Head and Neck Squamous Cell Carcinoma Subtypes Based on Immunologic and Hallmark Gene Sets in Tumor and Non-tumor Tissues

Abstract

BackgroundNon-tumor tissue has a significant impact on the prognosis of head and neck squamous cell carcinoma (HNSCC). Previous studies for HNSCC have mainly focused on tumor tissue, greatly neglecting the role of non-tumor tissue. This study aimed to identify HNSCC subtypes and prognostic gene sets based on activity changes of immunologic and hallmark gene sets in tumor and adjacent non-tumor tissues to improve patient prognosis.MethodsIn the study, we used gene set variation analysis (GSVA) to estimate the relative enrichment of gene sets over the sample population, and identified relevant subtypes of HNSCC by Cox regression analysis and the non-negative matrix factorization (NMF) method. The representative gene sets were identified by calculating the differential enrichment score of gene sets between each of the two subgroups, intersecting them, and screening them using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen out potential prognostic gene sets and establish a risk model. Finally, genes encompassed in each prognostic gene set were obtained and subjected to enrichment analysis and protein–protein interaction (PPI) in tumor and non-tumor tissues.ResultsWe identified three subtypes of HNSCC based on gene sets in tumor and non-tumor tissues, and patients with subtype 1 had a higher survival rate than subtypes 2 and 3. The subtypes were related to the survival status, pathological stage, and T stage of HNSCC patients. In total 450 differentially gene sets and 39 representative gene sets were obtained by calculating the differential enrichment score of gene sets between each of the two subgroups, intersecting them, and screening them using univariate Cox regression analysis. The prognostic model was constructed by LASSO regression analysis, including five prognostic gene sets. Kaplan-Meier analysis indicated that different risk groups and the five prognostic gene sets were associated with survival status in the model. Finally, enrichment analysis and PPI indicated that non-tumor and tumor tissues affect the prognosis of HNSCC patients in different ways.ConclusionIn conclusion, we provide a novel insight for rational treatment strategies and precise prognostic assessments based on tumor and adjacent non-tumor tissues, suggesting that more emphasis should be placed on changes in adjacent non-tumor and tumor tissues, rather than just the tumor itself.

Identification of molecular classification and gene signature for predicting prognosis and immunotherapy response in HNSCC using cell differentiation trajectories

“…During the malignant transformation of primary cells, new genetic mutations and molecular phenotypes emerge with each generation of offspring, leading to differences in clinical characteristics, therapeutic response, drug resistance, and prognosis of patients 17 – 19 . In the research, HNSCC cells in different differentiation phases were divided into four subsets on the basis of cell trajectory analysis.…”

“…In the study, we identified 4 molecular subtypes of HNSCC by cell differentiation trajectory, developed a signature based on differentially expressed prognostic DRGs, and constructed a nomogram integrating the signature and prognostic clinical features for predicting clinical outcomes and response to immunotherapy. During the malignant transformation of primary cells, new genetic mutations and molecular phenotypes emerge with each generation of offspring, leading to differences in clinical characteristics, therapeutic response, drug resistance, and prognosis of patients [17][18][19] . In the research, HNSCC cells in different differentiation phases were divided into four subsets on the basis of cell trajectory analysis.…”

Multiple programmed cell death patterns and immune landscapes in bladder cancer: Evidence based on machine learning and multi‐cohorts

“…Based on the obtained prognostic PCDRGs, consensus unsupervised clustering analysis were employed with the K ‐means algorithm using the R package “ConsensusClusterPlus.” 40 Criteria for selecting K values were small intra group differences, large inter group differences, and ensuring a sizable sample size in each group 41 . Survival disparities between different PCD clusters were analyzed using survival analysis and a chi‐squared test.…”

“…40 Criteria for selecting K values were small intra group differences, large inter group differences, and ensuring a sizable sample size in each group. 41 Survival disparities between different PCD clusters were analyzed using survival analysis and a chi-squared test. The distribution of different PCD clusters were analyzed using PCA, tSNE, and BLCAAP.…”

Gastric Cancer Subtypes in Tumour and Nontumour Tissues by Immunologic and Hallmark Gene Sets

“…Moreover, the tumour cells scattered in blood are likely to pass through the blood vessels and fix again in the residual stomach, leading to the recurrence of GC. All these circumstances indicate that immune and molecular changes in nontumour tissues have a great influence in the prognosis of GC patients [ 14 , 15 ].…”