Comprehensive characterization of the tumor microenvironment for assessing immunotherapy outcome in patients with head and neck squamous cell carcinoma

Construction of a prognosis model of head and neck squamous cell carcinoma pyroptosis and an analysis of immuno-phenotyping based on bioinformatics

Background

Head and neck squamous cell carcinoma (HNSCC) is currently the sixth most common cancer worldwide, and its prevalence and recurrence rates are gradually increasing. To study the relationship between HNSCC and cell pyroptosis and provide new treatment options for HNSCC, a prognostic model of pyroptosis-related genes (PRGs) was established to predict the prognosis of patients with HNSCC, and an immune correlation analysis was performed.

Methods

A total of 53 PRGs were selected. We comprehensively analyzed the role of these PRGs in HNSCC through multiple omics data-set integration. We then identified two different molecular subtypes and found that changes in multi-layer PRGs were associated with clinicopathological characteristics, prognosis, and tumor microenvironment cell-infiltration characteristics in patients. Next, prognostic models were generated for nine PRGs; that is, cytotoxic T lymphocyte antigen 4 (CTLA4), V-set and immunoglobulin domain containing 4 (VSIG4), heparin-binding-epidermal growth factor (HBEGF), aquaporin-1 (AQP1), sodium channel epithelial 1 subunit delta (SCNN1D), argininosuccinate synthase 1 (ASS1), family with sequence similarity 83 member (FAM83), cyclin dependent kinase inhibitor 2A (CDKN2A), and serine protease inhibitor Kazal 6 (SPINK6). Finally, a risk-score model was constructed, and the Kaplan-Meier method was used to evaluate overall survival. In addition, the immune environment and drug sensitivity were analyzed.

Results

This study showed that pyroptosis is closely related to HNSCC. The scores generated by the risk markers based on the new nine PRGs were identified as independent risk factors for predicting HNSCC. The differentially expressed genes between the low- and high-risk groups were further found to be related to the tumor immune cells and pathways. In addition, the risk score was found to be significantly correlated with chemosensitivity.

Conclusions

Our comprehensive analysis of PRGs revealed their potential role in the tumor immune microenvironment, clinicopathological characteristics, and prognosis. These findings may improve our understanding of pyroptosis in HNSCC and may provide new ideas for evaluating prognosis and developing more effective immunotherapy strategies.

Expanding the role of combined immunochemotherapy and immunoradiotherapy in the management of head and neck cancer (Review)

Immunotherapy has become one of the most promising approaches in tumor therapy, and there are numerous associated clinical trials in China. As an immunosuppressive tumor, head and neck squamous cell carcinoma (HNSCC) carries a high mutation burden, making immune checkpoint inhibitors promising candidates in this field due to their unique mechanism of action. The present review outlines a comprehensive multidisciplinary cancer treatment approach and elaborates on how combining immunochemotherapy and immunoradiotherapy guidelines could enhance clinical efficacy in patients with HNSCC. Furthermore, the present review explores the immunology of HNSCC, current immunotherapeutic strategies to enhance antitumor activity, ongoing clinical trials and the future direction of the current immune landscape in HNSCC. Advanced-stage HNSCC presents with a poor prognosis, low survival rates and minimal improvement in patient survival trends over time. Understanding the potential of immunotherapy and ways to combine it with surgery, chemotherapy and radiotherapy confers good prospects for the management of human papillomavirus (HPV)-positive HNSCC, as well as other HPV-positive malignancies. Understanding the immune system and its effect on HNSCC progression and metastasis will help to uncover novel biomarkers for the selection of patients and to enhance the efficacy of treatments. Further research on why current immune checkpoint inhibitors and targeted drugs are only effective for some patients in the clinic is needed; therefore, further research is required to improve the overall survival of affected patients.

Construction and analysis of a conjunctive diagnostic model of HNSCC with random forest and artificial neural network

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor that is highly aggressive and ranks fifth among the most common cancers worldwide. Although, the researches that attempted to construct a diagnostic model were deficient in HNSCC. Currently, the gold standard for diagnosing head and neck tumors is pathology, but this requires a traumatic biopsy. There is still a lack of a noninvasive test for such a high-incidence tumor. In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized. The data of HNSCC gene expression was accessed from Gene Expression Omnibus (GEO) database; we selected three datasets totally, and we combined 2 datasets (GSE6631 and GSE55547) for screening differentially expressed genes (DEGs) and chose another dataset (GSE13399) for validation. Firstly, the 6 DEGs (CRISP3, SPINK5, KRT4, MMP1, MAL, SPP1) were screened by RF. Subsequently, ANN was applied to calculate the weights of 6 genes. Besides, we created a diagnostic model and nominated it as neuralHNSCC, and the performance of neuralHNSCC by area under curve (AUC) was verified using another dataset. Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort. Furthermore, we used the Cell-type Identification using Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to investigate the difference in immune cell infiltration between HNSCC and normal tissues initially. The selected 6 DEGs and the constructed novel diagnostic model of HNSCC would make contributions to the diagnosis.

