Dataset Information

Expression data from different grades (WHO) of astrocytomas (ACM)

ABSTRACT: The aim of the study was to identify significant alterations in genes and molecular functional pathways in comparison with normal and ACM tissue, and detect the marker genes to differentiate the different stage astrocytomas Total RNA was isolated from Seventeen tumor tissue of patients, which included two WHO grade I(T1) and five WHO grade II(T2), III(T3) and IV(T4) samples, and four pooled normal tissue samples. The genome-wide expression analysis was first performed by directly comparing the expression profile of highly enriched different grade astrocytomas and pooled normal tissues, we then applied various data-mining methods to process the 15 different grades tumor tissues sample. Paired T-test and Q-cluster method was performed to explore mark genes validated by qRT-PCR. This study utilized a pathway-specific enrichment analysis of the microarray data and quantified the gene expression difference between astrocytomas tumor and pooled normal tissues. BioCarta and KEGG pathways of the ACM compared to normal tissue were identified through enrichment tests on gene lists obtained using SAM, while GO is organized into hierarchical annotations in the context of normal cellular function, the BioCarta and KEGG database organizes the genes(gen products) into pathway reaction maps and functional complexes, including some disease-specific pathway

ORGANISM(S): Homo sapiens

SUBMITTER: Yao Zhiqiang

PROVIDER: S-ECPF-GEOD-19728 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Astrocytoma is the most common glioma, accounting for half of all primary brain and spinal cord tumors. Late detection and the aggressive nature of high-grade astrocytomas contribute to high mortality rates. Though many studies identify candidate biomarkers using high-throughput transcriptomic profiling to stratify grades and subtypes, few have resulted in clinically actionable results. This shortcoming can be attributed, in part, to pronounced lab effects that reduce signature robustness and varied individual gene expression among patients with the same tumor. We addressed these issues by uniformly preprocessing publicly available transcriptomic data, comprising 306 tumor samples from three astrocytoma grades (Grade 2, 3, and 4) and 30 non-tumor samples (normal brain as control tissues). Utilizing Differential Rank Conservation (DIRAC), a network-based classification approach, we examined the global and individual patterns of network regulation across tumor grades. Additionally, we applied gene-based approaches to identify genes whose expression changed consistently with increasing tumor grade and evaluated their robustness across multiple studies using statistical sampling. Applying DIRAC, we observed a global trend of greater network dysregulation with increasing tumor aggressiveness. Individual networks displaying greater differences in regulation between adjacent grades play well-known roles in calcium/PKC, EGF, and transcription signaling. Interestingly, many of the 90 individual genes found to monotonically increase or decrease with astrocytoma grade are implicated in cancer-affected processes such as calcium signaling, mitochondrial metabolism, and apoptosis. The fact that specific genes monotonically increase or decrease with increasing astrocytoma grade may reflect shared oncogenic mechanisms among phenotypically similar tumors. This work presents statistically significant results that enable better characterization of different human astrocytoma grades and hopefully can contribute towards improvements in diagnosis and therapy choices. Our results also identify a number of testable hypotheses relating to astrocytoma etiology that may prove helpful in developing much-needed biomarkers for earlier disease detection.

Dataset Information

Expression data from different grades (WHO) of astrocytomas (ACM)

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets