XBP1 impacts lung adenocarcinoma progression by promoting plasma cell adaptation to the tumor microenvironment

Integrated analysis of single‑cell and bulk RNA sequencing data to construct a risk assessment model based on plasma cell immune‑related genes for predicting patient prognosis and therapeutic response in lung adenocarcinoma

Plasma cells serve a crucial role in the human immune system and are important in tumor progression. However, the specific role of plasma cell immune-related genes (PCIGs) in tumor progression remains unclear. Therefore, the present study aimed to establish a risk assessment model for patients with lung adenocarcinoma (LUAD) based on PCIGs. The data used in the present study were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. After identifying nine PCIGs, a risk assessment model was constructed and a nomogram was developed for predicting patient prognosis. To explore the molecular mechanism and clinical significance, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis and drug sensitivity prediction were performed. Furthermore, the accuracy of the model was validated using reverse transcription-quantitative PCR (RT-qPCR). The present study constructed a risk assessment model consisting of nine PCIGs. Kaplan-Meier survival curves indicated a worse prognosis in the high-risk subgroup (risk score ≥0.982) compared with that in the low-risk subgroup. The nomogram exhibited predictive value for survival prediction (area under the curve=0.727). GSEA enrichment analysis revealed enrichment of the focal adhesion and extracellular matrix-receptor interaction pathways in the high-risk group. Moreover, the high-risk group exhibited a higher TMB, as demonstrated by the TME analysis showing lower ESTIMATE scores. Drug sensitivity prediction facilitated potential drug selection. Subsequently, differential gene expression was validated in multiple LUAD cell lines using RT-qPCR. In conclusion, the risk assessment model based on nine PCIGs may be used to predict the prognosis and drug selection in patients with LUAD.

The role of B cell immunity in lung adenocarcinoma

Lung cancer is the deadliest cancer globally. Non-small cell lung cancer (NSCLC), including adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, constitutes a significant portion of cases. Adenocarcinoma, the most prevalent type, has seen a rising incidence. Immune checkpoint inhibitors (ICIs) have improved outcomes in lung adenocarcinoma (LUAD), yet response rates remain unsatisfactory. PD-1/PD-L1 inhibitors are primary ICIs for LUAD, targeting the PD-1/PD-L1 pathway between CD8+ T cells and tumor cells. However, LUAD presents a "cold tumor" phenotype with fewer CD8+ T cells and lower PD-1 expression, leading to resistance to ICIs. Thus, understanding the function of other immune cell in tumor microenvironment is crucial for developing novel immunotherapies for LUAD. B cells, which is part of the adaptive immune system, have gained attention for its role in cancer immunology. While research on B cells lags behind T cells, recent studies reveal their close correlation with prognosis and immunotherapy effectiveness in various solid tumors, including lung cancer. B cells show higher abundance, activity, and prognostic significance in LUAD than that in LUSC. This review summarizes the difference of B cell immunity between LUAD and other lung cancers, outlines the role of B cell immunity in LUAD.

Identification of atrial fibrillation-related genes through transcriptome data analysis and Mendelian randomization

Background

Atrial fibrillation (AF) is a common persistent arrhythmia characterized by rapid and chaotic atrial electrical activity, potentially leading to severe complications such as thromboembolism, heart failure, and stroke, significantly affecting patient quality of life and safety. As the global population ages, the prevalence of AF is on the rise, placing considerable strains on individuals and healthcare systems. This study utilizes bioinformatics and Mendelian Randomization (MR) to analyze transcriptome data and genome-wide association study (GWAS) summary statistics, aiming to identify biomarkers causally associated with AF and explore their potential pathogenic pathways.

