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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Zhang W, Zhou D, Song S, Hong X, Xu Y, Wu Y, Li S, Zeng S, Huang Y, Chen X, Liang Y, Guo S, Pan H, Li H. Prediction and verification of the prognostic biomarker SLC2A2 and its association with immune infiltration in gastric cancer. Oncol Lett 2024; 27:70. [PMID: 38192676 PMCID: PMC10773219 DOI: 10.3892/ol.2023.14203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/15/2023] [Indexed: 01/10/2024] Open
Abstract
Gastric cancer (GC) is the fifth most common cause of cancer-associated deaths; however, its treatment options are limited. Despite clinical improvements, chemotherapy resistance and metastasis are major challenges in improving the prognosis and quality of life of patients with GC. Therefore, effective prognostic biomarkers and targets associated with immunological interventions need to be identified. Solute carrier family 2 member 2 (SLC2A2) may serve a role in tumor development and invasion. The present study aimed to evaluate SLC2A2 as a prospective prognostic marker and chemotherapeutic target for GC. SLC2A2 expression in several types of cancer and GC was analyzed using online databases, and the effects of SLC2A2 expression on survival prognosis in GC were investigated. Clinicopathological parameters were examined to explore the association between SLC2A2 expression and overall survival (OS). Associations between SLC2A2 expression and immune infiltration, immune checkpoints and IC50 were estimated using quantification of the tumor immune contexture from human RNA-seq data, the Tumor Immune Estimation Resource 2.0 database and the Genomics of Drug Sensitivity in Cancer database. Differential SLC2A2 expression and the predictive value were validated using the Human Protein Atlas, Gene Expression Omnibus, immunohistochemistry and reverse transcription-quantitative PCR. SLC2A2 expression was downregulated in most types of tumor but upregulated in GC. Functional enrichment analysis revealed an association between SLC2A2 expression and lipid metabolism and the tumor immune microenvironment. According to Gene Ontology term functional enrichment analysis, SLC2A2-related differentially expressed genes were enriched predominantly in 'chylomicron assembly', 'plasma lipoprotein particle assembly', 'high-density lipoprotein particle', 'chylomicron', 'triglyceride-rich plasma lipoprotein particle', 'very-low-density lipoprotein particle'. 'intermembrane lipid transfer activity', 'lipoprotein particle receptor binding', 'cholesterol transporter activity' and 'intermembrane cholesterol transfer activity'. In addition, 'cholesterol metabolism', and 'fat digestion and absorption' were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Patients with GC with high SLC2A2 expression had higher levels of neutrophil and M2 macrophage infiltration and a significant inverse correlation was observed between SLC2A2 expression and MYC targets, tumor mutation burden, microsatellite instability and immune checkpoints. Furthermore, patients with high SLC2A2 expression had worse prognosis, including OS, disease-specific survival and progression-free interval. Multivariate regression analysis demonstrated that SLC2A2 could independently prognosticate GC and the nomogram model showed favorable performance for survival prediction. SLC2A2 may be a prospective prognostic marker for GC. The prediction model may improve the prognosis of patients with GC in clinical practice, and SLC2A2 may serve as a novel therapeutic target to provide immunotherapy plans for GC.
