Prognostic analysis of lung adenocarcinoma based on cancer-associated fibroblasts genes using scRNA-sequencing

Results

Identification of CAFs marker genes

We obtained single-cell sequencing data from 10 surgically resected primary LAUDs (73,566 cells) without specific treatment [36]. After strict quality control (removing nFeature > 10,000 or 500, nCount > 100,000 or 1,000, mitochondrial gene > 30%, and erythrocyte gene > 5%), 62115 high-quality cells were obtained. Figure 1E depicts the quality control results for each patient as a violin plot. The SCTransform algorithm identified 3,000 hypervariable genes and data homogenization and normalization. For the 3,000 genes mentioned above, 30 PCs were used to reduce dimensionality, yielding 29 cell clusters (Figure 1A). Subsequently, annotation with canonical cell markers (Figure 1D) yielded seven cell subsets, with the 19th cluster annotated as CAFs. However, the cell distribution existed between different patients, but there was no significant batch effect (Figure 1C). Analysis of seven cell subsets and 50 Hallmarkers revealed a significant positive correlation between CAFs and epithelial-mesenchymal transition (EMT), myogenesis, and Wnt/β-catenin signaling (Figure 1F). Ultimately, the ‘FindAllMarkers’ function yielded 120 CAFs-related genes.

Figure 1. Visualization of scRNA-seq data from 10 LUAD patients. (A) The U-MAP algorithm identified 29 cell subsets. (B) Seven cell types were identified based on marker genes. (C) UMAP plot of 62,115 cells, colored by patients. (D) Marker genes for different cell subsets. (E) Quality control results. (F) Association between cell subsets and Hallmarkers.

Prognostic signature based on CAFs marker genes construction and validation

Univariate COX regression analysis of 120 CAFs-related genes in the TCGA database revealed that 32 genes were significantly related to OS, with seven genes representing protective factors and 25 genes representing risk factors (Supplementary Figure 1A). These 32 genes were further analyzed using LassoCOX regression, and 13 were obtained according to the optimal λ value (logλ = -4). Then, the abovementioned 13 genes were used for stepwise multivariable COX regression analysis. Finally, eight genes, TIMP1, TPM2, NR2F2, MFAP4, SOD3, CAV1, SERPINH1, and FMO2, were used to build a prognosis model. The ‘Featureplot’ function was applied to display these eight gene expressions in different cell subsets. Online TIMER (http://timer.comp-genomics.org) was accessed to investigate these eight abnormal gene expressions in pan-cancer (Supplementary Figure 1D, 1E). The final model combined with the coefficient of each gene is: Risk = (0.2403052*TIMP1 expression) + (0.1475574*TPM2 expression) + (0.2686883*NR2F2 expression) + (-0.1635410*MFAP4 expression) + (-0.1219534*SOD3 expression) + (0.2009316*CAV1 expression) + (0.1449876*SERPINH1 expression) + (-0.1816038*FMO2 expression).

An increased risk score indicates an increase in the number of patients who have died, indicating a high risk and poor OS positive association. The high-risk group had significant TIMP1, TPM2, NR2F2, CAV1, and SERP1NH1 expressions, whereas the low-risk group had significant MFAP4, SOD3, and FMO2 expressions (Figure 2A). The Kaplan-Meier (K-M) survival curve demonstrated that high-risk patients had significantly lower OS (p < 0.001, Figure 2B), PFS (p = 0.012), and DSS (p < 0.001, Figure 2C, 2E). Despite this, the two groups did not differ significantly in DFS (p = 0.056). The ‘timeROC’ R package was employed to evaluate the model diagnostic efficacy and related clinical characteristics for survival outcomes, revealing that pathological stage (AUC = 0.705) and risk (AUC = 0.688, Figure 2F) were the two indicators with good diagnostic efficiency. After 10-fold cross-validation, the model’s performance was evaluated, and its mean AUC values were 0.688, 0.680, and 0.646 over one, three, and five years, respectively.

