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Systematic Analysis and Identification of Dysregulated Panel lncRNAs Contributing to Poor Prognosis in Head-Neck Cancer

ABSTRACT: Head and neck cancer (HNC) is one of the most prevalent cancers worldwide, accounting for approximately 5% of all cancers. While the underlying molecules and their pathogenetic mechanisms in HNC have yet to be well elucidated, recent studies have shown that dysregulation of lncRNAs may disrupt the homeostasis of various biological pathways. However, the understanding of lncRNAs in HNC is still limited by the lack of expression profiling. In the present study, we employed a systematic strategy to identify a panel of lncRNA associated with HNC. A cancer-related lncRNA profile PCR array was screened to explore potential molecules specific for HNC. A total of 55 lncRNAs were found to be dysregulated in HNC cells when compared to normal keratinocytes. Further analysis of the prognostic significance using The Cancer Genome Atlas (TCGA) database revealed 15 lncRNAs highly correlated with overall survival in HNC patients. Additionally, clinical sample expression analysis of the TCGA-HNSC cohort revealed 16 highly dysregulated lncRNAs in HNC, resulting in a combined 31-lncRNA signature panel that could predict prognosis. Validation of these molecules confirmed the considerable level of altered expressions in HNC cells, with XIST, HOXA11-AS, TSIX, MALAT1, WT1-AS, and IPW being the most prominently dysregulated. We further selected a molecule from our panel (XIST) to confirm the validity of these lncRNAs in the regulation of cancer aggressiveness. Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses demonstrated that XIST participated in various cancer-related functions, including cell proliferation and metastasis. XIST silencing with the RNAi technique substantially reduced invasion and migration in several HNC cell lines. Thus, our study defined a 31-lncRNA panel as prognostic signatures in HNC. These perspective results provide a knowledge foundation for further application of these molecules in precision medicine.

SUBMITTER: Tang S

PROVIDER: S-EPMC8558550 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeImmunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first-line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non-coding RNAs (lncRNAs) impact every step of cancer immunity. Therefore, reliable immune-lncRNAs able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management.MethodsIn the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Atlas and identified differentially expressed immune-related lncRNAs (DEir-lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir-lncRNAs to construct optimal risk model. Low-risk and high-risk groups were classified based on the optimal cut-off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan-Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, and chemotherapeutic and immunotherapeutic efficacy between two groups.ResultsIn our study, we identified 256 Deir-lncRNAs in HNSCC. A total of 18 Deir-lncRNA pairs (consisting of 35 Deir-lncRNAs) were used to construct a risk model significantly associated with survival of HNSCC. Cox proportional hazard regression analysis confirmed that our risk model could be served as an independent prognostic indicator. Besides, HNSCC patients with low-risk score significantly enriched of CD8+ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity.ConclusionOur risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.

Project description:Background: Cuproptosis is a new type of cell death that induces protein toxic stress and eventually leads to cell death. Hence, regulating cuproptosis in tumor cells is a new therapeutic approach. However, studies on cuproptosis-related long noncoding RNA (lncRNA) in head and neck squamous cell carcinoma (HNSC) have not been found. This study aimed to explore the cuproptosis-related lncRNAs prognostic marker and their relationship to immune microenvironment in HNSC by using bioinformatics methods. Methods: RNA sequencing, genomic mutations, and clinical data of TCGA_HNSC were downloaded from The Cancer Genome Atlas. HNSC patients were randomly assigned to either a training group or a validation cohort. The least absolute shrinkage and selection operator Cox regression and multivariate Cox regression models were used to determine the prognostic model in the training cohort, and its independent prognostic effect was further confirmed in the validation and entire cohorts. Results: Based on previous literature, we collected 19 genes associated with cuproptosis. Afterward, 783 cuproptosis-related lncRNAs were obtained through coexpression. Cox model revealed and constructed eight cuproptosis-related lncRNAs prognostic marker (AL132800.1, AC090587.1, AC079160.1, AC011462.4, AL157888.1, GRHL3-AS1, SNHG16, and AC021148.2). Patients were divided into high- and low-risk groups based on the median risk score. The Kaplan–Meier survival curve revealed that the overall survival between the high- and low-risk groups was statistically significant. The receiver operating characteristic curve and principal component analysis demonstrated the accurate prognostic ability of the model. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor. In addition, we used multivariate Cox regression to establish a nomogram of the predictive power of prognostic markers. The tumor mutation burden showed significant differences between the high- and low-risk groups. HNSC patients in the high-risk group responded better to immunotherapy than those in the low-risk group. We also found that risk scores were significantly associated with drug sensitivity in HNSC. Conclusion: In summary, our study identified eight cuprotosis-related lncRNAs signature of HNSC as the prognostic predictor, which may be promising biomarkers for predicting the benefit of HNSC immunotherapy as well as drug sensitivity.

Project description:Background: Head and neck squamous carcinoma (HNSC) is one of the most common malignant tumors with high incidence and poor prognosis. Transmembrane emp24 structural domain (TMED) proteins are involved in protein transport and vesicle budding processes, which have implicated various malignancies' progression. However, the roles of TMEDs in HNSC, especially in terms of development and prognosis, have not been fully elucidated. Methods: We applied TIMER 2.0, UALCAN, GEPIA 2, Kaplan-Meier plotter, GEO, The Human Protein Atlas (HPA), cBioPortal, Linkedomics, Metascape, GRNdb, STRING, and Cytoscape to investigate the roles of TMED family members in HNSC. Results: Compared with normal tissues, the mRNA expression levels of TMED1/2/4/5/7/8/9/10 were significantly increased in the TCGA HNSC dataset. And we combined GEPIA 2 and Kaplan-Meier Plotter to select TMED2/9/10 with prognostic value. Then we detected the levels of mRNA in the GEO HNSC database and the protein expression in HPA. It was found that the mRNA and protein expression levels of TMED2/9/10 were increased in HNSC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that TMED2/9/10 and their co-expressed genes promoted the malignant behavior of tumors by participating in biological processes such as intracellular transferase complex, protein transport, focal adhesion, intracellular protein processing. Single-cell analysis and immune infiltration analysis suggested that immune responses of cancer-associated fibroblasts and endothelial cells might be associated with prognosis. Finally, the transcription factors-genes network and protein-protein functional interaction network pointed to genes such as X-box binding protein 1 (XBP1) and TMED7, which might cooperate with TMED2/9/10 to change the progression of HNSC. Conclusions: Our study implied that TMED2/9/10 and related genes mightjointly affect the prognosis of HNSC, providing specific clues for further experimental research, personalized diagnosis strategies, and targeted clinical therapy for HNSC.

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Systematic Analysis and Identification of Dysregulated Panel lncRNAs Contributing to Poor Prognosis in Head-Neck Cancer

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