ArticlePloS one2016
Integrated Multiple "-omics" Data Reveal Subtypes of Hepatocellular Carcinoma.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 33 citations in OpenAlex.
- Interpretable machine learning-based survival prediction and key gene identification in cancer using gene expression and clinical data.Translational cancer research · 2026Article
- Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors.Biometrics · 2024Article
- Multi-Omics Profiling for Health.Molecular & cellular proteomics : MCP · 2023Review
- Genetic and Epigenetic Mechanisms Deregulate the CRL2Frontiers in genetics · 2022Article
- Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection.Frontiers in cardiovascular medicine · 2022Review
- The Metallome as a Link Between the "Omes" in Autism Spectrum Disorders.Frontiers in molecular neuroscience · 2021Review
- Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma.Nature reviews. Gastroenterology & hepatology · 2020Review
- Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data.BMC medical informatics and decision making · 2020Article
- Multi-omics Data Integration, Interpretation, and Its Application.Bioinformatics and biology insights · 2020Review
- Article
- [Research progress of microRNAs in regulation of lipid metabolism-related proteins for influencing the pathogenesis of hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2019Review
- SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.Frontiers in genetics · 2019Article
- Time for change: a new training programme for morpho-molecular pathologists?Journal of clinical pathology · 2018Review
- Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018Article
- Significant associations between driver gene mutations and DNA methylation alterations across many cancer types.PLoS computational biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Hepatocellular carcinoma is one of the most heterogeneous cancers, as reflected by its multiple grades and difficulty to subtype. In this study, we integrated copy number variation, DNA methylation, mRNA, and miRNA data with the developed "cluster of cluster" method and classified 256 HCC samples from TCGA (The Cancer Genome Atlas) into five major subgroups (S1-S5). We observed that this classification was associated with specific mutations and protein expression, and we detected that each subgroup had distinct molecular signatures. The subclasses were associated not only with survival but also with clinical observations. S1 was characterized by bulk amplification on 8q24, TP53 mutation, low lipid metabolism, highly expressed onco-proteins, attenuated tumor suppressor proteins and a worse survival rate. S2 and S3 were characterized by telomere hypomethylation and a low expression of TERT and DNMT1/3B. Compared to S2, S3 was associated with less copy number variation and some good prognosis biomarkers, including CRP and CYP2E1. In contrast, the mutation rate of CTNNB1 was higher in S3. S4 was associated with bulk amplification and various molecular characteristics at different biological levels. In summary, we classified the HCC samples into five subgroups using multiple "-omics" data. Each subgroup had a distinct survival rate and molecular signature, which may provide information about the pathogenesis of subtypes in HCC.
Indexed as
Identifiers
What OpenQuestion holds
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.