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Affiliation(s)
- Weijian Zhang
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, P.R. China
| | - Dishu Zhou
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, P.R. China
| | - Shuya Song
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, P.R. China
| | - Xinxin Hong
- Department of Gastroenterology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Yifei Xu
- Department of Gastroenterology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Yuqi Wu
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Shiting Li
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Sihui Zeng
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Yanzi Huang
- Department of Gastroenterology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Xinbo Chen
- Department of Gastroenterology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Yizhong Liang
- Department of Gastroenterology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Shaoju Guo
- Department of Gastroenterology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
| | - Huafeng Pan
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, P.R. China
| | - Haiwen Li
- Department of Gastroenterology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, P.R. China
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Shen C, Bi Y, Chai W, Zhang Z, Yang S, Liu Y, Wu Z, Peng F, Fan Z, Hu H. Construction and validation of a metabolism-associated gene signature for predicting the prognosis, immune landscape, and drug sensitivity in bladder cancer. BMC Med Genomics 2023; 16:264. [PMID: 37880682 PMCID: PMC10601123 DOI: 10.1186/s12920-023-01678-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/30/2023] [Indexed: 10/27/2023] Open
Abstract
Tumor Metabolism is strongly correlated with prognosis. Nevertheless, the prognostic and therapeutic value of metabolic-associated genes in BCa patients has not been fully elucidated. First, in this study, metabolism-related differential expressed genes DEGs with prognostic value in BCa were determined. Through the consensus clustering algorithm, we identified two molecular clusters with significantly different clinicopathological features and survival prognosis. Next, a novel metabolism-related prognostic model was established. Its reliable predictive performance in BCa was verified by multiple external datasets. Multivariate Cox analysis exhibited that risk score were independent prognostic factors. Interestingly, GSEA enrichment analysis of GO, KEGG, and Hallmark gene sets showed that the biological processes and pathways associated with ECM and collagen binding in the high-risk group were significantly enriched. Notely, the model was also significantly correlated with drug sensitivity, immune cell infiltration, and immunotherapy efficacy prediction by the wilcox rank test and chi-square test. Based on the 7 immune infiltration algorithm, we found that Neutrophils, Myeloid dendritic cells, M2 macrophages, Cancer-associated fibroblasts, etc., were more concentrated in the high-risk group. Additionally, in the IMvigor210, GSE111636, GSE176307, or our Truce01 (registration number NCT04730219) cohorts, the expression levels of multiple model genes were significantly correlated with objective responses to anti-PD-1/anti-PD-L1 immunotherapy. Finally, the expression of interested model genes were verified in 10 pairs of BCa tissues and para-carcinoma tissues by the HPA and real-time fluorescent quantitative PCR. Altogether, the signature established and validated by us has high predictive power for the prognosis, immunotherapy responsiveness, and chemotherapy sensitivity of BCa.
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Affiliation(s)
- Chong Shen
- Department of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China
- Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin, 300211, China
| | - Yuxin Bi
- Department of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China
- Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin, 300211, China
| | - Wang Chai
- Department of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China
- Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin, 300211, China
| | - Zhe Zhang
- Department of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China
- Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin, 300211, China
| | - Shaobo Yang
- Department of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China
- Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin, 300211, China
| | - Yuejiao Liu
- Department of Pharmacy, Zhu Xianyi Memorial Hospital of Tianjin Medical University, Tianjin, China
| | - Zhouliang Wu
- Department of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China
- Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin, 300211, China
| | - Fei Peng
- Department of Critical Care Medicine, the Peoples Hospital of Yuxi City, Yunnan, China
| | - Zhenqian Fan
- Department of Endocrinology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China.
| | - Hailong Hu
- Department of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Jianshan Street, Hexi, Tianjin, 300211, People's Republic of China.
- Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin, 300211, China.
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Liu Y, Zhou Z, Wang Z, Yang H, Zhang F, Wang Q. Construction and clinical validation of a nomogram-based predictive model for diabetic retinopathy in type 2 diabetes. Am J Transl Res 2023; 15:6083-6094. [PMID: 37969206 PMCID: PMC10641351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 09/14/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE This study aimed to identify risk factors for diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM) and construct a nomogram prediction model for DR. METHODS T2DM patients (n = 520) who underwent funduscopic examinations from June 2020 to June 2022 were included. Of these patients, 220 had DR, yielding a disease rate of 40.38%. Patients were divided into a training set (n = 364) and a validation set (n = 156) at a 7:3 ratio. Feature variables were selected using LASSO regression, random forests, and decision trees. Venn diagrams identified common DR feature variables. The prediction model's validity was assessed using the C-index, decision curve analysis (DCA), receiver operating characteristic (ROC) curves, and calibration curves. RESULTS Factors influencing DR were age, Diabetic Peripheral Neuropathy (DPN), Hemoglobin A1C (HbA1C) levels, High-Density Lipoprotein (HDL) cholesterol, Low-Density Lipoprotein (LDL) cholesterol, Neutrophil-to-Lymphocyte Ratio (NLR), Triglycerides (TG), Blood Urea Nitrogen (BUN), and disease duration. Univariate analysis excluded LDL as being unrelated to DR. The DR prediction model, constructed using the remaining eight variables, showed internal validation metrics with a C-index of 0.937, area under the ROC curve (AUC) of 0.773, and DCA net benefit of 11%-95%. The external validation metrics demonstrated a C-index of 0.916, AUC of 0.735, and DCA net benefit of 17%-93%. Calibration curves indicated high consistency. CONCLUSION This study developed a nomogram prediction model to assess the risk of DR in patients with T2DM. The model demonstrated high precision through internal validation.
