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Ghosh A, Jaaback K, Boulton A, Wong-Brown M, Raymond S, Dutta P, Bowden NA, Ghosh A. Fusobacterium nucleatum: An Overview of Evidence, Demi-Decadal Trends, and Its Role in Adverse Pregnancy Outcomes and Various Gynecological Diseases, including Cancers. Cells 2024; 13:717. [PMID: 38667331 PMCID: PMC11049087 DOI: 10.3390/cells13080717] [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: 03/07/2024] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
Gynecological and obstetric infectious diseases are crucial to women's health. There is growing evidence that links the presence of Fusobacterium nucleatum (F. nucleatum), an anaerobic oral commensal and potential periodontal pathogen, to the development and progression of various human diseases, including cancers. While the role of this opportunistic oral pathogen has been extensively studied in colorectal cancer in recent years, research on its epidemiological evidence and mechanistic link to gynecological diseases (GDs) is still ongoing. Thus, the present review, which is the first of its kind, aims to undertake a comprehensive and critical reappraisal of F. nucleatum, including the genetics and mechanistic role in promoting adverse pregnancy outcomes (APOs) and various GDs, including cancers. Additionally, this review discusses new conceptual advances that link the immunomodulatory role of F. nucleatum to the development and progression of breast, ovarian, endometrial, and cervical carcinomas through the activation of various direct and indirect signaling pathways. However, further studies are needed to explore and elucidate the highly dynamic process of host-F. nucleatum interactions and discover new pathways, which will pave the way for the development of better preventive and therapeutic strategies against this pathobiont.
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Affiliation(s)
- Arunita Ghosh
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW 2308, Australia;
- Drug Repurposing and Medicines Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia;
| | - Ken Jaaback
- Hunter New England Centre for Gynecological Cancer, John Hunter Hospital, Newcastle, NSW 2305, Australia;
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Angela Boulton
- Newcastle Private Hospital, Newcastle, NSW 2305, Australia; (A.B.); (S.R.)
| | - Michelle Wong-Brown
- Drug Repurposing and Medicines Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia;
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Steve Raymond
- Newcastle Private Hospital, Newcastle, NSW 2305, Australia; (A.B.); (S.R.)
| | - Partha Dutta
- Department of Medicine, Division of Cardiology, University of Pittsburgh, Pittsburgh, PA 15261, USA;
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Nikola A. Bowden
- Drug Repurposing and Medicines Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia;
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Arnab Ghosh
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW 2308, Australia;
- Drug Repurposing and Medicines Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia;
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Ye L, Jiang Z, Zheng M, Pan K, Lian J, Ju B, Liu X, Tang S, Guo G, Zhang S, Hong X, Lu W. Fatty acid metabolism-related lncRNA prognostic signature for serous ovarian carcinoma. Epigenomics 2024; 16:309-329. [PMID: 38356435 DOI: 10.2217/epi-2023-0388] [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] [Indexed: 02/16/2024] Open
Abstract
Background: To explore the role of fatty acid metabolism (FAM)-related lncRNAs in the prognosis and antitumor immunity of serous ovarian cancer (SOC). Materials & methods: A SOC FAM-related lncRNA risk model was developed and evaluated by a series of analyses. Additional immune-related analyses were performed to further assess the associations between immune state, tumor microenvironment and the prognostic risk model. Results: Five lncRNAs associated with the FAM genes were found and used to create a predictive risk model. The patients with a low-risk profile exhibited favorable prognostic outcomes. Conclusion: The established prognostic risk model exhibits better predictive capabilities for the prognosis of patients with SOC and offers novel potential therapy targets for SOC.
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Affiliation(s)
- Lele Ye
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Zhuofeng Jiang
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Mengxia Zheng
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Kan Pan
- First Clinical College, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jingru Lian
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Bing Ju
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Xuefei Liu
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Sangsang Tang
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Gangqiang Guo
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research & Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens & Immunity, Department of Microbiology & Immunology, Institute of Molecular Virology & Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Songfa Zhang
- Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Xin Hong
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
- Key University Laboratory of Metabolism & Health of Guangdong, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
- Guangdong Provincial Key Laboratory of Cell Microenvironment & Disease Research, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Weiguo Lu
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
- Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
- Center of Uterine Cancer Diagnosis & Therapy of Zhejiang Province, Hangzhou, 310006, Zhejiang, China
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Salamini-Montemurri M, Lamas-Maceiras M, Lorenzo-Catoira L, Vizoso-Vázquez Á, Barreiro-Alonso A, Rodríguez-Belmonte E, Quindós-Varela M, Cerdán ME. Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis. Int J Mol Sci 2023; 24:10798. [PMID: 37445988 PMCID: PMC10341812 DOI: 10.3390/ijms241310798] [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: 05/15/2023] [Revised: 06/19/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.
