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Search Results (10)
  • Open Access


    An inflammatory-related genes signature based model for prognosis prediction in breast cancer


    Oncology Research, Vol.31, No.2, pp. 157-167, 2023, DOI:10.32604/or.2023.027972

    Abstract Background: Breast cancer has become the most common malignant tumor in the world. It is vital to discover novel prognostic biomarkers despite the fact that the majority of breast cancer patients have a good prognosis because of the high heterogeneity of breast cancer, which causes the disparity in prognosis. Recently, inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer, so we set out to investigate the predictive usefulness of inflammatory-related genes in breast malignancies. Methods: We assessed the connection between Inflammatory-Related Genes (IRGs) and breast cancer by studying the TCGA database.… More > Graphic Abstract

    An inflammatory-related genes signature based model for prognosis prediction in breast cancer

  • Open Access


    Increased MAD2L2 expression predicts poor clinical outcome in Colon Adenocarcinoma


    BIOCELL, Vol.47, No.3, pp. 607-618, 2023, DOI:10.32604/biocell.2023.026445

    Abstract Background: Colon adenocarcinoma (COAD) is the second leading cause of cancer death worldwide thus, identification of COAD biomarkers is critical. Mitotic Arrest Deficient 2 Like 2 (MAD2L2) is a key factor in mammalian DNA damage repair and is highly expressed in many malignant tumors. This is a comprehensive study of MAD2L2 expression, its diagnostic value, prognostic analysis, potential biological function, and impact on the immune system of patients with COAD. Methods: Gene expression, clinical relevance, prognostic analysis, diagnostic value, GO/KEGG cluster analysis, data obtained from TCGA, and bioinformatics statistical analysis were performed using the R package. Immune responses to MAD2L2More >

  • Open Access


    Comprehensive Analysis of the Expression and Clinical Significance of a Ferroptosis-Related Genome in Ovarian Serous Cystadenocarcinoma: A Study Based on TCGA Data

    Hua Yang*

    Oncologie, Vol.24, No.4, pp. 835-863, 2022, DOI:10.32604/oncologie.2022.026447

    Abstract Background: Epithelial ovarian cancer (EOC) is the deadliest malignancy among the gynecologic tumors, and ovarian serous cystadenocarcinoma (OV) is the dominant histological type. Ferroptosis is a novel iron-dependent, programmed form of cell death, and agents that trigger ferroptosis may constitute potential anti-cancer therapies. Materials and Methods: We herein extracted the genes that participate in the process of ferroptosis from the online FerrDb database to create a ferroptosis-related genome (FRG), and then comprehensively analyzed the relationship between the mRNA expression of each gene and the clinicopathologic features of The Cancer Genome Atlas (TCGA)-OV cohort. Results: We found that most of the… More >

  • Open Access


    A Metabolism-Related Gene Signature Predicts the Prognosis of Breast Cancer Patients: Combined Analysis of High-Throughput Sequencing and Gene Chip Data Sets

    Lei Hu1,2,#, Meng Chen2,3,#, Haiming Dai2,3,4, Hongzhi Wang2,3,4,*, Wulin Yang2,3,4,*

    Oncologie, Vol.24, No.4, pp. 803-822, 2022, DOI:10.32604/oncologie.2022.026419

    Abstract Background and Aim: Hundreds of consistently altered metabolic genes have been identified in breast cancer (BC), but their prognostic value remains to be explored. Therefore, we aimed to build a prediction model based on metabolism-related genes (MRGs) to guide BC prognosis. Methods: Current work focuses on constructing a novel MRGs signature to predict the prognosis of BC patients using MRGs derived from the Virtual Metabolic Human (VMH) database, and expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Results: The 3-MRGs-signature constructed by SERPINA1, QPRT and PXDNL was found to be an… More >

  • Open Access


    The Transcriptional and Immunological Roles of Six2 in Clear Cell Renal Cell Carcinoma

    Dayu Tian1, Yang Shi1, Li Lei1, Xiangmin Qiu1, Tao Song2,*, Qianyin Li1,*

    Oncologie, Vol.24, No.2, pp. 261-282, 2022, DOI:10.32604/oncologie.2022.022838

    Abstract Background: Six2, a transcription factor, exerts an oncogenic role in clear cell renal cell carcinoma (ccRCC). Increased Six2 expression could enhance cancer metastasis. However, the regulatory mechanism of Six2 in promoting metastasis remains unclear. The purpose of this study is to analyze the regulatory pattern of Six2 and the potential role of Six2 in the tumor immune microenvironment. Materials and Methods: Firstly, transcriptional data in TCGA-KIRC cohorts was used to analyze the relationship between Six2 expression and clinical information. Secondly, we detect the association between Six2 and the tumor immune microenvironment in ccRCC. Then, we analyzed Six2-related differentially expressed genes… More >

