Protein-coding genes, long non-coding RNAs combined with microRNAs as a novel clinical multi-dimension transcriptome signature to predict prognosis in ovarian cancer

Deciphering the Long Non-Coding RNAs and MicroRNAs Coregulation Networks in Ovarian Cancer Development: An Overview

Non-coding RNAs are emergent elements from the genome, which do not encode for proteins but have relevant cellular functions impacting almost all the physiological processes occurring in eukaryotic cells. In particular, microRNAs and long non-coding RNAs (lncRNAs) are a new class of small RNAs transcribed from the genome, which modulate the expression of specific genes at transcriptional and posttranscriptional levels, thus adding a new regulatory layer in the flux of genetic information. In cancer cells, the miRNAs and lncRNAs interactions with its target genes and functional pathways are deregulated as a consequence of epigenetic and genetic alterations occurring during tumorigenesis. In this review, we summarize the actual knowledge on the interplay of lncRNAs with its cognate miRNAs and mRNAs pairs, which interact in coregulatory networks with a particular emphasis on the mechanisms underlying its oncogenic behavior in ovarian cancer. Specifically, we reviewed here the evidences unraveling the relevant roles of lncRNAs/miRNAs pairs in altered regulation of cell migration, angiogenesis, therapy resistance, and Warburg effect. Finally, we also discussed its potential clinical implications in ovarian cancer and related endocrine disease therapies.

AKT-mediated regulation of chromatin ubiquitylation and tumorigenesis through Mel18 phosphorylation

Polycomb repressor complex 1 (PRC1) is linked to the regulation of gene expression and histone ubiquitylation conformation, which contributes to carcinogenesis. However, the upstream regulators of PRC1 biogenesis machinery remain obscure. Here, we report that the polycomb group-related mammalian gene Mel18 is a target of the protein kinase AKT. AKT phosphorylates Mel18 at T334 to disrupt the interaction between Mel18 and other PRC1 members, leading to attenuated PRC1-dependent ubiquitylation of histone H2A at Lys119. As such, PRC1 target genes, many of which are known oncogenes, are derepressed upon T334-Mel18 phosphorylation, which promotes malignant behaviours, including cell proliferation, tumour formation, migration and invasion, bone and brain metastatic lesion formation. Notably, a positive correlation between AKT activity and pT334-Mel18 is observed, and prognostic models based on p-AKT and pT334-Mel18 that predicted overall survival and distant metastasis-free survival in breast cancer patients are established. These findings have implications for understanding the role of AKT and its associated proteins in chromatin ubiquitylation, and also indicate the AKT-Mel18-H2AK119ub axis as a novel prognostic biomarker and therapeutic target for cancer patients.

Identification of a novel CpG methylation signature to predict prognosis in lung squamous cell carcinoma

BACKGROUND

DNA methylation plays a vital role in modulating genomic function and warrants evaluation as a biomarker for the diagnosis and treatment of lung squamous cell carcinoma (LUSC).

OBJECTIVE

In this study, we aimed to identify effective potential biomarkers for predicting prognosis and drug sensitivity in LUSC.

METHODS

A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO).

RESULTS

A methylated region consisting of 3 sites (cg06675147, cg07064331, cg20429172) was selected. Patients were divided into a high-risk group and a low-risk group in the training dataset. High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82-4.07, P< 0.001) compared with low-risk patients. The accuracy of the prognostic signature was validated in the test and validation cohorts (TCGA, n= 94; GSE56044, n= 23). Gene set variation analysis (GSVA) showed that activity in the cell cycle/mitotic, ERBB, and ERK/MAPK pathways was higher in the high-risk compared with the low-risk group, which may lead to differences in OS.Interestingly, we observed that patients in the high-risk group were more sensitive to gemcitabine and docetaxel than the low-risk group, which is consistent with results of the GSVA.

CONCLUSION

We report novel methylation sites that could be used as powerful tools for predicting risk factors for poorer survival in patients with LUSC.

Circulating Adiponectin and Its Association with Metabolic Traits and Type 2 Diabetes: Gene-Diet Interactions Focusing on Selected Gene Variants and at the Genome-Wide Level in High-Cardiovascular Risk Mediterranean Subjects