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Affiliation(s)
- Martín Salamini-Montemurri
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Mónica Lamas-Maceiras
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Lidia Lorenzo-Catoira
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Ángel Vizoso-Vázquez
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Aida Barreiro-Alonso
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Esther Rodríguez-Belmonte
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
| | - María Quindós-Varela
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
- Complexo Hospitalario Universitario de A Coruña (CHUAC), Servizo Galego de Saúde (SERGAS), 15006 A Coruña, Spain
| | - M Esperanza Cerdán
- Centro Interdisciplinar de Química e Bioloxía (CICA), As Carballeiras, s/n, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
- Facultade de Ciencias, A Fraga, s/n, Campus de A Zapateira, Universidade da Coruña, 15071 A Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña (INIBIC), As Xubias de Arriba 84, 15006 A Coruña, Spain
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Gao K, Lian W, Zhao R, Huang W, Xiong J. The joint role of methylation and immune-related lncRNAs in ovarian cancer: Defining molecular subtypes and developing prognostic signature. Transl Oncol 2023; 34:101704. [PMID: 37257331 DOI: 10.1016/j.tranon.2023.101704] [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: 03/10/2023] [Revised: 05/12/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023] Open
Abstract
INTRODUCTION Complex outcome of ovarian cancer (OC) stems from the tumor immune microenvironment (TIME) influenced by genetic and epigenetic factors. This study aimed to comprehensively explored the subclasses of OC through lncRNAs related to both N6-methyladenosine (m6A)/N1-methyladenosine (m1A)/N7-methylguanosine (m7G)/5-methylcytosine (m5C) in terms of epigenetic variability and immune molecules and develop a new set of risk predictive systems. MATERIAL AND METHODS The lncRNA data of OC were collected from TCGA. Spearman correlation analysis on lncRNA data of OC with immune-related gene expression and with m6A/m5C/m1A/m7G were respectively conducted. The m6A/m5C/m1A/m7G-related m6A/m5C/m1A/m7G related immune lncRNA subtypes were identified on the basis of the prognostic lncRNAs. Heterogeneity among subtypes was evaluated by tumor mutation analysis, tumor microenvironment (TME) component analysis, response to immune checkpoint blocked (ICB) and chemotherapeutic drugs. A risk predictive system was developed based on the results of Cox regression analysis and random survival forest analysis of the differences between each specific cluster and other clusters. RESULTS Three m6A/m5C/m1A/m7G-related immune lncRNA subtypes of OC showing distinct differences in prognosis, mutation pattern, TIME components, immunotherapy and chemotherapy response were identified. A set of risk predictive system consisting of 10 lncRNA for OC was developed, according to which the risk score of samples in each OC dataset was calculated and risk type was defined. CONCLUSIONS This study classified three m6A/m5C/m1A/m7G-related immune lncRNA subtypes with distinct heterogeneous mutation patterns, TME components, ICB therapy and immune response, and provided a set of risk predictive system consisted of 10 lncRNA for OC.
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Affiliation(s)
- Kefei Gao
- Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Wenqin Lian
- Department of Burns and Plastic & Wound Repair Surgery, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361100, China
| | - Rui Zhao
- Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Weiming Huang
- Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Jian Xiong
- Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
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5
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Distefano R, Ilieva M, Madsen JH, Ishii H, Aikawa M, Rennie S, Uchida S. T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes. Noncoding RNA 2023; 9:30. [PMID: 37218990 PMCID: PMC10204529 DOI: 10.3390/ncrna9030030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/01/2023] [Accepted: 05/04/2023] [Indexed: 05/24/2023] Open
Abstract
Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1, using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.
