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Zhu L, Lin Z, Wang K, Gu J, Chen X, Chen R, Wang L, Cheng X. A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer. Front Endocrinol (Lausanne) 2024; 15:1372413. [PMID: 38529390 PMCID: PMC10961354 DOI: 10.3389/fendo.2024.1372413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 02/15/2024] [Indexed: 03/27/2024] Open
Abstract
Introduction Ovarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response. Methods Ovarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor. Results This study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.
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Affiliation(s)
- Linhua Zhu
- Department of Obstetrics, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhuoqun Lin
- Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kai Wang
- Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Department of Obstetrics and Gynecology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Jiaxin Gu
- Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaojing Chen
- Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ruizhe Chen
- Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Lingfang Wang
- Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaodong Cheng
- Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Liu S, He K, Yang L, Xu F, Cui X, Qu L, Li X, Ren BC. Endoplasmic reticulum stress regulators exhibit different prognostic, therapeutic and immune landscapes in pancreatic adenocarcinoma. J Cell Mol Med 2024; 28:e18092. [PMID: 38303549 PMCID: PMC10902308 DOI: 10.1111/jcmm.18092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/24/2023] [Accepted: 12/15/2023] [Indexed: 02/03/2024] Open
Abstract
Endoplasmic reticulum stress (ERS) and unfolded protein response are the critical processes of tumour biology. However, the roles of ERS regulatory genes in pancreatic adenocarcinoma (PAAD) remain elusive. A novel ERS-related risk signature was constructed using the Lasso regression analysis. Its prognostic value, immune effect, metabolic influence, mutational feature and therapeutic correlation were comprehensively analysed through multiple bioinformatic approaches. The biofunctions of KDELR3 and YWHAZ in pancreatic cancer (PC) cells were also investigated through colony formation, Transwell assays, flow cytometric detection and a xenograft model. The upstream miRNA regulatory mechanism of KDELR3 was predicted and validated. ERS risk score was identified as an independent prognostic factor and could improve traditional prognostic model. Meanwhile, it was closely associated with metabolic reprogramming and tumour immune. High ERS risk enhanced glycolysis process and nucleotide metabolism, but was unfavourable for anti-tumour immune response. Moreover, ERS risk score could act as a potential biomarker for predicting the efficacy of ICBs. Overexpression of KDELR3 and YWHAZ stimulated the proliferation, migration and invasion of SW1990 and BxPC-3 cells. Silencing KDELR3 suppressed tumour growth in a xenograft model. miR-137 could weaken the malignant potentials of PC cells through inhibiting KDELR3 (5'-AGCAAUAA-3'). ERS risk score greatly contributed to PAAD clinical assessment. KDELR3 and YWHAZ possessed cancer-promoting capacities, showing promise as a novel treatment target.
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Affiliation(s)
- Shanshan Liu
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Kaini He
- Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Longbao Yang
- Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Fangshi Xu
- Department of Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Xiaoguang Cui
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Li Qu
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Xueyi Li
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Bin-Cheng Ren
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
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Zhao H, Jiang R, Feng Z, Wang X, Zhang C. Transcription factor LHX9 (LIM Homeobox 9) enhances pyruvate kinase PKM2 activity to induce glycolytic metabolic reprogramming in cancer stem cells, promoting gastric cancer progression. J Transl Med 2023; 21:833. [PMID: 37980488 PMCID: PMC10657563 DOI: 10.1186/s12967-023-04658-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/25/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Glycolytic metabolic reprogramming is a phenomenon in which cells undergo altered metabolic patterns during malignant transformation, mainly involving various aspects of glycolysis, electron transport chain, oxidative phosphorylation, and pentose phosphate pathway. This reprogramming phenomenon can be used as one of the markers of tumorigenesis and development. Pyruvate kinase is the third rate-limiting enzyme in the sugar metabolism process by specifically catalyzing the irreversible conversion of PEP to pyruvate. PURPOSE This study aimed to reveal the critical mediator(s) that regulate glycolytic metabolism reprogramming in gastric cancer and their underlying molecular mechanism and then explore the molecular mechanisms by which LHX9 may be involved in regulating gastric cancer (GC) progression. METHODS Firstly, we downloaded the GC and glycolysis-related microarray datasets from TCGA and MSigDB databases and took the intersection to screen out the transcription factor LHX9 that regulates GC glycolytic metabolic reprogramming. Software packages were used for differential analysis, single gene predictive analysis, and Venn diagram. In addition, an enrichment analysis of the glycolytic pathway was performed. Immunohistochemical staining was performed for LHX9 and PKM2 protein expression in 90 GC patients, and the association between their expressions was evaluated by Spearman's correlation coefficient