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Wang Y, Chen X, Tang N, Guo M, Ai D. Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis. Int J Mol Sci 2024; 25:4134. [PMID: 38612943 PMCID: PMC11012314 DOI: 10.3390/ijms25074134] [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: 02/17/2024] [Revised: 03/26/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.
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Affiliation(s)
| | | | | | | | - Dongmei Ai
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China; (Y.W.); (X.C.); (N.T.); (M.G.)
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2
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Cusenza VY, Tameni A, Neri A, Frazzi R. The lncRNA epigenetics: The significance of m6A and m5C lncRNA modifications in cancer. Front Oncol 2023; 13:1063636. [PMID: 36969033 PMCID: PMC10033960 DOI: 10.3389/fonc.2023.1063636] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/10/2023] [Indexed: 03/12/2023] Open
Abstract
Most of our transcribed RNAs are represented by non-coding sequences. Long non-coding RNAs (lncRNAs) are transcripts with no or very limited protein coding ability and a length >200nt. They can be epigenetically modified. N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G) and 2’-O-methylation (Nm) are some of the lncRNAs epigenetic modifications. The epigenetic modifications of RNA are controlled by three classes of enzymes, each playing a role in a specific phase of the modification. These enzymes are defined as “writers”, “readers” and “erasers”. m6A and m5C are the most studied epigenetic modifications in RNA. These modifications alter the structure and properties, thus modulating the functions and interactions of lncRNAs. The aberrant expression of several lncRNAs is linked to the development of a variety of cancers and the epigenetic signatures of m6A- or m5C-related lncRNAs are increasingly recognized as potential biomarkers of prognosis, predictors of disease stage and overall survival. In the present manuscript, the most up to date literature is reviewed with the focus on m6A and m5C modifications of lncRNAs and their significance in cancer.
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Affiliation(s)
- Vincenza Ylenia Cusenza
- Laboratory of Translational Research, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Annalisa Tameni
- Laboratory of Translational Research, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Antonino Neri
- Scientific Directorate, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Raffaele Frazzi
- Scientific Directorate, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
- *Correspondence: Raffaele Frazzi,
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3
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Wang Y, Ji H, Zhu B, Xing Q, Xie H. Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma. Eur J Immunol 2023; 53:e2250105. [PMID: 36367018 DOI: 10.1002/eji.202250105] [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: 07/29/2022] [Revised: 10/30/2022] [Accepted: 11/09/2022] [Indexed: 11/13/2022]
Abstract
Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.
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Affiliation(s)
- Yi Wang
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China.,Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Ji
- Department of Urology, Tumor Hospital Affiliated to Nantong University, Nantong, Jiangsu Province, China
| | - Bingye Zhu
- Department of Urology, Affiliated Nantong Hospital of Shanghai University (The Sixth People's Hospital of Nantong), Nantong, Jiangsu Province, China
| | - Qianwei Xing
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Huyang Xie
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
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4
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Jiang D, Wu T, Shi N, Shan Y, Wang J, Jiang H, Wu Y, Wang M, Li J, Liu H, Chen M. Development of genomic instability-associated long non-coding RNA signature: A prognostic risk model of clear cell renal cell carcinoma. Front Oncol 2022; 12:1019011. [DOI: 10.3389/fonc.2022.1019011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/23/2022] [Indexed: 12/24/2022] Open
Abstract
PurposeRenal clear cell carcinoma (ccRCC) is the most lethal of all pathological subtypes of renal cell carcinoma (RCC). Genomic instability was recently reported to be related to the occurrence and development of kidney cancer. The biological roles of long non-coding RNAs (lncRNAs) in tumorigenesis have been increasingly valued, and various lncRNAs were found to be oncogenes or cancer suppressors. Herein, we identified a novel genomic instability-associated lncRNA (GILncs) model for ccRCC patients to predict the overall survival (OS).MethodsThe Cancer Genome Atlas (TCGA) database was utilized to obtain full transcriptome data, somatic mutation profiles, and clinical characteristics. The differentially expressed lncRNAs between the genome-unstable-like group (GU) and the genome-stable-like group (GS) were defined as GILncs, with |logFC| > 1 and an adjusted p-value< 0.05 for a false discovery rate. All samples were allocated into GU-like or GS-like types based on the expression of GILncs observed using hierarchical cluster analyses. A genomic instability-associated lncRNA signature (GILncSig) was constructed using parameters of the included lncRNAs. Quantitative real-time PCR analysis was used to detect the in vitro expression of the included lncRNAs. Validation of the risk model was performed by the log-rank test, time-dependent receiver operating characteristic (ROC) curves analysis, and multivariate Cox regression analysis.ResultsForty-six lncRNAs were identified as GILncs. LINC00460, AL139351.1, and AC156455.1 were employed for GILncSig calculation based on the results of Cox analysis. GILncSig was confirmed as an independent predictor for OS of ccRCC patients. Additionally, it presented a higher efficiency and accuracy than other RCC prognostic models reported before.ConclusionGILncSig score was qualified as a critical indicator, independent of other clinical factors, for prognostic prediction of ccRCC patients.
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Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma. Vaccines (Basel) 2022; 10:vaccines10071161. [PMID: 35891325 PMCID: PMC9325030 DOI: 10.3390/vaccines10071161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/11/2022] [Accepted: 07/15/2022] [Indexed: 01/13/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma. It is particularly important to accurately judge the prognosis of patients. Since most tumor prediction models depend on the specific expression level of related genes, a better model therefore needs to be constructed. To provide an immune-related lncRNA (irlncRNAs) tumor prognosis model that is independent of the specific gene expression levels, we first downloaded and sorted out the data on ccRCC in the TCGA database and screened irlncRNAs using co-expression analysis and then obtained the differently expressed irlncRNA (DEirlncRNA) pairs by means of univariate analysis. In addition, we modified LASSO penalized regression. Subsequently, the ROC curve was drawn, and we compared the area under the curve, calculated the Akaike information standard value of the 5-year receiver operating characteristic curve, and determined the cut-off point to establish the best model to distinguish the high- or low-disease-risk group of ccRCC. Subsequently, we reassessed the model from the perspectives of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. A total of 17 DEirlncRNAs pairs (AL031710.1|AC104984.5, AC020907.4|AC127-24.4,AC091185.1|AC005104.1, AL513218.1|AC079015.1, AC104564.3|HOXB-AS3, AC003070.1|LINC01355, SEMA6A-AS1|CR936218.1, AL513327.1|AS005785.1, AC084876.1|AC009704.2, IGFL2-AS1|PRDM16-DT, AC011462.4|MMP25-AS1, AL662844.3I|TGB2-AS1, ARHGAP27P1|AC116914.2, AC093788.1|AC007098.1, MCF2L-AS1|AC093001.1, SMIM25|AC008870.2, and AC027796.4|LINC00893) were identified, all of which were included in the Cox regression model. Using the cut-off point, we can better distinguish patients according to different factors, such as survival status, invasive clinic-pathological features, tumor immune infiltration, whether they are sensitive to chemotherapy or not, and expression of immunosuppressive biomarkers. We constructed the irlncRNA model by means of pairing, which can better eliminate the dependence on the expression level of the target genes. In other words, the signature established by pairing irlncRNA regardless of expression levels showed promising clinical prediction value.
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6
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Wang PY, Yang S, Bao YJ. An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:671-686. [PMID: 35063405 DOI: 10.1016/j.ajpath.2021.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/13/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.
