Xin Su

Decoding the genetic links between substance use disorder and cancer vulnerability

Cancer remains a leading cause of mortality and morbidity worldwide, imposing a significant public health burden. While cannabis and opioids are widely used in cancer pain management, their potential for abuse and addiction has raised concerns regarding their long-term health effects, including possible associations with cancer risk. However, the relationship between substance use disorders (SUDs) and cancer susceptibility remains controversial. This Mendelian randomization (MR) study aimed to investigate the potential causal effects of cannabis use disorder (CUD) and opioids use disorder (OUD) on cancer vulnerability. We conducted a two-sample MR study using summary statistics from genome-wide association studies, including data from FinnGen and UK Biobank. The primary analytical approach was the inverse-variance weighted (IVW), complemented by a range of sensitivity analyses to assess the robustness of the findings. IVW analysis identified a causal association between OUD and bladder cancer (OR = 1.040, 95% CI 1.004-1.078, P = 0.029, adj. P = 0.125), acute myeloid leukemia (OR = 0.931, 95% CI 0.885-0.978, P = 0.005, adj. P = 0.061) and ovarian cancer (OR = 0.937, 95% CI 0.891-0.984, P = 0.010, adj. P = 0.064). Sensitivity analysis yielded directionally consistent results. Reverse MR analysis provided no statistically significant evidence supporting a causal effect of these cancers on OUD (all P > 0.05). Additionally, no evidence of a significant causal relationship was observed between CUD and any cancer type (P > 0.05). This study suggests a potential causal link between OUD and increased susceptibility to bladder cancer, acute myeloid leukemia, and ovarian cancer, warranting further investigation in larger, multi-ethnic population studies. These results contribute to the ongoing discourse on the long-term health impacts of substance use disorders and highlight the need for further research to elucidate their potential oncogenic effects.

Reaction Pathway Differentiation Enabled Fingerprinting Signal for Single Nucleotide Variant Detection

AbstractAccurate identification of single‐nucleotide variants (SNVs) is paramount for disease diagnosis. Despite the facile design of DNA hybridization probes, their limited specificity poses challenges in clinical applications. Here, a differential reaction pathway probe (DRPP) based on a dynamic DNA reaction network is presented. DRPP leverages differences in reaction intermediate concentrations between SNV and WT groups, directing them into distinct reaction pathways. This generates a strong pulse‐like signal for SNV and a weak unidirectional increase signal for wild‐type (WT). Through the application of machine learning to fluorescence kinetic data analysis, the classification of SNV and WT signals is automated with an accuracy of 99.6%, significantly exceeding the 80.7% accuracy of conventional methods. Additionally, sensitivity for variant allele frequency (VAF) is enhanced down to 0.1%, representing a ten‐fold improvement over conventional approaches. DRPP accurately identified D614G and N501Y SNVs in the S gene of SARS‐CoV‐2 variants in patient swab samples with accuracy over 99% (n = 82). It determined the VAF of ovarian cancer‐related mutations KRAS‐G12R, NRAS‐G12C, and BRAF‐V600E in both tissue and blood samples (n = 77), discriminating cancer patients and healthy individuals with significant difference (p < 0.001). The potential integration of DRPP into clinical diagnostics, along with rapid amplification techniques, holds promise for early disease diagnostics and personalized diagnostics.