Introduction
Lung cancer is one of the malignant tumors with the highest morbidity and mortality worldwide; the overall 5-year survival rate is about 20% [
1]. Nonsmall cell lung cancers (NSCLCs) represent 85% of lung tumors. They encompass multiple cancer types, such as lung adenocarcinomas (LUADs), lung squamous cell carcinomas (LUSCs), and large-cell cancers. Among them, LUADs and LUSCs are the largest NSCLC subgroups [
2]. Therefore, effective treatments for LUAD have always been the focus of research. Over the past 10 years, the understanding of the immune system and its role in the development and progression of cancer has continued to deepen, leading to remarkable progress in the field of cancer immunotherapy [
3]. Immunotherapy has been widely used in the first-line and second-line treatments of NSCLC [
4‐
6], which has inspired people’s enthusiasm for elucidating the prognostic and pathophysiological effects of the tumor microenvironment (TME). The TME, including cancer-associated fibroblasts [
7,
8], extracellular matrix [
9], epithelial cells [
10], myeloid cells [
11], and tumor-infiltrating lymphocytes [
12], affects the malignant progression and immune response of lung cancer. T cells and B cells are important components of tumor-infiltrating immune cells. The research on the functions and mechanisms of T cells is relatively comprehensive; however, the research on B cells is still insufficient.
Tumor-infiltrating B cells have emerged as key players in the TME. Chen and colleagues performed a single-cell RNA-seq analysis of cells isolated from patients with NSCLC and identified two major subtypes of B cells, namely the naïve-like and plasma-like B cells [
13]. They found that the naïve-like B cells suppressed growth, while the plasma-like B cells promoted cell growth in the advanced stage of NSCLC, but inhibited cancer cell growth in the early stage of NSCLC. Wang and colleagues conducted a comprehensive genomic landscape of 149 NSCLC cases and revealed that highly clustered
EGFR mutations were associated with inflammatory tumor-infiltrating B lymphocytes, which was also confirmed in the TCGA dataset [
14]. Tumor-infiltrating B cells also served as local antigen-presenting cells by providing secondary stimulation to Immune infiltrating cells (TILs). Bruno and colleagues demonstrated that tumor-infiltrating B cells efficiently presented antigens to CD4
+ TILs and identified three CD4
+ TIL responses to tumor-infiltrating B cells, which were categorized as activated, antigen-associated, and nonresponsive [
15]. Hence, a new role was suggested for tumor-infiltrating B cells in their interplay with CD4
+ TIL in the TME. Whether tumor-infiltrating B cells have protumor or antitumor effect is still controversial.
Considering the important roles of B cells in the TME, which constitutes a potential novel therapeutic in NSCLC immunotherapy, urged us to construct a comprehensive approach to identify various charatacteristics of LUAD including B cell function, patients outcome and immunotherapy benefits. Therefore, a prognosis signature based on B-cell proportion was established, which was a robust prognostic biomarker and predictive factor that could be used in the clinic.
Materials and methods
RNA-sequencing data used to assess the abundance of immune-infiltrating cells
The gene expression data (workflow type: HTSeq-Counts) and the corresponding clinical information from the Cancer Genome Atlas (TCGA) website (
https://gdc.cancer.gov/) were downloaded using the “TCGAbiolinks” R package (Version 2.14.1). Entrez IDs were converted into gene symbols using the Bioconductor package “org. Hs.eg.db” (Version 3.10.0). Genes with low expression were removed from the profile. The abundance of immune-infiltrating cells in each sample was assessed with the MCP-counter [
16], which provided the abundance score for eight immune populations (T cells, CD8+ T cells, cytotoxic lymphocytes, natural killer cells, B lineages, monocytic lineage, myeloid dendritic cells, and neutrophils) and two stromal populations (endothelial cells and fibroblasts). The assessment of these cell subpopulations was based on the analysis of gene expression of cell markers. The MCP-counter signature composition of B lineages was as follows:
BANK1,
CD19,
CD22,
CD79A,
CR2,
FCRL2,
IGKC,
MS4A1, and
PAX5. The transcripts of other cell subpopulations were published by the algorithm’s author. All cell subpopulation abundances were normalized using the
Z score.
