Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma

BackgroundIntravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.MethodsEighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.ResultsThe radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).ConclusionsThe IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.     

A Novel Nomogram for Predicting the Overall Survival in Patients with Unresectable HCC after TACE plus Hepatic Arterial Infusion Chemotherapy

Background and aimTransarterial chemoembolization combined with hepatic arterial infusion chemotherapy (TACE-HAIC) has shown encouraging efficacy in the treatment of unresectable hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict overall survival (OS) of patients with unresectable HCC treated with TACE-HAIC.MethodsA total of 591 patients with unresectable HCC treated with TACE-HAIC between May 2009 and September 2020 were enrolled. These patients were randomly divided into training and validation cohorts. The independent prognostic factors were identified with Cox proportional hazards model. The model's discriminative ability and accuracy were validated using concordance index (C-index), calibration plots, the area under the time-dependent receiver operating characteristic curve (AUC) and decision curve analyses (DCAs).ResultsThe median OS was 15.6 months. A nomogram was established based on these factors, including tumor size, vein invasion, extrahepatic metastasis, tumor number, alpha fetoprotein (AFP), and albumin-bilirubin (ALBI), to predict OS for patients with unresectable HCC treated with TACE-HAIC. The C-index of the nomogram were 0.717 in the training cohort and 0.724 in validation cohort. The calibration plots demonstrated good agreement between the predicted outcomes and the actual observations. The AUC values were better than those of three conventional staging systems. The results of DCA indicated that the nomogram may have clinical usefulness. The patients in the low-risk group had a longer OS than those in intermediate-risk and high-risk groups (P<0.001).ConclusionA prognostic nomogram was developed and validated to assist clinicians in accurately predicting the OS of patients with unresectable HCC after TACE-HAIC.     

Nomogram to Assess the Risk of Central Cervical Lymph Node Metastasis in Patients With Clinical N0 Papillary Thyroid Carcinoma

ObjectiveTo develop and validate an individualized risk prediction model for the need for central cervical lymph node dissection in patients with clinical N0 papillary thyroid carcinoma (PTC) diagnosed using ultrasound.MethodsUpon retrospective review, derivation and internal validation cohorts comprised 1585 consecutive patients with PTC treated from January 2017 to December 2019 at hospital A. The external validation cohort consisted of 406 consecutive patients treated at hospital B from January 2016 to June 2020. Independent risk factors for central cervical lymph node metastasis (CLNM) were determined through univariable and multivariable logistic regression analysis. An individualized risk prediction model was constructed and illustrated as a nomogram, which was internally and externally validated.ResultsThe following risk factors of CLNM were established: a solitary primary thyroid nodule’s diameter, shape, calcification, and capsular abutment-to-lesion perimeter ratio. The areas under the receiver operating characteristic curves of the risk prediction model for the internal and external validation cohorts were 0.921 and 0.923, respectively. The calibration curve showed good agreement between the nomogram-estimated probability of CLNM and the actual CLNM rates in the 3 cohorts. The decision curve analysis confirmed the clinical usefulness of the nomogram.ConclusionThis study developed and validated a model for predicting the risk of CLNM in individual patients with clinical N0 PTC, which should be an efficient tool for guiding clinical treatment.     

Differential expression and bioinformatics analysis of exosome circRNAs in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a fatal malignant tumor with an unfavorable prognosis. Increasing evidence indicated circRNAs were associated with the pathogenesis and progression of tumors, but data on the expression of serum exosomal circRNAs in PDAC are scarce. This study attempted to explore the prognostic value and function of serum exosomes in PDAC patients.MethodsMicroarray-based circRNA expression was determined in PDAC and paired with normal serum samples, and the intersection of differentially expressed circRNAs (DECs) in serum exosomal samples and GSE79634 tissue samples was conducted. A specific CircRNA database was applied to investigate DECs binding miRNAs. Target genes were predicted using the R package multiMiR. Cox regression analyses were applied for constructing a prognostic model. The immunological characteristics analysis was carried out through the TIMER, QUANTISEQ, XCELL, EPIC, and ssGSEA algorithms.Results15 DECs were finally identified, and a circRNA-miRNA-mRNA network was established. A prognostic risk model was developed to categorize patients according to the risk scores. Furthermore, the association between risk score and immune checkpoint genes including CD80, TNFSF9, CD276, CD274, LGALS9, and CD44 were significantly elevated in the high-risk group, while ICOSLG and ADORA2A were upregulated in the low-risk group.ConclusionsOur results may provide new clues for the prognosis and treatment of PDAC.     

