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Discovery of a Novel Immune Gene Signature with Profound Prognostic Value in Colorectal Cancer: A Model of Cooperativity Disorientation Created in the Process from Development to Cancer.

An N, Shi X, Zhang Y, Lv N, Feng L, Di X, Han N, Wang G, Cheng S, Zhang K - PLoS ONE (2015)

Bottom Line: We originally established Spearman correlation transition model to quantify the cooperativity disorientation associated with the transition from normal to precancerous to cancer tissue, in conjunction with miRNA-mRNA regulatory network and machine learning algorithm to identify genes with prognostic value.Using the log-rank test, the 12-gene signature was closely related to overall survival in four datasets (GSE17536, n = 177, p = 0.0054; GSE17537, n = 55, p = 0.0039; GSE39582, n = 562, p = 0.13; GSE39084, n = 70, p = 0.11), and significantly associated with disease-free survival in four datasets (GSE17536, n = 177, p = 0.0018; GSE17537, n = 55, p = 0.016; GSE39582, n = 557, p = 4.4e-05; GSE14333, n = 226, p = 0.032).Cox regression analysis confirmed that the 12-gene signature was an independent factor in predicting colorectal cancer patient's overall survival (hazard ratio: 1.759; 95% confidence interval: 1.126-2.746; p = 0.013], as well as disease-free survival (hazard ratio: 2.116; 95% confidence interval: 1.324-3.380; p = 0.002).

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Molecular Oncology, Cancer Institute (Hospital), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

ABSTRACT
Immune response-related genes play a major role in colorectal carcinogenesis by mediating inflammation or immune-surveillance evasion. Although remarkable progress has been made to investigate the underlying mechanism, the understanding of the complicated carcinogenesis process was enormously hindered by large-scale tumor heterogeneity. Development and carcinogenesis share striking similarities in their cellular behavior and underlying molecular mechanisms. The association between embryonic development and carcinogenesis makes embryonic development a viable reference model for studying cancer thereby circumventing the potentially misleading complexity of tumor heterogeneity. Here we proposed that the immune genes, responsible for intra-immune cooperativity disorientation (defined in this study as disruption of developmental expression correlation patterns during carcinogenesis), probably contain untapped prognostic resource of colorectal cancer. In this study, we determined the mRNA expression profile of 137 human biopsy samples, including samples from different stages of human colonic development, colorectal precancerous progression and colorectal cancer samples, among which 60 were also used to generate miRNA expression profile. We originally established Spearman correlation transition model to quantify the cooperativity disorientation associated with the transition from normal to precancerous to cancer tissue, in conjunction with miRNA-mRNA regulatory network and machine learning algorithm to identify genes with prognostic value. Finally, a 12-gene signature was extracted, whose prognostic value was evaluated using Kaplan-Meier survival analysis in five independent datasets. Using the log-rank test, the 12-gene signature was closely related to overall survival in four datasets (GSE17536, n = 177, p = 0.0054; GSE17537, n = 55, p = 0.0039; GSE39582, n = 562, p = 0.13; GSE39084, n = 70, p = 0.11), and significantly associated with disease-free survival in four datasets (GSE17536, n = 177, p = 0.0018; GSE17537, n = 55, p = 0.016; GSE39582, n = 557, p = 4.4e-05; GSE14333, n = 226, p = 0.032). Cox regression analysis confirmed that the 12-gene signature was an independent factor in predicting colorectal cancer patient's overall survival (hazard ratio: 1.759; 95% confidence interval: 1.126-2.746; p = 0.013], as well as disease-free survival (hazard ratio: 2.116; 95% confidence interval: 1.324-3.380; p = 0.002).

No MeSH data available.


