ITPRIPL1: A tumor immune-associated biomarker with prognostic and therapeutic implications in gastrointestinal cancer

Abstract

Inositol 1,4,5-trisphosphate receptor-interacting protein-like 1(ITPRIPL1) has recently been implicated in tumor-immune regulation, yet its tumor-type specificity and clinical relevance in gastrointestinal malignancies remain unclear. Here, we performed an integrative analysis of ITPRIPL1 across stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), rectal adenocarcinoma (READ), and esophageal carcinoma (ESCA) using bulk transcriptomics, immune pathway analyses, survival modeling, single-cell RNA sequencing, immunofluorescence validation, and therapeutic correlation analyses. Although ITPRIPL1 was upregulated across gastrointestinal cancers, its prognostic significance was highly tumor-specific, with elevated expression consistently predicting unfavorable survival only in STAD. In gastric cancer, ITPRIPL1 expression was closely associated with immune-related pathways and genomic instability features, and its prognostic association varied across immune contexts, particularly according to CD8⁺/CD4⁺ T-cell abundance, with an exploratory association also observed for zeta-chain-associated protein kinase 70 (ZAP70) expression. Single-cell and immunofluorescence analyses demonstrated preferential enrichment of ITPRIPL1 in T cells and tumor-adjacent immune structures. Notably, Exploratory analyses further showed that higher ITPRIPL1 expression was associated with favorable survival outcomes in selected external pretreatment immunotherapy cohorts and with lower IC50 values for several agents in cancer cell-line pharmacogenomic datasets. Collectively, these findings identify ITPRIPL1 as an immune-associated biomarker with primary clinical relevance in gastric cancer.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-64178-4.

Introduction

Gastrointestinal malignancies, including gastric, colorectal, rectal, and esophageal cancers, constitute a major global health burden and remain leading causes of cancer-related mortality worldwide. Despite advances in surgical techniques, chemotherapy, and immunotherapy, patient prognosis, particularly in gastric cancer, remains highly heterogeneous¹. This variability is largely driven by the complex genetic background of tumors and the dynamic tumor microenvironment (TME), which are not fully captured by conventional clinicopathological staging systems².

Recent studies have further shown that gastric cancer progression and treatment response are shaped by epigenetic dysregulation, regulated cell-death pathways, metabolic adaptation, immune activation, and spatial microenvironmental remodeling. These advances highlight the biological complexity of gastric cancer and the need to identify immune-associated molecular determinants that link tumor progression, prognosis, and therapeutic response^3–8. Against this increasingly complex biological background, high-throughput sequencing technologies and bioinformatic approaches have provided new opportunities to characterize tumor biology and immune regulation at a system level⁹. Increasing evidence suggests that immune-related genes beyond classical immune checkpoints play critical roles in shaping the TME and influencing clinical outcomes^10,11. However, identifying immune-associated molecular determinants with robust prognostic and therapeutic relevance across gastrointestinal cancers remains challenging¹².

ITPRIPL1, also known as CD3L1, has recently emerged as a gene of interest in cancer research. Pan-cancer analyses have reported aberrant expression of ITPRIPL1 and suggested potential associations with immune infiltration and patient survival¹³. More recent experimental studies further indicated that ITPRIPL1 may participate in immune regulation by modulating T-cell receptor signaling¹⁴. Nevertheless, the biological and clinical significance of ITPRIPL1 within gastrointestinal malignancies remains largely unexplored. It is unclear whether ITPRIPL1 exerts consistent prognostic and immunological effects across different gastrointestinal tumor types or whether its role is context dependent.

To address these gaps, we first performed a pan-gastrointestinal analysis of ITPRIPL1 expression and clinical relevance across gastric, colorectal, rectal, and esophageal cancers. The observed tumor-type-specific patterns subsequently guided our focused investigation of gastric cancer. Building on these observations, we integrated bulk transcriptomic analyses, immune pathway profiling, genomic feature assessment, survival modeling, single-cell RNA sequencing, immunofluorescence-based spatial analysis, and pharmacogenomic correlation analyses to comprehensively characterize ITPRIPL1 across multiple biological dimensions.

An overall workflow illustrates the design of this study, from pan-cancer screening to gastrointestinal-specific analyses and gastric cancer-focused investigation. Through this integrative approach, we aimed to clarify the immune-context-associated role of ITPRIPL1 within the tumor-immune microenvironment and to assess its potential value as an immune-associated biomarker with prognostic and therapeutic implications in gastrointestinal malignancies.

Methods

Data acquisition and preprocessing

RNA-sequencing data, corresponding clinical information, and somatic mutation data for gastrointestinal cancers (STAD, COAD, READ, ESCA) were obtained from The Cancer Genome Atlas (TCGA) database¹⁵. Normal tissue transcriptomes were retrieved from the Genotype-Tissue Expression (GTEx) project to enable tumor–normal comparisons¹⁶. In addition, expression levels of the target gene in both normal and cancer tissues were independently validated using transcriptomic data from the Human Protein Atlas (HPA) database¹⁷. Expression matrices were normalized to Transcripts Per Million (TPM) and log2-transformed for downstream analyses. Datasets for single-cell analysis were downloaded from the Gene Expression Omnibus (GEO) and processed using the “limma” package, applying quantile normalization and batch-correction where necessary^18,19. Functional state analyses of tumor cells were conducted using the CancerSEA database, which provides curated functional annotations at the single-cell level²⁰. Furthermore, drug sensitivity analyses were performed based on pharmacogenomic data derived from the Genomics of Drug Sensitivity in Cancer (GDSC) database to assess the association between gene expression and chemotherapeutic response²¹.

Pan-cancer and gastrointestinal expression profiling

Pan-cancer expression analysis was conducted by integrating TCGA and GTEx datasets. Differential expressions between tumor and normal tissues were assessed using the Wilcoxon rank-sum test and paired Wilcoxon signed-rank test where matched samples were available. Z-score-standardized median expression values were projected onto organ-level anatomical maps using the “gganatogram” package to visualize distribution across digestive system tissues. Protein-level expression patterns were retrieved from the Human Protein Atlas (HPA) for independent validation^17,22,23.

Single-cell RNA sequencing analysis

Single-cell transcriptomic datasets from STAD were obtained from GEO and processed using the Seurat pipeline. After quality filtering (nFeature_RNA > 200, mitochondrial content < 15%), data were normalized using SCTransform and integrated via canonical correlation analysis. Dimensional reduction was performed using PCA and UMAP. Cell-type annotation was guided by canonical markers and verified using SingleR. ITPRIPL1 expression across clusters was visualized using feature plots, bubble plots, and heatmaps. Cell composition differences between high- and low-ITPRIPL1 groups were evaluated using χ² tests. Correlations between ITPRIPL1 and immune cell signatures were computed using Spearman coefficients^24,25.

