In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Context: Nonviral vectors based on polyethylenimine (PEI) usually contain an excess of PEI that is not complexed to DNA. Since unbound PEI contributes to cellular and systemic toxicity, purification of polyplexes from unbound PEI is desirable.', 'Size exclusion chromatography (SEC) was used to purify PEI polyplexes of free PEI. Transfection properties of purified polyplexes and the effect of free PEI on gene delivery were studied in vitro and in vivo after systemic application into mice.', 'SEC did not change the size and zeta-potential of polyplexes. Independent of the amount of PEI used for complex formation, purified PEI polyplexes had the same final PEI nitrogen/DNA phosphate ratio of 2.5. Notably, purified PEI polyplexes demonstrated low cellular and systemic toxicity. High transfection efficiency was achieved with purified polyplexes at high DNA concentrations (8-15 microg/ml). At low DNA concentrations (2-4 microg/ml) gene transfer with purified particles was less efficient than with polyplexes containing free PEI both in vitro and in vivo. Mechanistic studies showed that free PEI partly blocked cellular association of DNA complexes but was essential for the following intracellular gene delivery. Adding free PEI to cells treated with purified particles with a delay of up to 4 h resulted in significantly enhanced transfection efficiency compared with non-purified particles or purified particles without free PEI.\Question: Does purification of polyethylenimine polyplexes highlight the role of free polycations in gene transfer?
This study presents an efficient method to remove free PEI from PEI polyplexes by SEC. Our results from transfection experiments demonstrate that free PEI substantially contributes to efficient gene expression but also mediates toxic effects in a dose-dependent manner. Purified polyplexes without free PEI have to be applied at increased concentrations to achieve high transfection levels, but exhibit a greatly improved toxicity profile.

Context: The human RECQ DNA helicase family is involved in genomic stability. Gene mutations of RECQL2, RECQL3, and RECQL4 are associated with genetic disorders and induce early aging and carcinogenesis. Although previous studies have reported that the level of RECQL1 expression is correlated with the prognosis of some of malignancies, the function of RECQL1 is not yet clarified. The present study aimed to examine the relationship between prognosis and the level of RECQL1 expression in epithelial ovarian cancer (EOC), and to identify the role of RECQL1 in EOC cells.', 'The level of RECQL1 expression was determined immunohistochemically in 111 patients with EOC who received initial treatment at Hirosaki University hospital between 2006 and 2011. Effects of RECQL1 on cell growth or apoptosis were examined in vitro using wild-type and OVCAR-3 cells (RECQL1(+) cells) and similar cells transfected with RECQL1 siRNA transfected (RECQL1(-) cells).', 'The level of RECQL1 expression was not related to histological type, clinical stage, or retroperitoneal lymph node metastasis, but the expression level was significantly higher (P\u2009=\u20090.002) in patients with recurrence than those without recurrence, and progression-free survival and complete response rate to chemotherapy were also improved in patients with RECQL1-low expression (n = 39) stage III/IV EOC (P = 0.02 and P <0.05 vs RECQL1-high expression patients (n = ), respectively). A cell proliferation and colony formation assays revealed significantly less growth of RECQL1(-) cells compared to RECQL1(+) cells. A flow cytometry using annexin V -FITC and propidium iodide (PI) staining revealed a significant increase in apoptotic RECQL1(-) cells. Cell cycle analysis showed a significantly greater distribution in subG1 phase indicating apoptotic cells in RECQL1(-) cells than in RECQL1(+) cells.\Question: Is the level of RECQL1 expression a prognostic factor for epithelial ovarian cancer?
These results suggest that RECQL1 is a prognostic factor for EOC and that RECQL1 contributes to potential malignancy by inhibiting apoptosis.

Context: Network component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.', 'To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subnetwork using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed.\Question: Does iterative sub-network component analysis enable reconstruction of large scale genetic networks?

Our framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.