Immune-related genes have prognostic significance in head and neck squamous cell carcinoma

The value of subcutaneous xenografts for individualised radiotherapy in HNSCC: Robust gene signature correlates with radiotherapy outcome in patients and xenografts

PURPOSE

To assess the robustness of prognostic biomarkers and molecular tumour subtypes developed for patients with head and neck squamous cell carcinoma (HNSCC) on cell-line derived HNSCC xenograft models, and to develop a novel biomarker signature by combining xenograft and patient datasets.

MATERIALS AND METHODS

Mice bearing xenografts (n = 59) of ten HNSCC cell lines and a retrospective, multicentre patient cohort (n = 242) of the German Cancer Consortium-Radiation Oncology Group (DKTK-ROG) were included. All patients received postoperative radiochemotherapy (PORT-C). Gene expression analysis was conducted using GeneChip Human Transcriptome Arrays. Xenografts were stratified based on their molecular subtypes and previously established gene classifiers. The dose to control 50 % of tumours (TCD50) was compared between these groups. Using differential gene expression analyses combining xenograft and patient data, a gene signature was developed to define risk groups for the primary endpoint loco-regional control (LRC).

RESULTS

Tumours of mesenchymal subtype were characterized by a higher TCD50 (xenografts, p < 0.001) and lower LRC (patients, p < 0.001) compared to the other subtypes. Similar to previously published patient data, hypoxia- and radioresistance-related gene signatures were associated with high TCD50 values. A 2-gene signature (FN1, SERPINE1) was developed that was prognostic for TCD50 (xenografts, p < 0.001) and for patient outcome in independent validation (LRC: p = 0.007).

CONCLUSION

Genetic prognosticators of outcome for patients after PORT-C and subcutaneous xenografts after primary clinically relevant irradiation show similarity. The identified robust 2-gene signature may help to guide patient stratification, after prospective validation. Thus, xenografts remain a valuable resource for translational research towards the development of individualized radiotherapy.

Exploring the frontiers: tumor immune microenvironment and immunotherapy in head and neck squamous cell carcinoma

The global prevalence of head and neck malignancies positions them as the sixth most common form of cancer, with the head and neck squamous cell carcinoma (HNSCC) representing the predominant histological subtype. Despite advancements in multidisciplinary approaches and molecular targeted therapies, the therapeutic outcomes for HNSCC have only marginally improved, particularly in cases of recurrent or metastatic HNSCC (R/MHNSCC). This situation underscores the critical necessity for the development of innovative therapeutic strategies. Such strategies are essential not only to enhance the efficacy of HNSCC treatment but also to minimize the incidence of associated complications, thus improving overall patient prognosis. Cancer immunotherapy represents a cutting-edge cancer treatment that leverages the immune system for targeting and destroying cancer cells. It's applied to multiple cancers, including melanoma and lung cancer, offering precision, adaptability, and the potential for long-lasting remission through immune memory. It is observed that while HNSCC patients responsive to immunotherapy often experience prolonged therapeutic benefits, only a limited subset demonstrates such responsiveness. Additionally, significant clinical challenges remain, including the development of resistance to immunotherapy. The biological characteristics, dynamic inhibitory changes, and heterogeneity of the tumor microenvironment (TME) in HNSCC play critical roles in its pathogenesis, immune evasion, and therapeutic resistance. This review aims to elucidate the functions and mechanisms of anti-tumor immune cells and extracellular components within the HNSCC TME. It also introduces several immunosuppressive agents commonly utilized in HNSCC immunotherapy, examines factors influencing the effectiveness of these treatments, and provides a comprehensive summary of immunotherapeutic strategies relevant to HNSCC.

Identification of immune-related genes in the prognosis of head and neck cancer using a novel prognostic signature model

BACKGROUND

Increasing evidence indicates that the immune response plays a critical role in the development of head and neck cancer (HNC). We aimed to develop an immune-related gene signature and evaluate its prognostic value in patients with HNC.

METHODS

We retrieved an HNC cohort from The Cancer Genome Atlas database and divided the samples into high-risk and low-risk groups based on the median of the immune and stromal scores. We performed Venn and Cox analyses to identify the immune-related DEGs to use in our prognostic model. We evaluated the correlation between the model and immune-cell infiltration and validated the prognostic value of the model by applying it to 2 external HNC cohorts.

RESULTS

We identified 7 DEGs-CCR4, WDFY4, VCAM1, LYZ, VSIG4, XIRP1, and CMKLR1-to use in our prognostic model and validated the model by applying it to 2 external HNC cohorts. We found that risk scores based on the model could reflect the status of the tumor microenvironment and that VSIG4 might be associated with lymph node metastasis in HNC.

CONCLUSIONS

We developed a highly accurate immune-related prognostic 7-gene model in HNC predication, indicating that these 7 genes play critical roles in the tumor microenvironment.

