SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT

Epithelial–mesenchymal transition(EMT) is a complex nonlinear biological process that plays essential roles in fundamental biological processes such as embryogenesis, wounding healing, tissue regeneration,and cancer metastasis. A hallmark of EMT is the switch-like behavior during state transition, which is characteristic of phase transitions. Hence, detecting the tipping point just before mesenchymal state transition is critical for understanding molecular mechanism of EMT. Through dynamic network biomarkers(DNB) model, a DNB group with 37 genes was identified which can provide the early-warning signals of EMT. Particularly, we found that two DNB genes, i.e., SMAD7 and SERPINE1 promoted EMT by switching their regulatory network which was further validated by biological experiments. Survival analyses revealed that SMAD7 and SERPINE1 as DNB genes further acted as prognostic biomarkers for lung adenocarcinoma.

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.

Dysfunction of PLA2G6 and CYP2C44-associated network signals imminent carcinogenesis from chronic inflammation to hepatocellular carcinoma

Little is known about how chronic inflammation contributes to the progression of hepatoceUular carcinoma （HCC）, especially the initiation of cancer. To uncover the critical transition from chronic inflammation to HCC and the molecular mechanisms at a network level, we analyzed the time-series proteomic data of woodchuck hepatitis virus/c.myc mice and age-matched wt-C57BL/6 mice using our dynamical network biomarker （DNB） model. DNB analysis indicated that the 5th month after birth of transgenic mice was the critical period of cancer initiation, just before the critical transition, which is consistent with clinical symptoms. Meanwhile, the DNB-associated network showed a drastic inversion of protein expression and coexpression levels before and after the critical transition. Two members of DNB, PLA2G6 and CYP2C44, along with their associated differentially expressed proteins, were found to induce dysfunction of arachidonic acid metabolism, further activate inflammatory responses through inflammatory mediator regulation of transient receptor potential channels, and finally lead to impairments of liver detoxification and malignant transition to cancer. As a c-Myc target, PLA2G6 positively correlated with c-Myc in expression, showing a trend from decreasing to increasing during carcinogenesis, with the minimal point at the critical transition or tipping point. Such trend of homologous PLA2G6 and c-Myc was also observed during human hepatocarcinogenesis, with the minimal point at high-grade dysplastic nodules （a stage just before the carcinogenesis）. Our study implies that PLA2G6 might function as an oncogene like famous c-Myc during hepatocar- cinogenesis, while downregulation of PLA2G6 and c-Myc could be a warning signal indicating imminent carcinogenesis.

Edge biomarkers for classification and prediction of phenotypes

In general,a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network,which can be considered as a set of interactions or edges among molecules.Thus,instead of individual molecules,networks or edges are stable forms to reliably characterize complex diseases.This paper reviews both traditional node biomarkers and edge biomarkers,which have been newly proposed.These biomarkers are classified in terms of their contained information.In particular,we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample.First,we categorize the biomarkers based on the information used in the learning and prediction steps.We then briefly introduce conventional node biomarkers,or molecular biomarkers without network information,and their computational approaches.The main focus of this paper is edge and network biomarkers,which exploit network information to improve the accuracy of diagnosis and prognosis.Moreover,by extracting both network and dynamic information from the data,we can develop dynamical network and edge biomarkers.These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state.The identified critical molecules can be used as drug targets,and the critical state indicates the critical point of disease control.The paper also discusses representative biomarker-based methods.