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Zhou M, Li H, Gao B, Zhao Y. The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma. Comput Biol Med 2024; 178:108692. [PMID: 38879932 DOI: 10.1016/j.compbiomed.2024.108692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/22/2024] [Accepted: 06/01/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear. This study seeks to elucidate the regulatory connections among critical pathogenic genes and their associated expression variations within distinct stromal cell subtypes. METHOD Analysis and investigation were conducted on a total of 114,019 single-cell RNA data and 346 The Cancer Genome Atlas (TCGA) LUAD-related samples using bioinformatics and statistical algorithms. Differential gene expression analysis was performed for tumor samples and controls, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential genes between stromal cells and other cell clusters were identified and intersected with the differential genes from TCGA. We employed a combination of LASSO regression and multivariable Cox regression to identify the ultimate set of pathogenic gene. Survival models were trained to predict the relationship between patient survival and these pathogenic genes. Analysis of transcription factor (TF) cell specificity and pseudotime trajectories within stromal cell subpopulations revealed that vascular endothelial cells (ECs) and matrix cancer-associated fibroblasts (CAFs) are key in regulation of the prognosis-associated genes CAV2, COL1A1, TIMP1, ETS2, AKAP12, ID1 and COL1A2. RESULTS Seven pathogenic genes associated with LUAD in stromal cells were identified and used to develop a survival model. High expression of these genes is linked to a greater risk of poor survival. Stromal cells were categorized into eight subtypes and one unannotated cluster. Mesothelial cells, vascular endothelial cells (ECs), and matrix cancer-associated fibroblasts (CAFs) showed cell-specific regulation of the pathogenic genes. CONCLUSIONS The seven disease-causing genes in vascular ECs and matrix CAFs can be used to detect the survival status of LUAD patients, providing new directions for future targeted drug design.
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Affiliation(s)
- Murong Zhou
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China; College of Life Science, Northeast Forestry University, Harbin, 150040, China
| | - Hongfei Li
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China; College of Life Science, Northeast Forestry University, Harbin, 150040, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, 150040, China
| | - Yuming Zhao
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
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2
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Bhattacharjya A, Islam MM, Uddin MA, Talukder MA, Azad A, Aryal S, Paul BK, Tasnim W, Almoyad MAA, Moni MA. Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma. FEBS Open Bio 2024. [PMID: 38783639 DOI: 10.1002/2211-5463.13807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 01/30/2024] [Accepted: 04/16/2024] [Indexed: 05/25/2024] Open
Abstract
Hypopharyngeal cancer gene regulatory networks and therapeutic molecules are a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.
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Affiliation(s)
- Abanti Bhattacharjya
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Md Manowarul Islam
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Md Ashraf Uddin
- School of Information Technology, Deakin University, Geelong, Australia
| | - Md Alamin Talukder
- Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Akm Azad
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Sunil Aryal
- School of Information Technology, Deakin University, Geelong, Australia
| | - Bikash Kumar Paul
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Wahia Tasnim
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | | | - Mohammad Ali Moni
- Artificial Intelligence & Data Science, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, Australia
- AI & Digital Health Technology, Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, Australia
- Rural Health Research Institute, Charles Sturt University, Orange, Australia
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Hu J, Yang X, Ren J, Zhong S, Fan Q, Li W. Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis. Toxicol Mech Methods 2023; 33:781-795. [PMID: 37488941 DOI: 10.1080/15376516.2023.2240879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/17/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND This study identified and verified the characteristic differentially expressed ferroptosis-related genes (CDEFRGs) in osteosarcoma (OS). METHODS We extracted ferroptosis-related genes (FRGs), identified differentially expressed FRGs (DEFRGs) in OS, and conducted correlation analysis between DEFRGs. Next, we conducted GO and KEGG analyses to explore the biological functions and pathways of DEFRGs. Furthermore, we used LASSO and SVM-RFE algorithms to screen CDEFRGs, and evaluated its accuracy in diagnosing OS through ROC curves. Then, we demonstrated the molecular function and pathway enrichment of CDEFRGs through GSEA analysis. In addition, we evaluated the differences in immune cell infiltration between OS and NC groups, as well as the correlation between CDEFRGs expressions and immune cell infiltrations. Finally, the expression of CDEFRGs was verified through qRT-PCR, western blotting, and immunohistochemistry experiments. RESULTS We identified 51 DEFRGs and the expression relationship between them. GO and KEGG analysis revealed their key functions and important pathways. Based on four CDEFRGs (PEX3, CPEB1, NOX1, and ALOX5), we built the OS diagnostic model, and verified its accuracy. GSEA analysis further revealed the important functions and pathways of CDEFRGs. In addition, there were differences in immune cell infiltration between OS group and NC group, and CDEFRGs showed significant correlation with certain infiltrating immune cells. Finally, we validated the differential expression levels of four CDEFRGs through external experiments. CONCLUSIONS This study has shed light on the molecular pathological mechanism of OS and has offered novel perspectives for the early diagnosis and immune-targeted therapy of OS patients.
