CHRM3 is a novel prognostic factor of poor prognosis and promotes glioblastoma progression via activation of oncogenic invasive growth factors

Glioblastoma (GBM) is the most aggressive cancer of the brain and has a high mortality rate due to the lack of effective treatment strategy. Clarification of molecular mechanisms of GBM's characteristic invasive growth is urgently needed to improve the poor prognosis. Single-nuclear sequencing of primary and recurrent GBM samples revealed that levels of M3 muscarinic acetylcholine receptor (CHRM3) were significantly higher in the recurrent samples than in the primary samples. Moreover, immunohistochemical staining of an array of GBM samples showed that high levels of CHRM3 correlated with poor prognosis, consistent with The Cancer Genome Atlas database. Knockdown of CHRM3 inhibited GBM cell growth and invasion. An assay of orthotopic GBM animal model in vivo indicated that inhibition of CHRM3 significantly suppressed GBM progression with prolonged survival time. Transcriptome analysis revealed that CHRM3 knockdown significantly reduced an array of classic factors involved in cancer invasive growth, including MMP1/MMP3/MMP10/MMP12 and CXCL1/CXCL5/CXCL8. Taken together, CHRM3 is a novel and vital factor of GBM progression via regulation of multiple oncogenic genes and may serve as a new biomarker for prognosis and therapy of GBM patients.

Trends and variations in breast and colorectal cancer incidence from 1995 to 2011: a comparative study between Texas Cancer Registry and National Cancer Institute's Surveillance, Epidemiology and End Results data

Few studies have examined the cancer incidence trends in the state of Texas, and no study has ever been conducted to compare the temporal trends of breast and colorectal cancer incidence in Texas with those of the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) in the United States. This study aimed to conduct a parallel comparison between the Texas Cancer Registry and the National Cancer Institute's SEER on cancer incidence from 1995 to 2011. A total of 951,899 breast and colorectal cancer patients were included. Age-adjusted breast cancer incidence was 134.74 per 100,000 in Texas and 131.78 per 100,000 in SEER in 1995-2011, whereas age-adjusted colorectal cancer incidence was 50.52 per 100,000 in Texas and 49.44 per 100,000 in SEER. Breast cancer incidence increased from 1995 to 2001, decreased from 2002 to 2006, and then remained relatively stable from 2007 to 2011. For colorectal cancer, the incidence increased in 1995-1997, and then decreased continuously from 1998 to 2011 in Texas and SEER areas. Incidence rates and relative risks by age, gender and ethnicity were identical between Texas and SEER.

Ranking gene-drug relationships in biomedical literature using Latent Dirichlet Allocation

Drug responses vary greatly among individuals due to human genetic variations, which is known as pharmacogenomics (PGx). Much of the PGx knowledge has been embedded in biomedical literature and there is a growing interest to develop text mining approaches to extract such knowledge. In this paper, we present a study to rank candidate gene-drug relations using Latent Dirichlet Allocation (LDA) model. Our approach consists of three steps: 1) recognize gene and drug entities in MEDLINE abstracts; 2) extract candidate gene-drug pairs based on different levels of co-occurrence, including abstract level, sentence level, and phrase level; and 3) rank candidate gene-drug pairs using multiple different methods including term frequency, Chi-square test, Mutual Information (MI), a reported Kullback-Leibler (KL) distance based on topics derived from LDA (LDA-KL), and a newly defined probabilistic KL distance based on LDA (LDA-PKL). We systematically evaluated these methods by using a gold standard data set of gene-drug relations derived from PharmGKB. Our results showed that the proposed LDA-PKL method achieved better Mean Average Precision (MAP) than any other methods, suggesting its promising uses for ranking and detecting PGx relations.

HIGHLY ORDERED MICRO-ARRAYS OF CdSe QUANTUM DOTS ON CHEMICALLY PATTERNED SUBSTRATES

Highly ordered arrays consisting uniform fluorescent cadmium selenide ( CdSe ) quantum dots (QDs) ring or dot structures were obtained by self-assembly of QDs on chemically patterned substrates. In this method, Au substrates with alternating hydrophobic and hydrophilic square patterns are firstly fabricated by microcontact printing, which allows water droplets to condense on the hydrophilic regions to provide two-dimensional template arrays. The CdSe QDs are then assembled at the liquid/liquid interfaces to give uniform micro or nanostructures. The shape and size of the rings and dots can be tailored by controlling the relative evaporation speed of the water and the organic solvents. The obtained nanostructures have ideal topography to avoid substrate-induced fluorescence quenching.