Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

Cuproptosis and Immune-Related Gene Signature Predicts Immunotherapy Response and Prognosis in Lung Adenocarcinoma

Cuproptosis and associated immune-related genes (IRG) have been implicated in tumorigenesis and tumor progression. However, their effects on lung adenocarcinoma (LUAD) have not been elucidated. Therefore, we investigated the impact of cuproptosis-associated IRGs on the immunotherapy response and prognosis of LUAD using a bioinformatical approach and in vitro experiments analyzing clinical samples. Using the cuproptosis-associated IRG signature, we classified LUAD into two subtypes, cluster 1 and cluster 2, and identified three key cuproptosis-associated IRGs, NRAS, TRAV38-2DV8, and SORT1. These three genes were employed to establish a risk model and nomogram, and to classify the LUAD cohort into low- and high-risk subgroups. Biofunctional annotation revealed that cluster 2, remarkably downregulating epigenetic, stemness, and proliferation pathways activity, had a higher overall survival (OS) and immunoinfiltration abundance compared to cluster 1. Real-time quantitative PCR (RT-qPCR) validated the differential expression of these three genes in both subgroups. scRNA-seq demonstrated elevated expression of NRAS and SORT1 in macrophages. Immunity and oncogenic and stromal activation pathways were dramatically enriched in the low-risk subgroup, and patients in this subgroup responded better to immunotherapy. Our data suggest that the cuproptosis-associated IRG signature can be used to effectively predict the immunotherapy response and prognosis in LUAD. Our work provides enlightenment for immunotherapy response assessment, prognosis prediction, and the development of potential prognostic biomarkers for LUAD patients.

Construction and Validation of a Novel Immune-Related Gene Pairs-Based Prognostic Model in Lung Adenocarcinoma

OBJECT

Focus on immune-related gene pairs (IRGPs) and develop a prognostic model to predict the prognosis of patients with lung adenocarcinoma (LUAD).

METHODS

First, the LUAD patient dataset was downloaded from The Cancer Genome Atlas database, and paired analysis of immune-related genes was subsequently conducted. Then, LASSO regression was used to screen prognostic IRGPs for building a risk prediction model. Meanwhile, the Gene Expression Omnibus database was used for external validation of the model. Next, the clinical predictive power of IRGPs features was assessed by uni-multivariate Cox regression analysis, the infiltration of key immune cells in high and low IRGPs risk groups was analyzed with CIBERSORT, quanTIseq, and Timer, and the key pathways enriched for IRGPs were assessed using the Kyoto Encyclopedia of Genes and Genomes. Finally, the expression and related functions of key immune cells and genes were verified by immunofluorescence and cell experiments of tissue samples.

RESULTS

It was revealed that the risk score of 19 IRGPs could be used as accurate indicators to evaluate the prognosis of LUAD patients, and the risk score was mainly related to T cell infiltration based on CIBERSORT analysis. Two genes of IRGPs, IL6, and CCL2, were found to be closely associated with the expression of PD-1/PD-L1 and the function of T-cells. Depending on the results of tissue immunofluorescence, IL6, CCL2, and T cells were highly expressed in the LUAD tissues of patients. Furthermore, IL6 and CCL2 were positively correlated with the expression of T cells. Besides, qRT-PCR assay in four different LUAD cells proved that IL6 and CCL2 were positively correlated with the expression of PD-L1 (P < .001).

CONCLUSIONS

Based on 19 IRGPs, an effective prognosis model was established to predict the prognosis of LUAD patients. In addition, IL6 and CCL2 are closely related to the function of T-cells.

Single-Cell Transcriptomics of Immune Cells Reveal Diversity and Exhaustion Signatures in Non-Small-Cell Lung Cancer

Understanding immune cell phenotypes in the tumor microenvironment (TME) is essential for explaining and predicting progression of non-small cell lung cancer (NSCLC) and its response to immunotherapy. Here we describe the single-cell transcriptomics of CD45⁺ immune cells from tumors, normal tissues and blood of NSCLC patients. We identified three clusters of immune cells exerting immunosuppressive effects: CD8⁺ T cells with exhausted phenotype, tumor-associated macrophages (TAMs) with a pro-inflammatory M2 phenotype, and regulatory B cells (B regs) with tumor-promoting characteristics. We identified genes that may be mediating T cell phenotypes, including the transcription factors ONECUT2 and ETV4 in exhausted CD8⁺ T cells, TIGIT and CTL4 high expression in regulatory T cells. Our results highlight the heterogeneity of CD45⁺ immune cells in the TME and provide testable hypotheses about the cell types and genes that define the TME.

