Causal links between immune cells and asthma: Insights from a Mendelian Randomization analysis.

OBJECTIVES: Recent studies suggest immunophenotypes may play a role in asthma, but their causal relationship has not been thoroughly examined. METHODS: We used single nucleotide polymorphism (SNP)-derived instrumental variables. Summary data from 731 immune cell profiles and asthma cases were analyzed from genome-wide association studies (GWAS) of European populations. Mendelian Randomization (MR) analyses included inverse variance weighted (IVW), weighted median, and MR-Egger methods. Pleiotropy was assessed using the MR-Egger intercept and MR pleiotropy residual sum and outlier (MR-PRESSO) tests. Reverse MR analysis explored bidirectional causation between asthma and immunophenotypes. All statistical analyses were conducted using R software. RESULTS: MR analysis identified 108 immune signatures potentially contributing to asthma. Two immunophenotypes were significantly associated with asthma risk: CD4+ secreting Treg cells in allergic asthma (ORIVW = 1.078; 95% CI: 1.036-1.122; PIVW = 0.0002) and IgD + CD38- %lymphocyte cells in non-allergic asthma (ORIVW = 1.123; 95% CI: 1.057-1.194; PIVW = 0.0002). CONCLUSIONS: This study highlights the causal associations between specific immunophenotypes and asthma risk, providing new insights into asthma pathogenesis.

Multi­omics identification of a signature based on malignant cell-associated ligand-receptor genes for lung adenocarcinoma.

PURPOSE: Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. METHODS: We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. RESULTS: We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. CONCLUSION: We have identified malignant cell-associated ligand-receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.

Comprehensive pan-cancer analysis reveals EPHB2 is a novel predictive biomarker for prognosis and immunotherapy response.

PURPOSE: Recent studies have increasingly linked Ephrin receptor B2 (EPHB2) to cancer progression. However, comprehensive investigations into the immunological roles and prognostic significance of EPHB2 across various cancers remain lacking. METHODS: We employed various databases and bioinformatics tools to investigate the impact of EPHB2 on prognosis, immune infiltration, genome instability, and response to immunotherapy. Validation of the correlation between EPHB2 expression and M2 macrophages included analyses using bulk and single-cell transcriptomic datasets, spatial transcriptomics, and multi-fluorescence staining. Moreover, we performed cMap web tool to screen for EPHB2-targeted compounds and assessed their potential through molecular docking and dynamics simulations. Additionally, in vitro validation using lung adenocarcinoma (LUAD) cell lines was conducted to confirm the bioinformatics predictions about EPHB2. RESULTS: EPHB2 dysregulation was observed across multiple cancer types, where it demonstrated significant diagnostic and prognostic value. Gene Set Enrichment Analysis (GSEA) indicated that EPHB2 is involved in enhancing cellular proliferation, invasiveness of cancer cells, and modulation of the anti-cancer immune response. Furthermore, it is emerged as a pan-cancer marker for M2 macrophage infiltration, supported by integrated analyses of transcriptomics and multiple fluorescence staining. In LUAD cells, knockdown of EPHB2 expression led to a decrease in both cell proliferation and migratory activity. CONCLUSION: EPHB2 expression may serve as a pivotal indicator of M2 macrophage infiltration, offering vital insights into tumor dynamics and progression across various cancers, including lung adenocarcinoma, highlighting its significant prognostic and therapeutic potential for further exploration.

Causal association between immune cells and lung cancer risk: a two-sample bidirectional Mendelian randomization analysis.

Background: Previous studies have highlighted the crucial role of immune cells in lung cancer development; however, the direct link between immunophenotypes and lung cancer remains underexplored. Methods: We applied two-sample Mendelian randomization (MR) analysis, using genetic variants as instruments to determine the causal influence of exposures on outcomes. This method, unlike traditional randomized controlled trials (RCTs), leverages genetic variants inherited randomly at conception, thus reducing confounding and preventing reverse causation. Our analysis involved three genome-wide association studies to assess the causal impact of 731 immune cell signatures on lung cancer using genetic instrumental variables (IVs). We initially used the standard inverse variance weighted (IVW) method and further validated our findings with three supplementary MR techniques (MR-Egger, weighted median, and MR-PRESSO) to ensure robustness. We also conducted MR-Egger intercept and Cochran's Q tests to assess heterogeneity and pleiotropy. Additionally, reverse MR analysis was performed to explore potential causality between lung cancer subtypes and identified immunophenotypes, using R software for all statistical calculations. Results: Our MR analysis identified 106 immune signatures significantly associated with lung cancer. Notably, we found five suggestive associations across all sensitivity tests (P<0.05): CD25 on IgD- CD24- cells in small cell lung carcinoma (ORIVW =0.885; 95% CI: 0.798-0.983; P IVW =0.022); CD27 on IgD+ CD24+ cells in lung squamous cell carcinoma (ORIVW =1.054; 95% CI: 1.010-1.100; P IVW =0.015); CCR2 on monocyte cells in lung squamous cell carcinoma (ORIVW =0.941; 95% CI: 0.898-0.987; P IVW =0.012); CD123 on CD62L+ plasmacytoid dendritic cells (ORIVW =0.958; 95% CI: 0.924-0.992; P IVW =0.017) as well as on plasmacytoid dendritic cells (ORIVW =0.958; 95% CI: 0.924-0.992; P IVW =0.017) in lung squamous cell carcinoma. Conclusion: This study establishes a significant genomic link between immune cells and lung cancer, providing a robust basis for future clinical research aimed at lung cancer management.

Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer.

BACKGROUND: Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments. MATERIALS AND METHODS: In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments. RESULTS: In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions. CONCLUSION: Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.