Methods

We obtained AF microarray datasets GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database, merged them, and corrected for batch effects to pinpoint differentially expressed genes (DEGs). We gathered exposure data from expression quantitative trait loci (eQTL) and outcome data from AF GWAS through the IEU Open GWAS database. We employed inverse variance weighting (IVW), MR-Egger, weighted median, and weighted model approaches for MR analysis to assess exposure-outcome causality. IVW was the primary method, supplemented by other techniques. The robustness of our results was evaluated using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analysis. A "Veen" diagram visualized the overlap of DEGs with significant eQTL genes from MR analysis, referred to as common genes (CGs). Additional analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and immune cell infiltration studies, were conducted on these intersecting genes to reveal their roles in AF pathogenesis.

Results

The combined dataset revealed 355 differentially expressed genes (DEGs), with 228 showing significant upregulation and 127 downregulated. Mendelian randomization (MR) analysis identified that the autocrine motility factor receptor (AMFR) [IVW: OR = 0.977; 95% CI, 0.956-0.998; P = 0.030], leucine aminopeptidase 3 (LAP3) [IVW: OR = 0.967; 95% CI, 0.934-0.997; P = 0.048], Rab acceptor 1 (RABAC1) [IVW: OR = 0.928; 95% CI, 0.875-0.985; P = 0.015], and tryptase beta 2 (TPSB2) [IVW: OR = 0.971; 95% CI, 0.943-0.999; P = 0.049] are associated with a reduced risk of atrial fibrillation (AF). Conversely, GTPase-activating SH3 domain-binding protein 2 (G3BP2) [IVW: OR = 1.030; 95% CI, 1.004-1.056; P = 0.024], integrin subunit beta 2 (ITGB2) [IVW: OR = 1.050; 95% CI, 1.017-1.084; P = 0.003], glutaminyl-peptide cyclotransferase (QPCT) [IVW: OR = 1.080; 95% CI, 1.010-0.997; P = 1.154], and tripartite motif containing 22 (TRIM22) [IVW: OR = 1.048; 95% CI, 1.003-1.095; P = 0.035] are positively associated with AF risk. Sensitivity analyses indicated a lack of heterogeneity or horizontal pleiotropy (P > 0.05), and leave-one-out analysis did not reveal any single nucleotide polymorphisms (SNPs) impacting the MR results significantly. GO and KEGG analyses showed that CG is involved in processes such as protein polyubiquitination, neutrophil degranulation, specific and tertiary granule formation, protein-macromolecule adaptor activity, molecular adaptor activity, and the SREBP signaling pathway, all significantly enriched. The analysis of immune cell infiltration demonstrated associations of CG with various immune cells, including plasma cells, CD8T cells, resting memory CD4T cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells, activated mast cells, and neutrophils.

Conclusion

By integrating bioinformatics and MR approaches, genes such as AMFR, G3BP2, ITGB2, LAP3, QPCT, RABAC1, TPSB2, and TRIM22 are identified as causally linked to AF, enhancing our understanding of its molecular foundations. This strategy may facilitate the development of more precise biomarkers and therapeutic targets for AF diagnosis and treatment.

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Tumor immune microenvironment (TIME) and its indications for lung cancer patient prognosis and therapeutic response have become new hotspots in cancer research in recent years. Tumor cells, immune cells, various regulatory factors, and their interactions in the TIME have been suggested to commonly influence lung cancer development and therapeutic outcome. The heterogeneity of TIME is composed of dynamic immune-related components, including various cancer cells, immune cells, cytokine/chemokine environments, cytotoxic activity, or immunosuppressive factors. The specific composition of cell subtypes may facilitate or hamper the response to immunotherapy and influence patient prognosis. Various markers have been found to stratify the patient prognosis or predict the therapeutic outcome. In this article, we systematically reviewed the recent advancement of TIME studies in lung adenocarcinoma (LUAD) using single-cell RNA sequencing (scRNA-seq) techniques, with specific focuses on the roles of TIME in LUAD development, TIME heterogeneity, indications of TIME in patient prognosis and therapeutic response during immunotherapy and drug resistance. The main findings in TIME heterogeneity and relevant markers or models for prognosis stratification and response prediction have been summarized. We hope that this review provides an overview of TIME status in LUAD and an inspiration for future development of strategies and biomarkers in LUAD treatment.

Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

PURPOSE

This study aims to identify key genes regulating tumor infiltrating plasma cells (PC) and provide new insights for innovative immunotherapy.

METHODS

Key genes related to PC were identified using machine learning in lung adenocarcinoma (LUAD) patients. A prognostic model called PC scores was developed using TCGA data and validated with GEO cohorts. We assessed the molecular background, immune features, and drug sensitivity of the high PC scores group. Real-time PCR was utilized to assess the expression of hub genes in both localized LUAD patients and LUAD cell lines.

RESULTS

We constructed PC scores based on seventeen PC-related hub genes (ELOVL6, MFI2, FURIN, DOK1, ERO1LB, CLEC7A, ZNF431, KIAA1324, NUCB2, TXNDC11, ICAM3, CR2, CLIC6, CARNS1, P2RY13, KLF15, and SLC24A4). Higher age, TNM stage, and PC scores independently predicted shorter overall survival. The AUC value of PC scores for one year, three years, and five years of overall survival were 0.713, 0.716, and 0.690, separately. The nomogram model that integrated age, stage, and PC scores showed significantly higher predictive value than stage alone (P < 0.01). High PC scores group exhibited an immune suppressing microenvironment with lower B, CD8 + T, CD4 + T, and dendritic cell infiltration. Docetaxel, gefitinib, and erlotinib had lower IC50 in high PC groups (P < 0.001). After validation through the local cohort and in vitro experiments, we ultimately confirmed three key potential targets: MFI2, KLF15, and CLEC7A.

CONCLUSION

We proposed a prediction mode which can effectively identify high-risk LUAD patients and found three novel genes closely correlated with PC tumor infiltration.

The relationship between tumor infiltrating immune cells and the prognosis of patients with lung adenocarcinoma

Background

To depict the immune infiltration characteristics of tumor cells in patients with lung adenocarcinoma (LUAD) and evaluate the predictive value and significance of tumor immune cells on the prognosis of LUAD patients.

Methods

The clinical characteristics and transcriptome of LUAD patients were obtained from The Cancer Genome Atlas (TCGA), and the immune cell abundance in LUAD tissue was evaluated using the CIBERSORT algorithm. We created a simplified immune cell-based Cox regression model according to the survival status of patients and clarified the correlation between the survival status of patients and seven types of immune cells. An immune cell-based risk prediction model was created by Cox proportional hazards regression. Subsequently, the gene expression profile of LUAD patients was obtained from the Gene Expression Omnibus (GEO) database to validate the tumor immune infiltration and patient prognosis prediction model attained using the CIBERSORT algorithm.

Results

The abundance of 22 tumor-infiltrating immune cells in these patients was detected using the CIBERSORT algorithm. According to Pearson correlation analysis, the immune cells appeared to be closely related to each other. The immune cell composition was remarkably different between the LUAD tumor tissue and paracancerous tissue. The simplified COX model showed that seven kinds of immune cells have predictive value for the prognosis and survival status of LUAD. The receiver operating characteristic curve (ROC) curve confirmed that the prediction model performed well for 1-, 3-, and 5-year survival status. The calibration curve suggested that the prediction model was consistent with the clinical results. Correlation analysis revealed that the clinical features were significantly related to immune cell infiltration. A total of 246 LUAD specimens were from the GEO database, and the risk score model suggested that high risk scores were indicative of a poor prognosis. Finally, enzyme-linked immunosorbent assay (ELISA) revealed that the expressions of tumor necrosis factor-α (TNF-α), interleukin 8 (IL-8), IL-6, and interferon-γ (IFN-γ) in tumor tissues were remarkably higher compared with those in adjacent tissues.

Conclusions

There is a close correlation between the tumor-infiltrating immune cells and the prognosis and clinical characteristics of LUAD patients. The risk score model based on TCGA and GEO designed in this study can be applied in clinical practice.