Identification of immune-related lncRNA panel for predicting immune checkpoint blockade and prognosis in head and neck squamous cell carcinoma.

Immunotherapy 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. In 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. In 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. Our 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.

Li Q ，Shen Z ，Shen Y ，Deng H ，Shen Y ，Wang J ，Zhan G ，Zhou C ... -  《-》

A novel immune-related long non-coding RNA signature improves the prognosis prediction in the context of head and neck squamous cell carcinoma.

Chen L ，Cai Z ，Lyu K ，Cai Z ，Lei W ... -  《-》

Necrotic related-lncRNAs: Prediction of prognosis and differentiation between cold and hot tumors in head and neck squamous cell carcinoma.

Treatment of head and neck squamous cell carcinoma (HNSCC) is a substantial clinical challenge due to the high local recurrence rate and chemotherapeutic resistance. This project seeks to identify new potential biomarkers of prognosis prediction and precision medicine to improve this condition. A synthetic data matrix for RNA transcriptome datasets and relevant clinical information on HNSCC and normal tissues was downloaded from the Genotypic Tissue Expression Project and The Cancer Genome Atlas (TCGA). The necrosis-associated long-chain noncoding RNAs (lncRNAs) were identified by Pearson correlation analysis. Then 8-necrotic-lncRNA models in the training, testing and entire sets were established through univariate Cox (uni-Cox) regression and Lasso-Cox regression. Finally, the prognostic ability of the 8-necrotic-lncRNA model was evaluated via survival analysis, nomogram, Cox regression, clinicopathological correlation analysis, and receiver operating characteristic (ROC) curve. Gene enrichment analysis, principal component analysis, immune analysis and prediction of risk group semi-maximum inhibitory concentration (IC50) were also conducted. Correlations between characteristic risk score and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anti-cancer drug sensitivity were analyzed. Eight necrosis-associated lncRNAs (AC099850.3, AC243829.2, AL139095.4, SAP30L-AS1, C5orf66-AS1, LIN02084, LIN00996, MIR4435-2HG) were developed to improve the prognosis prediction of HNSCC patients. The risk score distribution, survival status, survival time, and relevant expression standards of these lncRNAs were compared between low- and high-risk groups in the training, testing and entire sets. Kaplan-Meier analysis showed the low-risk patients had significantly better prognosis. The ROC curves revealed the model had an acceptable predictive value in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 8 necrosis-associated lncRNAs were risk factors independent of various clinical parameters. We recombined the patients into 2 clusters through Consensus ClusterPlus R package according to the expressions of necrotic lncRNAs. Significant differences were found in immune cell infiltration, immune checkpoint molecules, and IC50 between clusters, suggesting these characteristics can be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. This risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.

Shi Y ，Zhang Y ，Zuo N ，Wang L ，Sun X ，Liang L ，Ju M ，Di X ... -  《-》

A pyroptosis-related lncRNA signature predicts prognosis and immune microenvironment in head and neck squamous cell carcinoma.

Pyroptosis, a type of regulated cell death controlled by the gasdermin family of proteins to form plasma membrane pores is known. Nonetheless, the function of pyroptosis-related long non-coding RNAs (lncRNAs) in this process still lacks exhaustive elucidation in head and neck squamous cell carcinoma (HNSCC). Other attributes encompassing the function of such lncRNAs in the immune microenvironment and potential prognosis for HNSCC are yet to see the light of the day. This work was designed to probe the possible prognostic worth of pyroptosis-related lncRNAs along with their impact on the immune microenvironment in the case of HNSCC. The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of HNSCC patients. Pearson correlation analysis was employed to scour for lncRNAs linked to pyroptosis based on 40 genes related to the process. Following the construction of a pyroptosis-related lncRNA signature employing univariate, Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, survival and nomogram analyses ensued to scrutinize the predictive value of the prognostic signature. The segregation of patients into two risk groups was done by the constructed pyroptosis-related-lncRNA signature (encompassing 14 lncRNAs). The prognosis was poorer for individuals of the high-risk group versus (vs.) the low-risk group with the risk score emerging as an independent prognostic factor by regression analyses. The accuracy of this signature was corroborated by Receiver operating characteristic curve (ROC) analysis with the three-years area under time-dependent ROC curve (AUC) reaching 0.767. Further analyses unveiled a conspicuous enrichment of immune-related pathways in the low-risk group. The high-risk group demonstrated an immunologically "cold" profile based on the immune cell infiltration landscape. The lncRNA signature encompassing 14 pyroptosis-related lncRNAs could be a prognostic marker for HNSCC, suggesting pyroptosis might be a promising therapeutic target in HNSCC.

Zhu W ，Ye Z ，Chen L ，Liang H ，Cai Q ... -  《-》

Construction of an m6A-related lncRNA pair prognostic signature and prediction of the immune landscape in head and neck squamous cell carcinoma.

Mounting evidence indicates that aberrantly expressed N6-methylandenosine (m6A) modification regulators and long noncoding RNA (lncRNA) influence the development of head and neck squamous cell carcinoma (HNSCC). However, the prognosis of m6A-related lncRNA (mrlncRNA) in HNSCC has not yet been evaluated. We retrieved transcriptome, somatic mutation, and clinical information from The Cancer Genome Atlas database and established a differently expressed mrlncRNA (DEmrlncRNA) pair signature based on least absolute shrinkage and selection operator Cox regression and multivariate Cox analyses. Each sample's risk score was computed premised on the signature, which accurately classified patients into low- and high-risk group by the cut-off point. The signature was evaluated from the perspective of survival, clinicopathological characteristics, tumor mutation burden (TMB), immune cell infiltration, efficacy of chemotherapeutics, tumor immune microenvironment, and immune checkpoint inhibitor (ICI)-related genes. 11 DEmrlncRNA pairs were identified and were used to construct the prediction signature. Kaplan-Meier plotter revealed a worse prognosis in high-risk patients over low-risk patients (log rank p < 0.001). According to multivariate Cox regression analysis, the hazard ratio of the risk score and 95% confidence interval of 1.722 and (1.488-1.992) (p < 0.001) were obtained. Furthermore, an increased risk score was associated with aggressive clinicopathological features, specific tumor immune infiltration status, increased expression of ICI-related genes, higher TMB, and higher chemotherapeutics sensitivity (all p < 0.05). This research demonstrated that the signature premised on DEmrlncRNA pairs was an efficient independent prognostic indicator and may provide a rationale for research on immunotherapeutic and chemotherapeutics strategies for HNSCC patients.

Zhou C ，Wang S ，Shen Z ，Shen Y ，Li Q ，Shen Y ，Huang J ，Deng H ，Ye D ，Zhan G ，Li J ... -  《-》