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Yu X, Du Z, Zhu P, Liao B. Diagnostic, prognostic, and therapeutic potential of exosomal microRNAs in renal cancer. Pharmacol Rep 2024; 76:273-286. [PMID: 38388810 DOI: 10.1007/s43440-024-00568-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/24/2024]
Abstract
Renal cell carcinoma (RCC) arises from the tubular epithelial cells of the nephron. It has the highest mortality rate among urological cancers. There are no effective therapeutic approaches and no non-invasive biomarkers for diagnosis and follow-up. Thus, suitable novel biomarkers and therapeutic targets are essential for improving RCC diagnosis/prognosis and treatment. Circulating exosomes such as exosomal microRNAs (Exo-miRs) provide non-invasive prognostic/diagnostic biomarkers and valuable therapeutic targets, as they can be easily isolated and quantified and show high sensitivity and specificity. Exosomes secreted by an RCC can exhibit alterations in the miRs' profile that may reflect the cellular origin and (patho)physiological state, as a ''signature'' or ''fingerprint'' of the donor cell. It has been shown that the transportation of renal-specific miRs in exosomes can be rapidly detected and measured, holding great potential as biomarkers in RCC. The present review highlights the studies reporting tumor microenvironment-derived Exo-miRs with therapeutic potential as well as circulating Exo-miRs as potential diagnostic/prognostic biomarkers in patients with RCC.
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Affiliation(s)
- Xiaodong Yu
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Zhongbo Du
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Pingyu Zhu
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Bo Liao
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
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Wang K, Ding Y, Liu Y, Ma M, Wang J, Kou Z, Liu S, Jiang B, Hou S. CPA4 as a biomarker promotes the proliferation, migration and metastasis of clear cell renal cell carcinoma cells. J Cell Mol Med 2024; 28:e18165. [PMID: 38494845 PMCID: PMC10945090 DOI: 10.1111/jcmm.18165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/07/2024] [Accepted: 01/24/2024] [Indexed: 03/19/2024] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is a commonly occurring and highly aggressive urological malignancy characterized by a significant mortality rate. Current therapeutic options for advanced ccRCC are limited, necessitating the discovery of novel biomarkers and therapeutic targets. Carboxypeptidase A4 (CPA4) is a zinc-containing metallocarboxypeptidase with implications in various cancer types, but its role in ccRCC remains unexplored. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized in order to investigate the differential expression patterns of CPA4. The expression of CPA4 in ccRCC patients was further verified using immunohistochemical (IHC) examination of 24 clinical specimens. A network of protein-protein interactions (PPI) was established, incorporating CPA4 and its genes that were expressed differentially. Functional enrichment analyses were conducted to anticipate the contribution of CPA4 in the development of ccRCC. To validate our earlier study, we conducted real-time PCR and cell functional tests on ccRCC cell lines. Our findings revealed that CPA4 is overexpressed in ccRCC, and the higher the expression of CPA4, the worse the clinical outcomes such as TNM stage, pathological stage, histological grade, etc. Moreover, patients with high CPA4 expression had worse overall survival, disease-specific survival and progress-free interval than patients with low expression. The PPI network analysis highlighted potential interactions contributing to ccRCC progression. Functional enrichment analysis indicated the involvement of CPA4 in the regulation of key pathways associated with ccRCC development. Additionally, immune infiltration analysis suggested a potential link between CPA4 expression and immune response in the tumour microenvironment. Finally, cell functional studies in ccRCC cell lines shed light on the molecular mechanisms underlying the role of CPA4 in promoting ccRCC formation. Overall, our study unveils CPA4 as a promising biomarker with prognostic potential in ccRCC. The identified interactions and pathways provide valuable insights into its implications in ccRCC development and offer a foundation for future research on targeted therapies. Further investigation of CPA4's involvement in immune responses may contribute to the development of immunotherapeutic strategies for ccRCC treatment.
