Ultraviolet radiation-induced non-melanoma skin cancer:Regulation of DNA damage repair and inflammation

Exposure to ultraviolet(UV)radiation is associated with approximately 65%of melanoma cases,and 90%of non-melanoma skin cancers(NMSC),including basal cell carcinoma(BCC)and squamous cell carcinoma(SCC).While the incidence of most other malignancies has either stabilized or declined,that of NMSC has increased and is developing even in younger age groups.NMSCs account for nearly 15,000 deaths,3.5 million new cases,and more than 3 billion dollars a year in medical costs in the United States alone,representing a major public health concern.As sun protection efforts have not been proven effective,targeted chemoprevention strategies are much needed.Skin carcinogenesis by DNA damage is considered a predominant paradigm for UV toxicity.Exposure to UV radiation can activate various oncogenes while inactivating tumor suppressor genes,resulting in inappropriate survival and proliferation of keratinocytes that harbor these damages.Moreover,increasing evidence demonstrate that inflammatory responses by the immune cells within the tumor microenvironment also contribute significantly to skin tumorigenesis.Initiation and progression of skin carcinogenesis mediated by UV radiation involve complex pathways,including those of apoptosis,proliferation,autophagy,DNA repair,checkpoint signaling,metabolism,and inflammation.In this review,we highlight the recent advances in two of these key molecular processes that result in UV-mediated skin carcinogenesis.In particular,we discuss 1)pathways that regulate DNA damage repair and 2)the regulation of the inflammatory process its crosstalk with DNA repair potentially leading to non-melanoma skin carcinogenesis.

Scalable nanolaminated SERS multiwell cell culture assay

This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy(SERS)analysis of biological samples.The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane(PDMS)multiwells for the simultaneous analysis of cultured cells.Multiple cell lines,including breast normal and cancer cells and prostate cancer cells,were used to validate the applicability of this unique platform.The cell lines were cultured in different wells.The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12h of introducing the cells to the assay.The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells.A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra.Specifically,the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables.The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%.The prediction rate between breast cancer cells and prostate cancer cells reached 94%.Four blind test groups were used to evaluate the prediction power of the SERS spectra.The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm.The blind testing groups were correctly predicted 100% of the time,demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.