Bioinformatics analysis of immune infiltrates and tripartite motif (TRIM) family genes in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the world’s fourth leading cause of cancer-related mortality (1). Liver transplantation and hepatectomy are curative treatments for HCC, and the indications have been safely expanded (2,3). However, some tumors are still too advanced to be cured by surgical resection and orthotopic liver transplantation at diagnosis. Therefore, it is important to administer palliative treatments to achieve downstaging for surgical therapy or delay the progression of tumors. Combination therapy improves the prognosis outcomes of patients with advanced HCC better than single-agent therapy (4), implying that combined therapy could be a promising treatment option for some HCC patients.

In the past few decades, cancer immunotherapy has become one of the most effective treatments and has been validated in various tumors (5,6). Since the advent of immune checkpoint inhibitors (ICIs), the concept of normalizing the tumor immune microenvironment (TIME) by correcting dysfunctions of the immune response has drawn attention again to immunotherapy. Immune checkpoint therapy, which is at the forefront of immunotherapy, has demonstrated clinical activity in several malignancies, including HCC, although the response rate to ICIs varies in patients (7,8). The encouraging results from clinical trials of immune checkpoint therapy have resulted in increased clinical implementation in various types of cancer, including HCC. However, only approximately 20% of advanced HCC patients benefit from ICIs, and most of them have disease progression after 3–9 months (9). These results indicate that a substantial proportion of patients treated with ICIs suffer primary or acquired resistance. Therefore, studying the underlying mechanism and maximizing the curative effect of immune checkpoint therapy has become a focus in the field of HCC treatment.

Members of the tripartite motif (TRIM) protein family are engaged in a wide range of cellular functions and share several functional characteristics (10). The TRIM family, consisting of roughly 80 members, is structurally a highly conserved gene family whose family members all contain the RING finger domain, a basic composition of 1 or 2 zinc-finger domains called B boxes, and a coiled-coil region (11). Diverse C-terminal domains determine the primary structural distinctions within the TRIM family, and TRIM proteins are divided into 11 classes based on their C-terminus (from C-I to C-XI) (12). To date, TRIM proteins have been shown to regulate cell proliferation (13,14), facilitate or prevent cancer cell transformation (15), and directly interact with innate immunity (11), among many other roles. It has been shown that multiple TRIM genes play a significant function in liver cancer development as well as its immunotherapy (16,17). However, the relationship between TRIM genes and the effect of treatment as well as prognosis in HCC is not clear.

Although single TRIM family gene has been investigated in various solid tumors, no systematical and comprehensive analysis has been performed to identify the role of TRIMs in HCC. Our study aimed to systematically assess TRIM family correlations with prognosis, checkpoints, and TIME in HCC. The relationships between clustering subgroups, risk models, checkpoints, immune scores, and immune cell infiltration, and the responsiveness of sorafenib and transarterial chemoembolization (TACE) treatment were subsequently thoroughly analyzed based on TRIM family gene-related signatures to further investigate TRIM genes’ effect on TIME. The development of risk models for TRIM genes is vital for helping to improve risk stratification and clinical decision-making in HCC. We present the following article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-619/rc).

Methods

Datasets

The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases (https://daco.icgc.org/ and https://portal.gdc.cancer.gov/) were used to obtain RNA sequencing (RNA-seq) transcriptome data and clinical data of HCC patients. The inclusion criteria were: (I) histologically confirmed HCC, and (II) data on mRNA expression profiles and overall survival (OS) available at the same time. Ultimately, 231 samples of HCC were acquired, together with clinicopathological characteristics such as age, sex, grade, and TNM stage. A total of 231 ICGC HCC patients were assigned to the training cohort, while 370 TCGA patients were assigned to the validation cohort. The baseline clinicopathological features are shown in Table 1. The GSE109211 and GSE104580 datasets from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database were used to analyze the responsiveness of sorafenib, TACE, and ICI treatment. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

TRIM genes selection

Based on previously published literature, 62 TRIM genes were selected (11). On the basis of mRNA expression results of liver hepatocellular carcinoma (LIHC) from ICGC, a total of 62 TRIM genes were identified. Next, the differential expression of 62 TRIM genes in tumor tissues and adjacent normal tissues was analyzed.

