Search

BACKGROUND: In recent years, immunotherapy has made great progress, and the regulatory role of epigenetics has been verified. However, the role of 5-methylcytosine (m(5)C) in the tumor microenvironment (TME) and immunotherapy response remains unclear. METHODS: Based on 11 m(5)C regulators, we evaluated the m(5)C modification patterns of 572 lung adenocarcinoma (LUAD) patients. The m(5)C score was constructed by principal component analysis (PCA) algorithms in order to quantify the m(5)C modification pattern of individual LUAD patients. RESULTS: Two m(5)C methylation modification patterns were identified according to 11 m(5)C regulators. The two patterns had a remarkably distinct TME immune cell infiltration characterization. Next, 226 differentially expressed genes (DEGs) related to the m(5)C phenotype were screened. Patients were divided into three different gene cluster subtypes based on these genes, which had different TME immune cell infiltration and prognosis characteristics. The m(5)C score was constructed to quantify the m(5)C modification pattern of individual LUAD patients. We found that the high m(5)C score group had a better prognosis. The role of the m(5)C score in predicting prognosis was also verified in the dataset GSE31210. CONCLUSIONS: Our study revealed that m(5)C modification played a significant role in TME regulation of LUAD. Investigation of the m(5)C regulation mode may have some implications for tumor immunotherapy in the future.

Nan Jiang,1,2,* Xiao-Lin Ge,2,* Zhao-Yue Zhang,2 Jia Liu,2 Pei-Pei Wang,2 Xin-Chen Sun,1,2 Min Yang1 1The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, 210000, People’s Republic of China; 2Department of Radiotherapy, Jiangsu Province Hospital, Nanjing, Jiangsu, 210000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Min YangThe First School of Clinical Medicine, Nanjing Medical University, No. 818 of Tianyuan Road, Jiangning District, Nanjing, Jiangsu, 210000, People’s Republic of ChinaTel +86 13701516659Email yangmin@jsinm.orgXin-Chen SunDepartment of Radiotherapy, Jiangsu Province Hospital, No. 300 of Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210000, People’s Republic of ChinaTel +86 13770662828Email sunxinchen2012@163.comPurpose: Intensity-modulated radiotherapy (IMRT) can improve the prognosis of patients with esophageal cancer. This study aimed to evaluate clinical factors relevant to the prognosis of patients with esophageal cancer who received intensity-modulated radiotherapy (IMRT) alone.Patient and Methods: Data of 103 patients with pathologically confirmed esophageal cancer who were admitted to our hospital between October 2011 and November 2017 were retrospectively reviewed. All patients had squamous cell carcinoma. All patients received IMRT. Patients with stage I–IVA tumors were included to represent the real-world clinical practice. We performed univariate and multivariate analyses to identify prognostic factors for overall survival (OS) and progression-free survival (PFS). In univariate analyses, the Kaplan–Meier method was used to estimate OS and PFS for various subgroups. In multivariate analyses, hazard ratios were calculated.Results: Single-factor analysis revealed that T stage (P=0.019), N stage (P =0.047), and lesion length (P =0.000) were associated with the prognosis of esophageal cancer patients who received IMRT. Cox regression analysis revealed that T stage (odds ratio [OR] = 4.68; P < 0.05), N stage (OR = 0.28; P < 0.05), and lesion length (OR = 0.09; P < 0.05) were independent factors relevant to prognosis.Conclusion: T stage, N stage, and lesion length influenced the long-term curative effects of IMRT for esophageal cancer and were prognostic factors for patients with esophageal cancer receiving definitive radiotherapy alone. The higher the stage and the longer the tumor, the lower the survival rate.Keywords: esophageal neoplasms, prognosis, intensity-modulated radiotherapy, retrospective studies

