Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer.

Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs. We find that the networks possess a two-level bipartite structure: common competing endogenous network (CCEN) composed of an invariant set of microRNAs over all the stages and stage-dependent, unique competing endogenous networks (UCENs). A systematic enrichment analysis of the pathways of the mRNAs in CCEN reveals that they are strongly associated with cancer development. We also find that the microRNA-linked mRNAs from UCENs have a higher enrichment efficiency. A key finding is six microRNAs from CCEN that impact patient survival at all stages, and four microRNAs that affect the survival from a specific stage. The ten microRNAs can then serve as potential biomarkers and prognostic tools for LUAD.

Non-pharmacological interventions for older adults with depressive symptoms: a network meta-analysis of 35 randomized controlled trials.

Objective: To assess the effectiveness of non-pharmacological interventions for seniors with depressive symptoms.Methods: A comprehensive literature search was performed. We conducted network meta-analysis in two ways, intervention classes (psychosocial, psychotherapy, physical activity, combined, treatment as usual) and individual intervention (11 categories). Whenever included studies used different scales, the different instruments were converted to the units of the scale most frequently used (the Geriatric Depression Scale), such that the effect size was reported as a mean difference (MD) with 95% confidence interval (CI). The risk of bias of RCTs included in this review was assessed according to the Cochrane Handbook. Bayesian NMA was conducted using R-3.4.0 software.Results: A total of 35 RCTs with 3,797 enrolled patients were included. Compared to conventional treatment, physical activity and psychotherapy resulted in significant improvements in depressive symptoms (MD: 2.25, 95%CrI: 0.99-3.56; SUCRA = 86.07%; MD: 1.75, 95% CrI: 0.90-2.64; SUCRA = 66.44%, respectively). Similar results were obtained for music (MD: 2.6; 95% CrI: 0.84-4.35;SUCRA = 80.53%), life review (MD:1.92; 95% CrI:0.71-3.14; SUCRA = 65.62%), cognitive behavioral therapy (MD: 1.27; 95% CrI: 0.23-2.38; SUCRA = 45.4%), aerobic (MD: 1.84; 95% CrI: 0.39-3.36; SUCRA = 63%) and resistance training (MD: 1.72; 95% CrI: 0.06-3.42; SUCRA = 59.24%). Network meta-regression showed that there were no statistically significant subgroup effects.Conclusions: Physical activity and psychotherapy demonstrated statistically significant superiority over conventional treatment. Music and life review therapy proved the most promising individual interventions. However, conclusions are limited by the lack of sufficient sample size and consensus regarding intervention categories and so an adequately powered study is necessary to consolidate these findings.

A network approach to quantifying radiotherapy effect on cancer: Radiosensitive gene group centrality.

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends to be more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients.

Mechanism of Fuzheng Kang'ai Formula Regulating Tumor Microenvironment in Non-Small Cell Lung Cancer.

OBJECTIVE: To study the mechanism of Chinese herbal medicine Fuzheng Kang'ai Formula (, FZKA) on tumor microenvironment (TME). METHODS: CIBERSORTx was used for analysis of TME. Traditional Chinese Medicine Systems Pharmacology and Analysis Platform was applied to identify compounds-targets network and the Cancer Genome Atlas (TCGA) was employed to identify the differential expression genes (DEGs) between tumor and paracancerous tissues in lung adenocarcinoma (LUAD) from TCGA-LUAD. Additionally, DEGs with prognosis in LUAD was calculated by univariable and multivariate Cox regression. The core targets of FZKA were analyzed in lung adenocarcinoma TME. Protein-protein interaction database was employed to predict down-stream of target. Quantitative reverse transcription polymerase chain reaction was employed for biological experiment in A549, H1299 and PC9 cell lines. RESULTS: The active and resting mast cells were significantly associated with prognosis of LUAD (P<0.05). Of the targets, CCNA2 as an important target of FZKA (hazard ratio=1.41, 95% confidential interval: 1.01-2.01, P<0.05) was a prognostic target and significantly associated with mast cells. CCNA2 was positively correlated with mast cell activation and negatively correlated with mast cell resting state. BCL1L2, ACTL6A and ITGAV were down-stream of CCNA2, which were validated by qRT-PCR in A549 cell. CONCLUSION: FZKA could directly bind to CCNA2 and inhibit tumor growth by regulating CCNA2 downstream genes and TME of NSCLC closely related to CCNA2.

The Efficacy and Safety of Carbon Ion Radiotherapy for Meningiomas: A Systematic Review and Meta-Analysis.

