Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale, and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n = 1,878). The identified lncRNAs are significantly associated (all P ≤ 1.48E-11) with overall survival (OS) of the TCGA cohort (n = 4,231). The signature stratified the cohort into low- and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P = 1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n = 1,122; CPTAC, n = 391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; log-rank P = 8.6E-09 and P = 2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

Differentially Expressed mRNAs and Their Long Noncoding RNA Regulatory Network with Helicobacter pylori-Associated Diseases including Atrophic Gastritis and Gastric Cancer

Background

Helicobacter pylori (Hp) infection is the strongest risk factor for gastric cancer (GC). However, the mechanisms of Hp-associated GC remain to be explored.

Methods

The gene expression profiling (GSE111762) data were downloaded from the GEO database. Differentially expressed genes (DEGs) between normal samples (NO) and Hp-atrophic gastritis (GA) or Hp-GA and Hp-GC were identified by GEO2R. Gene Ontology and pathway enrichment analysis were performed using the DAVID database. lncRNA-TF-mRNA and ceRNA regulation networks were constructed using Cytoscape. The cross-networks were obtained by overlapping molecules of the above two networks. GSE27411 and GSE116312 datasets were employed for validation.

Results

DEGs between NO and Hp-GA are linked to the activity of inward rectifying potassium channels, digestion, etc. DEGs between Hp-GA and Hp-GC were associated with digestion, positive regulation of cell proliferation, etc. According to the lncRNA-TF-mRNA network, 63 lncRNAs, 12 TFs, and 209 mRNAs were involved in Hp-GA while 16 lncRNAs, 11 TFs, and 92 mRNAs were contained in the Hp-GC network. In terms of the ceRNA network, 120 mRNAs, 18 miRNAs, and 27 lncRNAs were shown in Hp-GA while 72 mRNAs, 8 miRNAs, and 1 lncRNA were included in the Hp-GC network. In the cross-network, we found that immune regulation and differentiation regulation were important in the process of NO-GA. Neuroendocrine regulation was mainly related to the process of GA-GC. In the end, we verified that CDX2 plays an important role in the pathological process of NO to Hp-GA. Comparing Hp-GA with Hp-GC, DEGs (FPR1, TFF2, GAST, SST, FUT9, and SHH), TF, and GATA5 were of great significance.

Conclusions

We identified the DEGs, and their lncRNA regulatory network of Hp-associated diseases might provide insights into the mechanism between Hp infection and GC. Furthermore, in-depth studies of the molecules might be useful to explore the multistep process of gastric diseases.