scholarly journals Identification of NAA40 as a Potential Prognostic Marker for Aggressive Liver Cancer Subtypes

2021 ◽  
Vol 11 ◽  
Author(s):  
Costas Koufaris ◽  
Antonis Kirmizis
Keyword(s):  
Liver Cancer ◽  
In Silico Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Subtypes ◽  
Eukaryotic Proteins ◽  
Cancer Genome Atlas ◽  
Functional Inactivation ◽  
Tcga Dataset ◽  
Liver Hepatocellular Carcinoma ◽  
Significant Disease

Liver hepatocellular carcinoma (LIHC) is a leading cause of cancer-related mortality. In this study we initially interrogated the Cancer Genome Atlas (TCGA) dataset to determine the implication of N-terminal acetyltransferases (NATs), a family of enzymes that modify the N-terminus of the majority of eukaryotic proteins, in LIHC. This examination unveiled NAA40 as the NAT family member with the most prominent upregulation and significant disease prognosis for this cancer. Focusing on this enzyme, which selectively targets histone proteins, we show that its upregulation occurs from early stages of LIHC and is not specifically correlated with any established risk factors such as viral infection, obesity or alcoholic disease. Notably, in silico analysis of TCGA and other LIHC datasets found that expression of this epigenetic enzyme is associated with high proliferating, poorly differentiating and more aggressive LIHC subtypes. In particular, NAA40 upregulation was preferentially linked to mutational or non-mutational P53 functional inactivation. Accordingly, we observed that high NAA40 expression was associated with worse survival specifically in liver cancer patients with inactivated P53. These findings define NAA40 as a NAT with potentially oncogenic functions in LIHC and uncover its prognostic value for aggressive LIHC subtypes.

scholarly journals Novel cancer subtyping method based on patient-specific gene regulatory network

2021 ◽  
Author(s):  
Mai Adachi Nakazawa ◽  
Yoshinori Tamada ◽  
Yoshihisa Tanaka ◽  
Marie Ikeguchi ◽  
Kako Higashihara ◽  
...  
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Cancer Subtypes ◽  
Molecular Systems ◽  
Molecular Features ◽  
Cancer Genome Atlas

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Scaling concepts in ‘omics: Nuclear lamin-B scales with tumor growth and often predicts poor prognosis, unlike fibrosis

2021 ◽  
Vol 118 (48) ◽  
pp. e2112940118
Author(s):  
Manasvita Vashisth ◽  
Sangkyun Cho ◽  
Jerome Irianto ◽  
Yuntao Xia ◽  
Mai Wang ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Power Laws ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Structural Factors ◽  
Lamin B ◽  
Nuclear Lamin ◽  
Survival Gene ◽  
Cancer Genome Atlas ◽  
Tumor Types

Physicochemical principles such as stoichiometry and fractal assembly can give rise to characteristic scaling between components that potentially include coexpressed transcripts. For key structural factors within the nucleus and extracellular matrix, we discover specific gene-gene scaling exponents across many of the 32 tumor types in The Cancer Genome Atlas, and we demonstrate utility in predicting patient survival as well as scaling-informed machine learning (SIML). All tumors with adjacent tissue data show cancer-elevated proliferation genes, with some genes scaling with the nuclear filament LMNB1, including the transcription factor FOXM1 that we show directly regulates LMNB1. SIML shows that such regulated cancers cluster together with longer overall survival than dysregulated cancers, but high LMNB1 and FOXM1 in half of regulated cancers surprisingly predict poor survival, including for liver cancer. COL1A1 is also studied because it too increases in tumors, and a pan-cancer set of fibrosis genes shows substoichiometric scaling with COL1A1 but predicts patient outcome only for liver cancer—unexpectedly being prosurvival. Single-cell RNA-seq data show nontrivial scaling consistent with power laws from bulk RNA and protein analyses, and SIML segregates synthetic from contractile cancer fibroblasts. Our scaling approach thus yields fundamentals-based power laws relatable to survival, gene function, and experiments.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

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

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.

The impact of BMI on outcomes of patients with metastatic renal cell carcinoma treated with targeted therapy: An external validation data set and analysis of underlying biology from The Cancer Genome Atlas.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 405-405 ◽  
Author(s):  
Laurence Albiges ◽  
A. Ari Hakimi ◽  
Xun Lin ◽  
Ronit Simantov ◽  
Emily C. Zabor ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Targeted Therapy ◽  
Prognostic Factor ◽  
Renal Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Tcga Dataset ◽  
The Impact ◽  
Genome Atlas ◽  
High Bmi

405 Background: Obesity is a risk factor for renal cell carcinoma (RCC) and a poor prognostic factor across many tumor types. However, reports have suggested that RCC developing in an obesogenic environment may be more indolent. We recently reported on the favorable impact of body mass index (BMI) on survival in the International mRCC Database Consortium (IMDC). The current work aims to externally validate this finding and characterize the underlying biology. Methods: We conducted an analysis of 4,657 metastatic RCC (mRCC) patients (pts) treated on phase II-III clinical trials sponsored by Pfizer from 2003-2013. We assessed the impact of BMI on overall survival (OS), progression-free survival (PFS) and overall response rate (ORR). Additionally, we analysed metastatic pts from the clear cell RCC (ccRCC) cohort of TCGA dataset to correlate the expression of Fatty Acid Synthase (FASN) with BMI and OS. Results: At targeted therapy (TT) initiation, 1,829 (39%) pts were normal or underweight (BMI <25 kg/m2) and 2,828 (61%) were overweight or obese (BMI ≥25 kg/m2). Overall, the high BMI group had a longer median OS (23.4 months) than the low BMI group (14.5 months) (hazard ratio (HR) = 0.830, p= 0.0008, 95% CI 0.743-0.925) after adjusting for the IMDC prognostic risk group and other risks factors. In addition, pts with high BMI had improved PFS (HR=0.821, 95% CI 0.746-0.903, p<0.0001) and ORR (odds ratio =1.527, 95% CI 1.258-1.855, p<0.001). These results remain valid when stratified by line of therapy. When stratified by histological subtype, the favorable outcome associated with high BMI was only observed in ccRCC. Toxicity patterns did not differ between BMI groups. In the the Cancer Genome Atlas (TCGA) dataset (n=61), there was a trend towards improved OS in the high BMI group (p=0.07). FASN gene expression inversely correlated with both OS (p=0.002) and BMI (p=0.034). Conclusions: In an external cohort,we validate BMI as an independent prognostic factor for improved survival in mRCC. Given that this finding was observed in ccRCC only, we hypothesize that lipid metabolism may be modulated by the fat laden tumors cells. FASN staining in the IMDC cohort is ongoing to better investigate the obesity paradox in mRCC.

