In vivo single-molecule analysis reveals COOLAIR RNA structural diversity.

Cellular RNAs are heterogeneous with respect to their alternative processing and secondary structures, but the functional importance of this complexity is still poorly understood. A set of alternatively processed antisense non-coding transcripts, which are collectively called COOLAIR, are generated at the Arabidopsis floral-repressor locus FLOWERING LOCUS C (FLC)1. Different isoforms of COOLAIR influence FLC transcriptional output in warm and cold conditions2-7. Here, to further investigate the function of COOLAIR, we developed an RNA structure-profiling method to determine the in vivo structure of single RNA molecules rather than the RNA population average. This revealed that individual isoforms of the COOLAIR transcript adopt multiple structures with different conformational dynamics. The major distally polyadenylated COOLAIR isoform in warm conditions adopts three predominant structural conformations, the proportions and conformations of which change after cold exposure. An alternatively spliced, strongly cold-upregulated distal COOLAIR isoform6 shows high structural diversity, in contrast to proximally polyadenylated COOLAIR. A hyper-variable COOLAIR structural element was identified that was complementary to the FLC transcription start site. Mutations altering the structure of this region changed FLC expression and flowering time, consistent with an important regulatory role of the COOLAIR structure in FLC transcription. Our work demonstrates that isoforms of non-coding RNA transcripts adopt multiple distinct and functionally relevant structural conformations, which change in abundance and shape in response to external conditions.

REGγ Mitigates Radiation-Induced Enteritis by Preserving Mucin Secretion and Sustaining Microbiome Homeostasis.

Radiation-induced enteritis, a significant concern in abdominal radiation therapy, is associated closely with gut microbiota dysbiosis. The mucus layer plays a pivotal role in preventing the translocation of commensal and pathogenic microbes. Although significant expression of REGγ in intestinal epithelial cells is well established, its role in modulating the mucus layer and gut microbiota remains unknown. The current study revealed notable changes in gut microorganisms and metabolites in irradiated mice lacking REGγ, as compared to wild-type mice. Concomitant with gut microbiota dysbiosis, REGγ deficiency facilitated the infiltration of neutrophils and macrophages, thereby exacerbating intestinal inflammation after irradiation. Furthermore, fluorescence in situ hybridization assays unveiled an augmented proximity of bacteria to intestinal epithelial cells in REGγ knockout mice after irradiation. Mechanistically, deficiency of REGγ led to diminished goblet cell populations and reduced expression of key goblet cell markers, Muc2 and Tff3, observed in both murine models, minigut organoid systems and human intestinal goblet cells, indicating the intrinsic role of REGγ within goblet cells. Interestingly, although administration of broad-spectrum antibiotics did not alter the goblet cell numbers or mucin 2 (MUC2) secretion, it effectively attenuated inflammation levels in the ileum of irradiated REGγ absent mice, bringing them down to the wild-type levels. Collectively, these findings highlight the contribution of REGγ in counteracting radiation-triggered microbial imbalances and cell-autonomous regulation of mucin secretion.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction. METHODS: A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups. RESULTS: The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS). CONCLUSION: The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

[18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study.

