ABSTRACT
The current study's objective was to elucidate some currently unknown biological indicators to evaluate the biological nature of cancer-associated fibroblasts (CAFs). For this purpose, four different CAFs, CAFS1, CAFS2, SCC17F and MO-1000, were established using surgical specimens from oral squamous cell carcinomas (OSCC) with different clinical malignant stages (CAFS1 and CAFS2, T2N0M0, stage II; SCC17F and MO-1000, T4aN2bM0, stage IVA). Fibroblasts unrelated to cancer (non-CAFs) were also prepared and used as controls. Initially, confirmation that these four fibroblasts were indeed CAFs was obtained by their mRNA expression using positive and negative markers for the CAF or fibroblasts. To elucidate possible unknown biological indicators, these fibroblasts were subjected to a cellular metabolic analysis by a Seahorse bioanalyzer, in conjugation with 3D spheroid cultures of the cells and co-cultures with a pancreas ductal carcinoma cell line, MIA PaCa-2. The mitochondrial and glycolytic functions of human orbital fibroblasts (HOF) were nearly identical to those of Graves'-disease-related HOF (GOF). In contrast, the characteristics of the metabolic functions of these four CAFs were different from those of human conjunctival fibroblasts (HconF), a representative non-CAF. It is particularly noteworthy that CAFS1 and CAFS2 showed markedly reduced ratios for the rate of oxygen consumption to the extracellular acidification rate, suggesting that glycolysis was enhanced compared to mitochondrial respiration. Similarly, the physical aspects, their appearance and stiffness, of their 3D spheroids and fibroblasts that were induced effects based on the cellular metabolic functions of MIA PaCa-2 were also different between CAFs and non-CAFs, and their levels for CAFS1 or SCC17F were similar to those for CAFS2 or MO-1000 cells, respectively. The findings reported herein indicate that cellular metabolic functions and the physical characteristics of these types of 3D spheroids may be valuable and useful indicators for estimating potential biological diversity among various CAFs.
