J Han / N Uberoi (@1.57) vs Y Zhang / Y Zhao C (@2.25)
10-09-2019

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J Han / N Uberoi will win
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J Han / N Uberoi – Y Zhang / Y Zhao C Match Prediction | 10-09-2019 01:00

In this study, we used iTRAQ labeling and 2D-LC-MS/MS to compare protein expression between pooled lung adenocarcinoma and matched normal lung tissue samples. As a result, S100A14 was significantly upregulated in lung adenocarcinoma (2.10-fold) compared with normal lung tissues. Of the differentially expressed proteins, 234 (41%) were identified by more than five unique peptides, 42 (7.4%) by four unique peptides, 48 (8.4%) by three unique peptides, 101 (14.7%) by two unique peptides, and 143 (25.2%) by one peptide. MS/MS spectra of the four peptides used for identification of S100A14 are shown in Figure 2. Among the 2486 proteins, 568 proteins were considered differentially expressed between lung adenocarcinoma and normal lung tissue according to ratios of fold-change (1.5 or 0.66). Two hundred fifty-seven proteins were upregulated and 311 were downregulated (Table 1, Supplementary Data). A total of 2486 proteins from both tumor and normal tissue were respectively identified using at least one peptide with 95% confidence.

a The number of genes present in an individual using different CDS coverage threshold (80%, 85%, 90%, 95%, and 100%) versus the sequencing depth. c The number of core genes and the total number of genes in the pan-genome determined with different number of individuals. b The gene PAV distributed across 185 individuals with the CDS coverage of 0.95. These processes repeated 100 times. PAV profile analysis of 185 deep sequencing Han Chinese genomes. Each time we randomly increased one individual and calculated the number of core genes and the total number of genes. d PAV profile of 606 distributed genes.

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The cell membrane plays a critical role in cell signaling transduction and signal pathways closely associated with carcinogenesis and tumor development. Several studies have already shown that nicotinamide is involved in tumor formation and tumor cell apoptosis [33,34]. The results obtained from KEGG enrichment analysis revealed pathways associated with nicotinate and nicotinamide metabolism, which have been implicated in lung adenocarcinoma carcinogenesis. PID is a growing collection of human cellular signaling pathways. Therefore, we performed pathway enrichment analysis using PID and KEGG. Our study offered valuable information for further exploring the underlying mechanism in carcinogenesis of lung adenocarcinoma. KEGG, however, focuses on metabolic processes and generic mechanisms such as transcription and translation [32]. The database focuses on signaling and regulatory pathways, particularly those impacting cancer research and treatment. In this study, the enrichment analysis results derived from PID indicated that the enriched pathways were involved in many membrane proteins, which shows the importance of these proteins in carcinogenesis.

Blots were probed with anti-Na+/K+-ATPase for the plasma membrane and anti-prohibitin for mitochondria. T-M and T-C indicate the solution of membrane proteins and the solution of cytoplasm proteins in tumor tissue, respectively. N-M and N-C indicate the solution of membrane proteins and the solution of cytoplasm proteins in normal lung tissue, respectively. Verification of membrane protein purification using Western blotting analysis.

Cheng, V. Tatsuma, J. Li, R. Johan, L. Liu, Y. Garro, A. Lai, B. L. Li, C. R. Pickup, X. Castellani S. Bu, U. Sun, P. Giachetti, A. Rosin, R. Litman, X. Liu, Z. Han, H. Godil, J. Aono, A. Ben Hamza, A. Bronstein, S. Lian, M. Lu, A. Cheng, Z. Li, H. Martin, Z. Bronstein, M. SHREC'14 Track: Shape Retrieval of Non-Rigid 3D Human Models D.