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Affiliation(s)
- Jianhua Hu
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
| | - Xi Yang
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Jing Ren
- Department of Spinal Surgery, Qujing No. 1 Hospital, Affiliated Qujing Hospital of Kunming Medical University, Qujing, P. R. China
| | - Shixiao Zhong
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Qianbo Fan
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Weichao Li
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
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Fu L, Li M, Lv J, Yang C, Zhang Z, Qin S, Li W, Wang X, Chen L. Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma. Front Endocrinol (Lausanne) 2023; 14:1270772. [PMID: 37955007 PMCID: PMC10634586 DOI: 10.3389/fendo.2023.1270772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023] Open
Abstract
Introduction Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy. Methods This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites. Results Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models. Conclusion To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.
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Affiliation(s)
- Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Manshi Li
- Department of Radiation Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chengcheng Yang
- Department of Respiratory, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zihan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xinyan Wang
- Department of Respiratory, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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Zeng S, Xu Z, Liang Q, Thakur A, Liu Y, Zhou S, Yan Y. The prognostic gene CRABP2 affects drug sensitivity by regulating docetaxel-induced apoptosis in breast invasive carcinoma: A pan-cancer analysis. Chem Biol Interact 2023; 373:110372. [PMID: 36736488 DOI: 10.1016/j.cbi.2023.110372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 01/21/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023]
Abstract
Cellular retinoic acid-binding protein 2 (CRABP2), a specific transporter of retinoic acid, has been shown to have an important biological role in human cancers. However, due to the substantial variability among different tumors, the role of CRABP2 remains uncertain and has not yet been subjected to systematic analysis. Utilizing The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Kaplan-Meier Plotter, Biomarker Exploration of Solid Tumors (BEST), Cancer Cell Line Encyclopedia (CCLE), Receiver Operating Characteristic plotter (ROC plotter), and other online public tools, expression levels of CRABP2 in breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), and ovarian serous cystadenocarcinoma (OV) were found to be significantly greater than those in adjacent normal tissues, suggesting a correlation to poor prognosis. Among the three, CRABP2 expression in BRCA was most closely associated with clinical prognosis. In a study of docetaxel-treated BRCA patients, CRABP2 expression was significantly higher in the drug-resistant group. Colony formation and flow cytometry analysis were used to further investigate the relationship between CRABP2 and docetaxel sensitivity in BRCA cells MDA-MB-231and BT549. The knockdown of CRABP2 expression significantly reduced cell growth and increased sensitivity to the chemotherapeutic agent docetaxel in BRCA cells. Furthermore, CRABP2 knockdown augmented docetaxel-induced apoptosis. Molecular docking using SwissDock tool revealed that CRABP2 had a greater binding affinity to docetaxel than docetaxel-targeted proteins. This research provides an insight into the expression and prognostic potential of CRABP2 in cancers and suggests that CRABP2 may control docetaxel sensitivity in BRCA cells through apoptosis, warranting further investigation.
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Affiliation(s)
- Shuangshuang Zeng
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Zhijie Xu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China; Department of Pathology, Xiangya Changde Hospital, Changde, 415000, Hunan, China
| | - Qiuju Liang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Abhimanyu Thakur
- Ben May Department for Cancer Research, Pritzker School of Molecular Engineering, University of Chicago, Illinois, USA
| | - Yuanhong Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Shangjun Zhou
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Yuanliang Yan
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
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Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma. Genet Res (Camb) 2023; 2023:6585109. [PMID: 36793937 PMCID: PMC9904923 DOI: 10.1155/2023/6585109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 02/03/2023] Open
Abstract
Background Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD. Methods RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465. Results A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues. Conclusion Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.
