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LAPTM5 Plays a Key Role in the Diagnosis and Prognosis of Testicular Germ Cell Tumors

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LAPTM5 Plays a Key Role in the Diagnosis and Prognosis of Testicular Germ Cell Tumors Hindawi International Journal of Genomics Volume 2021, Article ID 8816456, 18 pages https://doi.org/10.1155/2021/8816456 Research Article LAPTM5 Plays a Key Role in the Diagnosis and Prognosis of Testicular Germ Cell Tumors Xiunan Li,1 Yu Su,2 Jiayao Zhang,3 Ye Zhu,4 Yingkun Xu ,5 and Guangzhen Wu 1 1Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China 2Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China 3Department of Hepatobiliary Surgery and Center of Organ Transplantation, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China 4Department of Orthopedic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China 5Department of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China Correspondence should be addressed to Yingkun Xu; [email protected] and Guangzhen Wu; [email protected] Received 22 September 2020; Revised 11 November 2020; Accepted 22 December 2020; Published 13 January 2021 Academic Editor: Xingshun Xu Copyright © 2021 Xiunan Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. Testicular germ cell tumors (TGCT) are a serious malignant tumor with low early diagnosis rates and high mortality. Methods. To investigate novel biomarkers to predict the diagnosis and prognosis of this cancer, bioinformatics analysis was used as an accurate, efficient, and economical method. Results. Our study detected 39 upregulated and 589 downregulated differentially expressed genes (DEGs) using the GEO and TCGA databases. To identify the function of DEGs, GO functional analysis, three pathway analysis (KEGG, REACTOME, and PANTHER), and protein-protein interaction network were performed using the KOBAS website, as well as the String database. After a series of analyses in GEPIA and TIMER, including differential expression, we found one candidate gene related to the prognosis and diagnosis of TGCT. LAPTM5 was also associated with CD8+ T cell and PDCD1 expression, which suggests that it may affect immune infiltration. Conclusions. LAPTM5 was identified as a hub gene, which could be used as a potential biomarker for TGCT diagnosis and prognosis. 1. Introduction detected in most patients with TGCT [6]. The upregulation of cellular tumor antigen (FGFR3) [7] and proliferation Testicular germ cell tumors (TGCT) are clinically rare malig- marker protein (BCL10), [8, 9] are thought to be involved nant endocrine tumors [1], with an incidence of 0.002‰– in the development of multiple tumors, including TGCT. 0.005% in the worldwide. This cancer has various clinical Additionally, Wilkie et al. also verified that the expression manifestations and is prone to invasion and metastasis [2]. of FGFR3 and HRAS was increased in TGCT compared to Due to the low rate of early diagnosis and high mortality, normal tissues [10]. However, it is still necessary to discover the survival period is generally no more than three years more representative candidate biomarkers for high-risk [3]. Complete surgical resection is regarded as the only TGCT assessment and inadequate prognosis predictions. potentially curative treatment for TGCT [4, 5]. Therefore, Currently, the discovery of crucial tumor biomarkers finding novel biomarkers for effective screening in the earlier using bioinformatics analysis has become a reliable and prof- stages of TGCT can be a powerful tool to improve the long- itable method [11–13], providing a variety of basic informa- term survival. tion about gene functions, regulatory pathways, cellular Many studies have shown that prognosis is related to the network, and prognostic results of diverse diseases, which levels of multiple molecules. Overexpression of KIT can be play an important role in practical research. In our study, 2 International Journal of Genomics Volcano GSE3218 GSE3218 5 0 logFC –5 0204060 80 Adj.P.val (a) (b) TCGA TCGA e differentially expressed genes on chromosomes 123456789101112 13 14 15 16 17 18 19 20 21 22 X Y GSE3218 Up-regulated DEGs 10 39 795 e gene positions are based on GRCh38.p2(NCBI).6127 genes. Over-expressed genes Under-expressed genes (c) (d) TCGA GSE3218 497 589 4691 Down-regulated DEGs (e) Figure 1: Identification of differentially expressed genes in TGCT. (a) Heatmap of GSE3218. (b) Volcano plot of GSE3218. (c) The location of differentially expressed genes of TCGA on chromosomes. Red represents overexpressed genes, and blue represents underexpressed genes. (d, e) Venn diagram of DEGs among GSE3218 and TCGA data. we first downloaded raw data