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Shan H, Wang X, Yin F, Zhou Y, Mao L, Zhu X, Liu C. Combination of transcriptome and Mendelian inheritance reveals novel prognostic biomarker of CTLA-4-related lncRNAs and protective role of nitrogen metabolism pathway in lung adenocarcinoma development. BMC Cancer 2024; 24:1009. [PMID: 39143529 PMCID: PMC11323378 DOI: 10.1186/s12885-024-12777-7] [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: 04/12/2024] [Accepted: 08/07/2024] [Indexed: 08/16/2024] Open
Abstract
OBJECTIVE Since in the cancer setting, tumor cells may use cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) to evade the immune system. This study aimed to identify CTLA-4-related long non-coding RNAs (lncRNAs) and assess their roles in lung adenocarcinoma (LUAD) development. METHODS Clinical and genomic data were obtained from The Cancer Genome Atlas (TCGA), MSigDB and Gene Weaver. CTLA-4-related lncRNA-based gene signatures (CTLA4LncSigs) were identified using Cox regression, establishing a risk score model and an independent prognostic model. Enrichment analysis (GO/KEGG) was performed. Mendelian randomization (MR) analysis investigated the nitrogen metabolism and lung cancer relationship, with Bayesian weighted MR (BWMR) addressing uncertainties. Correlations with tumor microenvironment and drug sensitivity were explored. RESULTS Nineteen CTLA4LncSigs significantly influenced LUAD prognosis. The risk score demonstrated independence as a prognostic factor. Functional analysis revealed lncRNAs' impact on nitrogen metabolism. MR and BWMR confirmed the protective role of the nitrogen metabolism pathway in lung cancer. CONCLUSION Our study identifies CTLA-4-related lncRNAs associated with LUAD prognosis and uncovers a previously undiscovered protective role of the nitrogen metabolism pathway in combating LUAD development, providing new insights into potential therapeutic targets and prognostic biomarkers for this aggressive cancer subtype.
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Affiliation(s)
- Huisi Shan
- Department of Clinical Laboratory, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China
- Department of Radiation Oncology, Guangdong Second People's Hospital, Jinan University, Guangzhou, China
| | - Xiaocong Wang
- Department of Pathology, Qingdao Municipal Hospital Group, Qingdao, China
| | - Fei Yin
- Department of Clinical Laboratory, Qingdao Sixth People's Hospital, Qingdao, China
| | - Yiting Zhou
- The Second Affiliated Hospital, Guangdong Medical University, Zhanjiang, China
- Department of Internal Medicine, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Liuhan Mao
- The Second Affiliated Hospital, Guangdong Medical University, Zhanjiang, China
| | - Xiao Zhu
- The Second Affiliated Hospital, Guangdong Medical University, Zhanjiang, China.
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China.
| | - Caixin Liu
- Department of Clinical Laboratory, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China.
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Xu Y, Xin X, Tao T. Decoding the neurotoxic effects of propofol: insights into the RARα-Snhg1-Bdnf regulatory cascade. Am J Physiol Cell Physiol 2024; 326:C1735-C1752. [PMID: 38618701 DOI: 10.1152/ajpcell.00547.2023] [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: 10/16/2023] [Revised: 12/26/2023] [Accepted: 01/27/2024] [Indexed: 04/16/2024]
Abstract
The potential neurotoxic effects of propofol, an extensively utilized anesthetic, underline the urgency to comprehend its influence on neuronal health. Insights into the role of the retinoic acid receptor-α, small nucleolar RNA host gene 1, and brain-derived neurotrophic factor (RARα-Snhg1-Bdnf) network can offer significant advancements in minimizing these effects. The study targets the exploration of the RARα and Snhg1 regulatory network's influence on Bdnf expression in the realm of propofol-induced neurotoxicity. Harnessing the Gene Expression Omnibus (GEO) database and utilizing JASPAR and RNA-Protein Interaction Prediction (RPISeq) database for projections, the study embarks on an in-depth analysis employing both in vitro and in vivo models. The findings draw a clear link between propofol-induced neurotoxicity and the amplification of RAR signaling pathways, impacting hippocampal development and apoptosis and leading to increased RARα and Snhg1 and decreased Bdnf. Propofol is inferred to accentuate neurotoxicity by heightening RARα and Snhg1 interactions, culminating in Bdnf suppression.NEW & NOTEWORTHY This study aimed to decode propofol's neurotoxic effects on the regulatory cascade, provide insights into the RARα-Snhg1-Bdnf interaction, apply extensive validation techniques, provide a detailed analysis and exploration of propofol's neurotoxicity, and offer a comprehensive approach to understanding molecular interactions.
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Affiliation(s)
- Yuhai Xu
- Department of Anesthesiology, Air Force Medical Center, Beijing, People's Republic of China
| | - Xin Xin
- Department of Anesthesiology, Air Force Medical Center, Beijing, People's Republic of China
| | - Tianzhu Tao
- Department of Anesthesiology, Air Force Medical Center, Beijing, People's Republic of China
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Ye W, Li H, Zhao J, Lu D, Tao T, Zhu X. Graphene therapy-related lncRNAs as prognostic and immune microenvironmental biomarkers in hepatocellular carcinoma. Transl Oncol 2024; 43:101915. [PMID: 38368713 PMCID: PMC10884496 DOI: 10.1016/j.tranon.2024.101915] [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: 08/17/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Graphene materials have the capacity to influence the tumor microenvironment and intracellular signaling responsiveness. However, the process of graphene-assisted liver cancer treatment still lacks specific biomarkers for assessing its efficacy. METHODS We identified graphene therapy-related lncRNAs (GTLncRNAs) through gene analysis and correlation tests. Multivariate COX and LASSO regression analyses yielded significant lncRNAs for a risk score model. We evaluated clinicopathological factors and tumor microenvironment using ssGSEA. We scrutinized the pathways of immune function, the evasion of tumor immunity, and the potential for immunotherapy. GTLncRNAs with differential expression were subjected to GO/KEGG analysis, and prospective chemotherapy drugs were discerned utilizing the pRRophetic algorithm. The prognostic model was authenticated through the examination of the Imvigor210 cohort, and an analysis of mRNA stemness was executed. RESULTS The researchers constructed a prognostic model based on 22 graphene therapy-related lncRNAs. Protective lncRNAs (AC010280.2, AL365361.1, and LINC01549) and negative lncRNAs (AC026412.3, AL031985.3, ELFN1-AS1, SNHG4, and EB2-AS1) were identified. Higher risk scores correlated with shorter survival. Low-risk immune pathways included Type_II_IFN_Reponse and cytolytic_activity. Subgroups differed significantly in TMB, TIDE, MDSC, exclusion, and dysfunction. Low TMB values correlated with longer survival. The high-risk subgroup showed increased sensitivity to screened compounds, and mRNAsi was higher in cancer tissue. CONCLUSIONS Our GTLncRNAs-based model accurately predicted survival of HCC patients and underscored the influence of graphene therapy-related genes on the tumor microenvironment. Potential treatment compounds were identified, and the mRNAsi index demonstrated prognostic value.
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Affiliation(s)
- Weilong Ye
- Computational Medicine and Epidemiology Laboratory (CMEL), The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, PR China
| | - Hui Li
- Department of Internal Medicine, Huadu District People's Hospital of Guangzhou (The Third School of Clinical Medicine, Southern Medical University), PR China
| | - Juan Zhao
- Department of the Clinical Laboratory, Huadu District People's Hospital of Guangzhou (The Third School of Clinical Medicine, Southern Medical University), PR China
| | - Deshuai Lu
- Computational Medicine and Epidemiology Laboratory (CMEL), The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, PR China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, Zibo, PR China.
| | - Xiao Zhu
- Computational Medicine and Epidemiology Laboratory (CMEL), The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, PR China.
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Guo X, Bian X, Li Y, Zhu X, Zhou X. The intricate dance of tumor evolution: Exploring immune escape, tumor migration, drug resistance, and treatment strategies. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167098. [PMID: 38412927 DOI: 10.1016/j.bbadis.2024.167098] [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: 11/16/2023] [Revised: 01/14/2024] [Accepted: 02/19/2024] [Indexed: 02/29/2024]
Abstract
Recent research has unveiled fascinating insights into the intricate mechanisms governing tumor evolution. These studies have illuminated how tumors adapt and proliferate by exploiting various factors, including immune evasion, resistance to therapeutic drugs, genetic mutations, and their ability to adapt to different environments. Furthermore, investigations into tumor heterogeneity and chromosomal aberrations have revealed the profound complexity that underlies the evolution of cancer. Emerging findings have also underscored the role of viral influences in the development and progression of cancer, introducing an additional layer of complexity to the field of oncology. Tumor evolution is a dynamic and complex process influenced by various factors, including immune evasion, drug resistance, tumor heterogeneity, and viral influences. Understanding these elements is indispensable for developing more effective treatments and advancing cancer therapies. A holistic approach to studying and addressing tumor evolution is crucial in the ongoing battle against cancer. The main goal of this comprehensive review is to explore the intricate relationship between tumor evolution and critical aspects of cancer biology. By delving into this complex interplay, we aim to provide a profound understanding of how tumors evolve, adapt, and respond to treatment strategies. This review underscores the pivotal importance of comprehending tumor evolution in shaping effective approaches to cancer treatment.
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Affiliation(s)
- Xiaojun Guo
- Department of Immunology, School of Medicine, Nantong University, Nantong, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiaonan Bian
- Department of Immunology, School of Medicine, Nantong University, Nantong, China
| | - Yitong Li
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiao Zhu
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China.
| | - Xiaorong Zhou
- Department of Immunology, School of Medicine, Nantong University, Nantong, China.
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Wei C, Tao T, Zhou J, Zhu X. Leveraging a Genomic Instability-Derived Signature to Predict the Prognosis and Therapy Sensitivity of Clear Cell Renal Cell Carcinoma. Clin Genitourin Cancer 2024; 22:134-148.e8. [PMID: 37919101 DOI: 10.1016/j.clgc.2023.10.004] [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: 05/18/2023] [Revised: 10/04/2023] [Accepted: 10/08/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Kidney cancer is a significant health concern with growing treatment resistance, often linked to genomic instability. This study used datasets from 72 renal and 952 clear cell renal cell carcinoma samples to identify genomic instability-derived lncRNAs and develop a prognostic index (GILPI). METHODS The study involved differential expression analysis, weighted gene co-expression network analysis, Cox analyses to construct GILPI, and its validation through survival analysis. SNP, TMB, and MSI data were integrated, and GSEA analysis explored associated pathways. A predictive nomogram was created, and immune cell infiltration was assessed. Targeted treatments for low-GILPI patients were identified through molecular docking and network pharmacology. RESULTS GILPI proved reliable in predicting prognosis (P<0.001, AUC=0.68) and in combination with other factors. GSEA revealed distinct pathway enrichments for different GILPI subgroups. The nomogram exhibited strong predictive performance (AUC=0.902). Immune cell differences suggest potential for immunotherapy in high-GILPI patients and targeted treatment in low-GILPI patients. Lapatinib and nilotinib were identified as effective drugs for low-GILPI patients. CONCLUSION This study identified a GILPI for kidney cancer prognosis, integrating various factors for a comprehensive assessment. It highlighted potential treatment strategies based on GILPI subgroups, enhancing personalized treatment approaches.
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Affiliation(s)
- Chuzhong Wei
- Kidney Department, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China; Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Tao Tao
- Department of Gastroenterology, Clinical Research Center, Zibo Central Hospital, Zibo, China
| | - Jiajun Zhou
- Kidney Department, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China.
| | - Xiao Zhu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China; Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou Medical College, Hangzhou, China.
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Zhong J, Kong Y, Li R, Feng M, Li L, Zhu X, Chen L. Identification and Functional Characterization of PI3K/Akt/mTOR Pathway-Related lncRNAs in Lung Adenocarcinoma: A Retrospective Study. CELL JOURNAL 2024; 26:13-27. [PMID: 38351726 PMCID: PMC10864771 DOI: 10.22074/cellj.2023.2007918.1378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/08/2023] [Accepted: 11/18/2023] [Indexed: 02/18/2024]
Abstract
OBJECTIVE This paper aimed to investigate the PI3K/Akt/mTOR signal-pathway regulator factor-related lncRNA signatures (PAM-SRFLncSigs), associated with regulators of the indicated signaling pathway in patients with lung adenocarcinoma (LUAD) undergoing immunotherapy. MATERIALS AND METHODS In this retrospective study, we employed univariate Cox, multivariate Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses to identify prognostically relevant long non-coding RNAs (lncRNAs), construct prognostic models, and perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Subsequently, immunoassay and chemotherapy drug screening were conducted. Finally, the prognostic model was validated using the Imvigor210 cohort, and tumor stem cells were analyzed. RESULTS We identified seven prognosis-related lncRNAs (AC084757.3, AC010999.2, LINC02802, AC026979.2, AC024896.1, LINC00941 and LINC01312). We also developed prognostic models to predict survival in patients with LUAD. KEGG enrichment analysis confirmed association of LUAD with the PI3K/Akt/mTOR signaling pathway. In the analysis of immune function pathways, we discovered three good prognostic pathways (Cytolytic_activity, Inflammation-promoting, T_cell_co-inhibition) in LUAD. Additionally, we screened 73 oncology chemotherapy drugs using the "pRRophetic" algorithm. CONCLUSION Identification of seven lncRNAs linked to regulators of the PI3K/Akt/mTOR signaling pathway provided valuable insights into predicting the prognosis of LUAD, understanding the immune microenvironment and optimizing immunotherapy strategies.
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Affiliation(s)
- Jiaqi Zhong
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Ying Kong
- Department of Clinical Laboratory, The Third People's Hospital of Hubei Province, Wuhan, China
| | - Ruming Li
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Minghan Feng
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Liming Li
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiao Zhu
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China.
| | - Lianzhou Chen
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
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Dong J, Tao T, Yu J, Shan H, Liu Z, Zheng G, Li Z, Situ W, Zhu X, Li Z. A ferroptosis-related LncRNAs signature for predicting prognoses and screening potential therapeutic drugs in patients with lung adenocarcinoma: A retrospective study. Cancer Rep (Hoboken) 2024; 7:e1925. [PMID: 38043920 PMCID: PMC10809199 DOI: 10.1002/cnr2.1925] [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: 06/25/2023] [Revised: 09/22/2023] [Accepted: 10/16/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) has a high mortality rate. Ferroptosis is linked to tumor initiation and progression. AIMS This study aims to develop prognostic models of ferroptosis-related lncRNAs, evaluate the correlation between differentially expressed genes and tumor microenvironment, and identify prospective drugs for managing LUAD. METHODS AND RESULTS In this study, transcriptomic and clinical data were downloaded from the TCGA database, and ferroptosis-related genes were obtained from the FerrDb database. Through correlation analysis, Cox analysis, and the LASSO algorithm for constructing a prognostic model, we found that ferroptosis-related lncRNA-based gene signatures (FLncSig) had a strong prognostic predicting ability in the LUAD patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments reconfirmed that ferroptosis is related to receptor-ligand activity, enzyme inhibitor activity, and the IL-17 signaling pathway. Next, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) algorithms, and pRRophetic were used to predict immunotherapy response and chemotherapy sensitivity. The IMvigor210 cohort was also used to validate the prognostic model. In the tumor microenvironment, Type_II_IFN_Response and HLA were found to be a group of low-risk pathways, while MHC_class_I was a group of high-risk pathways. Patients in the high-risk subgroup had lower TIDE scores. Exclusion, MDSC, CAF, and TAMM2 were significantly and positively correlated with risk scores. In addition, we found 15 potential therapeutic drugs for LUAD. Finally, differential analysis of stemness index based on mRNA expression (mRNAsi) indicated that mRNAsi was correlated with gender, primary tumor (T), distant metastasis (M), and the tumor, node, and metastasis (TNM) stage in LUAD patients. CONCLUSIONS In conclusion, the prognostic model based on FLncSig can alleviate the difficulty in predicting the prognosis and immunotherapy of LUAD patients. The identified FLncSig and the screened drugs exhibit potential for clinical application and provide references for the treatment of LUAD.
