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.

Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage

Skin,as the outmost layer of human body,is frequently exposed to environmental stressors including pollutants and ultraviolet(UV),which could lead to skin disorders.Generally,skin response process to ultraviolet B(UVB)irradiation is a nonlinear dynamic process,with unknown underlying molecular mechanism of critical transition.Here,the landscape dynamic network biomarker(lDNB)analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels.The advanced l-DNB analysis approach showed that:(i)there was a tipping point before critical transition state during pigmentation process,validated by 3D skin model;(ii)13 core DNB genes were identified to detect the tipping point as a network biomarker,supported by computational assessment;(iii)core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening,validated by independent human skin data.Overall,this study provides new insights for skin response to repetitive UVB irradiation,including dynamic pathway pattern,biphasic response,and DNBs for skin lightening change,and enables us to further understand the skin resilience process after external stress.