Non-coding RNAs (ncRNAs)-mediated high expression of Ras-related C3 botulinum toxin substrate 1 (RAC1) correlates with poor prognosis and tumor immune infiltration of liver hepatocellular carcinoma

Introduction

Globally ranking as the fourth leading cause of cancer mortality, liver hepatocellular carcinoma (LIHC) accounts for 75–85% of primary liver malignancies (1). Despite advancements in diagnostic and therapeutic modalities over the past decade, persistent high mortality rates of LIHC underscore the need for improved prognostic biomarkers (2,3). LIHC pathogenesis involves heterogeneous etiological factors including viral hepatitis, metabolic dysfunction, and environmental carcinogens (4-7). While current staging systems [e.g., tumor node metastasis (TNM) classification] guide clinical decisions, their prognostic limitations necessitate molecularly driven alternatives (8,9). Therefore, novel biomarkers are urgently needed to improve the clinical benefits for LIHC patients.

Ras-related C3 botulinum toxin substrate 1 (RAC1), a pivotal Rho GTPase, orchestrates diverse cellular processes spanning cytoskeletal dynamics, oxidative stress responses, and immune surveillance (10). Its oncogenic roles are well-documented in human cancers, involving breast cancer (11), esophageal squamous cell carcinoma (12), colorectal cancer (13), non-small cell lung cancer (14) and pancreatic cancer (15). In addition, emerging evidence implicating RAC1 in LIHC proliferation and therapeutic resistance (16). Nevertheless, current study still lacks a comprehensive analysis of the expression patterns, regulatory mechanisms, and immune interactions of RAC1 in LIHC.

In our investigation, we employed bioinformatics approaches to examine the association of RAC1 expression with clinicopathological features and prognostic outcomes across human malignancies, with a specific focus on liver hepatocellular carcinoma (LIHC). Furthermore, our analysis clarified the regulatory influence of non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), on the expression patterns of RAC1 in LIHC. Finally, systematic evaluation revealed significant associations between RAC1 expression gradients and three key immunophenotypic features: (I) immune cell infiltration patterns, (II) immune subset-specific biomarkers, and (III) checkpoint inhibitor profiles in LIHC. Importantly, ncRNA-mediated RAC1 dysregulation emerged as a master regulator linking immune-evasion phenotypes to reduced survival rates. This translational framework enables biomarker-guided therapeutic strategies to overcome LIHC treatment resistance. We present this article in accordance with the TRIPOD and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2848/rc).

Methods

RNA isolation, reverse-transcription and quantitative real-time polymerase chain reaction (qRT-PCR)

RNA isolation and the PCR amplification conditions were conducted according to established protocols as referenced previously (17). The qRT-PCR assay (SRBY Green) was performed on Applied Biosystem 7500. The relative expression of RAC1 was determined and quantified by applying the the 2^−ΔΔCT method. GAPDH employed as the internal reference gene for normalization. The primer sequences were designed by Primer 5.0 and are listed in Table S1.

Cell culture

The cell lines (HepG2, Huh7, HCCLM3, SK-Hep1, and LO2) used in our study were obtained from the Cell Bank of the Type Culture Collection (Chinese Academy of Sciences, Shanghai, China). These cells were maintained in DMEM/high glucose medium (Hyclone, Logan, UT, USA) enriched with 10% fetal bovine serum (PAN-Biotek, Aidenbach, Bavaria) and 1% penicillin-streptomycin (Hyclone). Incubation was carried out at 37 ℃ in a humidified environment with 5% CO₂.

Clinical samples

Tissues of 3 liver cancer and adjacent normal tissues were obtained from Division of Hepatobiliary and Pancreaticosplenic Surgery, Department of General Surgery, Beijing Chao-Yang Hospital from January 2026 to February 2026. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethical Committee of Beijing Chao-Yang Hospital (No. 2025-Ke-1098). Informed consent was taken from all the patients.

Western blot analysis

Western blot analysis was performed as described previously (17,18). Whole-cell lysates with approximately 40 µg of proteins were resolved on 10% and 12% SDS-PAGE and were subjected to western blot assay using the antibody RAC1(Proteintech, China, 24072-1-AP). After appropriate secondary antibody incubation, the bands were visualized with the Molecular Imager System (BIO-RAD, Hercules, USA) using an enhanced chemiluminescence method (Thermo Fisher Scientific, Massachusetts, USA).

Data collection and processing

The transcriptomic profiles and corresponding clinical information of hepatocellular carcinoma patients were acquired from two publicly available genomic repositories: The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) (19) and the Genotype-Tissue Expression (GTEx) project (https://commonfund.nih.gov/gtex) (20). Additionally, normalized RNA-seq data in transcripts per million (TPM) format were extracted from the GTEx database to facilitate comprehensive pan-cancer investigations.

