A comprehensive prognostic and immune analysis of LAPTM4B in pan-cancer and Philadelphia chromosome-positive acute lymphoblastic leukemia

A comprehensive prognostic and immune analysis of LAPTM4B in pan-cancer and Philadelphia chromosome-positive acute lymphoblastic leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A comprehensive prognostic and immune analysis of LAPTM4B in pan-cancer and Philadelphia chromosome-positive acute lymphoblastic leukemia Hui Zhou, Yuyao Yi, Wei He, Li Zheng, Yiguo Hu, Ting Niu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4502403/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Lysosomal-associated protein transmembrane-4 beta (LAPTM4B) protein expression was increased in solid tumors, whereas few studies were performed in hematologic malignancies. We aimed to study the effect of the LAPTM4B gene in pan-cancer and Philadelphia chromosome-positive acute B cell lymphoblastic leukemia (Ph + B-ALL). The differential expression, diagnosis, prognosis, genetic and epigenetic alterations, tumor microenvironment, stemness, immune infiltration cells, function enrichment, single-cell analysis, and drug response across cancers were conducted based on multiple computational tools. Additionally, Ph + B-ALL transgenic mouse model with Laptm4b knockout was used to analyze the function of LAPTM4B in vivo. BrdU incorporation method, flow cytometry, and Witte-lock Witte culture were used to evaluate the roles of LAPTM4B in vitro. We identified that LAPTM4B expression was increased in various cancers, with significant associations with clinical outcomes. LAPTM4B expression correlated with DNA and RNA methylation patterns and was associated with drug resistance. It also influenced the tumor immune microenvironment, with implications for immunotherapy response. In leukemia, LAPTM4B was expressed in stem cells and associated with specific subtypes. Knockout of LAPTM4B impeded B-ALL progression in mice and reduced cell proliferation and caused G0/G1 arrest in vitro. Our study elucidated the role LAPTM4B that promoted the development and progression in Ph + B-ALL. Furthermore, LAPTM4B played a diagnostic, prognostic, and immunological factor. LAPTM4B Ph + B-ALL pan-cancer diagnosis prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Tumorigenesis is a multifaceted process influenced by a dynamic interplay between internal factors and the tumor microenvironment. Internal factors encompass genetic mutations, epigenetic changes, and the dysregulation of signaling pathways (Fujii et al., 2023 ; Fulton-Ward and Middleton, 2023 ). Tumor microenvironment is comprised of various factors including metabolomics, inflammation, angiogenesis, immune system modulation, extracellular matrix (ECM) (Berrell et al., 2023 ; Choi and Jung, 2023 ; Mempel et al., 2024 ; Sleeboom et al., 2024 ; Zhang et al., 2023 ). Crucially, membrane proteins play a pivotal role during tumorigenesis by facilitating the transmission of signals between the extracellular environment and the cell's interior (Lu et al., 2024 ). These proteins participate some fundamental cellular processes such as growth, differentiation, and survival (Lu et al., 2024 ; Malla et al., 2023 ; Wang et al., 2023 ). Therefore, investigating the intricate interplay between internal factors, the tumor microenvironment, and the role of membrane proteins is imperative for comprehending the intricacies of tumorigenesis. Such understanding lays the foundation for the development of targeted strategies aimed at preventing and treating cancer effectively. One of the research interests in our laboratory is to uncover the roles and mechanisms of membrane proteins in the initiation and development of tumors. Building upon the reported biological functions of lysosomal membrane-associated protein transmembrane-4 beta ( LAPTM4B ) in existing studies, we aim to comprehensively understand its involvement in cancer, especially Philadelphia chromosome-positive acute B cell lymphoblastic leukemia (Ph + B-ALL). LAPTM4B is recognized as a late endosomal protein, and it is also distributed in the plasma membrane. It exhibits widespread expression in various tissues throughout the body, with predominant levels observed in the heart, kidney, skeletal muscle, and hematopoietic stem cells (HSCs). In contrast, its expression is relatively lower in peripheral blood leukocytes, spleen, and thymus (Meng et al., 2016 ). LAPTM4B involves in multiple biological processes, including cell cycle, cell growth and proliferation, and autophagy. LAPTM4B interacts with and integrin and promotes cell growth and proliferation through a series of enzyme-linked reactions within the membrane (Peng et al., 2005 ). LAPTM4B also regulates cell cycle and engages in growth signaling pathways, such as PI3K/AKT and MAPK (Ji et al., 2022 ). LAPTM4B also promotes autophagy through the EGFR signaling pathway (Ji et al., 2022 ; Wu and Zhang, 2020 ), and loss of LAPTM4B inhibited later stages of autophagy by blocking maturation of the autophagosome (Li et al., 2011 ). Increased LAPTM4B expression has been observed in various cancers, including breast, liver, lung, ovarian, uterine, and gastric cancers (Liao et al., 2023 ; Meng et al., 2016 ; Su et al., 2021 ; Wang et al., 2022 ). Notably, elevated LAPTM4B levels contribute to chemotherapy resistance in breast cancer. The overexpression of LAPTM4B induces resistance to anthracyclines (such as doxorubicin, daunorubicin, and epirubicin) by retaining the drug in the cytoplasm and reducing its nuclear localization, thereby diminishing drug-induced DNA damage (Li et al., 2010 ). In addition to solid tumors, LAPTM4B is also highly expressed in hematologic malignancies. LAPTM4B promoted AML progression by regulating the RPS9/STAT3 axis (Huang et al., 2023 ). Elevated LAPTM4B expression is associated with AML patients harboring NPM1 mutations in conjunction with FLT3-ITD mutations (Huang et al., 2012 ). In chronic myeloid leukemia (CML) bone marrow (BM) cells, LAPTM4B expression levels were significantly higher than those in normal individuals (Haferlach et al., 2010 ). Similar to observations in solid tumors, CML patients with higher LAPTM4B expression were associated with resistance to tyrosine kinase inhibitor (TKI) treatment (Singh et al., 2018 ). Most studies on LAPTM4B have primarily focused on intracellular signaling in certain types of cancers, and a comprehensive understanding of LAPTM4B in tumorigenesis is still lacking. In this study, we aimed to elucidate the expression, clinical characteristics and immunological characteristics of LAPTM4B across various cancers. In particular, our investigation unveiled a significant correlation between LAPTM4B expression and survival outcomes in Ph + B-ALL patients. Moreover, it was confirmed that the loss of the Laptm4b impeded BCR-ABL-induced B-ALL progression, both in vitro and in vivo . Materials and Methods Data Acquisition and analysis The standardized pan-cancer dataset was downloaded from UCSC ( https://xenabrowser.net/ ): TCGA TARGET GTEx (PANCAN, N = 19131, G = 60499). A log2(x + 1) transformation was applied to each expression value, and cancer types with fewer than 3 samples were excluded, resulting in the final expression data for 34 cancer types. Additionally, prognostic data for TCGA were sourced from prior studies (Liu et al., 2018 ). Simultaneously, TARGET follow-up data were supplemented from the UCSC database. Samples with a follow-up time of less than 30 days were excluded, and cancer types with fewer than 10 samples were also excluded. The abbreviations section provides the full names and corresponding abbreviations of the tumors. The Ph + B-ALL data was downloaded from the GEO database. RMA normalization was performed using the RMA algorithm the NimbleScan 2.5 software. The dataset GSE34861 comprises 191 adult B-ALL samples, among which 78 are Ph + B-ALL samples. Genetic and Epigenetic Alterations in Pan-cancer Genomic alteration data and methylation data were download from cBioPortal database ( https://www.cbioportal.org/ ). The correlation between LAPTM4B expression and gene promoter methylation was evaluated using Spearman rank correlation. Kaplan‒Meier analysis was performed to analyze the relationship between LAPTM4B methylation and the prognosis of patients. Clinical Characteristics LAPTM4B in Pan-cancer We developed the Cox proportional hazards regression model to analyze overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) of LAPTM4B across cancers. Kaplan‒Meier analysis was performed to analyze the prognostic significance. The diagnostic significance of LAPTM4B across cancers was assessed by the Receiver Operator Characteristic (ROC) curve via “pROC” (v1.17.0.1). The diagnosis accuracy was evaluated by the Area under Curve (AUC). The AUC is closer to 1, the diagnosis accuracy is better. The IC 50 values of various compounds in cancer cell lines were obtained from the GDSC dataset ( https://www.cancerrxgene.org ), to assess the relationship between DLAT and the drug response of tumor cells by the Spearman correlation coefficient. A higher IC50 indicates that cancers are less sensitive to the compounds. Tumor immune microenvironment Analysis Tumor-infiltrating lymphocytes (TILs) participated in predicting sentinel node status and associated with prognosis (Hall et al., 2016 ). ssGSEA scores of the correlation between LAPTM4B and immune cell infiltration for Ph + B-ALL were calculated using the xCELL algorithm and TILs. Spearman rank correlation was employed to assess the association between LAPTM4B expression and immune cell infiltration in pan-cancer, utilizing the xCELL and CIBERSORT algorithms. Immune checkpoint-related genes (ICGs) were obtained from a previous study (Hu et al., 2021 ). Immune-related genes were downloaded from the TISIDB database ( http://cis.hku.hk/TISIDB/index.php ). The relationship of immune-related genes and LAPTM4B in Ph + B-ALL was evaluated using ssGSEA. Immune regulatory genes are distributed in five immune pathways, including chemokine (41 genes), receptor (18 genes), MHC (21 genes), immunoinhibitor (24 genes) and immunostimulator (46 genes). The relationship between immune-related genes and LAPTM4B expression in pan-cancer was evaluated by Spearman rank correlation. Tumor Microenvironment Analysis in Pan-cancer We obtained 10,180 tumor samples from a total of 44 tumor types for immune infiltration scores. ESTIMATE was used to reflect the degree of infiltration of stromal or immune cells into tumors. The ESTIMATE algorithm included stromal, immune, and ESTIMATE scores. Spearman rank correlation was used to evaluate the correlation between LAPTM4B expression and these three scores by the R software packages “estimate” and “psych”. We downloaded all level 4 simple nucleotide variation data of TCGA samples from GDC ( https://portal.gdc.cancer.gov/ ). Tumor mutation burden (TMB) was analyzed using MAftools package (Version 2.8.05) of R software. Tumor stem cell infiltration analysis was performed based on DNA methylation dry score (DNAss) and RNA dry score (RNAss) (Malta et al., 2018 ). Spearman rank correlation was used to evaluate the correlation between LAPTM4B expression and TMB, microsatellite instability (MSI), purity, DNAss and RNAss. Single-cell Analysis and Enrichment Analysis We conducted the single-cell level expression of LAPTM4B at in leukemia using TISCH2 (Han et al., 2023 ). TISCH2 encompasses 190 tumor scRNA sequence datasets with 6 million cells across 50 cancer types. To assess the functional and signaling aspects with LAPTM4B , we conducted Gene Set Enrichment Analysis (GSEA) on HALLMARK and KEGG pathways. Based on the median expression of LAPTM4B in cancer, the group was divided into high and low expression groups. The Development of LAPTM4B Knockout Ph + B-ALL Model and in vitro assay The B6. LAPTM4B loxp/loxp mice were generated at Biocytogen Pharmaceuticals (Beijing) Co., Ltd, which were intercrossed with B6.CMV-Cre mice