Characterization of immune landscape in papillary thyroid cancer reveals distinct tumor immunogenicity and implications for immunotherapy

Abstract

Although the vast majority of patients with papillary thyroid cancer (PTC) have a favorable prognosis when conventional treatments are implemented, local recurrence and distant metastasis of advanced PTCs still hamper the survival and clinical management in certain patients. As immune checkpoint blockade (ICB) therapy achieves a great success in some advanced cancers, we aimed to investigate the immune landscape in PTC and its potential implications for prognosis and immunotherapy. In this study, different algorithms were conducted to estimate immune infiltration in PTC samples. A series of bioinformatic and machine learning approaches were performed to identify PTC-specific immune-related genes (IRGs) and distinct immune clusters. Differences in intrinsic tumor immunogenicity and potential immunotherapy response were observed between distinct immune clusters. A prognostic immune-related signature (IRS) was established to predict progression-free survival (PFS). IRS exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, an integrated survival decision tree and a scoring nomogram were constructed to improve prognostic stratification and predictive accuracy for individual patients. In addition, altered pathways, mutational patterns, and potential applicable drugs were analyzed in different immune-related risk groups. Our study gained some insight into the immune landscape of PTC, and provided some useful clues for introducing immune-based molecular classification into risk stratification and guiding ICB decision-making.

APOBEC-mediated mutagenesis is a favorable predictor of prognosis and immunotherapy for bladder cancer patients: evidence from pan-cancer analysis and multiple databases

“…Functional enrichment analysis of DEGs was performed using R package “clusterProfiler”. Protein-protein interaction (PPI) network which depicts the interactions among representative DEGs was generated using R package “STRINGdb” and “ggraph” as previously reported 21 .…”

“…Using R package “coxph”, hazard ratio and p value were calculated for each DEG, and candidate genes with p < 0.01 were further submitted to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis as previously reported 20 , 21 . LASSO regularization adds a penalty parameter (λ) to Cox regression model, and this action can lead to zero coefficients, i.e.…”

Papillary Thyroid Cancer Prognosis: An Evolving Field

“…Several experimental and clinical data suggest that other molecules may have a role in the prognostic stratification of TC patients. These include immune checkpoint factors and immune-related signature (IRS), estrogen receptors, Vitamin D, tumor angiogenic microenvironment, circular RNAs, long noncoding RNAs, and the angiotensin converting enzymes ACE and ACE2 [ 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 ]. However, all of them are awaiting validation in large case studies.…”

Identification of a Tumor Immunological Phenotype-Related Gene Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Hepatocellular Carcinoma

“…To explore the somatic mutations regarding TIPRGPI, the “maftools” R package was used to depict the waterfall plots manifesting the mutation landscape for the high- and low-risk groups of HCC patients. TMB values reflecting total mutation numbers for each HCC patient were calculated with non-synonymous mutations using 38MB as the estimate of the exome size ( 44 , 45 ). Somatic copy number alterations between the two different risk groups were investigated via the GISTIC2.0 algorithm.…”