Novel RNA N6-methyladenosine regulator related signature for predicting clinical and immunological characteristics in breast cancer

BACKGROUND

Epigenetic mechanismshave been reported to involve in shaping tumor immune microenvironment (TME). However, the role of RNA N6-methyladenosine (m6A) modification in breast cancerhas not been fully explored.

METHODS

Based on m6A modification and TME infiltration characteristics of 2249 breast cancer patients, we comprehensively correlated m6A modification with immune landscapeby screeningcandidate genes, function analysis and constructing m6Asignatures. Principal component analysis was used to establish the m6Ascore. Both LASSO and Cox regression analyses were used to evaluate its prognostic value.Functional assays and immunohistochemistry were used to evaluate the expression of m6A regulators and immune cell infiltration.

RESULTS

Based on the dysregulated expression of m6A, three distinct clusters were identified that displayed diverse types of tumour-associated TME cell infiltration in breast cancer.Gene signatures, stromal activity, and clinical prognosis were assessed by the m6Ascore. m6Ascore could function as a biomarker for predicting the therapeutic response to targeted therapy and immunotherapy.The dysregulated expression of m6Aregulators mediated the immune cell infiltration in the TME.

CONCLUSION

Basedonthestudy,weidentified the signature and potential mechanism of m6AmodificationsthatmodifyTME cell infiltration. Thus, targeting m6A regulators may provide a promisingmethodoftreatingBRCA.

Identification of Novel Biomarkers for Predicting Prognosis and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma Based on ceRNA Network and Immune Infiltration Analysis

Objectives

Patients with head and neck squamous cell carcinoma (HNSCC) have poor prognosis and show poor responses to immune checkpoint (IC) inhibitor (ICI) therapy. Competing endogenous RNA (ceRNA) networks, tumor-infiltrating immune cells (TIICs), and ICIs may influence tumor prognosis and response rates to ICI therapy. This study is aimed at identifying prognostic and IC-related biomarkers and key TIIC signatures to improve prognosis and ICI therapy response in HNSCC patients.

Methods and Results

Ninety-five long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and 1746 mRNAs were identified using three independent methods. We constructed a ceRNA network and estimated the proportions of 22 immune cell subtypes. Ten ceRNAs were related to prognosis according to Kaplan–Meier analysis. Two risk signatures based, respectively, on nine ceRNAs (ANLN, CFL2, ITGA5, KDELC1, KIF23, NFIA, PTX3, RELT, and TMC7) and three immune cell types (naïve B cells, neutrophils, and regulatory T cells) via univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses could accurately and independently predict the prognosis of HNSCC patients. Key mRNAs in the ceRNA network were significantly correlated with naïve B cells and regulatory T cells and with stage, grade, and immune and molecular subtype. Eight IC genes exhibited higher expression in tumor tissues and were correlated with eight key mRNAs in the ceRNA network in HNSCC patients with different HPV statuses according to coexpression and TIMER 2.0 analyses. Most drugs were effective in association with expression of these key signatures (ANLN, CFL2, ITGA5, KIF23, NFIA, PTX3, RELT, and TMC7) based on GSCALite analysis. The prognostic value of key biomarkers and associations between key ceRNAs and IC genes were validated using online databases. Eight key ceRNAs were confirmed to predict response to ICI in other cancers based on TIDE analysis.

Conclusions

We constructed two risk signatures to accurately predict prognosis in HNSCC. Key IC-related signatures may be associated with response to ICI therapy. Combinations of ICIs with inhibitors of eight key mRNAs may improve survival outcomes of HNSCC patients.

Identification of Clinical and Tumor Microenvironment Characteristics of Hypoxia-Related Risk Signature in Lung Adenocarcinoma

Background: Lung cancer is the leading cause of cancer-related death globally. Hypoxia can suppress the activation of the tumor microenvironment (TME), which contributes to distant metastasis. However, the role of hypoxia-mediated TME in predicting the diagnosis and prognosis of lung adenocarcinoma (LUAD) patients remains unclear.

Methods: Both RNA and clinical data from the LUAD cohort were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Both univariate and multivariate Cox regression analyses were used to further screen prognosis-related hypoxia gene clusters. Time-dependent receiver operation characteristic (ROC) curves were established to evaluate the predictive sensitivity and specificity of the hypoxia-related risk signature. The characterization of gene set enrichment analysis (GSEA) and TME immune cell infiltration were further explored to identify hypoxia-related immune infiltration.

