mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma.
biomarkers
complete transcriptomic profiles
generalization ability
prognosis
sample classification
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2021
2021
Historique:
received:
12
03
2021
accepted:
06
05
2021
entrez:
13
8
2021
pubmed:
14
8
2021
medline:
14
8
2021
Statut:
epublish
Résumé
In this work, an algorithm named mRBioM was developed for the identification of potential mRNA biomarkers (PmBs) from complete transcriptomic RNA profiles of gastric adenocarcinoma (GA). mRBioM initially extracts differentially expressed (DE) RNAs (mRNAs, miRNAs, and lncRNAs). Next, mRBioM calculates the total information amount of each DE mRNA based on the coexpression network, including three types of RNAs and the protein-protein interaction network encoded by DE mRNAs. Finally, PmBs were identified according to the variation trend of total information amount of all DE mRNAs. Four PmB-based classifiers without learning and with learning were designed to discriminate the sample types to confirm the reliability of PmBs identified by mRBioM. PmB-based survival analysis was performed. Finally, three other cancer datasets were used to confirm the generalization ability of mRBioM. mRBioM identified 55 PmBs (41 upregulated and 14 downregulated) related to GA. The list included thirteen PmBs that have been verified as biomarkers or potential therapeutic targets of gastric cancer, and some PmBs were newly identified. Most PmBs were primarily enriched in the pathways closely related to the occurrence and development of gastric cancer. Cancer-related factors without learning achieved sensitivity, specificity, and accuracy of 0.90, 1, and 0.90, respectively, in the classification of the GA and control samples. Average accuracy, sensitivity, and specificity of the three classifiers with machine learning ranged within 0.94-0.98, 0.94-0.97, and 0.97-1, respectively. The prognostic risk score model constructed by 4 PmBs was able to correctly and significantly ( GA-related PmBs have high specificity and sensitivity and strong prognostic risk prediction. MRBioM has also good generalization. These PmBs may have good application prospects for early diagnosis of GA and may help to elucidate the mechanism governing the occurrence and development of GA. Additionally, mRBioM is expected to be applied for the identification of other cancer-related biomarkers.
Identifiants
pubmed: 34386038
doi: 10.3389/fgene.2021.679612
pmc: PMC8354214
doi:
Types de publication
Journal Article
Langues
eng
Pagination
679612Informations de copyright
Copyright © 2021 Dong, Rao, Du, Gao, Lv, Wang and Zhang.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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