Background: No effective molecular targeted therapy has been established for SCC. We conducted a comprehensive study of SCC patients using RNA-sequencing and TCGA dataset to clarify the driver oncogene of SCC.
Method: Forty-six samples of 23 patients were totally analyzed with RNA-sequencing. We then searched for candidate-oncogenes of SCC using the TCGA database. To identify candidate oncogenes, we used the following 2 criteria: (1) the genes of interest were overexpressed in tumor tissues of SCC patients in comparison to normal tissues; and (2) using an integrated mRNA expression and DNA copy number profiling analysis using the TCGA dataset, the DNA copy number of the genes was positively correlated with the mRNA expression.
Result: We identified 188 candidate-oncogenes. Among those, the high expression of SLC38A7 was a strong prognostic marker that was significantly associated with a poor prognosis in terms of both overall survival (OS) and recurrence-free survival in the TCGA dataset (P < 0.05). Additionally, 202 resected SCC specimens were also subjected to an immunohistochemical analysis. Patients with the high expression of SLC38A7 (alternative name is sodium-coupled amino acid transporters 7) protein showed significantly shorter OS in comparison to those with the low expression of SLC38A7 protein [median OS 3.9 years (95% confidence interval, 2.4-6.4 years) vs 2.2 years (95% confidence interval, 1.9-4.1 years); log rank test: P = 0.0021].
Conclusion: SLC38A7, which is the primary lysosomal glutamine transporter required for the extracellular protein-dependent growth of cancer cells, was identified as a candidate therapeutic target of SCC.
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http://dx.doi.org/10.1097/SLA.0000000000005001 | DOI Listing |
Curr Pharm Biotechnol
January 2025
Department of Intensive Care Unit, Affiliated Hospital of Guangdong Medical University, 524000 Zhanjiang, China.
Objectives: This study aimed to comprehensively investigate the molecular landscape of gastric cancer (GC) by integrating various bioinformatics tools and experimental validations.
Methodology: GSE79973 dataset, limma package, STRING, UALCAN, GEPIA, OncoDB, cBioPortal, DAVID, TISIDB, Gene Set Cancer Analysis (GSCA), tissue samples, RT-qPCR, and cell proliferation assay were employed in this study.
Results: Analysis of the GSE79973 dataset identified 300 differentially expressed genes (DEGs), from which COL1A1, COL1A2, CHN1, and FN1 emerged as pivotal hub genes using protein-protein interaction network analysis.
BMC Med Genomics
January 2025
Department of Oncology, The First People's Hospital of Yibin, No.65, Wenxing Street, Cuiping District, Yibin, 644000, China.
Background: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC.
View Article and Find Full Text PDFAccurate 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 PDFBiochem Biophys Rep
March 2025
Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Introduction: Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients.
Materials And Methods: GC RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to compute hypoxia and stemness scores using Gene Set Variation Analysis (GSVA) and the mRNA expression-based stemness index (mRNAsi).
J Transl Med
January 2025
School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, Guizhou, 550000, China.
Background: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies.
Methods: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC.
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