In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

Background: The optimal therapeutic strategy in locally advanced esophageal squamous cell carcinoma (ESCC) primarily treated by surgery remains unknown. This study was designed to evaluate the impact of postoperative chemoradiotherapy and postoperative sequential chemoradiotherapy on survival in this population. Methods: The study included a total of 228 consecutive patients who underwent radical esophagectomy and were confirmed to have stage pT3–4 or pN+ ESCC from September 2011 to September 2017 at our institution. All patients received postoperative radiotherapy with or without concurrent or sequential chemotherapy after esophagectomy. Univariate and multivariate analyses were used to compare the survival of patients with postoperative radiotherapy, postoperative concurrent chemoradiotherapy, and postoperative sequential chemoradiotherapy. Results: After a median follow-up of 52 months, the 3- and 5-year overall survival (OS) rates were 70.2% [95% confidence interval (CI), 63.7–76.7%] and 62.2% (95% CI, 54.6–69.8%), respectively. The disease-free survival (DFS) rates at 3 and 5 years were 65.2% (95% CI, 58.7–71.7%) and 55.2% (95% CI, 47.6–62.8%), respectively. The 3- and 5-year locoregional recurrence-free survival (LRFS) rates were 65.1% (95% CI, 58.4–71.8%) and 55.5% (95% CI, 47.7–63.3%). Of the 228 patients, 38 (16.7%) had distant metastases. Subgroup analysis showed that being male and having a higher T stage were independent poor prognostic factors for OS and DFS in patients with pN+ or stage III + IVA ESCC. The results also showed that in patients with stage III + IVA ESCC, the DFS of the patients in the concurrent chemotherapy (CCT) group was improved compared with that in the no CCT group [hazard ratio (HR), 0.551; 95% CI, 0.323–0.938; P=0.028]. Multivariate analysis showed that sequential chemoradiotherapy was associated with poor LRFS (HR, 2.312; 95% CI, 1.078–4.959; P=0.031), especially in stage T3–4 patients, and it was also related to the poor DFS (HR, 1.781; 95% CI, 1.086–2.921; P=0.022) in patients with stage T3–4 ESCC. Conclusions: In patients with locally advanced ESCC, those who underwent sequential chemoradiotherapy had a worse LRFS. Postoperative concurrent chemoradiotherapy was the most effective adjuvant therapy for resected stage III–IVA ESCC. In addition, being male, having a higher T stage, and being node-positive were independent poor prognostic factors for OS and DFS.

BackgroundConcurrent chemoradiotherapy (CCRT) is the standard treatment for nonsurgical esophageal cancer (EC). However, esophageal cancer patients receiving CCRT alone are still unsatisfactory in terms of local control and overall survival (OS) benefit. Clinicians generally add consolidation chemotherapy (CCT) after CCRT. It remains controversial whether CCT following CCRT is beneficial for esophageal cancer. We, therefore, undertook a meta-analysis to assess the need for CCT in inoperable esophageal cancer.Materials and MethodsWe combed PubMed, Embase, Cochrane Library, Web of Science, and CNKI for relevant published articles up to July 2020 that compared CCRT plus CCT to CCRT alone for patients with nonsurgical EC. Our primary endpoint was OS and progression-free survival (PFS), and the secondary endpoint was treatment toxicity. We analyzed the hazard ratio (HR) to estimate the time-to-event data and the odds ratio (OR) to compare the treatment-related effect. To assess heterogeneity, we performed the I2 test and examined publication bias using funnel plots analysis.ResultsThe 11 retrospective studies involved 2008 patients. Of these 2008 patients, 1018 received CCRT plus CCT, and 990 received CCRT. Compared to CCRT alone, CCT after CCRT did not improve disease control rate (DCR) (OR 1.66; 95% CI 0.53–5.15, p=0.384) and objective response rate (ORR) (OR 1.44; 95% CI 0.62–3.35, p=0.393). However, OS (HR 0.72; 95% CI 0.59–0.86, p < 0.001) and PFS (HR 0.61; 95% CI 0.44–0.84, p=0.003) did increase. Our results show that CCT plus CCRT had a clear survival advantage over CCRT alone. The risk of treatment toxicity did not increase for EC patients who received CCT.ConclusionCCT after CCRT significantly increases OS and PFS in patients with nonsurgical EC and could provide them remarkable survival benefits. The results provide an evidence-based framework for the use of CCT after CCRT.

Contralateral axillary lymph node metastasis (CAM) in breast cancer is rare, and the reason is unclear. CAM may be found at the time of primary breast cancer diagnosis or following primary tumor treatment. CAM staging and treatment methods are controversial. Hence, we summarized the features of CAM patients and explored the therapeutic options. We report the case of an 82-year-old woman with right breast cancer accompanied by CAM. The records of breast cancer patients with CAM in PubMed (January 2000-May 2020) were also reviewed. After undergoing comprehensive treatments (neoadjuvant chemotherapy, surgery, radiotherapy, endocrine therapy, and targeted therapy), no signs of recurrence and metastasis were noted during the 25-month follow-up. Overall, 17 studies (36 patients) were selected for the review, of which 15 had synchronous CAM and 21 had metachronous CAM. Nineteen of 22 patients had histologic grade 3. Forty percent (12/30) had ≥4 positive lymph nodes. Among these 36 patients, 9 had triple-negative breast cancers, and 9 were human epidermal growth factor receptor 2 positive. Among the 30 patients staged, 6 (20%) were in stage I; 7 (23.33%), stage II; and 17 (56.67%), stage III. Two patients received systemic therapy, while one received an unknown treatment. The remaining 33 patients all received local treatment (surgery or radiotherapy). The survival time of synchronous and metachronous CAM patients was 12-72 months with the death rate of 1/7 and 12-144 months with that of 3/12, respectively. CAM may be a regional manifestation rather than a distant metastasis. Comprehensive treatment, including surgery and radiotherapy, may provide better control.

