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Zhang C, Su Y, Wang H, Dang D, Huang X, Shi S, Shi Y, Zhang P, Yang M. Characterization of a ferroptosis-related gene signature predicting survival and immunotherapeutic response in lung adenocarcinoma. Aging (Albany NY) 2024; 16:12608-12622. [PMID: 39311766 PMCID: PMC11466487 DOI: 10.18632/aging.206110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 07/11/2024] [Indexed: 10/08/2024]
Abstract
Lung cancer remains the leading cause of cancer-related death worldwide, and drug resistance represents the main obstacle responsible for the poor mortality and prognosis. Here, to identify a novel gene signature for predicting survival and drug response, we jointly investigated RNA sequencing data of lung adenocarcinoma patients from TCGA and GEO databases, and identified a ferroptosis-related gene signature. The signature was validated in the validation set and two external cohorts. The high-risk group had a reduced survival than the low-risk group (P < 0.05). Moreover, the established gene signature was associated with tumor mutation burden, microsatellite instability, and response to immune checkpoint blockade. In addition, four candidate oncogenes (RRM2, SLC2A1, DDIT4, and VDAC2) were identified to be candidate oncogenes using in silico and wet experiments, which could serve as potential therapeutic targets. Collectively, this study developed a novel ferroptosis-related gene signature for predicting prognosis and drug response, and identified four candidate oncogenes for lung adenocarcinoma.
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Affiliation(s)
- Chuan Zhang
- Department of Pediatric Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yingying Su
- Department of Anatomy, College of Basic Medical Sciences, Jilin University, Jilin, China
| | - Hongrui Wang
- Department of Molecular Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Dan Dang
- Department of Neonatology, The First Hospital of Jilin University, Changchun, China
| | - Xin Huang
- Department of Molecular Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Shuyou Shi
- Department of Molecular Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Yue Shi
- Department of Microbiology and Immunology, Changchun University of Chinese Medicine, Changchun, China
| | - Peng Zhang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Ming Yang
- Department of Molecular Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
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Liu Y, Zhao J, Huang B, Liang Y, Jiang G, Zhou X, Chen Y, He T, Zheng M, Huang Z. Identification and validation of an immunotherapeutic signature for colon cancer based on the regulatory patterns of ferroptosis and their association with the tumor microenvironment. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2024; 1871:119698. [PMID: 38387508 DOI: 10.1016/j.bbamcr.2024.119698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 02/04/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
The integrated landscape of ferroptosis regulatory patterns and their association with colon microenvironment have been demonstrated in recent studies. However, the ferroptosis-related immunotherapeutic signature for colon cancer (CC) remains unclear. We comprehensively evaluated 1623 CC samples, identified patterns of ferroptosis modification based on ferroptosis-associated genes, and systematically correlated these patterns with tumor microenvironment (TME) cell infiltration characteristics. In addition, the ferroptosis-regulated gene score (FRG-score) was constructed to quantify the pattern of ferroptosis alterations in individual tumors. Three distinct patterns of ferroptosis modification were identified, including antioxidant defense, iron toxicity, and lipid peroxidation. The characteristics of TME cell infiltration under these three patterns were highly consistent with the three immune phenotypes of tumors, including immune-inflamed, immune-excluded and immune-desert phenotypes. We also demonstrated that evaluation of ferroptosis regulatory patterns within individual tumors can predict tumor inflammatory status, tumor subtype, TME stromal activity, genetic variation, and clinical outcome. Immunotherapy cohorts confirmed that patients with low FRG-scores showed remarkable therapeutic and clinical benefits. Furthermore, the hub gene apolipoprotein L6 (APOL6), a drug-sensitive target associated with cancer cell ferroptosis, was identified through our proposed novel key gene screening process and validated in CC cell lines and scRNA-seq.
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Affiliation(s)
- Yong Liu
- Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523710, Guangdong, PR China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, Guangdong, PR China
| | - Junzhang Zhao
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong, PR China
| | - Baoxiang Huang
- Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523710, Guangdong, PR China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, Guangdong, PR China
| | - Youcheng Liang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong, PR China
| | - Guanming Jiang
- Dongguan Institute of Clinical Oncology Research in Dongguan People's Hospital, Dongguan 523018, Guangdong, PR China
| | - Xinglin Zhou
- Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523710, Guangdong, PR China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, Guangdong, PR China
| | - Yilin Chen
- Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523710, Guangdong, PR China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, Guangdong, PR China
| | - Tao He
- School of Basic Medicine, Guangdong Medical University, Dongguan 523018, Guangdong, PR China
| | - Mingbin Zheng
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, Guangdong, PR China; National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518112, Guangdong, PR China.
| | - Zunnan Huang
- Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523710, Guangdong, PR China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, Guangdong, PR China.
