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Liu P, Liu J, Liu J, Yu X. Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell RNA sequencing and bulk RNA sequencing. Aging (Albany NY) 2024; 16:4736-4758. [PMID: 38461424 PMCID: PMC10968697 DOI: 10.18632/aging.205628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 02/02/2024] [Indexed: 03/12/2024]
Abstract
Ovarian cancer stands as a prevalent malignancy within the realm of gynecology, and the emergence of resistance to chemotherapeutic agents remains a pivotal impediment to both prognosis and treatment. Through a single-cell level investigation, we scrutinize the drug resistance and mitotic activity of the core tumor cells in ovarian cancer. Our study revisits the interrelationships and temporal trajectories of distinct epithelial cells (EPCs) subpopulations, while identifying genes associated with ovarian cancer prognosis. Notably, our findings establish a strong association between the drug resistance of EPCs and oxidative phosphorylation pathways. Subsequently, through subpopulation and temporal trajectory analysis, we confirm the intermediate position of EPCs subpopulation C0. Furthermore, we delve into the immunological functions and differentially expressed genes associated with the prognosis of C0, shedding light on the potential for constructing novel ovarian cancer prognosis models and identifying new therapeutic targets.
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Affiliation(s)
- Pengfei Liu
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jinbao Liu
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jinxing Liu
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiao Yu
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
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Wang K, Zhang Y, Ao M, Luo H, Mao W, Li B. Multi-omics analysis defines a cuproptosis-related prognostic model for ovarian cancer: Implication of WASF2 in cuproptosis resistance. Life Sci 2023; 332:122081. [PMID: 37717621 DOI: 10.1016/j.lfs.2023.122081] [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: 07/16/2023] [Revised: 08/31/2023] [Accepted: 09/08/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Ovarian cancer (OVC) is one of the deadliest and most aggressive tumors in women, with an increasing incidence in recent years. Cuproptosis, a newly discovered type of programmed cell death, is caused by intracellular copper-mediated lipoylated protein aggregation and proteotoxic stress. However, the role of cuproptosis-related features in OVC remains elusive. METHODS The single-cell sequencing data from GSE154600 and bulk transcriptome data of 378 OVC patients from TCGA database. The RNA-seq and clinical data of 379 OVC patients in GSE140082 and 173 OV patients in GSE53963. The PROGENy score was calculated to assess tumor-associated pathways. Based on gene set enrichment analysis (GSEA) of the cuproptosis pathway, the single cells were divided into the cuproptosishigh and cuproptosislow groups. The differentially expressed genes (DEGs) between the two groups were screened, and 47 prognosis-related genes were identified based on univariate cox regression analysis. Randomforest was used to construct a prognostic model. Immuno-infiltration analysis was performed using ssGSEA and xCell algorithms. In vitro and in vivo experiments were used for functional verification. RESULTS Six major cell populations was identified, including fibroblast, T cell, myeloid, epithelial cell, endothelial cell, and B cell populations. The PROGENy score which revealed significant activation of the PI3K pathway in T and B cells, and activation of the TGF-β pathway in endothelial cells and fibroblasts. TIMM8B, COX8A, SSR4, HIGD2A, WASF2, PRDX5 and CLDN4 were selected to construct a prognostic model from the identified 47 prognosis-related genes. Furthermore, the cuproptosishigh and cuproptosislow groups showed significant differences in the expression levels of the model genes, immune cell infiltration, and sensitivity to six potential drug candidates. The functional experiments showed that WASF2 is associated with cuproptotic resistance and promotes cancer cell proliferation and resistance to platinum, and its high expression is associated with poor prognosis of OVC patients. CONCLUSION A clinically significant cuproptosis-related prognostic model was identified which can accurately predict the prognosis and immune characteristics of OVC patients. WASF2, one of the cuproptosis-related gene in the risk model, promotes the proliferation and platinum resistance of OVC cells, and leads poor prognosis.
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Affiliation(s)
- Kunyu Wang
- Department of Gynecological Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yanan Zhang
- Department of Gynecological Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Miao Ao
- Department of Gynecological Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Haixia Luo
- Department of Gynecological Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wei Mao
- Department of Gynecological Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bin Li
- Department of Gynecological Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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Zhao B, Pei L. A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning. BMC Med Genomics 2023; 16:230. [PMID: 37784081 PMCID: PMC10544447 DOI: 10.1186/s12920-023-01671-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy. METHODS Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC, GSE91061 and IMvigor210 dataset. RESULTS The optimal MRS developed by the combination of CoxBoost and StepCox[forward] algorithm served as an independent risk factor in ovarian cancer. Compared with stage, grade and other established prognostic signatures, the current MRS had a better performance in predicting the overall survival rate of ovarian cancer patients. Low risk score indicated a higher TME score, higher level of immune cells, higher immunophenoscore, higher tumor mutational burden, lower TIDE score and lower IC50 value in ovarian cancer. The survival prediction nomogram had a good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of ovarian cancer patients. CONCLUSION All in all, the current study constructed a powerful prognostic MRS for ovarian cancer patients using 10 machine learning algorithms. This MRS could predict the prognosis and drug sensitivity in ovarian cancer.
