ACSM6 overexpression indicates a non-inflammatory tumor microenvironment and predicts treatment response in bladder cancer: results from multiple real-world cohorts

Abstract

Background: ACSMs play critical roles in lipid metabolism; however, their immunological function within the tumor microenvironment (TME) remains unclear, especially that of ACSM6. In this study, we investigate the latent effect of ACSM6 on bladder cancer (BLCA). Methods: Several real-world cohorts, including the Xiangya (in-house), The Cancer Genome Atlas (TCGA-BLCA), and IMvigor210 cohorts, with TCGA-BLCA cohort serving as the discovery cohort were compared. We investigated the potential immunological effects of ACSM6 in regulating the BLCA tumor microenvironment by analyzing its correlation with immunomodulators, anti-cancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, we assessed the precision of ACSM6 in predicting BLCA molecular subtypes and responses to several treatments using ROC analysis. To ensure the robustness of our findings, all results were confirmed in two independent external cohorts: the IMvigor210 and Xiangya cohorts. Results: ACSM6 expression was markedly upregulated in BLCA. Our analysis suggests that ACSM6 might have significant impact to promote the formation of a non-inflamed tumor microenvironment because of its negative correlation with immunomodulators, anticancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, high ACSM6 expression levels in BLCA may predict the luminal subtype, which is typically associated with resistance to chemotherapy, neoadjuvant chemotherapy, and radiotherapy. These findings were consistent across both the IMvigor210 and Xiangya cohorts. Conclusion: ACSM6 has the potential to serve as a valuable predictor of the tumor microenvironment phenotypes and treatment outcomes in BLCA, thereby contributing to more precise treatment.

Keywords: ACSM6; bladder cancer; chemothearpy bladder cancer; chemotherapy; immunotherapy; tumor microenvironment.

FIGURE 1

Immunological characteristics in pan-cancers correlated with ACSM6. (A) Correlation of ACSM6 with immunomodulators, including chemokines, receptors, MHC, and immunostimulators. (B–E) Correlation of ACSM6 expression with PD-L1, CTLA-4, PD-1, and LAG-3.

FIGURE 2

(A–C) Correlation between ACSM6 expression and TME scores, including ESTIMATE, immune, and stromal scores.

FIGURE 3

In BLCA, the tumor immune microenvironment correlates with ACSM6 expression. (A) ACSM6 was highly expressed in the Xiangya cohort. (B) ACSM6 expression is different in different tissues in BLCA. (C) ACSM6 expression is different in various steps of the anti-tumor immune cycle. (D) ACSM6 expression correlates with infiltration levels of five tumor-infiltrating immune cell types (TIICs), as measured by various algorithms. (E) High- and low-ACSM6 tissues in BLCA show differential expression of effector genes of the five TIICs mentioned above. (F) ACSM6 expression correlates with 20 inhibitory immune checkpoints in BLCA.

FIGURE 4

In BLCA, ACSM6 expression predicts molecular subtype and response to multiple therapies. (A, B) ACSM6 expression correlates with tumor immune subtype and their corresponding effector genes. (C) ACSM6 expression correlates with molecular subtypes as defined by seven different subtyping systems. (D) ROC analysis demonstrates the prediction accuracy of ACSM6 for molecular subtypes using different systems. (E) Mutational profiles of neoadjuvant chemotherapy-related genes differ between low- and high-ACSM6 tissues. (F) ACSM6 expression correlates with enrichment scores of therapeutic signatures. (G) ACSM6 expression correlates with drug-target genes for various therapies.

FIGURE 5

Validation of the prediction accuracy of ACSM6 for molecular subtypes and response to therapies in the Xiangya Cohort. (A) ACSM6 expression correlates with different steps of the anti-cancer immune cycle. (B) ACSM6 expression correlates with multiple ssGSEA immune cells. (C) ACSM6 expression correlates with 20 immune checkpoints. (D) ACSM6 expression correlates with TIS-related genes.

FIGURE 6

ACSM6 expression correlates with TIS-related effector genes. (A) ACSM6 expression correlates with seven different molecular subtyping systems. (B) The molecular subtype prediction accuracy of ACSM6 is evaluated. (C) ACSM6 expression correlates with enrichment scores of therapeutic signatures.

FIGURE 7

Validation of prediction for low immune infiltration and molecular subtypes by ACSM6 in the IMvigor210 cohort. (A) ACSM6 expression correlates with different steps of the anti-cancer immune cycle. (B) ACSM6 expression correlates with several immune-related cells. (C) ACSM6 expression correlates with immune checkpoint inhibitors effector genes. (D) ACSM6 expression correlates with TIS-related genes.

FIGURE 8

Correlations between ACSM6 and seven molecular subtype systems, three immune phenotypes and the clinical response of tumor immunotherapy in the desert group. (A, B) ACSM6 expression correlates with seven different molecular subtyping systems, and its prediction accuracy is evaluated by ROC analysis. (C) ACSM6 expression correlates with enrichment scores of therapeutic signatures. (D) ACSM6 expression shows differential expression in different immune checkpoint groups. (E) ACSM6 expression shows differential expression in different IC groups. (F) ACSM6 expression shows differential expression in three immune phenotypes: the desert, excluded, and inflamed. (G) ACSM6 expression correlates with the clinical response of tumor immunotherapy in the desert group.