Laboratory Investigation最新文献

The risk of developing cervical squamous lesions in women with multiple high-risk human papillomavirus (hrHPV) infections is uncertain. The aim of this retrospective study was to investigate the type-specific attribution and phylogenetic effects of single and multiple hrHPV subtypes in cervical squamous lesions. All cases with cervical histopathologic diagnosis and human papillomavirus (HPV) genotyping results in the 6 months preceding biopsy from October 2018 to December 2022 were studied and analyzed. Over the study period, 70,361 cases with histopathologic follow-up and prior HPV genotyping were identified. The hrHPV-positive rate was 55.6% (39,104/70,361), including single hrHPV detected in 27,182 (38.6%), 2 types of hrHPV detected in 8158 (11.6%), and 3 types of hrHPV detected in 2486 (3.5%). Among 16,457 cases with a histologically diagnosed squamous lesion (cervical intraepithelial neoplasia 1: 11411; cervical intraepithelial neoplasia 2/3: 4192; squamous cell carcinoma: 854 cases), the prevalence of single hrHPV infection increased, but the rate of multiple concomitant hrHPV infections showed negative association as the degree of squamous lesions increased. Among women with a single HPV16 infection, cervical intraepithelial neoplasia 2/3 and squamous cell carcinoma (CIN2+) diagnostic rate was 30.6%, and it increased to 47.6% when coinfected with HPV33 (P < .001) but significantly decreased when coinfected with all other hrHPV types (P < .05). By comparing CIN2+ diagnostic rates in 40 most common 2 types of hrHPV infections with related single hrHPV infection, CIN2+ rates were decreased in 12 combinations (30.0%), equivalent in 26 combinations (65.0%), and increased in 2 combinations (5.0%). The cases with 3 types of HPV infections reduced the risk for CIN2+ compared with related single HPV infections. HPV16+52+53, HPV16+52+68, HPV16+52+51, HPV16+39+52, and HPV16+58+53 significantly decreased the risk of CIN2+ compared with HPV16 single infection (P < .05). This study demonstrates that multiple hrHPV infections are not associated with cumulatively higher risk for CIN2+ development, suggesting that oncogenic progression of multiple hrHPV-associated cervical squamous lesions is neither synergistic nor a cumulative effect at the phylogenetic level, possibly a way of competitive interference.

Metabolic syndrome (MetS) is a worldwide challenge that is closely associated with obesity, nonalcoholic liver disease, insulin resistance, and type 2 diabetes. Boosting nicotinamide adenine dinucleotide (NAD⁺) presents great potential in preventing MetS. However, the function of nuclear NAD⁺ in the development of MetS remains poorly understood. In this study, hepatocyte-specific Nmnat1 knockout mice were used to determine a possible link between nuclear NAD⁺ and high-fat diet (HFD)-induced MetS. We found that Nmnat1 knockout significantly reduced hepatic nuclear NAD⁺ levels but did not exacerbate HFD-induced obesity and hepatic triglycerides accumulation. Interestingly, loss of Nmnat1 caused insulin resistance. Further analysis revealed that Nmnat1 deletion promoted gluconeogenesis but inhibited glycogen synthesis in the liver. Moreover, Nmnat1 deficiency induced mitochondrial dysfunction by decreasing mitochondrial DNA (mtDNA)-encoded complexes Ⅰ and Ⅳ, suppressing mtDNA replication and mtRNA transcription and reducing mtDNA copy number. In addition, Nmnat1 depletion affected the expression of hepatokines in the liver, particularly downregulating the expression of follistatin. These findings highlight the importance of nuclear NAD⁺ in maintaining insulin sensitivity and provide insights into the mechanisms underlying HFD-induced insulin resistance.

