Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

The pathological tissue expression pattern and clinical significance of m6A-regulatory genes in non-small cell lung cancer.

BACKGROUND: Aberrant expression of m6A-related proteins contributes to the occurrence and progression of non-small cell lung cancer (NSCLC). Current studies mainly focus on single m6A regulatory genes and their underlying mechanisms, and the expression of multiple m6A regulatory proteins in NSCLC remains unclear. Therefore, it is necessary to systematically examine these proteins, particularly in clinical specimens. METHODS: Bioinformatic analysis was used to determine the expression of m6A regulatory genes and their correlation with common gene mutations, such as TP53, EGFR and KRAS, using The Cancer Genome Atlas (TCGA) and the AE-meta databases. Immunohistochemistry was employed to analyze the protein expression of m6A regulatory proteins in 61 benign lung tissues and 316 NSCLC tissues. Statistical analysis was performed to calculate the correlation between the expression of m6A regulatory proteins and clinicopathological features, survival, and common gene mutations in lung carcinoma patients. RESULTS: Analysis of the mRNA levels of 13 core m6A regulators, using information from TCGA and the AE-meta databases, revealed that YTHDF1 levels were upregulated in NSCLC compared to those in adjacent normal tissues. Immunohistochemical staining showed that the expression of METTL3, ALKBH5, YTHDC2 and YTHDF1 was significantly upregulated in NSCLC tissues. Further analyses demonstrated a positive correlation between differentially expressed m6A regulatory proteins, including METTL3, ALKBH5, YTHDC2 and YTHDF1, and the poor clinicopathological features and survival of NSCLC patients. According to the statistics of NSCLC patients enrolled in the present study, the protein levels of METTL3 in patients with EGFR exon-19 mutation were higher than those in patients with wild-type EGFR. CONCLUSIONS: Our results indicate that m6A regulators, including METTL3, ALKBH5, YTHDC2 and YTHDF1, could serve as predictive markers of NSCLC, which will facilitate the early detection and diagnosis of NSCLC.

Small lesion classification on abbreviated breast MRI: training can improve diagnostic performance and inter-reader agreement.

OBJECTIVE: To determine whether the diagnostic performance and inter-reader agreement for small lesion classification on abbreviated breast MRI (AB-MRI) can be improved by training, and can achieve the level of full diagnostic protocol MRI (FDP-MRI). METHODS: This retrospective study enrolled 1165 breast lesions (≤ 2 cm; 409 malignant and 756 benign) from 1165 MRI examinations for reading test. Twelve radiologists were assigned into a trained group and a non-trained group. They interpreted each AB-MRI twice, which was extracted from FDP-MRI. After the first read, the trained group received a structured training for AB-MRI interpretation while the non-trained group did not. FDP-MRIs were interpreted by the trained group after the second read. BI-RADS category for each lesion was compared to the standard of reference (histopathological examination or follow-up) to calculate diagnostic accuracy. Inter-reader agreement was assessed using multirater k analysis. Diagnostic accuracy and inter-reader agreement were compared between the trained and non-trained groups, between the first and second reads, and between AB-MRI and FDP-MRI. RESULTS: After training, the diagnostic accuracy of AB-MRI increased from 77.6 to 84.4%, and inter-reader agreement improved from 0.410 to 0.579 (both p < 0.001), which were higher than those of the non-trained group (accuracy, 84.4% vs 78.0%; weighted k, 0.579 vs 0.461; both p < 0.001). The post-training accuracy and inter-reader agreement of AB-MRI were lower than those of FDP-MRI (accuracy, 84.4% vs 92.8%; weighted k, 0.579 vs 0.602; both p < 0.001). CONCLUSIONS: Training can improve the diagnostic performance and inter-reader agreement for small lesion classification on AB-MRI; however, it remains inferior to those of FDP-MRI. KEY POINTS: • Training can improve the diagnostic performance for small breast lesions on AB-MRI. • Training can reduce inter-observer variation for breast lesion classification on AB-MRI, especially among junior radiologists. • The post-training diagnostic performance and inter-reader agreement of AB-MRI remained inferior to those of FDP-MRI.

Overexpression of the SARS-CoV-2 receptor ACE2 is induced by cigarette smoke in bronchial and alveolar epithelia.

Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a target for disease prevention. However, the relationship between ACE2 expression and its clinical implications in SARS-CoV-2 pathogenesis remains unknown. Here, we explored the location and expression of ACE2, and its correlation with gender, age, and cigarette smoke (CS), in a CS-exposed mouse model and 224 non-malignant lung tissues (125 non-smokers, 81 current smokers, and 18 ex-smokers) by immunohistochemistry. Moreover, the correlations of ACE2 with CS-induced oxidative stress-related markers, hypoxia-inducible factor-1α (HIF-1α), inducible nitric oxide synthase (iNOS), and 4-hydroxynonenal (4-HNE) were investigated. Chromatin immunoprecipitation and luciferase reporter assays identified the cause of ACE2 overexpression in human primary lung epithelial cells. We demonstrated that ACE2 was predominantly overexpressed on the apical surface of bronchial epithelium, while reduced in alveolar epithelium, owing to the dramatically decreased abundance of alveolar type II pneumocytes in CS-exposed mouse lungs. Consistent with this, ACE2 was primarily significantly overexpressed in human bronchial and alveolar epithelial cells in smokers regardless of age or gender. Decreased ACE2 expression was observed in bronchial epithelial cells from ex-smokers compared with current smokers, especially in those who had ceased smoking for more than 10 years. Moreover, ACE2 expression was positively correlated with the levels of HIF-1α, iNOS, and 4-HNE in both mouse and human bronchioles. The results were further validated using a publicly available dataset from The Cancer Genome Atlas (TCGA) and our previous integrated data from Affymetrix U133 Plus 2.0 microarray (AE-meta). Finally, our results showed that HIF-1α transcriptionally upregulates ACE2 expression. Our results indicate that smoking-induced ACE2 overexpression in the apical surface of bronchial epithelial cells provides a route by which SARS-CoV-2 enters host cells, which supports clinical relevance in attenuating the potential transmission risk of COVID-19 in smoking populations by smoking cessation. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

MIP image derived from abbreviated breast MRI: potential to reduce unnecessary sub-nipple biopsies during nipple-sparing mastectomy for breast cancer.

OBJECTIVE: To determine the value of a maximum-intensity projection (MIP) image derived from abbreviated breast MRI for excluding occult nipple-areolar complex (NAC) involvement in patients with breast cancer. METHODS: This prospective study included breast cancer patients with clinically normal NACs between April 2016 and May 2019. Abbreviated breast MRI was performed, and an MIP image was generated for each patient. MIP images were examined for the following features: asymmetric nipple enhancement, tumor-nipple distance (TND), tumor diameter, lesion type, location, and multifocality. Independent predictive MIP features for occult NAC involvement were identified by univariable and multivariable logistic regression analyses. Models based on independent predictive MIP features were developed, and their diagnostic performances were evaluated using ROC analysis. The utility of an MIP image for excluding occult NAC involvement was assessed by considering NPVs across patient subgroups. RESULTS: Eight hundred forty-three patients (67 NAC-positive and 776 NAC-negative) were enrolled. On MIP images, asymmetric nipple enhancement (odds ratio, 6.098; p < 0.001) and TND (odds ratio, 0.564; p = 0.003) were independent predictors of occult NAC involvement. A parallel test model of "asymmetric nipple enhancement or TND ≤ 15 mm" yielded the highest AUC value (0.838) among prediction models. The NPV of MIP images for excluding occult NAC involvement was 99.5%, which was applicable across various patient subgroups. CONCLUSIONS: A single MIP image derived from abbreviated breast MRI has utility for excluding occult NAC involvement in breast cancer patients and reducing the number of unnecessary sub-nipple biopsies in nipple-sparing mastectomy. KEY POINTS: • On MIP images derived from abbreviated breast MRI, asymmetric nipple enhancement and tumor-nipple distance were independent predictors for occult nipple involvement in patients with breast cancer. • Negative findings on MIP image can help select patients at minimal risk of occult nipple involvement, for whom unnecessary intraoperative sub-nipple biopsies in nipple-sparing mastectomy can be omitted.

Ectodysplasin A receptor (EDAR) promotes colorectal cancer cell proliferation via regulation of the Wnt/ß-catenin signaling pathway.

