Caste Gender and Occupational Outcomes

This chapter discusses an important concern of public policy in India which is to ensure that all persons, regardless of gender, caste, or religion, are treated fairly in the jobs market. A key aspect of this relates to inter-group differences in the likelihood of attaining different levels of occupational success. The issue here is whether these differences in likelihood are justified by differences in the distribution of employee attributes or whether they are, wholly or in part, due to employer bias. This chapter attempts to answer these questions using unit record data from the Indian Human Development Survey relating to the period 2011–12. Of particular interest to this chapter is that the Survey provides details about the occupations of approximately 62,500 persons by placing them in one or more of 99 occupations; these are aggregated in chapter 4 into six broad occupational categories. Using these data, the chapter (focusing on men and women between the ages of 21 and 60) employs the methods of multinomial logit to estimate the probabilities of persons being in these occupational categories, after controlling for their gender/caste/religion and their employment-related attributes. The main focus is the issue of differences between men and women, and differences between persons belonging to different social groups, in their likelihood of being in the different employment categories. Data on these men and women were used to decompose the observed difference between the groups, in their average proportions in the different occupations, into an “employer bias” and an “employee attributes” effect

Gene expression meta-analysis supports existence of molecular apocrine breast cancer with a role for androgen receptor and implies interactions with ErbB family

<p>Abstract</p> <p>Background</p> <p>Pathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology. Oncogenic signaling pathways are commonly inferred by comparison with signatures derived from cell lines. We use the Molecular Apocrine subtype of breast cancer to demonstrate our ability to infer pathways directly from patients' gene expression data with pattern analysis algorithms.</p> <p>Methods</p> <p>We combine data from two studies that propose the existence of the Molecular Apocrine phenotype. We use quantile normalization and XPN to minimize institutional bias in the data. We use hierarchical clustering, principal components analysis, and comparison of gene signatures derived from Significance Analysis of Microarrays to establish the existence of the Molecular Apocrine subtype and the equivalence of its molecular phenotype across both institutions. Statistical significance was computed using the Fasano & Franceschini test for separation of principal components and the hypergeometric probability formula for significance of overlap in gene signatures. We perform pathway analysis using LeFEminer and Backward Chaining Rule Induction to identify a signaling network that differentiates the subset. We identify a larger cohort of samples in the public domain, and use Gene Shaving and Robust Bayesian Network Analysis to detect pathways that interact with the defining signal.</p> <p>Results</p> <p>We demonstrate that the two separately introduced ER^-breast cancer subsets represent the same tumor type, called Molecular Apocrine breast cancer. LeFEminer and Backward Chaining Rule Induction support a role for AR signaling as a pathway that differentiates this subset from others. Gene Shaving and Robust Bayesian Network Analysis detect interactions between the AR pathway, EGFR trafficking signals, and ErbB2.</p> <p>Conclusion</p> <p>We propose criteria for meta-analysis that are able to demonstrate statistical significance in establishing molecular equivalence of subsets across institutions. Data mining strategies used here provide an alternative method to comparison with cell lines for discovering seminal pathways and interactions between signaling networks. Analysis of Molecular Apocrine breast cancer implies that therapies targeting AR might be hampered if interactions with ErbB family members are not addressed.</p

Prevention and early detection of prostate cancer

Prostate cancer is a common malignancy in men and the worldwide burden of this disease is rising. Lifestyle modifications such as smoking cessation, exercise, and weight control off er opportunities to reduce the risk of developing prostate cancer. Early detection of prostate cancer by prostate-specific antigen (PSA) screening is controversial, but changes in the PSA threshold, frequency of screening, and the use of other biomarkers have the potential to minimise the overdiagnosis associated with PSA screening. Several new biomarkers for individuals with raised PSA concentrations or those diagnosed with prostate cancer are likely to identify individuals who can be spared aggressive treatment. Several pharmacological agents such as 5 alpha-reductase inhibitors and aspirin could prevent development of prostate cancer. In this Review, we discuss the present evidence and research questions regarding prevention, early detection of prostate cancer, and management of men either at high risk of prostate cancer or diagnosed with low-grade prostate cancer

DNA methylation gene-based models indicating independent poor outcome in prostate cancer

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise statedCancer Research UK [Grant number C569/A10404] and The Orchid Foundation [ONAG1I6R, ONAG1I7R]. CSF is in addition supported by grants from the National Cancer Research Institute-Medical Research Council Prostate Cancer Collaborative [MRC093X] and from the North West Cancer Research Fund UK [CR901]