
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00237-7
10.1016/j.tranon.2024.102110
102110
Original Research
The immunological landscape and silico analysis of key paraptosis regulator LPAR1 in gastric cancer patients
Dai Ya-Jie yajie094584@163.com
ab⁎
Tang Hao-Dong ab
Jiang Guang-Qing ab
Xu Zhai-Yue ab
a Department of General Surgery, Zhongda Hospital, Southeast University, Nanjing, Jiangsu 210009, PR China
b Department of Surgery, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, PR China
⁎ Corresponding author. yajie094584@163.com
24 8 2024
11 2024
24 8 2024
49 1021108 6 2024
10 8 2024
21 8 2024
© 2024 Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Highlights

• LPAR1 consistently ranked highest in feature importance for paraptosis regulation in gastric cancer using machine learning models.

• High LPAR1 expression in cancer-associated fibroblasts (CAFs) is linked to advanced tumor grades and poor prognosis.

• LPAR1 serves as a potential biomarker for drug sensitivity and immunotherapy response in gastric cancer.

This study aims to identify key regulators of paraptosis in gastric cancer (GC) and explore their potential in guiding therapeutic strategies, especially in stomach adenocarcinoma (STAD). Genes associated with paraptosis were identified from the references and subjected to Cox regression analysis in the TCGA-STAD cohort. Using machine learning models, LPAR1 consistently ranked highest in feature importance. Multiple sequencing data showed that LPAR1 was significantly overexpressed in cancer-associated fibroblasts (CAFs). LPAR1 expression was significantly higher in normal tissues, and ROC analysis demonstrated its discriminative ability. Copy number alterations and microsatellite instability were significantly associated with LPAR1 expression. High LPAR1 expression correlated with advanced tumor grades and specific cancer immune subtypes, and multivariate analysis confirmed LPAR1 as an independent predictor of poor prognosis. LPAR1 expression was associated with different immune response metrics, including immune effector activation and upregulated chemokine secretion. High LPAR1 expression also correlated with increased sensitivity to compounds, such as BET bromodomain inhibitors I-BET151 and RITA, suggesting LPAR1 as a biomarker for predicting drug activity. FOXP2 showed a strong positive correlation with LPAR1 transcriptional regulation, while increased methylation of LPAR1 promoter regions was negatively correlated with gene expression. Knockdown of LPAR1 affected cell growth in most tumor cell lines, and in vitro experiments demonstrated that LPAR1 influenced extracellular matrix (ECM) contraction and cell viability in the paraptosis of CAFs. These findings suggest that LPAR1 is a critical regulator of paraptosis in GC and a potential biomarker for drug sensitivity and immunotherapy response. This underscores the role of CAFs in mediating tumorigenic effects and suggests that targeting LPAR1 could be a promising strategy for precision medicine in GC.

Keywords

Gastric cancer
Paraptosis
Silico analysis
CAFs
LPAR1
Abbreviations

AUC Area under the curve

CAFs Cancer-associated fibroblasts

CNA Copy number alteration

cMAP Connectivity map

CHX Cycloheximide

CI Confidence interval

COAD Colorectal adenocarcinoma

DCs Dendritic cells

DSS Disease-specific survival

ECM Extracellular matrix

ESCA Esophageal carcinoma

FGG Fraction of genome gained

FGL Fraction of genome lost

FGA Fraction of genome altered

FPKM Fragments per kilobase million

GC Gastric cancer

GEO Gene expression omnibus

GISTIC Genomic identification of significant targets in cancer

GLM Generalized linear model

GSVA Gene set variation analysis

HR Hazard ratio

IFN-γ Interferon gamma

KEGG Kyoto encyclopedia of genes and genomes

KNN K-nearest neighbors

LPAR1 Lysophosphatidic acid receptor 1

MFI Mean fluorescence intensity

MSI Microsatellite instability

MSS Microsatellite stable

NK Natural killer (cells)

OS Overall survival

PCR Polymerase chain reaction

PFI Progression-free interval

PLS Partial least squares

RF Random forest

ROC Receiver operating characteristic

RPKM Reads per kilobase million

SVM Support vector machine

STAD Stomach adenocarcinoma

TCGA The cancer genome atlas

TME Tumor microenvironment

TIP Tracking tumor immunophenotype

TISIDB Tumor-immune system interactions database

TLS Tertiary lymphoid structures

TPM Transcripts per million

UMAP Uniform manifold approximation and projection

Z-Score Standard score
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pmcIntroduction

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, particularly in East Asia [1]. Despite advancements in therapeutic strategies, the prognosis for patients with advanced GC remains poor, especially in stomach adenocarcinoma (STAD) [2]. GC is a complex and heterogeneous disease influenced by genetic, epigenetic, and environmental factors [3]. The current standard treatments for GC include surgery, chemotherapy, and radiotherapy, either alone or in combination with targeted therapies for specific targets [4]. Studies have explored the efficacy of immune checkpoint inhibitors, targeted therapies, and combination regimens in improving survival rates [[5], [6], [7], [8]]. These evolving treatments underscore the importance of identifying key molecular regulators.

In recent years, significant attention has been directed towards immunotherapy, particularly immune checkpoint inhibitors, for treating various tumors, including GC [9]. However, response rates to these treatments vary, and not all patients experience benefits. This variability highlights the importance of understanding the tumor microenvironment (TME) and the immunological landscape in GC [10,11]. The TME in GC comprises a complex interplay among tumor cells, immune cells, and stromal cells. Immune cells, such as T cells, natural killer cells, macrophages, and dendritic cells, play crucial roles in tumor progression and response to therapy [12]. The immunosuppressive microenvironment in GC often leads to immune evasion, resulting in resistance to immunotherapy [11].

Paraptosis is a non-apoptotic form of programmed cell death characterized by cytoplasmic vacuolation, mitochondrial swelling, and the absence of apoptotic features such as DNA fragmentation and caspase activation [13]. Unlike apoptosis, paraptosis does not involve caspase activation and is insensitive to traditional apoptosis inhibitors. This form of cell death is regulated by distinct molecular pathways and is often associated with endoplasmic reticulum (ER) stress and mitochondrial dysfunction [14,15]. Many studies have demonstrated that prolonged activation of paraptosis enhances tumor immunogenicity, replicating the vaccinating effects of mM-CSF-transduced cells [16]. This highlights the paraptosis process as a valuable strategy for clinical immunotherapy against cancer. However, a key regulator of paraptosis in STAD has not yet been identified. Furthermore, the immunomodulatory capabilities of such a key regulator remain entirely unknown.