Figure 2. The prediction model of CAFs-related genes was established in the TCGA database. (A) Distribution of risk scores and OS status. 8 gene expressions were involved in the model construction in the high- and low-risk groups. Kaplan-Meier curve illustrated the differences between high- and low-risk groups for (B) OS, (C) PFS, (D) DFS, and (E) DSS. (F) ROC curves for risk scores and clinical characteristics. (G) ROC curves for one-, three-, and five-year OS of risk scores in the TCGA database.

Next, four independent GEO datasets verified the model’s validity and stability. In GSE37745, the high-risk patient had a worse prognosis (p < 0.001, Figure 3A), with AUC values of 0.554, 0.616, and 0.631 at one-, three-, and five-year, respectively (Figure 3B). In GSE72094, the low-risk patient OS was significantly prolonged (p < 0.001, Figure 3C), with AUC values of 0.664, 0.709, and 0.638 at one-, three-, and five-year, respectively (Figure 3D). In GSE50081, high-risk patients had poor OS (p = 0.01, Figure 3E), with AUC values of 0.649, 0.572, and 0.584 at one-, three-, and five-year, respectively (Figure 3F). In GSE31210, the high-risk patient had poor OS (p = 0.016, Figure 3G) and AUC values of 0.876, 0.649, and 0.600 with the one-, three-, and five-year, respectively (Figure 3H). In conclusion, high-risk patients had poor long-term survival outcomes in validation and training sets. Furthermore, our model has a good prediction efficiency for the patient survival outcome.

Figure 3. Validation of the model using four independent GEO database cohorts. In the GSE37745 cohort, (A) Kaplan-Meier curves depict the prognosis for the high- and low-risk groups, and (B) the AUC values at one-, three-, and five-year. In the GSE72094 cohort, (C) Kaplan-Meier curves show the prognosis of the high- and low-risk groups, and (D) the AUC values at one-, three-, and five-year. In the GSE50081 cohort, (E) K-M curves display the prognosis for the high- and low-risk groups, and (F) the AUC values at one-, three-, and five-year. In the GSE31210 cohort, (G) Kaplan-Meier curves depict the prognosis of the high- and low-risk groups, and (H) the AUC values at one-, three-, and five-year.

The model diagnostic and prognostic ability combined with clinical features

Univariate COX regression was applied in the TCGA database by five clinical features, including age, gender, stage, tumor site, and smoking status. Besides the model risk score, OS risk factors revealed stage (p < 0.001, HR = 1.763, 95% CI = 1.441–1.942) with risk score (p < 0.001, HR = 2.787, 95% CI = 2.091–3.713, Figure 4A). Meanwhile, Multivariate COX regression findings revealed that the OS risk factors were age (p = 0.024, HR = 1.019, 95% CI = 1.002–1.035), stage (p < 0.001, HR = 1.587, 95% CI = 1.360–1.853), and risk score (p < 0.001, HR = 2.526, 95% CI = 1.876–3.402, Figure 4B). The model risk score was a survival outcome risk factor in both COX analyses. Next, the constructed nomogram revealed that a patient’s gender, age, risk score, and pathology stage are as follows: male, 60 years old, low risk, and stage III. Then, the patient’s total score was 144, and the one-, three-, and five-year survival rates were 0.876, 0.581, and 0.327, respectively (Figure 4C). The calibration curve exhibited a high correlation between predicted and actual values, revealing good predictive nomogram performance (Figure 4D). Figure 4E demonstrates that patients with a later pathological stage had a higher risk score, partly explaining the poor prognosis of the high-risk patient. Risk scores did not differ significantly between patients aged 65 years and those aged more than or less than 65 years (p = 0.44) and between EGFR mutation and non-mutation groups (p = 0.15). Patients with a K-RAS mutation exhibited a higher risk score (p = 0.043), as did patients with a higher smoking score (p = 0.0098). Next, patients were stratified using the statistically significant clinical characteristics and risk scores described above. High-risk + stage III−IV exhibited a poor prognosis, while low-risk + stage I−II exhibited the best prognosis (Figure 4F). Meanwhile, the K-RAS mutation + high-risk group had poor survival prognosis than the low-risk and K-RAS non-mutation groups (Figure 4G). Finally, the high-risk + high-smoking score patients also have a poor survival prognosis (Figure 4H).