HUPAN: a pan-genome analysis pipeline for human genomes

The non-reference sequences and novel predicted gene sequences from 275 Han Chinese individuals also have been deposited at http://cgm.sjtu.edu.cn/hupan/ and NODE database (http://www.biosino.org/node) with the accession OEP000301 [44]. An archival version of HUPAN is available on Zenodo with DOI https://doi.org/10.5281/zenodo.2593453 [42]. The data sets of 90 assembled Han Chinese genomes were downloaded from http://gigadb.org/dataset/100302. The raw sequencing data of this paper have been deposited in the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/) under accession EGAS00001003657 [43]. The raw data and assembled contigs of 185 newly sequenced Han Chinese have also been deposited at http://cgm.sjtu.edu.cn/hupan/ and NODE database (http://www.biosino.org/node) with the accession OEP000301 [44]. HUPAN is implemented in Perl, R, and C++ languages, and the source code is freely available under the MIT license at http://cgm.sjtu.edu.cn/hupan/ and https://github.com/SJTU-CGM/HUPAN [41].

The explosive growth of human whole-genome sequencing data brings significant challenges and tremendous opportunities to study the pan-genome of a specific population [21]. Instead of using all reads, only the unmapped reads were extracted to conduct de novo assembly [8, 20]. See more details in Additionalfile1: Supplementary methods). We compared the assembled results using all reads and unmapped reads with simulated sequencing data, and suggested that pseudo de novo assembly method may underestimate the size of non-reference sequences and produce more misassembled sequences at the meantime (Additional file 1: Table S1). Several previous studies reported non-reference genome sequences using the approach of pseudo de novo assembly [4, 6, 8, 20]. Nevertheless, due to the large size of the human genome, EUPAN cannot be applied for human pan-genome analysis because of the huge memory size requirement of the de novo assembly step (more than 500Gb memory is needed to assemble a human genome from a 30-fold sequencing data. Recently, we reported a tool EUPAN [22] based on a map-to-pan strategy and applied it to more than 3000 rice genomes [13]. However, constructing the pan-genome sequences from hundreds of individual genomes is a huge challenge. If all reads were used, aligning hundreds of assembled genomes to the human reference genome to extract the non-reference sequences and distinguishing the non-human sequences contaminated in sampling, sequencing, and other procedures are other challenges that need to be addressed.

NSCLC includes adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and other cell types. Lung cancer is divided into two classes: non-small cell lung cancer (NSCLC) and small cell lung cancer. Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer death worldwide [1]. Although many treatments are available, its prognosis is still poor. Smoking is the most common cause of lung cancer overall, but lung adenocarcinoma is the most frequently occurring cell type in nonsmokers, and its pathogenesis remains unclear. Lung adenocarcinoma is the most common type of lung cancer and has been increasing in recent years. The 5-year survival of all lung cancer patients is only approximately 16% [2].

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Furthermore, we verified the differential expression of S100A14 and found the protein may be a suitable biomarker potentially involved in tumor cell differentiation. Our findings in this study helped elucidate the underlying carcinogenesis of lung cancer by providing a potential novel biomarker and new therapeutic targets. In conclusion, a large number of differential proteins were identified in the membrane fraction from lung adenocarcinoma and normal lung tissue samples using the iTRAQ-coupled 2D-LC-MS/MS technique. However, a larger group of lung cancer samples is needed to confirm the results.

S100A14 is a novel member of the S100 protein family [35]. The study by Lukanidin [37] showed that the S100 family is pivotal in cell migration, invasion, and cancer metastasis. S100 is a subfamily of proteins related by Ca2+-binding to the EF-hand superfamily that appear to be involved in the regulation of many cellular processes (e.g., cell cycle progression, differentiation, cell-cell communication, intracellular signaling, energy metabolism) [35,36].

We performed manual microdissection to collect cells of interest from lung adenocarcinoma and matched adjacent normal lung tissue, as previously described by Nowak [6] and Chen [20]. Under the guidance of an H&E slide, the adjacent unstained 10 to 14 m thick continuous frozen sections were dissected with a syringe needle and/or scalpel from the area identified by a pathologist, transferred to an Eppendorf tube, and stored at -80C until ready for use. Frozen sections (5 m each) from lung adenocarcinoma and matched normal lung tissues were cut in a Microm HM500 Cryostat at -25C and identified by routine H&E staining.