Search

Colorectal cancer (CRC) is one of the most prominent malignant diseases, with a high incidence and a dismal prognosis. Metastasis to the liver is the leading cause of death in CRC patients. This study aimed to identify accurate metastatic biomarkers of CRC and investigate the potential molecular mechanisms of liver metastasis of colorectal cancer (LMCRC).Three independent datasets were screened and downloaded from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to identify differentially expressed genes (DEGs) in CRC tissues and liver metastases. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Furthermore, the protein-protein interactions (PPIs) of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database, Cytoscape, and Molecular Complex Detection (MCODE). Next, the expression levels and Kaplan-Meier survival analysis of the target gene between normal colon and CRC tissues were performed by UALCAN. The expression of the target gene in tissues and cell lines was verified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry (IHC) assay. The impact of the target gene on the proliferation, invasion, and migration ability of COAD cells was exploredA total of 92 common DEGs were found in the three independent datasets. GO/KEGG enrichment analysis showed that the DEGs were mainly involved in 14 different pathways. The protein-protein interaction (PPI) network revealed that complement 5 (C5), the upstream gene of C8A in the complement system, was associated with C8 and other key hub genes. Meanwhile, the online UALCAN resource showed that C5 was up-regulated and facilitated malignant progression in COAD samples. Next, we confirmed that C5 remarkably increased and promoted cell proliferation, migration, and invasion in CRC cell lines, SW620 and SW480. The IHC assay showed C5 was also highly expressed in a majority of LMCRC tissues compared with paired CRC tissues.The findings of our integrated bioinformatics study suggest that complement C5 might serve as a potential therapeutic target in patients with CRC.

As a disorder of the myocardium caused by diabetes mellitus, DCM has become a key health concern in the world. Autophagy plays an important role in the pathogenesis of DCM. Sphingosine kinase 1 benefits to cell survival and growth, and regulates the pathogenesis of many diseases, including diabetes and cardiovascular disease. Silibinin is widely used as a hepatoprotective and antioxidant agent in Asia and Europe. However, how silibinin governs DCM remains poorly understood. To this aim, the present study is to mine the role of Sphk1 in autophagy induction and cell survival in myocardial cells under high glucose treatment, and how silibinin regulates DCM. Herein, the study used the embryonic rat heart-derived myogenic H9C2 cells and adenovirus based gene manipulation was utilized. It was found that silibinin could induce autophagy in a dose-dependent manner. Overexpression of Sphk1 significantly increased mRNA expression of autophagy gene markers in H9C2 cells. Gain of function of Sphk1 significantly increased protein level of Beclin1 in H9C2 cells under treatments of low (5.5 mM) and high (25 mM) glucose. High glucose inhibited autophagy induction in H9C2 cells. Gain of function of Sphk1 compromised inhibition of on autophagy induction by high glucose. The results of the present study may assist in attaining an improved understanding of the pathogenesis of DCM, and developing novel therapies for treatment of DCM.

Autophagy in tumor was also found to influence immune microenvironment. The relation between autophagy and cancer intrinsic PD1 and PD-L1 expression was not clear.With data from TCGA and GTEx databases, mRNA expression levels of autophagy-related genes were compared between tumor samples and normal tissues, which were also correlated with survival status. Expression of autophagy-related genes were also associated with clinical traits in datasets of GSE14520 and ICGC LIRI. Single sample gene set enrichment analysis (ssGSEA) was used to calculate autophagy scores in tumor samples, using signatures from MSigDB database. Lentivirus (PD1 and PD-L1), siRNA (ATG13) and plasmids (LC3A/B) were used to target specific genes in tumor cells; Western blot was used to examine protein expression accordingly. Co-immunoprecipitation was performed to find PD1 or PD-L1 interacting proteins; colony formation and EdU analysis were used to evaluate tumor cell growth abilities.mRNA levels of autophagy markers were increased in tumor and correlated with worse survival of cancer patients. In hepatocellular carcinoma (HCC), high mRNA expression of autophagy markers was related to poor clinical status; increasing LC3 expression in HCC cell lines could promote tumor growth. Tumor intrinsic PD1 or PD-L1 were related to higher autophagy levels in specific tumor types; over-expression of PD1 or PD-L1 could increase autophagy in tumor cells through ATG13 interaction.Autophagy could promote tumor growth in specific cancer types. Tumor intrinsic PD1 or PD-L1 could both increase autophagy through ATG13 interaction.

PURPOSE Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size. MATERIALS AND METHODS Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance. RESULTS A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B. CONCLUSION A machine learning radiomics model may help differentiate SCLC from other lung lesions.