Colorectal cancer is the second leading cause of cancer mortality worldwide with poor prognosis and high recurrence. Aberrant Wnt/ß-catenin signaling promotes oncogenesis by transcriptional activation of c-Myc and its downstream signals. EDAR is characterized as an important effector of canonical Wnt signaling in developing skin appendages, but the interplay between EDAR and Wnt signaling in tumorigenesis and progression remains to be elucidated. In this study, we revealed that EDAR expression is prevalently elevated in colorectal cancer tissues compared with normal tissues. Further analysis suggests there is a strict correlation between EDAR expression and colorectal cancer progression. EDAR silencing by shRNA in colorectal cancer cells showed proliferative suppression via retarding cell cycle at G1 phase. Xenograft mice transplanted with shEDAR-transduced tumor cells significantly alleviated tumor burden in comparison with control mice. Furthermore, downregulation of EDAR was accompanied by reduction of ß-catenin, c-Myc and other G1 cell cycle regulators, while ß-catenin agonist restored the expression of these proteins and overrode the proliferative block induced by EDAR knockdown. These findings indicate that EDAR functions as a component of Wnt/ß-catenin signaling pathway, and is a potential modulator in colorectal carcinogenesis.

A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer.

OBJECTIVE: This study aimed to preoperatively differentiate primary gastric lymphoma from Borrmann type IV gastric cancer by heterogeneity nomogram based on routine contrast-enhanced computed tomographic images. METHODS: We enrolled 189 patients from 2 hospitals (90 in the training cohort and 99 in the validation cohort). Subjective findings, including high-enhanced mucosal sign, high-enhanced serosa sign, nodular or an irregular outer layer of the gastric wall, and perigastric fat infiltration, were assessed to construct a subjective finding model. A deep learning model was developed to segment tumor areas, from which 1680 three-dimensional heterogeneity radiomic parameters, including first-order entropy, second-order entropy, and texture complexity, were extracted to build a heterogeneity signature by least absolute shrinkage and selection operator logistic regression. A nomogram that integrates heterogeneity signature and subjective findings was developed by multivariate logistic regression. The diagnostic performance of the nomogram was assessed by discrimination and clinical usefulness. RESULTS: High-enhanced serosa sign and nodular or an irregular outer layer of the gastric wall were identified as independent predictors for building the subjective finding model. High-enhanced serosa sign and heterogeneity signature were significant predictors for differentiating the 2 groups (all, P < 0.05). The area under the curve with heterogeneity nomogram was 0.932 (95% confidence interval, 0.863-0.973) in the validation cohort. Decision curve analysis and stratified analysis confirmed the clinical utility of the heterogeneity nomogram. CONCLUSIONS: The proposed heterogeneity radiomic nomogram on contrast-enhanced computed tomographic images may help differentiate primary gastric lymphoma from Borrmann type IV gastric cancer preoperatively.

LINC00173.v1 promotes angiogenesis and progression of lung squamous cell carcinoma by sponging miR-511-5p to regulate VEGFA expression.

BACKGROUND: Anti-angiogenic therapy represents a promising strategy for non-small-cell lung cancer (NSCLC) but its application in lung squamous cell carcinoma (SQC) is limited due to the high-risk adverse effects. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) mediate in tumor progression by participating in the regulation of VEGF in NSCLC, which might guide the development of new antiangiogenic strategies. METHODS: Differential lncRNA expression in SQC was analyzed in AE-meta and TCGA datasets, and further confirmed in lung cancer tissues and adjacent normal tissues with RT-qPCR and in-situ hybridization. Statistical analysis was performed to evaluate the clinical correlation between LINC00173.v1 expression and survival characteristics. A tube formation assay, chick embryo chorioallantoic membrane assay and animal experiments were conducted to detect the effect of LINC00173.v1 on the proliferation and migration of vascular endothelial cells and tumorigenesis of SQC in vivo. Bioinformatics analysis, RNA immunoprecipitation and luciferase reporter assays were performed to elucidate the downstream target of LINC00173.v1. The therapeutic efficacy of antisense oligonucleotide (ASO) against LINC00173.v1 was further investigated in vivo. Chromatin immunoprecipitation and high throughput data processing and visualization were performed to identify the cause of LINC00173.v1 overexpression in SQC. RESULTS: LINC00173.v1 was specifically upregulated in SQC tissues, which predicted poorer overall and progression-free survival in SQC patients. Overexpression of LINC00173.v1 promoted, while silencing LINC00173.v1 inhibited the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC cells in vitro and in vivo. Our results further revealed that LINC00173.v1 promoted the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC cells by upregulating VEGFA expression by sponging miR-511-5p. Importantly, inhibition of LINC00173.v1 via the ASO strategy reduced the tumor growth of SQC cells, and enhanced the therapeutic sensitivity of SQC cells to cisplatin in vivo. Moreover, our results showed that squamous cell carcinoma-specific factor ΔNp63α contributed to LINC00173.v1 overexpression in SQC. CONCLUSION: Our findings clarify the underlying mechanism by which LINC00173.v1 promotes the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC, demonstrating that LINC00173.v1-targeted drug in combination with cisplatin may serve as a rational regimen against SQC.

