Comprehensive profiling of mRNA splicing indicates that GC content signals altered cassette exon inclusion in Ewing sarcoma

ABSTRACT Ewing sarcoma (EwS) is a small round blue cell tumor and is the second most frequent pediatric bone cancer. 85% of EwS tumors express the fusion oncoprotein EWS-FLI1, the product of a t(11;22) reciprocal translocation. Prior work has indicated that transcription regulation alone does not fully describe the oncogenic capacity of EWS-FLI1, nor does it provide an effective means to stratify patient tumors. Research using EwS cell lines and patient samples has suggested that EWS-FLI1 also disrupts mRNA biogenesis. In this work we both describe the underlying characteristics of mRNA that are aberrantly spliced in EwS tumor samples as well as catalogue mRNA splicing events across other pediatric tumor types. Here, we also use short- and long-read sequencing to identify cis-factors that contribute to splicing profiles we observe in Ewing sarcoma. Our analysis suggests that GC content upstream of cassette exons is a defining factor of mRNA splicing in EwS. We also describe specific splicing events that discriminate EwS tumor samples from the assumed cell of origin, human mesenchymal stem cells derived from bone marrow (hMSC-BM). Finally, we identify specific splicing factors PCBP2, RBMX, and SRSF9 by motif enrichment and confirm findings from tumor samples in EwS cell lines.

Cancer Cell Detection in Bone Using ANN

A vigorous disease is bone cancer results in deaths of many people. The identification and classification system must be done at its early stage to diagnose. The early detection plays an important role to safe guard the patient from death. And also cancer categorization is one of the toughest tasks in clinical analysis. This paper deals with MR images of various patients used to identify the tumor and classify cancer using Artificial Neural Network algorithm. The proposed methodology uses filtering as preprocessing techniques followed by gray conversion and other image processing methods like edge detection, morphological operation, segmentation, feature extraction and classification are prepared for the identification of bone cancer. By this method time required is reduced for identification and classification of bone cancer.

Differential Proteomic Analysis of the Spinal Cord in Bone Cancer Pain Rats by Two-Dimensional Gel Electrophoresis

Background: Bone cancer pain (BCP) is a common chronic pain that is caused by a primary or metastatic bone tumor. It is refractory to currently available clinical treatment owing to its complicated underlying mechanisms. Methods: In this study, we used proteomics approaches to investigate expressional changes of the rat spinal cord proteome from 7 to 21 d after inoculation. Proteins from the rat L4-6 spinal cord homogenates of BCP and Sham animals were fractionated by two-dimensional (2-DE) gel electrophoresis to produce a high-resolution map of the spinal cord soluble proteins. Proteins showing altered expression levels between BCP and Sham were selected. Results: A total of 60 spots were obtained, and isolated proteins were in-gel trypsin-digested and the resulting peptides were analyzed by matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry. Using the mass spectrometric data, 34 differentially expressed proteins (DEPs) were identified. GO analysis of the identified proteins allowed us to explore the function of the represented proteins. Conclusions: Based on these results, the identified proteins may contribute to the maintenance of BCP, and may provided new or valuable information in the discovery of new therapeutic targets for BCP.