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Affiliation(s)
- Kongjia Wang
- Department of UrologyQingdao Municipal HospitalQingdao UniversityQingdaoChina
| | - Yixin Ding
- Department of OncologyThe Affiliated Hospital of Qingdao UniversityQingdaoChina
| | - Yunbo Liu
- Department of UrologyThe Affiliated Hospital of Qingdao UniversityQingdaoChina
| | - Mingyu Ma
- Department of UrologyQingdao Municipal HospitalQingdao UniversityQingdaoChina
| | - Ji Wang
- Department of UrologyQingdao Municipal HospitalQingdao UniversityQingdaoChina
| | - Zengshun Kou
- Department of UrologyQingdao Municipal HospitalQingdao UniversityQingdaoChina
| | - Shuo Liu
- Department of UrologyQingdao Municipal HospitalQingdao UniversityQingdaoChina
| | - Bo Jiang
- Department of UrologyQingdao Municipal HospitalQingdao UniversityQingdaoChina
| | - Sichuan Hou
- Department of UrologyQingdao Municipal HospitalQingdao UniversityQingdaoChina
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Wang HX, Zhao ZP, Du XY, Peng SL, Xu HY, Tang W, Yang L. SLFN11 promotes clear cell renal cell carcinoma progression via the PI3K/AKT signaling pathway. Med Oncol 2024; 41:54. [PMID: 38206539 DOI: 10.1007/s12032-023-02262-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/18/2023] [Indexed: 01/12/2024]
Abstract
SLFN11 is abnormally expressed and associated with survival outcomes in various human cancers. However, the role of SLFN11 in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to investigate the clinical value and potential functions of SLFN11 in ccRCC. Comprehensive bioinformatics analyses were performed using online databases. Quantitative real-time PCR (qPCR) and western blotting were used to validate the expression data. CCK8, flow cytometry analysis, and EdU staining were performed to determine the level of cell proliferation. Flow cytometry analysis was also used to detect cell apoptosis. Wound-healing assay and Transwell assays were performed to assess cell migration and invasion capability, respectively. SLFN11 was overexpressed and was an independent prognostic factor in ccRCC. SLFN11 knockdown inhibited cell proliferation, migration, and invasion and promoted apoptosis. Functional and pathway enrichment analyses suggested that SLFN11 may have an impact on tumorigenesis in ccRCC through regulation of the inflammatory response, the PI3K/AKT signaling pathway and other effectors. Furthermore, SLFN11 knockdown inhibited the phosphorylation of the PI3K/AKT signaling pathway and could be activated by 740 Y-P. Finally, we demonstrated that miR-183 may specifically target SLFN11, and miR-183 expression was correlated with predicted survival. SLFN11 may play a critical role in ccRCC progression and may serve as a novel prognostic biomarker in ccRCC.
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Affiliation(s)
- He-Xi Wang
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Zhi-Peng Zhao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Xiao-Yi Du
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Sen-Lin Peng
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Hao-Yu Xu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Wei Tang
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
| | - Lei Yang
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
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Wang B, Li M, Li R. Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma. Front Oncol 2023; 13:1169395. [PMID: 37091151 PMCID: PMC10113630 DOI: 10.3389/fonc.2023.1169395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 03/17/2023] [Indexed: 04/25/2023] Open
Abstract
Background Identifying Kidney Renal Papillary Cell Carcinoma (KIRP) patients with high-risk, guiding individualized diagnosis and treatment of patients, and identifying effective prognostic targets are urgent problems to be solved in current research on KIRP. Methods In this study, data of multi omics for patients with KIRP were collected from TCGA database, including mRNAs, lncRNAs, miRNAs, data of methylation, and data of gene mutations. Data of multi-omics related to prognosis of patients with KIRP were selected for each omics level. Further, multi omics data related to prognosis were integrated into cluster analysis based on ten clustering algorithms using MOVICS package. The multi omics-based cancer subtype (MOCS) were compared on biological characteristics, immune microenvironmental cell abundance, immune checkpoint, genomic mutation, drug sensitivity using R packages, including GSVA, clusterProfiler, TIMER, CIBERSORT, CIBERSORT-ABS, quanTIseq, MCPcounter, xCell, EPIC, GISTIC, and pRRophetic algorithms. Results The top ten OS-related factors for KIRP patients were annotated. Patients with KIRP were divided into MOCS1, MOCS2, and MOCS3. Patients in the MOCS3 subtype were observed with shorter overall survival time than patients in the MOCS1 and MOCS2 subtypes. MOCS1 was negatively correlated with immune-related pathways, and we found global dysfunction of cancer-related pathways among the three MOCS subtypes. We evaluated the activity profiles of regulons among the three MOCSs. Most of the metabolism-related pathways were activated in MOCS2. Several immune microenvironmental cells were highly infiltrated in specific MOCS subtype. MOCS3 showed a significantly lower tumor mutation burden. The CNV occurrence frequency was higher in MOCS1. As for treatment, we found that these MOCSs were sensitive to different drugs and treatments. We also analyzed single-cell data for KIRP. Conclusion Based on a variety of algorithms, this study determined the risk classifier based on multi-omics data, which could guide the risk stratification and medication selection of patients with KIRP.
