Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

Gao, Li; Du, Xinyue; Wu, Xiaoxiong; Wu, Shen; Zhang, Yufei; Xu, Jianing; Wang, Hao; Chen, Tingsong

doi:10.3389/fphar.2021.638622

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Some of these have documented roles in HCC development. For example, A1CF promotes NASH and HCC 64 , ESRP2 loss promotes HCC and lower expression predicts worse survival 32 , HNRNPAB induces EMT and promotes HCC metastasis 65 , the poly(C)-binding protein PCBP2 is upregulated in HCC and promotes proliferation 66 , SFPQ and RBM8A mediate resistance to platinum drugs in HCC 67 , 68 , RBMS1 blocks ferroptosis in HCC 69 , MATR3 promotes HCC progression 70 , SART3 is an immunotherapy target in HCC 71 , ZCRB1 is a signature gene for HCC 72 , and down regulation of CELF2 or CPEB3 promotes HCC 73 , but the targets for many of these splicing factors remain to be determined. The role of these splicing factors in early liver disease is less clear but mouse studies have highlighted a potential causative role for some splicing factors in the pathogenesis of NAFLD and NASH.…”

Section: Discussionmentioning

confidence: 99%

Dysregulation of RNA splicing in early non-alcoholic fatty liver disease through hepatocellular carcinoma

Webster,

Kumar,

2024

Sci Rep

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While changes in RNA splicing have been extensively studied in hepatocellular carcinoma (HCC), no studies have systematically investigated changes in RNA splicing during earlier liver disease. Mouse studies have shown that disruption of RNA splicing can trigger liver disease and we have shown that the splicing factor SRSF3 is decreased in the diseased human liver, so we profiled RNA splicing in liver samples from twenty-nine individuals with no-history of liver disease or varying degrees of non-alcoholic fatty liver disease (NAFLD). We compared our results with three publicly available transcriptome datasets that we re-analyzed for splicing events (SEs). We found many changes in SEs occurred during early liver disease, with fewer events occurring with the onset of inflammation and fibrosis. Many of these early SEs were enriched for SRSF3-dependent events and were associated with SRSF3 binding sites. Mapping the early and late changes to gene ontologies and pathways showed that the genes harboring these early SEs were involved in normal liver metabolism, whereas those harboring late SEs were involved in inflammation, fibrosis and proliferation. We compared the SEs with HCC data from the TCGA and observed that many of these early disease SEs are found in HCC samples and, furthermore, are correlated with disease survival. Changes in splicing factor expression are also observed, which may be associated with distinct subsets of the SEs. The maintenance of these SEs through the multi-year oncogenic process suggests that they may be causative. Understanding the role of these splice variants in metabolic liver disease progression may shed light on the triggers of liver disease progression and the pathogenesis of HCC.

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Section: Discussionmentioning

confidence: 99%

Dysregulation of RNA splicing in early non-alcoholic fatty liver disease through hepatocellular carcinoma

Webster,

Kumar,

2024

Sci Rep

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“…In the precision medicine era, studies on genetic characteristics may provide us with new directions. A growing number of studies have proposed new biomarkers for liver cancer, such as the cancer driver gene [ 44 ], autophagy [ 45 ], and ferroptosis-related gene signature [ 46 ]. The present study identified novel transcriptional addiction gene-related signatures and nomograms.…”

Section: Discussionmentioning

confidence: 99%

Profiling and integrated analysis of transcriptional addiction gene expression and prognostic value in hepatocellular carcinoma

Wang

et al. 2023

Aging

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Transcriptional dysregulation caused by genomic and epigenetic alterations in cancer is called "transcriptional addiction". Transcriptional addiction is an important pathogenic factor of tumor malignancy. Hepatocellular carcinoma (HCC) genomes are highly heterogeneous, with many dysregulated genes. Our study analyzed the possibility that transcriptional addiction-related genes play a significant role in HCC. All data sources for conducting this study were public cancer databases and tissue microarrays. We identified 38 transcriptional addiction genes, and most were differentially expressed genes. Among patients of different groups, there were significant differences in overall survival rates. Both nomogram and risk score were independent predictors of HCC outcomes. Transcriptional addiction gene expression characteristics determine the sensitivity of patients to immunotherapy, cisplatin, and sorafenib. Besides, HDAC2 was identified as an oncogene, and its expression was correlated with patient survival time. Our study conclusively demonstrated that transcriptional addiction is crucial in HCC. We provided biomarkers for predicting the prognosis of HCC patients, which can more precisely guide the patient's treatment.

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“…All data from the TCGA and CGGA databases were transformed into log2 (x + 1) form for subsequent analyses. After that, the data on gene expression profiles from various databases were batchnormalized utilising the "Surrogate Variable Analysis (sva)" program that is included in R (Li et al, 2021). After intersecting all genes from the two datasets, 17,818 genes were defined as common genes.…”

Section: Introductionmentioning

confidence: 99%

Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Miao

Liu

et al. 2022

Front. Genet.

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Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.

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Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

Cited by 5 publications

References 40 publications

Dysregulation of RNA splicing in early non-alcoholic fatty liver disease through hepatocellular carcinoma

Dysregulation of RNA splicing in early non-alcoholic fatty liver disease through hepatocellular carcinoma

Profiling and integrated analysis of transcriptional addiction gene expression and prognostic value in hepatocellular carcinoma

Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

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