Introduction

Cancer remains a global health challenge with high incidence and mortality rates1. Its development involves complex multigene processes, emphasizing the need for novel diagnostic biomarkers and therapeutic targets. Whole-genome analysis, supported by advancements in high-throughput sequencing and bioinformatics, enables large-scale projects like TCGA to collect extensive genomic and clinical data2. Integrating multi-omics data allows researchers to profile gene expression, link it to prognosis, and explore signaling pathways, advancing cancer research from traditional pathology to molecular diagnosis.

The RIN family, including RIN1, RIN2, and RIN3, plays a key role in regulating endocytosis and endocytic transport. The RH domain of RIN proteins forms the Rab5 binding region essential for GEF activity3. Each member has distinct functions: RIN1 activates ABL tyrosine kinase and regulates epithelial adhesion and migration4; RIN2 links Ras to Rab5 during HGF-induced E-cadherin endocytosis5; RIN3, a Rab5-GEF, activates Rab5 by facilitating GDP-to-GTP exchange for RAB5B and RAB313,6. RIN3 methylation is linked to TIA/MIS, early cognitive impairment 7, and chronic obstructive pulmonary disease8. As a Ras effector, RIN3 is crucial for RAB31 function, with Rab31 overexpression enhancing STAD cell migration and metastasis9. RIN3 also activates Ras, influencing multiple signaling pathways10. RAS mutations, the most frequent genetic cause of cancer11, disrupt the MAPK pathway in many cancers12, with K-Ras-independent pathways limiting pancreatic cancer therapies13. Differences in Ras-Raf binding preferences impact oncogenic signaling14, and RAS mutations drive pro-tumorigenic stresses while enhancing cellular resilience15,16. Investigating RIN3 in cancer holds significant therapeutic potential.

Research on RIN3 in cancer is limited, and its role remains unclear. We analyzed RIN3 expression and prognosis across multiple cancers using TCGA, HPA, CCLE, GTEx, and TISCH databases, exploring correlations with DNA methylation, CNVs, TMB, MSI, and immune cell infiltration. Single-cell analysis across 33 cancer types revealed co-expression with immune-related genes and DNA repair mechanisms. In breast cancer, siRNA-mediated knockdown of RIN3 reduced cell proliferation, migration, and invasion, underscoring its potential as a therapeutic target. These findings suggest RIN3 is a promising prognostic biomarker and may guide immunotherapy strategies, especially in BRCA-associated tumors.

Methods

Publicly available datasets and analytical approaches

A bubble plot linking diseases or phenotypes to RIN3 was generated using the Open Targets platform (https://platform.opentargets.org/). RIN3’s subcellular localization was confirmed via the Human Protein Atlas (https://www.ProteinAtlas.org/). RNA-seq data (TPM) for 33 cancer types and their matched normal tissues were obtained from the UCSC XENA database (https://xena.ucsc.edu/) and standardized through the Toil pipeline17. RIN3 mRNA expression differences between cancer and adjacent normal tissues were analyzed using the Gene_DE module of TIMER2.018. Cancer cell line data from 29 tissue sources were sourced from the CCLE (https://portals.broadinstitute.org/ccle/) to assess RIN3 expression19. Subsequent TPM mRNA and clinical data were derived from the TCGA via the GDC portal20. A protein–protein interaction network for RIN3 was constructed using GeneMANIA (http://www.genemania.org) to elucidate its interactions21. Potential functions of RIN3 were predicted by analyzing various associations including physical interactions, co-expression, co-localization, and shared protein domains. Two independent immunotherapy cohorts (IMvigor210 and GSE91061) were used for validation. Specific cancer abbreviations are listed in Supplementary Table S1.

Single-cell and spatial transcriptome analysis

Using the TISCH web tool (URL: http://tisch1.comp-genomics.org/)22, an investigation was conducted into how the RIN3 gene is expressed across different cell types. A heatmap was generated to visualize RIN3 gene expression across all cancer types, grouped by primary cell type. For a deeper understanding of RIN3’s tumor localization, the SpatialDB online tool (URL: https://www.spatialomics.org/SpatialDB/)23 was utilized to analyze spatial transcriptome data from SKCM and HNSC. Specifically, the positioning of RIN3 alongside M2 macrophage markers CD68 and CD163 in HNSC tissue, as well as its association with CD8 T cell markers in SKCM tissue, was analyzed.

