Intratumoural microbiota: a new frontier in cancer development and therapy.

Human microorganisms, including bacteria, fungi, and viruses, play key roles in several physiological and pathological processes. Some studies discovered that tumour tissues once considered sterile actually host a variety of microorganisms, which have been confirmed to be closely related to oncogenesis. The concept of intratumoural microbiota was subsequently proposed. Microbiota could colonise tumour tissues through mucosal destruction, adjacent tissue migration, and hematogenic invasion and affect the biological behaviour of tumours as an important part of the tumour microenvironment. Mechanistic studies have demonstrated that intratumoural microbiota potentially promote the initiation and progression of tumours by inducing genomic instability and mutations, affecting epigenetic modifications, promoting inflammation response, avoiding immune destruction, regulating metabolism, and activating invasion and metastasis. Since more comprehensive and profound insights about intratumoral microbiota are continuously emerging, new methods for the early diagnosis and prognostic assessment of cancer patients have been under examination. In addition, interventions based on intratumoural microbiota show great potential to open a new chapter in antitumour therapy, especially immunotherapy, although there are some inevitable challenges. Here, we aim to provide an extensive review of the concept, development history, potential sources, heterogeneity, and carcinogenic mechanisms of intratumoural microorganisms, explore the potential role of microorganisms in tumour prognosis, and discuss current antitumour treatment regimens that target intratumoural microorganisms and the research prospects and limitations in this field.

Lipid metabolism of plasma-derived small extracellular vesicles in COVID-19 convalescent patients.

The coronavirus disease 2019 (COVID-19), which affects multiple organs, is causing an unprecedented global public health crisis. Most COVID-19 patients recover gradually upon appropriate interventions. Viruses were reported to utilize the small extracellular vesicles (sEVs), containing a cell-specific cargo of proteins, lipids, and nucleic acids, to escape the attack from the host's immune system. This study aimed to examine the sEVs lipid profile of plasma of recovered COVID-19 patients (RCs). Plasma sEVs were separated from 83 RCs 3 months after discharge without underlying diseases, including 18 recovered asymptomatic patients (RAs), 32 recovered moderate patients (RMs), and 33 recovered severe and critical patients (RSs), and 19 healthy controls (HCs) by Total Exosome Isolation Kit. Lipids were extracted from sEVs and then subjected to targeted liquid chromatography-mass spectrometry. The size, concentration, and distribution of sEVs did not differ in RCs and HCs as validated by transmission electron microscopy, nanoparticle tracking analysis, and immunoblot analysis. Fifteen subclasses of 508 lipids were detected in plasma sEVs from HCs, RAs, RMs, and RSs, such as phosphatidylcholines (PCs) and diacylglycerols (DAGs), etc. Total lipid intensity displayed downregulation in RCs compared with HCs. The relative abundance of DAGs gradually dropped, whereas PCs, lysophosphatidylcholines, and sphingomyelins were higher in RCs relative to HCs, especially in RSs. 88 lipids out of 241 in sEVs of RCs were significantly different and a conspicuous increase was revealed with disease status. The sEVs lipids alternations were found to be significantly correlated with the clinical indices in RCs and HCs, suggesting that the impact of COVID-19 on lipid metabolism lingered for a long time. The lipid abnormalities bore an intimate link with glycerophospholipid metabolism and glycosylphosphatidylinositol anchor biosynthesis. Furthermore, the lipidomic analysis showed that RCs were at higher risk of developing diabetes and sustaining hepatic impairment. The abnormality of immunomodulation in RCs might still exist. The study may offer new insights into the mechanism of organ dysfunction and help identify novel therapeutic targets in the RCs.

Prognosis prediction of icotinib as targeted therapy for advanced EGFR-positive non-small cell lung cancer patients.

