ViMIC: a database of human disease-related virus mutations, integration sites and <i>cis</i>-effects

Wang, Ying; Tong, Yuantao; Zhang, Zeyu; Zheng, Rongbin; Huang, Danqi; Yang, Jiancheng; Zong, Hui; Tan, Fanglin; Xie, Yujia; Huang, Honglian; Zhang, Xiaoyan

doi:10.1093/nar/gkab779

Cited by 21 publications

(14 citation statements)

References 70 publications

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“…Secondly, this study lacks data on immunotherapy responses and validation of prognostic models. Thirdly, the process of immune cell infiltration is complex, and some oncogenic pathways of driving mutations [ 51 ] or virus infections may also influence the activation of the immune response [ 56 ]. Thus, experimental validation will be needed in future work [ 57 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

Wang

Tong

Zong

et al. 2022

Genes

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Tumor mutational burden (TMB) is considered a potential biomarker for predicting the response and effect of immune checkpoint inhibitors (ICIs). However, there are still inconsistent standards of gene panels using next-generation sequencing and poor correlation between the TMB genes, immune cell infiltrating, and prognosis. We applied text-mining technology to construct specific TMB-associated gene panels cross various cancer types. As a case exploration, Pearson’s correlation between TMB genes and immune cell infiltrating was further analyzed in colorectal cancer. We then performed LASSO Cox regression to construct a prognosis predictive model and calculated the risk score of each sample for receiver operating characteristic (ROC) analysis. The results showed that the assessment of TMB gene panels performed well with fewer than 500 genes, highly mutated genes, and the inclusion of synonymous mutations and immune regulatory and drug-target genes. Moreover, the analysis of TMB differentially expressed genes (DEGs) suggested that JAKMIP1 was strongly correlated with the gene expression level of CD8+ T cell markers in colorectal cancer. Additionally, the prognosis predictive model based on 19 TMB DEGs reached AUCs of 0.836, 0.818, and 0.787 in 1-, 3-, and 5-year OS models, respectively (C-index: 0.810). In summary, the gene panel performed well and TMB DEGs showed great potential value in immune cell infiltration and in predicting survival.

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Section: Discussionmentioning

confidence: 99%

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

Wang

Tong

Zong

et al. 2022

Genes

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“…Globally, approximately 350 million people are infected with HBV, and 20%-30% of chronically HBV-infected individuals may develop cirrhosis and HCC. 33,34 About 80% of HCC cases are related to HBV infection in China. 35 No effective screening strategy for HBV-related HCC exists, 3,27 with insufficient sensitivity of current US-and AFP-based approaches to detect early HCC.…”

Section: Discussionmentioning

confidence: 99%

Validation of the GALAD model for early diagnosis and monitoring of hepatocellular carcinoma in Chinese multicenter study

Huang

Fang

Xiao

et al. 2021

Liver International

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Background GALAD is an algorithm model estimating the presence of hepatocellular carcinoma (HCC). However, the participants enrolled in the GALAD differ from those of Chinese subjects whose HCCs are mainly hepatitis B virus infection related. Therefore, the cross‐sectional as well as longitudinal multicenter study was designed to assess the clinical performances of GALAD in the Chinese population. Methods A case‐control study of 602 patients with HCC (34.10% within Barcelona Clinic Liver Cancer 0‐A stage) and 923 subjects without HCC from five Chinese medical centres was conducted. Longitudinally the performances of GALAD identifying HCC were assessed using receiver operating characteristic curves analyses. Furthermore, the surveillance performance of GALAD for 204 HCC patients after radical surgery and for the early detection of HCC prospectively in an independent cohort of chronic hepatitis B were analysed, respectively. Results We found the GALAD identified early stage HCC at an area under the receiver operating characteristic curve (AUC) above 0.85 and outperformed significantly than AFP, PIVKAII, AFP‐L3 and BALAD‐2 respectively. Meanwhile the GALAD could stratify HCC into two distinct subgroups with high or low risks of overall survival and recurrence. The GALAD could detection HCC 24 (AUC: 0.848) or even 48 (AUC: 0.833) weeks before clinical diagnosis. Conclusions Our study indicates that the GALAD exhibits outstanding performance in the early diagnosis, prognosis prediction as well as risk monitoring of HCC in our cross‐sectional and longitudinal multicenter study of 1561 patients. GALAD should be implanted into clinical practice early so as to improve the clinical efficacy of individual biomarkers in HCC early monitoring and prognosis prediction.

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“…Furthermore, due to the inconsistency of virus mutations and the lack of standard mutation nomenclature, the conventional ‘Keyword Search’ may be difficult to quickly and non-redundantly get desired mutation information from literature in PubMed. As such, extracting, mining and integrating virus mutation-related information from those extremely large amounts of literature has become an increasingly important task in many downstream applications, such as virus mutation database development ( Davey et al , 2014 ; Wang et al , 2022 ), immune escape mechanism exploration ( Huang et al , 2020 ), vaccine design and updating ( Xie et al , 2021 ), drug resistance mutation analysis and assistance in clinical personalized medicine ( Chen et al , 2020 ; Yeo et al , 2020 ).…”

Section: Introductionmentioning

confidence: 99%

ViMRT: a text-mining tool and search engine for automated virus mutation recognition

et al. 2022

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Motivation Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations. Results We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1,662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases. Availability ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.

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ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects

Cited by 21 publications

References 70 publications

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

Validation of the GALAD model for early diagnosis and monitoring of hepatocellular carcinoma in Chinese multicenter study

ViMRT: a text-mining tool and search engine for automated virus mutation recognition

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