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Liu X, Feng Y, Wang L, Shi L, Ji K, Hu N, Du Y, Liu M, Wang M. Silencing of circ_0088036 inhibits growth and invasion of lung adenocarcinoma through miR-203/SP1 axis. J Biochem Mol Toxicol 2024; 38:e23684. [PMID: 38533528 DOI: 10.1002/jbt.23684] [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: 03/26/2023] [Revised: 02/06/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. Circular RNA (circRNA) circ_0088036 is a recently discovered circRNA known for its roles in rheumatoid arthritis. The study aimed to study the function of circ_0088036 in lung adenocarcinoma (LUAD). Circ_0088036 expressions were analyzed in the Gene Expression Omnibus (GEO) database. The relationship between circ_0088036 expressions and clinicopathological data of LUAD was assessed. The messenger RNA and protein levels were analyzed by quantitative real-time polymerase chain reaction and Western blot. Cell viability, apoptosis, and invasion were tested by Cell Counting Kit-8, flow cytometry, and transwell assay. The direct interaction between microRNA-203 (miR-203) and circ_0088036 or specificity protein 1 (SP1) was confirmed by dual-luciferase reporter assay, RNA pull-down, and RNA immunoprecipitation assays. Circ_0088036 was overexpressed in LUAD from the analysis of the GEO database. The poor prognosis was found in the patients with high expressions of circ_0088036. The level of Circ_0088036 was increased in LUAD tissues and cells. In terms of function, the deletion of circ_0088036 inhibited LUAD tumorigenesis in vitro by repressing cell growth, invasion, and epithelial-mesenchymal transition (EMT). In mechanism, circ_0088036 could competitively sponge miR-203, thereby affecting the expressions of the target gene SP1. In addition, lessening of miR-203 and enlarging of SP1 could eliminate the anticancer effect of short hairpin RNA-circ_0088036 on LUAD cells. Besides, the knockout of circ_0088036 hindered the growth of xenografted tumors in vivo. Circ_0088036 promoted the LUAD cell growth, invasion, and EMT via modulating the miR-203/SP1 axis in LUAD.
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Affiliation(s)
- Xiuhua Liu
- Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yan Feng
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, Heilongjiang, China
| | - Linna Wang
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, Heilongjiang, China
| | - Lei Shi
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, Heilongjiang, China
| | - Kunxiang Ji
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, Heilongjiang, China
| | - Nan Hu
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, Heilongjiang, China
| | - Yang Du
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, Heilongjiang, China
| | - Mingyang Liu
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, Heilongjiang, China
| | - Man Wang
- Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
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Xin R, Cheng Q, Chi X, Feng X, Zhang H, Wang Y, Duan M, Xie T, Song X, Yu Q, Fan Y, Huang L, Zhou F. Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer. Genes (Basel) 2023; 14:2169. [PMID: 38136991 PMCID: PMC10742656 DOI: 10.3390/genes14122169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.
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Affiliation(s)
- Ruihao Xin
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Qian Cheng
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Xiaohang Chi
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin 132000, China;
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, China;
| | - Hang Zhang
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Yueying Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Meiyu Duan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Tunyang Xie
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK;
| | - Xiaonan Song
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130012, China;
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, China;
| | - Yusi Fan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130012, China;
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
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Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8579651. [PMID: 33299877 PMCID: PMC7700720 DOI: 10.1155/2020/8579651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/19/2020] [Accepted: 10/29/2020] [Indexed: 02/07/2023]
Abstract
Objective Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need. Methods The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape. Results Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions. Conclusion This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.
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Yu X, Zhang Y. Identification of a long non-coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma. Oncol Lett 2020; 19:2793-2800. [PMID: 32218832 PMCID: PMC7068299 DOI: 10.3892/ol.2020.11400] [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: 07/26/2019] [Accepted: 01/16/2020] [Indexed: 12/12/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high- and low-risk groups based on median risk scores calculated using a prognosis-related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan-Meier (K-M) survival analysis of the high- and low-risk cohorts. Potential biomarkers associated with survival status were then identified using K-M survival analysis and potential biomarker functions were predicted using enrichment analysis of co-expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3- and 5-year survival with the model were 0.836 and 0.818, respectively, of a time-dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K-M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.
