Melatonin inhibiting the survival of human gastric cancer cells under ER stress involving autophagy and Ras‐Raf‐MAPK signalling

Huang, Yongye; Yuan, Kexun; Tang, Meifang; Yue, Jiaming; Bao, Lijun; Wu, Shuang; Zhang, Yanxin; Li, Yin; Wang, Yihang; Ou, Xu; Gou, Jiaxin; Zhao, Qi; Yuan, Lin

doi:10.1111/jcmm.16237

Cited by 67 publications

(42 citation statements)

References 36 publications

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“…GSEA (Mootha et al, 2003;Subramanian et al, 2005;Huang et al, 2020) is an analysis method for genome-wide expression profile chip data, which compares genes with a predefined set of genes. Synthesize the existing information base of gene location, nature, function, biological significance, etc., to build a molecular tag database (MSigDB), in which known genes are identified by chromosomal location, established gene set, and model sequence.…”

Section: Gene Set Enrichment Analysismentioning

confidence: 99%

Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification

Feng

Jiang

et al. 2021

Front. Genet.

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The multiple sources of cancer determine its multiple causes, and the same cancer can be composed of many different subtypes. Identification of cancer subtypes is a key part of personalized cancer treatment and provides an important reference for clinical diagnosis and treatment. Some studies have shown that there are significant differences in the genetic and epigenetic profiles among different cancer subtypes during carcinogenesis and development. In this study, we first collect seven cancer datasets from the Broad Institute GDAC Firehose, including gene expression profile, isoform expression profile, DNA methylation expression data, and survival information correspondingly. Furthermore, we employ kernel principal component analysis (PCA) to extract features for each expression profile, convert them into three similarity kernel matrices by Gaussian kernel function, and then fuse these matrices as a global kernel matrix. Finally, we apply it to spectral clustering algorithm to get the clustering results of different cancer subtypes. In the experimental results, besides using the P-value from the Cox regression model and survival analysis as the primary evaluation measures, we also introduce statistical indicators such as Rand index (RI) and adjusted RI (ARI) to verify the performance of clustering. Then combining with gene expression profile, we obtain the differential expression of genes among different subtypes by gene set enrichment analysis. For lung cancer, GMPS, EPHA10, C10orf54, and MAGEA6 are highly expressed in different subtypes; for liver cancer, CMYA5, DEPDC6, FAU, VPS24, RCBTB2, LOC100133469, and SLC35B4 are significantly expressed in different subtypes.

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Section: Gene Set Enrichment Analysismentioning

confidence: 99%

Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification

Feng

Jiang

et al. 2021

Front. Genet.

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“…With the development of high-throughput sequencing technologies, it has become convenient and cost efficient to thoroughly profile the microbial community composition and diversity in various environmental habitats (Deshpande et al, 2018;Escalona et al, 2018;Rodriguez-R et al, 2018;Fritz et al, 2019;Huang et al, 2021). Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists (Szalkai and Grolmusz, 2018;Qu et al, 2019b).…”

Section: Discussionmentioning

confidence: 99%

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

et al. 2021

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With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.

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“…Natural compounds were widely used in food, cosmetics and other industries for many years. Because of their high safety, many clinical and preclinical studies begun to pay attention to them (60)(61)(62)(63)(64). As a natural polysaccharide extracted from brown algae, fucoidan was proved to have many biological effects (12)(13)(14)(15)(16).…”

Section: Discussionmentioning

confidence: 99%

Fucoidan Inhibits the Progression of Hepatocellular Carcinoma via Causing lncRNA LINC00261 Overexpression

Wei

Chen

et al. 2021

Front. Oncol.

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Hepatocellular carcinoma (HCC) as a main type of primary liver cancers has become one of the most deadly tumors because of its high morbidity and poor prognosis. Fucoidan is a family of natural, heparin-like sulfated polysaccharides extracted from brown algae. It is not only a widely used dietary supplement, but also participates in many biological activities, such as anti-oxidation, anti-inflammation and anti-tumor. However, the mechanism of fucoidan induced inhibition of HCC is elusive. In our study, we demonstrated that fucoidan contributes to inhibiting cell proliferation in vivo and in vitro, restraining cell motility and invasion and inducing cell cycle arrest and apoptosis. According to High-Throughput sequencing of long-non-coding RNA (lncRNA) in MHCC-97H cells treated with 0.5 mg/mL fucoidan, we found that 56 and 49 lncRNAs were correspondingly up- and down-regulated. LINC00261, which was related to the progression of tumor, was highly expressed in fucoidan treated MHCC-97H cells. Moreover, knocking down LINC00261 promoted cell proliferation by promoting the expression level of miR-522-3p, which further decreased the expression level of downstream SFRP2. Taken together, our results verified that fucoidan effectively inhibits the progression of HCC via causing lncRNA LINC00261 overexpression.

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Melatonin inhibiting the survival of human gastric cancer cells under ER stress involving autophagy and Ras‐Raf‐MAPK signalling

Cited by 67 publications

References 36 publications

Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification

Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

Fucoidan Inhibits the Progression of Hepatocellular Carcinoma via Causing lncRNA LINC00261 Overexpression

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