Integrated single-cell and bulk RNA sequencing revealed the molecular characteristics and prognostic roles of neutrophils in pancreatic cancer

Results

Identification of neutrophil marker gene expression profiles

The GSE111672 dataset was obtained, which contains the RNA expression data of 27,000 cells from three individuals with PC. The range of genes discovered in each sample, the sequencing depth, and the proportion of mitochondrial content are shown in Figure 1A–1C, respectively. Single cells with a total number of identified genes more than 50, mitochondrial gene expression of less than 5%, and genes found in less than three cells were chosen (Figure 1A–1C). As seen in Figure 1D, 1E, sequencing depth was favorably connected with gene number and mitochondrial content, with correlation values of 0.92 and 0.2, respectively. After this, ten of the original 1500 highly variable genes between cells were labeled (Figure 1F). PCA was used to illustrate the top 15 principal components with P-value < 0.05 for the leading 1500 genes (Figure 1G, 1H). On the basis of the PCA findings, t-SNE analysis was done to further categorize all cells into 15 groups (Figure 1I). Each cell cluster was annotated using Human Primary Cell Atlas reference data. In clusters 2 and 5, neutrophils were found (Figure 1J). 350 NRGs were identified in total (Supplementary Table 1). GO functional enrichment and KEGG pathway enrichment studies were then used to study the biological function of these marker genes. Neutrophil marker genes were mostly engaged in biological processes such as leukocyte mobility, leukocyte chemotaxis, ribosomes, and plaque formation, as shown by GO functional enrichment analysis (Figure 2A). KEGG pathway enrichment analysis showed that these genes were largely associated with COVID-19, ribosomes, leukocyte trans endothelial migration, IL17 signaling pathway, TNF signaling route, and apoptosis (Figure 2B).

Figure 1. Single-cell analysis and screening of neutrophils-related genes (NRGs). (A) A visualization showing the number of genes in each cell as a violin. (B) A violin plot showing the total of each cell’s gene expression levels. (C) Violin plot showing each cell’s proportion of mitochondrial genes. (D) Scatterplot of the total gene expression levels and the number of genes present in each cell. (E) Scatterplot comparing the total gene expression levels in each cell to the proportion of mitochondrial genes. (F) Genes that vary significantly between cells. Red dots denote 1500 genes whose expression levels have changed significantly. The top ten most variable genes are labeled in the graph. (G) The cell distribution in the PC1 and PC2 dimensions. (H) Principal component analysis identified the top 15 principal components with a P-value < 0.05. (I) The study of t-Distributed stochastic neighbor embedding (t-SNE) identified 15 cell clusters. (J) Using maker genes, cell subtypes were further annotated and labelled.

Figure 2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. (A) GO enrichment analysis. (B) KEGG enrichment analysis.

Classification and characteristics based on the expression characteristics of neutrophil marker genes

Using PCA, the TCGA, GSE28735, GSE62452, GSE57495 and GSE85916 datasets were combined to correct the data. After adjustment, the batch effect was effectively eliminated (Figure 3A, 3B). According to the expression of NRGs, the samples from GSE28735, GSE57495, GSE62452, GSE8516 and TCGA data sets were identified as two immune subtypes, NRGcluster A and NRGcluster B, which respectively represent high and low expression of NRGs. The k value should be adjusted to 2 for best performance, as evidenced by the area under the CDF curve and its relative change (Figure 3C–3E). In Figure 3C, when the slope of the curve in the CDF diagram is the most gentle, the clustering analysis results are the most reliable, and the corresponding k value is the best k value; Figure 3D shows the relative change of area under the CDF curve compared to k and k-1, and since there is no k = 1, the first point represents the total area under the CDF curve for k = 2; Figure 3E shows the matrix heat map when k = 2, and the white part rarely has blue infection, indicated that there is less interference between subgroups. To sum up, k = 2 is the optimal k value, and when k is 3 or other values, the interference between subgroups cannot be minimized. The Kaplan-Meir survival analysis indicated that patients with the NRGcluster A subtype had a poorer survival rate than those with the NRGcluster B subtype (p < 0.001) (Figure 3F). The PCA findings indicated a batch effect between NRGcluster A and B, suggesting that both clusters had strong internal consistency (Figure 3G). The survival and mortality status of the two patient groups were shown as heat maps in Figure 3H, along with the expression of neutrophil marker genes such as SRGN, BCL2A1, ALOX5AP, FPR1, BASP1, and CSFR. Individuals with the NRGcluster A subtype demonstrated elevated expression levels of neutrophil marker genes, suggesting that these patients may have aggressive neutrophil infiltration (Figure 3H).

