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A prognostic index based on an eleven gene signature to predict systemic recurrences in colorectal cancer.

ABSTRACT: Approximately half of colorectal cancer (CRC) patients experience disease recurrence and metastasis, and these individuals frequently fail to respond to treatment due to their clinical and biological diversity. Here, we aimed to identify a prognostic signature consisting of a small gene group for precisely predicting CRC heterogeneity. We performed transcriptomic profiling using RNA-seq data generated from the primary tissue samples of 130 CRC patients. A prognostic index (PI) based on recurrence-associated genes was developed and validated in two larger independent CRC patient cohorts (n?=?795). The association between the PI and prognosis of CRC patients was evaluated using Kaplan-Meier plots, log-rank tests, a Cox regression analysis and a RT-PCR analysis. Transcriptomic profiling in 130 CRC patients identified two distinct subtypes associated with systemic recurrence. Pathway enrichment and RT-PCR analyses revealed an eleven gene signature incorporated into the PI system, which was a significant prognostic indicator of CRC. Multivariate and subset analyses showed that PI was an independent risk factor (HR?=?1.812, 95% CI?=?1.342-2.448, P?

SUBMITTER: Kim SK

PROVIDER: S-EPMC6802642 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A prognostic index based on an eleven gene signature to predict systemic recurrences in colorectal cancer.

Kim Seon-Kyu SK Kim Seon-Young SY Kim Chan Wook CW Roh Seon Ae SA Ha Ye Jin YJ Lee Jong Lyul JL Heo Haejeong H Cho Dong-Hyung DH Lee Ju-Seog JS Kim Yong Sung YS Kim Jin Cheon JC

Experimental & molecular medicine 20191002 10

Approximately half of colorectal cancer (CRC) patients experience disease recurrence and metastasis, and these individuals frequently fail to respond to treatment due to their clinical and biological diversity. Here, we aimed to identify a prognostic signature consisting of a small gene group for precisely predicting CRC heterogeneity. We performed transcriptomic profiling using RNA-seq data generated from the primary tissue samples of 130 CRC patients. A prognostic index (PI) based on recurrenc ...[more]

PMID: 31578316

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Project description:BackgroundPrognosis varies among patients within the same colon adenocarcinoma (COAD) stage, indicating the need for reliable molecular markers to enable individualized treatment. This study aimed to investigate gene signatures that can be used for better prognostic prediction of COAD.MethodsGene-expression profiles of COAD patients were obtained from the Gene Expression Omnibus database (n = 332) and The Cancer Genome Atlas database (n = 431). The relationship between gene signature and relapse-free survival was analysed in the training set (n = 93) and validated in the internal validation set (n = 94) and external validation sets (n = 145 and 431).ResultsOverall, 11 genes (N-myc downstream regulated gene 1 [NDRG1], fms-like tyrosine kinase 1 [FLT1], lipopolysaccharide binding protein [LBP], fatty acid binding protein 4 [FABP4], adiponectin gene [ADIPOQ], angiotensinogen gene [AGT], activin A receptor, type II-like kinase 1 [ACVRL1], CC chemokine ligand 11 [CCL11], cell division cycle 42 [CDC42], T-cell receptor alpha variable 9_2 [TRAV9_2], and proopiomelanocortin [POMC]) were identified by univariable and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Based on the risk-score model, the patients were grouped into the high-risk or low-risk groups using the median risk score as the cut-off. The area under the curve (AUC) values for 1-, 3-, and 5-year recurrence were 0.970, 0.849, and 0.859, respectively. Patients in the high-risk group had significantly poorer relapse-free survival than did those in the low-risk group. The predictive accuracy of the 11-gene signature was proven in the validation sets. Our gene signature showed better predictive performance for 1-, 3-, and 5-year recurrence than did the other four models.ConclusionsThe 11-gene signature showed good performance in predicting recurrence in COAD. The accuracy of the signature for prognostic classification requires further confirmation.