method. Three human GC cell lines (AGS, NCI-N87, HGC-27) were selected for in vitro experimental validation. Flow cytometry was utilized to determine the stem cell marker CD44 expression status in GCSCs. A sphere formation assay was performed to evaluate the sphere-forming capabilities of GCSCs. In addition, RT-qPCR and Western blot experiments were employed to investigate the tumor stem cell markers OCT4 and SOX2 expression levels in GCSCs. Furthermore, a lentiviral expression vector was constructed to assess the impact of downregulating LHX9 or PKM2 on the glycolytic metabolic reprogramming of GCSCs. The proliferation, migration, and invasion of GCSCs were then detected by CCK-8, EdU, and Transwell assays. Subsequently, the mutual binding of LHX9 and PKM2 was verified using chromatin immunoprecipitation and dual luciferase reporter genes. In vivo experiments were verified by establishing a subcutaneous transplantation tumor model in nude mice, observing the size and volume of tumors in vivo in nude mice, and obtaining fresh tissues for subsequent experiments. RESULTS Bioinformatics analysis revealed that LHX9 might be involved in the occurrence and development of GC through regulating glycolytic metabolism. High LHX9 expression could be used as a reference marker for prognosis prediction of GC patients. Clinical tissue assays revealed that LHX9 and PKM2 were highly expressed in GC tissues. Meanwhile, GC tissues also highly expressed glycolysis-associated protein GLUT1 and tumor cell stemness marker CD44. In vitro cellular assays showed that LHX9 could enhance its activity and induce glycolytic metabolic reprogramming in GCSCs through direct binding to PKM2. In addition, the knockdown of LHX9 inhibited PKM2 activity and glycolytic metabolic reprogramming and suppressed the proliferation, migration, and invasive ability of GCSCs. In vivo animal experiments further confirmed that the knockdown of LHX9 could reduce the tumorigenic ability of GCSCs in nude mice by inhibiting PKM2 activity and glycolytic metabolic reprogramming. CONCLUSION The findings suggest that both LHX9 and PKM2 are highly expressed in GCs, and LHX9 may induce the reprogramming of glycolytic metabolism through transcriptional activation of PKM2, enhancing the malignant biological properties of GCSCs and ultimately promoting GC progression.
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Affiliation(s)
- Hongying Zhao
- Department of Oncology, Xuzhou City Cancer Hospital, Xuzhou Third People's Hospital, Jiangsu Province, Xuzhou Hospital Affiliated to Jiangsu University, No. 131, Huancheng Road, Gulou District, Xuzhou, 221000, People's Republic of China.
| | - Rongke Jiang
- Department of Oncology, Xuzhou City Cancer Hospital, Xuzhou Third People's Hospital, Jiangsu Province, Xuzhou Hospital Affiliated to Jiangsu University, No. 131, Huancheng Road, Gulou District, Xuzhou, 221000, People's Republic of China
| | - Zhijing Feng
- Jiangsu University, Zhenjiang, 212013, People's Republic of China
| | - Xue Wang
- Department of Oncology, Xuzhou City Cancer Hospital, Xuzhou Third People's Hospital, Jiangsu Province, Xuzhou Hospital Affiliated to Jiangsu University, No. 131, Huancheng Road, Gulou District, Xuzhou, 221000, People's Republic of China
| | - Chunmei Zhang
- Department of Oncology, Xuzhou City Cancer Hospital, Xuzhou Third People's Hospital, Jiangsu Province, Xuzhou Hospital Affiliated to Jiangsu University, No. 131, Huancheng Road, Gulou District, Xuzhou, 221000, People's Republic of China
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Liang J, Wang X, Yang J, Sun P, Sun J, Cheng S, Liu J, Ren Z, Ren M. Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer. Front Immunol 2023; 14:1198826. [PMID: 38035071 PMCID: PMC10684933 DOI: 10.3389/fimmu.2023.1198826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 10/31/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Breast cancer (BC) is now the most common type of cancer in women. Disulfidptosis is a new regulation of cell death (RCD). RCD dysregulation is causally linked to cancer. However, the comprehensive relationship between disulfidptosis and BC remains unknown. This study aimed to explore the predictive value of disulfidptosis-related genes (DRGs) in BC and their relationship with the TME. Methods This study obtained 11 disulfidptosis genes (DGs) from previous research by Gan et al. RNA sequencing data of BC were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) databases. First, we examined the effect of DG gene mutations and copy number changes on the overall survival of breast cancer samples. We then used the expression profile data of 11 DGs and survival data for consensus clustering, and BC patients were divided into two clusters. Survival analysis, gene set variation analysis (GSVA) and ss GSEA were used to compare the differences between them. Subsequently, DRGs were identified between the clusters used to perform Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a prognosis model. Finally, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. CCK-8 and a colony assay obtained by knocking down genes and gene sequencing were used to validate the model. Result Two DG clusters were identified based on the expression of 11DGs. Then, 225 DRGs were identified between them. RS, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-RS shows a better prognosis and higher immunotherapy response than high-RS. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. CCK-8 and colony assay obtained by knocking down genes have demonstrated that the knockdown of high-risk genes in the RS model significantly inhibited cell proliferation. Discussion This study elucidates the potential relationship between disulfidptosis-related genes and breast cancer and provides new guidance for treating breast cancer.
