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).

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).

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.

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.