Somatic mutation and genomic instability analysis

Mutation annotation format (MAF) files for TCGA gastrointestinal cancers were processed using the “maftools” package. The frequencies of nonsynonymous variants and mutation spectra were compared across tumor types. Genomic instability metrics—including Aneuploidy Score, Tumor Ploidy, and Homologous Recombination Defect (HRD) scores—were obtained from published TCGA pan-cancer studies. Differences in ITPRIPL1-associated genomic instability features among microsatellite instability groups were assessed using Wilcoxon rank-sum tests. Differences between ITPRIPL1 high- and low-expression groups were assessed using the Wilcoxon test. Co-occurrence analyses for driver gene mutations were also performed^26–28.

Pathway enrichment and immune correlation analyses

Gene set enrichment analysis (GSEA) was performed using Hallmark gene sets from MSigDB via the “clusterProfiler” package, and enrichment significance was evaluated using Benjamini-Hochberg-adjusted P values to control the false discovery rate. For immune-related gene correlation analyses, correlation P values were adjusted within each predefined comparison family using the Benjamini-Hochberg procedure. Associations with adjusted P values below 0.05 were considered statistically significant. Immune subtype assignment for STAD followed the six-class TCGA immune classification framework. Pearson correlation analyses evaluated associations between ITPRIPL1 and immune-related genes. Immune cell infiltration was estimated using five deconvolution algorithms (CIBERSORT, EPIC, TIMER, quanTIseq, and xCell). HPA immune cell–type abundance data were integrated to confirm immune cellular patterns^10,29–32.

Tumor functional state analyses and proteomic validation

Functional state scores for 14 tumor-related processes—including angiogenesis, proliferation, inflammation, differentiation, stemness, apoptosis, quiescence, and DNA damage—were obtained from the CancerSEA database. Spearman correlations between functional states and ITPRIPL1 expression were calculated across gastrointestinal cancers. Proteomic correlation analyses were performed using Reverse Phase Protein Array (RPPA) data from the TCGA-based TCPA platform, assessing the association between ITPRIPL1 and signaling, cell-cycle, and immune regulatory proteins^20,33,34.

Survival and prognostic evaluation

Univariate Cox regression and Kaplan-Meier survival analyses were conducted using the “survival” and “survminer” packages^35,36. Hazard ratios and log-rank p-values were calculated for each gastrointestinal cancer³⁷. The minimum proportion of patients assigned to either expression group was restricted to 30% (minprop = 0.30). Patients were subsequently classified into high- and low-expression groups according to the identified cut-off. The proportional hazards assumption was evaluated using Schoenfeld residuals implemented in the cox.zph function of the R package “survival.” In the cross-cancer survival analyses, Benjamini-Hochberg correction was applied to the Cox regression P values across the four cancer types to control the false discovery rate arising from multiple comparisons. Multivariable Cox proportional hazards model simultaneously included ITPRIPL1 expression, ZAP70 expression, CD8⁺ T-cell abundance, CD4⁺ T-cell abundance, stromal score, age, sex, and pathological stage. Continuous variables were Z-score standardized before model fitting, and complete cases with available OS and covariate information were included. Female sex and pathological stage I were used as the reference categories. The proportional hazards assumption was evaluated using Schoenfeld residuals. Combined prognostic analyses integrating ITPRIPL1 with ZAP70 expression or CD8⁺ T-cell infiltration were performed by stratifying patients into four groups based on dichotomized expression levels. Correlation between ITPRIPL1 and ZAP70 was assessed by linear regression. These analyses were designed to evaluate immune-associated prognostic patterns rather than formally test statistical interactions.

Clinical association and therapeutic response analyses

Clinical stage–dependent expression patterns were evaluated using GSE34942 and GSE62254 cohorts. Tumor–normal comparisons in TCGA-STAD were examined using paired and unpaired Wilcoxon tests. Immunotherapy outcome analysis was conducted using cohorts derived from the Tumor Immune Dysfunction and Exclusion (TIDE) database³⁸, which include patients receiving immune checkpoint inhibitors. Kaplan-Meier survival analysis was applied to evaluate the impact of different ITPRIPL1 expression levels on overall survival. Survival differences between the high-ITPRIPL1 and low-ITPRIPL1 expression groups were statistically compared using the log-rank test. Subtype-specific expression patterns across STAD, COAD, and READ were analyzed using their respective molecular subtype annotations^39,40.

Immunofluorescence staining

Formalin-fixed, paraffin-embedded colorectal cancer tissue specimens were obtained from Fujian Provincial Hospital affiliated with Fuzhou University. Colorectal cancer specimens were used for immunofluorescence staining to characterize the spatial localization and tissue distribution of ITPRIPL1. Representative images from the most informative specimen are presented. Written informed consent was obtained from all participants prior to sample collection, and the study was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committee. Tissue sections were deparaffinized, rehydrated, and subjected to antigen retrieval, followed by incubation with a primary antibody against ITPRIPL1 (ITPRIPL1 Antibody, Boster Biological Technology, Wuhan, China; Catalog #A17509-2; 1:20 dilution) and fluorophore-conjugated secondary antibodies. Nuclei were counterstained with DAPI, and stained sections were examined by fluorescence microscopy. Representative fields were selected to illustrate the spatial distribution of ITPRIPL1.

Immunotherapy cohort analysis

Publicly available immunotherapy cohorts were obtained through the Sparkle platform and their original GEO accessions or published sources.Within each cohort, patients were stratified into high- and low-ITPRIPL1 expression groups, and associations with overall survival or progression-free survival were evaluated using Kaplan-Meier analysis and the log-rank test. Detailed information on cancer type, treatment regimen, sampling time point, endpoint, sample size, and original dataset source is provided in Supplementary Table 1.

Drug sensitivity and CMap perturbation analysis

Drug response correlations were analyzed using the Genomics of Drug Sensitivity in Cancer (GDSC) database²¹. Pearson correlations were computed between ITPRIPL1 expression and IC50 values for common chemotherapy and targeted agents⁴¹. Connectivity Map (CMap) analysis was performed using the L1000 platform to identify compounds capable of reversing ITPRIPL1-associated transcriptional signatures⁴². Compounds with negative enrichment scores were considered potential antagonists of ITPRIPL1-related pathways.

Pan-cancer expression profiling of ITPRIPL1

To characterize the expression pattern of ITPRIPL1 across human malignancies, we integrated transcriptomic data from the TCGA and GTEx databases. Pan-cancer analysis revealed significant differences in ITPRIPL1 expression between tumor and normal tissues, with gastrointestinal malignancies showing consistently higher expression in tumor tissues than in normal counterparts (all p < 0.001; Fig. 1A).

Fig. 1.