Value of digital mammography in predicting lymphovascular invasion of breast cancer.

BACKGROUND: Lymphovascular invasion (LVI) has never been revealed by preoperative scans. It is necessary to use digital mammography in predicting LVI in patients with breast cancer preoperatively. METHODS: Overall 122 cases of invasive ductal carcinoma diagnosed between May 2017 and September 2018 were enrolled and assigned into the LVI positive group (n = 42) and the LVI negative group (n = 80). Independent t-test and χ2 test were performed. RESULTS: Difference in Ki-67 between the two groups was statistically significant (P = 0.012). Differences in interstitial edema (P = 0.013) and skin thickening (P = 0.000) were statistically significant between the two groups. Multiple factor analysis showed that there were three independent risk factors for LVI: interstitial edema (odds ratio [OR] = 12.610; 95% confidence interval [CI]: 1.061-149.922; P = 0.045), blurring of subcutaneous fat (OR = 0.081; 95% CI: 0.012-0.645; P = 0.017) and skin thickening (OR = 9.041; 95% CI: 2.553-32.022; P = 0.001). CONCLUSIONS: Interstitial edema, blurring of subcutaneous fat, and skin thickening are independent risk factors for LVI. The specificity of LVI prediction is as high as 98.8% when the three are used together.

Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.

OBJECTIVE: To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS: Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation. RESULTS: Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC. CONCLUSIONS: MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging. KEY POINTS: • Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

Comparison of the clinical and MRI features of patients with hepatic hemangioma, epithelioid hemangioendothelioma, or angiosarcoma.

BACKGROUND: Comparisons of hepatic epithelioid hemangioendothelioma (HEHE), hepatic hemangioma, and hepatic angiosarcoma (HAS) have rarely been reported. The purpose of our study was to analyze the clinical and magnetic resonance imaging (MRI) findings of these conditions. METHODS: A total of 57 patients (25 with hemangioma, 13 with HEHE, and 19 with HAS) provided hepatic vascular endothelial cell data between June 2006 and May 2017. RESULTS: The proportions of cases with circumscribed margins were 88% (22/25), 84.6% (11/13), and 31.6% (6/19) for hemangioma, HEHE, and HAS, respectively (P < 0.001). HAS lesions were less likely to have circumscribed margins. The proportions of lesions with hemorrhaging were 4% (1/25), 30.8% (4/13), and 36.8% (7/19) for hemangioma, HEHE, and HAS, respectively (P = 0.014). HEHE and HAS cases were more likely to show heterogeneous signals on T1-weighted (T1WI) MRI. HEHE and HAS cases were more likely to show heterogeneous signals on T2-weighted (T2WI) MRI. Centripetal enhancement was the most common pattern in vascular tumors, with proportions of 100, 46.2% (6/13), and 68.4% (13/19) for hemangioma, HEHE, and HAS, respectively. The difference in enhancement pattern between HEHE and HAS was not significant, but rim enhancement was more common for HEHE (46.2%, 6/13). CONCLUSIONS: Our study revealed clinical and imaging differences between HEHE and HAS. The platelet count (PLT) and coagulation function of the HAS group decreased, whereas the alpha-fetoprotein (AFP) level increased. The 5-year survival rate for HAS was significantly lower than that of HEHE. A higher malignancy degree indicated a more blurred lesion margin, easier occurrence of hemorrhaging, and more heterogeneous T1WI and T2WI signals.

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.

BACKGROUND: Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection. PURPOSE: To explore the use of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)-based radiomics for preoperative prediction of LVI in invasive breast cancer. STUDY TYPE: Prospective. POPULATION: Ninety training cohort patients (22 LVI-positive and 68 LVI-negative) and 59 validation cohort patients (22 LVI-positive and 37 LVI-negative) were enrolled. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, T1 -weighted DCE-MRI. ASSESSMENT: Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE-MRI. A radiomics signature was constructed in the training cohort with 10-fold cross-validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort. STATISTICAL TESTS: Mann-Whitney U-test, chi-square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA). RESULTS: Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone. DATA CONCLUSION: The DCE-MRI-based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847-857.

