Molecular biomarkers,network biomarkers,and dynamic network biomarkers for diagnosis and prediction of rare diseases

The difficulty of converting scientific research findings into novel pharmacological treatments for rare and life-threatening diseases is enormous.Biomarkers related to multiple biological processes involved in cell growth,proliferation,and disease occurrence have been identified in recent years with the development of immunology,molecular biology,and genomics technologies.Biomarkers are capable of reflecting normal physiological processes,pathological processes,and the response to therapeutic intervention;as such,they play vital roles in disease diagnosis,prevention,drug response,and other aspects of biomedicine.The discovery of valuable biomarkers has become a focal point of current research.Numerous studies have identified molecular biomarkers based on the differential expression/concentration of molecules(e.g.,genes/proteins)for disease state diagnosis,characterization,and treatment.Although technological breakthroughs in molecular analysis platforms have enabled the identification of a large number of candidate biomarkers for rare diseases,only a small number of these candidates have been properly validated for use in patient treatment.The traditional molecular biomarkers may lose vital information by ignoring molecular associations/interactions,and thus the concept of network biomarkers based on differential associations/correlations of molecule pairs has been established.This approach promises to be more stable and reliable in diagnosing disease states.Furthermore,the newly-emerged dynamic network biomarkers(DNBs)based on differential fluctuations/correlations of molecular groups are able to recognize pre-disease states or critical states instead of disease states,thereby achieving rare disease prediction or predictive/preventative medicine and providing deep insight into the dynamic characteristics of disease initiation and progression.

The single-sample network module biomarkers(sNMB)method reveals the pre-deterioration stage of disease progression

The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration,which allows the timely implementation of appropriate measures to prevent a catastrophic transition.However,identifying the pre-deterioration stage is a challenging task in clinical medicine,especially when only a single sample is available for most patients,which is responsible for the failure of most statistical methods.In this study,a novel computational method,called single-sample network module biomarkers(sNMB),is presented to predict the pre-deterioration stage or critical point using only a single sample.Specifically,the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples.Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets,including acute lung injury,stomach adenocarcinoma,esophageal carcinoma,and rectum adenocarcinoma.In addition,it provides signaling biomarkers for further practical application,which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.