High expression of PARD3 predicts poor prognosis in hepatocellular carcinoma

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.

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CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

10.21203/rs.3.rs-150866/v1 ◽

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.

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

PeerJ ◽

10.7717/peerj.3089 ◽

2017 ◽

Vol 5 ◽

pp. e3089 ◽

Cited By ~ 12

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.

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Prognostic Significance of NBEAL2 in Liver hepatocellular carcinoma

10.21203/rs.3.rs-471663/v1 ◽

2021 ◽

Author(s):

Yanghui Wen ◽

Hui Su ◽

Wuke Wang ◽

Feng Ren ◽

Haitao Jiang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Expression Profiles ◽

Prognostic Significance ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Chi Square ◽

Kaplan Meier ◽

Chi Square Test ◽

Liver Hepatocellular Carcinoma ◽

The Relationship

Abstract Background: NBEAL2 is a member of the BEACH domain–containing protein (BDCP) family and little is known about the relationship between NBEAL2 and malignancy.Methods: We downloaded the Gene expression profiles and clinical data of Liver hepatocellular carcinoma(LIHC) form the Cancer Genome Atlas (TCGA) dataset. The expression difference of NBEAL2 in LIHC tissues and adjacent nontumor tissues was analyzed by R software. The relationship between NBEAL2 expression and clinicopathological parameters was evaluate by Chi-square test. The effect of NBEAL2 expression on survival were assessed by Kaplan–Meier survival analysis and Cox proportional hazards regression model. GSEA was used to explore the potential molecular mechanism of NBEAL2 in LIHC.Results: Up-regulation of NBEAL2 expression was detected in the LIHC tissue compared with adjacent nontumor tissues(P < 0.001). The chi-square test showed that no significant correlation between the expression level of NBEAL2 and various clinicopathological parameters (including T, N and M classifications) were detected. The Kaplan–Meier curves suggested that lower NBEAL2 expression was related with poor prognosis. The results of Multivariate analysis revealed that a lower expression of NBEAL2 in LIHC was an independent risk of poor overall survival (HR, 8.873; 95% CI, 1.159-67.936; P = 0.035). GSEA suggested that multiple tumor-related metabolic pathways were evidently enriched in samples with the low-NBEAL2 expression phenotype. Conlusion: NBEAL2 might act as an tumor suppressor gene in the progression of LIHC but the precise role of NBELA2 in LIHC needs further vertification.

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The Prediction and Prognostic Significance of INPP5K Expression in Patients with Liver Cancer

BioMed Research International ◽

10.1155/2020/9519235 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Ruobing Wang ◽

Yan Jiao ◽

Yanqing Li ◽

Siyang Ye ◽

Guoqiang Pan ◽

...

Keyword(s):

Liver Cancer ◽

Mrna Expression ◽

Poor Prognosis ◽

Prognostic Significance ◽

The Cancer Genome Atlas ◽

Inositol Polyphosphate ◽

Chi Square ◽

Cancer Genome Atlas ◽

Poor Outcomes ◽

The Relationship

Liver cancer is a devastating disease for humans with poor prognosis. Although the survival rate of patients with liver cancer has improved in the past decades, the recurrence and metastasis of liver cancer are still obstacles for us. Inositol polyphosphate-5-phosphatase K (INPP5K) belongs to the family of phosphoinositide 5-phosphatases (PI 5-phosphatases), which have been reported to be associated with cell migration, polarity, adhesion, and cell invasion, especially in cancers. However, there have been few studies on the correlation of INPP5K and liver cancer. In this study, we explored the prognostic significance of INPP5K in liver cancer through bioinformatics analysis of data collected from The Cancer Genome Atlas (TCGA) database. Chi-square and Fisher exact tests were used to evaluate the relationship between INPP5K expression and clinical characteristics. Our results showed that low INPP5K expression was correlated with poor outcomes in liver cancer patients. Univariate and multivariate Cox analyses demonstrated that low INPP5K mRNA expression played a significant role in shortening overall survival (OS) and relapse-free survival (RFS), which might serve as the useful biomarker and prognostic factor for liver cancer. In conclusion, low INPP5K mRNA expression is an independent risk factor for poor prognosis in liver cancer.

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CPXM1: a novel biomarker for gastric cancer prognosis

10.21203/rs.3.rs-337128/v1 ◽

2021 ◽

Author(s):

Jun Du ◽

Mengxiang Zhu ◽

Wenwu Yan ◽

Changsheng Yao ◽

Qingyi Li ◽

...

