Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.Funding: This work was supported by the Spanish Ministry of Science, Innovation and Universities (grant PID2019-105010RB-I00, grant PID2019- 108096RB-C21), the Andalusian Government and FEDER (grants UMA18-FEDERJA-102, UMA18-FEDERJA-220, PY20_00257, PY20_00372, RH-0079-2021 and funds from the group PAIDI BIO 267); the Spanish Ministry of Economy and Competitiveness (grant PID2019-108096RB-C21), the Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, ‘‘A way to make Europe"); and the European Union (HORIZON-HLTH-2022-DISEASE-06, Project ID: 101080580) to JAGR. JRP holds a research grant from the Andalusian Government (Fundacion Progreso y Salud) [PI-0075- 2017]. BM is awarded of the Ayudas para la formación del profesorado universitario (FPU18/00755, Ministerio de Universidades). The ‘‘CIBER de Enfermedades Raras’’ is an initiative from the ISCIII (Spain). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscrip