RESUMEN
BACKGROUND: Lung cancer is associated with a high incidence of mortality worldwide. Molecular mechanisms governing the disease have been explored by genomic studies; however, several aspects remain elusive. The integration of genomic profiling with in-depth proteomic profiling has introduced a new dimension to lung cancer research, termed proteogenomics. The aim of this review article was to investigate proteogenomic approaches in lung cancer, focusing on how elucidation of proteogenomic features can evoke tangible clinical outcomes. METHODS: A strict methodological approach was adopted for study selection and key article features included molecular attributes, tumor biomarkers, and major hallmarks involved in oncogenesis. RESULTS: As a consensus, in all studies it becomes evident that proteogenomics is anticipated to fill significant knowledge gaps and assist in the discovery of novel treatment options. Genomic profiling unravels patient driver mutations, and exploration of downstream effects uncovers great variability in transcript and protein correlation. Also, emphasis is placed on defining proteogenomic traits of tumors of major histological classes, generating a diverse portrait of predictive markers and druggable targets. CONCLUSIONS: An up-to-date synthesis of landmark lung cancer proteogenomic studies is herein provided, underpinning the importance of proteogenomics in the landscape of personalized medicine for combating lung cancer.
RESUMEN
OBJECTIVES: The impact of depression on post-menopausal women is an important public health issue but remains largely unknown. The purpose of this study was to identify the prevalence of post-menopausal depression in Greece and outline the profile of the women it affects. STUDY DESIGN: A sample of post-menopausal women completed an anonymous, self-administered, web-based survey which included the Beck Depression Inventory-ΙΙ (BDI-II) and questions regarding socio-demographic data. MAIN OUTCOME MEASURES: The dependent variable of interest was a BDI-II score ≥ 20 (the cut-off for moderate depression according to the BDI). RESULTS: Overall, 502 post-menopausal women participated in the study. The median BDI-II score was 13 (range 0-50); 136 (27.1%) of the women scored ≥ 20 and were considered screen-positive for depression. According to the multivariate logistic regression model, age< 55 years (OR: 1.621; 95% CI: 1.036-2.535), not working (OR: 1.580; 95% CI: 1.013-2.465), smoking (OR: 1.656; 95% CI: 1.081-2.536) and history of depression (OR: 1.650; 95% CI: 1.045-2.604) were independently associated with post-menopausal depression. Subgroup analyses revealed that current smokers (OR: 2.514; 95% CI: 1.485-4.256) had higher odds of moderate depression, while obesity (OR: 2.455; 95% CI: 1.206-4.996), absence of healthcare insurance (OR: 4.413; 95% CI: 1.970-9.887) and a history of depression (OR: 2.253; 95% CI: 1.212-4.190) were identified as independent risk factors for severe post-menopausal depression. CONCLUSIONS: More than one out of four post-menopausal women were screen-positive for symptoms indicative of depression, while a personal history of depression, age < 55 years, smoking and current working status were independent predictors of its emergence.