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Affiliation(s)
- Rebecca Distefano
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, DK-2200 Copenhagen, Denmark;
| | - Mirolyuba Ilieva
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, DK-2450 Copenhagen, Denmark; (M.I.); (J.H.M.)
| | - Jens Hedelund Madsen
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, DK-2450 Copenhagen, Denmark; (M.I.); (J.H.M.)
| | - Hideshi Ishii
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan;
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Center for Excellence in Vascular Biology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sarah Rennie
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, DK-2200 Copenhagen, Denmark;
| | - Shizuka Uchida
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, DK-2450 Copenhagen, Denmark; (M.I.); (J.H.M.)
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Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA. Biomolecules 2023; 13:biom13020306. [PMID: 36830675 PMCID: PMC9953467 DOI: 10.3390/biom13020306] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/23/2022] [Accepted: 01/04/2023] [Indexed: 02/10/2023] Open
Abstract
(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.
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Hu XQ, Zhang XC, Li ST, Hua T. Construction and validation of a histone acetylation-related lncRNA prognosis signature for ovarian cancer. Front Genet 2022; 13:934246. [PMID: 36313424 PMCID: PMC9596759 DOI: 10.3389/fgene.2022.934246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
Abstract
Ovarian cancer (OC) leads to the most deaths among gynecological malignancies. The various epigenetic regulatory mechanisms of histone acetylation in cancer have attracted increasing attention from scientists. Long non-coding RNA (lncRNA) also plays an important role in multiple biology processes linked to OC. This study aimed to identify the histone acetylation-related lncRNAs (HARlncRNAs) with respect to the prognosis in OC. We obtained the transcriptome data from Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA); HARlncRNAs were first identified by co-expression and differential expression analyses, and then univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to construct the HARlncRNAs risk signature. Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analysis (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. A risk signature with 14 HARlncRNAs in OC was finally established and further validated in the International Cancer Genome Consortium (ICGC) cohort; the 1-, 3-, and 5-year ROC value, nomogram, and calibration results confirmed the good prediction power of this model. The patients were grouped into high- and low-risk subgroups according to the risk score by the median value. The low-risk group patients exhibited a higher homologous recombination deficiency (HRD) score, LOH_frac_altered, and mutLoad_nonsilent. Furthermore, consensus clustering analysis was employed to divide OC patients into three clusters based on the expression of the 14 HARlncRNAs, which presented different survival probabilities. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were also performed to evaluate the three clusters. In conclusion, the risk signature composed of 14 HARlncRNAs might function as biomarkers and prognostic indicators with respect to predicting the response to the anti-cancer drugs in OC.
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Affiliation(s)
- Xiao-Qian Hu
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Xiao-Chong Zhang
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Shao-Teng Li
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Tian Hua
- Department of Gynecology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
- *Correspondence: Tian Hua,
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An Immune-Related lncRNA Pairing Model for Predicting the Prognosis and Immune-Infiltrating Cell Condition in Human Ovarian Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3168408. [PMID: 36033566 PMCID: PMC9400430 DOI: 10.1155/2022/3168408] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/20/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022]
Abstract
Ovarian cancer is the second common cancer among the gynecological tumors. It is difficult to be found and diagnosed in the early stage and easy to relapse due to chemoresistance and deficiency in choices of treatment. Therefore, future exploring the biomarkers for diagnosis, treatment, and prognosis prediction of ovarian cancer is significant to women in the world. We downloaded data from TCGA and GTEx and used R “limma” package for analyzing the differentially expressed immune-related lncRNA in ovarian cancer and finally got 7 downregulated and 171 upregulated lncRNA. Then, we paired the differentially expressed immune-related lncRNA and constructed a novel lncRNA pairing model containing 7 lncRNA pairs. Based on the cut-off point with the highest AUC value, 102 patients were selected in high-risk group and 272 in low-risk group. The KM analysis suggested that the patients in the low-risk group had a longer overall survival. Future analysis showed the correlations between risk scores and clinicopathological parameters and infiltrating immune cells. In conclusion, we identified an immune-related lncRNA pairing model for predicting the prognosis and immune-infiltrating cell condition in human ovarian cancer, which thus further can instruct immunotherapy.