Pelvic mass: Schwannoma of the left seminal vesicle.

Schwannomas rarely occur in seminal vesicles. Here, we report a schwannoma of the left seminal vesicle. A 55-year-old man presented no clinical symptoms, and a mass in the left region of the seminal vesicle was found incidentally in a medical examination. A computed tomography and magnetic resonance imaging of pelvic were obtained and revealed a 5.17 × 2.59 × 3.5 cm mass on the left seminal vesicle. Transrectal ultrasound-guided seminal biopsy revealed a diagnosis of seminal vesical schwannoma. Laparoscopic resection of the tumour was performed. Postoperative pathology and immunohistochemical analysis revealed schwannoma arising from seminal vesical.

Phospholipid Phosphatase 4 promotes proliferation and tumorigenesis, and activates Ca2+-permeable Cationic Channel in lung carcinoma cells.

BACKGROUND: Phospholipid phosphatase 4 (PPAPDC1A or PLPP4) has been demonstrated to be involved in the malignant process of many cancers. The purpose of this study was to investigate the clinical significance and biological roles of PLPP4 in lung carcinoma. METHODS: PLPP4 expression was examined in 8 paired lung carcinoma tissues by real-time PCR and in 265 lung carcinoma tissues by immunohistochemistry (IHC). Statistical analysis was performed to evaluate the clinical correlation between PLPP4 expression and clinicopathological features and survival in lung carcinoma patients. In vitro and in vivo assays were performed to assess the biological roles of PLPP4 in lung carcinoma. Fluorescence-activated cell sorting, Western blotting and luciferase assays were used to identify the underlying pathway through which PLPP4 silencing mediates biological roles in lung carcinoma. RESULTS: PLPP4 is differentially elevated in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQC) tissues. Statistical analysis demonstrated that high expression of PLPP4 significantly and positively correlated with clinicopathological features, including pathological grade, T category and stage, and poor overall and progression-free survival in lung carcinoma patients. Silencing PLPP4 inhibits proliferation and cell cycle progression in vitro and tumorigenesis in vivo in lung carcinoma cells. Our results further reveal that PLPP4 silencing inhibits Ca2+-permeable cationic channel, suggesting that downregulation of PLPP4 inhibits proliferation and tumorigenesis in lung carcinoma cells via reducing the influx of intracellular Ca2+. CONCLUSION: Our results indicate that PLPP4 may hold promise as a novel marker for the diagnosis of lung carcinoma and as a potential therapeutic target to facilitate the development of novel treatment for lung carcinoma.

Identification of gene variation feature for targeted therapy of non-small cell lung cancer through combined method of DNA and RNA sequencing.

Next generation sequencing (NGS) is typically used to reveal tumor gene variation feature for targeted therapy of various types of human cancers, including non-small cell lung cancer (NSCLC). Here, we report the role and potential applicable value of combining DNA and RNA sequencing in gene variation detection in NSCLC. 386 NSCLC patients with stage II-IV were enrolled and detected using NGS sequencing of DNA and RNA panels that covered all well-documented target driver genes from the Chinese Society of Clinical Oncology (CSCO). The rate of epidermal growth factor receptor (EGFR) single nucleotide variation (SNV)/indel, mesenchymal-epithelial transition factor (MET) copy number variation (CNV) and anaplastic lymphoma kinase (ALK) fusion were 52.1%, 4.1% and 6.0% in the NSCLC cohort. The landscapes of SNV/indel, CNV and gene fusion in the cohort were depicted as well. Meanwhile, we assessed detection efficacy of DNA and RNA sequencing in gene fusion. Detected number and types of gene fusion using the RNA sequencing were better than those using the DNA sequencing. Gene fusion with intergenic region was only detected by DNA sequencing and MET exon 14 skipping (METΔex14) was more easily identified by RNA sequencing. Finally, we investigated clinical correlations of SNV/indel/CNV/fusion with clinicopathologic features in the NSCLC cohort. Taken together, RNA sequencing significantly complements deficiency of DNA sequencing for gene fusion, which cooperatively presents comprehensive and reliable gene variation features and facilitate the identification of potential drug targets for NSCLC patients.

