Integrated evaluation of targeted and non-targeted therapies in a network meta-analysis

Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.     

A prognostic five long–noncoding RNA signature for patients with rectal cancer

This study aimed to identify prognostic long noncoding RNAs (lncRNAs) signature for predicting the prognosis of patients with rectal cancer. LncRNA-sequencing data and clinicopathological data of patients with rectal cancer were retrieved from The Cancer Genome Atlas database. Univariate and multivariate Cox proportional hazards regression analysis, the least absolute shrinkage, and selection operator analysis and the Kaplan-Meier curve method were employed to identify prognostic lncRNAs and construct multi-lncRNA signature. Finally, five lncRNAs (AC079789.1, AC106900.2, AL121987.1, AP004609.1, and LINC02163) were identified to construct a five-lncRNA signature. According to the five-lncRNA signature, patients with rectal cancer were divided into a high-risk group and low-risk group. Patients with rectal cancer had significantly poorer overall survival in the high-risk group than in the low-risk group. We used a time-dependent receiver operating characteristic curve to assess the power of the five-lncRNA signature by calculating the area under the curve (AUC). The AUCs for predicting 3-year survival and 5-year survival were 0.742 and 0.935, respectively, which indicated a good performance of the five-lncRNA signature. The five-lncRNA signature was independently associated with the prognosis of patients with rectal cancer through using univariate and multivariate Cox regression analysis. The biological function of the five lncRNAs was enriched in some cancer-related biological processes and pathways by performing functional enrichment analysis of their correlated protein-coding genes. In conclusion, we developed a five-lncRNA signature as a potential indicator for rectal cancer.     

Circulating and tissue microRNAs as a potential diagnostic biomarker in patients with thrombotic events

Venous and arterial thrombosis are conditions that have a considerable burden if left untreated. The hypoxia-induced by the occluded vessel can disrupt the circulation of any organ, the cornerstone of treating thrombosis is rapid diagnosis and appropriate treatment. Diagnosis of thrombosis may be made by using laboratory tests or imaging techniques in individuals who have clinical manifestations of a thrombotic event. The use of serum micro ribonucleic acids (RNAs) has recently been applied to the diagnosis of thrombosis. These small RNA molecules are emerging as new diagnostic markers but have had very limited applications in vascular disease. Most of the articles provided various microRNAs with different levels of accuracy. However, there remains a lack of an appropriate panel of the most specific microRNA in the literature. The purpose of the present review was to summarize the existing data on the use of microRNAs as a diagnostic biomarker for venous thrombosis.     

LncRNAs as potential diagnostic and prognostic biomarkers in gastric cancer: A novel approach to personalized medicine

Gastric cancer is the third leading cause of cancer death with 5-year survival rate of about 30–35%. Since early detection is associated with decreased mortality, identification of novel biomarkers for early diagnosis and proper management of patients with the best response to therapy is urgently needed. Long noncoding RNAs (lncRNAs) due to their high specificity, easy accessibility in a noninvasive manner, as well as their aberrant expression under different pathological and physiological conditions, have received a great attention as potential diagnostic, prognostic, or predictive biomarkers. They may also serve as targets for treating gastric cancer. In this review, we highlighted the role of lncRNAs as tumor suppressors or oncogenes that make them potential biomarkers for the diagnosis and prognosis of gastric cancer. Relatively, lncRNAs such as H19, HOTAIR, UCA1, PVT1, tissue differentiation-inducing nonprotein coding, and LINC00152 could be potential diagnostic and prognostic markers in patients with gastric cancer. Also, the impact of lncRNAs such as ecCEBPA, MLK7-AS1, TUG1, HOXA11-AS, GAPLINC, LEIGC, multidrug resistance-related and upregulated lncRNA, PVT1 on gastric cancer epigenetic and drug resistance as well as their potential as therapeutic targets for personalized medicine was discussed.     

