1
|
Huang Q, Liang Z, Huang Q, Li X, Xia J, Huang L, Huang LB, Ou C. Involvement of lncRNAs in the regulation of aerobic glycolysis in hepatocellular carcinoma: Main functions, regulatory mechanisms and potential therapeutic implications (Review). Oncol Rep 2024; 51:84. [PMID: 38666534 PMCID: PMC11082637 DOI: 10.3892/or.2024.8743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 04/11/2024] [Indexed: 05/04/2024] Open
Abstract
Even under aerobic conditions, tumor cells can reprogram their metabolism to preferentially metabolize glucose into lactic acid. This abnormal metabolic pattern, known as the 'Warburg' effect or aerobic glycolysis, promotes cancer progression. Long non‑coding RNAs (lncRNAs) are RNAs that are >200 nucleotides in length and do not have protein‑coding capabilities. However, these RNAs play a key role in tumor development. There is increasing evidence to indicate that lncRNAs regulate glucose metabolism in tumor cells by affecting metabolic enzymes and some signaling pathways, thereby regulating the occurrence and progression of hepatocellular carcinoma (HCC). Therefore, it is crucial to understand which lncRNAs play a regulatory role in HCC glycolysis and to determine the related molecular mechanisms. The present review summarized and discussed the functions of lncRNAs, focusing on the regulatory mechanisms of lncRNAs in the process of glycolysis in HCC. In addition, the present review suggests the importance of lncRNAs as future therapeutic targets for antitumor cell metabolism.
Collapse
Affiliation(s)
- Qiongqing Huang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| | - Zhengui Liang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| | - Qiqi Huang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| | - Xueyu Li
- Experimental Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| | - Jingjing Xia
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| | - Lining Huang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| | - Lin Bing Huang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| | - Chao Ou
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China
| |
Collapse
|
2
|
Gao X, Ren X, Wang F, Ren X, Liu M, Cui G, Liu X. Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma. Sci Rep 2024; 14:9509. [PMID: 38664521 PMCID: PMC11045740 DOI: 10.1038/s41598-024-59877-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most significant causes of cancer-related deaths in the worldwide. Currently, predicting the survival of patients with HCC and developing treatment drugs still remain a significant challenge. In this study, we employed prognosis-related genes to develop and externally validate a predictive risk model. Furthermore, the correlation between signaling pathways, immune cell infiltration, immunotherapy response, drug sensitivity, and risk score was investigated using different algorithm platforms in HCC. Our results showed that 11 differentially expressed genes including UBE2C, PTTG1, TOP2A, SPP1, FCN3, SLC22A1, ADH4, CYP2C8, SLC10A1, F9, and FBP1 were identified as being related to prognosis, which were integrated to construct a prediction model. Our model could accurately predict patients' overall survival using both internal and external datasets. Moreover, a strong correlation was revealed between the signaling pathway, immune cell infiltration, immunotherapy response, and risk score. Importantly, a novel potential drug candidate for HCC treatment was discovered based on the risk score and also validated through ex vivo experiments. Our finds offer a novel perspective on prognosis prediction and drug exploration for cancer patients.
Collapse
Affiliation(s)
- Xiaoge Gao
- Cancer Institute, Xuzhou Medical University, Xuzhou, 221002, Jiangsu Province, People's Republic of China
| | - Xin Ren
- Cancer Institute, Xuzhou Medical University, Xuzhou, 221002, Jiangsu Province, People's Republic of China
- Department of Oncology, Jiangyin Clinical College, Xuzhou Medical University, Jiangyin, 214400, Jiangsu Province, People's Republic of China
| | - Feitong Wang
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu Province, People's Republic of China
| | - Xinxin Ren
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, People's Republic of China
| | - Mengchen Liu
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519040, Guangdong Province, People's Republic of China
| | - Guozhen Cui
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519040, Guangdong Province, People's Republic of China
| | - Xiangye Liu
- Jiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Xuzhou, 221004, Jiangsu Province, People's Republic of China.
