Cutibacterium acnes invades prostate epithelial cells to induce BRCAness as a possible pathogen of prostate cancer.

BACKGROUND: Abundant evidence suggests that chronic inflammation is linked to prostate cancer and that infection is a possible cause of prostate cancer. METHODS: To identify microbiota or pathogens associated with prostate cancer, we investigated the transcriptomes of 20 human prostate cancer tissues. We performed de novo assembly of nonhuman sequences from RNA-seq data. RESULTS: We identified four bacteria as candidate microbiota in the prostate, including Moraxella osloensis, Uncultured chroococcidiopsis, Cutibacterium acnes, and Micrococcus luteus. Among these, C. acnes was detected in 19 of 20 prostate cancer tissue samples by immunohistochemistry. We then analyzed the gene expression profiles of prostate epithelial cells infected in vitro with C. acnes and found significant changes in homologous recombination (HR) and the Fanconi anemia pathway. Notably, electron microscopy demonstrated that C. acnes invaded prostate epithelial cells and localized in perinuclear vesicles, whereas analysis of γH2AX foci and HR assays demonstrated impaired HR repair. In particular, BRCA2 was significantly downregulated in C. acnes-infected cells. CONCLUSIONS: These findings suggest that C. acnes infection in the prostate could lead to HR deficiency (BRCAness) which promotes DNA double-strand breaks, thereby increasing the risk of cancer development.

Prognostic significance of chronic kidney disease and impaired renal function in Japanese patients with COVID-19.

BACKGROUND: Renal impairment is a predictor of coronavirus disease (COVID-19) severity. No studies have compared COVID-19 outcomes in patients with chronic kidney disease (CKD) and patients with impaired renal function without a prior diagnosis of CKD. This study aimed to identify the impact of pre-existing impaired renal function without CKD on COVID-19 outcomes. METHODS: This retrospective study included 3,637 patients with COVID-19 classified into three groups by CKD history and estimated glomerular filtration rate (eGFR) on referral: Group 1 (n = 2,460), normal renal function without a CKD history; Group 2 (n = 905), impaired renal function without a CKD history; and Group 3 (n = 272), history of CKD. We compared the clinical characteristics of these groups and assessed the effect of CKD and impaired renal function on critical outcomes (requirement for respiratory support with high-flow oxygen devices, invasive mechanical ventilation, or extracorporeal membrane oxygen, and death during hospitalization) using multivariable logistic regression. RESULTS: The prevalence of comorbidities (hypertension, diabetes, and cardiovascular disease) and incidence of inflammatory responses (white blood counts, and C-reactive protein, procalcitonin, and D-dimer levels) and complications (bacterial infection and heart failure) were higher in Groups 2 and 3 than that in Group 1. The incidence of critical outcomes was 10.8%, 17.7%, and 26.8% in Groups 1, 2, and 3, respectively. The mortality rate and the rate of requiring IMV support was lowest in Group 1 and highest in Group 3. Compared with Group 1, the risk of critical outcomes was higher in Group 2 (adjusted odds ratio [aOR]: 1.32, 95% confidence interval [CI]: 1.03-1.70, P = 0.030) and Group 3 (aOR: 1.94, 95% CI: 1.36-2.78, P < 0.001). Additionally, the eGFR was significantly associated with critical outcomes in Groups 2 (odds ratio [OR]: 2.89, 95% CI: 1.64-4.98, P < 0.001) and 3 (OR: 1.87, 95% CI: 1.08-3.23, P = 0.025) only. CONCLUSIONS: Clinicians should consider pre-existing CKD and impaired renal function at the time of COVID-19 diagnosis for the management of COVID-19.

Differential Gene Regulatory Network Analysis between Azacitidine-Sensitive and -Resistant Cell Lines.

