Right- and left-sided colorectal cancers display distinct expression profiles and the anatomical stratification allows a high accuracy prediction of lymph node metastasis.

Accurate preoperative prediction of lymph node metastasis and degree of tumor invasion would facilitate an appropriate decision of the extent of surgical resection of cancers to reduce unnecessary complication or to minimize the risk of recurrence in patients. We analyzed gene expression profiles characteristic of the invasiveness of colorectal carcinoma in a total of 89 cases, using a cDNA array and pattern classification algorithms. We set binary classes for a panel of clinicopathologic parameters, each of which was divided at different levels for categories (discrete) or values (continuous). We searched an optimal combination of genes to discriminate the classes by using of a feature subset selection algorithm, which was applied to a set of genes preselected on the basis of statistical difference in expression (two-sided t test, P < or = 0.05). We used a sequential forward feature selection which additively searched a combination of genes, giving a minimal leave-one-out classification error rate of a k-nearest neighbor classifier. In the process of gene preselection, we found a remarkable difference in the expression pattern of genes according to the anatomical location of cancers. The difference was most prominent when the classes were set for cecum, ascending colon, transverse colon, and descending colon (CATD) versus sigmoid colon and rectum (SR). By stratifying these two locations, we were able to extract gene expression profiles characteristic of the classes of the presence versus absence of lymph node metastasis, lymphatic invasion, vascular invasion and degree of mural invasion, and pathological stages, with an accuracy of more than 90%. These results suggest that colorectal cancers harbor distinct molecular pathophysiological statuses according to their right-to-left locations, of which stratification is important for pattern classification of cDNA array data.

Prediction of lymphatic invasion/lymph node metastasis, recurrence, and survival in patients with gastric cancer by cDNA array-based expression profiling.

BACKGROUND: We assessed the predictability of various classes of gastric carcinoma defined by clinicopathological parameters, such as invasiveness and clinical outcomes, using cDNA array data obtained from 54 cases. MATERIALS AND METHODS: We searched an optimal combination of genes to discriminate the classes defined with the clinicopathological parameters by using a feature subset selection algorithm, which was applied to a set of genes preselected on the basis of statistical difference in expression (two-sided t test, P < or = 0.05). With the selected features (gene set), we evaluated the predictability of each parameter in a leave-one-out cross-validation test. RESULTS: We successfully selected sets of genes for which the classifier predicted better versus worse overall survival (tumor-specific death) and tumor-free survival (recurrence), with respective classification rates of 94 and 92%. A contingency table analysis (chi2 test) and Cox proportional hazard model analysis revealed that lymph node metastasis is the most important factor (confounding factor) in patients' prognoses and risks of recurrence. The feature subset selection procedure successfully extracted expression patterns characteristic of lymph node metastasis and lymphatic vessel invasion, yielding 92 and 98% prediction accuracies for these respective factors. CONCLUSION: We conclude that expression profiling using feature subset selection provides a powerful means of stratification of gastric cancer patients in regard to the prognostic factors. Further studies should be warranted to apply this method to personalization of the treatment options.

Aberrant expression of HOX genes in human invasive breast carcinoma.

HOX genes are known not only as master genes that control the morphogenesis, but also as regulator genes that maintain tissue or organ specificity in the adult body. We hypothesized that dysregulated expression of HOX genes was associated with tumor development and malignant progression such as invasion and metastasis. In this study, we analyzed the expression patterns of 39 HOX genes in human invasive ductal breast cancer tissues and normal tissues by the real-time RT-PCR method. We found 11 HOX genes (HOXA1, A2, A3, A5, A9, C11, D3, D4, D8, D9 and D10) expression levels of which were significantly different between cancerous and normal tissues. All 10 genes except HOXC11 were expressed at lower levels in cancerous tissues than normal tissues. Comparing expression levels of each HOX gene among the different types of cancer tissues, the expression level of HOXB7 was lower in lymph node metastasis-positive cancer tissues than negative cancer tissues; those of HOXD12 and D13 were higher in progesterone receptor-positive cancer tissues than negative cancer tissues; and the expression level of HOXC5 was lower in cancerous tissues with mutated-type p53 than in normal and cancerous tissues with wild-type p53. These results suggest that the aberrant expression of HOX genes is related to the development of breast cancer and malignant behavior of cancer cells.

Prediction of lymph node metastasis by analysis of gene expression profiles in non-small cell lung cancer.