Bioinformatics analysis

We used the “ConsensusClusterPlus” program to classify HCC patients into different subtypes in order to explore the biological functions of TRIM genes in HCC. To examine gene expression patterns among different HCC subtypes, principal component analysis (PCA) was performed using R (v4.1.0). Pathways analysis for different HCC subtypes was carried out using the R software package “GSVA”.

The immune score for each patient was estimated using the R “estimate package” and an algorithm (18). Cell-type identification by calculating relative subsets of RNA transcripts (CIBERSORT; https://cibersort.stanford.edu/) was used to develop the fraction of 22 immune cell types for each tumor specimen. With 1,000 permutations, the samples were chosen based on P<0.05.

In the ICGC training cohort, we performed K-M survival analysis for all TRIM genes, and we used least absolute shrinkage and selection operator (LASSO) regression analysis to identify predictive risk signatures for above TRIM genes (P<0.05). Ten cross-validations were used to select suitable values for the penalty parameter. The LASSO regression approach yielded the coefficients, and the risk score was obtained using the following formula: Riskscore=&sum;i=1ncodfi*xi where codfi is the coefficient and xi is the transformed relative expression value of each selected TRIM genes. This formula was used to generate a risk score for each patient in the training and validation cohorts. The samples were then separated into high-risk and low-risk categories based on the cutoff (median value).

Data from GEO and Array Express were collected by Tumor Immune Single-cell Hub (TISCH) to formulate a single-cell RNA-seq (scRNA-seq) atlas. TISCH compares different patients, therapy and response groups, tissue origins, cell types, and even cancer types by visualizing gene expression across several data sets at the single-cell or cluster level. In this study, we employed TISCH datasets to unravel the TME heterogeneity of 8 TRIM genes at the single-cell level.

The role of copy number alternations (CNAs) of the TRIM family on immune cell infiltration levels was evaluated by applying the Tumor Immune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/).

Statistical analysis

R version 4.1.0 and GraphPad Prism 9.2 were used for statistical analysis. A Student’s t-test, chi-square test, and Mann-Whitney-Wilcoxon test were used for comparisons between 2 groups, and a one-way analysis of variance (ANOVA) test was utilized for analysis with multiple comparisons. Survival curves were generated and compared using the Kaplan-Meier method. Univariate and multivariate analyses were conducted with Cox proportional hazards regression models. Receiver operating characteristic (ROC) curves were employed to compare the predictive accuracy of the TRIM gene-relevant signatures. P<0.05 (two-sided) indicated statistical significance.

Results

Expression of TRIM genes in HCC

Based on the ICGC dataset, we systematically investigated the expression patterns of 62 TRIM genes between HCC (n=240) and normal tissues (n=197) to assess the biological function of TRIM genes in the initiation and development of HCC. The expression levels of TRIM genes in HCC and normal tissues were evident (Figure S1A,S1B). The expression levels of most TRIM genes (45 of 62) were higher in HCC tissues than in normal adjacent tissues. Some TRIM genes (7 of 62) were lower in HCC tissues than in normal tissues (Figure S1A,S1B, Table S1). Additionally, there were also TRIM genes (10 of 62) with no statistically significant difference (P>0.05). The above results revealed that TRIM genes might possess essential biological roles in HCC development.

Significant correlation of consensus clustering for TRIM genes with the characteristics and survival of HCC patients

To achieve optimum clustering stability, k=2 was determined, and the samples from 231 patients with HCC were divided into 2 subgroups (Figure 1A). Individual TRIM gene expression was lower in cluster 1 than it was in cluster 2 (Figure 1B). Next, the clinicopathological characteristics of the 2 subgroups were compared (Figure 1B). Cluster 2 was more significantly related to higher stage (P<0.01) and higher mortality than cluster 1. Cluster 1 had a superior OS (P<0.001; Figure 1C). The results of PCA found that the gene expression profiles of the 2 groups were well differentiated (Figure 1D).

Figure 1 Differential clinicopathological features and survival of HCC in cluster 1/2 subtypes in ICGC cohort. (A) Consensus clustering matrix for k=2. (B) Heatmap and clinicopathologic features of the 2 clusters (cluster 1/2). (C) Kaplan-Meier curves of OS for patients with HCC in 2 clusters (cluster 1/2). (D) Principal component analysis of the total mRNA expression profile in 231 patients with HCC. HCC, hepatocellular carcinoma; ICGC, International Cancer Genome Consortium; OS, overall survival; TRIM, tripartite-motif; PC, principal component.