Related in: MedlinePlus

Gene signature optimization based on Spearman correlation transition model and AUC-RF algorithm.(A) The 665 DVIGs were projected onto a Spearman correlation transition coordinate system based on their cooperativity disorientation between the consecutive stages. Genes were colored in the same way as in the development heatmap. (B) The AUC-RF algorithm was used for gene signature optimization. Genes were recursively removed from an importance-ordered gene list until the largest AUC value was met. (C) The biggest AUC of 0.904 (95% CI: 0.799~1.000) was obtained when the number of genes were reduced to 12, with 81.8% sensitivity (95% CI: 0.636–0.955) and 89.5% specificity (95% CI: 0.737–1.000). Dev, development; Prog, progression; TPS, theoretically stable point; AUC, area under curve; DVIG, development varying immune gene; CI, confidence interval.
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pone.0137171.g003: Gene signature optimization based on Spearman correlation transition model and AUC-RF algorithm.(A) The 665 DVIGs were projected onto a Spearman correlation transition coordinate system based on their cooperativity disorientation between the consecutive stages. Genes were colored in the same way as in the development heatmap. (B) The AUC-RF algorithm was used for gene signature optimization. Genes were recursively removed from an importance-ordered gene list until the largest AUC value was met. (C) The biggest AUC of 0.904 (95% CI: 0.799~1.000) was obtained when the number of genes were reduced to 12, with 81.8% sensitivity (95% CI: 0.636–0.955) and 89.5% specificity (95% CI: 0.737–1.000). Dev, development; Prog, progression; TPS, theoretically stable point; AUC, area under curve; DVIG, development varying immune gene; CI, confidence interval.

Mentions: Pearson correlation heatmaps of DVIGs during the progression and cancer stages were reordered to make all three stages (Fig 2E and 2F, described in S1 Methods). As shown in Fig 3A, the 665 DVIGs were projected onto a Spearman transition coordinate system, with the Spearman transition between development and progression (STD-P, S1 Methods) and between progression and cancer (STP-C, S1 Methods) as the x and y axis coordinates, respectively. Genes were colored in the same way as in the developmental heatmap clustering in Fig 2A. Of the 665 DVIGs, 385 (termed “obedient genes”) fell within the quarter circle’s arc, while the remaining 280 (termed “diversion genes”) fell outside this arc and were used as candidates for downstream selection procedures.


Discovery of a Novel Immune Gene Signature with Profound Prognostic Value in Colorectal Cancer: A Model of Cooperativity Disorientation Created in the Process from Development to Cancer.

An N, Shi X, Zhang Y, Lv N, Feng L, Di X, Han N, Wang G, Cheng S, Zhang K - PLoS ONE (2015)

Gene signature optimization based on Spearman correlation transition model and AUC-RF algorithm.(A) The 665 DVIGs were projected onto a Spearman correlation transition coordinate system based on their cooperativity disorientation between the consecutive stages. Genes were colored in the same way as in the development heatmap. (B) The AUC-RF algorithm was used for gene signature optimization. Genes were recursively removed from an importance-ordered gene list until the largest AUC value was met. (C) The biggest AUC of 0.904 (95% CI: 0.799~1.000) was obtained when the number of genes were reduced to 12, with 81.8% sensitivity (95% CI: 0.636–0.955) and 89.5% specificity (95% CI: 0.737–1.000). Dev, development; Prog, progression; TPS, theoretically stable point; AUC, area under curve; DVIG, development varying immune gene; CI, confidence interval.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4556644&req=5

pone.0137171.g003: Gene signature optimization based on Spearman correlation transition model and AUC-RF algorithm.(A) The 665 DVIGs were projected onto a Spearman correlation transition coordinate system based on their cooperativity disorientation between the consecutive stages. Genes were colored in the same way as in the development heatmap. (B) The AUC-RF algorithm was used for gene signature optimization. Genes were recursively removed from an importance-ordered gene list until the largest AUC value was met. (C) The biggest AUC of 0.904 (95% CI: 0.799~1.000) was obtained when the number of genes were reduced to 12, with 81.8% sensitivity (95% CI: 0.636–0.955) and 89.5% specificity (95% CI: 0.737–1.000). Dev, development; Prog, progression; TPS, theoretically stable point; AUC, area under curve; DVIG, development varying immune gene; CI, confidence interval.
Mentions: Pearson correlation heatmaps of DVIGs during the progression and cancer stages were reordered to make all three stages (Fig 2E and 2F, described in S1 Methods). As shown in Fig 3A, the 665 DVIGs were projected onto a Spearman transition coordinate system, with the Spearman transition between development and progression (STD-P, S1 Methods) and between progression and cancer (STP-C, S1 Methods) as the x and y axis coordinates, respectively. Genes were colored in the same way as in the developmental heatmap clustering in Fig 2A. Of the 665 DVIGs, 385 (termed “obedient genes”) fell within the quarter circle’s arc, while the remaining 280 (termed “diversion genes”) fell outside this arc and were used as candidates for downstream selection procedures.

