1Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;
2Immunology Research Center for Oral and Systemic Health, Beijing Friendship Hospital, Capital Medical University, Beijing, China;
3Department of Gastroenterology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Contributions: (I) Conception and design: Y Zhou, Y Song; (II) Administrative support: S Zheng, Q Guo; (III) Provision of study materials or patients: N Wei, Z Xiao; (IV) Collection and assembly of data: Y Zhou, Y Song; (V) Data analysis and interpretation: Y Zhou, Y Song; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.
Correspondence to: Yuhu Song, MD, PhD. Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Jianghan District, Wuhan 430022, China. Email: yuhusong@163.com.
Background: Hepatocellular carcinoma (HCC) is a primary liver cancer with high mortality worldwide. Even though the patients with HCC received standard therapies, patients with HCC continue to experience unsatisfactory therapy outcomes. Presently, immune therapy acts as a novel therapeutic management for HCC. The aim of this study was to determine overexpressed genes in HCC and evaluate the association between overexpressed genes with the prognosis and immune infiltration.
Methods: Gene expression profiles of HCC were analyzed using multiple online databases, and then confirmed by qualitative real time-polymerase chain reaction (RT-qPCR) and immunohistochemical analysis. The correlations of gene overexpression with the prognosis and immune infiltration were determined.
Results: Top 11 common differentially expressed genes were identified in HCC, and RACGAP1 was selected for further analysis. RACGAP1 expression was significantly higher in HCC tissues than that in normal tissues. Upregulation of RACGAP1 was correlated with clinical stage and poor prognosis. Additionally, RACGAP1 overexpression was positively related to the infiltration of suppressive immune cells. Moreover, we speculate RACGAP1 may promote tumorigenesis of HCC through immunosuppression mediated by YAP activation.
Conclusions: Overexpression of RACGAP1 was associated with unfavorable prognosis and immune infiltration in HCC, which indicated that RACGAP1 could be a molecular target for HCC.
Submitted Aug 15, 2023. Accepted for publication Dec 13, 2023. Published online Feb 27, 2024.
doi: 10.21037/tcr-23-1474
Highlight box
Key findings
• This study found that RACGAP1 has predictive value for outcomes of hepatocellular carcinoma (HCC) patients.
• Interestingly, RACGAP1 may promote tumorigenesis of HCC through immunosuppression mediated by YAP activation.
What is known and what is new?
• HCC is a primary liver cancer with high mortality worldwide. Even though the patients with HCC received standard therapies, patients with HCC continue to experience unsatisfactory therapy outcomes. Presently, immune therapy acts as a novel therapeutic management for HCC.
• We found overexpression of RACGAP1 was correlated with clinical stage and poor prognosis.
What is the implication, and what should change now?
• The RACGAP1 is a potential prognostic signature or therapeutic strategy and is significantly associated with the malignant biology of HCC.
Introduction
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and a leading cause of death around the world (1). Even though the patients with HCC received standard therapies including open/laparoscopic resection, transcatheter arterial chemoembolization (TACE), and radiofrequency ablation (RFA), patients with HCC continue to experience unsatisfactory therapy outcomes (2). Presently, immune therapy acts as a novel therapeutic management for HCC.
RACGAP1, a component of the centralspindlin complex, regulates rho-mediated signal pathways. RACGAP1 plays essential roles in cytokinesis, cell growth and differentiation. Previous studies have reported that RACGAP1 plays a critical role in the pathogenesis of cancers and is identified as a prognosis biomarker (3-7). A study performed by Pliarchopoulou et al. showed that RACGAP1 overexpression was associated with poor overall survival (OS) and recurrence-free survival (RFS) in breast cancer (8). Saigusa et al. reported that the overexpression of RACGAP1 was related to lymph node metastasis and unfavorable outcomes in gastric cancer (4). Moreover, some recent findings revealed that knockdown of RACGAP1 in HCC cells induced cytokinesis failure and cell apoptosis (9). All of these findings indicate the involvement of RACGAP1 in HCC. Thus, our study sought to examine the specific role of RACGAP1 in the pathogenesis of HCC. Firstly, differential expression gene (DEG) analysis was performed based on Gene Expression Omnibus (GEO) database, and RACGAP1 was chosen as the candidate gene for further research. Then, the expression of RACGAP1 was assessed, and the relationship between RACGAP and the outcomes of HCC was analyzed. Furthermore, the correlation between immune infiltration and RACGAP1 expression was evaluated using Tumor Immune Estimation Resource (TIMER) or Gene Expression Profiling Interactive Analysis (GEPIA) database. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-1474/rc).
