scholarly journals Etiology-Specific Analysis of Hepatocellular Carcinoma Transcriptome Reveals Genetic Dysregulation in Pathways Implicated in Immunotherapy Efficacy

Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1273
Author(s):  
Wei Tse Li ◽  
Angela E. Zou ◽  
Christine O. Honda ◽  
Hao Zheng ◽  
Xiao Qi Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Advanced Hepatocellular Carcinoma ◽  
Sequencing Data ◽  
Genes Expression ◽  
Specific Analysis ◽  
Cancer Genome Atlas

Immunotherapy has emerged in recent years as arguably the most effective treatment for advanced hepatocellular carcinoma (HCC), but the failure of a large percentage of patients to respond to immunotherapy remains as the ultimate obstacle to successful treatment. Etiology-associated dysregulation of immune-associated (IA) genes may be central to the development of this differential clinical response. We identified immune-associated genes potentially dysregulated by alcohol or viral hepatitis B in HCC and validated alcohol-induced dysregulations in vitro while using large-scale RNA-sequencing data from The Cancer Genome Atlas (TCGA). Thirty-four clinically relevant dysregulated IA genes were identified. We profiled the correlation of all genomic alterations in HCC patients to IA gene expression while using the information theory-based algorithm REVEALER to investigate the molecular mechanism for their dysregulation and explore the possibility of genome-based patient stratification. We also studied gene expression regulators and identified multiple microRNAs that were implicated in HCC pathogenesis that can potentially regulate these IA genes’ expression. Our study identified potential key pathways, including the IL-7 signaling pathway and TNFRSF4 (OX40)- NF-κB pathway, to target in immunotherapy treatments and presents microRNAs as promising therapeutic targets for dysregulated IA genes because of their extensive regulatory roles in the cancer immune landscape.

Upregulation of SGOL2 Predicts Poor Prognosis and Facilitates Hepatocellular Carcinoma Progression via Dysregulating the Cell Cycle by Directly Activating MAD2

2021 ◽  
Author(s):  
Qingqing Hu ◽  
Xiaochu Hu ◽  
Yalei Zhao ◽  
Lingjian Zhang ◽  
Ya Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
The Cancer Genome Atlas ◽  
Loss Of Function ◽  
Protein Levels ◽  
Cancer Genome Atlas ◽  
Centromeric Protein ◽  
Hcc Cells

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Shugoshin-like protein 2 (SGOL2) is a centromeric protein that ensures the correct and orderly process of mitosis by protecting and maintaining centripetal adhesions during meiosis and mitosis. However, the role of SGOL2 in cancer is not well understood. Methods: The mRNA and protein levels of SGOL2 and survival analysis were conducted in The Cancer Genome Atlas (TCGA) and further validated in 2 independent cohorts. Differential genes correlated with SGOL2 and mitotic arrest deficient 2 like 1 (MAD2) were obtained using LinkedOmics. Subsequently, loss-of-function and rescue assays were carried out in vitro and in vivo to assess the functions of SGOL2 in hepatic tumorigenisis. Findings: We found that SGOL2 was significantly overexpressed in HCC and predicted unfavorable overall survival in HCC patients. Next, we identified 47 differentially expressed genes positively correlated with both SGOL2 and MAD2 to be mainly involved in the cell cycle. In addition, SGOL2 downregulation suppressed the migration, invasion, proliferation, stemness and EMT of HCC cells and inhibited tumorigenesis in vivo. Furthermore, SGOL2 promoted tumor proliferation by activating MAD2-induced cell cycle dysregulation, which could be reversed by the MAD2 inhibitor M2I-1. We also proved that SGOL2 activated MAD2 by directly binding with MAD2. Conclusions: The results of this study showed that SGOL2 acts as an oncogene in HCC cells by directly activating MAD2 and then dysregulating the cell cycle, thereby providing a potential target for HCC patients in the future.

