Competing endogenous RNAs in head and neck squamous cell carcinoma: a review.

Our understanding of RNA biology has evolved with recent advances in research from it being a non-functional product to molecules of the genome with specific regulatory functions. Competitive endogenous RNA (ceRNA), which has gained prominence over time as an essential part of post-transcriptional regulatory mechanism, is one such example. The ceRNA biology hypothesis states that coding RNA and non-coding RNA co-regulate each other using microRNA (miRNA) response elements. The ceRNA components include long non-coding RNAs, pseudogene and circular RNAs that exert their effect by interacting with miRNA and regulate the expression level of its target genes. Emerging evidence has revealed that the dysregulation of the ceRNA network is attributed to the pathogenesis of various cancers, including the head and neck squamous cell carcinoma (HNSCC). This is the most prevalent cancer developed from the mucosal epithelium in the lip, oral cavity, larynx and pharynx. Although many efforts have been made to comprehend the cause and subsequent treatment of HNSCC, the morbidity and mortality rate remains high. Hence, there is an urgent need to understand the holistic progression of HNSCC, mediated by ceRNA, that can have immense relevance in identifying novel biomarkers with a defined therapeutic intervention. In this review, we have made an effort to highlight the ceRNA biology hypothesis with a focus on its involvement in the progression of HNSCC. For the identification of such ceRNAs, we have additionally highlighted a number of databases and tools.

BCLncRDB: a comprehensive database of LncRNAs associated with breast cancer.

Breast cancer, the most common cancer in women, is characterized by high morbidity and mortality worldwide. Recent evidence has shown that long non-coding RNAs (lncRNAs) play a crucial role in the development and progression of breast cancer. However, despite increasing data and evidence indicating the implication of lncRNAs in breast cancer, no web resource or database exists primarily for lncRNAs associated with only breast cancer. Therefore, we developed a manually curated, comprehensive database, "BCLncRDB," for lncRNAs associated with breast cancer. For this, we collected, processed, and analyzed available data on breast cancer-associated lncRNAs from different sources, including previously published research articles, the Gene Expression Omnibus (GEO) Database of the National Centre for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), and the Ensembl database; subsequently, these data were hosted at BCLncRDB for public access. Currently, the database contains 5324 unique breast cancer-lncRNA associations and has the following features: (i) a user-friendly, easy-to-use web interface for searching and browsing about lncRNAs of the user's interest, (ii) differentially expressed and methylated lncRNAs, (iii) stage- and subtype-specific lncRNAs, and (iv) drugs, subcellular localization, sequence, and chromosome information of these lncRNAs. Thus, the BCLncRDB provides a one-stop dedicated platform for exploring breast cancer-related lncRNAs to advance and support the ongoing research on this disease. The BCLncRDB is publicly available for use at http://sls.uohyd.ac.in/new/bclncrdb_v1 .

A comprehensive structure-activity analysis of protein kinase B-alpha (Akt1) inhibitors.

Protein kinase B (PKB, also known as Akt) belongs to the AGC subfamily of the protein kinase superfamily. Akt1 has been reported as a central player in regulation of metabolism, cell survival, motility, transcription and cell-cycle progression, among the signalling proteins that respond to a large variety of signals. In this study an attempt was made to understand structural requirements for Akt1 inhibition using conventional QSAR, k-nearest neighbour QSAR and novel GQSAR methods. With this intention, a wide variety of structurally diverse Akt1 inhibitors were collected from various literature reports. The conventional QSAR analyses revealed the key role of Baumann's alignment independent topological descriptors along with other descriptors such as the number of hydrogen bond acceptors, hydrogen bond donors, rotatable bonds and aromatic oxygen (SaaOcount) along with molecular branching (chi3Cluster), alkene carbon atom type (SdsCHE-index) in governing activity variation. Further, the GQSAR analyses show that chemical variations like presence of hetero-aromatic ring, flexibility, polar surface area and fragment length present in the hinge binding fragment (in the present case fragment D) are highly influential for achieving highly potent Akt1 inhibitors. In addition, this study resulted in a k-nearest neighbour classification model with three descriptors suggesting the key role of oxygen (SssOE-index) and aromatic carbon (SaaCHE-index and SaasCE-index) atoms electro-topological environment that differentiate molecules binding to Akt1 kinase or PH domain. The developed models are interpretable, with good statistical and predictive significance, and can be used for guiding ligand modification for the development of potential new Akt1 inhibitors.

3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists.

3D-QSAR studies on the derivatives of 1-(3,3-diphenylpropyl)-piperidinyl amide and urea as CCR5 receptor antagonists were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The global minimum energy conformer of the template molecule, the most active and pharmacokinetically stable molecule of the series, was obtained by systematic search and used to build structures of the molecules in the dataset. The best predictions for the CCR5-receptor were obtained with the CoMFA standard model (q (2) = 0.787, r (2) = 0.962) and CoMSIA model combined steric, electrostatic and hydrophobic fields (q (2) = 0.809, r (2) = 0.951). The predictive ability of CoMFA and CoMSIA were determined using a test set of 12 compounds giving predictive correlation coefficients of 0.855 and 0.83, respectively, indicating good predictive power. Further, the robustness of the model was verified by bootstrapping analysis. The contour maps produced by the CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series. Based on the CoMFA and CoMSIA analysis, we have identified some key features in the series that are responsible for CCR5 antagonistic activity which may be used to design more potent 1-(3,3-diphenylpropyl)-piperidinyl derivatives and predict their activity prior to synthesis.