Phenomic Studies on Diseases: Potential and Challenges

Abstract

The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.

Phenomics demonstrates cytokines additive induction of epithelial to mesenchymal transition

“…Phenomics is a promising multi-omics approach for scientific and clinical field. [16,47] Existent work focus on the integration of phenomics imaging to predict clinical outcome. [48] A similar project exist for the description of EMT in lung cancer through a single cell mass cytometry.…”

Exosomal circRNAs in the plasma serve as novel biomarkers for IPF diagnosis and progression prediction

“…Exosomes, which are small extracellular vesicles secreted by nearly all cells, play a significant role in intercellular communication [ 4 ]. They contain various biomolecules, such as proteins, lipids, nucleic acids, and noncoding RNAs, including long noncoding RNAs (lncRNA), microRNAs (miRNAs), and circular RNAs (circRNAs) [ 4 , 5 ]. These exosome-derived inclusions have been implicated in the development and progression of various diseases, and can be found in biological fluids like urine, blood, saliva, and breast milk [ 4 ].…”

Identification of phenomic data in the pathogenesis of cancers of the gastrointestinal (GI) tract in the UK biobank

“…These traits arise from intricate interplays among genetic factors, environmental conditions, dietary influences, and symbiotic microorganisms 6 . Phenomic studies, in contrast to traditional biomedical research, exhibit unique features such as meticulous standardization in measurements, data management and analysis; relying on extensive big data, encompassing multi-dimensional and well-organized datasets 7 . In the domain of cancer, comprehending the entire spectrum of phenotypic irregularities associated with malignancies, including the intricate details revealed by biomarkers, is imperative for advancing our knowledge of carcinogenesis, especially the mechanisms underlying the relationships among phenome, genome and environmental impact.…”

“…Following the completion of the Human Genome Project, comprehensive explorations into the human phenome have become crucial, forming a foundational framework for deciphering the intricacies of human health codes, especially the complex relationships among the phenome, genome and environmental influences 7 . Previous research has documented the use of phenomic data to investigate the correlation between environmental factors, such as diet and lifestyle, and the development of various cancers 8 – 12 .…”