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An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer.

Cui J, Chen Y, Chou WC, Sun L, Chen L, Suo J, Ni Z, Zhang M, Kong X, Hoffman LL, Kang J, Su Y, Olman V, Johnson D, Tench DW, Amster IJ, Orlando R, Puett D, Li F, Xu Y - Nucleic Acids Res. (2010)

Bottom Line: This report describes an integrated study on identification of potential markers for gastric cancer in patients' cancer tissues and sera based on: (i) genome-scale transcriptomic analyses of 80 paired gastric cancer/reference tissues and (ii) computational prediction of blood-secretory proteins supported by experimental validation.Our findings show that: (i) 715 and 150 genes exhibit significantly differential expressions in all cancers and early-stage cancers versus reference tissues, respectively; and a substantial percentage of the alteration is found to be influenced by age and/or by gender; (ii) 21 co-expressed gene clusters have been identified, some of which are specific to certain subtypes or stages of the cancer; (iii) the top-ranked gene signatures give better than 94% classification accuracy between cancer and the reference tissues, some of which are gender-specific; and (iv) 136 of the differentially expressed genes were predicted to have their proteins secreted into blood, 81 of which were detected experimentally in the sera of 13 validation samples and 29 found to have differential abundances in the sera of cancer patients versus controls.Overall, the novel information obtained in this study has led to identification of promising diagnostic markers for gastric cancer and can benefit further analyses of the key (early) abnormalities during its development.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.

ABSTRACT
This report describes an integrated study on identification of potential markers for gastric cancer in patients' cancer tissues and sera based on: (i) genome-scale transcriptomic analyses of 80 paired gastric cancer/reference tissues and (ii) computational prediction of blood-secretory proteins supported by experimental validation. Our findings show that: (i) 715 and 150 genes exhibit significantly differential expressions in all cancers and early-stage cancers versus reference tissues, respectively; and a substantial percentage of the alteration is found to be influenced by age and/or by gender; (ii) 21 co-expressed gene clusters have been identified, some of which are specific to certain subtypes or stages of the cancer; (iii) the top-ranked gene signatures give better than 94% classification accuracy between cancer and the reference tissues, some of which are gender-specific; and (iv) 136 of the differentially expressed genes were predicted to have their proteins secreted into blood, 81 of which were detected experimentally in the sera of 13 validation samples and 29 found to have differential abundances in the sera of cancer patients versus controls. Overall, the novel information obtained in this study has led to identification of promising diagnostic markers for gastric cancer and can benefit further analyses of the key (early) abnormalities during its development.

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Related in: MedlinePlus

(A) The red curve represents the overall classification accuracies of k-gene markers (k = 1,…, 100), which is the average of the best accuracies of 500 randomly selected subsets; the blue curve represents the best 5-fold cross validation accuracy of k-gene markers (k = 1, 2,…, 8), identified through an exhaustive search. (B) The heatmap for the best 28-gene marker, comprising of 13 up-regulated and 15 down-regulated genes. Among them, NKAP, TMEM185B, C14orf104 and C1orf96 are up-regulated, while KLF15, PI16 and GADD45B are down-regulated across >89% early-stage patients.
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Figure 2: (A) The red curve represents the overall classification accuracies of k-gene markers (k = 1,…, 100), which is the average of the best accuracies of 500 randomly selected subsets; the blue curve represents the best 5-fold cross validation accuracy of k-gene markers (k = 1, 2,…, 8), identified through an exhaustive search. (B) The heatmap for the best 28-gene marker, comprising of 13 up-regulated and 15 down-regulated genes. Among them, NKAP, TMEM185B, C14orf104 and C1orf96 are up-regulated, while KLF15, PI16 and GADD45B are down-regulated across >89% early-stage patients.

Mentions: A number of genes were found to have distinguishing expression patterns between the cancer and the reference tissues, based on a classification analysis using RFE-SVM (see ‘Materials and Methods’ section). Figure 2A (red curve) summarizes the classification accuracies for the optimal k-gene combinations (markers) for k from 1 to 100, derived using our gene-signature identification program, where a 28-gene combination gives the best accuracy, having 95.9 and 97.9% agreement with the cancer and reference tissues, respectively (Figure 2B).Figure 2.


An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer.