To address this gap, we collected an extensive list of genes associated with paraptosis from the references [17,18]. Using a combination of bioinformatics and machine learning techniques, we screened these genes in GC cohorts. Through this comprehensive analysis, LPAR1 emerged as a significant regulator of paraptosis and was selected for further investigation. High-throughput sequencing data from public databases, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), were used to perform in-depth analyses of LPAR1 expression patterns, genetic alterations, and related signaling pathways in GC. These analyses revealed that LPAR1 overexpression in GC is associated with specific genetic and epigenetic changes. Integrative analyses identified potential upstream regulators and downstream effectors of LPAR1, elucidating its role in the biology of GC.

Materials and methods

Data collection

Gene expression data were sourced from the TCGA-STAD datasets provided by the PanCanAtlas project [19], including 373 tumor cases and 32 normal cases. Matrix files were generated by standardizing the gene expression data to Z-scores using the Firehose pipeline. The Firehose pipeline is an automated computational framework developed by the Broad Institute as part of the TCGA project. For the GSE19826 dataset, which includes 12 matched pairs of adjacent normal and tumor gastric tissues, the probe matrices were annotated to gene matrices, averaging expression values for genes with multiple probes, and then standardized to Z-scores for each sample. Specifically, the Z-scores were calculated using the formula (x-μ)/σ, where x represents the expression value, μ the mean, and σ the standard deviation of the expression values within the sample. This standardization was applied to each gene across all samples in the cohort to facilitate comparative analysis. Additionally, we collected an extensive list of genes associated with paraptosis from the references [17].

Machine learning screening

The TCGA-STAD dataset was randomly divided into two subsets, with 50 % of the data used for training and the remaining 50 % for testing. The classification variable was whether the sample was a tumor tissue. Various machine learning models were trained using the train function from the “caret” package. The explain function from the “DALEX” package was used to interpret the trained models [20]. Model accuracy on the test set was predicted using the predict function. The importance of variables within the models was assessed using the variable_importance function. Additionally, residual box plots were employed to visualize and evaluate model performance. More importantly, we showed R-square and Adjusted R-square values for each model in Supplementary file 1.

Differential expression analysis

The Wilcoxon Rank Sum Tests were applied to compare the LPAR1 expression level between the tumor and normal tissues. These tests were chosen due to the non-normal distribution of the data, as assessed by the Shapiro-Wilk test. The top 30 % of samples (n = 112) with high expression of LPAR1 were compared to the bottom 30 % of samples (n = 112) with low expression of LPAR1 using the “limma” package [21]. The genes with |logFC| > 0.585, with adjusted p-value < 0.05, are considered significantly different, and their results are represented in a volcano plot.

Prognosis and clinical analysis

Univariate Cox survival analysis, conducted with the survival package, assessed gene expression and clinical variables. Hazard ratios (HR) and 95 % confidence intervals (CI) were calculated and visualized using a forest plot generated by the “forestplot” package. Meta-analysis of Cox results used the inverse variance method, with log HR as the primary metric, categorizing HR values as <1 or >1. Statistical analysis and visualization were performed using the "meta" package. Patients were divided into high and low LPAR1 expression groups based on the median value, with chi-square tests assessing the statistical significance of clinical variables. Six immune subtypes were identified [22]: C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte-depleted), C5 (immunologically quiet), and C6 (TGF-β dominant). The Kruskal-Wallis Rank Sum Test compared LPAR1 expression across these subtypes.

Enrichment analysis

The “limma” package provided log2FC for each gene, and gene set enrichment analysis was conducted using the “fgsea” package with Kyoto Encyclopedia of Genes and Genomes (KEGG) [23] gene sets. Enrichment scores (ES) and significance tests were calculated. The “GSVA” package [24] scored 73 KEGG metabolic gene sets, comparing high- and low-LPAR1 groups. Differences in GSVA scores were analyzed using the “limma” package. CancerSEA integrated data from HCMDB, Cyclebase, and StemMapper [25], redefining fourteen functional states to reflect pathway activity through integrated feature gene expression. Values were enumerated as z-scores using the GSVA algorithm, with Pearson correlation analysis calculating the statistical correlation between LPAR1 and each gene set's z-scores.

Drug sensitivity evaluation

Spearman correlation analysis assessed the relationship between gene expression and dose-response curves (area under the curve - AUC) in the PRISM database [26]. A negative correlation indicated increased drug sensitivity with higher gene expression, while a positive correlation indicated increased drug resistance. cMAP analysis explored potential therapeutic options counteracting gene-mediated tumor promotion [27]. The XSum (eXtreme Sum) method matched gene-related features with cMAP gene features [27], producing similarity scores for compounds, where lower scores indicated potential inhibition of gene-mediated tumorigenic effects. ROC analysis using the pROC package calculated total area under the curve to evaluate gene expression's diagnostic performance for immune therapy response groups.

Immune microenvironment algorithm

MeTIL scores were compared between these groups using the Wilcoxon Rank Sum Test [28]. The anticancer immune response was quantified using the Tracking Tumor Immunophenotype (TIP) framework [29], which includes seven steps. Spearman correlation analysis assessed the relationship between LPAR1 expression and TIP scores, and the autocorrelation among TIP scores, with results visualized using the “linkET” package. Tertiary lymphoid structures (TLS), which are immune cell aggregates forming in non-lymphoid tissues postnatally, were evaluated using the easier package [30]. TLS levels were compared between high and low LPAR1 expression groups using the Wilcoxon Rank Sum and Signed Rank Tests.

Patients were divided into quartiles based on gene expression, with Q1 representing the highest 25 % and Q4 the lowest 25 %. Our method is consistent with the TISIDB database [31], which includes immunomodulatory genes, chemokines, and human leukocyte antigens. Differential expression analysis of immune-related molecules between high and low LPAR1 expression groups was conducted using the Wilcoxon test, and results were visualized in a heatmap. Following Thorsson et al.'s methodology [22], the mean score of each quartile was calculated and visualized using the pheatmap package to explore the relationship between gene expression, immune infiltration, and genomic status, including immunogenicity and DNA damage scores. Immune infiltration data for all TCGA-STAD samples were collected from the TIMER2.0 database [32], which integrates various immune infiltration algorithms. Spearman correlation coefficients were visualized using bar scatter plots. Significant differences in immune cell content were determined using the Wilcoxon test and summarized in a heatmap.

