In forensic science, accurate estimation of the age of a victim or suspect can facilitate the investigators to narrow a search and aid in solving a crime. Aging is a complex process associated with various molecular regulation on DNA or RNA levels. Recent studies have shown that circular RNAs (circRNAs) upregulate globally during aging in multiple organisms such as mice and elegans because of their ability to resist degradation by exoribonucleases. In the current study, we attempted to investigate circRNAs’ potential capability of age prediction. Here, we identified more than 40,000 circRNAs in the blood of thirteen Chinese unrelated healthy individuals with ages of 20-62 years according to their circRNA-seq profiles. Three methods were applied to select age-related circRNAs candidates including false discovery rate, lasso regression, and support vector machine. The analysis uncovered a strong bias for circRNA upregulation during aging in human blood. A total of 28 circRNAs were chosen for further validation in 50 healthy unrelated subjects aged between 19 and 72 years by RT-qPCR and finally, 7 age-related circRNAs were chosen for final age prediction models. Several different algorithms including multivariate linear regression (MLR), regression tree, bagging regression, random forest regression (RFR), and support vector regression (SVR) were compared based on root mean square error (RMSE) and mean average error (MAE) values. Among five modeling methods, random forest regression (RFR) performed better than the others with an RMSE value of 5.072 years and an MAE value of 4.065 years (R2 = 0.902). In this preliminary study, we firstly used circRNAs as additional novel age-related biomarkers for developing forensic age estimation models. We propose that the use of circRNAs to obtain additional clues for forensic investigations and serve as aging indicators for age prediction would become a promising field of interest.Author summaryIn forensic investigations, estimation of the age of biological evidence recovered from crime scenes can provide additional information such as chronological age or the appearance of a culprit, which could give valuable investigative leads especially when there is no eyewitness available. Hence, generating an accurate model for age prediction using body fluids such as blood commonly seen at a crime scene can be of vital importance. Various molecular changes on DNA or RNA levels were discovered that they upregulated or downregulated during a person’s lifetime. Although some biomarkers have been proved to be associated with aging and used to predict age, several disadvantages such as low sensitivity, prediction accuracy, instability and susceptibility of diseases or immune states, thus limiting their applicability in the field of age estimation. Here, we utilized a novel biomarker namely circular RNA (circRNA) to generate highly accurate age prediction models. We propose that circRNA is more suitable for forensic degradation samples because of its unique molecular structure. This preliminary research offers a new thought for exploring potential biomarker for age prediction.

BackgroundMultiplexed immunofluorescence (mIF) allows the visualization of multiple biomarkers in a single tumor tissue section, while at the same time preserving the spatial biology of the tumor microenvironment (TMI). CO-Detection by indEXing (CODEX®) and Phenoptics™ platforms are complementary mIF technologies that span the full spectrum of cancer research, from discovery to translational and clinical research. CODEX® is ultra-high plex and allows imaging of up to 40 antigens on a single tissue section with single-cell resolution. Phenoptics™ is an established mIF platform that enables high-throughput whole slide multispectral image acquisition and tissue interrogation with up to 8 markers plus DAPI. Here we present a study that compares shared sets of immune and tumor markers between the CODEX® and Phenoptics™ platforms. This cross-platform comparison provides a conceptual framework for researchers to translate biomarker signatures from discovery to high-throughput translational studies.Materials and MethodsSerial sections of human formalin-fixed paraffin embedded non-small cell lung cancer (NSCLC) and tonsils were analyzed. An initial screen with a 28-plex CODEX® antibody panel revealed multiple biomarkers of interest, including CK, CD8, Ki67, PD-L1 and PD-1; all of these biomarkers showed abundant expression in the TMI. Building on this result, we next developed a 6-plex Opal™ Phenotpics™ panel. This panel was screened and analyzed via high-throughput whole slide scanning of sample tissues. Image processing and data analysis were conducted similarly for both datasets so that repeatability and consistency of measurements could be established.ResultsBoth CODEX® and Phenoptics™ detected the same cell phenotypes and displayed similar frequencies of cells expressing CK, CD8, Ki67, PD-L1 and PD-1 in serial sections of tonsil and NSCLC tissues. These observations were consistent and cross-validated in data from CODEX® and Phenoptics™ platforms. Crucially, this means that the two approaches can be made analytically equivalent, and hence, that they can be used in conjunction with each other as research progresses along the continuum from discovery to translational and clinical research.ConclusionsOur cross-platform comparison provides a conceptual framework for biomarkers discovered on the CODEX® platform to be translated to the Phenoptics™ platform for high-throughput translational studies. The resulting comprehensive phenotyping and quantification data retain spatial context and provide unprecedented insight into tumor biology.Abstract P01.04 Figure 1Disclosure InformationO. Braubach: A. Employment (full or part-time); Significant; Akoya Biosciences. M. Gallina: A. Employment (full or part-time); Significant; Akoya Biosciences. B. Remeniuk: A. Employment (full or part-time); Significant; Akoya Biosciences. C. Wang: A. Employment (full or part-time); Significant; Akoya Biosciences. N. Nikulina: A. Employment (full or part-time); Significant; Akoya Biosciences. R. Bashier: A. Employment (full or part-time); Significant; Akoya Biosciences. J. Kennedy-Darling: A. Employment (full or part-time); Significant; Akoya Biosciences. C. Hoyt: A. Employment (full or part-time); Significant; Akoya Biosciences.