J Han / N Uberoi (@1.57) vs Y Zhang / Y Zhao C (@2.25)

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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

The patients signed informed consents. For immunohistochemical analysis, the formalin-fixed and paraffin-embedded 62 lung cancer specimens (41 lung adenocarcinoma, 21 lung squamous cell carcinoma) and 24 normal lung tissues from surgical resections were obtained from the Second Affiliated Hospital, Xian Jiaotong University and Shaanxi Cancer Hospital for this retrospective study. Lung adenocarcinoma and matched adjacent normal lung tissue samples were obtained from 10 patients who underwent surgery at the Second Affiliated Hospital of the Medical School of Xian Jiaotong University. All samples were immediately snap-frozen in liquid nitrogen and stored at -80C until analyzed. Adjacent normal tissue was obtained at least 5 cm away from the primary tumor. All patients with lung adenocarcinoma were confirmed by pathological diagnosis. The 10 pairs of lung cancer and matched normal lung tissue were used for comparative proteomic analysis and Western blotting. None of the patients had received chemotherapy or radiotherapy before surgery. This study was approved by the local ethnics committee.

Compared with normal lung specimens, the expression level of S100A14 in adenocarcinoma significantly increased (p

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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].

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.

As a result, a total of 568 differential expression proteins were identified between the two types of tissues, including well-known membrane markers Protein S100-P, Ig kappa chain V-I region Lay, Caveolin-1, Voltage-dependent calcium channel subunit alpha-2/delta-2, Annexin A3, Claudin-18, etc. WEGO has been applied in several genomics studies, including those of the rice genome [29], Arabidopsis genome [30], and the silkworm genome [31]. To better understand the basic biological information of differential expression proteins identified in our study, we further analyzed the proteins using WEGO. In addition, the differential proteins involved with binding, catalysis, molecular transduction, and transport were detected. WEGO provides a visualization of the annotation sets of genes, comparing the provided gene datasets by plotting a histogram of the distribution of GO annotation [22]. These results indicated that proteins engaged in these functions may play important roles in carcinogenesis. In this study, we isolated tumor cells from adjacent non-tumor cells and used iTRAQ labeling 2D-LC-MS/MS to identify the differential membrane proteins between lung adenocarcinoma tissue and matched normal lung tissue.


We decreased the threshold of sequence identity to explore the similarity between the fully unaligned sequences and the human reference genome. In order to understand the characteristics of the fully unaligned sequences, we ran CDHIT to further remove redundant sequences with lower identity levels and explored the similarity among the fully unaligned sequences. To estimate whether the fully unaligned sequences would continue to grow as the individuals increased, we added the fully unaligned sequences of each individual to run another round of clustering and remove the redundant sequences until the fully unaligned sequences from all individuals have merged into the non-redundant sequence dataset. We explored the repetitive elements of these sequences by RepeatMasker (http://www.repeatmasker.org/) and compared them with that of reference genome (both the primary assembly sequences and decoy sequences (hs38d1)) to characterize the compositions of repetitive sequences in fully unaligned sequences. Finally, we aligned these fully unaligned sequences to the patch sequence, alternative loci and decoy sequences (hs38d1) [9] as well as existing assembled individual genomes [2, 26,27,28,29,30] to determinate whether the fully unaligned sequences could be identified in other individuals.

The six individual assembled genomes were also downloaded from NCBI. The novel sequences of hs38d1 [9] were downloaded from NCBI with accession number GCA_000786075.2. The pan-genome of 910 Africans [20] were downloaded from NCBI under accession PDBU01000000. The six primate reference genomes were downloaded from NCBI with accession numbers GCA_000001515.5 (chimpanzee [45]), GCA_000151905.3 (gorilla [46]), GCF_000258655.2 (bonobo [47]), GCA_002880775.3 (orangutan [48]), GCA_000772875.3 (rhesus [49]), and GCF_000264685.3 (baboon [50]).