Survival and prognosis analysis

To evaluate the impact of RIN3 gene expression on patient survival across different cancer types, the TCGA database and the PanCanSurvPlot platform (https://smuonco.shinyapps.io/PanCanSurvPlot/) were used. Cox proportional hazards models were applied for analysis. Illumina HiSeq-generated high-throughput sequencing data were selected, with optimal cut-off values for RIN3 expression determined for each cancer type to define low and high expression groups. Survival outcomes—overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and progression-free survival (PFS)—were compared between expression groups. Hazard ratios (HR) and their 95% confidence intervals were visualized through forest plots generated using R.

Overview of genomic alterations in cancer and RIN3 mutations

A systematic analysis of four major genomic alterations (mutations, amplifications, deep deletions, and multiple alterations) in tumors was performed using the cancer type summary module of the cBioPortal database (https://www.cbioportal.org/)24. We focused on the RIN3 gene and used the GSCA25 platform (http://bioinfo.life.hust.edu.cn/GSCA) to explore its copy number variations (CNVs) and the correlation between CNVs and RIN3 mRNA expression. The GSCA platform also compared RIN3 methylation levels across cancer types, examined the link between methylation and mRNA expression, and evaluated its effect on patient survival. Spearman correlation analysis assessed the relationships between RIN3 mRNA expression and CNVs or methylation, with p-values adjusted by the Benjamini–Hochberg method. Methylation differences were analyzed with the t-test and Benjamini–Hochberg correction, and survival differences (OS, DSS, PFS) by methylation levels were evaluated using the log-rank test.

Immune therapy response prediction analysis

Somatic mutation data were obtained from the TCGA database (https://tcga.xenahubs.net), and TMB and MSI for each tumor sample were analyzed using the maftools package in R. Spearman correlation was used to explore the relationship between RIN3 expression and TMB/MSI, with results shown in a radar plot. Patients were also grouped into PD, SD, CR, and PR to investigate the link between RIN3 expression and immunotherapy response. The survminer package determined the optimal RIN3 expression cutoff, classifying patients into high and low expression groups. Survival rates, treatment responses, and the expression of MMR genes (MLH1, MSH2, MSH6, PMS2, EPCAM) were then compared, and heatmaps were generated using the tidyverse and ggnewscale packages.

Evaluation of the impact of RIN3 expression on immunity

The correlation between RIN3 expression and immune, stromal, and ESTIMATE scores was analyzed for each tumor sample26. Immune cell infiltration across cancers was further examined using TIMER2.0 immune cell scores, systematically assessing RIN3’s relationship with various immune cell types. To further understand RIN3’s role in the tumor immune microenvironment, 150 immune-related genes—including those encoding MHC molecules, immune inhibitors, chemokine receptors, activators, and chemokines—were selected from the TISDB database27. Correlation analyses between RIN3 and these genes were performed using R packages (limma, pheatmap, and ggplot2) to explore its potential regulatory function in tumor immunity.

Biological significance analysis of RIN3 expression

The biological functions of the RIN3 gene in various cancer types were analyzed using Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) with R packages including "tidyverse," "limma," "org.Hs.eg.db," "gseaplot2," and “clusterProfiler”28,29,30,31. The analysis utilized the C2 and C5 gene sets from the Molecular Signatures Database for data processing and visualization. Samples were divided into high and low RIN3 expression groups based on the median expression level, and normalized enrichment scores were calculated. Functional differences were assessed using FDR values for differentially expressed genes, while GSVA scoring evaluated RIN3’s impact on biological processes across all cancer samples. Additional packages, such as "GSVA," "ggprism," and "clusterProfiler," ensured result reliability.

Correlation between RIN3 expression and drug sensitivity

The CellMiner database (http://discover.NCI.NIH.gov/cellminer/) links RIN3 protein expression to drug response by integrating molecular and pharmacological data from the NCI-60 cancer cell lines. A processed dataset containing RNA-seq gene expression and drug activity data for these cell lines was downloaded32. Using the “limma” package in R, FDA-approved or clinical trial drugs were screened after excluding data with over 80% missing values, with remaining missing values imputed using the “impute” package. Data visualization was performed with “ggplot2” and “ggpubr” (p < 0.05). Molecular docking simulations between PAC-1 and RIN3 were conducted using Autodock433. The molecular structure of PAC-1 was obtained from PubChem34, and the RIN3 3D structure was predicted using AlphaFold35. The final docking model was visualized with Pymol.

