Clinical usefulness of metagenomic next-generation sequencing for Talaromyces marneffei diagnosis in China: a retrospective study.

PURPOSE: Metagenomic next-generation sequencing (mNGS) has been widely used in the diagnosis of infectious diseases. However, studies on Talaromyces marneffei detection using mNGS remain scarce. Therefore, this study aimed to explore the diagnostic performance of mNGS in T. marneffei. METHODS: Between March 2021 and June 2023, patients who were discharged with a final diagnosis of talaromycosis, or confirmed T. marneffei infection by mNGS, culture or pathological examination were included in the study. Culture and mNGS were performed simultaneously for all patients. Clinical data were retrieved for analysis. RESULTS: A total of 78 patients were enrolled, with 40 in the talaromycosis group and 38 in the suspected-talaromycosis group. In the talaromycosis group, mNGS showed a higher positivity rate(40/40, 100.0%) compared to culture(34/40, 85.0%)(P = 0.111). All patients in the suspected-talaromycosis group tested negative via culture, while mNGS yielded positive results. The T. marneffei reads in the talaromycosis group were significantly higher than in the suspected-talaromycosis group (4399 vs. 28, P < 0.001). In the suspected-talaromycosis group, of the four patients with low reads who did not receive antifungal therapy, one died and one lung lesion progressed; most patients(31/34, 91.2%) recovered after receiving appropriate antifungal therapy. CONCLUSION: mNGS proves to be a rapid and highly sensitive method for detecting T. marneffei. Higher reads of T. marneffei correspond to a higher likelihood of infection. However, cases with low reads necessitate a comprehensive approach, integrating clinical manifestations, laboratory tests, and imaging examinations to confirm T. marneffei infection.

Erratum: ß-elemene promotes the senescence of glioma cells through regulating YAP-CDK6 signaling.

[This corrects the article on p. 370 in vol. 11, PMID: 33575077.].

Synergistic Effect of Lenvatinib and Chemotherapy in Hepatocellular Carcinoma Using Preclinical Models.

Purpose: The current study aimed to evaluate the synergistic efficacy of lenvatinib and FOLFOX (infusional fluorouracil (FU), folinic acid, and oxaliplatin) in hepatocellular carcinoma (HCC) using patient-derived xenograft (PDX) and PDX-derived organotypic spheroid (XDOTS) models in vivo and in vitro. Methods: PDX and matched XDOTS models originating from three patients with HCC were established. All models were divided into four groups and treated with drugs alone or in combination. Tumor growth in the PDX models was measured and recorded, and angiogenesis and phosphorylation of the vascular endothelial growth factor receptor (VEGFR2), rearranged during transfection (RET), and extracellular signal-regulated kinase (ERK) were detected using immunohistochemistry and Western blot assays. The proliferative ability of XDOTS was evaluated through active staining and immunofluorescence staining, and the effect of the combined medication was evaluated using the Celltiter-Glo luminescent cell viability assay. Results: Three PDX models with genetic characteristics similar to those of the original tumors were successfully established. Combining lenvatinib with FOLFOX led to a higher tumor growth inhibition rate than individual therapies (P < 0.01). Immunohistochemical analysis demonstrated that the combined treatment significantly inhibited the proliferation and angiogenesis of PDX tissues (P < 0.05), and Western blot analysis showed that the combined treatment significantly inhibited the phosphorylation of VEGFR2, RET, and ERK compared with single-agent treatment. Additionally, all three matched XDOTS models were successfully cultured with satisfactory activity and proliferation, and the combined therapies led to better suppression of XDOTS growth compared with individual therapy (P < 0.05). Conclusion: Lenvatinib combined with FOLFOX had a synergistic antitumor effect in HCC PDX and XDOTS models by inhibiting the phosphorylation of VEGFR, RET, and ERK.

Prediction of Response to Lenvatinib Monotherapy for Unresectable Hepatocellular Carcinoma by Machine Learning Radiomics: A Multicenter Cohort Study.

