Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma.

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data.

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.

Long non-coding RNAs: controversial roles in drug resistance of solid tumors mediated by autophagy.

Current genome-wide studies have indicated that a great number of long non-coding RNAs (lncRNAs) are transcribed from the human genome and appeared as crucial regulators in a variety of cellular processes. Many studies have displayed a significant function of lncRNAs in the regulation of autophagy. Autophagy is a macromolecular procedure in cells in which intracellular substrates and damaged organelles are broken down and recycled to relieve cell stress resulting from nutritional deprivation, irradiation, hypoxia, and cytotoxic agents. Autophagy can be a double-edged sword and play either a protective or a damaging role in cells depending on its activation status and other cellular situations, and its dysregulation is related to tumorigenesis in various solid tumors. Autophagy induced by various therapies has been shown as a unique mechanism of resistance to anti-cancer drugs. Growing evidence is showing the important role of lncRNAs in modulating drug resistance via the regulation of autophagy in a variety of cancers. The role of lncRNAs in drug resistance of cancers is controversial; they may promote or suppress drug resistance via either activation or inhibition of autophagy. Mechanisms by which lncRNAs regulate autophagy to affect drug resistance are different, mainly mediated by the negative regulation of micro RNAs. In this review, we summarize recent studies that investigated the role of lncRNAs/autophagy axis in drug resistance of different types of solid tumors.