The expression and prognostic value of IFIT3 in esophageal squamous cell carcinoma
Highlight box
Key findings
• IFIT3 can be used as a potential biomarker for patient risk stratification and local regional metastasis in esophageal squamous cell carcinoma (ESCC).
What is known and what is new?
• Some bioinformatics analyses have suggested that IFIT3 may play a role in tumor progression (including ESCC).
• We have verified the role of IFIT3 in promoting the progression of ESCC through clinical tissue samples for the first time.
What is the implication, and what should change now?
• IFIT3 may become a target for ESCC treatment to improve the prognosis of clinical patients. More research is needed to explore the mechanism of IFIT3 affecting ESCC.
Introduction
Esophageal cancer (EC) is one of the most common malignant tumors in the world, and its incidence rate and mortality rate rank seventh and sixth, respectively, among all malignant tumors (1). According to epidemiology and pathology, EC can be divided into two types: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) (2-4). ESCC accounts for 70% of global EC cases, and is particularly common in the so-called “esophageal cancer belt”, which extends from northern China (with an annual incidence rate of up to 100/100,000 people) through Central Asia to northern Iran (5). As one of the regions with the highest incidence rate of EC, more than 90% of EC cases in China are ESCC, and the overall 5-year survival rate of patients is less than 30% (6).
For the treatment of ESCC, surgical resection is considered the standard protocol for early EC (stage I) (7,8), whereas synchronous radiotherapy and chemotherapy have been shown to be able to relatively prolong the survival time of patients with advanced EC (9,10). Despite improvements in the management and treatment of EC, the overall prognosis remains poor. Study of the molecular biological mechanisms involved in the occurrence, development, recurrence, and metastasis of EC (11) had led to the emergence of molecular targeted therapy. Molecular targeted therapy utilizes the molecular biological differences between tumor cells and normal cells to target the malignant phenotype molecules of tumor cells, using methods such as blocking receptors, inhibiting angiogenesis, and blocking signal transduction pathways to inhibit tumor cell growth and promote apoptosis (8,11,12). To date, the targeted drugs that have been extensively studied in molecular targeted therapy mainly include cetuximab, apatinib, trastuzumab, bevacizumab, and pabolizumab (13-17). Although these targeted drugs have been shown to be effective in some studies, shortcomings such as high resistance and incidence of adverse events have been observed in the real world, casting doubt over their true efficacy. Therefore, finding safer and more effective therapeutic targets remains the focus of current research.
In the past, IFIT3 has been widely studied in the field of antivirals, and in recent years, some bioinformatics analyses have suggested that IFIT3 may play a role in tumor progression (including ESCC), but this has not been verified through relevant research. In this study, we not only elucidated the role of IFIT3 in promoting the progression of ESCC through bioinformatics analysis, but also verified this result through clinical tissue samples for the first time. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-233/rc).
Methods
Patients
All patients were diagnosed with ESCC by pathological examination. A total of 102 patients with ESCC who underwent surgical treatment in the Department of Thoracic Surgery, General Hospital of Southern Theater Command, PLA from October 2015 to June 2021 were selected as the study group. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by General Hospital of Southern Theater Command, PLA (No. 2017-532-18) and informed consent was provided by all individual participants.
Specimen characteristics
After surgery, the EC tissue and paracancerous tissues of the patients were collected and stored in liquid nitrogen.
Assay methods
The esophageal tissue was taken from the liquid nitrogen and cut and ground under sterile conditions. According to the manufacturer’s instructions, TRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to extract total RNA from the tissue. RNA concentration was detected and screened with a Multiscan Spectrum microplate reader (BioTek, Winooski, VT, USA). The GoScript™ Reverse Transcription System Kit (Promega, Madison, WI, USA) was used to prepare complementary DNA (cDNA) from total RNA. Reverse transcription (RT) was carried out by the following steps: the initial denaturation step was for 5 minutes at 70 ℃, followed by 5 minutes at 4 ℃, annealing for 5 minutes at 25 ℃, and extension for 1 hour at 70 ℃, with holding at 4 ℃. An Applied Biosystems™ 7500 Fast Dx Real-Time Polymerase Chain Reaction (PCR) instrument (Thermo Fisher Scientific, Waltham, MA, USA) and GoTaq qPCR Master Mix (Promega) were used for quantitative (q) PCR to measure gene expression levels. qPCR was performed using the following cycle parameters: denaturation at 95 ℃ for 10 minutes, followed by 40 cycles of denaturation at 95 ℃ for 15 seconds, annealing at 55 ℃ for 1 minute, and extension at 72 ℃ for 1 minute. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the endogenous control. The expression data were calculated using the 2−ΔΔCt method. The PCR primer sequences used are listed below:
IFIT3 sense 5'- AAAAGCCCAACAACCCAGAAT-3';
Anti-sense 5'- CGTATTGGTTATCAGGACTCAGC-3';
GAPDH sense 5'- ACAGTCAGCCGCATCTTCTT-3';
Anti-sense 5'- GACTCCGACCTTCACCTTCC-3'.
