Evidence map›Paper›PMID 30107644›Full record

ArticleMolecular genetics & genomic medicine2018

Genomic landscape and mutational impacts of recurrently mutated genes in cancers.

Baolin Liu, Fei-Fei Hu, Qiong Zhang, Hui Hu, Zheng Ye, Qin Tang, An-Yuan Guo

Open access · goldAbstract read
In one paragraph

Article in Molecular genetics & genomic medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.6field-weighted citation impact, top 37% of its field
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

11 citing papers in PubMed, 15 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Baolin LiuDepartment of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Fei-Fei HuDepartment of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Qiong ZhangDepartment of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Hui HuDepartment of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Zheng YeDepartment of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Qin TangDepartment of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0001-8902-5316
An-Yuan GuoDepartment of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0002-5099-7465
Huazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer genes tend to be highly mutated under positive selection. Better understanding the recurrently mutated genes (RMGs) in cancer is critical for explicating the mechanisms of tumorigenesis and providing vital clues for therapy. Although some studies have investigated functional impacts of RMGs in specific cancer types, a comprehensive analysis of RMGs and their mutational impacts across cancers is still needed.

methodsWe obtained data from The Cancer Genome Atlas (TCGA) and calculated mutation rate of each gene in 31 cancer types. Functional analysis was performed to identify the important signaling pathways and enriched protein types of RMGs. In order to evaluate functional impacts of RMGs, differential expression, survival, and pairwise mutation patterns analyses were performed.

resultsTotally, we identified 897 RMGs and 624 of them were specifically mutant in only a single cancer type. Functional analysis demonstrated that these RMGs were enriched in hydrolases, cytoskeletal protein, and pathways like MAPK, cell cycle, PI3K-Akt, ECM receptor interaction, and energy metabolism. The differentially expressed genes potentially affected by the same common RMG showed a relatively low overlap across different cancer types. For the 19 Mucin (MUC) family genes, nine of them were RMGs and four of them (MUC17, MUC5B, MUC4, and MUC16) were common RMGs shared in 8 to 17 cancer types. The results showed that recurrent mutations in MUC genes were significantly associated with better survival prognosis. Only a small part of RMGs was differentially expressed due to their own mutations and most of them were downregulated. In addition, pairwise mutation pattern analysis revealed the high frequency of co-occurred mutations among RMGs in STAD.

conclusionThrough the functional analysis of RMGs, we found that six signaling pathways were disrupted in most cancer types and that energy metabolism was abnormal in tumors. The results also revealed a strong correlation between recurrently mutated genes from MUC family and human survival. In addition, gene expression and survival prognosis were associated with different mutation types of RMGs.

Indexed as

Mutation RateDatabases, GeneticGenome, HumanGenotypeHumansNeoplasmsSurvival AnalysiscancerDNA mutationgene mutationgenomic

Identifiers

PMID30107644
PMCPMC6305651
OpenAlexW2886328774

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Registered trials

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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.