from GEO and TCGA data- Tumor Immune Estimation Resource (TIMER) was used bases to obtain differentially expressed genes (DEGs) in to determine the distribution in pan cancers, pathway TGCT. We performed gene ontology, pathway enrichment enrichment, features in pathological parameters, and their analysis, and a protein-protein interaction (PPI) network. relationship with other genes. We attempted to identify GEPIA was adopted to observe differential expression precise hub genes that may serve as novel biomarkers for and prognostic characteristics of these genes. In addition, TGCT. International Journal of Genomics 3 GO Pathway enrichment –log10 (Pvalue) Spermatogenesis Single organism reproductive process 120 Single-organism process Single-organism developmental process Single-organism cellular process Single-multicellular organism process Sexual reproduction Reproductive process Reproduction Regulation of cellular process Regulation of biological process Protein binding Primary metabolic process 100 Organic substance metabolic process Organelle part Organelle Nucleus Non-membrane-bounded organelle Multicellular organismal reproductive process Multicellular organismal process Multicellular organism reproduction Multicellular organism development Multi-organism reproductive process Multi-organism process 80 Metabolic process Membrane-bounded organelle Male gamete generation Intracellular part Pathway name Intracellular organelle part Intracellular organelle Intracellular non-membrane-bounded organelle Intracellular membrane-bounded organelle Intracellular Gamete generation Developmental process 60 Cytoplasmic part Cytoplasm Cilium Cellular process Cellular metabolic process Cellular developmental process Cellular component organization or biogenesis Cellular component organization Cell projection Cell part 40 Cell differentiation Cell Biological regulation Binding Anatomical structure development –2.30500e-34 1.03725e-33 2.30500e-33 3.57275e-33 4.84050e-33 Pvalue Count 100 400 200 500 300 (a) Figure 2: Continued. 4 International Journal of Genomics REACTOME Pathway enrichment –log10 (Pvalue) Synthesis of PE Signaling by hedgehog Signal transduction Reproduction PIWI-interacting RNA (piRNA) biogenesis Peptide hormone metabolism 6 Organelle biogenesis and maintenance MHC class II antigen presentation Metabolism of lipids and lipoproteins Metabolism Meiotic synapsis 4 Meiosis Pathway name Pathway Intraflagellar transport Glutathione conjugation Fertilization Biological oxidations Assembly of the primary cilium 2 Anchoring of the basal body to the plasma membrane @@Pyruvate metabolism @@Disease 0.000 0.025 0.050 0.075 0.100 Pvalue Count 20 40 60 (b) Figure 2: Continued. International Journal of Genomics 5 PANTHER Pathway enrichment –log10 (Pvalue) 2.2 p53 pathway feedback loops 2 2.0 Huntington disease 1.8 Pathway name Pathway 1.6 CCKR signaling map 1.4 0.01 0.02 0.03 0.04 Pvalue Count 4 8 5 9 6 7 10 (c) KEGG Pathway enrichment –log10 (Pvalue) Synthesis and degradation of ketone bodies 4.0 Salivary secretion Pyruvate metabolism Protein processing in endoplasmic reticulum Ovarian steroidogenesis 3.5 Metabolic pathways Longevity regulating pathway - multiple species Longevity regulating pathway Leukocyte transendothelial migration 3.0 Influenza A Huntington's disease Glycolysis / Gluconeogenesis 2.5 Pathway name Pathway Glutathione metabolism Glucagon signaling pathway Fc gamma R-mediated phagocytosis Epstein-Barr virus infection 2.0 Endocytosis Carbon metabolism Bacterial invasion of epithelial cells Adherens junction 1.5 0.00 0.01 0.02 0.03 Pvalue Count 10 30 20 40 (d) Figure 2: Function analysis and pathway analysis of DEGs. (a–d) Bubble plots of GO, REACTOME, PANTHER, and KEGG analysis. 6 International Journal of Genomics FDNC3 DEGs ⁎ OS 8 491 9 619 6 (TPM+1) 2 4 Expression−log 2 0 TGCT (num(T) = 137; num(N) = 165) (a) (b) SYNGR4 FSCN3 ⁎ ⁎ 8 6 6 (TPM+1) (TPM+1) 2 2 4 4 Expression−log Expression−log 2 2 0 0 TGCT TGCT (num(T) = 137; num(N) = 165) (num(T) = 137; num(N) = 165) (c) (d) Figure 3: Continued. International Journal of Genomics 7 GLB1L GSTA1 ⁎ ⁎ 7 10 6 8 5 (TPM+1) (TPM+1) 2 2 6 4 4 Expression−log Expression−log 3 2 2 0 TGCT TGCT (num(T) = 137; num(N) = 165) (num(T) = 137; num(N) = 165) (e) (f) VAMP2 LAPTM5 ⁎ ⁎ 7 8 6 6 (TPM+1) (TPM+1) 2 2 5 4 4 Expression−log Expression−log 3 2 TGCT TGCT (num(T) = 137; num(N) = 165) (num(T) = 137; num(N) = 165) (g) (h) Figure 3: Continued. 8 International Journal of Genomics MAMLD1 PRM1 ⁎ ⁎ 15 6 5 10 (TPM+1) 4 (TPM+1) 2 2 3 5 Expression−log Expression−log 2 1 0 0 TGCT TGCT (num(T) = 137; num(N) = 165) (num(T) = 137; num(N) = 165) (i) (j) Figure 3: Cancer-related
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