Abstract
Background
COVID-19 has created a significant risk to worldwide public health. According to recent research, C-type lectins may be SARS-CoV-2 receptors. Layilin (LAYN), a broadly expressed integral membrane hyaluronan receptor with a C-type lectin structural domain, is a gene related to cell senescence. There are a few studies on C-type lectins in pan-cancer, and no pan-cancer analysis has been conducted for LAYN.
Methods
The genotype tissue expression (GTEx) portal and the cancer genome map (TCGA) database were used to collect samples from healthy and cancer patients. Bioinformatics methods are used to construct immune landscape, mutation landscape, and stemness landscape of LAYN. The single-cell sequencing data were used from the CancerSEA website to analyze the functions of LAYN. The prognosis potential of LAYN was discussed based on machine learning.
Results
LAYN is differentially expressed among cancers. Survival analysis indicated that LAYN was related to a poor overall survival (OS) rate in cancers, like HNSC, MESO, and OV. Mutational landscapes of LAYN in SKCM and STAD were constructed. LAYN was negatively related to Tumor Mutation Burden (TMB) in THCA, PRAD, and UCEC, and with the Microsatellite Instability (MSI) in STAD, LUAD, and UCEC. The immune landscape in pan-cancer suggested that LAYN may be involved in tumor immune escape. LAYN plays a crucial role in the infiltration of immune cells in malignant tumors. LAYN participates in methylation modifications and affects tumor proliferation and metastasis by regulating stemness. Analysis of single-cell sequencing data suggests that LAYN may participate in several biological processes, like stemness, apoptosis, and DNA repair. LAYN transcript was predicted as a liquid–liquid phase separation (LLPS)-related RNA. The results of KIRC were verified in the GEO and ArrayExpress databases. Furthermore, prognostic models based on machine learning of LAYN-related genes were established. Hsa-miR-153-5p and hsa-miR-505-3p may be the upstream miRNAs of LAYN and have a high value for tumor prognosis.
Conclusion
This study elucidated the functional mechanisms of LAYN from a pan-cancer perspective and provided novel insights into cancer prognosis, metastasis, and immunotherapy. LAYN has the potential to become a new target of mRNA vaccines and molecular therapies in tumors.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00432-023-04986-7.
Keywords: Immune infiltration, Stemness, Pan-cancer, LAYN, Prognostic marker
Introduction
The International Agency for Research on Cancer (IARC) reported 19.3 million new cases of cancer and 10 million deaths from cancer, across the globe, in 2020 (Siegel et al. 2022). By 2022, China and the United States are expected to have ≈4,820,000 and 2,370,000 new cancer cases, and 3,210,000 and 640,000 cancer-related deaths, respectively (Xia et al. 2022). Despite the remarkable advancements in oncology treatment strategies in recent years, cancer remains the biggest health risk to human beings. Tumors arise by complex mechanisms, and individual genes may play similar or opposite roles in different tumors (Wang et al. 2019). Therefore, there is an urgent need to draw rigorous pan-cancer conclusions by understanding the expression of genes in multiple tumors.
LAYN, a commonly expressed integral membrane hyaluronan receptor, encodes the membrane glycoprotein, Layilin, with the structural domain of C-type lectin (Weng et al. 2002). LAYN is related to cell senescence and expressed in several types of cells and organs and plays a crucial role in regulating motility, cell adhesion, and migration (Rovillain et al. 2011; Bono et al. 2001). In addition, LAYN is significantly involved in cancer and promotes antitumor immunity by enhancing integrin activation (Mahuron et al. 2020). Pan et al. (2019) revealed that LAYN could be considered a prognostic biomarker and was related to the infiltration levels of immune cells in colon and gastric cancers. Inhibition of LAYN expression also significantly inhibited in vitro invasion and migration and in vivo lymphatic metastasis of the human lung cell line, i.e., A549 cells (Chen et al. 2008). However, none of the researchers have extensively investigated the functional mechanism of LAYN in a majority of cancers.
In this study, LAYN expression was comprehensively analyzed in a variety of cancers and identified its potential value in prognosis. Subsequently, the immune microenvironment and cell stemness of LAYN were analyzed. Additionally, enrichment analysis of the LAYN co-expressed genes was carried out, and upstream miRNAs that regulate LAYN were detected. LAYN may function as a prognosis- and immune infiltration-associated biomarker in pan-cancer and could be involved in metastases of malignant tumors, by reducing cellular stemness.
Material and methods
Data sources
The molecular and clinical data related to 33 different cancers were obtained from The Cancer Genome Atlas (TCGA), and all the data were combined and examined (The TCGA Legacy 2018). The GTEx tissue repository and data resource (https://gtexportal.org/) consist of more than 7000 samples from 53 normal human tissues (GTEx Consortium 2013). The normal tissue data collected from the TCGA and GTEx databases were combined, and the expression and function of DLAT were investigated in different types of normal and malignant tissues. The expression of LAYN was examined using GEPIA (GEPIA, http://gepia2.cancer-pku.cn, ver. 2) and the TIMER databases (https://cistrome.shinyapps.io/timer/). The results of LAYN in KIRC were verified using GSE68417 and GSE53000 from the GEO database and E-MTAB-1980 from the ArrayExpress database. Table 1 lists the complete names of the cancers that were the subject of this investigation, along with their acronyms.
Table 1.