Cell culture and transfection

The human breast cancer cell line MCF-7 was obtained from the Shanghai Cell Bank of the Chinese Academy of Sciences. Cells were cultured in DMEM medium with 10% fetal bovine serum (FBS; Procell) at 37 °C in a humidified atmosphere with 5% CO2, with routine checks for mycoplasma contamination. siRNA targeting human RIN3 and its negative control were purchased from GenePharma (Shanghai, China) and transfected into cells using Lipofectamine 3000 reagent (Invitrogen, USA) following the manufacturer’s instructions. The siRNA sequences are provided in Supplementary Table S2.

Reverse transcription quantitative PCR (RT-qPCR)

Total RNA was extracted from samples using TRIzol reagent (TaKaRa Bio, Japan) and quantified for purity with a NanoDrop 2000 spectrophotometer (Thermo Scientific). RNA was then reverse transcribed into cDNA using PrimeScript™ RT Master Mix (Takara, RR036A). Real-time quantitative PCR (qPCR) was conducted with SYBR® Premix Ex Taq™ II (TaKaRa, RR820A), using ACTB as the reference gene. Relative expression levels of target genes were analyzed using the 2-ΔΔCt method. Primer sequences for RT-qPCR are provided in Supplementary Table S3.

Cell viability assay

Cell viability was assessed using the CCK-8 kit (GK10001, GLPBIO, Montclair, California, USA). Cells were seeded in 96-well plates at 2 × 103 cells per well and incubated for 24 or 48 h. After incubation, 10 μL of CCK-8 reagent was added to each well, followed by a 2-h incubation. Absorbance was measured at 450 nm using a microplate reader.

Colony formation

MCF-7 cells were seeded at 1000 cells per well in 6-well plates to assess clonogenic capacity. After one week of culture with medium changes every 3 days, cells were fixed, stained with crystal violet, and quantified using ImageJ software.

Cell scratch wound healing assay

Cells transfected with the indicated siRNA were seeded in 6-well plates at a density of 1 × 105 cells per well. To inhibit proliferation, the culture medium was supplemented with 2% fetal bovine serum. A scratch was made in the cell monolayer using a pipette tip. Wound healing was monitored with a Nikon Ti-E inverted microscope (Nikon Instruments, Florence, Italy), with images captured at 0 and 48 h post-scratch. Wound area was quantified using ImageJ software and normalized to the area at T0.

Statistical analysis

To validate our bioinformatics analysis, we preprocessed the dataset by removing samples with missing values or duplicates and log2-transformed the TPM values (log2(TPM + 1)) to stabilize variance. The Mann–Whitney U test compared RIN3 expression between normal and tumor tissues. For data from different tissue types in the CCLE database, the Kruskal–Wallis test assessed RIN3 expression variability. Paired or unpaired t-tests were used to compare RIN3 expression between groups (e.g., different tissue types or tumor vs. normal tissues). All statistical analyses were conducted using R software (version 4.4.0; https://www.R-project.org), with a significance threshold of P < 0.05.

Results

The expression of RIN3 in pan-cancer and protein–protein interaction (PPI) network

Figure S1 depicts the overall process. Using the OpenTarget web tool, we identified potential links between RIN3 and diseases such as breast carcinoma, lung adenocarcinoma, and colorectal cancer (Fig. 1A). Immunohistochemistry data from the HPA database showed elevated RIN3 protein levels in endometrial and breast cancer tissues compared to normals (Fig. 1B). Pan-cancer analysis of TCGA and GTEx databases revealed that RIN3 was significantly upregulated in ESCA, GBM, HNSC, KIRC, KIRP, LAML, LGG, PAAD, SKCM, and STAD, with downregulation in ACC, BLCA, BRCA, COAD, DLBC, KICH, LUAD, LUSC, PCPG, PRAD, READ, TGCT, THCA, THYM, UCEC, and UCS (Fig. 1C). TIMER2.0 validation confirmed higher expression in CHOL, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, and THCA, and lower expression in BLCA, BRCA, COAD, KICH, LUAD, LUSC, PAAD, PCPG, PRAD, and UCEC (Fig. 1D). Notably, RIN3 expression was highest in myeloid cells (Fig. 1E). PPI network analysis identified key interactions with proteins such as RAB31, RIN1, RAB5A, RAB5B, BIN1, SLC24A4, ZCWPW1, CASS4, NME8, and CD2AP (Fig. 1F), suggesting that RIN3 plays a critical role in tumor progression via cellular signaling networks.