Project description:BackgroundColorectal cancer (CRC) is the second leading cause of cancer-related deaths in the world. This study aimed to develop a urea cycle (UC)-related gene signature that provides a theoretical foundation for the prognosis and treatment of patients with CRC.MethodsDifferentially expressed UC-related genes in CRC were confirmed using differential analysis and Venn diagrams. Univariate Cox and least absolute shrinkage and selection operator regression analyses were performed to identify UC-related prognostic genes. A UC-related signature was created and confirmed using distinct datasets. Independent prognostic predictors were authenticated using Cox analysis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts algorithm and Spearman method were applied to probe the linkage between UC-related prognostic genes and tumor immune-infiltrating cells. The Human Protein Atlas database was used to determine the protein expression levels of prognostic genes in CRC and normal tissues. Verification of the expression levels of UC-related prognostic genes in clinical tissue samples was performed using real-time quantitative polymerase chain reaction (qPCR).ResultsA total of 49 DEUCRGs in CRC were mined. Eight prognostic genes (TIMP1, FABP4, MMP3, MMP1, CD177, CA2, S100P, and SPP1) were identified to construct a UC-related gene signature. The signature was then affirmed using an external validation set. The risk score was demonstrated to be a credible independent prognostic predictor using Cox regression analysis. Functional enrichment analysis revealed that focal adhesion, ECM-receptor interaction, IL-17 signaling pathway, and nitrogen metabolism were associated with the UC-related gene signature. Immune infiltration and correlation analyses revealed a significant correlation between UC-related prognostic genes and differential immune cells between the two risk subgroups. Finally, the qPCR results of clinical samples further confirmed the results of the public database.ConclusionTaken together, this study authenticated UC-related prognostic genes and developed a gene signature for the prognosis of CRC, which will be of great significance in the identification of prognostic molecular biomarkers, clinical prognosis prediction, and development of treatment strategies for patients with CRC.

Project description:IntroductionColorectal cancer shows high incidence and mortality rates. Immune checkpoint blockade can be used to treat colorectal carcinoma (CRC); however, it shows limited effectiveness in most patients.MethodsTo identify patients who may benefit from immunotherapy using immune checkpoint inhibitors, we constructed an immune-related gene prognostic index (IRGPI) for predicting the efficacy of immunotherapy in patients with CRC. Transcriptome datasets and clinical information of patients with CRC were used to identify differential immune-related genes between tumor and para-carcinoma tissue. Using weighted correlation network analysis and Cox regression analysis, the IRGPI was constructed, and Kaplan-Meier analysis was used to evaluate its predictive ability. We also analyzed the molecular and immune characteristics between IRGPI high-and low-risk subgroups, performed sensitivity analysis of ICI treatment, and constructed overall survival-related receiver operating characteristic curves to validate the IRGPI. Finally, IRGPI genes and tumor immune cell infiltration in CRC model mice with orthotopic metastases were analyzed to verify the results.ResultsThe IRGPI was constructed based on the following 11 hub genes: ADIPOQ, CD36, CCL24, INHBE, UCN, IL1RL2, TRIM58, RBCK1, MC1R, PPARGC1A, and LGALS2. Patients with CRC in the high-risk subgroup showed longer overall survival than those in the low-risk subgroup, which was confirmed by GEO database. Clinicopathological features associated with cancer progression significantly differed between the high- and low-risk subgroups. Furthermore, Kaplan-Meier analysis of immune infiltration showed that the increased infiltration of naïve B cells, macrophages M1, and regulatory T cells and reduced infiltration of resting dendritic cells and mast cells led to a worse overall survival in patients with CRC. The ORC curves revealed that IRGPI predicted patient survival more sensitive than the published tumor immune dysfunction and rejection and tumor inflammatory signature.DiscussionThus, the low-risk subgroup is more likely to benefit from ICIs than the high-risk subgroup. CRC model mice showed higher proportions of Tregs, M1 macrophages, M2 macrophages and lower proportions of B cells, memory B cell immune cell infiltration, which is consistent with the IRGPI results. The IRGPI can predict the prognosis of patients with CRC, reflect the CRC immune microenvironment, and distinguish patients who are likely to benefit from ICI therapy.

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A prognostic index based on an eleven gene signature to predict systemic recurrences in colorectal cancer.

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A prognostic index based on an eleven gene signature to predict systemic recurrences in colorectal cancer.

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