Targeting inhibition of prognosis-related lipid metabolism genes including CYP19A1 enhances immunotherapeutic response in colon cancer

The RNA-sequencing data and clinical information of colon cancer patients were downloaded from the TCGA database (https://​portal.​gdc.​cancer.​gov). The RNA expression profiles contained 494 samples. We obtained 453 colon cancer samples and 41 adjacent normal tissue samples. 435 colon cancer patients with survival data were included in the following study. Gene expression profiles of GSE41258 dataset based on the Affymetrix human genome U133A array platform, GSE38832 and GSE39582 datasets based on the Affymetrix human genome U133 Plus 2.0 array platform and clinical data were downloaded from the GEO database (https://​www.​ncbi.​nlm.​nih.​gov/​geo/​). A list of LMGs was collected from the “metabolism of lipids” in Reactome (https://​reactome.​org/​download-data/​). Genes not included in TCGA or GEO databases were excluded, and 731 genes related to lipid metabolism were obtained.

The “edgeR” [22] package was used to identify differentially expressed LMGs between the tumor and adjacent normal tissue samples. Adjusted P-value < 0.05 and |log2 (fold change) |> 1 were chosen as the cut-off threshold. The protein–protein interaction network of differentially expressed LMGs was analyzed by STRING database (https://​string-db.​org/​). Principal component analysis (PCA) was utilized to analyze the expression pattern in the colon cancer and normal tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed with the differentially expressed LMGs by using the “clusterProfiler” R package [23]. False discovery rate < 0.05 was considered statistically significant.

Univariate Cox regression analysis was performed to identify the prognosis-related LMGs. The P-value < 0.05 in univariate Cox regression analysis was considered statistically significant. The “glmnet” R package was used to perform a least absolute shrinkage and selection operator (LASSO)-Cox regression model analysis. The weighted LASSO-Cox coefficients based on individual gene expression levels were used to calculate the lipid metabolism-related risk score (LMrisk) as follows: LMrisk = ∑ (expression of gene_i × Coefficient of gene_i). The patients in the TCGA database were stratified into the low- and high-LMrisk groups according to median LMrisk value, and their survival were analyzed using the Kaplan–Meier method. Log-rank test was used to compare the survival curves of two or more groups. The specificity and sensitivity of the LMrisk in predicting 3-, 5- and 10-year survival were determined by receiver operating characteristic (ROC) analysis using the “survivalROC” R package, and the areas under curves (AUC) were calculated. We used a similar approach in the GSE41258, GSE38832 and GSE39582 datasets to verify the applicability of the LMrisk. To study whether the LMrisk is an independent predictor for overall survival of colon cancer patients, univariate and multivariate Cox regression analyses were conducted. The LMrisk, age, gender and TNM stage were used as covariates.

After testing for collinearity, all independent prognostic parameters were included in the construction of a nomogram to predict 3- and 5-year overall survival of colon cancer patients. Age, TNM stage and LMrisk were used to construct the nomogram using the “rms” and “survival” packages in R. The discrimination power of the predictive model was evaluated by Harrell’s concordance index (C-index). We calculated the C-index with 95% confidence interval using the bootstrap approach with 1000 resamples. Then, calibration curves were drawn to assess the consistency between actual and predicted survival. The nomogram performance was validated by ROC curves at 3 and 5 year using the TNM stage as control.

The Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) was used to evaluate the immune score, stromal score and ESTIMATE score of each sample [24]. Based on the gene expression data in the cancer tissues, the xCell and TIMER algorithms were applied to estimate the infiltration of immune cells in each sample [25]. The R package “maftools” was used to evaluate and sum the mutation data.

Letrozole (T1590) was purchased from TargetMol (Bellingham, WA, USA). CYP19A1 siRNA (sc-41498) and CYP19A1 antibody (sc-374176) were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). PD-L1 (13684) antibody was purchased from Cell Signaling Technology (Danvers, MA, USA). PE-conjugated anti-CD3 antibody (555340), APC-conjugated anti-CD8 antibody (566852), PE-Cy7-conjugated anti-IFN-γ antibody (557643) were purchased from BD Biosciences (San Jose, CA, USA). FITC-conjugated anti-CD107a antibody (328606) and Zombie NIR (423105) were purchased from BioLegend (San Diego, CA, USA). Opal Polaris 7 Color IHC Detection Kit (NEL861001KT) was purchased from Akoya Bioscience (Menlo Park, CA, USA). Chitosan (CS, deacetylation 98%, Mw = 50 KDa) and sodium tripolyphosphate (TPP) were purchased from Sigma Chemical Co (St. Louis, MO, USA). Hyaluronic acid (HA) was purchased from Meryer Chemical Technology Co Ltd (Shanghai, China).