Construction of an immune-related lncRNA signature pair for predicting oncologic outcomes and the sensitivity of immunosuppressor in treatment of lung adenocarcinoma

Background

Although immunotherapy has shown clinical activity in lung adenocarcinoma (LUAD), LUAD prognosis has been a perplexing problem. We aimed to construct an immune-related lncRNA pairs (IRLPs) score for LUAD and identify what immunosuppressor are appropriate for which group of people with LUAD.

Methods

Based on The Cancer Genome Atlas (TCGA)-LUAD cohort, IRLPs were identified to construct an IRLPs scoring system by Cox regression and validated in the Gene Expression Omnibus (GEO) dataset using log-rank test and the receiver operating characteristic curve (ROC). Next, we used spearman’s correlation analysis, t-test, signaling pathways analysis and gene mutation analysis to explore immune and molecular characteristics in different IRLP subgroups. The “pRRophetic” package was used to predict the sensitivity of immunosuppressant.

Results

The IRLPs score was constructed based on eight IRLPs calculated as 2.12 × (MIR31HG|RRN3P2) + 0.43 × (NKX2-1-AS1|AC083949.1) + 1.79 × (TMPO-AS1|LPP-AS2) + 1.60 × (TMPO-AS1|MGC32805) + 1.79 × (TMPO-AS1|PINK1-AS) + 0.65 × (SH3BP5-AS1|LINC01137) + 0.51 × (LINC01004|SH3PXD2A-AS1) + 0.62 × (LINC00339|AGAP2-AS1). Patients with a lower IRLPs risk score had a better overall survival (OS) (Log-rank test P_{TCGA train dataset} < 0.001, P_{TCGA test dataset} = 0.017, P_{GEO dataset} = 0.027) and similar results were observed in the AUCs of TCGA dataset and GEO dataset (AUC _{TCGA train dataset} = 0.777, AUC _{TCGA test dataset} = 0.685, AUC _{TCGA total dataset} = 0.733, AUC _{GEO dataset} = 0.680). Immune score (Cor = -0.18893, P < 0.001), stoma score (Cor = -0.24804, P < 0.001), and microenvironment score (Cor = -0.22338, P < 0.001) were significantly decreased in the patients with the higher IRLP risk score. The gene set enrichment analysis found that high-risk group enriched in molecular changes in DNA and chromosomes signaling pathways, and in this group the tumor mutation burden (TMB) was higher than in the low-risk group (P = 0.0015). Immunosuppressor methotrexate sensitivity was higher in the high-risk group (P = 0.0052), whereas parthenolide (P < 0.001) and rapamycin (P = 0.013) sensitivity were lower in the high-risk group.

Conclusions

Our study established an IRLPs scoring system as a biomarker to help in the prognosis, the identification of molecular and immune characteristics, and the patient-tailored selection of the most suitable immunosuppressor for LUAD therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02043-4.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

Prognostic and immune implications of a novel ferroptosis-related ten-gene signature in lung adenocarcinoma

Background

Lung cancer has been the focus of attention for many researchers in recent years due to its leading contribution to cancer-related death worldwide, with lung adenocarcinoma (LUAD) being the most common histological type. Ferroptosis, a novel iron-dependent form of regulated cell death, can be induced by sorafenib. Emerging evidence shows that triggering ferroptosis has potential as a cancer therapy. This work aimed to build a ferroptosis-related gene signature for predicting the outcome of LUAD.

Methods

The TCGA-LUAD dataset was set as the training cohort, and the GSE72094 and GSE68465 datasets were set as the validation cohorts. Sixty-two ferroptosis-related genes were retrieved from the literature. A univariate Cox regression model was constructed for the training cohort to preliminarily screen for potential prognostic ferroptosis-related genes. A gene signature was generated from a LASSO Cox regression model and assessed with the training and validation cohorts through Kaplan-Meier, Cox, and ROC analyses. In addition, the correlation between the risk score and autophagy-related genes was determined by the Pearson test. Finally, GSEA and immune infiltrating analyses were performed to better study the functional annotation of the signature and the role of each kind of immune cell.

Results

A ten-gene signature was constructed from the training cohort and validated in three cohorts by Kaplan-Meier and Cox regression analyses, revealing its independent prognostic value in LUAD. Moreover, a ROC analysis conducted with all cohort data confirmed the predictive ability of the ten-gene signature for LUAD prognosis. A total of 62.85% (308/490) of autophagy-related genes were found to be significantly correlated with risk scores. GSEA detailed the exact pathways related to the gene signature, and immune-infiltrating analyses identified crucial roles for resting mast cells and resting dendritic cells in the prognosis of LUAD.

Conclusions

We identified a novel ferroptosis-related ten-gene signature (PHKG2, PGD, PEBP1, NCOA4, GLS2, CISD1, ATP5G3, ALOX15, ALOX12B, and ACSL3) that can accurately predict LUAD prognosis and is closely linked to resting mast cells and resting dendritic cells.