Identification of microRNA expression signature for the diagnosis and prognosis of cervical squamous cell carcinoma

A systematic review and meta-analysis of miRNAs for the detection of cervical cancer

Aim: This study aimed to critically appraise the evidence of the diagnostic effectiveness of miRNAs for the detection of cervical cancer. Methods & materials: A systematic review and meta-analysis was performed, searching PubMed, EMBASE and Web of Science. An umbrella meta-analysis of meta-analyses of individual biomarkers was performed. A Grading of Recommendations, Assessment, Development and Evaluations (GRADE) assessment of evidence was also performed. Results: A total of 52 miRNAs were included. Umbrella meta-analysis revealed significant heterogeneity in terms of sensitivity, specificity, receiver operating characteristic (ROC), positive predictive value and/or negative predictive value. Umbrella effects were 0.76 (95% CI: 0.73-0.78), 0.78 (95% CI: 0.75-0.81), 0.77 (95% CI: 0.75-0.80), 0.75 (95% CI: 0.71-0.79) and 0.76 (95% CI: 0.74-0.79), respectively. Conclusion: Moderate quality evidence suggested miR199a-5p, miR21-5p and miR-141a had excellent diagnostic performance.

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

BACKGROUND

Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5-year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute.

METHODS

The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver-operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively.

RESULTS

A total of 13,802 patients were involved and classified into training (N = 11,041) and validation (N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models (p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors.

CONCLUSIONS

The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision-making for nonmetastatic CC patients in the future.

The role of miR-200 family in the regulation of hallmarks of cancer

MiRNAs are short non-coding RNAs that regulate gene expression post-transcriptionally contributing to the development of different diseases including cancer. The miR-200 family consists of five members, miR-200a, miR-200b, miR-200c, miR-141, and miR-429. Their expression is dysregulated in cancer tissue and their level is altered in the body fluids of cancer patients. Moreover, the levels of miR-200 family members correlate with clinical parameters such as cancer patients' survival which makes them potentially useful as diagnostic and prognostic biomarkers. MiRNAs can act as either oncomiRs or tumor suppressor miRNAs depending on the target genes and their role in the regulation of key oncogenic signaling pathways. In most types of cancer, the miR-200 family acts as tumor suppressor miRNA and regulates all features of cancer. In this review, we summarized the expression pattern of the miR-200 family in different types of cancer and their potential utility as biomarkers. Moreover, we comprehensively described the role of miR-200 family members in the regulation of all hallmarks of cancer proposed by Hanahan and Weinberg with the focus on the epithelial-mesenchymal transition, invasiveness, and metastasis of tumor cells.

MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to Identify Key miRNAs (microRNAs)

Identifying cancer-related miRNAs (or microRNAs) that precisely target mRNAs is important for diagnosis and treatment of cancer. Creating novel methods to identify candidate miRNAs becomes an imminent Frontier of researches in the field. One major obstacle lies in the integration of the state-of-the-art databases. Here, we introduce a novel method, MIMRDA, which incorporates the miRNA and mRNA expression profiles for predicting miRNA-disease associations to identify key miRNAs. As a proof-of-principle study, we use the MIMRDA method to analyze TCGA datasets of 20 types (BLCA, BRCA, CESE, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, THCA and UCEC) of cancer, which identified hundreds of top-ranked miRNAs. Some (as Category 1) of them are endorsed by public databases including TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR and mTD; others (as Category 2) are supported by literature evidences. miR-21 (representing Category 1) and miR-1258 (representing Category 2) display the excellent characteristics of biomarkers in multi-dimensional assessments focusing on the function similarity analysis, overall survival analysis, and anti-cancer drugs’ sensitivity or resistance analysis. We compare the performance of the MIMRDA method over the Limma and SPIA packages, and estimate the accuracy of the MIMRDA method in classifying top-ranked miRNAs via the Random Forest simulation test. Our results indicate the superiority and effectiveness of the MIMRDA method, and recommend some top-ranked key miRNAs be potential biomarkers that warrant experimental validations.

Identification of miRNA signatures and their therapeutic potentials in prostate cancer

BACKGROUND

Herein, we identified miRNA signatures that were able to differentiate malignant prostate cancer from benign prostate hyperplasia and revealed the therapeutic potential of these miRNAs against prostate cancer development.