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Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S, Favali M, Spagnoletti A, Castelo-Branco L, Dolezal J, Pearson AT, Lo Russo G, Proto C, Ganzinelli M, Giani C, Ambrosini E, Turajlic S, Au L, Koopman M, Delaloge S, Kather JN, de Braud F, Garassino MC, Pentheroudakis G, Spencer C, Pedrocchi ALG. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol 2024; 35:29-65. [PMID: 37879443 DOI: 10.1016/j.annonc.2023.10.125] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
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Affiliation(s)
- A Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.
| | - V Miskovic
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - M Zanitti
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - F Trovo
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - C Genova
- UO Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genoa; Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa
| | - G Viscardi
- Precision Medicine Department, Università degli Studi della Campania Luigi Vanvitelli, Naples
| | - S E Rebuzzi
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa; Medical Oncology Unit, Ospedale San Paolo, Savona, Italy
| | - L Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - L Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - S Kosta
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - M Favali
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - A Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - L Castelo-Branco
- ESMO European Society for Medical Oncology, Lugano, Switzerland; NOVA National School of Public Health, Lisboa, Portugal
| | - J Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - A T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - G Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - E Ambrosini
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - S Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London
| | - L Au
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne; Sir Peter MacCallum Department of Medical Oncology, The University of Melbourne, Melbourne, Australia
| | - M Koopman
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - S Delaloge
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - J N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - F de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M C Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | | | - C Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London.
| | - A L G Pedrocchi
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
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Cheng Z, Chen Y, Huang H. Identification and Validation of a Novel Prognostic Signature Based on Ferroptosis-Related Genes in Ovarian Cancer. Vaccines (Basel) 2023; 11:vaccines11020205. [PMID: 36851083 PMCID: PMC9962729 DOI: 10.3390/vaccines11020205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/08/2023] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Ovarian cancer is the most lethal gynecological tumor, with a poor prognosis due to the lack of early symptoms, resistance to chemotherapy, and recurrence. Ferroptosis belongs to the regulated cell death family, and is characterized by iron-dependent processes. Here, comprehensive bioinformatics analysis was applied to explore a valuable prognostic model based on ferroptosis-related genes, which was further validated in clinical OC samples. METHODS mRNA data of normal and ovarian tumor samples were obtained separately from the GTEx and TCGA databases. The least absolute shrinkage and selection operator (LASSO) cox regression was applied to construct the prognostic model based on ferroptosis-associated genes. Expression of ALOX12 in OC cell lines, as well as cell functions, including proliferation and migration, were examined. Finally, the prognostic efficiency of the model was assessed in the clinical tissues of OC patients. RESULTS A gene signature consisting of ALOX12, RB1, DNAJB6, STEAP3, and SELENOS was constructed. The signature divided TCGA, ICGC, and GEO cohorts into high-risk and low-risk groups separately. Receiver operating characteristic (ROC) curves and independent prognostic factor analysis were carried out, and the prognostic efficacy was validated. The expression levels of ALOX12 in cell lines were examined. Inhibition of ALOX12 attenuated cell proliferation and migration in HEY cells. Moreover, the prognostic value of ALOX12 expression was examined in clinical samples of OC patients. CONCLUSION This work constructed a novel ferroptosis-associated gene model. Furthermore, the clinical predictive role of ALOX12 was identified in OC patients, suggesting that ALOX12 might act as a potential prognostic tool and therapeutic target for OC patients.