Differential expression analysis and construction of the B-lineage-associated risk signature
Differential expression analysis between high B-lineage infiltration group and low B-lineage infiltration group was performed using the “DESeq2” R package (Version 1.26.0) with the standard comparison mode between the two experimental conditions. FoldChange > 3 and P value < 0.01 genes were selected for followup research. LASSO algorithm, using the R package “glmnet” (Version 3.0), was built to construct a B-lineage-associated risk signature. The “survival” R package (Version 3.5) was used to select the optimal cutoff value and plot Kaplan–Meier survival curves. The “timeROC” R package (Version 0.4) was used to conduct a time-dependent receiver operating characteristic (ROC) curve analysis.
Microarray data
The transcript expression matrixes from GSE31908, GSE29013, and GSE30219 based on the GPL570 platform, including 131 patients with LUAD, were downloaded from the Gene Expression Omnibus (GEO) database. In these matrixes, the gene expression data for three matrixes were subjected to log2 transformation. The scale method of the “limma” R package (Version 3.42.2) was used to normalize the data.
Patients with LUAD from Cancer hospital affiliated to Nanjing Medical University
A total of 12 patients who underwent surgery without neoadjuvant chemotherapy and were diagnosed with LUAD at Cancer Hospital Affiliated to Nanjing Medical University (Nanjing, China) were included. The Nanjing cohort consisted of formalin-fixed paraffin-embedded (FFPE) specimens collected from patients who underwent radical surgery between 2018 and 2020. Each patient underwent a standard radical surgical procedure, and all specimens were evaluated by expert pathologists according to eighth edition of the Union for International Cancer Control Tumor-Node-Metastasis (TNM) grading system. All patients underwent regional lymphadenectomy, and the existence of Tumor-Lymph Node-Metastasis (TNM) was pathologically examined. Total RNA was extracted from 4-μm-thick FFPE specimens by manual microdissection using an RNeasy FFPE Kit (Qiagen, Hilden, Germany). The complementary DNA (cDNA) synthesis was performed using PrimeScript RT Master Mix (RR036A) (Takara, Dalian, China). The quantitative reverse transcription–polymerase chain reaction (qRT-PCR) assays were performed with a ViiA 7 Dx RT-PCR System (Applied Biosystems, Foster City,USA) using PowerUp SYBR Green Master Mix (Applied Biosystems, Vilnius, Lithyania). The cycling conditions were as follows: 40 cycles of 95 °C for 15 s and 60 °C for 60 s. The relative expression of target genes was normalized against glyceraldehyde-3-phosphate dehydrogenase using the 2
–ΔCT method. Primer sequences are provided in Table S
1.
Immunohistochemistry
LUAD tissues were fixed with 10% formalin and embedded in paraffin. Then, the tissues were cut into 5-μm-thick sections and incubated overnight with primary antibodies anti-CD3, anti-CD4, anti-CD8, anti-CD19, anti-CD20, anti-PD1 (Abcam,UK). The sections were subsequently incubated with a secondary antibody (Abcam,UK) at 37 °C for 1.5 h and stained with a 3,3-diaminobenzidine solution.
Function enrichment and gene interaction analyses
Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using “clusterProfiler” R package (Version 3.11) based on differentially expressed genes (absolute value of logFC > 1.5;
P value < 0.01). GeneMANIA (
https://genemania.org/) was used to find other genes related to a set of input genes using a very large set of functional association data. Association data included protein and genetic interactions, pathways, co-expression, co-localization, and protein domain similarity.
Development and validation of the nomogram
Univariate and multivariate Cox analyses were performed to assess the independent prognostic ability of B-lineage-associated risk signature using “survival” R package (Version 3.5). Then, a concise nomogram of predicting the OS of LUAD was established using R package “rms” (Version 2.10), including four factors. In addition, the predictive accuracies of the nomogram and separate prognostic factors were compared using ROC analyses.
Statistical analysis
Statistical analyses were performed using R (Version 3.6.3) and GraphPad Prism 8. The Wilcoxon rank-sum test and Student t test were used to determine differences in comparison of two groups. All statistical tests were two-tailed with a statistical significance level set at 0.05 in this study.
