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Biomedical subjects

Takuya Higashiyama

Publications and source records attributed to Takuya Higashiyama.

4 recordsLinked to original sources
PubMed2006

Clinical significance of lymph node metastasis of thyroid papillary carcinoma located in one lobe.

BACKGROUND: Previous studies have shown that lymph node metastasis can be of prognostic value. In this study, we investigated the clinical significance of node metastasis focusing on metastasis in the central compartment for patients with papillary carcinoma located in one lobe. PATIENTS AND METHODS: We investigated the frequency of lymph node metastasis in 759 patients with papillary carcinoma to determine whether and how such metastasis affects disease-free survival (DFS). RESULTS: Central node metastasis was observed in 63% of patients, and the frequency was increased in relation to tumor size. The frequency of lateral node metastasis was 62.0%, which was also directly related to tumor size. On multivariate analysis of cases showing tumor larger than 1 cm, central node metastasis was recognized as an independent prognostic factor of DFS. The frequency of metastasis to the paratracheal nodes contralateral to the tumor was drastically elevated for tumors larger than 1 cm, but metastasis to this region did not independently predict worse DFS. CONCLUSION: Central node metastasis independently predicts a worse DFS for patients with papillary carcinoma larger than 1 cm.

Adult↗
PubMed2006

Delta Np73 expression in thyroid neoplasms originating from follicular cells.

AIMS: p73, a homologue of p53, is known as a negative regulator of tumour progression. However, delta Np73, an isoform of p73 lacking the NH2-terminal transactivation domain plays an oncogenic role by interfering with the activity of p53 and TA (full-length transactivating isoforms) p73. In this study, we investigated the expression of delta Np73 in human thyroid neoplasms originating from follicular cells. METHODS: We immunohistochemically investigated delta Np73 expression in 223 thyroid neoplasms. Delta Np73 expression level was evaluated as the sum of positivity score and intensity score. RESULTS: Normal follicular cells did not express delta Np73, but 27.3% of follicular adenoma, 85.4% of follicular carcinoma, 99.2% of papillary carcinoma, and 95.7% of anaplastic carcinoma were positive for the transcript. Delta Np73 expression level did not differ between widely invasive and minimally invasive follicular carcinomas. In papillary carcinoma, the level was inversely linked to tumour size, extrathyroid extension, and clinically apparent metastasis. Furthermore, in anaplastic carcinoma, delta Np73 expression level was significantly lower than that in papillary carcinoma. CONCLUSIONS: Our findings indicate that delta Np73 plays a role predominantly in the early phase of papillary carcinoma progression.

Adenocarcinoma, Follicular↗
PubMed2004

Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm.

PURPOSE: Selection of treatment options with the highest likelihood of successful outcome for individual breast cancer patients is based to a large degree on accurate classification into subgroups with poor and good prognosis reflecting a different probability of disease recurrence and survival after therapy. Here we propose a breast cancer classification algorithm taking into account three main prognostic features determined at the time of diagnosis: estrogen receptor (ER) status; lymph node (LN) status; and gene expression signatures associated with distinct therapy outcome. EXPERIMENTAL DESIGN: Using microarray expression profiling and quantitative reverse transcription-PCR analyses, we compared expression profiles of the 70-gene breast cancer survival signature in established breast cancer cell lines and primary breast carcinomas from cancer patients. We classified 295 breast cancer patients using 14-, 13-, 6-, and 4-gene survival predictor signatures into subgroups having statistically distinct probability of therapy failure (P < 0.0001). We evaluated the prognostic power of breast cancer survival predictor signatures alone and in combination with ER and LN status using Kaplan-Meier analysis. RESULTS: The breast cancer survival predictor algorithm allowed highly accurate classification into subgroups with dramatically distinct 5- and 10-year survival after therapy of a large cohort of 295 breast cancer patients with either ER+ or ER- tumors as well as LN+ or LN- disease (P < 0.0001, log-rank test). CONCLUSIONS: Our data imply that quantitative laboratory tests measuring expression profiles of a limited set of identified small gene clusters may be useful in stratification of breast cancer patients at the time of diagnosis into subgroups with statistically distinct probability of positive outcome after therapy and assisting in selection of optimal treatment strategies. The estimated increase in survival due to the optimization of treatment protocols may reach many thousands of breast cancer survivors every year at the 10-year follow-up check point.

Algorithms↗