Genetic alteration analysis

The CBioPortal platform (http://cbioportal.org) (21) was employed to systematically analyze genomic alterations across TCGA tumor datasets, including mutation frequency profiles, variant classification, specific amino acid changes, and protein three-dimensional (3D) structures for the selected candidate genes.

GEPIA database analysis

GEPIA (http://gepia.cancer-pku.cn/) is an online platform designed for the analysis and visualization of gene expression profiles in both cancerous and normal tissues, utilizing data from TCGA and the GTEx project (22). This resource was employed to examine the expression levels of RAC1 and lncRNAs across multiple human cancer types, with statistical significance defined as a P value less than 0.05. Furthermore, GEPIA facilitated survival analyses for RAC1 in various malignancies, assessing overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). The platform was also applied to evaluate the prognostic significance of selected lncRNAs in LIHC, where a log-rank P value <0.05 was deemed statistically significant. Additionally, the correlation between RAC1 expression and immune checkpoint markers in hepatocellular carcinoma (HCC) was investigated using the GEPIA database.

Functional enrichment analysis

To elucidate the biological significance of differentially expressed genes (DEGs), comprehensive functional annotation was performed through Gene Ontology (GO) categorization and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping, utilizing the GO plot package (v1.0.2) in R. Additionally, gene set enrichment assessment was conducted with the cluster Profiler package, applying stringent statistical thresholds where functional terms and pathways with an adjusted P value below 0.05 and a false discovery rate (FDR) less than 0.25 were considered significantly enriched.

DNA methylation analysis

To elucidate the molecular mechanisms of RAC1 in LIHC, we employed the UALCAN platform (http://ualcan.path.uab.edu) to analyze RAC1 promoter methylation patterns (23). Furthermore, we evaluated the clinical prognostic significance of RAC1 methylation status through the MethSurv database, a comprehensive web-based resource for performing multivariate survival analysis using DNA methylation profiles.

Construction and validation of the nomogram

To estimate the probability of OS, a predictive nomogram was constructed utilizing significant prognostic factors identified through multivariate Cox regression analysis. The model’s performance was subsequently evaluated using calibration curves, while its discriminative ability was quantified through the concordance index (C-index). These analytical procedures were implemented using the RMS package (version 5.1-4) in R statistical software. Additionally, time-dependent receiver operating characteristic (ROC) analysis was conducted to assess the model’s predictive accuracy, employing the ROC package for this purpose (24).

Candidate miRNA prediction

To identify upstream miRNAs of RAC1, we integrated predictions from seven algorithms: PITA, RNA22, miRmap, microT, miRanda, PicTar, and TargetScan. Only miRNAs predicted by at least three algorithms were considered. Subsequently, we performed correlation analysis using starBase, and only miRNAs showing significant negative correlation with RAC1 (|R| >0.1, P<0.05) and differential expression in LIHC were retained.

starBase database analysis

starBase (http://starbase.sysu.edu.cn/) serves as a comprehensive platform for investigating interactions involving miRNAs (25). This database was employed to conduct correlation analyses of expression patterns between miRNA and RAC1, as well as between LINC00662 and RAC1, specifically in LIHC. Furthermore, starBase facilitated the examination of hsa-miR-101-3p expression levels, comparing LIHC samples with normal tissue controls. For lncRNA prediction, starBase was used to identify lncRNAs with potential binding to hsa-miR-101-3p, and those with significant expression differences and prognostic value were selected.

Kaplan-Meier plotter analysis

The Kaplan-Meier survival analysis tool (http://kmplot.com/analysis/) is a publicly available web-based platform that enables comprehensive evaluation of the prognostic significance of genes or miRNAs across multiple malignancies, with LIHC being one of the included cancer types (26). Statistical significance was determined using a log-rank test threshold of P<0.05.

TIMER database analysis

The TIMER platform (https://cistrome.shinyapps.io/timer/) represents a comprehensive web-based resource for systematic evaluation of tumor-infiltrating immune cell populations, as described in reference (27). This analytical tool was employed to investigate potential associations between RAC1 gene expression patterns and both immune cell infiltration densities as well as immune checkpoint molecule expression profiles in LIHC. Statistical significance was determined using a threshold of P<0.05 for all analyses.

Statistical analysis

In this investigation, statistical analyses were performed automatically using the aforementioned online database. All statistical analyses were performed using R software (version 3.6.3). To evaluate the statistical significance of RAC1 expression in non-paired tissues, the Wilcoxon rank-sum test was applied, while the paired sample t-test was utilized for paired tissue comparisons. Associations between clinical characteristics and RAC1 expression levels were examined using the Wilcoxon rank-sum test and logistic regression analysis (28,29). All statistical tests were two-sided, and a P value of less than 0.05 was considered indicative of statistical significance.