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Affiliation(s)
- Bo Zhao
- Department of Obstetrics and Gynecology, General Hospital of Northern Theater Command, Shenyang, 110016, China
| | - Lipeng Pei
- Department of Obstetrics and Gynecology, General Hospital of Northern Theater Command, Shenyang, 110016, China.
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Wang K, Hou H, Zhang Y, Ao M, Luo H, Li B. Ovarian cancer-associated immune exhaustion involves SPP1+ T cell and NKT cell, symbolizing more malignant progression. Front Endocrinol (Lausanne) 2023; 14:1168245. [PMID: 37143732 PMCID: PMC10151681 DOI: 10.3389/fendo.2023.1168245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023] Open
Abstract
Background Ovarian cancer (OC) is highly heterogeneous and has a poor prognosis. A better understanding of OC biology could provide more effective therapeutic paradigms for different OC subtypes. Methods To reveal the heterogeneity of T cell-associated subclusters in OC, we performed an in-depth analysis of single-cell transcriptional profiles and clinical information of patients with OC. Then, the above analysis results were verified by qPCR and flow cytometry examine. Results After screening by threshold, a total of 85,699 cells in 16 ovarian cancer tissue samples were clustered into 25 major cell groups. By performing further clustering of T cell-associated clusters, we annotated a total of 14 T cell subclusters. Then, four distinct single-cell landscapes of exhausted T (Tex) cells were screened, and SPP1 + Tex significantly correlated with NKT cell strength. A large amount of RNA sequencing expression data combining the CIBERSORTx tool were labeled with cell types from our single-cell data. Calculating the relative abundance of cell types revealed that a greater proportion of SPP1 + Tex cells was associated with poor prognosis in a cohort of 371 patients with OC. In addition, we showed that the poor prognosis of patients in the high SPP1 + Tex expression group might be related to the suppression of immune checkpoints. Finally, we verified in vitro that SPP1 expression was significantly higher in ovarian cancer cells than in normal ovarian cells. By flow cytometry, knockdown of SPP1 in ovarian cancer cells could promote tumorigenic apoptosis. Conclusion This is the first study to provide a more comprehensive understanding of the heterogeneity and clinical significance of Tex cells in OC, which will contribute to the development of more precise and effective therapies.
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Lutgendorf SK, Thaker PH, Goodheart MJ, Arevalo JM, Chowdhury MA, Noble AE, Dahmoush L, Slavich GM, Penedo FJ, Sood AK, Cole SW. Biobehavioral factors predict an exosome biomarker of ovarian carcinoma disease progression. Cancer 2022; 128:4157-4165. [PMID: 36251340 PMCID: PMC9744596 DOI: 10.1002/cncr.34496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND Biobehavioral factors such as social isolation and depression have been associated with disease progression in ovarian and other cancers. Here, the authors developed a noninvasive, exosomal RNA profile for predicting ovarian cancer disease progression and subsequently tested whether it increased in association with biobehavioral risk factors. METHODS Exosomes were isolated from plasma samples from 100 women taken before primary surgical resection or neoadjuvant (NACT) treatment of ovarian carcinoma and 6 and 12 months later. Biobehavioral measures were sampled at all time points. Plasma from 76 patients was allocated to discovery analyses in which morning presurgical/NACT exosomal RNA profiles were analyzed by elastic net machine learning to identify a biomarker predicting rapid (≤6 months) versus more extended disease-free intervals following initial treatment. Samples from a second subgroup of 24 patients were analyzed by mixed-effects linear models to determine whether the progression-predictive biomarker varied longitudinally as a function of biobehavioral risk factors (social isolation and depressive symptoms). RESULTS An RNA-based molecular signature was identified that discriminated between individuals who had disease progression in ≤6 months versus >6 months, independent of clinical variables (age, disease stage, and grade). In a second group of patients analyzed longitudinally, social isolation and depressive symptoms were associated with upregulated expression of the disease progression propensity biomarker, adjusting for covariates. CONCLUSION These data identified a novel exosome-derived biomarker indicating propensity of ovarian cancer progression that is sensitive to biobehavioral variables. This derived biomarker may be potentially useful for risk assessment, intervention targeting, and treatment monitoring.