Study design
This was a retrospective study. Stratified analyses were performed based on clinicopathological data. The follow-up time was up to May 2023, with a total of 102 participants. Disease-free survival (DFS) represented the time from surgical resection to local recurrence, and overall survival (OS) represented the time from surgical resection to death due to any reason. The clinicopathological data of patients including age, sex, maximum diameter of tumor, degree of differentiation, tumor location, pathological morphology, neoadjuvant immunotherapy, T stage, lymph node metastasis, distant metastasis, and version 9 American Joint Committee on Cancer tumor, node, metastasis staging (AJCC TNM) were collected. The follow-up of survival time was mainly based on inpatient medical records and telephone follow-up. The sample size was calculated according to G*Power software (18).
Bioinformatics analysis
The Gene Expression Omnibus (GEO) is a public functional genomics database (https://www.ncbi.nlm.nih.gov/geo/). Based on the key words “esophageal squamous cell carcinoma” and “human”, we obtained three messenger RNA (mRNA) series from the GEO database. GSE161533, GSE45670, and GSE20347 contained the mRNA expression profiles of cancer and paracancerous tissues of 28, 28, and 17 patients with ESCC, respectively. The differentially expressed genes (DEGs) between cancer and paracancerous tissues were analyzed by GEO2R for each mRNA series. Log fold change (FC) >1 and P <0.05 indicated the existence of a DEG. The results were drawn using R language and a Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) was drawn of the intersecting DEGs.
The online database Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; https://string-db.org/cgi/input.pl) was used for evaluating and predicting protein-protein interaction (PPI) and to conduct Markov clustering (MCL) analysis on DEGs. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses were conducted on each cluster to understand gene function annotation and function enrichment, respectively. Fisher’s exact test was used to analyze the pathways, with a P<0.05 indicating significant enrichment. The top 10 genes in GO and KEGG pathways were visualized using the R language ggplot2 package. The results of the DEG interaction network were downloaded from the STRING database and imported into Cytoscape (version 3.7.2; https://cytoscape.org/) for visualization.
The Cancer Genome Atlas (TCGA) database, one of the largest databases of cancer gene information, was used to analyze the expression of IFIT3 in clinical cancer patients.
Statistical analysis
The software SPSS 22.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis. The differences between groups were compared by Mann-Whitney U test, and the data were expressed by median (first quartile, third quartile); Kaplan-Meier analysis data were represented by median [95% confidence interval (CI)], and Cox proportional hazards model analysis data were represented by hazard ratio (HR) (95% CI). A statistically significant difference was indicated by two-tailed P<0.05.
Results
Differential expression gene analysis
This study used bioinformatics analysis to find potential immunotherapeutic targets and prognostic markers, and the process is shown in Figure 1. According to the standard of logFC >1 and adjusted P<0.05, GEO2R analysis showed that there were “1,107”, “4,637”, and “1,362” DEGs between ESCC and paracancerous tissues in GSE161533 (Figure 2A), GSE45670 (Figure 2B), and GSE20347 (Figure 2C), respectively. A total of 279 DEGs (Figure 2D) were obtained by intersecting the DEGs of the three GEO mRNA series.
Clustering analysis, enrichment analysis, and PPI network construction of DEGs
The MCL algorithm of STRING was used to cluster the DEGs, and the top 10 clusters were obtained by sorting according to the number of clustered genes (Figure 3A). Through GO and KEGG enrichment analysis of each cluster gene, it was found that cluster 4 was mainly enriched in tumor-related pathways (Figure 3B,3C). Therefore, we selected cluster 4 for further research. In order to explore the relationship between genes in cluster 4, our research constructed a PPI network. The results show that the PPI network consists of 14 nodes and 44 lines (Figure 3D), and IFIT3 may be a gene with key regulatory functions.
Expression of IFIT3 in cancer patients
Using the TCGA, we could observe the expression of IFIT3 in a variety of cancers (Figure 4A,4B), which suggested that IFIT3 is highly expressed in EC patients (Figure 4C), especially in stage II patients (Figure 4D).
Verification of the differential expression of IFIT3 in esophageal tissues of clinical patients
Mann-Whitney U test showed that IFIT3 was highly expressed in ESCC and low expressed in paracancerous tissues. The median relative expression level of IFIT3 in 102 cases of ESCC was 2.679 (P25–P75: 0.988–7.333) times, and the result was statistically significant (P<0.0001, Figure 5).
