Applications of Next-Generation Sequencing in Cancer Research and Molecular Diagnosis

Next-generation sequencing (NGS) technologies including DNA sequencing and RNA sequencing provide “omics” approaches to reveal genomic, transcriptomic, and epigenomic landscapes of individual cancers. A variety of genomic aberrations can be screened simultaneously, such as common and rare variants, structural variations (e.g. insertions and deletions), copy-number variation, and fusion transcripts. NGS technologies together with bioinformatics analysis, which expand our knowledge, are increasingly used to simultaneously analyze multiple genes in a cost and time-effective manner and have been applied in analyzing clinical cancer samples and offering NGS-based molecular diagnosis. Therefore, NGS is increasingly valuable as a tool for diagnosis for a number of cancers. Here we briefly introduce NGS technologies and summarize the recent applications in cancer research and molecular diagnosis in breast and prostate cancers

Diisopropyl­ammonium nitrite

In the title mol­ecular salt, C6H16N+·NO2 −, the cation forms two N—H⋯O hydrogen bonds to nearby nitrite anions which link the ionic units into chains propagating along the b-axis direction

Revisiting the Ω(2012)\Omega(2012) as a hadronic molecule and its strong decays

Recently, the Belle collaboration measured the ratios of the branching fractions of the newly observed $\Omega(2012)$ excited state. They did not observe significant signals for the $\Omega(2012) \to \bar{K} \Xi^*(1530) \to \bar{K} \pi \Xi$ decay, and reported an upper limit for the ratio of the three body decay to the two body decay mode of $\Omega(2012) \to \bar{K} \Xi$. In this work, we revisit the newly observed $\Omega(2012)$ from the molecular perspective where this resonance appears to be a dynamically generated state with spin-parity $3/2^-$ from the coupled channels interactions of the $\bar{K} \Xi^*(1530)$ and $\eta \Omega$ in $s$-wave and $\bar{K} \Xi$ in $d$-wave. With the model parameters for the $d$-wave interaction, we show that the ratio of these decay fractions reported recently by the Belle collaboration can be easily accommodated.Comment: Published version. Published in Eur.\ Phys.\ J.\ C {\bf 80}, 361 (2020

Electrical transport across metal/two-dimensional carbon junctions: Edge versus side contacts

Metal/two-dimensional carbon junctions are characterized by using a nanoprobe in an ultrahigh vacuum environment. Significant differences were found in bias voltage (V) dependence of differential conductance (dI/dV) between edge- and side-contact; the former exhibits a clear linear relationship (i.e., dI/dV \propto V), whereas the latter is characterized by a nonlinear dependence, dI/dV \propto V3/2. Theoretical calculations confirm the experimental results, which are due to the robust two-dimensional nature of the carbon materials under study. Our work demonstrates the importance of contact geometry in graphene-based electronic devices

Bayesian Survival Analysis of Genetic Variants in PTPRN2 Gene for Age at Onset of Cancer

Background: The protein tyrosine phosphatase, receptor type, N polypeptide 2 (PTPRN2) gene may play a role in cancer; however, no study has focused on the associations of genetic variants within the PTPRN2 gene with age at onset (AAO) of cancer. Methods: This study examined 220 single nucleotide polymorphisms (SNPs) within the PTPRN2 gene in the Marshfield sample with 716 cancer cases (any diagnosed cancer, excluding minor skin cancer) and 2,848 non-cancer controls. Multiple logistic regression model and linear regression model in PLINK software were used to examine the association of each SNP with the risk of cancer and AAO, respectively. For survival analysis of AAO, both classic Cox regression and Bayesian survival analysis using the Cox proportional hazards model in SAS v. 9.4 were applied to detect the association of each SNP with AAO. The hazards ratios (HRs) with 95% confidence intervals (CIs) were estimated. Results: Single marker analysis identified 10 SNPs associated with the risk of cancer and 9 SNPs associated with AAO (p \u3c 0.05). SNP rs7783909 revealed the strongest association with cancer (p = 6.52x10-3); while the best signal for AAO was rs4909140 (p = 6.18x10-4), which was also associated with risk of cancer (p = 0.0157). Classic Cox regression model showed that 11 SNPs were associated with AAO (top SNP rs4909140 with HR = 1.38, 95%CI = 1.11-1.71, p = 3.3x10-3). Bayesian Cox regression model showed similar results to those using the classic Cox regression (top SNP rs4909140 with HR = 1.39, 95%CI = 1.1-1.69). Conclusions: This study provides evidence of several genetic variants within the PTPRN2 gene influencing the risk of cancer and AAO, and will serve as a resource for replication in other populations