塚本 徹哉

Abstract Currently, human papillomavirus tests and cytology are used to screen for cervical cancer. However, more accurate ancillary screening tests are needed. MicroRNAs (miRNAs) and cytokines are promising biomarkers that are aberrantly expressed in cervical cancer. Therefore, the potential of developing new screening markers based on the levels of miRNAs and cytokines in serum and local mucus samples from the same patients with cervical neoplasia was investigated. miRNA screening was performed by microarray and measurement using real‐time reverse‐transcriptase PCR. Cytokine were measured using multiplex bead assay, and changes in expressions were analyzed based on disease severity. As lesions progressed, miR‐20b‐5p, −155‐5p, −144‐3p, −451a, and −126‐3p expression levels were increased in mucus, and miR‐16‐5p, −223‐3p, and ‐451a expression levels were decreased in serum. Regarding cytokines, IL‐6, IL‐8, monocyte chemoattractant protein‐1, Eotaxin, interferon‐γ, and RANTES were increased, whereas granulocyte–colony‐stimulating factor (G‐CSF) was significantly decreased in mucus. miRNAs and cytokines in serum did not have high diagnostic accuracy. However, a combination of miR‐20b‐5p, ‐451a, ‐126‐3p, Eotaxin, as well as G‐CSF in mucus samples, had high diagnostic accuracy with an area under the receiver operating characteristic curve of 0.989 (0.979–0.999). Our results suggest that using mucus for this ancillary test is more beneficial than serum.

Helicobacter pylori induces DNA methylation in gastric mucosa, which links to gastric cancer (GC) risk. In contrast, CpG island methylator phenotype (CIMP) is defined as high levels of cancer-specific methylation and provides distinct molecular and clinicopathological features of GC. The association between those two types of methylation in GC remains unclear. We examined DNA methylation of well-validated H. pylori infection associated genes in GC and its adjacent mucosa and investigated its association with CIMP, various molecular subtypes and clinical features. We studied 50 candidate loci in 24 gastric samples to identify H. pylori infection associated genes. Identified loci were further examined in 624 gastric tissue from 217 primary GC, 217 adjacent mucosa, and 190 mucosae from cancer-free subjects. We identified five genes (IGF2, SLC16A2, SOX11, P2RX7, and MYOD1) as hypermethylated in H. pylori infected gastric mucosa. In non-neoplastic mucosa, methylation of H. pylori infection associated genes was higher in patients with GC than those without. In primary GC tissues, higher methylation of H. pylori infection associated genes correlated with CIMP-positive and its related features, such as MLH1 methylated cases. On the other hand, GC with lower methylation of these genes presented aggressive clinicopathological features including undifferentiated histopathology, advanced stage at diagnosis. H. pylori infection associated DNA methylation is correlated with CIMP, specific molecular and clinicopathological features in GC, supporting its utility as promising biomarker in this tumor type.

BACKGROUND: Distinguishing between central nervous system lymphoma (CNSL) and CNS infectious and/or demyelinating diseases, although clinically important, is sometimes difficult even using imaging strategies and conventional cerebrospinal fluid (CSF) analyses. To determine whether detection of genetic mutations enables differentiation between these diseases and the early detection of CNSL, we performed mutational analysis using CSF liquid biopsy technique. METHODS: In this study, we extracted cell-free DNA from the CSF (CSF-cfDNA) of CNSL (N = 10), CNS infectious disease (N = 10), and demyelinating disease (N = 10) patients, and performed quantitative mutational analysis by droplet-digital PCR. Conventional analyses were also performed using peripheral blood and CSF to confirm the characteristics of each disease. RESULTS: Blood hemoglobin and albumin levels were significantly lower in CNSL than CNS infectious and demyelinating diseases, CSF cell counts were significantly higher in infectious diseases than CNSL and demyelinating diseases, and CSF-cfDNA concentrations were significantly higher in infectious diseases than CNSL and demyelinating diseases. Mutation analysis using CSF-cfDNA detected MYD88L265P and CD79Y196 mutations in 60% of CNSLs each, with either mutation detected in 80% of cases. Mutual existence of both mutations was identified in 40% of cases. These mutations were not detected in either infectious or demyelinating diseases, and the sensitivity and specificity of detecting either MYD88/CD79B mutations in CNSL were 80% and 100%, respectively. In the four cases biopsied, the median time from collecting CSF with the detected mutations to definitive diagnosis by conventional methods was 22.5 days (range, 18-93 days). CONCLUSIONS: These results suggest that mutation analysis using CSF-cfDNA might be useful for differentiating CNSL from CNS infectious/demyelinating diseases and for early detection of CNSL, even in cases where brain biopsy is difficult to perform.