells in humans and it has also been implicated in tumorigenesis. However, the underlying mechanism of Linc-RoR-mediated gene expression in cancer is poorly understood. The present study demonstrates that Linc-RoR plays an oncogenic role in part through regulation of c-Myc expression. Linc-RoR knockout (KO) suppresses cell proliferation and tumor growth. In particular, Linc-RoR KO causes a significant decrease in c-Myc whereas re-expression of Linc-RoR in the KO cells restores the level of c-Myc. Mechanistically, Linc-RoR interacts with heterogeneous nuclear ribonucleoprotein (hnRNP) I and AU-rich element RNA-binding protein 1 (AUF1), respectively, with an opposite consequence to their interaction with c-Myc mRNA. While Linc-RoR is required for hnRNP I to bind to c-Myc mRNA, interaction of Linc-RoR with AUF1 inhibits AUF1 to bind to c-Myc mRNA. As a result, Linc-RoR may contribute to the increased stability of c-Myc mRNA. Although hnRNP I and AUF1 can interact with many RNA species and regulate their functions, with involvement of Linc-RoR they would be able to selectively regulate mRNA stability of specific genes such as c-Myc. Together, these results support a role for Linc-RoR in c-Myc expression in part by specifically enhancing its mRNA stability, leading to cell proliferation and tumorigenesis.

Targeting non-coding RNAs with the CRISPR/Cas9 system in human cell lines.

The CRISPR/Cas has been recently shown to be a powerful genome-editing tool in a variety of organisms. However, these studies are mainly focused on protein-coding genes. The present study aims to determine whether this technology can be applied to non-coding genes. One of the challenges for knockout of non-coding genes is that a small deletion or insertion generated by the standard CRISPR/Cas system may not necessarily lead to functional loss of a given non-coding gene because of lacking an open reading frame, especially in polyploidy human cell lines. To overcome this challenge, we adopt a selection system that allows for marker genes to integrate into the genome through homologous recombination (HR). Moreover, we construct a dual guide RNA vector that can make two cuts simultaneously at designated sites such that a large fragment can be deleted. With these approaches, we are able to successfully generate knockouts for miR-21, miR-29a, lncRNA-21A, UCA1 and AK023948 in various human cell lines. Finally, we show that the HR-mediated targeting efficiency can be further improved by suppression of the non-homologous end joining pathway. Together, these results demonstrate the feasibility of knockout for non-coding genes by the CRISPR/Cas system in human cell lines.

Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study.

BACKGROUND: Genome-wide transcriptome profiling generated by microarray and RNA-Seq often provides deregulated genes or pathways applicable only to larger cohort. On the other hand, individualized interpretation of transcriptomes is increasely pursued to improve diagnosis, prognosis, and patient treatment processes. Yet, robust and accurate methods based on a single paired-sample remain an unmet challenge. METHODS: "N-of-1-pathways" translates gene expression data profiles into mechanism-level profiles on single pairs of samples (one p-value per geneset). It relies on three principles: i) statistical universe is a single paired sample, which serves as its own control; ii) statistics can be derived from multiple gene expression measures that share common biological mechanisms assimilated to genesets; iii) semantic similarity metric takes into account inter-mechanisms' relationships to better assess commonality and differences, within and cross study-samples (e.g. patients, cell-lines, tissues, etc.), which helps the interpretation of the underpinning biology. RESULTS: In the context of underpowered experiments, N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within-and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets (Odds-Ratios≥13, p-values≤1 × 10-5), such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples. CONCLUSIONS: N-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods. Further, it identifies unique sample/ patient mechanisms, a requirement for precision medicine.