The complete names of the cancers and their acronyms
| ACC | Adrenocortical carcinoma |
| BLCA | Bladder urothelial carcinoma |
| BRCA | Breast invasive carcinoma |
| CESC | Cervical squamous cell carcinoma and endocervical adenocarcinoma |
| CHOL | Cholangiocarcinoma |
| COAD | Colon adenocarcinoma |
| COADREAD | Colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma |
| DLBC | Lymphoid neoplasm diffuse large B cell lymphoma |
| ESCA | Esophageal carcinoma |
| GBM | Glioblastoma multiforme |
| GBMLGG | Glioma |
| HNSC | Head and neck squamous cell carcinoma |
| KICH | Kidney chromophobe |
| KIPAN | Pan-kidney cohort (KICH + KIRC + KIRP) |
| KIRC | Kidney renal clear cell carcinoma |
| KIRP | Kidney renal papillary cell carcinoma |
| LAML | Acute myeloid leukemia |
| LGG | Brain lower grade glioma |
| LIHC | Liver hepatocellular carcinoma |
| LUAD | Lung adenocarcinoma |
| LUSC | Lung squamous cell carcinoma |
| MESO | Mesothelioma |
| OV | Ovarian serous cystadenocarcinoma |
| PAAD | Pancreatic adenocarcinoma |
| PCPG | Pheochromocytoma and paraganglioma |
| PRAD | Prostate adenocarcinoma |
| READ | Rectum adenocarcinoma |
| SARC | Sarcoma |
| SKCM | Skin cutaneous melanoma |
| STAD | Stomach adenocarcinoma |
| STES | Stomach and esophageal carcinoma |
| TGCT | Testicular germ cell tumors |
| THCA | Thyroid carcinoma |
| THYM | Thymoma |
| UCEC | Uterine corpus endometrial carcinoma |
| UCS | Uterine carcinosarcoma |
| UVM | Uveal melanoma |
| COADREAD | Colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma |
| GBMLGG | Glioma |
| KIPAN | Pan-kidney cohort |
| STES | Stomach and esophageal carcinoma |
| EAC | Esophageal adenocarcinoma |
Prognostic analysis
The OS and Disease-Free Survival (DFS) data from the GEPIA database were collected to determine the potential value of LAYN in cancer prognosis (Tang et al. 2019a). Additionally, Cox regression was used to examine the predictive value of LAYN in pan-cancer.
Mutation analysis
The cBioPortal program (http://www.cbioportal.org/) (Gao et al. 2013) was used to examine the mutational features of LAYN in various cancers. The “TCGA pan-cancer mapping study” was chosen as the dataset in this study. Subsequently, “LAYN” was used as the keyword in the “Query” module. The site, type, and the number of LAYN alterations were determined in the “Cancer Type Summary” and “Mutations” modules.
Immune infiltration analysis
The TIMER database, which is an online resource, contains 10,897 tissue samples from various types of cancer (Li et al. 2020). The “GENE” module in the TIMER database was utilized for assessing the immune cell infiltration levels in different cancers.
Methylation analysis
The LAYN gene expression data as well as the expression data of the 44 marker genes for the 3 classes of RNA-modified (m1A, m5C, and m6A) genes were retrieved from the uniformly normalized pan-cancer dataset that was downloaded from the UCSC (https://xenabrowser.net/) database. Then, the expression values were transformed by a log2(x + 0.001) factor, and the Pearson correlation coefficient existing between the LAYN and marker genes for the RNA-modified genes was calculated.
Protein–protein interaction network analysis
An online tool called the STRING database (https://string-db.org/) was used for designing the organism-wide Protein–Protein Interaction (PPI) network (Szklarczyk et al. 2021). The STRING program was used to obtain DLAT-related proteins and construct the PPI networks.
Functional enrichment analyses
Gene correlation analysis of LAYN was performed using the GEPIA database, and the data regarding the leading 100 related genes were extracted. Pearson test was employed to analyze the relationship between LAYN and other associated genes. The correlation between the functional states of LAYN and 14 other types of cancers was assessed using the single-cell sequencing data from the “correlation graph” module, on the CancerSEA website (biocc.hrbmu.edu.cn/CancerSEA/home.jsp) (Yuan et al. 2019).
Stemness analysis
Six tumor stemness indices calculated by mRNA expression and methylation signature were obtained from a previous study (Malta et al. 2018), including DNAss, RNAss, DMPss, EREG-METHss, ENHss, and EREG.EXPss. The Pearson correlation between LAYN expression and tumor stemness indices was analyzed.
Drug susceptibility analysis
The Genomics of Drug Sensitivity in Cancer (GDSC) project collected the half-maximal Inhibitory Concentration (IC50) and the corresponding mRNA gene expression levels of the 265 small molecules from the 860 cell lines (Yang et al. 2013). The Cancer Therapeutics Response Portal (CTRP) was used to gather the IC50 values and corresponding mRNA gene expression levels of 481 small compounds in 1001 cell lines. The data on drug sensitivity and mRNA expression were then combined. Finally, using Pearson correlation analysis, it was possible to determine the relationship between the gene mRNA expression levels and the IC50 values of the drugs.
Liquid–liquid phase separation (LLPS) analysis
LLPS is the key to assemble membrane-free organelles. Multiple biological processes, including tumorigenesis and autophagy, have been shown to utilize the principle of LLPS to facilitate these processes (Peng et al. 2021). The RPS (http://rps.renlab.org) and UCSC Genome Browser (https://genome.ucsc.edu) were used to explore the relationship between LAYN and LLPS (Liu et al. 2022; Navarro Gonzalez et al. 2021).
The human protein atlas (HPA) analysis
The subcellular localization of LAYN were analyzed by HPA (www.proteinatlas.org) (Uhlen et al. 2017). It is noted that the samples in HPA did not correspond to the samples in TCGA dataset.
miRNA prediction and analysis
The miRDB website, which acted as the online platform for miRNA target prediction and functional annotation, was used to predict the miRNAs with potential regulatory effects on LAYN (Chen and Wang 2020). Finally, a Kaplan–Meier (KM) plotter (http://kmplot.com) was used to evaluate the prognostic value of the miRNAs.
Statistical analysis
Using the t test, differences across groups were determined, whereas the Pearson test was used to evaluate the correlation between the groups. The R programming language (https://www.r-project.org/) (ver. 4.0.3) was used to execute each R package. The statistical analysis was done using SPSS 24.0 software. The result was significant if the P value was less than 0.05.