Clinical trials on icotinib, a first-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), have shown promising results as targeted therapy for non-small cell lung cancer (NSCLC). This study aimed to establish an effective scoring system to predict the one-year progression-free survival (PFS) of advanced NSCLC patients with EGFR mutations treated with icotinib as targeted therapy. A total of 208 consecutive patients with advanced EGFR-positive NSCLC treated with icotinib were enrolled in this study. Baseline characteristics were collected within 30 days before icotinib treatment. PFS was taken as the primary endpoint and the response rate as the secondary endpoint. Least absolute shrinkage and selection operator (LASSO) regression analysis and Cox proportional hazards regression analysis were used to select the optimal predictors. We evaluated the scoring system using a five-fold cross-validation. PFS events occurred in 175 patients, with a median PFS of 9.9 months (interquartile range, 6.8-14.5). The objective response rate (ORR) was 36.1%, and the disease control rate (DCR) was 67.3%. The final ABC-Score consisted of three predictors: age, bone metastases and carbohydrate antigen 19-9 (CA19-9). Upon comparison of all three factors, the combined ABC-score (area under the curve (AUC)= 0.660) showed a better predictive accuracy than age (AUC = 0.573), bone metastases (AUC = 0.615), and CA19-9 (AUC = 0.608) individually. A five-fold cross-validation showed good discrimination with AUC = 0.623. The ABC-score developed in this study was significantly effective as a prognostic tool for icotinib in advanced NSCLC patients with EGFR mutations.

Nanopore membrane chip-based isolation method for metabolomic analysis of plasma small extracellular vesicles from COVID-19 survivors.

Multiple studies showed that metabolic disorders play a critical role in respiratory infectious diseases, including COVID-19. Metabolites contained in small extracellular vesicles (sEVs) are different from those in plasma at the acute stage, while the metabolic features of plasma sEVs of COVID-19 survivors remain unknown. Here, we used a nanopore membrane-based microfluidic chip for plasma sEVs separation, termed ExoSEC, and compared the sEVs obtained by UC, REG, and ExoSEC in terms the time, cost, purity, and metabolic features. The results indicated the ExoSEC was much less costly, provided higher purity by particles/proteins ratio, and achieved 205-fold and 2-fold higher sEVs yield, than UC and REG, respectively. Moreover, more metabolites were identified and several signaling pathways were significantly enriched in ExoSEC-sEVs compared to UC-sEVs and REG-sEVs. Furthermore, we detected 306 metabolites in plasma sEVs using ExoSEC from recovered asymptomatic (RA), moderate (RM), and severe/critical COVID-19 (RS) patients without underlying diseases 3 months after discharge. Our study demonstrated that COVID-19 survivors, especially RS, experienced significant metabolic alteration and the dysregulated pathways mainly involved fatty acid biosynthesis, phenylalanine metabolism, etc. Metabolites of the fatty acid biosynthesis pathway bore a significantly negative association with red blood cell counts and hemoglobin, which might be ascribed to hypoxia or respiratory failure in RM and RS but not in RA at the acute stage. Our study confirmed that ExoSEC could provide a practical and economical alternative for high throughput sEVs metabolomic study.

Differentiation of malignant from benign pleural effusions based on artificial intelligence.

INTRODUCTION: This study aimed to construct artificial intelligence models based on thoracic CT images to perform segmentation and classification of benign pleural effusion (BPE) and malignant pleural effusion (MPE). METHODS: A total of 918 patients with pleural effusion were initially included, with 607 randomly selected cases used as the training cohort and the other 311 as the internal testing cohort; another independent external testing cohort with 362 cases was used. We developed a pleural effusion segmentation model (M1) by combining 3D spatially weighted U-Net with 2D classical U-Net. Then, a classification model (M2) was built to identify BPE and MPE using a CT volume and its 3D pleural effusion mask as inputs. RESULTS: The average Dice similarity coefficient, Jaccard coefficient, precision, sensitivity, Hausdorff distance 95% (HD95) and average surface distance indicators in M1 were 87.6±5.0%, 82.2±6.2%, 99.0±1.0%, 83.0±6.6%, 6.9±3.8 and 1.6±1.1, respectively, which were better than those of the 3D U-Net and 3D spatially weighted U-Net. Regarding M2, the area under the receiver operating characteristic curve, sensitivity and specificity obtained with volume concat masks as input were 0.842 (95% CI 0.801 to 0.878), 89.4% (95% CI 84.4% to 93.2%) and 65.1% (95% CI 57.3% to 72.3%) in the external testing cohort. These performance metrics were significantly improved compared with those for the other input patterns. CONCLUSIONS: We applied a deep learning model to the segmentation of pleural effusions, and the model showed encouraging performance in the differential diagnosis of BPE and MPE.

Fasudil Increased the Sensitivity to Gefitinib in NSCLC by Decreasing Intracellular Lipid Accumulation.

