ArticlePLoS computational biology2016
A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 35 citations in OpenAlex.
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
- Antibody reliability influences observed mRNA-protein correlations in tumour samples.Life science alliance · 2023Article
- SLFN11 captures cancer-immunity interactions associated with platinum sensitivity in high-grade serous ovarian cancer.JCI insight · 2021Article
- Changes in Immune Cell Types with Age in Breast are Consistent with a Decline in Immune Surveillance and Increased Immunosuppression.Journal of mammary gland biology and neoplasia · 2021Article
- Article
- Phosphoproteome Response to Dithiothreitol Reveals UniqueJournal of proteome research · 2020Article
- Insulin-like growth factor 1-induced enolase 2 deacetylation by HDAC3 promotes metastasis of pancreatic cancer.Signal transduction and targeted therapy · 2020Article
- Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics.Statistical applications in genetics and molecular biology · 2019Article
- Lysosomal acid lipase promotes cholesterol ester metabolism and drives clear cell renal cell carcinoma progression.Cell proliferation · 2018Article
- Gene co-expression analysis for functional classification and gene-disease predictions.Briefings in bioinformatics · 2018Article
- INBIA: a boosting methodology for proteomic network inference.BMC bioinformatics · 2018Article
- Article
- Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning.Frontiers in pharmacology · 2018Article
- Revealing protein networks and gene-drug connectivity in cancer from direct information.Scientific reports · 2017Article
- Estimation of the proteomic cancer co-expression sub networks by using association estimators.PloS one · 2017Article
- Harnessing Integrative Omics to Facilitate Molecular Imaging of the Human Epidermal Growth Factor Receptor Family for Precision Medicine.Theranostics · 2017Review
- Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems.Advances in bioinformatics · 2017Article
- Findings from the Section on Bioinformatics and Translational Informatics.Yearbook of medical informatics · 2016Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.