A Comprehensive Study on Aptasensors For Cancer Diagnosis.

Cancer is the most devastating disease in the present scenario, killing millions of people every year. Early detection, accurate diagnosis, and timely treatment are considered to be the most effective ways to control this disease. Rapid and efficient detection of cancer at their earliest stage is one of the most significant challenges in cancer detection and cure. Numerous diagnostic modules have been developed to detect cancer cells early. As nucleic acid equivalent to antibodies, aptamers emerge as a new class of molecular probes that can identify cancer-related biomarkers or circulating rare cancer/ tumor cells with very high specificity and sensitivity. The amalgamation of aptamers with the biosensing platforms gave birth to "Aptasensors." The advent of highly sensitive aptasensors has opened up many new promising point-of-care diagnostics for cancer. This comprehensive review focuses on the newly developed aptasensors for cancer diagnostics.

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

Profiling of 3D Genome Organization in Nasopharyngeal Cancer Needle Biopsy Patient Samples by a Modified Hi-C Approach.

Nasopharyngeal cancer (NPC), a cancer derived from epithelial cells in the nasopharynx, is a cancer common in China, Southeast Asia, and Africa. The three-dimensional (3D) genome organization of nasopharyngeal cancer is poorly understood. A major challenge in understanding the 3D genome organization of cancer samples is the lack of a method for the characterization of chromatin interactions in solid cancer needle biopsy samples. Here, we developed Biop-C, a modified in situ Hi-C method using solid cancer needle biopsy samples. We applied Biop-C to characterize three nasopharyngeal cancer solid cancer needle biopsy patient samples. We identified topologically associated domains (TADs), chromatin interaction loops, and frequently interacting regions (FIREs) at key oncogenes in nasopharyngeal cancer from the Biop-C heatmaps. We observed that the genomic features are shared at some important oncogenes, but the patients also display extensive heterogeneity at certain genomic loci. On analyzing the super enhancer landscape in nasopharyngeal cancer cell lines, we found that the super enhancers are associated with FIREs and can be linked to distal genes via chromatin loops in NPC. Taken together, our results demonstrate the utility of our Biop-C method in investigating 3D genome organization in solid cancers.