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MESH Headings
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Carcinoma, Renal Cell/diagnosis
- Carcinoma, Renal Cell/genetics
- Carcinoma, Renal Cell/metabolism
- Female
- Gene Expression Regulation, Neoplastic
- Gene Regulatory Networks
- Humans
- Kaplan-Meier Estimate
- Male
- MicroRNAs/genetics
- MicroRNAs/metabolism
- Prognosis
- RNA, Long Noncoding/genetics
- RNA, Long Noncoding/metabolism
- RNA, Messenger/genetics
- RNA, Messenger/metabolism
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Affiliation(s)
- Peng-Ying Wang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, School of Life Sciences, Hubei University, Wuhan, China
| | - Shihui Yang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, School of Life Sciences, Hubei University, Wuhan, China
| | - Yun-Juan Bao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, School of Life Sciences, Hubei University, Wuhan, China.
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7
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Cui T, Guo J, Sun Z. A computational prognostic model of lncRNA signature for clear cell renal cell carcinoma with genome instability. Expert Rev Mol Diagn 2021; 22:213-222. [PMID: 34871123 DOI: 10.1080/14737159.2021.1979960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Long non-coding RNAs (lncRNAs) play a critical role in genomic instability and prognosis of cancer patients, but the methods to identify genomic instability-related lncRNAs have yet to be established. In the present study, to assess the prognostic value of lncRNAs associated with genomic instability in clear cell renal cell carcinoma (ccRCC).A computational framework was established based on the mutation hypothesis and combined lncRNA expression and somatic mutation profiles of the ccRCC genome. Furthermore, a prognostic model was developed using the genome instability-derived lncRNA signature GILncSig based on three lncRNA genes (LINC02471, LINC01234, and LINC00460) and verified using multiple independent patient cohorts.This study established an effective computational method to study the role of lncRNAs in genomic instability, with potential applications in identifying new genomic instability-related cancer biomarkers.
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Affiliation(s)
- Tingting Cui
- Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Jiantao Guo
- Department of Cardiac Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Zhixia Sun
- Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
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8
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Ma T, Wang X, Wang J, Liu X, Lai S, Zhang W, Meng L, Tian Z, Zhang Y. N6-Methyladenosine-Related Long Non-coding RNA Signature Associated With Prognosis and Immunotherapeutic Efficacy of Clear-Cell Renal Cell Carcinoma. Front Genet 2021; 12:726369. [PMID: 34721523 PMCID: PMC8554127 DOI: 10.3389/fgene.2021.726369] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 09/30/2021] [Indexed: 12/24/2022] Open
Abstract
Increasing evidence suggests that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in cancer progression and immunotherapeutic efficacy in clear-cell renal cell carcinoma (ccRCC). In this study, we conducted a comprehensive ccRCC RNA-seq analysis using The Cancer Genome Atlas data to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for ccRCC. Forty-four prognostic m6A-related lncRNAs (m6A-RLs) were screened using Pearson correlation analysis (|R| > 0.7, p < 0.001) and univariable Cox regression analysis (p < 0.01). Using consensus clustering, the patients were divided into two clusters with different overall survival (OS) rates and immune status according to the differential expression of the lncRNAs. Gene set enrichment analysis corroborated that the clusters were enriched in immune-related activities. Twelve prognostic m6A-RLs were selected and used to construct the m6A-RLPS through least absolute shrinkage and selection operator Cox regression. We validated the differential expression of the 12 lncRNAs between tumor and non-cancerous samples, and the expression levels of four m6A-RLs were further validated using Gene Expression Omnibus data and Lnc2Cancer 3.0 database. The m6A-RLPS was verified to be an independent and robust predictor of ccRCC prognosis using univariable and multivariable Cox regression analyses. A nomogram based on age, tumor grade, clinical stage, and m6A-RLPS was generated and showed high accuracy and reliability at predicting the OS of patients with ccRCC. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. In conclusion, we established a novel m6A-RLPS with a favorable prognostic value for patients with ccRCC. The 12 m6A-RLs included in the signature may provide new insights into the tumorigenesis and allow the prediction of the treatment response of ccRCC.
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Affiliation(s)
- Tianming Ma
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaonan Wang
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.,Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jiawen Wang
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaodong Liu
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shicong Lai
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Wei Zhang
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Lingfeng Meng
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zijian Tian
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yaoguang Zhang
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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9
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Wei X, Wang Y, Ji C, Luan J, Yao L, Zhang X, Wang S, Yao B, Qin C, Song N. Genomic Instability Promotes the Progression of Clear Cell Renal Cell Carcinoma Through Influencing the Immune Microenvironment. Front Genet 2021; 12:706661. [PMID: 34712264 PMCID: PMC8546190 DOI: 10.3389/fgene.2021.706661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Long non-coding RNAs (lncRNAs) are now under discussion as novel promising biomarkers for clear cell renal cell carcinoma (ccRCC). However, the role of genomic instability-associated lncRNA signatures in tumors has not been thoroughly uncovered. The purpose of our study is to probe the role of genomic instability-derived lncRNA signature (GILncSig) and to further investigate the mechanism of genomic instability-mediated ccRCC progression. Methods: The transcriptome data and somatic mutation profiles of ccRCC as well as clinical characteristics used in this study were obtained from The Cancer Genome Atlas database and Gene Expression Omnibus database. Lasso regression analysis was performed to construct the GILncSig. Gene set enrichment analysis (GSEA) was performed to elucidate the biological functions and relative pathways. CIBERSORT and EPIC algorithm were applied to calculate the proportion of immune cells in ccRCC. ESTIMATE algorithm was utilized to compute the immune microenvironment scores. Results: In total, 148 novel genomic instability-derived lncRNAs in ccRCC were identified. Immediately, on the basis of univariate cox analysis and lasso analysis, a GILncSig was appraised, through which the patients were allocated into High-Risk and Low-Risk groups with significantly different characteristics and prognoses. In addition, we confirmed that the somatic mutation count, tumor mutation burden, and the expression of UBQLN4, which were ascertainably associated with genomic instability, were significantly correlated with the GILncSig, indicating its reliability as a measurement of the genomic instability. Furthermore, the efficiency of GILncSig in prognostic aspects was better than the single mutation gene in ccRCC. In addition, MNX1-AS1 was defined to be a potential biomarker characterized by strong correlation with clinical features. Moreover, GSEA results indicated that the IL6/JAK/STAT3/SIGNALING pathway could be considered as a potential mechanism of genomic instability to influence tumor progression. Besides, the immune microenvironment showed significant differences between the GS-like group and the GU-like group, which was specifically manifested as high expression of CTLA4, GITR, TNFSF14, and regulatory T cells (Tregs) as well as low expression of endothelial cells (ECs) in the GU-like group. Finally, the prognostic value and clinical relevance of GILncSig were verified in GEO datasets and other urinary tumors in TCGA dataset. Conclusion: In conclusion, our study provided a new perspective for the role of lncRNAs in genomic instability and revealed that genomic instability may mediate tumor progression by affecting immunity. Besides, MNX1-AS1 played critical roles in promoting the progression of ccRCC, which may be a potential therapeutic target. What is more, the immune atlas of genomic instability was characterized by high expression of CTLA4, GITR, TNFSF14, and Tregs, and low expression of ECs.