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Affiliation(s)
- Zhe Cheng
- Department of Oncology, NHC Key Laboratory of Cancer Proteomics, Laboratory of Structural Biology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yongheng Chen
- Department of Oncology, NHC Key Laboratory of Cancer Proteomics, Laboratory of Structural Biology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Huichao Huang
- Department of Infectious Disease, NHC Key Laboratory of Cancer Proteomics, Laboratory of Structural Biology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Correspondence:
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Shen C, Wang Y. Ferroptosis Biomarkers for Predicting Prognosis and Immunotherapy Efficacy in Adrenocortical Carcinoma. Arch Med Res 2023; 54:45-55. [PMID: 36528469 DOI: 10.1016/j.arcmed.2022.12.003] [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: 04/14/2022] [Revised: 10/17/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Numerous studies have suggested that ferroptosis plays an important regulatory role in cancer cell death. Nonetheless, the potential effects of ferroptosis regulators on the prognosis, the expression of immunomodulatory factors in the tumor microenvironment and on the efficacy of immunotherapy in adrenocortical carcinoma (ACC) remain largely unknown. METHODS Public ACC datasets were used to investigate the relationship between ferroptosis regulators and prognosis and clinical features. A ferroptosis scoring system was established for individual cases of ACC using principal component analysis algorithms. Hub ferroptosis-related genes involved in immunoregulation and immunotherapy efficacy in ACC were further identified. RESULTS Twenty ferroptosis regulators were differentially expressed in ACC and 17 ferroptosis regulators were closely related to prognosis in ACC. A ferroptosis scoring system was developed based on ACSL4, FANCD2, and SLC7A1 expression, and the ferroptosis regulators could serve as an independent prognostic factor for ACC. Further analyses indicated that the ferroptosis score integrated with the tumor mutation burden (TMB), and immune-checkpoint gene expression could predict prognosis in ACC. RNA isolation and reverse transcription‑quantitative polymerase chain reaction (RT-qPCR) demonstrated significant differences in the expression levels of ACSL4, FANCD2, and SLC7A1 between ACC and normal tissues. Furthermore, FANCD2 was significantly related to immunotherapy efficacy and prognosis in ACC. CONCLUSION Our study demonstrated that ferroptosis was significantly associated with prognosis, clinical characteristics, immune-checkpoint gene expression, and tumor microenvironment immune cell infiltration in ACC. The current study provides comprehensive evidence for further research on ferroptosis regulators in ACC and provides new insight into the epigenetic regulation of the antitumor immune response.
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Affiliation(s)
- Chengquan Shen
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China; Key Laboratory of Urology and Andrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yonghua Wang
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China; Key Laboratory of Urology and Andrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
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Correlation between Ferroptosis-Related Gene Signature and Immune Landscape: Prognosis in Breast Cancer. J Immunol Res 2022; 2022:6871518. [PMID: 36313179 PMCID: PMC9613394 DOI: 10.1155/2022/6871518] [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: 12/23/2021] [Revised: 08/15/2022] [Accepted: 09/07/2022] [Indexed: 12/02/2022] Open
Abstract
Breast cancer (BC) is the most commonly diagnosed cancer and second leading cause of cancer-related death in women worldwide. Ferroptosis, an iron-dependent newly discovered mode of cell death, can be induced by lenaltinib and plays an important role in the biological behaviors of BC. Therefore, the prognostic value of ferroptosis-related genes (FRGs) in BC warrants further investigation. FRG expression profiles and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO). Immune-related pathways were found in the functional analysis. Significant differences in enrichment scores for immune cells were observed. Some patients from TCGA-BRCA were included as the training cohort. A six-gene prediction signature was constructed with the least absolute shrinkage and selection operator Cox regression. This model was validated in the rest of the TCGA-BRCA and GEO cohort. The expressions of the six FRGs were verified with real-time quantitative polymerase chain reaction and immunohistochemistry in the Human Protein Atlas. Relapse or metastasis was more likely in the high-risk group. Risk score was an independent predictor of disease-free survival. Collectively, the ferroptosis-related risk model established in this study may serve as an effective tool to predict the prognosis in BC.