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Affiliation(s)
- Susan K. Lutgendorf
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA
| | - Premal H. Thaker
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO
| | - Michael J. Goodheart
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA
| | - Jesusa M.G. Arevalo
- Division of Hematology/Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Mamur A. Chowdhury
- Departments of Gynecologic Oncology, Cancer Biology and Center for RNA Interference and Noncoding RNA, University of Texas M.D. Anderson Cancer Center, Houston TX
| | - Alyssa E. Noble
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA
| | - Laila Dahmoush
- Department of Pathology, University of Iowa, Iowa City, IA
| | - George M. Slavich
- Cousins Center for Psychoneuroimmunology, University of California, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA
| | - Frank J. Penedo
- Department of Psychology and Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL
| | - Anil K. Sood
- Departments of Gynecologic Oncology, Cancer Biology and Center for RNA Interference and Noncoding RNA, University of Texas M.D. Anderson Cancer Center, Houston TX
| | - Steven W. Cole
- Division of Hematology/Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA
- Cousins Center for Psychoneuroimmunology, University of California, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA
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Xiong L, Tan J, Feng Y, Wang D, Liu X, Feng Y, Li S. Protein expression profiling identifies a prognostic model for ovarian cancer. BMC Womens Health 2022; 22:292. [PMID: 35840928 PMCID: PMC9284690 DOI: 10.1186/s12905-022-01876-x] [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: 02/09/2022] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.
Methods
Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.
Results
394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.
Conclusions
The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.
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Kashikar R, Kotha AK, Shah S, Famta P, Singh SB, Srivastava S, Chougule MB. Advances in nanoparticle mediated targeting of RNA binding protein for cancer. Adv Drug Deliv Rev 2022; 185:114257. [PMID: 35381306 DOI: 10.1016/j.addr.2022.114257] [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: 12/13/2021] [Revised: 02/28/2022] [Accepted: 03/30/2022] [Indexed: 12/24/2022]
Abstract
RNA binding proteins (RBPs) enact a very crucial part in the RNA directive processes. Atypical expression of these RBPs affects many steps of RNA metabolism, majorly altering its expression. Altered expression and dysfunction of RNA binding proteins lead to cancer progression and other diseases. We enumerate various available interventions, and recent findings focused on targeting RBPs for cancer therapy and diagnosis. The treatment, sensitization, chemoprevention, gene-mediated, and virus mediated interventions were studied to treat and diagnose cancer. The application of passively and actively targeted lipidic nanoparticles, polymeric nanoparticles, virus-based particles, and vaccine-based immunotherapy for the delivery of therapeutic agent/s against cancer are discussed. We also discuss the formulation aspect of nanoparticles for achieving delivery at the site of action and ongoing clinical trials targeting RBPs.
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Wu J, Wu Y, Guo Q, Wang S, Wu X. RNA-binding proteins in ovarian cancer: a novel avenue of their roles in diagnosis and treatment. J Transl Med 2022; 20:37. [PMID: 35062979 PMCID: PMC8783520 DOI: 10.1186/s12967-022-03245-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/11/2022] [Indexed: 12/18/2022] Open
Abstract
Ovarian cancer (OC), an important cause of cancer-related death in women worldwide, is one of the most malignant cancers and is characterized by a poor prognosis. RNA-binding proteins (RBPs), a class of endogenous proteins that can bind to mRNAs and modify (or even determine) the amount of protein they can generate, have attracted great attention in the context of various diseases, especially cancers. Compelling studies have suggested that RBPs are aberrantly expressed in different cancer tissues and cell types, including OC tissues and cells. More specifically, RBPs can regulate proliferation, apoptosis, invasion, metastasis, tumorigenesis and chemosensitivity and serve as potential therapeutic targets in OC. Herein, we summarize what is currently known about the biogenesis, molecular functions and potential roles of human RBPs in OC and their prospects for application in the clinical treatment of OC.
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Affiliation(s)
- Jiangchun Wu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People's Republic of China
| | - Yong Wu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People's Republic of China
| | - Qinhao Guo
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People's Republic of China
| | - Simin Wang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xiaohua Wu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. .,Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People's Republic of 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] [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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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: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [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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He T, Huang L, Li J, Wang P, Zhang Z. Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System. Front Med (Lausanne) 2021; 8:587496. [PMID: 34109184 PMCID: PMC8180546 DOI: 10.3389/fmed.2021.587496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 04/19/2021] [Indexed: 12/24/2022] Open
Abstract
Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms. Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system. Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer. Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.
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Affiliation(s)
- Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
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Wei C, Liu X, Wang Q, Li Q, Xie M. Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer. Int J Endocrinol 2021; 2021:4156187. [PMID: 34950205 PMCID: PMC8692015 DOI: 10.1155/2021/4156187] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC. METHODS The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis. RESULTS 8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK. CONCLUSION The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.
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Affiliation(s)
- Chunyan Wei
- Department of Gynaecology and Obstetrics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoqing Liu
- Department of Gynaecology and Obstetrics, Maternal and Child Health Hospital of Shangzhou District, Shangluo, Shanxi Province, China
| | - Qin Wang
- Department of Gynaecology and Obstetrics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qipei Li
- Department of Gynaecology and Obstetrics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Min Xie
- Department of Gynaecology and Obstetrics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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