scholarly journals Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method

2019 ◽  
Author(s):  
Ka-Lok Ng ◽  
Y-h Taguchi
Keyword(s):  
Feature Extraction ◽  
Cell Carcinoma ◽  
Complex Disease ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Tensor Decomposition ◽  
Renal Clear Cell Carcinoma ◽  
Integrated Analysis ◽  
Unsupervised Feature Extraction

AbstractCancer is a highly complex disease caused by multiple genetic factors. MicroRNA (miRNA) and mRNA expression profiles are useful for identifying prognostic biomarkers for cancer. Kidney renal clear cell carcinoma (KIRC), which accounts for more than 70% of all renal malignant tumour cases, was selected for our analysis.Traditional methods of identifying cancer prognostic markers may not be accurate. Tensor decomposition (TD) is a useful method uncovering the underlying low-dimensional structures in the tensor. The TD-based unsupervised feature extraction method was applied to analyse mRNA and miRNA expression profiles. Biological annotations of the prognostic miRNAs and mRNAs were examined utilizing the pathway and oncogenic signature databases DIANA-miRPath and MSigDB.TD identified the miRNA signatures and the associated genes. These genes were found to be involved in cancer-related pathways, and 23 genes were significantly correlated with the survival of KIRC patients. We demonstrated that the results are robust and not highly dependent upon the databases we selected. Compared with traditional supervised methods tested, TD achieves much better performance in selecting prognostic miRNAs and mRNAs.These results suggest that integrated analysis using the TD-based unsupervised feature extraction technique is an effective strategy for identifying prognostic signatures in cancer studies.

scholarly journals Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ka-Lok Ng ◽  
Y.-H. Taguchi
Keyword(s):  
Feature Extraction ◽  
Cell Carcinoma ◽  
Complex Disease ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Tensor Decomposition ◽  
Renal Clear Cell Carcinoma ◽  
Integrated Analysis ◽  
Unsupervised Feature Extraction

Abstract Cancer is a highly complex disease caused by multiple genetic factors. MicroRNA (miRNA) and mRNA expression profiles are useful for identifying prognostic biomarkers for cancer. Kidney renal clear cell carcinoma (KIRC), which accounts for more than 70% of all renal malignant tumour cases, was selected for our analysis. Traditional methods of identifying cancer prognostic markers may not be accurate. Tensor decomposition (TD) is a useful method uncovering the underlying low-dimensional structures in the tensor. The TD-based unsupervised feature extraction method was applied to analyse mRNA and miRNA expression profiles. Biological annotations of the prognostic miRNAs and mRNAs were examined utilizing the pathway and oncogenic signature databases DIANA-miRPath and MSigDB. TD identified the miRNA signatures and the associated genes. These genes were found to be involved in cancer-related pathways, and 23 genes were significantly correlated with the survival of KIRC patients. We demonstrated that the results are robust and not highly dependent upon the databases we selected. Compared with traditional supervised methods tested, TD achieves much better performance in selecting prognostic miRNAs and mRNAs. These results suggest that integrated analysis using the TD-based unsupervised feature extraction technique is an effective strategy for identifying prognostic signatures in cancer studies.

scholarly journals LncRNA CDKN2B-AS1 stabilized by IGF2BP3 drives the malignancy of renal clear cell carcinoma through epigenetically activating NUF2 transcription

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Xina Xie ◽  
Jiatian Lin ◽  
Xiaoqin Fan ◽  
Yuantang Zhong ◽  
Yequn Chen ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Survival Data ◽  
Kinase Inhibitor ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Binding Protein ◽  
Renal Clear Cell Carcinoma ◽  
Receiver Operating Curve ◽  
Migration And Invasion ◽  
Integrated Analysis

