Functional Clustering Analysis Identifies Aldehyde Dehydrogenase Phenotypes Associated with Tumor Immunity

2019 ◽  
Author(s):  
Haoyu Jiang ◽  
Guanzhang Li ◽  
Zhiliang Wang ◽  
Ruoyu Huang ◽  
Qiangwei Wang ◽  
...  
Keyword(s):  
Aldehyde Dehydrogenase ◽  
Tumor Immunity ◽  
Clustering Analysis ◽  
Functional Clustering ◽  
Functional Clustering Analysis

scholarly journals Functional clustering analysis identifies specific subtypes of aldehyde dehydrogenase associated with glioma immunity

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Di Wang ◽  
Haoyu Jiang ◽  
Zhiliang Wang ◽  
Ruoyu Huang ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Aldehyde Dehydrogenase ◽  
Clustering Analysis ◽  
Functional Clustering ◽  
Functional Clustering Analysis

scholarly journals Anticolorectal Cancer Effects of AUCAN: Effects to Suppress Proliferation, Metastasis, and Invasion of Tumor Cells

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Liu-Lin Xiong ◽  
Ruo-Lan Du ◽  
Jun-Jie Chen ◽  
Ya Jiang ◽  
Lu-Lu Xue ◽  
...  
Keyword(s):  
Side Effects ◽  
Liver Function ◽  
Clustering Analysis ◽  
Proteomic Analysis ◽  
Functional Clustering ◽  
Tumor Tissues ◽  
Hct 116 ◽  
Functional Clustering Analysis ◽  
Blood Routine

Background. Colorectal cancer (CRC) is an underlying deadly malignancy with poor prognosis, lacking effective therapies currently available to improve the prognosis. C18H17NO6 (AUCAN), a kind of dibenzofuran extracted from a special plant in Yunnan Province (China), is identified as a natural anticancer agent exerting strong inhibitory activities on various cancers. Our study was committed to investigating the potency of AUCAN against colorectal cancers and further exploring the potential mechanisms via proteomic analysis. Methods. Cell Counting Kit-8 assay and immunofluorescence staining were used to investigate the effect of AUCAN on the viability and proliferation of HCT-116 cells and RKO cells. The apoptosis of HCT-116 and RKO cells after AUCAN administration was determined by the flow cytometry test. The effects of AUCAN on invasion and migration of tumor cells were investigated by the colony formation assay, wound healing test, and Transwell invasion test. Meanwhile, the energy metabolism and growth of tumor tissues after AUCAN administration with 10 mg/kg and 20 mg/kg were examined by PET-CT in vivo. The side effects of AUCAN treatment were also evaluated through blood routine and liver function examination. RKO cell proliferation and apoptosis in vivo were further determined by hematoxylin and eosin staining, TUNEL staining, and immunohistochemistry. Furthermore, the differentially expressed proteins (DEPs) involved in AUCAN treatment were determined by proteomic analysis followed by functional clustering analysis. Results. The results showed that AUCAN suppressed the migratory abilities and enhanced apoptosis of HCT-116 and RKO cell lines. Meanwhile, AUCAN treatment dramatically depressed the growth and volume of colorectal tumors in nude mice and suppressed the survival of RKO cells in tumor tissues without any side effects on the blood routine and liver function. In addition, twenty-four upregulated and forty-two downregulated proteins were identified. Additionally, functional clustering analysis concealed enriched biological processes, cellular components, molecular functions, and related pathways of these proteins involved in cellular metabolic. Finally, the protein-protein interaction analysis revealed the regulatory connection among these DEPs. Conclusions. Taken together, AUCAN exerted its significant antitumor effect without side effects in the blood routine and liver function and the underlying mechanisms were preliminarily investigated by proteomic analysis.

Multilevel Functional Clustering Analysis

Biometrics ◽  
2012 ◽  
Vol 68 (3) ◽  
pp. 805-814 ◽  
Author(s):  
Nicoleta Serban ◽  
Huijing Jiang
Keyword(s):  
Clustering Analysis ◽  
Functional Clustering ◽  
Functional Clustering Analysis

scholarly journals Functional clustering analysis of Chinese provincial wind power generation

2020 ◽  
pp. 014459872090917
Author(s):  
Yizheng Fu ◽  
Zhifang Su
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Wind Power ◽  
Clustering Analysis ◽  
Energy Generation ◽  
Wind Power Generation ◽  
Monthly Data ◽  
Functional Clustering ◽  
Wind Energy Generation ◽  
Functional Clustering Analysis

China is a broad territory country. There are significant differences in the terrain, climate, and other environmental factors between different provinces, which affect wind power generation. In order to better analyze the situation of wind power generation in Chinese provinces, this paper uses the functional clustering analysis to classify the monthly data of wind power generation in 30 Chinese provinces from March 2013 to October 2019. The empirical results of this paper show that the wind energy generation in Chinese provinces can be divided into three categories, and the results are consistent with the actual situation. In this paper, functional clustering analysis is used to analyze monthly data, compared with the traditional clustering analysis to analyze annual data which are obtained by accumulated monthly data. Higher-dimensional data can be used for analysis to reduce information loss. Moreover, data can be viewed as functions, and more information can be mined by analyzing derivative functions, and so on. The analysis of wind energy generation has certain guiding significance for the development and utilization of renewable energy.

scholarly journals Genomics and Prognosis Analysis of N6-Methyladenosine Regulators in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yanpin Ma ◽  
Huping Zhang
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathways ◽  
Tumor Immunity ◽  
Risk Score ◽  
Clustering Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Indicators ◽  
Consensus Clustering ◽  
Cox Regression Analysis

Objective: N6-methyladenosine (m6A) modification is involved in modulating various biological processes in human cancers. But the implication of m6A modification in lung adenocarcinoma (LUAD) is still unclear. Hence, this study conducted a comprehensive analysis of the expression and clinical implication of m6A regulators in LUAD.Methods: Consensus clustering analysis of 502 LUAD samples in the TCGA dataset was presented based on the expression profiles of 20 m6A regulators using ConsensusClusterPlus package. Overall survival (OS), activation of signaling pathways and tumor immunity (immune/stromal score, tumor purity, expression of HLA and immune checkpoints, and immune cell infiltration) were compared between m6A modification patterns. The m6A-related genes between patterns were identified and prognostic m6A-related genes were imported into LASSO-cox regression analysis. The m6A risk score was developed and its prognostic implication was evaluated and externally verified in the GSE30219 and GSE72094 dataset. Furthermore, a nomogram that contained independent prognostic indicators was established, followed by external verification.Results: Two m6A modification patterns were clustered across LUAD based on the expression similarity of the m6A regulators via consensus clustering analysis, with distinct OS, activation of signaling pathways and tumor immunity. Totally, 213 m6A-related genes that were identified by comparing two patterns were significantly related to LUAD prognosis. By LASSO method, we constructed the m6A risk score that was a reliable and independent prognostic factor for LUAD. Patients with low m6A risk score displayed a prominent survival advantage. After incorporating independent clinical features, we developed the prognostic nomogram that exhibited high predictive accuracy and the best clinical net benefit for OS.Conclusion: Collectively, our study may provide a clinically useful tool for precise prognostic management and optimization of immunotherapeutic strategies for LUAD patients.

Escape Mechanisms in Tumor Immunity: An Update

2002 ◽  
Vol 21 (4) ◽  
pp. 54 ◽  
Author(s):  
Ludmila Muller ◽  
Rolf Kiessling ◽  
Robert C. Rees ◽  
Graham Pawelec
Keyword(s):  
Tumor Immunity
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