The DDUP protein encoded by the DNA damage-induced CTBP1-DT lncRNA confers cisplatin resistance in ovarian cancer.

Sustained activation of DNA damage response (DDR) signaling has been demonstrated to play vital role in chemotherapy failure in cancer. However, the mechanism underlying DDR sustaining in cancer cells remains unclear. In the current study, we found that the expression of the DDUP microprotein, encoded by the CTBP1-DT lncRNA, drastically increased in cisplatin-resistant ovarian cancer cells and was inversely correlated to cisplatin-based therapy response. Using a patient-derived human cancer cell model, we observed that DNA damage-induced DDUP foci sustained the RAD18/RAD51C and RAD18/PCNA complexes at the sites of DNA damage, consequently resulting in cisplatin resistance through dual RAD51C-mediated homologous recombination (HR) and proliferating cell nuclear antigen (PCNA)-mediated post-replication repair (PRR) mechanisms. Notably, treatment with an ATR inhibitor disrupted the DDUP/RAD18 interaction and abolished the effect of DDUP on prolonged DNA damage signaling, which resulted in the hypersensitivity of ovarian cancer cells to cisplatin-based therapy in vivo. Altogether, our study provides insights into DDUP-mediated aberrant DDR signaling in cisplatin resistance and describes a potential novel therapeutic approach for the management of platinum-resistant ovarian cancer.

Application of Ultrasound-Guided Cervical Plexus Block in Type I Thyroid Cartilage Laryngoplasty and Vocal Cord Medialization Surgery.

BACKGROUND: Under the background that cervical plexus block (CPB) is often adopted for type I thyroid cartilage laryngoplasty (TCL) and vocal cord medialization (VCM), the present study sought to investigate whether ultrasound-guided CPB (USCPB) could improve the efficiency of type I TCL and VCM. METHODS: Patients with TCL were enrolled and subjected to deep and superficial USCPBs. Intravenous dexmedetomidine pumping was used to assist the painless sedation and ensure the patients to be awake for phonation during surgery. Blood pressure, electrocardiogram, heart rate (HR), and blood oxygen saturation (SpO2) of patients were recorded. The complications, like local anesthetic toxicity and total spinal anesthesia, were monitored. RESULTS: All patients underwent CPB without infiltration anesthesia and complication. The use of Sufentanil at the dose of 5-10 µg was reported in 2 of 15 patients. No Horner syndrome was discovered in patients after anesthesia, and total intravenous anesthesia with intravenous pumping of dexmedetomidine was effective. During surgery, HR, diastolic blood pressure and mean blood pressure were barely changed, but systolic blood pressure was decreased. CONCLUSION: Ultrasound-guided CPB with the intravenous dexmedetomidine pumping is a safe anesthesia method for patients during TCL.

Nanoarchitectonics for High Adsorption Capacity Carboxymethyl Cellulose Nanofibrils-Based Adsorbents for Efficient Cu2+ Removal.

In the present study, carboxymethyl cellulose nanofibrils (CMCNFs) with different carboxyl content (0.99-2.01 mmol/g) were prepared via controlling the ratio of monochloroacetic acid (MCA) and sodium hydroxide to Eucalyptus bleached pulp (EBP). CMCFs-PEI aerogels were obtained using the crosslinking reaction of polyethyleneimine (PEI) and CMCNFs with the aid of glutaraldehyde (GA). The effects of pH, contact time, temperature, and initial Cu2+ concentration on the Cu2+ removal performance of CMCNFs-PEI aerogels was highlighted. Experimental data showed that the maximum adsorption capacity of CMCNF30-PEI for Cu2+ was 380.03 ± 23 mg/g, and the adsorption results were consistent with Langmuir isotherm (R2 > 0.99). The theoretical maximum adsorption capacity was 616.48 mg/g. After being treated with 0.05 M EDTA solution, the aerogel retained an 85% removal performance after three adsorption-desorption cycles. X-ray photoelectron spectroscopy (XPS) results demonstrated that complexation was the main Cu2+ adsorption mechanism. The excellent Cu2+ adsorption capacity of CMCNFs-PEI aerogels provided another avenue for the utilization of cellulose nanofibrils in the wastewater treatment field.

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics.

Objective: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC). Methods: Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI. Results: In the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p < 0.001; IDI = 0.092, p < 0.001; NRI = 0.554, p < 0.001). The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type (all p > 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility. Conclusions: An AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC.