Abstract 3578: Comprehensive pharmacogenomic analysis establishes lncRNAs as protein-coding independent biomarker of drug response in human cancers

AbstractThe Drug Response Gene Expression Associated Map, also referred as “DREAM” (http://bio-big-data.cn:8080/DREAM), is a manually curated database of experimentally supported protein-coding RNAs and drugs associations in human cancers. The current version of the DREAM documents 3048 entries about scientific literatures supported drug sensitivity or drug intervention related protein-coding RNAs from PubMed database and 195 high-throughput microarray data about drug sensitivity or drug intervention related protein-coding RNAs data from GEO database. Each entry in DREAM database contains detailed information on protein-coding RNA, drug, cancer, and other information including title, PubMed ID, journal, publish time. The DREAM database also provides some data visualization and online analysis services such as volcano plot, GO/KEGG enrichment function analysis, and novel drug discovery analysis. We hope the DREAM database should serve as a valuable resource for clinical practice and basic research, which could help researchers better understand the effects of protein-coding RNAs on drug response in human cancers.

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Screening and survival analysis of melanoma immunodrug response-related genes and the function of magnetic nanoparticles in gene extraction

Materials Express ◽

10.1166/mex.2021.2037 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1306-1312

Author(s):

Li Song ◽

Ningchao Du ◽

Haitao Luo ◽

Furong Li

Keyword(s):

Survival Analysis ◽

Magnetic Nanoparticles ◽

Drug Response ◽

High Throughput Sequencing ◽

Cox Proportional Hazards ◽

Sequencing Data ◽

Protein Coding ◽

Non Coding Rna ◽

Long Non Coding Rna ◽

Rna Genes

This study aimed to identify the association of protein coding and long non coding RNA genes with immunotherapy response in melanoma. Based on RNA sequencing data of melanoma specimens, the expression levels of protein coding and long non coding RNA genes were calculated using the Kallisto RNA-seq quantification method, and differently expressed genes were detected using the DESeq2 method. Cox proportional hazards regression was used to evaluate the effects of gene expression on survival. According to the clinical data of 14 patients with drug response and 11 patients without drug response, 18 protein coding genes and 14 long non coding RNAs showed differential expressions (multiple of difference > 2 and P < 0.01 after correction), among which the coding genes of differential expression were significantly enriched through the process of cell adhesion (P < 0.01). The results of survival analysis showed that 18 coding genes and 14 long non coding RNA genes had significant effects on patient survival (P < 0.01). In this study, magnetic nanoparticles can be used to extract genomic DNA and total RNA due to their paramagnetism and biocompatibility, then transcriptome high-throughput sequencing was performed. The method has the advantages of removing dangerous reagents such as phenol and chloroform, replacing inorganic coating such as silica with organic oil, and shortening reaction time. Protein coding and long non coding RNA genes as well as magnetic nanoparticles may serve as potential cancer immune biomarker targets for developing future oncological treatments.

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Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1909998116 ◽

2019 ◽

Vol 116 (44) ◽

pp. 22020-22029 ◽

Cited By ~ 9

Author(s):

Aritro Nath ◽

Eunice Y. T. Lau ◽

Adam M. Lee ◽

Paul Geeleher ◽

William C. S. Cho ◽

...

Keyword(s):

Anticancer Drug ◽

Noncoding Rna ◽

Large Scale ◽

Drug Response ◽

Cancer Cell Line ◽

Systematic Evaluation ◽

Protein Coding ◽

Protein Coding Genes ◽

Clinical Biomarkers ◽

Response Predictors

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

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Temozolomide-Induced RNA Interactome Uncovers Novel LncRNA Regulatory Loops in Glioblastoma

Cancers ◽

10.3390/cancers12092583 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2583

Author(s):

Sabrina Fritah ◽

Arnaud Muller ◽

Wei Jiang ◽

Ramkrishna Mitra ◽

Mohamad Sarmini ◽

...

Keyword(s):