Association of immune check points with TRIM family

We looked at differential expression in 2 subtypes and the relationship between immune checkpoints and TRIM genes to see whether immune checkpoints were related. The expression level of KIR2DL1, KIR2DL3, KIR2DL2, KLRC1, LAG3, CD274, CTLA4, and TIGIT were downregulated in HCC tissues compared with normal tissues (P<0.05; Figure 2A). CTLA4, HAVCR2, and PDCD1 expression levels in cluster 2 were significantly higher than in cluster 1 (P<0.05; Figure 2B). NT5E expression, on the other hand, was lower in cluster 2 than in cluster 1 (P<0.05; Figure 2B). We then analyzed the correlation between TRIM genes and the immune checkpoints (PDCD1, NT5E, HAVCR2, and CTLA4) in ICGC and TCGA datasets, which showed that a number of TRIM genes had a significant correlation with immune checkpoints, as shown in Figure 2C,2D. The above results suggested that TRIM family genes may improve immunotherapy for HCC.

Figure 2 Association of immune check points with TRIM genes and the landscape of immune cell infiltration in HCC. (A) Tumor vs. normal; (B) cluster 1 vs. cluster 2; (C) ICGC dataset; (D) TCGA dataset; (E) landscape of immune cell infiltration in cluster 1/2. *P<0.05, **P<0.01, and ***P<0.001. TRIM, tripartite-motif; HCC, hepatocellular carcinoma; ICGC, International Cancer Genome Consortium; TCGA, The Cancer Genome Atlas; ns, no significance.

Consensus clustering for TRIM genes associated with distinct immune cell infiltration

To investigate the effect of TRIM genes on the TIME of HCC, we compared the immune infiltrate levels in cluster 1 and cluster 2 (Figure 2E). This analysis showed a significant difference in naive B cells, memory B cells, regulatory T cells (Tregs), gamma delta T cells, M0 macrophages, M1 macrophages, resting dendritic cells, and stromal score between the 2 clusters (Figure 2E). Cluster 1, with a higher stromal score, had a better prognosis than cluster 2, with a lower stromal score (P<0.05). We further performed gene set variation analysis (GSVA) and the results showed that the spliceosome, homologous recombination, and DNA replication pathway might be implicated in the distinct TIME of cluster 2 (P<0.0001; Figure 3A), while amino acid metabolism, fatty acid metabolism, and the drug metabolism cytochrome P450 pathway might be implicated in the distinct TIME of cluster 1 (P<0.0001; Figure 3A, Table S2). Hence, the metabolism-related signaling pathways might be implicated in the distinct TIME of cluster 1.

Figure 3 The potential regulatory mechanisms resulting in differences in TIME. (A) The potential regulatory mechanisms resulting in differences in TIME between the 2 subgroups by performing GSVA; (B) univariate analyses in the ICGC training cohort. TIME, tumor immune microenvironment; GSVA, gene set variation analysis; ICGC, International Cancer Genome Consortium.

Construction and validation of prognostic signatures for TRIM genes

A total of 231 ICGC HCC patients were assigned to the training group, while 370 TCGA patients were assigned to the validation cohort (Table 1). We conducted univariate analysis for TRIM genes, and the results showed that 12 TRIM genes (MID1, TRIM11, TRIM21, TRIM22, TRIM24, TRIM28, TRIM31, TRIM37, TRIM42, TRIM47, TRIM5, are TRIM74) were related to survival in HCC (all P<0.05; Figure 3B, Table S3).

