Pan-Cancer Analyses Reveal Prognostic Value of Osteomimicry Across 20 Solid Cancer Types

Background

Osteomimicry of cancer cells had been widely reported in prostate cancer and breast cancer. However, the prognostic value of osteomimicry in various cancer types remained unclear. We hypothesized that osteomimicry would result in remodeling of the tumor microenvironment and was eligible to predict patient prognosis.

Methods

A comprehensive transcriptomic analysis of the osteomimicry, which was characterized by mRNA expression of SPARC, SPP1, and BGLAP, across 20 solid tumors (7564 patients) using RNA-seq data from The Cancer Genome Atlas (TCGA) was conducted. Samples of each cancer type were classified into subgroups (high vs. low) based on median value of osteomimetic markers, the associations of these markers with clinical outcomes, immune cell infiltration and immune checkpoints expression were explored.

Results

Each osteomimetic marker harbored prognostic value in the pan-cancer analyses [SPARC: hazard ratio (HR) = 1.10, p = 0.028; SPP1: HR = 1.25, p < 0.001; BGLAP: HR = 1.13, p = 0.005]. Patients with high expression of all the three genes also had significantly unfavorable survival (HR = 1.61, p < 0.0001) compared with those of low expression. Correlation analyses demonstrated that osteomimicry was closely related to tumor purity, dendritic cells (DC) infiltration and expression of immune checkpoints.

Conclusion

Osteomimicry had prognostic value in various cancer types and the underlying mechanism might correlate to the trapping and dysfunction of DCs in the tumor microenvironment, revealing the potential of osteomimicry as a target of immunotherapy.

Pan-cancer genomic analyses reveal prognostic and immunogenic features of the tumor melatonergic microenvironment across 14 solid cancer types

We performed comprehensive genomic analyses of the melatonergic system within the tumor microenvironment and their clinical relevance across a broad spectrum of solid tumors. RNA-seq data from The Cancer Genome Atlas (TCGA) of 14 solid tumors representing 6658 human samples were analyzed. The tumor melatonergic system was characterized by the rates of melatonin synthesis and metabolism using a two-gene expression model (melatonin synthesis/metabolism Index). We calculated three indexes according to different melatonin metabolism isoenzymes (Index-I [ASMT:CYP1A1], Index-II [ASMT:CYP1A2], and Index-III [ASMT:CYP1B1]). Samples of each cancer type were classified into two subgroups (high vs low) based on median values. Clinical outcomes, mutational burden, and neoepitope abundance were analyzed and compared. We found that the ability of the tumor microenvironment to synthesize and accumulate melatonin varied across cancer types and negatively correlated with tumor burden. Kaplan-Meier survival analyses and multivariable modeling showed that the three indexes played different roles across different cancers and harbored prognostic values in breast cancer (adjusted hazard ratio [AHR]_Index-II  = 0.65 [0.44-0.97]; P = 0.03), cervical cancer (AHR_Index-I  = 0.62 [0.39-0.98]; P = 0.04), lung squamous cell carcinoma (AHR_Index-III  = 0.75 [0.56-0.99]; P = 0.04), melanoma (AHR_Index-I  = 0.74 [0.55-0.98]; P = 0.04), and stomach adenocarcinoma (AHR_Index-III  = 0.68 [0.41-0.94]; P = 0.02). We further investigated its clinical relevance with tumor immunogenic features (mutational burden and neoantigen abundance), which may predict immunotherapy benefits. We observed significant negative correlations with mutational burden in the majority of tumors (P < 0.05), except cervical cancer, pancreatic adenocarcinoma, and thyroid carcinoma. Our study provides a systematic overview of the oncostatic values of the melatonergic system and highlights the utilization of this simple and promising gene signature as a prognosticator and potential predictor of response to immunotherapy.