LncRNA Sequencing Data Analysis

What is LncRNA Data Analysis?

Long non-coding RNA (LncRNA) is a non-coding RNA with unique regulatory functions that has attracted widespread attention in recent years. LncRNA is over 200 nt in length. It does not encode proteins, and is poorly conserved among species. The sequence structure of some LncRNAs is similar to that of mRNA (containing polyA tails, with variable splicing). By combining with DNA, RNA or protein, long non-coding RNA can regulate gene expression at the level of epigenetics, transcription and post-transcription.

The cutting-edge bioinformatic analysis technology can be used to accurately screen lncRNA, predict target genes, analyze the mRNAs in transcripts, comparative analysis of mRNA and lncRNA, and construct a co-expression network about mRNA and lncRNA. At present, sequencing analysis of lncRNA has been widely used in medical and agricultural research fields. Studies have shown that lncRNA is closely related to the growth and development of animals and plants, and the occurrence of human diseases. It can also be used as a marker or an important target for disease diagnosis.

Fig 1. Heatmap depiction of the differentially expressed lncRNAs among different samples.(Niknafs Y S, et al. 2016)

Application of LncRNA Data Analysis

Long non-coding RNA data analysis can be used for but not limited to the following research:

Medical research

Cancer research

Agricultural Research Field

Species improvement

Advantages of CD Genomics

Scientific scheme design.

Strict quality control management.

Professional analysis team.

Rich project experience.

High-quality project service.

CD Genomics Data Analysis Pipeline

Sample Submission Guidelines of Sequencing

Bioinformatic Analysis Content

CD Genomics has adopted a rigorous analysis process. The system collects the lncRNA database to identify known lncRNA and sets strict screening conditions for discovering new lncRNA. In addition, the analysis content is comprehensive. We carry out the correlation analysis of lncRNA and mRNA, and deeply analyze the lncRNA regulatory network.

If you have other omics data, such as circular RNA omics data, we can provide joint analysis of multiple omics. In addition, if any custom service on IncRNA sequencing is needed, please contact our technical support. We will customize scientific analysis strategies and provide reliable analysis results for you. For analysis content, price, cycle, or any other questions, please click online inquiry.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

CD Genomics provides general analysis and customized analysis of lncRNA sequencing data analysis. Experienced teams of scientists, researchers, and technicians, we provide fast turnaround, high-quality data reports at competitive prices for worldwide customers. Customers can contact our employees directly and we will respond promptly. If you are interested in our services, please feel free to contact us for more detailed information.

Reference

1. Niknafs Y S, et al. The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression[J]. Nature Communications, 2016, 7:12791.

What Does Analysis of LncRNA Sequenceing Show?

Basic Data Analysis

Distribution of Sequencing Quality

Fig 2. Sequencing quality distribution.

Distribution of A/T/G/C Base

Fig 3. A/T/G/C Distribution.

Evaluation of the Transcriptomic Library

Insert Length Test

Fig 4. Distribution of insert size for a sample. The distance between the start and end sites of the paired-end Reads on the reference genome is on the horizontal axis and the the number of paired-end Reads or inserts at different distances between the start and end sites is on the vertical axis.

mRNA Analysis

Gene Expression Distribution

Fig 5. FPKM density distribution.

Fig 6. Boxplot of FPKM for each sample.

Differential Expression Analysis

Correlation between Samples

Fig 7. Correlation analysis between samples.

Screening Differentially Expressed Genes

Fig 8. MA plot of differentially expressed genes

Title: Sequencing and Bioinformatics Analysis of lncRNA/circRNA-miRNA-mRNA in Glioblastoma multiforme

Publication: Metabolic Brain Disease

Main Methods: RNA-sequencing, Functional enrichment analysis, Construction of the lncRNA/circRNA-miRNA-mRNA ceRNA Network

Abstract: This study investigates the role of non-coding RNAs in glioblastoma multiforme (GBM) by analyzing RNA-sequencing data to identify differentially expressed circRNAs, lncRNAs, miRNAs, and mRNAs. Researchers found significant differences between GBM patients and healthy controls, identifying 1224 DECs, 1406 DELs, 229 DEMs, and 2740 DEGs. Key hub genes were identified through PPI network analysis, and a ceRNA network was constructed. These findings highlight potential therapeutic targets within the ceRNA interaction axes for GBM treatment.

