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Background: Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS. Methods: The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform. Results: We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression. Conclusions: Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: Previous studies have shown that TBX21 (T-Box Transcription Factor 21) plays a vital role in coordinating multiple aspects of the immune response especially type 1 immune response as well as tumor progression. However, the function of TBX21 in colorectal cancer (CRC) remains unclear. Methods: IHC to investigate TBX21 expression in CRC tissues. Cell proliferation and apoptosis assays to validate TBX21 function in vitro and in vivo. RNA-seq assay to explore target genes of TBX21. Human phospho-kinase array assay to explore down-stream signaling of TBX21. Results: We disclosed that the expression of TBX21 was marked decreased in CRC versus normal tissue, and negatively correlated with CRC TNM stages. Surprisingly, we found that the CRC and normal cell lines show no TBX21 expression levels. Ectopic expression of TBX21 inhibited cell proliferation and promoted cell apoptosis in vitro. Moreover, RNA-sequence data first time showed that ARHGAP29 acts as the target gene of TBX21 to mediate down-stream signaling activation. Human phospho-kinase array data first time displayed that ectopic expression of TBX21 reduced kinase RSK and GSK3β activation. In contrast, knocked down the expression of TBX21 or ARHGAP29 alternatively abolished TBX21 mediated cell proliferation suppression, cell apoptosis enhancement and RSK/GSK3β activation. In addition, xenograft model studies demonstrated that TBX21 inhibits colorectal tumor progression via ARHGAP29/ RSK/ GSK3β signaling in vivo. Conclusions: In summary, the aforementioned findings suggest a model of TBX21 in suppressing CRC progression. This may provide a promising target for CRC therapy. [ABSTRACT FROM AUTHOR]

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods. Author summary: With the advent and evolution of large-scale and comprehensive biobanks, there come up unprecedented opportunities for researchers to further uncover the complex landscape of human genetics. One major direction that attracts long-standing interest is the investigation of the relationships between genotypes and phenotypes. This includes but doesn't limit to the identification of genotypes that are significantly associated with the phenotypes, and the prediction of phenotypic values based on the genotypic information. Genome-wide association studies (GWAS) is a very powerful and widely used framework for the former task, having produced a number of very impactful discoveries. However, when it comes to the latter, its performance is fairly limited by the univariate nature. To address this, multiple regression methods have been suggested to fill in the gap. That said, challenges emerge as the dimension and the size of datasets both become large nowadays. In this paper, we present a novel computational framework that enables us to solve efficiently the entire lasso or elastic-net solution path on large-scale and ultrahigh-dimensional data, and therefore make simultaneous variable selection and prediction. Our approach can build on any existing lasso solver for small or moderate-sized problems, scale it up to a big-data solution, and incorporate other extensions easily. We provide a package snpnet that extends the glmnet package in R and optimizes for large phenotype-genotype data. On the UK Biobank, we observe competitive prediction performance of the lasso and the elastic-net for all four phenotypes considered from the UK Biobank. That said, the scope of our approach goes beyond genetic studies. It can be applied to general sparse regression problems and build scalable solution for a variety of distribution families based on existing solvers. [ABSTRACT FROM AUTHOR]

Previous studies have identified several ICAM3 transcript variants and mainly investigated the function of the longest transcript of ICAM3 in various tumor progressions. However, the role of the other ICAM3 transcript variants remains unclear. Herein, we detected the expression of ICAM3 transcript variants 1–4 in DLBCL cells and tumor tissues, disclosed that variants 1, 3, and 4 were expressed in normal B cell lines and 3 DLBCL cell lines except SU-DHL-2 as well as tumor tissues, while variant 2 was not detected. Moreover, we found that ectopic expression of variants 1–4 enhanced cell proliferation by accelerating the cell cycle in SU-DHL2 cells in vitro. In addition, variants 1–4 overexpression showed no effects on SU-DHL2 cell apoptosis. Interestingly, the expression of variants 1, 3, and 4 promoted cell migration and EMT process while variant 2 had no effects. Collectively, the above results displayed the different roles of ICAM3 transcript variants in mediating DLBCL progression. [ABSTRACT FROM AUTHOR]

Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) can profile genome-wide epigenetic marks associated with regulatory genomic elements. However, conventional ChIP-seq is challenging when examining limited numbers of cells. Here, we developed a new technique by supplementing carrier materials of both chemically modified mimics with epigenetic marks and dUTP-containing DNA fragments during conventional ChIP procedures (hereafter referred to as 2cChIP-seq), thus dramatically improving immunoprecipitation efficiency and reducing DNA loss of low-input ChIP-seq samples. Using this strategy, we generated high-quality epigenomic profiles of histone modifications or DNA methylation in 10–1000 cells. By introducing Tn5 transposase-assisted fragmentation, 2cChIP-seq reliably captured genomic regions with histone modification at the single-cell level in about 100 cells. Moreover, we characterized the methylome of 100 differentiated female germline stem cells (FGSCs) and observed a particular DNA methylation signature potentially involved in the differentiation of mouse germline stem cells. Hence, we provided a reliable and robust epigenomic profiling approach for small cell numbers and single cells. [ABSTRACT FROM AUTHOR]

Background: Cadherins play a pivotal role in facilitating intercellular interactions between spermatogonial progenitor cells (SPCs) and their surrounding microenvironment. Specifically, E-cadherin serves as a cellular marker of SPCs in many species. Depletion of E-cadherin in mouse SPCs showed no obvious effect on SPCs homing and spermatogenesis. Results: Here, we investigated the regulatory role of E-cadherin in regulating SPCs fate. Specific deletion of E-cadherin in germ cells was shown to promote SPCs differentiation, evidencing by reduced PLZF+ population and increased c-Kit+ population in mouse testes. E-cadherin loss down-regulated the expression level of β-catenin, leading to the reduced β-catenin in nuclear localization for transcriptional activity. Remarkably, increasing expression level of Cadherin-22 (CDH22) appeared specifically after E-cadherin deletion, indicating CDH22 played a synergistic effect with E-cadherin in SPCs. By searching for the binding partners of β-catenin, Lymphoid enhancer-binding factor 1 (LEF1), T-cell factor (TCF3), histone deacetylase 4 (HDAC4) and signal transducer and activator 3 (STAT3) were identified as suppressors of SPCs differentiation by regulating acetylation of differentiation genes with PLZF. Conclusions: Two surface markers of SPCs, E-cadherin and Cadherin-22, synergically maintain the undifferentiation of SPCs via the pivotal intermediate molecule β-catenin. LEF1, TCF3, STAT3 and HDAC4 were identified as co-regulatory factors of β-catenin in regulation of SPC fate. These observations revealed a novel regulatory pattern of cadherins on SPCs fate. [ABSTRACT FROM AUTHOR]