Gene Networks ◽

Drug Response ◽

Transcriptional Response ◽

Rna Seq ◽

Protein Coding ◽

Transcriptome Regulation ◽

Regulatory Circuits ◽

Functional Relevance ◽

Level Analysis ◽

Resistance To Chemotherapy

Resistance to chemotherapy by temozolomide (TMZ) is a major cause of glioblastoma (GBM) recurrence. So far, attempts to characterize factors that contribute to TMZ sensitivity have largely focused on protein-coding genes, and failed to provide effective therapeutic targets. Long noncoding RNAs (lncRNAs) are essential regulators of epigenetic-driven cell diversification, yet, their contribution to the transcriptional response to drugs is less understood. Here, we performed RNA-seq and small RNA-seq to provide a comprehensive map of transcriptome regulation upon TMZ in patient-derived GBM stem-like cells displaying different drug sensitivity. In a search for regulatory mechanisms, we integrated thousands of molecular associations stored in public databases to generate a background “RNA interactome”. Our systems-level analysis uncovered a coordinated program of TMZ response reflected by regulatory circuits that involve transcription factors, mRNAs, miRNAs, and lncRNAs. We discovered 22 lncRNAs involved in regulatory loops and/or with functional relevance in drug response and prognostic value in gliomas. Thus, the investigation of TMZ-induced gene networks highlights novel RNA-based predictors of chemosensitivity in GBM. The computational modeling used to identify regulatory circuits underlying drug response and prioritizing gene candidates for functional validation is applicable to other datasets.

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Prognostic significance of long non-coding RNA DANCR expression in human cancers: A systematic review and meta-analysis

Bioscience Reports ◽

10.1042/bsr20181627 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wei Liu ◽

Qin-Peng Wang ◽

Jia Guo

Keyword(s):

Meta Analysis ◽

Clinical Stage ◽

Prognostic Significance ◽

Disease Free Survival ◽

Cancer Prognosis ◽

Advanced Clinical Stage ◽

Protein Coding ◽

Human Cancers ◽

Non Coding Rna ◽

Long Non Coding Rna

Background: Several studies demonstrated that long non-coding RNA differentiation antagonizing non-protein coding RNA (lncRNA DANCR) expression might have the potential capacity to predict the cancer prognosis, however, definite conclusion has not been obtained. The aim of this meta-analysis was to evaluate the prognostic value of lncRNA DANCR expression in cancers. Methods: PubMed, Web of Science, Scopus and Embase were comprehensively searched for relevant studies. Studies meeting all inclusion standards were included into this meta-analysis. The analysis of overall survival (OS), disease-free survival (DFS) or clinicopathological features was conducted. Results: Eleven studies containing 1,154 cancer patients were analyzed in this meta-analysis. The results showed, compared with low lncRNA DANCR expression, high lncRNA DANCR expression was significantly associated with shorter OS (HR=1.85, 95%CI=1.52-2.26, P<0.01) and DFS (HR=1.82, 95%=1.43-2.32, P<0.01) in cancers. Besides, high lncRNA DANCR expression predicted deeper tumor invasion (P<0.01), earlier lymph node metastasis (P<0.01), earlier distant metastasis (P<0.01) and more advanced clinical stage (P<0.01) compared with low lncRNA DANCR expression in cancer populations. Conclusion: High lncRNA DANCR expression was associated with worse prognosis compared with low lncRNA DANCR expression in cancers. LncRNA DANCR expression could serve as a prognostic factor of human cancers.

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MicroRNA Gene Dosage Alterations and Drug Response in Lung Cancer

Journal of Biomedicine and Biotechnology ◽

10.1155/2011/474632 ◽

2011 ◽

Vol 2011 ◽

pp. 1-15 ◽

Cited By ~ 10

Author(s):

Katey S. S. Enfield ◽

Greg L. Stewart ◽

Larissa A. Pikor ◽

Carlos E. Alvarez ◽

Stephen Lam ◽

...

Keyword(s):

Lung Cancer ◽

Mirna Expression ◽

Drug Response ◽

Gene Dosage ◽

Chemotherapy Resistance ◽

Genetic Alterations ◽

Chemotherapeutic Agents ◽

Chemotherapy Response ◽

Protein Coding ◽

Lung Cancer Patients

Chemotherapy resistance is a key contributor to the dismal prognoses for lung cancer patients. While the majority of studies have focused on sequence mutations and expression changes in protein-coding genes, recent reports have suggested that microRNA (miRNA) expression changes also play an influential role in chemotherapy response. However, the role of genetic alterations at miRNA loci in the context of chemotherapy response has yet to be investigated. In this study, we demonstrate the application of an integrative, multidimensional approach in order to identify miRNAs that are associated with chemotherapeutic resistance and sensitivity utilizing publicly available drug response, miRNA loci copy number, miRNA expression, and mRNA expression data from independent resources. By instigating a logical stepwise strategy, we have identified specific miRNAs that are associated with resistance to several chemotherapeutic agents and provide a proof of principle demonstration of how these various databases may be exploited to derive relevant pharmacogenomic results.

