Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Liu, Hongbo; Hei, Guoli; Zhang, Lu; Jiang, Yanxia; Lü, Haijun

doi:10.1038/s41598-022-21473-0

Cited by 2 publications

(4 citation statements)

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“…Overall, most of the genes which were obtained from our study have been reported as promising candidate for biomarkers in various studies [2,[35][36][37][38][39][40]. However, some genes have not yet been reported in connection with Head and Neck cancer.…”

Section: Discussionmentioning

confidence: 66%

“…The gene PLAC9's overexpression has been reported in connection with the inhibition of cell growth regulation and has also been reported in connection with cancers such as ovarian cancer and breast cancers as prognostic biomarkers [34]. Gene "ACKR1", along with other 3 genes in a study, was reported to be downregulated in HNSCC patients, which was correlated with poor prognosis (p<0.05) [35]. Also, gene "AQP7", which is involved in physiologically functional cell migration, was upregulated in MSR of patients with ten tumors [36].…”

Section: Discussionmentioning

confidence: 91%

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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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Section: Discussionmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 91%

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

Jarwal,

Dhall,

Arora

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Overall, most of the genes which were obtained from our study have been reported as promising candidate for biomarkers in various studies ( Zivicova et al, 2018 ; Broutian et al, 2020 ; Sun et al, 2020 ; Wang et al, 2020 ; Zhao et al, 2020 ; Jin et al, 2021 ; Liu et al, 2022 ). However, some genes have not yet been reported in connection with Head and Neck cancer.…”

Section: Discussionmentioning

confidence: 70%

“…The gene PLAC9’s overexpression has been reported in connection with the inhibition of cell growth regulation and has also been reported in connection with cancers such as ovarian cancer and breast cancers as prognostic biomarkers ( Ouyang et al, 2018 ). Gene “ACKR1”, along with other 3 genes in a study, was reported to be downregulated in HNSCC patients, which was correlated with poor prognosis ( p < 0.05) ( Liu et al, 2022 ). Also, gene “AQP7,” which is involved in physiologically functional cell migration, was upregulated in MSR of patients with ten tumors ( Zivicova et al, 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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Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Cited by 2 publications

References 54 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

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

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