LncRNA DCST1-AS1 functions as a competing endogenous RNA to regulate FAIM2 expression by sponging miR-1254 in hepatocellular carcinoma

2019 ◽  
Vol 133 (2) ◽  
pp. 367-379 ◽  
Author(s):  
Jing Chen ◽  
Di Wu ◽  
Yue Zhang ◽  
Yong Yang ◽  
Yunfei Duan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Clinical Significance ◽  
Biological Function ◽  
The Cancer Genome Atlas ◽  
Cycle Arrest ◽  
Cancer Genome Atlas ◽  
Apoptosis Inhibitor ◽  
Hcc Cells

Abstract Long non-coding RNAs (lncRNAs) play important roles in a variety of tumours; however, their biological function and clinical significance in hepatocellular carcinoma (HCC) are still unclear. In the present study, the clinical significance, biological function and regulatory mechanisms of lncRNA DCST1-AS1 in HCC were investigated. Differential lncRNAs in HCC were identified based on The Cancer Genome Atlas (TCGA) database. The biological function and mechanism of DCST1-AS1 were studied in vitro and in vivo. LncRNA DCST1-AS1 was highly expressed in HCC tissues, and the high expression of DCST1-AS1 was significantly correlated with larger tumours and shorter survival time. Moreover, DCST1-AS1 knockout significantly inhibited proliferation, promoted apoptosis and cycle arrest of HCC cells, and inhibited tumour growth in vivo. According to functional analysis, DCST1-AS1 competitively bound miR-1254, thus blocking the silencing effect of miR-1254 on the target gene Fas apoptosis inhibitor 2 (FAIM2). A novel lncRNA DCST1-AS1 that functions as an oncogene in HCC was discovered. DCST1-AS1 up-regulates the expression of FAIM2 by up-regulating the expression of miR-1254, ultimately promoting the proliferation of HCC cells. This research provides new therapeutic targets for HCC.

scholarly journals Identification of Distinct Heterogenic Subtypes and Molecular Signatures Associated With African Ancestry in Triple Negative Breast Cancer Using Quantified Genetic Ancestry Models in Admixed Race Populations

2020 ◽  
Author(s):  
Melissa Davis ◽  
Rachel Martini ◽  
Lisa Newman ◽  
Olivier Elemento ◽  
Jason White ◽  
...  
Keyword(s):  
Gene Expression ◽  
Triple Negative ◽  
African Ancestry ◽  
Variant Calling ◽  
Genetic Ancestry ◽  
The Cancer Genome Atlas ◽  
Breast Cancers ◽  
Sequencing Data ◽  
Cancer Genome Atlas ◽  
Ethnic Variations

Triple negative breast cancers (TNBCs) are molecularly heterogeneous, and the link between their aggressiveness with African ancestry is not established. We investigated primary TNBCs for gene expression among self-reported race (SRR) groups of African American (AA, n=42) and European American (EA, n=33) women. Using The Cancer Genome Atlas (TCGA) approaches, we analyzed RNA sequencing data to measure changes in genome-wide expression and used logistic regressions to identify ancestry-associated gene expression signatures. To determine global ancestry, GATK best practices were followed for variant calling, and used the 1000 Genomes Project as reference data. We identified >150 African ancestry-associated genes and found that, compared to SRR, quantitative genetic analysis was a more robust method to identify racial/ethnic-specific genes that were differentially expressed. A subset of African ancestry-specific genes that were upregulated in TNBCs of our AA patients were validated in TCGA data. In AA patients, there was a higher incidence of basal-like 2 tumors and altered TP53, NFB1, and AKT pathways. The distinct distribution of TNBC subtypes and altered oncologic pathways show that the ethnic variations in TNBCs are driven by shared genetic ancestry. Thus, to appreciate the molecular diversity of TNBCs, tumors from patients of various ancestral origins should be evaluated.

scholarly journals Gene expression based inference of drug resistance in cancer

2021 ◽  
Author(s):  
Smriti Chawla ◽  
Anja Rockstroh ◽  
Melanie Lehman ◽  
Ellca Rather ◽  
Atishay Jain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Cell Lines ◽  
Large Scale ◽  
Activity Patterns ◽  
The Cancer Genome Atlas ◽  
Learning Approaches ◽  
Expression Data ◽  
Sequencing Data ◽  
Pathway Activity

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

scholarly journals Blocking siglec-10hi tumor-associated macrophages improves anti-tumor immunity and enhances immunotherapy for hepatocellular carcinoma

