The Emerging Role of Histone Demethylases in Renal Cell Carcinoma

Guo, Xiaoqiang; Zhang, Qiaoxia

doi:10.15586/jkcvhl.2017.56

Cited by 31 publications

(24 citation statements)

References 55 publications

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“…The set of constraints (12)(13)(14)(15)(16)(17)(18) ensures that all ancestor-descendant pairs of nodes in T S represent high-confidence temporal orderings. Specifically, constraint (18) eliminates from consideration all such pairs with insufficient support from tumor graphs.…”

Section: Ordering Nodes Into a Conserved Evolutionary Trajectory Treementioning

confidence: 99%

“…Specifically, constraint (18) eliminates from consideration all such pairs with insufficient support from tumor graphs. We introduce auxiliary variables P A u,v,w , set to 1 if u is an ancestor of w and w is an ancestor of v and set to 0 otherwise -by constraints (12), (13) and (14). A parent of a node is its ancestor by default, by constraint (15).…”

Section: Ordering Nodes Into a Conserved Evolutionary Trajectory Treementioning

confidence: 99%

“…Additionally, CONETT also detected two conserved evolutionary trajectories terminating with a sequence alteration in gene KDM5C that collectively cover 80% of tumors where it is present: VHL & loss 3p → gain 5q → KDM5C in 8 tumors and VHL → PBRM1 → KDM5C in 9 tumors; these trajectories were not reported in the original study. Note that KDM5C is a chromatin modifier, similar to BAP1; its dysregulation alters the activity of many other genes [12]. Additionally, VHL → PBRM1 → TSC1 is found in 9 tumors, representing 75% tumors where TSC1 is present.…”

Section: Conserved Evolutionary Trajectories In Ccrccmentioning

confidence: 99%

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Identification of Conserved Evolutionary Trajectories in Tumors

Hodzic

Shrestha

Malikić

et al. 2020

Preprint

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Motivation: As multi-region, time-series, and single cell sequencing data become more widely available, it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics). Results: In this paper, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 nonsmall-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories. Availability: CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.

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Section: Ordering Nodes Into a Conserved Evolutionary Trajectory Treementioning

confidence: 99%

Section: Ordering Nodes Into a Conserved Evolutionary Trajectory Treementioning

confidence: 99%

Section: Conserved Evolutionary Trajectories In Ccrccmentioning

confidence: 99%

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Identification of Conserved Evolutionary Trajectories in Tumors

Hodzic

Shrestha

Malikić

et al. 2020

Preprint

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“…Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system, accounting for 2%–3% of adult malignancies, 1 and more than 100,000 people die of kidney cancer every year worldwide. 2 The most common subtype of RCC is clear cell renal cell carcinoma (ccRCC), which is associated with high morbidity and poor prognosis. 3 …”

Section: Introductionmentioning

confidence: 99%

Identification and validation of a four-miRNA(miRNA-21-5p, miRNA-9-5p, miR-149-5p, and miRNA-30b-5p) prognosis signature in clear cell renal cell carcinoma

Xie¹,

Lv²,

Liu³

et al. 2018

CMAR

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PurposeClear cell renal cell carcinoma (ccRCC) is one of the most common cancers with high mortality worldwide. However, biomarkers for predicting prognosis in ccRCC are limited. In this study, we attempted to identify potential prognostic biomarkers of ccRCC.MethodsClinical information and the preprocessed ccRCC mature miRNA expression profiles in The Cancer Genome Atlas database were downloaded from UCSC Xena. The miRNAs differentially expressed between ccRCCs and matched normal tissues were analyzed using the “limma” package. A miRNA-based signature was constructed using the multivariate Cox regression model with prognosis index (PI) formula. Patients with ccRCC were divided into low-risk and high-risk subgroups according to median PI. The survival times were compared between the two groups using Kaplan–Meier analysis with log-rank test. The training set was used to construct a miRNA-based signature for predicting prognosis. The test set was used to verify the signature. Target gene prediction and functional enrichment analysis of the four miRNAs were performed using miRNet.ResultsWe identified four miRNAs, miRNA-21-5p, miRNA-9-5p, miR-149-5p, and miRNA-30b-5p, as independent prognostic indicators. Next, we used these four miRNAs to construct a four-miRNA PI for each patient. Results revealed that patients in the high-risk group (n=119) had significantly shorter survival time than those in the low-risk group (n=118) (high-risk/low-risk group log-rank P=0.000). This four-miRNA signature is an independent prognostic factor compared with routine clinicopathological features in the test set. These miRNAs targeted 1,634 genes, and a miRNA-target gene network was constructed using miRNet. The target genes of these four miRNAs were involved in various pathways related to cancer.ConclusionOur observations suggest that the four-miRNA signature correlated with the survival of patients with ccRCC and can be used as a prognostic biomarker of ccRCC.