Pan-gastrointestinal expression landscape of ITPRIPL1. (A) Pan-cancer differential expression of ITPRIPL1 based on integrated TCGA and GTEx datasets, showing elevated expression in multiple gastrointestinal malignancies. (B) Paired tumor-adjacent normal comparisons of ITPRIPL1 expression in STAD, COAD, READ, and ESCA cohorts from TCGA. (C) Organ-level anatomical mapping of Z-score-standardized ITPRIPL1 expression in normal and tumor tissues, highlighting gastrointestinal enrichment in cancer. (D) Logistic regression analysis evaluating the association between ITPRIPL1 expression and tumor status across gastrointestinal cancers. (E-F) Independent validation of ITPRIPL1 expression in normal and tumor tissues using transcriptomic data from the Human Protein Atlas and GTEx databases.

Focusing on digestive system tumors, paired tumor-adjacent tissue comparisons within TCGA cohorts further confirmed that ITPRIPL1 expression was significantly elevated in STAD, COAD, READ, and ESCA (Wilcoxon signed-rank test; Fig. 1B), indicating that its upregulation is a common feature of gastrointestinal cancers.

To examine organ-level distribution patterns, Z-score-standardized TPM data from TCGA and GTEx were projected onto an anatomical atlas. In normal tissues, ITPRIPL1 expression was relatively low in gastrointestinal organs compared with other tissues, particularly in the stomach. In contrast, tumor tissues exhibited pronounced enrichment of ITPRIPL1 in gastrointestinal cancers, with the strongest enrichment observed in colorectal tumors (Fig. 1C), suggesting a tumor-associated redistribution of expression toward the gastrointestinal tract.

We next assessed the association between ITPRIPL1 expression and tumor status using logistic regression analysis based on combined TCGA and GTEx datasets. In all four gastrointestinal cancers, higher ITPRIPL1 expression was significantly associated with tumor status, with odds ratios consistently greater than 1 (Fig. 1D), indicating a positive correlation between elevated ITPRIPL1 expression and malignancy.

Independent validation using transcriptomic data from the Human Protein Atlas and GTEx further supported these findings. Normal gastrointestinal tissues showed relatively low normalized TPM (nTPM) values of ITPRIPL1, whereas substantially higher expression levels were observed in corresponding cancers, including colorectal, gastric, and esophageal carcinomas (Fig. 1E-F).

Mutation landscape of ITPRIPL1 in gastrointestinal cancers

To delineate the mutational characteristics of ITPRIPL1 across cancers, we first examined its alteration frequency and mutation types in pan-cancer cohorts. Overall, ITPRIPL1 exhibited a low mutation frequency across tumor types, with gastrointestinal cancers, including colorectal and esophageal cancers, showing total alteration rates below 2% (Fig. 2A).

Fig. 2.

Mutation profile and genomic instability features associated with ITPRIPL1. (A) Pan-cancer alteration frequency of ITPRIPL1 across TCGA cohorts. (B) Distribution of alteration types affecting ITPRIPL1, including amplification, mutation, and deletion events. (C) Lollipop plot showing the distribution of ITPRIPL1 mutation sites along the protein sequence. (D) Comparative mutation frequency of ITPRIPL1 relative to canonical oncogenic pathway genes. (E) Correlation between ITPRIPL1 expression and genomic instability indicators (aneuploidy score, tumor ploidy, HRD score) across gastrointestinal cancers. (F) Association between ITPRIPL1 expression and microsatellite instability (MSI) status in STAD, COAD, and READ.

We next analyzed the distribution of alteration types affecting ITPRIPL1. Among all detected events, copy number amplification represented the predominant alteration type, whereas point mutations and deletions occurred at much lower frequencies (Fig. 2B). Consistently, the distribution of mutation sites across the ITPRIPL1 protein showed sparse and dispersed patterns without obvious recurrent hotspots (Fig. 2C).

To place these observations in a broader oncogenic context, we compared the mutation frequency of ITPRIPL1 with that of genes involved in canonical cancer signaling pathways across pan-cancer cohorts. In contrast to classical oncogenic pathways, which exhibited high mutation burdens, ITPRIPL1 showed markedly lower mutation frequencies in gastrointestinal cancers (Fig. 2D), suggesting that it is unlikely to function as a mutation-driven oncogene in these tumor types.

Given the low mutation burden, we next investigated whether ITPRIPL1 expression was associated with genomic instability-related features in gastrointestinal cancers. Correlation analysis revealed that in STAD, higher ITPRIPL1 expression was strongly associated with increased aneuploidy score, tumor ploidy, and homologous recombination defect (HRD) scores, all reaching statistical significance (Fig. 2E). These associations were less pronounced or absent in other digestive system tumors, indicating a tumor type-dependent relationship between ITPRIPL1 expression and genomic instability.

Finally, we evaluated the relationship between ITPRIPL1 expression and microsatellite instability (MSI) status. In COAD and READ, MSI-high (MSI-H) tumors exhibited significantly higher MSI scores compared with MSI-low and microsatellite-stable (MSS) groups (p < 0.05). In contrast, in STAD, MSI-H tumors showed significantly lower MSI scores compared with MSS tumors (p < 0.01; Fig. 2F), highlighting distinct patterns of genomic instability associated with ITPRIPL1 across gastrointestinal cancers.

Pathway enrichment and immune association analyses of ITPRIPL1

To investigate the biological pathways and immune-related processes associated with ITPRIPL1, we performed integrative pathway enrichment and immune correlation analyses across gastrointestinal cancers.

Immune subtype classification of STAD samples revealed that ITPRIPL1 expression was preferentially enriched in immune-inflamed subtypes (Fig. 3A), indicating an association with immune-active tumor microenvironments. To further characterize pathway-level features, gene set enrichment analysis (GSEA) was conducted using Hallmark gene sets across TCGA gastrointestinal cohorts (STAD, COAD, READ, and ESCA). Distinct cancer type-specific enrichment patterns were observed for KRAS signaling pathways: KRAS signaling up showed stronger positive associations with ITPRIPL1 expression in COAD and READ, whereas KRAS signaling down exhibited stronger positive associations in STAD and ESCA (Fig. 3B).

Fig. 3.

Pathway enrichment and immune associations of ITPRIPL1. (A) Distribution of ITPRIPL1 expression across TCGA immune subtypes in gastric cancer. (B) Gene set enrichment analysis (GSEA) illustrating pathways positively and negatively associated with ITPRIPL1 expression across STAD, COAD, READ, and ESCA. (C) Heatmap showing correlations between ITPRIPL1 and immune-related genes, including chemokines and HLA family members. (D) Spearman correlations between ITPRIPL1 expression and immune cell infiltration estimated by multiple deconvolution algorithms. (E) Immune cell-specific expression patterns of ITPRIPL1 derived from the Human Protein Atlas.

