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.

RETRIEVAL OF MULTIMEDIA WEB DOCUMENTS AND REMOVAL OF REDUNDANT INFORMATION

This paper describes a search engine for multimedia web documents and a methodology for removing (partially or totally) redundant information from multiple documents in an effort to synthesize new documents. In this paper, a typical multimedia document contains free text and images and additionally has associating well-structured data. An SQL-like query language, WebSSQL, is proposed to retrieve this type of documents. The main differences between WebSSQL and other proposed SQL extensions for retrieving web documents are that WebSSQL is similarity-based and supports conditions on images. This paper also deals with the detection and removal of redundant information (text paragraphs and images) from multiple retrieved documents. Documents reporting the same or related events and stories may contain substantial redundant information. The removal of the redundant information and the synthesis of these documents into a single document can not only save a user's time to acquire the information but also storage space to archive the data. The methodology reported here consists of techniques for analyzing text paragraphs and images as well as a set of similarity criteria used to detect redundant paragraphs and images. Examples are provided to illustrate these techniques.