Abstract
One of the vital challenges for cancer diseases is efficient biomarkers monitoring formation and development are limited. Omics data integration plays a crucial role in the mining of biomarkers in the human condition. As the link between omics study on biomarkers discovery and cancer diseases is deepened, defining the principal technologies applied in the field is a must not only for the current period but also for the future. We utilize topic modeling to extract topics (or themes) as a probabilistic distribution of latent topics from the dataset. To predict the future trend of related cases, we utilize the Prophet neural network to perform a prediction correction model for existing topics. A total of 2,318 pieces of literature (from 2006 to 2020) were retrieved from MEDLINE with the query on “omics” and “cancer.” Our study found 20 topics covering current research types. The topic extraction results indicate that, with the rapid development of omics data integration research, multi-omics analysis (Topic 11) and genomics of colorectal cancer (Topic 10) have more studies reported last 15 years. From the topic prediction view, research findings in multi-omics data processing and novel biomarker discovery for cancer prediction (Topic 2, 3, 10, 11) will be heavily focused in the future. From the topic visuallization and evolution trends, metabolomics of breast cancer (Topic 9), pharmacogenomics (Topic 15), genome-guided therapy regimens (Topic 16), and microRNAs target genes (Topic 17) could have more rapidly developed in the study of cancer treatment effect and recurrence prediction.
Highlights
Genomics, proteomics, metabolomics, transcriptomics, and other -omics studies involve comprehensive investigations (Saito et al, 2013)
These topics include: biomarker discovery in early diagnostics (86 studies); machine learning for cancer prediction (148 studies); novel biomarker identify technology (183 studies); multi-omics in Hepatocellular Carcinoma (97 studies); genomics in clinical application (74 studies); genomics in tumor heterogeneity discovery (76 studies); proteomics in post-translational modification (70 studies); transcriptional and metabolic processes (114 studies); metabolomics of breast cancer (121 studies); genomics of colorectal cancer (213 studies); multi-omics analysis (299 studies); cancer vaccines (63 studies); tumor immunotherapy (88 studies); metabolomics in prostate cancer (58 studies); pharmacogenomics (125 studies); genome-guided therapy regimens (132 studies); non-coding RNA target genes (153 studies); microbial metabolomics (70 studies); metagenomics (71 studies); genomics of colorectal cancer (77 studies)
The scientific panorama involved in studying the omics data integration toward the mining of biomarkers in cancers is described in the 20 extracted topics
Summary
Proteomics, metabolomics, transcriptomics, and other -omics studies involve comprehensive investigations (Saito et al, 2013). Advances in high-throughput technology have shown promise for discovering biomarkers (Njoku et al, 2020). Biomarkers are useful tools as indicators/predictors of disease severity and drug reactivity, and are expected to be used for diagnostic or prognostic purposes for all different types of complex diseases. With the discovery and identification of HRAS and TP53, more proto-oncogenes, tumor suppressor genes, and susceptibility genes have been discovered (Hanahan and Weinberg, 2000, 2011). The essential characteristics of tumor cells have been elucidated at the molecular level. The molecular mechanism and evolutionary dynamics of tumor development and cell heterogeneity can be observed (Urh and Kunej, 2016). Omics data integration and machine learning algorithm can be utilized to improve the predicting accuracy of familial tumor patients
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