AbstractBecause of the lack of sensitivity to radiotherapy and chemotherapy, therapeutic options for renal clear cell carcinoma (KIRC) are scarce. Long noncoding RNAs (lncRNAs) play crucial roles in the progression of cancer. However, their functional roles and upstream mechanisms in KIRC remain largely unknown. Exploring the functions of potential essential lncRNAs may lead to the discovery of novel targets for the diagnosis and treatment of KIRC. Here, according to the integrated analysis of RNA sequencing and survival data in TCGA-KIRC datasets, cyclin-dependent kinase inhibitor 2B antisense lncRNA (CDKN2B-AS1) was discovered to be the most upregulated among the 14 lncRNAs that were significantly overexpressed in KIRC and related to shorter survival. Functionally, CDKN2B-AS1 depletion suppressed cell proliferation, migration, and invasion both in vitro and in vivo. Mechanistically, CDKN2B-AS1 exerted its oncogenic activity by recruiting the CREB-binding protein and SET and MYND domain-containing 3 epigenetic-modifying complex to the promoter region of Ndc80 kinetochore complex component (NUF2), where it epigenetically activated NUF2 transcription by augmenting local H3K27ac and H3K4me3 modifications. Moreover, we also showed that CDKN2B-AS1 interacted with and was stabilized by insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), an oncofetal protein showing increased levels in KIRC. The Kaplan–Meier method and receiver operating curve analysis revealed that patients whose IGF2BP3, CDKN2B-AS1 and NUF2 are all elevated showed the shortest survival time, and the combined panel (containing IGF2BP3, CDKN2B-AS1, and NUF2) possessed the highest accuracy in discriminating high-risk from low-risk KIRC patients. Thus, we conclude that the stabilization of CDKN2B-AS1 by IGF2BP3 drives the malignancy of KIRC through epigenetically activating NUF2 transcription and that the IGF2BP3/CDKN2B-AS1/NUF2 axis may be an ideal prognostic and diagnostic biomarker and therapeutic target for KIRC.

scholarly journals Identification of Tumor Microenvironment-Related Genes in Kidney Renal Clear Cell Carcinoma Patients via Bioinformatic Analysis

2021 ◽  
Author(s):  
Rongjiong Zheng ◽  
Yaosen SHao ◽  
Mingming Wang ◽  
Yeli Tang ◽  
Meiling Hu
Keyword(s):  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment

Abstract BackgroundTumor microenvironment has been implicated in the development and progression of cancers. However, the prognostic significance of tumor microenvironment-related genes in kidney renal clear cell carcinoma (KIRC) remains unclear. MethodsIn this study, we obtained and analyzed gene expression profiles from The Cancer Genome Atlas database. Stromal and immune scores were calculated based on the ESTIMATE algorithm. ResultsIn the discovery series of 537 patients, we identified a list of differentially expressed genes which was significantly associated with prognosis in KIRC patients. Protein-protein interaction networks and functional enrichment analysis were both performed, indicating that these identified genes were related to the immune response. ConclusionsThe tumor microenvironment-related genes could serve as the potential biomarkers for KIRC.

scholarly journals Integrated analysis of the underlying immunomodulator and survival signature of STON1 gene in kidney renal clear cell carcinoma

2021 ◽  
Author(s):  
Axiu Zheng ◽  
Jianrong Bai ◽  
Yanping Ha ◽  
Bingshu Wang ◽  
Yuan Zou ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Clinicopathological Characteristics ◽  
Risk Scores ◽  
Integrated Analysis

Abstract Background Stonin 1 (STON1) is an endocytic protein but its role in cancer remains unclear. Here, we investigated the role of STON1 in kidney renal clear cell carcinoma (KIRC). Methods We undertook bioinformatics analyses of a series of public databases to determine the expression and clinical significance of STON1 in KIRC and focused on STON1-related immunomodulator and survival signatures. A nomogram model integrating clinicopathological characteristics and risk scores for KIRC was established and validated. Results Through TGCA and GEO databases, STON1 mRNA was found to be significantly downregulated in KIRC compared with normal controls, and decreased STON1 was related to grade, TNM stage, distant metastasis, and vital status of KIRC. Furthermore, OncoLnc, UALCAN, Kaplan–Meier, and GEPIA2 analyses supported that KIRC patients with high STON1 expression had better overall survival. STON1 was positively associated with mismatch proteins including MLH1, PMS2, MSH2, MSH6 and EpCAM, and was negatively correlated with tumor mutational burden. Interestingly, arm-level deletion of STON1 was clearly related to the abundance of immune cells and the infiltration abundance in the majority of 26 immune cell types that were positively related to STON1 mRNA level in the TIMER database. The TISIDB database revealed 21 immunostimulators and 10 immunoinhibitors that were obviously related to STON1 in KIRC. In univariate and multivariate Cox regression analyses, CTLA4 , KDR , LAG3 , PDCD1 , HHLA2 , TNFRSF25 , and TNFSF14 were screened to establish a risk score model, and the low-risk group had a better prognosis for KIRC. Furthermore, a nomogram integrating clinicopathological characteristics and risk score had better accuracy and practicability in predicating the survival of KIRC patients. Conclusions Decreased STON1 expression in KIRC leads to clinical progression and poor survival. Mechanically, loss of STON1 is associated with the aberrant immune microenvironment in KIRC. Integrated clinicopathological characteristics and risk score derived from STON1 -related immunomodulators can aid the prediction of KIRC survival.