Formalin-fixed paraffin-embedded (FFPE) tissues stored in biobanks and pathology archives are a vast but underutilized source for molecular studies on different diseases. Beyond being the “gold standard” for preservation of diagnostic human tissues, FFPE samples retain similar genetic information as matching blood samples, which could make FFPE samples an ideal resource for genomic analysis. However, research on this resource has been hindered by the perception that DNA extracted from FFPE samples is of poor quality. Here, we show that germline disease-predisposing variants and polygenic risk scores (PRS) can be identified from FFPE normal tissue (FFPE-NT) DNA with high accuracy. We optimized the performance of FFPE-NT DNA on a genome-wide array containing 657,675 variants. Via a series of testing and validation phases, we established a protocol for FFPE-NT genotyping with results comparable with blood genotyping. The median call rate of FFPE-NT samples in the validation phase was 99.85% (range 98.26%-99.94%) and median concordance with matching blood samples was 99.79% (range 98.85%-99.9%). We also demonstrated that a rare pathogenic PALB2 genetic variant predisposing to cancer can be correctly identified in FFPE-NT samples. We further imputed the FFPE-NT genotype data and calculated the FFPE-NT genome-wide PRS in 3 diseases and 4 disease risk variables. In all cases, FFPE-NT and matching blood PRS were highly concordant (all Pearson’s r > 0.95). The ability to precisely genotype FFPE-NT on a genome-wide array enables translational genomics applications of archived FFPE-NT samples with the possibility to link to corresponding phenotypes and longitudinal health data.

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.

Recent studies have described several molecular subtypes and deregulation of immuno-oncologic signaling pathways in angiosarcoma. Interestingly, mast cells were enriched in subsets of angiosarcoma, although their significance remains unknown. In this study, we aim to verify this observation using immunohistochemistry (H scores) and NanoString transcriptomic profiling and explore the association between mast cells with clinical and biological features. In the study cohort (N = 60), H scores showed a significant moderate correlation with NanoString mast cell scores (r = 0.525; P < .001). Both H score and NanoString mast cell scores showed a significant positive correlation (P < .05) with head and neck location, nonepithelioid morphology, and lower tumor grade. Mast cell enrichment significantly correlated with higher NanoString regulatory T-cell scores (H score, r = 0.32; P = .01; NanoString mast cell score, r = 0.27; P = .04). NanoString mast cell scores positively correlated with signaling pathways relating to antigen presentation (r = 0.264; P = .0414) and negatively correlated with apoptosis (r = −0.366; P = .0040), DNA damage repair (r = −0.348; P = .0064), and cell proliferation (r = −0.542; P < .001). Interestingly, in the metastatic setting, patients with mast cell–enriched angiosarcoma showed poorer progression-free survival (median, 0.2 vs 0.4 years; hazard ratio = 3.05; P = .0489) along with a trend toward worse overall survival (median, 0.2 vs 0.6 years; hazard ratio, 2.86; P = .0574) compared with patients with mast cell–poor angiosarcoma. In conclusion, we demonstrated the presence of mast cells in human angiosarcoma and provided initial evidence of their potential clinical and biological significance. Future research will be required to elucidate their specific roles and mechanisms, which may uncover novel avenues for therapeutic intervention.

Glomerular fibrillary deposits have occasionally been reported in diabetic nephropathy, but no large-scale, ultrastructural evaluation of these deposits has been reported so far. Here, we report our study of glomerular non-Congophilic, DnaJ homolog subfamily B member 9 negative fibrillary deposits in diabetic nephropathy as characterized by transmission electron microscopy. Clinical data from 55 patients with biopsy-confirmed diabetic nephropathy and 18 healthy living donors were reviewed, and their biopsies were evaluated by light microscopy, immunofluorescence, and electron microscopy. Small fibrillary structures with a diameter of 10 ± 1 nm were present in all cases with diabetic nephropathy, regardless of the histologic class. In addition, glomerular fibrillary structures with a diameter of 23 ± 5 nm or 30 ± 7 nm were present in 35 cases. Interestingly, especially the small- and medium-sized fibrils, usually without apparent organization, were comparable with fibrils in fibrillary glomerulopathy. We conclude that glomerular fibrillary deposits occur far more commonly in renal biopsies of patients with diabetic nephropathy than generally considered. This is an important finding because their similarity to fibrils in fibrillary glomerulonephritis may complicate the histologic diagnostic process, especially in cases of overlapping clinical manifestations. Therefore, when encountering fibrillary deposits on electron microscopy, it is important to consider diabetic nephropathy as an alternative diagnosis.