Comprehensive analysis of 33 human cancers reveals clinical implications and immunotherapeutic value of the solute carrier family 35 member A2.

Background: Solute carrier family 35 member A2 (SLC35A2), which belongs to the SLC35 solute carrier family of human nucleoside sugar transporters, has shown regulatory roles in various tumors and neoplasms. However, the function of SLC35A2 across human cancers remains to be systematically assessed. Insights into the prediction ability of SLC35A2 in clinical practice and immunotherapy response remains limited. Materials and methods: We obtained the gene expression and protein levels of SLC35A2 in a variety of tumors from Molecular Taxonomy of Breast Cancer International Consortium, The Cancer Genome Atlas, Gene Expression Omnibus, Chinese Glioma Genome Atlas, and Human Protein Atlas databases. The SLC35A2 level was validated by immunohistochemistry. The predictive value for prognosis was evaluated by Kaplan-Meier survival and Cox regression analyses. Correlations between SLC35A2 expression and DNA methylation, genetic alterations, tumor mutation burden (TMB), microsatellite instability (MSI), and tumor microenvironment were performed using Spearman's correlation analysis. The possible downstream pathways of SLC35A2 in different human cancers were explored using gene set variation analysis. The potential role of SLC35A2 in the tumor immune microenvironment was evaluated via EPIC, CIBERSORT, MCP-counter, CIBERSORT-ABS, quanTIseq, TIMER, and xCell algorithms. The difference in the immunotherapeutic response of SLC35A2 under different expression conditions was evaluated by the tumor immune dysfunction and exclusion (TIDE) score as well as four independent immunotherapy cohorts, which includes patients with bladder urothelial carcinoma (BLCA, N = 299), non-small cell lung cancer (NSCLC, N = 72 and N = 36) and skin cutaneous melanoma (SKCM, N = 25). Potential drugs were identified using the CellMiner database and molecular docking. Results: SLC35A2 exhibited abnormally high or low expression in 23 cancers and was significantly associated with the prognosis. In various cancers, SLC35A2 expression and mammalian target of rapamycin complex 1 signaling were positively correlated. Multiple algorithmic immune infiltration analyses suggested an inverse relation between SLC35A2 expression and infiltrating immune cells, which includes CD4+T cells, CD8+T cells, B cells, and natural killer cells (NK) in various tumors. Furthermore, SLC35A2 expression was significantly correlated with pan-cancer immune checkpoints, TMB, MSI, and TIDE genes. SLC35A2 showed significant predictive value for the immunotherapy response of patients with diverse cancers. Two drugs, vismodegib and abiraterone, were identified, and the free binding energy of cytochrome P17 with abiraterone was higher than that of SLC35A2 with abiraterone. Conclusion: Our study revealed that SLC35A2 is upregulated in 20 types of cancer, including lung adenocarcinoma (LUAD), breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC). The upregulated SLC35A2 in five cancer types indicates a poor prognosis. Furthermore, there was a positive correlation between the overexpression of SLC35A2 and reduced lymphocyte infiltration in 13 cancer types, including BRCA and COAD. Based on data from several clinical trials, patients with LUAD, LUSC, SKCM, and BLCA who exhibited high SLC35A2 expression may experience improved immunotherapy response. Therefore, SLC35A2 could be considered a potential predictive biomarker for the prognosis and immunotherapy efficacy of various tumors. Our study provides a theoretical basis for further investigating its prognostic and therapeutic potentials.

A Novel Signature Integrated of Immunoglobulin, Glycosylation and Anti-Viral Genes to Predict Prognosis for Breast Cancer.

Background: Aberrant glycosylation is significantly related to the occurrence, progression, metastasis, and drug resistance of tumors. It is essential to identify glycosylation and related genes with prognostic value for breast cancer. Objective: We aimed to construct and validate a prognostic model based on glycosylation and related genes, and further investigate its prognosis values in validation set and external independent cohorts. Materials and Methods: The transcriptome and clinical data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA, n = 1072), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, n = 1451), and GSE2741 (n = 120). Glycosylation-related genes were downloaded from the Genecards website. Differentially expressed glycosylation-related geneswere identified by comparing the tumor tissues with the adjacent tissues. The TCGA data were randomly divided into training set and validation set in a 1:1 ratio for further analysis. The glycosylation risk-scoring prognosis model was constructed by univariate and multivariate Cox regression analysis, followed by confirmation in TCGA validation, METABRIC, and GEO datasets. Gene set enrichment analysis (GSEA) and Gene ontology analysis for identifying the affected pathways in the high- and low-risk groups were performed. Results: We attained 1072 breast cancer samples from the TCGA database and 786 glycosylation genes from the Genecards website. A signature contains immunoglobulin, glycosylation and anti-viral related genes was constructed to separate BRCA patients into two risk groups. Low-risk patients had better overall survival than high-risk patients (p < 0.001). A nomogram was constructed with risk scores and clinical characteristics. The area under time-dependent ROC curve reached 0.764 at 1 year, 0.744 at 3 years, and 0.765 at 5 years in the training set. Subgroup analysis showed differences in OS between the high- and low-risk patients in different subgroups. Moreover, the risk score was confirmed as an independent prognostic indicator of BRCA patients and was potentially correlated with immunotherapy response and drug sensitivity. Conclusion: We identified a novel signature integrated of immunoglobulin (IGHA2), glycosylation-related (SLC35A2) and anti-viral gene (BST2) that was an independent prognostic indicator for BRCA patients. The risk-scoring model could be used for predicting prognosis and immunotherapy in BRCA, thus providing a powerful instrument for combating BRCA.