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Affiliation(s)
- Baodong Wang
- Department of Nephrology, Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan, China
| | - Mei Li
- Department of Laboratory Medicine, Shanxi Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Taiyuan, China
| | - Rongshan Li
- Department of Nephrology, Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan, China
- *Correspondence: Rongshan Li,
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Ding R, Wei H, Jiang X, Wei L, Deng M, Yuan H. Prognosis and pain dissection of novel signatures in kidney renal clear cell carcinoma based on fatty acid metabolism-related genes. Front Oncol 2022; 12:1094657. [PMID: 36568252 PMCID: PMC9780486 DOI: 10.3389/fonc.2022.1094657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Renal cell carcinoma (RCC) is a malignant tumor that is characterized by the accumulation of intracellular lipid droplets. The prognostic value of fatty acid metabolism-related genes (FMGs) in RCC remains unclear. Alongside this insight, we collected data from three RCC cohorts, namely, The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts, and identified a total of 309 FMGs that could be associated with RCC prognosis. First, we determined the copy number variation and expression levels of these FMGs, and identified 52 overall survival (OS)-related FMGs of the TCGA-KIRC and the E-MTAB-1980 cohort data. Next, 10 of these genes-FASN, ACOT9, MID1IP1, CYP2C9, ABCD1, CPT2, CRAT, TP53INP2, FAAH2, and PTPRG-were identified as pivotal OS-related FMGs based on least absolute shrinkage and selection operator and Cox regression analyses. The expression of some of these genes was confirmed in patients with RCC by immunohistochemical analyses. Kaplan-Meier analysis showed that the identified FMGs were effective in predicting the prognosis of RCC. Moreover, an optimal nomogram was constructed based on FMG-based risk scores and clinical factors, and its robustness was verified by time-dependent receiver operating characteristic analysis, calibration curve analysis, and decision curve analysis. We have also described the biological processes and the tumor immune microenvironment based on FMG-based risk score classification. Given the close association between fatty acid metabolism and cancer-related pain, our 10-FMG signature may also serve as a potential therapeutic target with dual effects on ccRCC prognosis and cancer pain and, therefore, warrants further investigation.
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Affiliation(s)
- Ruifeng Ding
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Huawei Wei
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Xin Jiang
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Liangtian Wei
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, China
| | - Mengqiu Deng
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Hongbin Yuan
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China,*Correspondence: Hongbin Yuan,
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Prognostic Gene Expression-Based Signature in Clear-Cell Renal Cell Carcinoma. Cancers (Basel) 2022; 14:cancers14153754. [PMID: 35954418 PMCID: PMC9367562 DOI: 10.3390/cancers14153754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/21/2022] [Accepted: 07/22/2022] [Indexed: 02/01/2023] Open
Abstract
The inaccuracy of the current prognostic algorithms and the potential changes in the therapeutic management of localized ccRCC demands the development of an improved prognostic model for these patients. To this end, we analyzed whole-transcriptome profiling of 26 tissue samples from progressive and non-progressive ccRCCs using Illumina Hi-seq 4000. Differentially expressed genes (DEG) were intersected with the RNA-sequencing data from the TCGA. The overlapping genes were used for further analysis. A total of 132 genes were found to be prognosis-related genes. LASSO regression enabled the development of the best prognostic six-gene panel. Cox regression analyses were performed to identify independent clinical prognostic parameters to construct a combined nomogram which includes the expression of CERCAM, MIA2, HS6ST2, ONECUT2, SOX12, TMEM132A, pT stage, tumor size and ISUP grade. A risk score generated using this model effectively stratified patients at higher risk of disease progression (HR 10.79; p < 0.001) and cancer-specific death (HR 19.27; p < 0.001). It correlated with the clinicopathological variables, enabling us to discriminate a subset of patients at higher risk of progression within the Stage, Size, Grade and Necrosis score (SSIGN) risk groups, pT and ISUP grade. In summary, a gene expression-based prognostic signature was successfully developed providing a more precise assessment of the individual risk of progression.
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Han W, Fan B, Huang Y, Wang X, Zhang Z, Gu G, Liu Z. Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma. BMC Nephrol 2022; 23:172. [PMID: 35513791 PMCID: PMC9069774 DOI: 10.1186/s12882-022-02801-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/25/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The dysfunction of RNA binding proteins (RBPs) is associated with various inflammation and cancer. The occurrence and progression of tumors are closely related to the abnormal expression of RBPs. There are few studies on RBPs in clear cell renal carcinoma (ccRCC), which allows us to explore the role of RBPs in ccRCC. METHODS We obtained the gene expression data and clinical data of ccRCC from the Cancer Genome Atlas (TCGA) database and extracted all the information of RBPs. We performed differential expression analysis of RBPs. Risk model were constructed based on the differentially expressed RBPs (DERBPs). The expression levels of model markers were examined by reverse transcription-quantitative PCR (RT-qPCR) and analyzed for model-clinical relevance. Finally, we mapped the model's nomograms to predict the 1, 3 and 5-year survival rates for ccRCC patients. RESULTS The results showed that the five-year survival rate for the high-risk group was 40.2% (95% CI = 0.313 ~ 0.518), while the five-year survival rate for the low-risk group was 84.3% (95% CI = 0.767 ~ 0.926). The ROC curves (AUC = 0.748) also showed that our model had stable predictive power. Further RT-qPCR results were in accordance with our analysis (p < 0.05). The results of the independent prognostic analysis showed that the model could be an independent prognostic factor for ccRCC. The results of the correlation analysis also demonstrated the good predictive ability of the model. CONCLUSION In summary, the 4-RBPs (EZH2, RPL22L1, RNASE2, U2AF1L4) risk model could be used as a prognostic indicator of ccRCC. Our study provides a possibility for predicting the survival of ccRCC.
