Genetic polymorphisms of FCGR2A, ORAI1 and CD40 are associated with risk of lung cancer

The Tumor Stroma of Squamous Cell Carcinoma: A Complex Environment That Fuels Cancer Progression

The tumor microenvironment (TME), a complex assembly of cellular and extracellular matrix (ECM) components, plays a crucial role in driving tumor progression, shaping treatment responses, and influencing metastasis. This narrative review focuses on the cutaneous squamous cell carcinoma (cSCC) tumor stroma, highlighting its key constituents and their dynamic contributions. We examine how significant changes within the cSCC ECM-specifically, alterations in fibronectin, hyaluronic acid, laminins, proteoglycans, and collagens-promote cancer progression, metastasis, and drug resistance. The cellular composition of the cSCC TME is also explored, detailing the intricate interplay of cancer-associated fibroblasts (CAFs), mesenchymal stem cells (MSCs), endothelial cells, pericytes, adipocytes, and various immune cell populations. These diverse players modulate tumor development, angiogenesis, and immune responses. Finally, we emphasize the TME's potential as a therapeutic target. Emerging strategies discussed in this review include harnessing the immune system (adoptive cell transfer, checkpoint blockade), hindering tumor angiogenesis, disrupting CAF activity, and manipulating ECM components. These approaches underscore the vital role that deciphering TME interactions plays in advancing cSCC therapy. Further research illuminating these complex relationships will uncover new avenues for developing more effective treatments for cSCC.

Multi-omics comprehensive analyses of programmed cell death patterns to regulate the immune characteristics of head and neck squamous cell carcinoma

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.

Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing

Background

To construct a predictive model of immunotherapy efficacy for patients with lung squamous cell carcinoma (LUSC) based on the degree of tumor-infiltrating immune cells (TIIC) in the tumor microenvironment (TME).

Methods

The data of 501 patients with LUSC in the TCGA database were used as a training set, and grouped using non-negative matrix factorization (NMF) based on the degree of TIIC assessed by single-sample gene set enrichment analysis (GSEA). Two data sets (GSE126044 and GSE135222) were used as validation sets. Genes screened for modeling by least absolute shrinkage and selection operator (LASSO) regression and used to construct a model based on immunophenotyping score (IPTS). RNA extraction and qPCR were performed to validate the prognostic value of IPTS in our independent LUSC cohort. The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy. Kaplan–Meier survival curve analysis was performed to evaluate the prognostic predictive ability. Correlation analysis and enrichment analysis were used to explore the potential mechanism of IPTS molecular typing involved in predicting the immunotherapy efficacy for patients with LUSC.

Results

The training set was divided into a low immune cell infiltration type (C1) and a high immune cell infiltration type (C2) by NMF typing, and the IPTS molecular typing based on the 17-gene model could replace the results of the NMF typing. The area under the ROC curve (AUC) was 0.82. In both validation sets, the IPTS of patients who responded to immunotherapy were significantly higher than those who did not respond to immunotherapy (P = 0.0032 and P = 0.0451), whereas the AUC was 0.95 (95% CI = 1.00–0.84) and 0.77 (95% CI = 0.58–0.96), respectively. In our independent cohort, we validated its ability to predict the response to cancer immunotherapy, for the AUC was 0.88 (95% CI = 1.00–0.66). GSEA suggested that the high IPTS group was mainly involved in immune-related signaling pathways.

Conclusions

IPTS molecular typing based on the degree of TIIC in the TME could well predict the efficacy of immunotherapy in patients with LUSC with a certain prognostic value.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03565-7.