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Affiliation(s)
- Jiahui Liang
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xin Wang
- Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Jing Yang
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Peng Sun
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jingjing Sun
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shengrong Cheng
- Department of Plastic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jincheng Liu
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhiyao Ren
- Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Min Ren
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
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Lian B, Yan S, Li J, Bai Z, Li J. HNRNPC promotes collagen fiber alignment and immune evasion in breast cancer via activation of the VIRMA-mediated TFAP2A/DDR1 axis. Mol Med 2023; 29:103. [PMID: 37528369 PMCID: PMC10394847 DOI: 10.1186/s10020-023-00696-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/07/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Cancers aggressively reorganize collagen in their microenvironment, leading to the evasion of tumor cells from immune surveillance. However, the biological significance and molecular mechanism of collagen alignment in breast cancer (BC) have not been well established. METHODS In this study, BC-related RNA-Seq data were obtained from the TCGA database to analyze the correlation between DDR1 and immune cells. Mouse BC cells EO771 were selected for in vitro validation, and dual-luciferase experiments were conducted to examine the effect of TFAP2A on DDR1 promoter transcription activity. ChIP experiments were performed to assess TFAP2A enrichment on the DDR1 promoter, while Me-RIP experiments were conducted to detect TFAP2A mRNA m6A modification levels, and PAR-CLIP experiments were conducted to determine VIRMA's binding to TFAP2A mRNA and RIP experiments to investigate HNRNPC's recognition of m6A modification on TFAP2A mRNA. Additionally, an in vivo mouse BC transplant model and the micro-physiological system was constructed for validation, and Masson staining was used to assess collagen fiber arrangement. Immunohistochemistry was conducted to identify the number of CD8-positive cells in mouse BC tumors and Collagen IV content in ECM, while CD8 + T cell migration experiments were performed to measure CD8 + T cell migration. RESULTS Bioinformatics analysis showed that DDR1 was highly expressed in BC and negatively correlated with the proportion of anti-tumor immune cell infiltration. In vitro cell experiments indicated that VIRMA, HNRNPC, TFAP2A, and DDR1 were highly expressed in BC cells. In addition, HNRNPC promoted TFAP2A expression and, therefore, DDR1 transcription by recognizing the m6A modification of TFAP2A mRNA by VIRMA. In vivo animal experiments further confirmed that VIRMA and HNRNPC enhanced the TFAP2A/DDR1 axis, promoting collagen fiber alignment, reducing anti-tumor immune cell infiltration, and promoting immune escape in BC. CONCLUSION This study demonstrated that HNRNPC promoted DDR1 transcription by recognizing VIRMA-unveiled m6A modification of TFAP2A mRNA, which enhanced collagen fiber alignment and ultimately resulted in the reduction of anti-tumor immune cell infiltration and promotion of immune escape in BC.
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Affiliation(s)
- Bin Lian
- Department of Surgical Oncology, General Hospital of Ningxia Medical University, No. 804, Shengli Street, Xingqing District, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Shuxun Yan
- Ningxia Medical University, Yinchuan, 750004, China
| | - Jiayi Li
- Northwest University for Nationalities, Lanzhou, 730030, China
| | | | - Jinping Li
- Department of Surgical Oncology, General Hospital of Ningxia Medical University, No. 804, Shengli Street, Xingqing District, Yinchuan, 750004, Ningxia Hui Autonomous Region, China.
