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Li Z, Fei H, Lei S, Hao F, Yang L, Li W, Zhang L, Fei R. Identification of HMMR as a prognostic biomarker for patients with lung adenocarcinoma via integrated bioinformatics analysis. PeerJ 2022; 9:e12624. [PMID: 35036134 PMCID: PMC8710063 DOI: 10.7717/peerj.12624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/19/2021] [Indexed: 12/11/2022] Open
Abstract
Background Lung adenocarcinoma (LUAD) is the most prevalent tumor in lung carcinoma cases and threatens human life seriously worldwide. Here we attempt to identify a prognostic biomarker and potential therapeutic target for LUAD patients. Methods Differentially expressed genes (DEGs) shared by GSE18842, GSE75037, GSE101929 and GSE19188 profiles were determined and used for protein-protein interaction analysis, enrichment analysis and clinical correlation analysis to search for the core gene, whose expression was further validated in multiple databases and LUAD cells (A549 and PC-9) by quantitative real-time PCR (qRT-PCR) and western blot analyses. Its prognostic value was estimated using the Kaplan-Meier method, meta-analysis and Cox regression analysis based on the Cancer Genome Atlas (TCGA) dataset and co-expression analysis was conducted using the Oncomine database. Gene Set Enrichment Analysis (GSEA) was performed to illuminate the potential functions of the core gene. Results A total of 115 shared DEGs were found, of which 24 DEGs were identified as candidate hub genes with potential functions associated with cell cycle and FOXM1 transcription factor network. Among these candidates, HMMR was identified as the core gene, which was highly expressed in LUAD as verified by multiple datasets and cell samples. Besides, high HMMR expression was found to independently predict poor survival in patients with LUAD. Co-expression analysis showed that HMMR was closely related to FOXM1 and was mainly involved in cell cycle as suggested by GSEA. Conclusion HMMR might be served as an independent prognostic biomarker for LUAD patients, which needs further validation in subsequent studies.
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Affiliation(s)
- Zhaodong Li
- Department of Cell Biology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Hongtian Fei
- Department of Pharmacology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Siyu Lei
- Department of Cell Biology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Fengtong Hao
- Department of Cell Biology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Lijie Yang
- Department of Cell Biology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Wanze Li
- Department of Cell Biology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Laney Zhang
- The College of Arts and Sciences, Cornell University, New York, USA
| | - Rui Fei
- Department of Cell Biology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China.,Key Laboratory of Lymphatic Surgery Jilin Province, Jilin University, Changchun, Jilin, China
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Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5516218. [PMID: 34671675 PMCID: PMC8523254 DOI: 10.1155/2021/5516218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 08/26/2021] [Indexed: 11/24/2022]
Abstract
Background The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach. Methods We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log2 fold change (FC) | >2. Results A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape. Conclusions We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.
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Yang D, He Y, Wu B, Liu R, Wang N, Wang T, Luo Y, Li Y, Liu Y. Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma. Cancer Biomark 2021; 29:399-416. [PMID: 32741804 DOI: 10.3233/cbm-200133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to construct a competing endogenous RNA (ceRNA) network to predict the progression in LUAD. METHODS Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC|> 1.0 and a false discovery rate (FDR) < 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs. RESULTS A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were associated with the KEGG pathway terms "microRNAs in cancer", "pathways in cancer", "cell cycle", "HTLV-1 infection", and the "PI3K-Akt signalling pathway". K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P< 0.05). Furthermore, 15 LUAD drugs interacting with 29 significant DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, PRKCE, DLC1, LATS2, and DPY19L1 were incorporated into the risk score system, and the results suggested that LUAD patients who had the high-risk score always suffered from a poorer overall survival. Additionally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 significant DEL-DEG pairs, NAV2-AS2 - PRKCE (r= 0.430, P< 0.001) and NAV2-AS2 - LATS2 (r= 0.338, P< 0.001). And NAV2-AS2 - mir-31 - PRKCE and NAV2-SA2 - mir-31 - LATS2 were finally identified as ceRNA network involved in the progression of LUAD. CONCLUSIONS The lncRNA-miRNA-mRNA ceRNA network plays an essential role in predicting the progression of LUAD. These results may improve our understanding and provide novel mechanistic insights to explore prognosis and therapeutic drugs for LUAD patients.