Ultrasound-Based Risk Stratification System for the Assessment of Partially Cystic Thyroid Nodules

ObjectiveTo develop and validate a risk stratification system for the prediction of malignancy in partially cystic thyroid nodules (PCTNs).MethodsWe retrospectively reviewed the sonography data of patients with PCTNs from 2 medical centers—Hangzhou Traditional Chinese Medicine Hospital and Hangzhou First People’s Hospital—from January 2020 to December 2021. The independent risk factors for malignant PCTNs were evaluated using the univariate and multivariate logistic regression analyses. The nomogram prediction efficiency was assessed using the area under the curve and calibration curves. The decision curve analysis was used to determine the clinical value of the predictive model.ResultsA total of 285 patients were enrolled in this retrospective study, and of 301 PCTNs, 242 were benign and 59 were malignant. Younger age, hypoechoic, irregular margin, and microcalcifications were found to be the independent risk factors for malignant PCTNs. The area under the curve, sensitivity, and specificity were 0.860, 77.1%, and 84.7% in the training data set and 0.897, 91.7%, and 87.0% in the external validation data set, respectively. The total point of nomogram was >161, which showed the best to predict malignancy in PCTNs.ConclusionOur findings demonstrated that the risk stratification system for the assessment of PCTNs showed good prediction capacities.     

Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis

PurposeThe prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.Patients and methodsThe gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.ResultsA total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.ConclusionOur study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.     

Immune-pyroptosis-related genes predict the prognosis of kidney renal clear cell carcinoma

BackgroundKidney renal clear cell carcinoma (KIRC) is a common cancer of the adult urological system. Recent developments in tumor immunology and pyroptosis biology have provided new directions for kidney cancer treatment. Therefore, there is an urgent need to identify potential targets and prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy.MethodsThe expression of immune-pyroptosis-related differentially expressed genes (IPR-DEGs) between KIRC and healthy tissues was examined using the Gene Expression Omnibus datasets. The GSE168845 dataset was selected for subsequent analyses. Data of 1793 human immune-related genes were downloaded from the ImmPort database (https://www.immport.org./home), while those of 33 pyroptosis-related genes were extracted from previous reviews. The independent prognostic value of IPR-DEGs was determined using differential expression, prognostic, and univariate and multivariate Cox regression analyses. The GSE53757 dataset was used to further verify the GSDMB and PYCARD levels. In our cohorts, the association among DEGs and clinicopathological features and overall survival was analyzed. The least absolute shrinkage and selection operator Cox regression model was established to evaluate the correlation of IPR-DEGs with the immune score, immune checkpoint gene expression, and one-class logistic regression (OCLR) score. KIRC cells and clinical tissue samples were subjected to quantitative real-time polymerase chain reaction to examine the GSDMB and PYCARD mRNA levels. The GSDMB and PYCARD levels in a healthy kidney cell line (HK-2 cells) and two KIRC cell lines (786-O and Caki-1 cells) were verified. The tissue levels of GSDMB and PYCARD were evaluated using immunohistochemical analysis. GSDMB and PYCARD were knocked down in 786-O cells using short-interfering RNA. Cell proliferation was examined using the cell counting kit-8 assay. Cell migration was measured by transwell migration assaysResultsGSDMB and PYCARD were determined to be IPR-DEGs with independent prognostic values. A risk prognostic model based on GSDMB and PYCARD was successfully established. In the GSE53757 dataset, the GSDMB and PYCARD levels in KIRC tissues were significantly higher than those in healthy tissues. The GSDMB and PYCARD expression was related to T stage and OS in our cohort. The GSDMB and PYCARD levels were significantly correlated with the immune score, immune checkpoint gene expression, and OCLR score. The results of experimental studies were consistent with those of bioinformatics analysis. The GSDMB and PYCARD levels in KIRC cells were significantly upregulated when compared with those in healthy kidney cells. Consistently, GSDMB and PYCARD in KIRC tissues were significantly upregulated when compared with those in adjacent healthy kidney tissues. GSDMB and PYCARD knockdown significantly decreased 786-O cell proliferation (p < 0.05). Transwell migration result reflects that silencing GSDMB and PYCARD inhibited 786-O cell migration (p < 0.05) .ConclusionsGSDMB and PYCARD are potential targets and effective prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy in KIRC.     