Main Research Results:

Overview of the differentially expressed lncRNAs, mRNAs, circRNAs and miRNAs based on gene sequencing: RNA-sequencing analyses were conducted to investigate DEGs in the GBM. With a fold change > 2.0 and P < 0.05, we found that 1406 lncRNAs (243 upregulated and 1163 downregulated), 1224 circRNAs (24 upregulated and 1200 downregulated), 2740 mRNAs (1149 upregulated and 1591 downregulated), and 229 miRNAs (132 upregulated and 97 downregulated) were differentially expressed.

Fig 9. Differentially expressed genes and transcripts in GBM. Hierarchical clustering analysis of (A) lncRNAs, (B) circRNAs, (C) mRNAs, and (D) miRNAs. lncRNA, long non-coding RNA; circRNA, circular RNA; miRNA, microRNA.

GO and KEGG pathway analyses of differentially expressed mRNAs in GBM: Differentially expressed mRNAs in glioblastoma multiforme (GBM) were analyzed using KEGG and GO methods. GO analysis revealed that DEGs were enriched in processes like GTPase activity regulation, signal transduction, and cell cycle transitions, primarily located in extracellular spaces and cell junctions. Molecular functions included DNA binding and protein binding. KEGG pathway analysis showed significant enrichment in pathways related to chromosomes, cell cycle, DNA replication, and immune responses, highlighting key areas for GBM research.

Fig 10. The overlapped GBM DEGs were analyzed for function and signaling pathways. (A) Biological processes. (B) Cellular components. (C) Molecular function. (D) KEGG pathways.

Global signal transduction network and pathway network: A global signal transduction network analysis of glioblastoma multiforme (GBM) revealed PRKACB as the central gene among key DEGs, including GNG13, MAPK3, and GNAI1. Pathway analysis highlighted significant enrichment in calcium signaling, MAPK, PI3K-Akt signaling, and cell cycle pathways, emphasizing their roles in GBM pathogenesis and therapeutic potential.

Fig 11. Global signal transduction network and pathway network

LncRNA target pathway network: A lncRNA target pathway network was constructed to explore lncRNA regulation in glioblastoma multiforme (GBM). Key lncRNAs, including NONHSAT159592.1 and ENST00000608442, were found to significantly impact GBM pathogenesis through various signaling pathways. These pathways included exosome BR, transcription factors BR, ECM-receptor interaction, focal adhesion, PI3K-Akt, IL-17, and TNF signaling pathways, highlighting the diverse regulatory roles of lncRNAs in GBM and their potential as therapeutic targets.

Fig 12. Examination of lncRNA-target pathways as a network. Circles stand for signaling pathways, while square boxes denote lncRNAs. The central nodes represent the most important routes, and each node's degree represents the individual lncRNA's contribution to the pathway.

miRNA target pathway network: Topological analysis of networks identified the degree of connectivity between differentially expressed miRNA (DEmiRNA) nodes and canonical pathway nodes. Greater significance was associated with increased node connectivity. The top DEmiRNAs, including hsa-miR-195-3p and hsa-miR-519a-3p, displayed varying degrees of connectivity, with the highest being 43. Pathways such as membrane trafficking, chromosome-associated proteins, and cAMP signaling were regulated by multiple miRNAs, with connectivity degrees of 46, 35, 33, 30, and 24, respectively.

