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Huang P, Wen F, Li Q. Current concepts of the crosstalk between lncRNA and E2F1: shedding light on the cancer therapy. Front Pharmacol 2024; 15:1432490. [PMID: 39119602 PMCID: PMC11306149 DOI: 10.3389/fphar.2024.1432490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
Long noncoding RNAs (lncRNAs) constitute a distinctive subset of RNA molecules with limited protein-coding potential, which exert crucial impacts on various biological activities. In the context of cancer, dysregulated lncRNAs function as essential regulators that affect tumor initiation and malignant progression. These lncRNAs serve as competitive endogenous RNAs (ceRNAs) through sponging microRNAs and regulating the expression of targeted genes. Moreover, they also directly bind to RNA-binding proteins, which can be integrated into a complex mechanistic network. E2F1, an extensively studied transcription factor, mediates multiple malignant behaviors by regulating cell cycle progression, tumor metastasis, and therapeutic response. Emerging evidence suggests that lncRNAs play a pivotal role in regulating the E2F1 pathway. This review aims to elucidate the intricate gene regulatory programs between lncRNAs and E2F1 in cancer progression. We elaborate on distinct mechanistic networks involved in cancer progression, emphasizing the potential of the lncRNAs/E2F1 axes as promising targets for cancer therapy. Additionally, we provide novel perspectives on current evidence, limitations, and future directions for targeting lncRNAs in human cancers. Fully deciphering the intricate network of lncRNA/E2F1-mediated regulatory mechanisms in cancer could facilitate the translation of current findings into clinical course, such efforts ultimately significantly improve the clinical prognosis of cancer patients.
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Affiliation(s)
- Peng Huang
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Feng Wen
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiu Li
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Chen Q, Qiu J, Lan W, Cao J. Similarity-guided graph contrastive learning for lncRNA-disease association prediction. J Mol Biol 2024:168609. [PMID: 38750722 DOI: 10.1016/j.jmb.2024.168609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.
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Affiliation(s)
- Qingfeng Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junlai Qiu
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Wei Lan
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junyue Cao
- College of Life Science and Technology, Guangxi University, Nanning 530004, Guangxi, China.
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Rinaldi S, Moroni E, Rozza R, Magistrato A. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. J Chem Theory Comput 2024; 20:993-1018. [PMID: 38287883 DOI: 10.1021/acs.jctc.3c01239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.
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Affiliation(s)
- Silvia Rinaldi
- National Research Council of Italy (CNR) - Institute of Chemistry of OrganoMetallic Compounds (ICCOM), c/o Area di Ricerca CNR di Firenze Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Elisabetta Moroni
- National Research Council of Italy (CNR) - Institute of Chemical Sciences and Technologies (SCITEC), via Mario Bianco 9, 20131 Milano, Italy
| | - Riccardo Rozza
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
| | - Alessandra Magistrato
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
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Yan J, Qu W, Li X, Wang R, Tan J. GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions prediction. Comput Biol Chem 2024; 108:108000. [PMID: 38070456 DOI: 10.1016/j.compbiolchem.2023.108000] [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: 07/16/2023] [Revised: 11/27/2023] [Accepted: 12/03/2023] [Indexed: 01/22/2024]
Abstract
Non-coding RNA (ncRNA) plays an important role in many fundamental biological processes, and it may be closely associated with many complex human diseases. NcRNAs exert their functions by interacting with proteins. Therefore, identifying novel ncRNA-protein interactions (NPIs) is important for understanding the mechanism of ncRNAs role. The computational approach has the advantage of low cost and high efficiency. Machine learning and deep learning have achieved great success by making full use of sequence information and structure information. Graph neural network (GNN) is a deep learning algorithm for complex network link prediction, which can extract and discover features in graph topology data. In this study, we propose a new computational model called GATLGEMF. We used a line graph transformation strategy to obtain the most valuable feature information and input this feature information into the attention network to predict NPIs. The results on four benchmark datasets show that our method achieves superior performance. We further compare GATLGEMF with the state-of-the-art existing methods to evaluate the model performance. GATLGEMF shows the best performance with the area under curve (AUC) of 92.41% and 98.93% on RPI2241 and NPInter v2.0 datasets, respectively. In addition, a case study shows that GATLGEMF has the ability to predict new interactions based on known interactions. The source code is available at https://github.com/JianjunTan-Beijing/GATLGEMF.
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Affiliation(s)
- Jing Yan
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
| | - Wenyan Qu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
| | - Xiaoyi Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
| | - Ruobing Wang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
| | - Jianjun Tan
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
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Huang L, Zeng X, Liang W, Chen J, Zhong C, Cai W, Wang X, Zhu Z, Su L, Liu Z, Peng H. Dissecting the role of lactate metabolism LncRNAs in the progression and immune microenvironment of osteosarcoma. Transl Oncol 2023; 36:101753. [PMID: 37549606 PMCID: PMC10423928 DOI: 10.1016/j.tranon.2023.101753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/15/2023] [Accepted: 08/01/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND The process of lactate metabolism has been proved to play a critical role in the progression of various cancers and to influence the immune microenvironment, but its potential role in osteosarcoma remains unclear. METHODS We have acquired transcriptomic and clinical data from 84 osteosarcoma samples and 70 normal bone samples from the TARGET and GTEx databases. We identified differentially expressed lactate metabolism-related LncRNAs (LRLs) in osteosarcoma and performed Cox regression and LASSO regression to establish LRLs prognostic signature (LRPS). The reliability of LRPS performance was examined by separate prognostic analysis, viability curves and receiver operating characteristic (ROC) curves. Furthermore, the effects of LRPS on the immune microenvironment of osteosarcoma were investigated, and the functions of the focal genes were experimentally validated. RESULT A total of 856 differentially expressed LRLs were identified and 5 of them were selected to construct LRPS, which was a better prognostic predictor for osteosarcoma compared with other published prognostic signatures (AUC up to 0.947 and 0.839 in the training and test groups, respectively, with adj-p<0.05 for KM curves). We found that LRPS significantly affected the immune infiltration of osteosarcoma, while RP11-472M19.2 significantly promoted the metastasis of osteosarcoma, which was well validated experimentally. Encouragingly, a number of sensitive drugs were identified for LRPS and RP11-472M19.2 high-risk groups. CONCLUSION Our study shows that lactate metabolism plays a crucial role in the development of osteosarcoma and has been well validated experimentally, providing extremely important insights into the clinical treatment and in-depth research of osteosarcoma.
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Affiliation(s)
- Liangkun Huang
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Xiaoshuang Zeng
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Wanting Liang
- Department of Clinical Medicine, Xianyue Hospital of Xiamen Medical College, Xiamen, 310058, China
| | - Junwen Chen
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Changheng Zhong
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Wenxiang Cai
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Xuezhong Wang
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Zhengjie Zhu
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Li Su
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China
| | - Zilin Liu
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China.
| | - Hao Peng
- Department of Orthopedics Surgery, Renmin Hospital of Wuhan University, Wuhan Hubei, 430060, China.
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