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Huang Y, Qiu L, Liang X, Zhao J, Chen H, Luo Z, Li W, Lin X, Jin J, Huang J, Zhang G. Identifying a 6-Gene Prognostic Signature for Lung Adenocarcinoma Based on Copy Number Variation and Gene Expression Data. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:6962163. [PMID: 36211815 PMCID: PMC9535135 DOI: 10.1155/2022/6962163] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/12/2022] [Accepted: 08/21/2022] [Indexed: 11/20/2022]
Abstract
The occurrence of lung adenocarcinoma (LUAD) is a complicated process, involving the genetic and epigenetic changes of proto-oncogenes and oncogenes. The objective of this study was to establish new predictive signatures of lung adenocarcinoma based on copy number variations (CNVs) and gene expression data. Next-generation sequencing was implemented to obtain gene expression and CNV information. According to univariate, multivariate survival Cox regression analysis, and LASSO analysis, the expression profiles of lung adenocarcinoma patients were screened and a risk score formula was established and experimentally validated in a local cohort. The model was evaluated by three independent cohorts (TCGA-LUAD, GSE31210, and GSE30219), and then validated by clinical samples from LUAD patients. A total of 844 CNV-related differentially expressed genes (CNV-related DEGs) were identified. These genes are significantly associated with the imbalance of various oxidative stress pathways. A CNV-associated-six gene signature was dramatically linked to overall survival in lung adenocarcinoma samples from both training and validation groups. Functional enrichment analysis further revealed involvement of genes in p53 signaling pathway and cell cycle as well as the mismatch repair pathway. Risk score is an independent marker considering clinical parameters and had better prediction in clinical subpopulation. The same signature also classified tumor tissues of clinical patients with CNV detected from their corresponding nontumorous tissues with an accuracy of 0.92. In conclusion, we identified a new class of 6 CNV-related gene markers that may act as efficient prognostic predictors of lung adenocarcinoma, thus contributing to individualized treatment decisions in patients.
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Affiliation(s)
- Yisheng Huang
- Postdoctoral Innovation Center of Zhongshan Chenxinghai Hospital, Jinan University, Guangzhou, China
- Department of Oncology, Maoming People's Hospital, Maoming City, China
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, China
| | - Liling Qiu
- Department of Endocrinology, Zhongshan Hospital of Sun Yat-Sen University, Zhongshan City People's Hospital, Zhongshan City, China
| | - Xiaoye Liang
- Department of Oncology, Maoming People's Hospital, Maoming City, China
| | - Jing Zhao
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, China
| | - Haoting Chen
- Translational Medicine Center, Key Laboratory of Molecular Target and Clinical Pharmacology, School of Pharmaceutical Sciences, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhiqiang Luo
- Department of Thoracic Surgery, Maoming People's Hospital, Maoming City, China
| | - Wanzhen Li
- Department of Oncology, Maoming People's Hospital, Maoming City, China
| | - Xiaohua Lin
- Department of Oncology, Maoming People's Hospital, Maoming City, China
| | - Jingjie Jin
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, China
| | - Jian Huang
- Department of Thoracic Surgery, Maoming People's Hospital, Maoming City, China
| | - Gong Zhang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, China
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Huang F, Zhang S, Li X, Huang Y, He S, Luo L. STAT3-mediated ferroptosis is involved in ulcerative colitis. Free Radic Biol Med 2022; 188:375-385. [PMID: 35779691 DOI: 10.1016/j.freeradbiomed.2022.06.242] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/16/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022]
Abstract
Ferroptosis is a form of iron-dependent lipid peroxidation cell death that plays an important role in inflammation. However, the mechanism of ferroptosis in ulcerative colitis (UC) remains to be further investigated. In the present study, we merged the differentially expressed genes (DEGs) of UC in GEO database with the ferroptosis-related genes of FerrDb for bioinformatics analysis and successfully screened out the ferroptosis-related hub gene STAT3 (signal transducer and activator of transcription 3). Then we further validated the role of STAT3-mediated ferroptosis in vitro and in vivo models of colitis. The results showed that ferroptosis was increased in DSS-induced colitis, salmonella typhimurium (S. Tm) colitis and H2O2-induced IEC-6 cells. And the phosphorylation level of the hub gene STAT3 was down-regulated in IEC-6 cells treated with H2O2, while Fer-1, an ferroptosis inhibitor, reactivated the phosphorylation level of STAT3. In addition, co-treatment of cells with H2O2 and STAT3 inhibitor (stattic) showed an additive effect on the extent of ferroptosis. Taken together, these findings suggest that ferroptosis is closely associated with the development of colitis and ferroptosis-related gene STAT3 could serve as a potential biomarker for diagnosis and treatment of ulcerative colitis.
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Affiliation(s)
- Fangfang Huang
- Graduate School, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China; Department of Pediatrics, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Suzhou Zhang
- The First Clinical College, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Xiaoling Li
- Experimental Animal Center, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Yuge Huang
- Department of Pediatrics, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524023, China.
| | - Shasha He
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Chinese Medicine, Beijing, 100000, China.
| | - Lianxiang Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, Guangdong, 524023, China.