OBJECTIVE: The purpose of this systematic review and meta-analysis is to evaluate the efficacy and safety of carbon ion radiotherapy (CI-RT) in improving meningioma by comparing photon and protons radiotherapy. METHODS: A comprehensive search for relevant studies published until March 17, 2021, was conducted in PubMed, the Cochrane Library, Chinese Biomedical Literature Database and EMBASE. Statistical analyses were performed with R 4.0.3. RESULTS: We identified 396 studies, of which 18 studies involving 985 participants were included. Except for one low quality study, the quality of the included studies was found to be either moderate or high quality. The analyses conducted according random effects model indicated that the 1-year overall survival rate (OS) of benign and non-benign meningiomas after the CI-RT treatment was 99% (95%CL=.91-1.00, I 2 = 0%). The overall average 5-year OS for meningiomas was 72% (95%CL=0.52-0.86, I 2 = 35%), not as effective as proton radiotherapy (PR-RT) 85% (95%CL=.72-.93, I 2 = 73, Q=4.17, df=2, p=.12). Additionally, 5-year OS of atypical meningiomas (81%) was found to be significantly higher than anaplastic meningiomas (52%). The 10-year OS after CI-RT of patients with mixed grade meningioma was 91% (95%CL=.75-.97, I 2 = 73%). The 15-year OS after CI-RT 87% (95%CL=.11-1.00) or PR-RT 87% (95%CL=.23-.99, I 2 = 79%) were the same (Q=0, df=1, p=.99). After undergoing CI-RT for 3 and 5 years, the LC for benign meningioma was 100% and 88%, respectively, while the 2-year LC of non-benign meningiomas (atypical/anaplastic) was 33%. Headache, sensory impairment, cognitive impairment, and hearing impairment were found to be the most common adverse reactions, with individual incidences of 19.4%, 23.7%, 9.1%, and 9.1%, respectively. CONCLUSION: CI-RT is a rapidly developing technique that has been proven to be an effective treatment against meningioma. The efficacy and safety of CI-RT for meningiomas were similar to those of PR-RT, better than photon radiotherapy (PH-RT). However, there is a need for more prospective trials in the future that can help provide more supportive evidence.

Exercise Training for Improving Patient-Reported Outcomes in Patients With Advanced-Stage Cancer: A Systematic Review and Meta-Analysis.

CONTEXT: Patients with advanced-stage cancer often suffer many physical and psychological symptoms. Exercise has been shown to improve quality of life (QoL), decrease cancer-related symptoms, and maintain or improve functional status in cancer survivors or patients with early stage cancer. However, the effect of exercise on these outcomes in patients with advanced-stage cancer is unclear. OBJECTIVES: This meta-analysis aimed to assess the effectiveness of exercise interventions for patients with advanced-stage cancer in improving cancer-related symptoms and functional status outcomes. METHODS: We conducted a comprehensive literature search in PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and Web of Science from their inception to February 3, 2019, to include randomized controlled trials (RCTs) comparing exercise and usual care for improving outcomes in patients with advanced-stage cancer. Two reviewers independently screened the studies, extracted data of interest, and assessed the risk of bias of individual RCTs using the Cochrane Handbook, Version 5.1.0. RESULTS: About 15 RCTs enrolling 1208 patients were included. Compared with usual care, exercise showed a significant improvement in QoL (standardized mean difference [SMD] 0.22; 95% CI 0.06-0.38; P = 0.009), fatigue (SMD -0.25; 95% CI -0.45 to -0.04; P = 0.02), insomnia (SMD -0.36; 95% CI -0.56 to -0.17; P = 0.0002), physical function (SMD 0.22; 95% CI 0.05-0.38; P = 0.009), social function (SMD 0.18; 95% CI 0.02-0.34; P = 0.03), and dyspnea reduction (SMD -0.18; 95% CI -0.34 to -0.01; P = 0.03). CONCLUSION: Exercise serves as an effective intervention to improve QoL and alleviate fatigue, insomnia, dyspnea, and physical and social functions for patients with advanced-stage cancer.

RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer.