PIK3C2A is a prognostic biomarker that is linked to immune infiltrates in kidney renal clear cell carcinoma

Background

Phosphoinositide 3-kinases (PI3Ks) are lipid enzymes that regulate a wide range of intracellular functions. In contrast to Class I and Class III PI3K, which have more detailed descriptions, Class II PI3K has only recently become the focus of functional research. PIK3C2A is a classical member of the PI3Ks class II. However, the role of PIK3C2A in cancer prognosis and progression remains unknown.

Methods

The expression pattern and prognostic significance of PIK3C2A in human malignancies were investigated using multiple datasets and scRNA-seq data. The PIK3C2A expression in renal clear cell carcinoma (KIRC) was then validated utilizing Western blot. The functional role of PIK3C2A in KIRC was assessed using combined function loss experiments with in vitro experiments. Furthermore, the correlation of PIK3C2A expression with tumor immunity was investigated in KIRC. The TCGA database was employed to investigate PIK3C2A functional networks.

Results

Significant decrease in PIK3C2A expression in KIRC, demonstrated that it potentially influences the prognosis of diverse tumors, particularly KIRC. In addition, PIK3C2A was significantly correlated with the T stage, M stage, pathologic stage, and histologic grade of KIRC. Nomogram models were constructed and used to predict patient survival based on the results of multivariate Cox regression analysis. PIK3C2A knockdown resulted in significantly increased KIRC cell proliferation. Of note, PIK3C2A expression demonstrated a significant correlation with the infiltrating levels of primary immune cells in KIRC.

Conclusion

These findings support the hypothesis that PIK3C2A is a novel biomarker for tumor progression and indicates dynamic shifts in immune infiltration in KIRC. Furthermore, aberrant PIK3C2A expression can influence the biological activity of cancer cells.

An Immune Feature-Based, Three-Gene Scoring System for Prognostic Prediction of Head-and-Neck Squamous Cell Carcinoma

Head-and-neck squamous cell carcinoma (HNSCC) is characterized by a high frequency of neck lymph node metastasis (LNM), a key prognostic factor. Therefore, identifying the biological processes during LNM of HNSCC has significant clinical implications for risk stratification. This study performed Gene Ontology enrichment analysis of differentially expressed genes between tumors with LNM and those without LNM and identified the involvement of immune response in the lymphatic metastasis of HNSCC. We further identified greater infiltrations of CD8⁺ T cells in tumors than in adjacent normal tissues through immunochemistry in the patient cohort (n = 62), indicating the involvement of CD8⁺ T cells in the antitumor immunity. Hierarchical clustering analysis was conducted to initially identify the candidate genes relevant to lymphocyte-mediated antitumor response. The candidate genes were applied to construct a LASSO Cox regression analysis model. Three genes were eventually screened out as progression-related differentially expressed candidates in HNSCC and a risk scoring system was established based on LASSO Cox regression model to predict the outcome in patients with HNSCC. The score was calculated using the formula: 0.0636 × CXCL11 - 0.4619 × CXCR3 + 0.2398 × CCR5. Patients with high scores had significantly worse overall survival than those with low scores (p < 0.001). The risk score showed good performance in characterizing tumor-infiltrating lymphocytes and provided a theoretical basis for stratifying patients receiving immune therapies. Additionally, a nomogram including the risk score, age, and TNM stage was constructed. The prediction model displayed marginally better discrimination ability and higher agreement in predicting the survival of patients with HNSCC compared with the TNM stage.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

Development and validation of a hypoxia-related gene pair signature to predict overall survival in head and neck squamous cell carcinoma

OBJECTIVE

Head and neck squamous cell carcinoma (HNSCC) are a highly aggressive tumor with an extremely poor prognosis. Thus, we aimed to develop and validate a robust prognostic signature that can estimate the prognosis for HNSCC.

METHODS

Data on gene expressions and clinical were downloaded from TCGA and GEO database. To develop the best prognosis signature, a LASSO Cox Regression model was employed. Time-dependent receiver-operating characteristic (ROC) was used to determine the best cut-off value. Patients were divided into high-risk and low-risk hypoxia groups according to cut-off value. Survival differences were evaluated by log-rank test, while multivariate analysis was performed by a Cox proportional hazards model.

RESULTS

A 17-HRGPs composed of 24 unique genes was constructed, which was significantly related to OS. In the TCGA and GEO datasets, patients in the high hypoxia risk group have a poor prognosis (TCGA: P < 0.001, GEO: P < 0.05). After adjusting for other clinicopathological parameters, the 17-HRGP signature was independent prognostic factors in patients with HNSCC (P < 0.05). Functional analysis revealed that mRNA binding, gene silencing by RNA, RNA binding involved in posttranscriptional gene silencing signaling pathway were enriched in the low-risk groups. For this model, C-index was 0.684, which was higher than that of many established risk models. Macrophages M0, Mast cells activated, NK cells resting, T cells CD4 memory resting, etc. were significantly higher in the high-risk group, and B cells memory, Plasma cells, T cells follicular helper, T cells gamma delta, T cells CD8, etc. were significantly higher in the low-risk group.

CONCLUSION

In summary, our study constructed a robust HRGPs signature as molecular markers for predicting the outcome of HNSCC patients.