In silico perturbation of drug targets in pan-cancer analysis combining multiple networks and pathways

Pan-cancer gene expression analysis: Identification of deregulated autophagy genes and drugs to target them

Identifying suitable deregulated targets in autophagy pathway is essential for developing autophagy modulating cancer therapies. With this aim, we systematically analyzed the expression levels of genes that contribute to the execution of autophagy in 21 cancers. Deregulated genes for 21 cancers were analyzed using the level 3 mRNA data from TCGAbiolinks. A total of 574 autophagy genes were mapped to the deregulated genes across 21 cancers. PPI network, cluster analysis, gene enrichment, gene ontology, KEGG pathway, patient survival, protein expression and cMap analysis were performed. Among the autophagy genes, 260 were upregulated, and 43 were downregulated across pan-cancer. The upregulated autophagy genes - CDKN2A and BIRC5 - were the most frequent signatures in cancers and could be universal cancer biomarkers. Significant involvement of autophagy process was found in 8 cancers (CHOL, HNSC, GBM, KICH, KIRC, KIRP, LIHC and SARC). Fifteen autophagy hub genes (ATP6V0C, BIRC5, HDAC1, IL4, ITGB1, ITGB4, MAPK3, mTOR, cMYC, PTK2, SRC, TCIRG1, TP63, TP73 and ULK1) were found to be linked with patients survival and also expressed in cancer patients tissue samples, making them as potential drug targets for these cancers. The deregulated autophagy genes were further used to identify drugs Losartan, BMS-345541, Embelin, Abexinostat, Panobinostat, Vorinostat, PD-184352, PP-1, XMD-1150, Triplotide, Doxorubicin and Ouabain, which could target one or more autophagy hub genes. Overall, our findings shed light on the most frequent cancer-associated autophagy genes, potential autophagy targets and molecules for cancer treatment. These findings can accelerate autophagy modulation in cancer therapy.

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

Multi-Approach Bioinformatics Analysis of Curated Omics Data Provides a Gene Expression Panorama for Multiple Cancer Types

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.

Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Metastases are the main cause of death in advanced breast cancer (BC) patients. Although chemotherapy and hormone therapy are current treatment strategies, drug resistance is frequent and still not completely understood. In this study, a bioinformatics analysis was performed on BC patients to explore the molecular mechanisms associated with BC metastasis. Microarray gene expression profiles of metastatic and non metastatic BC patients were downloaded from Gene Expression Omnibus (GEO) dataset. Raw data were normalized and merged using the Combat tool. Pathways enriched with differently expressed genes were identified and a pathway co-expression network was generated using Pearson's correlation. We identified from this network, which includes 17 pathways and 128 interactions, the pathways that most influence the network efficiency. Moreover, protein interaction network was investigated to identify hub genes of the pathway network. The prognostic role of the network was evaluated with a survival analysis using an independent dataset. In conclusion, the pathway co-expression network could contribute to understanding the mechanism and development of BC metastases.