Identification of differentially expressed genes in microsatellite stable HNPCC and sporadic colon cancer.

BACKGROUND: Some sporadic colon cancers and hereditary nonpolyposis colorectal cancer (HNPCC) have been known by a constitutional defect in mismatch gene repair (MMR) and dysfunction in of this MMR system, which lead to an aberrant phenotype that can cause microsatellite instability. However, the clinicopathologic features of still existing microsatellite stable (MSS) colon cancer remain to be investigated. METHOD: We compared the gene expression patterns of nine tumor tissues of HNPCC and nine tumor tissues of sporadic colon cancer with their adjacent pathologically normal, MSS tissues by modified differential display-polymerase chain reaction, selected four potential marker genes, and confirmed their reproducibility by reverse transcriptase-polymerase chain reaction. RESULTS: Reg I, MLCK, and MYH11 in tumors of MSS HNPCC showed significant differences in gene expression pattern compared with the adjacent pathologically normal tissues (P = 0.028, P = 0.002, and P = 0.001, respectively). Similar differences in expression patterns for the same set of genes were seen between the sporadic colon cancer group and their adjacent pathologically normal tissues (P = 0.012, P = 0.003, and P = 0.002, respectively). CONCLUSION: We suggest that the three cancer-associated differentially expressed genes in MSS sporadic colon cancer or MSS HNPCC might be potential tumor markers.

Clinical validity of the lung cancer biomarkers identified by bioinformatics analysis of public expression data.

Identification of molecular markers often leads to important clinical applications such as early diagnosis, prognosis, and drug targeting. Lung cancer, the leading cause of cancer-related deaths, still lacks reliable molecular markers. We have combined the bioinformatics analysis of the public gene expression data and clinical validation to identify biomarker genes for non-small-cell lung cancer. The serial analysis of gene expression and the expressed sequence tag data were meta-analyzed to produce a list of the differentially expressed genes in lung cancer. Through careful inspection of the predicted genes, we selected 20 genes for experimental validation using semiquantitative reverse transcriptase-PCR. The microdissected clinical specimens used in the study consisted of three groups: lung tissues from benign diseases and the paired (cancer and pathologic normal) tissues from non-small-cell lung cancer patients. After extensive statistical analyses, seven genes (CBLC, CYP24A1, ALDH3A1, AKR1B10, S100P, PLUNC, and LOC147166) were identified as potential diagnostic markers. Quantitative real-time PCR was carried out to additionally assess the value of the seven identified genes leading to the confirmation of at least two genes (CBLC and CYP24A1) as highly probable novel biomarkers. The gene properties of the identified markers, especially their relationship to lung cancer and cell signaling pathway regulation, further suggest their potential value as drug targets as well.

The signature from messenger RNA expression profiling can predict lymph node metastasis with high accuracy for non-small cell lung cancer.

BACKGROUND: The extent of regional lymph node (LN) metastasis is the most important factor in the evaluation of resectability and prognosis of non-small cell lung cancer (NSCLC) to increase the chance of complete cure. The authors attempted to deduce a group of genes from the analysis of mRNA expression profiles of the tumor tissues of NSCLC patients with or without LN metastasis, and make a classification model for better prediction of LN metastasis. METHODS: The authors analyzed mRNA expression profiles of 79 NSCLC patients with or without LN metastasis, and deduced the gene signature for the predictive model of LN metastasis in lung cancer. The authors evaluated the predictive accuracy of each of four algorithms by applying them to another set of 33 NSCLC patients. Each algorithm's accuracy was calculated by 10-fold cross-validation, and a combined model showed a level of accuracy that was higher than any one of the better three component algorithms (i.e., ANN, DT, or NB). Avadis, SAS, ArrayXPath, and R-package were the statistical analysis software packages used. RESULTS: The authors selected 949 genes using a classical permutation t test (p < 0.01) and finally obtained a gene signature consisting of 31 genes by adjustment of multiple-hypothesis testing. The LN metastasis prediction model derived from the signature (31 genes) and their characteristic interactions provided a predictive accuracy of 84.85% when applied to a test set of 33 patients. CONCLUSIONS: The authors have demonstrated that their gene signature developed by the expression profiling of mRNAs from the primary tissue could predict the LN metastasis status of NSCLC.

Determination of substrate specificity and putative substrates of Chk2 kinase.

Chk2/hCds1, the human homolog of Saccharomyces cerevisiae Rad53p and Schizosaccharomyces pombe Cds1p, plays a critical role in the DNA damage checkpoint pathway. While several in vivo targets of Chk2 have been identified, the other target proteins of Chk2 responsible for multiple functions, such as cell cycle arrest, DNA repair, and apoptosis, remain to be elucidated. We utilized the GST-peptide approach to identify physiological substrates for Chk2. Mutational analyses using GST-linked Cdc25A containing serine 123 revealed that residues at positions -5 and -3 are critical determinants for the recognition of the Chk2 substrate. We determined the general phosphorylation consensus sequence and identified in vitro targets of Chk2 using GST peptides as substrates. The newly identified in vitro target proteins include Abl1, Bub1R, Bub1, Bub3, Psk-H1, Smc3, Plk1, Cdc25B, Dcamkl1, Mre11, Pms1, and Xrcc9.