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

Abstract

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.

Biological Roles and Pathogenic Mechanisms of LncRNA MIR4435-2HG in Cancer: A Comprehensive Review

“…Individuals with elevated levels of MIR4435-2HG demonstrate significantly worse prognosis in contrast to those with lower levels, indicating the potential of MIR4435-2HG as a promising biomarker for head and neck squamous cell carcinoma. This biomarker can be utilized for prognosis prediction and precision medicine [46]. These investigations open up novel avenues and potentialities for the treatment and control of head and neck squamous cell carcinoma.…”

“…In-depth research into its mechanisms is expected to provide new targets and strategies for tumor immunotherapy. Furthermore, MIR4435-2HG may be involved in certain cell death processes, such as ferroptosis, pyroptosis, and necroptosis [46,116,117]. MIR4435-2HG potentially participates in cell death processes by regulating specific signaling pathways, gene expression, or cellular signaling molecules.…”

A five necroptosis-related lncRNA signature predicts the prognosis of bladder cancer and identifies hot or cold tumors

“…With the increasing application of immunotherapy in BC, how to identify hot and cold tumors is very important. [ 16 ] However, a convenient but accurate method for distinguishing tumors is still lacking. [ 17 ] Therefore, the establishment of a necroptosis-related lncRNA model to accurately distinguish tumors and predict the prognosis of BC is very important to guide clinical diagnosis and treatment.…”