OBJECTIVES: This study aims to develop and validate a radiomics model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) images to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: One hundred eighty-five patients receiving neoadjuvant chemoimmunotherapy for NSCLC at 5 centers from January 2019 to December 2022 were included and divided into a training cohort and a validation cohort. Radiomics models were constructed via the least absolute shrinkage and selection operator (LASSO) method. The performances of models were evaluated by the area under the receiver operating characteristic curve (AUC). In addition, genetic analyses were conducted to reveal the underlying biological basis of the radiomics score. RESULTS: After the LASSO process, 9 PET-CT radiomics features were selected for pCR prediction. In the validation cohort, the ability of PET-CT radiomics model to predict pCR was shown to have an AUC of 0.818 (95% confidence interval [CI], 0.711, 0.925), which was better than the PET radiomics model (0.728 [95% CI, 0.610, 0.846]), CT radiomics model (0.732 [95% CI, 0.607, 0.857]), and maximum standard uptake value (0.603 [95% CI, 0.473, 0.733]) (p < 0.05). Moreover, a high radiomics score was related to the upregulation of pathways suppressing tumor proliferation and the infiltration of antitumor immune cell. CONCLUSION: The proposed PET-CT radiomics model was capable of predicting pCR to neoadjuvant chemoimmunotherapy in NSCLC patients. CLINICAL RELEVANCE STATEMENT: This study indicated that the generated 18F-fluorodeoxyglucose positron emission tomography-computed tomography radiomics model could predict pathological complete response to neoadjuvant chemoimmunotherapy, implying the potential of our radiomics model to personalize the neoadjuvant chemoimmunotherapy in lung cancer patients. KEY POINTS: • Recognizing patients potentially benefiting neoadjuvant chemoimmunotherapy is critical for individualized therapy of lung cancer. • [18F]FDG PET-CT radiomics could predict pathological complete response to neoadjuvant immunotherapy in non-small cell lung cancer. • [18F]FDG PET-CT radiomics model could personalize neoadjuvant chemoimmunotherapy in lung cancer patients.

Risks for prolonged mechanical ventilation and reintubation after cervical malignant tumor surgery: a nested case-control study.

PURPOSE: Prolonged mechanical ventilation (PMV) and reintubation are among the most serious postoperative adverse events associated with malignant cervical tumors. In this study, we aimed to clarify the incidence, characteristics, and risk factors for PMV and reintubation in target patients. METHODS: This retrospective nested case-control study was performed between January 2014 and January 2020 at a large spinal tumor center in China. Univariate analysis was used to identify the possible risk factors associated with PMV and reintubation. Logistic regression analysis was performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) with covariates of a probability < 0.05 in univariate analysis. RESULTS: From a cohort of 560 patients with primary malignant (n = 352) and metastatic (n = 208) cervical tumors, 27 patients required PMV and 20 patients underwent reintubation. The incidence rates of PMV and reintubation were 4.82% and 3.57%, respectively. Three variables (all p < 0.05) were independently associated with an increased risk of PMV: Karnofsky Performance Status < 50 compared to ≥ 80, operation duration ≥ 8 h compared to < 6 h, and C4 nerve root encased by the tumor. Longer operative duration and preoperative hypercapnia (all p < 0.05) were independent risk factors for postoperative reintubation, both of which led to longer length of stay (32.6 ± 30.8 vs. 10.7 ± 5.95 days, p < 0.001), with an in-hospital mortality of 17.0%. CONCLUSION: Our results demonstrate the risk factors for PMV or reintubation after surgery for malignant cervical tumors. Adequate assessment, early detection, and prevention are necessary for this high-risk population.

The predictive value of inflammatory biomarkers for major pathological response in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy and its association with the immune-related tumor microenvironment: a multi-center study.

BACKGROUND: Inflammatory biomarkers in the peripheral blood have been established as predictors for immunotherapeutic efficacy in advanced non-small cell lung cancer (NSCLC). Whether they can also predict major pathological response (MPR) in neoadjuvant setting remains unclear. METHODS: In this multi-center retrospective study, 122 and 92 stage I-IIIB NSCLC patients from six hospitals who received neoadjuvant chemoimmunotherapy followed by surgery were included in the discovery and external validation cohort, respectively. Baseline and on-treatment neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII) were calculated and associated with MPR. Furthermore, resected tumor samples from 37 patients were collected for RNA-sequencing to investigate the immune-related tumor microenvironment. RESULTS: In both the discovery and validation cohorts, the on-treatment NLR, dNLR, PLR, and SII levels were significantly lower in the patients with MPR versus non-MPR. On-treatment SII remained an independent predictor of MPR in multivariate logistic regression analysis. The area under the curve (AUC) of on-treatment SII for predicting MPR was 0.75 (95%CI, 0.67-0.84) in the discovery cohort. Moreover, the predictive value was further improved by combining the on-treatment SII and radiological tumor regression data, demonstrating an AUC of 0.82 (95%CI, 0.74-0.90). The predictive accuracy was validated in the external cohort. Compared with the SII-high group, patients with SII-Low were associated with the activated B cell receptor signaling pathway and a higher intratumoral immune cell infiltration level. CONCLUSIONS: On-treatment SII was independently associated with MPR in NSCLC patients receiving neoadjuvant chemoimmunotherapy. Further prospective studies are warranted.

