A transcriptional network signature characterizes lung cancer subtypes

Abstract

BACKGROUND: Transcriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC). METHODS: A transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas. The authors tested its classification accuracy in 7 independent cohorts, for a total of 422 subjects of Caucasian, African, and Asian descent. RESULTS: The model for distinguishing AC from SCC was a 25-gene network signature. Its performance on the 7 independent cohorts achieved 95.2% classification accuracy. Even more surprisingly, 95% of this accuracy was explained by the interplay of 3 genes (KRT6A, KRT6B, KRT6C) on a narrow cytoband of chromosome 12. The role of this chromosomal region in distinguishing AC and SCC was further confirmed by the analysis of another group of 28 independent subjects assayed by DNA copy number changes. The copy number variations of bands 12q12, 12q13, and 12q12-13 discriminated these samples with 84% accuracy. CONCLUSIONS: These results suggest the existence of a robust signature localized in a relatively small area of the genome, and show the clinical potential of reverse engineering transcriptional networks from molecular profiles. Cancer 2011;117:353-60.

Comprehensive transcriptome profiling of squamous cell carcinoma of horn in Bos indicus

“…In human, the interplay of these three genes ( KRT6A, KRT6B and KRT6C ) was reported in cancer. Similar type of expression pattern was also observed in SCC of lung . Over and above these keratin protein family members, we also found up‐regulation of a novel candidate KRT84 which is a possible candidate involved in cancer pathways in Bos indicus .…”

Phenotype prediction by integrative network analysis of SNP and gene expression microarrays

“…Single-nucleotide polymorphism (SNP) microarrays interrogate DNA at a specific nucleotide, allowing genome-wide association studies to identify SNPs associated with disease formation in a hypothesis-free manner [1]. Gene expression chips record RNA transcripts from DNA, allowing differential expression analysis [2–3] to identify genes active or repressed in disease processes. While the techniques of analyzing each individual type of data have been well established, much work remains to usefully aggregate SNP and gene expression data to explain how genetic mutations and aberrant transcription result in disease formation.…”

Transcriptome alterations induced by cigarette smoke

“…A transcriptional network‐signature of 25 genes has been shown to distinguish adeno from SCC driven primarily by three genes in a narrow band of chromosome 12 72. Further information about transcriptional changes associated with disease progression comes from a recent study of adenocarcinoma, comparing transcriptomes of non‐involved vs .…”