To achieve flight risk identification and assessment in the new field of UAVs, two index systems of risk prediction and safety evaluation are constructed in this work by categorizing the set of UAV flight risks, and the identification subset is set according to the UAV characteristics and use. Risk identification elements are set for the identification subset, and the qualitative attributes of risk identification elements are quantified by set-valued statistics. A general quantitative model for the flight risk identification of UAVs is established. According to the different flight risk factors, UAV flight risks are categorized, the fuzzy comprehensive evaluation method is adopted, and case data are used to quantitatively evaluate different types of risks. Finally, the application of the techno-economic evaluation method to the feasibility evaluation of the UAV flight risk prevention scheme is proposed to provide support for UAV flight risk management.

Background To determine the diagnostic accuracy of preoperative T/N stage with magnetic resonance imaging (MRI) in lower and middle rectal cancer patients and the impacts on clinical decision‐making. Patients and methods A total of 211 patients were recruited from October 2015 to March 2017 in this retrospective study. High‐resolution MRI was performed within 2 weeks before surgery. Histopathologic results were evaluated for the postoperative T/N stage and the diagnostic accuracy of MRI was assessed according to the postoperative histopathologic results. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value were evaluated for T/N staging and κ values were used to evaluate MRI consistent analysis compared with postoperative histopathologic staging. Results The overall MRI diagnostic accuracy was 79.62% for T1‐4 staging and 54.50% for N0‐2 staging. The κ values were 0.619 and 0.255 for T1‐4 and N0‐2 staging, respectively. The diagnostic accuracy of MRI for treatment decision‐making was 80.57%. Conclusion MRI allows a highly accurate preoperative assessment of T stage but only a fairly accurate preoperative assessment of N stage for rectal cancer. The diagnostic accuracy of MRI for treatment decision‐making is promising, but additional studies are needed to validate these findings in a larger sample size from multiple centers.

Background Endometrial cancer (UCEC) is a complex malignant tumor characterized by both genetic level and clinical trial. Patients with UCEC exhibit the similar clinical features, however, they have distinct outcomes due to molecular heterogeneity. The aim of this study was to access the prognostic value of long non-coding RNAs (lncRNAs) in UCEC patients and to identify potential lncRNA signature for predicting patients’ survival and improving patient-tailored treatment. Methods We performed a comprehensive genome-wide analysis of lncRNA expression profiles and clinical data in a large cohort of 301 UCEC patients. UCEC patients were randomly divided into the discovery cohort (n = 150) and validation cohort (n = 151). A novel lncRNA-focus expression signature was identified in the discovery cohort, and independently accessed in the validation cohort. Additionally, the lncRNA signature was evaluated by multivariable Cox regression and stratification analysis as well as functional enrichment analysis. Results We detected a novel lncRNA-focus expression signature (LFES) consisting of 11 lncRNAs that were associated with survival based on risk scoring strategy in UCEC. The risk score based on the LFES was able to separate patients of discovery cohort into high-risk and low-risk groups with significantly different overall survival and progression-free survival, and has been successfully confirmed in the validation cohort. Furthermore, the LFES is an independent prognostic predictor of survival and demonstrates superior prognostic performance compared with the clinical covariates for predicting 5-year survival (AUC = 0.887). Functional analysis has linked the expression of prognostic lncRNAs to well-known tumor suppressor or ontogenetic pathways in endometrial carcinogenesis. Conclusions Our study revealed a novel 11-lncRNA signature to predict survival of UCEC patient. This lncRNA signature may be a valuable and alternative marker for risk evaluation to aid patient-tailored treatment and improve the outcome of patients with UCEC. Electronic supplementary material The online version of this article (10.1186/s12885-017-3983-0) contains supplementary material, which is available to authorized users.