Results
LAYN expression in human tissues
According to the HPA dataset, LAYN is widely expressed in human tissues. LAYN has high RNA expression in female tissues, gastrointestinal tract and endocrine tissues, and high protein expression in gastrointestinal tract, female tissues, and muscle tissues (Fig. 1A). Subcellular localization showed that LAYN was localized to cytosol (Fig. 1B–D). The highly conserved expression of LAYN was found in Euteleostomi (Fig. S1). The conserved regions of 100 vertebrates analyzed by PhyloP can be seen that the regions corresponding to exons are highly conserved (Fig. 1E). The extensive expression and high conservatism suggest that LAYN may play an important role in human beings and even in Euteleostomi.
Fig. 1.
LAYN expression in human tissues and its conservation. A LAYN expression in human tissues. Subcellular localization of LAYN in A-431 (B), U-2 OS (C), and U-251 MG (D) cell lines. E The conserved regions of vertebrates analyzed by PhyloP
LAYN expression in human cancers
To investigate the potential involvement of LAYN in carcinogenesis, the differential expression of LAYN in various malignant and normal tissue samples was examined using GEPIA. In comparison to normal tissues, LAYN was expressed at a high level in eight different cancer types (ACC, GBM, DLBC, HNSC, KIRC, PAAD, LGG, and THYM). Additionally, lower LAYN expression levels were observed in 12 types of cancer, like BRCA, BLCA, CESC, COAD, KICH, LUAD, OV, READ, PRAD, THCA, UCEC, and UCS (Fig. 2A). This result was validated in the Timer database. The expression of LAYN differed between 16 malignant and normal tissue samples (Fig. 2B). Using the “Pathological Stage Map” module of GEPIA, the relationship between the LAYN expression and the pathological stages of cancer was determined. Differential LAYN expression levels were noted during different stages of cancers, like KIRP, BLCA, STAD, THCA, and UCEC (Fig. 2C; all P < 0.05).
Fig. 2.
A Expression profile of LAYN in pan-cancer. LAYN expression was determined using the GEPIA database pan-cancer. B LAYN expression was determined using the TIMER database pan-cancer. C Relationship between the LAYN expression and different cancer stages
Survival analysis of LAYN in human cancer
Patients with cancer were classified into the high- and low-expression categories (groups) based on LAYN expression levels to investigate the correlation between LAYN expression and the prognosis among patients with 33 tumors. As depicted in Fig. 3A, elevated LAYN expression level was related to a poor OS value among patients with HNSC, MESO, OV, STAD, and UVM, whereas low LAYN expression levels were related to the poor OS value of SKCM patients. The DFS data indicated that a high LAYN expression could be related to the poor prognosis of patients with GBM, OV, and UVM, whereas the low LAYN expression was related to the poor prognosis of patients suffering from KIRP and THCA (Fig. 3B). In addition, the prognostic value of LAYN in different tumors was verified using the COX regression analysis. Similar to the KM curve results, LAYN expression could be associated with the OS values of BLCA, MESO, OV, STAD, and UVM patients (Fig. 3C). Furthermore, LAYN expression was associated with the DFS values of the OV and THCA patients (Fig. 3D). Therefore, LAYN had a certain prognostic value. However, the specific role of LAYN (a risk factor or a protective factor) depends on the type of cancer.
Fig. 3.
Prognostic value of LAYN in pan-cancer. A GEPIA analysis indicates the relationship between the LAYN expression levels and OS of the patients. B GEPIA analysis denotes the relationship between the LAYN expression levels and DFS. C Univariate Cox analysis of the relationship between the LYN expression levels and OS. D Univariate Cox analysis of the relationship between the LAYN expression level and the DFS
Genetic variations in LAYN expression are noted in different human cancers
The genetic variation in the LAYN expression levels in various tumor samples derived from the TCGA cohort was observed. As presented in Fig. 4A, the SKCM patients who showed “mutation” as the dominating type of genetic variation showed the highest frequency of LAYN genetic alterations (> 5%). In addition, other types of cancers, like CESC, TGCT, SARC, UCS, and UVM, showed “deep deletion” as the predominant form of genetic alteration, with a frequency of > 2%. LAYN copy number deletion was present in all TGCT, UVM, PRAD, and KIRP individuals with genetic alterations. The two-dimensional (Fig. 4B) and three-dimensional (Fig. 4C) structures of LAYN were visualized and the types and sites of mutations in the two-dimensional structure were marked. The missense mutation was the predominant form of mutation in patients with LAYN tumors. Site 216 was the most frequently mutated and could be mutated from E (Glu) to K (Lys) or D (Asp). Furthermore, the two cancers (SKCM and STAD) with the highest genetic variation frequency were selected to construct a mutational landscape of LAYN. As shown in Fig. 4D–E, the Top-3 mutated genes existing in SKCM were seen to be TTN, MUC16, and BRAF, while TTN, TP53, and MUC16 were the Top 3 mutated genes in STAD. Finally, the isomer landscape of LAYN was summarized (Fig. S2).
Fig. 4.
Mutation characteristics of LAYN in different types of cancers, based on the TCGA data, were determined by cBioPortal. The genetic alteration frequency based on the A Mutation type; and B Mutation site has been presented. In addition, B 2D and C 3D structures of LAYN were visualized. D Mutation landscape of LAYN in SKCM. E Mutation landscape of LAYN in STAD
Relationship between LAYN expression and TMB/MSI
The TMB, which is a measurable biomarker, characterizes the number of genetic mutations in tumor cells. MSI refers to a change in microsatellite length caused by mismatch repair mechanism failure during DNA replication. TMB/MSI is valuable for determining the efficacy of immunotherapy. The correlation between the LAYN expression levels and TMB was determined and assessed using the Spearman rank correlation coefficients (Fig. 5A). The results indicated that the LAYN gene expression levels were negatively related to the TMB in BRCA, PRAD, THCA, STAD, UCEC, LUSC, ESCA, LUAD, DLBC, CESC, BLCA, and UVM (Fig. 5B). Similarly, the correlation between LAYN gene expression levels and MSI was assessed (Fig. 5C). LAYN gene expression levels were negatively related to MSI in STAD, LUAD, and UCEC, but positively related to MSI in COAD and LAYN (Fig. 5D). These results, especially those of TMB, indicate that LAYN could play a vital role in immunotherapy.