Discussion
In our study, the MCP-counter algorithm was used to evaluate the immune cell infiltration of each sample in the TCGA-LUAD RNA-seq dataset. Among those microenvieonment cells, B-cell abundance significantly correlated with OS in patients with LUAD. A B-lineage-associated risk signature was constructed based on the TCGA cohort and validated in the meta-GEO cohort, which was significantly related to prognosis. The prognostic value of this signature was also independent of the known strong prognostic factors, such as sex, age, and tumor grade. In addition, this signature affected tumor immune-related pathways and immune cell infiltration in tumor tissues. Moreover, B-lineage-associated risk signature were positive correlate with several TILs marker, immune checkpoints and immunotherapy benefits. The molecular targets and several clinical factors were integrated into a new nomogram model with robust survival prediction, taking advantage of their complementary values.
LUAD is a malignant tumor with a high incidence worldwide. Early assessment of patient prognosis and effective immunotherapy biomarkers are very important. Traditional classification methods, including the TNM staging system, cannot cover the heterogeneity in molecular biology. Meanwhile, the research on the heterogeneity of the TME has become a hot issue in the field of tumor malignant progression, patient prognosis, and tumor immunotherapy [
17‐
22]. Evaluating the prognosis of patients with LUAD from the perspective of molecular biology and TME is very meaningful for individualized diagnosis and treatment.
A large number of clinical trials have shown that the combination of immune checkpoint inhibitors and chemotherapy can significantly improve the progression-free survival of patients with advanced NSCLC compared with conventional chemotherapy alone [
23,
24]. However, only part of patients can achieve a long-term, effective immune response from immunotherapy, and therefore a new immunotherapy strategy and research perspective is necessary [
25]. As an important component of tumor-infiltrating immune cells, B cells may become a breakthrough in regulating immune-related therapeutic targets.
B cells can regulate immune response function through a variety of signaling pathways. Tumor-infiltrating B cells have been reported to exist in a variety of solid tumors [
26]. B cells can inhibit the malignant progression of tumors by secreting immunoglobulins, promoting T-cell immune response, presenting tumor antigens, and directly killing tumor cells [
27]. B cells and their related pathways work together to promote the aggregation, maturation, and maintenance of tertiary lymphatic structures (TLS) [
28]. TLS is the lymphocyte aggregate formed in the chronic inflammatory response and similar in structure to the secondary lymphoid organs [
29]. TLS is defined as a CD20+ B-cell follicle surrounded by a CD3+ T-cell aggregate of DC-LAMP+ mature dendritic cells [
28,
30‐
32]. In many solid tumors including NSCLC, TLS is associated with improved prognosis and immune response [
32,
33]. A total of 13 B-cell-associated transcripts were screened using bioinformatics methods, revealing that they had a strong interaction and co-regulation relationship. Hence, it was possible that they were from the same structure in the sample.
In the pathway enrichment analysis, we also found that a large number of RNA splicing-related pathways were enriched (Fig. S
3A). Previous studies have shown that the RNA-binding protein hnRNPLL could splice and edit RNA in B cells, promoting the production of Ig and the loss of BCL6 expression, which indicated plasma cell maturation [
34].. Meanwhile, previous studies revealed a total different RNA splicing status between B cell and plasma cell [
35]. Those studies suggested us that, not only B cell infiltration, also B cell to matural plasma cell transforming, were different between two risk groups.
Our research still has some limitation and need further validation. Both our training cohort and the external validation cohort are carried out in a high-throughput public data queue. We need to use PCR to verify the effectiveness of B-lineage-associated risk signature in a larger real-world cohort. Otherwise, because it is difficult to obtain tissue samples from LUAD patients undergo immunotherapy, the correlation between B-lineage-associated risk signature and the benefit of immunotherapy is based on a small sample size public data cohort. We will verify B-lineage-associated risk signature in the large-scale immunotherapy cohort in the future.
In conclusion, the B-lineage-associated risk signature is a promising biomarker that divides patients into two subgroups with completely different clinical prognosis and immune status. It provides a view of the transcriptome level and TME to clarify the mechanism underlying different prognosis and efficacy of LUAD after immunotherapy.
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