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A novel 4-gene prognostic signature for hypermutated colorectal cancer.

ABSTRACT: Background:Hypermutated colorectal cancer (CRC) reportedly accounts for 15%-17% of all cases of CRC. However, the proportion and number of patients with hypermutated CRC cannot be unappreciated. Additionally, therapy options for these patients differ from those for CRC patients, with a greater potential benefit from immunotherapy. Materials and methods:We sequenced the tumor mucosa of CRC patients with >24 months of follow-up data at our center and identified mutation profiles of hypermutated CRC as a training data set (Zhejiang University [ZJU]); we then collected patients from The Cancer Genome Atlas (TCGA) as a validation data set. Recurrently mutated genes were combined to calculate a compound score via Cox proportional hazards model. Patients with higher-than-median scores were segregated as the high-risk group. Outcomes were analyzed by Kaplan-Meier and Cox regression analyses using Python (3.6.0) and R (3.4.0). Results:We constructed a 4-gene signature (ACVR2A, APC, DOCK2, and POLE), with training in 45 hypermutated patients at ZJU and validation in 24 hypermutated patients from TCGA. Patients in the high-risk group showed poor survival (adjusted HR =9.85, 95% CI: 2.07-46.81, P=0.004). Further subgroup analysis was performed for stage II and III colon cancer (HR =10.91, 95% CI: 1.36-87.5, P=0.005) and high microsatellite instability (MSI-H) CRC (HR =12.57, 95% CI: 1.57-100.69, P=0.002) subgroups, which verified that our signature is universal. We then compared our prognostic signature with other risk factors (including MSI status, POLE driver mutation, BRAF-p.V600E, tumor mutational burden, and TNM staging). The results proved that our 4-gene signature is better than the other risk factor for prognosis in hypermutated CRC. Conclusion:Our 4-gene signature is a good predictor of survival for hypermutated CRC, and this signature is powerful in stage II and III colon cancer and MSI-H CRC. Future prospective studies are needed to confirm the power of the 4-gene signature in patients receiving immunotherapy.

SUBMITTER: Ge W

PROVIDER: S-EPMC6407520 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A novel 4-gene prognostic signature for hypermutated colorectal cancer.

Ge Weiting W Cai Wen W Bai Rui R Hu Wangxiong W Wu Dehao D Zheng Shu S Hu Hanguang H

Cancer management and research 20190304

<h4>Background</h4>Hypermutated colorectal cancer (CRC) reportedly accounts for 15%-17% of all cases of CRC. However, the proportion and number of patients with hypermutated CRC cannot be unappreciated. Additionally, therapy options for these patients differ from those for CRC patients, with a greater potential benefit from immunotherapy.<h4>Materials and methods</h4>We sequenced the tumor mucosa of CRC patients with >24 months of follow-up data at our center and identified mutation profiles of ...[more]

PMID: 30881123

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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:Background: Anoikis is a type of apoptosis associated with cell detachment. Resistance to anoikis is a focal point of tumor metastasis. This study aimed to explore the relationship among anoikis-related genes (ARGs), immune infiltration, and prognosis in colorectal cancer (CRC). Methods: The transcriptome profile and clinical data on patients with CRC were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Patients were divided into two clusters based on the expression of ARGs. Differences between the two ARG molecular subtypes were analyzed in terms of prognosis, functional enrichment, gene mutation frequency, and immune cell infiltration. An ARG-related prognostic signature for predicting overall survival in patients with CRC was developed and validated using absolute value convergence and selection operator (LASSO) regression analysis. The correlation between the signature risk score and clinicopathological features, immune cell infiltration, immune typing, and immunotherapy response was analyzed. The risk score combined with clinicopathological characteristics was used to construct a nomogram to assess CRC patients' prognosis. Results: Overall, 151 ARGs were differentially expressed in CRC. Two ARG subtypes, namely, ARG-high and ARG-low groups, were identified and correlated with CRC prognosis. The gene mutation frequency and immune, stromal, and ESTIMATE scores of the ARG-high group were higher than those of the ARG-low group. Moreover, CD8, natural killer cells, M1 macrophages, human leukocyte antigen (HLA), and immune checkpoint-related genes were significantly increased in the ARG-high group. An optimized 25-gene CRC prognostic signature was successfully constructed, and its prognostic predictive ability was validated. The high-risk score was correlated with T, N, M, and TNM stages. Risk scores were negatively correlated with dendritic cells, eosinophils, and CD4 cells, and significantly positively correlated with regulatory T cells. Patients in the high-risk group were more likely to exhibit immune unresponsiveness. Finally, the nomogram model was constructed and showed good prognostic predictive power. Conclusion: ARGs are associated with clinicopathological features and the prognosis of CRC, and play important roles in the immune microenvironment. Herein, we underpinned the usefulness of ARGs in CRC to develop more effective immunotherapy techniques.

Project description:Background: Colorectal cancer (CRC) is a common malignant tumor that affects the large bowel or the rectum. Cuproptosis, recently discovered programmed cell death process, may play an important role in CRC tumorigenesis. Long non-coding RNAs (lncRNAs) can alter the proliferation of colorectal cancer cells through the control and activation of gene expression. To date, cuproptosis-related lncRNAs, have not been investigated as potential predictive biomarkers in colorectal cancer. Methods: The mRNA and lncRNA expression data of colorectal cancer were gathered from The Tumor Genome Atlas (TCGA) database, and Pearson correlation analysis and univariate Cox regression analysis were used to identify the lncRNAs with differential prognosis. Colorectal cancer was classified using consistent clustering, and the clinical significance of different types, tumor heterogeneity, and immune microenvironment differences was investigated. The differential lncRNAs were further screened using LASSO regression to develop a risk scoring model, which was then paired with clinicopathological variables to create a nomogram. Finally, the copy number changes in the high-risk and low-risk groups were compared. Results: Two clusters were formed based on the 28 prognostic cuproptosis-related lncRNAs, and the prognosis of cluster 2 was found to be significantly lower than that of cluster 1. Cluster 1 showed increased immune cell infiltration and immunological score, as well as strong enrichment of immune checkpoint genes. Next, LASSO regression was used to select 11 distinctive lncRNAs, and a risk score model was constructed using the training set to distinguish between high and low-risk groups. Patients in the high-risk group had a lower survival rate than those in the low-risk group, and both the test set and the total set produced consistent results. The AUC value of the ROC curve revealed the scoring model's efficacy in predicting long-term OS in patients. Moreover, the model could be used as an independent predictor when combined with a multivariate analysis of clinicopathological features, and our nomogram could be used intuitively to predict prognosis. Conclusion: Collectively, we developed a risk model using 11 differential lncRNAs and demonstrated that the model has predictive value as well as clinical and therapeutic implications for predicting prognosis in CRC patients.

Dataset Information

A novel 4-gene prognostic signature for hypermutated colorectal cancer.

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A novel 4-gene prognostic signature for hypermutated colorectal cancer.

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