Fig. 5.
Relationship between LAYN expression levels and TMB/MSI. A Relationship between LAYN expression levels and TMB in pan-cancer. B Pearson correlation analysis between the LAYN and tumors with TMB correlation. C Relationship between LAYN expression levels and MSI in pan-cancer. D Pearson correlation analysis of LAYN with tumors with MSI correlation
Association between LAYN and the immune checkpoint genes
Immune checkpoint genes are involved in maintaining the homeostasis of the immune functions of the body. However, many cancer cells use immune checkpoints to evade immune surveillance. PD1/PD-L1 is a key immune checkpoint responsible for tumor immune escape. The association between the LAYN and PDCD1, CD274, LAG3, and CTLA4 was evaluated to investigate the relationship between LAYN expression and the immune checkpoint genes. A positive relationship was noted between the LAYN expression and the immune checkpoint genes in most tumors (Fig. 6A). In particular, LAYN was positively correlated with PDCD1, CD274, LAG3, and CTLA4 in BLCA, COAD, LIHC, UVM, PAAD, PRAD, SKCM, STAD, and UCEC (Fig. 6B–E). In addition, the correlation of LAYN with some other immune checkpoints, such as TIGIT, PDCD1LG2, HAVCR2, and SIGLEC15, was evaluated (Fig. S3). These findings indicate that LAYN plays a key role in tumor immune escape.
Fig. 6.
Correlation between LAYN and the immune checkpoint genes. A Heatmap depicting the correlation between LAYN and PDCD1, CD274, LAG3, and CTLA4 expression in pan-cancer. LAYN was seen to be positively related to PDCD1, CD274, LAG3, and CTLA4 in B BLCA; C COAD; D LIHC; and E UVM
Relationship between LAYN and immune infiltration
Tumor-Infiltrating Immune Cells (TIICs) are a vital component of the Tumor Microenvironment (TME) and are noted to be closely related to the onset, progression, and metastasis of cancer. The Timer algorithm was used to determine the potential correlation between the infiltration level of various immune cells and LAYN gene expression levels in different types of cancers, derived from the TCGA dataset. A positive relationship was noted between LAYN and the infiltration levels of different immune cells in most types of cancer. In particular, LIHC, COAD, LUAD, PRAD, PAAD, SKCM, and UCEC were positively related to the infiltration of all 6 types of immune cells (Fig. 7A). In addition, a negative correlation was noted between LAYN and T cell CD4 + , myeloid dendritic cells, neutrophils, and B cells in UVM.
Fig. 7.
Correlation between the LAYN expression levels and the infiltration levels of different immune cells. A Immune cell infiltration of LAYN in pan-cancer. B Multiple algorithms show a positive correlation between LAYN and CAFs. C Correlation analysis of LAYN with the CAFs in BLCA, COAD, BRCA, PAAD, READ, PRAD, STAD, and TGCT
Cancer-Associated Fibroblasts (CAFs) existing in the TME stroma regulate the functioning of different TIICs. The correlation between LAYN and CAFs was assessed using multiple algorithms. LAYN was positively related to the CAFs in most tumors, except for ACC, DLBC, and SARC (Fig. 7B). The relationship between the LAYN expression levels and infiltration levels of CAFs in BLCA, COAD, BRCA, PAAD, READ, PRAD, STAD, and TGCT was visualized using different algorithms (Fig. 7C). The above findings showed that LAYN could play a crucial biological role in immune infiltration.
Relationship between LAYN and RNA methylation modifications
RNA methylation modification, one of the primary mechanisms of epigenetic regulation, plays a key role in the development of tumor cells. The expression levels of marker genes for m1A modification in RNA modification were extracted, including four writer genes, three reader genes, and one eraser gene. Subsequently, their correlation with LAYN in different cancer types was analyzed. m1A modification-related genes were correlated with LAYN in most of the tumors (Fig. 8A). Similarly, m5C- and m6A-related genes were analyzed and similar results were obtained as with m1A: LAYN was correlated with RNA methylation modification-related genes in almost all cancers (Fig. 8B–C).
Fig. 8.
Relationship between the LAYN expression levels and the RNA methylation modifications. Association between the LAYN expression and A m1A-modified marker genes; B m5C-modified marker genes; and C m6A-modified marker genes in pan-cancer
Relationship between LAYN and cellular stemness
Cellular stemness is closely related to the proliferative capacity of tumor cells. The tumor stemness scores of LAYN in pan-cancer were calculated using six different methods, and a Pearson correlation analysis was performed. As shown in Fig. 9A, LAYN was significantly correlated with DNAss tumor stemness scores in 21 tumors, with a significant positive correlation in 10 tumors, including GBMLGG, LGG, CESC, SARC, KIPAN, PRAD, KIRC, MESO, SKCM, and UVM, and significant negative correlation in 11 tumors, including COAD, COADREAD, BRCA, STES, STAD, UCEC, LIHC, OV, TGCT, PCPG, and BLCA. The results of EREG-METHss, DMPss, ENHss, RNAss, and EREG.EXPss were similar to those of DNAss (Fig. 9B–F). LAYN was correlated with stemness in most tumors. Surprisingly, LAYN was negatively correlated with all tumors, as evidenced by the RNAss tumor stemness score. Therefore, low expression of LAYN may be closely correlated with tumor immune evasion and treatment resistance.
Fig. 9.