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.     

A Novel Scoring System for the Risk of Papillary Thyroid Cancer Metastases in Lymph Nodes Posterior to the Right of the Recurrent Laryngeal Nerve

ObjectiveSome surgeons believe that dissection posterior to the right recurrent laryngeal nerve lymph node (PRRLN-LN) is unnecessary for the low metastasis rate and high complication risk. However, persistent metastatic lymph nodes may have a higher recurrence rate, surgical risk, and complications. Thus, it is important to distinguish patients who require PRRLN-LN dissection. To identify the risk factors for lymph nodes posterior to the right recurrent laryngeal nerve metastasis (LN-prRLN) and establish a scoring system to help determine whether PRRLN-LN dissection is required in patients with papillary thyroid carcinoma.Methods821 participants were randomly allocated to the development and validation cohorts in a 2:1 ratio. A nomogram-based predictive model for LN-prRLN was established based on the risk factors identified in the development cohort.ResultsLN-prRLN was diagnosed pathologically in 124 of 821 patients (15.1%) from the entire cohort. Multivariate analysis identified age (odds ratio [OR], 0.964; 95% CI, 0.945-0.983; P < .001), tumor size (OR, 1.536; 95% CI, 1.135-2.079; P = .005), extrathyroidal extension (OR 2.271, 95% CI, 1.368-3.770; P = .002), clinically involved right central compartment lymph node metastasis (OR 1.643, 95% CI, 1.055-2.559; P = .028), and right lateral lymph node metastasis (OR 4.271, 95% CI, 2.325-7.844; P < .001) as the predictors of LN-prRLN. A risk model was established and well validated. Calibration curves to evaluate the nomogram in both the development and validation cohorts revealed a concordance index of 0.756 ± 0.058 and 0.745 ± 0.042, respectively.ConclusionOur scoring system may be useful for helping the surgeons decide which patients should undergo the dissection of PRRLN-LN.     

Patterns of treatment failure for PD-(L)1 refractory extensive-stage small cell lung cancer in continued PD-(L)1 treatment

BackgroundAlthough immunotherapy greatly extends overall survival (OS) of patients with extensive-stage small cell lung cancer (ES-SCLC), a number of patients develop immunotherapy resistance (IR). Patterns of failure in ES-SCLC are not clarified. Our study aims to explore the clinical pattern of IR and prognostic factors for these patients.MethodsThe study was conducted from 117 ES-SCLC patients with immunotherapy between 2018 and 2022. Chi-square tests and Fishers' exact tests was used to explore failure patterns in different populations. Survival analyses of different progression patterns and subsequent treatment regimens were conducted by Kaplan–Meier curves and log-rank test.Results86 (73.5%) patients experienced IR. The patients with smoking (never smoker vs. current or ex-smoker, 59.5 % vs. 81.3%, P = 0.010), liver metastasis (extrahepatic metastasis vs. intrahepatic metastasis, 73.6 % vs. 90.9%, P = 0.050), and distant metastasis status (no distant metastasis vs. distant metastasis, 39.1 % vs. 81.9%, P<0.001) were associated with IR rates. Liver progression had a lower incidence in 1st line immunotherapy (1st line vs. ≥2nd lines, 14.0 % vs. 41.7%, P = 0.004) and a higher incidence in multiple progression (multiple progression vs. Oligo-progression, 39.4 % vs. 17.0%, P = 0.021). Cranial (41.7 % vs. 16.1%, P = 0.012) and distant lymph node (16.7 % vs. 3.2%, P = 0.049) progression were the main failure model for acquired IR in comparison to primary IR. Patients with new lesion progression only (17.73 vs. 9.17 months, P = 0.013) and non-hepatic progression (14.23 vs. 11.67 months, P = 0.042) had a longer OS. Patients in cross-line immunotherapy after IR had a favourable prognosis (17.07 vs. 11.93 months, P = 0.007).ConclusionThe most common failure pattern of immunotherapy for ES-SCLC was lung and regional lymph node progression. Brain and liver progression were the most common extra thoracic failure sites for 1st line and 2nd and more lines immunotherapy, respectively. There was a higher probability of primary IR in 2 lines and above immunotherapy. Patients with new only progression site and cross-line rechallenge immunotherapy had a better prognosis.     