PURPOSE: We aimed to construct machine learning (ML) radiomics models to predict response to lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC). EXPERIMENTAL DESIGN: Patients with HCC receiving lenvatinib monotherapy at three institutions were retrospectively identified and assigned to training and external validation cohorts. Tumor response after initiation of lenvatinib was evaluated. Radiomics features were extracted from contrast-enhanced CT images. The K-means clustering algorithm was used to distinguish radiomics-based subtypes. Ten ML radiomics models were constructed and internally validated by 10-fold cross-validation. These models were subsequently verified in an external validation cohort. RESULTS: A total of 109 patients were identified for analysis, namely, 74 in the training cohort and 35 in the external validation cohort. Thirty-two patients showed partial response, 33 showed stable disease, and 44 showed progressive disease. The overall response rate (ORR) was 29.4%, and the disease control rate was 59.6%. A total of 224 radiomics features were extracted, and 25 significant features were identified for further analysis. Two distant radiomics-based subtypes were identified by K-means clustering, and subtype 1 was associated with a higher ORR and longer progression-free survival (PFS). Among the 10 ML algorithms, AutoGluon displayed the highest predictive performance (AUC = 0.97), which was relatively stable in the validation cohort (AUC = 0.93). Kaplan-Meier analysis showed that responders had a better overall survival [HR = 0.21; 95% confidence interval (CI): 0.12-0.36; P < 0.001] and PFS (HR = 0.14; 95% CI: 0.09-0.22; P < 0.001) than nonresponders. CONCLUSIONS: Valuable ML radiomics models were constructed, with favorable performance in predicting the response to lenvatinib monotherapy for unresectable HCC.

Exploration and validation of a novel ferroptosis-related gene signature predicting the prognosis of intrahepatic cholangiocarcinoma.

Ferroptosis plays an important role in intrahepatic cholangiocarcinoma (ICC). We aim to develop a new ferroptosis-related gene signature predicting the prognosis of ICC. We download RNA expression profiles and clinical data of ICC from TCGA and GEO databases. Ferroptosis-related differentially expressed genes (DEGs) are screened by the Wilcoxon signed-rank test. GO and KEGG enrichment analyses are performed to understand the function of DEGs and co-expressed genes. Univariate Cox and LASSO regression are used to develop a ferroptosis-related gene signature. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analysis were used to evaluate the prognostic value. RNA sequencing is performed in 30 patients with ICC in our medical center to validate the prognostic value of the gene signature. We identify 44 ferroptosis-related DEGs, among which four (ACSL4, IREB2, NFE2L2, and TP53) are associated with overall survival (OS). Functional enrichment analysis shows that ferroptosis-associated DEGs have an important impact on ICC carcinogenesis. A new ferroptosis-related gene signature based on DEGs is built, and the prognostic ability is confirmed by KM and ROC curves (AUC=0.777, 0.75, 0.799 for 12, 24, and 36 months, respectively). Patients with high risk scores have worse OS ( P=0.0081). In the validation cohort, the expression of DEGs is in accordance with that in the exploration cohort. The four-gene signature is also demonstrated to have a favorable prognostic value (AUC=0.69). A new predictive model based on four ferroptosis-related genes (ACSL4, IREB2, NFE2L2, and TP53) is established and shows favorable prognostic value.

Diffuse reduction of spleen density is a novel prognostic marker for intrahepatic cholangiocarcinoma after curative resection.

BACKGROUND: Diffuse reduction of spleen density (DROSD) is related to cancer prognosis; however, its role in intrahepatic cholangiocarcinoma (ICC) remains unclear. AIM: To assess the predictive value of DROSD in the prognosis of ICC after curative resection. METHODS: In this multicenter retrospective cohort study, we enrolled patients with ICC who underwent curative hepatectomy between 2012 and 2019. Preoperative spleen density was measured using computed tomography. Overall survival (OS) and recurrence-free survival (RFS) rates were calculated and compared utilizing the Kaplan-Meier method. Univariable and multivariable Cox regression analyses were applied to identify independent factors for OS and RFS. A nomogram was created with independent risk factors to predict prognosis of patients with ICC. RESULTS: One hundred and sixty-seven ICC patients were enrolled. Based on the diagnostic cut-off values (spleen density ≤ 45.5 Hounsfield units), 55 (32.9%) patients had DROSD. Kaplan-Meier analysis indicated that patients with DROSD had worse OS and RFS than those without DROSD (P < 0.05). Cox regression analysis revealed that DROSD, carcinoembryonic antigen level, carbohydrate antigen 19-9 level, length of hospital stay, lymph node metastasis, and postoperative complications were independent predictors for OS (P < 0.05). The nomogram created with these factors was able to predict the prognosis of patients with ICC with good reliability (OS C-index = 0.733). The area under the curve for OS was 0.79. CONCLUSION: ICC patients with DROSD have worse OS and RFS. The nomogram is a simple and practical method to identify high-risk ICC patients with poor prognosis.