Single-cell and spatial transcriptome analysis

We obtained preprocessed and annotated pan-cancer single-cell and spatial transcriptomics data from the TISCH database [33] and conducted detailed analyses on the GSE167297 and GSE203612. Single-cell resolution data for LPAR1 expression across pan-cancer samples were extracted and visualized using heatmaps generated with the “pheatmap” package, illustrating the gene expression landscape in different cells. Cells were classified into positive and negative groups based on specific gene expression. The proportion of each cell type within these groups was calculated. The “AUCell” package was utilized to evaluate scores for various biological processes, including immune response, metabolism, signaling pathways, proliferation, cell death, and mitochondrial functions. Spearman correlation analysis assessed the relationships between cell contents in all spots and between cell contents and LPAR1 expression levels. The results were visualized using the “linkET” package.

Cell lines and cell culture

The SGC-7901 and MRC5 cell lines, provided by Dr. Feng from the School of Medicine at Southeast University, were authenticated using short tandem repeat (STR) typing. Both cell lines were maintained in DMEM/F12 medium (KGM12500S-500, KeyGEN, China) with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin at 37 °C in a humidified atmosphere of 5 % CO2. MRC5 cells were induced with TGF-β1 (50 ng/mL) for one week to transition into an activated phenotype, termed MRC5-CAFs [34,35]. Cycloheximide (CHX, HY-12320) was purchased from Sigma-Aldrich (St. Louis, MO). CHX is a protein synthesis inhibitor commonly used in biological research to study various cellular processes, including paraptosis [36]. MRC5-CAFs cells after a 48 h incubation period. Prior to incubation, the cells were pretreated with 5 µM cycloheximide (CHX) for 4 h.

RNA extraction and real-time polymerase chain reaction (PCR) assay

Total RNA was extracted using the FastPure Cell/Tissue Total RNA Isolation Kit V2 (RC112–01, Vazyme, China) following the manufacturer's instructions. Complementary DNA (cDNA) synthesis was performed using the HiScript III All-in-One RT SuperMix Perfect for qPCR Kit (R333–01, Vazyme, China). Quantitative real-time PCR was conducted using the Taq Pro Universal SYBR qPCR Master Mix Kit (Q712–02, Vazyme, China) on an ABI7500 system (Applied Biosystems, USA). Gene expression levels were quantified by the 2−ΔΔCT method. The primer sequences were sourced from PrimerBank [37], which contains over 306,800 primers covering most known human and mouse genes.

Cell transfection and collagen contraction

Short hairpin RNA (shRNA) targeting human LPAR1 (sh-LPAR1) and control shRNA (sh-NC) were procured from GenePharma. Transfection was carried out in six-well plates using Lipofectamine 3000 (Invitrogen, USA) as per the manufacturer's protocol. The contractile properties of MRC5-CAFs and primary CAFs were assessed using a floating collagen matrix assay. A suspension containing 6 × 10^4 cells in type I rat tail collagen (168.75 µL DMEM/F-12, 31.25 µL collagen, 0.72 µL 1 N NaOH) was plated into 96-well ultra-low attachment plates. After 24 h, images were captured to evaluate collagen contraction [38].

Statistical analysis

All statistical analyses in genome were performed using the R software (v.4.2.3). More detailed statistical methods are covered in the above section.

Results

Machine learning-based screening of key paraptosis regulator LPAR1

Firstly, genes associated with paraptosis were identified from the references and subjected to Cox regression analysis in the TCGA-STAD cohort for various survival outcomes. For overall survival (OS), HSPB8, MYLK, GPR15, LPAR1, MAPK14, and RYR2 were associated with increased risk, whereas PLPP2, MKNK2, and CASP3 were protective (Fig. 1A). For progression-free interval (PFI), CAMK2B, SSTR3, HSPB8, TNK2, CTDSP2, GPR15, LPAR1, and RYR1 were associated with increased risk, whereas UQCRC1 and TP53 were protective (Fig. 1B). For disease-specific survival (DSS), CAMK2B, MARK4, HSPB8, CTDSP2, GPR15, LPAR1, MAPK14, and RYR1 were associated with increased risk, whereas PLPP2, UQCRC1, CASP3, and TP53 were protective (Fig. 1C). Finally, we identified HSPB8, GPR15, and LPAR1 as significantly associated with all three survival outcomes (Fig. 1D). To evaluate their role in tumor/non-tumor diagnosis, we established a classification model using several machine learning models. The Random Forest (RF) model showed the lowest residuals, indicating the best performance, followed by Generalized Linear Model (GBM) and logistic regression (Fig. 1E). Feature importance analysis revealed that LPAR1 consistently ranked highest across the top-performing models, establishing it as a key regulator for further study in paraptosis (Fig. 1F).Fig. 1 Screening of Key Paraptosis Regulator LPAR1.

A. Forest plot of overall survival (OS) Cox regression analysis for key paraptosis regulators. B. Forest plot of progression-free interval (PFI) Cox regression analysis for key paraptosis regulators. C. Forest plot of disease-specific survival (DSS) Cox regression analysis for key paraptosis regulators. D. Venn diagram of genes significantly associated with all three survival outcomes (OS, PFI, DSS). E. Boxplot of residuals for different machine learning models used in the study. Models such as Random Forest (RF), Gradient Boosting Machine (GBM), logistic regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, Generalized Linear Model (GLM), Elastic Net, Stepwise Linear Discriminant Analysis (stepLDA), and Partial Least Squares (PLS) are compared. The red dots indicate the root mean square of residuals for each model. Note: The Random Forest model had the highest R-square and Adjusted R-square, indicating the best performance. F. Feature importance analysis for the machine learning models.