Results: Eight hypoxia-related genes (LDHA, DCN, PGK1, PFKP, FBP1, LOX, ENO3, and CXCR4) were identified and established to construct a hypoxia-related risk signature. The high-risk group showed a poor overall survival compared to that of the low-risk group in the TCGA and GSE68465 cohorts (p < 0.0001). The AUCs for 1-, 3-, and 5-year overall survival were 0.736 vs. 0.741, 0.656 vs. 0.737, and 0.628 vs. 0.649, respectively. The high-risk group was associated with immunosuppression in the TME.

Conclusion: The hypoxia-related risk signature may represent an independent biomarker that can differentiate the characteristics of TME immune cell infiltration and predict the prognosis of LUAD.

Molecular Characterization of the Clinical and Tumor Immune Microenvironment Signature of 5-methylcytosine-Related Regulators in non-small Cell Lung Cancer

Background: DNA methylation is an important epigenetic modification, among which 5-methylcytosine methylation (5mC) is generally associated with tumorigenesis. Nonetheless, the potential roles of 5mC regulators in the tumor microenvironment (TME) remain unclear.

Methods: The 5mC modification patterns of 1,374 lung adenocarcinoma samples were analyzed systematically. The correlation between the 5mC modification and tumor microenvironment cell infiltration was further assessed. The 5mCscore was developed to evaluate tumor mutation burden, immune check-point inhibitor response, and the clinical prognosis of individual tumors.

Results: Three 5mC modification patterns were established based on the clinical characteristics of 21 5mC regulators. According to the differential expression of 5mC regulators, three distinct 5mC gene cluster were also identified, which showed distinct TME immune cell infiltration patterns and clinical prognoses. The 5mCscore was constructed to evaluate the tumor mutation burden, immune check-point inhibitor response, and prognosis characteristics. We found that patients with a low 5mCscore had significant immune cell infiltration and increased clinical benefit.

Conclusion: This study indicated that the 5mC modification is involved in regulating TME infiltration remodeling. Targeting 5mC modification regulators might be a novel strategy to treat lung cancer.

The Effect of Smoking on the Immune Microenvironment and Immunogenicity and Its Relationship With the Prognosis of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer

Background: The emergence of immune checkpoint inhibitors (ICIs) has opened a new chapter for the treatment of non-small cell lung cancer (NSCLC), and the best beneficiaries of ICI treatment are still being explored. Smoking status has been repeatedly confirmed to affect the efficacy of ICIs in NSCLC patients, but the specific mechanism is still unclear.

Methods: We performed analysis on the Memorial Sloan Kettering Cancer Center (MSKCC) clinical NSCLC cohort receiving ICI treatment, The Cancer Genome Atlas (TCGA) Pan-Lung Cancer cohort, and Gene Expression Omnibus (GEO) database GSE41271 lung cancer cohort that did not receive ICI treatment, including survival prognosis, gene mutation, copy number variation, immunogenicity, and immune microenvironment, and explored the impact of smoking status on the prognosis of NSCLC patients treated with ICIs and possible mechanism. In addition, 8 fresh NSCLC surgical tissue samples were collected for mass cytometry (CyTOF) experiments to further characterize the immune characteristics and verify the mechanism.

Result: Through the analysis of the clinical data of the NSCLC cohort treated with ICIs in MSKCC, it was found that the smokers in NSCLC receiving ICI treatment had a longer progression-free survival (HR: 0.69, 95% CI: 0.49–0.97, p = 0.031) than those who never smoked. Further analysis of the TCGA and GEO validation cohorts found that the differences in prognosis between different groups may be related to the smoking group’s higher immunogenicity, higher gene mutations, and stronger immune microenvironment. The results of the CyTOF experiment further found that the immune microenvironment of smoking group was characterized by higher expression of immune positive regulatory chemokine, and higher abundance of immune activated cells, including follicular helper CD4⁺ T cells, gamma delta CD4⁺ T cells, activated DC, and activated CD8⁺ T cells. In contrast, the immune microenvironment of non-smoking group was significantly enriched for immunosuppressive related cells, including regulatory T cells and M2 macrophages. Finally, we also found highly enriched CD45RA^highCD4⁺ T cells and CD45RA^highCD8⁺ T cells in the non-smoking group.

Conclusion: Our research results suggest that among NSCLC patients receiving ICI treatment, the stronger immunogenicity and activated immune microenvironment of the smoking group make their prognosis better.