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Affiliation(s)
- Xiaolin Yu
- Department of Internal Medicine of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100078, P.R. China
| | - Yanxia Zhang
- Department of Respiratory, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, P.R. China
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Feng X, Hao X, Xin R, Gao X, Liu M, Li F, Wang Y, Shi R, Zhao S, Zhou F. Detecting Methylomic Biomarkers of Pediatric Autism in the Peripheral Blood Leukocytes. Interdiscip Sci 2019; 11:237-246. [DOI: 10.1007/s12539-019-00328-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 03/25/2019] [Accepted: 03/28/2019] [Indexed: 12/12/2022]
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Yang L, Yang Y, Ma P, Zheng B, Liu W, Zhang Z, Ding N, Liu L, Mao Y, Lv N. Spread through air spaces predicts a worse survival in patients with stage I adenocarcinomas >2 cm after radical lobectomy. J Thorac Dis 2018; 10:5308-5317. [PMID: 30416778 DOI: 10.21037/jtd.2018.09.22] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background The aim of this study was to evaluate the significance of spread through air spaces (STAS) in early lung adenocarcinomas after radical lobectomy and lymphadenectomy. Methods A total of 242 patients with lung adenocarcinomas less than 4 cm (8th pStage I) were selected from the lung cancer patients surgically treated from January, 2009 to September, 2011. Pathological review focused on STAS as well as histological subtypes, blood vessel & neural invasion, pathological tumor size etc. Recurrence or disease-free survival (DFS) and overall survival (OS) were compared between patients as stratified by STAS and tumor size. Results STAS was observed in 33.47% (81/242) patients, which was significantly correlated with histological predominant subtype (χ2=25.903, P=0.093×10-3) and differentiation grade (χ2=23.986, P=0.025×10-3). Patients with STAS (+) showed a comparable PFS (P=0.268) and OS rates (P=0.100) in all stage I cases, but a significant lower PFS (P=0.029) and OS (P=0.013) in tumors within 2< tumors ≤4 cm. Multivariate analysis revealed STAS to be an independent worse prognostic factor in lung adenocarcinomas within 2< tumors ≤4 cm, both for PFS (P=0.004) and OS (P=0.002), while no significant difference was found in patients with tumors ≤2 cm (PFS, P=0.537; OS, P=0.448), after adjusting by other clinicopathological parameters as age, gender, smoking etc. Conclusions Presence of STAS was a significant worse predictor for pStage I patients with lung adenocarcinoma >2 cm who underwent radical lobectomy, while it is not significant in patients with tumor ≤2 cm. These findings may be helpful in assessing postoperative therapy stratified by tumor size and STAS status.
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Affiliation(s)
- Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yikun Yang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Peiqing Ma
- Department of Pathology, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bo Zheng
- Department of Pathology, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wenchao Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Zhirong Zhang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ningning Ding
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Li Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.,Department of Clinical Medicine, North China University of Science and Technology, Tangshan 063200, China
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ning Lv
- Department of Pathology, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Ren Y, Feng X, Xia X, Zhang Y, Zhang W, Su J, Wang Z, Xu Y, Zhou F. Gender specificity improves the early-stage detection of clear cell renal cell carcinoma based on methylomic biomarkers. Biomark Med 2018; 12:607-618. [PMID: 29707986 DOI: 10.2217/bmm-2018-0084] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
AIM The two genders are different ranging from the molecular to the phenotypic levels. But most studies did not use this important information. We hypothesize that the integration of gender information may improve the overall prediction accuracy. MATERIALS & METHODS A comprehensive comparative study was carried out to test the hypothesis. The classification of the stages I + II versus III + IV of the clear cell renal cell carcinoma samples was formulated as an example. RESULTS & CONCLUSION In most cases, female-specific model significantly outperformed both-gender model, as similarly for the male-specific model. Our data suggested that gender information is essential for building biomedical classification models and even a simple strategy of building two gender-specific models may outperform the gender-mixed model.
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Affiliation(s)
- Yanjiao Ren
- College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.,College of Information Technology, Jilin Agricultural University, Changchun, Jilin 130118, China
| | - Xin Feng
- College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Xin Xia
- College of Software, Jilin University, Changchun, Jilin 130012, China
| | - Yexian Zhang
- College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Wenniu Zhang
- College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Jing Su
- Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, China
| | - Zhongyu Wang
- College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Ying Xu
- College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.,Computational Systems Biology Lab, Department of Biochemistry & Molecular Biology, University of Georgia, Athens, Georgia, 30602, USA.,College of Public Health, Jilin University, Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
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