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Affiliation(s)
- Wenkai Han
- Department of Clinical Medicine, Qingdao University, Qingdao, Shandong, 266000, China
| | - Bohao Fan
- Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China
| | - Yongshen Huang
- Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China
| | - Xiongbao Wang
- Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China
| | - Zhao Zhang
- Department of Clinical Medicine, Qingdao University, Qingdao, Shandong, 266000, China.,Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China
| | - Gangli Gu
- Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
| | - Zhao Liu
- Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
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Li M, He M, Xu F, Guan Y, Tian J, Wan Z, Zhou H, Gao M, Chong T. Abnormal expression and the significant prognostic value of aquaporins in clear cell renal cell carcinoma. PLoS One 2022; 17:e0264553. [PMID: 35245343 PMCID: PMC8896691 DOI: 10.1371/journal.pone.0264553] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/12/2022] [Indexed: 12/11/2022] Open
Abstract
Aquaporins (AQPs) are a kind of transmembrane proteins that exist in various organs of the human body. AQPs play an important role in regulating water transport, lipid metabolism and glycolysis of cells. Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the kidney, and the prognosis is worse than other types of renal cell cancer (RCC). The impact of AQPs on the prognosis of ccRCC and the potential relationship between AQPs and the occurrence and development of ccRCC are demanded to be investigated. In this study, we first explored the expression pattern of AQPs by using Oncomine, UALCAN, and HPA databases. Secondly, we constructed protein-protein interaction (PPI) network and performed function enrichment analysis through STRING, GeneMANIA, and Metascape. Then a comprehensive analysis of the genetic mutant frequency of AQPs in ccRCC was carried out using the cBioPortal database. In addition, we also analyzed the main enriched biological functions of AQPs and the correlation with seven main immune cells. Finally, we confirmed the prognostic value of AQPs throughGEPIA and Cox regression analysis. We found that the mRNA expression levels of AQP0/8/9/10 were up-regulated in patients with ccRCC, while those of AQP1/2/3/4/5/6/7/11 showed the opposite. Among them, the expression differences of AQP1/2/3/4/5/6/7/8/9/11 were statistically significant. The differences in protein expression levels of AQP1/2/3/4/5/6 in ccRCC and normal renal tissues were consistent with the change trends of mRNA. The biological functions of AQPs were mainly concentrated in water transport, homeostasis maintenance, glycerol transport, and intracellular movement of sugar transporters. The high mRNA expression levels of AQP0/8/9 were significantly correlated with worse overall survival (OS), while those of AQP1/4/7 were correlated with better OS. AQP0/1/4/9 were prognostic-related factors, and AQP1/9 were independent prognostic factors. In general, this research has investigated the values of AQPs in ccRCC, which could become new survival markers for ccRCC targeted therapy.
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Affiliation(s)
- Mingrui Li
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Minxin He
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Fangshi Xu
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Yibing Guan
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Juanhua Tian
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Ziyan Wan
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Haibin Zhou
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Mei Gao
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Tie Chong
- Department of Urology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
- * E-mail:
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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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10
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Jiang A, Meng J, Gong W, Zhang Z, Gan X, Wang J, Wu Z, Liu B, Qu L, Wang L. Elevated SNRPA1, as a Promising Predictor Reflecting Severe Clinical Outcome via Effecting Tumor Immunity for ccRCC, Is Related to Cell Invasion, Metastasis, and Sunitinib Sensitivity. Front Immunol 2022; 13:842069. [PMID: 35281041 PMCID: PMC8904888 DOI: 10.3389/fimmu.2022.842069] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 01/26/2022] [Indexed: 12/21/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and is associated with poor prognosis and notorious for its immune dysfunction characteristic. SNRPA1 is a spliceosome component responsible for processing pre-mRNA into mRNA, while the biological effect of SNRPA1 in ccRCC remains elusive. The aim of this study was to decipher the effect of SNRPA1 on clinical effect and tumor immunity for ccRCC patients. Multi-databases were collected to evaluate the different expression, prognostic value, DNA methylation, tumor immune microenvironment, and drug sensitivity of SNRPA1 on ccRCC. IHC was utilized to validate the expression and prognostic value of SNRPA1 in ccRCC patients from the SMMU cohort. The knockout expression of SNRPA by sgRNA plasmid inhibited the cell proliferation, migration, and metastasis ability and significantly increased the sensitivity of sunitinib treatment. In addition, we explored the role of SNRPA1 in pan-cancer level. The results indicated that SNRPA1 was differentially expressed in most cancer types. SNRPA1 may significantly influence the prognosis of multiple cancer types, especially in ccRCC patients. Notably, SNRPA1 was significantly correlated with immune cell infiltration and immune checkpoint inhibitory genes. In addition, the aggressive and immune inhibitory effects shown in SNRPA1 overexpression and the effect of SNRPA1 on ccRCC cell line invasion, metastasis, and drug sensitivity in vitro were observed. Moreover, SNRPA1 was related to Myc, MTORC, G2M, E2F, and DNA repair pathways in various cancer types. In all, SNRPA1 may prove to be a new biomarker for prognostic prediction, effect tumor immunity, and drug susceptibility in ccRCC.