Across all four gastrointestinal cancers, ITPRIPL1 expression was consistently and positively associated with multiple immune- and inflammation-related pathways, including Interferon Gamma Response, IL-6-JAK-STAT3 signaling, IL-2-STAT5 signaling, Inflammatory Response, Epithelial-Mesenchymal Transition, and Allograft Rejection (Fig. 3B). In contrast, negative correlations were observed with Fatty Acid Metabolism, Estrogen Response Early, and Estrogen Response Late, with the strongest inverse associations detected in ESCA.

We next performed gene-level co-expression analyses to further delineate immune-related associations. In COAD and READ, ITPRIPL1 exhibited stronger correlations with classical immune-related genes than in STAD and ESCA, particularly among members of the CCL chemokine family and the HLA gene family (Fig. 3C). Notably, in COAD, ITPRIPL1 showed the strongest correlation with PDCD1LG2 among all immune-related genes (correlation coefficient = 0.51). In STAD, although overall correlation strength was comparatively lower, several immune regulatory genes still demonstrated strong positive associations with ITPRIPL1, including ADORA2A (correlation coefficient = 0.50).

To assess immune cell infiltration, we applied multiple computational deconvolution algorithms to evaluate the Spearman correlations between ITPRIPL1 expression and immune cell abundance across pan-cancer cohorts. Among immune cell types, the strongest and most consistent positive correlations were observed with T cells, particularly CD8⁺ T cells, across all four gastrointestinal cancers (Fig. 3D).

Consistent with these findings, analysis of immune cell-specific expression data from the Human Protein Atlas revealed that immune cell populations with the highest ITPRIPL1 expression were predominantly T-cell subsets (Fig. 3E).

Functional state associations and proteomic validation of ITPRIPL1

To further delineate the biological roles of ITPRIPL1 in tumor progression, we investigated its association with cancer-related functional states across pan-cancer datasets. Correlation analyses demonstrated that ITPRIPL1 was strongly and positively associated with multiple tumorigenic processes, most notably angiogenesis, inflammation, proliferation, and differentiation (Fig. 4A and Supplementary Fig. 1A). Among these, the correlation with proliferation was particularly robust in gastrointestinal malignancies, indicating a potential role of ITPRIPL1 in driving tumor growth dynamics (Fig. 4B).

Fig. 4.

Important Tumor functional state associations validation of ITPRIPL1. (A) Correlations between ITPRIPL1 expression and cancer-related functional states based on CancerSEA analysis. (B) Functional state correlations stratified by gastrointestinal tumor type.

We next explored the relationship between ITPRIPL1 and protein-level signatures using data from the TCGA database. Proteomic correlation analyses revealed that ITPRIPL1 expression was significantly associated with several key regulatory proteins implicated in tumor biology, including markers of cell-cycle control, immune modulation, and signaling transduction (Supplementary Fig. 1B). These findings provide orthogonal validation at the protein level, supporting the notion that ITPRIPL1 is functionally connected to proliferative and immune-regulatory pathways in cancer.

Prognostic relevance of ITPRIPL1 across gastrointestinal malignancies

To evaluate the prognostic relevance of ITPRIPL1 across gastrointestinal malignancies, we performed univariate Cox regression analyses and Kaplan-Meier survival analyses based on TCGA datasets. Overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) were systematically assessed in four digestive system cancers, including STAD, COAD, READ, and ESCA (Fig. 5A-E).

Fig. 5.

Prognostic relevance of ITPRIPL1 across gastrointestinal cancers. (A-E) Univariate Cox regression analyses of overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) across STAD, COAD, READ, and ESCA. (F) Kaplan-Meier survival curves illustrating tumor-type-specific prognostic effects of ITPRIPL1 expression. (G) Multivariable Cox regression analysis of overall survival in TCGA-STAD. Forest plot showing hazard ratios and 95% confidence intervals for ITPRIPL1 expression, ZAP70 expression, CD8⁺ T-cell abundance, CD4⁺ T-cell abundance, stromal score, age, sex, and pathological stage. Continuous variables were Z-score standardized, and hazard ratios therefore represent the effect per 1-standard-deviation increase.

The prognostic impact of ITPRIPL1 expression exhibited marked tumor type-dependent heterogeneity. In STAD, elevated ITPRIPL1 expression was consistently associated with unfavorable outcomes. Both Cox regression and log-rank tests identified ITPRIPL1 as a high-risk factor for OS and DSS, while DFI association was significant only in the Kaplan-Meier analysis and was therefore interpreted cautiously as an exploratory, potentially threshold-dependent finding (Fig. 5F). These results suggest that increased ITPRIPL1 expression is associated with poorer clinical outcomes in gastric cancer across multiple survival endpoints.

In contrast, READ displayed an opposite prognostic pattern. Higher ITPRIPL1 expression was associated with improved prognosis, with both Cox regression and log-rank analyses identifying low-risk associations for OS. Additionally, DSS analysis based on the log-rank test demonstrated a favorable prognostic trend in READ patients with high ITPRIPL1 expression (Fig. 5F).

In ESCA, ITPRIPL1 expression showed limited prognostic relevance, with low-risk associations observed only in a subset of survival analyses, indicating a weaker and less consistent prognostic effect. Notably, in COAD, none of the four survival endpoints (OS, DSS, DFI, or PFI) demonstrated a significant association with ITPRIPL1 expression in either Cox regression or log-rank analyses, suggesting a lack of prognostic value in this tumor type (Supplementary Fig. 2).

Immune-modulated prognostic effects of ITPRIPL1 in gastrointestinal malignancies

Given the tumor-type-specific prognostic association observed in the preceding analyses, we next evaluated whether ITPRIPL1 retained prognostic value after adjustment for relevant clinical and immune-related variables in STAD. Multivariable Cox analysis in TCGA-STAD showed that higher ITPRIPL1 expression remained independently associated with poorer overall survival after adjustment for ZAP70 expression, CD8⁺ and CD4⁺ T-cell abundance, stromal score, age, sex, and pathological stage (HR = 1.23, 95% CI 1.04–1.47, P = 0.019; Fig. 5G). Stromal score, age, and advanced stage were also independently associated with worse survival.

To further investigate whether the prognostic impact of ITPRIPL1 was associated with different immune contexts, we performed stratified survival analyses across four gastrointestinal cancers (STAD, COAD, READ, and ESCA). For each cancer type, patients were dichotomized into high- and low-expression groups based on ITPRIPL1 levels and further stratified by four immune-related indicators: ZAP70 expression, CD8⁺ T-cell abundance, CD4⁺ T-cell abundance, and immune microenvironment scores. Each analysis generated four patient subgroups (high-high, high-low, low-high, and low-low), and OS was compared among these groups using Kaplan-Meier analysis.