The relationship between the relative expression of IFIT3 and the clinicopathology of ESCC
Mann-Whitney U test showed that the expression of IFIT3 was significantly correlated with the maximum diameter of tumor (P=0.01), the degree of differentiation (P=0.002), neoadjuvant immunotherapy (P=0.03), lymph node metastasis (P=0.005), distant metastasis (P=0.03), and TNM stage (P=0.03, Table 1).
Table 1
| Characteristic | All cases (n=102) | IFIT3 expression P50 (P25–P75) | P value |
|---|---|---|---|
| Age (years) | 0.57 | ||
| ≥60 | 60 | 2.62 (1.09–7.34) | |
| <60 | 42 | 2.71 (0.48–7.51) | |
| Sex | 0.34 | ||
| Male | 84 | 2.77 (1.04–7.42) | |
| Female | 18 | 1.26 (0.76–5.13) | |
| Maximum diameter of tumor | 0.01 | ||
| ≥4 cm | 50 | 3.82 (1.49–8.42) | |
| <4 cm | 52 | 1.54 (0.60–4.00) | |
| Degree of differentiation | 0.002 | ||
| G1–G2 | 54 | 1.53 (0.53–4.49) | |
| G3 | 48 | 3.89 (1.85–9.11) | |
| Tumor location | 0.92 | ||
| Upper | 13 | 2.73 (0.39–9.80) | |
| Middle | 59 | 2.48 (0.99–8.33) | |
| Lower | 30 | 2.94 (1.03–6.08) | |
| Pathological morphology | 0.63 | ||
| Ulcerative type | 49 | 2.70 (1.02–7.08) | |
| Fungating type | 12 | 5.29 (0.76–16.47) | |
| Medullary type | 26 | 2.30 (1.05–4.83) | |
| Constrictive type | 15 | 1.55 (0.34–8.33) | |
| Neoadjuvant immunotherapy | 0.03 | ||
| Yes | 13 | 1.04 (0.32–3.29) | |
| No | 89 | 2.79 (1.05–7.74) | |
| T stage | 0.08 | ||
| T1–2 | 43 | 1.84 (0.55–6.15) | |
| T3–4 | 59 | 3.09 (1.22–7.61) | |
| N stage | 0.005 | ||
| N0 | 44 | 1.29 (0.48–5.24) | |
| N+ | 58 | 3.46 (1.49–8.37) | |
| M stage | 0.03 | ||
| M0 | 94 | 2.37 (0.86–6.91) | |
| M1 | 8 | 7.50 (2.77–30.07) | |
| TNM stage | 0.03 | ||
| I–II | 42 | 1.54 (0.54–5.68) | |
| III–IV | 60 | 3.25 (1.23–8.04) |
ESCC, esophageal squamous cell carcinoma; TNM, tumor, node, metastasis.
The relationship between the relative expression of IFIT3 and the prognosis of patients with ESCC
Kaplan-Meier analysis showed that the median DFS of ESCC patients was 14 months (95% CI: 10.71–17.29, Figure 6A), and the median OS was 32 months (95% CI: 27.14–36.86, Figure 6B). Taking the median of IFIT3 relative expression in 102 patients with ESCC as the critical value, the relative expression of IFIT3 was divided into low level and high level. The median DFS of patients with IFIT3 low level was 18 months (95% CI: 3.63–22.37), the median OS was 41 months (95% CI: 31.11–50.89), the median DFS of patients with IFIT3 high level was 11 months (95% CI: 8.68–13.32), and the median OS was 27 months (95% CI: 2.27–31.73). The median DFS (P=0.003) and median OS (P=0.003) of patients with IFIT3 low level were significantly longer than those of patients with IFIT3 high level (Figure 6C,6D).
Subgroup analysis of the relationship between the relative expression of IFIT3 and the prognosis of patients with ESCC
Subgroup analysis showed that the correlation between the relative expression of IFIT3 and the prognosis of DFS and OS was significant in subgroups of age <60 years, male, G1–2, middle esophagus, ulcerative type, no neoadjuvant immunotherapy, N0, M0, and TNM I–II and maximum diameter of tumor ≥4 cm (Table 2, Figures 7-9).