Results

Pan-cancer analysis of RAC1 expression and mutation

To investigate the expression of RAC1 across various cancers, we firstly analyzed the expression levels of RAC1 in different human cancer types. As depicted in Figure 1A, RAC1 exhibited significant upregulation in several cancer types, including bladder urothelial carcinoma (BLCA), esophageal carcinoma (ESCA), pancreatic adenocarcinoma (PAAD), and skin cutaneous melanoma (SKCM). Subsequently, we explored the mutation status of RAC1 in diverse cancers using the cBioPortal platform, leveraging data from TCGA. Pan-cancer analysis revealed notable RAC1 amplification in ESCA, occurring in over 4% of cases, and a high mutation rate in SKCM, exceeding 6%. Additionally, OV exhibited the highest incidence of “deep deletion” at a frequency of 1% (Figure 1B). As described in Figure 1C, missense and truncating mutations were identified as the primary types of alterations in RAC1. For example, a missense mutation in the Ras domain, such as the P29S/L/T change, was observed. Figure 1D illustrates the P29S/L/T alteration within the 3D structure of RAC1 protein. Furthermore, we examined the expression of RAC1 across various cancers (Figures S1,S2) and explored the potential associations between RAC1 genetic alterations and patient survival outcomes in pan-cancer settings (Figures S3-S6).

Figure 1 Pan-cancer analysis of RAC1 expression and mutation. (A) The expression of RAC1 in different types of human cancer based on TCGA cancer and normal data. (B,C) cBioPortal was used to display the alteration frequency of different mutation types and mutation site of RAC1 in pan-cancer. (D) P29S/L/T mutation site was shown in the 3D protein structure of RAC1. ***, P<0.001. 3D, three-dimensional; ns, not significant; RAC1, Ras-related C3 botulinum toxin substrate 1; TCGA, The Cancer Genome Atlas.

Elevated expression and poor prognosis of RAC1 in LIHC

Comparative analysis revealed a marked elevation in RAC1 expression levels in hepatocellular carcinoma specimens relative to normal hepatic tissues (P<0.001) (Figure 2A). This differential expression pattern was further validated in 424 matched LIHC tissue pairs, demonstrating consistently higher RAC1 levels in tumor samples (P<0.001) (Figure 2B). ROC analysis indicated RAC1’s robust diagnostic potential, yielding an area under the curve (AUC) value of 0.915 [95% confidence interval (CI): 0.881–0.949] for discriminating malignant from normal tissues (Figure 2C). Survival analysis employing the Kaplan-Meier method, with stratification based on median RAC1 expression, revealed significant prognostic implications. Patients exhibiting lower RAC1 expression demonstrated superior clinical outcomes across all evaluated endpoints: OS [hazard ratio (HR) =1.90, 95% CI: 1.33–2.71, P<0.001], DSS (HR =1.82, 95% CI: 1.16–2.86, P=0.009), and PFI (HR =1.65, 95% CI: 1.23–2.21, P<0.001) (Figure 2D-2F). Subgroup analyses consistently demonstrated poorer prognosis associated with elevated RAC1 expression, irrespective of tumor stage (T1–T4), nodal status (N0–N1), metastatic status (M0–M1), histological differentiation (G1–G4), clinical staging (I–IV), vascular invasion status, or alpha-fetoprotein (AFP) levels (>400 ng/mL) (all P<0.05) (Figure S7). As shown in Table S2 and Figure S8, high expression of RAC1 was significantly associated with T stage (T1 vs. T3, P=0.007), pathologic stage (stage I vs. stage III, P=0.009), tumor status (tumor free vs. with tumor, P=0.01), Histologic grade (P<0.001), AFP expression (P<0.001), vascular invasion (P=0.043), OS (P<0.001), and DSS events (P=0.039). Univariate logistic regression analysis demonstrated significant clinicopathological variations between RAC1 high- and low-expression groups. Key differences were observed in T stage (OR =0.592, 95% CI: 0.365–0.952, P=0.032), pathological stage (OR =1.686, 95% CI: 1.039–2.761, P=0.036), tumor status (OR =1.665, 95% CI: 1.089–2.555, P=0.019), histological grade (OR =2.904, 95% CI: 1.871–4.554, P<0.001), AFP levels (OR =0.280, 95% CI: 0.151–0.503, P<0.001), and vascular invasion (OR =0.600, 95% CI: 0.374–0.956, P=0.032) (Table S3). In addition, the RAC1 showed high expression in four HCC cell lines (HepG2, SK-HepG1, Huh7 and HCCLM3) compared with healthy liver L02 cells (Figure 2G). For prognostic prediction in LIHC patients, we constructed a nomogram incorporating OS-independent factors, where higher cumulative scores correlated with unfavorable clinical outcomes (Figure 2H). The predictive accuracy of this nomogram was further validated through calibration curve analysis (Figure 2I-2K). Collectively, these findings establish a significant association between RAC1 overexpression and adverse prognosis in LIHC, suggesting its potential as a prognostic biomarker.