Fig 1

Silico analysis of LPAR1 in pan-digestive system tumors

Given the common origin of gastrointestinal cancers, we conducted a comprehensive analysis of LPAR1 across various digestive system tumors. LPAR1 expression levels were examined in colorectal adenocarcinoma (COAD), esophageal carcinoma (ESCA), rectal adenocarcinoma (READ), and STAD using data from TCGA. In all cancer types analyzed, LPAR1 expression was significantly higher in normal tissues compared to tumor tissues (Fig. 2A). Receiver operating characteristic (ROC) analysis revealed high area under the curve (AUC) values, underscoring the discriminative ability of LPAR1 expression to differentiate between tumor and normal tissues (Fig. 2B). Additionally, analysis of LPAR1 expression across different copy number alteration (CNA) categories in a pan-digestive cohort demonstrated significant variations (Fig. 2C). The CNA categories included C1 (Deep Deletion), C2 (Shallow Deletion), C3 (Diploid), C4 (Gain), and C5 (Amplification), with the highest LPAR1 expression observed in the C5 category. Furthermore, LPAR1 expression was evaluated in relation to microsatellite instability (MSI) status. Boxplot analysis revealed significant differences in LPAR1 expression among MSI-high (MSI-H), MSI-low (MSI-L), and microsatellite stable (MSS) groups, with the highest expression observed in MSS samples for STAD (Fig. 2D). ESCA exhibited a significant negative correlation with LPAR1 expression, indicated by the larger blue dot, while other cancer types showed minimal correlation (Fig. 2E). Minimal differences in LPAR1 methylation expression between tumor and normal tissues were observed across all cancer types, with small and non-significant delta values (Fig. 2F). A radar chart displaying the SNV neoantigens across different cancer types revealed that COAD and READ have higher neoantigen levels, suggesting higher mutation burdens compared to ESCA and STAD (Fig. 2G). LPAR1 has a somatic mutation rate of 1.44 %, with several missense mutations observed across the gene (Fig. 2H). LPAR1 expression varied across studies and cell types, with notable expression in fibroblasts (Fig. 2I).Fig. 2 LPAR1 Expression and Its Clinical Correlations in Pan-Digestive System Tumors.

A. LPAR1 expression levels in colorectal adenocarcinoma (COAD), esophageal carcinoma (ESCA), rectal adenocarcinoma (READ), and stomach adenocarcinoma (STAD) from TCGA data. Box plots show significantly higher LPAR1 expression in normal tissues compared to tumor tissues. Statistical significance: ***P < 0.001, **P < 0.01, *P < 0.05. B. Receiver operating characteristic (ROC) analysis of LPAR1 expression in COAD, ESCA, READ, and STAD. C. LPAR1 expression across different copy number alteration (CNA) categories in a pan-digestive cohort. Box plots demonstrate significant variations in LPAR1 expression among categories C1 (Deep Deletion), C2 (Shallow Deletion), C3 (Diploid), C4 (Gain), and C5 (Amplification). D. LPAR1 expression in relation to microsatellite instability (MSI) status. Box plots show significant differences in LPAR1 expression among MSI-high (MSI-H), MSI-low (MSI-L), and microsatellite stable (MSS) groups across different datasets. Statistical significance is indicated within each plot. E. Correlation between LPAR1 expression and various cancer types. Dot plots show the correlation coefficients with p-values for COAD, ESCA, READ, and STAD. Color intensity and dot size represent the strength and significance of the correlation. F. Comparative analysis of LPAR1 methylation expression between tumor and normal tissues in COAD, ESCA, READ, and STAD. Delta values (T - N) are depicted, with larger dots indicating higher p-values. G. Single nucleotide variant (SNV) neoantigen levels across different cancer types. The radar chart illustrates higher neoantigen levels in COAD and READ compared to ESCA and STAD, indicating higher mutation burdens. H. The lollipop plot displays several missense mutations across the LPAR1 gene. I. Heatmap of LPAR1 expression in various studies and cell types, with notable expression in fibroblasts. Studies include datasets from COAD, ESCA, READ, and STAD.

Fig 2

Localization analysis of LPAR1 in gastric cancer

The Uniform Manifold Approximation and Projection (UMAP) plot provides a two-dimensional visualization of the different cell types present in the GC dataset (Supplementary Figure 1A). Each color represents a distinct cell type, including plasma cells, epithelial cells, CD8 T cells, fibroblasts, dendritic cells (DCs), endothelial cells, and monocytes/macrophages. The contour plot illustrates the expression levels of LPAR1 across various cell types, with higher expression indicated by warmer colors (red and orange). The plot shows that LPAR1 expression is concentrated in specific cell clusters, particularly in fibroblasts (Supplementary Figure 1B). The accompanying bar chart compares the proportion of different cell types between LPAR1-positive and LPAR1-negative groups (Supplementary Figure 1C). The LPAR1-positive group (in red) shows a significant enrichment in fibroblasts (56.6 %), indicating that LPAR1 expression is predominantly associated with fibroblasts and certain epithelial cells. The box plot displays the mRNA levels of LPAR1 across different cell types, with fibroblasts showing significantly higher LPAR1 mRNA levels compared to other cell types, as indicated by the higher median and wider distribution range (Supplementary Figure 1D). Using the AUCell algorithm, a dot plot highlights the differential enrichment of various biological pathways across distinct cell types within the tumor microenvironment. Immune-related pathways are significantly enriched in immune cell populations such as plasma cells, monocytes/macrophages, and CD8+ T cells. Proliferation and baseline cellular function pathways are enriched in epithelial and endothelial cells, while metabolic pathways show high enrichment in monocytes/macrophages and epithelial cells (Supplementary Figure 1E). Additionally, we conducted spatial transcriptomics analysis. The spatial plot shows the localization of LPAR1 expression within a tissue section, highlighting specific regions with high expression levels. The results indicate that LPAR1 distribution is relatively sparse within the spatial spots (Supplementary Figure 1F). Furthermore, a bar chart compares the mean expression levels of LPAR1 in malignant, mixed, and normal tissues, revealing that LPAR1 expression is significantly higher in malignant tissues, moderate in mixed tissues, and lowest in normal tissues (Supplementary Figure 1G). Lastly, based on spatial data, LPAR1 expression shows a significant positive correlation with fibroblasts, macrophages, and epithelial cells, while a negative correlation is observed with CD4 T cells (Supplementary Figure 1H).

Clinical correlation analysis of LPAR1 in gastric cancer

The analysis suggests that LPAR1 expression levels are not significantly associated with age, metastasis, tumor size (T), lymph node involvement (N), metastasis (M), cancer stage, radiation therapy history, antireflux medication use, or gender. However, there is a potential association between higher LPAR1 expression and more aggressive tumor grades (Fig. 3A). Notably, LPAR1 expression peaks in Stage III, indicating a role in cancer progression (Fig. 3B). Furthermore, LPAR1 expression levels vary significantly among specific cancer subtypes. High LPAR1 expression is prevalent in C4 and C5 subtypes, while low expression is more common in C1 and C2 subtypes (Fig. 3C). Specifically: C4 (lymphocyte-depleted subtype) is characterized by prominent macrophage features, Th1 suppression, and high M2 response. C5 (immunologically quiet subtype) has the fewest lymphocytes. In STAD, LPAR1 expression is higher in tumor tissues compared to normal tissues, suggesting a potential role in tumorigenesis (Fig. 3D). Various studies (GSE14208, GSE62254, GSE84433, TCGA-STAD) provide log hazard ratios and their standard errors. The overall hazard ratio, calculated using a random effects model, is 1.16 (95 % CI: 1.08 to 1.24), indicating that higher LPAR1 expression is associated with worse survival outcomes (Fig. 3E). Univariate analysis shows LPAR1 has a hazard ratio (HR) of 1.144 (95 % CI: 1.013 to 1.291, p = 0.0297), marking it as a significant predictor of poor prognosis. Multivariate analysis confirms LPAR1 as an independent predictor with an HR of 1.548 (95 % CI: 1.114 to 2.152, p = 0.0093) after adjusting for other clinical variables, underscoring its prognostic value (Fig. 3F).Fig. 3 Clinical Correlations and Prognostic Significance of LPAR1 in Gastric Cancer.