Apoptosis detection via automated algorithms to analyze biomarker translocation in reporter cells

Rapid, efficient, and robust quantitative analyses of dynamic apoptotic events are essential in a high-throughput screening workflow. Currently used methods have several bottlenecks, specifically, limitations in available fluorophores for downstream assays and misinterpretation of statistical image data analysis. In this study, we developed cytochrome-C (Cyt-C) and caspase-3/-8 reporter cell lines using lung (PC9) and breast (T47D) cancer cells, and characterized them from the response to apoptotic stimuli. In these two reporter cell lines, the spatial fluorescent signal translocation patterns served as reporters of activations of apoptotic events, such as Cyt-C release and caspase-3/-8 activation. We also developed a vision-based, tunable, automated algorithm in MATLAB to implement the robust and accurate analysis of signal translocation in single or multiple cells. Construction of the reporter cell lines allows live monitoring of apoptotic events without the need for any other dyes or fixatives. Our algorithmic implementation forgoes the use of simple image statistics for more robust analytics. Our optimized algorithm can achieve a precision greater than 90% and a sensitivity higher than 85%. Combining our automated algorithm with reporter cells bearing a single-color dye/fluorophore, we expect our approach to become an integral component in the high-throughput drug screening workflow.     

Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival-related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival-related snoRNAs were identified, and a five-snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five-snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five-snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.     

Prognostic value of periostin in multiple solid cancers: A systematic review with meta-analysis

Previous studies have shown that the expression of periostin (POSTN) is significantly correlated with prognosis in multiple solid cancers. However, the function of POSTN in tumorigenesis and its relationship with clinical outcomes have not been systematically summarized and analyzed. Thus, a meta-analysis was performed to evaluate the prognostic pertinence of POSTN in solid cancer. We conducted a systematic search in the PubMed, EMBASE, Web of Science, and Cochrane library databases, and a total of 10 studies were used to assess the association of POSTN expression and patients’ overall survival (OS) and disease-free survival (DFS). The hazard ratio (HR) or odds ratio (OR) and their corresponding 95% confidence intervals (95% CIs) were further calculated to estimate the association between POSTN and relevant clinical parameters of solid cancer patients. The pooled results indicated that POSTN overexpression was associated with poor OS (HR = 2.35, 95% CI = 1.88–2.93, p < .00001) and DFS (HR = 2.70, 95% CI = 2.00–3.65, p < .00001) in a cohort of 993 patients with cancer. Subsequent analyses showed that the positive expression ratio of POSTN was evidently higher in cancer tissues than in normal tissues (OR = 7.44, 95% CI = 3.66–13.95, p < .00001). In addition, subgroup analysis showed that POSTN was related to microvascular invasion (OR = 5.09, 95% CI = 3.07–8.44, p < .00001), tumor differentiation (OR = 2.03, 95% CI = 1.41–2.91, p = .0001), and lymph node metastasis (OR = 3.05, 95% CI = 2.01–4.64, p < .00001). These data showed that POSTN could be a credible prognostic biomarker and a potential therapeutic target in human solid cancer.     

Profiling and integrated analysis of differentially expressed circRNAs as novel biomarkers for breast cancer

Breast cancer (BC) is a globally common cancer with the highest and increasing morbidity and mortality among females. Novel biomarkers are warranted to be discovered for the early detection, treatment, and prognosis of BC. In this study, we investigated the profiles of differentially expressed (DE) circular RNAs (circRNAs) by competing endogenous RNAs (ceRNA) microarray to construct a genome-wide circRNA profile. Then, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis of the host genes (HGs) of circRNAs. A total of 4,370 DE circRNAs were detected and GO and KEGG analysis showed that they were significantly associated with cell cycle, DNA replication, BC, and familial BC. We validated the differential circRNAs and relevant HGs through quantitative real-time polymerase chain reaction and constructed a putative circRNA–microRNA–messenger RNA regulatory network. Eight circRNAs, including hsa_circ_0069094, hsa_circ_0062558, hsa_circ_0074026, hsa_circ_0079876, hsa_circ_0017536, hsa_circ_0023302, hsa_circ_0017650, and hsa_circ_0017545, were validated significantly DE in BC tissue and associated with TNM staging, lymph node infiltration, and Ki67. Hsa_circ_0069094, hsa_circ_0079876, hsa_circ_0017650, and hsa_circ_0017526 were upregulated in plasma. This study revealed the general expression characteristics of specific DE circRNAs in BC and hsa_circ_0069094, hsa_circ_0079876, hsa_circ_0017650, and hsa_circ_0017526 might be promising candidate targets.