Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

Comprehensive analysis and identification of subtypes and hub genes of high immune response in lung adenocarcinoma

BACKGROUND

The advent of immunotherapy targeting immune checkpoints has conferred significant clinical advantages to patients with lung adenocarcinoma (LUAD); However, only a limited subset of patients exhibit responsiveness to this treatment. Consequently, there is an imperative need to stratify LUAD patients based on their response to immunotherapy and enhance the therapeutic efficacy of these treatments.

METHODS

The differentially co-expressed genes associated with CD8 + T cells were identified through weighted gene co-expression network analysis (WGCNA) and the Search Tool for the Retrieval of Interacting Genes (STRING) database. These gene signatures facilitated consensus clustering for TCGA-LUAD and GEO cohorts, categorizing them into distinct immune subtypes (C1, C2, C3, and C4). The Tumor Immune Dysfunction and Exclusion (TIDE) model and Immunophenoscore (IPS) analysis were employed to assess the immunotherapy response of these subtypes. Additionally, the impact of inhibitors targeting five hub genes on the interaction between CD8 + T cells and LUAD cells was evaluated using CCK8 and EDU assays. To ascertain the effects of these inhibitors on immune checkpoint genes and the cytotoxicity mediated by CD8 + T cells, flow cytometry, qPCR, and ELISA methods were utilized.

RESULTS

Among the identified immune subtypes, subtypes C1 and C3 were characterized by an abundance of immune components and enhanced immunogenicity. Notably, both C1 and C3 exhibited higher T cell dysfunction scores and elevated expression of immune checkpoint genes. Multi-cohort analysis of Lung Adenocarcinoma (LUAD) suggested that these subtypes might elicit superior responses to immunotherapy and chemotherapy. In vitro experiments involved co-culturing LUAD cells with CD8 + T cells and implementing the inhibition of five pivotal genes to assess their function. The inhibition of these genes mitigated the immunosuppression on CD8 + T cells, reduced the levels of PD1 and PD-L1, and promoted the secretion of IFN-γ and IL-2.

CONCLUSIONS

Collectively, this study delineated LUAD into four distinct subtypes and identified five hub genes correlated with CD8 + T cell activity. It lays the groundwork for refining personalized therapy and immunotherapy strategies for patients with LUAD.

Investigating subtypes of lung adenocarcinoma by oxidative stress and immunotherapy related genes

Lung adenocarcinoma (LUAD) is one of the most widespread and fatal types of lung cancer. Oxidative stress, resulting from an imbalance in the production and accumulation of reactive oxygen species (ROS), is considered a promising therapeutic target for cancer treatment. Currently, immune checkpoint blockade (ICB) therapy is being explored as a potentially effective treatment for early-stage LUAD. In this research, we aim to identify distinct subtypes of LUAD patients by investigating genes associated with oxidative stress and immunotherapy. Additionally, we aim to propose subtype-specific therapeutic strategies. We conducted a thorough search of the Gene Expression Omnibus (GEO) datasets. From this search, we pinpointed datasets that contained both expression data and survival information. We selected genes associated with oxidative stress and immunotherapy using keyword searches on GeneCards. We then combined expression data of LUAD samples from both The Cancer Genome Atlas (TCGA) and 11 GEO datasets, forming a unified dataset. This dataset was subsequently divided into two subsets, Dataset_Training and Dataset_Testing, using a random bifurcation method, with each subset containing 50% of the data. We applied consensus clustering (CC) analysis to identify distinct LUAD subtypes within the Dataset_Training. Molecular variances associated with oxidative stress levels, the tumor microenvironment (TME), and immune checkpoint genes (ICGs) were then investigated among these subtypes. Employing feature selection combined with machine learning techniques, we constructed models that achieved the highest accuracy levels. We validated the identified subtypes and models from Dataset_Training using Dataset_Testing. A hub gene with the highest importance values in the machine learning model was identified. We then utilized virtual screening to discover potential compounds targeting this hub gene. In the unified dataset, we integrated 2,154 LUAD samples from TCGA-LUAD and 11 GEO datasets. We specifically selected 1,311 genes associated with immune and oxidative stress processes. The expression data of these genes were then employed for subtype identification through CC analysis. Within Dataset_Training, two distinct subtypes emerged, each marked by different levels of immune and oxidative stress pathway values. Consequently, we named these as the OX+ and IM+ subtypes. Notably, the OX+ subtype showed increased oxidative stress levels, correlating with a worse prognosis than the IM+ subtype. Conversely, the IM+ subtype demonstrated enhanced levels of immune pathways, immune cells, and ICGs compared to the OX+ subtype. We reconfirmed these findings in Dataset_Testing. Through gene selection, we identified an optimal combination of 12 genes for predicting LUAD subtypes: ACP1, AURKA, BIRC5, CYC1, GSTP1, HSPD1, HSPE1, MDH2, MRPL13, NDUFS1, SNRPD1, and SORD. Out of the four machine learning models we tested, the support vector machine (SVM) stood out, achieving the highest area under the curve (AUC) of 0.86 and an accuracy of 0.78 on Dataset_Testing. We focused on HSPE1, which was designated as the hub gene due to its paramount importance in the SVM model, and computed the docking structures for four compounds: ZINC3978005 (Dihydroergotamine), ZINC52955754 (Ergotamine), ZINC150588351 (Elbasvir), and ZINC242548690 (Digoxin). Our study identified two subtypes of LUAD patients based on oxidative stress and immunotherapy-related genes. Our findings provided subtype-specific therapeutic strategies.

