Prognostic and predictive value of a five-molecule panel in resected pancreatic ductal adenocarcinoma: A multicentre study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a devastating prognosis. The performance of clinicopathologic parameters and molecules as prognostic factors remains limited and inconsistent. The present study aimed to construct a multi-molecule biomarker panel to more accurately predict post-resectional prognosis of PDAC patients. METHODS: Firstly, a novel computational strategy integrating prognostic evidence from omics and literature on the basis of bioinformatics prediction (CIPHER) to generate the network, was designed to systematically identify potential high-confidence PDAC-related prognostic candidates. After specimens from 605 resected PDAC patients were retrospectively collected, 23 candidates were detected immunohistochemically in tissue-microarrays for the development cohort to construct a multi-molecule panel. Lastly, the panel was validated in two independent cohorts. FINDINGS: According to the constructed five-molecule panel, disease-specific survival (DSS) was significantly poorer in high-risk patients than in low-risk ones in development cohort (HR 2.15, 95%CI 1.51-3.05, P<0.0001; AUC 0.67). In two validation cohorts, similar significant differences between the two groups were also observed (HR 3.18 and 3.31, 95%CI 1.89-5.37 and 1.78-6.16, All P<0.0001; AUC 0.72 and 0.73). In multivariate analyses, this panel was the sole prognosticator that was significant in each cohort. Furthermore, its predictive power for long-term survival, higher than its individual constituents, could be largely enhanced by combination with traditional clinicopathological variables. Finally, adjuvant chemotherapy (ACT) correlated with better DSS only in high-risk patients, uni- and multi-variately, in all the cohorts. INTERPRETATION: The novel prognostic panel developed by a systematically network-based strategy presents strong ability in prediction of post-resectional survival of PDAC patients. Furthermore, panel-defined high-risk patients might benefit more from ACT.

Dissecting the Single-Cell Transcriptome Network Underlying Gastric Premalignant Lesions and Early Gastric Cancer.

Intestinal-type gastric cancer is preceded by premalignant lesions, including chronic atrophic gastritis and intestinal metaplasia. In this study, we constructed a single-cell atlas for 32,332 high-quality cells from gastric antral mucosa biopsies of patients spanning a cascade of gastric premalignant lesions and early gastric cancer (EGC) using single-cell RNA sequencing. We then constructed a single-cell network underlying cellular and molecular characteristics of gastric epithelial cells across different lesions. We found that gland mucous cells tended to acquire an intestinal-like stem cell phenotype during metaplasia, and we identified OR51E1 as a marker for unique endocrine cells in the early-malignant lesion. We also found that HES6 might mark the pre-goblet cell cluster, potentially aiding identification of metaplasia at the early stage. Finally, we identified a panel of EGC-specific signatures, with clinical implications for the precise diagnosis of EGC. Our study offers unparalleled insights into the human gastric cellulome in premalignant and early-malignant lesions.

Network-based cancer precision medicine: A new emerging paradigm.

The complex interactions in biological systems have been shown to affect the response to single-targeted therapies which were initially developed under the "reductionist paradigm" of cancer precision medicine. To address these fundamental challenges, great efforts have been dedicated from a network perspective to explore the mechanisms underlying tumorigenesis and progression and to extend our understanding of cancer as a complex disease, which is exploiting new advances in cancer diagnosis, prevention, and treatment. This review summarizes recent progress of network applications in cancer precision medicine research, including biomarker identification, cancer patient stratification and network target recognition, highlights network-based systematic integrations across macro and micro networks, and discusses the tremendous potential of this new emerging network-based "systems paradigm" for precision medicine, which would ultimately make substantial progress for fighting cancer.

Loss of adult skeletal muscle stem cells drives age-related neuromuscular junction degeneration.

Neuromuscular junction degeneration is a prominent aspect of sarcopenia, the age-associated loss of skeletal muscle integrity. Previously, we showed that muscle stem cells activate and contribute to mouse neuromuscular junction regeneration in response to denervation (Liu et al., 2015). Here, we examined gene expression profiles and neuromuscular junction integrity in aged mouse muscles, and unexpectedly found limited denervation despite a high level of degenerated neuromuscular junctions. Instead, degenerated neuromuscular junctions were associated with reduced contribution from muscle stem cells. Indeed, muscle stem cell depletion was sufficient to induce neuromuscular junction degeneration at a younger age. Conversely, prevention of muscle stem cell and derived myonuclei loss was associated with attenuation of age-related neuromuscular junction degeneration, muscle atrophy, and the promotion of aged muscle force generation. Our observations demonstrate that deficiencies in muscle stem cell fate and post-synaptic myogenesis provide a cellular basis for age-related neuromuscular junction degeneration and associated skeletal muscle decline.

The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet contributes to improving the understanding of normal glomerular function and will be useful for detecting target cytoskeleton molecules of interest that may be involved in glomerular diseases in future studies.

Construction and Characterization of a Synthetic MicroRNA Cluster for Multiplex RNA Interference in Mammalian Cells.

RNA interference (RNAi) technology is widely used in basic and translational research. By mimicking a natural primary microRNA (pri-miRNA) cluster, multiple engineered hairpins can be transcribed as a single transcript from the same Pol II promoter, enabling the formation of multiplex RNAi in mammalian cells. However, constructing a synthetic miRNA cluster is still time-consuming, and the processing and function of a miRNA cluster are incompletely understood. Here, we identified a miRNA precursor architecture that allows precise miRNA maturation. We established a hierarchical cloning method for the efficient construction of a synthetic miRNA cluster harboring up to 18 miRNA precursors. We demonstrated that the maturation and function of individual miRNA precursors were independent of their positions in the cluster. We then analyzed the integration efficiency of miRNA clusters having a varied number of miRNA precursors by using CRISPR/Cas9-mediated integration, a piggyBac transposon system, and a lentiviral system. This synthetic miRNA cluster system provides an important tool for multiplex RNAi in mammalian cells.