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Xie B, Wu T, Hong D, Lu Z. Comprehensive landscape of junctional genes and their association with overall survival of patients with lung adenocarcinoma. Front Mol Biosci 2024; 11:1380384. [PMID: 38841188 PMCID: PMC11150628 DOI: 10.3389/fmolb.2024.1380384] [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: 02/01/2024] [Accepted: 04/22/2024] [Indexed: 06/07/2024] Open
Abstract
Objectives Junctional proteins are involved in tumorigenesis. Therefore, this study aimed to investigate the association between junctional genes and the prognosis of patients with lung adenocarcinoma (LUAD). Methods Transcriptome, mutation, and clinical data were retrieved from The Cancer Genome Atlas (TCGA). "Limma" was used to screen differentially expressed genes. Moreover, Kaplan-Meier survival analysis was used to identify junctional genes associated with LUAD prognosis. The junctional gene-related risk score (JGRS) was generated based on multivariate Cox regression analysis. An overall survival (OS) prediction model combining the JGRS and clinicopathological properties was proposed using a nomogram and further validated in the Gene Expression Omnibus (GEO) LUAD cohort. Results To our knowledge, this study is the first to demonstrate the correlation between the mRNA levels of 14 junctional genes (CDH15, CDH17, CDH24, CLDN6, CLDN12, CLDN18, CTNND2, DSG2, ITGA2, ITGA8, ITGA11, ITGAL, ITGB4, and PKP3) and clinical outcomes of patients with LUAD. The JGRS was generated based on these 14 genes, and a higher JGRS was associated with older age, higher stage levels, and lower immune scores. Thus, a prognostic prediction nomogram was proposed based on the JGRS. Internal and external validation showed the good performance of the prediction model. Mechanistically, JGRS was associated with cell proliferation and immune regulatory pathways. Mutational analysis revealed that more somatic mutations occurred in the high-JGRS group than in the low-JGRS group. Conclusion The association between junctional genes and OS in patients with LUAD demonstrated by our "TCGA filtrating and GEO validating" model revealed a new function of junctional genes.
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Affiliation(s)
- Bin Xie
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China
| | - Ting Wu
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China
| | - Duiguo Hong
- Jincheng Community Health Service Center, Hangzhou, China
| | - Zhe Lu
- Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Hangzhou Normal University, Hangzhou, China
- School of Basic Medicine, Hangzhou Normal University, Hangzhou, China
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Du Y, Chen X, Zhang B, Jin X, Wan Z, Zhan M, Yan J, Zhang P, Ke P, Huang X, Han L, Zhang Q. Identification of Copper Metabolism Related Biomarkers, Polygenic Prediction Model, and Potential Therapeutic Agents in Alzheimer's Disease. J Alzheimers Dis 2023; 95:1481-1496. [PMID: 37694370 DOI: 10.3233/jad-230565] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
BACKGROUND The underlying pathogenic genes and effective therapeutic agents of Alzheimer's disease (AD) are still elusive. Meanwhile, abnormal copper metabolism is observed in AD brains of both human and mouse models. OBJECTIVE To investigate copper metabolism-related gene biomarkers for AD diagnosis and therapy. METHODS The AD datasets and copper metabolism-related genes (CMGs) were downloaded from GEO and GeneCards database, respectively. Differentially expressed CMGs (DE-CMGs) performed through Limma, functional enrichment analysis and the protein-protein interaction were used to identify candidate key genes by using CytoHubba. And these candidate key genes were utilized to construct a prediction model by logistic regression analysis for AD early diagnosis. Furthermore, ROC analysis was conducted to identify a single gene with AUC values greater than 0.7 by GSE5281. Finally, the single gene biomarker was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. Additionally, immune cell infiltration in AD samples and potential therapeutic drugs targeting the identified biomarkers were further explored. RESULTS A polygenic prediction model for AD based on copper metabolism was established by the top 10 genes, which demonstrated good diagnostic performance (AUC values). COX11, LDHA, ATOX1, SCO1, and SOD1 were identified as blood biomarkers for AD early diagnosis. 20 agents targeting biomarkers were retrieved from DrugBank database, some of which have been proven effective for the treatment of AD. CONCLUSIONS The five blood biomarkers and copper metabolism-associated model can differentiate AD patients from non-demented individuals and aid in the development of new therapeutic strategies.