Despite the use of machine learning tools, it is challenging to properly model cause-specific deaths in colorectal cancer (CRC) patients and choose appropriate treatments. Here, we propose an interesting feature selection framework, namely union with recursive feature elimination (U-RFE), to select the union feature sets that are crucial in CRC progression-specific mortality using The Cancer Genome Atlas (TCGA) dataset. Based on the union feature sets, we compared the performance of 5 classification algorithms, including logistic regression (LR), support vector machines (SVM), random forest (RF), eXtreme gradient boosting (XGBoost), and Stacking, to identify the best model for classifying 4-category deaths. In the first stage of U-RFE, LR, SVM, and RF were used as base estimators to obtain subsets containing the same number of features but not exactly the same specific features. Union analysis of the subsets was then performed to determine the final union feature set, effectively combining the advantages of different algorithms. We found that the U-RFE framework could improve various models’ performance. Stacking outperformed LR, SVM, RF, and XGBoost in most scenarios. When the target feature number of the RFE was set to 50 and the union feature set contained 298 deterministic features, the Stacking model achieved F1_weighted, Recall_weighted, Precision_weighted, Accuracy, and Matthews correlation coefficient of 0.851, 0.864, 0.854, 0.864, and 0.717, respectively. The performance of the minority categories was also significantly improved. Therefore, this recursive feature elimination–based approach of feature selection improves performances of classifying CRC deaths using clinical and omics data or those using other data with high feature redundancy and imbalance.

With more novel drugs being approved for the treatment of ovarian carcinoma, the question remains to what extent patients benefit from antiangiogenic treatment with bevacizumab, either in combination with poly-(ADP-ribose) polymerase inhibitors or as single-agent maintenance. As fibroblast growth factor receptors and their ligands (FGFRs/FGFs) are key players in angiogenic signaling and have been linked to resistance to several drugs, we investigated the prognostic or predictive potential of FGFs/FGFRs signaling in the context of bevacizumab treatment within the prospective phase III AGO-OVAR11/ICON-7 study. FGFR1, FGFR2, FGFR3, FGFR4, FGF1, and FGF19 gene expressions were determined in 380 ovarian carcinoma tumor samples collected from German centers in the multicenter phase III AGO-OVAR11 trial/ICON-7 trial. All patients received carboplatin and paclitaxel, administered every 3 weeks for 6 cycles, and were randomized to bevacizumab. Expressions of FGFR1, FGFR2, FGF1, and FGF19 were associated with progression-free survival in both uni- and multivariate (FGFR1: HR, 1.6, P < .001; FGFR2: HR, 1.6, P = .002; FGF1: HR, 2.3, P < .001; and FGF19: HR, 0.7; P = .007) analysis. A signature built by FGFR1, FGFR4, and FGF19 defined a subgroup (n = 62) of patients that derived the greatest bevacizumab-associated improvement of progression-free survival (HR, 0.3; P = .004). In this exploratory analysis of a prospective randomized phase III trial, we provide evidence that the expression of FGFRs/FGFs might have independent prognostic values. An FGFR/FGF-based gene signature identified in our study appears to predict long-term benefit from bevacizumab. This observation is hypothesis-generating and requires validation on independent cohorts.

Effective inhibition of macrophage activation is critical for resolving inflammation and restoring pulmonary function in patients with chronic obstructive pulmonary disease (COPD). In this study, we identified the dual-enhanced cyclooxygenase-2 (COX-2)/soluble epoxide hydrolase (sEH) as a novel regulator of macrophage activation in COPD. Both COX-2 and sEH were found to be increased in patients and mice with COPD and in macrophages exposed to cigarette smoke extract. Pharmacological reduction of the COX-2 and sEH by 4-(5-phenyl-3-{3-[3-(4-trifluoromethylphenyl)-ureido]-propyl}-pyrazol-1-yl)-benzenesulfonamide (PTUPB) effectively prevented macrophage activation, downregulated inflammation-related genes, and reduced lung injury, thereby improving respiratory function in a mouse model of COPD induced by cigarette smoke and lipopolysaccharide. Mechanistically, enhanced COX-2/sEH triggered the activation of the NACHT, LRR, and PYD domains-containing protein 3 inflammasome, leading to the cleavage of pro-IL-1β into its active form in macrophages and amplifying inflammatory responses. These findings demonstrate that targeting COX-2/sEH-mediated macrophage activation may be a promising therapeutic strategy for COPD. Importantly, our data support the potential use of the dual COX-2 and sEH inhibitor PTUPB as a therapeutic drug for the treatment of COPD.

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.