Across the four gastrointestinal malignancies, statistically significant survival stratification was observed exclusively in STAD, whereas no significant differences were detected in COAD, READ, or ESCA. Therefore, subsequent analyses focused on STAD-specific results (Fig. 6A-D).

Fig. 6.

Immune–associated prognostic stratification of ITPRIPL1 in gastric cancer. (A) Combined survival analysis of ITPRIPL1 expression and CD8⁺ T-cell abundance in STAD. (B) Combined survival analysis of ITPRIPL1 expression and CD4⁺ T-cell abundance. (C) Stratified survival analysis based on ITPRIPL1 expression and stromal score. (D) Joint prognostic evaluation of ITPRIPL1 and ZAP70 expression in gastric cancer.

First, we evaluated the combined prognostic effect of ITPRIPL1 expression and CD8⁺ T-cell abundance in STAD. Patients with low ITPRIPL1 expression and high CD8⁺ T-cell infiltration (ITPRIPL1⁻/CD8⁺) exhibited the most favorable OS. In contrast, both ITPRIPL1⁺/CD8⁺ and ITPRIPL1⁺/CD8⁻ subgroups displayed significantly poorer survival outcomes (Fig. 6A).

Similarly, stratification based on CD4⁺ T-cell abundance revealed that the ITPRIPL1⁻/CD4⁺ subgroup was associated with the best prognosis in STAD. Patients with high ITPRIPL1 expression showed comparatively worse survival regardless of CD4⁺ T-cell levels (Fig. 6B).

We next examined the joint prognostic influence of ITPRIPL1 expression and immune microenvironment scores. Among the evaluated scores, stromal score-based stratification yielded statistically significant survival differences. Patients classified as ITPRIPL1⁻/stromal score-low demonstrated the most favorable OS, whereas ITPRIPL1⁺/stromal score-low and ITPRIPL1⁺/stromal score–high subgroups exhibited significantly poorer outcomes (Fig. 6C).

Finally, we assessed the combined prognostic effect of ITPRIPL1 and ZAP70, a key gene involved in T-cell receptor signaling. In STAD, patients with low ITPRIPL1 expression and high ZAP70 expression (ITPRIPL1⁻/ZAP70⁺) showed the most favorable survival, whereas both ITPRIPL1⁺/ZAP70⁻ and ITPRIPL1⁺/ZAP70⁺ subgroups were associated with the poorest prognosis (Fig. 6D). In addition, correlation analysis demonstrated a moderate positive association between ITPRIPL1 and ZAP70 expression in STAD (Pearson correlation coefficient R = 0.33).

Single-cell transcriptomic characterization of ITPRIPL1 across gastrointestinal cancers

To validate the cellular distribution of ITPRIPL1 at single-cell resolution, we analyzed multiple single-cell RNA sequencing datasets spanning gastrointestinal malignancies, including colorectal cancer (CRC: EMTAB8107, GSE146771_Smartseq2, GSE166555), esophageal cancer (ESCA: GSE160269), and gastric cancer (STAD: GSE134520). Among these datasets, EMTAB8107 (CRC) and GSE134520 (STAD) exhibited more distinct expression patterns and cellular heterogeneity and were therefore selected for detailed visualization, while the remaining datasets were included for integrative analyses.

UMAP-based clustering and cell-type annotation of the STAD dataset (GSE134520) revealed diverse immune and non-immune cell populations. Visualization of ITPRIPL1 expression overlaid on the UMAP embedding demonstrated that its expression was predominantly localized within CD8⁺ T cells and CD4⁺ T cells (Fig. 7A). A similar distribution pattern was observed in the CRC dataset EMTAB8107, where ITPRIPL1 expression was again enriched within T-cell populations on the UMAP projection (Fig. 7B).

Fig. 7.

Single-cell transcriptomic characterization of ITPRIPL1. (A) UMAP visualization of ITPRIPL1 expression across cell populations in gastric cancer single-cell dataset GSE134520. (B) UMAP visualization of ITPRIPL1 expression in colorectal cancer single-cell dataset EMTAB8107. (C) Average ITPRIPL1 expression across annotated cell types in gastric cancer. (D) Integrated heatmap summarizing ITPRIPL1 expression across major cell populations from multiple gastrointestinal single-cell datasets. (E) Comparison of cellular composition between ITPRIPL1-high and ITPRIPL1-low groups. (F) Correlation analysis between ITPRIPL1 expression and immune/non-immune cell populations.

To quantitatively validate these observations, we calculated the average expression of ITPRIPL1 across annotated cell types. In GSE134520, ITPRIPL1 expression was highest in T-cell subsets, confirming the visual UMAP patterns, while lower but detectable expression was also observed in malignant cells and mast cells (Fig. 7C). Consistent results were obtained in EMTAB8107, with expression distributions shown in Supplementary Fig. 3.

We next integrated all selected single-cell datasets and generated a heatmap summarizing ITPRIPL1 expression across major cell types. Across datasets, ITPRIPL1 exhibited consistently high relative expression in multiple T-cell subtypes, including regulatory T cells (Treg), conventional CD4⁺ T cells (CD4Tconv), exhausted CD8⁺ T cells (CD8Tex), and proliferating T cells (Tprolif). In contrast, relatively lower expression levels were observed in malignant cells, mast cells, fibroblasts, plasma cells, and other non-T-cell populations (Fig. 7D).

To further explore how ITPRIPL1 expression relates to cellular composition, we stratified all cells from GSE134520 and EMTAB8107 into ITPRIPL1-high and ITPRIPL1-low groups and compared their cellular composition. In the CRC dataset EMTAB8107, the proportion of T cells was markedly increased in the ITPRIPL1-high group, accompanied by reduced proportions of malignant cells, fibroblasts, and plasma cells. In the STAD dataset GSE134520, CD8⁺ T cells were also enriched in the ITPRIPL1-high group, while increased proportions of malignant cells, plasma cells, and mast cells were observed. Notably, the proportion of gastric gland cells decreased sharply from approximately 70% in the ITPRIPL1-low group to approximately 10% in the ITPRIPL1-high group (Fig. 7E).

Finally, we assessed correlations between ITPRIPL1 expression and immune cell abundance across all analyzed single-cell datasets. ITPRIPL1 expression showed significant positive correlations with CD8⁺ T cells (R = 0.56) and CD4Tconv cells (R = 0.41). In contrast, negative correlations were observed with multiple non-T-cell populations, including malignant cells, endothelial cells, epithelial cells, fibroblasts, myofibroblasts, mast cells, and monocyte-macrophage populations. Among these, particularly strong negative correlations were detected with myofibroblasts (R =-0.45) and mast cells (R = -0.36) (Fig. 7F).

