9,539 research outputs found
Identification of a Proliferation Gene Cluster Associated with HPV E6/E7 Expression Level and Viral DNA Load in Invasive Cervical Carcinoma
Specific HPV DNA sequences are associated with more than 90% of invasive
carcinomas of the uterine cervix. Viral E6 and E7 oncogenes are key mediators
in cell transformation by disrupting TP53 and RB pathways. To investigate
molecular mechanisms involved in the progression of invasive cervical
carcinoma, we performed a gene expression study on cases selected according to
viral and clinical parameters. Using Coupled Two-Way Clustering and Sorting
Points Into Neighbourhoods methods, we identified a Cervical Cancer
Proliferation Cluster composed of 163 highly correlated transcripts, many of
which corresponded to E2F pathway genes controlling cell proliferation, whereas
no primary TP53 targets were present in this cluster. The average expression
level of the genes of this cluster was higher in tumours with an early relapse
than in tumours with a favourable course (P=0.026). Moreover, we found that
E6/E7 mRNA expression level was positively correlated with the expression level
of the cluster genes and with viral DNA load. These findings suggest that HPV
E6/E7 expression level plays a key role in the progression of invasive
carcinoma of the uterine cervix via the deregulation of cellular genes
controlling tumour cell proliferation. HPV expression level may thus correspond
to a biological marker useful for prognosis assessment and specific therapy of
the disease
Network-based stratification of tumor mutations.
Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
- …