U2-snRNP B" protein gene is an early growth-inducible gene

Identification of a biomarker panel for colorectal cancer diagnosis

BACKGROUND

Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries.

METHODS

A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables.

RESULTS

After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples.

CONCLUSIONS

We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

Molecular signature of MT1-MMP: transactivation of the downstream universal gene network in cancer

Invasion-promoting MT1-MMP is directly linked to tumorigenesis and metastasis. Our studies led us to identify those genes, the expression of which is universally linked to MT1-MMP in multiple tumor types. Genome-wide expression profiling of MT1-MMP-overexpressing versus MT1-MMP-silenced cancer cells and a further data mining analysis of the preexisting expression database of 190 human tumors of 14 cancer types led us to identify 11 genes, the expression of which correlated firmly and universally with that of MT1-MMP (P < 0.00001). These genes included regulators of energy metabolism (NNT), trafficking and membrane fusion (SLCO2A1 and ANXA7), signaling and transcription (NR3C1, JAG1, PI3K delta, and CK2 alpha), chromatin rearrangement (SMARCA1), cell division (STK38/NDR1), apoptosis (DAPK1), and mRNA splicing (SNRPB2). Our subsequent extensive analysis of cultured cells, tumor xenografts, and cancer patient biopsies supported our data mining. Our results suggest that transcriptional reprogramming of the specific downstream genes, which themselves are associated with tumorigenesis, represents a distinctive "molecular signature" of the proteolytically active MT1-MMP. We suggest that the transactivation activity of MT1-MMP contributes to the promigratory cell phenotype, which is induced by this tumorigenic proteinase. The activated downstream gene network then begins functioning in unison with MT1-MMP to rework the signaling, transport, cell division, energy metabolism, and other critical cell functions and to commit the cell to migration, invasion, and, consequently, tumorigenesis.