Targeting Cell Cycle Proteins in Breast Cancer Cells with siRNA by Using Lipid-Substituted Polyethylenimines

Parmar, Manoj B.; Aliabadi, Hamidreza Montazeri; Mahdipoor, Parvin; Kucharski, Cezary; Maranchuk, Robert A.; Hugh, Judith; Uludağ, Hasan

doi:10.3389/fbioe.2015.00014

Cited by 22 publications

(25 citation statements)

References 47 publications

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“…OlapModule 1 was enriched for DNA replication and nucleotide binding, while olapModule 2 was enriched in cell division. The deregulation of cell cycle progression is one of the causes of breast cancer (22). It was previously demonstrated that cell cycle abnormalities in G1/S or G2/M phases are frequently observed in breast cancer (24)(25)(26).…”

Section: Resultsmentioning

confidence: 99%

“…Kaplan-Meier survival analysis was performed according to the survival time of patients in each class, to assess the significance of the effect of each module on survival time. The difference between the survival times of the two classes was compared with a log-rank test (22). P<0.05 was considered to represent a statistically significant difference.…”

Section: Identification Of Breastmentioning

confidence: 99%

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Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks

Bai

et al. 2017

Oncology Letters

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Abstract. Breast cancer is one of the leading causes of mortality in females. A number of prognostic markers have been identified, including single genes, multi-gene signatures and network modules; however, the robustness of these prognostic markers is insufficient. Thus, the present study proposed a more robust method to identify breast cancer prognostic modules based on weighted protein-protein interaction networks, by integrating four sets of disease-associated expression profiles. Three identified prognostic modules were closely associated with prognosis-associated functions and survival time, as determined by Cox regression and Kaplan-Meier survival analyses. The robustness of these modules was verified with an independent profile from another platform. Genes from these modules may be useful as breast cancer prognostic markers. The prognostic modules could be used to determine the prognoses of patients with breast cancer and characterize patient recovery. IntroductionBreast cancer is one of the leading causes of female mortality (1-3). Prognosis reflects the outlook and chance of recovery from breast cancer. It is therefore critical to acquire prognostic information that represents the physical condition of the patients. Understanding prognostic information assists in increasing the survival rate and prolongs the life expectancy of patients (4).With the advent of microarray and next-generation sequencing technology, an increased volume of genomic data is available, including data relevant to breast cancer prognosis. In recent decades, a number of prognostic marker identification methods based on a single gene have been proposed (5). However, the detection of the expression of a single gene does not effectively characterize the expression of a gene cluster composed of tens or hundreds of genes. It has been demonstrated that prognostic information from multi-gene signatures, including network modules, can reduce the heterogeneity of diseases with greater accuracy than data of a single gene (6). For example, a classifying system for breast cancer profiles was constructed based on 70 genes in a study by van de Vijver et al (7). A treatment response prediction method for patients with breast cancer was developed based on 64 genes by Pawitan et al (8). The 21-gene signature has now been intensively studied and is widely used in clinical decisions regarding breast cancer (9).Prognostic marker identification methods have the problem of low robustness (10), and prognostic information obtained from certain patients may not be applicable to other patients (11). Thus, the present study aimed to develop a method to identify robust prognostic modules based on four breast cancer expression profiles and protein-protein interaction networks (PPINs). Experimental detection based on these prognostic modules could aid clinicians in the diagnosis of patients who are at risk of developing malignant disease, and allow the provision of prevention and treatment for these patients as early as possible. Materials and methodsExpressio...

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Section: Resultsmentioning

confidence: 99%

Section: Identification Of Breastmentioning

confidence: 99%

Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks

Bai

et al. 2017

Oncology Letters

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“…Treated cells were then incubated for another 48 hr, before the final evaluation of cell growth by 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT) (Sigma‐Aldrich, St. Louis, MO) assay. To ensure the efficiency of siRNA silencing using the selected delivery system, CDC20 silencing siRNA (Cat # HSC.RNAI.N001255.12.1, IDT) was used as positive control, which was the most powerful siRNA we identified from a siRNA library against cell cycle proteins . In addition, we used 2 scrambled siRNAs as negative controls in our screens.…”

Section: Methodsmentioning

confidence: 99%

Novel targets for sensitizing breast cancer cells to TRAIL‐induced apoptosis with siRNA delivery

