Revealing a cancer-associated fibroblast-based risk signature for pancreatic adenocarcinoma through single-cell and bulk RNA-seq analysis

Data collection and processing

We obtained scRNA-seq data (GSE155698) from the Gene Expression Omnibus (GEO) database, encompassing pancreatic cancer tumor samples from 16 patients alongside 3 adjacent normal pancreas samples. Scanning the scRNA-seq data involved an initial screening of single cells, ensuring each gene was expressed in at least three cells, while maintaining a minimum threshold of 250 expressed genes per cell. To determine the relative abundance of mitochondria and rRNA, the Seurat R package was employed [18]. Each single cell was then configured to express at least 6000 genes and have a UMI greater than 100 for further screening. After everything was filtered out, 50,527 cells were left. Utilizing The Cancer Genome Atlas (TCGA) database, we accessed transcriptome data, single-nucleotide variant (SNV) data, copy number variant (CNV) data, and associated clinical information for PAAD. Additionally, expression matrices from GSE78229 and GSE85916 were acquired from the GEO database for validation purposes.

Clustering of CAFs

In an effort to better describe the CAF signature, we redeployed the scRNA-seq data of PAAD. After log normalization, cells that had either over 6000 or under 250 genes being actively expressed were filtered out. We addressed batch effects and conducted non-linear dimensional reduction. Using the FindNeighbors and FindClusters functions, individual cells were partitioned into separate subgroups. Subsequently, this subdivision was visualized via t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction. Six genes were used to annotate fibroblasts, including ACTA2, FAP, PDGFRB, NOTCH3, DCN, and COL1A2. Using the same approach, FindNeighbors and FindClusters were used to re-cluster the fibroblasts, which was visualized with the TSNE dimensionality reduction. We utilized the FindAllMarkers function to identify specific marker genes within each CAF cluster. Subsequently, functional enrichments were conducted on these CAF marker genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis via the clusterProfiler package [19]. The CopyKAT R package was used to distinguish tumor cells from normal cells by analyzing CNV features among CAFs clusters [20].

Identification of prognostic genes

Differentially expressed genes (DEGs) between 177 pancreatic tumor samples and 4 normal tissue samples were first identified in the TCGA-PAAD cohort. We then analyzed the DEGs for CAF clusters to determine which genes are most strongly correlated with CAFs (P < 0.001, cor > 0.4). Further prognosis-associated genes were identified through univariate Cox regression analysis, considering a significance threshold of P < 0.05. To reduce the pool of prognostic genes, initial analysis involved least absolute shrinkage and selection operator (LASSO) Cox regression, yielding seven genes. Subsequently, a multivariate Cox regression employing stepwise regression constructed the risk signature, calculating coefficients via the formula: risk score = Σβi*Expi. Patients were stratified into high- or low-risk groups based on their risk scores. An evaluation of the risk signature’s predictive performance was conducted using receiver operating characteristic (ROC) analysis. Analyses in the validation cohorts were carried out in a similar manner.

Nomogram construction

To create a clinically applicable nomogram model, we initiated univariate and multivariate Cox regression analyses incorporating the risk signature and clinicopathological factors such as age, gender, stage, and race. The resulting multivariate Cox model, including variables with P < 0.05, was utilized to devise a prognostic nomogram for PAAD using the rms package [21]. The prediction efficacy of the model was assessed by the calibration curve and the reliability was assessed using decision curve analysis (DCA).

Immune infiltration analysis

The immune and stromal scores of the tumor microenvironment (TME) were calculated using the ESTIMATE algorithm and the relative abundance of infiltrating immune cells in the TME was evaluated by the CIBERSORT and MCPcounter algorithms [22].

Evaluation of responses to immunotherapy

We collected clinical and transcriptomic data of PAAD patients treated with anti-PD-L1 therapies from the IMvigor210 cohort available at http://research-pub.gene.com/IMvigor210CoreBiologies. Furthermore, GSE78220 comprises pre-treatment melanoma samples that underwent anti-PD-1 checkpoint blockade (ICB) immunotherapy. This transcriptomic dataset can be accessed to assess the potential utility of the risk signature in predicting response to ICB therapy [23].

Statistical analysis

R software (version 3.6.3) was used for all statistical testing. The Pearson or Spearman correlation method was used for correlation analysis. The Wilcoxon test was used to examine the differences between the two groups. K-M curves with Log-rank testing were used to compare survival times across groups. Statistical significance was assumed at a P-value of less than 0.05.