Exploratory spatial characterization of ITPRIPL1 in colorectal cancer tissues

Following the single-cell analyses, immunofluorescence staining was performed in colorectal cancer specimens to explore the tissue-level spatial distribution of ITPRIPL1 within a gastrointestinal tumor context. Representative immunofluorescence images from the most colorectal cancer specimen are shown. Representative images at low (10×) and high (40×) magnifications are presented in Fig. 8 to illustrate both global tissue architecture and cellular-level localization, while intermediate magnification images (20×) are provided in Supplementary Fig. 4.

Fig. 8.

Exploratory immunofluorescence characterization of the spatial distribution of ITPRIPL1 in colorectal cancer tissues. Representative immunofluorescence images showing ITPRIPL1 localization in colorectal cancer tissues at low (10×), and high (40×) magnifications. Images shown in this figure were obtained from the same representative tumor specimen. ITPRIPL1 immunofluorescence signals are shown, with nuclei counterstained by DAPI. (A) Tumor regions showing strong and widespread ITPRIPL1 signals in adenocarcinoma cells. (B) Adjacent non-tumorous colonic epithelium displaying markedly reduced ITPRIPL1 signals with weak or sparse staining. (C) Prominent ITPRIPL1 signals within tumor-adjacent tertiary lymphoid structures (TLS), characterized by intense and localized distribution. (D) ITPRIPL1 signals detected in neurovascular components, including nerve bundles and vascular endothelial cells, with comparatively weaker signals observed in smooth muscle tissues.

In tumor regions, ITPRIPL1 exhibited strong and widespread fluorescence signals within adenocarcinoma cells, which were consistently observed at both low and high magnifications (Fig. 8A). In contrast, adjacent non-tumorous colonic epithelium displayed markedly reduced ITPRIPL1 staining, with only weak or sparse signals detectable (Fig. 8B). Prominent ITPRIPL1 expression was also observed within tumor-adjacent tertiary lymphoid structures (TLS), characterized by intense and localized signals (Fig. 8C). In addition, strong ITPRIPL1 positivity was detected in neurovascular components, including nerve bundles and vascular endothelial cells, whereas comparatively weaker staining was observed in smooth muscle tissues (Fig. 8D). Intermediate magnification views confirming these distribution patterns are shown in Supplementary Fig. 4.

Scale bars: 100 μm (10×), and 20 μm (40×).

Clinical relevance and therapeutic implications of ITPRIPL1 in gastrointestinal cancers

To further investigate the clinical relevance of ITPRIPL1 expression and its potential implications for therapy, we performed integrative analyses focusing on tumor stage, molecular subtypes, immunotherapy response, and pharmacogenomic associations.

We first examined the relationship between ITPRIPL1 expression and tumor stage using two independent gastric cancer cohorts, GSE34942 and GSE62254. In both datasets, ITPRIPL1 expression exhibited a consistent stage-associated pattern, characterized by a gradual decrease from stage I to stage III, followed by a modest increase at stage IV (Fig. 9A). This trend suggests that ITPRIPL1 expression dynamically changes during tumor progression.

Fig. 9.

Clinical associations and immunotherapy relevance of ITPRIPL1. (A) Stage-dependent expression patterns of ITPRIPL1 in independent gastric cancer cohorts. (B) Distribution of ITPRIPL1 expression across molecular subtypes of gastrointestinal cancers. (C) Kaplan-Meier analysis of overall survival according to ITPRIPL1 expression in the pretreatment anti-PD-1 clear-cell renal cell carcinoma cohort reported in PMID32472114. (D) Connectivity Map (CMap) analysis identifying candidate compounds associated with ITPRIPL1-related transcriptional signatures. (E-H) Drug sensitivity correlations between ITPRIPL1 expression and IC50 values across gastrointestinal cancers.

Next, we explored the distribution of ITPRIPL1 expression across tumor molecular subtypes using TCGA data. Among the four gastrointestinal cancers analyzed, STAD exhibited the most comprehensive molecular subtype classification, with relatively comparable ITPRIPL1 expression levels across subtypes. In contrast, in both COAD and READ, the GI.HM-indel subtype showed the highest ITPRIPL1 expression, whereas the GI.GS subtype consistently displayed the lowest expression levels. In ESCA, molecular subtype-based differences in ITPRIPL1 expression did not reach statistical significance (Fig. 9B).

To assess the association between ITPRIPL1 expression and immunotherapy outcomes, we analyzed pretreatment cohorts from immunotherapy datasets available through the Sparkel database (Supplementary Table 1). Survival analysis revealed that patients with higher ITPRIPL1 expression exhibited significantly improved survival outcomes following immunotherapy in pretreatment cohorts (Fig. 9C and Supplementary Fig. 5).

We further investigated potential therapeutic agents associated with ITPRIPL1 expression by leveraging the Connectivity Map (CMap) framework to identify small molecules predicted to reverse biological effects linked to ITPRIPL1 dysregulation. Candidate compounds were identified (Fig. 9D), and subsequent drug sensitivity analyses were conducted across the four gastrointestinal cancers (Fig. 9E-H). Among these, AH.6809 emerged as the most relevant compound in STAD and ESCA, imatinib showed the strongest association in READ, and W.13 demonstrated the highest relevance in COAD.

In addition, we interrogated pharmacogenomic datasets including PRISM, CTRP, and GDSC to evaluate the association between ITPRIPL1 expression and chemotherapeutic drug sensitivity. Correlation analyses revealed that ITPRIPL1 expression was negatively correlated with the IC50 values of most chemotherapeutic agents in the GDSC database, indicating that higher ITPRIPL1 expression is generally associated with increased chemosensitivity (Fig. 10A-C).

Fig. 10.

Pharmacogenomic associations of ITPRIPL1. (A-C) Correlation between ITPRIPL1 expression and chemotherapeutic drug sensitivity based on GDSC, PRISM, and CTRP datasets, indicating increased chemosensitivity in ITPRIPL1-high tumors.

Discussion

In this study, we performed a comprehensive, integrative analysis of ITPRIPL1 across major gastrointestinal malignancies by combining transcriptomic profiling, genomic instability assessment, immune pathway and cell-type analyses, survival modeling, spatial immunofluorescence validation, and therapeutic correlation analyses. By adopting a pan-gastrointestinal framework while maintaining a focused emphasis on gastric cancer, our work aimed to resolve the tumor-type specificity and immune-context dependency of ITPRIPL1 and to clarify its clinical relevance beyond descriptive pan-cancer associations.

Recent studies have progressively expanded the relevance of ITPRIPL1 across multiple cancer types, from early transcriptomic and epigenetic observations to emerging evidence linking its expression to tumor immunity. While pan-cancer analyses and tumor-specific investigations have suggested associations between ITPRIPL1, immune infiltration, and clinical outcome, these findings have largely been derived from isolated analytical frameworks or single-cancer contexts. Although recent work in gastric cancer highlighted the immunoregulatory and prognostic relevance of ITPRIPL1, it remains unclear whether such effects are conserved across anatomically related gastrointestinal malignancies or are restricted to specific tumor contexts⁴³. Moreover, how immune-related signals observed at the bulk level relate to cellular composition, spatial distribution, and therapeutic relevance has not been systematically examined. Addressing these questions, our study defines the tumor-type-specific role of ITPRIPL1 across gastrointestinal cancers, with particular relevance to gastric cancer.

