Objectives: Many studies have shown that dysregulation of metabolism contributes to oncogenesis. However, the exact roles of metabolism-related genes (MRGs) in oral squamous cell carcinoma (OSCC) remain unclear. Thus, we aimed to identify a prognostic signature related to MRGs in OSCC.
Methods: The gene sequencing data of OSCC samples and the MRG set were downloaded from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB). The Wilcoxon rank-sum test was used to identify differentially expressed MRGs. Then, a prognostic signature was established by multivariate Cox regression analysis. Finally, prognosis-related MRGs were selected and further validated in OSCC tissues and cell lines.
Results: A prognostic signature that included 8 MRGs was constructed. Multiple survival analysis revealed that only HPRT1 might be an independent biomarker and indicator of poor overall survival in OSCC patients. The expression of HPRT1 was then found to be upregulated in OSCC tissues and cell lines, and suppression of HPRT1 gene expression by siRNA inhibited the proliferation, migration, and invasion of OSCC cells in vitro.
Conclusions: MRGs play an important role in the development of OSCC. Furthermore, HPRT1 might be an independent biomarker of OSCC and enhance OSCC proliferation, migration, and invasion in vitro; these results emphasize the potential utility of HPRT1 in OSCC therapy.
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http://dx.doi.org/10.1155/2022/7453185 | DOI Listing |
Cancer Cell Int
January 2025
Department of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510080, China.
Background: Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients.
View Article and Find Full Text PDFMol Cancer
January 2025
Department of Cell Biology, Physiology, and Immunology, University of Córdoba, CIBER Pathophysiology of Obesity and Nutrition (CIBERobn), Córdoba, 14004, Spain.
Background: Hepatocellular carcinoma (HCC) genetic/transcriptomic signatures have been widely described. However, its proteomic characterization is incomplete. We performed non-targeted quantitative proteomics of HCC samples and explored its clinical, functional, and molecular consequences.
View Article and Find Full Text PDFLife Sci
January 2025
Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China. Electronic address:
Aims: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. This study utilized a multi-center and multi-omics analysis to establish and validate a prognosis and treatment vulnerability signature (PTVS) capable of effectively predicting patient prognosis and drug responsiveness.
Materials And Methods: To address this complexity, we constructed an integrative multi-omics analysis using 10 clustering algorithms on ccRCC patient data.
Accurate prediction of survival in patients with acute myelogenous leukemia (AML) is challenging. Therefore, we developed a predictive survival model using endocrine-related gene expression to identify an endocrine signature for accurate stratification of AML prognosis. RNA matrices and clinical data for AML were downloaded from a training dataset (GEO) and two validation datasets (TCGA and TARGET).
View Article and Find Full Text PDFMedicine (Baltimore)
November 2024
Department of Gastrointestinal Oncology, Affiliated Hospital of Qinghai University, Xining, China.
Ovarian cancer (OC) is a malignant gynecological cancer with an extremely poor prognosis. Stress granules (SGs) are non-membrane organelles that respond to stressors; however, the correlation between SG-related genes and the prognosis of OC remains unclear. This systematic analysis aimed to determine the expression levels of SG-related genes between high- and low-risk groups of patients with OC and to explore the prognostic value of these genes.
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