Deubiquitinating enzyme OTU domain‐containing ubiquitin aldehyde‐binding proteins 1 (OTUB1) has been shown to have an essential role in multiple carcinomas. However, the function of OTUB1 in papillary thyroid cancer (PTC) and the underlying mechanisms regulating PTC cells proliferation remain poorly understood. In this study, OTUB1 was significantly upregulated in papillary thyroid carcinoma tissues and cells. Through in vitro and in vivo experiments, knockdown of OTUB1 suppressed PTC cells growth whereas OTUB1 overexpression enhanced the proliferation ability of PTC cells. Moreover, the eyes absent homologue 1 (EYA1) was recognized as a potential target of OTUB1 through mass spectrometry analysis, and we further verified that EYA1 protein level was positively correlated with OTUB1 expression in PTC cells and clinical samples. Mechanistically, OTUB1 could interact with EYA1 directly and deubiquitinate EYA1 to stabilize it. At last, EYA1 was found to play an essential role in OTUB1‐derived PTC cells growth. Overall, our investigation reveals that OTUB1 is a previously unrecognized oncogenic factor in PTC cells proliferation and suggests that OTUB1 might be a novel therapeutic target in PTC.

Background The prevention and prognosis of the onset or recurrence of acute myocardial infarction (AMI) is a difficult problem in contemporary research. Methods In this study, peripheral blood samples were collected from seven patients with AMI and nine healthy adults, and exosome microRNAs (miRNAs) were extracted. The miRNA differential expression profiles of serum exosomes in patients with AMI were obtained by using the next-generation sequencing technology combined with bioinformatics analysis. Reverse-transcriptase polymerase chain reaction (RT-PCR) was used to verify the primary screening of differential exosome miRNAs to reveal the possible mechanism of their action on AMI. Results Compared with healthy individuals, 544 miRNAs were upregulated and 518 miRNAs were downregulated in AMI patients preoperatively. Among these miRNAs, we selected miR-6718 and miR-4329 for qPCR verification. The expression of miR6718 and miR-4329 in patients with myocardial infarction was significantly lower than that in normal controls.

Hongjiang Pu,1,* Peiyi Xie,2,* Yaxue Chen,3,* Yanrong Zhao,4 Xi Ye,5 Guiyu Lu,4 Dafu Zhang,6 Zhenhui Li6 1Department of Oncology, Dazhou Central Hospital, Dazhou, Sichuan, 635000, People’s Republic of China; 2Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, People’s Republic of China; 3Department of Nursing, Dazhou Vocational and Technical College, Dazhou, Sichuan, 635000, People’s Republic of China; 4Department of Colorectal Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, 650118, People’s Republic of China; 5Department of Oncology, The First People’s Hospital of Honghe State, Mengzi, 661100, People’s Republic of China; 6Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, 650118, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dafu Zhang; Zhenhui LiDepartment of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, Yunnan, 650118, People’s Republic of ChinaTel/ Fax +86 87168179594; +86 87168179549Email tosney@126.com; lizhenhui621@qq.comPurpose: Based on a multi-centered and a large sample size, this study aims to analyze the relationship between preoperative and postoperative serum CEA and recurrence of rectal cancer without preoperative therapy.Methods: This retrospective cohort study enrolled stage I to III rectal cancer patients without preoperative therapy (N = 1,022) who received radical resection of rectal cancer from 2 hospitals in China. Based on the preoperative and postoperative serum carcinoembryonic antigen, the patients were subdivided into 3 groups ie, normal preoperative CEA (≤ 5.0 ng/mL, N = 627), elevated preoperative (> 5.0 ng/mL) but normalized postoperative CEA (normalized postoperative CEA, N = 255), as well as elevated preoperative and postoperative CEA (elevated postoperative CEA, N = 67). The generalized additive model was used to assess the relationship between carcinoembryonic antigen and the risk of recurrence. Further, the Cox regression model was used to evaluate the relationship between carcinoembryonic antigen and 3-year recurrence-free survival (RFS) after adjusting for potential confounders.Results: The 3-year RFS of patients with elevated postoperative CEA was 45.8% (95% CI, 35.2% − 59.5%), which was significantly lower compared to the other two groups of patients (normalized postoperative CEA: 75.9%, 95% CI, 70.8%-81.4%; and normal preoperative CEA: 84.9%, 95% CI, 82.2%-87.8%) (P < 0.001). Based on multivariable Cox model analysis, the elevated postoperative CEA was a prognostic factor for 3 years RFS (hazard ratio [HR], 3.08; 95% CI, 2.05– 4.66; P< 0.001). At the same time, normalized postoperative CEA was insignificantly correlated with 3-year RFS (HR, 1.38; 95% CI, 1.00– 1.92; P = 0.05) and was not an independent risk factor.Conclusion: We found that preoperative and postoperative serum CEA of rectal cancer patients were related to the 3-year recurrence-free survival rate. Moreover, the risk of recurrence in the normalized postoperative CEA group of patients was insignificantly different from that of the normalized preoperative CEA patients. Therefore, it is necessary to combine preoperative and postoperative CEA to predict the prognosis of patients with rectal cancer, rather than using it alone.Keywords: preoperative and postoperative serum CEA, rectal cancer, RFS

One of the main challenges in applying the immune checkpoint blockade to treat colorectal cancer (CRC) is the immunosuppressive tumor microenvironment. Owing to its excellent cancer cell killing ability and immune activation, mild photothermal therapy (PTT) has shown bright promise to sensitize tumors to immune checkpoint inhibition through turning the immunologically "cold" tumors into "hot" ones. Herein, a mild photothermal effect-assisted theragnostic nanodrug (MnO

BACKGROUND: The potential of using magnetic resonance image tumour-regression grading (MRI-TRG) system to predict pathological TRG is debatable for locally advanced rectal cancer treated by neoadjuvant radiochemotherapy. METHODS: Referring to the American Joint Committee on Cancer/College of American Pathologists (AJCC/CAP) TRG classification scheme, a new four-category MRI-TRG system based on the volumetric analysis of the residual tumour and radiochemotherapy induced anorectal fibrosis was established. The agreement between them was evaluated by Kendall’s tau-b test, while Kaplan–Meier analysis was used to calculate survival outcomes. RESULTS: In total, 1033 patients were included. Good agreement between MRI-TRG and AJCC/CAP TRG classifications was observed (k = 0.671). Particularly, as compared with other pairs, MRI-TRG 0 displayed the highest sensitivity [90.1% (95% CI: 84.3–93.9)] and specificity [92.8% (95% CI: 90.4–94.7)] in identifying AJCC/CAP TRG 0 category patients. Except for the survival ratios that were comparable between the MRI-TRG 0 and MRI-TRG 1 categories, any two of the four categories had distinguished 3-year prognosis (all P

Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1–2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control., Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Here, the authors developed and validated a radiomic signature (RS) for prediction of DM within a multicenter dataset, and suggest that it may help with stratification of patients who might benefit from adjuvant chemotherapy for DM.

Background Atypical tumor response patterns during immune checkpoint inhibitor therapy pose a challenge to clinicians and investigators in immuno-oncology practice. This study evaluated tumor burden dynamics to identify imaging biomarkers for treatment response and overall survival (OS) in advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors. Methods This retrospective study enrolled a total of 198 target lesions in 75 patients with advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors between January 2017 and March 2021. Tumor diameter changes as defined by immunotherapy Response Evaluation Criteria in Solid Tumors (iRECIST) were studied to determine treatment response and association with OS. Results Based on the best overall response, the tumor diameter ranged from − 100 to + 135.3% (median: − 9.6%). The overall response rate was 32.0% (24/75), and the rate of durable disease control for at least 6 months was 30.7% (23/75, one (iCR, immune complete response) or 20 iPR (immune partial response), or 2iSD (immune stable disease). Using univariate analysis, patients with a tumor diameter maintaining a P = 0.034). The differences in age (HR = 1.09, P = 0.01), combined surgery (HR = 0.15, P = 0.01) and cancer type (HR = 0.23, P = 0.001) were significant. In multivariable analysis, patients with a tumor diameter with a P = 0.01) after adjusting for age, combined surgery, KRAS status, cancer type, mismatch repair (MMR) status, treatment course and cancer differentiation. Two patients (2.7%) showed pseudoprogression. Conclusions Tumor diameter with a

The patient monitors were used to explore the alarm fatigue in a cardiac care unit and to investigate the awareness and reaction of nurse to alarms.A semi-structured survey was taken to acquire nurses' feeling and knowledge about monitoring alarm. Three full-time researchers were scheduled to track the alarms with annotations, and analyze the alarm data of 12 patient monitors using central monitoring system.A total of 72 310 unique alarms occurred in the 67-day study period. About 75.7% of them were physiological alarms and less than 10% of medium-low alarms were false positives. The average alarm rate was 128 alarms/patient-day.There remains alarm fatigue in CCU, the alarm accuracy has improved than the past by applying new technologies. In some cases, clinicians will pay more attention to trend alarm and combination alarm.

Throughout the world, chronic obstructive pulmonary disease (COPD) is the cause of substantial morbidity and mortality and represents a large public health burden. Previous studies reported that inflammation is intimately involved in COPD, but the underlying mechanism remains unclear. As macrophage polarization was demonstrated to regulate inflammation, we hypothesized that COPD alters macrophage polarization. To test this, we investigated in individuals with COPD the relationship between S1PR1 and HADC1 expression and macrophage inflammation via flow cytometry, qRT-PCR, gene knockdown/overexpression, and western blot analysis. We found that macrophages from COPD subjects were polarized towards an M1 phenotype with increased S1PR1 and decreased HADC1experssion. S1PR1 also inhibited HADC1 expression. Thus, S1PR1/HDAC1 signaling regulates macrophage polarization. The results from this study provide insights into the pathogenesis of COPD and suggest possible therapeutic opportunities.

