A Novel Immune-Related Gene Signature to Identify the Tumor Microenvironment and Prognose Disease Among Patients With Oral Squamous Cell Carcinoma Patients Using ssGSEA: A Bioinformatics and Biological Validation Study

Abstract

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model’s predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

Immunosuppression by Inflammation-Stimulated Amplification of Myeloid-Derived Suppressor Cells and Changes in Expression of Immune Checkpoint HHLA2 in Chronic Obstructive Pulmonary Disease

“…The bar plot of immune cell proportions was visualized with the ggplot2. 19–21 After determining the immune cell scores in the various samples, the two-sample t -test was applied to analyze the differences in immune cells between COPD patients and healthy control groups. Pearson correlation analysis was thereafter used to analyze the correlation of immune cells in the dataset.…”

Immune infiltration associated C1q acts as a novel prognostic biomarker of cutaneous melanoma

“…The single-sample gene set enrichment analysis method of the GSVA package calculates immune cell type abundance, immune infiltration score, and immune cell score. [20] We use the "single-sample gene set enrichment analysis" algorithm to discover the association between c1q expression and various immune cells in the TME and to evaluate variations in immune cell abundance between groups with high and low C1q. [21,22] We also assessed the relationship between C1q and the checkpoint to determine how C1q affects SKCM individuals immune responses.…”

Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma

“…Gene set enrichment analysis (GSEA) is a computational method that classifies sets of genes with common functions (Subramanian et al, 2005), whereas ssGSEA analyses the absolute enrichment of one gene set per sample within a given data set (Barbie et al, 2009). In the present study, ssGSEA was used to generate the enrichment fraction of 29 immune cells in each sample (Chen et al, 2022). The patients in the TCGA cohort were further classified into lowand high-immune clusters using cluster analysis.…”