Background: The construction of realistic gene regulatory networks is currently hindered by the complex, often redundant, combinatorial and multi-layered nature of gene interdependencies, the limited availability of functional annotations, and the intrinsic shortcomings of co-expression-based approaches. Here, we introduce Gene Deregulation Networks (GDNs), a new structure in which linked genes indicate that an expression deregulation in one is likely to propagate to the other. GDNs are inferred directly from gene expression data using a probabilistic theory of causation, without requiring prior functional annotation or other domain knowledge. Methods: Using highly specific tissue markers—N-genes specifically expressed in normal tissues and T-genes specifically expressed in tumors—we construct separate GDNs for normal and tumor tissues. Data are obtained from TCGA RNA-Seq profiles of bulk tumor and normal tissue samples across multiple cancer localizations. GDN links are preliminarily identified via a statistical test of causal sufficiency between gene expression deregulation events, and then reassessed as spurious or redundant using additional causal criteria. A simple scheme based on the GDNs is implemented in order to describe the evolution dynamics. Results: The T-GDN in prostate adenocarcinoma comprises 6138 genes and 102362 directed edges (0.27% of possible connections). Genes with low expression deregulation frequency (< 0.2) exhibit high out-degrees, indicating high deregulation potential, while high-frequency genes (> 0.4) show high in-degrees, suggesting they are convergence points of deregulation cascades. EPHA10 (frequency 0.74, in-degree 182) and ENSG00000275479 (out-degree 209) represent these extremes. The N-GDN contains 1097 genes and 4984 edges, featuring compressed nodes reflecting the multi-step nature of somatic evolution. Analysis of tumor samples reveals that early-stage tumors rely primarily on spontaneous gene activations, while advanced tumors exhibit extensive cascade-driven deregulation. Preliminary simulations show qualitative agreement with experiments on gene knockdown in tumor cellular lines. Conclusions: GDNs provide a robust framework to understand cancer progression through deregulation cascades. The separation into N and T networks, connected by NT-genes, i.e. genes common to both networks, offers a systematic basis for modeling carcinogenesis and the possible outcomes of therapeutic interventions.

The innate immune system is the first line of defense against pathogens. Its composition includes barriers, mucus, and other substances as well as phagocytic and other cells. The purpose of the present paper is to compare tissues with regard to their immune response to infections and to cancer. Simple ideas and the qualitative theory of differential equations are used along with general principles such as the minimization of the pathogen load and economy of resources. In the simplest linear model, the annihilation rate of pathogens in any tissue should be greater than the pathogen’s average replication rate. When nonlinearities are added, a stability condition emerges, which relates the strength of regular threats, barrier height, and annihilation rate. The stability condition allows for a comparison of immunity in different tissues. On the other hand, in cancer immunity, the linear model leads to an expression for the lifetime risk, which accounts for both the effects of carcinogens (endogenous or external) and the immune response. The way the tissue responds to an infection shows a correlation with the way it responds to cancer. The results of this paper are formulated in the form of precise statements in such a way that they could be checked by present-day quantitative immunology.

Background: Face morphometry has been shown to work as a diagnosis tool in a set of syndromes. Face similarities are usually indications of more complete genetic similarities. Purpose: To show preliminary results on the face morphometry profile of the Cuban population and to argue that it could be used to define early markers for diseases like Alzheimer’s. Methods: A dataset composed of photos of 200000 men is processed. Facial landmarks are extracted by means of the DLIB library and distances between them are computed. By clustering samples with similar facial traits, groups are formed and their densities inside the population are computed. Results: The face morphometry profiles for two age cohorts are obtained, showing the population dynamics. Genes involved in facial development are shown to be related to Alzheimer’s disease. Conclusion: Late multifactorial diseases develop against the genetic background of each individual, which is expressed by its face morphometry. The latter can be thus considered a risk marker.

The innate immune system is the first line of defense against pathogens. It’s composition includes barriers, mucus and other substances as well as phagocytic and other cells. The purpose of the paper is to analyze in general grounds the immune system and the body immunity to cancer. Simple ideas and the qualitative theory of differential equations are used along with general principles such as the minimization of the pathogen load and economy of resources. In the simplest linear model, the annihilation rate of pathogens in any tissue should be greater than the pathogen’s average rate of growth. When nonlinearities are added, a reference value for the number of pathogens is set, and a stability condition emerges, which relates strength of regular threats, barrier height and annihilation rate. On the other hand, in cancer immunity, the linear model leads to an expression for the lifetime risk, which accounts for both the effects of carcinogens (endogenous or external) and the immune response. The stability condition allows a comparison of immunity in different tissues. The way the tissue responds to an infection shows a correlation with the way it responds to cancer. These statements are formulated at the qualitative level, but may and deserve to be quantitatively checked.