Gender specificity improves the early-stage detection of clear cell renal cell carcinoma based on methylomic biomarkers.

AIM: The two genders are different ranging from the molecular to the phenotypic levels. But most studies did not use this important information. We hypothesize that the integration of gender information may improve the overall prediction accuracy. MATERIALS & METHODS: A comprehensive comparative study was carried out to test the hypothesis. The classification of the stages I + II versus III + IV of the clear cell renal cell carcinoma samples was formulated as an example. RESULTS & CONCLUSION: In most cases, female-specific model significantly outperformed both-gender model, as similarly for the male-specific model. Our data suggested that gender information is essential for building biomedical classification models and even a simple strategy of building two gender-specific models may outperform the gender-mixed model.

Proteomic biomarkers for lung cancer progression.

AIM: Lung adenocarcinoma (LUAD) and lung squamous-cell carcinoma (LUSC) are two major subtypes of lung cancer and constitute about 70% of all the lung cancer cases. The patient's lifespan and living quality will be significantly improved if they are diagnosed at an early stage and adequately treated. METHODS & RESULTS: This study comprehensively screened the proteomic dataset of both LUAD and LUSC, and proposed classification models for the progression stages of LUAD and LUSC with accuracies 86.51 and 89.47%, respectively. DISCUSSION & CONCLUSION: A comparative analysis was also carried out on related transcriptomic datasets, which indicates that the proposed biomarkers provide discerning power for accurate stage prediction, and will be improved when larger-scale proteomic quantitative technologies become available.