Azacitidine, a DNA methylation inhibitor, is employed for the treatment of acute myeloid leukemia (AML). However, drug resistance remains a major challenge for effective azacitidine chemotherapy, though several studies have attempted to uncover the mechanisms of azacitidine resistance. With the aim to identify the mechanisms underlying acquired azacitidine resistance in cancer cell lines, we developed a computational strategy that can identify differentially regulated gene networks between drug-sensitive and -resistant cell lines by extending the existing method, differentially coexpressed gene sets (DiffCoEx). The technique specifically focuses on cell line-specific gene network analysis. We applied our method to gene networks specific to azacitidine sensitivity and identified differentially regulated gene networks between azacitidine-sensitive and -resistant cell lines. The molecular interplay between the metallothionein gene family, C19orf33, ELF3, GRB7, IL18, NRN1, and RBM47 were identified as differentially regulated gene network in drug resistant cell lines. The biological mechanisms associated with azacitidine and AML for the markers in the identified networks were verified through the literature. Our results suggest that controlling the identified genes (e.g., the metallothionein gene family) and "cellular response"-related pathways ("cellular response to zinc ion", "cellular response to copper ion", and "cellular response to cadmium ion", where the enriched functional-related genes are MT2A, MT1F, MT1G, and MT1E) may provide crucial clues to address azacitidine resistance in patients with AML. We expect that our strategy will be a useful tool to uncover patient-specific molecular interplay that provides crucial clues for precision medicine in not only gastric cancer but also complex diseases.

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Meta graphical lasso: uncovering hidden interactions among latent mechanisms.

In complex systems, it's crucial to uncover latent mechanisms and their context-dependent relationships. This is especially true in medical research, where identifying unknown cancer mechanisms and their impact on phenomena like drug resistance is vital. Directly observing these mechanisms is challenging due to measurement complexities, leading to an approach that infers latent mechanisms from observed variable distributions. Despite machine learning advancements enabling sophisticated generative models, their black-box nature complicates the interpretation of complex latent mechanisms. A promising method for understanding these mechanisms involves estimating latent factors through linear projection, though there's no assurance that inferences made under specific conditions will remain valid across contexts. We propose a novel solution, suggesting data, even from systems appearing complex, can often be explained by sparse dependencies among a few common latent factors, regardless of the situation. This simplification allows for modeling that yields significant insights across diverse fields. We demonstrate this with datasets from finance, where we capture societal trends from stock price movements, and medicine, where we uncover new insights into cancer drug resistance through gene expression analysis.

Inhibition of the galactosyltransferase C1GALT1 reduces osteosarcoma cell proliferation by interfering with ERK signaling and cell cycle progression.

Novel therapeutic strategies are urgently required for osteosarcoma, given the early age at onset and persistently high mortality rate. Modern transcriptomics techniques can identify differentially expressed genes (DEGs) that may serve as biomarkers and therapeutic targets, so we screened for DEGs in osteosarcoma. We found that osteosarcoma cases could be divided into fair and poor survival groups based on gene expression profiles. Among the genes upregulated in the poor survival group, siRNA-mediated knockdown of the glycosylation-related gene C1GALT1 suppressed osteosarcoma cell proliferation in culture. Gene expression, phosphorylation, and glycome array analyses also demonstrated that C1GALT1 is required to maintain ERK signaling and cell cycle progression. Moreover, the C1GALT1 inhibitor itraconazole suppressed osteosarcoma cell proliferation in culture, while doxycycline-induced shRNA-mediated knockdown reduced xenograft osteosarcoma growth in mice. Elevated C1GALT1 expression is a potential early predictor of poor prognosis, while pharmacological inhibition may be a feasible treatment strategy for osteosarcoma.

Immuno-genomic analysis reveals eosinophilic feature and favorable prognosis of female non-smoking esophageal squamous cell carcinomas.

Most of esophageal squamous cell carcinoma (ESCC) develop in smoking males in Japan, but the genomic etiology and immunological characteristics of rare non-smoking female ECSS remain unclear. To elucidate the genomic and immunological features of ESCC in non-smoking females, we analyzed whole-genome or transcriptome sequencing data from 94 ESCCs, including 20 rare non-smoking female cases. In addition, 31,611 immune cells were extracted from four ESCC tissues and subject to single-cell RNA-seq. We compared their immuno-genomic and microbiome profiles between non-smoking female and smoking ESCCs. Non-smoking females showed much better prognosis. Whole-genome sequencing analysis showed no significant differences in driver genes or copy number alterations depending on smoking status. The mutational signatures specifically observed in non-smoking females ESCC could be attributed to aging. Immune profiling from RNA-seq revealed that ESCC in non-smoking females had high tumor microenvironment signatures and a high abundance of eosinophils with a favorable prognosis. Single-cell RNA-sequencing of intratumor immune cells revealed gender differences of eosinophils and their activation in female cases. ESCCs in non-smoking females have age-related mutational signatures and gender-specific tumor immune environment with eosinophils, which is likely to contribute to their favorable prognosis.

Gastrointestinal symptoms in COVID-19 and disease severity: a Japanese registry-based retrospective cohort study.