Human colon carcinoma tissue microarray (HCol-Ade180Sur-05) containing 90 patients with colon cancer and adjacent normal tissues were obtained from Shanghai Outdo Biotech Co., Ltd. Each sample dot with a diameter of 1.5 mm and a thickness of 4 μm was prepared according to a standard method. Immunohistochemistry (IHC) of human colon cancer tissue microarrays were performed by using the CYP19A1 (1:100) and PD-L1 (1:300) antibodies. Immunostaining was graded using a two-score system based on intensity score and proportion score as previously described [26]. The two scores were then multiplied to yield a total immunoreactivity score regarding the protein expression in a sample. IHC score was assessed independently by two pathologists, and a consensus of grading was reached. Patients were divided into high- and low-expression groups according to the median IHC score.

Tumor tissues from 20 patients with colon cancer were obtained from Zhongnan Hospital, Wuhan University (Hubei, China) between 2020 and 2021. The histological diagnosis for each sample was reconfirmed using microscopic examination of hematoxylin/eosin-stained sections. All samples were collected from patients with informed consent, and all procedures were conducted with the approval of the Ethical Committee of the Medical School of Wuhan University and performed in accordance with relevant regulations and guidelines.

Quantitative multiplex immunofluorescence (mIF) was performed to characterize the immune landscape in human colon cancer tissues using Opal Polaris 7 Color IHC Detection Kits (NEL861001KT, Akoya Bioscience, CA, USA) as previously described [27]. Briefly, formalin-fixed paraffin-embedded sections were deparaffinized, followed by antigen retrieval with citrate acid buffer (pH 6.0)/Tris–EDTA buffer (pH 9.0) and then blocking with blocking/Ab diluent. Next, slides were incubated with primary antibodies against CYP19A1 (1:200), CD68 (Abcam, ab955, 1:800), CD31 (Abcam, ab24590, 1:300), α-SMA (Abcam, ab7817, 1:300), CD8 (DAKO, IR623, 1:5) and CK (AE1/AE3) (DAKO, IS05330-2, 1:3). Primary antibody was visualized using tyramide signal amplification linked to a specific fluorochrome from the mIF kit for each primary antibody. A stripping procedure based on microwaves was performed for each consecutive antibody staining. The stained slides were scanned using a Vectra® 3 multispectral microscope (Akoya Bioscience). From each slide, Vectra automatically captured the fluorescent spectra from 420 to 720 nm at 20-nm intervals with the same exposure time and then combined the captured images to create a single stack image that retained the particulate spectral signature of all markers. These data were analyzed using InForm 2.6 software.

Cell culture, cell proliferation assays, aromatase activity assays, CYP19A1 siRNA transfection, isolation of PBMCs and their coculture with colon cancer cells, flow cytometry analyses for tumor cell death and CD8⁺ T cell function, western blot analyses for CYP19A1 and PD-L1 expression, tumor models and therapeutic efficacy study, and statistical analysis were described in Supplementary Materials and Methods.

Expression data and clinical information of four cohorts were downloaded from the TCGA and GEO databases. The TCGA cohort consisted of 453 colon cancer and 41 normal samples, and was used for differential expression analysis. Seven hundred thirty-one LMGs were screened, and 178 LMGs were differentially expressed with 86 upregulation and 92 downregulation between colon cancer and normal samples (Fig. 1A). PCA revealed an obvious difference in a distinct cluster of these differentially expressed LMGs between the colon cancer tissues and adjacent normal tissues (Fig. 1B). The hub genes including CYP19A1 were identified by the protein–protein interaction network of differentially expressed LMGs in the colon cancer (Fig. 1C). Next, GO and KEGG pathway annotation analyses were performed to explore the biological functions of the differentially expressed LMGs in the colon cancer. GO analysis showed that these LMGs were enriched in the basic biological processes, including steroid metabolism, fatty acid metabolism, lipid catabolism and glycerolipid metabolism, and the most highly enriched terms for the cellular component and molecular function were lipid droplet and oxidoreductase activity, respectively (Fig. 1D). KEGG analysis showed that 178 LMGs were primarily related to AA metabolism, PPAR signaling pathway and bile secretion (Fig. 1E). These results suggest significant alterations of LMGs in human colon cancer.

