Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer

ncFANs v2.0: an integrative platform for functional annotation of non-coding RNAs

Increasing evidence proves the essential regulatory roles of non-coding RNAs (ncRNAs) in biological processes. However, characterizing the specific functions of ncRNAs remains a challenging task, owing to the intensive consumption of the experimental approaches. Here, we present an online platform ncFANs v2.0 that is a significantly enhanced version of our previous ncFANs to provide multiple computational methods for ncRNA functional annotation. Specifically, ncFANs v2.0 was updated to embed three functional modules, including ncFANs-NET, ncFANs-eLnc and ncFANs-CHIP. ncFANs-NET is a new module designed for data-free functional annotation based on four kinds of pre-built networks, including the co-expression network, co-methylation network, long non-coding RNA (lncRNA)-centric regulatory network and random forest-based network. ncFANs-eLnc enables the one-stop identification of enhancer-derived lncRNAs from the de novo assembled transcriptome based on the user-defined or our pre-annotated enhancers. Moreover, ncFANs-CHIP inherits the original functions for microarray data-based functional annotation and supports more chip types. We believe that our ncFANs v2.0 carries sufficient convenience and practicability for biological researchers and facilitates unraveling the regulatory mechanisms of ncRNAs. The ncFANs v2.0 server is freely available at http://bioinfo.org/ncfans or http://ncfans.gene.ac.

An efficient framework to identify key miRNA-mRNA regulatory modules in cancer

MOTIVATION

Micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA-mRNA regulatory modules.

RESULTS

We presented a two-step framework to model miRNA-mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supported miRTarBase database.

AVAILABILITY AND IMPLEMENTATION

Our implementation of proposed two-step RFR algorithm in R is available at https://github.com/MiladMokhtaridoost/2sRFR together with the scripts that replicate the reported experiments.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A four-gene signature associated with clinical features can better predict prognosis in prostate cancer

Prostate cancer (PCa) is one of the most deadly urinary tumors in men globally, and the 5‐year over survival is poor due to metastasis of tumor. It is significant to explore potential biomarkers for early diagnosis and personalized therapy of PCa. In the present study, we performed an integrated analysis based on multiple microarrays in the Gene Expression Omnibus (GEO) dataset and obtained differentially expressed genes (DEGs) between 510 PCa and 259 benign issues. The weighted correlation network analysis indicated that prognostic profile was the most relevant to DEGs. Then, univariate and multivariate COX regression analyses were conducted and four prognostic genes were obtained to establish a four‐gene prognostic model. And the predictive effect and expression profiles of the four genes were well validated in another GEO dataset, The Cancer Genome Atlas and the Human Protein Atlas datasets. Furthermore, combination of four‐gene model and clinical features was analyzed systematically to guide the prognosis of patients with PCa to a largest extent. In summary, our findings indicate that four genes had important prognostic significance in PCa and combination of four‐gene model and clinical features could achieve a better prediction to guide the prognosis of patients with PCa.

Clinical significance of ring finger protein 2 high expression in skin squamous cell carcinoma

Although ring finger protein 2 (RNF2) serves an important role in the occurrence, development and regulation of various types of cancer, RNF2 expression in skin squamous cell carcinoma (SCC) remains unknown. The aim of the present study was to investigate the role of RNF2 expression in SCC and adjacent tissues from patients. The protein and gene expression levels of RNF2 in SCC and adjacent tissues were detected by immunohistochemistry (IHC), western blot analysis and semi-quantitative reverse transcription (RT) PCR. Single factor analysis was used to study the association between RNF2 expression level and the clinicopathological characteristics of patients with SCC. Multifactor Cox survival analysis was used to examine the association between RNF2 expression and the overall survival rate of postoperative patients with SCC. The results from IHC staining demonstrated that the positive expression rate of RNF2 was 84.68% (210/248) and 56.05% (139/248) in SCC and in adjacent tissues, respectively. Furthermore, results from western blot analysis demonstrated that RNF2 protein expression in SCC tissues was significantly higher compared with that in the adjacent tissues (P<0.05). The positive rate of RNF2 mRNA in SCC was 81.05% (201/248), which was significantly higher compared with that in the adjacent tissues 54.44% (135/248; P<0.05). Furthermore, RNF2 protein and gene expression levels were associated with tumor diameter, tumor stage, tumor metastasis and the degree of tumor differentiation in patients with SCC. Patients exhibiting higher RNF2 protein expression in SCC tissues had a significantly shorter disease-specific survival rate compared with patients with low RNF2 expression. In addition, RNF2 protein expression, tumor diameter, tumors site and tumor stage were independent factors affecting the overall survival rate of postoperative patients. High protein and gene expression levels of RNF2 in SCC tissues may be associated with the occurrence and development of SCC and prognosis of patients. The results form this study may serve the development of novel therapeutic options and diagnostic strategies for patients with SCC.

Development of a four-gene prognostic model for pancreatic cancer based on transcriptome dysregulation

We systematically developed a prognostic model for pancreatic cancer that was compatible across different transcriptomic platforms and patient cohorts. After performing quality control measures, we used seven microarray datasets and two RNA sequencing datasets to identify consistently dysregulated genes in pancreatic cancer patients. Weighted gene co-expression network analysis was performed to explore the associations between gene expression patterns and clinical features. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct a prognostic model. We tested the predictive power of the model by determining the area under the curve of the risk score for time-dependent survival. Most of the differentially expressed genes in pancreatic cancer were enriched in functions pertaining to the tumor immune microenvironment. The transcriptome profiles were found to be associated with overall survival, and four genes were identified as independent prognostic factors. A prognostic risk score was then proposed, which displayed moderate accuracy in the training and self-validation cohorts. Furthermore, patients in two independent microarray cohorts were successfully stratified into high- and low-risk prognostic groups. Thus, we constructed a reliable prognostic model for pancreatic cancer, which should be beneficial for clinical therapeutic decision-making.

Function and regulation annotation of up-regulated long non-coding RNA LINC01234 in gastric cancer

Background

Accumulated evidences indicate that long non‐coding RNAs (lncRNAs) participate in many biological mechanisms. Moreover, it acts as an essential regulator in various human diseases such as gastric cancer (GC). Nevertheless, the comprehensive regulatory roles and clinical significance of most lncRNAs in GC are not fully understood.

Methods

In this research, our aim was to investigate the underlying mechanism of lncRNA LINC01234 in GC. Firstly, the usage of qRT‐PCR helped to establish expression pattern of LINC01234 in GC tissues. Following this, appropriate statistical tests were applied to analyze the relation between expression level and clinicopathological factors. Ultimately, potential functions and regulatory network of LINC01234 were concluded via GSEA and a series of bioinformatics tools or databases, respectively.

Results

Consequently, at the end of research we found LINC01234 is up‐regulated in GC tissues in comparison with adjacent normal tissues. Furthermore, its expression level is correlated with differentiation of patients with GC. It is also important to highlight bioinformatics analysis revealed that LINC01234 is involved in cancer‐associated pathways such as cell cycle and mismatch repair. Also, regulatory network of LINC01234 presented a probability in the involvement of tumorigenesis through regulating cancer‐associated genes.

Conclusion

Overall, our results suggested that LINC01234 may play a crucial role in GC.