Publications by authors named "S M Niaz Mowla"

Bladder cancer is a significant health concern worldwide, necessitating effective diagnostic and monitoring strategies. Biomarkers play a crucial role in the early detection, prognosis, and treatment of this disease. This review explores the current landscape of bladder cancer biomarkers, including FDA-approved molecular biomarkers and emerging ones.

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Background: DANCR is an oncogenic lncRNA associated with advanced colorectal cancer, one of the most common malignancies worldwide. This lncRNA has a new variant, DANCR-V1, whose function is not yet understood. In this study, we aimed to evaluate the expression pattern of DANCR-V1 and its regulatory mechanism in colorectal cancer.

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Cancer evolution is a multifaceted process leading to dysregulation of cellular expansion and differentiation through somatic mutations and epigenetic dysfunction. Clonal expansion and evolution is driven by cell-intrinsic and -extrinsic selective pressures, which can be captured with increasing resolution by single-cell and bulk DNA sequencing. Despite the extensive genomic alterations revealed in profiling studies, there remain limited experimental systems to model and perturb evolutionary processes.

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A unique approach is introduced for constructing gold nanocrystals (AuNCs) with RNA motif-directed morphologies in a sequence-independent manner and its applications in the clinical area are described. By using this method, a label-free LSPR-based detection method for the SOX2OT transcript, long non-coding RNAs (lncRNAs), which is a prognostic indicator of poor survival in lung cancer patients is presented. For the first time, we examined how the structural changes of RNA after the heteroduplex formation with a specific DNA probe can change the morphology and LSPR band of AuNCs.

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Article Synopsis
  • Accurate differentiation between lung adenocarcinoma (AC) and lung squamous cell carcinoma (SCC) is vital for proper treatment, with microRNAs (miRNAs) showing potential as biomarkers to distinguish between the two types.
  • The study utilized data from The Cancer Genome Atlas (TCGA) and employed various machine learning techniques to analyze miRNA expressions, validating findings through RT-qPCR and exploring their clinical significance.
  • Five specific miRNAs (miR-205-3p, miR-205-5p, miR-944, miR-375, and miR-326) were identified as potential biomarkers, with particular combinations correlating with survival outcomes in both AC and SCC, and involvement in important signaling
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