Euthyroid Sick Syndrome as a Prognostic Indicator of COVID-19 Pulmonary Involvement,Associated With Poorer Disease Prognosis and Increased Mortality

ObjectiveThe prevalence of euthyroid sick syndrome (ESS) and its association with the prognosis of COVID-19 and mortality in patients with lung involvement in COVID-19 have not yet been elucidated.MethodsClinical and laboratory data of patients with COVID-19 with or without ESS were collected retrospectively and analyzed on admission. All subjects were admitted to the Department of Internal Diseases and Clinical Pharmacology at Bieganski Hospital between December 2020 and April 2021.ResultsIn total, 310 medical records of patients with COVID-19 were analyzed retrospectively. Among 215 enrolled patients, 82 cases of ESS were diagnosed. The patients with ESS had higher pro-inflammatory factor levels, longer hospitalizations, and a higher risk of requiring high-flow nasal oxygen therapy or intubation than the patients without ESS. The Kaplan-Meier curve indicated that the patients with ESS had a lower probability of survival when computed tomography showed ≤50% parenchymal involvement compared with that in patients without ESS. However, no differences in mortality were noted in those with more than 50% parenchymal involvement. The survival curve showed that ESS was associated with a higher risk of mortality during hospitalization.ConclusionESS is closely associated with a poor prognosis, including longer hospitalizations, more frequent intubation, transfer to the intensive care unit, and a higher mortality rate in patients with COVID-19. ESS is a potential prognostic predictor of survival, regardless of lung involvement in COVID-19.     

Network pharmacology-based analysis of the role of tacrolimus in liver transplantation

BackgroundTacrolimus is a powerful immunosuppressant and has been widely used in organ transplantation. In order to further explore the role of tacrolimus in liver transplantation, we conducted network pharmacology analysis.MethodsGSE100155 was obtained from the GEO database, and the DEGs of liver transplantation were analyzed. The 2D structure of tacrolimus was obtained from the National Library of Medicine, and the pharmacophore model of tacrolimus was predicted using the online tool pharmmapper. Then a network of tacrolimus and target genes was constructed through network pharmacology, and visualization and GO enrichment analysis was performed through Cytoscape. In addition, we also analyzed the correlation between key genes and immune infiltrating cells. The data of GSE84908 was used to verify the changes of key gene expression levels after tacrolimus treatment.ResultsThe results of network pharmacological analysis showed that tacrolimus had 43 target genes, and the GO enrichment results showed many potential functions. Further analysis found that there were 5 key target genes in DEGs, and these 5 genes were significantly down-regulated in liver transplant patients. Another important finding was that 5 genes were significantly related to some immune infiltrating cells. The results of the GSE84908 data analysis showed that after tacrolimus treatment, the expression of DAAM1 was significantly increased (p = 0.015).ConclusionTacrolimus may inhibit the human immune response by affecting the expression of DAAM1 in liver transplant patients.     

LINC01140 regulates osteosarcoma proliferation and invasion by targeting the miR-139-5p/HOXA9 axis

Osteosarcoma is one of the commonest metastatic tumor in children and teenagers, and has a hopeless, prognosis. Long non-coding RNA (lncRNA) acts momentous roles as a regulator on the proliferation and migration of cancer. Here, we performed GEO database analysis and qPCR to identify differentially expressed lncRNAs in osteosarcoma cells. Knockdown of lncRNA LINC01140 was used to detect the effect of LINC01140 on the proliferation, invasion, and epithelial-mesenchymal transition (EMT) of osteosarcoma cells. Bioinformatics analysis and qPCR identified the LINC01140/miR-139-5p/Homeobox A9 (HOXA9) regulatory axis. RNA immunoprecipitation assay, Dual-luciferase assay, and rescue experiments confirmed the interaction of LINC01140/miR-139-5p/HOXA9 in osteosarcoma. LINC01140 was overexpressed in osteosarcoma and knocking down LINC01140 restrained the proliferation and invasion of osteosarcoma cells and EMT. In Saos2 and MG63 cells, LINC01140 sponged miR-139-5p, and a miR-139-5p inhibitor overturned the suppression of LINC01140 knockdown on the proliferation and migration of osteosarcoma cells. Moreover, miR-139-5p depressed the invasion, proliferation, and EMT of osteosarcoma cells via targeting HOXA9. Our results indicate that LINC01140 downregulation inhibits the invasion, proliferation, and EMT in osteosarcoma cells through targeting the miR-139-5p/HOXA9 axis. Therefore, LINC01140 is a potential therapeutic target for osteosarcoma.     