to generate B6. LAPTM4B −/− mice. BCR-ABL induced B-ALL model was developed as previously described (Hu et al., 2006 ). Briefly, bone marrow (BM) cells were collected from 8-week-old WT and LAPTM4B −/− mice (n = 3) and resuspended with BCR/ABL viral infection medium, centrifugation at 1000g for 90min at 37 ℃, then cultured at 37 ℃ for 3 h. Then, viral transfected cells were injected into lethally irradicated recipient mice at a dosed of 1x10^5 B cells/mouse via the tail vein. After the WT or LAPTM4B −/− BM cells were transfected with BCR/ABL, then seeded in DMEM medium containing 10% FBS in 24-well plates at series initial cell numbers of 5x10^5 (500k), 3x10^5 (300k), 1x10^5 (100k), 3x10^4 (30k), 1x10^4 (10k), and 2.5x10^3 (2.5k). Each well cell numbers were adjusted to 1x10^6 cells/well with WT mice BM cells and cultured with DMEM medium containing 10% FBS. The cell number in eahc well was counted on day 7 post seeding. Cell Cycle Experiments by the BrdU Incorporation Method BrdU was added directly into the prepared cell medium at final concentration of 10 uM, and incubated for 1h. After collection and washing, cells were suspended with PBS containing 0.5% paraformaldehyde on ice for 20 minutes. Then cells were washed with PBS and resuspended with 70% ethanol overnight. Next day, after washing with PBS, cells were resuspended with 2N HCL/0.5% triton X-100 at room temperature for 20 min to denature. After neutralization with 0.1M sodium borate, cells were suspended with PBS containing 0.5% BSA and 0.5%Tween 20 and stained with anti-BrdU antibody-FitC (BD Biosciences) at room temperature for 20min. The cells were resuspended with PBS containing RNase and incubated at 37 ℃. After adding PI, cells were analyzed using flow cytometry. Statistical Analysis R software (version 4.2.1) was utilized for this analysis. The Wilcoxon's test and analysis of variance (ANOVA) were applied for comparisons involving two and multiple groups, respectively. Spearman correlation coefficient was employed for correlation analysis. All experiments were conducted in triplicate. Results Expressions and Alterations of LAPTM4B in Human Cancers In order to examine the expression profile of LAPTM4B in pan-cancers, we evaluated its expression across 34 cancer types using data from TCGA, TARGET, and GTEx databases. Our findings revealed high LAPTM4B expression in 28 cancer types compared to normal tissues, including glioblastoma (GBM), lower-grade glioma (LGG), uterine corpus endometrial carcinoma (UCEC), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lung adenocarcinoma (LUAD), esophageal carcinoma (ESCA), stomach and esophageal carcinoma (STES), colon adenocarcinoma (COAD), colon adenocarcinoma/Rectum adenocarcinoma esophageal carcinoma (COADREAD), stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), liver hepatocellular carcinoma (LIHC), high-risk Wilms tumor (WT), skin cutaneous melanoma (SKCM), bladder urothelial carcinoma (BLCA), thyroid carcinoma (THCA), rectum adenocarcinoma (READ), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), testicular germ cell tumors (TGCT), uterine carcinosarcoma (UCS), acute lymphoblastic leukemia (ALL), acute myeloid leukemia (LAML), Adrenocortical carcinoma (ACC), and cholangiocarcinoma (CHOL). While, low LAPTM4B expression was observed in 4 cancer types, Pan-kidney cohort (KIPAN), prostate adenocarcinoma (PRAD), kidney renal clear cell carcinoma (KIRC), and kidney chromophobe (KICH) (Fig. 1 A). Specifically, LAPTM4B exhibited high expression in BLCA, BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, READ, STAD, and UCEC, while showing low expression in KICH, KIRC, PRAD, and THCA compared to adjacent paired normal tissues (Fig. 1 B). These results suggest that elevated LAPTM4B expression is associated with cancer progression in a majority of cases. The amplification of LAPTM4B was observed most frequently in UCS, BRCA, BLCA, OV, PRAD, and LIHC, (Fig. 1 C), and common in most cancers (Fig. 1 C, D). Moreover, we found 34 mutation sites between amino acids 0 and 317, including 24 missense mutations, 2 truncating, 8 SV/fusion, and S265N as the most frequent mutation sites within LAPTM4B across cancers (Fig. 1 E, F). To elucidate potential associations between LAPTM4B and intracellular epigenetic alterations, we examined the status of genomic methylation and the expression of genes involved in mRNA methylation in various types of cancer cells using data from cBioPortal database. We found that there were significant negative correlations between LAPTM4B expression and gene promotor methylation in most tumors (Supplementary Fig. 1A). Increased methylation of LAPTM4B mRNA was related to poorer OS in patients with GBM and LGG (Supplementary Figure 1B, C). Furthermore, the relationships between LAPTM4B and genes involved in mRNA m1A, m5C, m6A modifications were evaluated. LAPTM4B expression was significantly positively related to these RNA modification genes in almost all tumors (Supplementary Figure 1D). These results indicated that LAPTM4B could influence tumor development by regulating the repair of RNA and DNA methylation in cancers. The Clinical Features associated with LAPTM4B alterations in Pan-cancers To evaluate the clinical significance of elevated LAPTM4B expression in various cancers, we conducted a Cox proportional hazards model analysis encompassing OS, DSS, DFI, and PFI. Univariate Cox regression analysis of OS, DSS, PFI, and DFI revealed that LAPTM4B served as a significant risk factor for patients in multiple cancer types, including LIHC, B-ALL, SARC, GBMLGG, SKCM, AML, ACC, UVM, CESC, HNSC, KICH, MESO, UVM, BRCA, and PCPG (Fig. 2 A). Additionally, Kaplan‒Meier survival analyses of OS, DSS, and PFI were further explored across cancers (Fig. 2 B-D). The performance of the gene signature for diagnostic accuracy was evaluated by the ROC curves. Figure 3 showed that 17 types of cancer had high diagnostic accuracy (AUC > 0.9), including CHOL, ESCA, GBM, HNSC, LAML, LGG, LUAD, LUSC, OV, PAAD, READ, SKCM, STAD, TGCT, THYM, UCEC and UCS. These results suggested that LAPTM4B had good diagnostic value in a variety of cancers. The detailed results of all cancers were exhibited in the Supplementary table 1 . To assess the potential correlation between elevated LAPTM4B expression and the drug response of tumor cells, we conducted Spearman correlation coefficient analysis using data from the GDSC dataset. Our findings revealed increased LAPTM4B expression had increased IC50 values of 14 compounds, including rTRAIL, B-Raf inhibitors (PLX-4720, dabrafenib, SB590885), FTI-277 (FTase inhibitor), bexarotene (RXR agonist), dactolisib (PI3K/mTOR inhibitor), luminespib (HSP90 inhibitor), palbociclib (CDK4/6 inhibitor), (5Z)-7-Oxozeaenol (TAK1 inhibitor), QS11 (ARFGAP1 inhibitor), among others, which suggested that increased LAPTM4B lead drug resistance. Conversely, a negative association was observed with elesclomol and afatinib (EGFR/HER2 inhibitor) responses (Table 1). These results suggest that increased LAPTM4B expression may confer resistance to a broad spectrum of therapeutic agents in tumor cells. Moreover, we also found that LAPTM4B was positively correlated with RNAss and DNAss across most of the cancers (Supplementary Fig. 2A-B), which indicates that high expression of LAPTM4B might be associated with cancer tumor recurrence and metastasis. Immune Status Analysis of LAPTM4B in Pan-Cancer To explore the relationship between LAPTM4B expression and immune status in pan-cancer, we conducted a correlation analysis. Overall, we found that LAPTM4B expression was associated with immune subtypes in 19 cancer types and correlated with molecular subtypes in 14 cancer types (Supplementary Fig. 3A-B). Additionally, we analyzed stromal and immune cell scores to investigate the relationship between LAPTM4B expression and the tumor immune microenvironment (TIME) across cancers. We observed a positive correlation between LAPTM4B expression and StromalScore, ImmuneScore, and ESTIMATEScore in PAAD, OV, and UVM (Fig. 4 A). While, LAPTM4B expression showed a negative correlation with these scores in GBM, LGG, LAML, BRCA, CESC, LUAD, STES, SARC, KIRP, KIPAN, STAD, LUSC, WT, SKCM, SKCM-M, THCA, NB, and TCGT (Fig. 4 A). To explore the correlation between LAPTM4B expression and immune cells, we developed a heat map of LAPTM4B with immune cells by CIBERSORT and xCell. Our result revealed that LAPTM4B was associated with CD8 + T cells, macrophages M2 and Tregs in many cancers, which suggested that high LAPTM4B expression had inhibitory immune microenvironment (Fig. 4 B-C). Overall, our findings suggested that elevated LAPTM4B expression might be associated with a potential decrease in patients' immune anti-tumor capabilities. To investigate whether LAPTM4B expression levels are associated with TMB, MSI, and tumor purity, we conducted analyses using Spearman correlation analysis. The results showed that LAPTM4B expression was positively correlated with TMB in ACC, BRCA, GBMLGG, LAML, LGG, LUAD, PAAD, and THYM, while exhibiting a negative correlation in COAD, COADREAD, ESCA, PRAD, SKCM, and THCA (Fig. 5 A). The MSI analysis revealed a positive correlation of LAPTM4B expression with MSI in KIPAN, TGCT, and UVM, while a negative correlation in COAD, COADREAD, DLBC, GBMLGG, LGG, PAAD, PRAD, and THCA (Fig. 5 A). Additionally, LAPTM4B showed a significant correlation with tumor purity, with positive associations in CESC, ESCA, GBM, GBMLGG, HNSC, KIPAN, KIRP, LGG, LUAD, LUSC, SARC, SKCM, STAD, STES, TGCT, and THYM, and negative associations in BLCA, LIHC, OV, PCPG, PRAD, UCS, and UVM (Figs. 5 A). These findings suggest that LAPTM4B expression might serve as a potential biomarker for immunotherapy. Subsequently, the correlations of expression levels between LAPTM4B and immune checkpoint genes and immune regulatory genes in cancers were also investigated. We found that LAPTM4B expression was positively related to immune regulatory genes in majority tumor types, especially in PRAD, UVM, THYM, LIHC, BLCA, and OV. While, LAPTM4B expression was negatively related to immune regulatory genes in TGCT, GBM, LUAD, SARC, KIPAN, and SKCM (Fig. 5 B). Additionally, LAPTM4B expression was positively related to immune checkpoint genes in most types of tumors, except for some tumors, which were mainly TGCT, GBM, SKCM, and SARC (Fig. 5 C). In general, these results suggested that LAPTM4B might regulate immune cell infiltration and immune-related genes functions in most tumor types. Single-cell and Enrichment Analysis of LAPTM4B Expression in Leukemia To specifically and deeply depict the pictures of LAPTM4B involving in malignancies, then we focused on hematological malignancies, particularly Ph + B-ALL, to elucidate and clarify the biological functional characteristics of LAPTM4B in tumors. Taking the advantages of single-cell sequencing and open public data, we found that LAPTM4B was expressed mainly in normal HSCs, progenitors, and AML cells (Fig. 6 A-B). In an ALL sample, we found that LAPTM4B was highly expressed in proerythroblasts, but not malignant cells (Fig. 6 C-D). Interesting, an analysis based on an expression profiling of 191 B-ALL samples and 3 normal pre-B samples showed that LAPTM4B was more highly expressed in BCR/ABL B-ALL than other subtypes (Fig. 6 E). Then, we performed the analysis of the function and pathways of LAPTM4B -related genes in Ph + B-ALL. We found that genes associated with HSCs and leukemia stem cells (LSCs) were up-enriched in high LAPTM4B expression samples (Fig. 6 F-G), as well as genes associated with cell cycle, DNA replication, MYC target, E2F and G2M checkpoint pathways were also up-enriched in in Ph + B-ALL (Fig. 6 H-I). LAPTM4B deletion impairs the development and progression of Ph + B-ALL To instigate the involvement of LAPTM4B in the development of Ph + B-ALL, we employed a Ph + B-ALL mouse model. Bone marrow (BM) cells from wild type ( WT ) or LAPTM4B −/− mice were transfected with