Figure 2 Elevated expression and poor prognosis of RAC1 in LIHC. (A) Comparative analysis of RAC1 transcript levels in LIHC versus non-paired normal specimens, utilizing integrated data from the TCGA and GTEx repositories. (B) Differential RAC1 expression profiles between LIHC tumors and their corresponding adjacent normal tissues within the TCGA cohort. (C) ROC curves evaluating the diagnostic accuracy of RAC1 in distinguishing LIHC from normal hepatic tissue based on TCGA data. (D-F) Kaplan-Meier survival analyses illustrating the correlation between elevated versus suppressed RAC1 expression and clinical outcomes, specifically OS, DSS and PFI in LIHC patients. (G) Quantitative assessment of RAC1 protein abundance in the immortalized human hepatocyte line L02 compared to a panel of hepatocellular carcinoma cell lines, including HepG2, SK-HepG1, Huh7, and HCCLM3. (H) Construction of a prognostic nomogram designed to estimate individual probabilities of one-, three-, and five-year overall survival for patients diagnosed with LIHC. (I-K) Calibration plots validating the agreement between predicted and observed one-, three-, and five-year overall survival rates derived from the established nomogram in the LIHC population. *, P<0.05; **, P<0.01; ***, P<0.001. AFP, alpha-fetoprotein; AUC, area under the curve; CI, confidence interval; DSS, disease-specific survival; FPR, false positive rate; GTEx, Genotype Tissue Expression Project; HR, hazard ratio; LIHC, liver hepatocellular carcinoma; N, node; OS, overall survival; PFI, progression-free interval; RAC1, Ras-related C3 botulinum toxin substrate 1; ROC, receiver operating characteristic; T, tumor; TCGA, The Cancer Genome Atlas; TPM, transcripts per million; TPR, true positive rate.

Correlation between methylation and expression of RAC1

To elucidate the potential molecular mechanisms underlying RAC1 overexpression in hepatocellular carcinoma (LIHC), we employed bioinformatics tools to analyze the relationship between RAC1 expression and its DNA methylation patterns. Initial analysis using the UALCAN database demonstrated that LIHC tumor tissues exhibited significantly reduced DNA methylation levels at the RAC1 promoter region compared to normal liver tissues (P<0.001, Figure 3A). Further investigation revealed a significant association between methylation status and clinical outcomes (Figure 3B). Notably, patients with hypomethylation at specific CpG sites (cg01784548, cg02971328, cg05043952, cg11385938) showed significantly worse OS compared to those with hypermethylation (Figure 3C-3F). Interestingly, we also identified several hypermethylated sites (cg05725797, cg22104063, cg06200611) that paradoxically correlated with unfavorable prognosis (Figure 3G-3I), suggesting a complex regulatory role of DNA methylation in RAC1 expression and LIHC progression.

Figure 3 Interplay between RAC1 methylation status and transcriptional activity. (A) Promoter-specific methylation profiles of RAC1 in LIHC were retrieved from the UALCAN repository. (B) Statistical association analysis linking RAC1 mRNA abundance with corresponding methylation intensity. (C-I) Kaplan-Meier estimates depicting overall survival probabilities stratified by distinct RAC1 methylation sites. BMI, body mass index; HR, hazard ratio; LIHC, liver hepatocellular carcinoma; LR, log-rank; RAC1, Ras-related C3 botulinum toxin substrate 1; TCGA, The Cancer Genome Atlas; UTR, untranslated region.

Gene function annotation and pathway analysis

Linked Omics analysis revealed a significant positive correlation with the RAC1 gene, and the heatmap displayed 50 gene sets that were significantly positively or negatively correlated with RAC1 (Figure 4A-4C). Comparative transcriptomic analysis revealed significant differential expression of 435 genes when stratifying samples based on RAC1 expression levels [adjusted P<0.05, |Log₂ fold change (FC)| >2]. Among these DEGs, 395 transcripts (90.8%) exhibited increased expression in the high RAC1 group, while 40 genes (9.2%) showed decreased expression (Figure 4D). Next, the relationship between the top 10 DEGs (including CEACAM7, LGALS14, MAGEA4, WNT7B, PAGE1, CA9, NTS, BPIFA1, LINC01559, SST) and RAC1 are revealed in Figure 4E. Furthermore, GO enrichment analysis and KEGG pathway analysis were conducted (Figure 4F-4G). Gene set enrichment analysis (GSEA) was subsequently conducted to compare the high and low RAC1 expression groups. The analysis revealed a significant enrichment of immune-related biological pathways in tumors with low RAC1 expression. These findings imply that elevated RAC1 expression may correlate with an immunosuppressive tumor microenvironment in hepatocellular carcinoma (LIHC) (Figure 4H-4K).

