Immunogenomic Gene Signature of Cell-Death Associated Genes with Prognostic Implications in Lung Cancer

Abstract

Lung cancer is one of the leading causes of death worldwide. Cell death pathways such as autophagy, apoptosis, and necrosis can provide useful clinical and immunological insights that can assist in the design of personalized therapeutics. In this study, variations in the expression of genes involved in cell death pathways and resulting infiltration of immune cells were explored in lung adenocarcinoma (The Cancer Genome Atlas: TCGA, lung adenocarcinoma (LUAD), 510 patients). Firstly, genes involved in autophagy (n = 34 genes), apoptosis (n = 66 genes), and necrosis (n = 32 genes) were analyzed to assess the prognostic significance in lung cancer. The significant genes were used to develop the cell death index (CDI) of 21 genes which clustered patients based on high risk (high CDI) and low risk (low CDI). The survival analysis using the Kaplan–Meier curve differentiated patients based on overall survival (40.4 months vs. 76.2 months), progression-free survival (26.2 months vs. 48.6 months), and disease-free survival (62.2 months vs. 158.2 months) (Log-rank test, p < 0.01). Cox proportional hazard model significantly associated patients in high CDI group with a higher risk of mortality (Hazard Ratio: H.R 1.75, 95% CI: 1.28–2.45, p < 0.001). Differential gene expression analysis using principal component analysis (PCA) identified genes with the highest fold change forming distinct clusters. To analyze the immune parameters in two risk groups, cytokines expression (n = 265 genes) analysis revealed the highest association of IL-15RA and IL 15 (> 1.5-fold, p < 0.01) with the high-risk group. The microenvironment cell-population (MCP)-counter algorithm identified the higher infiltration of CD8+ T cells, macrophages, and lower infiltration of neutrophils with the high-risk group. Interestingly, this group also showed a higher expression of immune checkpoint molecules CD-274 (PD-L1), CTLA-4, and T cell exhaustion genes (HAVCR2, TIGIT, LAG3, PDCD1, CXCL13, and LYN) (p < 0.01). Furthermore, functional enrichment analysis identified significant perturbations in immune pathways in the higher risk group. This study highlights the presence of an immunocompromised microenvironment indicated by the higher infiltration of cytotoxic T cells along with the presence of checkpoint molecules and T cell exhaustion genes. These patients at higher risk might be more suitable to benefit from PD-L1 blockade or other checkpoint blockade immunotherapies.

An Integrated Pan-Cancer Analysis of 33 Human Cancers Reveals the Potential Clinical Implications and Immunotherapeutic Value of C-X-C Motif Chemokine Ligand 13

“…In cancers that CXCL13 harbor favorable prognosis, one study reported that CXCL13 was associated with better overall survival and positively correlated with infiltration of six immune cells (B cell, CD8+T cells, CD4+T cells, macrophages, neutrophils, dendritic cells) in SKCM ( 21 ). Another study showed that the higher infiltration of CD8+ T cells and macrophages were identified in the group with higher expression of immune checkpoint molecules (CD-274 (PD-L1), CTLA-4), and T cell exhaustion genes (HAVCR2, TIGIT, LAG3, PDCD1, CXCL13, and LYN) in LUSC ( 26 ), which was also consistent with our findings related to immunity genes. In cancers that CXCL13 harbor unfavorable prognosis, such as KIRC, intratumoral CXCL13 + CD8 + T cell infiltration determines poor clinical outcomes and immunoevasive contexture ( 27 ).…”

“…Pankaj et al. found higher expression of the immune checkpoint molecules CD-274 (PD-L1) and CTLA-4, and the T-cell depletion genes (TIGIT, LAG3, PDCD1, CXCL13, and Lyn) in the high-risk group of lung cancer, which may be more suitable for PD-L1 blocking or other checkpoint blocking immunotherapies ( 26 ). Li et al.…”

Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma

“…In recent years, great progress has been made with high-throughput sequencing technologies, such as microarrays and RNA-seq, and public databases consisting of large genetic and clinical datasets, such as the TCGA and GEO, have been established. In the field of lung cancer research, many studies are conducted using data obtained from public databases to identify key genes and pathways involved in cancer development and progression by analyzing gene expression profiles, and some researchers have predicted patient prognosis by constructing predictive models ( Zuo et al, 2020 ; Ahluwalia et al, 2021 ).…”

Prognostic Value of Genomic Instability of m6A-Related lncRNAs in Lung Adenocarcinoma

“…Recently, a study showed that CTLA4 can serve as a prognostic biomarker for predicting the survival of LUAD ( Wang et al, 2020b ). Furthermore, an immunogenic gene signature associated with immune checkpoint blockade provided novel therapeutic targets for immunology in LUAD ( Ahluwalia et al, 2021 ).…”