In this study, we constructed an eight-gene metabolic related signature for LUAD. The eight-gene prognostic signature (including PLAUR, F2, UGT2B17, GNG7, IDO2, ST3GAL6, PIK3CG, and GLS2) exhibited a good prognostic value in the TCGA LUAD training dataset and testing dataset. In addition, the risk score based on the signature model was significantly correlated with immune cell infiltration and expression levels of immune markers in LUAD patients. LUAD cohorts from GEO were used to validate the model, indicating the usefulness of the model. In summary, we developed and validated an eight-gene signature model for LUAD, which can reflect the immune microenvironment characteristics and predict the prognostic outcomes for LUAD patients.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034925 | PMC |
| http://dx.doi.org/10.18632/aging.202681 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer.
Nat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFCombinatorial functionomics identifies HDAC6-dependent molecular vulnerability of radioresistant head and neck cancer.
Exp Hematol Oncol
January 2025
Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Background: Radiotherapy is the primary treatment modality for most head and neck cancers (HNCs). Despite the addition of chemotherapy to radiotherapy to enhance its tumoricidal effects, almost a third of HNC patients suffer from locoregional relapses. Salvage therapy options for such recurrences are limited and often suboptimal, partly owing to divergent tumor and microenvironmental factors underpinning radioresistance.
View Article and Find Full Text PDFPredicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.
Geriatr Gerontol Int
January 2025
Division of Acute Care Surgery, Department of Surgery, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, Iowa, USA.
Aim: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could predict which older adults are at risk of fall-related hospital admission. We hypothesized that frailty, measured using the 5-item modified Frailty Index, could be utilized in combination with other factors as a predictor of admission for fall-related injuries.
View Article and Find Full Text PDFRadiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction.
J Immunother Cancer
January 2025
Vall d'Hebron Institute of Oncology, Barcelona, Spain.
Background: The efficacy of immune checkpoint inhibitors (ICIs) depends on the tumor immune microenvironment (TIME), with a preference for a T cell-inflamed TIME. However, challenges in tissue-based assessments via biopsies have triggered the exploration of non-invasive alternatives, such as radiomics, to comprehensively evaluate TIME across diverse cancers. To address these challenges, we develop an ICI response signature by integrating radiomics with T cell-inflamed gene-expression profiles.
View Article and Find Full Text PDFDeveloping and experimental validating a T cell senescence-related gene signature to predict prognosis and immunotherapeutic sensitivity in non-small cell lung cancer.
Gene
January 2025
Department of Thoracic Oncology Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350011 Fujian Province, PR China. Electronic address:
Background: T cell senescence affects non-small cell lung cancer (NSCLC) by compromising the anti-tumor immune response. However, the prognostic significance of T cell senescence-related genes in NSCLC remains unclear.
Methods: The scRNA-seq data from normal lung and NSCLC tissues, along with co-incubation experiments involving NSCLC cells and T cells, were utilized to identify T cell senescence characteristics.
Want 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!