Back

Discovery of new deregulated miRNAs in gingivo buccal carcinoma using Group Benjamini Hochberg method: a commentary on "A quest for miRNA bio-marker: a track back approach from gingivo buccal cancer to two different types of precancers"

Koner, S.; De Sarkar, N.; Laha, N.

2023-02-21 cancer biology
10.1101/2023.02.17.529013 bioRxiv
Show abstract

This formal comment is in response to "A quest for miRNA bio-marker: a track back approach from gingivo buccal cancer to two different types of precancers" written by De Sarkar and colleagues in 2014. The above-mentioned paper found seven miRNAs to be significantly deregulated in 18 gingivo-buccal cancer samples. However, they suspected more miRNAs to be deregulated based on their exploratory statistical analysis. To control the false discovery rate (FDR), the authors used the Benjamini Hochberg (BH) method, which does not leverage any available biological information on the miRNAs. In this work, we show that some specialized versions of the BH method, which can exploit positional information on the miRNAs, can lead to seven more discoveries with this data. Specifically, we group the closely located miRNAs, and use the group Benjamini Hochberg (GBH) methods (Hu et al., 2010), which reportedly have more statistical power than the BH method (Liu et al., 2019). The whole transcriptome analysis of Sing et al. (2017) and previous literature on the miRNAs suggest that most of the newly discovered miRNAs play a role in oncogenesis. In particular, the newly discovered miRNAs include hsa-miR-1 and hsa-miR-21-5p, whose cancer-related activities are well-established. Our findings indicate that incorporating the GBH method into suitable microarray studies may potentially enhance scientific discoveries via the exploitation of additional biological information.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
PeerJ
261 papers in training set
Top 0.1%
33.9%
2
Frontiers in Genetics
197 papers in training set
Top 1%
5.0%
3
Bioinformatics
1061 papers in training set
Top 5%
3.7%
4
PLOS ONE
4510 papers in training set
Top 38%
3.7%
5
Scientific Reports
3102 papers in training set
Top 34%
3.7%
6
BMC Bioinformatics
383 papers in training set
Top 3%
3.7%
50% of probability mass above
7
Computational Biology and Chemistry
23 papers in training set
Top 0.1%
3.2%
8
Frontiers in Oncology
95 papers in training set
Top 1%
2.8%
9
Cancers
200 papers in training set
Top 2%
2.5%
10
PLOS Computational Biology
1633 papers in training set
Top 13%
2.1%
11
Biochemistry and Biophysics Reports
28 papers in training set
Top 0.3%
2.1%
12
BMC Cancer
52 papers in training set
Top 1%
1.8%
13
Heliyon
146 papers in training set
Top 2%
1.7%
14
F1000Research
79 papers in training set
Top 2%
1.7%
15
iScience
1063 papers in training set
Top 17%
1.5%
16
G3 Genes|Genomes|Genetics
351 papers in training set
Top 2%
1.4%
17
Journal of Computational Biology
37 papers in training set
Top 0.4%
0.9%
18
BMC Genomics
328 papers in training set
Top 4%
0.9%
19
International Journal of Molecular Sciences
453 papers in training set
Top 12%
0.9%
20
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 5%
0.8%
21
Gene
41 papers in training set
Top 2%
0.8%
22
BioMed Research International
25 papers in training set
Top 3%
0.8%
23
Briefings in Bioinformatics
326 papers in training set
Top 6%
0.8%
24
Computational and Structural Biotechnology Journal
216 papers in training set
Top 10%
0.7%
25
Frontiers in Bioinformatics
45 papers in training set
Top 1%
0.5%
26
Genome Biology and Evolution
280 papers in training set
Top 2%
0.5%
27
Journal of Clinical Medicine
91 papers in training set
Top 8%
0.5%
28
IEEE Journal of Biomedical and Health Informatics
34 papers in training set
Top 3%
0.5%
29
Annals of Oncology
13 papers in training set
Top 1%
0.5%
30
Genes
126 papers in training set
Top 4%
0.5%