Identifying the critical states and dynamic network biomarkers of cancers based on network entropy

Abstract

Background There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems. Methods In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this method effectively explores the key associations among biomolecules and captures their dynamic abnormalities. Results Based on LNE, the pre-disease states of ten cancers were successfully detected. Two types of new prognostic biomarkers, optimistic LNE (O-LNE) and pessimistic LNE (P-LNE) biomarkers, were identified, enabling identification of the pre-disease state and evaluation of prognosis. In addition, LNE helps to find “dark genes” with nondifferential gene expression but differential LNE values. Conclusions The proposed method effectively identified the critical transition states of complex diseases at the single-sample level. Our study not only identified the critical transition states of ten cancers but also provides two types of new prognostic biomarkers, O-LNE and P-LNE biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Data-driven energy landscape reveals critical genes in cancer progression

“…There are two obvious transition pathways passing through attractor 3, which may indicate that it is an intermediate state of KIRC cancer evolution, and the cells in attractor 3 may return to a benign state (stage I, II) with appropriate intervention and treatment, or rapidly deteriorate to stage IV, if without timely treatment. Therefore, there is an opportunity to identify corresponding warning signals in stage III 29 .…”

Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein–protein interaction networks

“…Moreover, the final leader genes identified by our integrated procedure were compared with the result genes of methods from Bailey et al (2018 ), Ding et al (2021 ), and Liu et al (2022 ) according to the AUC values of SVM classifiers. The results of our genes and other methods all had good classification performance with no significant difference in terms of classification performance ( Table 8 ).…”

“…Other methods identify genes on the basis of their proximity to cancer genes from various evidence, such as biomolecular networks. Liu et al (2022 ) employed a model-free computational method to not only identify the critical transition states of ten cancers but also provide new biomarkers from a network perspective. Nie et al (2020 ) constructed a protein–protein interaction (PPI) network for differentially expressed genes (DEGs) between stomach cancer and normal tissues and identified ten hub genes highly related to stomach cancer.…”

Identifying the critical state of complex biological systems by the directed-network rank score method

“…Recently, network-based methods from the DNB framework have been developed to address different biological topics, such as the identification of pre-disease states for complex diseases ( Liu et al , 2022 ; Peng et al , 2022 ), the personalized characterization of diseases ( Liu et al , 2017 ; Zhang et al , 2021 ) and the discovery of personalized driver genes prioritization ( Guo et al , 2021b ). From the perspective of gene associative and cooperative effects, we presented a model-free computational method in this study, i.e.…”