Clinical utility of [18F]FDG PET/CT in the assessment of mediastinal lymph node disease after neoadjuvant chemoimmunotherapy for non-small cell lung cancer.

OBJECTIVES: The performance of positron emission tomography/computed tomography (PET/CT) for the prediction of ypN2 disease in non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy has not been reported. This multicenter study investigated the utility of PET/CT to assess ypN2 disease in these patients. METHODS: A total of 181 consecutive patients (chemoimmunotherapy = 86, chemotherapy = 95) at four institutions were enrolled in this study. Every patient received a PET/CT scan prior to surgery and complete resection with systematic nodal dissection. The diagnostic performance was evaluated through area under the curve (AUC). Kaplan-Meier method and Cox analysis were performed to identify the risk factors affecting recurrences. RESULTS: The sensitivity, specificity, and accuracy of PET/CT for ypN2 diseases were 0.667, 0.835, and 0.779, respectively. Therefore, the AUC was 0.751. Compared with the false positive cases, the mean value of max standardized uptake value (SUVmax) (6.024 vs. 2.672, p < 0.001) of N2 nodes was significantly higher in true positive patients. Moreover, the SUVmax of true positive (7.671 vs. 5.976, p = 0.365) and false (2.433 vs. 2.339, p = 0.990) positive cases were similar between chemoimmunotherapy and chemotherapy, respectively. Survival analysis proved that pathologic N (ypN) 2 patients could be stratified by PET/CT-N2(+ vs. -) for both chemoimmunotherapy (p = 0.023) and chemotherapy (p = 0.010). CONCLUSIONS: PET/CT is an accurate and non-invasive test for mediastinal restaging of NSCLC patients who receive neoadjuvant chemoimmunotherapy. The ypN2 patients with PET/CT-N2( +) are identified as an independent prognostic factor compared with PET/CT-N2(-). CLINICAL RELEVANCE STATEMENT: Imaging with 18F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) plays an integral role during disease diagnosis, staging, and therapeutic response assessments in patients with NSCLC. PET/CT could be an effective non-invasive tool for predicting ypN2 diseases after neoadjuvant chemoimmunotherapy. KEY POINTS: • PET/CT could serve as an effective non-invasive tool for predicting ypN2 diseases. • The ypN2 patients with PET/CT-N2( +) were a strong and independent prognostic factor. • The application of PET/CT for restaging should be encouraged in clinical practice.

Insights into the mechanism of persulfate activation with carbonated waste metal adsorbed resin for the degradation of 2,4-dichlorophenol.

Superabsorbent resin (SAR) saturated with heavy metals poses a threat to surrounding ecosystem. To promote the reutilization of waste, resins adsorbed by Fe2+ and Cu2+ were carbonized and used as catalysts (Fe@C/Cu@C) to activate persulfate (PS) for 2,4-dichlorophenol (2,4-DCP) degradation. The heterogeneous catalytic reaction was mainly responsible for 2,4-DCP removal. The synergistic effect of Fe@C and Cu@C was propitious to 2,4-DCP degradation. Fe@C/Cu@C with a ratio of 2:1 showed the highest performance of 2,4-DCP removal. 40 mg/L 2,4-DCP was completely removed within 90 min under reaction conditions of 5 mM PS, pH = 7.0 and T = 25 °C. The cooperation of Fe@C and Cu@C facilitated the redox cycling of Fe and Cu species to supply accessible PS activation sites, enhancing ROS generation for 2,4-DCP degradation. Carbon skeleton enhanced 2,4-DCP removal via radical/nonradical oxidation pathways and via its adsorption to 2,4-DCP. SO4˙-, HO˙ and O2•- were the dominate radical species involved in 2,4-DCP destruction. Meanwhile, the possible pathways of 2,4-DCP degradation were proposed based on GC-MS. Finally, recycling tests proved catalysts exhibited recyclable stability. Aiming to resource utilization, Fe@C/Cu@C with satisfactory catalysis and stability, is promising catalyst for contaminated water treatment.

