Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data

BACKGROUND

Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy.

METHODS

Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy.

RESULTS

A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy.

CONCLUSIONS

We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Single-cell and bulk RNA sequencing analysis of B cell marker genes in TNBC TME landscape and immunotherapy

Objective

This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing.

Methods

Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis.

Results

Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes.

Conclusion

This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

Comprehensive analysis of single-cell and bulk RNA-sequencing data identifies B cell marker genes signature that predicts prognosis and analysis of immune checkpoints expression in head and neck squamous cell carcinoma

Recent studies have shown that B cells and the associated tertiary lymphoid structures (TLS) correlate with the response of patients to immune checkpoint inhibitors (ICIs) and predict overall survival (OS) in cancer patients. We screened 145 B cell marker genes (BCMG) by a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of head and neck squamous cell carcinoma (HNSC) from the Gene Expression Omnibus (GEO) database. The BCMG signature (BCMGS) was established using The Cancer Genome Atlas (TCGA) dataset of HNSC and verified in four independent datasets. The multivariate Cox regression analysis identified the signature as an independent prognostic factor. A prognostic nomogram was constructed with independent prognostic factors using the TCGA dataset. GO and KEGG analysis revealed the underlying signaling pathways related to this signature. Study of immune profiles showed that patients in the low-risk group presented discriminative immune-cell infiltrations. Furthermore, the low-risk group was featured by higher TCR and BCR diversity, which suggested that low-risk patients may be more sensitive to ICIs. Immunohistochemistry was performed, and we found that high expression of FTH1 was significantly correlated with poor OS (P = 0.025). The expression of TIM-3, LAG-3 and PD-1 was positively correlated and associated with better OS in HNSC. However, there was no statistically significant difference between PD-L1, PD-L2, CTLA-4, TIGIT and prognosis. The BCMGS was a promising prognostic biomarker in HNSC, which may help to interpret the responses to immunotherapy and provide a new perspective for future research on the treatment in HNSC.

Construction of a B cell-related gene pairs signature for predicting prognosis and immunotherapeutic response in non-small cell lung cancer

BACKGROUND

Accumulating evidence indicates that the B cells play important roles in anti-tumor immunity and shaping tumor development. This study aimed to explore the expression profiles of B cell marker genes and construct a B cell-related gene pairs (BRGPs) signature associated with the prognosis and immunotherapeutic efficiency in non-small cell lung cancer (NSCLC) patients.

METHODS

B cell-related marker genes in NSCLC were identified using single-cell RNA sequencing data. TCGA and GEO datasets were utilized to identify the prognostic BRGPs based on a novel algorithm of cyclically single pairing along with a 0-or-1 matrix. BRGPs signature was then constructed using Lasso-Cox regression model. Its prognostic value, associated immunogenomic features, putative molecular mechanism and predictive ability to immunotherapy were investigated in NSCLC patients.

RESULTS

The BRGPs signature was composed of 23 BRGPs including 28 distinct B cell-related genes. This predictive signature demonstrated remarkable power in distinguishing good or poor prognosis and can serve as an independent prognostic factor for NSCLC patients in both training and validation cohorts. Furthermore, BRGPs signature was significantly associated with immune scores, tumor purity, clinicopathological characteristics and various tumor-infiltrating immune cells. Besides, we demonstrated that the tumor mutational burden scores and TIDE scores were positively correlated with the risk score of the model implying immune checkpoint blockade therapy may be more effective in NSCLC patients with high-risk scores.

CONCLUSIONS

This novel BRGPs signature can be used to assess the prognosis of NSCLC patients and may be useful in guiding immune checkpoint inhibitor treatment in our clinical practice.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on B cell marker genes to predict prognosis and immunotherapy response in lung adenocarcinoma

As an essential component of the tumor microenvironment, B cells exist in all stages of tumor and exert important roles in anti-tumor immunity and shaping tumor development. We aimed to explore the expression profile of B cell marker genes and construct a prognostic signature based on these genes in Lung adenocarcinoma (LUAD). A total of 1268 LUAD patients from different cohorts were enrolled in this study. We performed an analysis of single-cell RNA-sequencing (scRNA-seq) data from Gene expression omnibus (GEO) database to identify B cell marker genes in LUAD. TCGA database was used to construct signature, and six cohorts from GEO database were used for validation. We also investigated the association between this signature and immunotherapy response. Based on 258 B cell marker genes identified by scRNA-seq analysis, a nine-gene signature was constructed for prognostic prediction in TCGA dataset, which classified patients into high-risk and low-risk groups according to overall survival. The multivariate analysis demonstrated that the signature was an independent prognostic factor. The signature's predictive power was verified in other six independent cohorts and different clinical subgroups. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. More importantly, risk scores of the signature were closely correlated with PD-L1, tumor mutation burden, neoantigens, and tumor immune dysfunction and exclusion score. Our study proposed a novel prognostic signature based on B cell marker genes for LUAD patients. The signature could effectively indicate LUAD patients' survival and serve as a predictor for immunotherapy.