Research Paper Volume 16, Issue 8 pp 6954—6989

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

Yongqi Ding1,2, *, , Yike Jiang2, *, , Hong Zeng2, *, , Minqin Zhou2, , Xuanrui Zhou2, , Zichuan Yu2, , Jingying Pan2, , Xitong Geng2, , Yanting Zhu2, , Hao Zheng2, , Shuhan Huang2, , Yiyang Gong2, , Huabin Huang3, , Chengfeng Xiong1, , Da Huang1, ,

  • 1 Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
  • 2 Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330006, China
  • 3 Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
* Equal contribution

Received: November 14, 2023       Accepted: March 3, 2024       Published: April 12, 2024
How to Cite

Copyright: © 2024 Ding et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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.


LAPTM4A: lysosomal protein transmembrane 4; GMBLGG: lower grade glioma and glioblastoma; GBM: glioblastoma; LGG: lower grade glioma; WGCNA: weighted gene co-expression network analysis; TCGA: The Cancer Genome Atlas; GTEx: Genotype Tissue Expression; TME: tumor microenvironment; IDH: isocitrate dehydrogenase; PD-L1: programmed cell death protein 1 ligand; CTLA-4: cytotoxic T lymphocyte antigen 4; OS: overall survival; DSS: disease-specific survival; PFI: progression-free interval; ICIs: immune checkpoint inhibitors; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; m6A: N6-methyladenosine; m7G: N7-methylguanosine; CNV: copy number variation; ICB: immune checkpoint blockade; TILs: tumor-infiltrating lymphocytes; TAMs: tumor-associated macrophages; EMT: epithelial-mesenchymal transition.