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9
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Wu Z, Wang Y, Yan M, Liang Q, Li B, Hou G, Xia T, Lin Z, Xu W. Comprehensive analysis of the endoplasmic reticulum stress-related long non-coding RNA in bladder cancer. Front Oncol 2022; 12:951631. [PMID: 35992824 PMCID: PMC9386564 DOI: 10.3389/fonc.2022.951631] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/11/2022] [Indexed: 11/23/2022] Open
Abstract
Background Bladder cancer is ranked the second most frequent tumor among urological malignancies. The research strived to establish a prognostic model based on endoplasmic reticulum stress (ERS)-related long non-coding RNA (lncRNA) in bladder cancer. Methods We extracted the ERS-related genes from the published research and bladder cancer data from the Cancer Genome Atlas database. ERS-related lncRNAs with prognostic significance were screened by univariate Cox regression, least absolute shrinkage and selection operator regression analysis and Kaplan-Meier method. Multivariate Cox analysis was leveraged to establish the risk score model. Moreover, an independent dataset, GSE31684, was used to validate the model’s efficacy. The nomogram was constructed based on the risk score and clinical variables. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the ERS-related signature. Finally, we employed external datasets (GSE55433 and GSE89006) and qRT-PCR to investigate the expression profile of these lncRNAs in bladder cancer tissues and cells. Results Six ERS-related lncRNAs were identified to be closely coupled with patients’ prognosis. On this foundation, a risk score model was created to generate the risk score for each patient. The ERS-related risk score was shown to be an independent prognostic factor. And the results of GSE31684 dataset also supported this conclusion. Then, a nomogram was constructed based on risk scores and clinical characteristics, and proven to have excellent predictive value. Moreover, the gene function analysis demonstrated that ERS-related lncRNAs were closely linked to fatty extracellular matrix, cytokines, cell adhesion, and tumor pathways. Further analysis revealed the association of the 6-lncRNAs signature with gene mutations and immunity in bladder cancer. Finally, the external datasets and qRT-PCR verified high expressions of the ERS-related lncRNAs in bladder cancer tissues and cells. Conclusions Overall, our findings indicated that ERS-related lncRNAs, which may affect tumor pathogenesis in a number of ways, might be exploited to assess the prognosis of bladder cancer patients.
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Affiliation(s)
- Zhenyu Wu
- Department of Urology, The First People’s Hospital of Foshan, Foshan, China
| | - Yue Wang
- The First Clinical Medical College, GuangDong Medical University, ZhanJiang, China
| | - Mengxin Yan
- The First Clinical Medical College, GuangDong Medical University, ZhanJiang, China
| | - Quan Liang
- Department of Urology, The First People’s Hospital of Foshan, Foshan, China
| | - Bin Li
- Department of Urology, The First People’s Hospital of Foshan, Foshan, China
| | - Guoliang Hou
- Department of Urology, The First People’s Hospital of Foshan, Foshan, China
| | - Taolin Xia
- Department of Urology, The First People’s Hospital of Foshan, Foshan, China
| | - Zhe Lin
- Department of Urology, The First People’s Hospital of Foshan, Foshan, China
- *Correspondence: Wenfeng Xu, ; Zhe Lin,
| | - Wenfeng Xu
- Department of Urology, The First People’s Hospital of Foshan, Foshan, China
- *Correspondence: Wenfeng Xu, ; Zhe Lin,
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He YB, Fang LW, Hu D, Chen SL, Shen SY, Chen KL, Mu J, Li JY, Zhang H, Yong-lin L, Zhang L. Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer. Front Oncol 2022; 12:967207. [PMID: 35965557 PMCID: PMC9366220 DOI: 10.3389/fonc.2022.967207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 06/30/2022] [Indexed: 12/05/2022] Open
Abstract
Objective The mortality rate of ovarian cancer (OC) is the highest among all gynecologic cancers. To predict the prognosis and the efficacy of immunotherapy, we identified new biomarkers. Methods The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) databases were used to extract ovarian cancer transcriptomes. By performing the co-expression analysis, we identified necroptosis-associated long noncoding RNAs (lncRNAs). We used the least absolute shrinkage and selection operator (LASSO) to build the risk model. The qRT-PCR assay was conducted to confirm the differential expression of lncRNAs in the ovarian cancer cell line SK-OV-3. Gene Set Enrichment Analysis, Kaplan-Meier analysis, and the nomogram were used to determine the lncRNAs model. Additionally, the risk model was estimated to evaluate the efficacy of immunotherapy and chemotherapy. We classified necroptosis-associated IncRNAs into two clusters to distinguish between cold and hot tumors. Results The model was constructed using six necroptosis-associated lncRNAs. The calibration plots from the model showed good consistency with the prognostic predictions. The overall survival of one, three, and five-year areas under the ROC curve (AUC) was 0.691, 0.678, and 0.691, respectively. There were significant differences in the IC50 between the risk groups, which could serve as a guide to systemic treatment. The results of the qRT-PCR assay showed that AL928654.1, AL133371.2, AC007991.4, and LINC00996 were significantly higher in the SK-OV-3 cell line than in the Iose-80 cell line (P < 0.05). The clusters could be applied to differentiate between cold and hot tumors more accurately and assist in accurate mediation. Cluster 2 was more vulnerable to immunotherapies and was identified as the hot tumor. Conclusion Necroptosis-associated lncRNAs are reliable predictors of prognosis and can provide a treatment strategy by screening for hot tumors.