Methods
GEO datasets
The GEO database is a public database containing a large number of high-throughput gene expression data. GSE112791, GSE102079 and GSE62232 gene expression profile datasets were screened (10-12). GEO2R was applied for the DEG analysis.
Enrichment analysis
The database for annotation, visualization and integrated discovery (DAVID) was used for analyzing the selected genes (13).
Construction of protein-protein interaction (PPI) network and identification of hub gene
A PPI network with complex regulatory relationships was constructed using the STRING database (14). Cytoscape software (version 3.9.1) was employed to visualize the PPI network (15).
TIMER database
TIMER or TIMER2 is a comprehensive resource for analyzing immune infiltration in different tumor types (16,17).
GEPIA database
GEPIA is a user-friendly online tool for analyzing gene expression based on the data of The Cancer Genome Atlas (TCGA) and genotype-tissue expression (GTEx) (18). Cell type-level proportion analysis was analyzed using GEPIA2021 (19).
The University of Alabama at Birmingham Cancer (UALCAN) database
UALCAN is a web for in-depth analysis of TCGA and clinical proteomic tumor analysis consortium (CPTAC) data (20).
Tumor, normal and metastatic (TNM) plot database
TNM plot provides gene expressions in different tissues (21).
The Human Protein Atlas (HPA) database
HPA is a dataset which provides gene expression profiles at the protein level in different tissues (22).
Kaplan-Meier (KM) survival analysis
The KM plotter contains clinical profiles of patients, and was used to assess the relationship between the survival and genes in diverse cancers (23).
GeneMANIA database
GeneMANIA was applied to predict the function of genes or gene sets (24).
Tumor-Immune System Interaction Database (TISIDB)
TISIDB was used for analyzing the interaction between tumor and immune system (25).
Sangerbox database
The Sangerbox database is a comprehensive Chinese bioinformatics analysis platform which provides various visualization plots (26).
Rat HCC model
The male rats (6–8 weeks, Beijing Vital River Laboratory, Beijing, China) weighing 200 g were injected intraperitoneally with N-nitrosodiethylamine (DEN) (N0756; Sigma-Aldrich, St. Louis, MO, USA) or 0.9% NaCl (control) at a dose of 50 mg/kg once a week for 16 weeks (27).
Immunohistochemistry (IHC)
The IHC was applied to detect the protein expression of RACGAP1 in human and rat liver tissue. IHC was performed using a 2-step method as previously reported (27-29). RACGAP1 was used as the primary antibody for incubation and then appropriate secondary antibody was selected. The other steps followed that marked in the manual. The IHC scores were referred as the value of the staining intensity [0–3] and the portion of stained cells [1–4].
Statistical analysis
The different expression of RACGAP1 between tumors and normal tissues was analyzed using the Wilcoxon rank sum test or t-test, respectively. KM curve was plotted to assess the survival differences between the high expression and low expression groups. P<0.05 was considered as statistical significance.
Ethical statement
The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Ethical approval was not required because GEO and TCGA are publicly available sources of data. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
Results
Screen of hub genes
Three microarray datasets (GSE112791, GSE102079 and GSE62232) were screened for DEGs. In GSE112791, GSE102079 and GSE62232, 163 DEGs (60 upregulated, 103 downregulated), 93 DEGs (23 upregulated, 70 downregulated) and 130 DEGs (39 upregulated, 91 downregulated) were found, respectively (Figure 1A). Then, 65 common DEGs were identified through the intersection of the Venn diagram (Figure 1B). Next, the 65 overlapped genes were analyzed to establish PPI network (Figure 1C). The combined scores larger than 0.4 in PPIs were imported to Cytoscape for subsequent analysis. Using Cytohubba plug-in, 11 hub genes were identified (Figure 1D,1E). Some of the overlapped genes, such as ANLN, CDK1, and CCNB1 have been reported in several studies (30-32). However, the role of RACGAP1 in the pathogenesis of HCC has been rarely reported. Thus, RACGAP1 was selected as a candidate target gene for further analyses. As shown in Figure 1F, increased RACGAP1 expression in RNA level was observed in HCC tissues compared with non-cancer tissues, which was also confirmed in the three microarray datasets.