Figure 4 Gene function annotation and pathway analysis. (A-C) The correlation analysis of RAC1 expression and its top 100 co-expressed gene network. (D) Volcano plot of DEGs. Blue and red dots indicate the significantly down-regulated and up-regulated DEGs, respectively. (E) Heatmap of correlation between RAC1 expression and the top 10 DEGs. (F) GO analysis of DEGs. (G) KEGG analysis of DEGs. (H-K) GSEA of DEGs. *, P<0.05; **, P<0.01; ***, P<0.001. DEGs, differentially expressed genes; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; RAC1, Ras-related C3 botulinum toxin substrate 1; TPM, transcripts per million.

Prediction and analysis of upstream miRNAs of RAC1

It has been widely recognized that ncRNAs play a role in regulating gene expression. To investigate potential ncRNA-mediated regulation of RAC1, we performed computational prediction of miRNA binding sites and identified nine candidate miRNAs targeting RAC1. For improved graphical representation, we generated a miRNA-RAC1 interaction network using Cytoscape (Figure 5A). Based on the known inhibitory function of miRNAs on target gene expression, we hypothesized an inverse relationship between miRNA levels and RAC1 expression. Subsequent correlation analysis revealed statistically significant negative associations between RAC1 and two specific miRNAs (hsa-miR-101-3p and hsa-miR-365a-3p) in LIHC samples (Figure 5B), while no significant correlations were observed for the remaining seven predicted miRNAs. Further examination demonstrated that hsa-miR-101-3p exhibited significant downregulation in LIHC tissues (Figure 5C), with higher expression levels correlating with improved clinical outcomes (Figure 5D). A strong inverse relationship was confirmed between hsa-miR-101-3p and RAC1 expression patterns (Figure 5E). Interestingly, while hsa-miR-365a-3p showed similar inverse correlation with RAC1 and positive association with patient prognosis, its expression was paradoxically elevated in LIHC (Figure 5F-5H). Based on these comprehensive analyses, hsa-miR-101-3p was selected as the primary candidate for subsequent functional investigations.

Figure 5 Prediction and analysis of upstream miRNAs of RAC1. (A) The miRNA-RAC1 regulatory network established by cytoscape software. (B) The expression correlation between predicted miRNAs and RAC1 in LIHC analyzed by starBase database. (C) The expression of hsa-miR-101-3p in LIHC and control normal samples determined by starBase database. (D) The prognostic value of hsa-miR-101-3p in LIHC assessed by Kaplan-Meier plotter. (E) The expression correlation of hsa-miR-101-3p with RAC1 in LIHC determined by starBase database. (F) The expression of hsa-miR-365a-3p in LIHC and control normal samples determined by starBase database. (G) The prognostic value of hsa-miR-365a-3p in LIHC assessed by Kaplan-Meier plotter. (H) The expression correlation of hsa-miR-365a-3p with RAC1 in LIHC determined by starBase database. LIHC, liver hepatocellular carcinoma; miRNAs, microRNAs; RAC1, Ras-related C3 botulinum toxin substrate 1; RPM, reads per million mapped reads.

Prediction and analysis of upstream lncRNAs of hsa-miR-101-3p

Next, the starBase database was employed to predict the upstream lncRNAs of hsa-miR-101-3p. A total of 12 possible lncRNAs were identified (Table S4). The expression levels, prognostic values, and correlations with RAC1 and hsa-miR-101-3p in LIHC were then evaluated for these lncRNAs (Figures S9,S10). As depicted in Figure 6A, among all the 12 lncRNAs, only LINC00662 was significantly upregulated in LIHC compared to normal controls. Subsequently, the prognostic significance of LINC00662 in LIHC was assessed (Figure 6B,6C), revealing that patients with higher LINC00662 expression had poorer OS and DSS. The competing endogenous RNA (ceRNA) hypothesis posits that lncRNAs can enhance mRNA expression levels by sequestering miRNAs that would otherwise target those mRNAs, thereby establishing an inverse relationship between lncRNA and miRNA and a direct relationship between lncRNA and mRNA. This theoretical framework is corroborated by the experimental evidence presented in Figure 6D,6E. Integrating findings from expression profiling, survival analysis, and correlation studies, LINC00662 emerges as a plausible upstream regulatory lncRNA within the hsa-miR-101-3p/RAC1 signaling axis in LIHC.

Figure 6 Prediction and analysis of upstream lncRNAs of hsa-miR-101-3p. (A) The expression of LINC00662 in LIHC and control normal samples determined by starBase database. (B,C) The prognostic value of LINC00662 in LIHC assessed by Kaplan-Meier plotter. (D) The expression correlation of LINC00662 with hsa-miR-101-3p in LIHC determined by starBase database. (E) The expression correlation of LINC00662 with RAC1 in LIHC determined by starBase database. FPKM, fragments per kilobase of transcript per million mapped fragments; HR, hazard ratio; LIHC, liver hepatocellular carcinoma; lncRNAs, long non-coding RNAs; RAC1, Ras-related C3 botulinum toxin substrate 1; RPM, reads per million mapped reads.

