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Risk Predictive Model Based on Three DDR-Related Genes for Predicting Prognosis, Therapeutic Sensitivity, and Tumor Microenvironment in Hepatocellular Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:4869732. [PMID: 36213834 PMCID: PMC9546689 DOI: 10.1155/2022/4869732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/07/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022]
Abstract
Hepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Tumor mutational load, genomic instability, and tumor-infiltrating lymphocytes were associated with DNA damage response and repair gene changes. The goal of this study is to estimate the chances of patients with HCC surviving their disease by constructing a DNA damage repair- (DDR-) related gene profile. The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) provided us with the mRNA expression matrix as well as clinical information relevant to HCC patients. Using Cox regression and LASSO analysis, DEGs strongly related to general survival were discovered in the differentially expressed gene (DEG) study. In order to assess the model’s accuracy, Kaplan-Meier (KM) and receiver operating characteristic (ROC) were used. In order to compute the immune cell infiltration score and immune associated pathway activity, a single-sample gene set enrichment analysis was performed. A three-gene signature (CDC20, TTK, and CENPA) was created using stability selection and LASSO COX regression. In comparison to the low-risk group, the prognosis for the high-risk group was surprisingly poor. In the ICGC datasets, the predictive characteristic was confirmed. A receiver operating characteristic (ROC) curve was calculated for each cohort. The risk mark for HCC patients is a reliable predictor according to multivariate Cox regression analysis. According to ssGSEA, this signature was highly correlated with the immunological state of HCC patients. There was a significant correlation between the expression levels of prognostic genes and cancer cells’ susceptibility to antitumor therapies. Overall, a distinct gene profile associated with DDR was identified, and this pattern may be able to predict HCC patients’ long-term survival, immune milieu, and chemotherapeutic response.
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Xu Y, Dong Q, Li F, Xu Y, Hu C, Wang J, Shang D, Zheng X, Yang H, Zhang C, Shao M, Meng M, Xiong Z, Li X, Zhang Y. Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data. J Transl Med 2019; 17:255. [PMID: 31387579 PMCID: PMC6685260 DOI: 10.1186/s12967-019-2010-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 07/31/2019] [Indexed: 12/19/2022] Open
Abstract
Background Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy. Methods In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations. Results Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response. Conclusions Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions. Electronic supplementary material The online version of this article (10.1186/s12967-019-2010-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Qun Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yingqi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Congxue Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jingwen Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xuan Zheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Mengting Shao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Mohan Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Zhiying Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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