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. 2021 Mar 23:11:634732.
doi: 10.3389/fonc.2021.634732. eCollection 2021.

Using lncRNA Sequencing to Reveal a Putative lncRNA-mRNA Correlation Network and the Potential Role of PCBP1-AS1 in the Pathogenesis of Cervical Cancer

Linhan Li  1 , Qisong Peng  2 , Min Gong  1 , Ling Ling  1 , Yingxue Xu  1 , Qiaoling Liu  1
Affiliations

Using lncRNA Sequencing to Reveal a Putative lncRNA-mRNA Correlation Network and the Potential Role of PCBP1-AS1 in the Pathogenesis of Cervical Cancer

Linhan Li et al. Front Oncol. .

Abstract

Background/aims: Long non-coding RNAs (lncRNAs) play important roles in many diseases and participate in posttranscriptional regulatory networks in tumors. However, the functions of major lncRNAs in cervical cancer are unclear. Therefore, the aim of this study was to construct a lncRNA-mRNA coexpression functional network and analyze lncRNAs that might contribute to the pathogenesis of cervical cancer.

Methods: Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between three pairs of cervical cancer tissues and adjacent mucosa were identified by lncRNA microarray analysis. LncRNA-mRNA correlation analysis and functional enrichment were performed on the DEGs. From the correlation network, PCBP1-AS1 was selected as a candidate for further analysis. PCBP1-AS1 expression was examined by qPCR, and Kaplan-Meier survival, clinicopathology, GSEA, and immune infiltration analysis of PCBP1-AS1 were performed. The immune responses of PCBP1-AS1 expression in cervical cancer were analyzed using TIMER and western blot. PCBP1-AS1 was knocked down and overexpressed to evaluate its role in cell proliferation, migration, and invasion.

Results: A total of 130 lncRNAs were significantly differentially expressed in cervical cancer patient samples compared with control samples. Differentially expressed mRNAs in the lncRNA-mRNA interaction network were involved in the EMT process. Combined with the Kaplan-Meier survival analyses, the coexpression network revealed that PCBP1-AS1 was significantly associated with OS and clinicopathological parameters in cervical cancer patients. Moreover, PCBP1-AS1 expression was not only significantly increased in cervical cancer specimens but also associated with tumor stage, TNM, and invasion. GSEA revealed that PCBP1-AS1 is closely correlated with cell biological function via the p53 and notch signaling pathways. TIMER analysis revealed that the numbers of NK cells and M2 macrophages decreased when PCBP1-AS1 expression was high, which was consistent with the western blot results in clinical samples. Furthermore, in vitro experiments showed that high expression of PCBP1-AS1 promoted cell proliferation, migration, and invasion.

Conclusions: Transcriptomic and lncRNA-mRNA correlation analyses revealed that PCBP1-AS1 plays a key role as an independent prognostic factor in patients with cervical cancer. The identification of PCBP1-AS1 as a new biomarker for cervical cancer could help explain how changes in the immune environment promote cervical cancer development.

Keywords: cervical cancer; lncRNA sequencing; lncRNA-mRNA correlation network; long non-coding RNA; proliferation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
LncRNA sequencing of CESC. (A) Volcano plots of differential expression profiles of lncRNAs and mRNAs. The red dots represent upregulated genes. The blue dots represent downregulated genes. The gray dots represent genes that do not differ significantly. (B) Heat map of differential expression profiles of lncRNAs and mRNAs. (C) Chromosomal distribution of all differentially expressed lncRNAs. (D) Fraction distribution of all category-annotated DEG long non-coding RNAs (lncRNAs).
Figure 2
Figure 2
LncRNA-mRNA interaction network. (A) Correlation analysis was carried out with differentially expressed lncRNAs and mRNAs, and the cutoff value was set with a threshold of correlation >0.95. Red nodes represent lncRNAs, green nodes represent mRNAs, and red dotted boxes indicate trans and cis groups. (B) The red solid line box indicates the highly active subnetwork module identified by the jActive module.
Figure 3
Figure 3
GO and pathway analysis for differentially expressed mRNAs in the highly active subnetwork module.
Figure 4
Figure 4
(A) Kaplan–Meier analysis results of PCBP1-AS1, FAM222A, FHAD1, WDR62, and SBK1 in CESC. (B) Univariate Cox proportional hazards regression analysis of PCBP1-AS1, FAM222A, FHAD1, WDR62, and SBK1 in CESC. (C) Expression of PCBP1-AS1, FAM222A, FHAD1, WDR62, and SBK1 correlated significantly with clinicopathological parameters.
Figure 5
Figure 5
(A) Subcellular localizations of PCBP1-AS1 determined by using lncLocator. (B) Expression of PCBP1-AS1 in CESC cancer tissues assessed by qPCR. (C) Expression of PCBP1-AS1 in CESC from TCGA by GEPIA.*p<0.05. (D) Correlation analysis among PCBP1-AS1, FAM222A, FHAD1, and SBK1. (E) Multivariate Cox analysis of PCBP1-AS1 expression and other clinicopathological variables. (F) ROC curves of PCBP1-AS1. (G) Expression of PCBP1-AS1 correlated significantly with clinicopathological parameters.
Figure 6
Figure 6
Correlations between PCBP1-AS1 expression and immune infiltration levels, with purity adjustment.
Figure 7
Figure 7
(A) The varied proportions of 18 subtypes of immune cells in high and low PCBP1-AS1 expression groups in tumor samples. (B) Western blot results of CD4 (representing CD4+ T cells), CD19 (representing B cells), and CD56 (representing NK cells) protein expression in cervical cancer tissues and corresponding adjacent normal cervical cancer tissues. GAPDH was used as a control. (C) Gray value ratios of CD4/GAPDH, CD19/GAPDH, and CD56/GAPDH in cervical cancer tissues and corresponding adjacent normal cervical cancer tissues. **p < 0.01,***p < 0.001. (D) Heatmap of 18 infiltrating immune cells in tumor samples.
Figure 8
Figure 8
(A) KEGG pathway analysis showed five positively correlated groups and five negatively correlated groups. (B) GO term analysis revealed three positively correlated groups and three negatively correlated groups.
Figure 9
Figure 9
(A) Overexpression of PCBP1-AS1 in HeLa cells analyzed by fluorescence microscopy. (B) PCBP1-AS1 knockout efficiency in HeLa cells analyzed by qPCR. (C) and (D) The effects of PCBP1-AS1 knockdown or overexpression on HeLa cell migration measured using the migration assay. ⁎⁎⁎ p < 0.001. (E) The effects of PCBP1-AS1 knockdown and overexpression on the viability of HeLa cells measured using the CCK-8 assay. (F) and (G) Transwell invasion assay and average number of invasive PCBP1-AS1 knockdown and overexpression cells. ⁎⁎⁎p < 0.001.
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