scholarly journals From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3089 ◽  
Author(s):  
Hong Yang ◽  
Xin Zhang ◽  
Xiao-yong Cai ◽  
Dong-yue Wen ◽  
Zhi-hua Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Big Data ◽  
Natural Language Processing ◽  
Language Processing ◽  
Poor Prognosis ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Liver Hepatocellular Carcinoma ◽  
Genome Atlas

BackgroundLiver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.MethodsBig data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.ResultsA list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998],P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.DiscussionThe study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

scholarly journals High expression of PARD3 predicts poor prognosis in hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Songwei Li ◽  
Jian Huang ◽  
Fan Yang ◽  
Haiping Zeng ◽  
Yuyun Tong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dendritic Cells ◽  
Poor Prognosis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Predictive Capacity ◽  
Immune Infiltration ◽  
Cancer Genome Atlas ◽  
Liver Hepatocellular Carcinoma ◽  
The Relationship

AbstractHepatocellular carcinoma (HCC) is one of the most commonly cancers with poor prognosis and drug response. Identifying accurate therapeutic targets would facilitate precision treatment and prolong survival for HCC. In this study, we analyzed liver hepatocellular carcinoma (LIHC) RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), and identified PARD3 as one of the most significantly differentially expressed genes (DEGs). Then, we investigated the relationship between PARD3 and outcomes of HCC, and assessed predictive capacity. Moreover, we performed functional enrichment and immune infiltration analysis to evaluate functional networks related to PARD3 in HCC and explore its role in tumor immunity. PARD3 expression levels in 371 HCC tissues were dramatically higher than those in 50 paired adjacent liver tissues (p < 0.001). High PARD3 expression was associated with poor clinicopathologic feathers, such as advanced pathologic stage (p = 0.002), vascular invasion (p = 0.012) and TP53 mutation (p = 0.009). Elevated PARD3 expression also correlated with lower overall survival (OS, HR = 2.08, 95% CI = 1.45–2.98, p < 0.001) and disease-specific survival (DSS, HR = 2.00, 95% CI = 1.27–3.16, p = 0.003). 242 up-regulated and 71 down-regulated genes showed significant association with PARD3 expression, which were involved in genomic instability, response to metal ions, and metabolisms. PARD3 is involved in diverse immune infiltration levels in HCC, especially negatively related to dendritic cells (DCs), cytotoxic cells, and plasmacytoid dendritic cells (pDCs). Altogether, PARD3 could be a potential prognostic biomarker and therapeutic target of HCC.

Identification of prognostic and immune-related lncRNAs in hepatocellular carcinoma based on the cancer genome atlas data mining

2020 ◽  
Vol 10 (8) ◽  
pp. 1189-1196
Author(s):  
Kaikai Ren ◽  
Jiakang Ma ◽  
Bo Zhou ◽  
Xiaoyan Lin ◽  
Mingyu Hou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Prognostic Indicators ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Hepatocellular carcinoma (HCC) is a malignancy originating from hepatocytes with a high rate of distant metastasis and recurrence. HCC prognosis remains poorly understood, although its diagnosis and treatment have improved globally. Therefore, it is necessary to identify reliable predictive and prognostic indicators of HCC. HCC gene expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas. Seven lncRNAs (C10orf91, AC011352.3, AC015722.2, AC006372.1, PICSAR, AC110285.3, and AP001972.4) associated with immune and clinicopathological features were identified as biomarker candidates for HCC prognosis based on single-sample gene set enrichment analysis, the ESTIMATE algorithm, and Cox PHR analyses. Altogether, the findings revealed that the seven immune-related lncRNAs may provide a reference for improving HCC prognosis.

scholarly journals Study of the G Protein Nucleolar 2 Value in Liver Hepatocellular Carcinoma Treatment and Prognosis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yiwei Dong ◽  
Qianqian Cai ◽  
Lisheng Fu ◽  
Haojie Liu ◽  
Mingzhe Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
G Protein ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Migration And Invasion ◽  
Functional Assays ◽  
Close Relationship ◽  
Cancer Genome Atlas ◽  
And Function ◽  
Liver Hepatocellular Carcinoma