Purpose: This study aimed to develop and validate a nomogram for predicting 3-year disease-free survival (DFS) among a multicenter cohort of Chinese patients with locally advanced rectal cancer (LARC) who underwent preoperative therapy followed by surgery. This nomogram might help identify patients who would benefit from postoperative adjuvant chemotherapy and close follow-up. Materials and methods: All data from 228 patients in two independent Chinese cohorts (118 patients and 110 patients) were pooled and subjected to survival analysis. One cohort’s data were used to develop multivariate nomograms based on Cox regression, and the second cohort was used for external validation. The variables were sex, age, clinical tumor stage, tumor location, preoperative therapy protocol, adjuvant chemotherapy, surgical procedure, surgical approach, pTNM stage, tumor deposit, tumor regression grade, lymphovascular invasion, perineural invasion, pretreatment serum carcinoembryonic antigen (CEA) level, preoperative CEA level, and postoperative CEA level. The model’s performance was evaluated based on its discrimination, calibration, and clinical usefulness. Results: The nomogram was based on ypT stage and ypN stage, and the C-index values for 3-year DFS were 0.70 in the training cohort (95% confidence interval: 0.62–0.78) and 0.78 in the validation cohort (95% confidence interval: 0.68–0.89). The Hosmer-Lemeshow calibration test revealed good calibration for predicting 3-year DFS in the training and validation cohorts, and decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: This nomogram including the ypT stage and ypN stage could predict DFS at 3 years after surgery, which may help better identify Chinese patients who would benefit from additional postoperative adjuvant systemic treatment.

Myocardial ischemia triggers an inflammatory reaction and oxidative stress that increases apoptosis of myocardiocytes. It has been evidenced that tanshinone-IIA (Tan-IIA) protects against heart failure post-myocardial infarction via inhibition of the apoptotic pathway. The purpose of the present study was to investigate the therapeutic effect of Tan-IIA in a rat model of myocardial ischemia, and explore the possible mechanism of Tan-IIA in myocardiocytes. The rat model of myocardial ischemia was established by left anterior descending coronary artery and rats received treatment with either Tan-IIA (10 mg/kg) or PBS for 20 days continuously. The cardiac function in the experimental rat model was detected using the Sequoia 512 echocardiography system on day 21. The cell viability of myocardiocytes was assessed by CCK-8 assay. Apoptosis of myocardiocytes and myocardial tissue was evaluated by TUNEL assay. The infarct size of the myocardial ischemia rat was determined through 2,3,5-triphenyltetrazolium chloride (TTC) and Evan blue double staining assay. The expression levels of apoptotic factors were assessed by immunohistochemistry, western blotting and immunofluorescence. The results demonstrated that Tan-IIA reduced myocardial infarct size and improved the myocardial function in myocardial ischemia rats. Compared with PBS, Tan-IIA treatment decreased myocardial tissue apoptosis and the expression levels of caspase-3, Cyto c and Apaf-1 in myocardial tissue. Tan-IIA increased the viability of impaired myocardiocytes, inhibited apoptosis of impaired myocardiocytes and increased Bcl-2 and Bak expression in myocardiocytes. In addition, Tan-IIA increased Bim and CHOP, decreased TBARS, ROS and H2O2 production, decreased ATF4 and IRE1α expression, and reduced intracellular calcium and oxidative stress in myocardiocytes. Furthermore, caspase-3 overexpression blocked Tan-IIA-decreased apoptosis of myocardiocytes. In conclusion, the data in the present study indicated that Tan-IIA improved myocardial infarct and apoptosis via the endoplasmic reticulum stress-dependent pathway and mitochondrial apoptotic signaling pathway.

Background Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. Methods We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months’ postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. Findings DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P Interpretation MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. Funding The funding bodies that contributed to this study are listed in the Acknowledgements section.

The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor.

Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (HE)-stained biopsy slides.In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of HE-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825-0·912]), and in validation cohort 1 (0·860 [0·828-0·892]) and validation cohort 2 (0·872 [0·810-0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717-0·907), sensitivity of 0·888 (0·728-0·999), specificity of 0·740 (0·593-0·886), NPV of 0·929 (0·862-0·995), and PPV of 0·512 (0·313-0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507-0·754] for the pathomics microenvironment model, 0·716 [0·580-0·852] for the radiomics MRI model, and 0·733 [0·620-0·845] for the pathomics nucleus model; all p0·0001).RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer.National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.

