Head and neck squamous cell carcinoma (HNSCC) is a common malignant cancer that accounts for 5–10% of all cancers. This study aimed to identify essential genes associated with the prognosis of HNSCC and construct a powerful prognostic model for the risk assessment of HNSCC. RNAseq expression profile data for the patients with HNSCC were obtained from the TCGA database (GEO). A total of 500 samples with full clinical following-up were randomly divided into a training set and a validation set. The training set was used to screen for differentially expressed lncRNAs. Single-factor survival analysis was performed to obtain lncRNAs that associated with prognosis. A robust likelihood-based survival model was constructed to identify the lncRNAs that are essential for the prognosis of HNSCC. A co-expression network between genes and lncRNAs was also constructed to identify lncRNAs co-expressed with genes to serve as the final signature lncRNAs for prognosis. Finally, the prognostic effect of the signature lncRNAs was tested by multi-factor survival analysis and a scoring model for the prognosis of HNSCC was constructed. Moreover, the results of the validation set and the relative expression levels of the signature lncRNAs in the tumour and the adjacent tissue were consistent with the results of the training set. The 5 lncRNAs were distributed among 3 expression modules. Further KEGG pathway enrichment analysis showed that these 3 co-expressed modules participate in different pathways, and many of these pathways are associated with the development and progression of disease. Therefore, we proposed that the 5 validated lncRNAs can be used to predict the prognosis of HNSCC patients and can be applied in postoperative treatment and follow-up.

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Cervical cancer is one of the leading female health‐killers among all types of malignancies globally. Human papillomavirus infection combined with genetic and epigenetic alterations have been indicated to be closely associated with the pathogenesis, progression, and malignant transformation of cervical cancer. Notably, during the complex tumorigenesis process, a series of DNA methylations occurs early and is the most frequent molecular behavior. In this study, to exploit the specific DNA methylation sites influencing the prognosis of patients with cervical cancer, 275 samples were downloaded from The Cancer Genome Atlas database and further analyzed. As a result, 1253 CpGs were found to have a significant correlation with patient prognosis and were further selected for the consistent clustering of samples into six subgroups. Specifically, the samples in every subgroup were different regarding the following: race, age, tumor stage, receptor status, histological type, metastasis status, and patient prognosis. In addition, we calculated the levels of methylation sites in all subgroups, with 79 methylation sites (corresponding to 81 genes) screened as the intrasubgroup‐specific methylation sites. Moreover, signaling pathway enrichment analysis was conducted on the genes of the corresponding promoter regions of the above‐described specific methylation sites, revealing that these genes were enriched in biological pathways closely associated with tumors, such as the cyclic guanosine monophosphate–dependent protein kinase and focal adhesion signaling pathways. Finally, the least absolute shrinkage and selection operator algorithm was employed to establish a prognostic prediction model for cervical cancer patients, with training and test sets used for testing and validation, respectively. In summary, the specific DNA methylation site–based classification is able to reflect the heterogeneity of cervical cancer tissue, contributing to the development of personalized therapy and the accurate prediction of patient prognosis.

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