Keyword(s):

Gastric Cancer ◽

Poor Prognosis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Signal Pathways ◽

Data Set ◽

Kaplan Meier ◽

Cancer Genome Atlas ◽

Cox Proportional Hazard Regression ◽

The Relationship

Abstract Background The molecular role of carboxypeptidase X, M14 family member (CPXM1) in oncogenesis or tumor progression remains unclear. The aim of this study was to determine whether CPXM1 can be used as a potential prognostic biomarker for gastric cancer (GC). Methods We first demonstrated the relationship between CPXM1 expression and GC in various public databases. Secondly, the expression of CPXM1 in GC tissues was further verified by immunohistochemical staining using tissue microarray containing 96 cases of GC patients. Kaplan–Meier analysis and a Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CPXM1 and the survival of GC patients. Finally, we used the expression data of CPXM1 in The Cancer Genome Atlas database to predict CPXM1-related signaling pathways through bioinformatics analysis. Results The expression level of CPXM1 in GC tissues was significantly correlated with tumor size (p = 0.041) and lymph node metastasis (p = 0.014). In addition, Kaplan–Meier analysis showed that the expression of CPXM1 in GC tissues was significantly associated with poor prognosis (p = 0.011). Multivariate analysis indicated that CPXM1 is a potential predictor of poor prognosis in GC patients (p = 0.026). The results of biosynthesis analysis demonstrated that the data set of CPXM1 high expression was mainly enriched in cancer-related signal pathways. Conclusion CPXM1 is an effective biomarker for the prognosis of GC patients and may play a key role in the occurrence and progression of GC.

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Associations of Serum Tumor Biomarkers with Integrated Genomic and Clinical Characteristics of Hepatocellular Carcinoma

Liver Cancer ◽

10.1159/000516957 ◽

2021 ◽

pp. 1-13

Author(s):

Keun Soo Ahn ◽

Daniel R. O’Brien ◽

Yong Hoon Kim ◽

Tae-Seok Kim ◽

Hiroyuki Yamada ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Lens Culinaris ◽

Acid Metabolism ◽

The Cancer Genome Atlas ◽

Molecular Characteristics ◽

Genetic Profile ◽

Vegf Signaling ◽

The Usa ◽

Cancer Genome Atlas ◽

Liver Hepatocellular Carcinoma

Introduction: Serum α-fetoprotein (AFP), Lens culinaris agglutinin-reactive AFP (AFP-L3), and des-γ-carboxyprothrombin (DCP) are useful biomarkers of hepatocellular carcinoma (HCC). However, associations among molecular characteristics and serum biomarkers are unclear. We analyzed RNA expression and DNA variant data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) to examine their associations with serum biomarker levels and clinical data. Methods: From 371 TCGA-LIHC patients, we selected 91 seen at 3 institutions in Korea and the USA and measured AFP, AFP-L3, and DCP from preoperatively obtained serum. We conducted an integrative clinical and molecular analysis, focusing on biomarkers, and validated the findings with the remaining 280 patients in the TCGA-LIHC cohort. Results: Patients were categorized into 4 subgroups: elevated AFP or AFP-L3 alone (↑AFP&L3), elevated DCP alone (↑DCP), elevation of all 3 biomarkers (elevated levels of all 3 biomarkers [↑All]), and reference range values for all biomarkers (RR). CTNNB1 variants were frequently observed in ↑DCP patients (53.8%) and RR patients (38.5%), but ↑DCP patients with a CTNNB1 variant had worse survival than RR patients. TP53 sequence variants were associated with ↑AFP (30.8%) and ↑DCP (30.8%). The Wnt-β-catenin signaling pathway was activated in the ↑AFP&L3, whereas liver-related Wnt signaling was activated in the RR. TGF-β and VEGF signaling were activated in ↑AFP&L3, whereas dysregulated bile acid and fatty acid metabolism were dominant in ↑DCP. We validated these findings by showing similar results between the test cohort and the remainder of the TCGA-LIHC cohort. Conclusions: Serum AFP, AFP-L3, and DCP levels can help predict variants in the genetic profile of HCC, especially for TP53 and CTNNB1. These findings may facilitate development of an evidence-based approach to treatment.

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Identification of Key Prognostic Biomarker and Its Correlation with Immune Infiltrates in Pancreatic Ductal Adenocarcinoma

Disease Markers ◽

10.1155/2020/8825997 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Hong Luan ◽