Thapa

Uludağ

2017

Intl Journal of Cancer

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Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) induces apoptosis in variety of cancer cells without affecting most normal cells, which makes it a promising agent for cancer therapy. However, TRAIL therapy is clinically not effective due to resistance induction. To identify novel regulators of TRAIL that can aid in therapy, protein targets whose silencing sensitized breast cancer cells against TRAIL were screened with an siRNA library against 446 human apoptosis-related proteins in MDA-231 cells. Using a cationic lipopolymer (PEI-αLA) for delivery of library members, 16 siRNAs were identified that sensitized the TRAIL-induced death in MDA-231 cells. The siRNAs targeting BCL2L12 and SOD1 were further evaluated based on the novelty and their ability to sensitize TRAIL induced cell death. Silencing both targets sensitized TRAIL-mediated cell death in MDA-231 cells as well as TRAIL resistant breast cancer cells, MCF-7. Combination of TRAIL and siRNA silencing BCL2L12 had no effect in normal human umbilical vein cells and human bone marrow stromal cell. The silencing of BCL2L12 and SOD1 enhanced TRAIL-mediated apoptosis in MDA-231 cells via synergistically activating capsase-3 activity. Hence, here we report siRNAs targeting BCL2L12 and SOD1 as a novel regulator of TRAIL-induced cell death in breast cancer cells, providing a new approach for enhancing TRAIL therapy for breast cancer. The combination of siRNA targeting BCL2L12 and TRAIL can be a highly effective synergistic pair in breast cancer cells with minimal effect on the non-transformed cells.

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“…Several researchers used TNFα gene as a target for the treatment of inflammatory and autoimmune diseases [52][53][54]. Murine models are the most popular animal models among various studies that used dsiRNA in vivo [47][48][49][50][51][52][53][54]; however, there are studies where other animal models, for example, rats, were used [53,55]. An exciting example of dsiRNA application was described by Doré-Savard and his colleagues, who demonstrated, for the first time, the efficient suppression of target genes in central nervous system (CNS) of rats by dsiRNA [55].…”

Section: Dicer Substrate Interfering Rnasmentioning

confidence: 99%

“…Several studies use dsiRNA to silence therapeutically relevant genes in vivo (Table 1). Frequently, cancer-related genes and genes of viruses [50,51,[56][57][58][59] are chosen as targets for dsiRNAs [47][48][49]. Several researchers used TNFα gene as a target for the treatment of inflammatory and autoimmune diseases [52][53][54].…”

Section: Dicer Substrate Interfering Rnasmentioning

confidence: 99%

Noncanonical Synthetic RNAi Inducers

Gvozdeva¹,

Chernolovskaya²

2016

RNA Interference

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This review focuses on current strategies of development of noncanonical synthetic RNA interference (RNAi) inducers with structural modifications for promoting better gene silencing with low risk of side effects. A particular focus is on longer RNA duplexes 25-30 nucleotides (nt) in length that mimic Dicer substrates to improve interaction of RNAi inducers with RNAi machinery. Various design strategies of efficient Dicer substrate smallinterfering RNA (siRNA) are described. It was found that the length, chemical modifications, and overhang structure influence the gene silencing activity and RNA-induced silencing complex (RISC) assembly. Special attention is paid to the long doublestranded RNA duplexes that induce effective gene silencing in Dicer-dependent or Dicerindependent mode. Some structural variants of shorter siRNAs, including hairpin and dumbbell siRNAs and fork-siRNA (fsiRNA) with several nucleotide substitutions at the 3′ end of the sense strand, are also analyzed. These structural modifications provide efficiently increased gene silencing of targets with unfavorable duplex thermodynamic asymmetry. Recent data remove the length and structure limits for the design of RNAi effectors, and add another example in the list of novel RNAi-inducing molecules differing from the classical siRNA, which is discussed in this chapter.

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Targeting Cell Cycle Proteins in Breast Cancer Cells with siRNA by Using Lipid-Substituted Polyethylenimines

Cited by 22 publications

References 47 publications

Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks

Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks

Novel targets for sensitizing breast cancer cells to TRAIL‐induced apoptosis with siRNA delivery

Noncanonical Synthetic RNAi Inducers

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