Tyrosine kinase inhibitors (TKIs) resistance is a challenge in patients with epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC). Here, we examined the effect of Fasudil in reversing TKIs resistance. The results of CCK8 assay, clone formation assay, cell cycle arrest analysis, and apoptosis analysis show that Fasudil treatment effectively suppressed the growth and induced apoptosis of the EGFR-mutant NSCLC cells. Furthermore, Fasudil in combination with gefitinib showed a synergistic anti-tumor effect in gefitinib-resistant NSCLC cells. RNA-seq analysis and immunoblotting indicated that Fasudil treatment significantly inhibited intracellular lipid accumulation and EGFR/PI3K/AKT pathway activation. Mechanistic investigations showed that Fasudil regulated lipogenic gene expressions via AMPK signal pathway. In vivo, Fasudil and gefitinib co-administration significantly attenuated the growth of H1975 nude mouse xenograft models, suggesting that Fasudil treatment combined with gefitinib can be applied as a therapy for gefitinib-resistant NSCLC cells.

The potential applications of microparticles in the diagnosis, treatment, and prognosis of lung cancer.

Microparticles (MPs) are 100-1000 nm heterogeneous submicron membranous vesicles derived from various cell types that express surface proteins and antigenic profiles suggestive of their cellular origin. MPs contain a diverse array of bioactive chemicals and surface receptors, including lipids, nucleic acids, and proteins, which are essential for cell-to-cell communication. The tumour microenvironment (TME) is enriched with MPs that can directly affect tumour progression through their interactions with receptors. Liquid biopsy, a minimally invasive test, is a promising alternative to tissue biopsy for the early screening of lung cancer (LC). The diverse biomolecular information from MPs provides a number of potential biomarkers for LC risk assessment, early detection, diagnosis, prognosis, and surveillance. Remodelling the TME, which profoundly influences immunotherapy and clinical outcomes, is an emerging strategy to improve immunotherapy. Tumour-derived MPs can reverse drug resistance and are ideal candidates for the creation of innovative and effective cancer vaccines. This review described the biogenesis and components of MPs and further summarised their main isolation and quantification methods. More importantly, the review presented the clinical application of MPs as predictive biomarkers in cancer diagnosis and prognosis, their role as therapeutic drug carriers, particularly in anti-tumour drug resistance, and their utility as cancer vaccines. Finally, we discussed current challenges that could impede the clinical use of MPs and determined that further studies on the functional roles of MPs in LC are required.

Predictive Risk Factors at Admission and a "Burning Point" During Hospitalization Serve as Sequential Alerts for Critical Illness in Patients With COVID-19.

Background: We intended to establish a novel critical illness prediction system combining baseline risk factors with dynamic laboratory tests for patients with coronavirus disease 2019 (COVID-19). Methods: We evaluated patients with COVID-19 admitted to Wuhan West Union Hospital between 12 January and 25 February 2020. The data of patients were collected, and the illness severity was assessed. Results: Among 1,150 enrolled patients, 296 (25.7%) patients developed into critical illness. A baseline nomogram model consists of seven variables including age [odds ratio (OR), 1.028; 95% confidence interval (CI), 1.004-1.052], sequential organ failure assessment (SOFA) score (OR, 4.367; 95% CI, 3.230-5.903), neutrophil-to-lymphocyte ratio (NLR; OR, 1.094; 95% CI, 1.024-1.168), D-dimer (OR, 1.476; 95% CI, 1.107-1.968), lactate dehydrogenase (LDH; OR, 1.004; 95% CI, 1.001-1.006), international normalised ratio (INR; OR, 1.027; 95% CI, 0.999-1.055), and pneumonia area interpreted from computed tomography (CT) images (medium vs. small [OR, 4.358; 95% CI, 2.188-8.678], and large vs. small [OR, 9.567; 95% CI, 3.982-22.986]) were established to predict the risk for critical illness at admission. The differentiating power of this nomogram scoring system was perfect with an area under the curve (AUC) of 0.960 (95% CI, 0.941-0.972) in the training set and an AUC of 0.958 (95% CI, 0.936-0.980) in the testing set. In addition, a linear mixed model (LMM) based on dynamic change of seven variables consisting of SOFA score (value, 2; increase per day [I/d], +0.49), NLR (value, 10.61; I/d, +2.07), C-reactive protein (CRP; value, 46.9 mg/L; I/d, +4.95), glucose (value, 7.83 mmol/L; I/d, +0.2), D-dimer (value, 6.08 µg/L; I/d, +0.28), LDH (value, 461 U/L; I/d, +13.95), and blood urea nitrogen (BUN value, 6.51 mmol/L; I/d, +0.55) were established to assist in predicting occurrence time of critical illness onset during hospitalization. Conclusion: The two-checkpoint system could assist in accurately and dynamically predicting critical illness and timely adjusting the treatment regimen for patients with COVID-19.