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Affiliation(s)
- Yonghong Liu
- Department of Ophthalmology, Gansu Provincial Hospital of TCMNo. 418 Guazhou Road, Qilihe District, Lanzhou 730050, Gansu, China
| | - Zhaoling Zhou
- Department of Electrocardiogram, Gansu Provincial Hospital of TCMNo. 418 Guazhou Road, Qilihe District, Lanzhou 730050, Gansu, China
| | - Zhiyong Wang
- Trauma Disease Diagnosis and Treatment Center, Gansu Provincial Hospital of TCMNo. 418 Guazhou Road, Qilihe District, Lanzhou 730050, Gansu, China
| | - Huan Yang
- Department of Endocrinology, The Third People’s Hospital of Gansu ProvinceNo. 763 Duanjiatan Road, Chengguan District, Lanzhou 730020, Gansu, China
| | - Feifei Zhang
- Department of Endocrinology, The Third People’s Hospital of Gansu ProvinceNo. 763 Duanjiatan Road, Chengguan District, Lanzhou 730020, Gansu, China
| | - Qinhu Wang
- Department of Endocrinology, The Third People’s Hospital of Gansu ProvinceNo. 763 Duanjiatan Road, Chengguan District, Lanzhou 730020, Gansu, China
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Xu X, Zhang X, Lin Q, Qin Y, Liu Y, Tang W. Integrated single-cell and bulk RNA sequencing analysis identifies a prognostic signature related to ferroptosis dependence in colorectal cancer. Sci Rep 2023; 13:12653. [PMID: 37542061 PMCID: PMC10403602 DOI: 10.1038/s41598-023-39412-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 07/25/2023] [Indexed: 08/06/2023] Open
Abstract
Ferroptosis is an iron-dependent form of cell death induced by lipid oxidation with an essential role in diseases, including cancer. Since prognostic value of ferroptosis-dependent related genes (FDRGs) in colorectal cancer (CRC) remains unclear, we explored the significance of FDRGs in CRC through comprehensive single-cell analysis. We downloaded the GSE161277 dataset for single-cell analyses and calculated the ferroptosis-dependent gene score (FerrScore) for each cell type. According to each cell type-specific median FerrScore, we categorized the cells into low- and high-ferroptosis groups. By analyzing differentially-expressed genes across the two groups, we identified FDRGs. We further screened these prognosis-related genes used to develop a prognostic signature and calculated its correlation with immune infiltration. We also compared immune checkpoint gene efficacy among different risk groups, and qRT-PCR was performed in colorectal normal and cancer cell lines to explore whether the signature genes could be used as clinical prognostic indicators. In total, 523 FDRGs were identified. A prognostic signature including five signature genes was constructed, and patients were divided into two risk groups. The high-risk group had poor survival rates and displayed high levels of immune infiltration. Our newly developed ferroptosis-based prognostic signature possessed a high predictive ability for CRC.