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Affiliation(s)
- Dan Yang
- Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China.,Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Yang He
- Molecular Oncology Laboratory of Cancer Research Institute, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China.,Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Bo Wu
- Department of Anus and Intestine Surgery, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China
| | - Ruxi Liu
- Department of Rheumatology and Immunology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China
| | - Nan Wang
- Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Tieting Wang
- Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Yannan Luo
- Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Yunda Li
- Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Yang Liu
- Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China
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Su C, Liu WX, Wu LS, Dong TJ, Liu JF. Screening of Hub Gene Targets for Lung Cancer via Microarray Data. Comb Chem High Throughput Screen 2020; 24:269-285. [PMID: 32772911 DOI: 10.2174/1386207323666200808172631] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/24/2020] [Accepted: 06/16/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Lung cancer is one of the malignancies exhibiting the fastest increase in morbidity and mortality, but the cause is not clearly understood. The goal of this investigation was to screen and identify relevant biomarkers of lung cancer. METHODS Publicly available lung cancer data sets, including GSE40275 and GSE134381, were obtained from the GEO database. The repeatability test for data was done by principal component analysis (PCA), and a GEO2R was performed to screen differentially expressed genes (DEGs), which were all subjected to enrichment analysis. Protein-protein interactions (PPIs), and the significant module and hub genes were identified via Cytoscape. Expression and correlation analysis of hub genes was done, and an overall survival analysis of lung cancer was performed. A receiver operating characteristic (ROC) curve analysis was performed to test the sensitivity and specificity of the identified hub genes for diagnosing lung cancer. RESULTS The repeatability of the two datasets was good and 115 DEGs and 10 hub genes were identified. Functional analysis revealed that these DEGs were associated with cell adhesion, the extracellular matrix, and calcium ion binding. The DEGs were mainly involved with ECM-receptor interaction, ABC transporters, cell-adhesion molecules, and the p53 signaling pathway. Ten genes including COL1A2, POSTN, DSG2, CDKN2A, COL1A1, KRT19, SLC2A1, SERPINB5, DSC3, and SPP1 were identified as hub genes through module analysis in the PPI network. Lung cancer patients with high expression of COL1A2, POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 had poorer overall survival times than those with low expression (p <0.05). The CTD database showed that 10 hub genes were closely related to lung cancer. Expression of POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 was also associated with a diagnosis of lung cancer (p<0.05). ROC analysis showed that SPP1 (AUC = 0.940, p = 0.000*, 95%CI = 0.930-0.973, ODT = 7.004), SLC2A1 (AUC = 0.889, p = 0.000*, 95%CI = 0.791-0.865, ODT = 7.123), CDKN2A (AUC = 0.730, p = 0.000*, 95%CI = 0.465-1.000, ODT = 6.071) were suitable biomarkers. CONCLUSION Microarray technology represents an effective method for exploring genetic targets and molecular mechanisms of lung cancer. In addition, the identification of hub genes of lung cancer provides novel research insights for the diagnosis and treatment of lung cancer.
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Affiliation(s)
- Chang Su
- Department of Cardiothoracic Surgery, the 980 Hospital of PLA Joint Logistical Support Force (Bethune International Peace Hospital), Shijiazhuang, Hebei 050082, China
| | - Wen-Xiu Liu
- Department of Cardiology, the 980 Hospital of PLA Joint Logistical Support Force (Bethune International Peace Hospital), Shijiazhuang, Hebei 050082, China
| | - Li-Sha Wu
- Department of Emergency, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Xinhua District, Shijiazhuang 050000, China
| | - Tian-Jian Dong
- Department of Cardiothoracic Surgery, the 980 Hospital of PLA Joint Logistical Support Force (Bethune International Peace Hospital), Shijiazhuang, Hebei 050082, China
| | - Jun-Feng Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei 050011, China
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Wu Z, Zhang X, He Z, Hou L. Identifying candidate diagnostic markers for early stage of non-small cell lung cancer. PLoS One 2019; 14:e0225080. [PMID: 31726467 PMCID: PMC6855900 DOI: 10.1371/journal.pone.0225080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Accepted: 10/28/2019] [Indexed: 11/19/2022] Open
Abstract
We performed a series of bioinformatics analysis on a set of important gene expression data with 76 samples in early stage of non-small cell lung cancer, including 40 adenocarcinoma samples, 16 squamous cell carcinoma samples and 20 normal samples. In order to identify the specific markers for diagnosis, we compared the two subtypes with the normal samples respectively to determine the gene expression characteristics. Through the multi-dimensional scaling classification, we found that the samples were clustered well according to the disease cases. Based on the classification results and using empirical Bayes moderation and treat method, 486 important genes associated with the disease were identified. We constructed gene functions and gene pathways to verify our result and explain the pathogenicity factor and process. We generated a protein-protein interaction network based on the mutual interaction between the selected genes and found that the top thirteen hub genes were highly associated with lung cancer or some other cancers including five newly found genes through our method. The results of this study indicated that contrast on the gene expression between different subtypes and normal samples provides important information for the detection of non-small cell lung cancer and helps exploration of the disease pathogenesis.
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Affiliation(s)
- Zhen Wu
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
| | - Xu Zhang
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
| | - Zhihui He
- Department of Pediatric Respiration, Chongqing Ninth People’s Hospital, Chongqing 400700, China
| | - Liyun Hou
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
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