Translation element EIF4A1 is a potential divergent immune biomarker between colon cancer and rectal cancer

doi:10.21203/rs.3.rs-4821941/v1

Download PDF

Article

Translation element EIF4A1 is a potential divergent immune biomarker between colon cancer and rectal cancer

https://doi.org/10.21203/rs.3.rs-4821941/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Colorectal cancer is among the most common malignant tumors globally, and the distinct treatment requirements for colon and rectal cancers underscore the necessity to identify specific molecules for each type. Although EIF4A1 plays a crucial role in translational initiation, its therapeutic potential remains unclear. This study utilized the TCGA database to reveal that EIF4A1 expression is upregulated in both cancer types and is associated with poorer survival outcomes. KEGG and GO analyses indicate that EIF4A1 is primarily associated with the extracellular trap network of neutrophils in colon cancer. Additionally, a strong positive correlation with neutrophil infiltration is observed in both cancers. Notably, EIF4A1 exhibits varying immune infiltration patterns, showing a strong correlation with CD8+ T cells in colon cancer and a negative correlation with CD4+ T cells in rectal cancer. In colon cancer, EIF4A1 shows a strong positive correlation with PD-1, PD-L1, CTLA4, and LAG-3, contrasted by a weaker correlation in rectal cancer. Elevated EIF4A1 expression is potentially linked to immune checkpoints that facilitate immune escape, thereby possibly accelerating cancer progression. This implies varying immunotherapy strategies for colon and rectal cancers.

Biological sciences/Cancer/Gastrointestinal cancer

Biological sciences/Cancer/Tumour immunology

Translational Control

EIF4A1

Colon Cancer

Tumor Immunity

Differences in Tumors

Colorectal cancer (CRC) is one of the most common cancers and has one of the highest mortality rates. Its incidence has been steadily increasing in China, making it the third most prevalent cancer worldwide[1]. Colorectal cancer is subdivided into colon and rectal cancers, with treatment principles often differing in clinical practice based on the disease's location. Treatment for both colon and rectal cancers includes surgery, chemotherapy, and radiotherapy; however, specific surgical methods and treatment strategies vary with the tumor's location and stage. Surgery for rectal cancer can be particularly complex due to its anatomical specifics. Focusing on the biological and clinical differences and similarities between rectal and colon cancers is critical, as these aspects significantly affect current cancer staging and treatment, and may influence the design of future targeted drug trials[2, 3]. As society advances, an increasing number of risk factors for colorectal cancer are being identified. Notably, patients, including those with colon or rectal cancer, are increasingly younger at diagnosis, often at advanced stages. This trend significantly worsens their prognosis, treatment outcomes, and quality of life. Therefore, there is a critical need for early diagnosis and prognostic assessment in clinical settings, as well as for the development of novel diagnostic and prognostic markers to distinguish between colon and rectal cancers[4, 5].

Protein synthesis is frequently dysregulated in cancer; moreover, the translation of mRNA into proteins is a highly complex and crucial process[6]. mRNA translation includes the stages of initiation, elongation, and termination. Cells can rapidly adjust their proteomes to dynamic environments through the regulation of these stages[7]. A growing body of research indicates that translational dysregulation of mRNAs may be linked to cancer development, and regulatory elements at the translational level play a critical role[8–10]. Dysregulated translation initiation is widely associated with cancer initiation and progression, and the eukaryotic transcription initiation factor EIF4A1, as an important translation initiation regulatory element, is involved in the homeostatic regulation of translation initiation, which plays a crucial role in the translation initiation process by unraveling the secondary structure of the mRNAs upstream of the start codon through its own unraveling action, a mechanism that promotes the efficient recruitment of ribosomes to downstream gene sequences. This mechanism promotes the efficient recruitment of ribosomes to downstream gene sequences, thereby activating the dynamic process of protein synthesis[11–13]. The regulation of mRNA translation is frequently dysregulated in various cancers, including colorectal cancer. Targeting and inhibiting the translation of specific oncogenic mRNAs represents a novel anticancer therapeutic approach.

Eukaryotic initiation factor 4A-I (EIF4A1) is a member of the EIF4A family and plays a critical role in the initiation phase of translation by regulating the 5′ untranslated region (5′ UTR) of mRNA. It functions as an RNA helicase, unwinding RNA secondary structures in an ATP-dependent manner[14, 15]. EIF4A1, the enzymatic catalytic core of the EIF4F complex, is essential for translation initiation and plays a key role in reprogramming protein synthesis in cancer, thus making it a promising target for cancer therapy[16]. Numerous studies have shown that the expression of EIF4A1 is associated with epithelial-mesenchymal transition (EMT) in various cancers. EIF4A1 plays a crucial role in the EMT process of gastric cancer. In pancreatic cancer, EIF4A1 deletion impairs EMT and the metastatic ability of pancreatic cancer cells. Additionally, EIF4A1 can serve as a biomarker for the early diagnosis of tumors, histological classification, and clinical grading/staging, offering new tools for precision and individualized cancer treatment[14, 17–19].