Correlation of LAYN expression with tumor cell stemness. Correlation of LAYN with A DNAss; B EREG-METHss; C DMPss; D ENHss; E RNAss; and F EREG.EXPss scores in pan-cancer
Relationship between LAYN and drug susceptibility
The IC50 values of 265 small molecules and their LAYN mRNA expression were collected from GDSC in 860 cell lines, and thereafter, all mRNA expression data related to LAYN and drug susceptibility were combined. The relationship between LAYN mRNA expression levels and the drug IC50 values was determined using the Pearson correlation analysis. As presented in Fig. 10A, LAYN gene expression was seen to be positively related to drugs, such as WZ3105 and KIN001-102, and negatively related to drugs, such as Bleomycin and TGX221. The correlation between LAYN and drug susceptibility was determined by the treatment response portal (CTRP). A positive correlation was observed between LAYN and several drugs, such as GW-405833, COL-3, and belinostat (Fig. 10B).
Fig. 10.
Relationship between LAYN and drug susceptibility. A Correlation between LAYN expression and susceptibility of top 30 GDSC drugs in pan-cancer. B Relationship between LAYN expression levels and susceptibility of Top-30 CTRP drugs in pan-cancer
Enrichment analysis of LAYN-related genes
This study screened 50 LAYN-related proteins by the STRING tool and visualized the PPI networks between these proteins to further determine the probable molecular mechanisms of LAYN in tumor progression (Fig. 11A). All tumor data from TCGA were integrated using GEPIA to acquire the Top-100 genes related to DLAT expression. LAYN-related genes were assessed using the Gene Ontology (GO) analysis. The results of the enrichment analysis, with regard to Cellular Component (CC), Molecular Function (MF), and Biological Process (BP), are shown in Fig. 11B. LAYN may play a biological role via branching involved in blood vessel morphogenesis and other pathways. A further dendrogram analysis of the GO outcomes was performed (Fig. 11C). BP analysis indicated that LAYN may be involved in endothelial cell development and proliferation. KEGG analysis of LAYN-related genes was performed and the top 25 genes and related pathways were visualized (Fig. 11D). The top three LAYN-related pathways were ECM–receptor interaction, glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate, and pathways in cancer. The relevant functions of LAYN in 14 cancers were also explored by single-cell data. LAYN was negatively correlated with cell cycle, apoptosis, DNA damage, DNA repair, hypoxia, invasion, inflammation, and stemness, and positively correlated with angiogenesis, differentiation, and inflammation in some cancers (Fig. 11E).
Fig. 11.
Enrichment analysis of LAYN-related genes. A PPI network of LAYN and its associated genes. B GO enrichment analysis of LAYN-linked genes. C Functional dendrogram analysis of LAYN-related genes based on GO analysis. D KEGG analysis of LAYN-linked genes. E Functional analysis of LAYN in 14 types of cancers
The formation of biomolecular aggregates by LLPS has become a universal mechanism for the spatiotemporal coordination of biological activities in cells. Dysregulation of LLPS is a recessive driver of oncogenic activity. We built the interaction network of LAYN through RPS and found that LAYN interacted with LLPS-related RNA (Fig. 12A). We visualized the RNA secondary structure of LAYN and analyzed the LLPS-related binding sites on LAYN (Fig. 12B–C). All these results indicate that the LAYN transcript is most likely a LLPS-related RNA and play a biological role.
Fig. 12.
Correlation between LAYN and LLPS. A The interaction network of LAYN through RPS. B The RNA secondary structure of LAYN. C The LLPS-related binding sites on LAYN
Prognostic model construction for LAYN-related genes
Prognostic models of LAYN-related genes were developed by incorporating LAYN and 20 LAYN-related genes to determine the prognostic value of LAYN-related genes in tumors. Lasso regression method was used due to the significant multicollinearity between the genes. As shown in Fig. 13A–C, the prognostic modes established for LAYN and its associated genes in PAAD were as follows:
Fig. 13.
Prognostic model construction for LAYN-related genes. A LASSO coefficient mapping of prognosis-related genes in PAAD. B The plot of partial likelihood deviation in PAAD shows the minimum number of covariates corresponding to those used for LASSO Cox analysis. C Distribution of risk scores in PAAD, Kaplan–Meier plots for OS, and 1, 3 and 5 years ROC survival predictions. D LASSO coefficient mapping of prognosis-related genes in KIRC. E A plot of partial likelihood deviation in KIRC showing the minimum number of covariates corresponding to those used for LASSO Cox analysis. F Distribution of risk scores in KIRC, Kaplan–Meier plots for OS, and 1, 3 and 5 years ROC survival predictions
Risk score = (0.0802) * LAYN + (− 0.2923) * TMEM204 + (0.0155) * THY1 + (0.1214) * SMIM3 + (0.4973) * RBFOX2 + (0.029) * GALNT18 + (− 0.0525) * RGS4 + (− 0.2764) * SLC18A1 + (0.0842) * DNAJB4 + (− 0.7874) * SOBP, with a 5 years AUC value of 90.6%. Similarly, the prognosis model of LAYN and its associated genes in KIRC was developed as follows (Fig. 13D–F):
Risk score = (0.2472) * LAYN + (− 0.4404) * CDH13 + (− 0.2059) * TMEM204 + (0.1136) * THY1 + (0.0646) * SMIM3 + (0.0552) * RBFOX2 + (− 0.0522) * GALNT18 + (0.1142) * COL4A2 + (0.0363) * COPZ2 + (− 0.0072) * SOBP, with 1, 3 and 5 years AUC values of approximately 74%. The above results can indicate that the model constructed by LAYN and its related genes has a high value for survival prediction. We also constructed a nomogram based on the above genes in KIRC (Fig. S4–S7).
Validation in other databases
There are doubts about the reliability of a single database, so we used GEO and ArrayExpress databases to validate the expression and prediction model of LAYN in KIRC. We first standardized the data of GSE68417 and GSE53000, then removed batch effects, and finally obtained the expression differences of LAYN in KIRC and normal kidney tissue (Fig. 14A–D). Similar to the results in the TCGA database, LAYN is highly expressed in renal cell carcinoma. We validated the predictive model in E-MTAB-1980. The results show that the prediction model based on the above genes has a good predictive effect on the prognosis of KIRC patients (Fig. 14E).
Fig. 14.