Figure 1 Identification of hub genes. (A) The volcano map of GSE112791, GSE102079 and GSE62232. (B) The Venn diagram showing overlapping differential expression genes. (C) Protein-protein interaction network diagram. (D) The significant genes clustering modules. (E) Top 11 hub genes of the overlapping genes. (F) RACGAP1 RNA expression in HCC and normal tissues. ***, P<0.001. FDR, false discovery rate; GEO, gene expression omnibus; NT, normal tissue; HCC, hepatocellular carcinoma.
RACGAP1 message RNA (mRNA) and protein expression increased in HCC patients
To further examine RACGAP1 mRNA expression in HCC, the expression of RACGAP1 in the GEPIA, UALCAN and TNM plot was displayed. And we found increased expression of RACGAP1 mRNA in HCC tissues (Figure 2A,Figure S1A,S1B).
Figure 2 Expression of RACGAP1 in HCC. (A) RACGAP1 mRNA expression in HCC was examined by using the GEPIA. (B) RACGAP1 protein expression in HCC and normal tissues. (C) RACGAP1 protein expression in the HPA database by IHC with DAB staining. (D) RACGAP1 protein expression in human HCC tissues by IHC with DAB staining method. *, P<0.05; ***, P<0.001. HCC, hepatocellular carcinoma; mRNA, message RNA; GEPIA, Gene Expression Profiling Interactive Analysis; TPM, transcripts per million; CPTAC, clinical proteomic tumor analysis consortium; HPA, human protein atlas; IHC, immunohistochemistry; DAB, 3,3'-diaminobenzidine.
To detect the RACGAP1 protein expression, the CPTAC was applied through UALCAN website. The results revealed that the expression of RACGAP1 protein was significantly higher in HCC tissues compared to that of the normal tissues (Figure 2B). IHC results from HPA database showed RACGAP1 protein expression was overexpressed in HCC tissues (Figure 2C). To confirm the result, the RACGAP1 expression in protein level was also determined. Our results revealed an up-regulation of RACGAP1 expression at the protein level in HCC tissues (Figure 2D).
Increased RACGAP1 expression was correlated with unfavorable prognosis in HCC patients
To investigate the relationship between RACGAP1 and clinicopathological characteristics in HCC patients, the analysis was performed using the online tool UALCAN. No difference was found in the RACGAP1 expression in different genders (Figure 3A). Then, the expression of RACGAP1 was determined in different stages of HCC. RACGAP1 expression increased gradually in cancerous tissues, which was accompanied by the development of HCC. However, lower RACGAP1 expression was observed in stage 4 compared with stage 3, which might be attributed to small size of enrolled patients in stage 4 (Figure 3B). Similar pattern was observed in the correlation between tumor grade and RACGAP1 expression (Figure 3C). Increased RACGAP1 expression was accompanied by lymph nodes metastasis (Figure 3D).
Figure 3 RACGAP1 is associated with clinicopathological data in hepatocellular carcinoma patients. (A) Gender. (B) Tumor stage. (C) Tumor grade. (D) Lymph node metastasis. **, P<0.01; ***, P<0.001. TCGA, the cancer genome atlas.
KM plotter survival analysis was performed to assess the prognosis (using “auto select best cutoff” as group cutoff). Firstly, the survival was analyzed using the KM plotter online tool. And KM analysis revealed HCC patients with increased RACGAP1 expression had poor OS, progression-free survival (PFS), RFS and disease-specific survival (DSS) compared with patients with lower level of RACGAP1 expression (Figure 4A). The data of the GEPIA database showed similar pattern in HCC patients with different levels of RACGAP1 expression (Figure 4B).
Figure 4 RACGAP1 is correlated with the prognosis of HCC. (A) Kaplan-Meier curves revealed HCC patients with high RACGAP1 mRNA expression had a shorter OS, PFS, RFS, and DSS. (B) GEPIA survival analysis of OS and RFS for HCC patients. HCC, hepatocellular carcinoma; mRNA, message RNA; OS, overall survival; PFS, progression-free survival; RFS, recurrence-free survival; DSS, disease-specific survival; GEPIA, Gene Expression Profiling Interactive Analysis; HR, hazard ratio; TPM, transcripts per million.