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“…KDM3A (also known as JHDM2A or JMJD1A) is a histone demethylase that physically interacts with the androgen receptor (AR) to upregulate AR target gene expression through the demethylation of methylated histone H3 lysine 9 ( 76 ), a modification that is generally associated with transcriptional repression. More recent studies have revealed that histone lysine demethylases such as KDM3A play important roles in renal cell carcinoma, the most common kidney cancer, via hypoxia-mediated angiogenesis pathways ( 77–79 ). Smad3 is an intracellular signal transducer and transcriptional modulator activated by transforming growth factor (TGF)-β ( 80 ).…”

Section: Discussionmentioning

confidence: 99%

Role for first zinc finger of WT1 in DNA sequence specificity: Denys–Drash syndrome-associated WT1 mutant in ZF1 enhances affinity for a subset of WT1 binding sites

Wang

Horton

Zheng³

et al. 2017

Nucleic Acids Research

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Wilms tumor protein (WT1) is a Cys2-His2 zinc-finger transcription factor vital for embryonic development of the genitourinary system. The protein contains a C-terminal DNA binding domain with four tandem zinc-fingers (ZF1–4). An alternative splicing of Wt1 can add three additional amino acids—lysine (K), threonine (T) and serine (S)—between ZF3 and ZF4. In the −KTS isoform, ZF2–4 determine the sequence-specificity of DNA binding, whereas the function of ZF1 remains elusive. Three X-ray structures are described here for wild-type −KTS isoform ZF1–4 in complex with its cognate DNA sequence. We observed four unique ZF1 conformations. First, like ZF2–4, ZF1 can be positioned continuously in the DNA major groove forming a ‘near-cognate’ complex. Second, while ZF2–4 make base-specific interactions with one DNA molecule, ZF1 can interact with a second DNA molecule (or, presumably, two regions of the same DNA molecule). Third, ZF1 can intercalate at the joint of two tail-to-head DNA molecules. If such intercalation occurs on a continuous DNA molecule, it would kink the DNA at the ZF1 binding site. Fourth, two ZF1 units can dimerize. Furthermore, we examined a Denys–Drash syndrome-associated ZF1 mutation (methionine at position 342 is replaced by arginine). This mutation enhances WT1 affinity for a guanine base. X-ray crystallography of the mutant in complex with its preferred sequence revealed the interactions responsible for this affinity change. These results provide insight into the mechanisms of action of WT1, and clarify the fact that ZF1 plays a role in determining sequence specificity of this critical transcription factor.

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The Emerging Role of Histone Demethylases in Renal Cell Carcinoma

Cited by 31 publications

References 55 publications

Identification of Conserved Evolutionary Trajectories in Tumors

Identification of Conserved Evolutionary Trajectories in Tumors

Identification and validation of a four-miRNA(miRNA-21-5p, miRNA-9-5p, miR-149-5p, and miRNA-30b-5p) prognosis signature in clear cell renal cell carcinoma

Role for first zinc finger of WT1 in DNA sequence specificity: Denys–Drash syndrome-associated WT1 mutant in ZF1 enhances affinity for a subset of WT1 binding sites

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