Validation in other databases. A The boxplot of the normalized data (GEO database). B PCA results before batch removal for multiple datasets. C PCA results after batch removal. D The expression distribution of LAYN. G1: cancer, G2: normal. EValidation of the prognostic model for KIRC in E-MTAB-1980
Prediction and analysis of miRNAs upstream of LAYN
miRNAs can be involved in regulating the expression of genes. Upstream miRNAs that may bind to LAYN were predicted through the miRDB website to determine whether LAYN was regulated by certain miRNAs. In total, 68 miRNAs that may be involved in LAYN regulation were identified (Table 2). In addition, the prediction scores of miRDB are listed. As per the miRNA regulation mechanism underlying the target gene expression, a negative correlation was hypothesized between the miRNAs and LAYN. Hence, the relationship between the 10 miRNAs with the highest miRBD scores and LAYN in tumors was evaluated (Table 3). The hsa-miR-153-5p and hsa-miR-505-3p sequences were negatively related to LAYN expression in eight and ten tumors, respectively. After analyzing the remaining miRNAs, the findings indicated that LAYN expression was negatively related to a few tumors. The above results suggest that hsa-miR-153-5p and hsa-miR-505-3p are the most likely upstream miRNAs of LAYN. KM plots of hsa-miR-153 and hsa-miR-505 were plotted (Fig. 15A–B), highlighting the prognostic value of hsa-miR-153 in CSCC, ESCC, HNSC, KIRC, KIRP, LUAD, OV, PDAC, SARC, THYM, and UCEC, while hsa-miR-505 had a prognostic value in BLCA, EAC, LUSC, OV, READ, SARC, TGCT, and THYM.
Table 2.
Upstream miRNAs of LAYN
| Target score | miRNA name | Gene symbol | Gene description |
|---|---|---|---|
| 96 | hsa-miR-153-5p | LAYN | Layilin |
| 94 | hsa-miR-138-5p | LAYN | Layilin |
| 90 | hsa-miR-3617-5p | LAYN | Layilin |
| 90 | hsa-miR-641 | LAYN | Layilin |
| 89 | hsa-miR-5787 | LAYN | Layilin |
| 89 | hsa-miR-4505 | LAYN | Layilin |
| 87 | hsa-miR-450b-5p | LAYN | Layilin |
| 86 | hsa-miR-505-3p | LAYN | Layilin |
| 84 | hsa-miR-4999-5p | LAYN | Layilin |
| 83 | hsa-miR-6888-3p | LAYN | Layilin |
| 81 | hsa-miR-4743-3p | LAYN | Layilin |
| 79 | hsa-miR-3120-5p | LAYN | Layilin |
| 78 | hsa-miR-4663 | LAYN | Layilin |
| 77 | hsa-miR-619-5p | LAYN | Layilin |
| 75 | hsa-miR-6893-3p | LAYN | Layilin |
| 75 | hsa-miR-370-3p | LAYN | Layilin |
| 75 | hsa-miR-4283 | LAYN | Layilin |
| 74 | hsa-miR-6506-5p | LAYN | Layilin |
| 73 | hsa-miR-30e-3p | LAYN | Layilin |
| 73 | hsa-miR-30a-3p | LAYN | Layilin |
| 73 | hsa-miR-30d-3p | LAYN | Layilin |
| 73 | hsa-miR-6890-3p | LAYN | Layilin |
| 72 | hsa-miR-3915 | LAYN | Layilin |
| 71 | hsa-miR-5688 | LAYN | Layilin |
| 70 | hsa-miR-299-3p | LAYN | Layilin |
| 70 | hsa-miR-939-3p | LAYN | Layilin |
| 70 | hsa-miR-146a-3p | LAYN | Layilin |
| 68 | hsa-miR-4782-5p | LAYN | Layilin |
| 68 | hsa-miR-3065-5p | LAYN | Layilin |
| 68 | hsa-miR-4768-3p | LAYN | Layilin |
| 68 | hsa-miR-6839-5p | LAYN | Layilin |
| 68 | hsa-miR-5706 | LAYN | Layilin |
| 68 | hsa-miR-6834-5p | LAYN | Layilin |
| 67 | hsa-miR-6799-3p | LAYN | Layilin |
| 67 | hsa-miR-3144-5p | LAYN | Layilin |
| 67 | hsa-miR-2467-3p | LAYN | Layilin |
| 67 | hsa-miR-3173-5p | LAYN | Layilin |
| 65 | hsa-miR-3074-5p | LAYN | Layilin |
| 64 | hsa-miR-495-3p | LAYN | Layilin |
| 64 | hsa-miR-520g-5p | LAYN | Layilin |
| 64 | hsa-miR-510-3p | LAYN | Layilin |
| 64 | hsa-miR-4795-3p | LAYN | Layilin |
| 63 | hsa-miR-223-3p | LAYN | Layilin |
| 62 | hsa-miR-4296 | LAYN | Layilin |
| 62 | hsa-miR-378j | LAYN | Layilin |
| 61 | hsa-miR-627-3p | LAYN | Layilin |
| 61 | hsa-miR-6885-3p | LAYN | Layilin |
| 60 | hsa-miR-1303 | LAYN | Layilin |
| 60 | hsa-miR-4314 | LAYN | Layilin |
| 59 | hsa-miR-6856-3p | LAYN | Layilin |
| 58 | hsa-miR-3680-3p | LAYN | Layilin |
| 58 | hsa-miR-507 | LAYN | Layilin |
| 57 | hsa-miR-4265 | LAYN | Layilin |
| 57 | hsa-miR-4322 | LAYN | layilin |
| 57 | hsa-miR-557 | LAYN | Layilin |
| 56 | hsa-miR-320a-5p | LAYN | Layilin |
| 55 | hsa-miR-4680-3p | LAYN | Layilin |
| 54 | hsa-miR-492 | LAYN | Layilin |
| 53 | hsa-miR-133a-5p | LAYN | Layilin |
| 53 | hsa-miR-6131 | LAYN | Layilin |
| 53 | hsa-miR-100-3p | LAYN | Layilin |
| 53 | hsa-miR-3672 | LAYN | Layilin |
| 53 | hsa-miR-6864-3p | LAYN | Layilin |
| 52 | hsa-miR-1244 | LAYN | Layilin |
| 51 | hsa-miR-570-3p | LAYN | Layilin |
| 51 | hsa-miR-6755-3p | LAYN | Layilin |
| 50 | hsa-miR-19a-5p | LAYN | Layilin |
| 50 | hsa-miR-651-3p | LAYN | Layilin |
Table 3.