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Affiliation(s)
- Aimin Jiang
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Jialin Meng
- Department of Urology, The First Affiliated Hospital of Anhui Medical University; Institute of Urology, Anhui Medical University; Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei, China
| | - Wenliang Gong
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Zhonghua Zhang
- Department of Clinical Pharmacy, No. 988 Hospital of Joint Logistic Support Force, Zhengzhou, China
| | - Xinxin Gan
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Jie Wang
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Zhenjie Wu
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Bing Liu
- Department of Urology, The Third Affiliated Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Le Qu
- Department of Urology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Linhui Wang
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
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MicroRNA as a Biomarker for Diagnostic, Prognostic, and Therapeutic Purpose in Urinary Tract Cancer. Processes (Basel) 2021. [DOI: 10.3390/pr9122136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The incidence of urologic cancers, including kidney, upper tract urothelial, and bladder malignancies, is increasing globally, with a high percentage of cases showing metastasis upon diagnosis and low five-year survival rates. MicroRNA (miRNA), a small non-coding RNA, was found to regulate the expression of oncogenes and tumor suppressor genes in several tumors, including cancers of the urinary system. In the current review, we comprehensively discuss the recently reported up-or down-regulated miRNAs as well as their possible targets and regulated pathways involved in the development, progression, and metastasis of urinary tract cancers. These miRNAs represent potential therapeutic targets and diagnostic/prognostic biomarkers that may help in efficient and early diagnosis in addition to better treatment outcomes.
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12
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The Identification of Zinc-Finger Protein 433 as a Possible Prognostic Biomarker for Clear-Cell Renal Cell Carcinoma. Biomolecules 2021; 11:biom11081193. [PMID: 34439859 PMCID: PMC8392881 DOI: 10.3390/biom11081193] [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: 06/21/2021] [Revised: 07/31/2021] [Accepted: 08/06/2021] [Indexed: 11/18/2022] Open
Abstract
Clear-cell renal cell carcinoma (ccRCC) is the most common and aggressive form of all urological cancers, with poor prognosis and high mortality. At late stages, ccRCC is known to be mainly resistant to chemotherapy and radiotherapy. Therefore, it is urgent and necessary to identify biomarkers that can facilitate the early detection of ccRCC in patients. In this study, the levels of transcripts of ccRCC from The Cancer Genome Atlas (TCGA) dataset were used to identify prognostic biomarkers in this disease. Analyzing the data obtained indicated that the KRAB-ZNF protein is significantly suppressed in clear-cell carcinomas. Furthermore, ZNF433 is differentially expressed in ccRCC in a stage- and histological-grade-specific manner. In addition, ZNF433 expression was correlated with metastasis, with greater node involvement associated with lower ZNF433 expression (p < 0.01) and with a more unsatisfactory overall survival outcome (HR, 0.45; 95% CI, 0.33–0.6; p = 8.5 × 10−8). Since ccRCC is characterized by mutations in proteins that alter epigenetic modifications and /or chromatin remodeling, we examined the expression of ZNF433 transcripts in ccRCC with wildtype and mutated forms of BAP1, KDMC5, MTOR, PBRM1, SETD2, and VHL. Analysis revealed that ZNF433 expression was significantly reduced in ccRCC with mutations in the BAP1, SETD2, and KDM5C genes (p < 0.05). In addition, the ZNF433 promoter region was highly methylated, and hypermethylation was significantly associated with mRNA suppression (p < 2.2 × 10−16). In silico analysis of potential ZNF target genes found that the largest group of target genes are involved in cellular metabolic processes, which incidentally are particularly impaired in ccRCC. It was concluded from this study that gene expression of ZNF433 is associated with cancer progression and poorer prognosis, and that ZNF433 behaves in a manner that suggests that it is a prognostic marker and a possible tumor-suppressor gene in clear-cell renal cell carcinoma.
