RESUMO
Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous cancer with high morbidity and mortality. Triggering the programmed cell death (PCD) to enhance the anti-tumor therapies is being applied in multiple cancers. However, the limited understanding of genetic heterogeneity in HNSCC severely hampers the clinical efficacy. We systematically analyzed 14 types of PCD in HNSCC from The Cancer Genome Atlas (TCGA). We utilized ssGSEA to calculate the PCD scores and classify patients into two clusters. Subsequently, we displayed the genomic alteration landscape to unravel the significant differences in copy number alterations and gene mutations. Furthermore, we calculated the IC50 values of targeted drugs to predict the differences in sensitivity. To identify the immune-related prognostic types, we comprehensively estimated the relationship between immune indicators and all prognostic PCD in three datasets (TCGA, GSE65858, GSE41613). Finally, 7 regulators were filtered. Subsequently, we integrated 10 machine learning algorithms and 101 algorithm combinations to test the clinical predictive efficacy. Using WGCNA as a basis, we built a weighted co-expression network to identify modules involved in the immune landscape with different colors. Meanwhile, our results indicated that blue and red modules containing crucial regulators closely related to the CD4+, CD8+ T cells, TMB or PD-L1. FCGR2A from blue module, CSF2, INHBA, and THBS1 from the red module were determined. After verifying in vivo experiments, FCGR2A was identified as hub gene. In conclusion, our findings suggest a potential role of PCD in HNSCC, offering new insights into effective immunotherapy and anti-tumor therapies in HNSCC.
RESUMO
Background: Cancer associated fibroblasts (CAF), an important cancer-promoting and immunosuppressive component of the tumor immune microenvironment (TIME), have recently been found to infiltrate adult diffuse highest-grade gliomas (ADHGG) (gliomas of grade IV). Methods: Gene expression and clinical data of ADHGG patients were obtained from the CGGA and TCGA databases. Consensus clustering was used to identify CAF subtypes based on CAF key genes acquired from single-cell omics and spatial transcriptomomics. CIBERSORT, ssGSEA, MCPcounter, and ESTIMATE analyses were used to assess the TIME of GBM. Survival analysis, drug sensitivity analysis, TCIA database, TIDE and cMap algorithms were used to compare the prognosis and treatment response between patients with different CAF subtypes. An artificial neural network (ANN) model based on random forest was constructed to exactly identify CAF subtypes, which was validated in a real-world patient cohort of ADHGG. Results: Consensus clustering classified ADHGG into two CAF subtypes. Compared with subtype B, patients with ADHGG subtype A had a poorer prognosis, worse responsiveness to immunotherapy and radiotherapy, higher CAF infiltration in TIME, but higher sensitivity to temozolomide. Furthermore, patients with subtype A had a much lower proportion of IDH mutations. Finally, the ANN model based on five genes (COL3A1, COL1A2, CD248, FN1, and COL1A1) could exactly discriminate CAF subtypes, and the validation of the real-world cohort indicated consistent results with the bioinformatics analyses. Conclusion: This study revealed a novel CAF subtype to distinguish ADHGG patients with different prognosis and treatment responsiveness, which may be helpful for accurate clinical decision-making of ADHGG.