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. 2024 Jul 23:15:1441732.
doi: 10.3389/fgene.2024.1441732. eCollection 2024.

TRP channel-related LncRNAs, AC092535.4 and LINC01637, as novel prognostic biomarkers for uveal melanoma

Min Zhang  1 , Jinglan Ni  1 , Dongyue Liu  1 , Yubo Cui  2 , Xiaochen Ma  1 , Jun Zhao  2
Affiliations

TRP channel-related LncRNAs, AC092535.4 and LINC01637, as novel prognostic biomarkers for uveal melanoma

Min Zhang et al. Front Genet. .

Abstract

Introduction: Transient receptor potential (TRP) channels function as cellular sensors with a broad impact, and their dysregulation is linked to numerous cancers. The influence of TRP channel-related long noncoding RNAs (TCRLs) on uveal melanoma (UM) remains poorly understood.

Methods: We employed bioinformatics to examine the RNA-seq data and relevant clinical information of UM in the TCGA databases. By implementing coexpression analysis, we identified differentially expressed TCRLs. Using univariate Cox regression analysis, selection operator (LASSO) algorithm and stepwise regression, five key prognostic biomarkers were chosen. The high- and low-risk groups were divided based on the risk scores. Afterwards, the prediction performance of the signature was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier (K-M) survival analysis. The functional enrichment analysis of TCRLs was also investigated. Following that, we examined immune cell infiltration, immune checkpoint expression, and tumor immune microenvironment between patients in high and low risk groups. TCRLs were validated using Random forests and multifactor Cox analysis. Candidate biomarkers were identified and screened. Finally, the effects of the candidate biomarkers on the proliferation, migration and invasion of UM cells were detected by CCK-8 assay, migration assay and perforation invasion assay.

Results: The risk score generated by five TCRLs demonstrated robust predictive power. The high-risk group exhibited a poorer prognosis, increased immune cell infiltration, and an active tumor immune microenvironment compared to the low-risk group. Furthermore, two TCRLs of risk score, AC092535.4 and LINC01637, were screened to multiplex modelling. The in vitro experiments demonstrated that UM cells were suppressed following AC092535.4 or LINC01637 knockdown.

Discussion: Two TCRLs, AC092535.4 and LINC01637, serve as novel prognostic biomarkers for uveal melanoma and may present potential therapeutic targets.

Keywords: bioinformatics; immune microenvironment; lncRNA; prognosis; transient receptor potential channel; uveal melanoma.

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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
Construction of TCRL signature. (A) Flow diagram of the present study. (B) A co-expression network was constructed between TCRLs and their related mRNAs. (C) A forest map of TCRLs using univariate Cox analysis (p < 0.001). (D) LASSO coefficients produced by LASSO regression analysis. (E) LASSO coefficient profiles of eight TCRLs. (F) The Sankey diagram illustrates the connection between five TCRLs and their related TCRGs.
FIGURE 2
FIGURE 2
Validation of TCRL signature. The results of univariate Cox regression analysis (A) and multiple Cox regression analysis (B) of the training, testing, and entire sets; risk status (C), survival outcome (D), and expression levels of TCRLs in the signature (E).
FIGURE 3
FIGURE 3
The prognostic capacity of the TCRL signature and the Kaplan–Meier survival curves of patients in subset groups. (A) The results of the 1-, 2-, and 3-year ROC curves of the training set, testing set, and entire sample. (B) ROC curves of risk scores and clinical characteristics. (C) Kaplan–Meier survival between the low- and high-risk groups. (D) Progression-free survival between the low- and high-risk groups. K-M survival curves of patients in age ≤60 years and >60 years (E), in males and females (F), in stage II and stage III–IV (G), in T2-3 and T4 (H).
FIGURE 4
FIGURE 4
Results of GO and KEGG pathway enrichment analyses. (A) Circle diagrams of GO enriched pathways. (B) Bubble charts of GO enriched pathways. (C) Circle diagrams of KEGG enriched pathways. (D) Bubble charts of KEGG enriched pathways.
FIGURE 5
FIGURE 5
The immune landscape of UM. (A) Bubble diagram to verify the relationship between risk scores and immune components and heat map to demonstrate the distributions of infiltrating immune cell types in the two risk groups (B) and between two clusters (C). Detailed differences in 13 immune-related functions were analyzed between the two risk groups (D) and two clusters (E). Detailed differences in checkpoint genes were analyzed between the two risk groups (F) and two clusters (G). The differences in stomal score, immune score, and ESTIMATE score between the two risk groups (H) and two clusters (I).
FIGURE 6
FIGURE 6
Gene function verification experiment. (A) Verification of gene silencing using qRT-PCR. (B) Proliferation by CCK8 assay. (C) Representative images (×10) and cell counting results of transwell invasion assay. (D) Representative images (×10) and the wound closure rate of scratch wound assay. Data are shown as mean (standard deviation [SD]); ***p ≤ 0.001 or **p ≤ 0.01 compared to the control.
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Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Science, Technology, and Innovation Commission of Shenzhen Municipality (grant numbers JCYJ20210324113610029, GJHZ20220913142618036).

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