RAC1 positively correlates with immune cell infiltration in LIHC

RAC1 is a crucial member of the Rho GTPase family, plays an essential role in immune regulation. Figure 7A demonstrates that alterations in RAC1 copy number variations in LIHC significantly influenced immune cell infiltration patterns. To elucidate the biological significance of RAC1, we systematically examined the relationship between RAC1 expression profiles and immune cell infiltration dynamics. Our analysis revealed strong positive associations between RAC1 expression and multiple immune cell populations (Figure 7B-7G), encompassing B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells in LIHC specimens.

Figure 7 RAC1 associated immune cell infiltration in LIHC. (A) The infiltration level of various immune cells under different copy numbers of RAC1 in LIHC. (B-G) The correlation of RAC1 expression level with B cell (B), CD8⁺ T cell (C), CD4⁺ T cell (D), macrophage (E), neutrophil (F), or dendritic cell (G) infiltration level in LIHC. *, P<0.05; **, P<0.01. LIHC, liver hepatocellular carcinoma; RAC1, Ras-related C3 botulinum toxin substrate 1; TPM, transcripts per million.

Expression correlation of RAC1 and biomarkers of immune cells in LIHC

To elucidate the functional involvement of RAC1 in tumor immune regulation, we conducted a comprehensive analysis of its expression patterns in relation to immune cell markers in LIHC using the GEPIA database. Our findings revealed statistically significant positive associations between RAC1 expression levels and multiple immune cell biomarkers (Table 1). Specifically, strong correlations were observed with markers for B lymphocytes (CD19 and CD79A), CD8⁺ T cells (CD8A and CD8B), CD4⁺ T cells (CD4), polarized macrophages (IRF5 and PTGS2 for M1 phenotype; CD163, VSIG4, and MS4A4A for M2 phenotype), neutrophils (ITGAM), and antigen-presenting dendritic cells (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C, NRP1, and ITGAX). These data provide compelling evidence that RAC1 expression is closely linked to immune cell recruitment and infiltration within the tumor microenvironment.

Relationship between RAC1 and immune checkpoints in LIHC

The immune checkpoint molecules programmed death 1 (PD-1)/programmed death ligand-1 (PD-L1) and cytotoxic T-lymphocyte associated protein 4 (CTLA-4) play crucial roles in facilitating tumor immune evasion. Given the putative oncogenic function of RAC1 in hepatocellular carcinoma (LIHC), we examined potential associations between RAC1 expression and these immune checkpoint markers. Our analysis revealed statistically significant positive correlations between RAC1 levels and PD-1, PD-L1, and CTLA-4 expression in LIHC samples after adjusting for tumor purity (Figure 8A-8C). Consistent with TIMER database findings, we observed robust positive associations between RAC1 and these immune checkpoint molecules (Figure 8D-8F). These findings suggest that RAC1 may contribute to LIHC pathogenesis through mechanisms involving immune escape pathways.

Figure 8 RAC1 positively correlates with immune cell infiltration in LIHC. (A) Spearman correlation of RAC1 with expression of PD-1 in LIHC adjusted by purity using TIMER. (B) Spearman correlation of RAC1 with expression of PD-L1 in LIHC adjusted by purity using TIMER. (C) Spearman correlation of RAC1 with expression of CTLA-4 in LIHC adjusted by purity using TIMER. (D) The expression correlation of RAC1 with PD-1 in LIHC determined by GEPIA database. (E) The expression correlation of RAC1 with PD-L1 in LIHC determined by GEPIA database. (F) The expression correlation of RAC1 with CTLA-4 in LIHC determined by GEPIA database. CTLA-4, cytotoxic T-lymphocyte associated protein 4; LIHC, liver hepatocellular carcinoma; PD-1, programmed death 1; PD-L1, programmed death ligand-1; RAC1, Ras-related C3 botulinum toxin substrate 1; TPM, transcripts per million.

The expression of RAC1, LINC00662 and hsa-miR-101-3p in clinical samples

We examined the levels of RAC1, LINC00662 and hsa-miR-101-3p in LIHC and matched adjacent non-tumor tissues. Western blot analysis was conducted to assess RAC1 protein expression in a subset of these samples. Our results confirmed that RAC1 mRNA and protein levels are significantly upregulated in LIHC tissues compared to adjacent normal tissues, consistent with our TCGA and GTEx analyses (Figure 9A,9B). Furthermore, we observed that the expression level of hsa-miR-101-3p was significantly reduced in LIHC compared to adjacent normal tissues in clinical samples; The expression level of LINC00662 is significantly increased in LIHC (Figure 9C,9D). These experimental validations greatly enhance the reliability of our findings.

Figure 9 Expression analysis for RAC1, LINC00662 and hsa-miR-101-3p in clinical samples. (A-B) Expression levels of RAC1 in LIHC and adjacent tissue. (C) Expression levels of LINC00662 in LIHC and adjacent tissue. (D) Expression levels of hsa-miR-101-3p in LIHC and adjacent tissue. *, P<0.05; **, P<0.01. LIHC, liver hepatocellular carcinoma; RAC1, Ras-related C3 botulinum toxin substrate 1.