scholarly journals Identification of Candidate Drugs for Heart Failure using Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Integrated Analysis of Gene Expression between Heart Failure and DrugMatrix Datasets

2017 ◽  
Author(s):  
Y-h. Taguchi
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Feature Extraction ◽  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Unsupervised Feature Extraction

AbstractIdentifying drug target genes in gene expression profiles is not straightforward. Because a drug targets not mRNAs but proteins, mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I apply tensor decomposition-based unsupervised feature extraction to the integrated analysis of gene expression between heart failure and the DrugMatrix dataset where comprehensive data on gene expression during various drug treatments of rats were reported. I found that this strategy, in a fully unsupervised manner, enables us to identify a combined set of genes and compounds, for which various associations with heart failure were reported.

Abstract 3438: Integrated analysis of germline and somatic variants in renal clear cell carcinoma

2014 ◽  
Author(s):  
Jiayin Wang ◽  
Charles Lu ◽  
Mingchao Xie ◽  
Piyush Tripathi ◽  
Michael McLellan ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Integrated Analysis

scholarly journals Universal nature of drug treatment responses in drug-tissue-wide model-animal experiments using tensor decomposition-based unsupervised feature extraction

2020 ◽  
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Feature Extraction ◽  
Drug Treatment ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Model Animal ◽  
Multiple Drugs ◽  
Unsupervised Feature Extraction

ABSTRACTGene expression profiles of tissues treated with drugs have recently been used to infer clinical outcomes. Although this method is often successful from the application point of view, gene expression altered by drugs is rarely analyzed in detail, because of the extremely large number of genes involved. Here, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to the gene expression profiles of 24 mouse tissues treated with 15 drugs. TD-based unsupervised FE enabled identification of the common effects of 15 drugs including an interesting universal feature: these drugs affect genes in a gene-group-wide manner and were dependent on three tissue types (neuronal, muscular, and gastroenterological). For each tissue group, TD-based unsupervised FE enabled identification of a few tens to a few hundreds of genes affected by the drug treatment. These genes are distinctly expressed between drug treatments and controls as well as between tissues in individual tissue groups and other tissues. We also validated the assignment of genes to individual tissue groups using multiple enrichment analyses. We conclude that TD-based unsupervised FE is a promising method for integrated analysis of gene expression profiles from multiple tissues treated with multiple drugs in a completely unsupervised manner.

scholarly journals Identification of Potential Biomarkers For The Prognosis, Diagnosis, And Targeted Therapy in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Fang Cheng ◽  
Qiang Li ◽  
Jinglin Wang ◽  
Yumei Wang ◽  
Zhendi Wang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Signaling Pathway ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Reduction Process ◽  
Renal Clear Cell Carcinoma ◽  
Biological Functions ◽  
Hub Genes