To accurately predict the clinical prognosis of HCC patients, we performed K-M survival analysis for all TRIM genes, and we used least absolute shrinkage and selection operator (LASSO) regression analysis to identify predictive risk signatures for above TRIM genes (P<0.05). The results showed that 9 TRIM genes, namely MID1, TRIM38, TRIM37, TRIM47, TRIM24, TRIM28, TRIM22, TRIM5, and TRIM74, were identified. The median risk score (median =0.5656) was then used to separate patients into low- and high-risk groups (Table S4). The distribution of risk scores, OS, OS status, and expression profiles of the 9 TRIM gene-based signatures in the ICGC training and TCGA validation cohorts are shown in Figure 4. The heatmap data showed that TRIM genes, including MID1, TRIM28, TRIM31, TRIM37, and TRIM47, were substantially expressed in the high-risk group (Figure 4). In the ICGC training and TCGA validation cohorts, the low-risk group had a longer OS than the high-risk group (P<0.05; Figure 4). We then performed univariate and multivariate analyses and found that risk score was an independent prognostic factor in the ICGC and TCGA datasets (all P<0.05; Figure 5). We compared the respective area under curve (AUC) values in 1-, 3-, and 5-year ROC curve analyses to determine the prognostic accuracy of our model. The 1-, 3-, and 5-year AUC values for the 9 risk signatures in the ICGC training cohort were 0.789, 0.827, and 0.694, respectively (Figure 5), and 0.642, 0.571, and 0.533 in the TCGA dataset, respectively (Figure 5). Our model, based on the 9 TRIM genes, demonstrated favorable discrimination performance for the prognosis of patients with HCC, as evidenced by the AUC values. The results of PCA analysis corroborated the preceding findings (Figure S2). These findings suggested that the risk score derived from the 9 risk signatures might reliably predict HCC patients’ prognosis.

Figure 4 Construction and validation of prognostic signatures of TRIM genes in ICGC and TCGA cohorts. (A-D) Distribution of risk score, OS, and OS status and heatmap of the 9 prognostic TRIM genes in the ICGC training cohort; (E-H) distribution of risk score, OS, and OS status and heatmap of the 9 prognostic TRIM genes in the TCGA validation cohort. TRIM, tripartite-motif; ICGC, International Cancer Genome Consortium; TCGA, The Cancer Genome Atlas; OS, overall survival.

Figure 5 Univariate, multivariate Cox regression and ROC analyses in the 2 cohorts. Univariate (A) and multivariate (B) Cox regression analyses in the ICGC training cohort; univariate (D) and multivariate (E) Cox regression analyses in the TCGA validation cohort; receiver operating characteristic curves of 1, 3, and 5 years based on the risk score in the ICGC training cohort (C) and TCGA validation cohort (F). ICGC, International Cancer Genome Consortium; TCGA, The Cancer Genome Atlas.

Risk scores correlated with stage, immune score, TRIM cluster, and therapies in HCC

The association between risk score and clustering subtypes, stage, immune score, estimate score, stromal score, tumor purity, and OS status was also investigated. The cluster 2 risk score was significantly greater than the cluster 1 risk score (P<0.001; Figure 6). The high-risk group had a significantly lower immune score and higher TNM stage than the low-risk group (P<0.01; Figure 6).

Figure 6 Prognostic risk scores correlated with clinicopathological features, estimate score, stromal score, tumor purity, OS status, TNM stage, and immune score in TCGA training cohort. (A) Heatmap and clinicopathologic features of high- and low-risk groups. (B-H) Distribution of risk scores stratified by cluster 1/2 (B), estimate score (C), immune score (D), stromal score (E), tumor purity (F), OS status (G), and TNM stage (H). *P<0.05, and ***P<0.001. OS, overall survival; TCGA, The Cancer Genome Atlas.

The heatmap depicted the expression levels of 9 TRIM genes in the ICGC training cohort’s high- and low-risk groups (Figure 6). The high-risk group had lower levels of TRIM38, TRIM22, TRIM5, and TRIM74 expression than the low-risk group. TRIM31, TRIM47, TRIM28, TRIM37, and MID1 expression levels were low in the low-risk group. In addition, we analyzed the correlation of the 9 prognostic TRIM genes with TNM stage in the ICGC and TCGA datasets, and the result showed that in the ICGC dataset, there was a clear positive correlation between the expression of TRIM28 and TRIM47 and TNM stage, and similar results could be obtained in the TCGA dataset (Figure S3).

