An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma.

Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a feature selector based on the F-test is employed for the first stage selection. For the second stage, four feature subsets are formed using mutual information (MI), ReliefF, SURF, and SURF* filters in parallel. The third stage involves feature selection using a combinator based on GINI coefficient. Finally, a soft voting approach is proposed to employ for classification, including decision tree, naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN) and random forest classifiers. To demonstrate the effectiveness and efficiency of the proposed algorithm, eight high-dimensional datasets are used and five feature selection methods are employed to compare with our proposed algorithm. Experimental results show that our method effectively enhances the accuracy and speed of feature selection. Moreover, to explore the biological significance of the proposed algorithm, we apply it on the renal cell carcinoma dataset GSE40435 from the Gene Expression Omnibus database. Two feature genes, NOP2 and NSUN5, are selected by our proposed algorithm. They are directly involved in regulating m5c RNA modification, which reveals the biological importance of EFS-GINI. Through bioinformatics analysis, we shows that m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.

Elevated expression of the MYB proto-oncogene like 2 (MYBL2)-encoding gene as a prognostic and predictive biomarker in human cancers.

Recently, MYBL2 is frequently found to be overexpressed and associated with poor patient outcome in breast cancer, colorectal cancer, bladder carcinoma, hepatocellular carcinoma, neuroblastoma and acute myeloid leukemia. In view of the fact that there is an association between MYBL2 expression and the clinicopathological features of human cancers, most studies reported so far are limited in their sample size, tissue type and discrete outcomes. Furthermore, we need to verify which additional cancer entities are also affected by MYBL2 deregulation and which patients could specifically benefit from using MYBL2 as a biomarker or therapeutic target. We characterized the up-regulated expression level of MYBL2 in a large variety of human cancer via TCGA and oncomine database. Subsequently, we verified the elevated MYBL2 expression effect on clinical outcome using various databases. Then, we investigate the potential pathway in which MYBL2 may participate in and find 4 TFs (transcript factors) that may regulate MYBL2 expression using bioinformatic methods. At last, we confirmed elevated MYBL2 expression can be useful as a biomarker and potential therapeutic target of poor patient prognosis in a large variety of human cancers. Additionally, we find E2F1, E2F2, E2F7 and ZNF659 could interact with MYBL2 promotor directly or indirectly, indicating the four TFs may be the upstream regulator of MYBL2. TP53 mutation or TP53 signaling altered may lead to elevated MYBL2 expression. Our findings indicate that elevated MYBL2 expression represents a prognostic biomarker for a large number of cancers. What's more, patients with both P53 mutation and elevated MTBL2 expression showed a worse survival in PRAD and BRCA.

Interfacial charge dominating major active species and degradation pathways: An example of carbon based photocatalyst.

In this work, the chlorine-doped and undoped hydrothermal carbonation carbon (Cl-HTCC, HTCC) photocatalysts were used to study the correlation of their interfacial charge and photocatalytic performance. For degradation of aromatic dye, rhodamine B (RhB), Cl-HTCC manifests much better photocatalytic performance compared with that of undoped HTCC. Besides the slightly enhanced charge transfer brought, the Cl-HTCC showed more negatively interfacial charge and thus a stronger adsorption of positively charged RhB. This made the photogenerated holes (h+) directly react with the adsorbed RhB, which does not require the h+ to produce hydroxyl radical (OH) and reduce its lost during the transformation, thus enhanced the performance of Cl-HTCC. While for undoped HTCC, it showed a weaker adsorption of RhB, and the photogenerated h+ firstly reacted with H2O molecules to produce OH. Then, the OH can attack the RhB. Besides, the intermediates and the degradation pathways are also evaluated here via UPLC-MS. Results show that the interfacial charge also dominated the degradation pathways. This work provides a novel metal-free photocatalyst for environmental remediation and will inspire further efforts to enhance the photocatalytic performance by concerning interfacial conditions.