Evidence map›Paper›PMID 34503185›Full record

ArticleCancers2021

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer.

Amin Ghareyazi, Amir Mohseni, Hamed Dashti, Amin Beheshti, Abdollah Dehzangi, Hamid R Rabiee, Hamid Alinejad-Rokny

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Pooled it
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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 4 institutions in 3 countries.

Amin GhareyaziBioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran.ORCID 0000-0003-1688-283X
Amir MohseniBioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran.
Hamed DashtiBioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran.
Amin BeheshtiDepartment of Computing, Macquarie University, Sydney, NSW 2109, Australia.ORCID 0000-0002-5988-5494
Abdollah DehzangiDepartment of Computer Science, Rutgers University, Camden, NJ 08102, USA.ORCID 0000-0001-8577-0271
Hamid R RabieeBioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran.ORCID 0000-0002-9835-4493
Hamid Alinejad-RoknyBioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.ORCID 0000-0002-2189-9153
Sharif University of Technology · IRMacquarie University · AURutgers, The State University of New Jersey · USUNSW Sydney · AU

Funding

University of New South Wales p34532
6 · The paper itself

Abstract

It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

Indexed as

cancer subtype identificationgenotype and phenotype characterizationpancreatic cancerpersonalized medicinesomatic point mutationstherapeutic targets

Identifiers

PMID34503185
PMCPMC8431675
OpenAlexW3196331722

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.