retrovirus containing BCR/ABL and then injected into lethally irradicated recipients (Fig. 7 A). Overall, the survival time of recipients receiving LAPTM4B −/− BM cells was significantly longer than that receiving WT BM cells (Fig. 7 B). We also monitored the number of leukemic cells with BCR/ABL (represented with GFP and B220) in peripheral blood of mice receiving BCR/ABL-transduced WT or LAPTM4B −/− BM cells on the day 10, 20 and 30 post-BM transplantation. We found that the percentages of B-lymphoid leukemic cells were significantly lower in mice receiving BCR/ABL-transduced WT or LAPTM4B −/− BM cells than in those receiving BCR/ABL-transduced WT BM cells at all time points measured (Fig. 7 C). To investigate the role of LAPTM4B in BCR/ABL-induced leukemogenesis, we conducted an in vitro assay for proliferation of BCR/ABL transformed BM B-lymphoid progenitors, as described in methods. BCR-ABL-transformed B-lymphoid progenitors from LAPTM4B −/− BM cells exhibited much lower number than it transformed those from WT BM cells (Fig. 7 D). Further, in vitro Brdu assays for cell proliferation rate showed that LAPTM4B deletion impaired Ph + B-ALL cell proliferation and caused G0/G1 arrest (Fig. 7 E). These findings demonstrated that LAPTM4B deletion significantly impaired the development and progression of Ph + B-ALL. Relationships between LAPTM4B Expression and Immune Status in Ph + B-ALL To evaluate the association of LAPTM4B expression with TME in Ph + B-ALL, we conducted an ESTIMATE analysis to calculate the stromal score, immune score, ESTIMATE score, and tumor purity within Ph + B-ALL. We found that LAPTM4B expression was not significantly associated with TME in Ph + B-ALL (Supplementary Fig. 4). Then, the relationship between LAPTM4B and immune cells in Ph + B-ALL was conducted using xCell algorithm method. The scores of CD4 + memory T cells, CD8 + T cells, HSC, preadipocytes, and Tgd cells were higher; while, the scores of CD4 + Tem, eosinophils, epithelial cells, MSC, and NKT were significantly lower in the high LAPTM4B expression patient samples (Fig. 8 A). LAPTM4B expression was negatively related to macrophages M2, NKT, mv endothelial cells, and CD4 + Tem; while they were positively correlated with CD4 + memory T cells, Th2 cells, Tgd cells, CD4 + T cells and microenvironment Score (Fig. 8 B). Moreover, immune infiltration scores of tumor-infiltrating lymphocytes (TILs) type in different LAPTM4B expression groups were also evaluated using ssGSEA. The central memory CD4 T cells, effector memory CD4 T cells, immature B cells, plasmacytoid dendritic cells, and immature dendritic cells were highly expressed in the high LAPTM4B expression patient samples (Fig. 8 C). The correlation between LAPTM4B expression and immune-related genes was also assessed. As previous reports, there were 79 genes related to immune checkpoint (Hu et al., 2021 ). We found that TNFRSF14 and TNFSF14 were lowly expressed in the high LAPTM4B expression samples (Supplementary Fig. 5A). Additionally, TNFRSF14 was negatively correlated with LAPTM4B expression; whereas, CTLA4 , HLA-E , and ICOS were positively associated with LAPTM4B expression (Supplementary Fig. 5B). Moreover, the correlation between LAPTM4B and the chemokine genes was also evaluated. We found that CCL1 , CCL11 , CCL15 , CCL19 , CCL21 , CCL22 , CCL24 and CCL25 were lowly expressed in high LAPTM4B expression samples (Supplementary Fig. 5C). But, no significant difference was observed on immunoinhibitory genes, immunostimulatory genes, receptor genes, and MHC genes except for IL6 , LTA , ULBP1 , and XCR1 (Supplementary Fig. 6A-D). These results suggested that the high expression of LAPTM4B might affected immune microenvironment in Ph + B-ALL. Discussion LAPTM4B is required for lysosomes function, participates in the cell death program, promotes autophagy and tolerance to metabolic stress in cancer cells (Vergarajauregui et al., 2011 ) (Blom et al., 2015 ), and is an essential gene for adjuvant drug resistance(Li et al., 2012 ; Li et al., 2011 ). Our results revealed the role LAPTM4B plays in pan-cancer and Ph + B-ALL. We determined the expression levels of LATPM4B mRNA in various cancers, and confirmed the most common types of LAPTM4B mutations and their locations. The correlation between epigenetic alterations and LAPTM4B were evaluated. We also assessed the diagnostic, prognostic and therapeutic values of LAPTM4B expression across cancers and differentiated its expression levels across several immune and cellular subtypes of cancers. We associated LAPTM4B expression levels with tumor microenvironment and the infiltration levels of immune cells and genes in various cancers. Besides, the expression of LAPTM4B at the single-cell level and function in B-ALL were explored. We identified that the loss of the LAPTM4B gene impeded BCR-ABL-induced B-ALL progression, both in vitro and in vivo . We also explored the immunological function of LAPTM4B in Ph + B-ALL. LAPTM4B was not highly expressed in all tissues, while we found that it was highly expressed in most cancers. And the amplification was the most frequent alteration. However, the expression of LAPTM4B was discovered lowly expressed in KIPAN, KIRC, KICH and PRAD, while the genetic alterations were mainly amplification in these cancers. Therefore, there were other factors affected the expression of LAPTM4B . As we known, abnormal methylation of DNA and RNA promotes various diseases and cancers (Jones and Baylin, 2007 ; Klutstein et al., 2016 ; Li et al., 2022 ; Qu et al., 2022 ; Xu et al., 2023 ; Zhao et al., 2023 ; Zhao et al., 2017 ). And we found that LAPTM4B expression was significantly related to DNA methylation and RNA modifications in these cancers. Theses might partly explain the inconsistencies between alteration and expression. A total of 17 types of cancer had high diagnostic accuracy (AUC > 0.9), suggesting that LAPTM4B had good diagnostic value. Meanwhile, LAPTM4B was a risk factor in many cancers. Besides, we found that increased LAPTM4B expression led resistance to a broad spectrum of therapeutic agents in tumor cells. These results were consistent with previous studies that LAPTM4B could be a diagnostic, prognostic and therapeutic factor in hepatocellular carcinoma, breast cancer, hepatocellular carcinoma, bladder cancer and renal cell carcinoma and so on (Dong et al., 2017 ; Gan et al., 2023 ; Maki et al., 2015 ; Meng et al., 2018 ; Ren et al., 2021 ; Su et al., 2021 ; Wang et al., 2019 ; Wang et al., 2022 ; Yang et al., 2018 ; Yang et al., 2019 ; Zhong et al., 2021 ). LAPTM4B expression was different in different molecular or immune subtypes of cancer, which results in different survival in the overall population and particular subtype of cancer. Therefore, immune features should be also considered in the further study. TMB can reflect the proportion of somatic mutations in tumors (Choucair et al., 2020 ). MSI refers to the arbitrary length change of microsatellites in tumor tissue due to insertion or deletion of repeat units (Bonneville et al., 2017 ). The purity of the tumor usually related to prognosis (Thorsson et al., 2018 ). TMB, MSI, and tumor purity are emerging biomarkers associated with the immunotherapy response. ESTIMATE reflects the degree of infiltration of stromal or immune cells into tumors. High stemness scores represent the activity of tumor stem cells, and are associated with drug resistance and the continuous proliferation of tumor cells, and are correlated with poorer survival (Malta et al., 2018 ). Our results exhibited that LAPTM4B was significant correlated with these indexs. Besides, we showed that LAPTM4B was related to different immune cells in various cancers. In general, the infiltration of activated CD8 + T cells, Tem and Tcm CD8 + cells, and Tem CD4 + cells was associated with good prognosis, whereas MDSCs and Tregs were correlated with bad prognosis (Charoentong et al., 2017 ). A previous study identified that LAPTM4B inhibited human regulatory T cells produced TGF-β1 (Huygens et al., 2015 ). Besides, LAPTM4B was upregulated in CML TKI-resistant patients (Singh et al., 2018 ). Therefore, LAPTM4B might be an immunotherapeutic factor in various cancers. Then, we found that LAPTM4B is expressed primarily in stem cells at single-cell level in leukemia, and highly expressed in BCR/ABL subtype of B-ALL, and upregulated stem cell pathway in Ph + B-ALL. A previous study also identified LAPTM4B as a candidate gene related to stemness, which was the downstream target of HOXB4 in hematopoietic progenitor cells (Lee et al., 2010 ). Similarly, the study verified that LAPTM4B was closely related to the stemness of HCC (Liao et al., 2023 ). These studies illustrated that LAPTM4B might regulate stem cell-related genes. Additionally, LAPTM4B might participated in the signaling pathways of MYC, E2F, cell cycle, and T/B cell receptor. And in the Ph + B-ALL mouse model, LAPTM4B knockout prolonged survival, inhibited cell proliferation and arrest of G0/G1. The results were similar to the previous study, which exhibited that LAPTM4B promoted the entry of cells from the G1 into the S phase in breast cancer (Tao et al., 2019 ). In B-cell malignancies, leukemic cells can alter the normal microenvironment, in favor of their growth, survival and resistance to cytotoxic therapies (Hughes et al., 2022 ). Immune cells are important constituents of the tumor stroma and play a crucial role in tumor development and progression60 (Hinshaw and Shevde, 2019 ; Lei et al., 2020 ). Our study showed that LAPTM4B expression was associated with HSC, preadipocytes, immature B cells, and immature dendritic cells in Ph + B-ALL. These results validated that LAPTM4B was related to stem cell pathways as before analyzed. Moreover, the results demonstrated that high LAPTM4B expression had low TNFRSF14 and TNFSF14 expression, and was positively correlated with the CTLA4 and ICOS checkpoint gene. TNFSF14 was an immune-activating gene, mainly expressed in activated T cells, activated natural killer cells and immature dendritic cells (Ware and Sedý). TNFRSF14 is a receptor for BTLA, TNFSF14/LIGHT, and homotrimeric TNFSF1, is involved in lymphocyte activation. CTLA4 also known as CD152, is a protein receptor that acts as an immune checkpoint and negatively regulates the immune response (Patwekar et al.). ICOS, also called CD278, ICOS were immune checkpoint proteins expressed on activated T cells. Tumor-infiltrating Tregs expressed high levels of cell surface molecules associated with T-cell activation, such as CTLA4, PD-1, LAG3, TIGIT, ICOS, and TNF receptor superfamily members (Li et al.). These results suggested that LAPTM4B might influence the efficacy of immunotherapy. In summary, our study systematically performed a comprehensive pan-cancer analysis of LAPTM4B , and explored the expression, immunological features, and functions of the LAPTM4B gene in the Ph + B-ALL mouse model. We demonstrated the abnormal expression profiles of LAPTM4B and it was related to clinical diagnosis, prognosis, genetic and epigenetic alterations, immunological features, and drug response. Additionally, we identified that increased LAPTM4B expression was associated with an unfavorable prognosis and promoted the development and progression in Ph + B-ALL, and was related to immune status. These results could help illustrate the underlying oncogenic role and immunological function of LAPTM4B in cancers. Declarations Competing Interests 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. Author s’ Contributions HZ and YY have contributed equally to this work and share the first authorship. HZ and YY participated in the study design and wrote the manuscript. HZ and YY acquired the data, and analysed and interpreted the data. YY, WH, LZ and HZ performed the experiments. YH and TN revised the manuscript. All authors contributed to the manuscript and approved the submitted version. Funding This work was supported by Natural Science Foundation of Sichuan Province (NO. 24NSFSC3632), 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (No. ZYJC21007), National Key Research and Development Program of China (No. 2022YFC2502600, 2022YFC2502603), and National Natural Science Foundation of China (No. 82370192). Data availability statements Not applicable. Ethics approval The animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC), West China Hospital, Sichuan University. All animal experimental procedures were conducted in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council of People’s Republic of China. Acknowledgments The authors acknowledge the databases in this article for providing their platforms and those contributors for uploading their valuable datasets. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4502403","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331379682,"identity":"b0749771-263f-4f42-94cc-56d30257599a","order_by":0,"name":"Hui Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACAwbGBhBi4GdgYCNRi2QD8VqAAKTF4ACxWswlkhsYf+6wSdx8I/nZwx81hxn4Zzfg12I5I7GBQfJMWuK2G2nmxjzHDjNI3DlAwGE3gFoM2w4nbrudwybN2HCYwUAigQgtiUAtm2fnsEn+JFrLQaCWDdI5bBK8RGk587CBsbEtzXjG/Wdm0jzH0nkkbhDScjz9AePPNhvZ/p7DzyR/1FjL8c8goAUI2H8g83gIqh8Fo2AUjIJRQBgAAMQvRJAEFCUhAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-3598-9934","institution":"Sichuan University West China Hospital Department of Hematology","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhou","suffix":""},{"id":331379683,"identity":"a8d322d0-4692-46e5-9cf7-3aba291ae81e","order_by":1,"name":"Yuyao Yi","email":"","orcid":"","institution":"Sichuan University West China Hospital Department of Hematology","correspondingAuthor":false,"prefix":"","firstName":"Yuyao","middleName":"","lastName":"Yi","suffix":""},{"id":331379684,"identity":"9dd75eda-1332-4d45-97bb-22fe39adcb7c","order_by":2,"name":"Wei He","email":"","orcid":"","institution":"Sichuan University West China Hospital Department of Thyroid Surgery","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"He","suffix":""},{"id":331379685,"identity":"f56e4cce-e3dc-40b3-a2aa-75e80dc60bdc","order_by":3,"name":"Li Zheng","email":"","orcid":"","institution":"Sichuan University West China Hospital Department of Thyroid Surgery","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zheng","suffix":""},{"id":331379686,"identity":"e9beed3c-ebc2-4837-9483-a4773e40ed5c","order_by":4,"name":"Yiguo Hu","email":"","orcid":"","institution":"Sichuan University West China Hospital Department of Thyroid Surgery","correspondingAuthor":false,"prefix":"","firstName":"Yiguo","middleName":"","lastName":"Hu","suffix":""},{"id":331379687,"identity":"8740934c-bd97-445b-bb0d-dfb050b93cca","order_by":5,"name":"Ting Niu","email":"","orcid":"https://orcid.org/0000-0003-1580-1014","institution":"Sichuan University West China Hospital Department of Hematology","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Niu","suffix":""}],"badges":[],"createdAt":"2024-05-30 10:42:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4502403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4502403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62943234,"identity":"c4b910f3-3685-4eae-8fe0-8bcdf5212d8f","added_by":"auto","created_at":"2024-08-21 09:54:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression and genetic alteration analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLAPTM4B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e across cancers. \u003c/strong\u003e(A) \u003cem\u003eLAPTM4B\u003c/em\u003e expressionlevels in tumor and normal samples. (B) Paired differential analysis of \u003cem\u003eLAPTM4B\u003c/em\u003e expression in matched tumor and normal samples from TCGA. (C) Bar chart of \u003cem\u003eLAPTM4B\u003c/em\u003e mutations across cancers. (D) Mutation counts and types of \u003cem\u003eLAPTM4B\u003c/em\u003e across cancers. (E) Mutation diagram of \u003cem\u003eLAPTM4B\u003c/em\u003e across protein domains. (F) Landscape of genetic mutation of \u003cem\u003eLAPTM4B\u003c/em\u003e across cancers.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/54dc773a739640445cd1fdcc.png"},{"id":62942413,"identity":"733315e4-c5ef-4236-9048-59c391e8488d","added_by":"auto","created_at":"2024-08-21 09:46:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":668036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prognostic analysis of LAMPTM4B across cancers. \u003c/strong\u003e(A) Forest plots of \u003cem\u003eLAPTM4B\u003c/em\u003e by univariate Cox regression analysis across cancers. OS, DSS, PFI, and DFI. Kaplan‒Meier curves showing the relationships of \u003cem\u003eLAPTM4B\u003c/em\u003e expression with (B) OS. (C) DSS. (D) PFI in pan-cancer.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/5c751fa8df19b54d9c44e7c9.png"},{"id":62943931,"identity":"ae4bfd77-1c28-4e74-b795-372fa7381f23","added_by":"auto","created_at":"2024-08-21 10:02:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":451857,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of \u003cem\u003eLAPTM4B\u003c/em\u003e expression in the TCGA and GTEx database in pan-cancer. Cancers with AUC \u0026gt; 0.9.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/2f5a8153d8136c3a71bc5aaf.png"},{"id":62942421,"identity":"5b2e3a24-0780-48a2-8bec-e6ea8abdcf8b","added_by":"auto","created_at":"2024-08-21 09:46:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1225337,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between LAPTM4B expression and immune status across cancers. (A) Relationship between LAPTM4B expression and the StromalScore, ImmuneScore, and ESTIMATEScore. Relationships between LAPTM4B expression and the immune cells by CIBERSORT algorithm (B), and the xCell algorithm (C).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/4e05c1d7688fd8a4fe32f4f3.png"},{"id":62943233,"identity":"347beaf6-4709-4556-9812-e70ac993b4eb","added_by":"auto","created_at":"2024-08-21 09:54:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1343534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLAPTM4B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression and genes in microenvironment immune cells across cancers. \u003c/strong\u003eRelationship between \u003cem\u003eLAPTM4B\u003c/em\u003e and TBM, MSI, and purity. Correlation between \u003cem\u003eLAPTM4B\u003c/em\u003eand immune regulatory genes (A), and immune checkpoint genes (B, C). *p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/d80aab4ebaad49b2dcaa518c.png"},{"id":62942419,"identity":"e6c06884-bbbc-47d7-826b-358bfc065675","added_by":"auto","created_at":"2024-08-21 09:46:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":822864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLAPTM4B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at the single-cell level and its related signaling in B-ALL. \u003c/strong\u003e\u003cem\u003eLAPTM4B\u003c/em\u003e expression profiles at single-cell level in AML (A-B), and B-ALL (C-D). The expression of \u003cem\u003eLAPTM4B\u003c/em\u003e in different subtypes in B-ALL (E). GSEA analysis of \u003cem\u003eLAPTM4B\u003c/em\u003e was related to hematopoietic stem cells (F), and leukemia stem cells (G) in Ph+ B-ALL. KEGG (H) and HALLMARK (I) analysis suggested that \u003cem\u003eLAPTM4B\u003c/em\u003e was correlated with the cell cycle, MYC, E2F, and G2M checkpoint pathways in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/eeae1658cce1cdc66cde8a6f.png"},{"id":62942415,"identity":"7884b158-c5b2-4144-a0a6-6fa45c373098","added_by":"auto","created_at":"2024-08-21 09:46:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":688617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLAPTM4B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e promoted the development and progression of Ph\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e B-ALL. \u003c/strong\u003e(A) The schematic diagram to establish the mouse model. The Kaplan-Meier curve (B), and the counts of GFP\u003csup\u003e+\u003c/sup\u003e/B22\u003csup\u003e+\u003c/sup\u003e/IgM\u003csup\u003e-\u003c/sup\u003e Ph\u003csup\u003e+\u003c/sup\u003e B-ALL cells (C) in \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e-/- \u003c/sup\u003eand wild type Ph\u003csup\u003e+\u003c/sup\u003e B-ALL mouse model. The cell counting (D), and the cell cycle (E) in \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e-/-\u003c/sup\u003e and wild-type Ph\u003csup\u003e+\u003c/sup\u003e B-ALL cells \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/c54e00a0d84abd2e5b7bc1b0.png"},{"id":62943231,"identity":"e0dda43d-3f5c-4e65-b128-801c9a7592b2","added_by":"auto","created_at":"2024-08-21 09:54:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":598549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLAPTM4B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression and immune-related cells in ph+ B-ALL.\u003c/strong\u003e (A) Boxplots of immune cells in different \u003cem\u003eLAPTM4B\u003c/em\u003eexpression groups by xCell algorithm. (B) Scatterplot of \u003cem\u003eLAPTM4B\u003c/em\u003e correlated with immune cells. (C) Boxplots of TILs in different \u003cem\u003eLAPTM4B\u003c/em\u003e expression groups.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/4620672351e3e24f8e80f1eb.png"},{"id":62944670,"identity":"3cf06098-6a74-45e1-bbc9-6763dff4d9d4","added_by":"auto","created_at":"2024-08-21 10:10:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6525685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/4c83ef86-9d4a-417b-8885-6a3a0dbe07c9.pdf"},{"id":62943230,"identity":"ed1d8189-3b63-44dd-801b-bd8d27cf8042","added_by":"auto","created_at":"2024-08-21 09:54:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11461,"visible":true,"origin":"","legend":"","description":"","filename":"Keymessages.docx","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/5726292fa8aa48182847da95.docx"},{"id":62942417,"identity":"c9e27ecf-6f7c-479f-a1a7-c964b17e5905","added_by":"auto","created_at":"2024-08-21 09:46:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2019992,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4502403/v1/aacad206d41171b34e1e9bed.docx"}],"financialInterests":"","formattedTitle":"A comprehensive prognostic and immune analysis of LAPTM4B in pan-cancer and Philadelphia chromosome-positive acute lymphoblastic leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTumorigenesis is a multifaceted process influenced by a dynamic interplay between internal factors and the tumor microenvironment. Internal factors encompass genetic mutations, epigenetic changes, and the dysregulation of signaling pathways (Fujii et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fulton-Ward and Middleton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Tumor microenvironment is comprised of various factors including metabolomics, inflammation, angiogenesis, immune system modulation, extracellular matrix (ECM) (Berrell et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Choi and Jung, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mempel et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sleeboom et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Crucially, membrane proteins play a pivotal role during tumorigenesis by facilitating the transmission of signals between the extracellular environment and the cell's interior (Lu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These proteins participate some fundamental cellular processes such as growth, differentiation, and survival (Lu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Malla et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, investigating the intricate interplay between internal factors, the tumor microenvironment, and the role of membrane proteins is imperative for comprehending the intricacies of tumorigenesis. Such understanding lays the foundation for the development of targeted strategies aimed at preventing and treating cancer effectively. One of the research interests in our laboratory is to uncover the roles and mechanisms of membrane proteins in the initiation and development of tumors. Building upon the reported biological functions of lysosomal membrane-associated protein transmembrane-4 beta (\u003cem\u003eLAPTM4B\u003c/em\u003e) in existing studies, we aim to comprehensively understand its involvement in cancer, especially Philadelphia chromosome-positive acute B cell lymphoblastic leukemia (Ph\u003csup\u003e+\u003c/sup\u003e B-ALL).