30 research outputs found

    Additional file 2: of Downregulation of castor zinc finger 1 predicts poor prognosis and facilitates hepatocellular carcinoma progression via MAPK/ERK signaling

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    Table S1. Clinicopathologcal characteristics of HCC patients in training cohort and validation cohort. Table S2 Correlation between CASZ1 expression and clinicopathologic characteristics of HCC patients in training cohort and validation cohort. Table S3 Univariate and multivariate analyses of risk factors associated with overall survival and disease-free survival of HCC patients in training cohort. Table S4 Univariate and multivariate analyses of risk factors associated with overall survival and disease-free survival of HCC patients in validation cohort. (DOC 282 kb

    Additional file 3: of Downregulation of castor zinc finger 1 predicts poor prognosis and facilitates hepatocellular carcinoma progression via MAPK/ERK signaling

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    Figure S2. The efficacy of CASZ1 ectopic expression or silence was determined in HCC cells. A-B. qRT-PCR (A) and western blot (B) confirmed CASZ1 mRNA and protein levels in HCCLM3CASZ1, PLC/PRF/5shCASZ1 and their respective control cells. (TIFF 8263 kb

    Additional file 6: of Downregulation of castor zinc finger 1 predicts poor prognosis and facilitates hepatocellular carcinoma progression via MAPK/ERK signaling

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    Figure S5. The efficacy of RAF1 ectopic expression or silence is determined in CASZ1-interfered HCC cells. A-B. qRT-PCR (A) and western blot (B) confirmed RAF1 mRNA and protein levels in HCCLM3CASZ1 cells with RAF1 overexpression or PLC/PRF/5shCASZ1 cells with RAF1 knockdown. C. The wound closure rate of CASZ1-interfered HCC cells with RAF1 ectopic expression or knockdown. * P < 0.05, ** P < 0.01. (TIFF 2192 kb

    Additional file 4: of Downregulation of castor zinc finger 1 predicts poor prognosis and facilitates hepatocellular carcinoma progression via MAPK/ERK signaling

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    Figure S3. CASZ1 inhibits HCC progression by inactivating the MAPK/ERK pathway. A EMT genes including E-cadherin, N-cadherin and vimentin were detected by western blot in HCCLM3CASZ1, PLC/PRF/5shCASZ1 and their control cells. B Cell morphological changes in HCCLM3CASZ1, PLC/PRF/5shCASZ1 and their control cells was examined by phase-contrast photomicrographs. C IHC staining showed that the expression of p-ERK, cyclinD1, MMP2 and MMP9 was reduced in the CASZ1-overexpressed HCCLM3 xenograft tumors, but increased in the CASZ1-silenced PLC/PRF/5 xenograft tumors (magnification, × 400). (TIFF 11458 kb

    Resolution for varying relatedness using GRM, encGRM and <i>encG-reg</i>.

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    The figure shows the resolution for detecting relatives or overlapping samples with respect to varying number of markers at every row (for better illustration me was twice that of Eq 3) and the degree of relatives to be detected (r = 0, 1, and 2). The y axis is the relatedness calculated from GRM and the x axis is the estimated relatedness calculated from encG-reg (A) and encGRM (B). Each point represents an individual pair between cohort 1 and cohort 2 (there are 200 × 200 = 40,000 pairs in total), given the simulated relatedness. The dotted line indicates the 95% confidence interval of the relatedness directly estimated from the original genotype (blue) and the encrypted genotype (red). The table provides how m and k are estimated. The columns “under minimal me” provide benchmark for a parameter, and it is practically to choose 2×me and then estimate k as shown under the column “practical me”.</p

    <i>m</i><sub><i>e</i></sub> estimation in UK Biobank and Chinese cohorts.

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    Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier.</div

    Cohort-level genetic background analyses for Chinese cohorts under parsimony encG-reg analysis.

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    (A) Overview of the intersected SNPs across cohorts, a black dot indicated its corresponding cohort was included. Each row represented one cohort while each column represented one combination of cohorts. Dots linked by lines suggested cohorts in this combination. The height of bars represented the cohort’s SNP numbers (rows) or SNP intersection numbers (columns). Inset histogram plots show the distribution of the 7,009 intersected SNPs and the 500 SNPs randomly chosen from the 7,009 SNPs for encG-reg analysis. (B) 7,009 SNPs were used to estimate fPC from the intersection of SNPs for the 9 cohorts. Each triangle represented one Chinese cohort and was placed according to their first two principal component scores (fPC1 and fPC2) derived from the received allele frequencies. (C) Five private datasets have been pinned onto the base map from GADM (https://gadm.org/data.html) using R language. The size of point indicates the sample size of each dataset. (D) Global fStructure plot indicates global-level Fst-derived genetic composite projected onto the three external reference populations: 1KG-CHN (CHB and CHS), 1KG-EUR (CEU and TSI), and 1KG-AFR (YRI), respectively; 4,296 of the 7,009 SNPs intersected with the three reference populations were used. (E) Within Chinese fStructure plot indicates within-China genetic composite. The three external references are 1KG-CHB (North Chinese), 1KG-CHS (South Chinese), and 1KG-CDX (Southwest minority Chinese Dai), respectively; 4,809 of the 7,009 SNPs intersected with these three reference populations were used. Along x axis are 9 Chinese cohorts and the height of each bar represents its proportional genetic composition of the three reference populations. Cohort codes: YRI, Yoruba in Ibadan representing African samples; CHB, Han Chinese in Beijing; CHS, Southern Han Chinese; CHN, CHB and CHS together; CEU, Utah Residents with Northern and Western European Ancestry; TSI, Tuscani in Italy; CDX, Chinese Dai in Xishuangbanna.</p
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