HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data

Tran, Anh; Dussaq, Alex M.; Kennell, Timothy; Willey, Christopher D.; Hjelmeland, Anita B.

doi:10.1186/s12859-019-3059-z

Cited by 49 publications

(32 citation statements)

References 19 publications

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“…As an additional level of verification, we used HPA data (v19.3) obtained via HPA analyze [ 40 ], a Bioconductor program that runs in R, to assess protein expression levels across multiple tissues among the two genesets, and used ggplot2 [ 41 ] to visualise the data.…”

Section: Methodsmentioning

confidence: 99%

Coupling of autism genes to tissue-wide expression and dysfunction of synapse, calcium signalling and transcriptional regulation

et al. 2020

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Autism Spectrum Disorder (ASD) is a heterogeneous disorder that is often accompanied with many co-morbidities. Recent genetic studies have identified various pathways from hundreds of candidate risk genes with varying levels of association to ASD. However, it is unknown which pathways are specific to the core symptoms or which are shared by the co-morbidities. We hypothesised that critical ASD candidates should appear widely across different scoring systems, and that comorbidity pathways should be constituted by genes expressed in the relevant tissues. We analysed the Simons Foundation for Autism Research Initiative (SFARI) database and four independently published scoring systems and identified 292 overlapping genes. We examined their mRNA expression using the Genotype-Tissue Expression (GTEx) database and validated protein expression levels using the human protein atlas (HPA) dataset. This led to clustering of the overlapping ASD genes into 2 groups; one with 91 genes primarily expressed in the central nervous system (CNS geneset) and another with 201 genes expressed in both CNS and peripheral tissues (CNS+PT geneset). Bioinformatic analyses showed a high enrichment of CNS development and synaptic transmission in the CNS geneset, and an enrichment of synapse, chromatin remodelling, gene regulation and endocrine signalling in the CNS+PT geneset. Calcium signalling and the glutamatergic synapse were found to be highly interconnected among pathways in the combined geneset. Our analyses demonstrate that 2/3 of ASD genes are expressed beyond the brain, which may impact peripheral function and involve in ASD co-morbidities, and relevant pathways may be explored for the treatment of ASD co-morbidities.

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Section: Methodsmentioning

confidence: 99%

Coupling of autism genes to tissue-wide expression and dysfunction of synapse, calcium signalling and transcriptional regulation

et al. 2020

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“… 28 - 30 Although the HPA database provided immunohistochemistry images, proteomics, and transcriptomics data, it may not be optimal for dataset analysis and automatic retrieval of images. In this study, we performed data mining for critical genes by using HPA analyze packager 31 (version 1.4.3, http://www.bioconductor.org/packages/release/bioc/html/HPAanalyze.html ), a software package designed for easy retrieval and analysis of HPA data.…”

Section: Methodsmentioning

confidence: 99%

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

Jia

Shu

et al. 2020

Evol Bioinform Online

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Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in “oxidation reduction,” “steroid hormone biosynthesis,” “fatty acid metabolic process,” and “linoleic acid metabolism.” Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.

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“…proteinatlas.org/) (Persson et al, 2006). This comprehensive database contained a large amount of proteomics and transcriptomics data from antibody-based tissue microarray analysis and deep RNA-sEquation (Tran et al, 2019).…”

Section: Expression Level Of Genes Correlated With Survival In the Hpmentioning

confidence: 99%

Identification and analysis of genes associated with lung adenocarcinoma by integrated bioinformatics methods

Xie

Zhang

2021

Annals of Human Genetics

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Lung adenocarcinoma (LUAD) is one of the most common forms of lung cancer, with a very high mortality rate. Although the treatments available for LUAD have become more effective in recent years, significant improvement is still needed. Advances in sequencing technologies and bioinformatics analysis have enabled new approaches to be developed for identifying drug targets. In this work we utilized data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify hub genes related to LUAD through Weighted Gene Correlation Network Analysis (WGCNA) and other bioinformatics methods, with the goal of identifying new drug targets for cancer treatment.

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HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data

Cited by 49 publications

References 19 publications

Coupling of autism genes to tissue-wide expression and dysfunction of synapse, calcium signalling and transcriptional regulation

Coupling of autism genes to tissue-wide expression and dysfunction of synapse, calcium signalling and transcriptional regulation

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

Identification and analysis of genes associated with lung adenocarcinoma by integrated bioinformatics methods

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