\u003c/p\u003e \u003cp\u003e \u003cem\u003eLAPTM4B\u003c/em\u003e is recognized as a late endosomal protein, and it is also distributed in the plasma membrane. It exhibits widespread expression in various tissues throughout the body, with predominant levels observed in the heart, kidney, skeletal muscle, and hematopoietic stem cells (HSCs). In contrast, its expression is relatively lower in peripheral blood leukocytes, spleen, and thymus (Meng et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eLAPTM4B\u003c/em\u003e involves in multiple biological processes, including cell cycle, cell growth and proliferation, and autophagy. \u003cem\u003eLAPTM4B\u003c/em\u003e interacts with and integrin and promotes cell growth and proliferation through a series of enzyme-linked reactions within the membrane (Peng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). \u003cem\u003eLAPTM4B\u003c/em\u003e also regulates cell cycle and engages in growth signaling pathways, such as PI3K/AKT and MAPK (Ji et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eLAPTM4B\u003c/em\u003e also promotes autophagy through the EGFR signaling pathway (Ji et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu and Zhang, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and loss of \u003cem\u003eLAPTM4B\u003c/em\u003e inhibited later stages of autophagy by blocking maturation of the autophagosome (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncreased LAPTM4B expression has been observed in various cancers, including breast, liver, lung, ovarian, uterine, and gastric cancers (Liao et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, elevated LAPTM4B levels contribute to chemotherapy resistance in breast cancer. The overexpression of LAPTM4B induces resistance to anthracyclines (such as doxorubicin, daunorubicin, and epirubicin) by retaining the drug in the cytoplasm and reducing its nuclear localization, thereby diminishing drug-induced DNA damage (Li et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition to solid tumors, LAPTM4B is also highly expressed in hematologic malignancies. LAPTM4B promoted AML progression by regulating the RPS9/STAT3 axis (Huang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Elevated LAPTM4B expression is associated with AML patients harboring NPM1 mutations in conjunction with FLT3-ITD mutations (Huang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In chronic myeloid leukemia (CML) bone marrow (BM) cells, LAPTM4B expression levels were significantly higher than those in normal individuals (Haferlach et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similar to observations in solid tumors, CML patients with higher LAPTM4B expression were associated with resistance to tyrosine kinase inhibitor (TKI) treatment (Singh et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost studies on LAPTM4B have primarily focused on intracellular signaling in certain types of cancers, and a comprehensive understanding of LAPTM4B in tumorigenesis is still lacking. In this study, we aimed to elucidate the expression, clinical characteristics and immunological characteristics of \u003cem\u003eLAPTM4B\u003c/em\u003e across various cancers. In particular, our investigation unveiled a significant correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and survival outcomes in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL patients. Moreover, it was confirmed that the loss of the \u003cem\u003eLaptm4b\u003c/em\u003e impeded BCR-ABL-induced B-ALL progression, both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition and analysis\u003c/h2\u003e \u003cp\u003eThe standardized pan-cancer dataset was downloaded from UCSC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): TCGA TARGET GTEx (PANCAN, N\u0026thinsp;=\u0026thinsp;19131, G\u0026thinsp;=\u0026thinsp;60499). A log2(x\u0026thinsp;+\u0026thinsp;1) transformation was applied to each expression value, and cancer types with fewer than 3 samples were excluded, resulting in the final expression data for 34 cancer types. Additionally, prognostic data for TCGA were sourced from prior studies (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Simultaneously, TARGET follow-up data were supplemented from the UCSC database. Samples with a follow-up time of less than 30 days were excluded, and cancer types with fewer than 10 samples were also excluded. The abbreviations section provides the full names and corresponding abbreviations of the tumors.\u003c/p\u003e \u003cp\u003eThe Ph\u003csup\u003e+\u003c/sup\u003e B-ALL data was downloaded from the GEO database. RMA normalization was performed using the RMA algorithm the NimbleScan 2.5 software. The dataset GSE34861 comprises 191 adult B-ALL samples, among which 78 are Ph\u003csup\u003e+\u003c/sup\u003e B-ALL samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenetic and Epigenetic Alterations in Pan-cancer\u003c/h2\u003e \u003cp\u003eGenomic alteration data and methylation data were download from cBioPortal database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and gene promoter methylation was evaluated using Spearman rank correlation. Kaplan‒Meier analysis was performed to analyze the relationship between \u003cem\u003eLAPTM4B\u003c/em\u003e methylation and the prognosis of patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical Characteristics\u003c/b\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003ein Pan-cancer\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe developed the Cox proportional hazards regression model to analyze overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) of \u003cem\u003eLAPTM4B\u003c/em\u003e across cancers. Kaplan‒Meier analysis was performed to analyze the prognostic significance.\u003c/p\u003e \u003cp\u003eThe diagnostic significance of \u003cem\u003eLAPTM4B\u003c/em\u003e across cancers was assessed by the Receiver Operator Characteristic (ROC) curve via \u0026ldquo;pROC\u0026rdquo; (v1.17.0.1). The diagnosis accuracy was evaluated by the Area under Curve (AUC). The AUC is closer to 1, the diagnosis accuracy is better.\u003c/p\u003e \u003cp\u003eThe IC\u003csub\u003e50\u003c/sub\u003e values of various compounds in cancer cell lines were obtained from the GDSC dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), to assess the relationship between \u003cem\u003eDLAT\u003c/em\u003e and the drug response of tumor cells by the Spearman correlation coefficient. A higher IC50 indicates that cancers are less sensitive to the compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune microenvironment Analysis\u003c/h2\u003e \u003cp\u003eTumor-infiltrating lymphocytes (TILs) participated in predicting sentinel node status and associated with prognosis (Hall et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). ssGSEA scores of the correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e and immune cell infiltration for Ph\u003csup\u003e+\u003c/sup\u003e B-ALL were calculated using the xCELL algorithm and TILs. Spearman rank correlation was employed to assess the association between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and immune cell infiltration in pan-cancer, utilizing the xCELL and CIBERSORT algorithms.\u003c/p\u003e \u003cp\u003eImmune checkpoint-related genes (ICGs) were obtained from a previous study (Hu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Immune-related genes were downloaded from the TISIDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/index.php\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The relationship of immune-related genes and \u003cem\u003eLAPTM4B\u003c/em\u003e in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL was evaluated using ssGSEA. Immune regulatory genes are distributed in five immune pathways, including chemokine (41 genes), receptor (18 genes), MHC (21 genes), immunoinhibitor (24 genes) and immunostimulator (46 genes). The relationship between immune-related genes and \u003cem\u003eLAPTM4B\u003c/em\u003e expression in pan-cancer was evaluated by Spearman rank correlation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTumor Microenvironment Analysis in Pan-cancer\u003c/h2\u003e \u003cp\u003eWe obtained 10,180 tumor samples from a total of 44 tumor types for immune infiltration scores. ESTIMATE was used to reflect the degree of infiltration of stromal or immune cells into tumors. The ESTIMATE algorithm included stromal, immune, and ESTIMATE scores. Spearman rank correlation was used to evaluate the correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and these three scores by the R software packages \u0026ldquo;estimate\u0026rdquo; and \u0026ldquo;psych\u0026rdquo;.\u003c/p\u003e \u003cp\u003eWe downloaded all level 4 simple nucleotide variation data of TCGA samples from GDC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Tumor mutation burden (TMB) was analyzed using MAftools package (Version 2.8.05) of R software. Tumor stem cell infiltration analysis was performed based on DNA methylation dry score (DNAss) and RNA dry score (RNAss) (Malta et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Spearman rank correlation was used to evaluate the correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and TMB, microsatellite instability (MSI), purity, DNAss and RNAss.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell Analysis and Enrichment Analysis\u003c/h2\u003e \u003cp\u003eWe conducted the single-cell level expression of \u003cem\u003eLAPTM4B\u003c/em\u003e at in leukemia using TISCH2 (Han et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). TISCH2 encompasses 190 tumor scRNA sequence datasets with 6\u0026nbsp;million cells across 50 cancer types. To assess the functional and signaling aspects with \u003cem\u003eLAPTM4B\u003c/em\u003e, we conducted Gene Set Enrichment Analysis (GSEA) on HALLMARK and KEGG pathways. Based on the median expression of \u003cem\u003eLAPTM4B\u003c/em\u003e in cancer, the group was divided into high and low expression groups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Development of\u003c/b\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003eKnockout Ph\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eB-ALL Model and in vitro assay\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe B6. \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u003cem\u003eloxp/loxp\u003c/em\u003e\u003c/sup\u003e mice were generated at Biocytogen Pharmaceuticals (Beijing) Co., Ltd, which were intercrossed with B6.CMV-Cre mice to generate B6. \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice. BCR-ABL induced B-ALL model was developed as previously described (Hu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Briefly, bone marrow (BM) cells were collected from 8-week-old \u003cem\u003eWT\u003c/em\u003e and \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e mice (n\u0026thinsp;=\u0026thinsp;3) and resuspended with BCR/ABL viral infection medium, centrifugation at 1000g for 90min at 37 ℃, then cultured at 37 ℃ for 3 h. Then, viral transfected cells were injected into lethally irradicated recipient mice at a dosed of 1x10^5 B cells/mouse via the tail vein.