Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expressed genes in making present/absent calls. Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. Support vector machine RFEs are greedy methods that attempt to find superlative possible combinations leading to binary classification, which may not be biologically significant. To overcome this limitation of SVM-RFE, we propose a novel feature selection algorithm, termed as “sigFeature” (https://bioconductor.org/packages/sigFeature/), based on SVM and t statistic to discover the differentially significant features along with good performance in classification. The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. Using six publicly available microarray data sets (downloaded from Gene Expression Omnibus) with different biological attributes, we further compared the performance of “sigFeature” to three other feature selection algorithms. A small number of selected features (by “sigFeature”) also show higher classification accuracy. For further downstream evaluation of its biological signature, we conducted gene set enrichment analysis with the selected features (genes) from “sigFeature” and compared it with the outputs of other algorithms. We observed that “sigFeature” is able to predict the signature of four out of six microarray data sets accurately, whereas the other algorithms predict less data set signatures. Thus, “sigFeature” is considerably better than related algorithms in discovering differentially significant features from microarray data sets.

Mutations in the TP53 gene are one of the most frequent events in cancers. Certain missense mutant p53 proteins gain oncogenic functions (gain-of-functions) and drive tumorigenesis. Apart from the coding genes, a few non-coding microRNAs (miRNAs) are implicated in mediating mutant p53-driven cancer phenotypes. Here, we identified miRNAs in mutant p53^R273H bearing non-small cell lung carcinoma (NSCLC) cells while using small RNA deep sequencing. Differentially regulated miRNAs were validated in the TCGA lung adenocarcinoma patients with p53 mutations and, subsequently, we identified specific miRNA signatures that are associated with lymph node metastasis and poor survival of the patients. Pathway analyses with integrated miRNA-mRNA expressions further revealed potential regulatory molecular networks in mutant p53 cancer cells. A possible contribution of putative mutant p53-regulated miRNAs in epithelial-to-mesenchymal transition (EMT) is also predicted. Most importantly, we identified a novel miRNA from the unmapped sequencing reads through a systematic computational approach. The newly identified miRNA promotes proliferation, colony-forming ability, and migration of NSCLC cells. Overall, the present study provides an altered miRNA expression profile that might be useful in biomarker discovery for non-small cell lung cancers with TP53 mutations and discovers a hitherto unknown miRNA with oncogenic potential.

Cancer stem cells (CSCs) are known to potentiate tumor initiation and maintenance in Oral Squamous Cell Carcinoma (OSCC). Increasing evidences suggest that CD44 ^high CD24 ^low population in OSCC are potential CSCs. MicroRNAs (miRNAs) have emerged as crucial players in tumor development. However, their role in maintenance of OSCC stem cells remains unclear. Here we report that CD44 ^high CD24 ^low population within OSCC cells and primary HNSCC tumors have an elevated expression of miR-146a. Moreover, over-expression of miR-146a results in enhanced stemness phenotype by augmenting CD44 ^high CD24 ^low population. We demonstrate that miR-146a induces stemness by stabilizing β-catenin with concomitant loss of E-cadherin and CD24. Interestingly, CD24 is identified as a novel functional target of miR-146a and ectopic expression of CD24 abrogates miR-146a driven potential CSC phenotype. Mechanistic analysis reveals that higher CD24 levels inhibit AKT phosphorylation leading to β-catenin degradation. Using stably expressing miR-146a/CD24 OSCC cell lines, we also validate that the miR-146a/ CD24/AKT loop significantly alters tumorigenic ability in vivo . Furthermore, we confirmed that β-catenin trans-activates miR-146a, thereby forming a positive feedback loop contributing to stem cell maintenance. Collectively, our study demonstrates that miR-146a regulate CSCs in OSCC through CD24-AKT-β-catenin axis. Highlights: MiR-146a induces cancer stem cell characteristics in OSCC by targeting CD24 CD24 abrogates miR-146a mediated stemness via β-catenin degradation in non-CSCs Akt/Wnt pathway is critical for sustenance of miR-146a driven potential CSCs The miR-146a/CD24/AKT loop significantly alters tumorigenic ability in vivo