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Affiliation(s)
- Xiyi Wei
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yichun Wang
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chengjian Ji
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiaocheng Luan
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Liangyu Yao
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xi Zhang
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shuai Wang
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Bing Yao
- Department of Medical Genetics, Nanjing Medical University, Nanjing, China
| | - Chao Qin
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ninghong Song
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.,The Affiliated Kezhou People's Hospital of Nanjing Medical University, Kezhou, China
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10
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Wang G, Qu F, Liu S, Zhou J, Wang Y. Nucleolar protein NOP2 could serve as a potential prognostic predictor for clear cell renal cell carcinoma. Bioengineered 2021; 12:4841-4855. [PMID: 34334108 PMCID: PMC8806646 DOI: 10.1080/21655979.2021.1960130] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
As an indispensable part for cancer precision medicine, biomarkers and signatures for predicting cancer prognosis and therapeutic benefits were urgently required. The purpose of this study was to investigate the prognostic roles of NOP2 in renal clear cell carcinoma (ccRCC) for overall survival (OS) and its relationships with immunity. NOP2-related gene expression matrix associated with clinical information was obtained from the Cancer Genome Atlas (TCGA) ccRCC dataset and NOP2-related pathways were identified by gene set enrichment analysis (GSEA). Associations among the NOP2 expression and MSI, TMB, TNB, and immunity were also explored. Both the NOP2 mRNA and protein/phosphoprotein had a higher expression in ccRCC tumor tissues than in normal kidney tissues (both P < 0.001) and elevated NOP2 expression was associated with poor OS (P < 0.001). Logistic regression analysis revealed the NOP2 expression was significantly linked to stage, age, grade, N stage, T stage, and M stage (all P < 0.05). Univariate/multivariate Cox hazard regression analysis results indicated that NOP2 was an independent prognostic factor for OS in ccRCC and GSEA revealed five NOP2-related signaling pathways. Nomogram based on NOP2 and eight clinical characteristic parameters (grade, age, stage, gender, T stage, race, M stage, N stage) was constructed and carefully evaluated. Furthermore, NOP2 gene expression was also found to be significantly related to MSI, TMB, and immunity. Our findings revealed that NOP2 might be a potential prognostic factor for OS in ccRCC and it was significantly associated with immunity, MSI, and TMB.
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Affiliation(s)
- Gang Wang
- Department of Urology, The Affiliated Jianhu Hospital of Nantong University, Jiangsu Province, China
| | - Fangfang Qu
- Department of Anesthesiology, The Affiliated Jianhu Hospital of Nantong University, Jiangsu Province, China
| | - Shouyong Liu
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Jincai Zhou
- Department of Urology, The Affiliated Jianhu Hospital of Nantong University, Jiangsu Province, China
| | - Yi Wang
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
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11
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Chen H, Pan Y, Jin X, Chen G. Identification of a Four Hypoxia-Associated Long Non-Coding RNA Signature and Establishment of a Nomogram Predicting Prognosis of Clear Cell Renal Cell Carcinoma. Front Oncol 2021; 11:713346. [PMID: 34386428 PMCID: PMC8353455 DOI: 10.3389/fonc.2021.713346] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/09/2021] [Indexed: 12/21/2022] Open
Abstract
To identify novel hypoxia-associated long non-coding RNAs (lncRNAs) as potential biomarkers, we developed a risk stratification signature and constructed a prognosis prediction nomogram of clear cell renal cell carcinoma (ccRCC). Hypoxia-related lncRNAs were identified through Pearson correlation analysis between the expression profiles of hypoxia-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Then, a signature of four key lncRNAs (COMETT, EMX2OS, AC026462.3, and HAGLR) was developed. The four lncRNAs were downregulated in high-grade, advanced stage, and high-risk ccRCC. The signature had an independent and long-standing prognosis prediction ability up to a 10-year follow-up. Notably, the risk score was significantly positively correlated with the infiltration abundances of six immune cells from the Tumor IMmune Estimation Resource (TIMER). The gene set enrichment analysis (GSEA) also suggested that the signature was involved in metabolism and tumorigenesis, which were closely related to the hypoxic tumor microenvironment. Ultimately, a nomogram of signature, age, stage, and grade, was built to predict the individual long-term survival possibility. Finally, the expressions of four lncRNAs were validated by quantitative real-time PCR (qRT-PCR). Our study identified a four-lncRNA signature and established a prognostic nomogram that reliably predicts survival in ccRCC. The findings may be beneficial to therapeutic customization and medical decision-making.
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Affiliation(s)
- Hualin Chen
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Pan
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoxiang Jin
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Gang Chen
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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12
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Dzobo K, Dandara C. Broadening Drug Design and Targets to Tumor Microenvironment? Cancer-Associated Fibroblast Marker Expression in Cancers and Relevance for Survival Outcomes. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 24:340-351. [PMID: 32496971 DOI: 10.1089/omi.2020.0042] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Solid tumors have complex biology and structure comprising cancer cells, stromal cells, and the extracellular matrix. While most therapeutics target the cancer cells, recent data suggest that cancer cell behavior and response to treatment are markedly influenced by the tumor microenvironment (TME). In particular, the cancer-associated fibroblasts (CAFs) are the most abundant stromal cells, and play a significant contextual role in shaping tumor initiation, progression, and metastasis. CAFs have therefore emerged as part of the next-generation cancer drug design and discovery innovation strategy. We report here new findings on differential expression and prognostic significance of CAF markers in several cancers. We utilized two publicly available resources: The Cancer Genomic Atlas and Gene Expression Profiling Interactive Analysis. We examined the expression of CAF markers, ACTA2, S100A4, platelet-derived growth factor receptor-beta [PDGFR-β], CD10, and fibroblast activation protein-alpha (FAP-α), in tumor tissues versus the adjacent normal tissues. We found that CAF markers were differentially expressed in various different tumors such as colon, breast, and esophageal cancers and melanoma. No CAF marker is expressed in the same pattern in all cancers, however. Importantly, we report that patients with colon adenocarcinoma and esophageal carcinoma expressing high FAP-α and CD10, respectively, had significantly shorter overall survival, compared with those with low levels of these CAF markers (p < 0.05). We call for continued research on TME biology and clinical evaluation of the CAF markers ACTA2, S100A4, PDGFR-β, CD10, and FAP-α in relation to prognosis of solid cancers in large population samples. An effective cancer drug design and discovery roadmap in the 21st century ought to be broadly framed, and include molecular targets informed by both cancer cell and TME variations.
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Affiliation(s)
- Kevin Dzobo
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town, South Africa.,Faculty of Health Sciences, Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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13
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De Silva RA, Gorin MA, Mease RC, Minn I, Lisok A, Plyku D, Nimmagadda S, Allaf ME, Yang X, Sgouros G, Rowe SP, Pomper MG. Process validation, current good manufacturing practice production, dosimetry, and toxicity studies of the carbonic anhydrase IX imaging agent [ 111 In]In-XYIMSR-01 for phase I regulatory approval. J Labelled Comp Radiopharm 2021; 64:243-250. [PMID: 33576099 PMCID: PMC8129612 DOI: 10.1002/jlcr.3906] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 01/02/2023]
Abstract
[111 In]In-XYIMSR-01 is a promising single-photon emission computed tomography (SPECT) imaging agent for identification of tumors that overexpress carbonic anhydrase IX. To translate [111 In]In-XYIMSR-01 to phase I trials, we performed animal toxicity and dosimetry studies, determined the maximum dose for human use, and completed the chemistry, manufacturing, and controls component of a standard regulatory application. The production process, quality control testing, stability studies, and specifications for sterile drug product release were based on United States Pharmacopeia chapters <823> and <825>, FDA 21 CFR Part 212. Toxicity was evaluated by using nonradioactive [113/115 In]In-XYIMSR-01 according to 21 CFR Part 58 guidelines. Organ Level INternal Dose Assessment/EXponential Modeling (OLINDA/EXM) was used to calculate the maximum single dose for human studies. Three process validation runs at starting radioactivities of ~800 MBq were completed with a minimum concentration of 407 MBq/ml and radiochemical purity of ≥99% at the end of synthesis. A single intravenous dose of 55 μg/ml of [113/115 In]In-XYIMSR-01 was well tolerated in male and female Sprague-Dawley rats. The calculated maximum single dose for human injection from dosimetry studies was 390.35 MBq of [111 In]In-XYIMSR-01. We have completed toxicity and dosimetry studies as well as validated a manufacturing process to test [111 In]In-XYIMSR-01 in a phase I clinical trial.
