Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Identification of lung adenocarcinoma subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes

Background: Lung adenocarcinoma (LUAD) is the most common variant of non-small cell lung cancer (NSCLC) across the world. Recently, the rapid development of immunotherapy has brought a new dawn for LUAD patients. Closely related to the tumor immune microenvironment and immune cell functions, more and more new immune checkpoints have been discovered, and various cancer treatment studies targeting these novel immune checkpoints are currently in full swing. However, studies on the phenotype and clinical significance of novel immune checkpoints in LUAD are still limited, and only a minority of patients with LUAD can benefit from immunotherapy. Methods: The LUAD datasets were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, and the immune checkpoints score of each sample were calculated based on the expression of the 82 immune checkpoints-related genes (ICGs). The weighted gene co-expression network analysis (WGCNA) was used to obtain the gene modules closely related to the score and two different LUAD clusters were identified based on these module genes by the Non-negative Matrix Factorization (NMF) Algorithm. The differentially expressed genes between the two clusters were further used to construct a predictive signature for prognosis, immune features, and the response to immunotherapy for LUAD patients through a series of regression analyses. Results: A new immune checkpoints-related signature was finally established according to the expression of 7 genes (FCER2, CD200R1, RHOV, TNNT2, WT1, AHSG, and KRTAP5-8). This signature can stratify patients into high-risk and low-risk groups with different survival outcomes and sensitivity to immunotherapy, and the signature has been well validated in different clinical subgroups and validation cohorts. Conclusion: We constructed a novel immune checkpoints-related LUAD risk assessment system, which has a good predictive ability and significance for guiding immunotherapy. We believe that these findings will not only aid in the clinical management of LUAD patients but also provide some insights into screening appropriate patients for immunotherapy.

Acircadian rhythm-related gene signature for predicting survival and drug response in HNSC

Head and neck squamous cell carcinoma (HNSC) represents one of the most common malignant carcinomas worldwide. Because the 5-year survival rate of patients with HNSC is poor, it is necessary to develop an effective signature for predicting the risk of HNSC. To identify a circadian rhythm (CR)-related predictive signature, we analyzed the RNA-seq data of patients with HNSC from The Cancer Genome Atlas and Gene Expression Omnibus cohorts. Nine CR-related genes (PER2, PER3, GHRL, CSF2, HDAC3, KLF10, PRKAA2, PTGDS, and RORB) were identified to develop a CR-related signature. The area under the curve values for 5-year overall survival were 0.681, 0.700, and 0.729 in the training set, validation set, and an external independent test set (GSE41613), respectively. The Kaplan‒Meier curve analysis showed that the high-risk group had a reduced relapse-free survival compared with the low-risk group in the training set, validation set, and test set (P < 0.05). Finally, we observed that the CR-related gene signature was associated with the tumor immune microenvironment, somatic nucleotide variation, and drug response in HNSC. In conclusion, we developed a circadian rhythm-related gene signature for predicting overall survival in HNSC.

Genomic Analysis of Waterpipe Smoke-Induced Lung Tumor Autophagy and Plasticity

The role of autophagy in lung cancer cells exposed to waterpipe smoke (WPS) is not known. Because of the important role of autophagy in tumor resistance and progression, we investigated its relationship with WP smoking. We first showed that WPS activated autophagy, as reflected by LC3 processing, in lung cancer cell lines. The autophagy response in smokers with lung adenocarcinoma, as compared to non-smokers with lung adenocarcinoma, was investigated further using the TCGA lung adenocarcinoma bulk RNA-seq dataset with the available patient metadata on smoking status. The results, based on a machine learning classification model using Random Forest, indicate that smokers have an increase in autophagy-activating genes. Comparative analysis of lung adenocarcinoma molecular signatures in affected patients with a long-term active exposure to smoke compared to non-smoker patients indicates a higher tumor mutational burden, a higher CD8+ T-cell level and a lower dysfunction level in smokers. While the expression of the checkpoint genes tested-PD-1, PD-L1, PD-L2 and CTLA-4-remains unchanged between smokers and non-smokers, B7-1, B7-2, IDO1 and CD200R1 were found to be higher in non-smokers than smokers. Because multiple factors in the tumor microenvironment dictate the success of immunotherapy, in addition to the expression of immune checkpoint genes, our analysis explains why patients who are smokers with lung adenocarcinoma respond better to immunotherapy, even though there are no relative differences in immune checkpoint genes in the two groups. Therefore, targeting autophagy in lung adenocarcinoma patients, in combination with checkpoint inhibitor-targeted therapies or chemotherapy, should be considered in smoker patients with lung adenocarcinoma.