- National Demonstration Center for Experimental Basic Medical Science Education (Xuzhou Medical University), Xuzhou, 221002, Jiangsu Province, People's Republic of China.
| |
Collapse
|
3
|
Suthen S, Lim CJ, Nguyen PHD, Dutertre CA, Lai HLH, Wasser M, Chua C, Lim TKH, Leow WQ, Loh TJ, Wan WK, Pang YH, Soon G, Cheow PC, Kam JH, Iyer S, Kow A, Tam WL, Shuen TWH, Toh HC, Dan YY, Bonney GK, Chan CY, Chung A, Goh BKP, Zhai W, Ginhoux F, Chow PKH, Albani S, Chew V. Hypoxia-driven immunosuppression by Treg and type-2 conventional dendritic cells in HCC. Hepatology 2022; 76:1329-1344. [PMID: 35184329 DOI: 10.1002/hep.32419] [Citation(s) in RCA: 73] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/25/2022] [Accepted: 02/07/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND AND AIMS Hypoxia is one of the central players in shaping the immune context of the tumor microenvironment (TME). However, the complex interplay between immune cell infiltrates within the hypoxic TME of HCC remains to be elucidated. APPROACH AND RESULTS We analyzed the immune landscapes of hypoxia-low and hypoxia-high tumor regions using cytometry by time of light, immunohistochemistry, and transcriptomic analyses. The mechanisms of immunosuppression in immune subsets of interest were further explored using in vitro hypoxia assays. Regulatory T cells (Tregs) and a number of immunosuppressive myeloid subsets, including M2 macrophages and human leukocyte antigen-DR isotype (HLA-DRlo ) type 2 conventional dendritic cell (cDC2), were found to be significantly enriched in hypoxia-high tumor regions. On the other hand, the abundance of active granzyme Bhi PD-1lo CD8+ T cells in hypoxia-low tumor regions implied a relatively active immune landscape compared with hypoxia-high regions. The up-regulation of cancer-associated genes in the tumor tissues and immunosuppressive genes in the tumor-infiltrating leukocytes supported a highly pro-tumorigenic network in hypoxic HCC. Chemokine genes such as CCL20 (C-C motif chemokine ligand 20) and CXCL5 (C-X-C motif chemokine ligand 5) were associated with recruitment of both Tregs and HLA-DRlo cDC2 to hypoxia-high microenvironments. The interaction between Tregs and cDC2 under a hypoxic TME resulted in a loss of antigen-presenting HLA-DR on cDC2. CONCLUSIONS We uncovered the unique immunosuppressive landscapes and identified key immune subsets enriched in hypoxic HCC. In particular, we identified a potential Treg-mediated immunosuppression through interaction with a cDC2 subset in HCC that could be exploited for immunotherapies.
Collapse
Affiliation(s)
- Sheena Suthen
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore
| | - Chun Jye Lim
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore
| | - Phuong H D Nguyen
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore
| | - Charles-Antoine Dutertre
- Gustave Roussy Cancer Campus, Villejuif, France.,Institut National de la Santé Et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale Contre le Cancer, Villejuif, France
| | - Hannah L H Lai
- Agency for Science, Technology and Research, Genome Institute of Singapore, Singapore
| | - Martin Wasser
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore
| | - Camillus Chua
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore
| | - Tony K H Lim
- Duke-NUS Medical School, Singapore.,Department of Anatomical Pathology, Singapore General Hospital, Singapore
| | - Wei Qiang Leow
- Duke-NUS Medical School, Singapore.,Department of Anatomical Pathology, Singapore General Hospital, Singapore
| | - Tracy Jiezhen Loh
- Duke-NUS Medical School, Singapore.,Department of Anatomical Pathology, Singapore General Hospital, Singapore
| | - Wei Keat Wan
- Duke-NUS Medical School, Singapore.,Department of Anatomical Pathology, Singapore General Hospital, Singapore
| | - Yin Huei Pang
- Department of Pathology, National University Hospital, Singapore
| | - Gwyneth Soon
- Department of Pathology, National University Hospital, Singapore
| | - Peng Chung Cheow
- Duke-NUS Medical School, Singapore.,Division of Surgery and Surgical Oncology, Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Center Singapore, Singapore
| | - Juinn Huar Kam
- Duke-NUS Medical School, Singapore.,Division of Surgery and Surgical Oncology, Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Center Singapore, Singapore
| | - Shridhar Iyer