Adiponectin is gaining renewed interest since, in addition to its possible protective role against insulin resistance and arteriosclerosis, recent studies suggest other additional favorable effects. However, the influence of gene-diet interactions on plasma adiponectin levels is still little understood. We analyzed the association between plasma adiponectin levels and various metabolic traits in a high-cardiovascular risk Mediterranean population, as well as the genetic effect of four candidate single-nucleotide polymorphisms (SNPs) in the adiponectin gene (ADIPOQ) and their interactions with the Mediterranean dietary pattern. Additionally, we explored, at the genome-wide level, the SNPs most associated with plasma adiponectin levels, as well as gene-diet interactions with the Mediterranean diet. In the 954 participants studied (aged 55-80 years), plasma adiponectin levels were strongly associated with plasma HDL-C concentrations (p = 6.6 × 10^-36) and inversely related to triglycerides (p = 4.7 × 10^-18), fasting glucose (p = 3.5 × 10^-16) and type 2 diabetes (p = 1.4 × 10^-7). Of the four pre-selected ADIPOQ candidate SNPs, the one most associated with plasma adiponectin was the -11391G > A (rs17300539) promoter SNP (p = 7.2 × 10^-5, in the multivariable adjusted model). No significant interactions with the Mediterranean diet pattern were observed for these SNPs. Additionally, in the exploratory genome-wide association study (GWAS), we found new SNPs associated with adiponectin concentrations at the suggestive genome-wide level (p < 1 × 10^-5) for the whole population, including the lead SNP rs9738548 (intergenic) and rs11647294 in the VAT1L (Vesicle Amine Transport 1 Like) gene. We also found other promising SNPs on exploring different strata such as men, women, diabetics and non-diabetics (p = 3.5 × 10^-8 for rs2850066). Similarly, we explored gene-Mediterranean diet interactions at the GWAS level and identified several SNPs with gene-diet interactions at p < 1 × 10^-5. A remarkable gene-diet interaction was revealed for the rs2917570 SNP in the OPCML (Opioid Binding Protein/Cell Adhesion Molecule Like) gene, previously reported to be associated with adiponectin levels in some populations. Our results suggest that, in this high-cardiovascular risk Mediterranean population, and even though adiponectin is favorably associated with metabolic traits and lower type 2 diabetes, the gene variants more associated with adiponectin may be population-specific, and some suggestive gene-Mediterranean diet interactions were detected.

A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

BACKGROUND

In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide.

METHODS

Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined.

RESULTS

A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development.

CONCLUSION

In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

Cancer Biomarker Discovery for Precision Medicine: New Progress

BACKGROUND

Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued.

OBJECTIVE

This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine.

RESULTS

The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients' survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients' survival.

CONCLUSION

Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis

Background: Breast cancer is one of the deadliest malignant tumors worldwide. Due to its complex molecular and cellular heterogeneity, the efficacy of existing breast cancer risk prediction models is unsatisfactory. In this study, we developed a new lncRNA model to predict the prognosis of patients with BRCA.

Methods: BRCA-related differentially-expressed long non-coding RNA were screened from the Cancer Genome Atlas database. A novel lncRNA model was developed by univariate and multivariate analyses to predict the prognosis of patients with BRCA. The efficacy of the model was verified by TCGA-based breast cancer samples. Identified lncRNA-related mRNA based on the co-expression method.

Results: We constructed a 7-lncRNA breast cancer prediction model including LINC00377, LINC00536, LINC01224, LINC00668, LINC01234, LINC02037, and LINC01456. The breast cancer samples were divided into high-risk and low-risk groups based on the model, which verified the specificity and sensitivity of the model. The Area Under Curve (AUC) of the 3- and 5-year Receiver Operating Characteristic curve were 0.711 and 0.734, respectively, indicating that the model has good performance.

Conclusion: We constructed a 7-lncRNA model to predict the prognosis of patients with BRCA, and suggest that these lncRNAs may play a specific role in the carcinogenesis of BRCA.

Analysis of Promoter-Associated Chromatin Interactions Reveals Biologically Relevant Candidate Target Genes at Endometrial Cancer Risk Loci

The identification of target genes at genome-wide association study (GWAS) loci is a major obstacle for GWAS follow-up. To identify candidate target genes at the 16 known endometrial cancer GWAS risk loci, we performed HiChIP chromatin looping analysis of endometrial cell lines. To enrich for enhancer-promoter interactions, a mechanism through which GWAS variation may target genes, we captured chromatin loops associated with H3K27Ac histone, characteristic of promoters and enhancers. Analysis of HiChIP loops contacting promoters revealed enrichment for endometrial cancer GWAS heritability and intersection with endometrial cancer risk variation identified 103 HiChIP target genes at 13 risk loci. Expression of four HiChIP target genes (SNX11, SRP14, HOXB2 and BCL11A) was associated with risk variation, providing further evidence for their targeting. Network analysis functionally prioritized a set of proteins that interact with those encoded by HiChIP target genes, and this set was enriched for pan-cancer and endometrial cancer drivers. Lastly, HiChIP target genes and prioritized interacting proteins were over-represented in pathways related to endometrial cancer development. In summary, we have generated the first global chromatin looping data from normal and tumoral endometrial cells, enabling analysis of all known endometrial cancer risk loci and identifying biologically relevant candidate target genes.