1.
Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities.
Yan, J, Zhao, Y, Chen, Y, Wang, W, Duan, W, Wang, L, Zhang, S, Ding, T, Liu, L, Sun, Q, et al
EBioMedicine. 2021;:103583
Abstract
BACKGROUND To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. METHODS The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). FINDINGS The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). INTERPRETATION DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. FUNDING A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
2.
Roles and Mechanisms of Human Cathelicidin LL-37 in Cancer.
Chen, X, Zou, X, Qi, G, Tang, Y, Guo, Y, Si, J, Liang, L
Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology. 2018;(3):1060-1073
Abstract
LL-37, the C-terminal peptide of human cathelicidin antimicrobial peptide (CAMP, hCAP18), reportedly increases resistance to microbial invasion and exerts important physiological functions in chemotaxis, promotion of wound closure, and angiogenesis. Accumulating evidence indicates that LL-37 also plays a significant role in human cancer. LL-37 induces tumorigenic effects in cancers of the ovary, lung, breast, prostate, pancreas, as well as in malignant melanoma and skin squamous cell carcinoma. In contrast, LL-37 displays an anti-cancer effect in colon cancer, gastric cancer, hematologic malignancy and oral squamous cell carcinoma. Mechanistically, LL-37-induced activation of membrane receptors and subsequent signaling pathways lead to alteration of cellular functions. Different membrane receptors on various cancer cells appear to be responsible for the tissue-specific effects of LL-37. Meanwhile, the findings that vitamin D-dependent induction of cathelicidin in human macrophages activates the anti-cancer activity of tumor-associated macrophages (TAMs) and enhances antibody-dependent cellular cytotoxicity (ADCC) support critical roles of vitamin D-dependent induction of cathelicidin in cancer progression. This review describes novel advances involving the roles and mechanisms of human cathelicidin LL-37 in cancer.