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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

Forest plot of the association between individual genes in the 12-gene signature and CRC survival.(A) Forest plot of the association between individual genes and OS with a fixed-effect model in datasets containing OS information (GSE17536, GSE17537, GSE39582 and GSE39084). Meta-analysis of these 12 genes in four independent datasets was conducted, and HR, 95% CI of each gene and corresponding p value were calculated and plotted in the forest plot. (B) Forest plot of the association between individual genes and DFS with a random-effect model in four datasets containing DFS information (GSE17536, GSE17537, GSE39582 and GSE14333). CRC, colorectal cancer; HR, hazard ratio; CI; confidence interval; OS, overall survival; DFS, disease-free survival.
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pone.0137171.g006: Forest plot of the association between individual genes in the 12-gene signature and CRC survival.(A) Forest plot of the association between individual genes and OS with a fixed-effect model in datasets containing OS information (GSE17536, GSE17537, GSE39582 and GSE39084). Meta-analysis of these 12 genes in four independent datasets was conducted, and HR, 95% CI of each gene and corresponding p value were calculated and plotted in the forest plot. (B) Forest plot of the association between individual genes and DFS with a random-effect model in four datasets containing DFS information (GSE17536, GSE17537, GSE39582 and GSE14333). CRC, colorectal cancer; HR, hazard ratio; CI; confidence interval; OS, overall survival; DFS, disease-free survival.

Mentions: Kaplan–Meier survival analysis was conducted to evaluate the prognostic value of the 12-gene signature in five Affymetrix datasets retrieved from the GEO database. The log-rank test results confirmed that the 12-gene signature was closely related to OS in four datasets (Fig 5A; GSE17536, n = 177, p = 0.0054; GSE17537, n = 55, p = 0.0039; GSE39582, n = 562, p = 0.13; GSE39084, n = 70, p = 0.11). Furthermore, this 12-gene signature was significantly associated with DFS in four datasets (Fig 5B; 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 also confirmed that the 12-gene signature was an independent factor in predicting CRC patient’s OS [Table 2; hazard ratio (HR): 1.759; 95% CI: 1.126–2.746; p = 0.013], as well as DFS (Table 2; HR: 2.116; 95% CI: 1.324–3.380; p = 0.002). Meta-analysis was conducted to evaluate the correlation between each of the 12 genes and survival (OS: GSE17536, GSE17537, GSE39582 and GSE39084; and DFS: GSE17536, GSE17537, GSE39582 and GSE14333) of CRC patients with fixed-effect model (Fig 6A) and random-effect model (Fig 6B).


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)

Forest plot of the association between individual genes in the 12-gene signature and CRC survival.(A) Forest plot of the association between individual genes and OS with a fixed-effect model in datasets containing OS information (GSE17536, GSE17537, GSE39582 and GSE39084). Meta-analysis of these 12 genes in four independent datasets was conducted, and HR, 95% CI of each gene and corresponding p value were calculated and plotted in the forest plot. (B) Forest plot of the association between individual genes and DFS with a random-effect model in four datasets containing DFS information (GSE17536, GSE17537, GSE39582 and GSE14333). CRC, colorectal cancer; HR, hazard ratio; CI; confidence interval; OS, overall survival; DFS, disease-free survival.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4556644&req=5

pone.0137171.g006: Forest plot of the association between individual genes in the 12-gene signature and CRC survival.(A) Forest plot of the association between individual genes and OS with a fixed-effect model in datasets containing OS information (GSE17536, GSE17537, GSE39582 and GSE39084). Meta-analysis of these 12 genes in four independent datasets was conducted, and HR, 95% CI of each gene and corresponding p value were calculated and plotted in the forest plot. (B) Forest plot of the association between individual genes and DFS with a random-effect model in four datasets containing DFS information (GSE17536, GSE17537, GSE39582 and GSE14333). CRC, colorectal cancer; HR, hazard ratio; CI; confidence interval; OS, overall survival; DFS, disease-free survival.
Mentions: Kaplan–Meier survival analysis was conducted to evaluate the prognostic value of the 12-gene signature in five Affymetrix datasets retrieved from the GEO database. The log-rank test results confirmed that the 12-gene signature was closely related to OS in four datasets (Fig 5A; GSE17536, n = 177, p = 0.0054; GSE17537, n = 55, p = 0.0039; GSE39582, n = 562, p = 0.13; GSE39084, n = 70, p = 0.11). Furthermore, this 12-gene signature was significantly associated with DFS in four datasets (Fig 5B; 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 also confirmed that the 12-gene signature was an independent factor in predicting CRC patient’s OS [Table 2; hazard ratio (HR): 1.759; 95% CI: 1.126–2.746; p = 0.013], as well as DFS (Table 2; HR: 2.116; 95% CI: 1.324–3.380; p = 0.002). Meta-analysis was conducted to evaluate the correlation between each of the 12 genes and survival (OS: GSE17536, GSE17537, GSE39582 and GSE39084; and DFS: GSE17536, GSE17537, GSE39582 and GSE14333) of CRC patients with fixed-effect model (Fig 6A) and random-effect model (Fig 6B).

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