Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis

Yinchu Chen; Chen Li; Nana Wang; Zhenghao Wu; Jin Zhang; Jiawei Yan; Yuanfeng Wei; Qunlong Peng; Jing Qi

doi:10.3389/fonc.2022.883301

Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis

Front Oncol. 2022 May 26:12:883301. doi: 10.3389/fonc.2022.883301. eCollection 2022.

Authors

Yinchu Chen¹, Chen Li², Nana Wang¹, Zhenghao Wu³, Jin Zhang⁴, Jiawei Yan¹, Yuanfeng Wei¹, Qunlong Peng⁵, Jing Qi¹

Affiliations

¹ Department of Hematology, The First Affiliated Hospital of Wannan Medical College, WuHu, China.
² Department of Biology, Chemistry, Pharmacy, Free University of Berlin, Berlin, Germany.
³ Department of Clinical and Surgery, Moscow State First Medical University, Moscow, Russia.
⁴ Department of Surgery, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
⁵ College of Pharmacy, Xiangnan University, Chenzhou, China.

Abstract

Background: The long non-coding RNA (lncRNA)-mRNA regulation network plays an important role in the development of diffuse large B-cell lymphoma (DLBCL). This study uses bioinformatics to find an innovative regulation axis in DLBCL that will provide a positive reference for defining the mechanism of disease progression.

Methods: Batch Cox regression was used to screen prognosis-related lncRNAs, and a random forest model was used to identify hub lncRNA. The clinical value of the lncRNA was evaluated and Spearman correlation analysis was used to predict the candidate target genes. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were used to define the biological function of the lncRNA. A batch Cox regression model, expression validation, and Spearman correlation analysis were used to select the best downstream target genes. The expression and prognostic value validation of this gene was conducted using public data. Gene Set Enrichment Analysis (GSEA) was performed to explore potential mechanisms for this gene in DLBCL.

Results: LINC00654 was identified as the hub lncRNA and 1443 mRNAs were selected as downstream target genes of the lncRNA. The target genes were enriched in the regulation of GTPase and Notch signaling pathways. After validation, the ninein-like (NINL) gene was selected as the potential target of LINC00654 and the LINC00654-NINL axis was constructed. Patients with better responses to therapy were shown to have high NINL gene expression (p-value = 0.036). NINL also had high expression in the DB cell line and low expression in the OCILY3 cell line. Survival analysis showed that high NINL expression was a risk factor for overall survival (OS) and disease-specific survival (DSS) within older patients and those with advanced-stage cancer. GSEA results showed that NINL may be involved in neutrophil-mediated immunity and NF-κB signaling.

Conclusion: This study identified a novel LncRNA00654-NINL regulatory axis in DLBCL, which could provide a favorable reference for exploring the possible mechanisms of disease progression.

Keywords: DLBCL; LINC00654; NINL; mechnism; random forest.