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation

PurposeEvaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment).MethodsA large annotated adult knee MRI database was built combining medical expertise of radiologists and data scientists’ tools. Coronal and sagittal proton density fat suppressed-weighted images of 11,353 knee MRI examinations (10,401 individual patients) paired with their standardized structured reports were retrospectively collected. After database curation, deep learning models were trained and validated on a subset of 8058 examinations. Algorithm performance was evaluated on a test set of 299 examinations reviewed by 5 musculoskeletal specialists and compared to general radiologists’ reports. External validation was performed using the publicly available MRNet database. Receiver Operating Characteristic (ROC) curves results and Area Under the Curve (AUC) values were obtained on internal and external databases.ResultsA combined architecture of meniscal localization and lesion classification 3D convolutional neural networks reached AUC values of 0.93 (95% CI 0.82, 0.95) for medial and 0.84 (95% CI 0.78, 0.89) for lateral meniscal tear detection, and 0.91 (95% CI 0.87, 0.94) for medial and 0.95 (95% CI 0.92, 0.97) for lateral meniscal tear migration detection. External validation of the combined medial and lateral meniscal tear detection models resulted in an AUC of 0.83 (95% CI 0.75, 0.90) without further training and 0.89 (95% CI 0.82, 0.95) with fine tuning.ConclusionOur deep learning algorithm demonstrated high performance in knee menisci lesion detection and characterization, validated on an external database.     

Epigenetic regulator KDM4A activates Notch1-NICD-dependent signaling to drive tumorigenesis and metastasis in breast cancer

BackgroundAltered epigenetic reprogramming and events contribute to breast cancer (Bca) progression and metastasis. How the epigenetic histone demethylases modulate breast cancer progression remains poorly defined. We aimed to elucidate the biological roles of KDM4A in driving Notch1 activation and Bca progression.MethodsThe KDM4A expression in Bca specimens was analyzed using quantitative PCR and immunohistochemical assays. The biological roles of KDM4A were evaluated using wound-healing assays and an in vivo metastasis model. The Chromatin Immunoprecipitation (ChIP)-qPCR assay was used to determine the role of KDM4A in Notch1 regulation.ResultsHere, we screened that targeting KDM4A could induce notable cell growth suppression. KDM4A is required for the growth and progression of Bca cells. High KDM4A enhances tumor migration abilities and in vivo lung metastasis. Bioinformatic analysis suggested that KDM4A was highly expressed in tumors and high KDM4A correlates with poor survival outcomes. KDM4A activates Notch1 expressions via directly binding to the promoters and demethylating H3K9me3 modifications. KDM4A inhibition reduces expressions of a list of Notch1 downstream targets, and ectopic expressions of ICN1 could restore the corresponding levels. KDM4A relies on Notch1 signaling to maintain cell growth, migration and self-renewal capacities. Lastly, we divided a panel of cell lines into KDM4A^high and KDM4A^low groups. Targeting Notch1 using specific LY3039478 could efficiently suppress cell growth and colony formation abilities of KDM4A^high Bca.ConclusionTaken together, KDM4A could drive Bca progression via triggering the activation of Notch1 pathway by decreasing H3K9me3 levels, highlighting a promising therapeutic target for Bca.     