BackgroundDeubiquitinase (DUB) zinc finger RANBP2-type containing 1 (ZRANB1/TRABID) has been reported to have a close relationship with cancers. However, its underlying role and molecular mechanisms in hepatocellular carcinoma (HCC) remain elusive. MethodsGene and protein expression of ZRANB1 in HCC tissues were determined by qRT-PCR, western blot and immunohistochemistry. A series of gain- and loss-of-function assays were used to investigated the role of ZRANB1 in HCC cells progression. Moreover, RNA-seq were used to identify the downstream targets of ZRANB1 in HCC cells. The interaction between ZRANB1 and SP1 was examined through co-IP experiment and in vitro ubiquitination assay.ResultsZRANB1 was highly expressed in HCC tissues and ZRANB1 can regulate HCC cell growth and metastasis in vitro and in vivo. Through RNA-seq, we identified that Lysyl oxidase-like 2 (LOXL2) was the most significantly downregulated gene after ZRANB1 knockdown. Furthermore, the scatter plots indicated a significant positive correlation between ZRANB1 and LOXL2 expression in clinical HCC specimens. Additionally, LOXL2 was essential for ZRANB1-mediated HCC growth and metastasis. More importantly, specificity protein 1 (SP1) was critical in ZRANB1-mediated regulation of LOXL2 expression. Mechanistically, ZRANB1 bound with SP1 directly and stabilized the SP1 protein by deubiquitinating it. The expression patterns of ZRANB1, SP1 and LOXL2 were evaluated in HCC patients. ConclusionZRANB1 overexpression facilitates the carcinogenesis of HCC through stabilizing and upregulating SP1 to promote LOXL2 expression, suggesting ZRANB1 can be novel prognostic biomarker for HCC treatment.

OTU domain-containing protein 3 (OTUD3), a deubiquitinating enzyme, has been shown to participate in progression of multiple malignancies. The accurate function of OTUD3 in hepatocellular carcinoma (HCC) progression remains elusive. We found that OTUD3 was significantly overexpressed in HCC, and higher OTUD3 expression was correlated with larger tumor size, more distant metastasis, and worse TNM stage. A series of gain- and loss-of-function assays were also performed to examine the oncogenic function of OTUD3 in promoting HCC cell growth and metastasis in vitro. Using a xenograft mouse model, we showed that OTUD3 accelerated HCC progression in vivo. Furthermore, alpha-actinin 4 (ACTN4) was identified as a downstream target of OTUD3 through mass spectrometry analysis, and the ACTN4 protein level was significantly related to OTUD3 expression. Additionally, OTUD3 directly bound with ACTN4 and deubiquitinated ACTN4 to stabilize it. Finally, ACTN4 was found to be essential for OTUD3-mediated HCC proliferation and metastasis in vitro and in vivo. Collectively, our findings identify the oncogenic role of OTUD3 in HCC and suggest that OTUD3 can be considered as a pivotal prognostic biomarker and a potential therapeutic target.

Background: OTUD3, a deubiquitinating enzyme, has emerged as important role in some cancer. It showed that OTUD3 plays suppressive role in breast cancer whereas oncogenic role in lung cancer. However, the function and mechanism of OTUD3 in hepatocellular carcinoma (HCC) progression remain elusive. Methods: Gene and protein expression of OTUD3 in HCC tissues were determined by qRT-PCR, western blot and immunohistochemistry. A series of gain- and loss-of-function assays were used to investigated the role of OTUD3 in HCC cells progression. Moreover, mass spectroscopic analysis and RNA-seq were used to identify the downstream targets of OTUD3 in HCC cells. The interaction between OTUD3 and ACTN4 was examined through co-IP experiment and in vitro ubiquitination assay.Results: In our research, OTUD3 was significantly overexpressed in HCC tissues and higher OTUD3 expression was correlated with bigger tumor size, more distant metastasis, and worse TNM stage. Additionally, OTUD3 promoted HCC cells growth and metastasis in vitro and in vivo. Furthermore, ACTN4 was identified as a downstream target of OTUD3 and ACTN4 protein level was significantly related to OTUD3 expression. Rescue experiments indicated that ACTN4 was essential for OTUD3-mediated HCC proliferation and metastasis in vitro and in vivo. Moreover, we identified that NF-κB signaling pathway was activated by OTUD3 through ACTN4 to promote HCC cells progression. Importantly, OTUD3 protein level was correlated with ACTN4 protein level and activity of NF-κB signaling pathway in HCC tissues. Conclusion: Our findings identify the oncogenic role of OTUD3 in HCC and suggest that OTUD3 can be considered as a pivotal prognostic biomarker and a potential therapeutic target.

Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.

