Description:  Cancer is a significant plague to modern civilization. 1 in 3 people will be diagnosed with cancer in their lifetime.  There is a dismal need for novel molecular diagnostic tools to detect and predict cancer disease more precisely. Two primary cancers that burden our economy and public health are prostate cancer and medulloblastoma (MB). MB is a central nervous system tumor that predominantly affects children and always requires aggressive therapy. Understanding and identifying novel disease-related molecular mechanisms and pathways are essential for developing optimal and novel therapies. To predict medulloblastoma from cerebral spinal fluid (CSF), we collected patient and normal CSF samples. We performed NGS transcriptomic sequencing and mass-spectrometry of metabolite/lipid to develop the molecular marker panel to predict MB from CSF accurately.  To identify MB subgroup-specific circRNAs, we subjected RNA-seq data from 175 clinical medulloblastoma samples representing the four subgroups to a statistical and machine learning (random forest classification) pipeline. Circular RNA circ_63706 expression was specific to the sonic hedgehog (SSH) group, which was confirmed through in situ hybridization analysis of clinical tissue samples.

Similarly, for Prostate Cancer, Urine samples are a non-invasive means to obtain abundant and readily accessible “liquid biopsies.” We used urine liquid biopsies to identify and characterize a novel group of urine-enriched RNAs with PCa and normal individuals with or without the benign prostatic disease. Differentially expressed RNAs/circRNAs were identified in urine samples by deep sequencing. These results pave the way for the specific identification and personalized treatment of MB and prostate cancer.

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