Dataset Information

Comparison of Familial Clustering of Anogenital and Skin Cancers Between In Situ and Invasive Types.

ABSTRACT: Literature on familial risk of carcinomas in situ (CISs) is limited because many cancer registries do not collect information on CIS. In Sweden CISs are collected, and we used these data to analyze familial relative risks (RRs) for concordant (CIS-CIS) types of anogenital (cervical, other female and male genital and anal) and skin squamous cell CIS; additionally RRs were assessed between CIS types and between CIS and invasive forms. RRs were calculated for the offspring generations when family members were diagnosed CIS. Case numbers for CIS ranged from 330 in anal to 177,285 in cervical CIS. Significant concordant CIS-CIS RRs were 2.74 for female genital, 1.77 for cervical and 2.29 for SCC skin CISs. The CIS forms associated also with each other, except for cervical and skin CIS types. RRs for concordant CIS-invasive cancer associations were lower than CIS-CIS associations. Cervical CIS associated with non-Hodgkin CIS which may suggest immune dysfunction as a contributing factors. The results for anogenital CIS types suggest that life style related human papilloma virus infections contributed to the observed familial associations. Lower risks for CIS-invasive cancer than CIS-CIS suggest that CIS and invasive cancers share only partially risk factors that underlie familial clustering.

SUBMITTER: Zhang L

PROVIDER: S-EPMC6834624 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Comparison of Familial Clustering of Anogenital and Skin Cancers Between In Situ and Invasive Types.

Zhang Luyao L Hemminki Otto O Zheng Guoqiao G Försti Asta A Sundquist Kristina K Sundquist Jan J Hemminki Kari K

Scientific reports 20191106 1

Literature on familial risk of carcinomas in situ (CISs) is limited because many cancer registries do not collect information on CIS. In Sweden CISs are collected, and we used these data to analyze familial relative risks (RRs) for concordant (CIS-CIS) types of anogenital (cervical, other female and male genital and anal) and skin squamous cell CIS; additionally RRs were assessed between CIS types and between CIS and invasive forms. RRs were calculated for the offspring generations when family m ...[more]

PMID: 31695117

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Project description:Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP-chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies.

Dataset Information

Comparison of Familial Clustering of Anogenital and Skin Cancers Between In Situ and Invasive Types.

Publications

Comparison of Familial Clustering of Anogenital and Skin Cancers Between In Situ and Invasive Types.

Similar Datasets

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