SummaryThe wave of COVID-19 continues to overwhelm the medical resources, especially the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). Here we performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from 9 external hospitals, achieved satisfying performance for predicting ICU, MV and death of COVID-19 patients (AUROC 0.916, 0.919 and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943 and 0.856). Both clinical and image features showed complementary roles in events prediction and provided accurate estimates to the time of progression (p

Ubiquitin-specific protease 7 (USP7/HAUSP) is known to regulate multiple cellular phenomena, including cell cycle progression and proliferation, and is involved in binding and stabilizing specific target proteins through deubiquitylation. However, the detailed role of USP7 in papillary thyroid carcinoma (PTC) remains to be investigated. In this study, our results showed that USP7 was upregulated in PTC tissues compared with adjacent nontumour tissues. Consistently, a series of gain/loss functional assays in vivo and in vitro demonstrated the role of USP7 in promoting PTC cell proliferation. Furthermore, we showed that there was a negative correlation between USP7 and the CDK inhibitor p57

To develop and validate radiomic models in evaluating biological characteristics of rectal cancer based on multiparametric magnetic resonance imaging (MP-MRI). This study consisted of 345 patients with rectal cancer who underwent MP-MRI. We focused on evaluating five postoperative confirmed characteristics: lymph node (LN) metastasis, tumor differentiation, fraction of Ki-67-positive tumor cells, human epidermal growth factor receptor 2 (HER-2), and KRAS-2 gene mutation status. Data from 197 patients were used to develop the biological characteristics evaluation models. Radiomic features were extracted from MP-MRI and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by using two feature-ranking methods (MRMR and WLCX) and three classifiers (RF, SVM, and LASSO). Multivariable logistic regression was used to build an integrated evaluation model combining radiomic signatures and clinical characteristics. The performance was evaluated using an independent validation dataset comprising 148 patients. The MRMR and LASSO regression produced the best-performing radiomic signatures for evaluating HER-2, LN metastasis, tumor differentiation, and KRAS-2 gene status, with AUC values of 0.696 (95% CI, 0.610–0.782), 0.677 (95% CI, 0.591–0.763), 0.720 (95% CI, 0.621–0.819), and 0.651 (95% CI, 0.539–0.763), respectively. The best-performing signatures for evaluating Ki-67 produced an AUC value of 0.699 (95% CI, 0.611–0.786), and it was developed by WLCX and RF algorithm. The integrated evaluation model incorporating radiomic signature and MRI-reported LN status had improved AUC of 0.697 (95% CI, 0.612–0.781). Radiomic signatures based on MP-MRI have potential to noninvasively evaluate the biological characteristics of rectal cancer. • Radiomic features were extracted from MP-MRI images of the rectal tumor. • The proposed radiomic signatures demonstrated discrimination ability in identifying the histopathological, immunohistochemical, and genetic characteristics of rectal cancer. • All MRI sequences were important and could provide complementary information in radiomic analysis.

Protein arginine methyltransferases (PRMTs) have been implicated in the development of various cancers. PRMT2, a member of the type I PRMT family, is overexpressed in multiple tumors. However, the expression and role of PRMT2 in hepatocellular carcinoma (HCC) have not been studied. Here, we discovered that PRMT2 expression is elevated in HCC tissues compared to the corresponding non-tumor tissues, and PRMT2 overexpression is an independent predictor of poor prognosis in HCC patients. Depletion of PRMT2 in HCC cell lines inhibited their cell growth and induced apoptosis. Mechanistic investigations showed that PRMT2 is responsible for H3R8 asymmetric methylation (H3R8me2a). H3R8me2a enrichment at the Bcl2 promoter increases its accessibility to STAT3, promoting Bcl2 gene expression. In addition, our results confirmed that the catalytically inactive mutant of PRMT2 or the type I PRMT inhibitor MS023 impaired the pro-tumorigenic functions of PRMT2 in HCC cells. Overall, our findings showed that PRMT2 functions as an oncogenic gene in HCC, revealing its potential as a novel therapeutic target in HCC.

Objectives: The purpose of this study was to investigate the role of legumain in the formation and stability of atherosclerotic plaque, as well as to explore the association between legumain with Smad3 pathway in a rat atherosclerosis model. Methods: Rat with thoracic aorta atherosclerosis was established and received treatment with statin (n = 15 each) or controls (n = 10). Serum level of legumain was determined by enzyme-linked immunosorbent assay. Legumain and Smad3 aortic expression levels were assessed by immunohistochemistry and fluorescence microscopy. Protein and mRNA levels were analyzed using Western blot analysis and reverse transcriptase coupled polymerase chain reaction, respectively. Results: The atherosclerotic group showed higher serum legumain level than control and statin group. Expression of legumain and Smad3 in macrophages and foam cells was increased in atherosclerotic group compared to control and statin group. The protein and mRNA levels of legumain and Smad3 were significantly attenuated by statin treatment (p

Diabetic cardiomyopathy is the myocardium disorders caused by diabetes mellitus, which has become a key concern bringing heavy burden to the public health. Autophagy is one of activities involving in the pathogenesis of diabetic cardiomyopathy. Sphk1 gene plays a crucial role in cell survival and growth, and regulation of many diseases including diabetes and cardiovascular disease. However, the pathogenesis of diabetic cardiomyopathy remains poorly understood. To this aim, the study established adenovirus vectors expressing Sphk1 and shSphk1 to investigate the effects of Sphk1 on autophagy and cell survival in myocardial cells under high glucose conditions (25 mM). It was found that overexpression of Sphk1 promoted autophagy activity in H9c2 cells under high glucose treatment measured by various methodologies including qRT-PCR, western blot, fluorescence microscope, and so on. Inhibition of autophagy decreased cell vitality under high glucose condition. A broadly used medicine silibinin was demonstrated to induce autophagy in a dose-dependent manner. Herein, the findings in the present study may provide useful reference for unveiling the pathogenesis of diabetic cardiomyopathy and developing novel therapies treating diabetic cardiomyopathy.