In order to further investigate the role of RACGAP1 expression in HCC patient’s prognosis, the correlation between RACGAP1 mRNA expression and clinicopathological features was determined (Table 1). The overexpression of RACGAP1 was found to be significantly associated with worse OS and PFS. For HCC patients in Stages 1 to 3, high expression of RACGAP1 was significantly related to unfavorable OS and PFS. Additionally, increased expression of RACGAP1 was related to unfavorable OS and PFS in tumor grade and American Joint Committee on Cancer (AJCC) Tumor (T) stage but was not related to OS of grade 2 [hazard ratio (HR): 1.57, 95% CI: 0.93–2.62, P=0.086] and PFS of AJCC-T3 [HR: 1.94, 95% CI: 0.96–3.90, P=0.059]. These results revealed the expression of RACGAP1 was an important factor in the HCC patient’s prognosis.
Table 1
Correlation of RACGAP1 mRNA expression and clinical prognosis in HCC with different clinicopathological factors by Kaplan-Meier plotter
Factors
Overall survival (n=364)
Progress-free survival (n=366)
N
HR (95% CI)
P
N
HR (95% CI)
P
Gender
1.81 (1.31–2.81)
0.00068*
1.81 (1.31–2.81)
0.00068*
Male
246
4.04 (1.94–8.40)
5.3e–05*
246
1.92 (1.33–2.76)
0.00038*
Female
118
1.98 (1.08–3.62)
0.024*
120
2.63 (1.49–4.66)
0.00055*
Alcohol
Yes
115
3.26 (1.42–7.47)
0.0032*
115
2.90 (1.62–5.17)
0.00017*
None
202
2.05 (1.25–3.35)
0.0034*
204
2.06 (1.36–3.13)
0.00051*
Hepatitis virus
Yes
150
2.70 (1.18–6.15)
0.014*
152
1.92 (1.14–3.25)
0.013*
None
167
2.19 (1.37–3.52)
0.00087*
167
3.07 (1.91–4.91)
1e−06*
Stage
1
170
2.44 (1.23–4.88)
0.0088*
170
1.78 (1.06–3.00)
0.027*
2
83
2.75 (1.09–6.94)
0.025*
84
1.95 (1.00–3.80)
0.045*
3
83
2.60 (1.31–5.15)
0.0046*
83
2.27 (1.16–4.43)
0.014*
4
4
–
–
5
–
–
Grade
1
55
3.9 (1.33–11.48)
0.0088*
55
2.69 (1.02–7.11)
0.039*
2
174
1.57 (0.93–2.62)
0.086
175
2.37 (1.53–3.68)
6.8e−05*
3
118
3.14 (1.71–5.76)
0.00011*
119
3.28 (1.93–5.57)
3.6e−06*
4
12
–
–
12
–
–
AJCC tumor
1
180
2.31 (1.21–4.41)
0.0088*
180
1.80 (1.11–2.91)
0.015*
2
90
2.64 (1.12–6.21)
0.021*
92
1.98 (1.05–3.74)
0.031*
3
78
2.31 (1.22–4.4)
0.0085*
78
1.94 (0.96–3.90)
0.059
4
13
–
–
13
–
–
Vascular invasion
None
203
1.99 (1.07–3.70)
0.027*
204
1.98 (1.25–3.15)
0.0033*
Micro
90
2.30 (0.93–5.73)
0.065
91
1.95 (1.08–3.54)
0.024*
Macro
16
–
–
16
–
–
“–” means sample number too low for meaningful analysis. *, P<0.05. Only patients for whom explicit information is available are listed in this table. mRNA, message RNA; HCC, hepatocellular carcinoma; HR, hazard ratio; CI, confidence interval; AJCC, American Joint Committee on Cancer.