\u003c/p\u003e \u003cp\u003eAfter the \u003cem\u003eWT\u003c/em\u003e or \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e BM cells were transfected with BCR/ABL, then seeded in DMEM medium containing 10% FBS in 24-well plates at series initial cell numbers of 5x10^5 (500k), 3x10^5 (300k), 1x10^5 (100k), 3x10^4 (30k), 1x10^4 (10k), and 2.5x10^3 (2.5k). Each well cell numbers were adjusted to 1x10^6 cells/well with WT mice BM cells and cultured with DMEM medium containing 10% FBS. The cell number in eahc well was counted on day 7 post seeding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell Cycle Experiments by the BrdU Incorporation Method\u003c/h2\u003e \u003cp\u003eBrdU was added directly into the prepared cell medium at final concentration of 10 uM, and incubated for 1h. After collection and washing, cells were suspended with PBS containing 0.5% paraformaldehyde on ice for 20 minutes. Then cells were washed with PBS and resuspended with 70% ethanol overnight. Next day, after washing with PBS, cells were resuspended with 2N HCL/0.5% triton X-100 at room temperature for 20 min to denature. After neutralization with 0.1M sodium borate, cells were suspended with PBS containing 0.5% BSA and 0.5%Tween 20 and stained with anti-BrdU antibody-FitC (BD Biosciences) at room temperature for 20min. The cells were resuspended with PBS containing RNase and incubated at 37 ℃. After adding PI, cells were analyzed using flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eR software (version 4.2.1) was utilized for this analysis. The Wilcoxon's test and analysis of variance (ANOVA) were applied for comparisons involving two and multiple groups, respectively. Spearman correlation coefficient was employed for correlation analysis. All experiments were conducted in triplicate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eExpressions and Alterations of\u003c/b\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003ein Human Cancers\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn order to examine the expression profile of \u003cem\u003eLAPTM4B\u003c/em\u003e in pan-cancers, we evaluated its expression across 34 cancer types using data from TCGA, TARGET, and GTEx databases. Our findings revealed high \u003cem\u003eLAPTM4B\u003c/em\u003e expression in 28 cancer types compared to normal tissues, including glioblastoma (GBM), lower-grade glioma (LGG), uterine corpus endometrial carcinoma (UCEC), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lung adenocarcinoma (LUAD), esophageal carcinoma (ESCA), stomach and esophageal carcinoma (STES), colon adenocarcinoma (COAD), colon adenocarcinoma/Rectum adenocarcinoma esophageal carcinoma (COADREAD), stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), liver hepatocellular carcinoma (LIHC), high-risk Wilms tumor (WT), skin cutaneous melanoma (SKCM), bladder urothelial carcinoma (BLCA), thyroid carcinoma (THCA), rectum adenocarcinoma (READ), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), testicular germ cell tumors (TGCT), uterine carcinosarcoma (UCS), acute lymphoblastic leukemia (ALL), acute myeloid leukemia (LAML), Adrenocortical carcinoma (ACC), and cholangiocarcinoma (CHOL). While, low \u003cem\u003eLAPTM4B\u003c/em\u003e expression was observed in 4 cancer types, Pan-kidney cohort (KIPAN), prostate adenocarcinoma (PRAD), kidney renal clear cell carcinoma (KIRC), and kidney chromophobe (KICH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Specifically, \u003cem\u003eLAPTM4B\u003c/em\u003e exhibited high expression in BLCA, BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, READ, STAD, and UCEC, while showing low expression in KICH, KIRC, PRAD, and THCA compared to adjacent paired normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These results suggest that elevated \u003cem\u003eLAPTM4B\u003c/em\u003e expression is associated with cancer progression in a majority of cases.\u003c/p\u003e \u003cp\u003eThe amplification of \u003cem\u003eLAPTM4B\u003c/em\u003e was observed most frequently in UCS, BRCA, BLCA, OV, PRAD, and LIHC, (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), and common in most cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). Moreover, we found 34 mutation sites between amino acids 0 and 317, including 24 missense mutations, 2 truncating, 8 SV/fusion, and S265N as the most frequent mutation sites within \u003cem\u003eLAPTM4B\u003c/em\u003e across cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo elucidate potential associations between \u003cem\u003eLAPTM4B\u003c/em\u003e and intracellular epigenetic alterations, we examined the status of genomic methylation and the expression of genes involved in mRNA methylation in various types of cancer cells using data from cBioPortal database. We found that there were significant negative correlations between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and gene promotor methylation in most tumors (Supplementary Fig.\u0026nbsp;1A). Increased methylation of \u003cem\u003eLAPTM4B\u003c/em\u003e mRNA was related to poorer OS in patients with GBM and LGG (Supplementary Figure\u0026ensp;1B, C). Furthermore, the relationships between \u003cem\u003eLAPTM4B\u003c/em\u003e and genes involved in mRNA m1A, m5C, m6A modifications were evaluated. \u003cem\u003eLAPTM4B\u003c/em\u003e expression was significantly positively related to these RNA modification genes in almost all tumors (Supplementary Figure\u0026ensp;1D). These results indicated that \u003cem\u003eLAPTM4B\u003c/em\u003e could influence tumor development by regulating the repair of RNA and DNA methylation in cancers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Clinical Features associated with\u003c/b\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003ealterations in Pan-cancers\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the clinical significance of elevated \u003cem\u003eLAPTM4B\u003c/em\u003e expression in various cancers, we conducted a Cox proportional hazards model analysis encompassing OS, DSS, DFI, and PFI. Univariate Cox regression analysis of OS, DSS, PFI, and DFI revealed that \u003cem\u003eLAPTM4B\u003c/em\u003e served as a significant risk factor for patients in multiple cancer types, including LIHC, B-ALL, SARC, GBMLGG, SKCM, AML, ACC, UVM, CESC, HNSC, KICH, MESO, UVM, BRCA, and PCPG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, Kaplan‒Meier survival analyses of OS, DSS, and PFI were further explored across cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe performance of the gene signature for diagnostic accuracy was evaluated by the ROC curves. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed that 17 types of cancer had high diagnostic accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9), including CHOL, ESCA, GBM, HNSC, LAML, LGG, LUAD, LUSC, OV, PAAD, READ, SKCM, STAD, TGCT, THYM, UCEC and UCS. These results suggested that \u003cem\u003eLAPTM4B\u003c/em\u003e had good diagnostic value in a variety of cancers. The detailed results of all cancers were exhibited in the Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the potential correlation between elevated \u003cem\u003eLAPTM4B\u003c/em\u003e expression and the drug response of tumor cells, we conducted Spearman correlation coefficient analysis using data from the GDSC dataset. Our findings revealed increased \u003cem\u003eLAPTM4B\u003c/em\u003e expression had increased IC50 values of 14 compounds, including rTRAIL, B-Raf inhibitors (PLX-4720, dabrafenib, SB590885), FTI-277 (FTase inhibitor), bexarotene (RXR agonist), dactolisib (PI3K/mTOR inhibitor), luminespib (HSP90 inhibitor), palbociclib (CDK4/6 inhibitor), (5Z)-7-Oxozeaenol (TAK1 inhibitor), QS11 (ARFGAP1 inhibitor), among others, which suggested that increased \u003cem\u003eLAPTM4B\u003c/em\u003e lead drug resistance. Conversely, a negative association was observed with elesclomol and afatinib (EGFR/HER2 inhibitor) responses (Table\u0026nbsp;1). These results suggest that increased \u003cem\u003eLAPTM4B\u003c/em\u003e expression may confer resistance to a broad spectrum of therapeutic agents in tumor cells. Moreover, we also found that \u003cem\u003eLAPTM4B\u003c/em\u003e was positively correlated with RNAss and DNAss across most of the cancers (Supplementary Fig.\u0026nbsp;2A-B), which indicates that high expression of \u003cem\u003eLAPTM4B\u003c/em\u003e might be associated with cancer tumor recurrence and metastasis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmune Status Analysis of\u003c/b\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003ein Pan-Cancer\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore the relationship between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and immune status in pan-cancer, we conducted a correlation analysis. Overall, we found that \u003cem\u003eLAPTM4B\u003c/em\u003e expression was associated with immune subtypes in 19 cancer types and correlated with molecular subtypes in 14 cancer types (Supplementary Fig.\u0026nbsp;3A-B). Additionally, we analyzed stromal and immune cell scores to investigate the relationship between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and the tumor immune microenvironment (TIME) across cancers. We observed a positive correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and StromalScore, ImmuneScore, and ESTIMATEScore in PAAD, OV, and UVM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). While, \u003cem\u003eLAPTM4B\u003c/em\u003e expression showed a negative correlation with these scores in GBM, LGG, LAML, BRCA, CESC, LUAD, STES, SARC, KIRP, KIPAN, STAD, LUSC, WT, SKCM, SKCM-M, THCA, NB, and TCGT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). To explore the correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and immune cells, we developed a heat map of \u003cem\u003eLAPTM4B\u003c/em\u003e with immune cells by CIBERSORT and xCell. Our result revealed that \u003cem\u003eLAPTM4B\u003c/em\u003e was associated with CD8\u003csup\u003e+\u003c/sup\u003e T cells, macrophages M2 and Tregs in many cancers, which suggested that high \u003cem\u003eLAPTM4B\u003c/em\u003e expression had inhibitory immune microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). Overall, our findings suggested that elevated \u003cem\u003eLAPTM4B\u003c/em\u003e expression might be associated with a potential decrease in patients' immune anti-tumor capabilities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate whether \u003cem\u003eLAPTM4B\u003c/em\u003e expression levels are associated with TMB, MSI, and tumor purity, we conducted analyses using Spearman correlation analysis. The results showed that \u003cem\u003eLAPTM4B\u003c/em\u003e expression was positively correlated with TMB in ACC, BRCA, GBMLGG, LAML, LGG, LUAD, PAAD, and THYM, while exhibiting a negative correlation in COAD, COADREAD, ESCA, PRAD, SKCM, and THCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The MSI analysis revealed a positive correlation of \u003cem\u003eLAPTM4B\u003c/em\u003e expression with MSI in KIPAN, TGCT, and UVM, while a negative correlation in COAD, COADREAD, DLBC, GBMLGG, LGG, PAAD, PRAD, and THCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Additionally, \u003cem\u003eLAPTM4B\u003c/em\u003e showed a significant correlation with tumor purity, with positive associations in CESC, ESCA, GBM, GBMLGG, HNSC, KIPAN, KIRP, LGG, LUAD, LUSC, SARC, SKCM, STAD, STES, TGCT, and THYM, and negative associations in BLCA, LIHC, OV, PCPG, PRAD, UCS, and UVM (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These findings suggest that \u003cem\u003eLAPTM4B\u003c/em\u003e expression might serve as a potential biomarker for immunotherapy.