PURPOSE Accurate segmentation of rectal tumors is a basic and crucial task for diagnosis and treatment of rectal cancer. To avoid tedious manual delineation, an automatic rectal tumor segmentation model is proposed. METHODS A pretrained Resnet50 model was introduced for feature extraction. To reduce the complexity of the model, all layers after the 13th residual block of ResNet50 were removed, and three side-output modules were added to the hidden layer of ResNet50 to guide multiscale feature learning. The final boundaries of tumors were determined by fusion of the predictions from side-output modules. The proposed model was compared with two other models, and the effects of different region of interest (ROI) sizes, loss functions, and side-output fusion strategy were also evaluated. RESULTS The models were trained and evaluated on data from four clinical centers; T2-weighted magnetic resonance images (T2W-MRIs) from 461 patients in the first clinical center were used for training, while T2W-MRIs from 51 patients in the same clinical center and 56 patients in three other clinical centers were used for performance evaluation. The proposed model was superior to the two other models and achieved an average Dice similarity coefficient of 82.39%, sensitivity of 86.32%, specificity of 92.25%, and Hausdorff distance of 12.10 px. In addition, when the ROI contained rectal tumors, the smaller the ROI size, the higher the segmentation accuracy. For a certain ROI size, there were no considerable differences in segmentation results among the loss functions. Compared to the models with single side-output module, the proposed model performed better. CONCLUSIONS The results show that the proposed model has potential clinical applications in rectal cancer treatment, particularly with regard to therapeutic response evaluation and preoperative planning.

Objective: The goal of this study was to investigate whether neoadjuvant chemoradiotherapy (NCRT) plus total mesorectal excision (TME) would improve the outcome of patients with MRI-defined high-risk rectal cancer compared with TME plus adjuvant chemotherapy (ACT) or TME alone. Methods: We retrospectively enrolled 362 patients with MRI-defined high-risk rectal cancer who were treated with NCRT plus TME, TME plus ACT, or TME alone between January 2008 and August 2018. Cases with a high-risk tumor stage, positive extramural venous invasion, or mesorectal fascia involvement on baseline MRI were considered cases of high-risk rectal cancer. We matched patients treated with NCRT plus TME to patients treated with TME plus ACT and to those treated with TME alone. Kaplan–Meier curves were used to compare local recurrence (LR), disease-free survival (DFS), and overall survival (OS) rates. Results: The cumulative 3 year LR rate in the matched NCRT plus TME group was more favorable than in the TME plus ACT group (0% vs 5.1%; p = 0.037; n = 98) and in the TME alone group (0% vs 11.5%; p = 0.016; n = 61). Patients who received NCRT plus TME demonstrated better cumulative 3 year DFS rates than patients treated with TME plus ACT (85.7% vs 65.3%; p = 0.009) or with TME alone (86.9% vs 68.9%; p = 0.046). No difference in OS was observed among the groups. Conclusion: NCRT may improve DFS and LR rates in patients with MRI-defined high-risk rectal cancer when compared with TME plus ACT or TME alone. Advances in knowledge: This study illustrated the specific benefit of NCRT on the outcome measures of MRI-defined high-risk rectal cancer compared with TME plus ACT or TME alone, which was not clearly clarified in previous studies enrolling all patients with Stage II/III rectal cancer.

Ubiquitin-specific protease 7 (USP7/HAUSP) is known to regulate multiple cellular phenomena, including cell cycle progression and proliferation, and is involved in binding and stabilizing specific target proteins through deubiquitylation. However, the detailed role of USP7 in papillary thyroid carcinoma (PTC) remains to be investigated. In this study, our results showed that USP7 was upregulated in PTC tissues compared with adjacent nontumour tissues. Consistently, a series of gain/loss functional assays in vivo and in vitro demonstrated the role of USP7 in promoting PTC cell proliferation. Furthermore, we showed that there was a negative correlation between USP7 and the CDK inhibitor p57KIP2 expression in PTC tissues and that USP7 facilitated PTC cell proliferation by inhibiting p57KIP2. Mechanistically, USP7 inhibited p57KIP2 expression by modulating TBX3, directly binding to TBX3, and decreasing its ubiquitination and degradation. Our findings demonstrated that USP7 played a critical oncogenic role in PTC tumorigenesis, suggesting that USP7 might act as a prognostic and therapeutic target for PTC progression.

