ArticleOncology letters2020
Identification of osteosarcoma driver genes using a network method.
Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed, 1 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma (OS) is a severe disease that is generally caused by genetic alterations. Systematic identification of driver genes may be used to increase the understanding of the mechanisms underlying the disease. The present study identified a framework to predict driver genes, with the hypothesis that driver genes operate through a number of connected functional genes. OS-related genes were extracted from the Catalogue Of Somatic Mutations In Cancer and subsequently ranked by virtue of their effect on a set of functional genes using a network-based algorithm. This revealed the driver genes associated with dysregulated networks. In addition, compared with the Mutations For Functional Impact on Network Neighbors algorithm, the results obtained using the aforementioned network-based algorithm revealed that the proposed method is effective. Gene functional analysis demonstrated that the potential OS driver genes were involved in OS-associated pathways. Through the validation of the 15 candidate OS driver genes, the classifier constructed in the present study revealed that the identified driver genes were able to distinguish 184 cancer samples from controls. Therefore, the present study provided insights into the identification of driver genes from a vast amount of sequencing data.
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.