Background: Hyaluronan (HA) modulates key cancer cell functions through interaction with its CD44 and RHAMM receptors. Results: Low molecular weight HA (LMWHA) significantly increased (p Յ 0.01) the adhesion capacity of HT1080 cells in a RHAMM-dependent manner. Conclusion: RHAMM/HA interaction regulates fibrosarcoma cell adhesion via the activation of FAK and ERK1/2 signaling pathways. Significance: Identification of a novel HA-signaling pathway.
Collagen VI and hyaluronan are widely distributed extracellular matrix macromolecules that play a crucial role in tissue development and are highly expressed in cancers. Both hyaluronan and collagen VI are upregulated in breast cancer, generating a microenvironment that promotes tumour progression and metastasis. A growing number of studies show that these two molecules are involved in inflammation and angiogenesis by recruiting macrophages and endothelial cells, respectively. Additionally, collagen VI induces epithelial-mesenchymal transition that is correlated to increased synthesis of hyaluronan in mammary cells. Hyaluronan has also a specific role in cellular functions that depends mainly on the size of the polymer, whereas the effect of collagen VI in tumour progression may be the result of the intact molecule or the C5 peptide of α3(VI) chain, known as endotrophin. Collectively, these findings strongly support the parallel role of these molecules in tumour progression and suggest that they may be used as prognostic factors for the breast cancer treatment.
Fibrosarcomas are rare malignant mesenchymal tumors originating from fibroblasts. Importantly, fibrosarcoma cells were shown to have a high content and turnover of extracellular matrix (ECM) components including hyaluronan (HA), proteoglycans, collagens, fibronectin, and laminin. ECMs are complicated structures that surround and support cells within tissues. During cancer progression, significant changes can be observed in the structural and mechanical properties of the ECM components. Importantly, hyaluronan deposition is usually higher in malignant tumors as compared to benign tissues, predicting tumor progression in some tumor types. Furthermore, activated stromal cells are able to produce tissue structure rich in hyaluronan in order to promote tumor growth. Key biological roles of HA result from its interactions with its specific CD44 and RHAMM (receptor for HA-mediated motility) cell-surface receptors. HA-receptor downstream signaling pathways regulate in turn cellular processes implicated in tumorigenesis. Growth factors, including PDGF-BB, TGFβ2, and FGF-2, enhanced hyaluronan deposition to ECM and modulated HA-receptor expression in fibrosarcoma cells. Indeed, FGF-2 through upregulation of specific HAS isoforms and hyaluronan synthesis regulated secretion and net hyaluronan deposition to the fibrosarcoma pericellular matrix modulating these cells' migration capability. In this paper we discuss the involvement of hyaluronan/RHAMM/CD44 mediated signaling in the insidious pathways of fibrosarcoma progression.
The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting.
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