An improved total en bloc spondylectomy for L5 vertebral giant cell tumor through a single-stage posterior approach.

PURPOSE: Although total en bloc spondylectomy (TES) is strongly recommended for spinal giant cell tumor (GCT), it is extremely difficult to excise a L5 neoplasm intactly through the single-stage posterior approach. Given the risk of neurological and vascular injury, intralesional curettage (IC) is usually recommended for the treatment of L5 GCT. In this study, we presented our experience with the use of an improved TES to treat L5 GCT through the single-stage posterior approach. METHODS: This study included 20 patients with L5 GCT who received surgical treatment in our department between September 2010 and April 2021. Of them, seven patients received improved TES without iliac osteotomy, and the other 13 patients received IC (n = 8), sagittal en bloc resection (n = 1), TES with iliac osteotomy (n = 3), and TES with radicotomy (n = 1) as control. RESULTS: The mean operative time was 331.43 ± 92.95 min for improved TES group and 365.77 ± 85.17 min for the control group (p = 0.415), with the mean blood loss of 1142.86 ± 340.87 ml vs. 1969.23 ± 563.30 ml (p = 0.002). Postoperative treatment included bisphosphonates in nine patients and denosumab in 12 patients including one patient who changed from bisphosphonates to denosumab. Three patients who received IC experienced local recurrence, and no relapse was observed in improved TES group. CONCLUSION: Single-stage posterior TES for L5 GCT was previously considered impossible. In this study, we presented our experience with the use of an improved surgical technique for L5 TES through the single-stage posterior approach, which has proved to be superior to the conventional procedures in terms of blood loss control and complication and recurrence rates. LEVEL OF EVIDENCE: IV.

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinical stage I NSCLC. Materials and Methods In this retrospective study conducted from May 2020 to October 2020 in a population with clinical stage I NSCLC, an internal cohort was adopted to establish a deep learning signature. Subsequently, the predictive efficacy and biologic basis of the proposed signature were investigated in an external cohort. A multicenter diagnostic trial (registration number: ChiCTR2000041310) was also performed to evaluate its clinical utility. Finally, on the basis of the N2 risk scores, the instructive significance of the signature in prognostic stratification was explored. The diagnostic efficiency was quantified with the area under the receiver operating characteristic curve (AUC), and the survival outcomes were assessed using the Cox proportional hazards model. Results A total of 3096 patients (mean age ± standard deviation, 60 years ± 9; 1703 men) were included in the study. The proposed signature achieved AUCs of 0.82, 0.81, and 0.81 in an internal test set (n = 266), external test cohort (n = 133), and prospective test cohort (n = 300), respectively. In addition, higher deep learning scores were associated with a lower frequency of EGFR mutation (P = .04), higher rate of ALK fusion (P = .02), and more activation of pathways of tumor proliferation (P < .001). Furthermore, in the internal test set and external cohort, higher deep learning scores were predictive of poorer overall survival (adjusted hazard ratio, 2.9; 95% CI: 1.2, 6.9; P = .02) and recurrence-free survival (adjusted hazard ratio, 3.2; 95% CI: 1.4, 7.4; P = .007). Conclusion The deep learning signature could accurately predict N2 disease and stratify prognosis in clinical stage I non-small cell lung cancer. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Park and Lee in this issue.

Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study.

Our aim was to validate and analyze the prognostic impact of the novel International Association for the Study of Lung Cancer (IASLC) Pathology Committee grading system for invasive pulmonary adenocarcinomas (IPAs) in Chinese patients and to evaluate its utility in predicting a survival benefit from adjuvant chemotherapy (ACT). In this multicenter, retrospective, cohort study, we included 926 Chinese patients with completely resected stage I IPAs and classified them into three groups (Grade 1, n = 119; Grade 2, n = 431; Grade 3, n = 376) according to the new grading system proposed by the IASLC. Recurrence-free survival (RFS) and overall survival (OS) were estimated by the Kaplan-Meier method, and prognostic factors were assessed using univariable and multivariable Cox proportional hazards models. All included cohorts were well stratified in terms of RFS and OS by the novel grading system. Furthermore, the proposed grading system was found to be independently associated with recurrence and death in the multivariable analysis. Among patients with stage IB IPA (N = 490), the proposed grading system identified patients who could benefit from ACT but who were undergraded by the adenocarcinoma (ADC) classification. The novel grading system not only demonstrated prognostic significance in stage I IPA in a multicenter Chinese cohort but also offered clinical value for directing therapeutic decisions regarding adjuvant chemotherapy.

Lung cancer with PET/CT-defined occult nodal metastasis yields favourable prognosis and benefits from adjuvant therapy: a multicentre study.

PURPOSE: To investigate the surgical prognosis and efficacy of adjuvant therapy in non-small cell lung cancer (NSCLC) with occult lymph node metastasis (ONM) defined by positron emission tomography/computed tomography (PET/CT). METHODS: A total of 3537 NSCLC patients receiving surgical resection were included in this study. The prognosis between patients with ONM and evident nodal metastasis, ONM patients with and without adjuvant therapy was compared, respectively. RESULTS: ONM was associated with significantly better prognosis than evident nodal metastasis whether for patients with N1 (5-year OS: 56.8% versus 52.3%, adjusted p value = 0.267; 5-year RFS: 44.7% versus 33.2%, adjusted p value = 0.031) or N2 metastasis (5-year OS: 42.8% versus 32.3%, adjusted p value = 0.010; 5-year RFS: 31.3% versus 21.6%, adjusted p value = 0.025). In ONM population, patients receiving adjuvant therapy yielded better prognosis comparing to those without adjuvant therapy (5-year OS: 50.1% versus 33.5%, adjusted p value < 0.001; 5-year RFS: 38.4% versus 22.1%, adjusted p value < 0.001). CONCLUSIONS: ONM defined by PET/CT identifies a unique clinical subtype of lung cancer, ONM is a favorable prognostic factor whether for pathological N1 or N2 NSCLC and adjuvant therapy could provide additional survival benefits for ONM patients.

Intact RNA structurome reveals mRNA structure-mediated regulation of miRNA cleavage in vivo.

MicroRNA (miRNA)-mediated cleavage is involved in numerous essential cellular pathways. miRNAs recognize target RNAs via sequence complementarity. In addition to complementarity, in vitro and in silico studies have suggested that RNA structure may influence the accessibility of mRNAs to miRNA-induced silencing complexes (miRISCs), thereby affecting RNA silencing. However, the regulatory mechanism of mRNA structure in miRNA cleavage remains elusive. We investigated the role of in vivo RNA secondary structure in miRNA cleavage by developing the new CAP-STRUCTURE-seq method to capture the intact mRNA structurome in Arabidopsis thaliana. This approach revealed that miRNA target sites were not structurally accessible for miRISC binding prior to cleavage in vivo. Instead, we found that the unfolding of the target site structure plays a key role in miRISC activity in vivo. We found that the single-strandedness of the two nucleotides immediately downstream of the target site, named Target Adjacent nucleotide Motif, can promote miRNA cleavage but not miRNA binding, thus decoupling target site binding from cleavage. Our findings demonstrate that mRNA structure in vivo can modulate miRNA cleavage, providing evidence of mRNA structure-dependent regulation of biological processes.

Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study.

BACKGROUND: Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. METHODS: We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People's Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. RESULTS: We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. CONCLUSIONS: Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

YAP/TEAD4-induced KIF4A contributes to the progression and worse prognosis of esophageal squamous cell carcinoma.

Aberrant expression of kinesin family member 4A (KIF4A), which is associated with tumor progression, has been reported in several types of cancer. However, its expression and the underlying molecular mechanisms regulating the transcription of KIF4A in esophageal squamous cell carcinoma (ESCC) remain largely unclear. Here, we found that high KIF4A expression was positively correlated with tumor stage and poor prognosis in ESCC patients. KIF4A silencing significantly inhibited the growth and migration of ESCC cells, arrested cell cycle, and induced apoptosis. Interestingly, KIF4A expression was positively related to the expression of YAP in human ESCC tissues. YAP knockdown or disrupting YAP/TEAD4 interaction by verteporfin repressed KIF4A expression. Also, KIF4A knockdown significantly inhibited the cell growth induced by YAP overexpression. Mechanistically, YAP activated KIF4A transcriptional expression by TEAD4-mediated direct binding to KIF4A promoter. Finally, KIF4A knockdown and verteporfin treatment synergistically inhibited tumor growth in xenograft models. Together, these results indicated that KIF4A, a novel target gene of YAP/TEAD4, may be a progression and prognostic biomarker of ESCC. Targeting drugs for KIF4A combined with YAP inhibitor may be a novel therapeutic strategy for ESCC.

Brain CT registration using hybrid supervised convolutional neural network.

BACKGROUND: Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration. METHOD: HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference-moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively. RESULTS: HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s). CONCLUSION: The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration.

Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images.

BACKGROUND: The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT (computed tomography) examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. METHOD: The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are GGO (ground-glass opacity), cord, solid and subsolid. A computer-aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three-dimensional texture descriptors are applied on the volume data of lesions as well as shape and first-order features. The massive feature data are selected by HAFS (hybrid adaptive feature selection) algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. RESULTS: There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (93.06%, 96.84%, 99.58%, and 94.30%), the recall is (95.52%, 91.58%, 95.80% and 80.75%) and the f-score is (93.84%, 92.37%, 95.47%, and 84.42%). CONCLUSION: The three-dimensional radiomics features used in this paper can better express the high-level information of COVID-19 lesions in CT slices. HAFS method aggregates the results of multiple feature selection algorithms intersects with traditional methods to filter out redundant features more accurately. After selection, the subtype of COVID-19 lesion can be judged by inputting the features into the RF (random forest) model, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

G-quadruplex structures trigger RNA phase separation.

Liquid-liquid phase separation plays an important role in a variety of cellular processes, including the formation of membrane-less organelles, the cytoskeleton, signalling complexes, and many other biological supramolecular assemblies. Studies on the molecular basis of phase separation in cells have focused on protein-driven phase separation. In contrast, there is limited understanding on how RNA specifically contributes to phase separation. Here, we described a phase-separation-like phenomenon that SHORT ROOT (SHR) RNA undergoes in cells. We found that an RNA G-quadruplex (GQ) forms in SHR mRNA and is capable of triggering RNA phase separation under physiological conditions, suggesting that GQs might be responsible for the formation of the SHR phase-separation-like phenomenon in vivo. We also found the extent of GQ-triggered-phase-separation increases on exposure to conditions which promote GQ. Furthermore, GQs with more G-quartets and longer loops are more likely to form phase separation. Our studies provide the first evidence that RNA can adopt structural motifs to trigger and/or maintain the specificity of RNA-driven phase separation.

Knockdown of lncRNA ZFAS1-suppressed non-small cell lung cancer progression via targeting the miR-150-5p/HMGA2 signaling.

