We present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. This dataset is associated with the following publication: Watford, S., R. Grashow, V. De La Rosa, R. Rudel, K. Paul-Friedman, and M. Martin. Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: Use case in breast carcinogenesis. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 46-57, (2018).

Background We previously identified and characterized a novel 55 kDa nuclear protein, termed nmt55/p54nrb, whose expression was decreased in a subset of human breast tumors. The objective of this study was to determine if this reduced expression in human breast tumors was attributed to the regulation of mRNA transcription or the presence of altered forms of this protein. Results Northern blot analysis and ribonuclease protection assay indicated that nmt55/p54nrb mRNA is expressed at varying levels in estrogen receptor positive (ER+) and estrogen receptor negative (ER-) human breast tumors suggesting that reduced expression of nmt55/p54nrb protein in ER- tumors was not due to transcriptional regulation. To determine if multiple protein isoforms are expressed in breast cancer, we utilized Western blot and immunohistochemical analyses, which revealed the expression of an nmt55/p54nrb protein isoform in a subset of ER+ tumors. This subset of ER+ human breast tumors expressed an altered form of nmt55/p54nrb that was undetectable with an amino-terminal specific antibody suggesting that this isoform contains alterations or modifications within the amino terminal domain. Conclusions Our study indicates that nmt55/p54nrb protein is post-transcriptionally regulated in human breast tumors leading to reduced expression in ER- tumors and the expression of an amino terminal altered isoform in a subset of ER+ tumors. The potential involvement of nmt55/p54nrb in RNA binding and pre-mRNA splicing may be important for normal cell growth and function; thus, loss or alteration of protein structure may contribute to tumor growth and progression.

Background Certain genes from the glutathione S-transferase superfamily have been associated with several cancer types. It was the objective of this study to determine whether alleles of the glutathione S-transferase zeta 1 (GSTZ1) gene are associated with the development of sporadic breast cancer. Methods DNA samples obtained from a Caucasian population affected by breast cancer and a control population, matched for age and ethnicity, were genotyped for a polymorphism of the GSTZ1 gene. After PCR, alleles were identified by restriction enzyme digestion and results analysed by chi-square and CLUMP analysis. Results Chi-squared analysis gave a χ2 value of 4.77 (three degrees of freedom) with P = 0.19, and CLUMP analysis gave a T1 value of 9.02 with P = 0.45 for genotype frequencies and a T1 value of 4.77 with P = 0.19 for allele frequencies. Conclusion Statistical analysis indicates that there is no association of the GSTZ1 variant and hence the gene does not appear to play a significant role in the development of sporadic breast cancer.

GEO accession number of the microarray study. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Biserni, M. Arno, S. Balu, C. Corton, R. Ugarte, and M. Antoniou. Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 108: 30-42, (2017).

Supports finding human phenotype/genotype relationships with queries by phenotype, chromosome location, gene, and SNP identifiers. Currently includes information from dbGaP, the National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog, and Genotype - Tissue Expression (GTeX).

MedGen is NCBI's portal to information about conditions and phenotypes related to Medical Genetics. Terms from the NIH Genetic Testing Registry (GTR), UMLS, HPO, Orphanet, ClinVar and other sources are aggregated into concepts, each of which is assigned a unique identifier and a preferred name and symbol. The core content of the record may include names, identifiers used by other databases, mode of inheritance, clinical features, and map location of the loci affecting the disorder. The concept identifier (CUI) is used to aggregate information about that concept, similar to the way NCBI Gene serves as a gateway to gene-related information. MedGen provides links to such resources as: Genetic tests registered in the NIH Genetic Testing Registry (GTR), GeneReviews, ClinVar, OMIM, Related genes, Disorders with similar clinical features, Medical and research literature, Practice guidelines, Consumer resources, Ontologies such as HPO and ORDO. Links to the GTR, GeneReviews, and Practice Guidelines are based on curation by NCBI staff. Other data feeds are automated, but reviewed by NCBI staff and informed by feedback from the community.

Background Drug resistance in breast cancer is a major obstacle to successful chemotherapy. In this study we used cDNA microarray technology to examine gene expression profiles obtained from fine needle aspiration (FNA) of primary breast tumors before and after systemic chemotherapy. Our goal was to determine the feasibility of obtaining representative expression array profiles from limited amounts of tissue and to identify those expression profiles that correlate with treatment response. Methods Repeat presurgical FNA samples were taken from six patients who were to undergo primary surgical treatment. Additionally, a group of 10 patients who were to receive neoadjuvant chemotherapy underwent two FNAs before chemotherapy (adriamycin 60 mg/m2 and cyclophosphamide 600 mg/m2) followed by another FNA on day 21 after the first cycle. Total RNA was amplified with T7 Eberwine's procedure and labeled cDNA was hybridized onto a 7600-feature glass cDNA microarray. Results We identified candidate gene expression profiles that might distinguish tumors with complete response to chemotherapy from tumors that do not respond, and found that the number of genes that change after one cycle of chemotherapy was 10 times greater in the responding group than in the non-responding group. Conclusion This study supports the suitability of FNA-derived cDNA microarray expression profiling of breast cancers as a comprehensive genomic approach for studying the mechanisms of drug resistance. Our findings also demonstrate the potential of monitoring post-chemotherapy changes in expression profiles as a measure of pharmacodynamic effect and suggests that these approaches might yield useful results when validated by larger studies.

All data used for these analyses are publicly available, either through CPDat, ToxRefDB, or the CompTox Chemicals Dashboard. Script associated with these analyses are publicly available through the Ragerlab Github repository. Data that were combined and analyzed in generating results for this specific study are provided as supplemental material (Supplementary Tables 1–10, provided online through the Ragerlab-Dataverse repository). This dataset is associated with the following publication: Koval, L., K. Dionisio, K. Friedman, K. Isaacs, and J. Rager. Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 794-807, (2022).

All data used for these analyses are publicly available, either through CPDat, ToxRefDB, or the CompTox Chemicals Dashboard. Script associated with these analyses are publicly available through the Ragerlab Github repository. Data that were combined and analyzed in generating results for this specific study are provided as supplemental material (Supplementary Tables 1–10, provided online through the Ragerlab-Dataverse repository). This dataset is associated with the following publication: Koval, L., K. Dionisio, K. Friedman, K. Isaacs, and J. Rager. Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 794-807, (2022).