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Affiliation(s)
- Yi-bo He
- Department of Clinical Lab, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Lu-wei Fang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Dan Hu
- Department of Clinical Lab, The Cixi Integrated Traditional Chinese and Western Medicine Medical and Health Group Cixi Red Cross Hospital, Cixi, China
| | - Shi-liang Chen
- Department of Clinical Lab, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Si-yu Shen
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Kai-li Chen
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jie Mu
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jun-yu Li
- Department of Pharmacy, Sanya Women and Children Hospital Managed by Shanghai Children’s Medical Center, Sanya, China
| | - Hongpan Zhang
- Department of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- *Correspondence: Li Zhang, ; Hongpan Zhang, ; Liu Yong-lin,
| | - Liu Yong-lin
- Reproductive Centre, Sanya Women and Children Hospital Managed by Shanghai Children’s Medical Center, Sanya, China
- *Correspondence: Li Zhang, ; Hongpan Zhang, ; Liu Yong-lin,
| | - Li Zhang
- Obstetrics and Gynaecology, The First Affiliated Hospital of Zhejiang Chinese Medical, Hangzhou, China
- *Correspondence: Li Zhang, ; Hongpan Zhang, ; Liu Yong-lin,
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Xie J, Tian W, Tang Y, Zou Y, Zheng S, Wu L, Zeng Y, Wu S, Xie X, Xie X. Establishment of a Cell Necroptosis Index to Predict Prognosis and Drug Sensitivity for Patients With Triple-Negative Breast Cancer. Front Mol Biosci 2022; 9:834593. [PMID: 35601830 PMCID: PMC9117653 DOI: 10.3389/fmolb.2022.834593] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/04/2022] [Indexed: 12/25/2022] Open
Abstract
Background: Necroptosis has been an alternatively identified mechanism of programmed cancer cell death, which plays a significant role in cancer. However, research about necroptosis-related long noncoding RNAs (lncRNAs) in cancer are still few. Moreover, the potentially prognostic value of necroptosis-related lncRNAs and their correlation with the immune microenvironment remains unclear. The present study aimed to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship to immune microenvironment in triple-negative breast cancer (TNBC). Methods: The RNA expression matrix of patients with TNBC was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Finally, 107 patients of GSE58812, 159 patients of TCGA, and 143 patients of GSE96058 were included. Necroptosis-related lncRNAs were screened by Cox regression and Pearson correlation analysis with necroptosis-related genes. By LASSO regression analysis, nine necroptosis-related lncRNAs were employed, and a cell necroptosis index (CNI) was established; then, we evaluated its prognostic value, clinical significance, pathways, immune infiltration, and chemotherapeutics efficacy. Results: Based on the CNI value, the TNBC patients were divided into high- and low-CNI groups, and the patients with high CNI had worse prognosis, more lymph node metastasis, and larger tumor (p < 0.05). The receiver operating characteristic (ROC) analysis showed that the signature performed well. The result of the infiltration proportion of different immune cell infiltration further explained that TNBC patients with high CNI had low immunogenicity, leading to poor therapeutic outcomes. Moreover, we found significant differences of the IC50 values of various chemotherapeutic drugs in the two CNI groups, which might provide a reference to make a personalized chemotherapy for them. Conclusion: The novel prognostic marker CNI could not only precisely predict the survival probability of patients with TNBC but also demonstrate a potential role in antitumor immunity and drug sensitivity.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Xinhua Xie
- *Correspondence: Xinhua Xie, ; Xiaoming Xie,
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