| 1. |
- Yasuda, Kazuki, et al.
(författare)
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Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus
- 2008
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 40:9, s. 1092-1097
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Tidskriftsartikel (refereegranskat)abstract
- We carried out a multistage genome-wide association study of type 2 diabetes mellitus in Japanese individuals, with a total of 1,612 cases and 1,424 controls and 100,000 SNPs. The most significant association was obtained with SNPs in KCNQ1, and dense mapping within the gene revealed that rs2237892 in intron 15 showed the lowest P value (6.7 x 10(-13), odds ratio (OR) = 1.49). The association of KCNQ1 with type 2 diabetes was replicated in populations of Korean, Chinese and European ancestry as well as in two independent Japanese populations, and meta-analysis with a total of 19,930 individuals (9,569 cases and 10,361 controls) yielded a P value of 1.7 x 10(-42) (OR = 1.40; 95% CI = 1.34-1.47) for rs2237892. Among control subjects, the risk allele of this polymorphism was associated with impairment of insulin secretion according to the homeostasis model assessment of beta-cell function or the corrected insulin response. Our data thus implicate KCNQ1 as a diabetes susceptibility gene in groups of different ancestries.
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| 2. |
- Fukuda, Tetsuya, et al.
(författare)
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A selected reaction monitoring mass spectrometric assessment of biomarker candidates diagnosing large-cell neuroendocrine lung carcinoma by the scaling method using endogenous references
- 2017
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- Selected reaction monitoring mass spectrometry (SRM-MS) -based semi-quantitation was performed to assess the validity of 46 selected candidate proteins for specifically diagnosing large-cell neuroendocrine lung carcinoma (LCNEC) and differentiating it from other lung cancer subtypes. The scaling method was applied in this study using specific SRM peak areas (AUCs) derived from the endogenous reference protein that normalizes all SRM AUCs obtained for the candidate proteins. In a screening verification study, we found that seven out of the 46 candidate proteins were statistically significant for the LCNEC phenotype, including 4F2hc cell surface antigen heavy chain (4F2hc/CD98) (p-ANOVA ≤ 0.0012), retinal dehydrogenase 1 (p-ANOVA ≤ 0.0029), apolipoprotein A-I (p-ANOVA ≤ 0.0004), β-enolase (p-ANOVA ≤ 0.0043), creatine kinase B-type (p-ANOVA ≤ 0.0070), and galectin- 3-binding protein (p-ANOVA = 0.0080), and phosphatidylethanolamine-binding protein 1 (p-ANOVA ≤ 0.0012). In addition, we also identified candidate proteins specific to the smallcell lung carcinoma (SCLC) subtype. These candidates include brain acid soluble protein 1 (p-ANOVA < 0.0001) and γ-enolase (p-ANOVA ≤ 0.0013). This new relative quantitationbased approach utilizing the scaling method can be applied to assess hundreds of protein candidates obtained from discovery proteomic studies as a first step of the verification phase in biomarker development processes.
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| 3. |
- Nakayama, Noboru, et al.
(författare)
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Developments of mass spectrometry-based technologies for effective drug development linked with clinical proteomes.
- 2016
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Ingår i: Drug Metabolism and Pharmacokinetics. - : Elsevier BV. - 1347-4367. ; 31:1, s. 3-11
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Forskningsöversikt (refereegranskat)abstract
- A strong demand in drug discovery and development today is to overcome "Big Gaps" encountered by differences in species and races, to accelerate effective developments in cost and time, and to meet medical needs. Moreover, drugs of various types have emerged which cover middle-size molecules and polymers rather than conventional small molecules. Upon those challenges, mass spectrometry (MS)-based technologies, which will be described in this paper, will play an increasingly important role, among which the liquid chromatography-tandem mass spectrometry (LC/MS/MS) platform will be powerful as rapid and molecule-based analysis more than ever. nanoPore Optical Interferometry (nPOI) newly introduced can detect even weak interactions in protein-protein and protein-compound, and can be connected directly to LC/MS/MS for identification of binding molecular species, which will be quite useful for affinity ranking and high-throughput interaction screening. Imaging MS provides the molecular information and spatial distribution of targeted molecules within a tissue specimen. MS-based clinical proteomics utilizing clinical specimens and empowered by advanced bioinformatics can attain both key protein-protein interaction (PPI) networks with major protein players responsible for functional mechanisms of a disease subtype. An integration of those MS-based technologies will deliver a seamless platform of drug development from molecules identified in human clinical specimens.
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| 4. |
- Nishimura, Toshihide, et al.
(författare)
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Current status of clinical proteogenomics in lung cancer
- 2019
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Ingår i: Expert Review of Proteomics. - : Informa UK Limited. - 1478-9450 .- 1744-8387. ; 16:9, s. 761-772
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Forskningsöversikt (refereegranskat)abstract
- Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.
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| 5. |
- Nishimura, Toshihide, et al.
(författare)
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Protein co-expression network-based profiles revealed from laser-microdissected cancerous cells of lung squamous-cell carcinomas
- 2021
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- No therapeutic targets have been identified for lung squamous cell cancer (SqCC) which is the second most prevalent lung cancer because its molecular profiles remain unclear. This study aimed to unveil disease-related protein networks by proteomic and bioinformatic assessment of laser-microdissected cancerous cells from seven SqCCs compared with eight representative lung adenocarcinomas. We identified three network modules significant to lung SqCC using weighted gene co-expression network analysis. One module was intrinsically annotated to keratinization and cell proliferation of SqCC, accompanied by hypoxia-induced aerobic glycolysis, in which key regulators were activated (HIF1A, ROCK2, EFNA1-5) and highly suppressed (KMT2D). The other two modules were significant for translational initiation, nonsense-mediated mRNA decay, inhibited cell death, and interestingly, eIF2 signaling, in which key regulators, MYC and MLXIPL, were highly activated. Another key regulator LARP1, the master regulator in cap-dependent translation, was highly suppressed although upregulations were observed for hub proteins including EIF3F and LARP1 targeted ribosomal proteins, among which PS25 is the key ribosomal protein in IRES-dependent translation. Our results suggest an underlying progression mechanism largely caused by switching to the cap-independent, IRES-dependent translation of mRNA subsets encoding oncogenic proteins. Our findings may help to develop therapeutic strategies to improve patient outcomes.
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