Consistent with prior reports, we observed that ITPRIPL1 is upregulated in tumor tissues across gastric, colorectal, rectal, and esophageal cancers. Importantly, however, its prognostic significance was highly tumor specific. Only gastric cancer demonstrated a stable and reproducible association between high ITPRIPL1 expression and unfavorable survival across multiple endpoints, whereas colorectal and rectal cancers exhibited weak or inverse associations and esophageal cancer showed inconsistent trends. This divergence was not explained by differential mutation frequency, as ITPRIPL1 alterations were uniformly rare across gastrointestinal tumors, indicating that its functional relevance is driven by expression regulation rather than somatic mutation. These results support the conclusion that ITPRIPL1 should not be regarded as a universal gastrointestinal biomarker, but rather as a context-dependent factor with primary clinical relevance in gastric cancer.

Mechanistically, ITPRIPL1 expression was consistently associated with immune- and inflammation-related pathways, including interferon-γ response, IL-6-JAK-STAT3 and IL-2-STAT5 signaling, and inflammatory response programs. In gastric cancer, ITPRIPL1 further correlated with genomic instability features such as aneuploidy, tumor ploidy, and homologous recombination deficiency, suggesting that it marks a transcriptional state linked to chromosomal instability. These molecular features provide a plausible backdrop for the coexistence of aggressive tumor behavior and immune activation signals observed in this disease.

To reconcile immune associations with clinical outcomes, we evaluated immune-modulated prognostic effects in gastric cancer. Importantly, Multivariable analysis confirmed that ITPRIPL1 retained an adverse prognostic association independent of major clinical and immune-related covariates. This finding provides a bridge between the initial survival analysis and the subsequent stratified analyses examining how the immune context may modify its prognostic effect. Notably, the prognostic impact of ITPRIPL1 was strongly associated with immune context, particularly T-cell-related features. ZAP70, a key tyrosine kinase required for early TCR signal transduction and a canonical marker of T-cell activation, was associated with distinct survival patterns in combination with ITPRIPL1 expression⁴⁴. Although formal interaction testing was not performed, the multivariable Cox analysis and the consistent survival differences across CD8⁺ T-cell-, CD4⁺ T-cell-, stromal-, and ZAP70-defined subgroups collectively support the immune-context-dependent prognostic framework of ITPRIPL1.Similar joint biomarker stratification strategies have been used to characterize immune-related prognostic patterns in gastric cancer, colon cancer, and pan-cancer studies^45–47. Patients with low ITPRIPL1 expression combined with high ZAP70 expression or high CD8⁺/CD4⁺ T-cell abundance exhibited the most favorable outcomes, whereas high ITPRIPL1 expression consistently identified poor-prognosis groups. Meanwhile, Single-cell transcriptomic analyses further substantiated this interpretation by demonstrating preferential ITPRIPL1 expression in multiple T-cell subsets rather than exclusive localization to malignant epithelial cells. The colorectal immunofluorescence analysis provided exploratory information on the spatial distribution of ITPRIPL1 within a gastrointestinal tumor tissue context, including its localization in malignant regions, tumor-adjacent immune structures, and stromal-neurovascular components.

According to the above analysis, the enrichment of ITPRIPL1 in T-cell-associated immune niches may have important functional implications. Mechanistically, ITPRIPL1 has been reported to bind CD3ε and stabilize the CD3ε-Nck complex, thereby limiting ZAP70 recruitment and phosphorylation and suppressing downstream ERK, NF-κB, calcium signaling, cytokine production, and T-cell cytotoxicity. Because ZAP70 is a central mediator of proximal T-cell receptor signaling, disruption of this axis may impair immune surveillance even in tumors with substantial T-cell infiltration.

We therefore propose that ITPRIPL1-high tumors may represent an immune-infiltrated but functionally constrained state, in which CD8⁺ and CD4⁺ T cells are present but their antitumor activity is attenuated through inhibition of the CD3ε-ZAP70 signaling axis. Such proximal T-cell receptor suppression may facilitate tumor immune escape and may also contribute to dysfunctional or exhaustion-like T-cell phenotypes. However, this interpretation remains hypothetical in gastric cancer, as the present study did not directly assess CD3ε binding, ZAP70 phosphorylation, or T-cell effector function^14,48–50.

Beyond baseline prognosis, we observed that elevated ITPRIPL1 expression was associated with improved survival in pretreatment immunotherapy cohorts and with increased chemosensitivity, as reflected by negative correlations with IC50 values across multiple pharmacogenomic datasets. An intriguing finding is that ITPRIPL1 may link adverse baseline prognosis with potential treatment-related vulnerability. Although elevated ITPRIPL1 expression was associated with poorer survival in gastric cancer, it was also associated with more favorable outcomes in selected pretreatment immunotherapy cohorts and with lower IC50 values for several compounds across pharmacogenomic datasets. This apparent contrast suggests that ITPRIPL1-high tumors may exhibit an aggressive yet therapeutically susceptible state, possibly reflecting an immune-infiltrated but functionally constrained microenvironment that can be partially relieved by treatment.

Nevertheless, several limitations should be acknowledged. The present study is primarily retrospective and based on publicly available datasets, which limit causal inference. The integrated cohorts differed in patient characteristics, disease stage, treatment background, sample collection, clinical annotation, follow-up, and endpoint definition, introducing unavoidable inter-cohort heterogeneity. In addition, the datasets were generated using different transcriptomic platforms and preprocessing pipelines, including RNA-sequencing and microarray-based technologies. Although standardized processing, within-cohort analyses, and batch correction were applied where appropriate, platform-specific variation and residual batch effects cannot be completely excluded.

Subgroup findings in survival analysis are exploratory and do not formally demonstrate statistical interaction, further interaction modeling and independent validation are required to confirm whether the immune context modifies the prognostic effect of ITPRIPL1. In addition, tissue-level validation was not performed in gastric cancer specimens. Accordingly, the colorectal immunofluorescence findings cannot be directly extrapolated to gastric cancer, and future studies using independent gastric cancer tissues will be required to evaluate the spatial relationship of ITPRIPL1 with malignant cells, CD4⁺/CD8⁺ T cells, ZAP70-related signaling, and tertiary lymphoid structures. Meanwhile, prospective clinical cohorts, treatment-interaction analyses, and functional validation in experimental models will be required to determine whether ITPRIPL1 has genuine predictive or therapeutic value.