A. Donut charts representing the clinical characteristics of gastric cancer patients stratified by high and low LPAR1 expression. Variables include age, grade, metastasis (M), lymph node involvement (N), tumor size (T), cancer stage, history of radiation therapy, use of antireflux treatment, and gender. Each chart depicts the proportion of patients within each category, highlighting significant differences between high and low LPAR1 expression groups. B. Line and bar plot showing the median unit of LPAR1 expression (Z-score) across different cancer stages (I-IV). C. Heatmap illustrating the distribution of LPAR1 expression across six immune subtypes (C1 to C6) in the TCGA cohort. Subtypes include C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte-depleted), C5 (immunologically quiet), and C6 (TGF-β dominant). D. Density plot comparing the estimated expression of LPAR1 in tumor versus normal tissues in the STAD dataset (GSE19826). E. Forest plot of hazard ratios (HR) from univariate Cox regression analysis across multiple studies (GSE14208, GSE22354, GSE84433, TCGA-STAD) for overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). F. Univariate and multivariate Cox regression analysis for overall survival in gastric cancer patients. The forest plot summarizes hazard ratios (HR), 95 % confidence intervals (CI), and p-values for various clinical variables, including LPAR1 expression Bold values indicate statistically significant results (p < 0.05).

Fig 3

Molecular characterization of LPAR1 in gastric cancer

Fig. 4A illustrates the GISTIC scores for overall copy number alterations in STAD samples across different chromosomes. Significant regions of copy number gains and losses are observed, with notable gains on chromosomes 8, 17, and 20, and losses on chromosomes 3, 4, and 9. Higher LPAR1 levels (Q4) are associated with increased genome alterations, indicating genomic instability (Fig. 4B). Differential analysis of the top and bottom 20 % of LPAR1 expression samples in the TCGA-STAD cohort reveals significant genes, with 4480 upregulated (red) and 1027 downregulated (green) genes highlighted in the volcano plot (Fig. 4C). Enrichment analysis of these differentially expressed genes identifies significant pathways and biological processes, with upregulated pathways in high LPAR1 expression groups shown in red and downregulated pathways in blue (Fig. 4D). Pathways related to genetic information processing and metabolism are downregulated in the high LPAR1 expression group, whereas pathways related to environmental information processing and organismal systems are significantly enriched. Notably, detailed analysis of metabolic pathways reveals that glycosphingolipid biosynthesis (ganglio series) and glycosaminoglycan biosynthesis (chondroitin sulfate/dermatan sulfate) are significantly activated in the high LPAR1 group (Fig. 4E). Scatter plots demonstrate the correlation between LPAR1 expression (z-score) and various oncogenic pathways across multiple parameters. Significant positive correlations are observed for angiogenesis, hypoxia, invasion, metastasis, EMT, quiescence, and stemness. Conversely, significant negative correlations are observed for cell cycle, DNA damage, and DNA repair pathways (Fig. 4F).Fig. 4 Comprehensive Genomic and Transcriptomic Analysis of LPAR1 in Gastric Cancer.

A. GISTIC scores for overall copy number alterations in TCGA-STAD samples across different chromosomes. The plot highlights significant regions of copy number gains and losses, with notable gains observed on chromosomes 8, 17, and 20, and losses on chromosomes 3, 4, and 9. B. Bar plot representing the fraction of the genome altered (FGA) in TCGA-STAD samples. The plot shows a higher fraction of the genome altered in the LPAR1 high-expression group compared to the low-expression group. C. Bar plot representing the fraction of the genome gained or lost (FGG or FGL) in TCGA-STAD samples. The plot highlights significant differences in the fraction of genome alterations between LPAR1 high and low expression groups. D. Volcano plot illustrating differentially expressed genes between high and low LPAR1 expression groups in the TCGA-STAD cohort. Significantly upregulated genes (red) and downregulated genes (green) are indicated, with their corresponding log2 fold change and adjusted p-values. E. Heatmap showing the differential expression of genes in high LPAR1 expression groups, with significant upregulated pathways (red) and downregulated pathways (blue). F. Bar plot representing the enrichment scores of various pathways in high LPAR1 expression groups, with significantly activated metabolic pathways, including glycosphingolipid biosynthesis (ganglio series) and glycosaminoglycan biosynthesis (chondroitin sulfate/dermatan sulfate). G. Scatter plots demonstrating the correlation between LPAR1 expression (z-score) and various oncogenic pathways across multiple parameters. Significant positive correlations are observed for angiogenesis, hypoxia, invasion, metastasis, EMT, quiescence, and stemness. Conversely, significant negative correlations are observed for cell cycle, DNA damage, and DNA repair pathways. Each scatter plot includes a correlation coefficient (R) and p-value.

