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BACKGROUND: There is a great need for the development of personalized prediction models (PPMs) that can predict the rate of disease progression for persons with Parkinson's disease (PD), based on their individual characteristics. In this study, we aimed to clarify the perspective of persons diagnosed with PD on the value of such hypothetical PPMs. METHODS: We organized four focus group discussions, each including five persons with PD who were diagnosed within the last 5 years. The sessions focused on what they think of receiving a personalized prediction; what outcomes are important to them; if and how the possibility of influencing the prognosis would change the way they think of personalized predictions; how they deal with the uncertainty from a PPM; and what barriers and facilitators they expect for using a PPM. RESULTS: The wish of persons with PD for receiving personalized prognostic information was highly heterogenous, for various reasons. Most persons with PD would like to receive more personalized prognostic information, mainly to better prepare themselves and their loved ones for the future. The prediction provided should be as personalized as possible, and there should be adequate supervision and coaching by a professional when providing the information. They were particularly interested in receiving prognostic information when their interventions would be available that could subsequently influence the identified prognostic factor and thereby affect the disease course beneficially. CONCLUSION: Most persons with PD in this study want more insight into their own future by means of prediction models, provided that this is explained and supervized properly by professionals. PATIENT OR PUBLIC CONTRIBUTION: Two patient-researchers were involved in the study design, conduct of the study, interpretation of the data and in preparation of the manuscript.

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DNA repair-related genes (DRGs) have attracted much attention in the field of oncology. However, the prognostic role of DRGs and their biological function in lung adenocarcinoma (LUAD) remains rudimentary and inconclusive. In this study, 716 LUAD cases from two different cohorts were collected. Samples from The Cancer Genome Atlas (TCGA) were used as the training set, and data from Gene Expression Omnibus (GEO) datasets were used for validation. Using multivariate Cox analysis and LASSO regression, we constructed a DRG signature and used it, together with clinical indices, to develop a nomogram to predict 1-, 3-, and 5-year survival rates. We identified a six-DRG signature to estimate the survival of LUAD patients, which distinguished high-risk from low-risk patients with LUAD in both the training and validation cohorts. We also observed elevated levels of infiltrating CD4 memory activated T cells, resting NK cells, M0 and M1 macrophages, and activated mast cells in the high-risk group. Finally, a nomogram incorporating the signature and clinical parameters was superior to the American Joint Committee on Cancer (AJCC) staging system in predicting the survival of LUAD patients. The DRG prognostic signature and integrated nomogram could be a useful tool to predict prognosis in patients with LUAD.