Immunotherapy, TACE, and molecularly targeted therapies have been widely used in the treatment of patients with HCC and contribute to the prognosis of patients. We investigated whether TRIM genes harbored the same influence on ICIs, sorafenib, and TACE treatment in additional HCC cases. First, we employed the tumor immune dysfunction and exclusion (TIDE) score, a scoring system that integrates 2 tumor immune escape mechanisms, to analyze the response rate of HCC immunotherapy. A high TIDE score indicates a poor treatment effect for ICIs (19). The results showed that the TIDE score of high-risk patients was high, but the response rate of immunotherapy in low-risk patients was higher than that in high-risk patients (87% vs. 65%; Figure 7A,7B). Next, we selected the eligible GEO datasets, GSE109211 and GSE104580, as the external validation cohort. The response to sorafenib was not significantly different from the risk score, but the response rate was significantly higher in low-risk patients than in high-risk patients (42% vs. 24%; Figure S4A, Figure 7C, Table S5), which was possibly due to the small number of patients who responded to sorafenib. In addition, the response to TACE was significantly different from the risk score, and the response rate was higher in low-risk patients than in high-risk patients (65% vs. 39%; Figure S4B, Figure 7D, Table S6).

Figure 7 Prognostic risk scores correlated with TIDE score, sorafenib and TACE treatment. (A) Prognostic risk scores correlated with TIDE score; (B) the response to ICI treatment; the response to sorafenib treatment (C) and TACE treatment (D). TIDE, tumor cell dysfunction and exclusion; ICIs, immune checkpoint inhibitors; TACE, transarterial chemotherapy embolization.

Single-nucleotide variant (SNV) mutations are associated with HCC treatment efficacy prediction and immune infiltration (20), so specific mutations in high- and low-risk groups may bring benefits for the prognosis of patients. We compared HCC samples from the low TRIM score subgroup to those from the high TRIM score subgroup in terms of substantially modified genes (SMG). The SMG mutational landscapes revealed that in the high TRIM score group, TP53 (17% vs. 47%) had greater somatic mutation rates, whereas in the low TRIM score group, CTNNB1 (34% vs. 23%) had higher somatic mutation rates (Figure S5A,S5B).

Correlation between TRIM genes and the TISH database

We used TISCH to investigate the expression of the TRIM genes in the HCC tumor microenvironment at the single-cell level (Figure 8A-8I). In LIHC_GSE140228, most TRIM genes were mainly expressed in immune cells, including B cells, plasma cells, exhausted CD8T (Tex) cells, CD8T cells, CD4 conventional T (Tconv) cells, mono/macrophages, mast cells, Tpolif cells, natural killer (NK) cells, and regulatory T cells. These results suggested that the expression of TRIM genes in HCC was closely related to immune cell infiltration (Figure 8).

Figure 8 The cell types and their distribution in the LIHC_GSE140228 datasets. (A) The distribution of 8 TRIM genes in different cell types was analyzed using single-cell resolution in the LIHC_GSE140228 datasets. MID1 (B), TRIM5 (C), TRIM22 (D), TRIM28 (E), TRIM31 (F), TRIM37 (G), TRIM38 (H), and TRIM47 (I). LIHC, liver hepatocellular carcinoma; TRIM, tripartite-motif.

Effect of genetic alterations of the TRIM gene signatures on immune cell infiltration

We used TIMER 2.0 to analyze the relationship of 9 TRIM genes with infiltration levels of 6 immune cell types to assess the effect of the 9 TRIM genes on the HCC immune microenvironment. The results revealed that a significantly positive correlation was observed between almost all the immune cells and the 9 TRIM genes (Figures S6,S7). These results confirmed that TRIM gene-based risk signatures were implicated in the TIME of HCC.

Genome instability and immune cell infiltration are both promoted by somatic CNAs. The infiltration levels of B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells in HCC were significantly impacted by the CNAs of the identified TRIM gene signatures, including arm-level deletion and arm-level gain (Figure 9). These findings showed that TRIM genes were important regulators of TIME in HCC patients.

Figure 9 Effect of genetic alterations of TRIM gene-relevant signature on the immune cell infiltration. (A-I) MID1 (A), TRIM5 (B), TRIM22 (C), TRIM28 (D), TRIM31 (E), TRIM37 (F), TRIM38 (G), TRIM47 (H), and TRIM74 (I). *P<0.05, **P<0.01, and ***P<0.001. TRIM, tripartite-motif.