Fig 4

Immunological characterization of LPAR1 in gastric cancer

The association between LPAR1 expression and various immune response and genomic state metrics, categorized into quartiles (Q1-Q4). We calculated the average scores for each metric across the quartiles, standardizing them to ensure comparability. LPAR1 expression shows varying associations with immune response metrics (Fig. 5A). For example, lower LPAR1 expression (Q1) is associated with a higher leukocyte fraction and increased proliferation. Conversely, genomic instability metrics, such as the fraction altered and aneuploidy score, are higher with high LPAR1 expression (Q4). Additionally, low LPAR1 expression (Q1) is linked to greater TCR and BCR responses and higher immune infiltration. Significant associations are observed between LPAR1 expression and various immune molecules, including the high expression of co-stimulatory and antigen presentation genes (Fig. 5B). We analyzed the relationships between key immune molecule mRNA levels, methylation, amplification frequency, deletion frequency, and LPAR1 expression. While mRNA expression levels of various immune-related molecules do not significantly vary across different LPAR1 expression groups, methylation levels, and amplification and deletion frequencies are higher in the high LPAR1 expression group, indicating a higher immune activity. Furthermore, the relationship between LPAR1 expression and different steps of the cancer-immunity cycle, such as antigen presentation, T cell recruitment, and the killing of cancer cells, was examined (Fig. 5C). LPAR1 expression shows significant correlations with multiple steps of the cancer-immunity cycle, particularly in recruiting immune cells and presenting antigens. Moreover, high LPAR1 expression is associated with higher MeTIL estimates (Fig. 5D) and higher TLS levels (Fig. 5E), suggesting that LPAR1 may play a role in the formation or presence of tertiary lymphoid structures in STAD. Additionally, Wilcoxon differential analysis of immunomodulators and chemokines from the TISIDB database reveals that the high LPAR1 expression group exhibits increased immune activity, particularly in chemokine secretion, such as CCL, CXCL12, CXCL13, and CXCL14 (Supplementary Figure 2). This further supports the role of LPAR1 in modulating the immune microenvironment in gastric cancer.Fig. 5 Immune Response and Genomic State in Gastric Cancer with LPAR1 Expression.

A. Heatmap illustrating the association between LPAR1 expression quartiles (Q1-Q4) and various immune response metrics and genomic state parameters. Metrics include leukocyte fraction, stromal fraction, TIL (tumor-infiltrating lymphocytes) regional fraction, IFN-gamma response, proliferation, wound healing, TGF-beta response, SNV (single nucleotide variant) neoantigens, indel (insertion-deletion) neoantigens, fraction altered, homologous recombination defects, aneuploidy score, and TCR (T-cell receptor) and BCR (B-cell receptor) richness and evenness. B. Heatmap showing the expression of immune-related genes (co-stimulatory and co-inhibitory molecules, ligands, receptors, cell adhesion molecules, and antigen presentation molecules) and their association with LPAR1 expression levels. Metrics include mRNA expression, expression vs. methylation, amplification frequency, and deletion frequency. C. Network plot depicting the correlation between LPAR1 expression and various steps of the cancer-immunity cycle, such as antigen presentation, T cell recruitment, and the killing of cancer cells. Significant correlations are indicated by colored lines. D. Density plot comparing the estimated MeTIL (methylation of tumor-infiltrating lymphocytes) scores in high and low LPAR1 expression groups. The box plot below shows the statistical significance (p < 0.001) of the differences. E. Density plot comparing the estimated TLS (tertiary lymphoid structures) scores in high and low LPAR1 expression groups. The box plot below indicates the statistical significance (p < 0.001) of the differences.

Fig 5

Additionally, we categorized and compared different cell types to examine their differences. Red indicates significant enrichment in the high LPAR1 expression group, while blue indicates enrichment in the low expression group. The results consistently show that various cell types, such as B cells, cancer-associated fibroblasts (CAFs), endothelial cells, monocytes, and myeloid cells, are significantly enriched in the high LPAR1 expression group (Fig. 6A). The heatmap clearly illustrates the changes in immune cell content between different samples and highlights the differences between groups, with the high LPAR1 group exhibiting a "hot tumor" state (Fig. 6B). Correlation analysis revealed statistically significant associations for several immune cell types. Most cell types showed a positive correlation with LPAR1 expression, while others, such as CD4 Th1 and CD4 Th2 cells, showed a negative correlation (Fig. 6C). Interestingly, single-cell distribution analysis indicated that LPAR1 is predominantly expressed in CAFs (Fig. 2I). This finding was corroborated by immunological algorithm-based correlation analysis (Fig. 6D-E). Furthermore, single-cell pan-cancer data from the TISCH database supported the significant positive correlation between LPAR1 and CAFs (Fig. 6F).Fig. 6 Immunological Characterization of LPAR1 in Gastric Cancer.

A. Heatmap showing the abundance of various immune cell types across STAD samples with high and low LPAR1 expression levels. B. Heatmap illustrating the expression of immune-related genes across STAD samples. Gene categories include co-stimulatory and co-inhibitory molecules, ligands, receptors, and cell adhesion molecules. The heatmap shows differential expression patterns between high and low LPAR1 expression groups. C. Scatter plot displaying the correlation between LPAR1 expression and the infiltration levels of various immune cell types in the tumor microenvironment. Cell types include T cells, NK cells, macrophages, and fibroblasts. The correlation coefficients and statistical significance are indicated. D-E. Correlation scatter plots illustrating the relationship between LPAR1 expression and cancer-associated fibroblast (CAF) markers identified by different algorithms (MCP-COUNTER, xCELL). F. Correlation scatter plot depicting the association between LPAR1 expression and fibroblast activation in the tumor microenvironment in single cell data.

Fig 6

LPAR1 guides the treatment of gastric cancer

To investigate whether LPAR1 can guide drug selection for gastric cancer and achieve "precision medicine," we calculated dose-response curves (area under the curve - AUC) for various compounds from the PRISM database (Supplementary Figure 3A). Compounds were ranked based on their relevance to LPAR1, with I-BET151, RITA, and JIB04 showing the highest relevance (p < 0.001). Higher LPAR1 expression correlates with greater sensitivity to these compounds, as indicated by their negative AUC values. Conversely, compounds with lower relevance to LPAR1, such as navarixin and 7-hydroxystaurosporine, exhibit lower sensitivity (p < 0.001). This suggests that LPAR1 expression could serve as a potential biomarker for predicting the efficacy of specific drugs in gastric cancer treatment. For predicting response to immunotherapy, we analyzed AUC values for various datasets across different cancer types, including STAD, NSCLC, melanoma, LGG, KIRC, and GBM (Supplementary Figure 3B). The datasets were ranked by their AUC values, with higher AUC indicating better performance in distinguishing between tumor and normal samples. The STAD dataset (FR-JRJ-F25780) showed the highest AUC, followed by NSCLC datasets (GSE135222 and GSE120444). Melanoma datasets were also well-represented, highlighting the effectiveness of LPAR1 as a biomarker across multiple cancer types. Finally, a scatter plot based on the XSum algorithm illustrates the sensitivity scores of various compounds, focusing on those showing significant sensitivity (score > 1) or resistance (score < −1) to LPAR1 expression levels (Supplementary Figure 3C). Compounds such as X4.5.dianilinophthalimide and W.13 show significant sensitivity, while others like NU.1025 exhibit resistance.