Differences in Clinical Manifestations and Tumor Features Between Metastatic Pheochromocytoma/Paraganglioma Patients With and Without Germline SDHB Mutation

ObjectiveTo compare metastatic pheochromocytoma/paraganglioma (MPP) patients with germline SDHB mutations (SDHB MPP) and without SDHB mutations (non-SDHB MPP) in terms of baseline clinical manifestations, tumor characteristics, and outcomes.MethodsClinical data were retrospectively reviewed in 101 MPP patients, including 34 SDHB MPP patients and 61 non-SDHB MPP patients.ResultsSDHB MPP patients presented at a younger age at onset, diagnosis, or metastasis (25 ± 16 vs 36 ± 14, 28 ± 17 vs 38 ± 15, and 31 ± 17 vs 44 ± 14 years old, respectively, P < .01 for all) than non-SDHB patients. Compared with their non-SDHB counterparts, SDHB patients were more likely to have paragangliomas (83% vs 47%, P < .05), synchronous metastases (44% vs 23%, P < .05), bone metastases (80% vs 48%, P < .01), and a shorter progression-free survival (3 years vs 5 years, P < .01). The Ki-67 index was higher in SDHB tumors (P < .05). The 5- and 10-year survival rates were 79% and 74%, respectively, in all patients. Seventeen patients died from MPP, and the time from metastasis to death in patients who had received systemic therapy was significantly longer than in those who had not (3.1 ± 1.5 vs 1.4 ± 0.7 years, P < .01).ConclusionCompared with MPP patients without SDHB mutations, MPP patients with SDHB mutations were younger at onset, diagnosis, or metastasis; had a higher incidence of synchronous metastases, higher ratio of paraganglioma, and higher Ki-67 index; had a shorter postoperative progression-free survival; and were more likely to develop bone metastasis or sole liver metastasis. Our results suggest that patients with SDHB mutations should be identified early and monitored regularly to achieve optimal clinical outcomes.     

New Insights on Effects of Glucocorticoids in Patients With SARS-CoV-2 Infection

ObjectiveSince January 2020, the highly contagious novel coronavirus SARS-CoV-2 has caused a global pandemic. Severe COVID-19 leads to a massive release of proinflammatory mediators, leading to diffuse damage to the lung parenchyma, and the development of acute respiratory distress syndrome. Treatment with the highly potent glucocorticoid (GC) dexamethasone was found to be effective in reducing mortality in severely affected patients.MethodsTo review the effects of glucocorticoids in the context of COVID-19 we performed a literature search in the PubMed database using the terms COVID-19 and glucocorticoid treatment. We identified 1429 article publications related to COVID-19 and glucocorticoid published from 1.1.2020 to the present including 238 review articles and 36 Randomized Controlled Trials. From these studies, we retrieved 13 Randomized Controlled Trials and 86 review articles that were relevant to our review topics. We focused on the recent literature dealing with glucocorticoid metabolism in critically ill patients and investigating the effects of glucocorticoid therapy on the immune system in COVID-19 patients with severe lung injury.ResultsIn our review, we have discussed the regulation of the hypothalamic-pituitary-adrenal axis in patients with critical illness, selection of a specific GC for critical illness-related GC insufficiency, and recent studies that investigated hypothalamic-pituitary-adrenal dysfunction in patients with COVID-19. We have also addressed the specific activation of the immune system with chronic endogenous glucocorticoid excess, as seen in patients with Cushing syndrome, and, finally, we have discussed immune activation due to coronavirus infection and the possible mechanisms leading to improved outcomes in patients with COVID-19 treated with GCs.ConclusionFor clinical endocrinologists prescribing GCs for their patients, a precise understanding of both the molecular- and cellular-level mechanisms of endogenous and exogenous GCs is imperative, including timing of administration, dosage, duration of treatment, and specific formulations of GCs.     

Thyroid Immune-Related Adverse Events in Patients with Cancer Treated with anti-PD1/anti-CTLA4 Immune Checkpoint Inhibitor Combination: Clinical Course and Outcomes