Table 2
| Characteristic subgroup | Cases | DFS | OS | |||
|---|---|---|---|---|---|---|
| mDFS (95% CI) | P value | mOS (95% CI) | P value | |||
| Age <60 years | 0.001 | 0.008 | ||||
| IFIT3 low level | 21 | 28 (16.69–39.31) | 41 (29.78–52.22) | |||
| IFIT3 high level | 21 | 10 (5.51–14.49) | 28 (23.04–32.96) | |||
| Male | 0.004 | 0.008 | ||||
| IFIT3 low level | 40 | 17 (12.35–21.65) | 36 (28.03–43.97) | |||
| IFIT3 high level | 44 | 10 (8.14–11.86) | 26 (20.88–31.13) | |||
| Maximum diameter of tumor ≥4 cm | 0.047 | 0.09 | ||||
| IFIT3 low level | 18 | 16 (7.68–24.32) | 33 (16.87–49.13) | |||
| IFIT3 high level | 32 | 9 (4.84–13.16) | 23 (17.73–28.27) | |||
| G1–2 | 0.02 | 0.002 | ||||
| IFIT3 low level | 35 | 21 (16.36–25.64) | 41 (32.36–49.64) | |||
| IFIT3 high level | 19 | 11 (8.16–13.84) | 23 (18.73–27.27) | |||
| Middle esophagus | 0.01 | 0.006 | ||||
| IFIT3 low level | 31 | 21 (15.55–26.45) | 37 (29.75–44.25) | |||
| IFIT3 high level | 28 | 11 (6.85–15.15) | 23 (16.52–29.48) | |||
| Ulcerative type | 0.01 | 0.006 | ||||
| IFIT3 low level | 24 | 15 (4.20–25.80) | 41 (27.67–54.33) | |||
| IFIT3 high level | 25 | 11 (8.98–13.02) | 22 (19.55–24.45) | |||
| No neoadjuvant immunotherapy | 0.01 | 0.004 | ||||
| IFIT3 low level | 42 | 17 (12.77–21.23) | 34 (21.42–46.58) | |||
| IFIT3 high level | 47 | 11 (9.10–12.90) | 26 (21.50–30.50) | |||
| N0 | 0.037 | 0.03 | ||||
| IFIT3 low level | 30 | 28 (17.82–38.18) | 47 (36.71–57.29) | |||
| IFIT3 high level | 14 | 11 (8.25–13.75) | 34 (25.03–42.97) | |||
| M0 | 0.002 | 0.004 | ||||
| IFIT3 low level | 49 | 19 (14.44–23.57) | 41 (31.27–50.73) | |||
| IFIT3 high level | 45 | 11 (9.13–12.87) | 28 (21.78–34.22) | |||
| TNM I–II | 0.07 | 0.041 | ||||
| IFIT3 low level | 27 | 30 (16.12–43.88) | No outcome observed | |||
| IFIT3 high level | 15 | 12 (2.15–21.85) | 34 (25.00–43.00) | |||
ESCC, esophageal squamous cell carcinoma; DFS, disease-free survival; OS, overall survival; mDFS, median disease-free survival; mOS, median overall survival; CI, confidence interval; TNM, tumor, node, metastasis.
Analysis of prognosis of ESCC
Cox proportional hazards model univariate analysis showed that the maximum diameter of tumor ≥4 cm (P=0.02), T3–4 stage (P=0.006), N+ stage (P=0.007), TNM III–IV stage (P=0.002), and IFIT3 high level (P=0.004) were all risk factors affecting DFS in patients with ESCC. Meanwhile, male (P=0.042), T3–4 stage (P=0.008), N+ stage (P=0.005), M1 stage (P=0.007), TNM III–IV (P=0.003) stage, and IFIT3 high level (P=0.003) were all risk factors affecting OS in patients with ESCC (Table 3).
Table 3
| Characteristic | DFS | OS | |||
|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | ||
| Age (≥60 vs. <60 years) | 0.84 (0.67–1.05) | 0.13 | 0.82 (0.51–1.32) | 0.41 | |
| Sex (male vs. female) | 1.18 (0.89–1.56) | 0.25 | 1.47 (1.11–2.22) | 0.042 | |
| Maximum diameter of tumor (≥4 vs. <4 cm) | 1.66 (1.08–2.56) | 0.02 | 1.59 (1.00–2.54) | 0.05 | |
| Degree of differentiation (G3 vs. G1–2) | 1.53 (0.99–2.35) | 0.06 | 1.41 (0.89–2.24) | 0.15 | |
| Tumor location | |||||
| Upper | 0.92 (0.60–1.40) | 0.69 | 0.80 (0.50–1.28) | 0.36 | |
| Middle | 1 | 0.25 | 1 | 0.31 | |
| Lower | 1.28 (0.91–1.80) | 0.16 | 1.32 (0.92–1.90) | 0.13 | |
| Pathological morphology | |||||
| Ulcerative type | 1.46 (0.75–2.84) | 0.27 | 1.48 (0.71–3.08) | 0.29 | |
| Fungating type | 1.31 (0.56–3.09) | 0.54 | 0.99 (0.37–2.66) | 0.98 | |
| Medullary type | 1.36 (0.66–2.82) | 0.40 | 1.21 (0.55–2.69) | 0.63 | |
| Constrictive type | 1 | 0.74 | 1 | 0.60 | |
| Neoadjuvant immunotherapy (yes vs. no) | 0.81 (0.57–1.15) | 0.24 | 0.86 (0.59–1.24) | 0.42 | |
| T stage (T3–4 vs. T1–2) | 2.06 (1.31–3.26) | 0.006 | 1.92 (1.18–3.11) | 0.008 | |
| N stage (N+ vs. N0) | 3.09 (1.94–4.93) | 0.007 | 3.25 (1.96–5.39) | 0.005 | |
| M stage (M1 vs. M0) | 2.16 (0.99–4.73) | 0.009 | 2.73 (1.23–6.04) | 0.007 | |
| TNM stage (III–IV vs. I–II) | 3.47 (2.12–5.67) | 0.002 | 3.38 (2.00–5.70) | 0.003 | |
| IFIT3 (high level vs. low level) | 1.91 (1.23–2.96) | 0.004 | 2.00 (1.24–3.22) | 0.003 | |
ESCC, esophageal squamous cell carcinoma; DFS, disease-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval; TNM, tumor, node, metastasis.