Data availability statement

Publicly available datasets were analyzed in this study. The data can be found in GEO and TCGA databases.

Identification of four CAF-related clusters for PAAD

The single-cell RNA-seq dataset GSE155698 was employed to identify the CAF subpopulations in PAAD, which included 17 PAAD tumor samples from 16 patients (Patient 11 had samples A and B) and 3 adjacent normal pancreas samples. Following preliminary screening, a total of 50,527 cells were collected (Supplementary Figure 1A, 1B). Supplementary Figure 1C displayed the comprehensive outcomes of the data preprocessing, which demonstrated a high quality control for the subsequent analysis. The UMAP plot displayed the distributions of the 20 samples after removal of the batch effect (Supplementary Figure 1D). After log-normalization and reducing dimensions, a total of 31 distinct subpopulations were identifiable (Supplementary Figure 1E). Of note, six CAF marker genes, including ACTA2, FAP, PDGFRB, NOTCH3, DCN, and COL1A2, were observed to be highly expressed in the subpopulation 10 and 17, which were therefore considered as CAF populations (Supplementary Figure 1F).

Four CAF clusters were then identified after extracting cells from the two CAF populations (subpopulation 10 and 17) using the same clustering approach for dimensionality reduction (Figure 1A). The varying expression of the six CAF marker genes within the four CAF clusters suggests distinct marker gene expressions among different subpopulations (Figure 1B). A total of 860 DEGs were recognized within the four CAF clusters, and Figure 1C illustrates the expression patterns of the top 5 DEGs within these clusters, serving as the designated marker genes. The proportions of the four clusters in each sample were shown in Figure 1D. As determined by CNV characteristics, the four CAF clusters contained 1415 normal cells and 537 tumor cells (Figure 1E). Furthermore, KEGG enrichment analysis showed that DEGs were enriched in multiple pathways across the four clusters, including vascular smooth muscle contraction, focal adhesion, dilated cardiomyopathy, hypertrophic cardiomyopathy, ECM-receptor interaction, and Protein digestion and absorption (Figure 1F).

Figure 1.

Profiling of CAF subpopulations. (A) UMAP plot displaying the distribution of four distinct CAF subpopulations post-clustering. (B) UMAP plot illustrating the expression of CAF marker genes (ACTA2, FAP, PDGFRB, NOTCH3, DCN, and COL1A2). (C) Dot plot showcasing the top 5 marker gene expressions across the four CAF clusters. (D) Relative proportions and cell numbers within each sample for the four CAF clusters. (E) UMAP plot delineating the distribution between malignant and non-malignant cells. (F) KEGG enrichment analysis of DEGs observed across the four CAF clusters.

Expression of distinct signaling patterns in CAF clusters

To investigate the potential role of CAF clusters in tumorigenesis, the expression of ten tumor-related pathways were evaluated in the four CAF clusters, including WNT, NRF1, MYC, CellCycle, PI3K, HIPPO, NOTCH, RAS, TGF-Beta, and TP53 signaling pathways. The GSVA scores of these pathways across the four CAF clusters were presented in the heatmap (Figure 2A). CAF_2 cluster exhibited notably higher proportions of malignant cells compared to the other three clusters (Figure 2B). Moreover, the CAF_4 cluster displayed a higher proportion of malignant cells compared to both CAF_1 and CAF_3 clusters. Furthermore, we conducted a comparison of GSVA scores for ten tumor-related pathways between malignant and non-malignant cells within each CAF cluster (Figure 2C–2F). Non-malignant cells had considerably higher GSVA scores for most signaling pathways in all CAF clusters, while minor variations among groups were observed.

Figure 2.

Tumor-related pathway characteristics in CAF clusters. (A) Heatmap illustrating GSVA scores for ten tumor-related pathways enriched in both malignant and non-malignant CAF cells. (B) Comparison of malignant and non-malignant cell proportions across different CAF clusters. Comparative analysis of GSVA scores for ten tumor-related pathways between malignant and non-malignant cells within (C) CAF_1, (D) CAF_2, (E) CAF_3, and (F) CAF_4 clusters. *P < 0.05; **P < 0.01; ***P < 0.001; ns, not statistically significant.