To develop prognostic models in the TCGA colon cancer cohort, machine learning methods such as random survival forest, CoxBoost, support vector machine, gradient boosting machine, elastic net and LASSO-Cox were employed. A univariate combined LASSO-Cox regression model with the maximum C-index (Fig. 2A) was used for subsequent analysis. Univariate Cox regression analysis revealed that the LRP2, CYP19A1, SLCO1A2, FABP4, ALOXE3, PLAAT5 and SPHK1 were risk genes with hazard ratio (HR) > 1, while PPARGC1A was protective gene with HR < 1 among 178 differentially expressed LMGs (Fig. 2B). By using LASSO-Cox regression analysis to further reduce overfitting, the six LMGs, including LRP2, CYP19A1, SLCO1A2, FABP4, ALOXE3 and PPARGC1A, were identified to construct the optimal lipid metabolism-related prognostic signature (Fig. 2C and D). The expression levels and LASSO coefficients of six LMGs were extracted to calculate the LMrisk for each patient with the following formula: LMrisk = (0.124 × LRP2 expression) + (0.145 × CYP19A1 expression) + (0.0551 × SLCO1A2 expression) + (0.00237 × FABP4 expression) + (0.117 × ALOXE3 expression) + (-0.182 × PPARGC1A expression). Based on the median LMrisk, the patients were assigned to low LMrisk (n = 217) and high LMrisk groups (n = 218). We observed more deaths and shorter survival time in the colon cancer patients with the high LMrisk as compared with the low LMrisk (Fig. 2E and F). Next, time-dependent ROC curves were used to evaluate the predictive value of the LMrisk. As shown in Fig. 2G, the AUC of LMrisk at 3, 5 and 10 years were 0.68, 0.73 and 0.81, respectively. Consistently, Kaplan–Meier survival curves indicated that the survival time was shorter in the high LMrisk group than in the low LMrisk group in the GSE41258 (P = 0.0072), GSE38832 (P = 0.0099) and GSE39582 (P = 0.022) datasets (Fig. 2H–J). To validate the predictive ability of the LMrisk on colon cancer prognosis, we conducted a stratified analysis based on clinicopathological features including age, gender, TNM stage, N stage, M stage and T stage in the high and low LMrisk groups. Kaplan–Meier survival analyses revealed that the high-risk group had worse overall survival compared to the low LMrisk group in different strata of clinical characteristics including younger (< 66 years) or older (≥ 66 years), male or female, stage I–II or stage III–IV, N0 or N1–N2, M0 or M1 and T3–T4 patients (Fig. S1). These results suggest that the LMrisk has good robustness for predicting prognosis of colon cancer.

To identify the factors affecting the survival of patients with colon cancer, we analyzed the prognostic value of the LMrisk and clinicopathological parameters in the TCGA dataset. As shown in Fig. S2A, the LMrisk, in addition to clinical factors (age, T stage, N stage, M stage and TNM stage), was closely associated with survival outcomes. Multivariate analysis manifested that the LMrisk, age and TNM stage were independent prognostic indicators (Fig. S2B). The AUC of LMrisk was larger than other indicators including TNM stage, suggesting that the LMrisk has a better ability to predict prognosis in the colon cancer (Fig. S2C). The LMrisk was significantly correlated with the age, T stage, N stage, M stage and TNM stage. We also observed relatively low expression of PPARGC1A and high expression of CYP19A1, FABP4, ALOXE3, LRP2 and SLCO1A2 in the patients with high LMrisk (Fig. S2D). The patients had higher LMrisk in stage III–IV than in stage I–II (P = 0.023), and in T3–T4 than in T1–T2 (P = 0.016). The patients with lymph node metastasis (N1–N2) or distant metastasis (M1) had higher LMrisk than those without lymph node metastasis (N0) or distant metastasis (M0) (Fig. S2E). These data suggest that the LMrisk is an independent prognostic risk factor in the colon cancer.