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study.

BACKGROUND: Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs). METHODS: In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models. RESULTS: The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection. CONCLUSIONS: In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

Changes in glomerular filtration rate and metabolomic differences in severely ill coronavirus disease survivors 3 months after discharge.

To explore the recovery of renal function in severely ill coronavirus disease (COVID-19) survivors and determine the plasma metabolomic profile of patients with different renal outcomes 3 months after discharge, we included 89 severe COVID-19 survivors who had been discharged from Wuhan Union Hospital for 3 months. All patients had no underlying kidney disease before admission. At patient recruitment, renal function assessment, laboratory examination, chest computed tomography (CT) were performed. Liquid chromatography-mass spectrometry was used to detect metabolites in the plasma. We analyzed the longitudinally change in the estimated glomerular filtration rate (eGFR) based on serum creatinine and cystatin-c levels using the CKD-EPI equation and explored the metabolomic differences in patients with different eGFR change patterns from hospitalization to 3 months after discharge. Lung CT showed good recovery; however, the median eGFR significantly decreased at the 3-month follow-up. Among the 89 severely ill COVID-19 patients, 69 (77.5%) showed abnormal eGFR (<90 mL/min per 1.73 m2) at 3 months after discharge. Age (odds ratio [OR] = 1.26, 95% confidence interval [CI] = 1.08-1.47, p = 0.003), body mass index (OR = 1.97, 95% CI = 1.20-3.22, p = 0.007), and cystatin-c level (OR = 1.22, 95% CI = 1.07-1.39, p = 0.003) at discharge were independent risk factors for post-discharge abnormal eGFR. Plasma metabolomics at the 3-months follow-up revealed that ß-pseudouridine, uridine, and 2-(dimethylamino) guanosine levels gradually increased with an abnormal degree of eGFR. Moreover, the kynurenine pathway in tryptophan metabolism, vitamin B6 metabolism, cysteine and methionine metabolism, and arginine biosynthesis were also perturbed in survivors with abnormal eGFR.

Plasma Metabolomic Profiles in Recovered COVID-19 Patients without Previous Underlying Diseases 3 Months After Discharge.

BACKGROUND: It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities. METHODS: COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants. RESULTS: Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions. CONCLUSION: We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

Proteomics of extracellular vesicles in plasma reveals the characteristics and residual traces of COVID-19 patients without underlying diseases after 3 months of recovery.

More and more patients suffered from Coronavirus disease 2019 (COVID-19) have got recovery gradually due to suitable intervention. Increasing data mainly studies the clinical characteristics of recovered COVID-19 patients, and their molecular changes especially proteome changes also play the same important role in understanding of biological characteristics of recovered COVID-19 patients as clinical characteristics do. In our study, we reported the whole lung-ground glass-CT value-average of mild/severe recovered patients 3 months after discharge without underlying diseases was significantly lower than that of healthy subjects. Then we isolated the extracellular vesicles (EVs) of plasma from 19 healthy subjects and 67 recovered COVID-19 patients. Mass Spectrometry was used to catalogue the proteins of these EVs compared to a defined group of controls. Identified 174 proteins were differentially expressed in the EVs of COVID-19 patients compared with healthy subjects, which involved in lipid metabolic process, response to cellular, and response to stress oxygen-containing compound. Besides, we identified several protein of plasma EVs in recovered patients associated with coagulation activity, inflammatory reaction, immune response, and low organ function. In addition, proteins correlating with clinical index such as alkaline phosphatase (ALP) and alanine aminotransferase (ALT) were also detected. Moreover, we also identified many unique or characteristic associations found in the recovered COVID-19 patients, which especially involved the kidney, serum electrolyte levels, and inflammation functions. This finding suggests that monitoring the situation of recovered patients might be useful, especially the indexes of coagulation, inflammation, immunity, and organ function, which can prevent bleeding, reinfection and organ dysfunction.

Comparison of clinical characteristics among younger and elderly deceased patients with COVID-19: a retrospective study.

