High grade neuroendocrine lung cancer expression profile

ABSTRACT: Genomic gains and losses, particularly amplification of oncogenes and deletion of tumor suppressor genes, are critical molecular events involved in tumorigenesis and cancer progression. These genomic structural abnormalities trigger pathway alterations which activate/inactivate transcription factors along protein network, and then affect gene transcription profiles. Therefore, trace-back analysis of the pathway alteration by integrating genomic copy number, transcription profile, and known protein network data is expected to provide key information to interpret tumorigenesis and cancer progression processes. However, there are a number of pathway alteration candidates, so that it is difficult to understand overall picture. Primitive approaches such as filtering by arbitrary selection of thresholds involve a risk of overlooking important pathway alterations and their triggers. We proposed a visualization method for the trace-back analysis of pathway alterations, called a Cluster Overlap Distribution Map (CODM). We applied the CODM to trace-back analysis of pathway alterations related to subtype classifications of high grade neuroendocrine carcinoma samples; 1) small cell lung carcinoma (SCLC) vs. large cell neuroendocrine carcinoma (LCNEC), and 2) group1 vs. group2 (this is the classification based on transcription profiles and group2 has a higher survival rate than group1). By effective use of 3D and color spaces, the CODM allowed us to understand the overall picture of pathway alteration without arbitrary selection of thresholds and to extract 6 pathway alterations related to only group1 vs. groups2, 2 pathway alterations related to only SCLC vs. LCNEC, and 2 pathway alterations related to both group1 vs. group2 and SCLC vs. LCNEC. Keywords: lung cancer profile

ORGANISM(S): Homo sapiens

PROVIDER: GSE9074 | GEO | 2022/04/01

REPOSITORIES: GEO