BACKGROUND: Research on whether gastrointestinal symptoms correlate with the severity of Coronavirus Disease 2019 (COVID-19) has been inconclusive. This study aimed to clarify any associations between gastrointestinal symptoms and the prognosis of COVID-19. METHODS: We collected data from the Japanese nationwide registry for COVID-19 to conduct a retrospective cohort study. Data from 3498 Japanese COVID-19 patients, diagnosed at 74 facilities between February 2020 and August 2022, were analyzed in this study. Hospitalized patients were followed up until discharge or transfer to another hospital. Outpatients were observed until the end of treatment. Associations between gastrointestinal symptoms and clinical outcomes were investigated using multivariable-adjusted logistic regression models. RESULTS: The prevalence of diarrhea, nausea/vomiting, abdominal pain, and melena were 16.6% (581/3498), 8.9% (311/3498), 3.5% (121/3498), and 0.7% (23/3498), respectively. In the univariable analysis, admission to intensive care unit (ICU) and requirement for mechanical ventilation were less common in patients with diarrhea than those without (ICU, 15.7% vs. 20.6% (p = 0.006); mechanical ventilation, 7.9% vs. 11.4% (p = 0.013)). In the multivariable-adjusted analysis, diarrhea was associated with lower likelihood of ICU admission (adjusted odds ratio (aOR), 0.70; 95% confidence interval (CI), 0.53-0.92) and mechanical ventilation (aOR, 0.61; 95% CI, 0.42-0.89). Similar results were obtained in a sensitivity analysis with another logistic regression model that adjusted for 14 possible covariates with diarrhea (ICU; aOR, 0.70; 95% CI, 0.53-0.93; mechanical ventilation; aOR 0.62; 95% CI, 0.42-0.92). CONCLUSIONS: Diarrhea was associated with better clinical outcomes in COVID-19 patients.

Integrated assessment of computed tomography density in pectoralis and erector spinae muscles as a prognostic biomarker for coronavirus disease 2019.

BACKGROUND ＆ AIMS: Muscle quantification using chest computed tomography (CT) is a useful prognostic biomarker for coronavirus disease 2019 (COVID-19). However, no studies have evaluated the clinical course through comprehensive assessment of the pectoralis and erector spinae muscles. Therefore, we compared the impact of the areas and densities of these muscles on COVID-19 infection outcome. METHODS: This multicenter retrospective cohort study was conducted by the COVID-19 Task Force. A total of 1410 patients with COVID-19 were included, and data on the area and density of the pectoralis and erector spinae muscles on chest CT were collected. The impact of each muscle parameter on the clinical outcome of COVID-19 was stratified according to sex. The primary outcome was the percentage of patients with severe disease, including those requiring oxygen supplementation and those who died. Additionally, 167 patients were followed up for changes in muscle parameters at three months and for the clinical characteristics in case of reduced CT density. RESULTS: For both muscles, low density rather than muscle area was associated with COVID-19 severity. Regardless of sex, lower erector spinae muscle density was associated with more severe disease than pectoralis muscle density. The muscles were divided into two groups using the receiver operating characteristic curve of CT density, and the population was classified into four (Group A: high CT density for both muscles, Group B: low CT density for pectoralis and high for erector spinae muscle. Group C: high CT density for pectoralis and low for erector spinae muscle, Group D: low CT density for both muscles). In univariate analysis, Group D patients exhibited worse outcomes than Group A (OR: 2.96, 95% CI: 2.03-4.34 in men; OR: 3.02, 95% CI: 2.66-10.4 in women). Multivariate analysis revealed that men in Group D had a significantly more severe prognosis than those in Group A (OR: 1.82, 95% CI: 1.16-2.87). Moreover, Group D patients tended to have the highest incidence of other complications due to secondary infections and acute kidney injury during the clinical course. Longitudinal analysis of both muscle densities over three months revealed that patients with decreased muscle density over time were more likely to have severe cases than those who did not. CONCLUSIONS: Muscle density, rather than muscle area, predicts the clinical outcomes of COVID-19. Integrated assessment of pectoralis and erector spinae muscle densities demonstrated higher accuracy in predicting the clinical course of COVID-19 than individual assessments.

CT-derived vertebral bone mineral density is a useful biomarker to predict COVID-19 outcome.

