Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data

Muro, Shizuko; Takemasa, Ichiro; Oba, Shigeyuki; Matoba, Ryo; Ueno, Noriko; Maruyama, Chiyuri; Yamashita, Riu; Sekimoto, Mitsugu; Yamamoto, Hirofumi; Nakamori, Shoji; Monden, Morito; Ishii, Shin; Kato, Kikuya

doi:10.1186/gb-2003-4-3-r21

Cited by 59 publications

(22 citation statements)

References 32 publications

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“…With this procedure we have been able to develop diagnostic systems for the evaluation of prognosis for breast carcinoma [20], of the sensitivity to 5-fluorouracil and interferon-α treatment for hepatocellular carcinoma [22] and of the prognosis for colon carcinoma [25]. In the study reported here, we applied ATAC-PCR to the differential diagnosis of FTA and FTC.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, we successfully applied ATAC-PCR to the analysis of gene expression profiles in various human tumors, including breast carcinoma [21], hepatocellular carcinoma [22,23,24] and colon carcinoma [25]. With this procedure we have been able to develop diagnostic systems for the evaluation of prognosis for breast carcinoma [20], of the sensitivity to 5-fluorouracil and interferon-α treatment for hepatocellular carcinoma [22] and of the prognosis for colon carcinoma [25].…”

Section: Discussionmentioning

confidence: 99%

“…In our opinion, these considerations make ATAC-PCR more suitable for practical clinical application than cDNA microarray. Since we had already obtained successful results with ATAC-PCR for several human tumors [20,21,22,23,24,25], we decided to investigate the feasibility of applying ATAC-PCR to the analysis of gene expression profiles in FTA and FTC in order to identify differentially expressed genes and to construct a differential diagnostic system based on such genes.…”

Section: Introductionmentioning

confidence: 99%

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Differentiation of Follicular Thyroid Adenoma from Carcinoma by Means of Gene Expression Profiling with Adapter-Tagged Competitive Polymerase Chain Reaction

Taniguchi

Takano²,

Miyauchi³

et al. 2005

Oncology

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Objective: Since preoperative differentiation between follicular thyroid adenoma (FTA) and carcinoma (FTC) remains very difficult, the purpose of this study was to identify the genes differentially expressed in FTA and FTC in order to construct a diagnostic system based on such genes for differentiation of FTA and FTC. Methods:Gene expression profiles of 45 FTAs and 22 FTCs were analyzed by means of adapter-tagged competitive polymerase chain reaction (ATAC-PCR) with 2,516 genes (learning set). The genes differentially expressed in FTAs and FTCs were then used to construct a diagnostic system based on the weighted-voting algorithm. In addition, a validation study of this diagnostic system was conducted using 12 FTAs and 6 FTCs (validation set). Results: The diagnostic system for differentiation of FTA and FTC, constructed with the aid of the learning set samples, was based on 60 genes differentially expressed in FTA and FTC, which included several genes previously identified as overexpressed in FTC (DPP4, KRT19 and IGFBP3) or FTA (trefoil factor 3 and thyroid peroxidase).The leave-one-out cross-validation study showed that the accuracy of this diagnostic system was as high as 90% (sensitivity: 77.3% and specificity: 95.6%), and was confirmed by the validation study (diagnostic accuracy: 83.3%; 95% confidence interval: 62.8–95.4%, sensitivity: 66.7% and specificity: 91.2%). Conclusions: This diagnostic system using the ATAC-PCR assay is expected to be clinically useful for preoperative differentiation between FTA and FTC since ATAC-PCR can be used for the small amount of RNA obtained from fine needle aspiration biopsy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differentiation of Follicular Thyroid Adenoma from Carcinoma by Means of Gene Expression Profiling with Adapter-Tagged Competitive Polymerase Chain Reaction

Taniguchi

Takano²,

Miyauchi³

et al. 2005

Oncology

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“…Kernel density estimation is widely applied in various problems including Bioinformatics [3], [6], [12 ]. The idea of kernel density estimation is to model the density of the effective subspaces as a sum of the influences of the data points which is given by a kernel function, symmetric and has the maximum informative data [9].…”

Section: Introductionmentioning

confidence: 99%

Kernel Dimensionality Reduction evaluation on various dimensions of effective subspaces for cancer patient survival analysis

Chin

Wasito

Hashim

2010

10th International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2010)

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In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis. KDR method was extended and combined with the K-means clustering technique, Cox's proportional hazards regression model and log rank test where KDR contributes in gene classification to determine subgroups from the patient's group. Results from the experiments have indicated that our model has outperformed Support Vector Machines (SVM) in gene classification. We also observed that the best value for dimension of effective subspaces (K) for microarray DNA data is between 10%-20% of the total patients.

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“…This feature also eliminates potential effects of a racial background on gene expression. The high quality of the expression and clinical data is demonstrated by the successful identification of prognostic genes in breast (6), colorectal (7) and hepatocellular cancers (8). …”

Section: Introductionmentioning

confidence: 99%

Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues

Kato¹

2004

Nucleic Acids Research

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Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data

Cited by 59 publications

References 32 publications

Differentiation of Follicular Thyroid Adenoma from Carcinoma by Means of Gene Expression Profiling with Adapter-Tagged Competitive Polymerase Chain Reaction

Differentiation of Follicular Thyroid Adenoma from Carcinoma by Means of Gene Expression Profiling with Adapter-Tagged Competitive Polymerase Chain Reaction

Kernel Dimensionality Reduction evaluation on various dimensions of effective subspaces for cancer patient survival analysis

Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues

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