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LncAS2Cancer: a comprehensive database for alternative splicing of lncRNAs across human cancers

Briefings in Bioinformatics ◽

10.1093/bib/bbaa179 ◽

2020 ◽

Author(s):

Yulan Deng ◽

Hao Luo ◽

Zhenyu Yang ◽

Lunxu Liu

Keyword(s):

Alternative Splicing ◽

Rna Sequencing ◽

Protein Coding ◽

Functional Roles ◽

Comprehensive Database ◽

Human Cancers ◽

Manual Curation ◽

Annotation Information ◽

Cancer Types ◽

Splicing Patterns

Abstract Accumulating studies demonstrated that the roles of lncRNAs for tumorigenesis were isoform-dependent and their aberrant splicing patterns in cancers contributed to function specificity. However, there is no existing database focusing on cancer-related alternative splicing of lncRNAs. Here, we developed a comprehensive database called LncAS2Cancer, which collected 5335 bulk RNA sequencing and 1826 single-cell RNA sequencing samples, covering over 30 cancer types. By applying six state-of-the-art splicing algorithms, 50 859 alternative splicing events for 8 splicing types were identified and deposited in the database. In addition, the database contained the following information: (i) splicing patterns of lncRNAs under seven different conditions, such as gene interference, which facilitated to infer potential regulators; (ii) annotation information derived from eight sources and manual curation, to understand the functional impact of affected sequences; (iii) survival analysis to explore potential biomarkers; as well as (iv) a suite of tools to browse, search, visualize and download interesting information. LncAS2Cancer could not only confirm the known cancer-associated lncRNA isoforms but also indicate novel ones. Using the data deposited in LncAS2Cancer, we compared gene model and transcript overlap between lncRNAs and protein-coding genes and discusses how these factors, along with sequencing depth, affected the interpretation of splicing signals. Based on recurrent signals and potential confounders, we proposed a reliable score to prioritize splicing events for further elucidation. Together, with the broad collection of lncRNA splicing patterns and annotation, LncAS2Cancer will provide important new insights into the diverse functional roles of lncRNA isoforms in human cancers. LncAS2Cancer is freely available at https://lncrna2as.cd120.com/.

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The Emerging Roles of Long Noncoding RNA ROR (lincRNA-ROR) and its Possible Mechanisms in Human Cancers

Cellular Physiology and Biochemistry ◽

10.1159/000452539 ◽

2016 ◽

Vol 40 (1-2) ◽

pp. 219-229 ◽

Cited By ~ 65

Author(s):

Yan Pan ◽

Chen Li ◽

Jing Chen ◽

Kai Zhang ◽

Xiaoyuan Chu ◽

...

Keyword(s):

Noncoding Rna ◽

Massively Parallel Sequencing ◽

Hepatocellular Cancer ◽

Messenger Rnas ◽

Protein Coding ◽

Human Cancers ◽

Tumorigenesis And Progression ◽

Non Coding Rnas ◽

Underlying Mechanisms ◽

Induced Pluripotent

To date, there is only up to 2% of protein-coding genes that are stably transcribed, whereas the vast majority are non-coding RNAs (ncRNAs). These ncRNAs, also known as non-messenger RNAs (nmRNAs) or functional RNAs (fRNAs), include transfer RNAs, ribosomal RNAs, microRNAs and long non-coding RNAs (lncRNAs). With the advance of high-resolution microarrays and massively parallel sequencing technology, lncRNAs have gained extended attentions nowadays and are found to play important roles in tumorigenesis and progression of human cancers. Long intergenic non-protein coding RNA, regulator of reprogramming (linc-ROR), was first discovered in induced pluripotent stem cells (iPSCs), where it was controlled by the key pluripotency factors Oct4, Sox2 and Nanog. Linc-ROR has been shown to be dysregulated in many types of cancers, including breast cancer (BC), pancreatic cancer (PC), hepatocellular cancer (HCC), endometrial cancer (EC), and nasopharyngeal carcinoma (NPC). Also, linc-ROR functions as regulatory molecule in a large amount of biological processes. However, the underlying mechanisms of its contribution to carcinogenesis remain to be elucidated. In this review, we will emphasize on the characteristics of linc-ROR and their roles in different types of human cancers.

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Discovering novel long non-coding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

10.1101/666156 ◽

2019 ◽

Author(s):

Aritro Nath ◽

Eunice Y.T. Lau ◽

Adam M. Lee ◽

Paul Geeleher ◽

William C.S. Cho ◽

...

Keyword(s):

Anticancer Drug ◽

Large Scale ◽

Drug Response ◽

Cancer Cell Line ◽

Systematic Evaluation ◽

Protein Coding ◽

Protein Coding Genes ◽

Clinical Biomarkers ◽

Response Predictors ◽

Drugs Analysis

AbstractLarge-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long non-coding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue-lineage or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that two novel lncRNA, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-EGFR drug response.

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