Cui J, Chen Y, Chou WC, Sun L, Chen L, Suo J, Ni Z, Zhang M, Kong X, Hoffman LL, Kang J, Su Y, Olman V, Johnson D, Tench DW, Amster IJ, Orlando R, Puett D, Li F, Xu Y - Nucleic Acids Res. (2010)

(A) The red curve represents the overall classification accuracies of k-gene markers (k = 1,…, 100), which is the average of the best accuracies of 500 randomly selected subsets; the blue curve represents the best 5-fold cross validation accuracy of k-gene markers (k = 1, 2,…, 8), identified through an exhaustive search. (B) The heatmap for the best 28-gene marker, comprising of 13 up-regulated and 15 down-regulated genes. Among them, NKAP, TMEM185B, C14orf104 and C1orf96 are up-regulated, while KLF15, PI16 and GADD45B are down-regulated across >89% early-stage patients.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3045610&req=5

Figure 2: (A) The red curve represents the overall classification accuracies of k-gene markers (k = 1,…, 100), which is the average of the best accuracies of 500 randomly selected subsets; the blue curve represents the best 5-fold cross validation accuracy of k-gene markers (k = 1, 2,…, 8), identified through an exhaustive search. (B) The heatmap for the best 28-gene marker, comprising of 13 up-regulated and 15 down-regulated genes. Among them, NKAP, TMEM185B, C14orf104 and C1orf96 are up-regulated, while KLF15, PI16 and GADD45B are down-regulated across >89% early-stage patients.
Mentions: A number of genes were found to have distinguishing expression patterns between the cancer and the reference tissues, based on a classification analysis using RFE-SVM (see ‘Materials and Methods’ section). Figure 2A (red curve) summarizes the classification accuracies for the optimal k-gene combinations (markers) for k from 1 to 100, derived using our gene-signature identification program, where a 28-gene combination gives the best accuracy, having 95.9 and 97.9% agreement with the cancer and reference tissues, respectively (Figure 2B).Figure 2.

Bottom Line: This report describes an integrated study on identification of potential markers for gastric cancer in patients' cancer tissues and sera based on: (i) genome-scale transcriptomic analyses of 80 paired gastric cancer/reference tissues and (ii) computational prediction of blood-secretory proteins supported by experimental validation.Our findings show that: (i) 715 and 150 genes exhibit significantly differential expressions in all cancers and early-stage cancers versus reference tissues, respectively; and a substantial percentage of the alteration is found to be influenced by age and/or by gender; (ii) 21 co-expressed gene clusters have been identified, some of which are specific to certain subtypes or stages of the cancer; (iii) the top-ranked gene signatures give better than 94% classification accuracy between cancer and the reference tissues, some of which are gender-specific; and (iv) 136 of the differentially expressed genes were predicted to have their proteins secreted into blood, 81 of which were detected experimentally in the sera of 13 validation samples and 29 found to have differential abundances in the sera of cancer patients versus controls.Overall, the novel information obtained in this study has led to identification of promising diagnostic markers for gastric cancer and can benefit further analyses of the key (early) abnormalities during its development.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.

ABSTRACT
This report describes an integrated study on identification of potential markers for gastric cancer in patients' cancer tissues and sera based on: (i) genome-scale transcriptomic analyses of 80 paired gastric cancer/reference tissues and (ii) computational prediction of blood-secretory proteins supported by experimental validation. Our findings show that: (i) 715 and 150 genes exhibit significantly differential expressions in all cancers and early-stage cancers versus reference tissues, respectively; and a substantial percentage of the alteration is found to be influenced by age and/or by gender; (ii) 21 co-expressed gene clusters have been identified, some of which are specific to certain subtypes or stages of the cancer; (iii) the top-ranked gene signatures give better than 94% classification accuracy between cancer and the reference tissues, some of which are gender-specific; and (iv) 136 of the differentially expressed genes were predicted to have their proteins secreted into blood, 81 of which were detected experimentally in the sera of 13 validation samples and 29 found to have differential abundances in the sera of cancer patients versus controls. Overall, the novel information obtained in this study has led to identification of promising diagnostic markers for gastric cancer and can benefit further analyses of the key (early) abnormalities during its development.

Show MeSH
Related in: MedlinePlus