Figure 4. Establishment and validation of nomograms, risk score differences between different clinical characteristics. (A) Univariate COX regression analysis. (B) Multivariate COX regression analysis. (C) Nomogram constructed using risk score and clinical characteristics. (D) Calibration curve of the nomogram. (E) Risk score differences between different clinical characteristics. The K-M analysis curves for the patients stratified by (F) risk score and stage, (G) risk score and K-Ras mutation status, (H) risk score, and smoking level.

Enrichment analysis

GO analysis indicated the significance of the 120 CAFs-related genes enrichment in ECM organization and structural constituent besides the collagen-containing ECM and other pathways (Figure 5A). Moreover, KEGG analysis indicated that these genes significantly correlate with protein digestion and absorption, ECM-receptor interaction, complement and coagulation cascades, and other pathways (Figure 5B), indicating the CAF importance in ECM remodeling, consistent with former studies [13–15]. KEGG and GO analyses were also conducted to explore functional differences between the risk groups. According to Gene Set Enrichment Analysis (GSEA) analysis, the significant high-risk group enrichment in DNA replication, mitotic nuclear division, and other pathways (Figure 5C) indicates its significant correlation with cell replication pathways. Meanwhile, the significant low-risk group enrichment in the B cell receptor (BCR) and immunoglobulin complex pathways (Figure 5E) indicates its significant enrichment in immune-related pathways. GSEA analysis also indicated the significant enrichment of cell cycle, ECM receptor interaction, and focal adhesion pathway in the high-risk group (Figure 5D). Meanwhile, the low-risk group enrichment appeared in allograft rejection, asthma, and autoimmune thyroid disorder pathway (Figure 5F).

Figure 5. Enrichment analysis. (A) GO and (B) KEGG enrichment analysis of 120 CAFs-related genes. Gene Set Enrichment Analysis (GSEA) analysis for patients in the (C, D) high- and (E, F) low-risk groups.

Somatic mutation analysis

TMB is a quantitative biomarker that reflects the total tumor cell mutation count, the total count of somatic gene coding errors, base substitution, gene insertion, or deletion errors detected per million bases [37]. Higher TMB levels correlated with longer OS after immunotherapy across multiple cancers. Figure 6A, 6B depict a waterfall map of the top 20 genes mutation landscape with the highest mutation frequency. The high-risk patients exhibited increased TMB levels (p = 6.1e-05), suggesting their further benefit from the immunotherapy (Figure 6C). Meanwhile, Figure 6D depicts that the risk score positively correlates with the TMB level (R = 0.2, p = 5.9e-06). The median TMB level was utilized as the boundary to divide TMB into high and low groups. According to the K-M curve, the high TMB group has a better survival prognosis than the low TMB group (Figure 6E). When combined with the risk model, the low TMB+ high-risk group has the worst survival prognosis (Figure 6F).

Figure 6. Mutation analysis between the high- and low-risk groups. The mutational landscapes of the (A) high- and (B) low-risk groups. (C) TMB level comparison between high- and low-risk groups. (D) Correlation between risk score and TMB. (E) K-M curves for high and low TMB levels. (F) K-M curves for the patients stratified by risk score and TMB.

Tumor immune microenvironment estimate

The immune cell composition and abundance in the TME strongly influenced tumor progression and immunotherapy effusiveness. A heat map demonstrates the immune cells with significant differences (p < 0.05, Figure 7A). Overall, the low-risk group had a significant abundance of immune cell infiltration, including T and B cells, in the TIMER and XCELL algorithms. Next, the common risk group immune checkpoint gene expressions were assessed (Supplementary Table 2). Twenty genes displayed significant differences, with 15 upregulated in the low-risk group and 5 upregulated in the high-risk group (Figure 7B). The differential expression of common MHC molecules was also analyzed (Supplementary Table 2). Interestingly, all 15 results with significant differences were high in the low-risk group (Figure 7B).