Discussion

LIHC, the predominant form of primary liver cancer, represents a growing global health burden with rising incidence rates (30). The identification of reliable molecular markers for prognostic assessment and therapeutic optimization remains an urgent clinical need (31). Through comprehensive analysis of TCGA datasets, our investigation revealed a marked elevation in RAC1 expression levels in malignant hepatic tissues relative to their normal counterparts. As an oncogene, RAC1 has been reported to be highly expressed in various cancers, including bladder cancer (32), gastric cancer (33), breast cancer (34), colorectal cancer (35), lung adenocarcinoma (36) and clear cell renal cell carcinoma (37). However, the expression levels and prognostic significance of RAC1 in LIHC remain understudied and warrant further investigation.

Our study commenced with a comprehensive pan-cancer investigation of RAC1 expression patterns utilizing TCGA datasets, followed by rigorous validation through the GEPIA platform. Genetic alterations in RAC1, such as mutations and deep deletions, are known to contribute to various human diseases, including malignant melanoma and neurodevelopmental disorders (38,39). Our results demonstrated that RAC1 genetic alterations, including mutations and deep deletions, are present across multiple cancer types (Figure 1B-1D). Survival analysis revealed that LIHC patients with high RAC1 expression had a poorer prognosis. Additionally, elevated RAC1 expression was associated with adverse clinicopathological features, such as stage IV disease, G4 histologic grade, AFP levels >400 ng/mL, and vascular invasion. Furthermore, our findings indicated that high RAC1 expression serves as an independent prognostic biomarker for poor OS and DSS in LIHC patients. Collectively, these data suggest that RAC1 could be a promising and novel therapeutic target for LIHC treatment.

DNA methylation is a critical regulator of epigenetics, typically leading to gene silencing (40,41). However, studies on RAC1 methylation levels in solid tumors are sparse (42,43). In our investigation, we explored the mechanisms driving RAC1 overexpression in LIHC and found that it may be linked to DNA hypomethylation. Moreover, hypomethylation of RAC1 was associated with adverse prognosis in LIHC patients.

RAC1 has been shown to drive LIHC progression by modulating several cancer-related signaling pathways, such as Src/FAK signaling (44), mTORC2/Akt signaling pathway (45), and ERK-STAT3 signaling pathway (46). Nevertheless, the current understanding of RAC1’s role in LIHC remains incomplete, necessitating further investigation into its biological functions and associated signaling cascades. Through comprehensive GSEA analysis, our research identified several key pathways that were markedly enriched in samples exhibiting elevated RAC1 expression levels. These included processes related to immune-mediated allograft rejection, apical junction formation, E2F-mediated transcriptional regulation, epithelial-mesenchymal transition (EMT) progression, interferon-α signaling, and late-phase estrogen response. A previous research reported that RAC1 overexpression could be targeted to inhibit the EMT process via PAK1 and Snail suppression in LIHC (47). Further experimental validation of these findings is needed to expand our understanding of RAC1’s biological roles in LIHC.

ncRNAs, encompassing miRNAs, lncRNAs, and circular RNAs (circRNAs), are recognized as pivotal regulators of gene expression through their competitive interactions via the ceRNA mechanism (17,48-50). To systematically identify upstream miRNAs targeting RAC1 in hepatocellular carcinoma (LIHC), we integrated predictions from seven computational algorithms: PITA, RNA22, miRmap, microT, miRanda, PicTar, and TargetScan. Our integrative analysis revealed a significant negative correlation between RAC1 expression and hsa-miR-101-3p levels. Comprehensive multi-omics analyses- incorporating competitive endogenous RNA network comparison, co-expression pattern examination, and prognostic significance verification - identified hsa-miR-101-3p as the key miRNA modulating RAC1 activity in LIHC development. Consistent with our findings, previous studies also showed the tumor-suppressive role of hsa-miR-101-3p in suppressing LIHC proliferation and metastatic progression (51). Moreover, its expression is downregulated in LIHC compared to normal tissues. Survival analysis further indicated that higher expression of hsa-miR-101-3p is significantly associated with better prognosis in LIHC. Overall, our findings suggest that hsa-miR-101-3p may act as a negative regulator of LIHC by targeting RAC1.

Based on the ceRNA hypothesis, lncRNAs associated with the hsa-miR-101a-3p/RAC1 axis are likely to exhibit oncogenic properties in LIHC. To identify these lncRNAs, we utilized the starBase database and identified 12 potential candidates (Table S4). Through comprehensive analyses, including expression profiling, survival analysis, and correlation studies, LINC00662 was determined as the most probable upstream lncRNA in this axis. LINC00662 has been previously implicated as an oncogene in various cancers, including LIHC (52,53). Our results collectively demonstrate that the LINC00662/hsa-miR-101a-3p/RAC1 signaling axis plays a crucial regulatory role in hepatocellular carcinoma pathogenesis. This molecular cascade exhibits substantial functional importance in liver cancer progression, as evidenced by our comprehensive experimental data. The identified axis appears to constitute a key molecular network governing LIHC development and progression.