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Affiliation(s)
- Ravindra A. De Silva
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Michael A. Gorin
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Ronnie C. Mease
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Il Minn
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Ala Lisok
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Donika Plyku
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Sridhar Nimmagadda
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Mohamad E. Allaf
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Xing Yang
- Present address: Peking University First Hospital, Beijing 100034, China
| | - George Sgouros
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Steven P. Rowe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
| | - Martin G. Pomper
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD, 21287, USA
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14
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Li MX, Wang HY, Yuan CH, Ma ZL, Jiang B, Li L, Zhang L, Xiu DR. KLHDC7B-DT aggravates pancreatic ductal adenocarcinoma development via inducing cross-talk between cancer cells and macrophages. Clin Sci (Lond) 2021; 135:629-649. [PMID: 33538300 DOI: 10.1042/cs20201259] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/13/2021] [Accepted: 02/04/2021] [Indexed: 12/19/2022]
Abstract
Tumor microenvironment (TME) exerts key roles in pancreatic ductal adenocarcinoma (PDAC) development. However, the factors regulating the cross-talk between PDAC cells and TME are largely unknown. In the present study, we identified a long noncoding RNA (lncRNA) KLHDC7B divergent transcript (KLHDC7B-DT), which was up-regulated in PDAC and correlated with poor survival of PDAC patients. Functional assays demonstrated that KLHDC7B-DT enhanced PDAC cell proliferation, migration, and invasion. Mechanistically, KLHDC7B-DT was found to directly bind IL-6 promoter, induce open chromatin structure at IL-6 promoter region, activate IL-6 transcription, and up-regulate IL-6 expression and secretion. The expression of KLHDC7B-DT was positively correlated with IL-6 in PDAC tissues. Via inducing IL-6 secretion, KLHDC7B-DT activated STAT3 signaling in PDAC cells in an autocrine manner. Furthermore, KLHDC7B-DT also activated STAT3 signaling in macrophages in a paracrine manner, which induced macrophage M2 polarization. KLHDC7B-DT overexpressed PDAC cells-primed macrophages promoted PDAC cell proliferation, migration, and invasion. Blocking IL-6/STAT3 signaling reversed the effects of KLHDC7B-DT on macrophage M2 polarization and PDAC cell proliferation, migration, and invasion. In conclusion, KLHDC7B-DT enhanced malignant behaviors of PDAC cells via IL-6-induced macrophage M2 polarization and IL-6-activated STAT3 signaling in PDAC cells. The cross-talk between PDAC cells and macrophages induced by KLHDC7B-DT represents potential therapeutic target for PDAC.
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Affiliation(s)
- Mu-Xing Li
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Hang-Yan Wang
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Chun-Hui Yuan
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Zhao-Lai Ma
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Bin Jiang
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Lei Li
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Li Zhang
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Dian-Rong Xiu
- Department of General Surgery, Peking University Third Hospital, Beijing, China
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15
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Wu Y, Deng J, Lai S, You Y, Wu J. A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets. PeerJ 2021; 9:e10556. [PMID: 33614260 PMCID: PMC7879943 DOI: 10.7717/peerj.10556] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 11/22/2020] [Indexed: 12/12/2022] Open
Abstract
Background Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets. Methods We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups. Results Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II-IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways. Conclusion The risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.
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Affiliation(s)
- Yiguo Wu
- Department of Medicine, Nanchang University, Nan Chang, China
| | - Junping Deng
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nan Chang, China
| | - Shuhui Lai
- Department of Medicine, Nanchang University, Nan Chang, China
| | - Yujuan You
- Department of Anesthesiology, The Second Affiliated Hospital of Nanchang University, Nan Chang, China
| | - Jing Wu
- Shenzhen Prevention and Treatment Center for Occupational Diseases, Shen Zhen, China
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16
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Hao JF, Chen P, Li HY, Li YJ, Zhang YL. Effects of LncRNA HCP5/miR-214-3p/MAPK1 Molecular Network on Renal Cell Carcinoma Cells. Cancer Manag Res 2021; 12:13347-13356. [PMID: 33380840 PMCID: PMC7769072 DOI: 10.2147/cmar.s274426] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/04/2020] [Indexed: 12/21/2022] Open
Abstract
Background Recent researches have shown that long non-coding RNA (LncRNA) is often disordered and acts in many carcinomas. Clear cell renal cell carcinoma (ccRCC) is the main reason for carcinoma-related deaths, which are mainly caused by the metastasis. HCP5 is a newly discovered LcnRNA. Early studies have found that HCP5 acts in neoplasm metastasis, but the mechanism of HCP5 in ccRCC is still unclear. Methods The expression of HCP5 in human renal cell carcinoma (RCC) was detected by real-time quantitative PCR. The biological effect of LncRNAs in proliferation, migration, invasion and metastasis of RCC cells was explored by gain-of-function and loss-of-function tests. The molecular mechanism of LncRNAs was explored by RNA immunoprecipitation and Western blot. Results qRT-PCR revealed that HCP5 was enhanced in neoplasm tissues of ccRCC patients and correlated with the metastatic characteristics of RCC. Over-expression of HCP5 promoted the proliferation, migration and invasion of renal carcinoma cells. The deletion of HCP5 inhibited the proliferation, migration and invasion of RCC in vitro and the metastasis of RCC in vivo. Mechanically, HCP5 inhibited the growth and metastasis of ccRCC cells by regulating miR-214-3p/MAPK1 axis. Conclusion HCP5, as a key LncRNA, can promote ccRCC metastasis by regulating miR-214-3p/MAPK1 axis and may be a biomarker and be helpful for judging the prognosis of ccRCC.
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Affiliation(s)
- Jun-Feng Hao
- Department of Nephrology and Blood Purification Center, Jin Qiu Hospital of Liaoning Province (Geriatric Hospital of Liaoning Province), Shenyang City, Liaoning Province 110000, People's Republic of China
| | - Pei Chen
- Department of Basic Medical Sciences, Jiangsu College of Nursing, Huai'an, Jiangsu Province 223000, People's Republic of China
| | - He-Yi Li
- Department of Ophthalmology, Jin Qiu Hospital of Liaoning Province (Geriatric Hospital of Liaoning Province), Shenyang City, Liaoning Province 110000, People's Republic of China
| | - Ya-Jing Li
- Department of Nephrology and Blood Purification Center, Jin Qiu Hospital of Liaoning Province (Geriatric Hospital of Liaoning Province), Shenyang City, Liaoning Province 110000, People's Republic of China
| | - Yu-Ling Zhang
- Department of Basic Medical Sciences, Jiangsu College of Nursing, Huai'an, Jiangsu Province 223000, People's Republic of China
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17
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Xu G, Wei J, Huangfu B, Gao J, Wang X, Xiao L, Xuan R, Chen Z, Song G. Animal model and bioinformatics analyses suggest the TIMP1/MMP9 axis as a potential biomarker in oral squamous cell carcinoma. Mol Carcinog 2020; 59:1302-1316. [PMID: 33006223 DOI: 10.1002/mc.23258] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/29/2020] [Accepted: 09/10/2020] [Indexed: 12/15/2022]
Abstract
Oral squamous cell carcinoma (OSCC) is a common malignant tumor of the head and neck. However, the molecular mechanism underlying its development and progression is yet unclear. Genes that are differentially expressed, that is, differentially expressed genes (DEGs), between normal and diseased tissues are believed to be involved in disease development and progression. To identify the DEGs in OSCC and explore their role in occurrence and progression, we established a Chinese hamster OSCC model, determined the DEG, screened the identified DEGs, and performed Gene Ontology (GO) and KEGG enrichment analyses. A protein-protein interaction (PPI) network was generated to screen potential candidate genes. We then analyzed the expression, tumor stage and prognosis of candidate genes using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, we verified the candidate DEGs by quantitative real-time PCR and Gene Expression Omnibus analysis. The results showed 194 significantly DEGs, 140 enriched GO terms, and 8 KEGG pathways, which suggested that OSCC was closely related to the immune system, cell migration, and extracellular matrix. GEPIA and PPI network analysis revealed that SPP1, TNC, and ACTA1 were significantly related to tumor staging; SPP1, tissue inhibitors of matrix metallopeptidases (MMPs) 1 (TIMP1), and ACTA1 were closely related to prognosis. The scores for the top five highest degree genes were close, and the TIMP1/MMP9 axis appeared to be at the center of the PPI network, indicating that expression changes in the TIMP1/MMP9 axis and related genes may be involved in tumor invasion and metastasis. These findings provide novel insights into the mechanism of oral cancer.