Abstract Background: Kidney renal clear cell carcinoma (KIRC) is the most common renal cell carcinoma types. This work aims to find potential diagnostic biomarkers and explore the biological functions related to the prognosis of KIRC. Method: First, Gene expression profiles of GSE15641, GSE72304, GSE71963, GSE53757, and GSE36895 from GEO database. Differentially expressed genes (DEGs) were identified by the limma package in R software. Next, gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were performed. Then protein-protein interaction (PPI) and hub genes were visualized by Cytoscape with STRING database. Then, we evaluate the predictive potential of hub genes expressions in KIRC with TCGA data. In addition, the relevant biological functions were identified using GSEA. Finally, we examined the differences of hub genes expression between multiple tumor tissues and normal tissues.Results: A total of 141 DEGs (including 99 upregulated and 42 downregulated genes) were identified. GO analysis indicated that DEGs were mainly involved in oxidation-reduction process and response to hypoxia. The KEGG analysis primarily related to PPAR signaling pathway, and HIF-1 signaling pathway. Moreover, the PPI analysis revealed 5 hub genes (AOX1, ALDH6A1, ABAT, HADH, and PCCA). The 5 hub genes were significantly correlated with KIRC progression and might have prognostic significance for KIPC patients. And low expression of the hub genes associated biological pathways were enriched in the NF-KB activation, focal adhesion, and JAK-STAT signaling pathway, respectively. Conclusion: Our study demonstrated that AOX1, ALDH6A1, ABAT, HADH, and PCCA can be used as prognostic biomarkers for KIRC.

scholarly journals N6-Methyladenosine Writer Gene ZC3H13 Predicts Immune Phenotype and Therapeutic Opportunities in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Tao Guo ◽  
Hongxiang Duan ◽  
Jinbo Chen ◽  
Jinhui Liu ◽  
Belaydi Othmane ◽  
...  
Keyword(s):  
Targeted Therapy ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Cancer Specific Survival

BackgroundAlthough the RNA modification N6-methyladenosine ZC3H13 has been found to play vital regulatory roles in many types of cancers, its role in predicting the tumor immune microenvironment (TME) and response to immune checkpoint blockade (ICB) in kidney renal clear cell carcinoma (KIRC) remains unclear.MethodsWe comprehensively analyzed the expression, prognostic significance and immunological role of ZC3H13 in pan-cancers and systematically correlated ZC3H13 with TME cell-infiltration, ICB response and targeted therapy in KIRC. The data were collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx), Broad Institute Cancer Cell Line Encyclopedia (CCLE) and DrugBank database. Also, we performed RNA sequencing (RNA-seq) of 46 renal cell carcinoma tissues and 11 adjacent normal tissues to validate our result. All analyses were implemented using R software, version 3.6.3.ResultsZC3H13 was significantly differentially expressed in most tumors. However, its expression profiles and prognostic significance were consistent only in KIRC, regardless of overall survival, progression-free survival and cancer-specific survival. Additionally, ZC3H13 expression was correlated with clinicopathological factors in KIRC. Furthermore, we found that ZC3H13 might shape a noninflamed phenotype and could predict a lower response to ICB in KIRC. These results could be validated in our own RNA-seq data. Tumor mutation burden (TMB) was significantly higher in the low ZC3H13 group. Finally, we found that ZC3H13 could predict the sensitivity of targeted therapy for KIRC.ConclusionsZC3H13 might shape a noninflamed phenotype in KIRC. Moreover, ZC3H13 could predict the prognosis and clinical response of ICB and the sensitivity to targeted therapies in KIRC.

Delayed Pancreatic Metastasis of Renal Clear Cell Carcinoma

2013 ◽  
Vol 13 (2) ◽  
pp. 79-80
Author(s):  
Zane Simtniece ◽  
Gatis Kirsakmens ◽  
Ilze Strumfa ◽  
Andrejs Vanags ◽  
Maris Pavars ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Surgical Treatment ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Primary Tumour ◽  
Renal Clear Cell Carcinoma ◽  
Pancreatic Metastasis ◽  
Distal Pancreatic Resection

Abstract Here, we report surgical treatment of a patient presenting with pancreatic metastasis (MTS) of renal clear cell carcinoma (RCC) 11 years after nephrectomy. RCC is one of few cancers that metastasise in pancreas. Jaundice, abdominal pain or gastrointestinal bleeding can develop; however, asymptomatic MTS can be discovered by follow-up after removal of the primary tumour. The patient, 67-year-old female was radiologically diagnosed with a clinically silent mass in the pancreatic body and underwent distal pancreatic resection. The postoperative period was smooth. Four months after the surgery, there were no signs of disease progression.

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