Figure 3. Establishment of neutrophils-related genes (NRGs) cluster subtypes. (A) Principal component analysis (PCA) plot of the overall expression of all samples before batching. (B) PCA plot of the overall expression of all samples after batching. (C–E) Process and results of consistency cluster analysis, which classified the data into NRGclusters A and B. (F) Survival curves of patients with NRGclusters A and B. The prognosis of patients with NRGcluster B was significantly better than that of patients with NRGcluster A. (G) PCA plot of NRGclusters A and B. (H) The expression of neutrophil-associated genes in NRGclusters A and B, most neutrophil-associated genes are upregulated in NRGcluster A.

Subsequently, the unique biological processes and metabolic pathways of patients with distinct subtypes were investigated. The heat map generated by GSVA indicated substantial variations between the two groups in the activation and inhibition of 30 metabolic pathways. P53 signaling pathway, cell cycle, base excision repair, amino sugar and nucleotide sugar metabolism, glycolysis and gluconeogenesis, sphingolipid metabolism, endocytosis, glycerophospholipid metabolism, and linoleic acid metabolism were highly abundant in NRGcluster A. In contrast, NRGcluster B was markedly enriched in glycine, serine, and threonine metabolism, neuroactive ligand-receptor interaction, and calcium signaling pathway (Figure 4A). GSEA demonstrated that the neuroactive ligand-receptor interaction, the calcium signaling system, and the adipocytokine signaling pathway were significantly enriched in NRGcluster B patients. The cell cycle and O-glycan production were dramatically enhanced in NRGcluster A patients (Figure 4B).

Figure 4. Analysis of metabolic pathways and immunological infiltration for subtypes of the neutrophil-related genes (NRGs) cluster. (A) Gene set variation analysis (GSVA) results. (B) Gene set expression analysis (GSEA) results. (C) Single-sample GSEA (ssGSEA) results.

We also assessed the TME status of the two subtypes using ssGSEA. Immune cell infiltration differed dramatically across subtypes. Neutrophils, central memory CD8+ T cells, Th1 cells, Th17 cells, and eosinophils were the most abundant infiltrating cells in NRGcluster A, while activated CD8+ T cells were the least abundant (Figure 4C).

The Genetics of Drug Sensitivity in Cancer database was used to evaluate the relationship between the two categories and the sensitivity to numerous first-line chemotherapy treatments. The NRGcluster B subtype was more sensitive to 5-fluorouracil, cisplatin, KRAS (G12C) inhibitor-12, gemcitabine, irinotecan, oxaliplatin, and paclitaxel, but less sensitive to selumetinib and trametinib (Figure 5A–5I).

Figure 5. Correlation between neutrophil-related genes (NRGs) cluster subtypes and drug sensitivity. Increased sensitivity of NRGcluster B to 5-fluorouracil (A), cisplatin (B), KRAS (G12C) inhibitor-12 (C), gemcitabine (D), irinotecan (E), oxaliplatin (F), and paclitaxel (G), increased sensitivity of NRGcluster A to selumetinib (H) and trametinib (I).

Genotyping based on differentially expressed genes

Subsequently, the genetic alterations and underlying processes of NRG expression patterns were investigated. Using the “limma” R package, the differentially expressed genes of NRGcluster A and B were selected based on the following criteria: |log2 FC| > 1 and adjusted P-value < 0.05. A total of 285 genes with differential expression were found (Supplementary Table 2). The functions of these differentially expressed genes were then identified. The differentially expressed genes were mostly connected with epidermal development, the apical region of the cell, endopeptidase activity, extracellular matrix structural component, etc., as determined by GO functional enrichment analysis (Figure 6A). The majority of differentially expressed genes were involved in pancreatic secretion, protein digesting, and absorption-related processes, as shown by KEGG pathway enrichment analysis (Figure 6B). To examine further the influence of differently expressed genes on PC prognosis and tumor immune microenvironment, all patients were grouped using the “Consensus Cluster Plus” R program based on differentially expressed genes. Two immunological isotypes (geneClusters A and B) were distinguished (Figure 6C–6E). Patients with geneCluster A had a considerably shorter survival rate than those with geneCluster B (Figure 6F). The ssGSEA found distinct immune cell infiltration between the two categories. Neutrophils, activated CD4+ T cells, and central memory CD8+ T cells were the most abundant infiltrating cells in geneCluster A, whereas monocytes and activated CD8+ T cells were less abundant (Figure 6G).