Correlation between miRNAs and LAYN in tumors
| miRNAs | Cancer | Sample number | Coefficient-R | P value |
|---|---|---|---|---|
| hsa-miR-153-5p | PCPG | 183 | − 0.44 | 4.78E-10 |
| PAAD | 178 | − 0.356 | 1.09E-06 | |
| PRAD | 495 | − 0.355 | 3.95E-16 | |
| ESCA | 162 | − 0.348 | 5.80E-06 | |
| STAD | 372 | − 0.324 | 1.45E-10 | |
| READ | 161 | − 0.205 | 8.99E-03 | |
| COAD | 450 | − 0.16 | 6.72E-04 | |
| OV | 376 | − 0.101 | 4.96E-02 | |
| hsa-miR-138-5p | PCPG | 183 | − 0.309 | 2.05E-05 |
| THYM | 119 | − 0.259 | 4.43E-03 | |
| KIRC | 517 | − 0.207 | 1.95E-06 | |
| SKCM | 449 | − 0.141 | 2.67E-03 | |
| hsa-miR-3617-5p | LUSC | 475 | − 0.167 | 2.46E-04 |
| PCPG | 183 | − 0.15 | 4.21E-02 | |
| hsa-miR-641 | BRCA | 1085 | − 0.075 | 1.34E-02 |
| hsa-miR-4505 | UCS | 56 | − 0.306 | 2.19E-02 |
| hsa-miR-450b-5p | STAD | 372 | − 0.244 | 1.96E-06 |
| KIRP | 289 | − 0.194 | 9.13E-04 | |
| hsa-miR-505-3p | TGCT | 156 | − 0.39 | 4.80E-07 |
| THYM | 119 | − 0.385 | 1.50E-05 | |
| STAD | 372 | − 0.325 | 1.41E-10 | |
| COAD | 450 | − 0.303 | 5.02E-11 | |
| READ | 161 | − 0.26 | 8.81E-04 | |
| BLCA | 408 | − 0.217 | 1.00E-05 | |
| BRCA | 1085 | − 0.19 | 2.64E-10 | |
| LIHC | 370 | − 0.133 | 1.02E-02 | |
| LGG | 525 | − 0.13 | 2.90E-03 | |
| LUSC | 475 | − 0.113 | 1.38E-02 | |
| hsa-miR-4999-5p | BLCA | 408 | − 0.141 | 4.46E-03 |
Fig. 15.
Prognostic value of miRNAs upstream of LAYN. A KM plots of hsa-miR-153 in 11 tumors. B KM plots of hsa-miR-505 in eight tumors
Discussion
Layilin, which is a type I transmembrane protein, is seen to bind to cytoskeletal proteins, like merlin and talin, for regulating cell adhesion (Rovillain et al. 2011; Wegener et al. 2008). The rapid advancement of genomics and proteomics has led to the identification of numerous genes and their functions in tumors. In addition, LAYN has received much attention. LAYN is upregulated in 8 cancers, such as HNSC and KIRC, and significantly downregulated in 12 cancers, such as BLCA and BRCA, which was similar to previous findings. Differential expression of LAYN in tumors is common (Pan et al. 2019). Aberrant LAYN expression in tumor cells can regulate various signaling pathways and ultimately mediate the development of tumor cells into a more aggressive phenotype.
LAYN is a promising prognostic marker in tumors. LAYN overexpression is related to poor prognosis and low OS rates in patients with HNSC, OV, STAD, MESO, and UVM. However, higher LAYN expression levels were indicative of a better prognosis for SKCM patients. Therefore, LAYN may play different biological roles in different tumors. A study in malignant glioma revealed that LAYN improves the invasiveness of the malignant glioma cells, which was independent of its functioning as the hyaluronan receptor (Kaji et al. 2019). Thus, the functions of LAYN are diverse and complex. LAYN may also act as a disease marker in other diseases in addition to tumors. High LAYN expression is related to the severity of plastic bronchitis. LAYN could be regarded as a biomarker for the early prediction of mycoplasma pneumonia combined with plastic bronchitis (Ma et al. 2022). LAYN is hypothesized to play a key role in the progression of renal fibrosis in cellular assays, and 5 proteins, including LAYN, are seen to be independently related to the increased risk of diabetes progression and renal failure (Kobayashi et al. 2022).
TMB is defined as the number of somatic mutations in the tumor genome despite the removal of the germline mutations. A stronger ability of immune cells to recognize tumor cells suggests a better effect of immunotherapy. In the case of high TMB, tumors produce more neo-antigens, resulting in more tumors being recognized by immune cells. A significant relationship was noted between high TMB and improved survival in various cancer tissues (Samstein et al. 2019). In the present study, a negative relationship was observed between LAYN and TMB in a variety of tumors, suggesting that LAYN may improve the aggressiveness of tumor cells by decreasing TMB. MSI, primarily caused by the defective mismatch repair (MMR) function, is a variation in the MS sequence owing to the deletion or insertion mutations during the DNA replication process (Cortes-Ciriano et al. 2017). Most patients who display a higher MSI level show a better outcome and prognosis compared to patients with a low MSI level or stable microsatellites (Yang et al. 2019). LAYN does not correlate well with MSI, and it may only act through MSI in several tumors, such as STAD.
