scholarly journals Serological Biomarkers for Early Detection of Hepatocellular Carcinoma: A Focus on Autoantibodies against Tumor-Associated Antigens Encoded by Cancer Driver Genes

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1271 ◽  
Author(s):  
Keyan Wang ◽  
Miao Li ◽  
Jiejie Qin ◽  
Guiying Sun ◽  
Liping Dai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Early Detection ◽  
Protein Microarrays ◽  
Training Dataset ◽  
Validation Dataset ◽  
Driver Genes ◽  
Cancer Driver ◽  
Tumor Associated Antigens ◽  
Prediction Probability ◽  
Cancer Driver Genes

Substantial evidence manifests the occurrence of autoantibodies to tumor-associated antigens (TAAs) in the early stage of hepatocellular carcinoma (HCC), and previous studies have mainly focused on known TAAs. In the present study, protein microarrays based on cancer driver genes were customized to screen TAAs. Subsequently, autoantibodies against selected TAAs in sera were tested by enzyme-linked immunosorbent assays (ELISA) in 1175 subjects of three independent datasets (verification dataset, training dataset, and validation dataset). The verification dataset was used to verify the results from the microarrays. A logistic regression model was constructed within the training dataset; seven TAAs were included in the model and yielded an area under the receiver operating characteristic curve (AUC) of 0.831. The validation dataset further evaluated the model, exhibiting an AUC of 0.789. Remarkably, as the aggravation of HCC increased, the prediction probability (PP) of the model tended to decrease, the trend of which was contrary to alpha-fetoprotein (AFP). For AFP-negative HCC patients, the positive rate of this model reached 67.3% in the training dataset and 50.9% in the validation dataset. Screening TAAs with protein microarrays based on cancer driver genes is the latest, fast, and effective method for finding indicators of HCC. The identified anti-TAA autoantibodies can be potential biomarkers in the early detection of HCC.

scholarly journals Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

2021 ◽  
Vol 12 ◽  
Author(s):  
Di Jiang ◽  
Xue Zhang ◽  
Man Liu ◽  
Yulin Wang ◽  
Tingting Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Validation Cohort ◽  
Protein Array ◽  
Enzyme Linked Immunosorbent Assay ◽  
Training Set ◽  
Driver Genes ◽  
Test Set ◽  
Cancer Driver ◽  
Cancer Driver Genes

Substantial studies indicate that autoantibodies to tumor-associated antigens (TAAbs) arise in early stage of lung cancer (LC). However, since single TAAbs as non-invasive biomarkers reveal low diagnostic performances, a panel approach is needed to provide more clues for early detection of LC. In the present research, potential TAAbs were screened in 150 serum samples by focused protein array based on 154 proteins encoded by cancer driver genes. Indirect enzyme-linked immunosorbent assay (ELISA) was used to verify and validate TAAbs in two independent datasets with 1,054 participants (310 in verification cohort, 744 in validation cohort). In both verification and validation cohorts, eight TAAbs were higher in serum of LC patients compared with normal controls. Moreover, diagnostic models were built and evaluated in the training set and the test set of validation cohort by six data mining methods. In contrast to the other five models, the decision tree (DT) model containing seven TAAbs (TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1), built in the training set, yielded the highest diagnostic value with the area under the receiver operating characteristic curve (AUC) of 0.897, the sensitivity of 94.4% and the specificity of 84.9%. The model was further assessed in the test set and exhibited an AUC of 0.838 with the sensitivity of 89.4% and the specificity of 78.2%. Interestingly, the accuracies of this model in both early and advanced stage were close to 90%, much more effective than that of single TAAbs. Protein array based on cancer driver genes is effective in screening and discovering potential TAAbs of LC. The TAAbs panel with TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1 is excellent in early detection of LC, and they might be new target in LC immunotherapy.

scholarly journals Cooperation Between Distinct Cancer Driver Genes Underlies Intertumor Heterogeneity in Hepatocellular Carcinoma

2020 ◽  
Vol 159 (6) ◽  
pp. 2203-2220.e14
Author(s):  
Pedro Molina-Sánchez ◽  
Marina Ruiz de Galarreta ◽  
Melissa A. Yao ◽  
Katherine E. Lindblad ◽  
Erin Bresnahan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes

scholarly journals Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Gao Li ◽  
Xiaowei Du ◽  
Xiaoxiong Wu ◽  
Shen Wu ◽  
Yufei Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Critical Role ◽  
Risk Groups ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and heterogeneity. Genetic mutations caused by driver genes are important contributors to the formation of the tumor microenvironment. The purpose of this study is to discuss the expression of cancer driver genes in tumor tissues and their clinical value in predicting the prognosis of HCC.Methods: All data were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) public databases. Differentially expressed and prognostic genes were screened by the expression distribution of the cancer driver genes and their relationship with survival. Candidate genes were subjected to functional enrichment and transcription factor regulatory network. We further constructed a prognostic signature and analyzed the survival outcomes and immune status between different risk groups.Results: Most cancer driver genes are specifically expressed in cancer tissues. Driver genes may influence HCC progression through processes such as transcription, cell cycle, and T-cell receptor-related pathways. Patients in different risk groups had significant survival differences (p < 0.05), and risk scores showed high predictive efficacy (AUC>0.69). Besides, risk subgroups were also associated with multiple immune functions and immune cell content.Conclusion: We confirmed the critical role of cancer driver genes in mediating HCC progression and the immune microenvironment. Risk subgroups contribute to the assessment of prognostic value in different patients and explain the heterogeneity of HCC.

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

EBioMedicine ◽  
2018 ◽  
Vol 27 ◽  
pp. 156-166 ◽  
Author(s):  
Magali Champion ◽  
Kevin Brennan ◽  
Tom Croonenborghs ◽  
Andrew J. Gentles ◽  
Nathalie Pochet ◽  
...  
Keyword(s):  
Antiviral Response ◽  
Driver Genes ◽  
Cancer Driver ◽  
Module Analysis ◽  
Cancer Driver Genes
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