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Affiliation(s)
- Xiaochen Xu
- Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021, Guangxi Zhuang Autonomous Region, China
| | - Xinwen Zhang
- Department of Gastrointestinal Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi Zhuang Autonomous Region, China
| | - Qiumei Lin
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, 530021, Guangxi Zhuang Autonomous Region, China
| | - Yuling Qin
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, 530021, Guangxi Zhuang Autonomous Region, China
| | - Yihao Liu
- Department of Gastrointestinal Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi Zhuang Autonomous Region, China
| | - Weizhong Tang
- Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
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Wang J, Dong Y, Shang D. Rectum adenocarcinoma metabolic subtypes analysis and a risk prognostic model construction based on fatty acid metabolism genes. Medicine (Baltimore) 2023; 102:e33186. [PMID: 36930129 PMCID: PMC10019117 DOI: 10.1097/md.0000000000033186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/13/2023] [Indexed: 03/18/2023] Open
Abstract
Fatty acid metabolism is an essential part of cancer research due to its role in cancer initiation and progression. However, its characteristics and prognostic value in rectum adenocarcinoma have not been systematically evaluated. We collected fatty acid metabolism gene expression profiles and clinical information from the cancer genome atlas and gene expression omnibus databases. After excluding individuals lacking clinical information and the presence of genetic mutations, we performed consistent clustering of the remaining patients and selected stable clustering results to group patients. Differentially expressed genes and gene set enrichment analysis were compared between subgroups, while metabolic signature identification and decoding the tumor microenvironment were performed. In addition, we explored the survival status of patients among different subgroups and identified signature genes affecting survival by least absolute shrinkage and selection operator regression. Finally, we selected signature genes to construct a risk prognostic model by multivariate Cox regression and evaluated model efficacy by univariate Cox regression and the receiver operating characteristic curve. By consensus clustering, patients were distinguished into 2 stable subpopulations, gene set enrichment analysis and metabolic signature identification effectively defined 2 completely different subtypes of fatty acid metabolism: fatty acid catabolic subtype and fatty acid anabolic subtype. Among them, patients with the fatty acid catabolic subtype had a poorer prognosis, with a significantly lower proportion of myeloid dendritic cells infiltration within the tumor microenvironment. Aquaporin 7 (hazard ratio, HR = 2.064 (1.4408-4.5038); P < .01), X inactive specific transcript (HR = (0.3758-0.7564), P = .045) and interleukin 4 induced 1 (HR = 1.34 (1.13-1.59); P = .034), were selected by multivariate Cox regression, which constructed a risk prognostic model. The independent hazard ratio of the model was 2.72 and the area under curve was higher than age, gender and tumor stage, showing better predictive efficacy. Our study revealed the heterogeneity of fatty acid metabolism in rectum adenocarcinoma, defined 2 completely distinct subtypes of fatty acid metabolism, and finally established a novel fatty acid metabolism-related risk prognostic model. The study contributes to the early risk assessment and monitoring of individual prognosis and provides data to support individualized patient treatment.
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Affiliation(s)
- Jian Wang
- Department of Hernia and Colorectal Surgery, Dalian University Affiliated Xinhua Hospital, Dalian, Liaoning, China
| | - Yi Dong
- Department of Hernia and Colorectal Surgery, Dalian University Affiliated Xinhua Hospital, Dalian, Liaoning, China
| | - Dong Shang
- Department of General Surgery, Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
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Yuan Y, Zhang ZG, Ma B, Ji P, Ma S, Qi X. Effective oxygen metabolism-based prognostic signature for colorectal cancer. Front Oncol 2023; 13:1072941. [PMID: 36845724 PMCID: PMC9947833 DOI: 10.3389/fonc.2023.1072941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Backgroud Oxygen metabolism is an important factor affecting the development of tumors, but its roles and clinical value in Colorectal cancer are not clear. We developed an oxygen metabolism (OM) based prognostic risk model for colorectal cancer and explored the role of OM genes in cancer. Methods Gene expression and clinical data obtained from The Cancer Genome Atlas, Clinical Proteomic Tumor Analysis Consortium databases were consider as discovery and validation cohort, respectively. The prognostic model based on differently expressed OM genes between tumor and GTEx normal colorectal tissues were constructed in discovery cohort and validated in validation cohort. The Cox proportional hazards analysis was used to test clinical independent. Upstream and downstream regulatory relationships and interaction molecules are used to clarify the roles of prognostic OM genes in colorectal cancer. Results A total of 72 common differently expressed OM genes were detected in the discovery and validation set. A five-OM gene prognostic model including LRT2, ATP6V0E2, ODC1, SEL1L3 and VDR was established and validated. Risk score determined by the model was an independent prognostic according to routine clinical factors. Besides, the role of prognostic OM genes involves transcriptional regulation of MYC and STAT3, and downstream cell stress and inflammatory response pathways. Conclusions We developed a five-OM gene prognostic model and study the unique roles of oxygen metabolism in of colorectal cancer.