Finally, we performed copy number variation (CNV) analysis on 9 TRIM genes, and the results showed that all 9 genes had acquired mutations greater than deletion mutations (Figure 10A). In addition, we labeled the location of the 9 TRIM genes on the chromosome, as shown in Figure 10B. Further analysis showed that in MID1, the alteration frequency of deep deletion and amplification accounted for the vast majority, but in TRIM5 and TRIM28, both accounted for half. In addition, the alteration frequency of amplification occupied almost all of the other TRIM genes (Figure 10C). The results of the abovementioned studies indicated that the genomic and transcriptomic landscapes had significant differences and connections.

Figure 10 CNV frequency, location and alteration frequency of prognostic TRIM genes. CNV frequency of 9 prognostic TRIM genes (A). Location of TRIM genes on chromosomes (B). Alteration frequency of 9 prognostic TRIM genes (C). CNV, copy number variation; TRIM, tripartite-motif.

Discussion

The expression patterns, prognostic values, and effects on TIME of the TRIM genes in HCC were investigated in this study. In HCC tissues, the expression of 45 TRIM genes increased significantly, while the expression of 7 TRIM genes dropped dramatically. By using consensus clustering for TRIM genes, we were successful in identifying subgroups of HCC: cluster 1 and cluster 2. The cluster subtype affected the prognosis and different clinicopathological features of HCC and was closely related to immune cell infiltration levels. We characterized the effects of differential TRIM genes on different HCC subtypes by clustering TRIM genes. The patients in cluster 1 showed a lower TNM stage. Similarly, cluster 1 had a better survival rate compared with that of cluster 2.

Furthermore, we analyzed and summarized the prognostic predictive role of TRIM family genes in HCC, and finally derived 9 prognostic risk signatures from TRIM genes, which effectively stratified the OS of HCC patients in the ICGC and TCGA cohorts into high- and low-risk groups. The risk score was found to be an independent prognostic factor for HCC patients in both univariate and multivariate cox regression models. The high- and low-risk groups were also related to distinct clustering subtypes, TNM stage, immune score, estimate score, tumor purity, and stromal score. Among these risk signatures, MID1, known as TRIM18, functions as an oncogene in melanoma (21). TRIM31, TRIM28, TRIM37, and TRIM47 are involved in oncogenic regulation in HCC, gastric cancer, prostate cancer, and renal cell carcinoma, respectively (22-25). Interestingly, TRIM37 has emerged as a tumor-suppressive regulator in various tumors in TRIM37 knock-out mice. TRIM22 is a double-edged sword in that it is a tumor-suppressive regulator in endometrial cancer and gastric cancer (26,27) but is involved in oncogenic regulation in non-small cell lung cancer and chronic myeloid leukemia (28,29). There are few studies of TRIM38 and TRIM74 in tumors, and currently, research mainly focuses on innate immunity and inflammatory response (30,31). These findings demonstrated that deregulation of specific TRIM genes played separate functions in various cancers.

To further interrogate the mechanism of the role of TRIM family genes in HCC, we performed GSVA analysis and the results indicated that the malignant functional features of the tumor, including amino acid metabolism, fatty acid metabolism, and the drug metabolism cytochrome P450 pathway, were significantly enriched in cluster 1. This may be related to the high response of cluster 1 to sorafenib, TACE, as well as immunotherapy, which in turn had a better prognosis than cluster 2. Previous research has shown that RIPK3-dependent TRIM28 inhibition in cancer cells leads to increased immunostimulatory cytokine production in the tumor microenvironment, which contributes to strong cytotoxic antitumor immunity (32). Liu et al. discovered that TRIM28 knockdown increases sensitivity to etoposide by upregulating E2F1 in non-small cell lung cancer (33). Previous study indicated that the expression level of TRIM37 significantly increased in 354 HCC tissues and promoted peroxisomal matrix protein import via direct monoubiquitination of PEX5 at K464 and silencing of gene expression through monoubiquitination of histone H2A (34). Clinical data analysis has suggested that patients with high expression of TRIM37 have more sorafenib resistance and shorter disease-free survival (DFS) and OS (P<0.01) (34). In addition, previous research reported that TRIM47 overexpression played a role in colorectal cancer chemoresistance in response to fluorouracil (5-FU) therapy (35). To date, TRIM38 and TRIM74 have been poorly studied in tumors, and their role in tumors may require more attention and investigation in the future. The above results were consistent with our study results; that is, patients in the high-risk group had a low response to both sorafenib treatment and TACE treatment, and had a poor prognosis. This suggested that for high-risk patients, new treatment strategies may need to be developed to improve survival rates. The predictive significance of the TRIM gene-relevant signatures was assessed in HCC patients and validated in the TCGA cohort, as well as the external GSE109211 and GSE104580 cohorts. The results showed that the TRIM gene-associated risk profiles could effectively predict the prognosis of HCC patients, allowing for more personalized treatment options and greater insight into the advancement of therapeutic techniques.

