Utility of gene tumor expression of VEGF, FOXM1*3 and CD-133 on diagnosis and prognosis of brain gliomas
Feria-Romero, I.; Nettel-Rueda, B.; Rodriguez-Florido, M. A.; Felix-Espinoza, I.; Castellanos-Pallares, L.; Cienfuegos-Meza, J.; Orozco-Suarez, S.; Chavez, J. A.; Escamilla-Nunez, M. C.; Guinto, G.; Marquez-Gonzalez, H.; Rodea-Avila, C.; Grijalva, I.
Show abstract
ObjectiveThis paper seeks to quantify the normalized expression of transcripts FOXM1*3, VEGF, CD133, and MGMT and their relation with the histopathological and molecular diagnosis and with the probability of estimating tumor progression-free survival of gliomas. MethodsA cohort of patients was made up of patients aged over 18 years with a histological and molecular diagnosis of gliomas from the year 2011 to 2018. The patients had a complete tumor resection. Patients with high-grade glioma received adjuvant management (temozolamide and radiotherapy). Clinical and imaging follow-up was carried out periodically to identify the time of progression free survival (PFS). ResultsNinety-one patients (age range, 18-85 years) comprised the study cohort with a predominance of males. The expression of FOXM1*3, VEGF, and CD133 allowed the differentiation of astrocytomas grade II from GBM. ROC curves proved statistically significant in the GBM model (p < 0.05), demonstrating greatest sensitivity with FOXM1*3 (91%), and greatest specificity with VEGF (93%). The age-adjusted Cox multivariate model established that a PFS50% of 25 months corresponds to a median value of 5.3 for VEGF and 0.42 for CD133. ConclusionsThe normalized expression of transcripts FOXM1*3, VEGF, and CD133 allow us to estimate the probability of PFS, especially in gliomas grades II and IV; likewise, their overexpression defines the diagnosis of GBM. AuthorshipO_LISubstantial contributions to conception and design (IAFR, BNR, MARF, GG, IG), acquisition of data (IAFR, BNR, MARF, IFE, LCP, JCM, SOS, JAC, CRA), analysis and interpretation of data (IAFR, BNR, MARF, JCM, SOS, CEN, HMG, IG). C_LIO_LIDrafting the article (IAFR, BNR, MARF, IFE, LCP, SOS, JAC, IG), revising it critically for important intellectual content (IAFR, JCM, CEN, GG, HMG, CRA, IG) C_LIO_LIFinal approval of the version to be published (IAFR, BNR, MARF, IFE, LCP, JCM, SOS, JAC, CEN, GG, HMG, CRA, IG). C_LI
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Disrupting Akt-Wnt/β-catenin signaling suppresses glioblastoma stem cell growth and tumor progression in immunocompetent mice 93%
- Early Imaging Marker of Progressive Glioblastoma: a window of opportunity 92%
- Association of Extent of Resection and Functional Outcomes in Diffuse Low-Grade Glioma: Systematic Review & Meta-Analysis 91%
Similar papers in this journal
- Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning 92%
- Tumor Mutational Burden Predicts Survival In Patients With Low Grade Gliomas Expressing Mutated IDH1 92%
- Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study 92%
Similar papers in this journal
- A subset of pediatric thalamic gliomas share a distinct DNA methylation profile, H3K27me3 loss and frequent alteration of EGFR 93%
- Spatial Transcriptomics Characterisation of Radionecrotic Changes in Glioblastoma Patients 92%
- FYN tyrosine kinase, a downstream target of receptor tyrosine kinases, modulates anti-glioma immune responses 91%
Similar papers in this journal
Similar papers in this journal
- Assessment of Prognostic Value of Cystic Features in Glioblastoma Relative to Sex and Treatment with Standard-of-Care 94%
- Novel kinome profiling technology reveals drug treatment is patient and 2D/3D model dependent in GBM 90%
- Metabolic-imaging of human glioblastoma explants: a new precision-medicine model to predict tumor treatment response early 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.