Given a passage with a context and an answer, construct a question on the basis of the information present in the passage. Construct the question in such a way that (i) it is unambiguous, (ii) it is answerable from the passage, (iii) its answer is unique (iv) it answer uses text phrases from the passage. Avoid creating questions that (i) can't be answered correctly without actually understanding the passage and (ii) uses the same words or phrases given in the passage.

Q: Context: Formyl peptide receptor 1 (FPR1) as a regulator of innate inflammatory response has been implicated in tumor progression of gliomas. The purpose of the present study was to evaluate the prognostic significance and the ligand-receptor interaction of FPR1 in gastric cancer (GC).', 'FPR1 was immunohistochemically-analyzed in tissue sections originating from 116 GC patients. Reverse transcription-polymerase chain reaction (RT-PCR) was used for the assessment of interaction between FPR1 and the FPR1 ligand annexin A1 (AnxA1) in GC cells.', 'High FPR1 expression was significantly associated with stage IV disease, submucosal invasion, serosal invasion, and clinical outcome of GC. Multivariate analysis showed that high FPR1 expression was an independent risk factor of poor overall survival in GC patients. FPR1 expression increased significantly when AnxA1 overexpression was present in GC cells. A positive feedback regulation of FPR1 was involved in the AnxA1-FPR1 signal transduction.
Answer: FPR1 expression may be used as a novel indicator to predict outcome in GC patients after gastrectomy.

A: Is formyl Peptide receptor 1 expression associated with tumor progression and survival in gastric cancer?
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Q: Context: Vessel formation is a crucial event in tissue repair after injury. Thus, one assumption of innovative therapeutic approaches is the understanding of its molecular mechanisms. Notwithstanding our knowledge of the role of Protein Kinase C epsilon (PKCε) in cardio-protection and vascular restenosis, its role in vessel progenitor differentiation remains elusive.', 'Given the availability of PKCε pharmacological modulators already tested in clinical trials, the specific aim of this study is to unravel the role of PKCε in vessel progenitor differentiation, with implications in vascular pathology and vasculogenesis.', 'Mouse Peri-Vascular Adipose Tissue (PVAT) was used as source of mesenchymal vessel progenitors. VEGF-induced differentiation of PVAT cells down-regulates both PKCε and p-PAK1 protein expression levels. PKCε overexpression and activation: i) reduced the expression levels of SMA and PECAM in endothelial differentiation of PVAT cells; ii) completely abrogated tubules formation in collagen gel assays; iii) increased the expression of p-PAK1.
Answer: PKCε negatively interferes with vessel progenitor differentiation via interaction with PAK-1.

A: Is pKCε a negative regulator of PVAT-derived vessel formation?
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Q: Context: A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.', 'We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.
Answer: Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN.

A:
Do triangle network motifs predict complexes by complementing high-error interactomes with structural information?
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