0000000000482102
AUTHOR
Héctor Tejero
Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Este es el artículo que se ha publicado de forma definitiva en: https://molecularautism.biomedcentral.com/articles/10.1186/s13229-019-0262-8 En este artículo también participa Joan Climent, Vera Pancaldi, Lourdes Fañanás, Celso Arango, Mara Parellada, Anaïs Baudot, Daniel Vogt, John L. Rubenstein, Alfonso Valencia y Rafael Tabarés-Seisdedos. Background: Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Methods: Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer t…
Additional file 1: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Table S1. Datasets included in our study. Table S2. Age, gender and pmi distributions between cases and control samples at each step of the preprocessing procedure at the asd defferentail gene expression meta-analysis. Table S3. Final samples included in the asd meta-analysis. Table S4. Jointly same direction derregulated genes in asd ans sddcs and jointly oposite direction derregulated genes in asd and oddcs. Table S5. Genes jointly deregulated in asd and in sddcs and oddcs. Figure S1. Comaprison of differential gene expression analysis using limma with rlog transformed data and two state of the art rnaseq differential expression metdos. Figure S2. Patient overlap in the three asd studies.…
Abstract A22: PanDrugsDB: Identifying druggable genetic dependencies for personalized cancer therapy
Abstract The paradigm of personalized medicine is the identification of the appropriate drug for the right patient, using molecular profiles. In Oncology, it is well established that the anticancer drugs are effective in only a small subset of patients. Moreover, many of the new targeted therapies inhibit specific proteins, and they are only effective in tumors that are genetically altered. Consequently, the success of personalized treatment depends on each individual molecular profile, which a priori can be considered as very heterogeneous. Here, we present a new computational approach (PanDrugsDB) based on the analysis and integration of genomic data (mutations, copy number variations or …
Unveiling the molecular basis of disease co-occurrence: towards personalized comorbidity profiles
AbstractComorbidity is an impactful medical problem that is attracting increasing attention in healthcare and biomedical research. However, little is known about the molecular processes leading to the development of a specific disease in patients affected by other conditions. We present a disease interaction network inferred from similarities in patients’ molecular profiles, which significantly recapitulates epidemiologically documented comorbidities, providing the basis for their interpretation at a molecular level. Furthermore, expanding on the analysis of subgroups of patients with similar molecular profiles, our approach discovers comorbidity relations not previously described, implicat…
A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer's Disease, Glioblastoma and Lung cancer.
Epidemiological studies indicate that patients suffering from Alzheimer’s disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the molecular scenarios that might underlie direct and inverse co-morbidities between these diseases. Transcriptomic meta-analyses reveal significant numbers of genes with inverse patterns of expression in Alzheimer’s disease and lung cancer, and with similar patterns of expression in Alzheimer’s disease and glioblastoma. These observations support the existence of molecular substrates that could at least partially account for these direct and inverse co-morbidity relationships. A functional analy…
Additional file 5: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Full differential gene expression meta-analysis results of cancer data. (ZIP 31237 kb)
Additional file 3: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Full ASD GSEA pre-ranked enrichment results for different molecular signatures (C2, H, GO_BP, GO_CC, and GO_MF). (XLSX 698 kb)
Additional file 5: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Full differential gene expression meta-analysis results of cancer data. (ZIP 31237 kb)
Additional file 3: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Full ASD GSEA pre-ranked enrichment results for different molecular signatures (C2, H, GO_BP, GO_CC, and GO_MF). (XLSX 698 kb)
Additional file 4: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
gProfileR biological process overrepresentation results of the genes contained in the significant intersections of ASD, SDDCs, and ODDCs. (XLSX 233 kb)
Additional file 2: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Full ASD differential gene expression meta-analysis results. (CSV 1387 kb)
Additional file 2: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
Full ASD differential gene expression meta-analysis results. (CSV 1387 kb)
Additional file 4: of Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer
gProfileR biological process overrepresentation results of the genes contained in the significant intersections of ASD, SDDCs, and ODDCs. (XLSX 233 kb)