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Kerslake R, Belay B, Panfilov S, Hall M, Kyrou I, Randeva HS, Hyttinen J, Karteris E, Sisu C. Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models. Cancers (Basel) 2023; 15:3350. [PMID: 37444459 DOI: 10.3390/cancers15133350] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 06/01/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012-2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial-mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.
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Affiliation(s)
- Rachel Kerslake
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
| | - Birhanu Belay
- Computational Biophysics and Imaging Group, The Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Suzana Panfilov
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
| | - Marcia Hall
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
- Mount Vernon Cancer Centre, Rickmansworth Road, Northwood HA6 2RN, UK
| | - Ioannis Kyrou
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- Research Institute for Health & Wellbeing, Coventry University, Coventry CV1 5FB, UK
- Aston Medical School, College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK
- Laboratory of Dietetics and Quality of Life, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece
| | - Harpal S Randeva
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Jari Hyttinen
- Computational Biophysics and Imaging Group, The Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Emmanouil Karteris
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
| | - Cristina Sisu
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
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Geng T, Zheng M, Wang Y, Reseland JE, Samara A. An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma. Front Mol Biosci 2023; 10:1200354. [PMID: 37388244 PMCID: PMC10301747 DOI: 10.3389/fmolb.2023.1200354] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/31/2023] [Indexed: 07/01/2023] Open
Abstract
Background: Ovarian Serous Adenocarcinoma is a malignant tumor originating from epithelial cells and one of the most common causes of death from gynecological cancers. The objective of this study was to develop a prediction model based on extracellular matrix proteins, using artificial intelligence techniques. The model aimed to aid healthcare professionals to predict the overall survival of patients with ovarian cancer (OC) and determine the efficacy of immunotherapy. Methods: The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection was used as the study dataset, whereas the TCGA-Pancancer dataset was used for validation. The prognostic importance of 1068 known extracellular matrix proteins for OC were determined by the Random Forest algorithm and the Lasso algorithm establishing the ECM risk score. Based on the gene expression data, the differences in mRNA abundance, tumour mutation burden (TMB) and tumour microenvironment (TME) between the high- and low-risk groups were assessed. Results: Combining multiple artificial intelligence algorithms we were able to identify 15 key extracellular matrix genes, namely, AMBN, CXCL11, PI3, CSPG5, TGFBI, TLL1, HMCN2, ESM1, IL12A, MMP17, CLEC5A, FREM2, ANGPTL4, PRSS1, FGF23, and confirm the validity of this ECM risk score for overall survival prediction. Several other parameters were identified as independent prognostic factors for OC by multivariate COX analysis. The analysis showed that thyroglobulin (TG) targeted immunotherapy was more effective in the high ECM risk score group, while the low ECM risk score group was more sensitive to the RYR2 gene-related immunotherapy. Additionally, the patients with low ECM risk scores had higher immune checkpoint gene expression and immunophenoscore levels and responded better to immunotherapy. Conclusion: The ECM risk score is an accurate tool to assess the patient's sensitivity to immunotherapy and forecast OC prognosis.
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Affiliation(s)
- Tianxiang Geng
- Department of Biomaterials, FUTURE, Center for Functional Tissue Reconstruction, Faculty of Dentistry, University of Oslo, Oslo, Norway
| | - Mengxue Zheng
- Laboratory of Reproductive Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Yongfeng Wang
- Department of Obstetrics and Gynecology, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Janne Elin Reseland
- Department of Biomaterials, FUTURE, Center for Functional Tissue Reconstruction, Faculty of Dentistry, University of Oslo, Oslo, Norway
| | - Athina Samara
- Department of Biomaterials, FUTURE, Center for Functional Tissue Reconstruction, Faculty of Dentistry, University of Oslo, Oslo, Norway
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Huang J, Sigon G, Mullish BH, Wang D, Sharma R, Manousou P, Forlano R. Applying Lipidomics to Non-Alcoholic Fatty Liver Disease: A Clinical Perspective. Nutrients 2023; 15:nu15081992. [PMID: 37111211 PMCID: PMC10143024 DOI: 10.3390/nu15081992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 04/29/2023] Open
Abstract
The prevalence of Non-alcoholic fatty liver disease (NAFLD) and associated complications, such as hepatocellular carcinoma (HCC), is growing worldwide, due to the epidemics of metabolic risk factors, such as obesity and type II diabetes. Among other factors, an aberrant lipid metabolism represents a crucial step in the pathogenesis of NAFLD and the development of HCC in this population. In this review, we summarize the evidence supporting the application of translational lipidomics in NAFLD patients and NAFLD associated HCC in clinical practice.