Fig. 1
figure 1

(A) Using the OpenTarget web tool to analyze diseases related to RIN3. Red dashed lines represent cancers associated with RIN3. (B) Immunohistochemistry images of RIN3 in normal tissues (left) and in cancer tissues (right). (C) Differences in RIN3 expression between various tumor tissues and normal tissues. (D) RIN3 expression in pan-cancer tumors and normal tissues, using the TIMER2.0 method. (E) Differences in the expression levels of RIN3 in organs. (F) The protein associated with RIN3 was obtained by PPI network.

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Single-cell and spatial transcriptome database analysis

To identify the main cell types expressing RIN3 in the tumor microenvironment (TME), single-cell RNA sequencing was performed on 77 cancer datasets. RIN3 expression in 33 cell types (including immune, ductal, malignant, and functional cells) was visualized via the TISCH web tool, revealing predominant expression in immune cells, particularly monocytes/macrophages and CD8 + T cells (Fig. 2A). In the GSE103322 dataset (5902 cells from 18 oral cavity tumors), RIN3 was highly expressed in mast cells, monocytes/macrophages, and CD8 + T cells within the HNSC microenvironment (Fig. 2B). Similarly, analysis of 16,291 cells from 48 melanoma samples treated with checkpoint inhibitors (GSE120575) confirmed RIN3’s primary expression in monocytes/macrophages and CD8 + T cells (Fig. 2D). Spatial transcriptomics further demonstrated a strong spatial correlation between RIN3 and M2 macrophage markers (CD163, CD68) in HNSC tissues (Fig. 2C) and with CD8 + T cell markers (CD8A, CD8B) in SKCM tissues (Fig. 2E), indicating co-expression in these cancers.

Fig. 2
figure 2

(A) single cell of expression levels of RIN3 of 33 cell types in 77 single-cell datasets. (B) Using GSE103322 HNSC dataset, Scatter plot which the distributions of 11 different cell types were draw. (C) Spatial transcriptomics analysis revealed the spatial distribution of RIN3, CD163, CD68 markers gene. The colors of dots represent the expression levels of these macrophage marker genes. (D) Using GSE120575 SKCM dataset, Scatter plot which the distributions of 8 different cell types were draw. (E) Spatial transcriptomics analysis revealed the spatial distribution of RIN3, CD8A, CD8B markers gene. The colors of dots represent the expression levels of these CD8+T cell marker genes.

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Alteration frequency and methylation of RIN3 in pan-cancer

Genomic alterations, a key factor in cancer, were analyzed for RIN3 in TCGA. RIN3 mutations were most frequent in SKCM, LUSC, and UCEC (Fig. 3A). CNVs were most common in OV, UCS, LUSC, and ESCA (Fig. 3B). A positive correlation between CNV levels and RIN3 mRNA expression was observed (Fig. 3C). Survival analysis revealed that RIN3 CNVs were significantly associated with poorer prognosis, especially in KIRC (Fig. 3D–G). Methylation analysis showed higher methylation in LUSC, KIRP, and ESCA (Fig. 3H). Methylation negatively correlated with RIN3 mRNA in several cancers, including DLBC and PRAD, but positively in others like LIHC and HNSC (Fig. 3I). Dividing samples by methylation levels, we found that higher RIN3 methylation was linked to worse survival in KIRC (Fig. 3J–M). These results suggest RIN3 alterations and methylation influence cancer prognosis.

Fig. 3
figure 3

(A) The RIN3 of alteration frequency, containing mutations, amplifications, deep deletions, and multiple modifications was analyzed using TCGA database. (B) The CNVs percentage of RIN3 in each cancer and different colors representing different types of CNVs in the pie chart. (C) Correlations of CNVs with mRNA expression in the bubble pot. (D) Survival difference between CNVs groups of RIN3 in different cancer was analyzed by Cox regression. (E–G) By using the GSCA web tool to draw the Kaplan–Meier picture which represent prognostic significance of RIN3 CNVs in KIRC. (H) Methylation difference in each cancer was investigated by comparing the methylation of normal samples and cancer samples. (I) Correlation between methylation and mRNA expression of RIN3 in the bubble pot. (J) Survival difference between high and low methylation in each cancer was drawn by univariate Cox regression. (K–M) The Kaplan–Meier survival bights were used to describe RIN3 methylation in KIRC.

