↓ Skip to main content

Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma

Overview of attention for article published in BMC Medical Genomics, July 2015
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
44 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma
Published in
BMC Medical Genomics, July 2015
DOI 10.1186/s12920-015-0114-0
Pubmed ID
Authors

Vinay Randhawa, Vishal Acharya

Abstract

Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity but, OSCC can be difficult to detect at its earliest stage due to its molecular complexity and clinical behavior. Therefore, identification of key gene signatures at an early stage will be highly helpful. The aim of this study was to identify key genes associated with progression of OSCC stages. Gene expression profiles were classified into cancer stage-related modules, i.e., groups of genes that are significantly related to a clinical stage. For prioritizing the candidate genes, analysis was further restricted to genes with high connectivity and a significant association with a stage. To assess predictive power of these genes, a classification model was also developed and tested by 5-fold cross validation and on an independent dataset. The identified genes were enriched for significant processes and functional pathways, and various genes were found to be directly implicated in OSCC. Forward and stepwise, multivariate logistic regression analyses identified 13 key genes whose expression discriminated early- and late-stage OSCC with predictive accuracy (area under curve; AUC) of ~0.81 in a 5-fold cross-validation strategy. The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage; the classification model that is developed with these genes may help to distinguish cancer stages. The proposed genes and model hold promise for monitoring of OSCC stage progression, and our findings may facilitate cancer detection at an earlier stage, resulting in improved treatment outcomes.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
Unknown 43 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 16%
Student > Bachelor 6 14%
Student > Ph. D. Student 6 14%
Student > Postgraduate 5 11%
Student > Doctoral Student 4 9%
Other 7 16%
Unknown 9 20%
Readers by discipline Count As %
Medicine and Dentistry 10 23%
Agricultural and Biological Sciences 9 20%
Biochemistry, Genetics and Molecular Biology 8 18%
Computer Science 2 5%
Nursing and Health Professions 1 2%
Other 4 9%
Unknown 10 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 March 2016.
All research outputs
#13,441,810
of 22,817,213 outputs
Outputs from BMC Medical Genomics
#501
of 1,223 outputs
Outputs of similar age
#123,608
of 262,407 outputs
Outputs of similar age from BMC Medical Genomics
#16
of 23 outputs
Altmetric has tracked 22,817,213 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,223 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,407 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.