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Affiliation(s)
- Guoqiang Xu
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China
| | - Jianing Wei
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China
| | - Bing Huangfu
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China.,Taiyuan Zoo, Taiyuan, China
| | - Jiping Gao
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China
| | - Xiaotang Wang
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China
| | - Lanfei Xiao
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China
| | - Ruijing Xuan
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China
| | - Zhaoyang Chen
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China
| | - Guohua Song
- Laboratory Animal Center, Shanxi Medical University, Taiyuan, China.,Mental Health Hospital affiliated to Shanxi Medical University, Taiyuan, China
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18
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A novel prognostic model based on immunogenomics for clear cell renal cell carcinoma. Int Immunopharmacol 2020; 90:107119. [PMID: 33243605 DOI: 10.1016/j.intimp.2020.107119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/14/2020] [Accepted: 10/16/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Immune cell infiltration into tumor tissue is closely related to the clinical outcomes of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to screen out potential immune genes associated with ccRCC, analyze their relationships with clinical outcomes, and construct a signature to predict ccRCC. METHODS The transcriptome RNA-sequencing data in 539 ccRCC and 72 adjacent normal tissues were obtained from TCGA database. Biomedical computational algorithms were conducted to identify immune-related differential expressed genes (IRDGs) and enriched pathways. Then, LASSO Cox and multivariate Cox analyses were performed to screen out genes that were then used to construct the prognostic model. RESULTS A total of 116 down-regulated and 565 up-regulated IRDGs were identified. Pathway enrichment analysis suggested that IRDGs was mainly enriched in the pathway of "cytokines and cytokine receptors". The entire data of ccRCC were randomly divided into the training set and the test set with a ratio of 1:1. A 4-gene signature was then constructed using LASSO Cox analysis and multivariate Cox analysis in the training set. This prognostic signature could stratify patients into high- and low-risk groups successfully, and serve as an independent predictor when adjusted with clinical factors by univariate and multivariate Cox regression analysis. These results were verified in the test set and the entire set. Besides, the abundance of CD4 + T cells and dendritic cells increased in the high-risk group. Finally, we built a nomogram incorporating risk score and clinical factors to predict the overall survival of ccRCC patients. CONCLUSIONS These findings may contribute to the research of ccRCC in immunization part.
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19
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Nazari M, Shiri I, Zaidi H. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med 2020; 129:104135. [PMID: 33254045 DOI: 10.1016/j.compbiomed.2020.104135] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 10/21/2020] [Accepted: 11/11/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients. METHODS According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models. RESULTS The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively). CONCLUSIONS We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens.
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Affiliation(s)
- Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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20
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Yang F, Liu C, Zhao G, Ge L, Song Y, Chen Z, Liu Z, Hong K, Ma L. Long non-coding RNA LINC01234 regulates proliferation, migration and invasion via HIF-2α pathways in clear cell renal cell carcinoma cells. PeerJ 2020; 8:e10149. [PMID: 33088626 PMCID: PMC7568479 DOI: 10.7717/peerj.10149] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/21/2020] [Indexed: 12/21/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) have been proved to have an important role in different malignancies including clear cell renal cell carcinoma (ccRCC). However, their role in disease progression is still not clear. The objective of the study was to identify lncRNA-based prognostic biomarkers and further to investigate the role of one lncRNA LINC01234 in progression of ccRCC cells. We found that six adverse prognostic lncRNA biomarkers including LINC01234 were identified in ccRCC patients by bioinformatic analysis using The Cancer Genome Atlas database. LINC01234 knockdown impaired cell proliferation, migration and invasion in vitro as compared to negative control. Furthermore, the epithelial-mesenchymal transition was inhibited after LINC01234 knockdown. Additionally, LINC01234 knockdown impaired hypoxia-inducible factor-2a (HIF-2α) pathways, including a suppression of the expression of HIF-2α, vascular endothelial growth factor A, epidermal growth factor receptor, c-Myc, Cyclin D1 and MET. Together, these datas showed that LINC01234 was likely to regulate the progression of ccRCC by HIF-2α pathways, and LINC01234 was both a promising prognostic biomarker and a potential therapeutic target for ccRCC.
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Affiliation(s)
- Feilong Yang
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Cheng Liu
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Guojiang Zhao
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Liyuan Ge
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Yimeng Song
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Zhigang Chen
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Zhuo Liu
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Kai Hong
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Lulin Ma
- Department of Urology, Peking University Third Hospital, Beijing, China
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21
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Zhu Z, Xu C, Lin L, Lv T, Cai T, Lin J. Prognostic Value and Potential Biological Functions of CLDN8 in Patients with Clear Cell Renal Cell Carcinoma. Onco Targets Ther 2020; 13:9135-9145. [PMID: 32982302 PMCID: PMC7501992 DOI: 10.2147/ott.s266846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 08/12/2020] [Indexed: 01/21/2023] Open
Abstract
Purpose Clear cell renal cell carcinoma (ccRCC) is among the most common malignant tumors worldwide, with a high incidence rate and poor prognosis. Currently, there are no biomarkers that can accurately guide prognostic evaluation and therapeutic strategy for ccRCC. The prognostic value and potential biological function of claudin-8 (CLDN8), a critical component of tight junctions in ccRCC, remain unclear. Methods Sequencing data were obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, and Gene Expression Omnibus databases. R packages were used to explore CLDN8 mRNA expression levels and analyze differentially expressed genes. Results were validated in clinical specimens and cell lines, and bioinformatics analyses were conducted to explore the potential biological functions of CLDN8. Finally, functional analyses were carried out using 786–O ccRCC cell line. Results Both CLDN8 mRNA and protein expression levels were significantly lower in ccRCC compared with the normal control tissues. Kaplan–Meier analyses showed that low CLDN8 expression levels were associated with the poor overall survival, while univariate and multivariate Cox regression indicated that CLDN8 could serve as an independent prognostic factor in patient with ccRCC. Bioinformatic and Western blot analyses showed that CLDN8 suppressed proliferation, migration, and invasion of 786–O ccRCC cells through the epithelial–mesenchymal transition and AKT pathways. Conclusion CLDN8 could serve as an independent prognostic factor in ccRCC, in which it suppresses 786–O proliferation, migration, and invasion through EMT and AKT pathways.