Various factors, including reactive oxygen species, cytokines, metabolites, extracellular matrix, and inflammation, have an impact on TME, which is made up of stromal cells, immune cells, and tumor cells (Balta et al. 2021; Roma-Rodrigues et al. 2019). Tumor cells have developed several immune escape strategies, such as loss of antigenicity, immunogenicity, and synthesis of immunosuppressive TME (Jhunjhunwala et al. 2021; Drake et al. 2006). Immunotherapy has the potential to induce durable responses. However, in the past few years, very few patients have positively responded to immunotherapy owing to the immune evasion mechanisms implemented by tumor cells. Therefore, immunotherapy faces significant obstacles in cancer treatment (Murciano-Goroff et al. 2020). LAYN correlates with PDCD1, CD274, LAG3, and CTLA4 in most tumors, suggesting a possible role for LAYN in immunotherapy. In addition, LAYN is related to the infiltration level of immune cells, like CD4 + T cells, CD8 + T cells, neutrophils, and macrophages, in a majority of cancers. LAYN had a weak correlation with B cells but had a certain correlation with several cancers including COAD and LIHC. The above analyses suggest that LAYN might play a crucial role in the tumor immune infiltration level and immune microenvironment.
Methylation modifications are involved in innate immunity, carcinogenesis, and antitumor immune responses (Yang et al. 2021; Papanicolau-Sengos and Aldape 2022). Among the more than 100 different RNA chemical modifications that have been identified, 5-methylcytidine (C5-methylcytidine, m5C), 6-methyladenosine (N6-methyladenosine, m6A), and 1-methyladenosine (N1-methyladenosine, m1A) modifications play a key role. In mammals, the proportion of adenine A that undergoes m6A modification is 0.1–0.4%, with only 3–5 m6A methylation sites per mRNA. In addition, m6A modification can regulate stem cell differentiation, cell proliferation, metastasis, and homeostasis in different cancers by affecting cell biological functions (Ma et al. 2019). m5C and m1A are also involved in regulating cell proliferation and metastasis formation (Song et al. 2021; Haruehanroengra et al. 2020). Methylation studies on LAYN have not been reported yet. The present study confirmed that LAYN correlates with methylation modification-related genes in most tumors by analyzing the merged data from TCGA and GTEx. Therefore, LAYN may exert its effect by interacting with enzymes regulating RNA methylation modifications and may act as a new therapeutic target.
Cancer Stem Cells (CSCs) are a type of self-renewing cells that promote tumorigenesis, help in metastasis, and increase resistance to cancer therapy. Transcriptomic analysis of the various types of cancer highlighted the significant association between stemness and immune characteristics, suggesting biological interactions between these hallmark features of cancer (Chen et al. 2021; Romano et al. 2020). Since LAYN is seen to play a key role in TME, the relationship between LAYN and stemness was further analyzed. LAYN was negatively related to cell stemness in many tumors, and in particular, a significantly negative relationship was noted between LAYN and RNAss stemness scores in all tumors. Therefore, cellular stemness is an important pathway for LAYN to play its biological role. Low LAYN expression might be closely associated with tumor immune evasion and treatment resistance. Layilin co-localized with integrin αLβ2 (LFA-1) on the T cells at the molecular level, and cross-linking layilin stimulated the activation of this integrin. Thus, LAYN deficiency may lead to diminished LFA-1-dependent cell adhesion (Bono et al. 2001). The relevant functions of LAYN in 14 cancers were explored by single-cell data. Similar to the results of stemness analysis, LAYN was negatively correlated with pathways, such as stemness in some cancers. The TNF-α administration improved the LAYN expression in mouse renal tubular epithelial cells, suggesting that LAYN could be involved in the TNF-α-induced Epithelial–Mesenchymal Transition (EMT) of the renal tubular epithelial cells (Adachi et al. 2015). However, LAYN expression and EMT did not show any relationship in the single-cell sequencing-based pathway analyses. The pathway study of LAYN is currently inadequate, and the pathway via which LAYN exerts its biological function requires further exploration.miRNAs, small non-coding RNAs, regulate gene expression by identifying homologous sequences and by interfering with the transcriptional, translational, or epigenetic processes (Chen et al. 2019). Currently, no studies on miRNAs upstream of LAYN have been reported. In the present study, upstream miRNAs that may regulate LAYN were predicted by binding sites. hsa-miR-153-5p and hsa-miR-505-3p may be regulatory miRNAs of LAYN. In the past, many researchers have extensively explored the involvement of hsa-miR-153-5p and hsa-miR-505-3p in different tumors (Li et al. 2021; Wang et al. 2020; Zhao et al. 2019; Tang et al. 2019b). This study provides new corroboration for their prognostic value.
Conclusions
This study thoroughly examined the role played by LAYN in pan-cancer. The first pan-cancer analysis of LAYN showed that many human cancers showed a differential expression of LAYN. Expression of LAYN can be associated with the prognosis of patients with some types of cancers. We established the prognostic models of LAYN and related genes in PAAD and KIRC through machine learning, which showed that LAYN has good prognostic potential. LAYN expression is closely related to the infiltration level of different immune cells, especially CAFs. LAYN plays a vital role in immunotherapy. In addition, aberrant LAYN expression in various cancers was significantly related to TMB, MSI, RNA methylation, and cell stemness. Single-cell sequencing-based data indicated that LAYN could be involved in multiple biological roles, like stemness, apoptosis, and DNA repair. Consequently, LAYN was seen to be involved in prognosis prediction, immune microenvironment, tumor development, and metastasis. LAYN has the potential to become a new target of mRNA vaccines and molecular therapies in tumors.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Author contributions
WJ designed the work. WJ and WJ analyzed the data and prepared the manuscript. WJ, LJ, and ZY edited and revised the manuscript. All the authors read and approved the final manuscript.
Funding
This study was supported by the National Key Research and Development Program of China (grant number: 2018YFC2002202).
Data availability
The datasets presented in this study can be found in online repositories. The names of the repository/repositories can be found in the article/supplementary material.
Declarations
Conflict of interest
The authors declare that they have no competing interests.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Wang Jiawen and Wang Jinfu have contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories can be found in the article/supplementary material.