Collectively, our findings support a model in which ITPRIPL1 defines an immune-infiltrated yet functionally constrained tumor microenvironment, particularly in gastric cancer. We hypothesize that elevated ITPRIPL1 expression reflects a regulatory state in which active immune infiltration coexists with impaired T-cell surveillance, thereby permitting aggressive tumor behavior despite the presence of immune cells. In parallel, this immune-permissive condition may cooperate with TGF-β-driven stromal remodeling, MMP1/MMP2-mediated extracellular-matrix degradation, invadopodia formation, and metastatic niche development to facilitate invasion and dissemination. This integrated framework may explain the association of ITPRIPL1 with poor baseline prognosis, while the persistence of immune infiltration and treatment-related vulnerabilities may underlie the favorable responses observed in selected immunotherapy cohorts and pharmacogenomic analyses. Although these proposed links require direct experimental validation, they provide a testable mechanistic hypothesis positioning ITPRIPL1 as an immune-associated biomarker connecting immune dysfunction, metastatic progression, prognosis, and potential therapeutic susceptibility in gastric cancer^51–55.

Conclusion

In conclusion, our integrative pan-gastrointestinal analysis identifies ITPRIPL1 as a tumor-type-specific biomarker with its strongest clinical relevance in gastric cancer rather than as a universal marker across gastrointestinal malignancies. In STAD, elevated ITPRIPL1 was associated not only with immune- and inflammation-related programs and genomic instability features, but also with a distinctly immune-context-associated prognostic pattern. Importantly, its prognostic value was substantially refined by the gastric cancer immune microenvironment, particularly by CD8⁺/CD4⁺ T-cell abundance and ZAP70 expression, supporting the view that ITPRIPL1 reflects the functional state of antitumor immunity rather than immune infiltration alone. Moreover, the association of high ITPRIPL1 expression with improved outcomes in pretreatment immunotherapy cohorts and increased chemosensitivity suggests that this marker may connect adverse baseline prognosis with actionable therapeutic vulnerability. Together, these findings identify ITPRIPL1 as an immune-associated prognostic candidate in gastric cancer. The observed associations with immunotherapy outcomes and cell-line drug-response profiles provide hypothesis-generating translational clues but are insufficient to support clinical treatment selection at present. Prospective and functional validation will be required to determine whether ITPRIPL1 has predictive or therapeutically actionable value.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CD4Tconv

Conventional CD4⁺ T cells

CD8Tex

Exhausted CD8⁺ T cells

COAD

Colon adenocarcinoma

CMap

Connectivity Map

CRC

Colorectal cancer

CTRP

Cancer Therapeutics Response Portal

DAPI

4′,6-diamidino-2-phenylindole

DFI

Disease-free interval

DSS

Disease-specific survival

EMT

Epithelial–mesenchymal transition

EPIC

Estimating the Proportions of Immune and Cancer cells

ESCA

Esophageal carcinoma

GDSC

Genomics of Drug Sensitivity in Cancer

GEO

Gene Expression Omnibus

GSEA

Gene set enrichment analysis

GTEx

Genotype-Tissue Expression

Hallmark

Hallmark gene set collection

HPA

Human Protein Atlas

HRD

Homologous recombination deficiency

IF

Immunofluorescence

ITPRIPL1

Inositol 1,4,5-trisphosphate receptor-interacting protein-like 1

MAF

Mutation annotation format

MSI

Microsatellite instability

MSI-H

Microsatellite instability-high

MSS

Microsatellite stable

OS

Overall survival

PCA

Principal component analysis

PFI

Progression-free interval

PRISM

Profiling Relative Inhibition Simultaneously in Mixtures

READ

Rectal adenocarcinoma

RPPA

Reverse Phase Protein Array

scRNA-seq

Single-cell RNA sequencing

STAD

Stomach adenocarcinoma

TCGA

The Cancer Genome Atlas

TCPA

The Cancer Proteome Atlas

TIDE

Tumor Immune Dysfunction and Exclusion

TLS

Tertiary lymphoid structures

TME

Tumor microenvironment

TPM

Transcripts per million

Tprolif

Proliferating T cells

Treg

Regulatory T cells

UMAP

Uniform Manifold Approximation and Projection

ZAP70

Zeta-chain-associated protein kinase 70

Author contributions

Conceptualization, H.L., and Y.T.; Methodology, H.L., G.G., H.Z., Y.W., Q.H., and Y.T.; Investigation, Y.L., H.L., W.L., H.C., N.W. and Y.T.; Validation, Y.L., W.L., Y.W., H.C., F.X., and Y.T.; Formal analysis, Y.L., H.L., and G.G.; Resources, H.L., G.G., F.X., and N.W.; Visualization, H.L., G.G., W.L., N.W., and J.L.; Writing—Original Draft, H.L., G.G., N.W., Y.T.; Writing—Review & Editing, W.L., F.X., N.W., J.L., and Y.T.; Supervision, J.L., and Y.T.; Project administration, N.W., J.L., and Y.T.; Funding acquisition, N.W., and Y.T.; All authors read and approved the submitted version of manuscript.

Funding

This study was supported by the School Science Fund of Zhejiang Chinese Medical University (Grant number: 2023FSYYZY19); the Startup Fund for Scientific Research of Fujian Medical University (Grant number: 2025QH1036, to Yue Tian); the Natural Science Foundation of Fujian Province (Grant number: 2026J001210); the Joint Funds for the Innovation of Science and Technology, Fujian Province (Grant number: 2025Y9053, 2023Y9299).

Data availability

All datasets analyzed in this study are publicly available unless otherwise specified. Bulk RNA-sequencing, clinical, and somatic mutation data for STAD, COAD, READ, and ESCA were obtained from TCGA (https://portal.gdc.cancer.gov/), and normal tissue transcriptomes were retrieved from GTEx (https://gtexportal.org/). Single-cell RNA sequencing datasets (EMTAB8107, GSE146771, GSE166555, GSE160269, and GSE134520) were downloaded from GEO and ArrayExpress. Protein expression data were sourced from the Human Protein Atlas, tumor functional state data from CancerSEA, and drug sensitivity data from GDSC1/2, PRISM, and CTRP. Immunotherapy response data were accessed through publicly available cohorts integrated within the Sparkle platform. Immunofluorescence images generated from colorectal cancer specimens are available from the corresponding author upon reasonable request. No new sequencing data was generated in this study. The analytical workflow and code implementation received technical support from the Sparkle platform. Materials related to this study have been retained by the research team and are available from Hanzhang Lyu (hanzhanglyu@outlook.com) upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (Approval number: K2024-12-064). All procedures were conducted in accordance with institutional guidelines and relevant national regulations. All specimens were obtained from existing pathology archives, and no additional invasive procedures were performed on human participants. Written informed consent was obtained from all participants prior to sample collection, and the study was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committee.