RESUMEN
Multi-modal multi-objective problems (MMOPs) have gained much attention during the last decade. These problems have two or more global or local Pareto optimal sets (PSs), some of which map to the same Pareto front (PF). This article presents a new affinity propagation clustering (APC) method based on the Multi-modal multi-objective differential evolution (MMODE) algorithm, called MMODE_AP, for the suit of CEC'2020 benchmark functions. First, two adaptive mutation strategies are adopted to balance exploration and exploitation and improve the diversity in the evolution process. Then, the affinity propagation clustering method is adopted to define the crowding degree in decision space (DS) and objective space (OS). Meanwhile, the non-dominated sorting scheme incorporates a particular crowding distance to truncate the population during the environmental selection process, which can obtain well-distributed solutions in both DS and OS. Moreover, the local PF membership of the solution is defined, and a predefined parameter is introduced to maintain of the local PSs and solutions around the global PS. Finally, the proposed algorithm is implemented on the suit of CEC'2020 benchmark functions for comparison with some MMODE algorithms. According to the experimental study results, the proposed MMODE_AP algorithm has about 20 better performance results on benchmark functions compared to its competitors in terms of reciprocal of Pareto sets proximity (rPSP), inverted generational distances (IGD) in the decision (IGDX) and objective (IGDF). The proposed algorithm can efficiently achieve the two goals, i.e., the convergence to the true local and global Pareto fronts along with better distributed Pareto solutions on the Pareto fronts.
RESUMEN
Background: Genome instability plays a crucial role in promoting tumor development. Germline mutations in genes responsible for DNA repair are often associated with familial cancer syndromes. A noticeable exception is the CHEK1 gene. Despite its well-established role in homologous recombination, germline mutations in CHEK1 are rarely reported. Case presentation: In this report, we present a patient diagnosed with ovarian clear cell carcinoma who has a family history of cancer. Her relatives include a grandfather with esophageal cancer, a father with gastric cancer, and an uncle with a brain tumor. The patient carried a typical genomic profile of clear cell carcinoma including mutations in KRAS, PPP2R1A, and PIK3R1. Importantly, her paired peripheral blood cells harbored a germline CHEK1 mutation, CHEK1 exon 6 c.613 + 2T>C, which was also found in her father. Unfortunately, the CHEK1 status of her grandfather and uncle remains unknown due to the unavailability of their specimens. Further evaluation via RT-PCR confirmed a splicing error in the CHEK1 gene, resulting in truncation at the kinase domain region, indicative of a loss-of-function mutation. Conclusion: This case highlights a rare germline CHEK1 mutation within a family with a history of cancer. The confirmed splicing error at the mRNA level underscores the functional consequences of this mutation. Documenting such cases is vital for future evaluation of inheritance patterns, clinical penetrance of the mutation, and its association with specific cancer types.
RESUMEN
Background & Objective: Previous studies have suggested that the modified Glasgow Prognostic Score (mGPS) could be a potential biomarker for lung cancer (LC). However, the association between mGPS and overall survival (OS) or progression-free survival (PFS) in lung cancer patients remains unclear. The purpose of our study was to investigate possible correlation between mGPS and OS or PFS in LC patients. Methods: An extensive search of PubMed, Cochrane Library, EMbase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Trip Database, Worldwide Science, and Google Scholar databases was done for relevant articles, published prior to May 30, 2021, that report correlation between mGPS and OS or PFS in LC patients. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were used as the main parameters for evaluation. Results: A total of 28 studies involving 9,748 lung cancer patients were analysed. The pooled analysis revealed that elevated mGPS (≥ 0) was associated with poor OS (HR=1.54; 95% CI, 1.32-1.77) and PFS (HR=1.49; 95% CI, 1.17-1.82). Furthermore, a significant correlation between mGPS (1 or 2) and OS was observed. However, no significant correlation was found between mGPS (1 or 2) and PFS. Subgroup analysis based on ethnicity demonstrated that mGPS ≥ 0 was associated with worse OS compared to mGPS=0 in both Asian (HR=1.46; 95% CI, 1.04-1.89; p<0.05) and Caucasian (HR=1.64; 95% CI, 1.35-1.94; p<0.05) cohorts of LC patients. Conclusions: Our results demonstrate that positive mGPS is associated with poor survival results. Therefore, mGPS may be used as a biomarker for predicting prognosis in LC patients.