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Relationships between RIN3 and immune checkpoint

We examined the correlation between RIN3 expression, TMB, and MSI. As shown in Figs 4A, B, RIN3 expression positively correlated with TMB in SARC and negatively in ACC, DLBC, HNSC, LIHC, LUAD, PRAD, STAD, and THCA. RIN3 expression also showed a positive correlation with MSI in UVM, but negative correlations were found in DLBC, READ, SKCM, STAD, and UCEC. In melanoma, patients with high RIN3 expression had lower survival rates than those with low RIN3 expression in response to anti-PD-1 therapy. In the GSE91061 melanoma cohort, high RIN3 expression was associated with a significantly lower response rate to anti-PD-1 therapy (17.78%) compared to low RIN3 expression (50.00%) (Fig. 4C). Similarly, in urological tumors, high RIN3 expression was linked to a lower survival rate and a lower response to anti-PD-L1 therapy (19.81% vs. 38.89% in low RIN3 expression, Fig. 4D). MMR plays a crucial role in repairing DNA replication errors during cell division, and downregulation or dysfunction of MMR genes can lead to mutations in somatic cells36. Figure 4E illustrates the relationships between RIN3 expression levels and MMR genes expression levels across various cancer types, it was demonstrated that MMR gene was positively correlated with RIN3 in CHOL, KICH, LIHC, PAAD, THYM, while a strong negative relationship between MMR gene and RIN3 in BRCA, CESC, GBM, KIRP, LUAD.

Fig. 4
figure 4

Radar plots were employed to illustrate the association of RIN3 expression with TMB (A) and MSI (B) in a pan-cancer analysis. (C, D) Based on two public datasets, GSE91061-melanoma and IMvigor210-urological tumors, Kaplan–Meier survival analysis and stacked bar plot showed the survival outcomes and immunotherapeutic response rates between patients with low and high RIN3 expression levels. (E) The relationships between RIN3 expression levels and MMR genes (*p < 0.05, **p < 0.01, ***p < 0.001).

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Co-expression of RIN3 and immune genes

In order to further explore the role of RIN3 in tumor immunity, we used TIMER2.0 database to conduct Spearman correlation analysis on RIN3 expression and various levels of immune cell infiltration, including B cell, CAF, Progenitor, Dendritic Cell, Endo, Eos, CD4+T cell, HSC, NKT, Macrophage, Mast cell, CD8+T cell, Monocyte, MDSC, Neutrophil, NK cell, Tfh, g/dT, Tregs (Fig. 5). In most TCGA cancers, we observed that RIN3 expression has a positively correlation with the infiltration levels of most cells, except in MDSC cells. Recent studies indicated that immune cells, particularly CD4+T cells, CAFs, MDSCs, neutrophils, and macrophages, have been shown to play an increasingly important role in cancer immunotherapy. These results showed that the gene of RIN3 might have a significant affect for development, prognosis, and therapy of cancers by affecting immune cells.

Fig. 5
figure 5

RIN3 expression and various levels of immune cell infiltration, including B cell, CAF, Progenitor, Dendritic Cell, Endo, Eos, CD4+T cell, HSC, NKT, Macrophage, Mast cell, CD8+T cell, Monocyte, MDSC, Neutrophil, NK cell, Tfh, g/dT, Tregs by using TIMER2.0 database to conduct Spearman correlation analysis. In this heat map, red represents a positive correlation and blue represents a negative correlation.

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Function of RIN3 expression in tumors

To investigate the biological processes associated with RIN3 in tumors, cancer samples were divided into low and high RIN3 expression groups based on the median expression levels, and both GSEA and GSVA analyses were performed. The results indicated that RIN3 positively regulated several immune-related activities, including antigen processing and presentation, T cell-mediated immunity, B cell-mediated immunity, and humoral responses. Additionally, RIN3 was significantly linked to various KEGG pathways like antigen processing, the intestinal immune network for IgA production, and phagosome formation, underscoring its role in immune regulation. Notably, there is a hypothesis that RIN3 enhances B cell receptor signaling and B cell activation (Fig. 6A).