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Affiliation(s)
- Zhenpeng Zhu
- Department of Urology, Peking University First Hospital, Beijing 100034, People's Republic of China.,Institute of Urology, Peking University, Beijing 100034, People's Republic of China.,Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, People's Republic of China
| | - Chunru Xu
- Department of Urology, Peking University First Hospital, Beijing 100034, People's Republic of China.,Institute of Urology, Peking University, Beijing 100034, People's Republic of China.,Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, People's Republic of China
| | - Lanruo Lin
- College of Basic Medical Science, Capital Medical University, Beijing 100069, People's Republic of China
| | - Tongde Lv
- Department of Urology, Peking University First Hospital, Beijing 100034, People's Republic of China.,Institute of Urology, Peking University, Beijing 100034, People's Republic of China.,Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, People's Republic of China
| | - Tianyu Cai
- Department of Urology, Peking University First Hospital, Beijing 100034, People's Republic of China.,Institute of Urology, Peking University, Beijing 100034, People's Republic of China.,Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, People's Republic of China
| | - Jian Lin
- Department of Urology, Peking University First Hospital, Beijing 100034, People's Republic of China.,Institute of Urology, Peking University, Beijing 100034, People's Republic of China.,Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, People's Republic of China
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22
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Berglund A, Amankwah EK, Kim YC, Spiess PE, Sexton WJ, Manley B, Park HY, Wang L, Chahoud J, Chakrabarti R, Yeo CD, Luu HN, Pietro GD, Parker A, Park JY. Influence of gene expression on survival of clear cell renal cell carcinoma. Cancer Med 2020; 9:8662-8675. [PMID: 32986937 PMCID: PMC7666730 DOI: 10.1002/cam4.3475] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 09/03/2020] [Accepted: 09/05/2020] [Indexed: 12/14/2022] Open
Abstract
Approximately 10%‐20% of patients with clinically localized clear cell renal cell carcinoma (ccRCC) at time of surgery will subsequently experience metastatic progression. Although considerable progression was seen in the systemic treatment of metastatic ccRCC in last 20 years, once ccRCC spreads beyond the confines of the kidney, 5‐year survival is less than 10%. Therefore, significant clinical advances are urgently needed to improve overall survival and patient care to manage the growing number of patients with localized ccRCC. We comprehensively evaluated expression of 388 candidate genes related with survival of ccRCC by using TCGA RNAseq (n = 515), Total Cancer Care (TCC) expression array data (n = 298), and a well characterized Moffitt RCC cohort (n = 248). We initially evaluated all 388 genes for association with overall survival using TCGA and TCC data. Eighty‐one genes were selected for further analysis and tested on Moffitt RCC cohort using NanoString expression analysis. Expression of nine genes (AURKA, AURKB, BIRC5, CCNE1, MK167, MMP9, PLOD2, SAA1, and TOP2A) was validated as being associated with poor survival. Survival prognostic models showed that expression of the nine genes and clinical factors predicted the survival in ccRCC patients with AUC value: 0.776, 0.821 and 0.873 for TCGA, TCC and Moffitt data set, respectively. Some of these genes have not been previously implicated in ccRCC survival and thus potentially offer insight into novel therapeutic targets. Future studies are warranted to validate these identified genes, determine their biological mechanisms and evaluate their therapeutic potential in preclinical studies.
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Affiliation(s)
- Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Ernest K Amankwah
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Cancer and Blood Disorders Institute, Johns Hopkins All Children's Hospital, Saint Petersburg, FL, USA
| | - Young-Chul Kim
- Department of Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Philippe E Spiess
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Wade J Sexton
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Brandon Manley
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.,Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Hyun Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Liang Wang
- Department of Tumor Biology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jad Chahoud
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Ratna Chakrabarti
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, USA
| | - Chang D Yeo
- Division of Pulmonology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hung N Luu
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.,Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Giuliano D Pietro
- Department of Pharmacy, Universidade Federal de Sergipe, Sao Cristovao, Brazil
| | - Alexander Parker
- University of Florida College of Medicine, Jacksonville, FL, USA
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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23
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Zhong W, Chen B, Zhong H, Huang C, Lin J, Zhu M, Chen M, Lin Y, Lin Y, Huang J. Identification of 12 immune-related lncRNAs and molecular subtypes for the clear cell renal cell carcinoma based on RNA sequencing data. Sci Rep 2020; 10:14412. [PMID: 32879362 PMCID: PMC7467926 DOI: 10.1038/s41598-020-71150-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 07/20/2020] [Indexed: 12/29/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC). Despite the existing extensive research, the molecular and pathogenic mechanisms of ccRCC are elusive. We aimed to identify the immune-related lncRNA signature and molecular subtypes associated with ccRCC. By integrating 4 microarray datasets from Gene Expression Omnibus database, we identified 49 immune-related genes. The corresponding immune-related lncRNAs were further identified in the TCGA dataset. 12-lncRNAs prognostic and independent signature was identified through survival analysis and survival difference between risk groups was further identified based on the risk score. Besides, we identified 3 molecular subtypes and survival analysis result showed that cluster 2 has a better survival outcome. Further, ssGSEA enrichment analysis for the immune-associated gene sets revealed that cluster 1 corresponded to a high immune infiltration level. While cluster 2 and cluster 3 corresponded to low and medium immune infiltration level, respectively. In addition, we validated the 12-lncRNA prognostic signature and molecular subtypes in an external validation dataset from the ICGC database. In summary, we identified a 12-lncRNA prognostic signature which may provide new insights into the molecular mechanisms of ccRCC and the molecular subtypes provided a theoretical basis for personalized treatment by clinicians.
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Affiliation(s)
- Weimin Zhong
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China
| | - Bin Chen
- The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, 361003, Fujian Province, People's Republic of China
| | - Hongbin Zhong
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China
| | - Chaoqun Huang
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China
| | - Jianqiong Lin
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China
| | - Maoshu Zhu
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China
| | - Miaoxuan Chen
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China
| | - Ying Lin
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China
| | - Yao Lin
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, College of Life Sciences, Qishan Campus, Fujian Normal University, Fuzhou, 350117, Fujian Province, People's Republic of China.
| | - Jiyi Huang
- The Fifth Hospital of Xiamen, Xiamen, 361101, Fujian Province, People's Republic of China. .,Xiang'an Branch, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, 361101, Fujian Province, People's Republic of China.
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24
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Liu CZ, Guo WP, Peng JB, Chen G, Lin P, Huang XL, Liu XF, Yang H, He Y, Pang YY, Ma W. Clinical significance of CCNE2 protein and mRNA expression in thyroid cancer tissues. Adv Med Sci 2020; 65:442-456. [PMID: 33059229 DOI: 10.1016/j.advms.2020.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 06/22/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE Thyroid carcinoma (TC) is the most common endocrinal malignancy worldwide. Cyclin E2 (CCNE2), a member of the cyclin family, acts as a regulatory subunit of cyclin-dependent kinases (CDKs). It controls the transition of quiescent cells into the cell cycle, regulates the G1/S transition, promotes DNA replication, and activates CDK2. This study explored the role and potential molecular mechanisms of CCNE2 expression in TC tissues. MATERIAL/METHODS Immunohistochemistry was used to evaluate the CCNE2 protein expression levels in TC. High-throughput data on CCNE2 in TC were obtained from RNA sequencing (RNA-seq), microarray, and literature data. The CCNE2 expression levels in TC were comprehensively assessed through an integrated analysis. Analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPIs) data facilitated the investigation of the relative molecular mechanisms of CCNE2 in TC. RESULTS The immunohistochemical experiment showed a significant increase in the expression of CCNE2 in the TC tissues. For 505 TC and 59 non-cancerous samples from RNA-seq data, the area under the curve (AUC) was 0.8016 (95% confidence interval [CI] 0.742-0.8612; p<0.001). With another 14 microarrays, the pool standard mean difference [SMD] was 1.01 (95% CI [0.82-1.19]). The pooled SMD of CCNE2 was 1.12 (95% CI [0.60-1.64]), and the AUC was 0.87 (95% CI [0.84-0.90]) for 1157 TC samples and 366 non-cancerous thyroid samples from all possible sources. Nine hub genes were upregulated in TC. CONCLUSIONS A high expression of CCNE2 may lead to carcinogenesis and the development of TC.