Tumor-infiltrating Leukocyte Profiling Defines Three Immune Subtypes of NSCLC with Distinct Signaling Pathways and Genetic Alterations

Resistance to immune checkpoint blockade remains challenging in patients with non-small cell lung cancer (NSCLC). Tumor-infiltrating leukocyte (TIL) quantity, composition, and activation status profoundly influence responsiveness to cancer immunotherapy. This study examined the immune landscape in the NSCLC tumor microenvironment by analyzing TIL profiles of 281 fresh resected NSCLC tissues. Unsupervised clustering based on numbers and percentages of 30 TIL types classified adenocarcinoma (LUAD) and squamous cell carcinoma (LUSQ) into the cold, myeloid cell-dominant, and CD8⁺ T cell-dominant subtypes. These were significantly correlated with patient prognosis; the myeloid cell subtype had worse outcomes than the others. Integrated genomic and transcriptomic analyses, including RNA sequencing, whole-exome sequencing, T-cell receptor repertoire, and metabolomics of tumor tissue, revealed that immune reaction-related signaling pathways were inactivated, while the glycolysis and K-ras signaling pathways activated in LUAD and LUSQ myeloid cell subtypes. Cases with ALK and ROS1 fusion genes were enriched in the LUAD myeloid subtype, and the frequency of TERT copy-number variations was higher in LUSQ myeloid subtype than in the others. These classifications of NSCLC based on TIL status may be useful for developing personalized immune therapies for NSCLC.

Significance

The precise TIL profiling classified NSCLC into novel three immune subtypes that correlates with patient outcome, identifying subtype-specific molecular pathways and genomic alterations that should play important roles in constructing subtype-specific immune tumor microenvironments. These classifications of NSCLC based on TIL status are useful for developing personalized immune therapies for NSCLC.

M2 subtype tumor associated macrophages (M2-TAMs) infiltration predicts poor response rate of immune checkpoint inhibitors treatment for prostate cancer

BACKGROUND

Prostate cancer (PCa) is poor response to the immunotherapy for its high heterogeneity of immune microenvironment. In this study, we aim to introduce a new immune subtype for PCa involving M2 tumour associated macrophages (M2-TAMs).

METHODS

Three hundred and sixty-two PCa patients and matched normal prostate tissues were selected from the Cancer Genome Atlas and Gene Expression Omnibus databases. Patients' immune infiltration characters were then analyzed based on the gene expressions. The immune subtypes were identified by the method of unsupervised hierarchical clustering. Finally, the relationship between the M2-TAMs infiltration and anti-programmed death-ligand-1 (PD-L1) therapy was investigated in the IMvigor210 cohort.

RESULTS

PCa expressed lower immune-related genes levels compared with the adjacent normal tissues. Based on the proved immunosuppressive mechanisms in PCa, tumour patients were classified into three independent subclasses with high infiltrated cytolytic activity (CYT), M2-TAMs and regulatory T cell (Tregs), respectively. Among these subtypes, M2-TAMs infiltration subtype showed the worst clinicopathological features and prognosis compared with the other two subtypes. The results of the IMvigor210 cohort demonstrated poor response of anti-PD-L1 therapy for patients with high M2-TAMs infiltration.

CONCLUSION

Prostate tumours involved in significant immunosuppression, and high infiltration of M2-TAMs can be applied to predict the effect of anti-PD-L1 therapy.Key MessagesPCa patients can be classified into three immunotypes of high infiltrated CYT, M2-TAMS, and Tregs according to the immunosuppressive mechanisms.High M2-TAMs infiltration subtype reflected the worst clinical characters, immune infiltration, and lowest expression of immune checkpoint inhibitors among the three subclasses in PCa.High M2-TAMs infiltration predicts the low response rate of anti-PD-L1 therapy.

Bioinformatics Analysis of GFAP as a Potential Key Regulator in Different Immune Phenotypes of Prostate Cancer

Tumor immune escape plays an essential role in both cancer progression and immunotherapy responses. For prostate cancer (PC), however, the molecular mechanisms that drive its different immune phenotypes have yet to be fully elucidated. Patient gene expression data were analyzed from The Cancer Genome Atlas-prostate adenocarcinoma (TCGA-PRAD) and the International Cancer Genome Consortium (ICGC) databases. We used a Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis and an unsupervised clustering analysis to identify patient subgroups with distinct immune phenotypes. These distinct phenotypes were then explored for associations for differentially expressed genes (DEGs) and both epigenetic and genetic landscapes. Finally, we used a protein-protein interaction analysis to identify key hub genes. We identified two patient subgroups with independent immune phenotypes associated with the expression of Programmed death-ligand 1 (PD-L1). Patient samples in Cluster 1 (C1) had higher scores for immune-cell subsets compared to Cluster 2 (C2), and C2 samples had higher specific somatic mutations, MHC mutations, and genomic copy number variations compared to C1. We also found additional cluster phenotype differences for DNA methylation, microRNA (miRNA) expression, and long noncoding RNA (lncRNA) expression. Furthermore, we established a 4-gene model to distinguish between clusters by integrating analyses for DEGs, lncRNAs, miRNAs, and methylation. Notably, we found that glial fibrillary acidic protein (GFAP) might serve as a key hub gene within the genetic and epigenetic regulatory networks. These results improve our understanding of the molecular mechanisms underlying tumor immune phenotypes that are associated with tumor immune escape. In addition, GFAP may be a potential biomarker for both PC diagnosis and prognosis.

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory.

Methods

The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues.

Results

In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells.

Conclusions

In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07911-8.