Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization

Li, Jianfeng; Miao, Ben-Ben; Wang, S; Dong, Wei; Xu, Houshi; Si, Chenchen; Wang, Wei; Duan, Songqi; Lou, Jiacheng; Zhaorigetu, Bao; Zeng, Hailuan; Yang, Zengzeng; Cheng, Wen‐Yan; Zhao, Fei; Zeng, Jianming; Liu, Xue Song; Wu, Renxie; Shen, Yang; Chen, Zhu; Chen, Sai‐Juan; Wang, Mingjie

doi:10.1093/bib/bbac261

Cited by 149 publications

(90 citation statements)

References 35 publications

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“…Clinical data, CNA data and mRNA expression data were downloaded from TCGA data portal ( (accessed on 8 October 2021)). Differential expression analysis was performed using UCSCXenaShiny [ 30 ] ( (accessed on 10 October 2021)) and the results obtained are visualized by Hiplot [ 31 ] ( (accessed on 12 October 2021)). The procedure for CNA analysis was adopted from our previously work [ 32 ] and GISTIC2.0 ( (accessed on 8 October 2021)) was used to identify recurrent copy-number alterations.…”

Section: Methodsmentioning

confidence: 99%

Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis

Zhao

Chen

Sun

et al. 2022

Cancers

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Lymph nodes metastases are common in patients with lung cancer. Additionally, those patients are often at a higher risk for death from lung tumor than those with tumor-free lymph nodes. Somatic DNA alterations are key drivers of cancer, and copy number alterations (CNAs) are major types of DNA alteration that promote lung cancer progression. Here, we performed genome-wide DNA copy number analysis, and identified a novel lung-cancer-metastasis-related gene, EFNA4. The EFNA4 genome locus was significantly amplified, and EFNA4 mRNA expression was significantly up-regulated in lung cancer compared with normal lung tissue, and also in lung cancer with lymph node metastases compared with lung cancer without metastasis. EFNA4 encodes Ephrin A4, which is the ligand for Eph receptors. The function of EFNA4 in human lung cancer remains largely unknown. Through cell line experiments we showed that EFNA4 overexpression contributes to lung tumor cells growth, migration and adhesion. Conversely, EFNA4 knockdown or knockout led to the growth suppression of cells and tumor xenografts in mice. Lung cancer patients with EFNA4 overexpression have poor prognosis. Together, by elucidating a new layer of the role of EFNA4 in tumor proliferation and migration, our study demonstrates a better understanding of the function of the significantly amplified and overexpressed gene EFNA4 in lung tumor metastasis, and suggests EFNA4 as a potential target in metastatic lung cancer therapy.

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Section: Methodsmentioning

confidence: 99%

Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis

Zhao

Chen

Sun

et al. 2022

Cancers

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“…The heatmaps and PCA analysis were generated and visualized on Hiplot ( https://hiplot-academic.com/ ) 20 . Pathway enrichment analysis was conducted based on the Reactome database using Web-based gene set analysis toolkit (WebGestalt, http://www.webgestalt.org/ ) 21 , 22 and Gene Ontology (GO) analysis was conducted and visualized utilizing the R “clusterProfiler” package on Hiplot 23 .…”

Section: Methodsmentioning

confidence: 99%

Identification of a prognosis-related ceRNA network in cholangiocarcinoma and potentially therapeutic molecules using a bioinformatic approach and molecular docking

Gao

Zhang

Jia

et al. 2022

Sci Rep

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Cholangiocarcinoma (CCA) is a highly malignant disease with a poor prognosis, and mechanisms of initiation and development are not well characterized. It is long noncoding RNAs (lncRNAs) acting as miRNA decoys to regulate cancer-related RNAs in competing endogenous RNA (ceRNA) networks that suggest a possible molecular mechanism in CCA. The current study aims to find potential prognosis biomarkers and small molecule therapeutic targets based on the construction of a CCA prognosis-related ceRNA network. A transcriptome dataset for CCA was downloaded from the TCGA database. Differentially expressed lncRNAs (DElncRNAs), DEmiRNAs and DEmRNAs were identified based on the differential expression and a DEceRNA network was constructed using predicted miRNA-lncRNA and miRNA-mRNA interactions. Heat maps, PCA analysis, and Pathway enrichment analysis and GO enrichment analysis were conducted. The prognostic risk model and molecular docking were constructed based on identified key ceRNA networks. A DElncRNA-miRNA-mRNAs network consisting of 434 lncRNA-miRNA pairs and 284 miRNA-mRNA pairs with 200 lncRNAs, 21 miRNAs, and 245 mRNAs was constructed. There were three lncRNAs (AC090772.1, LINC00519, and THAP7-AS1) and their downstream mRNAs (MECOM, MBNL3, RCN2) screened out as prognostic factors in CAA. Three key networks (LINC00519/ hsa-mir-22/ MECOM, THAP7-AS1/hsa-mir-155/MBNL3, and THAP7-AS1/hsa-mir-155/RCN2) were identified based on binding sites prediction and survival analysis. A prognostic risk model was established with a good predictive ability (AUC = 0.66–0.83). Four anticancer small molecules, MECOM and 17-alpha-estradiol (−7.1 kcal/mol), RCN2 and emodin (−8.3 kcal/mol), RCN2 and alpha-tocopherol (−5.6 kcal/mol), and MBNL3 and 17-beta-estradiol (−7.1 kcal/mol) were identified. Based on the DEceRNA network and Kaplan–Meier survival analysis, we identified three important ceRNA networks associated with the poor prognosis of CCA. Four anti-cancer small molecules were screened out by computer-assisted drug screening as potential small molecules for the treatment of CCA. This study provides theoretical support for the development of ceRNA network-based drugs to improve the prognosis of CCA.

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“…Analyses were performed by using tools in Hiplot (https:// hiplot.org) (18), a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization. Gene expression data and clinical information were visualized by the R package "ggplot2" R package.…”

Section: Statistical Analysesmentioning

confidence: 99%

GPI: An indicator for immune infiltrates and prognosis of human breast cancer from a comprehensive analysis

Zeng

Tan³

et al. 2022

Front. Endocrinol.

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Glucose-6-phosphate isomerase (GPI) plays an important part in gluconeogenesis and glycolysis through the interconversion of d-glucose-6-phosphate and d-fructose-6-phosphate, and its clinical significance still remains unclear in breast cancer (BRCA). We analyzed the expressions of GPI in BRCA patients to determine prognostic values. Our results showed that the expression levels of GPI were upregulated in BRCA patients, and a high GPI expression is correlated with poor overall survival (OS) in BRCA. At the same time, a high GPI expression is correlated with poor clinicopathological characteristics, such as stage III, over 60 years old, N3, HER2 negative, and estrogen receptor (ER) positive. Further analysis of the influence of GPI on the prognosis of BRCA suggested that 50 genes and 10 proteins were positively correlated with GPI, and these genes and proteins were mainly involved in cell cycle signaling pathways. In addition, in this study, we observed that GPI was closely related to N6-methyladenosine (m6A) RNA methylation modification and immune cell infiltration and ferroptosis-related gene expression in BRCA, and there was a difference in m6A RNA methylation alterations, immune cell infiltration, and ferroptosis-related gene expression between the high GPI expression group and the low GPI expression group. Finally, we found that GPI in BRCA had 2.6% gene alterations, and BRCA patients with gene alteration of GPI had a poor prognosis in disease-free survival (DFS). Altogether, our work strongly suggested that GPI may serve as a new prognostic biomarker for BRCA patients.

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Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization

Cited by 149 publications

References 35 publications

Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis

Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis

Identification of a prognosis-related ceRNA network in cholangiocarcinoma and potentially therapeutic molecules using a bioinformatic approach and molecular docking

GPI: An indicator for immune infiltrates and prognosis of human breast cancer from a comprehensive analysis

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