Fig. 6
figure 6

(A) Function of RIN3 expression were observed in tumors by GO and KEGG pathways. Positive regulation is indicated by peaks on the curves, while negative regulation is indicated by downslopes. (B) Association of RIN3 expression with positively and negatively pathways by GSVA analysis where blue represent a significantly positive correlation, green represent a significantly negatively correlation, gray represent a insignificantly correlation.

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GSVA analysis further revealed the top 20 pathways correlated with RIN3 expression (Fig. 6B), with strong associations observed with immune-related pathways such as the response to fungal molecules, T cell receptor signaling, and immune synapse formation. Moreover, RIN3 was linked to pathways involved in T cell antigen processing, ATP synthesis, and VEGF receptor activity. However, an interesting observation was that in cancers such as BRCA and UCEC, RIN3 expression negatively correlated with energy metabolism and immune responses, for example, showing an inverse relationship with the respiratory chain complex III in LUSC. These findings collectively suggest RIN3 as a potential immune modulator, though functional validation is required to confirm its precise role in immune regulation.

Silencing RIN3 causes cell proliferation, migration

Using siRNA, we generated a RIN3 knockdown cancer cell line, MCF-7 (Fig. 7A). Our results showed that RIN3-deficient breast cancer cells had significantly reduced proliferation, migration, and invasion compared to control cells. As shown in Fig. 7B and D, the results of CCK-8 and colony formation assays proved that knockdown of RIN3 significantly inhibits the proliferation of breast cancer cells. In addition, as shown in Fig. 7C, with the reduction of RIN3 expression, the migration ability of cancer cells is significantly reduced.

Fig. 7
figure 7

Silencing of RIN3 results in changes in cell proliferation and migration. (A) The relative expression of RIN3 in the RIN3-sh1, RIN3-sh2, and NC groups within the RIN3 knockdown MCF-7 cancer cell lines was evaluated. (B) The CCK-8 assay demonstrated that knockdown of RIN3 notably inhibited the proliferation of breast cancer cells. (C) Wound healing experiments were conducted on cancer cells in the RIN3-sh1, RIN3-sh2, and NC groups. (D) A comparative analysis of clone formation was carried out among the RIN3-sh1, RIN3-sh2, and control cell lines.

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Association between RIN3 and patient prognosis

We analyzed TCGA prognosis data across cancer types using univariate Cox regression for OS, DFS, DSS, and PFS. For OS, RIN3 was a risk factor in LAML, KIRC, SKCM, STAD, GBM, LGG, MESO, PAAD, THCA, and UCS, but a protective factor in ACC, BRCA, KICH, DLBC, HNSC, PAAD, KIRP, PRAD, SARC, and THYM (Figure S2A). In DFS, high RIN3 correlated with poorer outcomes in BLCA, KIRC, STAD, GBM, LGG, LUSC, MESO, PAAD, and PCPG, but improved DFS in ACC, LIHC, KIRP, and UVM (Figure S2B). For DSS, RIN3 was a risk factor in KIRC, SKCM, STAD, GBM, LIHC, LGG, LUAD, LUSC, THCA, and UCS, but protective in ACC, DLBC, HNSC, MESO, PAAD, KIRP, and PRAD (Figure S2C). In PFS, RIN3 was a risk factor in BRCA, STAD, LUSC, PCPG, and SARC, but protective in ACC, COAD, and LIHC (Figure S2D).

Relationships between RIN3 expression levels and immune-related factors

The TME is composed of stromal cells, fibroblasts, endothelial cells, components of the innate immune system, and components of the adaptive immune system, collectively shaping tumor growth, invasion, prognosis, and therapy37. To better understand the role of RIN3 in tumors, we explored its relationship with the TME by analyzing Stromal Score, Immune Score, and ESTIMATE score across various cancers (Figure S3A). We found that RIN3 expression positively correlated with these scores in several cancers, including BLCA, BRCA, GBM, KICH, KIRC, KIRP, LGG, LUAD, LUSC, MESO, OV, PAAD, PCPG, PRAD, and THCA. To highlight the significant correlations, we focused on BRCA, KIRC, and LUSC (Figure S3B-J).