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Affiliation(s)
- Jian Huang
- Liver Unit, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W21NY, UK
| | - Giordano Sigon
- Liver Unit, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W21NY, UK
| | - Benjamin H Mullish
- Liver Unit, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W21NY, UK
| | - Dan Wang
- Liver Unit, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W21NY, UK
| | - Rohini Sharma
- Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W21NY, UK
| | - Pinelopi Manousou
- Liver Unit, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W21NY, UK
| | - Roberta Forlano
- Liver Unit, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W21NY, UK
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Li J, Wu F, Li C, Sun S, Feng C, Wu H, Chen X, Wang W, Zhang Y, Liu M, Liu X, Cai Y, Jia Y, Qiao H, Zhang Y, Zhang S. The cuproptosis-related signature predicts prognosis and indicates immune microenvironment in breast cancer. Front Genet 2022; 13:977322. [PMID: 36226193 PMCID: PMC9548612 DOI: 10.3389/fgene.2022.977322] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 09/06/2022] [Indexed: 11/20/2022] Open
Abstract
Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.
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Affiliation(s)
- Jia Li
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Fei Wu
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Chaofan Li
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Shiyu Sun
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Cong Feng
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Huizi Wu
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Xi Chen
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Weiwei Wang
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yu Zhang
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Mengji Liu
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Xuan Liu
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yifan Cai
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yiwei Jia
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Hao Qiao
- Department of Orthopedics, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yinbin Zhang
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Yinbin Zhang, ; Shuqun Zhang,
| | - Shuqun Zhang
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Yinbin Zhang, ; Shuqun Zhang,
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10
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Xiang J, Su R, Wu S, Zhou L. Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes. Front Oncol 2022; 12:967342. [PMID: 36185201 PMCID: PMC9520471 DOI: 10.3389/fonc.2022.967342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/31/2022] [Indexed: 11/21/2022] Open
Abstract
Background The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs). Methods Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed. Results A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024). Conclusions We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.
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Affiliation(s)
- Jiangdong Xiang
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Rongjia Su
- Department of Gynecologic Oncology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Sufang Wu
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Lina Zhou
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Lina Zhou,
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11
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Chen L, Gu H, Zhou L, Wu J, Sun C, Han Y. Integrating cell cycle score for precise risk stratification in ovarian cancer. Front Genet 2022; 13:958092. [PMID: 36061171 PMCID: PMC9428269 DOI: 10.3389/fgene.2022.958092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Ovarian cancer (OC) is a highly heterogeneous disease, of which the mesenchymal subtype has the worst prognosis, is the most aggressive, and has the highest drug resistance. The cell cycle pathway plays a vital role in ovarian cancer development and progression. We aimed to screen the key cell cycle genes that regulated the mesenchymal subtype and construct a robust signature for ovarian cancer risk stratification. Methods: Network inference was conducted by integrating the differentially expressed cell cycle signature genes and target genes between the mesenchymal and non-mesenchymal subtypes of ovarian cancer and identifying the dominant cell cycle signature genes. Results: Network analysis revealed that two cell cycle signature genes (POLA2 and KIF20B) predominantly regulated the mesenchymal modalities of OC and used to construct a prognostic model, termed the Cell Cycle Prognostic Signature of Ovarian Cancer (CCPOC). The CCPOC-high patients showed an unfavorable prognosis in the GSE26712 cohort, consistent with the results in the seven public validation cohorts and one independent internal cohort (BL-OC cohort, qRT-PCR, n = 51). Functional analysis, drug-sensitive analysis, and survival analysis showed that CCPOC-low patients were related to strengthened tumor immunogenicity and sensitive to the anti-PD-1/PD-L1 response rate in pan-cancer (r = −0.47, OC excluded), which indicated that CCPOC-low patients may be more sensitive to anti-PD-1/PD-L1. Conclusion: We constructed and validated a subtype-specific, cell cycle-based prognostic signature for ovarian cancer, which has great potential for predicting the response of anti-PD-1/PD-L1.
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Affiliation(s)
- Lingying Chen
- Department of Obstetrics and Gynecology, Beilun District People’s Hospital, Ningbo, China
| | - Haiyan Gu
- Department of Obstetrics and Gynecology, Beilun District People’s Hospital, Ningbo, China
| | - Lei Zhou
- Department of Obstetrics and Gynecology, Beilun District People’s Hospital, Ningbo, China
| | - Jingna Wu
- Department of Obstetrics and Gynecology, Beilun District People’s Hospital, Ningbo, China
| | - Changdong Sun
- Department of Obstetrics and Gynecology, Beilun District People’s Hospital, Ningbo, China
- *Correspondence: Changdong Sun, ; Yonggui Han,
| | - Yonggui Han
- Department of Obstetrics and Gynecology, Beilun No 3 People’s Hospital, Ningbo, China
- *Correspondence: Changdong Sun, ; Yonggui Han,
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12
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Zhang Y, Huang W, Chen D, Zhao Y, Sun F, Wang Z, Lou G. Identification of a Recurrence Gene Signature for Ovarian Cancer Prognosis by Integrating Single-Cell RNA Sequencing and Bulk Expression Datasets. Front Genet 2022; 13:823082. [PMID: 35754835 PMCID: PMC9214038 DOI: 10.3389/fgene.2022.823082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 04/28/2022] [Indexed: 12/24/2022] Open
Abstract
Ovarian cancer is one of the most common gynecological malignancies in women, with a poor prognosis and high mortality. With the expansion of single-cell RNA sequencing technologies, the inner biological mechanism involved in tumor recurrence should be explored at the single-cell level, and novel prognostic signatures derived from recurrence events were urgently identified. In this study, we identified recurrence-related genes for ovarian cancer by integrating two Gene Expression Omnibus datasets, including an ovarian cancer single-cell RNA sequencing dataset (GSE146026) and a bulk expression dataset (GSE44104). Based on these recurrence genes, we further utilized the merged expression dataset containing a total of 524 ovarian cancer samples to identify prognostic signatures and constructed a 13-gene risk model, named RMGS (recurrence marker gene signature). Based on the RMGS score, the samples were stratified into high-risk and low-risk groups, and these two groups displayed significant survival difference in two independent validation cohorts including The Cancer Genome Atlas (TCGA). Also, the RMGS score remained significantly independent in multivariate analysis after adjusting for clinical factors, including the tumor grade and stage. Furthermore, there existed close associations between the RMGS score and immune characterizations, including checkpoint inhibition, EMT signature, and T-cell infiltration. Finally, the associations between RMGS scores and molecular subtypes revealed that samples with mesenchymal subtypes displayed higher RMGS scores. In the meanwhile, the genomics characterization from these two risk groups was also identified. In conclusion, the recurrence-related RMGS model we identified could provide a new understanding of ovarian cancer prognosis at the single-cell level and offer a reference for therapy decisions for patient treatment.
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Affiliation(s)
- Yongjian Zhang
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wei Huang
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Dejia Chen
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yue Zhao
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Fusheng Sun
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Zhiqiang Wang
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ge Lou
- Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China
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Feng S, Xia T, Ge Y, Zhang K, Ji X, Luo S, Shen Y. Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer. Front Immunol 2022; 13:868067. [PMID: 35418998 PMCID: PMC8995567 DOI: 10.3389/fimmu.2022.868067] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
Purpose The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a non-invasive radiogenomics approach to identify a hypoxia pattern with potential application in patient prognostication. Methods Specific hypoxia-related genes (sHRGs) were identified based on RNA-seq of OC cell lines cultured with different oxygen conditions. Meanwhile, multiple hypoxia-related subtypes were identified by unsupervised consensus analysis and LASSO–Cox regression analysis. Subsequently, diversified bioinformatics algorithms were used to explore the immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms. Results One hundred forty sHRGs and three types of hypoxia-related subtypes were identified. Among them, hypoxia-cluster-B, gene-cluster-B, and high-risk subtypes had poor survival outcomes. The subtypes were closely related to each other, and hypoxia-cluster-B and gene-cluster-B had higher hypoxia risk scores. Notably, the low-risk subtype had an active immune microenvironment and may benefit from immunotherapy. Finally, a four-feature radiogenomics model was constructed to reveal hypoxia risk status, and the model achieved area under the curve (AUC) values of 0.900 and 0.703 for the training and testing cohorts, respectively. Conclusion As a non-invasive approach, computed tomography-based radiogenomics biomarkers may enable the pretreatment prediction of the hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in patients with OC.