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Song X, Wu L, Wang G, Liu B, Zhu W. Construction of a Novel Ferroptosis-Related Gene Signature for Predicting Survival of Patients With Lung Adenocarcinoma. Front Oncol 2022; 12:810526. [PMID: 35311093 PMCID: PMC8928751 DOI: 10.3389/fonc.2022.810526] [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: 11/07/2021] [Accepted: 01/31/2022] [Indexed: 12/24/2022] Open
Abstract
Lung adenocarcinoma (LUAD) is the most diagnosed subtype of lung cancer; ferroptosis is widely involved in the pathological cell death associated with various cancers, including lung cancer. However, the comprehensive relationship between ferroptosis and LUAD is little known in molecular levels until now. In the present study, 513 LUAD patients could be aggregated into three clusters by consensus clustering based on RNA sequencing data of 291 ferroptosis-related genes (FRGs) in The Cancer Genome Atlas (TCGA) database; cluster2 had significant survival advantage compared to the other two clusters. A novel prognostic model of 8 differential FRGs was constructed to effectively divide LUAD patients into high- or low-risk group according to the risk scores by the Cox and LASSO regression analyses. The overall survival of LUAD patients in the high-risk group was significantly worse in the TCGA and GEO cohorts. Moreover, patients with radiation therapy or high clinical stage had obviously higher risk scores. We validated the differential mRNA and protein expression of four FRGs in paired tumor and normal samples from our clinical cohort. Our study constructed a novel FRG signature to predict the prognosis of LUAD patients, which might provide a new prognostic tool and potential therapeutic targets for LUAD.
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Affiliation(s)
- Xiaojie Song
- Department of Respiratory Medicine, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Liqun Wu
- Department of Respiratory Medicine, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Guangqiang Wang
- Department of Respiratory Medicine, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Baoyi Liu
- Department of Respiratory Medicine, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Wenyong Zhu
- Department of Thoracic Surgery, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
- *Correspondence: Wenyong Zhu,
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Tabnak P, HajiEsmailPoor Z, Soraneh S. Ferroptosis in Lung Cancer: From Molecular Mechanisms to Prognostic and Therapeutic Opportunities. Front Oncol 2021; 11:792827. [PMID: 34926310 PMCID: PMC8674733 DOI: 10.3389/fonc.2021.792827] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022] Open
Abstract
Lung cancer is the second commonly diagnosed malignancy worldwide and has the highest mortality rate among all cancers. Tremendous efforts have been made to develop novel strategies against lung cancer; however, the overall survival of patients still is low. Uncovering underlying molecular mechanisms of this disease can open up new horizons for its treatment. Ferroptosis is a newly discovered type of programmed cell death that, in an iron-dependent manner, peroxidizes unsaturated phospholipids and results in the accumulation of radical oxygen species. Subsequent oxidative damage caused by ferroptosis contributes to cell death in tumor cells. Therefore, understanding its molecular mechanisms in lung cancer appears as a promising strategy to induce ferroptosis selectively. According to evidence published up to now, significant numbers of research have been done to identify ferroptosis regulators in lung cancer. Therefore, this review aims to provide a comprehensive standpoint of molecular mechanisms of ferroptosis in lung cancer and address these molecules’ prognostic and therapeutic values, hoping that the road for future studies in this field will be paved more efficiently.