Figure 7. Tumor microenvironment (TME) analysis. (A) The heatmap displays the differences in the number of immune cells among the eight algorithms. Differential expression of (B) immune checkpoint-related genes, (C) MHC-related genes, and other (D) Tumor-related gene sets between high- and low-risk groups. (E) Estimate score differences between the high- and low-risk groups. (F) Tumor purity differences between the high- and low-risk groups. (G) Correlation between risk-score and tumor purity.

Moreover, the other tumor-related gene differential expression was analyzed (Figure 7C). The low-risk group had higher immune and estimate scores through the ‘estimate’ algorithm, but the stromal score did not differ significantly (Figure 7D). Supplementary Table 3 provides ‘estimate’ score details for each LUAD patient in the TCGA data. Moreover, tumor purity was reduced in the low-risk group (Figure 7F). The risk score and tumor purity exhibited a significant positive association (Figure 7G). In summary, the low-risk group had greater immune infiltration and upregulation of immune checkpoint-related and MHC genes.

Immunotherapy efficacy prediction

LUAD patient IPS was downloaded from the TCIA database to assess the immunotherapy response, indicating the higher IPS of the low-risk patients regardless of whether CTLA-4 and PD1 were expressed or not and further benefit from immunotherapy (Figure 8A–8D). Moreover, the complete response (CR) + partial response (PR) group risk score was decreased than the stable disease (SD) + progression disease (PD) group (Figure 8E), revealing the strongest diagnostic power of neoantigen (AUC = 0.778), followed by TMB (AUC = 0.718). The risk score also showed a certain predictive power (AUC = 0.578, Figure 8F). K-M analysis demonstrated that the high-risk patients had poor survival outcomes in the IMvigor210 immunotherapy cohort (Figure 8G). The low-risk patients had higher TIDE scores, indicating a high susceptibility to immune escape (Figure 8H). Our study discovered immunotherapy benefits for low-risk patients besides better long-term survival after immunotherapy.

Figure 8. (A–D) IPS score differences. Validation of the model's ability to predict immunotherapy response in the IMvigor210 cohort, (E) Comparison of the risk score differences between CR + PR and SD + PD groups, (F) ROC curve illustrated the ability of TMB, Neoantigen, and risk-score to predict the efficacy of immunotherapy, (G) survival prognosis of high- and low-risk groups. (H) TIDE score differences in the TCGA database.

Prediction of chemotherapy response

Further analysis determined whether IC₅₀ levels differed between the LUAD patient risk groups. We selected 20 LUAD-related chemotherapy drugs with significant differences in drug sensitivity, revealing that the low-risk group had high sensitivity to the following 17 drugs: Gemcitabine, 5-Fluorouracil, Epirubicin, Savolitinib, AZD6738, Alisertib, AZD1332, I-BET-762, Ulixertinib, Trametinib, Cisplatin, Cediranib, Talazoparib, BI−2536, Crizotinib, Cytarabine, and Dasatinib (Figure 9A). In contrast, three drugs (Axitinib, ABT737, and AZD8055) demonstrated higher sensitivity in high-risk patients (Figure 9B). The above results indicate that our model shows promising potential in distinguishing the sensitivity of LUAD patients to chemotherapy drugs, thereby providing new avenues for future treatment strategies in LUAD. However, further validation through clinical trials and animal experiments is necessary to confirm the accuracy of our drug sensitivity predictions.

Figure 9. A drug sensitivity analysis. (A) Drugs with higher sensitivity in the low-risk group. (B) Drugs with higher sensitivity in the high-risk group.

Author Contributions

Han Zhang: conceived the study. Han Zhang, Yuhang Wang and Kai Wang: analyzed the data and made charts. Shuai Zhao, Xiaoteng Jia and Yun Ding: wrote the manuscript. XL and DS: revised and reviewed the manuscript. All authors contributed to the article and approved the submitted version.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This study was supported by the Natural Funding Project of Tianjin Science and Technology Bureau (No. 20JCYBJC01350), Tianjin Health Science and Technology Project Key Projects (ZD20023) and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-018A).

Editorial Note

This corresponding author has a verified history of publications using a personal email address for correspondence.

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