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Hong K, Chen Q, Zhang Y, Cheng X, Cen K, Dai Y, Mai Y, Guo Y. Prognostic implication and immunotherapy response prediction of a ubiquitination-related gene signature in breast cancer. Front Genet 2023; 13:1038207. [PMID: 36685928 PMCID: PMC9845272 DOI: 10.3389/fgene.2022.1038207] [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: 09/07/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023] Open
Abstract
Breast cancer (BC) is one of the most common tumor types and has poor outcomes. In this study, a ubiquitination-related prognostic signature was constructed, and its association with immunotherapy response in BC was explored. A list of ubiquitination-related genes was obtained from the molecular signatures database, and a ubiquitination-related gene signature was obtained by least absolute shrinkage and selection operator Cox regression. The genes, TCN1, DIRAS3, and IZUMO4, had significant influence on BC outcomes. Patients were categorized into two clusters-a high-risk group with poor survival and a low-risk group with greater chances of controlling BC progression. Univariate and multivariate Cox regression analyses revealed that the risk signature was an independent prognostic factor for BC. Gene set enrichment analysis suggested that the high-risk group was enriched in cell cycle and DNA replication pathways. The risk score was positively linked to the tumor microenvironment and negatively correlated with the immunotherapy response. The IC50 values for rapamycin were higher in the low-risk group, whereas those for axitinib, AZD6244, erlotinib, GDC0941, GSK650394, GSK269962A, lapatinib, and PD0325901 were higher in the high-risk group. Therefore, the ubiquitination-related signature is considered a promising tool for predicting a BC patient's immunotherapy response.
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Affiliation(s)
- Kai Hong
- Department of Thyroid and Breast Surgery, Ningbo First Hospital, Ningbo, China,Department of Thyroid and Breast Surgery, Ningbo Hospital of Zhejiang University, Ningbo, China
| | - Qiaoqiao Chen
- Reproductive Medicine Center, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China,Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province Assisted Reproduction Unit, Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingjue Zhang
- Department of Molecular Pathology, Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Xu Cheng
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Kenan Cen
- The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China
| | - Ying Dai
- The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China
| | - Yifeng Mai
- The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China,*Correspondence: Yangyang Guo, ; Yifeng Mai,
| | - Yangyang Guo
- Department of Thyroid and Breast Surgery, Ningbo First Hospital, Ningbo, China,Department of Thyroid and Breast Surgery, Ningbo Hospital of Zhejiang University, Ningbo, China,*Correspondence: Yangyang Guo, ; Yifeng Mai,
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Tong S, Xia M, Xu Y, Sun Q, Ye L, Cai J, Ye Z, Tian D. Identification and validation of a 17-gene signature to improve the survival prediction of gliomas. Front Immunol 2022; 13:1000396. [PMID: 36248799 PMCID: PMC9556650 DOI: 10.3389/fimmu.2022.1000396] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/08/2022] [Indexed: 11/23/2022] Open
Abstract
Gliomas are one of the most frequent types of nervous system tumours and have significant morbidity and mortality rates. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research was to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in the TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO and KEGG pathway analyses revealed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LASSO was performed to construct prognostic signatures in the TCGA cohort, and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate Cox regression analyses showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk score and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was found to be closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. And we found that knockdown of RPL39 inhibited the polarization and infiltration of M2 phenotype macrophages. In conclusion, our new prognosis-related model provides more potential therapeutic strategies for glioma patients.
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Affiliation(s)
- Shiao Tong
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Minqi Xia
- Department of Endocrinology & Metabolism, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yang Xu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qian Sun
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liguo Ye
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiayang Cai
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhang Ye
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Daofeng Tian
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Daofeng Tian,
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Liu P, Li H, Liao C, Tang Y, Li M, Wang Z, Wu Q, Zhou Y. Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis. PeerJ 2022; 10:e12731. [PMID: 35178291 PMCID: PMC8812315 DOI: 10.7717/peerj.12731] [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: 05/07/2021] [Accepted: 12/11/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis. METHODS In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues. RESULTS A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues. CONCLUSIONS The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.
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Affiliation(s)
- Ping Liu
- Department of Respiratory Medicine, The First Hospital of Changsha, Changsha, China
| | - Hui Li
- Department of Respiratory Medicine, The First Hospital of Changsha, Changsha, China
| | - Chunfeng Liao
- Department of Cardiology, The First Hospital of Changsha, Changsha, China
| | - Yuling Tang
- Department of Respiratory Medicine, The First Hospital of Changsha, Changsha, China
| | - Mengzhen Li
- MyGene Diagnostics Co., Ltd., Guangzhou, China
| | - Zhouyu Wang
- MyGene Diagnostics Co., Ltd., Guangzhou, China
| | - Qi Wu
- Department of Emergency, The First Hospital of Changsha, Changsha, China
| | - Yun Zhou
- Department of Spinal Surgery, The First Hospital of Changsha, Changsha, China
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