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Affiliation(s)
- Jiaxin Dong
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Tao Tao
- Medical Research Center, Department of GastroenterologyZibo Central HospitalZiboChina
| | - Jiaao Yu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Huisi Shan
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Ziyu Liu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Guangzhao Zheng
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Zhihong Li
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Wanyi Situ
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Xiao Zhu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research InstituteGuangdong Medical UniversityZhanjiangChina
| | - Zesong Li
- Guangdong Provincial Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, Shenzhen Key Laboratory of Genitourinary Tumor, Department of UrologyThe First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital (Shenzhen Institute of Translational Medicine)ShenzhenChina
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Ao YQ, Gao J, Jiang JH, Wang HK, Wang S, Ding JY. Comprehensive landscape and future perspective of long noncoding RNAs in non-small cell lung cancer: it takes a village. Mol Ther 2023; 31:3389-3413. [PMID: 37740493 PMCID: PMC10727995 DOI: 10.1016/j.ymthe.2023.09.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 09/24/2023] Open
Abstract
Long noncoding RNAs (lncRNAs) are a distinct subtype of RNA that lack protein-coding capacity but exert significant influence on various cellular processes. In non-small cell lung cancer (NSCLC), dysregulated lncRNAs act as either oncogenes or tumor suppressors, contributing to tumorigenesis and tumor progression. LncRNAs directly modulate gene expression, act as competitive endogenous RNAs by interacting with microRNAs or proteins, and associate with RNA binding proteins. Moreover, lncRNAs can reshape the tumor immune microenvironment and influence cellular metabolism, cancer cell stemness, and angiogenesis by engaging various signaling pathways. Notably, lncRNAs have shown great potential as diagnostic or prognostic biomarkers in liquid biopsies and therapeutic strategies for NSCLC. This comprehensive review elucidates the significant roles and diverse mechanisms of lncRNAs in NSCLC. Furthermore, we provide insights into the clinical relevance, current research progress, limitations, innovative research approaches, and future perspectives for targeting lncRNAs in NSCLC. By summarizing the existing knowledge and advancements, we aim to enhance the understanding of the pivotal roles played by lncRNAs in NSCLC and stimulate further research in this field. Ultimately, unraveling the complex network of lncRNA-mediated regulatory mechanisms in NSCLC could potentially lead to the development of novel diagnostic tools and therapeutic strategies.
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Affiliation(s)
- Yong-Qiang Ao
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jian Gao
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jia-Hao Jiang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hai-Kun Wang
- CAS Key Laboratory of Molecular Virology and Immunology, Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China
| | - Shuai Wang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Jian-Yong Ding
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
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Zhang J, Song L, Li G, Liang A, Cai X, Huang Y, Zhu X, Zhou X. Comprehensive assessment of base excision repair (BER)-related lncRNAs as prognostic and functional biomarkers in lung adenocarcinoma: implications for personalized therapeutics and immunomodulation. J Cancer Res Clin Oncol 2023; 149:17199-17213. [PMID: 37789154 DOI: 10.1007/s00432-023-05435-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 09/17/2023] [Indexed: 10/05/2023]
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, and comprehending its molecular mechanisms is pivotal for advancing treatment efficacy. This study aims to explore the prognostic and functional significance of base excision repair (BER)-related long non-coding RNAs (BERLncs) in LUAD. METHODS A risk score model for BERLncs was developed using the least absolute shrinkage and selection operator regression and Cox regression analysis. Model validation and prognostic evaluation were performed using Kaplan-Meier and receiver-operating characteristic curve analyses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential biological functions of BERLncs. Comparative analyses were carried out to investigate disparities in tumor mutation burden (TMB), immune infiltration, tumor immune dysfunction and exclusion (TIDE) score, chemosensitivity, and immune checkpoint gene expression between the two risk groups. RESULTS A predictive risk score model comprising 19 BERLncs was successfully developed. Patients were divided into high-risk and low-risk groups according to the median risk score. The high-risk subgroup exhibited significantly inferior overall survival. Functional enrichment analysis revealed pathways associated with lung cancer development, notably the neuroactive ligand-receptor interaction pathway. High-risk patients demonstrated elevated TMB, diminished TIDE scores, and an immunosuppressive tumor microenvironment, while low-risk patients displayed potential benefits from immunotherapy. Additionally, the risk model identified potential anticancer agents. CONCLUSION The risk score model based on BERLncs shows promise as a prognostic biomarker for LUAD patients, providing valuable insights for clinical decision-making, therapeutic strategies, and understanding of underlying biological mechanisms.