Prostate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely used as a biomarker for PCa diagnosis, its results can be controversial. Therefore, new biomarkers are needed to enhance the clinical management of PCa. From publicly available microarray data, differentially expressed genes (DEGs) were identified by meta-analysis with RankProd. Genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The diagnostic and prognostic capability of the prediction model and candidate genes were investigated in both GEO and TCGA datasets. Candidate genes were further validated by qPCR, Western Blot and Tissue microarray. By RankProd meta-analyses, 2306 significantly up- and 1311 down-regulated probes were found in 133 cases and 30 controls microarray data. The overall accuracy rate of the PCa diagnostic prediction model, consisting of a 15-gene signature, reached up to 100% in both the training and test dataset. The prediction model also showed good results for the diagnosis (AUC = 0.953) and prognosis (AUC of 5 years overall survival time = 0.808) of PCa in the TCGA database. The expression levels of three genes, FABP5, C1QTNF3 and LPHN3, were validated by qPCR. C1QTNF3 high expression was further validated in PCa tissue by Western Blot and Tissue microarray. In the GEO datasets, C1QTNF3 was a good predictor for the diagnosis of PCa (GSE6956: AUC = 0.791; GSE8218: AUC = 0.868; GSE26910: AUC = 0.972). In the TCGA database, C1QTNF3 was significantly associated with PCa patient recurrence free survival (P < .001, AUC = 0.57). In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa. This approach can be applied to other high-throughput data from different platforms for the discovery of oncogenes or biomarkers in different kinds of diseases.

A network meta-analysis on the efficacy of targeted agents in combination with chemotherapy for treatment of advanced/metastatic triple-negative breast cancer.

OBJECTIVE: Our network meta-analysis aimed to determine the assistant efficacy of targeted therapy in combined with chemotherapy for advanced/metastatic triple-negative breast cancer (TNBC). RESULTS: A total of 15 randomized controlled trials (RCTs), involving 2,410 patients, met our inclusion criteria. Eight targeted agents involving 11 treatment arms were included. The methodological quality of included RCTs was acceptable. The results of direct comparisons showed that progression-free survival (PFS) was significantly longer with bevacizumab+chemotherapy when compared to chemotherapy alone (hazard ratio [HR] = 0.62, 95% credible intervals [CrI]: 0.41-0.87). However, there were no statistically significant differences for all other direct comparison groups. The results of indirect comparison of different targeted agents revealed no significant differences regarding all outcomes of interest. According to ranking probabilities, all outcomes favored bevacizumab+chemotherapy and veliparib+chemotherapy. Bayesian and Frequentist network meta-analysis showed similar results, and the probability of bias of small-study effects was small. MATERIALS AND METHODS: A comprehensive literature search in PubMed, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science (via ISI Web of Knowledge), BIOSIS Previews (via ISI Web of Knowledge), and Chemical Abstracts (CA) was conducted to identify RCTs involving targeted agents in the treatment of advanced/metastatic TNBC. Two reviewers independently extracted related data and assessed the risk of bias of included studies. Bayesian network meta-analysis was conducted using R-3.3.2 software. CONCLUSIONS: Limited evidence showed that targeted agents combined with chemotherapy for advanced/metastatic TNBC were slightly effective. Further investigation of targeted therapies for TNBC is required to improve patient outcomes. The registration number was CRD42014014299.

Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co-expression analysis.

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival-related individual genes (or miRNAs) and miRNA -mRNA pairs in GBM using a multi-step approach. First, the weighted gene co-expression network analysis and survival analysis are applied to identify survival-related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival-related miRNA -mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient's survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin-1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA -mRNA regulatory networks that comprised 38 survival-related miRNA -mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.

Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis.

AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis (WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study. METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus (GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes. The function of the genes were annotated by gene ontology (GO). RESULTS: In this study, we identified four co-expression modules significantly correlated with clinic traits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location (sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter (LTD). Additionally, we identified the hug gene (top connectivity with other genes) in each module. The hub gene RPS15A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma. CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

Proteomic analysis of energy metabolism and signal transduction in irradiated melanoma cells.

AIM: To analyze proteomic and signal transduction alterations in irradiated melanoma cells. METHODS: We combined stable isotope labeling with amino acids in cell culture (SILAC) with highly sensitive shotgun tandem mass spectrometry (MS) to create an efficient approach for protein quantification. Protein-protein interaction was used to analyze relationships among proteins. RESULTS: Energy metabolism protein levels were significantly different in glycolysis and not significantly different in oxidative phosphorylation after irradiation. Conversely, tumor suppressor proteins related to cell growth and development were downregulated, and those related to cell death and cell cycle were upregulated in irradiated cells. CONCLUSION: Our results indicate that irradiation induces differential expression of the 29 identified proteins closely related to cell survival, cell cycle arrest, and growth inhibition. The data may provide new insights into the pathogenesis of uveal melanoma and guide appropriate radiotherapy.