Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0). Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances. Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods. Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC.

A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

AIM: Optimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated. METHODS:A matched (by variables of age and sex) case-control design (111 CRC and 227 non-cancer samples) was applied. Total RNAs isolated from the 338 blood samples were reverse-transcribed, and the relative transcript levels of candidate genes were analyzed. The training set was made of 162 random samples of the total 338 samples. A logistic regression analysis was performed, and odds ratios for each gene were determined between CRC and non-cancer. The samples (n = 176) in the testing set were used to validate the logistic model, and an inferred performance (generality) was verified. By pooling 12 public microarray datasets (GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105), which included 519 cases of adenocarcinoma and 88 controls of normal mucosa, we were able to verify the selected genes from logistic models and estimate their external generality. RESULTS:The logistic regression analysis resulted in the selection of five significant genes (P < 0.05; MDM2 , DUSP6 , CPEB4 , MMD , and EIF2S3 ), with odds ratios of 2.978, 6.029, 3.776, 0.538 and 0.138, respectively. The five-gene model performed stably for the discrimination of CRC cases from controls in the training set, with accuracies ranging from 73.9% to 87.0%, a sensitivity of 95% and a specificity of 95%. In addition, a good performance in the test set was obtained using the discrimination model, providing 83.5% ac- = 0.853, AUC = 0.978, accuracy = 0.949, specificity = 0.818 and sensitivity = 0.971). RETROSPECTIVE STUDY

Colorectal cancer (CRC) is a complex disease characterized by dynamically deregulated gene expression and crosstalk between signaling pathways. In this study, a new approach based on gene-function-based clusters was introduced to explore the CRC-associated networks of gene expression. Each cluster contained genes involved in coordinated regulatory activity, such as RAS signaling, the cell cycle process, transcription, or translation. A retrospective case–control study was conducted with the inclusion of 119 patients with histologically confirmed colorectal cancer and 308 controls. The quantitative expression data of 15 genes were obtained from the peripheral blood samples of all participants to investigate cluster–gene and gene–gene interactions. DUSP6, MDM2, and EIF2S3 were consistently selected as CRC-associated factors with high significance in all logistic models. CPEB4 became an insignificant factor only when combined with the clusters for cell cycle processes and for transcription. The CPEB4/DUSP6 complex was a prerequisite for the significance of MMD, whereas EXT2, RNF4, ZNF264, WEE1, and MCM4 were affected by more than two clusters. Intricate networks among MMD, RAS signaling factors (DUSP6, GRB2, and NF1), and translation factors (EIF2S3, CPEB4, and EXT2) were also revealed. Our results suggest that limited G1/S transition, uncontrolled DNA replication, and the cap-independent initiation of translation may be dominant and concurrent scenarios in circulating tumor cells derived from colorectal cancer. This gene-function-based cluster approach is simple and useful for revealing intricate CRC-associated gene expression networks. These findings may provide clues to the metastatic mechanisms of circulating tumor cells in patients with colorectal cancer.

Malignant pleural effusions (MPE) commonly result from malignant tumors and represent advanced-stage cancers. Thus, in clinical practice, early recognition of MPE is valuable. However, the current diagnosis of MPE is based on pleural fluid cytology or histologic analysis of pleural biopsies with a low diagnostic rate. This research aimed to assess the diagnostic ability of eight previously identified Non-Small Cell Lung Cancer (NSCLC)-associated genes for MPE. In the study, eighty-two individuals with pleural effusion were recruited. There were thirty-three patients with MPE and forty-nine patients with benign transudate. mRNA was isolated from the pleural effusion and amplified by Quantitative real-time PCR. The logistic models were further applied to evaluate the diagnostic performance of those genes. Four significant MPE-associated genes were discovered in our study, including Dual-specificity phosphatase 6 (DUSP6), MDM2 proto-oncogene (MDM2), Ring finger protein 4 (RNF4), and WEE1 G2 Checkpoint Kinase (WEE1). Pleural effusion with higher expression levels of MDM2 and WEE1 and lower expression levels of RNF4 and DUSP6 had a higher possibility of being MPE. The four-gene model had an excellent performance distinguishing MPE and benign pleural effusion, especially for pathologically negative effusions. Therefore, the gene combination is a suitable candidate for MPE screening in patients with pleural effusion. We also identified three survival-associated genes, WEE1, Neurofibromin 1 (NF1), and DNA polymerase delta interacting protein 2 (POLDIP2), which could predict the overall survival of patients with MPE.

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