\u003c/p\u003e \u003cp\u003eSubsequently, the correlations of expression levels between \u003cem\u003eLAPTM4B\u003c/em\u003e and immune checkpoint genes and immune regulatory genes in cancers were also investigated. We found that \u003cem\u003eLAPTM4B\u003c/em\u003e expression was positively related to immune regulatory genes in majority tumor types, especially in PRAD, UVM, THYM, LIHC, BLCA, and OV. While, \u003cem\u003eLAPTM4B\u003c/em\u003e expression was negatively related to immune regulatory genes in TGCT, GBM, LUAD, SARC, KIPAN, and SKCM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additionally, \u003cem\u003eLAPTM4B\u003c/em\u003e expression was positively related to immune checkpoint genes in most types of tumors, except for some tumors, which were mainly TGCT, GBM, SKCM, and SARC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In general, these results suggested that \u003cem\u003eLAPTM4B\u003c/em\u003e might regulate immune cell infiltration and immune-related genes functions in most tumor types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-cell and Enrichment Analysis of\u003c/b\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003eExpression in Leukemia\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo specifically and deeply depict the pictures of \u003cem\u003eLAPTM4B\u003c/em\u003e involving in malignancies, then we focused on hematological malignancies, particularly Ph\u003csup\u003e+\u003c/sup\u003e B-ALL, to elucidate and clarify the biological functional characteristics of \u003cem\u003eLAPTM4B\u003c/em\u003e in tumors. Taking the advantages of single-cell sequencing and open public data, we found that \u003cem\u003eLAPTM4B\u003c/em\u003e was expressed mainly in normal HSCs, progenitors, and AML cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). In an ALL sample, we found that \u003cem\u003eLAPTM4B\u003c/em\u003e was highly expressed in proerythroblasts, but not malignant cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). Interesting, an analysis based on an expression profiling of 191 B-ALL samples and 3 normal pre-B samples showed that \u003cem\u003eLAPTM4B\u003c/em\u003e was more highly expressed in BCR/ABL B-ALL than other subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Then, we performed the analysis of the function and pathways of \u003cem\u003eLAPTM4B\u003c/em\u003e-related genes in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL. We found that genes associated with HSCs and leukemia stem cells (LSCs) were up-enriched in high \u003cem\u003eLAPTM4B\u003c/em\u003e expression samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G), as well as genes associated with cell cycle, DNA replication, MYC target, E2F and G2M checkpoint pathways were also up-enriched in in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH-I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003edeletion impairs the development and progression of Ph\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eB-ALL\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo instigate the involvement of \u003cem\u003eLAPTM4B\u003c/em\u003e in the development of Ph\u003csup\u003e+\u003c/sup\u003e B-ALL, we employed a Ph\u003csup\u003e+\u003c/sup\u003e B-ALL mouse model. Bone marrow (BM) cells from wild type (\u003cem\u003eWT\u003c/em\u003e) or \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e mice were transfected with retrovirus containing BCR/ABL and then injected into lethally irradicated recipients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Overall, the survival time of recipients receiving \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e BM cells was significantly longer than that receiving \u003cem\u003eWT\u003c/em\u003e BM cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). We also monitored the number of leukemic cells with BCR/ABL (represented with GFP and B220) in peripheral blood of mice receiving BCR/ABL-transduced WT or \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e BM cells on the day 10, 20 and 30 post-BM transplantation. We found that the percentages of B-lymphoid leukemic cells were significantly lower in mice receiving BCR/ABL-transduced WT or \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e BM cells than in those receiving BCR/ABL-transduced \u003cem\u003eWT\u003c/em\u003e BM cells at all time points measured (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). To investigate the role of \u003cem\u003eLAPTM4B\u003c/em\u003e in BCR/ABL-induced leukemogenesis, we conducted an \u003cem\u003ein vitro\u003c/em\u003e assay for proliferation of BCR/ABL transformed BM B-lymphoid progenitors, as described in methods. BCR-ABL-transformed B-lymphoid progenitors from \u003cem\u003eLAPTM4B\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e BM cells exhibited much lower number than it transformed those from WT BM cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Further, \u003cem\u003ein vitro\u003c/em\u003e Brdu assays for cell proliferation rate showed that \u003cem\u003eLAPTM4B\u003c/em\u003e deletion impaired Ph\u003csup\u003e+\u003c/sup\u003e B-ALL cell proliferation and caused G0/G1 arrest (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). These findings demonstrated that \u003cem\u003eLAPTM4B\u003c/em\u003e deletion significantly impaired the development and progression of Ph\u003csup\u003e+\u003c/sup\u003e B-ALL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationships between\u003c/b\u003e \u003cb\u003eLAPTM4B\u003c/b\u003e \u003cb\u003eExpression and Immune Status in Ph\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eB-ALL\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the association of \u003cem\u003eLAPTM4B\u003c/em\u003e expression with TME in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL, we conducted an ESTIMATE analysis to calculate the stromal score, immune score, ESTIMATE score, and tumor purity within Ph\u003csup\u003e+\u003c/sup\u003e B-ALL. We found that \u003cem\u003eLAPTM4B\u003c/em\u003e expression was not significantly associated with TME in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL (Supplementary Fig.\u0026nbsp;4). Then, the relationship between \u003cem\u003eLAPTM4B\u003c/em\u003e and immune cells in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL was conducted using xCell algorithm method. The scores of CD4\u003csup\u003e+\u003c/sup\u003e memory T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, HSC, preadipocytes, and Tgd cells were higher; while, the scores of CD4\u003csup\u003e+\u003c/sup\u003e Tem, eosinophils, epithelial cells, MSC, and NKT were significantly lower in the high \u003cem\u003eLAPTM4B\u003c/em\u003e expression patient samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). \u003cem\u003eLAPTM4B\u003c/em\u003e expression was negatively related to macrophages M2, NKT, mv endothelial cells, and CD4\u003csup\u003e+\u003c/sup\u003e Tem; while they were positively correlated with CD4\u003csup\u003e+\u003c/sup\u003e memory T cells, Th2 cells, Tgd cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells and microenvironment Score (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Moreover, immune infiltration scores of tumor-infiltrating lymphocytes (TILs) type in different \u003cem\u003eLAPTM4B\u003c/em\u003e expression groups were also evaluated using ssGSEA. The central memory CD4 T cells, effector memory CD4 T cells, immature B cells, plasmacytoid dendritic cells, and immature dendritic cells were highly expressed in the high \u003cem\u003eLAPTM4B\u003c/em\u003e expression patient samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e expression and immune-related genes was also assessed. As previous reports, there were 79 genes related to immune checkpoint (Hu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We found that \u003cem\u003eTNFRSF14\u003c/em\u003e and \u003cem\u003eTNFSF14\u003c/em\u003e were lowly expressed in the high \u003cem\u003eLAPTM4B\u003c/em\u003e expression samples (Supplementary Fig.\u0026nbsp;5A). Additionally, \u003cem\u003eTNFRSF14\u003c/em\u003e was negatively correlated with \u003cem\u003eLAPTM4B\u003c/em\u003e expression; whereas, \u003cem\u003eCTLA4\u003c/em\u003e, \u003cem\u003eHLA-E\u003c/em\u003e, and \u003cem\u003eICOS\u003c/em\u003e were positively associated with \u003cem\u003eLAPTM4B\u003c/em\u003e expression (Supplementary Fig.\u0026nbsp;5B). Moreover, the correlation between \u003cem\u003eLAPTM4B\u003c/em\u003e and the chemokine genes was also evaluated. We found that \u003cem\u003eCCL1\u003c/em\u003e, \u003cem\u003eCCL11\u003c/em\u003e, \u003cem\u003eCCL15\u003c/em\u003e, \u003cem\u003eCCL19\u003c/em\u003e, \u003cem\u003eCCL21\u003c/em\u003e, \u003cem\u003eCCL22\u003c/em\u003e, \u003cem\u003eCCL24\u003c/em\u003e and \u003cem\u003eCCL25\u003c/em\u003e were lowly expressed in high \u003cem\u003eLAPTM4B\u003c/em\u003e expression samples (Supplementary Fig.\u0026nbsp;5C). But, no significant difference was observed on immunoinhibitory genes, immunostimulatory genes, receptor genes, and MHC genes except for \u003cem\u003eIL6\u003c/em\u003e, \u003cem\u003eLTA\u003c/em\u003e, \u003cem\u003eULBP1\u003c/em\u003e, and \u003cem\u003eXCR1\u003c/em\u003e (Supplementary Fig.\u0026nbsp;6A-D). These results suggested that the high expression of \u003cem\u003eLAPTM4B\u003c/em\u003e might affected immune microenvironment in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eLAPTM4B\u003c/em\u003e is required for lysosomes function, participates in the cell death program, promotes autophagy and tolerance to metabolic stress in cancer cells (Vergarajauregui et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) (Blom et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and is an essential gene for adjuvant drug resistance(Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our results revealed the role \u003cem\u003eLAPTM4B\u003c/em\u003e plays in pan-cancer and Ph\u003csup\u003e+\u003c/sup\u003e B-ALL. We determined the expression levels of \u003cem\u003eLATPM4B\u003c/em\u003e mRNA in various cancers, and confirmed the most common types of \u003cem\u003eLAPTM4B\u003c/em\u003e mutations and their locations. The correlation between epigenetic alterations and \u003cem\u003eLAPTM4B\u003c/em\u003e were evaluated. We also assessed the diagnostic, prognostic and therapeutic values of \u003cem\u003eLAPTM4B\u003c/em\u003e expression across cancers and differentiated its expression levels across several immune and cellular subtypes of cancers. We associated \u003cem\u003eLAPTM4B\u003c/em\u003e expression levels with tumor microenvironment and the infiltration levels of immune cells and genes in various cancers. Besides, the expression of \u003cem\u003eLAPTM4B\u003c/em\u003e at the single-cell level and function in B-ALL were explored. We identified that the loss of the \u003cem\u003eLAPTM4B\u003c/em\u003e gene impeded BCR-ABL-induced B-ALL progression, both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. We also explored the immunological function of \u003cem\u003eLAPTM4B\u003c/em\u003e in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLAPTM4B\u003c/em\u003e was not highly expressed in all tissues, while we found that it was highly expressed in most cancers. And the amplification was the most frequent alteration. However, the expression of \u003cem\u003eLAPTM4B\u003c/em\u003e was discovered lowly expressed in KIPAN, KIRC, KICH and PRAD, while the genetic alterations were mainly amplification in these cancers. Therefore, there were other factors affected the expression of \u003cem\u003eLAPTM4B\u003c/em\u003e. As we known, abnormal methylation of DNA and RNA promotes various diseases and cancers (Jones and Baylin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Klutstein et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). And we found that \u003cem\u003eLAPTM4B\u003c/em\u003e expression was significantly related to DNA methylation and RNA modifications in these cancers. Theses might partly explain the inconsistencies between alteration and expression.