Associations between CAF clusters and clinical characteristics of PAAD patients

To investigate potential associations between CAF clusters and clinical characteristics of PAAD patients, the ssGSEA scores for specific marker genes—C10orf10, COL14A1, COL4A2, COL4A1, and ANGPT2 for CAF_1; SFRP2, LUM, MMP11, CTHRC1, and COL1A1 for CAF_2; RASD1, MT1A, MT1M, JUNB, and RERGL for CAF_3; and LTB, CCL4, IL7R, CCL5, and CD52 for CAF_4 (as identified in Figure 1C)—were calculated within each CAF cluster based on the TCGA cohort. Supplementary Figure 2A demonstrated that the CAF_4 cluster displayed notably lower scores in tumor samples in comparison to normal samples, with no statistically significant differences observed in the remaining CAF clusters. Following this, to assess the prognostic significance, the PAAD samples within the TCGA cohort were stratified based on high and low CAF scores. There was no correlation between CAF_1 and CAF_3 clusters and PAAD prognosis, while the CAF_2 and CAF_4 clusters fared better for low-CAF score samples than high-CAF score samples. (Supplementary Figure 2B). Next, we set out to establish the clinical correlations of CAF clusters. When PAAD patients were segmented into two categories based on gender (female vs. male), the disparity of CAF_1 score, but not other CAF clusters, was observed (Supplementary Figure 2C–2F). However, no clinical associations between CAF clusters and age were discovered. Furthermore, CAF_3 and CAF_4 scores were shown to be considerably higher in white persons compared to Asian people, whereas no associations were found between CAF scores and pathological stages.

Construction of CAF-related risk signature

We initiated the identification of DEGs by comparing 177 tumor samples with 4 normal samples in the TCGA-PAAD cohort to establish a risk signature. As depicted in Figure 3A, a total of 207 DEGs were discerned, comprising 47 up-regulated and 160 down-regulated genes. Among these, 148 genes displayed significant associations with CAF clusters. Subsequently, univariate Cox regression analysis was conducted on these 148 genes to assess their prognostic significance. Out of these, 14 genes exhibited prognostic values; 5 genes were identified as risk-associated, while 9 genes were deemed protective (Figure 3B). Functional annotations for the 148 genes were presented in Figure 3C. The significantly enriched biological process terms were immune response-regulating signaling pathway, leukocyte mediated immunity, regulation of immune effector process, cell activation involved in immune response, and leukocyte activation involved in immune response. In the cellular component part, genes were particularly enriched in external side of plasma membrane, secretory granule membrane, endocytic vesicle, plasma membrane signaling receptor complex, and tertiary granule. Meanwhile, immune receptor activity, phosphatidylinositol binding, MHC class I receptor activity, MHC protein binding, and inhibitory MHC class I receptor activity were mainly enriched in the molecular function group. In addition, integrated DEGs were mainly involved in Chemokine signaling pathway, Osteoclast differentiation, B cell receptor signaling pathway, Cytokine-cytokine receptor interaction, and Neutrophil extracellular trap formation in KEGG pathway analysis. Subsequently, we utilized LASSO regression analysis to further screen 14 prognostic genes and finally obtained seven genes as lambda = 0.0335 in the model, including Toll-Like Receptor 1 (TLR1), Serpin Family B Member 5 (SERPINB5), Phospholipase D Family Member 4 (PLD4), CD36 Molecule (CD36), Phosphocholine Phosphatase 1 (PHOSPHO1), BCL11 Transcription Factor A (BCL11A), Ring Finger Protein 166 (RNF166) (Figure 3D, 3E). We utilized multivariate Cox regression analysis employing a stepwise regression method to establish the risk signature. The resulting final seven-gene signature formula is represented as follows: RiskScore = -0.129 * CD36 - 0.202 * PHOSPHO1 - 0.211 * PLD4 + 0.58 * TLR1 + 0.183 * SERPINB5 - 0.36 * BCL11A - 0.272 * RNF166 (Figure 3F). Following z-mean normalization, the risk score was computed for each sample and categorized into high- and low-risk groups. Kaplan-Meier survival analysis conducted on the TCGA cohort revealed notably poorer survival outcomes among individuals in the high-risk category compared to those in the low-risk group (Figure 3G). In the TCGA cohort, the model displayed varying 1- to 5-year AUC values ranging from 0.680 to 0.857 (Figure 3H). Figure 3I, 3J displayed the survival curves for validation datasets GSE78229 and GSE85916. The findings indicate notably poorer prognosis among patients in the high-risk group.

Figure 3.

