Revealing genes associated with cervical cancer in distinct immune cells: A comprehensive Mendelian randomization analysis

Human papillomavirus can be contracted by sexually active women. However, only a small proportion of these infections persist and have the potential to progress into cervical cancers, indicating a significant involvement of the immune system in cervical cancer development. Despite this, our understanding of the precise contributions of genes from different immune cell types in cervical cancers remains limited. Therefore, the primary objective of our study was to investigate the potential causal relationships between specific immune cell genes and the development of cervical cancers. By accessing expression quantitative trait loci datasets of 14 distinct immune cell types and genome wide association study of cervical cancers, we employed the summary data-based Mendelian randomization (SMR) along with multi-single nucleotide polymorphism (SNP)-based SMR to identify significant genes associated with cervical cancers. Colocalization analysis was further conducted to explore the shared genetic causality. A total of 10 genes across 11 immune cell types (26 significant gene-trait associations) were found to be associated with cervical cancers after false discovery rate correction. Notably, the ORMDL3, BRK1 and HMGN1 gene expression levels showed significant association with cervical cancer in specific immune cell types, respectively. These associations were supported by strong evidence of colocalization analyses. Our study has identified several genes in different immune cells that were associated with cervical cancer. However, further research is necessary to confirm these findings and provide more comprehensive insights into the association between these gene expressions and cervical cancer risk.

Hypoxia-Responsive Tetrameric Supramolecular Polypeptide Nanoprodrugs for Combination Therapy

Despite the intense progress of photodynamic and chemotherapy, however, they cannot prevent solid tumor invasion, metastasis, and relapse, along with inferior efficacy and severe side effects. The hypoxia-responsive nanoprodrugs integrating photodynamic functions are highly sought to address the above-mentioned problems and overcome the tumor hypoxia-reduced efficacy. Herein, a hypoxia-responsive tetrameric supramolecular polypeptide nanoprodrug (SPN-TAPP-PCB4) is constructed from the self-assembly of tetrameric porphyrin-central poly(l-lysine-azobenzene-chlorambucil) (TAPP-(PLL-Azo-CB)4) and an anionic water-soluble [2]biphenyl-extended-pillar[6]arene (AWBpP6) via the synergy of hydrophobic, π-π stacking, and host-guest interactions. Upon laser irradiation, the central TAPP can convert oxygen to generate single oxygen (1 O2 ) to kill tumor cells. Furthermore, under the acidic and PDT-aggravated hypoxia tumor cell microenvironment, SPN-TAPP-PCB4 is rapidly disassembled, and then efficiently releases activated CB through the hypoxic-responsive cleavage of azobenzene linkages. Both in vitro and in vivo biological studies showcase synergistic cancer-killing actions between photodynamic therapy (PDT) and chemotherapy (CT) with negligible toxicity. Consequently, this supramolecular polypeptide nanoprodrug offers an effective strategy to design a hypoxia-responsive nanoprodrug for a potential combo PDT-CT transition.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.