According to the results of univariate analysis, sex, maximum diameter of tumor ≥4 cm, T3–4 stage, N+ stage, M1 stage, TNM III–IV stage, and IFIT3 high level are all risk factors. Considering that TNM stage is highly correlated with T stage, N stage, and M stage, in order to eliminate the interference between independent variables, TNM stage was not included in the Cox proportional hazards model multivariate analysis, and the remaining variables were included in the Cox proportional hazards model multivariate analysis. The degree of differentiation was not statistically different in univariate analysis, but as it had been shown to be related to prognosis in clinical and past studies, it was also included in the Cox proportional hazards model multivariate analysis. The results showed that N+ stage was an independent risk factor affecting DFS and OS in patients with ESCC (HR >1, P=0.005, Table 4).
Table 4
| Characteristic | DFS | OS | |||
|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | ||
| Sex (male vs. female) | 1.19 (0.66–2.13) | 0.57 | 1.84 (0.86–3.95) | 0.12 | |
| Maximum diameter of tumor (≥4 vs. <4 cm) | 1.40 (0.89–2.20) | 0.14 | 1.20 (0.74–1.96) | 0.47 | |
| Degree of differentiation (G3 vs. G1–2) | 1.12 (0.69–1.84) | 0.64 | 0.88 (0.52–1.50) | 0.64 | |
| T stage (T3–4 vs. T1–2) | 1.47 (0.88–2.47) | 0.15 | 1.25 (0.72–2.18) | 0.42 | |
| N stage (N+ vs. N0) | 2.13 (1.20–3.79) | 0.01 | 2.34 (1.27–4.32) | 0.005 | |
| M stage (M1 vs. M0) | 1.11 (0.49–2.53) | 0.80 | 1.53 (0.66–3.56) | 0.32 | |
| IFIT3 (high level vs. low level) | 1.23 (0.75–2.01) | 0.42 | 1.37 (0.79–2.37) | 0.26 | |
ESCC, esophageal squamous cell carcinoma; DFS, disease-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval.
Discussion
The treatment of patients with ESCC is still a major challenge. Most patients are in the middle and advanced stage at the time of diagnosis, so they cannot be treated surgically. At present, there are many non-surgical treatment methods for EC available clinically, such as radiotherapy, chemotherapy, and targeted therapy, but due to many limitations, these treatment methods have not achieved the desired effect (19).
According to our research, the GO enrichment analysis of cluster 4 in the DEGs shows that it was highly correlated with the signal pathway of type I interferon (IFN). Some studies have shown that type I IFN activates the intracellular antibacterial program and affects the development of innate and adaptive immune responses (20). This indicates that cluster 4 genes may participate in regulation of the immune microenvironment. There were 14 genes in cluster 4, namely IFI35, IFI6, RSAD2, CXCL10, CXCL11, CXCL13, IFIT3, IFI44, UBL3, ISG15, STAT1, TNFAIP3, FHL1, and SOCS1. Among them, IFI35, IFI6, IFIT3, IFI44, and ISG15 are interferon-stimulated genes (ISGs), and the PPI network showed that IFIT3 was the hub gene. Liu, Zhen, and Wang found that IFIT3 is a regulatory gene for cutaneous squamous cell carcinoma (CSCC), thyroid carcinoma (THCA), and bladder cancer (BLCA) through GEO and TCGA databases, and is highly related to prognosis (21-23). Jiang et al. screened ESCC candidate biomarkers using topological difference analysis based on a gene–gene interaction network. According to this method, IFIT3 may be a biomarker of ESCC (24). The above studies support the results of this study, indicating that IFIT3 as a new target deserves further study.