Aims Deubiquitinase ubiquitin-specific protease 33 (USP33) is abnormally expressed in various tumors and participates in tumor progression. However, the expression and biological role of USP33 in hepatocellular carcinoma (HCC) are still unclear. Main methods We performed immunohistochemistry, western blotting, and qRT-PCR analysis to determine the expression of USP33 in HCC. We then analyzed the effects of USP33 expression on the prognosis of HCC. The roles of USP33 in regulating HCC cell migration and invasion were further explored in vitro. Animal studies were performed to investigate the effects of USP33 on tumor metastasis. RNA sequencing and luciferase reporter and immunofluorescence assays were used to identify the activation of the specificity protein 1 (SP1)/c-Met axis. Key findings Here, for the first time, we reported an abnormal increase in the expression of USP33 in HCC tissues and that USP33 may act as a prognostic biomarker for HCC patients. We found that USP33 knockdown inhibited the invasion and metastasis in HCC cells both in vitro and in vivo, which was partly dependent on c-Met. Further investigations revealed that USP33 regulated c-Met expression by enhancing the protein stability of the transcription factor SP1 in HCC cells. Mechanistically, USP33 directly bound SP1 and decreased its ubiquitination, thereby upregulating c-Met expression. Significance Our results reveal that USP33 acts as the deubiquitinating enzyme of SP1 and contributes to HCC invasion and metastasis through activation of the SP1/c-Met axis. These data indicate a previously unknown function of USP33, which may provide potential targets for the treatment of HCC patients.

Background Accurate lymph nodes (LNs) assessment is important for rectal cancer (RC) staging in multiparametric magnetic resonance imaging (mpMRI). However, it is incredibly time-consumming to identify all the LNs in scan region. This study aims to develop and validate a deep-learning-based, fully-automated lymph node detection and segmentation (auto-LNDS) model based on mpMRI. Methods In total, 5789 annotated LNs (diameter ≥ 3 mm) in mpMRI from 293 patients with RC in a single center were enrolled. Fused T2-weighted images (T2WI) and diffusion-weighted images (DWI) provided input for the deep learning framework Mask R-CNN through transfer learning to generate the auto-LNDS model. The model was then validated both on the internal and external datasets consisting of 935 LNs and 1198 LNs, respectively. The performance for LNs detection was evaluated using sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), and segmentation performance was evaluated using the Dice similarity coefficient (DSC). Findings For LNs detection, auto-LNDS achieved sensitivity, PPV, and FP/vol of 80.0%, 73.5% and 8.6 in internal testing, and 62.6%, 64.5%, and 8.2 in external testing, respectively, significantly better than the performance of junior radiologists. The time taken for model detection and segmentation was 1.3 s/case, compared with 200 s/case for the radiologists. For LNs segmentation, the DSC of the model was in the range of 0.81–0.82. Interpretation This deep learning–based auto-LNDS model can achieve pelvic LNseffectively based on mpMRI for RC, and holds great potential for facilitating N-staging in clinical practice.

Background Our previous study showed that overexpression of hepatocyte nuclear factor 4α (HNF4α) could directly promote mesenchymal stem cells (MSCs) to differentiate into hepatocyte-like cells. However, the efficiency of hepatic differentiation remains low. The purpose of our study was to establish an MSC cell line that overexpressed HNF4α and FOXA2 genes to obtain an increased hepatic differentiation efficiency and hepatocyte-like cells with more mature hepatocyte functions. Methods Successful establishment of high-level HNF4α and FOXA2 co-overexpression in human induced hepatocyte-like cells (hiHep cells) was verified by flow cytometry, immunofluorescence and RT-PCR. Measurements of albumin (ALB), urea, glucose, indocyanine green (ICG) uptake and release, cytochrome P450 (CYP) activity and gene expression were used to analyze mature hepatic functions of hiHep cells. Results hiHep cells efficiently express HNF4α and FOXA2 genes and proteins, exhibit typical epithelial morphology and acquire mature hepatocyte-like cell functions, including ALB secretion, urea production, ICG uptake and release, and glycogen storage. hiHep cells can be activated by CYP inducers. The percentage of both ALB and α-1-antitrypsin (AAT)-positive cells was approximately 72.6%. The expression levels of hepatocyte-specific genes (ALB, AAT, and CYP1A1) and liver drug transport-related genes (ABCB1, ABCG2, and SLC22A18) in hiHep cells were significantly higher than those in MSCs-Vector cells. The hiHep cells did not form tumors after subcutaneous xenograft in BALB/c nude mice after 2 months. Conclusion This study provides an accessible, feasible and efficient strategy to generate hiHep cells from MSCs.