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Affiliation(s)
- Yonghui Yuan
- Liaoning Cancer Hospital & Institute, Clinical Research Center for Malignant Tumor of Liaoning Province, Cancer Hospital of China Medical University, Shenyang, Liaoning, China,*Correspondence: Yonghui Yuan, ; Xun Qi,
| | - Zhong-guo Zhang
- Large-Scale Data Analysis Center of Cancer Precision Medicine, Cancer Hospital of Chinese Medical University, Liaoning Provincial Cancer Institute and Hospital, Shenyang, China
| | - Bin Ma
- Department of Colorectal Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Pengfei Ji
- Department of Medical Image of Liaoning Province, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Shiyang Ma
- Department of Radiology, Key Laboratory of Diagnostic Imaging and Interventional Radiology of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xun Qi
- Key Laboratory of Diagnostic Imaging and Interventional Radiology of Liaoning Province, Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China,*Correspondence: Yonghui Yuan, ; Xun Qi,
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Xie Y, Chen H, Fang JY. Amino acid metabolism-based molecular classification of colon adenocarcinomavia in silico analysis. Front Immunol 2022; 13:1018334. [DOI: 10.3389/fimmu.2022.1018334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 10/03/2022] [Indexed: 11/13/2022] Open
Abstract
Amino acid metabolism is closely related to the occurrence and development of colon adenocarcinoma (COAD). Studies on the relationship between COAD and the expression of amino acid metabolism are still rare. Based on in silico analysis, we used 358 amino acid metabolism-related genes (AAMRGs) to determine the amino acid metabolism characteristics and then classified COAD into two distinct subtypes, namely AA1 and AA2. Then we analyzed the clinical characteristics, somatic mutation landscape, transcriptome profile, metabolism signatures, immune infiltration, and therapy sensitivity of these two subtypes. The AA1 subtype had inferior overall survival and was characterized by lower amino acid metabolic activity, higher tumor mutation burden, and higher immune cell infiltration, while AA2 displayed higher metabolic activity and relatively better survival. Furthermore, the AA1 subtype was likely to benefit from irinotecan in chemotherapy and immune checkpoint blockade therapy including programmed cell death protein-1 (PD-1) and cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) immune checkpoint inhibitor but was resistant to targeted therapy cetuximab. The AA2 subtype showed higher sensitivity to 5-fluorouracil and oxaliplatin. To provide perspectives on cell-specific metabolism for further investigation, we explored metabolic activity in different cell types including lymphocytes, mast cells, myeloid cells stromal cells, and epithelial cells via colorectal cancer single-cell data. Additionally, to assist in clinical decision-making and prognosis prediction, a 60-AAMRG-based classifier was generated and validated in an independent cohort.
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Read GH, Bailleul J, Vlashi E, Kesarwala AH. Metabolic response to radiation therapy in cancer. Mol Carcinog 2022; 61:200-224. [PMID: 34961986 PMCID: PMC10187995 DOI: 10.1002/mc.23379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/01/2021] [Accepted: 12/01/2021] [Indexed: 11/11/2022]
Abstract
Tumor metabolism has emerged as a hallmark of cancer and is involved in carcinogenesis and tumor growth. Reprogramming of tumor metabolism is necessary for cancer cells to sustain high proliferation rates and enhanced demands for nutrients. Recent studies suggest that metabolic plasticity in cancer cells can decrease the efficacy of anticancer therapies by enhancing antioxidant defenses and DNA repair mechanisms. Studying radiation-induced metabolic changes will lead to a better understanding of radiation response mechanisms as well as the identification of new therapeutic targets, but there are few robust studies characterizing the metabolic changes induced by radiation therapy in cancer. In this review, we will highlight studies that provide information on the metabolic changes induced by radiation and oxidative stress in cancer cells and the associated underlying mechanisms.
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Affiliation(s)
- Graham H. Read
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Justine Bailleul
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Erina Vlashi
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California
| | - Aparna H. Kesarwala
- Department of Radiation Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
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