RIN3 expression was correlation with immune response genes

We investigated the RIN3 expression was relation to gene about immune response genes as like encode MHC genes, immune activating and inhibiting proteins, chemokines and their receptors by co-expression network analysis. As shown in Figure S4, we can found that expression of RIN3 co-expression with most immune response genes. Although RIN3 expression was positively correlation with most immune response genes in most cancer, in THYM, UVM was observed negative correlation.

Association of RIN3 expression with drug sensitivity

Molecular docking studies were carried out to identify potential drug candidates targeting RIN3 (Figure S5A). The analysis revealed that RIN3 expression correlated positively with the drug response for agents such as tivantinib, skepinone-L, PF-3758309, PAC-1, NVP-BEZ235, M1-2, LRRK2-1N-1, BRD-K51490254, BRD-A86708339, and belinostat, while a negative correlation was noted for drugs like XMD13-2, VNLG/124, Tubastatin A, TPCA-1, TL-1–85, selumetinib, PLX-4720, pluripotin, PIK-93, PHA-793887, PD318088, PD-0325901, OSI-930, Methotrexate, lovastatin, KIN001-260, JW-7–24-1, Ispinesib, Mesylate, fumonisin B1, dasatinib, CX-5461, CI-1040, BIX02189, BHG712, avicinD, AP-24534, and AICAR.

Focusing on PAC-1, a known chemotherapeutic agent that acts by activating caspase-3 to induce programmed cell death (with demonstrated efficacy in breast cancer38), Autodock4 was used to simulate its binding with RIN3. The docking results (Figure S5B) showed that PAC-1 binds to the RIN3 protein through significant hydrogen bonding and strong electrostatic interactions, with a calculated binding energy of 4.68 kJ/mol, suggesting a tight binding affinity.

Discussion

While immunotherapy, especially immune checkpoint blockade, has shown promise in cancer treatment, its effectiveness varies widely due to the heterogeneity of the tumor microenvironment. To improve patient outcomes, it is crucial to identify and validate new, specific biomarkers. RIN3, a GEF for the Rab5 small GTPase family39, affects the PICLAM pathway and regulates transcytosis in the blood–brain barrier40. Although RIN3 has been implicated in various diseases, its role in tumorigenesis remains poorly understood, highlighting its growing importance in pan-cancer research. This study conducted an extensive bioinformatics analysis of RIN3, including its expression, survival prognosis, genetic variations, immune infiltration, and protein phosphorylation, using multiple public databases (TCGA, HPA, CCLE, GTEx, and TISCH). We also examined RIN3’s genomic alterations across cancers and their correlation with patient prognosis, as well as its interactions with CNVs, DNA methylation, MSI, TMB, and the tumor immune microenvironment.

We analyzed the TCGA and GTEx databases to compare RIN3 expression between cancerous and normal tissues. RIN3 expression was significantly decreased in BLCA, BRCA, COAD, KICH, LUAD, and LUSC, while increased in other cancers, indicating diverse roles. TISCH single-cell analysis showed RIN3 enrichment in monocytes/macrophages and CD8 + T cells, especially in HNSC and SKCM. Survival analysis revealed high RIN3 expression correlates with poor prognosis in STAD and UCS but better outcomes in LIHC and ACC, highlighting its potential as a prognostic biomarker.

In KIRC, RIN3 variations, particularly CNVs, significantly impact prognosis. cBioPortal analysis identified CNVs as the most frequent alteration, with a positive correlation between RIN3 expression, CNVs, and DNA methylation. Patients with abnormal CNVs and high methylation had worse outcomes, suggesting RIN3 as a key prognostic marker for KIRC. While TMB is crucial for predicting immunotherapy response41, combining MSI, high TMB, and tumor-infiltrating lymphocytes enhances the prediction of PD-1 inhibitor efficacy42,42.

Our research demonstrates a significant link between RIN3 expression, TMB, and MSI across various cancers, indicating that RIN3 may impact immune checkpoint inhibitor response by modulating the tumor microenvironment and MSI. Notably, RIN3 expression strongly correlated with patient response to PD-1/PD-L1 inhibitors, particularly in melanoma. Furthermore, RIN3 showed a positive association with MMR gene expression, suggesting it may enhance TMB and MSI via the MMR system in certain cancers. These findings highlight RIN3’s potential as a biomarker for predicting immunotherapy response and support the development of RIN3-targeted therapies to improve the efficacy of combined immunotherapy strategies.