This result is consistent with previous studies [3, 9, 26] (Fig. 2b). In addition, the GC content (%) of fully unaligned sequences was slightly higher than that of partially unaligned sequences. Obvious stratification was observed in the fully unaligned sequences before removing contamination sequences (Additional file 1: Figure S3), which were mainly from the bacterium Helicobacter pylori, one majority infectious agent associated with gastric diseases in several individuals (Additional file 1: Figure S4). Comparing with EUPAN, this new strategy could severely reduce both CPU time and memory consumption but with little loss in precision (Table1 and Additional file 1: Figure S2). After discarding the potential contamination, ~5Mb fully unaligned sequences and ~6Mb partially unaligned sequences for each individual were obtained (Fig.2a). In HUPAN, we proposed a hierarchical strategy to extract the non-reference sequences (see the Methods section).


For example, more than 3700 non-repetitive non-reference (NRNR) sequences were called from whole-genome sequence data of 15,219 Icelanders by de novo assembly of the unmapped reads into contigs [4]. found that each genome carried an average of 0.7Mb sequences that were not found in the human reference genome [6]. Therefore, reference-based methods may miss some sequence variations within or between populations [2, 3]. However, most of these studies are based on the human reference genome, which was built from several individuals, and only a consensus of these genomes was included [1]. For example, a 766-bp non-repetitive non-reference sequence was found to have an association with myocardial infarction in Icelanders [4]. Actually, previous studies have discovered various types of novel sequences, which are not present in the human reference genome [4,5,6,7,8]. In another study, by analyzing the unmapped reads from ~10,000 deep sequencing human genomes, Telenti et al. The Simons Genome Diversity Project reported high-quality genomes of 300 individuals from 142 diverse populations and suggested at least 5.8Mb sequences from these genomes were not present in the human reference genome [9]. These novel sequences may harbor functional genomic elements that are ethnic specific, and may affect gene regulations or transcriptional diversity [2]. Adding these novel sequences into the human reference genome could improve the efficiency of mapping and variant calling process [9]. Single nucleotide variations (SNVs), small insertions and deletions (INDELs), and structural variations (SVs) of the human genome are routinely explored to study the genomic variations in biomedical studies.

We found that S100A14 expression was increased in lung adenocarcinoma and squamous cell carcinoma compared with that in normal lung tissue. These apparent discrepancies suggest that S100A14 plays different roles at different tumor and development stages, although the underlying mechanism remains unclear. Recently, some studies reported S100A14 expression in various cancers, but the results were inconsistent. The IHC data indicated that S100A14 may play a potential role in cell differentiation. Furthermore, our results showed that S100A14 was expressed at higher levels in well or moderately differentiated lung cancer than in poorly differentiated lung cancer, which was consistent with a previous report [40]. On the other hand, some data suggest that S100A14 is downregulated in kidney, colon, rectal, esophageal, and oral carcinoma [35,39]. Therefore, using IHC we further detected S100A14 expression in paraffin-embedded archival tissue specimens and evaluated the relationship between S100A14 and clinicopathological characteristics in patients with lung cancer. Although northern blot hybridization has shown that S100A14 mRNA expression is upregulated in lung tumors [35], S100A14 protein expression in lung cancer still remains unclear. Some data have indicated S100A14 is upregulated in several cancers, including ovarian, breast, and hepatocellular cancer [35,38].

After removing sequence contaminations from microorganisms and non-primate eukaryotes, we identified 28,622 fully unaligned sequences, with a total length of 30.72Mb and 8320 partially unaligned sequences, with a total length of 46.63Mb (Additional file 1: Figure S6). Majority of the partially unaligned sequences were classified into human and other primates (Additional file 1: Figure S5b), indicating these sequences are indeed from human genomes. After the non-reference sequenceswere merged, they were clustered to remove the redundant sequences across individuals. More than 20Mb of the 52.90Mb sequences were classified into microorganisms (Additional file 1: Figure S5a). We obtained 52.90Mb fully unaligned sequences and 46.76Mb partially unaligned sequences.