Extensive research has established that the infiltration of immune cells within tumor microenvironments significantly influences therapeutic outcomes, including responses to chemotherapeutic agents, radiation treatment, and immunotherapeutic approaches, while also serving as a prognostic indicator for patient survival (54-57). Our study identified a significant positive association between RAC1 expression and multiple immune cell populations in LIHC, encompassing B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells. Notably, our analysis revealed a significant positive correlation between RAC1 expression and markers of M2-type macrophages (CD163, VSIG4, MS4A4A), which are known to foster an immunosuppressive niche by inhibiting effector T-cell responses and promoting tumor progression. Furthermore, the strong positive association of RAC1 with immune checkpoint molecules like PD-1, PD-L1, and CTLA-4 suggests that RAC1 may play a pivotal role in facilitating tumor immune evasion. We hypothesize that elevated RAC1 expression might contribute to an immunosuppressive microenvironment through a dual mechanism: by promoting the infiltration and M2 polarization of tumor-associated macrophages, and by upregulating immune checkpoint pathways. This positions RAC1 as a potential molecular bridge connecting oncogenic signaling to the establishment of an immune-deserted or immune-excluded tumor phenotype in LIHC.

Furthermore, the therapeutic effectiveness of immunotherapeutic interventions is contingent upon two critical factors: robust infiltration of immune cells within the tumor microenvironment and optimal expression levels of immune checkpoint molecules (58). The findings of this study also have potential translational implications. First, the strong correlation between RAC1 and immune checkpoint molecules (PD-1, PD-L1, CTLA-4) suggests that RAC1 expression levels might serve as a predictive biomarker for response to immune checkpoint blockade therapy in LIHC. Patients with high RAC1 expression, who may exhibit a more immunosuppressive microenvironment, could potentially derive greater benefit from immunotherapy. Second, given its role as a key oncogenic driver linked to both tumor progression and immune evasion, RAC1 itself represents a promising therapeutic target. Several small molecule inhibitors targeting RAC1, such as NSC23766, are in preclinical development and could be repurposed for LIHC treatment. Furthermore, the correlation with immune checkpoints raises the intriguing possibility that combining RAC1 inhibition with immune checkpoint blockade could yield synergistic antitumor effects by simultaneously targeting tumor cell proliferation and reversing immune evasion. Future studies are warranted to explore these therapeutic strategies and validate RAC1 as a clinically useful biomarker in LIHC.

In summary, our research revealed elevated RAC1 expression across multiple human malignancies, with particularly pronounced levels in LIHC. Importantly, RAC1 overexpression showed significant association with unfavorable clinical outcomes in LIHC cases. Mechanistically, we uncovered the LINC00662/hsa-miR-101a-3p regulatory axis as a key upstream modulator of RAC1 in LIHC pathogenesis (Figure 10). Furthermore, our data indicate that RAC1 likely facilitates oncogenesis through mechanisms involving increased immune cell infiltration and upregulation of immune checkpoint molecules. These findings position RAC1 as a promising candidate for prognostic evaluation in LIHC management. Nevertheless, comprehensive mechanistic studies and prospective clinical investigations are warranted to substantiate these preliminary observations.

Figure 10 The model of LINC00662- hsa-miR-101-3p -RAC1 axis in carcinogenesis of LIHC. LIHC, liver hepatocellular carcinoma; RAC1, Ras-related C3 botulinum toxin substrate 1.

Conclusions

In multiple public databases, RAC1 exhibits significant dysregulation in hepatocellular carcinoma (LIHC) and is correlated with unfavorable patient outcomes. In LIHC, RAC1 serves as an independent prognostic indicator, associated with changes in DNA methylation and an immunosuppressive tumor microenvironment. Elevated RAC1 expression is linked to aggressive clinicopathological characteristics and increased expression of immune checkpoint molecules, highlighting its potential as both a prognostic biomarker and a therapeutic target. The discovery of the LINC00662/hsa-miR-101-3p regulatory axis offers new perspectives on the molecular mechanisms driving RAC1 overexpression in the pathogenesis of LIHC.

Acknowledgments

We would like to express our sincere thanks to the database of GEPIA 2, UALCAN, TIMER, TISIDB, CBioPortal for supporting our research. We would also like to express our gratitude to Professor Changyu Yao for his support in this research.

Footnote

Reporting Checklist: The authors have completed the TRIPOD and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2848/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2848/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2848/prf

Funding: This work was supported by the Natural Science Foundation of Capital Medical University (No. PYZ23087) and the National Natural Science Foundation of China (No. 82400757).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2848/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethical Committee of Beijing Chao-Yang Hospital (No. 2025-Ke-1098). Informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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