In the 1980s, IFIT1 was initially identified as an ISG, which encodes the protein with tetrapeptide repeat (TPR) after IFN treatment (25). Subsequently, three other members of the human IFIT gene family were discovered, namely IFIT2 (ISG54), IFIT3 (ISG60) and IFIT5 (ISG58). As an important member of the ISG family, IFITs not only directly interfere with the life cycle of viruses, but can also regulate the TBK1-IKKε-IRF3 signaling pathway, negatively affecting the type I IFN pathway (26). The antiviral effect of IFIT is significant among a variety of viruses, including influenza A virus (IAV), cytomegalovirus (CMV), and hantavirus (HTNV) (27).
At present, more and more studies have found that the IFIT family not only has antiviral effects, but also participates in the development of tumors, especially IFIT2 and IFIT3. For example, Su et al. found that the deletion of IFIT2 in human lung cancer cell lines A549, H1975, and SK-MES-1 significantly improved the viability, migration, and invasion of cells (28). Lai et al. found that oral squamous cell carcinoma (OSCC) patients with low IFIT2 expression level (IFIT2 <50%) had higher distant metastasis rate and poor prognosis, and that IFIT2 deletion activated the aPKC pathway, thus inducing epithelial-mesenchymal transition (EMT), cell migration, and invasion (29). Follow-up studies also found that IFIT2 deficiency led to tumor necrosis factor (TNF)-α upregulation, leading to angiogenesis and metastasis of OSCC cells (30). At present, there is no report on the related role of IFIT3 in ESCC. However, as IFIT2 is of the same family as IFIT3, some studies have confirmed that programmed cell death ligand 1 (PD-L1) mediates EMT in human EC through the STAT1/IFIT2 signal pathway (31). The invasion and metastasis of ESCC are closely related to EMT (32). The whole process involves a large number of growth factors, transcripts, signal pathways, and microRNAs (miRNAs). IFIT3 and IFIT2 have similar characteristics in structure and function, which also suggests that IFIT3 may also be the downstream gene of programmed cell death 1 (PD-1)/PD-L1 immunotherapy and play a role in the tumor immune microenvironment.
This study was specific to IFIT3. Among the study population, 82% were male and 59% were over 60 years old. Relevant research shows that among those with ESCC, there is a predominance of elderly (age ≥60 years) and male patients, indicating that the cases included in this study conform to the population distribution of ESCC in the real world (33). Mann-Whitney U test and Kaplan-Meier survival analysis showed that the expression of IFIT3 was correlated with T stage, lymph node metastasis, distant metastasis, OS, and DFS in patients with ESCC. Pidugu et al. also found in a clinical study that increased IFIT3 expression was significantly positively correlated with advanced T stage, lymph node metastasis, peripheral nerve invasion, lymphatic invasion, and poor OS rate in patients with OSCC (34). Zhang et al. found that the high expression of IFIT3 is independently related to the shortened survival period of pancreatic cancer patients, which can be used as a prognostic indicator (35). Other studies have shown that IFIT3 silencing can reduce the expression of interleukin (IL)-17 and IL-1β, and decreases the migration ability of liver cancer cells (36). Based on the existing studies, IFIT3 can promote the migration of various tumor cells, is highly correlated with the lymph node metastasis of patients in clinical practice, and can affect the prognosis of patients. This study confirmed the role of IFIT3 in ESCC and enriched our understanding of the tumor types affected by IFIT3.
In terms of subgroup analysis, there was no statistical difference detected between IFIT3 and prognosis in women and neoadjuvant immunotherapy subgroups. The reason may be that the subgroup sample was small and the results bias was likely to be large. The relative expression of IFIT3 in age <60 years, G1–2, N0, M0, and TNM I–II subgroups has a significant correlation with the prognosis of ESCC, and these patients are often in a mild stage, suggesting that IFIT3 is more likely to be used as a prognostic marker in these patients, whereas for patients in a severe stage, there are too many factors affecting the prognosis, and the predictive role of IFIT3 is limited. IFIT3 has a good prognostic role for patients with ulcerative pathomorphology, and may be more involved in the pathogenesis of ulcerative ESCC. At present, there is a lack of relevant research data. In addition, the subgroup of lower EC did not attain ideal results, which may be due to its proximity to the gastroesophageal junction and also the junction of squamous cell carcinoma and adenocarcinoma. It is not ruled out that some adenocarcinoma components in ESCC tissue affect the predictive effect of IFIT3 on prognosis. Cox multivariate analysis showed that lymph node metastasis was the only independent prognostic factor. Unfortunately, IFIT3 did not produce statistically significant results. The reason may be that the expression of IFIT3 was too closely related to lymph node metastasis, and the influence between variables made it insufficient to become an independent predictor.