Upstream regulation of LPAR1 in gastric cancer

The transcription initiation site of LPAR1, spanning 3000 base pairs (bp) upstream to 3000 bp downstream, was annotated using ATAC-seq, revealing eight peaks potentially related to the regulation of LPAR1 transcription (Fig. 7A). This bar chart illustrates the distribution of genomic features associated with LPAR1 in gastric cancer, categorized into four groups: Promoter (≤1 kb), Promoter (1–2 kb), 1st Intron, and Other Intron. Spearman rank correlation coefficients were used to measure the correlation between transcription factor expression and ATAC peaks (Fig. 7B). For each transcription factor, correlations with all peaks were calculated, revealing that FOXP2 had the highest positive correlation with the peak at chr9:111039034–111039533 (r = 0.739) (Fig. 7C-D). Additionally, based on the difference between DNA methylation age and chronological age, samples were classified into age-accelerated and age-decelerated groups. LPAR1 was highly expressed in the age-decelerated group, suggesting a potential role for LPAR1 in reversing cellular aging (Fig. 7E). The methylation analysis included regions such as TSS1500 (from −200 to −1500 bp upstream of TSS), TSS200 (from −200 bp upstream of TSS), the 1st Exon, and the 5′ untranslated region (5′UTR). Spearman correlation analysis showed a negative correlation between LPAR1 expression and methylation levels, indicating that increased methylation might reduce gene expression (Fig. 7F). Box plots were used to visualize methylation levels at each site. The promoter region of LPAR1 contains eight methylation sites, with cg14231369 and cg14429427 showing high methylation intensity, identifying them as potential methylation-regulated sites (Fig. 7G).Fig. 7 Epigenetic and Transcriptional Regulation of LPAR1 in Gastric Cancer.

A. Bar plot representing the ATAC-seq peaks over chromosomes, highlighting the genomic regions with open chromatin associated with LPAR1 transcription in gastric cancer cells. B. Bar chart showing the distribution of genomic features associated with LPAR1, categorized into four groups: Promoter (≤1 kb), Promoter (1–2 kb), 1st Intron, and Other Intron. This chart illustrates the percentage of each feature type. C. Heatmap displaying the correlation between transcription factor (TF) expression and ATAC-seq peaks at various chromosomal locations. The color scale indicates the strength of the correlation, with significant correlations marked by asterisks. D. Scatter plot showing the positive correlation between FOXP1 expression and an ATAC-seq peak at chr9:111039034–111039533, with a correlation coefficient (r) of 0.739 and a p-value of 1.971e-04. E. Violin plot comparing LPAR1 expression between age-accelerated and age-decelerated gastric cancer samples. The plot shows a significant difference in LPAR1 expression (P = 0.001). F. Scatter plot illustrating the negative correlation between LPAR1 expression and promoter methylation levels (median β value) in STAD (stomach adenocarcinoma) samples. The Spearman correlation coefficient (ρ) is −0.17 with a P-value of 0.00114. G. Dot plot displaying the methylation levels (beta values) of LPAR1 promoter regions across different methylation sites. Each color represents a different methylation site.

Fig 7

LPAR1 regulates the contractility of CAFs through paraptosis

Whole-genome CRISPR screening data were downloaded from the DepMap portal, and the dependency scores for approximately 17,000 candidate genes were calculated using the CERES algorithm [39]. Negative scores indicate that gene knockout inhibits cell growth. The results showed that knockout of LPAR1 affected the growth of most tumor cell lines (Fig. 8A). Consequently, we did not perform biological function experiments in gastric cancer cell lines but focused on in vitro experiments with CAFs that highly express LPAR1 in gastric cancer tissues. We hypothesized that CAFs influence paraptosis through LPAR1, leading to extracellular matrix changes and indirectly promoting tumor progression. Compared to gastric cancer cell lines, LPAR1 expression was significantly higher in MRC5 and MRC5-CAFs cell lines (Fig. 8B). LPAR1 was knocked down in activated phenotype MRC5-CAFs (Fig. 8C). Given that paraptosis is a non-apoptotic form of programmed cell death characterized by endoplasmic reticulum (ER) stress and extensive cytoplasmic vacuolation originating from the ER, we aimed to determine whether LPAR1 induces paraptosis. MRC5-CAFs were pretreated with cycloheximide (CHX) to block paraptotic vacuole formation. The results indicated that CHX pretreatment effectively mitigated the sh-LPAR1-induced decline in cell viability and reversed the sh-LPAR1-induced reduction in ECM contraction (Fig. 8D-F).Fig. 8 Functional Analysis of LPAR1 in Gastric Cancer-Associated Fibroblasts (CAFs).

A. Whole-genome CRISPR screening data showing the dependency scores in different cell lines. B. Bar plot showing the relative expression of LPAR1 in SGC-7901, MRC5, and MRC5-CAFs. The expression of LPAR1 is significantly higher in MRC5-CAFs compared to SGC-7901 and MRC5 cells (***P < 0.001, ****P < 0.0001). C. Bar plot displaying the relative expression of LPAR1 in MRC5-CAFs transfected with control shRNA (sh-NC) and shRNA targeting LPAR1 (sh-LPAR1). LPAR1 expression is significantly reduced in sh-LPAR1 transfected cells (***P < 0.001). D. Bar plot showing the cell viability of MRC5-CAFs treated with cycloheximide (CHX) at 0 µM, 5 µM, and 10 µM concentrations, with and without sh-LPAR1 transfection. Cell viability significantly decreases with CHX treatment and LPAR1 knockdown (**P < 0.01). E. Representative images of collagen contraction assays performed on MRC5-CAFs. Cells treated with 0 µM or 5 µM CHX, with or without sh-LPAR1 transfection, show different levels of collagen matrix contraction. Red dashed circles indicate the area of contraction. F. Bar plot quantifying the percentage of collagen contraction in MRC5-CAFs under the same conditions as panel E. LPAR1 knockdown and CHX treatment significantly reduce collagen contraction (**P < 0.01).