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

Liver Cancer ◽  
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

<b><i>Introduction:</i></b> Serum α-fetoprotein (AFP), <i>Lens culinaris</i> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> Patients were categorized into 4 subgroups: elevated AFP or AFP-L3 alone (↑AFP&amp;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). <i>CTNNB1</i> variants were frequently observed in ↑DCP patients (53.8%) and RR patients (38.5%), but ↑DCP patients with a <i>CTNNB1</i> variant had worse survival than RR patients. <i>TP53</i> sequence variants were associated with ↑AFP (30.8%) and ↑DCP (30.8%). The Wnt-β-catenin signaling pathway was activated in the ↑AFP&amp;L3, whereas liver-related Wnt signaling was activated in the RR. TGF-β and VEGF signaling were activated in ↑AFP&amp;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. <b><i>Conclusions:</i></b> Serum AFP, AFP-L3, and DCP levels can help predict variants in the genetic profile of HCC, especially for <i>TP53</i> and <i>CTNNB1</i>. These findings may facilitate development of an evidence-based approach to treatment.

scholarly journals Mast Cells Resting-Related Prognostic Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hao Zhang ◽  
Lin Sun ◽  
Xiao Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mast Cells ◽  
Liver Cancer ◽  
Differentially Expressed Genes ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Immune Checkpoints ◽  
New Ideas ◽  
Cancer Genome Atlas

The immune microenvironment of liver cancer is of great significance for the treatment of liver cancer. After evaluating the content of mast cells resting in the transcriptome data of The Cancer Genome Atlas database by CIBERSORT analysis, this study aimed to group the samples according to the content of mast cells resting in different samples to find the differentially expressed genes in the two groups. Significant prognostic differences were found between high and low mast cells resting infiltration groups. The prognostic model was constructed according to the differentially expressed genes. The model was validated using external independent datasets. The results revealed that the constructed model was reliable. It could well distinguish the prognostic differences of patients in different characteristic groups. The high-risk group was mainly concentrated in metabolic pathways. The risk score of this model was closely related to some immune cells, immune function, and immune checkpoints. Therefore, this model may provide new ideas for immunotherapy of hepatocellular carcinoma.

scholarly journals Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

2016 ◽  
Author(s):  
Nao Hiranuma ◽  
Jie Liu ◽  
Chaozhong Song ◽  
Jacob Goldsmith ◽  
Michael Dorschner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Triple Negative ◽  
The Cancer Genome Atlas ◽  
Breast Cancers ◽  
Single Nucleotide Variants ◽  
Primary Tumors ◽  
Cancer Subtypes ◽  
Multiple Biopsies ◽  
Cancer Genome Atlas

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

scholarly journals Identification of ST7 Alteration Profile, Frequency of Alteration and Correlation with ST7-Related Genes using TCGA Data

2019 ◽  
Vol 16 (3) ◽  
pp. 217-230
Author(s):  
Nurdina CHARONG ◽  
Moltira PROMKAN
Keyword(s):  
Disease Free Survival ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Using Data ◽  
Liver Hepatocellular Carcinoma ◽  
Disease Free ◽  
Genome Atlas

ST7 (Suppression of Tumorigenicity 7) was reported as a protein playing a role in maintaining cellular structure. This study aims to investigate the ST7 alteration profiles and frequency of alteration in different cancers using data from The Cancer Genome Atlas (TCGA). The correlation between alterations of ST7 and angiogenesis-related genes, SERPINE1, MMP13, and VEGFA, was determined and the relation between ST7 and genes involved in suppression of ST7 transcription, PRMT5 and SMARCA4, were also analyzed. Data of 6 cancer groups from The Cancer Genome Atlas (TCGA) including ovarian serous cystadenocarcinoma (OSC), liver hepatocellular carcinoma (LHC), bladder urothelial adenocarcinoma (BUA), stomach adenocarcinoma (SC), prostate adenocarcinoma (PRAD) and glioblastoma multiforme (GBM) were downloaded for this study. The results indicated that 3 alteration patterns including amplification, missense mutation, and deletion were observed in 6 cancer studies. Gene pair between ST7 and SERPINE1 indicated the co-occurrent alteration in BUC, OSC and SC (p < 0.05). However, no association between alterations of these 2 genes and survival events in our study was observed. Shorter overall survival rate and disease-free survival were found in BUC patients with ST7, PRMT5, and  SMARCA4 alterations. These findings suggest that using TCGA data can target the potential genes involved in carcinogenesis. Combining ST7 with PRMT5 and SMARCA4 could be used as indicators for analyzing the patient survival in BUC patients and may serve as the potential therapeutic target for cancer in the future.

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