ß-elemene promotes the senescence of glioma cells through regulating YAP-CDK6 signaling.

Glioma is currently the most widespread and malignant primary intracranial tumor, which is characterized by high heterogeneity and high fatality rates. ß-elemene, which is a bioactive compound extracted from a Chinese herb, Curcuma wenyujin, has been reported to reduce resistance of chemotherapeutic drugs and induce apoptosis in tumor cells. However, the role and mechanisms of ß-elemene in glioma senescence remains unknown. In the present study, we found that a low concentration of ß-elemene (10 µg/mL) induced senescence in glioma cells, including reduction of cell proliferation, hypertrophic morphology, increase of senescence-associated ß-galactosidase (SA-ß-Gal) activity, upregulation of several senescence-associated genes such as p16, p53 and NF-κB, and downregulation of Lamin B1. However, a high concentration of ß-elemene induced apoptosis in glioma cells. Treatment with ß-elemene caused a marked down-regulation of Yes-associated protein (YAP) expression in glioma cells, which is a key transcriptional co-activator in multiple cancers. Moreover, cyclin dependent kinase 6 (CDK6), which is a known downstream target of YAP, was decreased in glioma cells that treated with ß-elemene. The overexpression of YAP and CDK6 significantly rescued ß-elemene-induced senescence in glioma cells. Finally, ß-elemene treatment also induced the senescence of glioma cells in glioma xenograft model through inactivation of YAP-CDK6 pathways, which might inhibit the glioma growth. Taken together, these results reveal a previously unknown role of ß-elemene in glioma cell senescence in vitro and in vivo that is associated with YAP-CDK6 signaling pathway, which will enhance our understanding of glioma cell senescence, and provide novel strategies for the treatment of gliomas.

A novel five-lncRNA signature panel improves high-risk survival prediction in patients with cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a fatal disease with dismal survival rates. Long non-coding RNA (lncRNA) expression profiling as potential prognostic biomarkers play critical roles in tumor initiation, development, and poor prognosis. Identifying specific lncRNA to predict the prognosis of CCA patients in the early stages is very important for improving a patient's survival. In the current study, we aimed to establish a novel risk-stratification lncRNA signature panel in CCA. The initial lncRNA discovery was identified in The Cancer Genome Atlas database (TCGA cohort). The Cox regression analysis was used to establish the lncRNA prognostic model and the receiver operating characteristic (ROC) curve analysis was performed to assess the specificity and sensitivity of the model. This was followed by independent validation of the lncRNA signature in the CCA patients from the First Affiliated Hospital of Wenzhou Medical University (WMU cohort). Furthermore, by using the Gene Ontology function and Kyoto Encyclopedia Gene and Genome pathway enrichment analysis, we explored the potential function of prognosis lncRNA. Finally, five lncRNA (HULC; AL359715.5; AC006504.8; AC090114.2; AP00943.4) were screened to establish the predictive model that significantly associated with poor overall survival(HR:4.879;95%CI,1.587-14.996;p=0.006). This five-lncRNA signature model showed excellent accuracy in the TCGA cohort (AUC=0.938), and also robustly predicted survival in the validation WMU cohort(AUC=0.816). Functional enrichment analysis suggested prognostic lncRNA was primarily associated with CCA-related biological processes. Our data established a novel lncRNA signature model for CCA risk-stratification and robust identification of CCA patients with poor molecular genotypes. Moreover, it revealed new molecular mechanisms of CCA.