Method to predict pathological grade and to identify drug targets against glioma tumor

A method for determining the risk of tumor development grade of Glioblastoma (GBM or Low Grade Glioblastoma) from the general GBM developmental model, the method comprising;

• (i) collecting tumor samples from patients diagnosed with glioblastoma cancer followed by extracting and purifying whole cell mRNA form the samples;
• (ii) performing high-throughput mRNA sequencing process requiring purified and enriched mRNA library for generating the pair-end short reads of the sequences, which is further checked by measuring the library size and base quality and shared in a computer readable text file;
• (iii) processing RNA-Seq files of step (ii) for quality checking of the sequences, aligning of the sequence reads to the human reference genome and quantifying the transcripts/genes;
• (iv) performing differential gene expression analyses to determine the differential mRNA expression profile of the collected tumor specimen with respect to normal cells;
• and
• (v) producing a predictive computing module based on the tumor specimen, wherein the predictive computing module utilizes the mRNA expression profile of step (iv) comprising select input proteins, performs mechanistic simulations to compute the frequencies of tumorigenic [LGG (Low Grade Glioblastoma) and GBM] and non-tumorigenic cells, followed by computing a novel quantitative (i.e., phenotype predictor) score of each sub-types of glioma tumor cells to predict the chances of occurrence of glioma tumor of respective grades.