\u003c/p\u003e \u003cp\u003eA total of 17 types of cancer had high diagnostic accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9), suggesting that \u003cem\u003eLAPTM4B\u003c/em\u003e had good diagnostic value. Meanwhile, \u003cem\u003eLAPTM4B\u003c/em\u003e was a risk factor in many cancers. Besides, we found that increased \u003cem\u003eLAPTM4B\u003c/em\u003e expression led resistance to a broad spectrum of therapeutic agents in tumor cells. These results were consistent with previous studies that \u003cem\u003eLAPTM4B\u003c/em\u003e could be a diagnostic, prognostic and therapeutic factor in hepatocellular carcinoma, breast cancer, hepatocellular carcinoma, bladder cancer and renal cell carcinoma and so on (Dong et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Maki et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhong et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eLAPTM4B\u003c/em\u003e expression was different in different molecular or immune subtypes of cancer, which results in different survival in the overall population and particular subtype of cancer. Therefore, immune features should be also considered in the further study.\u003c/p\u003e \u003cp\u003eTMB can reflect the proportion of somatic mutations in tumors (Choucair et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MSI refers to the arbitrary length change of microsatellites in tumor tissue due to insertion or deletion of repeat units (Bonneville et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The purity of the tumor usually related to prognosis (Thorsson et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). TMB, MSI, and tumor purity are emerging biomarkers associated with the immunotherapy response. ESTIMATE reflects the degree of infiltration of stromal or immune cells into tumors. High stemness scores represent the activity of tumor stem cells, and are associated with drug resistance and the continuous proliferation of tumor cells, and are correlated with poorer survival (Malta et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our results exhibited that \u003cem\u003eLAPTM4B\u003c/em\u003e was significant correlated with these indexs. Besides, we showed that \u003cem\u003eLAPTM4B\u003c/em\u003e was related to different immune cells in various cancers. In general, the infiltration of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells, Tem and Tcm CD8\u0026thinsp;+\u0026thinsp;cells, and Tem CD4\u0026thinsp;+\u0026thinsp;cells was associated with good prognosis, whereas MDSCs and Tregs were correlated with bad prognosis (Charoentong et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A previous study identified that \u003cem\u003eLAPTM4B\u003c/em\u003e inhibited human regulatory T cells produced TGF-β1 (Huygens et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Besides, \u003cem\u003eLAPTM4B\u003c/em\u003e was upregulated in CML TKI-resistant patients (Singh et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, \u003cem\u003eLAPTM4B\u003c/em\u003e might be an immunotherapeutic factor in various cancers.\u003c/p\u003e \u003cp\u003eThen, we found that \u003cem\u003eLAPTM4B\u003c/em\u003e is expressed primarily in stem cells at single-cell level in leukemia, and highly expressed in BCR/ABL subtype of B-ALL, and upregulated stem cell pathway in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL. A previous study also identified \u003cem\u003eLAPTM4B\u003c/em\u003e as a candidate gene related to stemness, which was the downstream target of HOXB4 in hematopoietic progenitor cells (Lee et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similarly, the study verified that \u003cem\u003eLAPTM4B\u003c/em\u003e was closely related to the stemness of HCC (Liao et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies illustrated that \u003cem\u003eLAPTM4B\u003c/em\u003e might regulate stem cell-related genes. Additionally, \u003cem\u003eLAPTM4B\u003c/em\u003e might participated in the signaling pathways of MYC, E2F, cell cycle, and T/B cell receptor. And in the Ph\u0026thinsp;+\u0026thinsp;B-ALL mouse model, \u003cem\u003eLAPTM4B\u003c/em\u003e knockout prolonged survival, inhibited cell proliferation and arrest of G0/G1. The results were similar to the previous study, which exhibited that \u003cem\u003eLAPTM4B\u003c/em\u003e promoted the entry of cells from the G1 into the S phase in breast cancer (Tao et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn B-cell malignancies, leukemic cells can alter the normal microenvironment, in favor of their growth, survival and resistance to cytotoxic therapies (Hughes et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Immune cells are important constituents of the tumor stroma and play a crucial role in tumor development and progression60 (Hinshaw and Shevde, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lei et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our study showed that \u003cem\u003eLAPTM4B\u003c/em\u003e expression was associated with HSC, preadipocytes, immature B cells, and immature dendritic cells in Ph\u0026thinsp;+\u0026thinsp;B-ALL. These results validated that \u003cem\u003eLAPTM4B\u003c/em\u003e was related to stem cell pathways as before analyzed. Moreover, the results demonstrated that high \u003cem\u003eLAPTM4B\u003c/em\u003e expression had low \u003cem\u003eTNFRSF14\u003c/em\u003e and \u003cem\u003eTNFSF14\u003c/em\u003e expression, and was positively correlated with the \u003cem\u003eCTLA4\u003c/em\u003e and \u003cem\u003eICOS\u003c/em\u003e checkpoint gene. \u003cem\u003eTNFSF14\u003c/em\u003e was an immune-activating gene, mainly expressed in activated T cells, activated natural killer cells and immature dendritic cells (Ware and Sed\u0026yacute;). TNFRSF14 is a receptor for BTLA, TNFSF14/LIGHT, and homotrimeric TNFSF1, is involved in lymphocyte activation. CTLA4 also known as CD152, is a protein receptor that acts as an immune checkpoint and negatively regulates the immune response (Patwekar et al.). ICOS, also called CD278, ICOS were immune checkpoint proteins expressed on activated T cells. Tumor-infiltrating Tregs expressed high levels of cell surface molecules associated with T-cell activation, such as CTLA4, PD-1, LAG3, TIGIT, ICOS, and TNF receptor superfamily members (Li et al.). These results suggested that \u003cem\u003eLAPTM4B\u003c/em\u003e might influence the efficacy of immunotherapy.\u003c/p\u003e \u003cp\u003eIn summary, our study systematically performed a comprehensive pan-cancer analysis of \u003cem\u003eLAPTM4B\u003c/em\u003e, and explored the expression, immunological features, and functions of the \u003cem\u003eLAPTM4B\u003c/em\u003e gene in the Ph\u003csup\u003e+\u003c/sup\u003e B-ALL mouse model. We demonstrated the abnormal expression profiles of \u003cem\u003eLAPTM4B\u003c/em\u003e and it was related to clinical diagnosis, prognosis, genetic and epigenetic alterations, immunological features, and drug response. Additionally, we identified that increased \u003cem\u003eLAPTM4B\u003c/em\u003e expression was associated with an unfavorable prognosis and promoted the development and progression in Ph\u003csup\u003e+\u003c/sup\u003e B-ALL, and was related to immune status. These results could help illustrate the underlying oncogenic role and immunological function of \u003cem\u003eLAPTM4B\u003c/em\u003e in cancers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003es\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHZ and YY have contributed equally to this work and share the first authorship.\u0026nbsp;HZ and YY\u0026nbsp;participated in the study design and wrote the manuscript. HZ and YY acquired the data, and analysed and interpreted the data. YY, WH, LZ and HZ performed the experiments. YH and TN revised the manuscript. All authors contributed to the manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Sichuan Province (NO. 24NSFSC3632),\u0026nbsp;1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (No. ZYJC21007), National Key Research and Development Program of China (No. 2022YFC2502600, 2022YFC2502603), and National Natural Science Foundation of China (No. 82370192).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC), West China Hospital, Sichuan University. All animal experimental procedures were conducted in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council of People\u0026rsquo;s Republic of China. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the databases in this article for providing their platforms and those contributors for uploading their valuable datasets.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerrell, N., Sadeghirad, H., Blick, T., Bidgood, C., Leggatt, G.R., O\u0026apos;Byrne, K., and Kulasinghe, A. (2023). Metabolomics at the tumor microenvironment interface: Decoding cellular conversations. Medicinal research reviews.\u003c/li\u003e\n\u003cli\u003eBlom, T., Li, S., Dichlberger, A., B\u0026auml;ck, N., Kim, Y.A., Loizides-Mangold, U., Riezman, H., Bittman, R., and Ikonen, E. (2015). 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Analytical chemistry\u003cem\u003e 93\u003c/em\u003e, 9778-9787.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmme","sideBox":"Learn more about [Journal of Molecular Medicine](https://www.springer.com/journal/109)","snPcode":"109","submissionUrl":"https://submission.nature.com/new-submission/109/3","title":"Journal of Molecular Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"LAPTM4B, Ph + B-ALL, pan-cancer, diagnosis, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4502403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4502403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLysosomal-associated protein transmembrane-4 beta (LAPTM4B) protein expression was increased in solid tumors, whereas few studies were performed in hematologic malignancies. We aimed to study the effect of the LAPTM4B gene in pan-cancer and Philadelphia chromosome-positive acute B cell lymphoblastic leukemia (Ph\u0026thinsp;+\u0026thinsp;B-ALL). The differential expression, diagnosis, prognosis, genetic and epigenetic alterations, tumor microenvironment, stemness, immune infiltration cells, function enrichment, single-cell analysis, and drug response across cancers were conducted based on multiple computational tools. Additionally, Ph\u0026thinsp;+\u0026thinsp;B-ALL transgenic mouse model with Laptm4b knockout was used to analyze the function of LAPTM4B in vivo. BrdU incorporation method, flow cytometry, and Witte-lock Witte culture were used to evaluate the roles of LAPTM4B in vitro. We identified that LAPTM4B expression was increased in various cancers, with significant associations with clinical outcomes. LAPTM4B expression correlated with DNA and RNA methylation patterns and was associated with drug resistance. It also influenced the tumor immune microenvironment, with implications for immunotherapy response. In leukemia, LAPTM4B was expressed in stem cells and associated with specific subtypes. Knockout of LAPTM4B impeded B-ALL progression in mice and reduced cell proliferation and caused G0/G1 arrest in vitro. Our study elucidated the role LAPTM4B that promoted the development and progression in Ph\u0026thinsp;+\u0026thinsp;B-ALL. Furthermore, LAPTM4B played a diagnostic, prognostic, and immunological factor.\u003c/p\u003e","manuscriptTitle":"A comprehensive prognostic and immune analysis of LAPTM4B in pan-cancer and Philadelphia chromosome-positive acute lymphoblastic leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 09:45:55","doi":"10.21203/rs.3.rs-4502403/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-02-13T22:02:03+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-24T18:20:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-09T12:50:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Medicine","date":"2024-07-09T07:39:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmme","sideBox":"Learn more about [Journal of Molecular Medicine](https://www.springer.com/journal/109)","snPcode":"109","submissionUrl":"https://submission.nature.com/new-submission/109/3","title":"Journal of Molecular Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b0c606ca-8f24-41bc-825e-55e4c22bb29e","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-21T09:45:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-21 09:45:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4502403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4502403","identity":"rs-4502403","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}