  • Open Access


    Microenvironment Analysis of Prognosis and Molecular Signature of Immune-Related Genes in Lung Adenocarcinoma

    Bo Ling, Zuliang Huang, Suoyi Huang, Li Qian, Genliang Li, Qianli Tang

    Oncology Research, Vol.28, No.6, pp. 561-578, 2020, DOI:10.3727/096504020X15907428281601

    Abstract There is growing evidence on the clinical significance of tumor microenvironment (TME) cells in predicting prognosis and therapeutic effects. However, cell interactions in tumor microenvironments have not been thoroughly studied or systematically analyzed so far. In this study, 22 immune cell components in the lung adenocarcinoma (LUAD) TME were analyzed using gene expression profile from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TME-based molecular subtypes of LUAD were defined to evaluate further the relationship between molecular subtypes, prognosis, and clinical characteristics. A TME risk score model was constructed by using the differentially expressed genes (DEGs) of… More >

  • Open Access


    Exploration of Combinational Therapeutic Strategies for HCC Based on TCGA HCC Database

    Dong Yan1,#, Chunxiao Li2,#, Yantong Zhou2, Xue Yan1, Weihua Zhi1, Haili Qian2,*, Yue Han1,*

    Oncologie, Vol.24, No.1, pp. 101-111, 2022, DOI:10.32604/oncologie.2022.020357

    Abstract Hepatocellular carcinoma (HCC) is one of the most deadly types of cancer. Sorafenib is currently the only available first-line molecular targeted drug approved by the FDA for HCC. However, primary and secondary resistance is often encountered with treatment with sorafenib. Genomic alterations found in HCC represent potential targets to develop new drugs or new combinational strategies against this type of cancer. Here we analyzed genomic alterations from the TCGA database of HCC samples and the corresponding targeted drugs available to the clinic to identify candidate drugs that might hold promise when used in combination with sorafenib. Our results revealed that… More >

  • Open Access


    Racial Bias Can Confuse AI for Genomic Studies

    Beifen Dai1,#, Zhihao Xu2,#, Hongjue Li3, Bo Wang3, Jinsong Cai1, Xiaomo Liu4,*

    Oncologie, Vol.24, No.1, pp. 113-130, 2022, DOI:10.32604/oncologie.2022.020259

    Abstract Large-scale genomic studies are important ways to comprehensively decode the human genomics, and provide valuable insights to human disease causalities and phenotype developments. Genomic studies are in need of high throughput bioinformatics analyses to harness and integrate such big data. It is in this overarching context that artificial intelligence (AI) offers enormous potentials to advance genomic studies. However, racial bias is always an important issue in the data. It is usually due to the accumulation process of the dataset that inevitability involved diverse subjects with different races. How can race bias affect the outcomes of AI methods? In this work,… More >

  • Open Access


    Immune prognostic implications of PSMD14 and its associated genes signatures in hepatocellular carcinoma


    BIOCELL, Vol.45, No.6, pp. 1527-1541, 2021, DOI:10.32604/biocell.2021.016203

    Abstract PSMD14 played a vital role in initiation and progression of hepatocellular carcinoma (HCC). However, PSMD14 and its-related genes for the immune prognostic implications of HCC patients have rarely been analyzed. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database-Liver Hepatocellular Carcinoma (LIHC). Additionally, we used multi-dimensional bioinformatics analysis to construct and validate a PSMD14-based immune prognostic signature (including RBM45, PSMD1, OLA1, CCT6A, LCAT and IVD) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. Calibration curves confirmed… More >

  • Open Access


    Microenvironment and related genes predict outcomes of patients with cervical cancer: evidence from TCGA and bioinformatic analysis


    BIOCELL, Vol.44, No.4, pp. 597-605, 2020, DOI:10.32604/biocell.2020.011328

    Abstract Cervical cancer (CESC) is one of the most common cancers and affects the female genital tract. Consistent HPV infection status has been determined to be a vital cause of tumorigenesis. HPV infection may induce changes to the immune system and limit the host’s immune response. Immunotherapy is therefore essential to improving the overall survival of both locally advanced and recurrent CESC patients. Using 304 relevant samples from TCGA, we assessed immune cell function in CESC patients to better understand the status of both tumor micro-environment cells and immune cells in CESC. Functional enrichment analysis, pathway enrichment analysis, and PPI network… More >

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