The low vertebral bone computed tomography (CT) Hounsfield unit values measured on CT scans reflect low bone mineral density (BMD) and are known as diagnostic indicators for osteoporosis. The potential prognostic significance of low BMD defined by vertebral bone CT values for the coronavirus disease 2019 (COVID-19) remains unclear. This study aimed to assess the impact of BMD on the clinical outcome in Japanese patients with COVID-19 and evaluate the association between BMD and critical outcomes, such as high-flow nasal cannula, non-invasive and invasive positive pressure ventilation, extracorporeal membrane oxygenation, or death. We examined the effects of COVID-19 severity on the change of BMD over time. This multicenter retrospective cohort study enrolled 1132 inpatients with COVID-19 from the Japan COVID-19 Task Force database between February 2020 and September 2022. The bone CT values of the 4th, 7th, and 10th thoracic vertebrae were measured from chest CT images. The average of these values was defined as BMD. Furthermore, a comparative analysis was conducted between the BMD on admission and its value 3 months later. The low BMD group had a higher proportion of critical outcomes than did the high BMD group. In a subanalysis stratifying patients by epidemic wave according to onset time, critical outcomes were higher in the low BMD group in the 1st-4th waves. Multivariable logistic analysis of previously reported factors associated with COVID-19 severity revealed that low BMD, chronic kidney disease, and diabetes were independently associated with critical outcomes. At 3 months post-infection, patients with oxygen demand during hospitalization showed markedly decreased BMD than did those on admission. Low BMD in patients with COVID-19 may help predict severe disease after the disease onset. BMD may decrease over time in patients with severe COVID-19, and the impact on sequelae symptoms should be investigated in the future.

Lung volume measurement using chest CT in COVID-19 patients: a cohort study in Japan.

OBJECTIVE: This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients. METHODS: This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months. RESULTS: The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct (%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct (%pred) group. TLCct (%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months. CONCLUSION: Lower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.

Epicardial adipose tissue measured from analysis of adipose tissue area using chest CT imaging is the best potential predictor of COVID-19 severity.

BACKGROUND: Computed tomography (CT) imaging is widely used for diagnosing and determining the severity of coronavirus disease 2019 (COVID-19). Chest CT imaging can be used to calculate the epicardial adipose tissue (EAT) and upper abdominal visceral adipose tissue (Abd-VAT) areas. The EAT is the main source of inflammatory cytokines involved in chest inflammatory diseases; thus, the EAT area might be a more useful severity predictor than the Abd-VAT area for COVID-19. However, to the best of our knowledge, there are no large-scale reports that sufficiently consider this issue. In addition, there are no reports on the characteristics of patients with normal body mass index (BMI) and high adipose tissue. AIM: The purpose of this study was to analyze whether the EAT area, among various adipose tissues, was the most associated factor with COVID-19 severity. Using a multicenter COVID-19 patient database, we analyzed the associations of chest subcutaneous, chest visceral, abdominal subcutaneous, and Abd-VAT areas with COVID-19 outcomes. In addition, the clinical significance of central obesity, commonly disregarded by BMI, was examined. METHODS: This retrospective cohort study evaluated patients with COVID-19 aged ≥18 years In Japan. Data including from chest CT images collected between February 2020 and October 2022 in four hospitals of the Japan COVID-19 Task Force were analyzed. Patient characteristics and COVID-19 severity were compared according to the adipose tissue areas (chest and abdominal subcutaneous adipose tissue [Chest-SAT and Abd-SAT], EAT, and Abd-VAT) calculated from chest CT images. RESULTS: We included 1077 patients in the analysis. Patients with risk factors of severe COVID-19 such as old age, male sex, and comorbidities had significantly higher areas of EAT and Abd-VAT. High EAT area but not high Abd-VAT area was significantly associated with COVID-19 severity (adjusted odds ratio (aOR): 2.66, 95 % confidence interval [CI]: 1.19-5.93). There was no strong correlation between BMI and VAT. Patients with high VAT area accounted for 40.7 % of the non-obesity population (BMI < 25 kg/m2). High EAT area was also significantly associated with COVID-19 severity in the non-obesity population (aOR: 2.50, 95 % CI: 1.17-5.34). CONCLUSIONS: Our study indicated that VAT is significantly associated with COVID-19 severity and that EAT is the best potential predictor for risk stratification in COVID-19 among adipose tissue areas. Body composition assessment using EAT is an appropriate marker for identifying obesity patients overlooked by BMI. Considering the next pandemic of the global health crisis, our findings open new avenues for implementing appropriate body composition assessments based on CT imaging.