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Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases.

Kim S, Lee D - BMC Bioinformatics (2009)

Bottom Line: We found that our candidate genes for tissue specificity are consistent with the TiGER database.And we also found that the metastasis candidate genes from our method were more consistent with the known biological background and independent from other noise features.The proposed method attempts to minimize the influences from other factors except metastatic ability including tissue originality and tissue viability by confining the result of metastasis unrelated test combinations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-Dong, Yu-seong Gu, Daejeon, 305-701, Republic of Korea. swkim@biosoft.kaist.ac.kr

ABSTRACT

Background: Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in elucidating metastasis related genes and pathways, followed by predicting prognosis of cancer patients, but there still is a question whether the result genes or pathways contain enough information and noise features have been controlled appropriately.

Methods: We set four tumor type classes composed of various tumor characteristics such as tissue origin, cellular environment, and metastatic ability. We conducted a set of comparisons among the four tumor classes followed by searching for genes that are consistently up or down regulated through the whole comparisons.

Results: We identified four sets of genes that are consistently differently expressed in the comparisons, each of which denotes one of four cellular characteristics respectively - liver tissue, colon tissue, liver viability and metastasis characteristics. We found that our candidate genes for tissue specificity are consistent with the TiGER database. And we also found that the metastasis candidate genes from our method were more consistent with the known biological background and independent from other noise features.

Conclusion: We suggested a new method for identifying metastasis related genes from a large-scale database. The proposed method attempts to minimize the influences from other factors except metastatic ability including tissue originality and tissue viability by confining the result of metastasis unrelated test combinations.

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Related in: MedlinePlus

Heat map of A↔B comparison. Differently expressed genes were denoted in a heat map. Sample A (left cluster) is from primary liver tissues, sample B (right cluster) is from liver metastasis of colon cancer. As shown in Table 1, this result contains information of metastatic ability and tissues specificity (liver versus colon tissue). All heat maps from other comparison combinations are included in Additional file 2.
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Figure 3: Heat map of A↔B comparison. Differently expressed genes were denoted in a heat map. Sample A (left cluster) is from primary liver tissues, sample B (right cluster) is from liver metastasis of colon cancer. As shown in Table 1, this result contains information of metastatic ability and tissues specificity (liver versus colon tissue). All heat maps from other comparison combinations are included in Additional file 2.

Mentions: We scored 20606 genes using the function described in the last section. Total 54675 probe sets from an Affymetrix U133 Plus 2.0 chip were matched to their corresponding genes using GSEA v2 program's Collapse Dataset tool. In the case of many to gene matching, we used the maximum value of the probes. Enrichment scores have been calculated for six comparisons (A↔B, A↔C, A↔D, B↔C, B↔D, C↔D), and their differently expressed genes were denoted using six Heat Maps. The heat map of A↔B is shown in Figure 3. Remaining heat maps are shown in Additional file 2.


Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases.

Kim S, Lee D - BMC Bioinformatics (2009)

Heat map of A↔B comparison. Differently expressed genes were denoted in a heat map. Sample A (left cluster) is from primary liver tissues, sample B (right cluster) is from liver metastasis of colon cancer. As shown in Table 1, this result contains information of metastatic ability and tissues specificity (liver versus colon tissue). All heat maps from other comparison combinations are included in Additional file 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2665050&req=5

Figure 3: Heat map of A↔B comparison. Differently expressed genes were denoted in a heat map. Sample A (left cluster) is from primary liver tissues, sample B (right cluster) is from liver metastasis of colon cancer. As shown in Table 1, this result contains information of metastatic ability and tissues specificity (liver versus colon tissue). All heat maps from other comparison combinations are included in Additional file 2.
Mentions: We scored 20606 genes using the function described in the last section. Total 54675 probe sets from an Affymetrix U133 Plus 2.0 chip were matched to their corresponding genes using GSEA v2 program's Collapse Dataset tool. In the case of many to gene matching, we used the maximum value of the probes. Enrichment scores have been calculated for six comparisons (A↔B, A↔C, A↔D, B↔C, B↔D, C↔D), and their differently expressed genes were denoted using six Heat Maps. The heat map of A↔B is shown in Figure 3. Remaining heat maps are shown in Additional file 2.

Bottom Line: We found that our candidate genes for tissue specificity are consistent with the TiGER database.And we also found that the metastasis candidate genes from our method were more consistent with the known biological background and independent from other noise features.The proposed method attempts to minimize the influences from other factors except metastatic ability including tissue originality and tissue viability by confining the result of metastasis unrelated test combinations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-Dong, Yu-seong Gu, Daejeon, 305-701, Republic of Korea. swkim@biosoft.kaist.ac.kr

ABSTRACT

Background: Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in elucidating metastasis related genes and pathways, followed by predicting prognosis of cancer patients, but there still is a question whether the result genes or pathways contain enough information and noise features have been controlled appropriately.

Methods: We set four tumor type classes composed of various tumor characteristics such as tissue origin, cellular environment, and metastatic ability. We conducted a set of comparisons among the four tumor classes followed by searching for genes that are consistently up or down regulated through the whole comparisons.

Results: We identified four sets of genes that are consistently differently expressed in the comparisons, each of which denotes one of four cellular characteristics respectively - liver tissue, colon tissue, liver viability and metastasis characteristics. We found that our candidate genes for tissue specificity are consistent with the TiGER database. And we also found that the metastasis candidate genes from our method were more consistent with the known biological background and independent from other noise features.

Conclusion: We suggested a new method for identifying metastasis related genes from a large-scale database. The proposed method attempts to minimize the influences from other factors except metastatic ability including tissue originality and tissue viability by confining the result of metastasis unrelated test combinations.

Show MeSH
Related in: MedlinePlus