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MESH Headings
- Adenocarcinoma, Follicular/genetics
- Adenocarcinoma, Follicular/metabolism
- Adenocarcinoma, Follicular/pathology
- Apoptosis
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Carcinoma, Papillary/genetics
- Carcinoma, Papillary/metabolism
- Carcinoma, Papillary/pathology
- Cell Proliferation
- Cyclins/genetics
- Cyclins/metabolism
- Female
- Gene Expression Regulation, Neoplastic
- Humans
- Male
- Middle Aged
- Prognosis
- RNA, Messenger/genetics
- RNA, Messenger/metabolism
- Survival Rate
- Thyroid Neoplasms/genetics
- Thyroid Neoplasms/metabolism
- Thyroid Neoplasms/pathology
- Tumor Cells, Cultured
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Affiliation(s)
- Cui-Zhen Liu
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Wan-Ping Guo
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Jin-Bo Peng
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Gang Chen
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Peng Lin
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Xiao-Li Huang
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Xiao-Fan Liu
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Hong Yang
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Yun He
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Yu-Yan Pang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Wei Ma
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
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25
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Zhang D, Zeng S, Hu X. Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma. Cancer Cell Int 2020; 20:319. [PMID: 32694941 PMCID: PMC7367230 DOI: 10.1186/s12935-020-01423-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 07/14/2020] [Indexed: 02/06/2023] Open
Abstract
Background Long noncoding RNA (lncRNA) is generally identified as competing endogenous RNA (ceRNA) that plays a vital role in the pathogenesis of kidney renal clear cell carcinoma (KIRC), the most common subtype of renal cell carcinoma with poor prognosis and unclear pathogenesis. This study established a novel ceRNA network and thus identified a three-lncRNA prognostic model in KIRC patients. Methods Differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) database. The lncATLAS was applied to determine the differentially expressed lncRNAs (DElncRNAs) of the cytoplasm. The miRcode, miRDB, miRTarBase, and TargetScan databases were utilized to predict the interactions of DElncRNAs, DEmiRNAs, and DEmRNAs. Cytoscape was used to construct the ceRNA network. Then, a lncRNA prognostic model (LPM) was constructed based on ceRNA-related lncRNA that was significantly related to overall survival (OS), and its predictive ability was evaluated. Moreover, an LPM-based nomogram model was constructed. The significantly different expression of genes in the LPM was validated in an independent clinical cohort (N = 21) by quantitative RT-PCR. Results A novel ceRNA regulatory network, including 73 lncRNAs, 8 miRNAs, and 21 mRNAs was constructed. Functional enrichment analysis indicated that integral components of membrane and PI3K-Akt signaling pathway represented the most significant GO terms and pathway, respectively. The LPM established based on three lncRNAs (MIAT, LINC00460, and LINC00443) of great prognostic value from the ceRNA network was proven to be independent of conventional clinical parameters to differentiate patients with low or high risk of poor survival, with the AUC of 1-, 5- and 10-year OS were 0.723, 0.714 and 0.826 respectively. Furthermore, the nomogram showed a better predictive value in KIRC patients than individual prognostic parameters. The expression of MIAT and LINC00460 was significantly upregulated in the KIRC samples, while the expression of LINC00443 was significantly downregulated compared with the adjacent normal samples in the clinical cohort, TCGA, and GTEx. Conclusion This LPM based on three-lncRNA could serve as an independent prognostic factor with a tremendous predictive ability for KIRC patients, and the identified novel ceRNA network may provide insight into the prognostic biomarkers and therapeutic targets of KIRC.
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Affiliation(s)
- Di Zhang
- Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 GongTi South Road, 100020 Beijing, China.,Institute of Urology, Capital Medical University, Beijing, China
| | - Song Zeng
- Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 GongTi South Road, 100020 Beijing, China.,Institute of Urology, Capital Medical University, Beijing, China
| | - Xiaopeng Hu
- Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 GongTi South Road, 100020 Beijing, China.,Institute of Urology, Capital Medical University, Beijing, China
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26
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Chen M, Zhang S, Nie Z, Wen X, Gao Y. Identification of an Autophagy-Related Prognostic Signature for Clear Cell Renal Cell Carcinoma. Front Oncol 2020; 10:873. [PMID: 32547955 PMCID: PMC7274034 DOI: 10.3389/fonc.2020.00873] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/04/2020] [Indexed: 12/23/2022] Open
Abstract
Abnormal autophagy is closely related to the development of cancer. Many studies have demonstrated that autophagy plays an important role in biological function in clear cell renal cell carcinoma (ccRCC). This study aimed to construct a prognostic signature for ccRCC based on autophagy-related genes (ARGs) to predict the prognosis of ccRCC. Differentially expressed ARGs were obtained from ccRCC RNA-seq data in The Cancer Genome Atlas (TCGA) database. ARGs were enriched by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The prognostic ARGs used to construct the risk score models for overall survival (OS) and disease-free survival (DFS) were identified by Cox regression analyses. According to the median value of the risk score, patients were divided into a high-risk group and a low-risk group. The OS and DFS were analyzed by the Kaplan-Meier method. The predictive accuracy was determined by a receiver operating characteristic (ROC) curve analysis. Additionally, we performed stratification analyses based on different clinical variables and evaluated the correlation between the risk score and the clinical variables. The differentially expressed ARGs were mainly enriched in the platinum drug resistance pathway. The prognostic signatures based on 11 ARGs for OS and 5 ARGs for DFS were constructed and showed that the survive time was significantly shorter in the high-risk group than in the low-risk group (P < 0.001). The ROC curve for OS exhibited good predictive accuracy, with an area under the curve value of 0.738. In the stratification analyses, the OS time of the high-risk group was shorter than that of the low-risk group stratified by different clinical variables. In conclusion, an autophagy-related signature for OS we constructed can independently predict the prognosis of ccRCC patient, and provide a deep understanding of the potential biological mechanisms of autophagy in ccRCC.