In this study, bioinformatics analysis was performed using the TCGA database to examine the differential landscape of EIF4A1 in colon and rectal cancers. The analysis of EIF4A1 expression in relation to clinical data revealed upregulation in both colon and rectal cancers. This upregulation is more significant in colon cancer, suggesting greater value and specificity for predicting survival prognosis and serving as a diagnostic marker compared to rectal cancer. Functional enrichment analysis indicated that colon cancer is associated with DNA and protein interactions. Additionally, pathway enrichment analysis revealed significant connections with Neutrophil Extracellular Traps (NETs) and epigenetic modifications, such as methylation and acetylation. These findings suggest roles for EIF4A1 in cancer progression, immune evasion, and immune response mechanisms. Rectal cancer is primarily characterized by enrichment in pathways related to immune response recognition, immune and inflammatory responses, cellular stress, and energy metabolism. Notably, the Complement System and the MAPK signaling pathway play crucial roles in immune surveillance and immune response. Inhibition of these pathways may contribute to the development of cancer, highlighting their potential as therapeutic targets. Immunoassays revealed distinct immune infiltration patterns for EIF4A1 in colon and rectal cancers, with varying degrees of immune cell infiltration indicative of different immunotherapeutic responses. Correlation analysis between EIF4A1 and immune checkpoints indicated more complex interactions in colon cancer compared to rectal cancer, highlighting the potential for tailored immunotherapeutic strategies in colorectal cancers.

Screening for differential prognostic genes:

A total of 1,549 differential genes in HCT116 were obtained from the RBP database provided in the literature[22]. A total of 2,929 significant differential genes were identified by setting the parameters log2 fold change (|logFC|) > 1 and p.adj < 0.05 in the TCGA-COADREAD dataset. A list of prognostic genes related to colorectal cancer was identified by univariate Cox regression based on the TCGA-COADREAD dataset. A total of 631 prognostic genes were obtained by setting the parameters HR > 1 and p.adj < 0.05. The RBP library, TCGA-COADREAD differential genes, and prognostic genes were used to create Venn diagram intersections. The results showed that three related genes intersected (Fig. 1a): TSFM, EIF4A1, and ARL6IP4. Visualization of these differential genes was conducted using volcano plot analysis in the TCGA-COADREAD database (Fig. 1b). Using TCGA-COADREAD, the three differential genes were analyzed for co-expression with clinical Pathologic T stage, Pathologic N stage, and Pathologic M stage. The results indicated that TSFM, EIF4A1, and ARL6IP4 showed higher expression in the Pathologic T3 stage, were primarily expressed in the Pathologic N0 stage, and predominantly expressed in the Pathologic M0 stage (Fig. 1c). We focused on the eukaryotic initiation factor EIF4A1, which is associated with translation initiation, and continued to study it as a key molecule in our research.

Expression landscape of EIF4A1

The mRNA and protein levels of EIF4A1 are widely expressed across various organs and tissues (Fig. 2a), as shown in Fig. 2a. This analysis reveals that EIF4A1 mRNA is predominantly found in the proximal digestive tract, kidney, urinary bladder, connective and soft tissue, as well as bone marrow and lymphoid tissues. According to the HPA database, high protein presence was noted primarily in the stomach, colon, rectum, placenta, tonsil, and bone marrow (Fig. 2b). IHC-stained sections of EIF4A1 in normal colorectal tissues versus colorectal cancer, as per the HPA database (Fig. 2d-g), demonstrated that normal colon tissues exhibited higher expression, appearing darker than those of the rectum. Additionally, colorectal cancer tissues showed a higher expression level than normal tissues. Analysis from the Ualcan database indicates that EIF4A1 protein expression is significantly higher in cancerous tissues (Fig. 2h). Protein expression and localization data for EIF4A1 in CACO-2 cells, obtained from the HPA database, reveal that EIF4A1 is primarily localized in the cytoplasm (Fig. 2i).

