Dataset Information

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation.

ABSTRACT:

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, but in clinical practice, the lack of precise biomarkers often results in an advanced diagnosis. Hence, it is crucial to explore novel biomarkers to improve the clinical outcome of HNSCC patients.

Methods

We downloaded RNA-seq data consisting of 502 HNSCC tissues and 44 normal tissues from the TCGA database, and lncRNA genomic sequence information was downloaded from the GENECODE database for annotating lncRNA expression profiles. We used Cox regression analysis to screen prognostic lncRNAs, the threshold as HR >1 and p value <0.05. Subsequently, three survival outcomes (overall survival, progress-free interval, and disease-specific survival)-related lncRNAs overlapped to get the common lncRNAs. The hub biomarker was identified using LASSO and random forest models. Subsequently, we used a variety of statistical methods to validate the prognostic ability of the hub marker. In addition, Spearman correlation analysis between the hub marker expression and genomic heterogeneity was conducted, such as instability (MSI), homologous recombination deficiency (HRD), and tumor mutational burden (TMB). Finally, we used enrichment analysis, ssGSEA, and ESTIMATE algorithms to explore the changes in the underlying immune-related pathway and function. Finally, the MTT assay and transwell assay were performed to determine the effect of LINC01615 silencing on tumor cell proliferation, invasion, and migration.

Results

Cox regression analysis revealed 133 lncRNAs with multiple prognostic significance. The machine learning algorithm screened out the hub lncRNA with the highest importance in the RF model: LINC01615. Clinical correlation analysis revealed that the LINC01615 increased with increasing the T stage, N stage, pathology grade, and clinical stage. LINC01615 could be used as a predictor of HNSCC prognosis validating by a variety of statistical methods. Subsequently, when clinical indicators were combined with the LINC01615 expression, the visualization model (nomogram) was more applicable to clinical practice. Finally, immune algorithms indicated that LINC01615 may be involved in the regulation of lymphocyte recruitment and immunological infiltration in HNSCC, and the LINC01615 expression represented genomic heterogeneity in pan-cancer. Functionally, silencing of LINC01615 suppresses cell proliferation, invasion, and migration in HEP-2 and TU212 cells.

Conclusion

LINC01615 may play an important role in the prostromal cell enrichment and immunosuppressive state and serve as a prognostic biomarker in HNSCC.

SUBMITTER: Yin X

PROVIDER: S-EPMC9252709 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation.

Yin Xiaoyan X Wang Jingmiao J Bian Yanrui Y Jia Qiaojing Q Shen Ziyi Z Zhang Haizhong H

Journal of oncology 20220627

<h4>Background</h4>Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, but in clinical practice, the lack of precise biomarkers often results in an advanced diagnosis. Hence, it is crucial to explore novel biomarkers to improve the clinical outcome of HNSCC patients.<h4>Methods</h4>We downloaded RNA-seq data consisting of 502 HNSCC tissues and 44 normal tissues from the TCGA database, and lncRNA genomic sequence information was downloaded from the GENECODE database f ...[more]

PMID: 35794984

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Dataset Information

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation.

Background

Methods

Results

Conclusion

Publications

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation.

Similar Datasets

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