To predict overall survival probabilities of colon cancer patients, a prognostic nomogram was established by integrating the LMrisk with age and TNM stage in the TCGA dataset (Fig. S3A). We used calibration curve to verify the accuracy of the prediction model, and found that the predicted probability of 3- and 5-year survival fitted well with the observed survival (Fig. S3B and C). The AUC values of nomogram were higher than those of TNM stage in 3 and 5 year (Fig. S3D and E). The nomogram also showed satisfactory discrimination and calibration with a C-index of 0.79 (95% confidence interval: 0.74–0.84). These data suggest that our nomogram presents superior prognostic value than that of TNM stage in colon cancer.

The tumor immune microenvironment innately modulates tumor progression [28]. Thus, the ESTIMATE algorithm was used to evaluate the immune score and stromal score of colon cancer patients. Figure 3A showed that the LMrisk was positively correlated with the immune score, stroma score and ESTIMATE score in the TCGA dataset. Next, immune cell and stromal cell infiltration in colon cancer tissues were inferred by xCell and EPIC algorithms. We observed less infiltration of CD8⁺ T cells, but more proportions of immunosuppressive cells including macrophages, monocytes, CAFs and endothelial cells in the high LMrisk group compared to the low LMrisk group (Fig. 3B). These data confirm that the infiltration of CD8⁺ T cells is decreased but the infiltration of immunosuppressive cells is increased in the colon cancer with high LMrisk.

The TME includes immune checkpoint regulators as well as inflammatory mediators besides immune cells [13]. To understand the molecular mechanisms altering immunotherapy responsiveness, we evaluated the association of the LMrisk with immunotherapy response in colon cancer. As shown in Fig. 4A, PD-1, PD-L1, CTLA-4, LAG3 and HAVCR2 (TIM3) were significantly overexpressed in the high LMrisk group than in the low LMrisk group. The patients with low LMrisk and high PD-L1 had better survival than those with high LMrisk and high PD-L1, and patients with low LMrisk and low PD-L1 had better survival than those with high LMrisk and low PD-L1 (Fig. 4B). Similar results were obtained in the PD-1 and CTLA-4 stratifying groups (Fig. 4C and D). MSI status, consensus molecular subtypes (CMS) heterogeneity and TMB display different immune landscapes in the TME, and therefore are used as predictive biomarkers for response of ICIs [29‐32]. Accordingly, we compared the LMrisk between different MSI status, and found that the LMrisk was higher in the colon cancer with MSI-H and MSI-L as compared with MSS (Fig. 4E). The LMrisk was higher in CMS1 (immune activated phenotype) or CMS4 (immune inflamed phenotype) than in CMS2 (immune desert phenotype) and CMS3 (immune excluded phenotype) (Fig. 4F). A previous study shows that, in six immune subtypes, IFN-γ dominant subtype has better ICI outcomes [33, 34]. We observed the significantly increased LMrisk in IFN-γ dominant subtype than in wound healing subtype, though there was no significant difference of LMrisk between inflammatory and lymphocyte depleted subtypes (Fig. 4G). The TMB was significantly increased in the patients with high LMrisk as compared with low LMrisk (Fig. 4H). Maftools analysis results showed that TTN gene mutated more frequently in the high LMrisk than in the low LMrisk group (Fig. 4I). Because of unavailable datasets for colon cancer patients treated with immune checkpoint blockade, we analyzed publicly available datasets from urothelial cancer patients treated with anti-PD-1 therapy. As shown in Fig. 4J, the high LMrisk was correlated with a complete or partial response (CR/PR), while low LMrisk correlated with stable disease or progressive disease (SD/PD) when the patients received anti-PD-1 therapies. The high LMrisk also predicted significant improvement in overall survival for the patients received anti-PD-1 therapy compared with the low LMrisk. These results indicate that the LMrisk is a potential predictive biomarker of immunotherapeutic response.

