Identification of a four‐gene metabolic signature predicting overall survival for hepatocellular carcinoma

Liu, Gao-Min; Xie, Wenlin; Zhang, Caiyun; Xu, Jiachao

doi:10.1002/jcp.29081

Cited by 96 publications

(90 citation statements)

References 45 publications

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“…Meanwhile, a large number of cancer-related data are stored in publicly available databases, such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A reanalysis of these data could facilitate the discovery of a vast range of valuable biological information (Gao et al, 2018(Gao et al, , 2019Liu et al, 2019;Zhou et al, 2019). For instance, several novel genes associated with poor prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) were identified by analyzing the array data from GEO.…”

Section: Introductionmentioning

confidence: 99%

“…These genes might serve as potential diagnostic and prognostic biomarkers for PDAC (Zhou et al, 2019). Liu et al (2019) revealed a novel robust four-gene metabolic signature predicting overall survival for hepatocellular carcinoma (HCC) by mining mRNA expression profiles and clinicopathological data from TCGA and GSE14520 data sets. The signature would be very useful for reflecting the dysregulated metabolic microenvironment and providing potential biomarkers for metabolic therapy and treatment response prediction in HCC (Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Genes Related to Clinicopathological Characteristics and Prognosis of Patients with Colorectal Cancer

Gao

Yang

2020

DNA and Cell Biology

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The aim of this study was to identify genes with clinical significance in colorectal cancer (CRC). Gene expression profiles of 585 CRC tissues and 61 normal colorectal tissues from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to identify differentially expressed genes (DEGs) between CRC and normal colorectal tissues. DAVID and KOBAS tools were used to explore Gene Ontology (GO) and KEGG pathways enriched by DEGs, respectively. In addition, TCGA data sets were also used to identify prognostic factors and develop a prognostic prediction model for CRC. A total of 353 DEGs including 117 upregulated and 236 downregulated genes in CRC were identified based on GSE32323 data set. These DEGs were significantly enriched in the biological process related to the regulation of cell proliferation and 50 signaling pathways, such as ''TGF-beta signaling pathway,'' ''Wnt signaling pathway,'' and ''Jak-STAT signaling pathway.'' GCG, ADH1B, SLC4A4, ZG16, and CLCA4 were the top five downregulated in CRC. FOXQ1, LGR5, CLDN1, KRT23, and DPEP1 were the top five upregulated in CRC. KRT23 expression could affect tumor stage and regional lymph node metastasis in CRC patients. FOXQ1 expression could affect tumor distant metastasis in CRC patients. Survival analysis indicated that SLC4A4 expression was associated with the prognosis of CRC patients. Prognostic prediction model developed based on age, tumor stage, and SLC4A4 expression exhibited an efficient performance in predicting 1-, 3-, and 5-year overall survival of CRC patients. In conclusion, the current study identified several genes and pathways related to CRC, which provided new insight in understanding molecular mechanism of tumorigenesis and development of CRC.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Genes Related to Clinicopathological Characteristics and Prognosis of Patients with Colorectal Cancer

Gao

Yang

2020

DNA and Cell Biology

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“…In addition, a large amount of studies suggested that WTAP as a novel oncogenic protein played a vital role in AML, and it also was a crucial factor on poor prognosis of malignant glioma [43]. Based on gene expression profile, we established a risk score model via lasso COX regression that had some predicted performance for prognosis of GC patients [44,45]. The result of m6A risk score model suggested that m6A-related genes were significantly associated with prognosis of GC patients.…”

Section: Discussionmentioning

confidence: 99%

Expression Status And Prognostic Value Of M6A-associated Genes in Gastric Cancer

Guan¹,

Liu²,

Li³

et al. 2020

J. Cancer

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Purpose: Gastric cancer (GC) is a primary cause of cancer-associated mortality worldwide. N6-methyladenosine (m6A) is one of the most common RNA modifications that involves in the progression of numerous cancers. However, the expression status and function of m6A-related genes in gastric cancer is still not well understood. The current study is aimed to investigate the expression status and determinate prognostic value of m6A-related genes in gastric cancer. Methods: m6A-asssociated gene expression was evaluated via analyzing the expression data of GC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The protein expression levels of m6A-associated molecules were further validated by immunohistochemical (IHC) staining data from GC tissue microarray (TMA) cohort and Human Protein Atlas (HPA) database. Kaplan-Meier analysis was performed to assess the prognostic value of m6A-associated genes in gastric cancer. Risk score model was established by lasso COX regression analysis and its prognostic predicted efficiency was assessed by the receiver-operator characteristic (ROC) curve. Cox regression analyses were used for exploring risk factors related to GC patient prognosis. Results: Most of m6A-related genes were upregulated at both mRNA and protein levels in gastric cancer tissues compared with that in normal gastric tissues. The expression levels of m6A-related genes were associated with clinicopathological features including race, age and TNM stage. High expression of WTAP and FTO predicted poor prognosis of GC patients. Survival analysis demonstrated that patients with high-risk scores had worse overall survival (OS) and ROC curves suggested the prediction performance for gastric patients. Moreover, Cox regression analyses indicated that m6A risk model score was a prognostic factor for OS and FTO upregulation might be a potential independent prognostic factor for recurrence-free survival (RFS) in gastric cancer patients. Conclusion: m6A-related genes were dysregulated in GC and were closely associated with prognosis of GC patients. FTO might serve as a novel prognostic biomarker for gastric cancer, while the m6A-related risk score might be informative for risk assessment and prognostic stratification.

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“…Univariate and multivariate Cox regression were performed with a forward stepwise procedure. When the P-value was less than 0.05, it was considered statistically signi cant (17).…”

Section: Discussionmentioning

confidence: 99%

A prognostic model for predicting recurrence-free survival in hepatocellular carcinoma patients

Wang

Xie

et al. 2020

Preprint

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Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. Using the The Cancer Genome Atlas （TCGA） dataset, we identified genes that are associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. Validation in both the TCGA cohort and the GEO cohort demonstrated that the 7-gene prognostic model has the capability of predicting the RFS of HCC patients. Meanwhile, the result of multivariate Cox regression showed that the 7-gene prognostic model could work as an independent prognostic factor. In addition, according to the time dependent ROC curve, the 7-gene prognostic model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. What’s more, these seven genes were found to be related to the occurrence and development of liver cancer by exploring other three databases. Our study identified a seven-gene signature for HCC RFS prediction that is a novel and convenient prognostic tool. The seven genes might provide potential target genes for metabolic therapy and the treatment of HCC.

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Identification of a four‐gene metabolic signature predicting overall survival for hepatocellular carcinoma

Cited by 96 publications

References 45 publications

Identification of Genes Related to Clinicopathological Characteristics and Prognosis of Patients with Colorectal Cancer

Identification of Genes Related to Clinicopathological Characteristics and Prognosis of Patients with Colorectal Cancer

Expression Status And Prognostic Value Of M6A-associated Genes in Gastric Cancer

A prognostic model for predicting recurrence-free survival in hepatocellular carcinoma patients

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