This study had some limitations, and there is a lack of functional experimental research of IFIT3; we plan to address these issues in the future to better demonstrate our views.
Conclusions
This study suggests that IFIT3 may be a potential central gene during the development of ESCC. IFIT3 was found to be differentially expressed in EC and paracancerous tissues, and the relative expression was related to clinical pathology and prognosis (DFS and OS). IFIT3 can be used as a potential biomarker for patient risk stratification and local regional metastasis in ESCC. In subgroups with age <60 years, male, G1–2, moderate ESCC, ulcerative type, no neoadjuvant immunotherapy, N0, M0, TNM I–II, and tumor maximum diameter ≥4 cm, the correlation between IFIT3 and prognosis is more significant.
Based on the current research, IFIT3 is highly correlated with the prognosis of ESCC patients, which may promote the occurrence and development of ESCC. The prognosis of ESCC patients is poor, yet as more research is conducted to explore the mechanism of IFIT3 affecting ESCC, IFIT3 may become a target for ESCC treatment to bring better prognosis to clinical patients.
Acknowledgments
We sincerely thank the public databases mentioned in this study and the patients participating in the study.
Funding: This study was funded by
Footnote
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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-233/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). The study was approved by General Hospital of Southern Theater Command, PLA (No. 2017-532-18) and informed consent was obtained from all individual participants.
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/.
References
- Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
- Cook MB, Chow WH, Devesa SS. Oesophageal cancer incidence in the United States by race, sex, and histologic type, 1977-2005. Br J Cancer 2009;101:855-9. [Crossref] [PubMed]
- Edgren G, Adami HO, Weiderpass E, et al. A global assessment of the oesophageal adenocarcinoma epidemic. Gut 2013;62:1406-14. [Crossref] [PubMed]
- Lin Y, Totsuka Y, He Y, et al. Epidemiology of esophageal cancer in Japan and China. J Epidemiol 2013;23:233-42. [Crossref] [PubMed]
- Kamangar F, Dores GM, Anderson WF. Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol 2006;24:2137-50. [Crossref] [PubMed]
- Short MW, Burgers KG, Fry VT. Esophageal Cancer. Am Fam Physician 2017;95:22-8. [PubMed]
- Fokas E, Weiss C, Rödel C. The role of radiotherapy in the multimodal management of esophageal cancer. Dig Dis 2013;31:30-7. [Crossref] [PubMed]
- Assoun S, Lemiale V, Azoulay E. Molecular targeted therapy-related life-threatening toxicity in patients with malignancies. A systematic review of published cases. Intensive Care Med 2019;45:988-97. [Crossref] [PubMed]
- Lordick F, Hölscher AH, Haustermans K, et al. Multimodal treatment of esophageal cancer. Langenbecks Arch Surg 2013;398:177-87. [Crossref] [PubMed]
- Takahashi K, Osaka Y, Ota Y, et al. Phase II Study of Docetaxel, Cisplatin, and 5-Fluorouracil Chemoradiotherapy for Unresectable Esophageal Cancer. Anticancer Res 2020;40:2827-32. [Crossref] [PubMed]
- Quail DF, Joyce JA. Molecular Pathways: Deciphering Mechanisms of Resistance to Macrophage-Targeted Therapies. Clin Cancer Res 2017;23:876-84. [Crossref] [PubMed]
- Zhang L, Ma J, Han Y, et al. Targeted therapy in esophageal cancer. Expert Rev Gastroenterol Hepatol 2016;10:595-604. [Crossref] [PubMed]
- Cunningham D, Stenning SP, Smyth EC, et al. Peri-operative chemotherapy with or without bevacizumab in operable oesophagogastric adenocarcinoma (UK Medical Research Council ST03): primary analysis results of a multicentre, open-label, randomised phase 2-3 trial. Lancet Oncol 2017;18:357-70. [Crossref] [PubMed]
- Guo XF, Li SS, Zhu XF, et al. Lapatinib in combination with paclitaxel plays synergistic antitumor effects on esophageal squamous cancer. Cancer Chemother Pharmacol 2018;82:383-94. [Crossref] [PubMed]
