Publications by authors named "Chanwoong Hwa"
Article Synopsis
- Immunotherapy shows promise for treating gastric cancer, but resistance to immune checkpoint inhibitors (ICIs) remains a challenge, particularly due to issues with interferon-γ (IFN-γ) signaling.
- * The study reveals that YTHDF1, an m6A-binding protein, is overexpressed in gastric cancer tissues and negatively impacts IFN-γ responsiveness, correlating with reduced cancer immunity and lower survival rates.
- * YTHDFs, notably YTHDF1, destabilize IRF1 mRNA, a key player in IFN-γ signaling, indicating their potential as targets for enhancing the effectiveness of cancer immunotherapy.
Article Synopsis
- - Non-small cell lung cancer (NSCLC) is categorized into two main types: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), but some tumors display mixed characteristics that may be influenced more by other factors than histology alone.
- - A study using data from 691 patients identified five molecular subtypes of NSCLC, including one associated with the PI3K-Akt pathway that correlates with high rates of metastasis and poor survival, regardless of the specific type of NSCLC.
- - The research highlights the immune microenvironment's role, revealing different immune cell compositions and neoantigen levels across subtypes, which can help predict patient outcomes; immunological subtypes showed better responses to
N-Methyladenosine (m6A) RNA modification plays a critical role in the posttranscriptional regulation of gene expression. Alterations in cellular m6A levels and m6A-related genes have been reported in many cancers, but whether they play oncogenic or tumor-suppressive roles is inconsistent across cancer types. We investigated common features of alterations in m6A modification and m6A-related genes during carcinogenesis by analyzing transcriptome data of 11 solid tumors from The Cancer Genome Atlas database and our in-house gastric cancer cohort.
View Article and Find Full Text PDFMulti-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research.
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