Role of KCNB1 in the prognosis of gliomas and autophagy modulation

Wang, Hao‐Yuan; Wen, Wang; Liu, Yanwei; Li, Mingyang; Liang, Tingyu; Li, Jiye; Hu, Huimin; Lü, Yang; Yao, Chen; Ye, Yongyi; Wang, Yongzhi; Zhang, Shizhong

doi:10.1038/s41598-017-00045-7

Cited by 54 publications

(43 citation statements)

References 40 publications

(38 reference statements)

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“…The KBNB1 gene codes for the Potassium Voltage-Gated Channel Subfamily B Member 1 protein. KCNB1 has been previously identified as a prognostic factor in gliomas due to its tumor suppressor function 60 . It contains on average 2.14 protein altering somatic mutations per tumor sample in the 271 OV samples containing the TP53+KCNB1+TTN 3-hit combination, compared to 0.03 mutations per normal sample (p-value < 0.0001) (Fig.…”

Section: Discussionmentioning

confidence: 99%

Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU

hajri

Dash

Feng

et al. 2020

Sci Rep

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Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy. Cancer is one of the leading causes of death in the US with a projected 606,880 deaths in 2019 1. Despite significant progress, effective treatment in advanced cases remain elusive, with most progress coming from prevention and early detection 2,3. One of many possible reasons is that, although cancer is known to be caused primarily by multiple genetic mutations 4-9 , we cannot in general determine the specific combination of mutations responsible for a given instance of cancer 10,11. Knowing the specific combination of hits in individual cases would allow us to develop more effective targeted combination therapies 10,11. Although there are other factor that may contribute to cancer growth, such as tumor microenvironment, epigenetic modifications, gene fusion, germline defects, etc., the focus of this work is on somatic mutations 12-18. Current computational approaches search for cancer genes and mutations that increase cancer risk (the probability of getting cancer) 18-29. In addition to other considerations, these methods search for genes that are significantly more frequently mutated in tumor samples compared to an estimated background mutation rate. However, mutations in any one of these genes by themselves do not always result in cancer, suggesting that carcinogenesis may require additional mutations, as illustrated by the following exmples. Germline mut...

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Section: Discussionmentioning

confidence: 99%

Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU

hajri

Dash

Feng

et al. 2020

Sci Rep

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“…Moreover, it is related to the development of breast cancer and endometrial cancer (Kurozumi et al, 2017;He et al, 2019). As a prognostic biomarker for gliomas, KCNB1 suppresses tumor growth by inducing autophagy (Wang et al, 2017). So far, there is no study showing the effect of PGR and KCNB1 in COAD.…”

Section: Confirmation Of Hub Genesmentioning

confidence: 99%

MicroRNAs Associated With Colon Cancer: New Potential Prognostic Markers and Targets for Therapy

Zhu

Xü

Liu

et al. 2020

Front. Bioeng. Biotechnol.

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MicroRNAs (miRNAs) are a kind of non-coding RNA (ncRNA) that regulate the expression of target genes and play a role in the occurrence and development of cancers. Colon cancer (COAD) is the second most common cause of cancer-related mortality. However, the prognostic value of miRNAs in COAD is still confusing. In this study, we obtain miRNAs and messenger RNAs (mRNAs) expression profiles of COAD from the Cancer Genome Atlas (TCGA) database. After preliminary data screening and preprocessing, we acquire the expression data of 894 miRNAs and 17,019 mRNAs. Then, compared with the normal samples, 39 upregulated miRNAs and 54 downregulated miRNAs are identified by differential expression analysis. Furthermore, we obtain 1,487 upregulated mRNAs and 2,847 downregulated mRNAs. We confirm nine key miRNAs related to the survival rate of COAD patients. Moreover, by using bioinformatics methods, we get 461 common genes from both the target genes of these nine key miRNAs and differentially expressed mRNAs. Through analyzing the protein-protein interaction (PPI) network of these 461 common genes and survival analysis, we confirm five hub genes as promising biomarkers for COAD prognosis. It is worth mentioning that no previous reports have found that PGR and KCNB1 are related to COAD. We expect these key miRNAs and hub genes will provide a new way for the study of COAD.

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“…Fractal dimensions are commonly applied to determine the self-similarity properties and natural structural patterns. Recently, fractal dimension has been used to describe behavioral patterns of animals such as fruit flies [ 16 ] and chironomids [ 17 ]. Self-organizing map (SOM) is an artificial neural network that is trained through unsupervised learning to obtain a low-dimensional and discretized description of the input data and is therefore a process to conduct dimensionality reduction [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…SOM has primarily been used in analysis of complex behaviors in response to the extreme environmental stresses in ecological risk assessment, because it can efficiently identify the patterns of animal responses based on behavioral parameters [ 19 , 20 ]. SOM was also applied to differentiate animal behaviors with various genotypes in response to environmental pollutants [ 4 , 16 ]. In addition, behavioral classification with the SOM has been expanded for use of clinical applications [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Fractal Dimension and Self-Organizing Map to Detect Toxic Effects of Toluene on Movement Tracks of Daphnia magna

Liu

Xia

Fan

et al. 2018

Journal of Toxicology

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Movement behaviors of an indicator species, Daphnia magna, in response to contaminants have been implemented to monitor environmental disturbances. Complexity in movement tracks of Daphnia magna was characterized by use of fractal dimension and self-organizing map. The individual movement tracks of D. magna were continuously recorded for 24 hours before and after treatments with toluene at the concentration of 10 mg/L, respectively. The general complexity in movement tracks (10 minutes) was characterized by fractal dimension. Results showed that average fractal dimension of movement tracks was decreased from 1.62 to 1.22 after treatments. The instantaneous movement parameters of movement segments in 5 s were input into the self-organizing map to investigate the swimming pattern changes under stresses of toluene. Abnormal behaviors of D. magna are more frequently observed after treatments than before treatments. Computational methods in ecological informatics could be utilized to obtain the useful information in behavioral data of D. magna and would be further applied as an in situ monitoring tool in water environment.

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Role of KCNB1 in the prognosis of gliomas and autophagy modulation

Cited by 54 publications

References 40 publications

Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU

Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU

MicroRNAs Associated With Colon Cancer: New Potential Prognostic Markers and Targets for Therapy

Implementation of Fractal Dimension and Self-Organizing Map to Detect Toxic Effects of Toluene on Movement Tracks of Daphnia magna

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