We aimed to compare the age-related clinical characteristics between younger and elderly deceased COVID-19 patients. This single-center retrospective study included 163 adult deceased COVID-19 patients who were admitted to Wuhan Union Hospital West Campus from January 12, 2020, to March 30, 2020. Demographic and clinical features were collected by reviewing the medical records. The median age of the 163 deceased patients was 69 (interquartile range [IQR], 62-78) years. They were classified as younger (age 18-69 years; 86/163, 52.8%) and elderly (≥70 years; 77/163, 47.2%) subjects. Younger deceased patients were more likely to develop fever (72/86 vs 54/77, P=0.039) than elderly deceased patients were while anorexia was (29/77 vs 19/86, P=0.029) more common in elderly deceased patients than in younger deceased patients. In multivariate analyses, age was a protective factor for acute cardiac injury of deceased COVID-19 patients (odds ratio [OR] 0.968, [95% confidence interval (CI), 0.940-0.997]; P=0.033) while chronic cardiac disease was a risk factor for acute cardiac injury of deceased COVID-19 patients (OR 2.660 [95%CI, 1.034-6.843]; P=0.042). Our study described the clinical characteristics of younger and elderly deceased COVID-19 patients and demonstrated that younger deceased patients were more likely to develop an acute cardiac injury.

Development and validation of a risk factor-based system to predict short-term survival in adult hospitalized patients with COVID-19: a multicenter, retrospective, cohort study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a public health emergency of global concern. We aimed to explore the risk factors of 14-day and 28-day mortality and develop a model for predicting 14-day and 28-day survival probability among adult hospitalized patients with COVID-19. METHODS: In this multicenter, retrospective, cohort study, we examined 828 hospitalized patients with confirmed COVID-19 hospitalized in Wuhan Union Hospital and Central Hospital of Wuhan between January 12 and February 9, 2020. Among the 828 patients, 516 and 186 consecutive patients admitted in Wuhan Union Hospital were enrolled in the training cohort and the validation cohort, respectively. A total of 126 patients hospitalized in Central Hospital of Wuhan were enrolled in a second external validation cohort. Demographic, clinical, radiographic, and laboratory measures; treatment; proximate causes of death; and 14-day and 28-day mortality are described. Patients' data were collected by reviewing the medical records, and their 14-day and 28-day outcomes were followed up. RESULTS: Of the 828 patients, 146 deaths were recorded until May 18, 2020. In the training set, multivariate Cox regression indicated that older age, lactate dehydrogenase level over 360 U/L, neutrophil-to-lymphocyte ratio higher than 8.0, and direct bilirubin higher than 5.0 µmol/L were independent predictors of 28-day mortality. Nomogram scoring systems for predicting the 14-day and 28-day survival probability of patients with COVID-19 were developed and exhibited strong discrimination and calibration power in the two external validation cohorts (C-index, 0.878 and 0.839). CONCLUSION: Older age, high lactate dehydrogenase level, evaluated neutrophil-to-lymphocyte ratio, and high direct bilirubin level were independent predictors of 28-day mortality in adult hospitalized patients with confirmed COVID-19. The nomogram system based on the four factors revealed good discrimination and calibration, suggesting good clinical utility.

Facile modification of activated carbon with highly dispersed nano-sized α-Fe2O3 for enhanced removal of hexavalent chromium from aqueous solutions.

Activated carbon-coated α-Fe2O3 nanoparticles (nFe2O3@AC) were synthesized by a facile impregnation method to enhance hexavalent chromium (Cr(VI)) removal from water. The SEM images confirmed that α-Fe2O3 particles ranging from 90 to 500 nm were dispersedly loaded on the AC, which successfully amended Cr(VI) removal. The nFe2O3@AC was able to remove Cr(VI) with a 3 times higher efficiency of 94% in comparison with the AC. After adsorption, Cr(VI) reduction coupled with AC oxidation and low soluble (CrxFe1-x)(OH)3 precipitates were eventually formed. The Cr(VI) removal process was pH-dependent and could be well fitted to pseudo second-order kinetics. The nFe2O3@AC could be easily regenerated by 0.1 M HCl and showed a good stability as an 80% Cr(VI) removal efficiency was recorded after 4 desorption-adsorption cycles. In addition, this composite had a promising potential for repeated utilization because the AC of the adsorbed nFe2O3@AC could be refreshed and remodified with nFe2O3 after stripping all the nFe2O3 and (CrxFe1-x)(OH)3 precipitates from its surface by 1 M HCl and a Cr(VI) removal efficiency of 86% could be achieved. Our results demonstrated that the use of nFe2O3 is an efficient and promising method to modify AC and enhance Cr(VI) removal form aqueous solutions.