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13
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Wu Z, Zhang Y, Chen X, Tan W, He L, Peng L. Characterization of the Prognostic Values of the CXCR1-7 in Clear Cell Renal Cell Carcinoma (ccRCC) Microenvironment. Front Mol Biosci 2020; 7:601206. [PMID: 33324682 PMCID: PMC7724088 DOI: 10.3389/fmolb.2020.601206] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/23/2020] [Indexed: 12/15/2022] Open
Abstract
Background: As cancer immunotherapy has become a hot research topic, the values of CXC chemokine receptors (CXCRs) in tumor microenvironment have been increasingly realized. More and more evidence showed that the aberrant expression of CXCRs is closely related to the prognosis of various cancers. However, prognostic values and the exact roles of different CXCRs in clear cell renal cell carcinoma (ccRCC) have not yet been elucidated. Methods: To further evaluate the potential of seven CXCRs as prognostic biomarkers for ccRCC, multiple online analysis tools, including ONCOMINE, UALCAN (TCGA dataset), Kaplan–Meier Plotter, MethSurv, cBioPortal, GEPIA, Metascape, and TIMER databases, were utilized in our research. Results: The mRNA expression of CXCR4/6/7 was significantly increased in ccRCC patients, and all CXCRs are remarkably related to tumor stage or grade of ccRCC. Higher levels of CXCR3/4/5/6 expression were correlated with worse overall survival (OS) in patients with ccRCC, while higher expression of CXCR2 was associated with better OS. 23.14% mutation rate (118/510) of CXCR1-7 was observed in ccRCC patients, and the genetic alterations in CXCRs were related to worse OS and progression-free survival in ccRCC patients. Additionally, 53 CpGs of CXCR1-7 showed significant prognostic values. For functional enrichment, our results showed that CXCRs and their similar genes may be involved in cancer-associated pathways, immune process, and angiogenesis, etc. Besides, CXCRs were significantly correlated with multiple immune cells (e.g., CD8+ T cell, CD4+ cell, and dendritic cell). Conclusion: This study explored the potential prognostic values and roles of the CXCRs in ccRCC microenvironment. Our results suggested that CXCR4 and CXCR6 could be the prognostic biomarkers for the patients with ccRCC.
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Affiliation(s)
- Zhulin Wu
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Yingzhao Zhang
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xiang Chen
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Wanjun Tan
- Shenzhen Futian Center for Chronic Disease Control, Shenzhen, China
| | - Li He
- Department of Oncology and Haematology, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Lisheng Peng
- Department of Science and Education, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
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14
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A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma. Hereditas 2020; 157:38. [PMID: 32883362 PMCID: PMC7470605 DOI: 10.1186/s41065-020-00152-y] [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: 03/28/2020] [Accepted: 08/26/2020] [Indexed: 12/11/2022] Open
Abstract
Background Kidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients. Results We identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set. Conclusion Our findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients.
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15
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Hui Y, Wei PJ, Xia J, Wang YT, Zheng CH. MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks. BMC Med Genomics 2019; 12:140. [PMID: 31888623 PMCID: PMC6936061 DOI: 10.1186/s12920-019-0582-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 09/10/2019] [Indexed: 01/09/2023] Open
Abstract
Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. Methods We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. Results We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. Conclusions We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.
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Affiliation(s)
- Ying Hui
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Pi-Jing Wei
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Junfeng Xia
- Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Yu-Tian Wang
- School of Software Engineering, Qufu Normal University, Qufu, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China.
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16
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Mihaylov I, Kańduła M, Krachunov M, Vassilev D. A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models. Biol Direct 2019; 14:22. [PMID: 31752974 PMCID: PMC6868770 DOI: 10.1186/s13062-019-0249-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 09/20/2019] [Indexed: 12/17/2022] Open
Abstract
Background Recently high-throughput technologies have been massively used alongside clinical tests to study various types of cancer. Data generated in such large-scale studies are heterogeneous, of different types and formats. With lack of effective integration strategies novel models are necessary for efficient and operative data integration, where both clinical and molecular information can be effectively joined for storage, access and ease of use. Such models, combined with machine learning methods for accurate prediction of survival time in cancer studies, can yield novel insights into disease development and lead to precise personalized therapies. Results We developed an approach for intelligent data integration of two cancer datasets (breast cancer and neuroblastoma) − provided in the CAMDA 2018 ‘Cancer Data Integration Challenge’, and compared models for prediction of survival time. We developed a novel semantic network-based data integration framework that utilizes NoSQL databases, where we combined clinical and expression profile data, using both raw data records and external knowledge sources. Utilizing the integrated data we introduced Tumor Integrated Clinical Feature (TICF) − a new feature for accurate prediction of patient survival time. Finally, we applied and validated several machine learning models for survival time prediction. Conclusion We developed a framework for semantic integration of clinical and omics data that can borrow information across multiple cancer studies. By linking data with external domain knowledge sources our approach facilitates enrichment of the studied data by discovery of internal relations. The proposed and validated machine learning models for survival time prediction yielded accurate results. Reviewers This article was reviewed by Eran Elhaik, Wenzhong Xiao and Carlos Loucera.
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Affiliation(s)
- Iliyan Mihaylov
- Faculty of Mathematics and Informatics, Sofia University, "St. Kliment Ohridski", 5 James Bourchier Blvd., Sofia, 1164, Bulgaria
| | - Maciej Kańduła
- Department of Biotechnology, Boku University, Vienna, 1180, Austria.,Institute for Machine Learning, Johannes Kepler University, Linz, 4040, Austria
| | - Milko Krachunov
- Faculty of Mathematics and Informatics, Sofia University, "St. Kliment Ohridski", 5 James Bourchier Blvd., Sofia, 1164, Bulgaria
| | - Dimitar Vassilev
- Faculty of Mathematics and Informatics, Sofia University, "St. Kliment Ohridski", 5 James Bourchier Blvd., Sofia, 1164, Bulgaria.
