Dataset Information

Screening of tumor grade-related mRNAs and lncRNAs for Esophagus Squamous Cell Carcinoma.

ABSTRACT:

Background

The goal of our study was to screen tumor grade-related lncRNAs and mRNAs to reveal the underlying molecular mechanism of esophagus squamous cell carcinoma (ESCC).

Methods

The lncRNA and mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA). Tumor grade correlation analysis of lncRNAs and mRNAs was executed, followed by the functional enrichment analysis of all tumor grade-related mRNAs. The differentially expression mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) were obtained. PPI network and DEmRNA-DElncRNA interaction analysis were constructed. The functional annotation of the DEmRNAs co-expressed with DElncRNAs was performed. The expression levels of the candidate genes were validated using qRT-PCR.

Results

A total of 1864 tumor grade-related mRNAs (846 positively related and 1018 negatively related) and 552 tumor grade-related lncRNAs (331 positively related and 221 negatively related) were obtained. The top 10 significantly grade-related mRNAs and lncRNAs included CA12, FABP4, DECR1, BAIAP2, IL1RAPL2, PPARD, LAD1, TSPAN10, LDOC1, ZNF853, RP11-25G10.2, RP11-557H15.3, RP11-521D12.5, CHKB-AS1, RP11-219B4.3, CH17-335B8.4, RP11-99 J16-A.2, CTB-111H14.1, ADNP-AS1, and JHDM1D-AS1. SFN, IL1RAPL2, and RP11-25G10.2 were overlapped from grade 1, grade 2, and grade 3. PPI network showed that top 10 proteins with higher degrees, including GNAI1, RAP2B, GNAZ, SHH, ADCY1, PRKAR2B, SH3GL1, GNA15, and ARRB1. A DElncRNAs-nearby DEmRNAs network was constructed to obtain hub lncRNAs including ADAMTS9-AS2, RP11-210 M15.2, RP11-13 K12.1, ZBED3-AS1, and RP11-25G10.2. Except for RP11-25G10.2, ADAMTS9-AS1, ZBED3-AS1, SFN, ATP1A2, and GNA15 were consistent with our TCGA analysis.

Conclusions

Alterations of DEmRNAs and DElncRNAs may provide key insights into the molecular mechanisms of ESCC.

SUBMITTER: Gao X

PROVIDER: S-EPMC8183932 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is the main subtype of esophageal carcinoma. Protein coding genes and non-coding RNAs can be powerful prognostic factors in multiple cancers, including ESCC. However, there is currently no model that integrates multiple types of RNA expression signatures to predict clinical outcomes.MethodsThe sequencing data (RNA-sequencing and miRNA-sequencing) and clinical data of ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database, and Differential gene expression analysis, Cox regression analysis and Spearman correlation analysis were used to construct prognosis-related lncRNA-mRNA co-expression network and scoring system with multiple types of RNA. The potential molecular mechanisms of prognostic mRNAs were explored by functional enrichment analysis.ResultsA total of 62 prognostic lncRNAs, eight prognostic miRNAs and 66 prognostic mRNAs were identified in ESCC (P-value < 0.05) and a prognosis-related lncRNA-mRNA co-expression network was created. Five prognosis-related hub RNAs (CDCA2, MTBP, CENPE, PBK, AL033384.1) were identified. Biological process analysis revealed that mRNAs in prognosis-related co-expression RNA network were mainly enriched in cell cycle, mitotic cell cycle and nuclear division. Additionally, we constructed a prognostic scoring system for ESCC using ten signature RNAs (MLIP, TNFSF10, SIK2, LINC01068, LINC00601, TTTY14, AC084262.1, LINC01415, miR-5699-3p, miR-552-5p). Using this system, patients in the low-risk group had better long-term survival than those in the high-risk group (log-rank, P-value < 0.0001). The area under the ROC curve (AUCs) revealed that the accuracy of the prediction model was higher than the accuracy of single type of RNA prediction model.ConclusionIn brief, we constructed a prognostic scoring system based on multiple types of RNA for ESCC that showed high predicting prognosis performance, and deeply understood the regulatory mechanism of prognosis-related lncRNA-mRNA co-expression network.