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Affiliation(s)
- Songwei Feng
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyi Xia
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yu Ge
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ke Zhang
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xuan Ji
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shanhui Luo
- Department of Gynaecology, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
| | - Yang Shen
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Xiaowei W, Tong L, Yanjun Q, Lili F. PTH2R is related to cell proliferation and migration in ovarian cancer: a multi-omics analysis of bioinformatics and experiments. Cancer Cell Int 2022; 22:148. [PMID: 35410353 PMCID: PMC8996580 DOI: 10.1186/s12935-022-02566-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/30/2022] [Indexed: 12/23/2022] Open
Abstract
Background Ovarian cancer is a common gynecological disease and seriously endangers women's health. Currently, there is still a lack of effective molecular markers for the diagnosis and treatment of ovarian cancer. The present study aimed to investigate the molecular markers associated with ovarian cancer. Methods The molecular and gene related to ovarian cancer were extracted from GEO database and TCGA database by bioinformatics, and the related genes and functions were further analyzed. The results were verified by qPCR, WB, CCK-8 and Transwell experiments. Results Data analysis showed that PTH2R gene was highly expressed in tumors, and 51 HUB genes were obtained. Finally, experimental verification showed that PTH2R gene was highly expressed in ovarian cancer, and PTH2R gene was involved in the proliferation, invasion and metastasis of ovarian cancer cells. Conclusions After experimental verification, we found that knocking down the expression of PTH2R can inhibit the proliferation, invasion and migration of tumor cells.PTH2R is expected to become a new molecular marker for ovarian cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-022-02566-2.
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Affiliation(s)
- Wang Xiaowei
- Department of Ultrasnography in Gynecology and Obstetrics, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lu Tong
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qu Yanjun
- Department of Ultrasnography in Gynecology and Obstetrics, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Fan Lili
- Department of Children's and Adolescent Health, Public Health College, Harbin Medical University, Harbin, China.
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Cao X, Zhang Q, Zhu Y, Huo X, Bao J, Su M. Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer. Front Oncol 2022; 12:780950. [PMID: 35280739 PMCID: PMC8912994 DOI: 10.3389/fonc.2022.780950] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/25/2022] [Indexed: 12/18/2022] Open
Abstract
Background Pyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction. Methods Based on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan-Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and BRCA1/2 mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and in-vitro assays on alternative lncRNAs. Results A total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the immune response and BRCA1 mutations were identified in different groups distinguished by the risk signature (p < 0.05). Interestingly, in-vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote OC cell proliferation. Conclusion The PRL risk signature could independently predict overall survival and guide treatment in patients with OC.
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Affiliation(s)
- Xueyan Cao
- Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China.,Medical College, Nantong University, Nantong, China
| | - Qingquan Zhang
- Department of Cardiology, Affiliated Hospital of Nantong University, Nantong, China.,Medical College, Nantong University, Nantong, China
| | - Yu Zhu
- Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China.,Medical College, Nantong University, Nantong, China
| | - Xiaoqing Huo
- Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China.,Medical College, Nantong University, Nantong, China
| | - Junze Bao
- Medical College, Nantong University, Nantong, China
| | - Min Su
- Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China
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Feng S, Yin H, Zhang K, Shan M, Ji X, Luo S, Shen Y. Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer. J Ovarian Res 2022; 15:10. [PMID: 35057848 PMCID: PMC8772079 DOI: 10.1186/s13048-022-00944-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022] Open
Abstract
Background Ferroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction. Methods In multi-omics data from OC patients, FIRLs were identified by calculating Pearson correlation coefficients with ferroptosis and iron-metabolism related genes (FIRGs). Cox-Lasso regression analysis was performed on the FIRLs to screen further the lncRNAs participating in FIRLs signature. In addition, all patients were divided into two robust risk subtypes using the FIRLs signature. Receiver operator characteristic (ROC) curve, Kaplan–Meier analysis, decision curve analysis (DCA), Cox regression analysis and calibration curve were used to confirm the clinical benefits of FIRLs signature. Meanwhile, two nomograms were constructed to facilitate clinical application. Moreover, the potential biological functions of the signature were investigated by genes function annotation. Finally, immune microenvironment, chemotherapeutic sensitivity, and the response of PARP inhibitors were compared in different risk groups using diversiform bioinformatics algorithms. Results The raw data were randomized into a training set (n = 264) and a testing set (n = 110). According to Pearson coefficients between FIRGs and lncRNAs, 1075 FIRLs were screened for univariate Cox regression analysis, and then LASSO regression analysis was used to construct 8-FIRLs signature. It is worth mentioning that a variety of analytical methods indicated excellent predictive performance for overall survival (OS) of FIRLs signature (p < 0.05). The multivariate Cox regression analysis showed that FIRLs signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the abundance of immune cells, immune-related pathways, and drug response were excavated in different risk subtypes (p < 0.05). Conclusion The FIRLs signature can independently predict overall survival and therapeutic effect in OC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s13048-022-00944-y.