Identification of CAF-associated hub genes with prognostic significance. (A) Volcano plot illustrating DEGs between tumor and normal tissues in the TCGA-PAAD cohort. (B) Volcano plot showcasing prognosis-related genes identified through univariate Cox regression analysis. (C) Functional enrichment analyses encompassing GO (BP, CC, and MF) and KEGG analyses of CAF-related DEGs. (D) Trajectory plot depicting each independent variable with lambda in the LASSO model for PAAD. (E) LASSO coefficient profiles highlighting the seven genes in PAAD. The plot shows coefficient profiles against the log (lambda) sequence. (F) Multivariate Cox coefficients for each gene in the risk signature. (G) Kaplan-Meier curves illustrating the risk model constructed using the seven genes in the TCGA-PAAD cohort. (H) ROC curves displaying the risk model constructed with the seven genes in the TCGA-PAAD cohort. Kaplan-Meier curves of the risk model constructed with the seven genes in the validation datasets (I) GSE78229 and (J) GSE85916.

Development of nomogram based on CAF-related gene signature in PAAD patients

Utilizing both univariate and multivariate Cox regression analyses, we amalgamated clinicopathological characteristics to enhance the predictive capability of the CAF-related gene signature. In the initial phase of our investigation, a univariate analysis highlighted age [hazard ratio (HR) = 1.028, 95% confidence interval (CI): 1.007 – 1.049, P = 0.009] and risk score (HR = 2.718, 95% CI: 1.973 – 3.746, P < 0.001) as notably linked to PAAD patient survival (Figure 4A). Subsequent multivariate Cox regression analysis confirmed the risk score (HR = 2.683, 95% CI: 1.935 – 3.722, P < 0.001) as an independent prognostic indicator for PAAD, considering other influencing factors (Figure 4B). Consequently, we developed a visual nomogram incorporating age and risk score to predict individual survival at 1, 2, and 3 years, depicted in Figure 4C. The calibration curve illustrated satisfactory alignment between actual observations and predictions across 1-, 2-, and 3-year intervals (Figure 4D). Demonstrating superior discriminative performance for identifying high-risk patients, the DCA plot highlighted the nomogram’s superiority over age (Figure 4E). TimeROC analysis of the TCGA cohort exhibited higher AUC values for the risk score and nomogram compared to other indicators (Figure 4F).

Figure 4.

Creation of a nomogram using CAF-related gene signature for PAAD prognostication. (A) Univariate and (B) multivariate Cox regression analyses involving risk score and clinicopathological characteristics. (C) Development of a nomogram model amalgamating age and risk score. (D) Calibration plots showcasing the prediction accuracy for 1-, 2-, and 3-year survival probabilities. (E) Decision curve analysis illustrating the nomogram’s utility. (F) Time-ROC curve analysis comparing the predictive performance of the nomogram against other factors.

Mutation and pathway analyses of seven prognostic genes

The genetic mutations (SNV) within the seven genes forming the risk signature were subsequently examined. More samples were found to contain SNV mutations in TLR1, BCL11A, PHOSPHO1, and SERPINB5, however, no SNV mutation was found in CD36, PLD4, or RNF166 (Figure 5A). Pathway enrichment on SNV data showed that TGF-Beta and NRF2 pathways were the most affected pathways in PAAD, while more samples were involved in the RTK-RAS and TP53 pathways (Figure 5B). Within the seven genes comprising the risk signature, only a few samples displayed CNV alterations (Figure 5C). Hence, we explored the connections between these risk genes and diverse molecular indicators in PAAD, aiming to enhance our understanding of how these genes are linked to the disease. The results demonstrated that CD36, PHOSPHO1, PLD4, TLR1, BCL11A, and RNF166 were all significantly negatively correlated with Aneuploidy Score, Fraction Altered, and Number of Segments, whereas SERPINB5 exhibited notably positive correlations with Homologous Recombination Defects and Number of Segments (Figure 5D). Interestingly, the Nonsilent Mutation Rate displayed no correlation with any of these identified risk genes. Additionally, we delved into the potential pathways linked to each risk gene (Figure 5E, 5F). The heatmap revealed significant correlations of these seven genes with a total of 60 pathways, encompassing pathways like adipocytokine signaling pathway, base excision repair, and cell adhesion molecules cams.

Figure 5.