ObjectiveThyroid immune-related adverse events (irAEs) have been reported to have prognostic significance among patients with cancer treated with anti-programmed cell death-1 (PD1) and anti-programmed death-ligand 1 monotherapies. We evaluated the clinical course and predictors of thyroid irAEs in relation to outcomes of patients with advanced cancer treated with combination anti-PD1/anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA4).MethodsWe conducted a regional study and identified patients with advanced cancer who received ≥1 cycle of combination anti-PD1/anti-CTLA4 between 2015 and 2019 in Hong Kong. Thyroid function tests (TFTs) were monitored every 3 weeks. Thyroid irAE was defined by ≥2 abnormal TFTs after initiation of combination anti-PD1/anti-CTLA4 in the absence of other causes.ResultsOne hundred and three patients were included (median age: 59 years; 71.8% men). About 45% had prior anti-PD1 exposure. Upon median follow-up of 6.8 months, 17 patients (16.5%) developed thyroid irAEs, where 6 initially presented with thyrotoxicosis (overt, n = 4; subclinical, n = 2) and 11 with hypothyroidism (overt, n = 2; subclinical, n = 9). Eventually, 10 patients (58.8%) required continuous thyroxine replacement. Systemic steroid was not required in all cases. Prior anti-PD1 exposure (odds ratio, 3.67; 95% CI, 1.19–11.4; P = .024) independently predicted thyroid irAEs. Multivariable Cox regression analysis revealed that occurrence of thyroid irAEs was independently associated with better overall survival (adjusted hazard ratio, 0.34; 95% CI, 0.17–0.71; P = .004).ConclusionThyroid irAEs are common in routine clinical practice among patients with advanced cancer treated with anti-PD1/anti-CTLA4 combination and might have potential prognostic significance. Regular TFT monitoring is advised for timely treatment of thyroid irAEs to prevent potential morbidities.     

Prediction of response and adverse drug reaction of pemetrexed plus platinum-based chemotherapy in lung adenocarcinoma by serum metabolomic profiling

BackgroundPemetrexed plus platinum doublet chemotherapy regimen remains to be the standard first-line treatment for lung adenocarcinoma patients. However, few biomarkers can be used to identify potential beneficiaries with maximal efficacy and minimal toxicity. This study aimed to explore potential biomarker models predictive of efficacy and toxicity after pemetrexed plus platinum chemotherapy based on metabolomics profiling.MethodsA total of 144 patients who received at least two cycles of pemetrexed plus platinum chemotherapy were enroled in the study. Serum samples were collected before initial treatment to perform metabolomics profiling analysis. Logistic regression analysis was performed to establish prediction models.Results157 metabolites were found to be differentially expressed between the response group and the nonresponse group. A panel of Phosphatidylserine 20:4/20:1, Sphingomyelin d18:1/18:0, and Phosphatidic Acid 18:1/20:0 could predict pemetrexed and platinum chemotherapy response with an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.7968. 76 metabolites were associated with hematological toxicity of pemetrexed plus platinum chemotherapy. A panel incorporating triglyceride 14:0/22:3/22:5, 3-(3-Hydroxyphenyl) Propionate Acid, and Carnitine C18:0 was the best predictive ability of hematological toxicity with an AUROC of 0.7954. 54 differential expressed metabolites were found to be associated with hepatotoxicity of pemetrexed plus platinum chemotherapy. A model incorporating stearidonic acid, Thromboxane B3, l-Homocitrulline, and phosphoinositide 20:3/18:0 showed the best predictive ability of hepatotoxicity with an AUROC of 0.8186.ConclusionsThis study established effective and convenient models that can predict the efficacy and toxicity of pemetrexed plus platinum chemotherapy in lung adenocarcinoma patients before treatment delivery.     

Impact of baseline telomere length on survival and chemotherapy related toxicity in breast cancer patients receiving (neo)adjuvant anthracycline containing chemotherapy

PurposeThe aim of this study is to assess baseline mean leukocyte telomere length (TL) as a potential predictive factor for chemotherapy toxicity and a prognostic marker for long-term outcome in early breast cancer (BC) patients.Methods445 BC patients were selected, diagnosed between 2007 and 2010 with early BC and treated with (neo)adjuvant fluorouracil, epirubicin and cyclophosphamide (FEC) or with FEC and Docetaxel (FEC-D). RT-qPCR was performed on germline DNA samples collected at diagnosis before any treatment, to measure mean leukocyte TL. Uni- and multivariable logistic regression or Cox proportional hazard regression analyses were carried out to assess correlation between baseline TL and toxicity parameters (derived from the medical chart) or longer-term outcome.ResultsBaseline TL correlated with age as expected (p = 0.005), but not with febrile neutropenia (n = 97), left ventricular ejection fraction >10% decrease (n = 17) nor other toxicity endpoints measured (all p > 0.05). TL was neither associated with overall survival, breast cancer specific survival or distant disease-free survival (all p > 0.05).ConclusionsBaseline TL is not associated with chemotherapy-related toxicity nor long-term outcome in BC patients.