Figure 6. Formation of subtypes of gene clusters. (A) Analysis of Gene Ontology (GO) enrichment. (B) Enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG). (C–E) The methodology and outcomes of consistency cluster analysis, which categorized the data into geneClusters A and B. (F) The survival curves for patients with the A and B geneClusters. Patients with geneCluster B had a much better prognosis than those with geneCluster A. (G) Single-sample gene set enrichment analysis (ssGSEA) findings.

Establishment of the PC prognostic risk model based on NRGs

To further study the predictive usefulness of NRGs in PC, a univariate Cox regression analysis was conducted to identify NRGs linked with patient prognosis. Each potential gene is represented by a red dot in the volcano map (Figure 7A). Using the RSFVH method, the 10 most significant genes were analyzed further (Figure 7B). Using an algorithm, survival analysis p-values were analyzed. The optimal gene combination (LDHA, IL1R2, and TM4SF1) was determined (Figure 7C), and a predictive model was created using multivariate Cox regression. The correlation coefficients of the three model genes were shown in Figure 7D, and risk scores of each sample were computed with the help of Cox coefficients and gene expression profiles. The GEO database datasets GSE28735, GSE62452, GSE57495, and GSE85916 were utilized as the validation set. To evaluate the accuracy of the prognostic model, the training and validation sets were split into high-risk and low-risk groups based on the median risk score. Survival study of the TCGA and GEO datasets found that the survival time of high-risk patients was shorter than that of low-risk patients (Figure 7E, 7F). For all three model genes (LDHA, IL1R2, and TM4SF1), the high-expression group had a lower life period than the low-expression group (Figure 7G–7I). The alluvial plot indicated the link between NRGcluster typing, gene Cluster type, risk categorization, and sample survival status (survival or death) and supported the reliability and consistency of the analytical findings (Figure 7J). The majority of genes were elevated in the high-risk group, indicating that high-risk individuals may have aggressive neutrophil infiltration (Figure 7K).

Figure 7. Construction of the prognostic risk score model. (A) Volcano plot of univariate Cox regression analysis results. Red dots represent neutrophil-related genes associated with pancreatic cancer prognosis. (B) Random survival forest analysis identified the top 10 important genes. (C) After survival analysis of 2¹⁰−1 = 1023 combinations, the top 20 models were sorted according to P-values. The best model comprised three genes. (D) Coefficients of genes in the model. (E) The Cancer Genome Atlas (TCGA) dataset (Kaplan-Meir curve of the training set). (F) GSE28735, GSE62452, GSE57495, and GSE85916 datasets from the Gene Expression Omnibus (GEO) platform (Kaplan-Meir curves of the validation set). Survival curves of patients according to the expression of LDHA (G), IL1R2 (H), and TM4SF1 (I). (J) Sankey plots of different subtypes, risk groups, and survival states. (K) The expression of neutrophil-associated genes in high-risk and low-risk groups, most neutrophil-associated genes are upregulated in the high-risk groups.

Correlation between risk model and clinical pathological features

After merging clinical data and risk scores from TCGA datasets, the risk scores were compared across clinical groups using the Wilcoxon signed-rank test to investigate the association between the model and the clinical features of the patients. The risk ratings did not vary substantially across ages, genders, or M stages (Figure 8A, 8B and 8E). Due to the limited number of patients with M-stage tumors, the Wilcoxon signed-rank test findings might be distorted. The risk scores of patients with advanced T-stage, pathological grade, and clinical stage were considerably elevated (Figure 8C, 8F and 8G). The difference between N1 and N0 patients was close to statistical significance (p = 0.083), and stage N1 patients had a higher risk score (Figure 8D). Age, pathological grade, and risk score were substantially linked with the prognosis of patients with PC, as evidenced by the forest plot of univariate Cox analysis findings (Figure 8H). Furthermore, the forest plot of multivariate Cox analysis findings revealed that age and risk score are independent prognostic markers for PC patients (Figure 8I).

Figure 8. Correlation and independent prognostic analysis of clinical pathological features. Differential risk scores between age groups (A), gender groups (B), different T stages (C), different N stages (D), different M stages (E), different pathological grades (F), and different clinical stages (G). Forest plots of univariate Cox analysis (H) and multivariate Cox analysis results (I).