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

Objective

This study aimed to identify the programmed death ligand-1 (PDL1, also termed as CD274) and its positively correlated immune checkpoint genes (ICGs) and to determine the immune subtypes of CD274-centered ICG combinations in oral and squamous cell carcinoma (OSCC).

Materials and Methods

Firstly, the 95 ICGs obtained via literature reviews were identified in the Cancer Genome Atlas (TCGA) database in relation to OSCC, and such 88 ICG expression profiles were extracted. ICGs positively correlated with CD274 were utilized for subsequent analysis. The relationship between ICGs positively correlated with CD274 and immunotherapy biomarkers (tumor mutation burden (TMB), and adaptive immune resistance pathway genes) was investigated, and the relationships of these genes with OSCC clinical features were explored. The prognostic values of CD274 and its positively correlated ICGs and also their associated gene pairs were revealed using the survival analysis.

Results

Eight ICGs, including CTLA4, ICOS, TNFRSF4, CD27, B- and T-lymphocyte attenuator (BTLA), ADORA2A, CD40LG, and CD28, were found to be positively correlated with CD274. Among the eight ICGs, seven ICGs (CTLA4, ICOS, TNFRSF4, CD27, BTLA, CD40LG, and CD28) were significantly negatively correlated with TMB. The majority of the adaptive immune resistance pathway genes were positively correlated with ICGs positively correlated with CD274. The survival analysis utilizing the TCGA-OSCC data showed that, although CD274 was not significantly associated with overall survival (OS), the majority of ICGs positively correlated with CD274 (BTLA, CD27, CTLA4, CD40LG, CD28, ICOS, and TNFRSF4) were significantly correlated with OS, whereby their low-expression predicted a favorable prognosis. The survival analysis based on the gene pair subtypes showed that the combination subtypes of CD274_low/BTLA_low, CD274_low/CD27_low, CD274_low/CTLA4_low, CD8A_high/BTLA_low, CD8A_high/CD27_low, and CD8A_high/CTLA4_low predicted favorable OS.

Conclusion

The results in this study provide a theoretical basis for prognostic immune subtyping of OSCC and highlight the importance of developing future immunotherapeutic strategies for treating oral cancer.

Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Multiomics analysis of tumor mutational burden across cancer types

Whether tumor mutational burden (TMB) is related to improved survival outcomes or the promotion of immunotherapy in various malignant tumors remains controversial, and we lack a comprehensive understanding of TMB across cancers. Based on the data obtained from The Cancer Genome Atlas (TCGA), we conducted a multiomics analysis of TMB across 21 cancer types to identify characteristics related to TMB and determine the mechanism as it relates to prognosis, gene expression, gene mutation and signaling pathways. In our study, TMB was found to have a significant relationship with prognosis for 21 tumors, and the relationship was different in different tumors. TMB may also be related to different outcomes for patients with different tumor subtypes. TMB was confirmed to be correlated with clinical information, such as age and sex. Mutations in GATA3 and MAP3K1 in beast invasive carcinoma (BRCA), TCF7L2 in colon adenocarcinoma (COAD), NFE2L2 in esophageal carcinoma (ESCA), CIC and IDH1 in brain lower grade glioma (LGG), CDH1 in stomach adenocarcinoma (STAD), and TP53 in uterine corpus endometrial carcinoma (UCEC) were demonstrated to be correlated with lower TMB. Moreover, we identified differentially expressed genes (DEGs) and differentially methylated regions (DMRs) according to different TMB levels in 21 cancers. We also investigated the correlation between enrichment of signaling pathways, immune cell infiltration and TMB. In conclusion, we identified multiomic characteristics related to the TMB in 21 tumors, providing support for a comprehensive understanding of the role of TMB in different tumors.