Roles of Major RNA Adenosine Modifications in Head and Neck Squamous Cell Carcinoma

Huo, Xingxing; Wang, Shujie; Song, Hang; Li, Mingde; Yu, Hua; Wang, Meng; Gong, Hong-xiao; Qiu, Xiaoting; Zhu, Yongfu; Zhang, Jianye

doi:10.3389/fphar.2021.779779

Cited by 4 publications

(4 citation statements)

References 159 publications

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“…Growth differentiation factor-10 (GDF10), also known as BMP3b, is a tumor suppressor that belongs to the transforming growth factor-b (TGF-b) superfamily ( Cheng et al, 2016 ). CIB1, PIM3, SLC16A3, VOPP1, BMP4, TIGIT, ADAR, and LRRN4CL are studied as upregulated genes in various cancer types such as squamous carcinoma cells, breast cancer, head and neck squamous cell carcinoma, and pancreatic cancer ( Baras et al, 2011 ; Alarmo et al, 2013 ; Zheng and Tian, 2014 ; de Jong et al, 2018 ; Notarangelo, 2018 ; Broutian et al, 2020 ; Yu et al, 2020 ; Huo et al, 2021 ; Wen et al, 2021 ; Yang et al, 2022 ). A complex that is important in the keratinocyte-intrinsic immune response to human papillomaviruses (-HPVs) is formed when CIB1 interacts with the EVER1, and EVER2 proteins ( de Jong et al, 2018 ; Notarangelo, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Jarwal,

Dhall,

Arora

et al. 2024

Front. Mol. Biosci.

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BackgroundHead and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature.MethodsIn this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Furthermore, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV−) categories. In this study, we have used GSE181919 dataset, we have extracted 20 primary cancer (HNSCC) samples, and 9 normal tissues samples. The primary cancer samples contained 13 HPV− and 7 HPV+ samples. The models developed in this study have been trained on 80% of the dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes.ResultsArtificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV− patients on the validation set. In GO enrichment analysis, it was found that most genes were involved in binding and catalytic activities.ConclusionA software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/.

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Section: Discussionmentioning

confidence: 99%

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Jarwal,

Dhall,

Arora

et al. 2024

Front. Mol. Biosci.

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“…Previous studies have shown a significant correlation between increased ADAR expression and poorer survival outcomes in esophageal squamous cell carcinoma [ 12 , 36 ] and human hepatocellular carcinoma [ 10 ]. In this study, we demonstrated that total ADAR mRNAs and typical transcripts ( p110 ) were highly expressed in CRC compared with adjacent paracancerous tissues.…”

Section: Discussionmentioning

confidence: 99%

The Interplay between RNA Editing Regulator ADAR1 and Immune Environment in Colorectal Cancer

Zheng

Zhang

Zhao

et al. 2023

Journal of Oncology

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An abnormality in the regulation of adenosine deaminase acting on RNA (ADAR) enzymes, which catalyzed adenosine-to-inosine (A-to-I) RNA editing, was closely associated with the highly aggressive biologic behavior and poor prognosis in many malignancies. In the present study, we aimed to investigate the relationship among transcript factors-microRNAs regulatory network, immune environment, and ADAR gene in colorectal carcinoma (CRC). The association among the expression levels of ADAR mRNA and copy number variation, methylation, and mutation status were comprehensively analyzed using cBioPortal, Wanderer, and UALCAN databases in CRC datasets. ADAR-transcript factors (TFs) and ADAR-miRNA regulation networks were constructed by Cistrome Cancer and miRWalk2.0, respectively. The full network and subnetworks for ADAR coexpression genes were constructed using the STRING database and visualized by the MCODE module of the Cytoscape app. The relationship between ADAR mRNA expression and the abundance of infiltrating immune cells in CRC patients was explored by the Tumor Immune Estimation Resource, CIBERSORT, and single-gene gene set enrichment analysis (GSEA). ADAR mRNA was elevated and was a cancer essential gene in CRC. ADAR mRNA and transcripts P110 were significantly elevated in CRC compared to normal controls. Low-level methylation in the promoter region and high copy number amplification of ADAR were responsible for high levels of ADAR mRNA expression. ADAR coexpression genes were mainly involved in immunoregulation, especially T-lymphocyte activation. Hub genes, including CD2, CD274, and FASLG, were also significantly upregulated in the ADAR-high group compared to the control group. Besides, M1 macrophages were enriched in the ADAR-high group compared to the control group. This study demonstrated that ADAR, a new essential gene, was involved in the immune regulator and was a novel immune treatment target in CRC.

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“…Growth differentiation factor-10 (GDF10), also known as BMP3b, is a tumor suppressor that belongs to the transforming growth factor-b (TGF-b) superfamily [42]. CIB1, PIM3, SLC16A3, VOPP1, BMP4, TIGIT, ADAR, and LRRN4CL are studied as upregulated genes [2,[43][44][45][46][47][48][49][50][51]. A complex that is important in the keratinocyte-intrinsic immune response to human papillomaviruses (-HPVs) is formed when CIB1 interacts with the EVER1, and EVER2 proteins [43,44].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Jarwal,

Dhall,

Arora

et al. 2023

Preprint

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Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature. In this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Additionally, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories. The models developed in this study have been trained on 80% of the GSE181919 dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. Artificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV-patients on the validation set. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes and found that most genes were involved in binding and catalytic activities. To facilitate the scientific community, a software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available athttps://webs.iiitd.edu.in/raghava/hnscpred/.Key PointsApplication of single cell transcriptomics in cancer diagnosisDevelopment of models for predicting HNSCC patientsClassification of HPV+ and HPV-HNSCC patientsIdentification of gene biomarkers from single cell sequencingA standalone software package HNSCpred for predicting HNSCC patientsAuthor’s BiographyAkanksha Jarwal is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Anjali Dhall is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Akanksha Arora is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Sumeet Patiyal is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Aman Srivastava is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Gajendra P. S. Raghava is currently working as a Professor and Head of the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.

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Roles of Major RNA Adenosine Modifications in Head and Neck Squamous Cell Carcinoma

Cited by 4 publications

References 159 publications

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

The Interplay between RNA Editing Regulator ADAR1 and Immune Environment in Colorectal Cancer

A Deep Learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

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