OBJECTIVE: Non-small cell lung carcinoma (NSCLC) is one of the leading causes of death in the world. Lymph node metastasis is not only an important factor in estimating the extent and the metastatic potential of an NSCLC but also in prognosticating the patient outcome. Preoperative prediction of lymph node metastasis might greatly facilitate the choice of appropriate surgical and medical options in patients with NSCLC. METHODS AND RESULTS: Using a cDNA array, we analyzed the expression profiles of 1,289 genes in 92 cancer tissues of NSCLC (37 squamous cell carcinomas and 55 adenocarcinomas). We divided the patients into two groups (classes) for each of various pathological factors, such as lymph node metastasis and pT-stage. For each pair of classes, we searched for an optimal combination of genes to classify the cases using a sequential forward selection algorithm starting from a gene set that showed significant difference in expression between the classes. We used the leave-one-out error cross-validation on a k-nearest neighbor classifier to sequentially choose the gene. Using the optimized set of genes, it was possible to stratify the patients for lymph node metastasis (pN-stage) and pT-stage at, respectively, 100% (23 genes) and 100% (55 genes) for cases with squamous cell carcinomas and 94% (43 genes) and 92% (35 genes) for those with adenocarcinomas. CONCLUSION: We conclude that expression profiling using feature selection provides a powerful means of stratification (personalization) of NSCLC patients and choice in treatment options, particularly for factors such as lymph node metastasis whose radiological diagnosis is presently incomplete.

Prediction of lymph node metastasis and perineural invasion of biliary tract cancer by selected features from cDNA array data.

OBJECTIVE: To better understand the nature of the malignancy of biliary tract carcinoma and evaluate the feasibility of its prediction by gene expression profiles. METHODS AND RESULTS: We explored the gene expression profiles characteristic of progression and invasiveness in the cDNA array data obtained from 37 biliary tract carcinomas (15 bile duct, 11 gallbladder, 11 of ampulla of Vater). We pre-selected 51 and 100 genes for the presence versus absence of lymph node metastasis and perineural invasion on the basis of statistical difference. To search optimized sets of genes for prediction, we applied a sequential forward feature selection, minimizing leave-one-out error rates on a k-nearest neighbor classifier. We could predict lymph node metastasis and perineural invasion with an accuracy of 94 and 100%, respectively. When the 6-stage IA cancers without perineural invasion were precluded, a marked difference in gene expression (147 gene), discriminable with 100% accuracy, was noted between positive versus negative perineural invasion, suggesting that the acquisition of invasive character is rather a later molecular pathological event in biliary tract cancer. CONCLUSION: The present method provides a powerful means of classifying biliary tract carcinomas. We also suggest that perineural invasion is an important target of array databased pattern classification, which may predict patient outcomes and facilitate the determination of the extent of surgery to minimize the risk of recurrence.

Gene-expression profile changes correlated with tumor progression and lymph node metastasis in esophageal cancer.

PURPOSE: The purpose of this research was to identify molecular clues to tumor progression and lymph node metastasis in esophageal cancer and to test their value as predictive markers. EXPERIMENTAL DESIGN: We explored the gene expression profiles in cDNA array data of a 36-tissue training set of esophageal squamous cell carcinoma (ESCC) by using generalized linear model-based regression analysis and a feature subset selection algorithm. By applying the identified optimal feature sets (predictive gene sets), we trained and developed ensemble classifiers consisting of multiple probabilistic neural networks combined with AdaBoosting to predict tumor stages and lymph node metastasis. We validated the classifier abilities with 18 independent cases of ESCC. RESULTS: We identified 71 genes of 1289 cancer-related genes of which the expression correlated with tumor stages. Of the 71 genes, 47 significantly differed between the Tumor-Node-Metastasis pT1/2 and pT3/4 stages. Cell cycle regulators and transcriptional factors possibly promoting the growth of tumor cells were highly expressed in the early stages of ESCC, whereas adhesion molecules and extracellular matrix-related molecules possibly promoting invasiveness increased in the later stages. For lymph node metastasis, we identified 44 genes with predictive values, which included cell adhesion molecules and cell membrane receptors showing higher expression in node-positive cases and cell cycle regulators and intracellular signaling molecules showing higher expression in node-negative cases. The ensemble classifiers trained with the selected features predicted tumor stage and lymph node metastasis in the 18 validation cases with respective accuracies of 94.4% and 88.9%. This demonstrated the reproducibility and predictive value of the identified features. CONCLUSION: We suggest that these characteristic genes will provide useful information for understanding the malignant nature of ESCC as well as information useful for personalizing the treatments.