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Affiliation(s)
- Yuanyuan Du
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
- Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xi Chen
- Clinical Laboratory, Yangzhou Wutaishan Hospital, Yangzhou, Jiangsu, China
| | - Bin Zhang
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xing Jin
- The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Zemin Wan
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
| | - Min Zhan
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
| | - Jun Yan
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
| | - Pengwei Zhang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
| | - Peifeng Ke
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
| | - Xianzhang Huang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
| | - Liqiao Han
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
| | - Qiaoxuan Zhang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Minnai F, Noci S, Chierici M, Cotroneo CE, Bartolini B, Incarbone M, Tosi D, Mattioni G, Jurman G, Dragani TA, Colombo F. Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue. Cancer Sci 2022; 114:281-294. [PMID: 36114746 PMCID: PMC9807507 DOI: 10.1111/cas.15591] [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: 04/14/2022] [Revised: 08/23/2022] [Accepted: 09/12/2022] [Indexed: 01/07/2023] Open
Abstract
Emerging evidence suggests that the prognosis of patients with lung adenocarcinoma can be determined from germline variants and transcript levels in nontumoral lung tissue. Gene expression data from noninvolved lung tissue of 483 lung adenocarcinoma patients were tested for correlation with overall survival using multivariable Cox proportional hazard and multivariate machine learning models. For genes whose transcript levels are associated with survival, we used genotype data from 414 patients to identify germline variants acting as cis-expression quantitative trait loci (eQTLs). Associations of eQTL variant genotypes with gene expression and survival were tested. Levels of four transcripts were inversely associated with survival by Cox analysis (CLCF1, hazard ratio [HR] = 1.53; CNTNAP1, HR = 2.17; DUSP14, HR = 1.78; and MT1F: HR = 1.40). Machine learning analysis identified a signature of transcripts associated with lung adenocarcinoma outcome that was largely overlapping with the transcripts identified by Cox analysis, including the three most significant genes (CLCF1, CNTNAP1, and DUSP14). Pathway analysis indicated that the signature is enriched for ECM components. We identified 32 cis-eQTLs for CNTNAP1, including 6 with an inverse correlation and 26 with a direct correlation between the number of minor alleles and transcript levels. Of these, all but one were prognostic: the six with an inverse correlation were associated with better prognosis (HR < 1) while the others were associated with worse prognosis. Our findings provide supportive evidence that genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue.
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Affiliation(s)
- Francesca Minnai
- Institute for Biomedical TechnologiesNational Research CouncilSegrateItaly
| | - Sara Noci
- Department of ResearchFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Marco Chierici
- Data Science for Health Research UnitBruno Kessler FoundationTrentoItaly
| | | | - Barbara Bartolini
- Department of ResearchFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | | | - Davide Tosi
- Thoracic Surgery and Lung Transplantation UnitFondazione IRCCS Cà Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Giovanni Mattioni
- Thoracic Surgery and Lung Transplantation UnitFondazione IRCCS Cà Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Giuseppe Jurman
- Data Science for Health Research UnitBruno Kessler FoundationTrentoItaly
| | - Tommaso A. Dragani
- Department of ResearchFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Francesca Colombo
- Institute for Biomedical TechnologiesNational Research CouncilSegrateItaly
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Wang Y, Xu J, Fang Y, Gu J, Zhao F, Tang Y, Xu R, Zhang B, Wu J, Fang Z, Li Y. Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma. Front Immunol 2022; 13:950001. [PMID: 36091041 PMCID: PMC9455632 DOI: 10.3389/fimmu.2022.950001] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background As the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD). Methods First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation. Results LUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells. Conclusions We developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens.
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Affiliation(s)
- Yuli Wang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing Xu
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuan Fang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiefei Gu
- Information Center, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fanchen Zhao
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu Tang
- School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Rongzhong Xu
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bo Zhang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianchun Wu
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianchun Wu, ; Zhihong Fang, ; Yan Li,
| | - Zhihong Fang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianchun Wu, ; Zhihong Fang, ; Yan Li,
| | - Yan Li
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianchun Wu, ; Zhihong Fang, ; Yan Li,
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The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022; 14:cancers14143349. [PMID: 35884409 PMCID: PMC9321521 DOI: 10.3390/cancers14143349] [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: 06/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Abstract Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
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