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Nan Xiao ◽  
Xiaodong Zhu ◽  
Kangshuai Li ◽  
Yifan Chen ◽  
Xuefeng Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Function ◽  
Cox Regression ◽  
Human Tumor ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Tumor Associated Macrophages ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background Tumor-associated macrophages (TAMs) promote key processes in the modulation of tumor microenvironment (TME). However, the clinical significance of heterogeneous subpopulations of TAMs in hepatocellular carcinoma (HCC) remains unknown. Methods HCC tissues from Zhongshan Hospital and data from The Cancer Genome Atlas were obtained and analyzed. Immunohistochemistry and flow cytometry were performed to detect the characteristics of sialic acid-binding immunoglobulin-like lectin 10high (Siglec-10hi) TAMs and explore their impact on the TME of HCC. The effect of Siglec-10 blockade was evaluated in vitro based on fresh human tumor tissues. Results Our data revealed that Siglec-10 was abundant in a large proportion of HCC specimens and prominently distributed on macrophages. Kaplan–Meier curves and Cox regression analysis showed that intratumoral Siglec-10+ cell enrichment was associated with unfavorable prognosis in patients with HCC. Notably, multiple anti-inflammatory cytokines and inhibitory receptors were enriched in Siglec-10hi TAMs. RNA sequencing data also revealed that numerous M2-like signaling pathways were significantly upregulated in Siglec-10hi TAMs. High infiltration of Siglec-10hi TAMs was associated with impaired CD8+ T cell function in HCC. Of note, blocking Siglec-10 with the competitive binding antibody Siglec-10 Fc led to decreased expression of immunosuppressive molecules and increased the cytotoxic effects of CD8+ T cells against HCC cells. Moreover, blocking Siglec-10 promoted the anti-tumor efficacy of the programmed cell death protein 1 (PD-1) inhibitor pembrolizumab. Conclusions Siglec-10hi TAMs are associated with immune suppression in the TME, and indicate poor prognosis in patients with HCC. Targeting Siglec-10hi TAMs may serve as a promising immunotherapy approach for HCC.

scholarly journals CDKN3 expression predicates poor prognosis and regulates adriamycin sensitivity in hepatocellular carcinoma in vitro

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052093687
Author(s):  
Wei Dai ◽  
Shuo Fang ◽  
Guanhe Cai ◽  
Jialiang Dai ◽  
Guotai Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factor ◽  
Therapeutic Target ◽  
Bioinformatics Analysis ◽  
Kinase Inhibitor ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
The Relationship

Objective Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths worldwide. This study investigated the relationship between cyclin-dependent kinase inhibitor (CDKN)3 and prognosis and pathological characteristics in HCC patients to determine whether it could be used as a prognostic factor and/or therapeutic target for HCC drug development. Methods We previously showed that CDKN3 is deregulated in HCC tumor samples. Here, bioinformatics analysis was used to assess the relationship between CDKN3 gene expression and the characteristics of HCC patients from Gene Expression Omnibus and The Cancer Genome Atlas databases. Additionally, CDKN3 expression was silenced by small interfering RNA to determine its effect on HCC cell proliferation and on HCC cell sensitivity to adriamycin chemotherapy. Results Bioinformatics analysis showed a negative correlation between CDKN3 expression and both disease-free survival and overall survival. CDKN3 silencing did not significantly suppress the proliferation of HCC cells, but did decrease their sensitivity to adriamycin. Conclusions CDKN3 may have a dual role during the development of HCC, and could be used as an independent prognostic factor and therapeutic target for HCC treatment.

Identification of prognostic biomarkers of hepatocellular carcinoma via long noncoding RNA expression and copy number alterations

Epigenomics ◽  
2020 ◽  
Vol 12 (15) ◽  
pp. 1303-1315
Author(s):  
Weibo Du ◽  
Wenbiao Chen ◽  
Zheyue Shu ◽  
Dairong Xiang ◽  
Kefan Bi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Copy Number ◽  
Noncoding Rna ◽  
Noncoding Rnas ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Biomarkers ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
Cancer Genome Atlas

Aim: This study aimed to identify long noncoding RNAs (lncRNAs) with potential to be prognostic biomarkers of hepatocellular carcinoma (HCC) by analyzing copy number alterations (CNAs). Methods: RNA Sequencing data of 369 HCC patients was downloaded from The Cancer Genome Atlas database and analyzed with a series of systematic bioinformatics methods. Results: LncRNA-CNA association analysis revealed that many lncRNAs were located in sites frequently amplified or deleted. Three upregulated lncRNAs (LINC00689, SNHG20 and MAFG-AS1) with copy amplification and one downregulated lncRNA TMEM220-AS1 with copy deletion were associated with poor prognosis of HCC. Conclusion: This study reveals that differentially expressed lncRNAs correlate with CNAs in HCC. Moreover, the differentially expressed lncRNAs and their copy amplification/deletions could be promising prognostic biomarkers of HCC.

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