The advancements in (epi)genomics, transcriptomics, proteomics, metabolomics, and immunomics contributed greatly to the discovery of diagnostic, predictive, and prognostic biomarkers of human diseases such as cancer.
Tissue biopsy is a conventional method to collect solid tissue samples for cancer diagnosis, monitoring, and pathologic analysis. Diverse molecular biomarkers at different levels including variations, DNAs, mRNAs, ncRNAs, genes, proteins, and signal cells have been detected based on tissue biopsy omics data. As a new attempt, recently liquid biopsy (e.g. peripheral blood, serum, plasma, etc.) emerges as a promising non-invasive surrogate for solid tissue to investigate disease-associated molecular biomarkers, especially for tissues like the brain where tissue biopsy is frequently difficult.
Bioinformatics and computational methods and tools have played a major role in the detection of disease biomarkers for both population-level and individual-level analyses in precision medicine. For example, molecular signatures identified based on the computation of within-sample relative orderings can reliably predict the individuals and are easily translated into clinical practices.
With this Research Topic, we would like to contribute to the discovery of diagnostic, predictive, and prognostic biomarkers for cancer that are stable, effective, and interpretable.
We welcome original research, methods, and review articles on new approaches to detection, verification, and clinical application of such biomarkers, including among others:
• Analysis of disease aberrant signals in bodily fluids such as peripheral whole blood, serum, plasma, including quality control, differential analysis, pathway enrichment, and network analysis.
• Methods for identification of biomarkers or signatures, detection of pathways or networks for cancer diagnostic, predictive, therapeutic, or prognostic purposes.
The advancements in (epi)genomics, transcriptomics, proteomics, metabolomics, and immunomics contributed greatly to the discovery of diagnostic, predictive, and prognostic biomarkers of human diseases such as cancer.
Tissue biopsy is a conventional method to collect solid tissue samples for cancer diagnosis, monitoring, and pathologic analysis. Diverse molecular biomarkers at different levels including variations, DNAs, mRNAs, ncRNAs, genes, proteins, and signal cells have been detected based on tissue biopsy omics data. As a new attempt, recently liquid biopsy (e.g. peripheral blood, serum, plasma, etc.) emerges as a promising non-invasive surrogate for solid tissue to investigate disease-associated molecular biomarkers, especially for tissues like the brain where tissue biopsy is frequently difficult.
Bioinformatics and computational methods and tools have played a major role in the detection of disease biomarkers for both population-level and individual-level analyses in precision medicine. For example, molecular signatures identified based on the computation of within-sample relative orderings can reliably predict the individuals and are easily translated into clinical practices.
With this Research Topic, we would like to contribute to the discovery of diagnostic, predictive, and prognostic biomarkers for cancer that are stable, effective, and interpretable.
We welcome original research, methods, and review articles on new approaches to detection, verification, and clinical application of such biomarkers, including among others:
• Analysis of disease aberrant signals in bodily fluids such as peripheral whole blood, serum, plasma, including quality control, differential analysis, pathway enrichment, and network analysis.
• Methods for identification of biomarkers or signatures, detection of pathways or networks for cancer diagnostic, predictive, therapeutic, or prognostic purposes.