A 15-lncRNA signature predicts survival and functions as a ceRNA in patients with colorectal cancer

Purpose Colorectal cancer (CRC) is one of the most common malignant tumors worldwide. This study aimed to explore the prognostic value of lncRNAs in CRC. Material and methods We performed gene expression profiling to identify differentially expressed lncRNAs between 51 normal and 646 tumor tissues from The Cancer Genome Atlas database. Cox regression and robust likelihood-based survival models were used to find prognosis-related lncRNAs. A lncRNA signature was developed to predict the overall survival of patients with CRC. In addition, a receiver operating characteristic curve analysis was performed to identify the optimal cutoff with the best Youden index to divide patients into different groups based on risk level. Results Eighty survival-related lncRNAs were identified and a 15-lncRNA signature was developed on the basis of a risk score to comprehensively predict the overall survival of patients with CRC. The prognostic value of the 15-lncRNA risk score was validated using the internal testing set and total set. The risk indicator was shown to be an independent prognostic factor (hazard ratio =2.92; 95% CI: 1.73–4.94; P<0.001). Notably, all 15 lncRNAs (AC024581.1, FOXD3-AS1, AC012531.1, AC003101.2, LINC01219, AC083967.1, AL590483.1, AC105118.1, AC010789.1, AC067930.5, AC105219.2, LINC01354, LINC02474, LINC02257, and AC079612.1) were newly found to correlate with the prognosis of patients with CRC. Furthermore, the function of 15 lncRNAs was explored through the ceRNA network. These lncRNAs regulated coding genes that were involved in many key cancer pathways. Conclusion A 15-lncRNA expression signature was discovered as a prognostic indicator for patients with CRC, which may act as competing endogenous RNA (ceRNAs) to play a crucial role in the modulation of cancer-related pathways. These findings may allow a better understanding of the prognostic value of lncRNAs.

[1]  J. Hardcastle,et al.  Colorectal cancer , 1993, Europe Against Cancer European Commission Series for General Practitioners.

[2]  P. Grambsch,et al.  A Package for Survival Analysis in S , 1994 .

[3]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[4]  R. Gray Modeling Survival Data: Extending the Cox Model , 2002 .

[5]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[6]  A. Sparks,et al.  The Genomic Landscapes of Human Breast and Colorectal Cancers , 2007, Science.

[7]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[8]  Jaewoo Kang,et al.  Robust Likelihood-Based Survival Modeling with Microarray Data , 2009 .

[9]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[10]  P. Pandolfi,et al.  A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language? , 2011, Cell.

[11]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[12]  Davis J. McCarthy,et al.  Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.

[13]  Guangchuang Yu,et al.  clusterProfiler: an R package for comparing biological themes among gene clusters. , 2012, Omics : a journal of integrative biology.

[14]  Howard Y. Chang,et al.  Long Noncoding RNAs: Cellular Address Codes in Development and Disease , 2013, Cell.

[15]  Pier Paolo Pandolfi,et al.  ceRNA cross-talk in cancer: when ce-bling rivalries go awry. , 2013, Cancer discovery.

[16]  D. Bartel,et al.  Predicting effective microRNA target sites in mammalian mRNAs , 2015, eLife.

[17]  J. Carethers,et al.  Genetics and Genetic Biomarkers in Sporadic Colorectal Cancer. , 2015, Gastroenterology.

[18]  J. Zucman‐Rossi,et al.  Genetic Landscape and Biomarkers of Hepatocellular Carcinoma. , 2015, Gastroenterology.

[19]  Li Zhang,et al.  The lncRNA H19 promotes epithelial to mesenchymal transition by functioning as miRNA sponges in colorectal cancer , 2015, Oncotarget.

[20]  Xiaowei Wang,et al.  miRDB: an online resource for microRNA target prediction and functional annotations , 2014, Nucleic Acids Res..

[21]  Hsien-Da Huang,et al.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database , 2015, Nucleic Acids Res..

[22]  J. Beermann,et al.  Non-coding RNAs in Development and Disease: Background, Mechanisms, and Therapeutic Approaches. , 2016, Physiological reviews.

[23]  Yuan Tang,et al.  ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages , 2016, R J..

[24]  Xiaowei Wang,et al.  Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies , 2016, Bioinform..

[25]  Sarah C. Ayling,et al.  The Ensembl gene annotation system , 2016, Database J. Biol. Databases Curation.

[26]  Howard Y. Chang,et al.  Long Noncoding RNAs in Cancer Pathways. , 2016, Cancer cell.

[27]  A. Dreher Modeling Survival Data Extending The Cox Model , 2016 .

[28]  P. Gao,et al.  Non-coding RNAs participate in the regulatory network of CLDN4 via ceRNA mediated miRNA evasion , 2017, Nature Communications.

[29]  Pak Chung Sham,et al.  Exploring genetic associations with ceRNA regulation in the human genome , 2017, Nucleic acids research.

[30]  Scott W. Lowe,et al.  Putting p53 in Context , 2017, Cell.

[31]  Min Zhang,et al.  lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer. , 2018, Cancer cell.

[32]  Yi Pan,et al.  Prediction of lncRNA–disease associations based on inductive matrix completion , 2018, Bioinform..

[33]  K. Wiman,et al.  Targeting mutant p53 for efficient cancer therapy , 2017, Nature Reviews Cancer.

[34]  Astrid Gall,et al.  Ensembl 2018 , 2017, Nucleic Acids Res..

[35]  Clement Adebamowo,et al.  A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers. , 2018, Cancer cell.

[36]  Yuan Tang,et al.  Data Visualization Tools for Statistical Analysis Results [R package ggfortify version 0.4.11] , 2020 .