CTDquerier: a bioconductor R package for Comparative Toxicogenomics DatabaseTM data extraction, visualization and enrichment of environmental and toxicological studies

Global comparative transcriptomes uncover novel and population-specific gene expression in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and is one of the deadliest gastrointestinal malignancies. Despite numerous transcriptomics studies to understand its molecular basis, the impact of population-specific differences on this disease remains unexplored.

AIMS

This study aimed to investigate the population-specific differences in gene expression patterns among ESCC samples obtained from six distinct global populations, identify differentially expressed genes (DEGs) and their associated pathways, and identify potential biomarkers for ESCC diagnosis and prognosis. In addition, this study deciphers population specific microbial and chemical risk factors in ESCC.

METHODS

We compared the gene expression patterns of ESCC samples from six different global populations by analyzing microarray datasets. To identify DEGs, we conducted stringent quality control and employed linear modeling. We cross-compared the resulting DEG lists of each populations along with ESCC ATLAS to identify known and novel DEGs. We performed a survival analysis using The Cancer Genome Atlas Program (TCGA) data to identify potential biomarkers for ESCC diagnosis and prognosis among the novel DEGs. Finally, we performed comparative functional enrichment and toxicogenomic analysis.

RESULTS

Here we report 19 genes with distinct expression patterns among populations, indicating population-specific variations in ESCC. Additionally, we discovered 166 novel DEGs, such as ENDOU, SLCO1B3, KCNS3, IFI35, among others. The survival analysis identified three novel genes (CHRM3, CREG2, H2AC6) critical for ESCC survival. Notably, our findings showed that ECM-related gene ontology terms and pathways were significantly enriched among the DEGs in ESCC. We also found population-specific variations in immune response and microbial infection-related pathways which included genes enriched for HPV, Ameobiosis, Leishmaniosis, and Human Cytomegaloviruses. Our toxicogenomic analysis identified tobacco smoking as the primary risk factor and cisplatin as the main drug chemical interacting with the maximum number of DEGs across populations.

CONCLUSION

This study provides new insights into population-specific differences in gene expression patterns and their associated pathways in ESCC. Our findings suggest that changes in extracellular matrix (ECM) organization may be crucial to the development and progression of this cancer, and that environmental and genetic factors play important roles in the disease. The novel DEGs identified may serve as potential biomarkers for diagnosis, prognosis and treatment.

A comprehensive analysis of mRNA expression profiles of Esophageal Squamous Cell Carcinoma reveals downregulation of Desmoglein 1 and crucial genomic targets

AIM

Esophageal Squamous Cell Carcinoma (ESCC) is a histological subtype of esophageal cancer that begins in the squamous cells in the esophagus. In only 19% of the ESCC-diagnosed patients, a five-year survival rate has been seen. This necessitates the identification of high-confidence biomarkers for early diagnosis, prognosis, and potential therapeutic targets for the mitigation of ESCC.

METHOD

We performed a meta-analysis of 10 mRNA datasets and identified consistently perturbed genes across the studies. Then, integrated with ESCC ATLAS to segregate 'core' genes to identify consequences of primary gene perturbation events leading to gene-gene interactions and dysregulated molecular signaling pathways. Further, by integrating with toxicogenomics data, inferences were drawn for gene interaction with environmental exposures, trace elements, chemical carcinogens, and drug chemicals. We also deduce the clinical outcomes of candidate genes based on survival analysis using the ESCC related dataset in The Cancer Genome Atlas.

RESULT

We identified 237 known and 18 novel perturbed candidate genes. Desmoglein 1 (DSG1) is one such gene that we found significantly downregulated (Fold Change =-1.89, p-value = 8.2e-06) in ESCC across six different datasets. Further, we identified 31 'core' genes (that either harbor genetic variants or are regulated by epigenetic modifications) and found regulating key biological pathways via adjoining genes in gene-gene interaction networks. Functional enrichment analysis showed dysregulated biological processes and pathways including "Extracellular matrix", "Collagen trimmer" and "HPV infection" are significantly overrepresented in our candidate genes. Based on the toxicogenomic inferences from Comparative Toxicogenomics Database we report the key genes that interacted with risk factors such as tobacco smoking, zinc, nitroso benzylmethylamine, and drug chemicals such as cisplatin, Fluorouracil, and Mitomycin in relation to ESCC. We also point to the STC2 gene that shows a high risk for mortality in ESCC patients.

CONCLUSION

We identified novel perturbed genes in relation to ESCC and explored their interaction network. DSG1 is one such gene, its association with microbiota and a clinical presentation seen commonly with ESCC hints that it is a good candidate for early diagnostic marker. Besides, in this study we highlight candidate genes and their molecular connections to risk factors, biological pathways, drug chemicals, and the survival probability of ESCC patients.

Epidemiology meets toxicogenomics: Mining toxicologic evidence in support of an untargeted analysis of pesticides exposure and Parkinson's disease

BACKGROUND

Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson's disease (PD).

OBJECTIVES

Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases.

METHODS

We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays.

RESULTS

Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress.

CONCLUSIONS

Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms

The urinary proteome is a promising pool of biomarkers of kidney disease. However, the protein changes observed in urine only partially reflect the deregulated mechanisms within kidney tissue. In order to improve on the mechanistic insight based on the urinary protein changes, we developed a new prioritization strategy called PRYNT (PRioritization bY protein NeTwork) that employs a combination of two closeness-based algorithms, shortest-path and random walk, and a contextualized protein-protein interaction (PPI) network, mainly based on clique consolidation of STRING network. To assess the performance of our approach, we evaluated both precision and specificity of PRYNT in prioritizing kidney disease candidates. Using four urinary proteome datasets, PRYNT prioritization performed better than other prioritization methods and tools available in the literature. Moreover, PRYNT performed to a similar, but complementary, extent compared to the upstream regulator analysis from the commercial Ingenuity Pathway Analysis software. In conclusion, PRYNT appears to be a valuable freely accessible tool to predict key proteins indirectly from urinary proteome data. In the future, PRYNT approach could be applied to other biofluids, molecular traits and diseases. The source code is freely available on GitHub at: https://github.com/Boizard/PRYNT and has been integrated as an interactive web apps to improved accessibility ( https://github.com/Boizard/PRYNT/tree/master/AppPRYNT ).