Identification and functional analysis of RACGAP1
Firstly, the gene interaction network for RACGAP1 was generated through the GeneMania database. The results revealed that the top 20 genes were strongly related to RACGAP1, including the KIF23, ECT2, PRC1 and AURKB (Figure 5A). Then, the STRING tool was used to generate PPI network of RACGAP1. The network had 51 edges and 11 nodes, including KIF23, ECT2, ANLN and CDK1 (Figure 5B).
Figure 5 Identification of RACGAP1 interacted genes and proteins. (A) The gene-gene interaction network. (B) The protein-protein interaction network. (C) Co-expression heat map of the top 50 RACGAP1-related genes.
To further uncover the function of RACGAP1, we searched the related genes of RACGAP1 using UALCAN website. The top 300 genes related to RACGAP1 were identified. And the results showed that the top 300 genes were positively associated with RACGAP1. Then, the top 50 related genes were selected through heat map analysis (Figure 5C). Subsequently, the top 300 genes received enrichment analysis to assess the potential signal pathways and function. The representative Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways are shown in Figure S2A-S2D. These genes were involved in cell division/cycle and DNA repair.
Expression of RACGAP1 was positively associated with immune infiltration
The tumor microenvironment (TME) plays a crucial role in tumor progression and recurrence (33-35). TME is consisted of immune cells, abundant immune molecules and other regulating cytokines, which is related to the occurrence, development and metastasis of tumor. Previous studies reported immune infiltration was closely associated with the prognosis of cancers (36,37). The TIMER database was used to explore the relationship between the expression of RACGAP1 and immune infiltration. RACGAP1 expression was closely related to B cell, CD8+ T cell and CD4+ T cell (Figure 6A). Then, the results of TIMER2 database demonstrated RACGAP1 expression was positively related to the resting myeloid dendritic cell, M0 macrophage, myeloid derived suppressor cells (MDSCs), activated CD4+ memory T cell, Tfh, and Treg cell, and negatively associated with CD4+ memory resting T cell in TME.
Figure 6 Correlation of RACGAP1 with immune infiltration. (A) RACGAP1 is correlated with tumor purity and different immune infiltration cells. (B) Pan-cancer analysis revealing the association between RACGAP1 and tumor infiltrating lymphocytes through TISIDB database. (C) The proportion of T regulatory cells in the HCC and NT. ***, P<0.001. TPM, transcripts per million; TISIDB, Tumor-Immune System Interaction Database; HCC, hepatocellular carcinoma; NT, normal tissues.
Thereafter, the TISIDB database was applied to assess the relationship between RACGAP1 expression and tumor infiltrating lymphocytes (TILs). The result of heat map revealed the relationship between RACGAP1 and TILs across pan-cancers (Figure 6B). Meanwhile, the fraction of infiltrating immune cells was determined using the online tool of GEPIA2021. The result revealed that the proportion of Treg, macrophages M0, and mast cell resting was significantly higher in tumor tissues than that in normal tissues. However, the result for monocyte and mast cell activated showed the opposite trend (Figure 6C,Figure S3A-S3D). These findings indicated that RACGAP1 might regulate the infiltration of Treg, Tfh, Th2, M0 macrophage, MDSC in HCC TME.
RACGAP1 expression was positively correlated with immune suppressive microenvironment
It is well established that tumor immune escape involves Tregs which comprise an immune suppressive microenvironment. The TIMER and GEPIA datasets were utilized to explore the relationship between RACGAP1 expression and diverse immune signatures in HCC. Additionally, the association between RACGAP1 expression and the markers of diverse immune cells was explored.
The correlation between tumor purity and immune infiltration was studied. After adjusting for purity, there was a clear correlation between RACGAP1 expression and several immune cells, particularly Treg, T cell exhaustion, TAM, dendritic cell, M2 macrophage cell and MDSC (Table 2). In order to confirm the results, the association between RACGAP1 expression and the infiltration of immune cells in TME was analyzed using the GEPIA web. Similar results were observed in the TIMER web tool (Table 3).
Table 2
Correlation analysis between RACGAP1 and relate genes markers of immune cells using TIMER
The results of TIMER, TIMER2, GEPIA and GEPIA2021 revealed RACGAP1 was positively related to Treg cells in HCC. Moreover, further investigation was conducted to examine the relationship between RACGAP1 and the cytokine/receptor axis for Treg infiltration (CCR4-CCL22, CCR5-CCL5, CCR8-CCL1, and CCR10-CCL28). These results indicated RACGAP1 expression had a positive association with the expression levels of the aforementioned cytokine and receptors (Table S1).
Discussion
HCC is a common cancer with unfavorable prognosis (38). In China, the 5-year survival rate of HCC is still less than 5% (39). In this study, 65 common DEGs were screened out from three GSE datasets, and then the top 11 hub genes were identified. The hub genes, including ANLN, CDK1 and CCNB1, have been studied previously. However, the function of RACGAP1 has rarely been explored in HCC. Thus, RACGAP1 was selected to explore its value in hepatocarcinogenesis.
RACGAP1 is a Rac GTPase-activating protein involved in process of cell division, cell transformation and differentiation. Recent studies found that it had an important function in oncogenesis. RACGAP1 knockdown could inhibit development of various tumors, especially HCC (9,40,41). However, the potential molecular mechanisms for this process have not been profoundly known.
In this study, the expression of RACGAP1 was upregulated in HCC. Higher expression of RACGAP1 was associated with tumor aggressive behavior, which is similar to findings from previous studies (7,42). All these results indicated that RACGAP1 could be applied as a candidate biomarker for predicting HCC prognosis. Enrichment analyses revealed that RACGAP1 regulated the cell division process. Furthermore, the results indicated that RACGAP1 expression was correlated with immune infiltration. Therefore, this study could provide a novel view to the potential value of RACGAP1 in tumor immunology.
Tregs are involved in the maintenance of immunological self-tolerance through immunosuppressive effects in various types of tumors, thereby helping tumor cells to escape from immune killing (43-46). In this study, several results indicated the recruitment of immunosuppressive Tregs as a key mechanism of immune escape in HCC. In HCC, the secretion of various chemokines recruits Treg to TME (47). We found that RACGAP1 upregulation was positively related to the chemokines and receptors expression for Treg recruitment. Increased secretion of chemokines and corresponding receptors following RACGAP1 may contribute to the Treg enrichment in HCC TME.
The enrichment analysis revealed RACGAP1 was involved in cell division processes, which suggested its contribution to tumorigenesis. Yang et al. demonstrated that overexpression of RACGAP1 contributed to tumorigenesis by promoting cytokinesis via Hippo/YAP signaling (9). Hippo-YAP pathway is regarded as a regulator of cell proliferation, contact inhibition, organ size and tumorigenesis. Interestingly, recent studies demonstrated YAP activation enhanced the initiation or maintenance of Treg differentiation in multiple tumors including gastric cancer, HCC and melanoma (48-50). Knockdown or knockout YAP decreased the expression of RACGAP1 and improved antitumor immunity. Therefore, we speculate RACGAP1 may promote tumorigenesis of HCC through immunosuppression mediated by YAP activation.
This study has several limitations. Firstly, the correlation between RACGAP1 and prognosis factors was analyzed using online public databases, which requires clinical studies to validate the conclusion. Secondly, the analyses were mainly performed according to the expression of RACGAP1 at the mRNA levels. Thirdly, the precise molecular mechanisms of RACGAP1 in immunosuppression need further studies to validate.
We demonstrated positive correlation of RACGAP1 overexpression with HCC prognosis and immune infiltration in this study. Notably, we demonstrated that the immune suppressive effect of RACGAP1 was mediated by YAP activation. In summary, these results indicated that RACGAP1 could serve as a molecular target for HCC therapy.
Conclusions
In this study, we determined the prognostic value of RACGAP1 for HCC patients. Overexpression of RACGAP1 was associated with unfavorable prognosis and immune infiltration in HCC, which indicated that RACGAP1 could be a molecular target for HCC.
Acknowledgments
We would like to thank Robin Ahmed for his help in polishing our paper.
Funding:This work was supported by National Natural Science Foundation of China (No. 82070631).
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 (as revised in 2013). Ethical approval was not required because GEO and TCGA are publicly available sources of data. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
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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Cite this article as: Zhou Y, Zheng S, Guo Q, Wei N, Xiao Z, Song Y. Upregulation of RACGAP1 is correlated with poor prognosis and immune infiltration in hepatocellular carcinoma. Transl Cancer Res 2024;13(2):847-863. doi: 10.21037/tcr-23-1474