Bottom Line: We originally established Spearman correlation transition model to quantify the cooperativity disorientation associated with the transition from normal to precancerous to cancer tissue, in conjunction with miRNA-mRNA regulatory network and machine learning algorithm to identify genes with prognostic value.Using the log-rank test, the 12-gene signature was closely related to overall survival in four datasets (GSE17536, n = 177, p = 0.0054; GSE17537, n = 55, p = 0.0039; GSE39582, n = 562, p = 0.13; GSE39084, n = 70, p = 0.11), and significantly associated with disease-free survival in four datasets (GSE17536, n = 177, p = 0.0018; GSE17537, n = 55, p = 0.016; GSE39582, n = 557, p = 4.4e-05; GSE14333, n = 226, p = 0.032).Cox regression analysis confirmed that the 12-gene signature was an independent factor in predicting colorectal cancer patient's overall survival (hazard ratio: 1.759; 95% confidence interval: 1.126-2.746; p = 0.013], as well as disease-free survival (hazard ratio: 2.116; 95% confidence interval: 1.324-3.380; p = 0.002).

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Molecular Oncology, Cancer Institute (Hospital), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

ABSTRACT
Immune response-related genes play a major role in colorectal carcinogenesis by mediating inflammation or immune-surveillance evasion. Although remarkable progress has been made to investigate the underlying mechanism, the understanding of the complicated carcinogenesis process was enormously hindered by large-scale tumor heterogeneity. Development and carcinogenesis share striking similarities in their cellular behavior and underlying molecular mechanisms. The association between embryonic development and carcinogenesis makes embryonic development a viable reference model for studying cancer thereby circumventing the potentially misleading complexity of tumor heterogeneity. Here we proposed that the immune genes, responsible for intra-immune cooperativity disorientation (defined in this study as disruption of developmental expression correlation patterns during carcinogenesis), probably contain untapped prognostic resource of colorectal cancer. In this study, we determined the mRNA expression profile of 137 human biopsy samples, including samples from different stages of human colonic development, colorectal precancerous progression and colorectal cancer samples, among which 60 were also used to generate miRNA expression profile. We originally established Spearman correlation transition model to quantify the cooperativity disorientation associated with the transition from normal to precancerous to cancer tissue, in conjunction with miRNA-mRNA regulatory network and machine learning algorithm to identify genes with prognostic value. Finally, a 12-gene signature was extracted, whose prognostic value was evaluated using Kaplan-Meier survival analysis in five independent datasets. Using the log-rank test, the 12-gene signature was closely related to overall survival in four datasets (GSE17536, n = 177, p = 0.0054; GSE17537, n = 55, p = 0.0039; GSE39582, n = 562, p = 0.13; GSE39084, n = 70, p = 0.11), and significantly associated with disease-free survival in four datasets (GSE17536, n = 177, p = 0.0018; GSE17537, n = 55, p = 0.016; GSE39582, n = 557, p = 4.4e-05; GSE14333, n = 226, p = 0.032). Cox regression analysis confirmed that the 12-gene signature was an independent factor in predicting colorectal cancer patient's overall survival (hazard ratio: 1.759; 95% confidence interval: 1.126-2.746; p = 0.013], as well as disease-free survival (hazard ratio: 2.116; 95% confidence interval: 1.324-3.380; p = 0.002).

No MeSH data available.


Related in: MedlinePlus