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Affiliation(s)
- Junzheng Zhang
- Department of Immunology, School of Medicine, Nantong University, Nantong, China
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Lu Song
- Department of Clinical Laboratory, Qingdao City Sixth People's Hospital, Qingdao, China
| | - Guanrong Li
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Anqi Liang
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Xiaoting Cai
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Yaqi Huang
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
| | - Xiao Zhu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China.
- Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou Medical College, Hangzhou, China.
| | - Xiaorong Zhou
- Department of Immunology, School of Medicine, Nantong University, Nantong, China.
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Zou Z, Zhang M, Xu S, Zhang Y, Zhang J, Li Z, Zhu X. Computational identification of long non-coding RNAs associated with graphene therapy in glioblastoma multiforme. Brain Commun 2023; 6:fcad293. [PMID: 38162904 PMCID: PMC10754320 DOI: 10.1093/braincomms/fcad293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/26/2023] [Accepted: 10/24/2023] [Indexed: 01/03/2024] Open
Abstract
Glioblastoma multiforme represents the most prevalent primary malignant brain tumour, while long non-coding RNA assumes a pivotal role in the pathogenesis and progression of glioblastoma multiforme. Nonetheless, the successful delivery of long non-coding RNA-based therapeutics to the tumour site has encountered significant obstacles attributable to inadequate biocompatibility and inefficient drug delivery systems. In this context, the use of a biofunctional surface modification of graphene oxide has emerged as a promising strategy to surmount these challenges. By changing the surface of graphene oxide, enhanced biocompatibility can be achieved, facilitating efficient transport of long non-coding RNA-based therapeutics specifically to the tumour site. This innovative approach presents the opportunity to exploit the therapeutic potential inherent in long non-coding RNA biology for treating glioblastoma multiforme patients. This study aimed to extract relevant genes from The Cancer Genome Atlas database and associate them with long non-coding RNAs to identify graphene therapy-related long non-coding RNA. We conducted a series of analyses to achieve this goal, including univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression. The resulting graphene therapy-related long non-coding RNAs were utilized to develop a risk score model. Subsequently, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses on the identified graphene therapy-related long non-coding RNAs. Additionally, we employed the risk model to construct the tumour microenvironment model and analyse drug sensitivity. To validate our findings, we referenced the IMvigor210 immunotherapy model. Finally, we investigated differences in the tumour stemness index. Through our investigation, we identified four promising graphene therapy-related long non-coding RNAs (AC011405.1, HOXC13-AS, LINC01127 and LINC01574) that could be utilized for treating glioblastoma multiforme patients. Furthermore, we identified 16 compounds that could be utilized in graphene therapy. Our study offers novel insights into the treatment of glioblastoma multiforme, and the identified graphene therapy-related long non-coding RNAs and compounds hold promise for further research in this field. Furthermore, additional biological experiments will be essential to validate the clinical significance of our model. These experiments can help confirm the potential therapeutic value and efficacy of the identified graphene therapy-related long non-coding RNAs and compounds in treating glioblastoma multiforme.
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Affiliation(s)
- Zhuoheng Zou
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Ming Zhang
- Department of Physical Medicine and Rehabilitation, Zibo Central Hospital, Zibo 255000, China
| | - Shang Xu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Youzhong Zhang
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Junzheng Zhang
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Zesong Li
- Guangdong Provincial Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, Shenzhen Key Laboratory of Genitourinary Tumor, Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital (Shenzhen Institute of Translational Medicine), Shenzhen 518035, China
| | - Xiao Zhu
- Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
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Zhu X, Xu Z, Li B. Editorial: Epigenetics in cancer: mechanisms and drug development-volume II. Front Genet 2023; 14:1242960. [PMID: 37476412 PMCID: PMC10354629 DOI: 10.3389/fgene.2023.1242960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/22/2023] Open
Affiliation(s)
- Xiao Zhu
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Guangdong Medical University, Zhanjiang, China
- Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou Medical College, Hangzhou, China
| | - Zhenhua Xu
- Center for Cancer and Immunology, Children’s National Health System, Washington, DC, United States
| | - Biaoru Li
- Cancer Center, Medical College of Georgia, Augusta University, Augusta, GA, United States
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