Background Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

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Human papillomavirus (HPV) infects squamous epithelium and is a major cause of cervical cancer (CC) and a subset of head and neck cancer (HNC). Virus-induced tumourigenesis, molecular alterations, and related prognostic markers are expected to be similar between the two cancers, but they remain poorly understood. We present integrated molecular analysis of HPV-associated tumours from TCGA and GEO databases and identify prognostic biomarkers. Analysis of gene expression profiles identified common upregulated genes and pathways of DNA replication and repair in the HPV-associated tumours. We established 34 prognostic gene signatures with universal cut-off value in TCGA-CC using Elastic Net Cox regression analysis. We were externally validated in TCGA-HNC and several GEO datasets, and demonstrated prognostic power in HPV-associated HNC, but not in HPV-negative cancers. The HPV-related prognostic and predictive indicator did not discriminate other cancers, except bladder urothelial carcinoma. These results identify and completely validate a highly selective prognostic system and its cross-usefulness in HPV-associated cancers, regardless of the tumour’s anatomical subsite. Importance Persistent infection with high-risk HPV interferes with cell function regulation and causes cell mutations, which accumulate over the long term and eventually develop into cancer. Results of pathway enrichment analysis presumably showed that accumulation of intracellular damage during the chronic HPV infected state. We used highly advanced statistical method to identify the most appropriate genes and coefficients and developed the HPPI risk scoring system. We applied same cut-off value to training and validation sets and demonstrated good prognostic performance in both data sets, and confirmed a consistent trend in external validation. Moreover, HPPI presented significant validation result of bladder cancer suspected to be related to HPV. This suggested that our risk scoring system based on the prognostic gene signature could play an important role in the development of treatment strategies for patients with HPV-related cancer.