Background: Understanding common and unique mechanisms driving oncogenic processes in human tumors is indispensable to develop efficient therapies. Recent studies have proposed Twinfilin Actin Binding Protein 1 (TWF1) as a putative driver gene in lung cancer, pancreatic cancer and breast cancer, however a systematic pan-cancer analysis has not been carried out.
Methods: Here, we set out to explore the role of TWF1 in 33 tumor types using TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus) dataset, Human Protein Atlas (HPA), and several bioinformatic tools.
Results: As part of our analysis, we have assessed expression across tumors. We found that over-expression of generally predicted poor OS for patients with tumors with high expression, such as mesothelioma, lung adenocarcinoma, cervical cancer and pancreatic adenocarcinoma. We also assessed the mutation burden of in cancer and the -associated survival of cancer patients, compared the phosphorylation of TWF1 between normal and primary tumor tissues and explored putative functional mechanisms in TWF1-mediated oncogenesis.
Conclusions: Our pan-cancer analysis provides a comprehensive overview of the oncogenic roles of TWF1 in multiple human cancers.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185641 | PMC |
| http://dx.doi.org/10.3389/fonc.2021.692136 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A multidimensional pan-cancer analysis of NDUFA4L2 and verification of the oncogenic value in colon cancer.
FASEB J
January 2025
Department of General Surgery, Sir Run Run Hospital of Nanjing Medical University, Nanjing, Jiangsu, P. R. China.
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4-like 2 (NDUFA4L2) protein is located in the mitochondria and can regulate cell proliferation. Some studies have shown that the high NDUFA4L2 expression is linked with poor prognosis and cancer progression in various patients with cancers. However, the correlation between NDUFA4L2 and pan-cancer is unknown.
View Article and Find Full Text PDFST8SIA6 Sialylates CD24 to Enhance Its Membrane Localization in BRCA.
Cells
December 2024
Key Laboratory of Marine Drugs (Ministry of Education), Shandong Provincial Key Laboratory of Glycoscience and Glycoengineering, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China.
CD24, a highly sialylated glycosyl-phosphatidyl-inositol (GPI) cell surface protein that interacts with sialic acid-binding immunoglobulin-like lectins (Siglecs), serves as an innate immune checkpoint and plays a crucial role in inflammatory diseases and tumor progression. Recently, cytoplasmic CD24 has been observed in samples from patients with cancer. However, whether sialylation governs the subcellular localization of CD24 in cancer remains unclear, and the impact of CD24 expression and localization on the clinical prognosis of cancer remains controversial.
View Article and Find Full Text PDFMutation in CDC42 Gene Set as a Response Biomarker for Immune Checkpoint Inhibitor Therapy.
Cancer Med
January 2025
School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Background: Immune checkpoint inhibitors (ICIs) have achieved great success; however, a subset of patients exhibits no response. Consequently, there is a critical need for reliable predictive biomarkers. Our focus is on CDC42, which stimulates multiple signaling pathways promoting tumor growth.
View Article and Find Full Text PDFZP3 Expression in Pancreatic Adenocarcinoma: Its Implications for the Prognosis and Therapy.
Protein Pept Lett
January 2025
Scientific Research Center, Beijing ChosenMed Clinical Laboratory Co., Ltd. Beijing100176, China.
Background: The role of Zona pellucida glycoprotein 3 (ZP3) is unclear in pancreatic adenocarcinoma (PAAD).
Objective: This study aimed to explore the role of ZP3 in PAAD.
Methods: A comparative analysis of ZP3 gene expression was performed to discern differences between various types of cancer and PAAD, leveraging data sourced from The Cancer Genome Atlas (TCGA).
Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.
Nat Biomed Eng
January 2025
Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China.
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!