Molecular pathway and immune infiltration analysis based on the prognostic risk model

To assess the metabolic pathway activity in high-risk and low-risk samples, GSVA and GSEA were conducted. The high-risk group had considerably greater glycan biosynthesis, P53 signaling pathway, and cell cycle activity than the low-risk group. In contrast, the activities of neuroactive ligand-receptor interaction and calcium were increased in the group at low risk (Figure 9A). Cell cycle and olfactory transduction were typically active in the high-risk group, while maturity-onset diabetes of the young and neuroactive ligand-receptor interaction were passive (Figure 9B). To further study the link between risk score and TME in PC, ssGSEA was used to analyze immune cell infiltration across groups with high and low risk. The high-risk group had higher infiltration of activated CD4+ T cells, NK cells, and neutrophils, whereas the low-risk group demonstrated increased infiltration of activated CD8+ T cells and activated B cells (Figure 9C). In addition, the CIBERSORT method was used to calculate the infiltration scores of 22 immune cell subtypes in each sample (Supplementary Figure 1) (7 T cell subtypes, naïve and memory B cells, plasma cells, NK cells, bone marrow subsets, etc.). Neutrophils, M0 macrophages, active dendritic cells, and resting dendritic cells associated considerably and positively with the risk score, while CD8+ T cells, naïve B cells, and monocytes linked significantly and negatively with the risk score (Figure 9D–9J).

Figure 9. Analysis of metabolic pathways and immune infiltration using the prognostic models. (A) Gene set variation analysis (GSVA) results. (B) Gene set enrichment (GSEA) results. (C) Single-sample GSEA (ssGSEA) results. Risk scores were significantly and positively correlated with neutrophil infiltration levels (D), significantly and negatively correlated with CD8+ T cell infiltration levels (E), significantly and negatively correlated with naïve B cell infiltration levels (F), significantly and positively correlated with M0 macrophage infiltration levels (G), significantly and negatively correlated with monocyte infiltration levels (H), significantly and positively correlated with activated dendritic cell infiltration levels (I), and significantly and positively correlated with resting dendritic cell infiltration levels (J).

Drug sensitivity analysis

The R program “oncoPredict” was used to examine the differential medication sensitivity between high-risk and low-risk groups. Low-risk patients were sensitive to 5-fluorouracil, cisplatin, gemcitabine, irinotecan, oxaliplatin, and paclitaxel, whereas high-risk patients were susceptible to selumetinib and trametinib (Figure 10A–10H). These results help influence clinical medication for PC patients.

Figure 10. Correlation between drug sensitivity and prognostic risk scores. Low-risk patients were sensitive to 5-fluorouracil (A), cisplatin (B), gemcitabine (C), irinotecan (D), oxaliplatin (E), and paclitaxel (F). High-risk patients were sensitive to selumetinib (G) and trametinib (H).

Gene expression analysis

To evaluate the differential expression levels of model genes LDHA, IL1R2, and TM4SF1 across tumor and non-tumor tissues at the RNA level, scatter plots were generated using the GEPIA database. The expression of model genes was higher in tumor tissues compared to non-tumor tissues (Figure 11A–11C). The HPA database was queried for pictures of immunohistochemical investigation of LDHA, IL1R2, and TM4SF1. Figure 11D, 11E demonstrates that LDHA and IL1R2 were highly elevated in PC tissues, but TM4SF1 was moderately expressed in both PC and non-tumor tissues (Figure 11F).

Figure 11. Validation of levels of gene expression. In pancreatic tumor tissues, the expression levels of LDHA (A), IL1R2 (B), and TM4SF1 (C) were considerably greater than in healthy tissues. Immunohistochemical examination indicated that the expression levels of LDHA (D) and IL1R2 (E) were considerably elevated in pancreatic tumor tissues compared to healthy tissues, TM4SF1 (F) was moderately expressed in both pancreatic tumor tissues compared to healthy tissues.

Author Contributions

All authors participated in the research design and manuscript writing of this study. The data collection was mainly completed by the following personnel: Biao Zhang, Hewen Guan, Hui Guo, Bingqian Huang, Qihang Yuan. The data analysis was mainly completed by the following personnel: Biao Zhang, Xu Chen, Jiaao Sun, Hewen Guan. The tasks of image editing and beautification were primarily carried out by the following personnel: Hewen Guan, Hui Guo, Bingqian Huang. The paper submission was primarily completed by the following personnel: Xu Chen, Feng Chen, Qihang Yuan. Responding to the reviewers' comments and revising the paper were primarily completed by the following personnel: Biao Zhang, Jiaao Sun, Hewen Guan, Xu Chen, Feng Chen, Qihang Yuan. The payment of publication fees and proofreading work were mainly completed by the following personnel: Xu Chen, Feng Chen, Qihang Yuan.

Acknowledgments

We thank Bullet Edits Limited for the linguistic editing of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Funding

This manuscript is a bioinformatics analysis of neutrophil characteristics in pancreatic cancer, without involving molecular biology experiments or purchasing reagents. Therefore, this project does not require any research funding, as all data used are publicly available and free.

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