Non-small cell lung cancer (NSCLC) is the main type of lung malignancy. Early diagnosis and treatments for NSCLC are far from satisfactory due to the limited knowledge of the molecular mechanisms regarding NSCLC progression. Long noncoding RNA (lncRNA) ZNFX1 antisense RNA1 (ZFAS1) has been implicated for its functional role in the progression of malignant tumors. This study aimed to determine the ZFAS1 expression from lung cancer clinical samples and to explore the molecular mechanisms underlying ZFAS1-modulated NSCLC progression. Experimental assays revealed that clinical samples and cell lines of lung malignant tumors showed an upregulation of ZFSA1. ZFAS1 expression was markedly upregulated in the lung tissues from patients with advanced stage of this malignancy. The loss-of-function assays showed that knockdown of ZFAS1-suppressed NSCLC cell proliferative, as well as invasive potentials, increased NSCLC cell apoptotic rates in vitro and also attenuated tumor growth of NSCLC cells in the nude mice. Further experimental evidence showed that ZFAS1 inversely affected miR-150-5p expression and positively affected high-mobility group AT-hook 2 (HMGA2) expression in NSCLC cell lines. MiR-150-5p inhibition or HMGA2 overexpression counteracted the effects of ZFAS1 knockdown on NSCLC cell proliferative, invasive potentials and apoptotic rates. In light of examining the clinical lung cancer samples, miR-150-5p expression was downregulated and the HMGA2 expression was highly expressed in the lung cancer tissues compared with normal ones; the ZFAS1 expression showed a negative correlation with miR-150-5p expression but a positive correlation with HMGA2 expression in lung cancer tissues. To summarize, we, for the first time, demonstrated the inhibitory effects of ZFAS1 knockdown on NSCLC cell progression, and the results from mechanistic studies indicated that ZFAS1-mediated NSCLC progression cells via targeting miR-150-5p/HMGA2 signaling.

ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs.

Noncoding RNAs (ncRNAs) play critical roles in many critical biological processes and have become a novel class of potential targets and bio-markers for disease diagnosis, therapy, and prognosis. Annotating and analysing ncRNA-disease association data are essential but challenging. Current computational resources lack comprehensive database platforms to consistently interpret and prioritize ncRNA-disease association data for biomedical investigation and application. Here, we present the ncRPheno database platform (http://lilab2.sysu.edu.cn/ncrpheno), which comprehensively integrates and annotates ncRNA-disease association data and provides novel searches, visualizations, and utilities for association identification and validation. ncRPheno contains 482,751 non-redundant associations between 14,494 ncRNAs and 3,210 disease phenotypes across 11 species with supporting evidence in the literature. A scoring model was refined to prioritize the associations based on evidential metrics. Moreover, ncRPheno provides user-friendly web interfaces, novel visualizations, and programmatic access to enable easy exploration, analysis, and utilization of the association data. A case study through ncRPheno demonstrated a comprehensive landscape of ncRNAs dysregulation associated with 22 cancers and uncovered 821 cancer-associated common ncRNAs. As a unique database platform, ncRPheno outperforms the existing similar databases in terms of data coverage and utilities, and it will assist studies in encoding ncRNAs associated with phenotypes ranging from genetic disorders to complex diseases. ABBREVIATIONS: APIs: application programming interfaces; circRNA: circular RNA; ECO: Evidence & Conclusion Ontology; EFO: Experimental Factor Ontology; FDR: false discovery rate; GO: Gene Ontology; GWAS: genome wide association studies; HPO: Human Phenotype Ontology; ICGC: International Cancer Genome Consortium; lncRNA: long noncoding RNA; miRNA: micro RNA; ncRNA: noncoding RNA; NGS: next generation sequencing; OMIM: Online Mendelian Inheritance in Man; piRNA: piwi-interacting RNA; snoRNA: small nucleolar RNA; TCGA: The Cancer Genome Atlas.