METHODS AND RESULTS

MicroRNA expressions were determined by qPCR. MTT was used for cell viability analysis and immunohistochemistry was performed for Bax/Bcl-2 staining. ELISA was used to measure MMP2/9 levels. Wound healing assay was used for the evaluation of cell migration. Notably, expression levels of miR-125b-5p, miR-145-5p and miR-221-3p were significantly reduced in prostate cancer patients as compared to BPH patients. Moreover, ectopic expression of miR-125b-5p, miR-145-5p and miR-221-3p resulted in significant inhibition of cell proliferation and altered cell morphology. Also, expression level of Bax protein was increased while Bcl-2 level was reduced in cells treated with miR-125b-5p, miR-145-5p and miR-221-3p mimics. Enhanced expression of miR-125b-5p, miR-145-5p and miR-221-3p was also significantly altered the expression of caspase 3 and 8 levels. In addition, MMP9 levels were significantly reduced in cells ectopically expressing miR-221-3p. All miRNA mimics significantly interfered with the migration of prostate cancer cells.

CONCLUSIONS

Consequently, our findings point to an important role of these three miRNAs in prostate cancer and indicate that miR-125b-5p, miR-145-5p and miR-221-3p are potential therapeutic targets against prostate cancer.

Integrated whole transcriptome and small RNA analysis revealed multiple regulatory networks in colorectal cancer

Colorectal cancer (CRC) remains a global disease burden and a leading cause of cancer related deaths worldwide. The identification of aberrantly expressed messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA), and the resulting molecular interactions and signaling networks is essential for better understanding of CRC, identification of novel diagnostic biomarkers and potential development of therapeutic interventions. Herein, we performed microRNA (miRNA) sequencing on fifteen CRC and their non-tumor adjacent tissues and whole transcriptome RNA-Seq on six paired samples from the same cohort and identified alterations in miRNA, mRNA, and lncRNA expression. Computational analyses using Ingenuity Pathway Analysis (IPA) identified multiple activated signaling networks in CRC, including ERBB2, RABL6, FOXM1, and NFKB networks, while functional annotation highlighted activation of cell proliferation and migration as the hallmark of CRC. IPA in combination with in silico prediction algorithms and experimentally validated databases gave insight into the complex associations and interactions between downregulated miRNAs and upregulated mRNAs in CRC and vice versa. Additionally, potential interaction between differentially expressed lncRNAs such as H19, SNHG5, and GATA2-AS1 with multiple miRNAs has been revealed. Taken together, our data provides thorough analysis of dysregulated protein-coding and non-coding RNAs in CRC highlighting numerous associations and regulatory networks thus providing better understanding of CRC.

Circular dumbbell miR-34a-3p and -5p suppresses pancreatic tumor cell-induced angiogenesis and activates macrophages

Angiogenesis is a tightly regulated biological process by which new blood vessels are formed from pre-existing blood vessels. This process is also critical in diseases such as cancer. Therefore, angiogenesis has been explored as a drug target for cancer therapy. The future of effective anti-angiogenic therapy lies in the intelligent combination of multiple targeting agents with novel modes of delivery to maximize therapeutic effects. Therefore, a novel approach is proposed that utilizes dumbbell RNA (dbRNA) to target pathological angiogenesis by simultaneously targeting multiple molecules and processes that contribute to angiogenesis. In the present study, a plasmid expressing miR-34a-3p and -5p dbRNA (db34a) was constructed using the permuted intron-exon method. A simple protocol to purify dbRNA from bacterial culture with high purity was also developed by modification of the RNASwift method. To test the efficacy of db34a, pancreatic cancer cell lines PANC-1 and MIA PaCa-2 were used. Functional validation of the effect of db34a on angiogenesis was performed on human umbilical vein endothelial cells using a tube formation assay, in which cells transfected with db34a exhibited a significant reduction in tube formation compared with cells transfected with scrambled dbRNA. These results were further validated in vivo using a zebrafish angiogenesis model. In conclusion, the present study demonstrates an approach for blocking angiogenesis using db34a. The data also show that this approach may be used to targeting multiple molecules and pathways.