Previous studies have shown a close relationship between gene mutations and tumor development prognosis as well as treatment (36-38). Simultaneously, numerous research findings have revealed that the most common mutant gene mutations in HCC are, among others, TP53 and CTNNB1, which are closely connected to the prognosis and therapies of HCC (36-38). At the same time, we also found that the most frequent type of non-nonsense mutation, whether in the low- or high-risk group, was a missense mutation. The top 3 genes with the highest frequency were TP53, TTN, and CTNNB1, respectively, which was consistent with previous studies (39,40). However, we found that the genes with the highest mutation frequency among high- and low-risk groups differed, with CTNNB1 highest in the low-risk group and TP53 highest in the high-risk group. Therefore, we may be able to distinguish whether a patient belongs to a high- or low-risk group by the specific gene mutated.

Although there are many models related to the prognosis and treatment of HCC, in this study, the risk prognostic model could distinguish well high and low risks by prognosis-related TRIM genes. In addition, the model could also distinguish well the response of HCC patients to immunotherapy, targeted therapy, and TACE, providing new insights and theories for precise, personalized treatment of HCC patients. In this study, the risk score based on the 9 TRIM gene-based risk signatures was shown to be strongly related to immune cell infiltration. These findings suggested that TRIM genes are involved in TIME regulation to some extent. In addition, the advent of ICIs has brought great benefits to cancer patients, and HCC patients are no exception, but not all patients can benefit from the treatment of ICIs. The immune checkpoint (CTLA4, HAVCR2, and PDCD1) expression levels of cluster 2 were significantly higher than that of cluster 1. The immunotherapy response of HCC is very low (about 10%), and our risk model could distinguish well which patients responded to immunotherapy, which in turn may improve the immunotherapy effect. To evaluate the response of HCC patients to immunotherapy, we employed the TIDE score, with the results showing that the low-risk score group had a low TIDE score but a high response to ICI treatment. Therefore, we proved that the TRIM family gene-based prognostic model could evaluate well the degree of benefit of immunotherapy in patients at different risk levels, leading to the implementation of personalized treatment strategies for different patients, ultimately benefiting patients.

Our research did, however, have some limitations. For instance, our findings were confirmed in the ICGC, TCGA, and GEO cohorts, but we didn’t have any independent clinical sample data to support our claims and conclusions. Thus, the results of our research will need to be verified further, and we will continue to investigate TRIM gene correction and TIME in HCC in the future. In addition, the TRIM family’s regulatory mechanism in TIME should be explored further in order to restructure TIME and improve HCC precision immunotherapy.

Conclusions

In conclusion, this research examined the prognostic significance, immune checkpoint correlations, TIME relevance, and potential regulatory mechanisms of TRIM genes in HCC. The risk score established from 9 TRIM gene-based signatures was found to be an independent prognostic indicator for HCC patients. TACE and ICI treatment were more likely to benefit patients with a low-risk score. The levels of immune cell infiltration in patients with HCC were strongly associated with the TRIM gene-based signatures. Further, many signaling pathways may be implicated in the regulation of the HCC immune microenvironment by the TRIM family. The identification of TRIM genes that contribute to biochemical pathways controlling tumor immune responses, as well as examining their regulatory processes and responses, could provide potential targets for enhancing HCC’s immunotherapy responsiveness.

Acknowledgments

Funding: This work was funded by the National Natural Science Foundation of China (Nos. 81871909, 81702861), the “13th five-year Plan” Science and Education Strong Health Project Innovation team of Yangzhou (Nos. LJRC20181; YZCXTD201801), Provincial-level discipline leader of the NJPH (No. DTRC201809), and Beijing GanDanXiangZhao Public Welfare Foundation Project (No. GDXZ-08-19).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-619/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-619/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 (as revised in 2013).

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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(English Language Editor: A. Muijlwijk)