Genetic profile of the seven genes in the risk signature. (A) Waterfall diagram illustrating SNV mutations of the seven pivotal genes. (B) Enrichment heatmap displaying key pathways associated with SNV data in PAAD. (C) CNV alterations in the seven crucial genes, showcasing instances of gain, loss, and absence of alterations. (D) Heatmap visualizing correlations between the seven pivotal genes and Aneuploidy Score, Homologous Recombination Defects, Fraction Altered, Number of Segments, and Nonsilent Mutation Rate. (E) Heatmap revealing gene-pathway correlations. (F) Heatmap illustrating enrichment scores for pathways. *P < 0.05, **P < 0.01, ***P < 0.001.

Relationship between immune infiltration and seven prognostic genes

We then investigated the association between the seven prognostic genes and immune infiltration. Initially, we computed the immune score, stromal score, and ESTIMATE score utilizing the ESTIMATE algorithm (Supplementary Figure 3A–3D). Our correlation analysis revealed that CD36, PHOSPHO1, PLD4, TLR1, and RNF166 exhibited positive correlations with stromal score, immune score, and ESTIMATE score. Conversely, BCL11A showed positive correlations solely with immune score and ESTIMATE score. Notably, SERPINB5 did not display significant correlations with any of these three scores. We then compared the immune score across groups that were defined by the median expression level of respective genes. For the genes CD36, PHOSPHO1, PLD4, TLR1, BCL11A, and RNF166, individuals with high expression had significantly higher immune score than those with low expression (Supplementary Figure 3E–3K). However, individuals with low SERPINB5 expression had higher immune score. We utilized the CIBERSORT algorithm to compute the abundance of infiltrating immune cells and their correlations with the seven prognostic genes. The correlation heatmap showed that CD36, PHOSPHO1, PLD4, BCL11A, and RNF166 presented a significantly positive correlation with CD8⁺ T cells, naïve and memory B cells, whereas negative correlation with CD4⁺ memory resting T cells. TLR1 was positively correlated with CD4⁺ memory activated T cells, while negatively correlated with activated NK cells and activated mast cells, similar to SERPINB5 (Supplementary Figure 3L). We further validated our results using the MCPcounter algorithm. Significantly positive correlations were observed between RNF166, TLR1, PLD4, PHOSPHO1, CD36, and infiltrating immune cells, while SERPINB5 was only positively correlated with fibroblasts, which was consistent with previous results (Supplementary Figure 3M).

The prediction of risk signature to the efficacy of immunotherapy

The utilization of T-cell immunotherapy in cancer treatment has shown promise in extending patients’ survival. Consequently, we conducted an analysis on the IMvigor210 and GSE78220 cohorts to evaluate the predictive efficacy of the risk signature for immune-checkpoint therapy. Patients categorized in the IMvigor210 low-risk group exhibited better clinical outcomes and longer overall survival in comparison to those in the high-risk group (Figure 6A, P = 0.0049). However, no significant difference in risk score was observed between patients with complete/partial response (CR/PR) and those with progressive/stable disease (PD/SD), and the proportion of PD/SD patients in the high-risk group did not significantly differ from that in the low-risk group (Figure 6B, 6C). Particularly, substantial differences in survival probability were noted among various risk groups for patients diagnosed at Stages I and II (Figure 6D, P = 0.013), but not for those at Stages III and IV (Figure 6E, P = 0.19). These findings suggested higher sensitivity of the risk score for patients diagnosed at earlier stages. Similarly, patients within the low-risk group in the GSE78220 cohort displayed significantly prolonged overall survival compared to those in the high-risk group (Figure 6F, P = 0.033). Moreover, in contrast to IMvigor210, PD patients exhibited notably higher risk scores than PR/CR patients (Figure 6G), and the proportion of PD patients in the high-risk group was significantly elevated compared to the low-risk group in the GSE78220 cohort (Figure 6H). These outcomes indicate the effective predictive capacity of the risk signature in determining patient responses to immunotherapy.

Figure 6.

Risk signature response to immunotherapy in IMvigor210 and GSE78220 cohorts. (A) Prognostic differences among IMvigor210 cohort subgroups based on the risk score. (B) Variations in risk scores within IMvigor210 cohort responses to immunotherapy. (C) Distribution of immunotherapy responses among risk score groups in the IMvigor210 cohort. (D) Prognostic differences among subgroups of early-stage patients in the IMvigor210 cohort based on the risk score. (E) Prognostic differences among subgroups of advanced-stage patients in the IMvigor210 cohort based on the risk score. (F) Prognostic differences among subgroups of the GSE78220 cohort based on the risk score. (G) Variations in risk scores among GSE78220 cohort responses to immunotherapy. (H) Distribution of immunotherapy responses among risk score groups in the GSE78220 cohort.