iWith the rapid development and the increasing security threats of D2D communication, the D2D communication security has become more and more important. Device recognition is the foundation of communication security. Traditional device authentication is the cryptographic mechanism and the security protocol that are not enough for the new security threat. RF fingerprint of wireless device has physical characteristics, which is difficult to clone. Therefore, it can be used for D2D device recognition. In this paper, a recognition algorithm of D2D device based on RF fingerprint is proposed. First, Hilbert transform and principal component analysis is used to generate the RF fingerprint of D2D device. Second, support vector machine (SVM) is introduced and CV-SVM is used as the classifier. Finally, simulation results show that the recognition rate is more than 90% under SNR = 5 dB, which proves that the proposed method is effective for D2D device recognition. Therefore, it can be used as the trust management of D2D communication.

Automated modulation classification plays a very important part in cognitive radio networks. Deep learning is also a powerful tool that we could not overlook its potential in addressing signal modulation recognition problem. In our last work, we propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation, but we still believe that the convolution neural network (CNN) can work better. However, its application to signal modulation recognition is often hampered by insufficient data and overfitting. Here, we propose a smart approach to programmatic data augmentation method by using the auxiliary classifier generative adversarial networks (ACGANs). The famous CNN model, AlexNet, has been utilized to be the classifier and ACGAN to be the generator, which will enlarge our data set. In order to alleviate the common issues in the traditional generative adversarial nets training, such as discriminator overfitting, generator disconverge, and mode collapse, we apply several training tricks in our training. With the result on original data set as our baseline, we will evaluate our result on enlarged data set to validate the ACGAN’s performance. The result shows that we can gain 0.1~6% increase in the classification accuracy in the ACGAN-based data set.

1090ES ADS-B is a new technology in civil aviation, mainly used to monitor dynamic condition of aircrafts and share the flight information. However, the ADS-B signals are often interfered with other overlapped signals during the signal transmission, causing signal overlap and bringing difficulties to signal processing afterwards. This article applies Blind Source Separation (BSS) into the separation process of ADS-B overlapped signals and constructs the ADS-B overlapped signal separation model using Infomax algorithm, in order to separate ADS-B overlapped signals into single-way ADS-B signals and improve signal decoding rate.

With the popularization of self-driving cars and the rapid development of intelligent transportation, pedestrian detection shows more and more extensive application scenarios in daily life, which have higher and higher application values. It also raises more and more interest from academic community. Pedestrian detection is fundamental in many human-oriented tasks, including trajectory tracking of people, recognition of pedestrian gait, and autopilot recognition of pedestrians to take appropriate response measures. In this context, this paper studies the design and implementation of a pedestrian detection system. The pedestrian detection system of this article is mainly composed of two parts. The first part is a pedestrian detector based on deep learning, and the second part is a graphical interface that interacts with the user. The former part mainly uses the Faster R-RCNN learning model, which can use convolutional neural networks to learn features from the data and extract the features of the image. It can also search the image through RPN network for areas where the target is located and then classify them. In this paper, a complete pedestrian detection system is implemented on the basis of deep learning framework Caffe. Experiments show that the system has high recognition rate and fast recognition speed in real world.

Background To determine the diagnostic accuracy of preoperative T/N stage using MRI in lower and middle rectal cancer patients and the impacts on clinical decision-making. Patients and methods There were 354 patients recruited from May 2017 to February 2019. MRI was performed within 2 weeks before surgery. Histopathologic results were evaluated for the postoperative T/N stage and MRI diagnostic accuracy was assessed based on the postoperative histopathologic results. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Kappa values were used to evaluate MRI diagnostic accuracy and analysis consistency compared with postoperative histopathologic staging. Results Overall MRI diagnostic accuracy was 78.2% and 56.8% for T1–4 and N0–2 staging. The Kappa values were 0.625 and 0.323 for T1–4 and N0–2 staging, respectively. After combination, MRI diagnostic accuracy was 85% and 69.5% for T and N staging. The Kappa values were 0.693 and 0.4 for T and N staging. The diagnostic accuracy of MRI for treatment decision-making was 79.1%. Conclusion MRI enables a highly accurate preoperative assessment of T stage but only a fairly accurate preoperative assessment of the N stage for rectal cancer with surgery. The diagnostic accuracy of MRI for treatment decision-making is promising.

In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Renyi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is −10 dB.