Fig 13. Examination of the miRNA-pathway connection. The miRNA or route is represented by the diamond or rectangle node, respectively. Each color represents a different degree of regulation: red for increased, green for decreased. Canonical miRNA pathways or the top 10 differentially expressed miRNAs in the miRNA-pathway network

Construction of the lncRNA/circRNA-miRNA-mRNA regulation network: This research analyzed 57 lncRNA-miRNA-mRNA ceRNA networks and 24 circRNA-miRNA-mRNA ceRNA networks. In Fig. 6, we highlighted the top lncRNAs and circRNAs that regulate numerous miRNAs, including ENST00000608442, NONHSAT183359.1, and has-circ-0060927, which may be characterized as significant nodes of the ceRNA network. CEACAM5 and FAM83A were shown to be targets of both lncRNAs and circRNAs after integrating the lncRNA/circRNA-mRNA interactions.

Fig 14. Construction of the lncRNA/circRNA-miRNA-mRNA regulation network

Validation of key differentially expressed lncRNA/circRNA-miRNA-mRNA: Tumor samples from GBM patients were analyzed using real-time qPCR to assess the expression levels of key lncRNAs, circRNAs, and miRNAs. ENST00000608442, NONHSAT189677.1, NONHSAT183359.1, NONHSAT159592.1, and has-circ-0060927 were upregulated, while hsa-miR-338-3p, hsa-miR-539-3p, and others were downregulated. Western blotting confirmed the expression of CEACAM5, CXCL17, and other genes in GBM. High KRT80, EPHA1, and BIRC3 expression, as shown in the GEPIA database, predicted poor overall survival in GBM patients.

Fig 15. Validation of key differentially expressed lncRNA/circRNA, miRNA, and mRNA. (A) The expression of predicted lncRNAs was detected by qRT-PCR. (B) The expression of predicted circRNAs was detected by qRT-PCR. (C) The expression of predicted miRNAs was detected by qRT-PCR. (D) The protein expression and survival curve of key genes in GBM. Protein levels of CEACAM5, CXCL17, FAM83A, TMPRSS4, KRT80, GPRC5A, EPHA1 and BIRC3 were determined by western blotting. (E) The overall survival analysis of DEGs (CEACAM5, CXCL17, FAM83A, TMPRSS4, KRT80, GPRC5A, EPHA1 and BIRC3) for GBM.

Conclusions:

A ceRNA network comprising 8 DECs, 7 DELs, 16 DEMs, and 17 DEGs was constructed. In TCGA, 8 mRNAs were linked to GBM prognosis. This network offers insights into glioma differentiation and potential therapeutic targets and prognostic indicators for GBM.

1. How to detect lncRNA in tissues?

The detection of long non-coding RNAs (lncRNAs) in tissues can be achieved through several methodologies. These include full-length lncRNA sequencing, rapid amplification of cDNA ends (RACE), chromatin isolation by RNA purification (CHIRP), RNA fluorescence in situ hybridization (RNA FISH), and quantitative polymerase chain reaction (qPCR).

2. What are the principles and processes of lncRNA sequencing?

lncRNA sequencing involves RNA extraction, ribosomal RNA removal, cDNA synthesis, library construction, high-throughput sequencing, and data analysis to quantify lncRNA expression and functions.

3. What is the process of lncRNA sequencing data processing?

lncRNA sequencing data analysis involves preprocessing, transcript assembly, lncRNA identification, functional analysis, and visualization, utilizing bioinformatics tools to understand lncRNA roles and regulatory mechanisms.

4. What does the correlation analysis between lncRNA and mRNA mainly include?

Analyzing lncRNA-mRNA correlation involves differential expression analysis, correlation coefficient calculation, co-expression network construction, and experimental validation to understand their interactions and biological significance.

5. What are the species requirements of lncRNA?

lncRNA research in species requires high-quality RNA extraction, effective ribosomal RNA removal, and accurate reference genomes to ensure reliable identification and functional analysis.