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TCGA and ESTIMATE data mining to identify potential prognostic biomarkers in HCC patients.

ABSTRACT: Hepatocellular carcinoma (HCC) is an aggressive form of cancer characterized by a high recurrence rate following resection. Studies have implicated stromal and immune cells, which form part of the tumor microenvironment, as significant contributors to the poor prognoses of HCC patients. In the present study, we first downloaded gene expression datasets for HCC patients from The Cancer Genome Atlas database and categorized the patients into low and high stromal or immune score groups. By comparing those groups, we identified differentially expressed genes significantly associated with HCC prognosis. The Gene Ontology database was then used to perform functional enrichment analysis, and the STRING network database was used to construct protein-protein interaction networks. Our results show that most of the differentially expressed genes were involved in immune processes and responses and the plasma membrane. Those results were then validated using another a dataset from a HCC cohort in the Gene Expression Omnibus database and in 10 pairs of HCC tumor tissue and adjacent nontumor tissue. These findings enabled us to identify several tumor microenvironment-related genes that associate with HCC prognosis, and some those appear to have the potential to serve as HCC biomarkers.

SUBMITTER: He G

PROVIDER: S-EPMC7695391 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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TCGA and ESTIMATE data mining to identify potential prognostic biomarkers in HCC patients.

He Guolin G Fu Shunjun S Li Yang Y Li Ting T Mei Purong P Feng Lei L Cai Lei L Cheng Yuan Y Zhou Chenjie C Tang Yujun Y Huang Wenbin W Liu Haiyan H Cen Bohong B Pan Mingxin M Gao Yi Y

Aging 20201111 21

Hepatocellular carcinoma (HCC) is an aggressive form of cancer characterized by a high recurrence rate following resection. Studies have implicated stromal and immune cells, which form part of the tumor microenvironment, as significant contributors to the poor prognoses of HCC patients. In the present study, we first downloaded gene expression datasets for HCC patients from The Cancer Genome Atlas database and categorized the patients into low and high stromal or immune score groups. By comparin ...[more]

PMID: 33177245

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Project description:BACKGROUND:The E2F family of transcription factor 2 (E2F2) plays an important role in the development and progression of various tumors, but its association with hepatocellular carcinoma (HCC) remains unknown. Our study aimed to investigate the role and clinical significance of E2F2 in HCC. METHODS:HCC raw data were extracted from The Cancer Genome Atlas (TCGA). Wilcoxon signed-rank test, Kruskal-Wallis test and logistic regression were applied to analyze the relationship between the expression of E2F2 and clinicopathologic characteristics. Cox regression and Kaplan-Meier were employed to evaluate the correlation between clinicopathologic features and survival. The biological function of E2F2 was annotated by Gene Set Enrichment Analysis (GSEA). RESULTS:The expression of E2F2 was increased in HCC samples. The expression of elevated E2F2 in HCC samples was prominently correlated with histologic grade (OR?=?2.62 for G3-4 vs. G1-2, p?=?1.80E-05), clinical stage (OR?=?1.74 for III-IV vs. I-II, p?=?0.03), T (OR?=?1.64 for T3-4 vs.T1-2, p?=?0.04), tumor status (OR?=?1.88 for with tumor vs. tumor free, p?=?3.79E-03), plasma alpha fetoprotein (AFP) value (OR?=?3.18 for AFP???400 vs AFP<20, p?=?2.16E-04; OR?=?2.50 for 20???AFP<400 vs AFP<20, p?=?2.56E-03). Increased E2F2 had an unfavorable OS (p?=?7.468e-?05), PFI (p?=?3.183e-?05), DFI (p?=?0.001), DSS (p?=?4.172e-?05). Elevated E2F2 was independently bound up with OS (p?=?0.004, hazard ratio [HR]?=?2.4 (95% CI [1.3-4.2])), DFI (P?=?0.029, hazard ratio [HR]?=?2.0 (95% CI [1.1-3.7])) and PFI (P?=?0.005, hazard ratio [HR]?=?2.2 (95% CI [1.3-3.9])). GSEA disclosed that cell circle, RNA degradation, pyrimidine metabolism, base excision repair, aminoacyl tRNA biosynthesis, DNA replication, p53 signaling pathway, nucleotide excision repair, ubiquitin-mediated proteolysis, citrate cycle TCA cycle were notably enriched in E2F2 high expression phenotype. CONCLUSIONS:Elevated E2F2 can be a promising independent prognostic biomarker and therapeutic target for HCC. Additionally, cell cycle, pyrimidine metabolism, DNA replication, p53 signaling pathway, ubiquitin-mediated proteolysis, the citrate cycle TCA cycle may be the key pathway by which E2F2 participates in the initial and progression of HCC.

Project description:BackgroundHepatocellular carcinoma (HCC) is a highly malignant disease, and it is characterized by rapid progression and low five-year survival rate. At present, there are no effective methods for monitoring the treatment and prognosis of HCC.MethodsThe transcriptome and gene expression profiles of HCC were obtained from the Cancer Genome Atlas (TCGA) program, International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The random forest method was applied to construct a four-gene prognostic model based on RNA terminal phosphate cyclase like 1 (RCL1) expression. The Kaplan-Meier method was performed to evaluate the prognostic value of RCL1, long noncoding RNAs (AC079061, AL354872, and LINC01093), and four-gene signature (SPP1, MYBL2, TRNP1, and FTCD). We examined the relationship between RCL1 expression and immune cells infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI).ResultsThe results of multiple databases indicated that the aberrant expression of RCL1 was associated with clinical outcome, immune cells infiltration, TMB, and MSI in HCC patients. Meanwhile, we found that long noncoding RNAs (AC079061, AL354872, and LINC01093) and RCL1 were significantly coexpressed in HCC patients. We also confirmed that the four-gene signature was an independent prognostic factor for HCC patients. Ferroptosis potential index, immune checkpoint molecules, and clinical feature were found to have obvious correlations with risk score. The area under the receiver operating characteristic curve values for the model were 0.7-0.8 in the training set and the validation set, suggesting high robustness of the four-gene signature. We then built a nomogram for facilitating the use in clinical practice.ConclusionOur study demonstrated that RCL1 and a novel four-gene signature can be used as prognostic biomarkers for predicting clinical outcome in HCC patients; and this model may assist in individualized treatment monitoring of HCC patients in clinical practice.

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TCGA and ESTIMATE data mining to identify potential prognostic biomarkers in HCC patients.

Publications

TCGA and ESTIMATE data mining to identify potential prognostic biomarkers in HCC patients.

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