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Heawchaiyaphum C, Pientong C, Yoshiyama H, Iizasa H, Panthong W, Ekalaksananan T. General Features and Novel Gene Signatures That Identify Epstein-Barr Virus-Associated Epithelial Cancers. Cancers (Basel) 2021; 14:cancers14010031. [PMID: 35008199 PMCID: PMC8750470 DOI: 10.3390/cancers14010031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/16/2021] [Accepted: 12/19/2021] [Indexed: 11/16/2022] Open
Abstract
Epstein-Barr virus (EBV) is associated with various types of human malignancies, including nasopharyngeal carcinoma (NPC), EBV-associated gastric carcinoma (EBVaGC), and oral squamous cell carcinoma (OSCC). The present study aimed to identify gene signatures and common signaling pathways that can be used to predict the prognosis of EBV-associated epithelial cancers (EBVaCAs) by performing an integrated bioinformatics analysis of cell lines and tumor tissues. We identified 12 differentially expressed genes (DEGs) in the EBVaCA cell lines. Among them, only four DEGs, including BAMBI, SLC26A9, SGPP2, and TMC8, were significantly upregulated. However, SLC26A9 and TMC8, but not BAMBI and SGPP2, were significantly upregulated in EBV-positive tumor tissues compared to EBV-negative tumor tissues. Next, we identified IL6/JAK/STAT3 and TNF-α/NF-κB signaling pathways as common hallmarks of EBVaCAs. The expression of key genes related to the two hallmarks was upregulated in both EBV-infected cell lines and EBV-positive tumor tissues. These results suggest that SLC26A9 and TMC8 might be gene signatures that can effectively predict the prognosis of EBVaCAs and provide new insights into the molecular mechanisms of EBV-driven epithelial cancers.
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Affiliation(s)
- Chukkris Heawchaiyaphum
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand; (C.H.); (C.P.); (W.P.)
- HPV&EBV and Carcinogenesis (HEC) Research Group, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Chamsai Pientong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand; (C.H.); (C.P.); (W.P.)
- HPV&EBV and Carcinogenesis (HEC) Research Group, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Hironori Yoshiyama
- Department of Microbiology, Shimane University Faculty of Medicine, Izumo 693-8501, Japan; (H.Y.); (H.I.)
| | - Hisashi Iizasa
- Department of Microbiology, Shimane University Faculty of Medicine, Izumo 693-8501, Japan; (H.Y.); (H.I.)
| | - Watcharapong Panthong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand; (C.H.); (C.P.); (W.P.)
- HPV&EBV and Carcinogenesis (HEC) Research Group, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Tipaya Ekalaksananan
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand; (C.H.); (C.P.); (W.P.)
- HPV&EBV and Carcinogenesis (HEC) Research Group, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
- Correspondence: ; Tel.: +66-4336-3808; Fax:+66-4334-8385
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Zhang D, Li Y, Yang S, Wang M, Yao J, Zheng Y, Deng Y, Li N, Wei B, Wu Y, Zhai Z, Dai Z, Kang H. Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients. Cancer Med 2021; 10:8222-8237. [PMID: 34609082 PMCID: PMC8607265 DOI: 10.1002/cam4.4317] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 08/22/2021] [Accepted: 08/31/2021] [Indexed: 12/17/2022] Open
Abstract
Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.
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Affiliation(s)
- Dai Zhang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, The Air Force Medical University, Xi'an, China
| | - Yiche Li
- Department of Tumor Surgery, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Si Yang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Meng Wang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jia Yao
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Zheng
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yujiao Deng
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Na Li
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Bajin Wei
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Wu
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Zhen Zhai
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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