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Affiliation(s)
- Mei Chen
- Central Laboratory, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Shufang Zhang
- Central Laboratory, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Zhenyu Nie
- Central Laboratory, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Xiaohong Wen
- Central Laboratory, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Yuanhui Gao
- Central Laboratory, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
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27
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Comprehensive analysis of competitive endogenous RNAs network reveals potential prognostic lncRNAs in gastric cancer. Heliyon 2020; 6:e03978. [PMID: 32455175 PMCID: PMC7235626 DOI: 10.1016/j.heliyon.2020.e03978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 04/13/2020] [Accepted: 05/11/2020] [Indexed: 01/17/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) are key regulators of a range of human diseases, including various cancers, with multiple previous studies having explored lncRNA dysregulation in the context of gastric cancer (GC). The present study sought to expand upon these previous results by downloading lncRNA, mRNA, and microRNA (miRNA) expression profiles derived from 180 GC tissues and 24 normal control tissues within the Cancer Genome Atlas (TCGA) database. These datasets were then interrogated to identify GC-related differentially expressed (DE) RNAs (|fold change| ≥ 2, FDR< 0.01), leading to the identification of 1946 DE lncRNAs, 123 DE miRNAs, and 3159 DE mRNAs. These results were then used to generate a putative GC-related competitive endogenous RNA (ceRNA) network composed of 131 lncRNAs, 9 miRNAs, and 78 mRNAs. Subsequent survival analyses based upon this network revealed 17 of these lncRNAs to be significantly associated with GC patient survival (P < 0.05). Further multivariable Cox regression and lasso analyses allowed for the construction of an 8-lncRNA risk score that was able to effectively predict GC patient survival with good discriminative ability. The Kaplan-Meier Plotter database further confirmed that network hub genes that were related to these 8 lncRNAs were associated with GC patient prognosis (P < 0.05). As the ceRNA network in the present study was constructed with a focus on both disease stage and differential gene expression, it represents a key resource that will offer valuable insights into the mechanistic roles of ceRNA pathways in GC development and progression.
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28
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Zheng T, Wang X, Yue P, Han T, Hu Y, Wang B, Zhao B, Zhang X, Yan X. Prognostic Inflammasome-Related Signature Construction in Kidney Renal Clear Cell Carcinoma Based on a Pan-Cancer Landscape. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2020; 2020:3259795. [PMID: 32328125 PMCID: PMC7157792 DOI: 10.1155/2020/3259795] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 02/20/2020] [Accepted: 03/06/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To investigate the expression patterns and prognostic characteristics of inflammasome-related genes (IRGs) across cancer types and develop a robust biomarker for the prognosis of KIRC. METHODS The differentially expressed IRGs and prognostic genes among 10 cancers were analyzed based on The Cancer Genome Atlas (TCGA) dataset. Subsequently, an IRGs risk signature was developed in KIRC. Its prognostic accuracy was evaluated by receiver operating characteristic (ROC) analysis. The independent predictive capacity was identified by stratification survival and multivariate Cox analyses. The gene ontology (GO) analysis and principal component analysis (PCA) were performed to explore biological functions of the IRGs signature in KIRC. RESULTS The expression patterns and prognostic association of IRGs varied from different cancers, while KIRC showed the most abundant survival-related dysregulated IRGs. The IRG signature for KIRC was able to independently predict survival, and the signature genes were mainly involved inimmune-related processes. CONCLUSIONS The pan-cancer analysis provided a comprehensive landscape of IRGs across cancer types and identified a strong association between IRGs and the prognosis of KIRC. Further IRGs signature represented a reliable prognostic predictor for KIRC and verified the prognostic value of inflammasomes in KIRC, contributing to our understanding of therapies targeting inflammasomes for human cancers.
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Affiliation(s)
- Tianyu Zheng
- The VIP Department, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang 110002, China
| | - Xindong Wang
- Department of Orthopedics, The First Hospital of China Medical University, Shenyang 110001, China
| | - Peipei Yue
- Department of Biochemistry and Molecular Biology, China Medical University, Shenyang 110001, China
| | - Tongtong Han
- The VIP Department, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang 110002, China
| | - Yue Hu
- The VIP Department, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang 110002, China
| | - Biyao Wang
- The VIP Department, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang 110002, China
| | - Baohong Zhao
- Center of Implant Dentistry, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang 110002, China
| | - Xinwen Zhang
- Center of Implant Dentistry, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang 110002, China
| | - Xu Yan
- The VIP Department, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang 110002, China
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29
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Deciphering Immune-Associated Genes to Predict Survival in Clear Cell Renal Cell Cancer. BIOMED RESEARCH INTERNATIONAL 2019; 2019:2506843. [PMID: 31886185 PMCID: PMC6925759 DOI: 10.1155/2019/2506843] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 10/11/2019] [Accepted: 10/17/2019] [Indexed: 12/27/2022]
Abstract
Background To elucidate the correlations between tumor microenvironment and clinical characteristics as well as prognosis in clear cell renal cell cancer (ccRCC) and investigate the immune-associated genes by a comprehensive analysis of The Cancer Genome Atlas (TCGA) database. Methods We collected mRNA expression profiles of 537 ccRCC samples from the TCGA database. Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. The differentially expressed genes (DEGs) were screened between high immune/stromal score and low immune/stromal score groups by the cutoff of |log (fold change)| > 1, P value <0.05 by using package "limma" in R. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction network of intersected DEGs between stromal score and immune score groups was conducted using the STRING database. The Kaplan-Meier method was used to explore DEGs with predictive values in overall survival, and the prognostic DEGs were further validated in a Gene Expression Omnibus (GEO) dataset GSE29609. Results A higher immune score was associated with T3/4 (vs. T1/2, P < 0.001), N1 (vs. N0, P=0.05), M1 (vs. M0, P=0.004), G3/4 (vs. G1/2, P < 0.001), advanced AJCC stage (P < 0.001), and shorter overall survival (P=0.04). Intersected DEGs between immune and stromal score groups were 48 upregulated and 47 downregulated genes, with 43 DEGs associated with overall survival in ccRCC. After validation by a cohort of 39 ccRCC cases with detailed follow-up information from GSE29609, six immune-associated DEGs including CASP5, HSD11B1, VSIG4, HMGCS2, HSD11B2, and OGDHL were demonstrated to be predictive of prognosis in ccRCC. Conclusions Our study elucidated tight associations between immune score and clinical characteristics as well as prognosis in ccRCC. Moreover, six DEGs were explored and validated to exert predictive values in overall survival of ccRCC.
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30
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Zhang C, He H, Hu X, Liu A, Huang D, Xu Y, Chen L, Xu D. Development and validation of a metastasis-associated prognostic signature based on single-cell RNA-seq in clear cell renal cell carcinoma. Aging (Albany NY) 2019; 11:10183-10202. [PMID: 31747386 PMCID: PMC6914399 DOI: 10.18632/aging.102434] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 10/29/2019] [Indexed: 12/12/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) was recently adopted for deciphering intratumoral heterogeneity across cell sub-populations, including clear cell renal cell carcinoma (ccRCC). Here, we characterized the single-cell expression profiling of 121 cell samples and found 44 metastasis-associated marker genes. Accordingly, we trained and validated 17 pivotal metastasis-associated genes (MAGs) in 626 patients incorporating internal and external cohorts to evaluate the model for predicting overall survival (OS) and progression-free survival (PFS). Correlation analysis revealed that the MAGs correlated significantly with several risk clinical characteristics. Moreover, we conducted Cox regression analysis integrating these independent clinical variables into a MAGs nomogram with superior accuracy in predicting progression events. We further revealed the differential landscape of somatic tumor mutation burden (TMB) between two nomogram-score groups and observed that TMB was also a prognostic biomarker; patients with high MAGs-nomogram scores suffered from a higher TMB, potentially leading to worse prognosis. Last, higher MAGs-nomogram scores correlated with the upregulation of oxidative phosphorylation, the Wnt signaling pathway, and MAPK signaling crosstalk in ccRCC. Overall, we constructed the robust MAGs through scRNA-seq and validated the model in a large patient population, which was valuable for prognostic stratification and providing potential targets against metastatic ccRCC.
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Affiliation(s)
- Chuanjie Zhang
- Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Hongchao He
- Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Xin Hu
- First Clinical Medical College of Nanjing Medical University, Nanjing, China
| | - Ao Liu
- Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Da Huang
- Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yang Xu
- Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Lu Chen
- Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Danfeng Xu
- Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
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