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Wang K, Shen S, Dong L, Fang Q, Hou X, Shi X. Polo-like kinase (PLK) 5, a new member of the PLK family, serves as a biomarker to indicate anabatic tumor burden and poor prognosis for resectable non-small cell lung cancer. Front Surg 2023; 9:964044. [PMID: 36684318 PMCID: PMC9856523 DOI: 10.3389/fsurg.2022.964044] [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: 06/08/2022] [Accepted: 11/09/2022] [Indexed: 01/09/2023] Open
Abstract
Objective A review argues that polo-like kinase 5 (PLK5) may be linked to unfavorable prognosis in non-small cell lung cancer (NSCLC) patients, which contradicts the discoveries from The Human Protein Atlas database (derived from TCGA analysis). This study intended to comprehensively confirm the association of PLK5 with clinical characteristics and prognosis in NSCLC patients. Methods This two-center, retrospective, cohort study enrolled 210 NSCLC patients receiving surgical resection. PLK5 protein and mRNA were detected by immunohistochemistry and RT-qPCR in tumor and nontumor tissues. Moreover, RNA FPKM data for 994 lung cancer patients were obtained from The Human Protein Atlas database. Results PLK5 protein was decreased in tumor tissue compared to nontumor tissue (P < 0.001). Additionally, decreased PLK5 protein was linked with increased pathological grade (P = 0.002), lymph node metastasis presence (P = 0.001), elevated tumor-node-metastasis (TNM) stage (P = 0.003), and abnormal cancer antigen 125 (CA125) (P = 0.002). Meanwhile, low PLK5 protein was correlated with shortened disease-free survival (DFS) (P = 0.007) and overall survival (OS) (P = 0.038); further multivariable Cox regression analysis revealed that low PLK5 protein independently predicted unfavorable DFS (hazard ratio = 0.573, P = 0.022). PLK5 mRNA was reduced in tumor tissue compared with nontumor tissue (P < 0.001); its decline was linked with enhanced pathological grade (P = 0.034), climbed TNM stage (P = 0.032), and abnormal CA125 (P = 0.002). Furthermore, low PLK5 mRNA was correlated with unfavorable DFS (P = 0.046). The Human Protein Atlas database also disclosed the link between low PLK5 mRNA and worse OS (P = 0.046). Conclusion A PLK5 decrement reflects anabatic tumor burden and poor prognosis in NSCLC patients.
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Affiliation(s)
- Kaichao Wang
- Department of Cardiothoracic Surgery, Daqing Oilfield General Hospital, Daqing, China
| | - Shaohui Shen
- Department of Cardiothoracic Surgery, Longnan Hospital, Daqing, China,Correspondence: Shaohui Shen
| | - Liyuan Dong
- Department of Gynecology, Daqing Oilfield General Hospital, Daqing, China
| | - Qinmo Fang
- Department of Cardiothoracic Surgery, Daqing Oilfield General Hospital, Daqing, China
| | - Xinlei Hou
- Department of Cardiothoracic Surgery, Daqing Oilfield General Hospital, Daqing, China
| | - Xueliang Shi
- Department of Cardiothoracic Surgery, Daqing Oilfield General Hospital, Daqing, China
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Sultana A, Alam MS, Liu X, Sharma R, Singla RK, Gundamaraju R, Shen B. Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches. Transl Oncol 2022; 27:101571. [PMID: 36401966 PMCID: PMC9676382 DOI: 10.1016/j.tranon.2022.101571] [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: 03/30/2022] [Revised: 10/12/2022] [Accepted: 10/24/2022] [Indexed: 11/18/2022] Open
Abstract
Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.
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Affiliation(s)
- Adiba Sultana
- School of Biology and Basic Medical Sciences, Soochow University Medical College, 199 Ren'ai Road, Suzhou 215123, China; Center for Systems Biology, Soochow University, Suzhou 215006, China; Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China
| | - Md Shahin Alam
- School of Biology and Basic Medical Sciences, Soochow University Medical College, 199 Ren'ai Road, Suzhou 215123, China
| | - Xingyun Liu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China
| | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India.
| | - Rajeev K Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab 144411, India.
| | - Rohit Gundamaraju
- ER Stress and Mucosal Immunology Lab, School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, Tasmania, TAS 7248, Australia
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China.
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Bermúdez-Guzmán L. Pan-cancer analysis of non-oncogene addiction to DNA repair. Sci Rep 2021; 11:23264. [PMID: 34853396 PMCID: PMC8636604 DOI: 10.1038/s41598-021-02773-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/23/2021] [Indexed: 12/26/2022] Open
Abstract
Cancer cells usually depend on the aberrant function of one or few driver genes to initiate and promote their malignancy, an attribute known as oncogene addiction. However, cancer cells might become dependent on the normal cellular functions of certain genes that are not oncogenes but ensure cell survival (non-oncogene addiction). The downregulation or silencing of DNA repair genes and the consequent genetic and epigenetic instability is key to promote malignancy, but the activation of the DNA-damage response (DDR) has been shown to become a type of non-oncogene addiction that critically supports tumour survival. In the present study, a systematic evaluation of DNA repair addiction at the pan-cancer level was performed using data derived from The Cancer Dependency Map and The Cancer Genome Atlas (TCGA). From 241 DDR genes, 59 were identified as commonly essential in cancer cell lines. However, large differences were observed in terms of dependency scores in 423 cell lines and transcriptomic alterations across 18 cancer types. Among these 59 commonly essential genes, 14 genes were exclusively associated with better overall patient survival and 19 with worse overall survival. Notably, a specific molecular signature among the latter, characterized by DDR genes like UBE2T, RFC4, POLQ, BRIP1, and H2AFX showing the weakest dependency scores, but significant upregulation was strongly associated with worse survival. The present study supports the existence and importance of non-oncogenic addiction to DNA repair in cancer and may facilitate the identification of prognostic biomarkers and therapeutic opportunities.
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Affiliation(s)
- Luis Bermúdez-Guzmán
- Robotic Radiosurgery Center, International Cancer Center, San José, Costa Rica. .,Section of Genetics and Biotechnology, School of Biology, University of Costa Rica, San Pedro, San José, Costa Rica.
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Wang S, Wu J, Guo C, Shang H, Yao J, Liao L, Dong J. Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis. Cancer Manag Res 2020; 12:9787-9799. [PMID: 33116838 PMCID: PMC7550107 DOI: 10.2147/cmar.s250792] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 09/11/2020] [Indexed: 12/14/2022] Open
Abstract
Purpose The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC. Methods Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P‐values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cut-off criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan–Meier plotter from those hub genes and validated by RT-qPCR. Results A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan–Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3–1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells. Conclusion Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.
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Affiliation(s)
- Shengnan Wang
- Laboratory of Endocrinology, Medical Research Center, Shandong Provincial Qianfoshan Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, People's Republic of China.,Department of Occupational Disease, Yantai Shan Hospital, Yantai, People's Republic of China
| | - Jing Wu
- Laboratory of Endocrinology, Medical Research Center, Shandong Provincial Qianfoshan Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, People's Republic of China
| | - Congcong Guo
- Department of Endocrinology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, People's Republic of China
| | - Hongxia Shang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Jinming Yao
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Lin Liao
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.,Department of Endocrinology and Metabology, Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China
| | - Jianjun Dong
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China
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