bims-lorfki 2021-06-20 papers

Issue of 2021‒06‒20
three papers selected by
Nikita Dewani
Max Delbrück Centre for Molecular Medicine

Front Oncol. 2021 ;11 663263

Identification of an m6A-Related lncRNA Signature for Predicting the Prognosis in Patients With Kidney Renal Clear Cell Carcinoma.

JunJie Yu, WeiPu Mao, Si Sun, Qiang Hu, Can Wang, ZhiPeng Xu, RuiJi Liu, SaiSai Chen, Bin Xu, Ming Chen.

Purpose: This study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.Methods: The KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.
Results: We constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.
Conclusions: We constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

Keywords: M6A; The Cancer Genome Atlas; kidney renal clear cell carcinoma; long non-coding RNA; prognostic signature

DOI: https://doi.org/10.3389/fonc.2021.663263

Mol Cell Proteomics. 2021 Jun 12. pii: S1535-9476(21)00081-5. [Epub ahead of print] 100109

Deeply Mining a Universe of Peptides Encoded by Long Noncoding RNAs.

Qing Zhang, Erzhong Wu, Yiheng Tang, Lili Zhang, Jifeng Wang, Yajing Hao, Bao Zhang, Yue Zhou, Xiaojing Guo, Jianjun Luo, Tanxi Cai, Runsheng Chen, Fuquan Yang.

Many small open reading frames (smORFs) embedded in lncRNA transcripts have been shown to encode biologically functional polypeptides (smORFs-encoded polypeptides, SEPs) in different organisms. Despite significant advances in genomics, bioinformatics and proteomics that largely enabled the discovery of novel SEPs, their identification across different biological samples is still hampered by their poor predictability, diminutive size and low relative abundance. Here, we take advantage of NONCODE, a repository containing the most complete collection and annotation of lncRNA transcripts from different species, to build a novel database that attempts to maximize a collection of SEPs from human and mouse lncRNA transcripts. In order to further improve SEP discovery, we implemented two effective and complementary polypeptide enrichment strategies, 30 kDa MWCO filter and C8 SPE column. These combined strategies enabled us to discover 353 and 409 SEPs from, respectively, 8 human cell lines, and 3 mouse cell lines and 8 mouse tissues. Importantly, nineteen of the identified SEPs were then verified through in-vitro expression, immunoblotting, parallel reaction monitoring (PRM) and synthetic peptides. Subsequent bioinformatic analysis revealed that some of the physical and chemical properties of these novel SEPs, including amino acid composition and codon usage, are different from those commonly found in canonical proteins. Intriguingly, nearly 65% of the identified SEPs were found to be initiated with non-AUG start codons. Overall, the strategy presented in this study encompasses an efficient workflow that enabled us to identify 762 novel SEPs across multiple cell lines and tissues, which probably represents the largest number of SEPs detected by mass spectrometry reported to date. These novel SEPs might not only provide new clues for the annotation of noncoding elements in the genome but can also serve as a valuable resource for the functional characterization of individual SEPs.

Keywords: Long noncoding RNA (lncRNA); NONCODE database; enrichment; mass spectrometry; smORF encoded polypeptides (SEPs)

DOI: https://doi.org/10.1016/j.mcpro.2021.100109

Biomed Pharmacother. 2021 Jun 11. pii: S0753-3322(21)00594-1. [Epub ahead of print]141 111812

Roles of lncRNA MAGI2-AS3 in human cancers.

Liu Kai-Xing, Cheng Cheng, Li Rui, Shi Zheng-Wei, Tan Wen-Wen, Xu Peng.

Long noncoding RNAs (lncRNAs) are noncoding RNAs more than 200 nucleotides in length. A growing number of reports indicate that lncRNAs play a key role in multiple cancers by serving as oncogenes or tumor suppressor genes. MAGI2 antisense RNA 3 (MAGI2-AS3) is ubiquitously expressed in human cancers, and the level of MAGI2-AS3 expression is associated with the progression and prognosis of cancers. Moreover, dysregulation of MAGI2-AS3 has been found to regulate cancer cell proliferation, cell death, invasion and metastasis and treatment resistance by serving as a competing endogenous RNA (ceRNA), epigenomic regulator, and transcriptional regulator. Moreover, increasing evidence shows that MAGI2-AS3 may be a potential biomarker for cancer prognosis and a potential target for cancer therapy. In this review, we summarize current research on the functions, mechanisms and clinical significance of the lncRNA MAGI2-AS3 in cancer development.

Keywords: Biomarker; Long noncoding RNAs; MAGI2-AS3; Target

DOI: https://doi.org/10.1016/j.biopha.2021.111812