The modularity and dynamicity of miRNA-mRNA interactions in high-grade serous ovarian carcinomas and the prognostic implication

Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer

Ovarian cancer (OC), the eighth-leading cause of cancer-related death among females worldwide, is mainly represented by epithelial OC (EOC) that can be further subdivided into four subtypes: serous (75%), endometrioid (10%), clear cell (10%), and mucinous (3%). Major reasons for high mortality are the poor biological understanding of the OC mechanisms and a lack of reliable markers defining each EOC subtype. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression primarily by targeting messenger RNA (mRNA) transcripts. Their aberrant expression patterns have been associated with cancer development, including OC. However, the role of miRNAs in tumorigenesis is still to be determined, mainly due to the lack of consensus regarding optimal methodologies for identification and validation of miRNAs and their targets. Several tools for computational target prediction exist, but false interpretations remain a problem. The experimental validation of every potential miRNA-mRNA pair is not feasible, as it is laborious and expensive. In this study, we analyzed the correlation between global miRNA and mRNA expression patterns derived from microarray profiling of 197 EOC patients to identify the signatures of miRNA-mRNA interactions associated with overall survival (OS). The aim was to investigate whether these miRNA-mRNA signatures might have a prognostic value for OS in different subtypes of EOC. The content of our cohort (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas) reflects a real-world scenario of EOC. Several interaction pairs between 6 miRNAs (hsa-miR-126-3p, hsa-miR-223-3p, hsa-miR-23a-5p, hsa-miR-27a-5p, hsa-miR-486-5p, and hsa-miR-506-3p) and 8 mRNAs (ATF3, CH25H, EMP1, HBB, HBEGF, NAMPT, POSTN, and PROCR) were identified and the findings appear to be well supported by the literature. This indicates that our study has a potential to reveal miRNA-mRNA signatures relevant for EOC. Thus, the evaluation on independent cohorts will further evaluate the performance of such findings.

How hsa-miR-495 performed in the tumorigenesis of pancreatic adenocarcinoma by bioinformatics analysis

INTRODUCTION

Pancreatic adenocarcinoma (PAAD) is one of the most fatal cancers in the world for early metastasis, extensive invasion, and poor prognosis with a 5-year survival rate less than 5%. However, the underlying mechanisms are poorly understood. Therefore, it is urgent to explore molecular markers for early diagnosis or therapy target to improve the outcome of PAAD.

METHODS

We retrieved transcriptome data as well as clinical information from patients with PAAD in The Cancer Genome Altas (TCGA) database. Survival time associated microRNAs (miRNAs) and messenger RNAs (mRNAs) were initially identified, followed by enrichment analysis (Gene Ontology [GO] and pathway). The relationship between survival time associated miRNAs-mRNAs was also investigated to discover putative transcriptional control mechanisms of PAAD. Finally, by consulting the literature and retrieving the database, we found that hsa-miR-495 might have played an important role in PAAD.

RESULTS

In total, 146 miRNAs from 378 miRNAs and 580 mRNA from 17 100 mRNA, including 328 risk mRNA and 252 protective mRNA, were found to be associated with the survival time of PAAD. Eight hundred eighty-eight mRNA-miRNA pairs were related to the survival time of PAAD, involving in 755 mRNAs and 35 miRNAs. We chose 13 miRNAs predicted by target gene in the miRanda database for further research. Among these 13 miRNAs, hsa-miR-495 was identified as a good biomarker. Through GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the significantly enriched pathways involved in focal adhesion, Staphylococcus aureus infection, and Intestinal immune network for immunoglobulin A production. And four target genes and 87 pathways of the hsa-miR-495 were enriched in PAAD. Interestingly, we found hsa-miR-495 with a low expression having a poor overall survival and significantly different recurrence rate within 5 years.

CONCLUSION

Hsa-miR-495 and its target genes may serve as a prognostic and predictive marker in PAAD. Further research on the function of the hsa-miR-495 and its target genes in the KEGG pathway may provide references for treatment of PAAD.

Driver gene mutations based clustering of tumors: methods and applications

Motivation

Somatic mutations in proto-oncogenes and tumor suppressor genes constitute a major category of causal genetic abnormalities in tumor cells. The mutation spectra of thousands of tumors have been generated by The Cancer Genome Atlas (TCGA) and other whole genome (exome) sequencing projects. A promising approach to utilizing these resources for precision medicine is to identify genetic similarity-based sub-types within a cancer type and relate the pinpointed sub-types to the clinical outcomes and pathologic characteristics of patients.

Results

We propose two novel methods, ccpwModel and xGeneModel, for mutation-based clustering of tumors. In the former, binary variables indicating the status of cancer driver genes in tumors and the genes' involvement in the core cancer pathways are treated as the features in the clustering process. In the latter, the functional similarities of putative cancer driver genes and their confidence scores as the 'true' driver genes are integrated with the mutation spectra to calculate the genetic distances between tumors. We apply both methods to the TCGA data of 16 cancer types. Promising results are obtained when these methods are compared to state-of-the-art approaches as to the associations between the determined tumor clusters and patient race (or survival time). We further extend the analysis to detect mutation-characterized transcriptomic prognostic signatures, which are directly relevant to the etiology of carcinogenesis.

Availability and implementation

R codes and example data for ccpwModel and xGeneModel can be obtained from http://webusers.xula.edu/kzhang/ISMB2018/ccpw_xGene_software.zip.

Supplementary information

Supplementary data are available at Bioinformatics online.