Search for: All records

Abstract Background To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient. Results This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism. Conclusions DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA . 

It has been evident that N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) involves regulating tumorigenesis, invasion, and metastasis for various cancer types. In this study, we sought to pick computationally up a set of 13 hub m6A-lncRNAs in light of three state-of-the-art tools WGCNA, iWGCNA, and oCEM, and interrogated their prognostic values in brain low-grade gliomas (LGG). Of the 13 hub m6A-lncRNAs, we further detected three hub m6A-lncRNAs as independent prognostic risk factors, including HOXB-AS1, ELOA-AS1, and FLG-AS1 . Then, the m6ALncSig model was built based on these three hub m6A-lncRNAs. Patients with LGG next were divided into two groups, high- and low-risk, based on the median m6ALncSig score. As predicted, the high-risk group was more significantly related to mortality. The prognostic signature of m6ALncSig was validated using internal and external cohorts. In summary, our work introduces a high-confidence prognostic prediction signature and paves the way for using m6A-lncRNAs in the signature as new targets for treatment of LGG. 

Uveal melanoma (UM) is a comparatively rare cancer but requires serious consideration since patients with developing metastatic UM survive only for about 6–12 months. Fortunately, increasingly large multi-omics databases allow us to further understand cancer initiation and development. Moreover, previous studies have observed that associations between copy number aberrations (CNA) or methylation (MET) versus messenger RNA (mRNA) expression have affected these processes. From that, we decide to explore the effect of these associations on a case study of UM. Also, the current subtypes of UM display its weak association with biological phenotypes and its lack of therapy suggestions. Therefore, the re-identification of molecular subtypes is a pressing need. In this study, we recruit three omics profiles, including CNA, MET, and mRNA, in a UM cohort from The Cancer Genome Atlas (TCGA). Firstly, we identify two sets of genes, CNAexp and METexp, whose CNA and MET significantly correlated with their corresponding mRNA, respectively. Then, single and integrative analyses of the three data types are performed using the PINSPlus tool. As a result, we discover two novel integrative subgroups, IntSub1 and IntSub2, which could be a useful alternative classification for UM patients in the future. To further explore molecular events behind each subgroup, we identify their subgroup-specific genes computationally. Accordingly, the highest expressed genes among IntSub1-specific genes are mostly enriched with immune-related processes. On the other hand, IntSub2-specific genes are highly associated with cellular cation homeostasis, which responds effectively to chemotherapy using ion channel inhibitor drugs. In addition, we detect that the two integrative subgroups show different age-related risks and survival rates. These discoveries can influence the frequency of metastatic surveillance and support medical practitioners to choose an appropriate treatment regime.