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, University Surgical Cluster, National University Health System, Singapore
| | - Alfred Kow
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, University Surgical Cluster, National University Health System, Singapore
| | - Wai Leong Tam
- Agency for Science, Technology and Research, Genome Institute of Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Timothy W H Shuen
- Division of Medical Oncology, National Cancer Center Singapore, Singapore
| | - Han Chong Toh
- Duke-NUS Medical School, Singapore.,Division of Medical Oncology, National Cancer Center Singapore, Singapore
| | - Yock Young Dan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Glenn K Bonney
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, University Surgical Cluster, National University Health System, Singapore
| | - Chung Yip Chan
- Duke-NUS Medical School, Singapore.,Division of Surgery and Surgical Oncology, Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Center Singapore, Singapore
| | - Alexander Chung
- Duke-NUS Medical School, Singapore.,Division of Surgery and Surgical Oncology, Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Center Singapore, Singapore
| | - Brian K P Goh
- Duke-NUS Medical School, Singapore.,Division of Surgery and Surgical Oncology, Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Center Singapore, Singapore
| | - Weiwei Zhai
- Gustave Roussy Cancer Campus, Villejuif, France.,Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Florent Ginhoux
- Gustave Roussy Cancer Campus, Villejuif, France.,Institut National de la Santé Et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale Contre le Cancer, Villejuif, France
| | - Pierce K H Chow
- Division of Surgery and Surgical Oncology, Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Center Singapore, Singapore.,Academic Clinical Programme for Surgery, SingHealth Duke-NUS Academic Medical Centre, Singapore
| | - Salvatore Albani
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore
| | - Valerie Chew
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore
| |
Collapse
|
4
|
Zi Q, Cui H, Liang W, Chi Q. Machine learning algorithm and deep neural networks identified a novel subtype in hepatocellular carcinoma. Cancer Biomark 2022; 35:305-320. [DOI: 10.3233/cbm-220147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Due to the lack of specific characteristics in the early stage of the disease, patients are usually diagnosed in the advanced stage of disease progression. OBJECTIVE: This study used machine learning algorithms to identify key genes in the progression of hepatocellular carcinoma and constructed a prediction model to predict the survival risk of HCC patients. METHODS: The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differential expression analysis and COX proportional-hazards model participated in the identification of survival-related genes. K-Means, Random forests, and LASSO regression are involved in identifying novel subtypes of HCC and screening key genes. The prediction model was constructed by deep neural networks (DNN), and Gene Set Enrichment Analysis (GSEA) reveals the metabolic pathways where key genes are located. RESULTS: Two subtypes were identified with significantly different survival rates (p< 0.0001, AUC = 0.720) and 17 key genes associated with the subtypes. The accuracy rate of the deep neural network prediction model is greater than 93.3%. The GSEA analysis found that the survival-related genes were significantly enriched in hallmark gene sets in the MSigDB database. CONCLUSIONS: In this study, we used machine learning algorithms to screen out 17 genes related to the survival risk of HCC patients, and trained a DNN model based on them to predict the survival risk of HCC patients. The genes that make up the model are all key genes that affect the formation and development of cancer.
Collapse
Affiliation(s)
- Quan Zi
- Department of Engineering Structure and Mechanics, Wuhan University of Technology, Wuhan, Hubei, China
| | - Hanwei Cui
- Department of Science and Education, Shenzhen Samii Medical Center, Shenzhen, Guangdong, China
| | - Wei Liang
- Division of Nephrology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qingjia Chi
- Department of Engineering Structure and Mechanics, Wuhan University of Technology, Wuhan, Hubei, China
| |
Collapse
|