Fig 8

Discussion

Feature importance analysis revealed that LPAR1 consistently ranked highest across the top-performing models, establishing it as a key regulator for further study in paraptosis. LPAR1 is a G protein-coupled receptor activated by lysophosphatidic acid (LPA), a bioactive lipid mediator involved in various physiological processes. Shi et al. [40] investigated the expression and prognostic significance of LPAR1 in prostate cancer, finding that LPAR1 is significantly downregulated in tumors and associated with favorable overall survival. Ma et al. [41] demonstrated that silencing miR-501–5p increases cell apoptosis and arrests the cell cycle in the G2 phase, suggesting that the miR-501–5p/LPAR1 axis could be a potential therapeutic target for GC treatment. Juin et al. elucidated that N-WASP is crucial for pancreatic ductal adenocarcinoma metastasis by facilitating chemotaxis and matrix remodeling in response to LPA1. Abdelmessih et al. [42] found that the Ki16425 liposomal formulation significantly improved uptake by metastatic breast cancer (MBC) cells and reduced uptake by normal cells, suggesting a promising therapeutic approach for targeting the LPA-LPAR1 axis in MBC. Qian et al. [18] developed a paraptosis-based prognostic model for lower-grade glioma (LGG) patients, identifying 10 paraptosis-related gene (PRG) signatures, including LPAR1 to classify patients into high- and low-risk subgroups, showing high performance in predicting OS.

Paraptosis is a non-apoptotic type of programmed cell death mainly characterized by cytoplasm vacuolation and swelling of mitochondria without the typical characteristics of apoptosis, including DNA fragmentation and activation of caspases [13]. Our study found that LPAR1 is one of the predominant regulators of proptosis in GC, specifically in CAFs. CAFs represent one of the major constituents of the TME and have demonstrated critical roles in tumor progression, metastasis, and therapy resistance [34,43]. Consistent with this, we observed highly high LPAR1 expression in CAFs relative to other cell types, which helps to localize LPAR1 precisely to the TME. Our in vitro experiments showed that LPAR1 influenced ECM contraction and cell viability of CAFs during paraptosis. Knockdown of LPAR1 was found to affect cell growth in tumor cell lines, indicating that it may be crucial for maintaining the proliferative and contractility of CAFs.

The high-throughput sequencing information compiled from public databases, including TCGA and GEO, indicated that LPAR1 overexpression in GC is associated with specific genetic and epigenetic changes. These differences encompassed changes in the CNA regions and representation of microsatellite instability, which both proved to have a highly significant association with LPAR1 expression. Additionally, boxplot analysis revealed significant differences in LPAR1 expression levels between MSI-H, MSI-L, and MSS groups. The MSS group was the highest expresser of all. The methylation analysis graphically represented the negative correlation between increased methylation in the LPAR1 promoter region and gene expression. This inverse association would suggest that epigenetic alterations might play a vital regulatory role in LPAR1 expression in GC. Multiple methylation sites were found, mainly located in the promoter region of LPAR1, with cg14231369 and cg14429427 presenting a high intensity of methylation and, hence, potential candidates for methylation-regulated sites. In addition to LPAR1, we identified the transcription factor FOXP2 as a further marker displaying a robust positive correlation within the set of LPAR1-correlated transcriptional regulation. This means that the mediation of LPAR1 in GC may be conducted by FOXP2 expression and the related pathway; hence, the FOXP2 and its related regulatory network might serve as novel therapeutic possibilities to modulate LPAR1 activity for treatment efficacy.

The immunological microenvironment in GC is complex and involves nearly all kinds of immune cells and signaling pathways [44]. Our study continued with the relationship between LPAR1 expression and immune responses, immune effector activation, and chemokine secretion. High LPAR1 expression was related to high values of the immune effector activation signature, including chemokine secretion, playing critical roles in immune cell recruitment and activation at the TME. We further find that LPAR1 is associated with numerous metrics of immune response. Lower LPAR1 expression levels are associated with higher fractions of leukocytes and proliferation, while higher expression of LPAR1 is related to metrics of genomic instability, including fraction alteration and aneuploidy score. Such associations suggest that LPAR1 could possess a bifunctional modulatory role in immune response and genomic stability in GC. The expression of LPAR1 was upregulated in both the subtypes with lymphocyte-depleted (C4) and immunologically quiet (C5), characterized by prominent features of macrophages, Th1 suppression, and high M2 response. The upregulation of LPAR1 expression facilitates the development of an immunosuppressive microenvironment to enhance immune escape and tumor progression.

High LPAR1 expression was associated with high sensitivity to compounds I-BET151 and RITA, such as BET bromodomain inhibitors. Therefore, the results suggested that LPAR1 expression may be an important predictive factor for the efficacy of certain drugs against GC, which would help justify the potential role of precision medicine. The dose-response curves of PRISM database drugs further investigated the association between LPAR1 detection and chemosensitivity. Compounds of greater relevance to LPAR1, such as I-BET151 and RITA, showed less sensitivity in the low LPAR1 expression groups and, therefore, the possibility for therapeutic repositioning for such compounds. Conversely, compounds less relevant to LPAR1, such as navarixin and 7-hydroxystaurosporine, showed less sensitivity to high LPAR1 expression. Thus, again, we confirmed the specificity of LPAR1 as a biomarker of response.

Despite the strengths of this study, several limitations should be acknowledged. Firstly, the reliance on publicly available datasets may introduce biases related to sample selection and data quality. Secondly, while our bioinformatics analyses provide valuable insights, experimental validation in larger and more diverse cohorts is necessary to confirm our findings. Additionally, the functional mechanisms of LPAR1 in paraptosis and its interactions with the immune microenvironment require further investigation.

Our study, therefore, provides an in-depth view of the role of LPAR1 in GC and its potential as a therapeutic target and biomarker for drug sensitivity and response to immunotherapy. However, knowledge gaps remain, particularly in understanding the precise mechanisms by which LPAR1 influences immune cell interactions and tumor biology. We anticipate significant advancements in this area, with increased integration of molecular profiling and personalized medicine approaches in clinical practice, ultimately improving patient outcomes.

Conclusion

LPAR1 is one of the promising targets in precision medicine for GC. Its implication in paraptosis, association with diverse genomic and immunological features, and potential use as a drug-sensitive biomarker render it therapeutically relevant. Future work would now be directed toward more focused clinical validation studies, mechanistic investigations, and exploration of combination regimens, possibly targeting LPAR1 in conjunction with immune checkpoints to alter the landscape of GC therapeutics for better outcomes in the future.

Funding

Not applicable.

Ethics approval and consent to participate

Not applicable.

Availability of data and materials

The following information was supplied regarding data availability: Data is available at the TCGA (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/).The detailed data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Ya-Jie Dai: Conceptualization. Hao-Dong Tang: Data curation. Guang-Qing Jiang: Formal analysis. Zhai-Yue Xu: Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, application 1

Acknowledgements

We are grateful to the staff in Biobank of Zhongda Hospital Affiliated to Southeast University for technical assistance.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.102110.
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