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. 2024 Apr 12;16(8):6954-6989.
doi: 10.18632/aging.205736. Epub 2024 Apr 12.

Identification of a robust biomarker LAPTM4A for glioma based on comprehensive computational biology and experimental verification

Yongqi Ding  1   2 , Yike Jiang  2 , Hong Zeng  2 , Minqin Zhou  2 , Xuanrui Zhou  2 , Zichuan Yu  2 , Jingying Pan  2 , Xitong Geng  2 , Yanting Zhu  2 , Hao Zheng  2 , Shuhan Huang  2 , Yiyang Gong  2 , Huabin Huang  3 , Chengfeng Xiong  1 , Da Huang  1
Affiliations

Identification of a robust biomarker LAPTM4A for glioma based on comprehensive computational biology and experimental verification

Yongqi Ding et al. Aging (Albany NY). .

Abstract

Background: Glioma, a highly invasive and deadly form of human neoplasm, presents a pressing need for the exploration of potential therapeutic targets. While the lysosomal protein transmembrane 4A (LATPM4A) has been identified as a risk factor in pancreatic cancer patients, its role in glioma remains unexplored.

Methods: The analysis of differentially expressed genes (DEG) was conducted from The Cancer Genome Atlas (TCGA) glioma dataset and the Genotype Tissue Expression (GTEx) dataset. Through weighted gene co-expression network analysis (WGCNA), the key glioma-related genes were identified. Among these, by using Kaplan-Meier (KM) analysis and univariate/multivariate COX methods, LAPTM4A emerged as the most influential gene. Moreover, the bioinformatics methods and experimental verification were employed to analyze its relationships with diagnosis, clinical parameters, epithelial-mesenchymal transition (EMT), metastasis, immune cell infiltration, immunotherapy, drug sensitivity, and ceRNA network.

Results: Our findings revealed that LAPTM4A was up-regulated in gliomas and was associated with clinicopathological features, leading to poor prognosis. Furthermore, functional enrichment analysis demonstrated that LATPM4A played a role in the immune system and cancer progression. In vitro experiments indicated that LAPTM4A may influence metastasis through the EMT pathway in glioma. Additionally, we found that LAPTM4A was associated with the tumor microenvironment (TME) and immunotherapy. Notably, drug sensitivity analysis revealed that patients with high LAPTM4A expression were sensitive to doxorubicin, which contributed to a reduction in LAPTM4A expression. Finally, we uncovered the FGD5-AS1-hsa-miR-103a-3p-LAPTM4A axis as a facilitator of glioma progression.

Conclusions: In conclusion, our study identifies LATPM4A as a promising biomarker for prognosis and immune characteristics in glioma.

Keywords: LAPTM4A; ceRNA; glioma; immune infiltration; prognostic biomarker.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no conflicts of interest related to this study.

Figures

Figure 1
Figure 1
Identification of the key gene modules in WGCNA. (A) The volcano map showed differentially expressed genes. (B) Determination of the soft-thresholding power. (C) Dendrogram of differentially expressed genes clustered based on a dissimilarity measure (1-TOM). (D) The correlation of gene modules with clinical traits. (E) Gene correlation scatter plot of the turquoise module. (F) The 1-, 3-, and 5-years ROC for the top ten genes. (G) Comparison of the clinical significance of LAPTM4A and RP2.
Figure 2
Figure 2
Expression of LAPTM4A in glioma. (A) The expression level of LAPTM4A in different types of tumor tissues and normal tissues in the TIMER database. (p < 0.05) (B–D) Expression levels of LAPTM4A were higher than corresponding normal tissues in LGG, GBM, and GBMLGG samples. The box plot showed the association of LAPTM4A expression with clinicopathological characteristics. (E) WHO grade, (F) Histological type, (G) IDH status, (H) 1p/19q codeletion, (I) Age, (J) Gender, (K) OS event, (L) Primary therapy outcome.
Figure 3
Figure 3
Relationship between LAPTM4A and prognosis of glioma patients. LGG patients with higher expression levels of LAPTM4A had unfavorable (A) OS, (B) DSS, and (C) PFS. GBMLGG patients with higher expression levels of LAPTM4A had awful (D) OS, (E) DSS, and (F) PFS. GBM patients with higher expression levels of LAPTM4A had undesirable (G) OS, (H) DSS, and (I) PFS.
Figure 4
Figure 4
Pathway enrichment analysis of LAPTM4A. (A, B) Significantly enriched GO and KEGG pathways of LAPTM4A. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes. (C, D) Correlation of LAPTM4A expression and cancer-related pathways. (E) Functional relevance of LAPTM4A in pan-cancers from cancerSEA. (F) Functional relevance of LAPTM4A in GBMLGG from cancerSEA red plots suggested a positive correlation, while blue plots suggested a negative correlation.
Figure 5
Figure 5
LAPTM4A may affect the invasion and migration through the EMT pathway in glioma. Changes in the expression of n-cadherin, e-cadherin, and MMP 9 after LAPTM4A knockdown (A) with mRNA aspect, (B) with protein aspect. (C, D) Effect of LAPTM4A knockdown on glioma cell invasion and migration.
Figure 6
Figure 6
The association between LAPTM4A with the tumor microenvironment. (A) The relevance between LAPTM4A expression and the stromal score in GBM, LGG, and GBMLGG. (B) The relevance between LAPTM4A expression and the immune score in GBM, LGG, and GBMLGG. (C) The relevance between LAPTM4A expression and the ESTIMATE score in GBM, LGG, and GBMLGG.
Figure 7
Figure 7
Analysis of the correlation between LAPTM4A expression and immune cell infiltration. (A) The relevance between LAPTM4A expression and the infiltration of five immune cells. (B) The association between LAPTM4A expression and the infiltration of various immune cells in pan-cancers. (C) The connection between LAPTM4A expression and several notable biomarkers of Macrophage/Monocyte and cancer-associated fibroblast.
Figure 8
Figure 8
The relationship between LAPTM4A expression and immunotherapy. (A) LAPTM4A differential expression status of the immune checkpoint genes under the high and low expression groups. (B) The correlation between LAPTM4A and the immune checkpoint genes. (C) The TIDE score of the LAPTM4A.
Figure 9
Figure 9
Prediction of LAPTM4A expression-related drugs. (A) An advanced network diagram shows 37 cancer-related drugs that can modulate LAPTM4A expression. (B) A Venn diagram demonstrates drugs related to LAPTM4A expression in CTD and cgp2016. (C) Relationship between LAPTM4A expression and IC50 of doxorubicin. (D) LAPTM4A is resistant to 73 drugs and sensitive to 9 drugs.
Figure 10
Figure 10
Prediction of the ceRNA network in glioma. (A, B) The regulating relationship of has-miR-103a-3p and LAPTM4A was investigated by a dual-luciferase reporter gene system. (C, D) Real-time qPCR was used to determine LAPTM4A mRNA levels in U251 cells. (E) The regulating relationship of has-miR-103a-3p and FGD5-AS1 was investigated by a dual-luciferase reporter gene system. (F) RNA-pull down assay was performed to detect has-miR-103a-3p enrichment in FGD5-AS1. (G) Western blotting assay was performed to detect LAPTM4A expression levels in the control and FGD5-AS1 group. (H) Western blotting assay was performed to detect LAPTM4A expression levels in the control, FGD5-AS1, and has-miR-103a-3p group. (I) Transwell assay was used to detect U251 cell metastasis in the control, FGD5-AS1, inh-NC, and inh-has-miR-103a-3p group. *p < 0.05, **p < 0.01.
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