Biology subjects

Perez, G. J. G.

Publications and source records attributed to Perez, G. J. G..

3 recordsLinked to original sources

bioRxiv2024

Dynamics of N-genes and T-genes in cancer

Common knowledge states that the spontaneous somatic evolution of a normal tissue may lead to a tumor. Once the tumor is formed, it naturally evolves towards a state of higher malignancy. On the other hand, perfect gene expression markers for normal tissue and tumor--the so-called N-genes and T-genes--were recently introduced. We join these two pieces of knowledge in order to argue that: 1) Only N-markers participate in the spontaneous dynamics of a normal tissue. The number of active markers decreases as the tissue approaches the transition point where it becomes a tumor. 2) Only T-markers participate in the spontaneous dynamics of tumors. The number of markers increases as the tumor becomes more malignant. 3) Both sets of genes are connected by the so-called NT-genes, i.e., genes that are simultaneously N- and T-markers. They should play a crucial role at the transition point and, possibly, when the tumor is exposed to a drug or therapy. 4) The pathways or mechanisms protecting the normal tissue from becoming a tumor may be described by a small perfect panel of N-genes. 5) The pathways or mechanisms guiding the evolution of tumors in a tissue may be described by a small perfect panel of T-genes. We illustrate the above statements with the analysis of expression data for prostate adenocarcinoma, one of the most heterogeneous tumors. In this case, there are about 1000 N-genes and 6000 T-genes, and the perfect N- and T-panels contain 11 and 8 genes, respectively. Additionally, we provide examples from lung adenocarcinoma and liver hepatocarcinoma.

cancer biology↗

bioRxiv2023

Deregulation cascades in carcinogenesis

BackgroundThe 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. MethodsUsing 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. ResultsThe 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. ConclusionsGDNs 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.

systems biology↗

bioRxiv2022

Perfect genetic biomarkers for cancer from a fresh view of gene dysregulation

Over the last decades, a host of gene expression profiles of tumor and normal tissue samples have been recorded by many microarray and RNA-Seq projects. Much of this big data awaits a full understanding and exploitation for translational cancer research. In particular, the pressing need to discover gene panels for diagnosis and therapy have not received yet a definitive answer. Here, we tackle such a question through rigorous mining of some of the currently available data. Our mining scheme rests on formal concept analysis and rough set theory and allows us to identify perfect gene panels for twelve of the solid tumors reported in the TCGA database. We dub them perfect gene panels because they perfectly discriminate between normal and tumor samples. To wit, testing the gene expression profiles against a tumor or normal pattern provides no false positive and no false negative cases (i.e., 100% sensitivity and 100% specificity). Hence, perfect gene panels might be useful genetic markers for cancer diagnosis. Furthermore, we stress that such panels come in many flavors depending on the gene expression levels we choose as a pattern to check. For instance, there are perfect panels where a single gene over-expression signals a tumor and others where a single non-silenced gene is an indication of a tumor-free sample, just to mention two out of eight possible cases. Remarkably, some panels also suggest suitable genetic targets for therapeutic interventions, since they define normal samples by tuning the expression level of a single gene.

cancer biology↗