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17
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Volkmann A, De Bin R, Sauerbrei W, Boulesteix AL. A plea for taking all available clinical information into account when assessing the predictive value of omics data. BMC Med Res Methodol 2019; 19:162. [PMID: 31340753 PMCID: PMC6657034 DOI: 10.1186/s12874-019-0802-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 07/11/2019] [Indexed: 12/22/2022] Open
Abstract
Background Omics data can be very informative in survival analysis and may improve the prognostic ability of classical models based on clinical risk factors for various diseases, for example breast cancer. Recent research has focused on integrating omics and clinical data, yet has often ignored the need for appropriate model building for clinical variables. Medical literature on classical prognostic scores, as well as biostatistical literature on appropriate model selection strategies for low dimensional (clinical) data, are often ignored in the context of omics research. The goal of this paper is to fill this methodological gap by investigating the added predictive value of gene expression data for models using varying amounts of clinical information. Methods We analyze two data sets from the field of survival prognosis of breast cancer patients. First, we construct several proportional hazards prediction models using varying amounts of clinical information based on established medical knowledge. These models are then used as a starting point (i.e. included as a clinical offset) for identifying informative gene expression variables using resampling procedures and penalized regression approaches (model based boosting and the LASSO). In order to assess the added predictive value of the gene signatures, measures of prediction accuracy and separation are examined on a validation data set for the clinical models and the models that combine the two sources of information. Results For one data set, we do not find any substantial added predictive value of the omics data when compared to clinical models. On the second data set, we identify a noticeable added predictive value, however only for scenarios where little or no clinical information is included in the modeling process. We find that including more clinical information can lead to a smaller number of selected omics predictors. Conclusions New research using omics data should include all available established medical knowledge in order to allow an adequate evaluation of the added predictive value of omics data. Including all relevant clinical information in the analysis might also lead to more parsimonious models. The developed procedure to assess the predictive value of the omics data can be readily applied to other scenarios. Electronic supplementary material The online version of this article (10.1186/s12874-019-0802-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexander Volkmann
- Institute for Medical Information Processing, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, Munich, 81377, Germany. .,Chair of Statistics, School of Business and Economics, Humboldt-Universität zu Berlin, Spandauer Straße 1, Berlin, 10178, Germany.
| | - Riccardo De Bin
- Department of Mathematics, University of Oslo, Moltke Moes vei 35, Oslo, 0851, Norway
| | - Willi Sauerbrei
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Straße 26, Freiburg, 79104, Germany
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, Munich, 81377, Germany
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18
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Liu J, Liu B, Guo Y, Chen Z, Sun W, Gao W, Wu H, Wang Y. Key miRNAs and target genes played roles in the development of clear cell renal cell carcinoma. Cancer Biomark 2019; 23:279-290. [PMID: 30198869 DOI: 10.3233/cbm-181558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Clear cell renal cell carcinoma (CCRCC) is the most aggressive form of renal cell carcinoma (RCC). OBJECTIVE This study was aimed to identify the differentially expressed miRNAs and target genes in CCRCC. METHODS The miRNA and mRNA next-generation sequencing data were downloaded from The Cancer Genome Atlas (TCGA) dataset. Differential expression analysis was performed, followed by correlation analysis of miRNA-mRNA. Functional enrichment and survival analysis was also performed. RESULTS Seven hundred and eighty-seven patients with CCRCC from TCGA data portal were included in this study. A total of 52 differentially expressed miRNAs were identified in CCRCC. Then 2361 differentially expressed genes (DEGs) were identified. Prediction analysis and correlation analysis revealed that 89 miRNA-mRNA pairs were not only predicted by algorithms but also had a significant inverse relationship. Several differentially expressed miRNAs such as hsa-mir-501 and their target genes including AK1, SLC25A15 and PCDHGC3 had a significant prognostic value for CCRCC patients. CONCLUSIONS Alterations of differentially expressed miRNAs and target genes may be involved in the development of CCRCC and can be considered as the prognostic markers for CCRCC.
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19
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Shi X, Ma S, Huang Y. Promoting sign consistency in the cure model estimation and selection. Stat Methods Med Res 2019; 29:15-28. [PMID: 30600776 DOI: 10.1177/0962280218820356] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In survival analysis, when a subset of subjects has extremely long survival, the two-part cure rate model has been commonly adopted. In the two-part model, the first part is for a binary response and describes the probability of cure. The second part is for a survival response and describes the probability of survival. Despite their intuitive interconnections, most of the existing works estimate the two parts without any constraint. The existing works on proportionality promote similarity in magnitudes (i.e. quantitative similarity) and can be too restrictive. In this study, for the two-part cure rate model, we propose imposing a sign-based penalty to promote similarity in signs (i.e. qualitative similarity). The proposed strategy can be more informative than those that neglect the two-part interconnections and be less restrictive than the existing proportionality works. Penalty is also imposed to select relevant variables and accommodate high-dimensional data. Numerical studies, including simulation and two data analyses, demonstrate the advantageous performance of the proposed approach.