LIHC (liver hepatocellular carcinoma) mostly occurs in patients with chronic liver disease. It is primarily induced by a vicious cycle of liver injury, inflammation, and regeneration that usually last for decades. The G protein nucleolar 2 (GNL2), as a protein-encoding gene, is also known as NGP1, Nog2, Nug2, Ngp-1, and HUMAUANTIG. Few reports are shown towards the specific biological function of GNL2. Meanwhile, it is still unclear whether it is related to the pathogenesis of carcinoma up to date. Here, our study attempts to validate the role and function of GNL2 in LIHC via multiple databases and functional assays. After analysis of gene expression profile from The Cancer Genome Atlas (TCGA) database, GNL2 was largely heightened in LIHC, and its overexpression displayed a close relationship with different stages and poor prognosis of carcinoma. After enrichment analysis, the data revealed that the genes coexpressed with GNL2 probably participated in ribosome biosynthesis which was essential for unrestricted growth of carcinoma. Cell functional assays presented that GNL2 knockdown by siRNA in LIHC cells MHCC97-H and SMCC-7721 greatly reduced cell proliferation, migration, and invasion ability. All in all, these findings capitulated that GNL2 could be a promising treatment target and prognosis biomarker for LIHC.

Signatures of gene expression, DNA methylation and microRNAs of hepatocellular carcinoma with vascular invasion

2019 ◽  
Vol 15 (22) ◽  
pp. 2603-2617 ◽  
Author(s):  
Siti A Sulaiman ◽  
Nadiah Abu ◽  
Nurul-Syakima Ab-Mutalib ◽  
Teck Yew Low ◽  
Rahman Jamal
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Vascular Invasion ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Differentially Methylated Genes ◽  
Cancer Genome Atlas ◽  
Poor Outcomes ◽  
Liver Hepatocellular Carcinoma ◽  
Genome Atlas

Aim: Micro and macro vascular invasion (VI) are known as independent predictors of tumor recurrence and poor survival after surgical treatment of hepatocellular carcinoma (HCC). Here, we aimed to re-analyze The Cancer Genome Atlas of liver hepatocellular carcinoma datasets to identify the VI-expression signatures. Materials & methods: We filtered The Cancer Genome Atlas liver hepatocellular carcinoma (LIHC) datasets into three groups: no VI (NVI = 198); micro VI (MIVI = 89) and macro VI (MAVI = 16). We performed differential gene expression, methylation and microRNA analyses. Results & conclusion: We identified 12 differentially expressed genes and 55 differentially methylated genes in MAVI compared with no VI. The GPD1L gene appeared in all of the comparative analyses. Higher GPD1L expression was associated with VI and poor outcomes in the HCC patients.

scholarly journals High RPS27A Expression Predicts Poor Prognosis in Patients With HPV Type 16 Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiming Wang ◽  
Yan Cai ◽  
Xuewen Fu ◽  
Liang Chen
Keyword(s):  
Cervical Cancer ◽  
Poor Prognosis ◽  
Biological Significance ◽  
Enrichment Analysis ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Cervical Cancer Cells ◽  
Cancer Genome Atlas

In recent years, the incidence and the mortality rate of cervical cancer have been gradually increasing, becoming one of the major causes of cancer-related death in women. In particular, patients with advanced and recurrent cervical cancers present a very poor prognosis. In addition, the vast majority of cervical cancer cases are caused by human papillomavirus (HPV) infection, of which HPV16 infection is the main cause and squamous cell carcinoma is the main presenting type. In this study, we performed screening of differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) database and GSE6791, constructed a protein–protein interaction (PPI) network to screen 34 hub genes, filtered to the remaining 10 genes using the CytoHubba plug-in, and used survival analysis to determine that RPS27A was most associated with the prognosis of cervical cancer patients and has prognostic and predictive value for cervical cancer. The most significant biological functions and pathways of RPS27A enrichment were subsequently investigated with gene set enrichment analysis (GSEA), and integration of TCGA and GTEx database analyses revealed that RPS27A was significantly expressed in most cancer types. In this study, our analysis revealed that RPS27A can be used as a prognostic biomarker for HPV16 cervical cancer and has biological significance for the growth of cervical cancer cells.

scholarly journals CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

2021 ◽  
Author(s):  
Jun Du ◽  
Jinguo Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Early Stage ◽  
Human Tumor ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

Sign in / Sign up

Export Citation Format

Share Document