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Meta-Analysis
. 2019 Apr 8:10:17.
doi: 10.1186/s13229-019-0262-8. eCollection 2019.

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

Jaume Forés-Martos  1 , Ferrán Catalá-López  1   2   3   4 , Jon Sánchez-Valle  5 , Kristina Ibáñez  6 , Héctor Tejero  7 , Helena Palma-Gudiel  1   8 , Joan Climent  3   9 , Vera Pancaldi  5 , Lourdes Fañanás  1   8 , Celso Arango  1   10 , Mara Parellada  1   10 , Anaïs Baudot  11 , Daniel Vogt  12 , John L Rubenstein  13   14 , Alfonso Valencia  5   15 , Rafael Tabarés-Seisdedos  1   2   3
Affiliations
Meta-Analysis

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

Jaume Forés-Martos et al. Mol Autism. .

Abstract

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 types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them.

Results: Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD.

Conclusions: Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer.

Keywords: ASD; Autism; Cancer; Comorbidity; Gene expression; Meta-analysis; Multimorbidity; Transcriptome.

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Conflict of interest statement

Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
a Table showing the datasets included in the ASD differential gene expression meta-analysis. b Diagram depicting the workflow used to perform the ASD differential gene expression meta-analysis. c Summary of the cancer types, number of datasets and samples included in each cancer-specific differential gene expression meta-analysis
Fig. 2
Fig. 2
a) Table showing the significance of the intersections of upregulated and downregulated genes between ASD and the 22 cancer types included in our study, comprising, acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), bladder cancer (BLAD), brain cancer (BRAIN), breast cancer (BREAST), cervical cancer (CERVI), cholangiocarcinoma (CHOL), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), colorectal cancer (CRC), diffuse large b cell lymphoma (DLBCL), follicular lymphoma (FLYMPH), gastric cancer (GSTCA), head and neck carcinoma (HNC), kidney cancer (KIDN), liver cancer (LIV), lung cancer (LUNG), melanoma (MEL), ovarian cancer (OV), pancreatic cancer (PANC), prostate cancer (PROST), and thyroid cancer (THYR). Columns A, B, C, and D include the number of genes upregulated in both, downregulated in both, upregulated in ASD and downregulated in cancer, and downregulated in ASD and upregulated in cancer, respectively. Green cell colors indicate significant intersections (FDR corrected p-values from Fisher’s exact test lower than 0.05) with darker green tones indicating lower FDR corrected p-values. b) Venn diagrams showing the number of genes commonly deregulated in SDDCs and ODDCs. c) Scatter plots and correlation values, depicting the associations between ASD and all SDDC and ODDCs for cancer differential expression profiles. d) Heatmap showing the differential expression status of genes included in the KEGG hsa04151 pathways (PI3K-Akt signaling pathway), that were found to be differentially expressed in the ASD differential expression meta-analyses. White, gray and black cells indicate unaltered, downregulated and upregulated differential expression status, respectively
Fig. 3
Fig. 3
Top 10 ASD- and cancer-associated pathways extracted from 3 different molecular signature databases (Hallmarks, KEGG and Reactome). Yellow and purple segments indicate pathways downregulated and upregulated in cancer, respectively, whereas red and green segments denote pathways downregulated and upregulated in ASD, respectively. The length of the yellow and purple bars indicates the number of studied cancers that represent the reported direction of deregulation for this particular pathway. a Pathways jointly upregulated in ASD and cancer. b Pathways upregulated in ASD and downregulated in cancer. c Pathways jointly downregulated in ASD and cancer. d Pathways downregulated in ASD and upregulated in cancer
Fig. 4
Fig. 4
LINCS L1000-derived top related drug sets. Gold cells represent drug sets that produce differential expression profiles that mimic the differential expression profiles found in the disease of interest, light blue cells indicate drug sets that generate differential expression profiles opposite to those found in our disease of interest. Green, blue and red bars located on top of the heat map indicate ODDCs, SDDCs and ASD membership, respectively
Fig. 5
Fig. 5
Heatmap showing the pathways altered in ASD, SDDCs and ODDCs. Yellow and purple cells indicate pathways downregulated and upregulated in cancer, respectively, red and green cells denote pathways downregulated and upregulated in ASD, respectively
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