Chuang Zhang ◽

Tuo Zhang ◽

Ye He ◽

Yanna Su ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Poor Prognosis ◽

Immune Therapy ◽

The Cancer Genome Atlas ◽

Ductal Adenocarcinoma ◽

Immune Checkpoints ◽

Hub Genes ◽

Immune Infiltration ◽

The Relationship

Pancreatic ductal adenocarcinoma (PDAC) is an extremely malignant tumor. The immune profile of PDAC and the immunologic milieu of its tumor microenvironment (TME) are unique; however, the mechanism of how the TME engineers the carcinogenesis of PDAC is not fully understood. This study is aimed at better understanding the relationship between the immune infiltration of the TME and gene expression and identifying potential prognostic and immunotherapeutic biomarkers for PDAC. Analysis of data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases identified differentially expressed genes (DEGs), including 159 upregulated and 53 downregulated genes. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment were performed and showed that the DEGs were mainly enriched for the PI3K-Akt signaling pathway and extracellular matrix organization. We used the cytoHubba plugin of Cytoscape to screen out the most significant ten hub genes by four different models (Degree, MCC, DMNC, and MNC). The expression and clinical relevance of these ten hub genes were validated using Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas, respectively. High expression of nine of the hub genes was positively correlated with poor prognosis. Finally, the relationship between these hub genes and tumor immunity was analyzed using the Tumor Immune Estimation Resource. We found that the expression of SPARC, COL6A3, and FBN1 correlated positively with infiltration levels of six immune cells in the tumors. In addition, these three genes had a strong coexpression relationship with the immune checkpoints. In conclusion, our results suggest that nine upregulated biomarkers are related to poor prognosis in PDAC and may serve as potential prognostic biomarkers for PDAC therapy. Furthermore, SPARC, COL6A3, and FBN1 play an important role in tumor-related immune infiltration and may be ideal targets for immune therapy against PDAC.

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RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.686547 ◽

2021 ◽

Vol 9 ◽

Author(s):

Benchen Rao ◽

Jianhao Li ◽

Tong Ren ◽

Jing Yang ◽

Guizhen Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Tumor Progression ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

The Cancer Genome Atlas ◽

Bile Acid Metabolism ◽

Hub Genes ◽

Immune Infiltration ◽

Tumor Tissues

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.MethodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.ResultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.ConclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

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An independent poor-prognosis subtype of hepatocellular carcinoma based on the tumor microenvironment

Journal of International Medical Research ◽

10.1177/0300060520980646 ◽

2021 ◽

Vol 49 (2) ◽

pp. 030006052098064

Author(s):

Junfeng Wang ◽

Jianying Lou ◽

Lei Fu ◽

Qu Jin

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Tumor Microenvironment ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

Immune Cell ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Using Data

Background Hepatocellular carcinoma (HCC) is a highly malignant tumor with a particularly poor prognosis. The tumor microenvironment (TME) is closely associated with tumorigenesis, progression, and treatment. However, the relationship between TME genes and HCC patient prognosis is poorly understood. Methods In this study, we identified two prognostic subtypes based on the TME using data from The Cancer Genome Atlas and Gene Expression Omnibus. The Microenvironment Cell Populations-counter method was used to evaluate immune cell infiltration in HCC. Differentially expressed genes between molecular subtypes were calculated with the Limma package, and clusterProfiler was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses to identify genes related to the independent subtypes. We also integrated mRNA expression data into our bioinformatics analysis. Results We identified 4227 TME-associated genes and 640 genes related to the prognosis of HCC. We defined two major subtypes (Clusters 1 and 2) based on the analysis of TME-associated gene expression. Cluster 1 was characterized by increased expression of immune-associated genes and a worse prognosis than Cluster 2. Conclusions The identification of these HCC subtypes based on the TME provides further insight into the molecular mechanisms and prediction of HCC prognosis.

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Decreased expression of ARID1A invasively downregulates the expression of ribosomal proteins in hepatocellular carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2020-0464 ◽

2021 ◽

Author(s):

Jing Zhao ◽

Weiran Xu ◽

Yu Zhang ◽

Xiaomin Lv ◽

Yiran Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Tumor Cells ◽

Ribosomal Proteins ◽

Bioinformatic Analysis ◽

The Cancer Genome Atlas ◽

Reverse Transcription Pcr ◽

Adaptive Mechanisms ◽

Tumor Mutational Burden ◽

Cancer Genome Atlas ◽

Liver Hepatocellular Carcinoma

Background: There was increasing evidence showing that ARID1A alterations correlated with higher tumor mutational burden, but there were limited studies focusing on the adaptive mechanisms for tumor cells to survive under excessive genomic alterations. Materials & methods: To further explore the adaptive mechanisms under ARID1A alterations, we performed RNA sequencing in ARID1A knockdown hepatocellular carcinoma cell lines, and demonstrated that decreased expression of ARID1A controlled global ribosomal proteins synthesis. The results were further confirmed by quantitative reverse transcription-PCR and bioinformatic analysis in The Cancer Genome Atlas Liver Hepatocellular Carcinoma database. Conclusion: The present study was the first to demonstrate that ARID1A might be involved in the translation pathway and served as an adaptive mechanism for tumor cells to survive under stress.

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