Among the differentially expressed lipid metabolism genes in colon cancer and adjacent normal tissues, CYP19A1 is a key molecular node and has the largest prognostic hazard ratio (Figs. 1C and 2B). Therefore, CYP19A1, a risk factor of the LMrisk model, was used for subsequently experimental verification. We found that CYP19A1 gene was highly expressed in the colon cancer tissues compared to the adjacent normal tissues in the Gene Expression Profiling Interactive Analysis (GEPIA) webserver (Fig. S4A). CYP19A1 gene expression was a strong prognostic risk factor in the colon cancer (Fig. S4B), and positively correlated with PD-L1 expression in the GEPIA webserver (Fig. S4C). Subsequently, we conducted functional verification by using a tissue microarray of human colon cancer. Similarly, CYP19A1 protein expression was significantly increased in human colon cancer tissues (Fig. 5A and B), and positively correlated with PD-L1 expression (Fig. 5D). CYP19A1 protein expression in the colon cancer tissues was negatively correlated with overall survival (Fig. 5C), and positively associated with N stage, and an independent prognostic parameter of overall survival (Tables S1 and S2). Next, mIF staining was performed to explore the relationship between CYP19A1 expression and immune landscape in the colon cancer. We observed an apparent colocalization of CYP19A1 with CK (tumor cell marker), indicating that CYP19A1 is mainly expressed in tumor cells. The infiltration of CD8⁺ T cells was more but the proportions of CD68⁺ cells (macrophages), α-SMA⁺ cells (CAFs) and CD31⁺ cells (endothelial cells) were fewer in human colon cancer tissues with low CYP19A1 expression as compared with high CYP19A1 expression (Fig. 5E). We analyzed PD-L1 expression in the same biopsies, and found that PD-L1⁺ CAFs and macrophages were significantly higher in CYP19A1 high expression group as compared with low CYP19A1 expression group (Fig. 5F). These data indicate that high CYP19A1 expression leads to poor prognosis, accompanied by the low infiltration level of CD8⁺ T cells and high infiltration levels of immunosuppressive cells in colon cancer.

To gain insight into the role of CYP19A1 in tumor immunity, the inhibitor (letrozole) and siRNAs of CYP19A1 were used in the in vitro co-culture of human HT29 and HCT116 cells with PBMCs. We found that the letrozole (5 μM) significantly decreased aromatase activity in human HT29 and HCT116 cells (Fig. 6A) without affecting their proliferation (Fig. 6B). The CYP19A1 siRNAs significantly inhibited CYP19A1 expression (Fig. 6C). Unsurprisingly, CYP19A1 inhibition by the letrozole or siRNA in tumors increased the proportion of 7-AAD⁺ tumor cells after co-culture with PBMCs (1.5- and 2-fold, respectively) (Fig. 6D). The letrozole or CYP19A1 siRNA-treated HT29 and HCT116 cells significantly increased CD8⁺ T cell proliferation and the proportion of CD8⁺ CD107a⁺ and CD8⁺ IFN-γ⁺ T cells (Fig. 6E–G), and promoted pericyte cell migration and reduced endothelial cell migration (Fig. 6H). These data indicate that CYP19A1 inhibition in colon cancer cells enhances CD8⁺ T cell-mediated antitumor immunity.

Tumor cells upregulate the expression of immune checkpoint molecules or secrete cytokines to induce abnormal angiogenesis, thereby promoting tumor immune escape [35, 36]. We found that CYP19A1 expression level was significantly positively correlated with PD-L1, IL-6 and TGF-β levels in the TCGA colon cancer dataset (Fig. 7A). Next, we verified the factors derived from tumor cells in HT29 and HCT116 colon cancer cells, and comfirmed that CYP19A1 inhibition significantly reduced PD-L1, IL-6, and TGF-β expression in tumor cells (Fig. 7B–D). Neutralizing antibodies against PD-L1, IL-6 or TGF-β partially ameliorated the effects of CYP19A1 overexpression on the proliferation and effector function of CD8⁺ T cells (Fig. 7E and F). Neutralizing IL-6 and TGF-β also promoted pericyte cell migration and inhibited endothelial cell migration (Fig. 7G). Importantly, combined blockade of PD-L1, IL-6 and TGF-β exerted better efficacy than single blockade (Fig. 7E–G). These data highlight compelling evidence that CYP19A1 inhibits the proliferation and effector function of CD8⁺ T cells by upregulating PD-L1, IL-6 and TGF-β expression in colon cancer cells.