- Hecht JR, Bang YJ, Qin SK, et al. Lapatinib in Combination With Capecitabine Plus Oxaliplatin in Human Epidermal Growth Factor Receptor 2-Positive Advanced or Metastatic Gastric, Esophageal, or Gastroesophageal Adenocarcinoma: tRIO-013/LOGiC--A Randomized Phase III Trial. J Clin Oncol 2016;34:443-51. [Crossref] [PubMed]
- Joshi SS, Maron SB, Catenacci DV. Pembrolizumab for treatment of advanced gastric and gastroesophageal junction adenocarcinoma. Future Oncol 2018;14:417-30. [Crossref] [PubMed]
- Pietrantonio F, Caporale M, Morano F, et al. HER2 loss in HER2-positive gastric or gastroesophageal cancer after trastuzumab therapy: Implication for further clinical research. Int J Cancer 2016;139:2859-64. [Crossref] [PubMed]
- Faul F, Erdfelder E, Lang AG, et al. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007;39:175-91. [Crossref] [PubMed]
- Rogers JE, Sewastjanow-Silva M, Waters RE, et al. Esophageal cancer: emerging therapeutics. Expert Opin Ther Targets 2022;26:107-17. [Crossref] [PubMed]
- Ivashkiv LB, Donlin LT. Regulation of type I interferon responses. Nat Rev Immunol 2014;14:36-49. [Crossref] [PubMed]
- Liu H, Chen D, Liu P, et al. Secondary analysis of existing microarray data reveals potential gene drivers of cutaneous squamous cell carcinoma. J Cell Physiol 2019;234:15270-8. [Crossref] [PubMed]
- Zhen J, Song Z, Su W, et al. Integrated analysis of RNA-binding proteins in thyroid cancer. PLoS One 2021;16:e0247836. [Crossref] [PubMed]
- Wang Z, Tu L, Chen M, et al. Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer. BMC Cancer 2021;21:692. [Crossref] [PubMed]
- Jiang S, Zhang Q, Su Y, et al. Network-Based Differential Analysis to Identify Molecular Features of Tumorigenesis for Esophageal Squamous Carcinoma. Molecules 2018;23:88. [Crossref] [PubMed]
- Kusari J, Roy P. Molecular and genetic comparisons of two serotypes of epizootic hemorrhagic disease of deer virus. Am J Vet Res 1986;47:1713-7. [PubMed]
- Zhang N, Shi H, Yan M, et al. IFIT5 Negatively Regulates the Type I IFN Pathway by Disrupting TBK1-IKKε-IRF3 Signalosome and Degrading IRF3 and IKKε. J Immunol 2021;206:2184-97. [Crossref] [PubMed]
- Xu J, Hao X, Zhan R, et al. Effect of Lipoprotein(a) on Stroke Recurrence Attenuates at Low LDL-C (Low-Density Lipoprotein) and Inflammation Levels. Stroke 2022;53:2504-11. [Crossref] [PubMed]
- Su W, Xiao W, Chen L, et al. Decreased IFIT2 Expression In Human Non-Small-Cell Lung Cancer Tissues Is Associated With Cancer Progression And Poor Survival Of The Patients. Onco Targets Ther 2019;12:8139-49. [Crossref] [PubMed]
- Lai KC, Liu CJ, Chang KW, et al. Depleting IFIT2 mediates atypical PKC signaling to enhance the migration and metastatic activity of oral squamous cell carcinoma cells. Oncogene 2013;32:3686-97. [Crossref] [PubMed]
- Lai KC, Liu CJ, Lin TJ, et al. Blocking TNF-α inhibits angiogenesis and growth of IFIT2-depleted metastatic oral squamous cell carcinoma cells. Cancer Lett 2016;370:207-15. [Crossref] [PubMed]
- Chen J, Liu Y, Zhu Y, et al. STAT1/IFIT2 signaling pathway is involved in PD-L1-mediated epithelial-to-mesenchymal transition in human esophageal cancer. Clin Transl Oncol 2022;24:927-40. [Crossref] [PubMed]
- Liu J, Chen L, Deng H, et al. Epithelial-to-mesenchymal transition in human esophageal cancer associates with tumor progression and patient's survival. Int J Clin Exp Pathol 2014;7:6943-9. [PubMed]
- Lagergren J, Smyth E, Cunningham D, et al. Oesophageal cancer. Lancet 2017;390:2383-96. [Crossref] [PubMed]
- Pidugu VK, Wu MM, Yen AH, et al. IFIT1 and IFIT3 promote oral squamous cell carcinoma metastasis and contribute to the anti-tumor effect of gefitinib via enhancing p-EGFR recycling. Oncogene 2019;38:3232-47. [Crossref] [PubMed]
- Zhang Y, Molavi O, Su M, et al. The clinical and biological significance of STAT1 in esophageal squamous cell carcinoma. BMC Cancer 2014;14:791. [Crossref] [PubMed]
- Liu G, Sun J, Yang ZF, et al. Cancer-associated fibroblast-derived CXCL11 modulates hepatocellular carcinoma cell migration and tumor metastasis through the circUBAP2/miR-4756/IFIT1/3 axis. Cell Death Dis 2021;12:260. [Crossref] [PubMed]
