Molecular subtyping for lung adenocarcinoma and a novel prognostic model based on ligand-receptor pairs.

Lung adenocarcinoma (LUAD) is a leading cause of cancer death worldwide. Ligands and receptors play important roles in cell communication. This study aimed to demonstrate the importance of ligand-receptor (LR) pairs in LUAD development through constructing molecular subtypes and a prognostic model based on LR pairs. A total of 1110 LUAD samples with clinical and expression data were obtained from public databases. Unsupervised consensus clustering was applied to construct molecular subtypes based on LR pairs. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a prognostic model. Three molecular subtypes (C1, C2 and C3) were constructed based on 17 prognosis-related LR pairs. C1 subtype had the worst prognosis, while C3 subtype had the optimal prognosis. Oncogenic pathways such as epithelial-mesenchymal transition (EMT) were activated in C1 subtype. A prognostic model was built based on 8 LR pairs, and could classify samples into high- and low-LR score groups. Two groups had distinct overall survival and tumor microenvironment (TME). High-LR score group was more sensitive to chemotherapeutic drugs, while low-LR score group could benefit much from anti-PD-1/PD-L1 therapy. The study showed that LR pairs played critical roles in LUAD development. The prognostic model could predict prognosis and guide personalized therapy for LUAD patients.

Li D ，Ma X ，Ou S 《-》

The molecular subtypes of triple negative breast cancer were defined and a ligand-receptor pair score model was constructed by comprehensive analysis of ligand-receptor pairs.

Intercellular communication mediated by ligand-receptor interactions in tumor microenvironment (TME) has a profound impact on tumor progression. This study aimed to explore the molecular subtypes mediated by ligand-receptor (LR) pairs in triple negative breast cancer (TNBC), identify the most important LR pairs to construct a prognostic risk model, and study their effect on TNBC immunotherapy. LR pairs subclasses of TNBC were categorized by consensus clustering based on LR Pairs in METABRIC dataset. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a LR pairs score model. The relationship between LR pairs score and immune cell infiltration, stromal score and immune score associated with TME was analyzed, and the prediction of drug therapy and immunotherapy efficacy by LR pairs score was evaluated. According to the expression pattern of 145 TNBC prognostic LR pairs, the samples were divided into three subclasses with different survival outcomes, copy number variation (CNV), TME immune cell infiltration, stromal score and immune score. The LR pairs score model constructed in the METABRIC dataset was composed of four LR pairs, and its predictive significance for TNBC prognosis was verified in GSE58812 and GSE21653 cohorts. In addition, LR pairs score was negatively correlated with several immune pathways regulating immunity and immune score, and related to the sensitivity of anti-neoplastic drugs and the effect of anti-PD-L1 therapy. Our study confirmed the impact of LR pairs on the molecular heterogeneity of TNBC, characterized three LR pairs subtypes with different survival outcomes and TME patterns, and proposed a LR pairs score system with predictive significance for TNBC prognosis and anti-PD-L1 therapeutic effect, which provides a potential evaluation scheme for TNBC management.

Pan W ，Song K ，Zhang Y ，Yang C ，Zhang Y ，Ji F ，Zhang J ，Shi J ，Wang K ... -  《Frontiers in Immunology》

Molecular characterization based on tumor microenvironment-related signatures for guiding immunotherapy and therapeutic resistance in lung adenocarcinoma.

Background: Although the role of tumor microenvironment in lung adenocarcinoma (LUAD) has been explored in a number of studies, the value of TME-related signatures in immunotherapy has not been comprehensively characterized. Materials and Methods: Consensus clustering was conducted to characterize TME-based molecular subtypes using transcription data of LUAD samples. The biological pathways and immune microenvironment were assessed by CIBERSORT, ESTIMATE, and gene set enrichment analysis. A TME-related risk model was established through the algorithms of least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). Results: Four TME-based molecular subtypes including C1, C2, C3, and C4 were identified, and they showed distinct overall survival, genomic characteristics, DNA methylation pattern, immune microenvironment, and biological pathways. C1 had the worst prognosis and high tumor proliferation rate. C3 and C4 had higher enrichment of anti-tumor signatures compared to C1 and C2. C4 had evidently low enrichment of epithelial-mesenchymal transition (EMT) signature and tumor proliferation rate. C3 was predicted to be more sensitive to immunotherapy compared with other subtypes. C1 is more sensitive to chemotherapy drugs, including Docetaxel, Vinorelbine and Cisplatin, while C3 is more sensitive to Paclitaxel. A five-gene risk model was constructed, which showed a favorable performance in three independent datasets. Low-risk group showed a longer overall survival, more infiltrated immune cells, and higher response to immunotherapy than high-risk group. Conclusion: This study comprehensively characterized the molecular features of LUAD patients based on TME-related signatures, demonstrating the potential of TME-based signatures in exploring the mechanisms of LUAD development. The TME-related risk model was of clinical value to predict LUAD prognosis and guide immunotherapy.

Jie Y ，Wu J ，An D ，Li M ，He H ，Wang D ，Gu A ，E M ... -  《Frontiers in Pharmacology》

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis.

Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively. LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay. A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues. Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

Shu J ，Jiang J ，Zhao G 《Frontiers in Immunology》

Comprehensive Analysis of Ligand-receptor Interactions in Colon Adenocarcinoma to Identify of Tumor Microenvironment Oxidative Stress and Prognosis Model.

Single-cell technology enables a deep study on the mechanism of cancers. This work delineated the function of ligand-receptor interaction in colon adenocarcinoma (COAD), and developed a LR pairs-based prognostic model. For identifying important LR pairs, Single-cell RNA sequencing data of COAD was included. Unsupervised consensus clustering constructed molecular subtypes. LASSO established a prognostic model. Infiltration of 22 immune cells was evaluated by Cibersort. Enrichment score of oxidative stress related pathways was determined by SsGSEA in each patient. Forty-seven LR pairs were closely associated with the prognosis of COAD. Three molecular subtypes were differentiated according to 47 LR pairs, which displayed differential clinical features and molecular features. There were significant differences in immune T cell lytic activity among different subtypes. In clust1 with poor prognosis, significantly enriched oncogenic pathways were found, especially epithelial-mesenchymal transition (EMT). Additionally, it has been found that clust3 had significantly higher immune infiltration. A prognostic model containing eight LR pairs (PDGFB-PDGFRA, FLT4-VEGFC, CSF1R-CSF1, DLL1-NOTCH4, PDGFB-LRP1, DLL1- NOTCH3, FLT4-PDGFC, and NRP2-PGF) was established, which could effectively divide samples into low-risk and high-risk groups. Significantly higher oxidative stress was found among high-risk patients. This study integrated expression data and single-cell data for demonstrating the effectiveness of LR pairs in establishing the prognostic model and constructing molecular subtypes. Prognostic LR pairs may contribute to tumorigenesis and progression in COAD. The prognostic model was the potential for predicting prognosis and guiding immunotherapy for COAD patients.

Hu J ，Zhu W ，Wang W ，Yue X ，Zhao P ，Kong D ... -  《-》