Activation of G-protein coupled estrogen receptor 30 (GPR30) promotes colon cancer growth through mediating estrogenic activity in colon cancer cells [37], and Akt signaling is one of the key downstream pathways of GPR30 in cancer progression [38]. We discovered that CYP19A1 inhibition by letrozole or si-CYP19A1 downregulated GPR30, p-Akt and PD-L1 in HCT116 and HT29 colon cancer cells (Fig. 8A), which were greatly enhanced by exogenous addition of estradiol (Fig. 8B). GPR30 knockdown decreased p-Akt and PD-L1 levels (Fig. 8C and D), and Akt knockdown also inhibited PD-L1 protein expression in HCT116 and HT29 colon cancer cells (Fig. 8E and F). Exogenous supplementation of estradiol increased IL-6 and TGF-β expression in HCT116 and HT29 colon cancer cells (Fig. 8G). Importantly, GPR30 or Akt siRNA reversed the increased expression of IL-6 and TGF-β in HCT116 and HT29 colon cancer cells in response to estradiol stimulation (Fig. 8G). These results imply that GPR30-Akt signaling is crucial for CYP19A1/estradiol-mediated immunosuppression in colon cancer.

The established hyaluronic acid-modified chitosan nanoparticles are recently shown to be effective for the systemic administration of siRNA to tumors [39, 40]. To explore the possibility of using CYP19A1 as a target for sensitizing anti-PD-1 therapy, the effect of nanoparticle-encapsulated CYP19A1 siRNA on PD-1 blockade therapy was investigated in the orthotopic and subcutaneous colon cancer models. We detected obvious decreases in CYP19A1 protein expression and estradiol production in siCYP19A1-treated MC38 tumor as compared with the si-Scram (Fig. 9A-C). Knockdown of CYP19A1 by specific siRNA significantly impeded growth of orthotopic and subcutaneous MC38 colon cancer, but its combination with anti-PD-1 treatment was far more effective than monotherapy (Fig. 9D-H). CYP19A1 knockdown boosted tumor-infiltrating CD8⁺ T cells and enhanced GzmB and IFNγ production, indicative of CD8⁺ T cells-elicited adaptive immunity against colon cancer, which were amplyfied in combination therapy (Fig. 9I). Additionally, the CYP19A1 siRNA remarkably inhibited PD-L1 expression on tumor cells (Fig. 9J), induced vascular normalization, as manifested by an obvious decrease in the hypoxia-inducible factor (HIF) -1α level and an increase in CD31⁺ αSMA⁺ cells number (Fig. 9K and L). Together, our results demonstrate that CYP19A1 inhibition drastically enhances anti-PD-1 therapy for colon cancer.

Next, we further explored the possibility of using CYP19A1 as a target for sensitizing anti-PD-1 therapy, and found that the CYP19A1 inhibitor letrozole sensitized anti-PD-1 therapy in subcutaneous tumors derived from the MC38 and CT26 murine colon cancer (Fig. 10A and B). The combination of letrozole with PD-1 blockade promoted expression of IFNγ and CD107a and intratumoral infiltration of CD8⁺ T cells compared to the monotherapy (Fig. 10C and D). The combination treatment reduced GPR30 and p-Akt levels in MC38 and CT26 colon cancer tissues with an obvious decrease in estradiol production compared to the anti-PD-1 monotherapy (Fig. 10E and F). The letrozole inhibited PD-L1 expression in tumor cells (Fig. 10G), boosted tumor perfusion, promoted vascular normalization, and downregulated HIF-1α in CT26 and MC38 tumor (Fig. 10H–J). Similar results were obtained in the mice model of orthotopic MC38 colon cancer (Supplementary Fig. S5). The combination of letrozole with anti-PD-1 treatment did not result in any extra toxicity compared with the anti-PD-1 monotherapy (Supplementary Table S3). Overall, these findings strongly indicate that CYP19A1 inhibition greatly facilitates anti-PD-1 therapy for colon cancer.