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Affiliation(s)
- Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Soroush Soraneh
- Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran
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11
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Fu XW, Song CQ. Identification and Validation of Pyroptosis-Related Gene Signature to Predict Prognosis and Reveal Immune Infiltration in Hepatocellular Carcinoma. Front Cell Dev Biol 2021; 9:748039. [PMID: 34820376 PMCID: PMC8606409 DOI: 10.3389/fcell.2021.748039] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/22/2021] [Indexed: 12/26/2022] Open
Abstract
Background: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and accounts for the fourth common cause of cancer-related deaths. Recently, pyroptosis has been revealed to be involved in the progression of multiple cancers. However, the role of pyroptosis in the HCC prognosis remains elusive. Methods: The clinical information and RNA-seq data of the HCC patients were collected from the TCGA-LIHC datasets, and the differential pyroptosis-related genes (PRG) were firstly explored. The univariate Cox regression and consensus clustering were applied to recognize the HCC subtypes. The prognostic PRGs were then submitted to the LASSO regression analysis to build a prognostic model in the TCGA training cohort. We further evaluated the predictive model in the TCGA test cohort and ICGC validation cohort (LIRI-JP). The accuracy of prediction was validated using the Kaplan—Meier (K-M) and receiver operating characteristic (ROC) analyses. The single-sample gene set enrichment analysis (ssGSEA) was used to determine the differential immune cell infiltrations and related pathways. Finally, the expression of the prognostic genes was validated by qRT-PCR in vivo and in vitro. Results: We identified a total of 26 differential PRGs, among which three PRGs comprising GSDME, GPX4, and SCAF11 were subsequently chosen for constructing a prognostic model. This model significantly distinguished the HCC patients with different survival years in the TCGA training, test, and ICGC validation cohorts. The risk score of this model was confirmed as an independent prognostic factor. A nomogram was generated indicating the survival years for each HCC patient. The ssGSEA demonstrated several tumor-infiltrating immune cells to be remarkably associated with the risk scores. The qRT-PCR results also showed the apparent dysregulation of PRGs in HCC. Finally, the drug sensitivity was analyzed, indicating that Lenvatinib might impact the progression of HCC via targeting GSDME, which was also validated in human Huh7 cells. Conclusion: The PRG signature comprised of GSDME, GPX4, and SCAF11 can serve as an independent prognostic factor for HCC patients, which would provide further evidence for more clinical and functional studies.
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Affiliation(s)
- Xiao-Wei Fu
- Fudan University, Shanghai, China.,Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Laboratory of Gene Therapeutic Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Chun-Qing Song
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Laboratory of Gene Therapeutic Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
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12
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Ren Z, Hu M, Wang Z, Ge J, Zhou X, Zhang G, Zheng H. Ferroptosis-Related Genes in Lung Adenocarcinoma: Prognostic Signature and Immune, Drug Resistance, Mutation Analysis. Front Genet 2021; 12:672904. [PMID: 34434214 PMCID: PMC8381737 DOI: 10.3389/fgene.2021.672904] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/20/2021] [Indexed: 12/25/2022] Open
Abstract
It is reported that ferroptosis has close relation with tumorigenesis and drug resistance. However, the clinical significance of ferroptosis in lung adenocarcinoma (LUAD) remains elusive, and the potential targets for ferroptosis-based treatment are limited. In this study, we constructed a 15-gene prognostic signature predicting overall survival based on ferroptosis-related genes (ferroptosis driver genes VDAC2, GLS2, FLT3, TLR4, PHKG2, phosphogluconate dehydrogenase (PGD), PANX1, KRAS, PEBP1, ALOX15, and ALOX12B, and suppressor genes ACSL3, CISD1, FANCD2, and SLC3A2) in The Cancer Genome Atlas (TCGA)-LUAD cohort. The signature’s predictive ability was validated in the GSE68465 and GSE72094 cohorts by survival analysis and independent prognostic analysis with clinical features. Nomograms were provided for clinical reference. Functional analysis revealed that ferroptosis was closely related to cell cycle, cell metabolism, and immune pathways. Pan-cancer analysis comprehensively analyzed these 15 genes in 33 cancer types, indicating that the heterogeneity of 15 genes was evident across different cancer types. Besides, these genes were critical regulators modulating drug resistance, tumor microenvironment infiltration, and cancer stemness. Then, we screened 10 genes (TLR4, PHKG2, PEBP1, GLS2, FLT3, ALOX15, ACSL3, CISD1, FANCD2, and SLC3A2) as potential targets for further research because their biological functions in ferroptosis were consistent with their prognostic significance. Somatic mutation and copy number variation analysis revealed that the alteration rates of KRAS, PGD, and ALOX15 were more than 1% and significantly associated with overall survival in LUAD. Moreover, the expression of KRAS and PGD was positively related to tumor mutation burden, indicating that KRAS and PGD could serve as novel biomarkers for predicting immunotherapy response rate. Our study identified and validated a ferroptosis-related gene signature for LUAD, provided a 10-gene set for future research, and screened KRAS and PGD as potential novel immunotherapy biomarkers.
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Affiliation(s)
- Ziyuan Ren
- Basic Medical School, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Minghui Hu
- Clinical Lab, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhonglin Wang
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States
| | - Junpeng Ge
- Department of Biology Engineering, Shandong Jianzhu University, Jinan, China
| | - Xiaoyan Zhou
- Clinical Lab, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Guoming Zhang
- Basic Medical School, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hongying Zheng
- Clinical Lab, The Affiliated Hospital of Qingdao University, Qingdao, China
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