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Affiliation(s)
- Xingjie Shi
- Department of Statistics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China
| | - Shuangge Ma
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Yuan Huang
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
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20
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Cubuk C, Hidalgo MR, Amadoz A, Pujana MA, Mateo F, Herranz C, Carbonell-Caballero J, Dopazo J. Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. Cancer Res 2018; 78:6059-6072. [PMID: 30135189 DOI: 10.1158/0008-5472.can-17-2705] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 01/31/2018] [Accepted: 08/17/2018] [Indexed: 11/16/2022]
Abstract
Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies.Significance: Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. Cancer Res; 78(21); 6059-72. ©2018 AACR.
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Affiliation(s)
- Cankut Cubuk
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla, Spain
| | - Marta R Hidalgo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla, Spain
| | | | - Miguel A Pujana
- ProCURE, Catalan Institute of Oncology. Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, Barcelona, Spain
| | - Francesca Mateo
- ProCURE, Catalan Institute of Oncology. Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, Barcelona, Spain
| | - Carmen Herranz
- ProCURE, Catalan Institute of Oncology. Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, Barcelona, Spain
| | | | - Joaquin Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla, Spain. .,Functional Genomics Node, INB-ELIXIR-es, FPS, Hospital Virgen del Rocío, Sevilla, Spain.,Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Sevilla, Spain
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21
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Abstract
Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. While survival rates are high if RCC is diagnosed when still confined to the kidney and treated definitively, there are no specific diagnostic screening tests available and symptoms are rare in early stages of the disease. Management of advanced RCC has changed significantly with the advent of targeted therapies, yet survival is usually increased by months due to acquired resistance to these therapies. DNA methylation, the covalent addition of a methyl group to a cytosine, is essential for normal development and transcriptional regulation, but becomes altered commonly in cancer. These alterations result in broad transcriptional changes, including in tumor suppressor genes. Because DNA methylation is one of the earliest molecular changes in cancer and is both widespread and stable, its role in cancer biology, including RCC, has been extensively studied. In this review, we examine the role of DNA methylation in RCC disease etiology and progression, the preclinical use of DNA methylation alterations as diagnostic, prognostic and predictive biomarkers, and the potential for DNA methylation-directed therapies.
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Affiliation(s)
- Brittany N Lasseigne
- HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, AL, 35806-2908, USA.
| | - James D Brooks
- Department of Urology, Stanford University Medical Center, 300 Pasteur Drive, Stanford, CA, 94305-5118, USA
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22
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Du M, Giridhar KV, Tian Y, Tschannen MR, Zhu J, Huang CC, Kilari D, Kohli M, Wang L. Plasma exosomal miRNAs-based prognosis in metastatic kidney cancer. Oncotarget 2017; 8:63703-63714. [PMID: 28969022 PMCID: PMC5609954 DOI: 10.18632/oncotarget.19476] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 06/19/2017] [Indexed: 11/25/2022] Open
Abstract
Plasma exosomal miRNAs were evaluated for prognosis in an initial set of 44 metastatic renal cell cancer (mRCC) patients by RNA sequencing. Among ∼3.49 million mappable reads per patient, miRNAs accounted for 93.1% of the mapped RNAs. 227 miRNAs with high abundance were selected for survival analysis. Cox regression analysis identified association of 6 miRNAs with overall survival (OS) (P<0.01, False discovery rate (FDR) < 0.3). Five of the associated miRNAs were quantified in an independent follow-up cohort of 65 mRCC patients by TaqMan-based miRNA assays. Kaplan-Meier analysis confirmed the significant OS association of three miRs; miR-let-7i-5p (P=0.018, HR=0.49, 95% CI=0.21-0.84), miR-26a-1-3p (P=0.025, HR=0.43, 95% CI=0.10-0.84) and miR-615-3p (P=0.0007, HR=0.36, 95% CI=0.11-0.54). A multivariate analysis of miR-let-7i-5p with the clinical factor-based Memorial Sloan-Kettering Cancer Center (MSKCC) score improved survival prediction from an area under the curve (AUC) of 0.58 for MSKCC score to an average AUC of 0.64 across 48-month follow-up time. The multivariate model was able to define a high-risk group with median survival of 14 months and low risk group of 39 months (P=0.0002, HR=3.43, 95%CI, 2.73-24.15). Further validation of miRNA-based prognostic biomarkers are needed to improve current clinic-pathologic based prognostic models in patients with mRCC.
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Affiliation(s)
- Meijun Du
- Department of Pathology and MCW Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Yijun Tian
- Department of Pathology and MCW Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA.,Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Michael R Tschannen
- Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jing Zhu
- Department of Pathology and MCW Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Chiang-Ching Huang
- Department of Biostatistics, University of Wisconsin, Milwaukee, WI, USA
| | - Deepak Kilari
- Department of Oncology, Medical College of Wisconsin and Milwaukee VA Medical Center, Milwaukee, WI, USA
| | - Manish Kohli
- Department of Oncology, Mayo Clinic, Rochester, MN, USA
| | - Liang Wang
- Department of Pathology and MCW Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
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