Our study underscores the critical role of RIN3 in tumor immunity and its impact on the tumor microenvironment (TME), which is closely linked to immunotherapy response and patient outcomes44. Normally, the immune system can detect and eliminate tumor cells, but tumors evade immune surveillance through various mechanisms to grow and metastasize. By analyzing the TME across 33 cancer types, we found that RIN3 expression positively correlates with stromal score, immune score, and estimate score, strongly indicating its role in TME regulation.

Tumor-infiltrating lymphocytes are key components of the TME, capable of both promoting and inhibiting tumor progression45. Our TCGA analysis revealed that RIN3 expression is positively associated with the infiltration of diverse immune cells, including B cells, CAFs, dendritic cells, macrophages, CD4 + T cells, CD8 + T cells, monocytes, MDSCs, neutrophils, NK cells, and Tregs. These findings suggest that RIN3 may influence cancer progression and prognosis by modulating immune cell infiltration in the TME. Notably, increased M2 macrophage infiltration has been linked to early KIRC recurrence and poor prognosis46, aligning with our analysis, which suggests that RIN3 may affect KIRC prognosis by regulating M2 macrophage levels in the TME. We observed that RIN3 gene is upregulated in a majority of cancer types and is significantly positively correlated with various key genes involved in immune regulation, such as MHC and immune activation/inhibition molecules. This suggests that RIN3 protein might influence patient survival by regulating immune cell infiltration in the tumor microenvironment. These findings provide a new avenue for developing targeted immunotherapies against RIN3.

GSEA analysis showed that RIN3 expression is positively correlated with several immune-related pathways, including T cell-mediated immunity, cell-mediated cytotoxicity, B cell immunity, and immunoglobulin responses. These pathways are crucial for tumor response to stress, self-repair, and cell cycle regulation, suggesting that RIN3 may influence the tumor microenvironment. GSVA analysis further highlighted the association between RIN3 and immune pathways across various cancer types, positioning RIN3 as a potential immune regulator. Our GSVA analysis revealed that RIN3 expression inversely correlates with mitochondrial respiratory chain activity (e.g., oxidative phosphorylation in LUSC; Fig. 7B), suggesting it may promote a metabolic shift toward glycolysis—a hallmark of aggressive tumors. This is consistent with RIN3’s role in Ras signaling10, which drives aerobic glycolysis (Warburg effect) via HIF-1α stabilization. These findings offer new insights into RIN3’s mechanisms and its potential as a target for tumor immunotherapy.

To identify potential anticancer therapies, we screened drugs capable of modulating RIN3 expression. After assessing RIN3 levels across various tumors, we selected candidate drugs for further testing. In drug sensitivity experiments, PAC-1, a widely used chemotherapeutic, demonstrated effective inhibition of tumor cells with high RIN3 expression, indicating its potential for targeted therapy. PAC-1 is currently used in treating cancers such as breast cancer, metastatic neuroendocrine tumors, hemangiopericytoma, and uveal melanoma38,47. As the precursor to the apoptotic executioner caspase-3, procaspase-3 is overexpressed in various malignancies48. PAC-1 selectively induces apoptosis in cancer cells by activating overexpressed procaspase-3.

Our findings suggest PAC-1’s effectiveness may stem from its ability to inhibit RIN3, making RIN3 a potential therapeutic target, especially for BRCA-deficient cancers. This aligns with previous studies linking RIN3 to breast cancer38. Further research shows RIN3 inhibits breast cancer cell proliferation and migration, with reduced RIN3 expression in MCF-7 cells enhancing this effect, reinforcing its value as a drug target. However, limitations remain, including potential bias from transcriptome data analysis and the need for more in vitro experiments to validate these findings.

In summary, RIN3 plays a crucial role in cancer research. It is significantly associated with immune regulators, tumor-infiltrating immune cells, the tumor microenvironment, TMB, and MSI across cancers, influencing tumor immunity and immune escape. Additionally, RIN3 is vital in regulating endocytosis, trafficking, and cancer progression. Further exploration of RIN3 and its pathways could provide new strategies for cancer diagnosis, treatment, and prevention. However, conclusions regarding RIN3’s therapeutic potential are derived from computational analyses and require further experimental validation.