Recent studies have demonstrated the utility and superiority of long non-coding RNAs (lncRNAs) as novel biomarkers for cancer diagnosis, prognosis, and therapy. In the present study, the prognostic value of lncRNAs in glioblastoma multiforme was systematically investigated by performing a genome-wide analysis of lncRNA expression profiles in 419 glioblastoma patients from The Cancer Genome Atlas (TCGA) project. Using survival analysis and Cox regression model, we identified a set of six lncRNAs (AC005013.5, UBE2R2-AS1, ENTPD1-AS1, RP11-89C21.2, AC073115.6, and XLOC_004803) demonstrating an ability to stratify patients into high- and low-risk groups with significantly different survival (median 0.899 vs. 1.611 years, p = 3.87e−09, log-rank test) in the training cohort. The six-lncRNA signature was successfully validated on independent test cohort of 219 patients with glioblastoma, and it revealed superior performance for risk stratification with respect to existing lncRNA-related signatures. Multivariate Cox and stratification analysis indicated that the six-lncRNA signature was an independent prognostic factor after adjusting for other clinical covariates. Further in silico functional analysis suggested that the six-lncRNA signature may be involved in the immune-related biological processes and pathways which are very well known in the context of glioblastoma tumorigenesis. The identified lncRNA signature had important clinical implication for improving outcome prediction and guiding the tailored therapy for glioblastoma patients with further prospective validation.

Deregulation of long non-coding RNAs (lncRNAs) expression has been proven to be involved in the development and progression of cancer. However, expression pattern and prognostic value of lncRNAs in breast cancer recurrence remain unclear. Here, we analyzed lncRNA expression profiles of breast cancer patients who did or did not develop recurrence by repurposing existing microarray datasets from the Gene Expression Omnibus database, and identified 12 differentially expressed lncRNAs that were closely associated with tumor recurrence of breast cancer patients. We constructed a lncRNA-focus molecular signature by the risk scoring method based on the expression levels of 12 relapse-related lncRNAs from the discovery cohort, which classified patients into high-risk and low-risk groups with significantly different recurrence-free survival (HR = 2.72, 95% confidence interval 2.07–3.57; p = 4.8e-13). The 12-lncRNA signature also represented similar prognostic value in two out of three independent validation cohorts. Furthermore, the prognostic power of the 12-lncRNA signature was independent of known clinical prognostic factors in at least two cohorts. Functional analysis suggested that the predicted relapse-related lncRNAs may be involved in known breast cancer-related biological processes and pathways. Our results highlighted the potential of lncRNAs as novel candidate biomarkers to identify breast cancer patients at high risk of tumor recurrence.

Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology and have been implicated in the development and progression of cancers. However, the prognostic significance of lncRNAs in diffuse large-B-cell lymphoma (DLBCL) remains unclear and needs to be systematically investigated. In this study, we obtained and analyzed lncRNA expression profiles in three cohorts of 1043 DLBCL patients by repurposing the publicly available microarray datasets from the Gene Expression Omnibus (GEO) database. In the discovery series of 207 patients, we identified a set of six lncRNAs that was significantly associated with patients’ overall survival (OS) using univariate Cox regression analysis. The six prognostic lncRNAs were combined to form an expression-based six-lncRNA signature which classified patients of the discovery series into the high-risk group and low-risk group with significantly different survival outcome (HR = 2.31, 95% CI = 1.8 to 2.965, p

Aim This meta-analysis was designed to fully assess the curative effects of radiotherapy-based therapies for human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC). Methods English articles were retrieved through searching Cochrane library, PubMed, and Embase databases updated to February 2017. Studies were selected based on the inclusion and exclusion criteria. The curative effects of radiotherapy-based therapies forHER2+ BC patients were assessed using hazard rates (HRs) or odds ratios (ORs), as well as their 95% confidence intervals (CIs). In addition, Egger test was used to assess publication bias, followed by sensitivity analysis. All statistic methods were conducted using R 3.12 software. Results A total of 9 eligible studies were included into this meta-analysis, which involved 2236 HER2+ BC patients. Egger test showed that the eligible studies had no publication bias (t = 2.198, P = .05918). Sensitivity analysis demonstrated that the results were stable. HER2+ BC patients in radiotherapy group had lower locoregional recurrences than those in other groups. Moreover, meta-analysis showed that no significant difference was found between HER2+ BC patients in radiotherapy group and other groups on the 1-year overall survival (P = 0.5263, I = 65.4%), 3-year overall survival (P = 0.4591, I = 0), and 5-year overall survival (P = 0.06277, I = 0). Conclusion Radiotherapy-based therapies might have certain advantages in treating HER2+ BC patients.