Evidence map›Paper›PMID 27403431›Full record

ReviewBioMed research international2016

Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks.

Shengda Lin, Yi A Yin, Xiaoqian Jiang, Nidhi Sahni, Song Yi

Abstract readReview
In one paragraph

Review in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Precision medicine: Uses and challenges.Medical journal, Armed Forces India · 2021
    Review
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shengda LinDepartment of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Yi A YinDepartment of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Cancer Biology Program, Stanford University School of Medicine, Stanford, CA 94305, USA.
Xiaoqian JiangDivision of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA.
Nidhi SahniDepartment of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Song YiDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.

Funding

Protection of Records: Privacy (PReP) Technology for Medical ResearchR00LM011392 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN · 2013 to 2015
$650k
NLM NIH HHS R00 LM011392
6 · The paper itself

Abstract

The advent of the human genome sequence and the resulting ~20,000 genes provide a crucial framework for a transition from traditional biology to an integrative "OMICs" arena (Lander et al., 2001; Venter et al., 2001; Kitano, 2002). This brings in a revolution for cancer research, which now enters a big data era. In the past decade, with the facilitation by next-generation sequencing, there have been a huge number of large-scale sequencing efforts, such as The Cancer Genome Atlas (TCGA), the HapMap, and the 1000 genomes project. As a result, a deluge of genomic information becomes available from patients stricken by a variety of cancer types. The list of cancer-associated genes is ever expanding. New discoveries are made on how frequent and highly penetrant mutations, such as those in the telomerase reverse transcriptase (TERT) and TP53, function in cancer initiation, progression, and metastasis. Most genes with relatively frequent but weakly penetrant cancer mutations still remain to be characterized. In addition, genes that harbor rare but highly penetrant cancer-associated mutations continue to emerge. Here, we review recent advances related to cancer genomics, proteomics, and systems biology and suggest new perspectives in targeted therapy and precision medicine.

Indexed as

AnimalsChromosome MappingDNA, NeoplasmGene EditingGenes, NeoplasmGenetic Predisposition to DiseaseGenome, HumanGenomicsHigh-Throughput Nucleotide SequencingHumansLiver NeoplasmsNeoplasm ProteinsSignal TransductionDNA, NeoplasmNeoplasm Proteins

Identifiers

PMID27403431
PMCPMC4923561

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.