Analysis using the Timer 2.0 database revealed (see Fig. 3a) that EIF4A1 expression was considerably broader across various human cancers, including CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, STAD, UCEC (P < 0.001 for each), BRCA (P < 0.01), and READ (P < 0.05), generally displaying higher expression levels compared to normal tissues. Conversely, its expression was notably lower in KICH (P < 0.001) and PCPG (P < 0.05) relative to normal tissues. Pairwise tissue analysis based on TCGA database 11123 revealed (Fig. 3b): EIF4A1 is highly expressed in COAD (P < 0.001), CHOL (P < 0.05), KIRC (P < 0.001), and READ (P < 0.01), while it shows low expression in BRCA (P < 0.001) and THCA (P < 0.05). Based on the TCGA-COADREAD data of 698 samples, 51 cases of paraneoplastic were analyzed for differences, and the results suggested (Fig. 3c) that EIF4A1 showed high expression in COADREAD with significance P < 0.001, which was downloaded from the TCGA database ( https://portal.gdc.cancer.gov ) and organized RNAseq data from the STAR process of TCGA-COAD and TCGA-READ projects and extracted the data in TPM format, extracted the corresponding numbered paired paraneoplastic and cancer samples of TCGA-COADREAD dataset to perform the paired samples t-test, the results suggested that EIF4A1 was significantly higher in cancer tissues of COADREAD than in paraneoplastic tissues (Fig. 3c), with significant P < 0.001 and P < 0.001. The TCGA-COAD,TCGA-READ datasets were downloaded and organized from the TCGA database, and the data were log2(value + 1) processed, and cancer and paracancer difference analysis and paired analysis were performed in the same way, respectively. The results of the difference and paired analysis showed that the expression of EIF4A1 in the COAD and READ tissues was significantly higher than that in the paracancerous tissues and the significance of the EIF4A1 in the The significance of P < 0.001 in COAD was more statistically significant than that of P < 0.01 in READ, and these results may suggest that the differential value of EIF4A1 expression in colon tissues was greater than that in rectum (Fig. 3d-e).

Clinical diagnosis and prognostic value evaluation of EIF4A1

We conducted Cox proportional hazards regression analysis utilizing the 'survival' package to fit the survival models. Results were visualized employing the 'survivminer' and 'ggplot2' packages. Survival analyses encompassed the COADREAD, COAD, and READ datasets, evaluating three parameters: Overall Survival (OS), Disease-Specific Survival (DSS), and Progress-Free Interval (PFI). The overall survival results showed that the survival prognosis of low expression of EIF4A1 in COADREAD (P = 0.045) and COAD (P = 0.007) was better (Fig. 4b-c), and had statistical significance, while high expression of EIF4A1 in READ (P = 0.041) had better prognosis (Fig. 4d). Disease specific surviva1l analysis showed that high expression of EIF4A1 in COADREAD (P = 0.024) and COAD (P = 0.012) had a poor survival prognosis (Fig. 4f-g), but there was no statistical significance in READ (P = 0.778) (Fig. 4h). Progress-free interval analyses demonstrated (Fig. 4j-l) that high EIF4A1 expression is associated with poorer survival outcomes in COADREAD (P < 0.001) and COAD (P = 0.001). However, in READ, these differences were not statistically significant (P = 0.256). To synthesize and assess the prognostic significance of EIF4A1, we employed 'ggplot2' to visualize the Kaplan-Meier estimates for OS, DSS, and PFI, alongside Cox regression outcomes, including hazard ratios (HR) and P values (Fig. 4a, e, i).

Utilizing clinical data from the TCGA-COADREAD, TCGA-COAD, and TCGA-READ datasets, ROC analysis was conducted via the 'pROC' package, with the ROC curves subsequently visualized using 'ggplot2'. The analysis results show that (Fig. 4o-m), AUC (Area Under the Curve) of EIF4A1 in COADREAD: 0.827, AUC: 0.836 in COAD, AUC: 0.797 in READ. The results suggest that EIF4A1 has clinical diagnostic value and diagnostic potential in COADREAD, COAD, and READ. Compared with READ, COAD has higher diagnostic value, and EIF4A1 has more potential in the diagnosis of colon.

To elucidate the impact of EIF4A1 expression and clinicopathological factors on survival, we explored survival disparities between colon and rectal cancers through both univariate and multivariate Cox regression analyses. In the univariate analysis of colon cancer, pathological T stage (especially T4), pathological N stage (especially N1), advanced pathological stage, age, and EIF4A1 expression were significantly associated with the mortality risk of colon cancer patients.In rectal cancer, higher mortality risks were correlated with the T4 stage, N2 stage, and older age, as documented in Tables 1 and 2, with P-values < 0.05 confirming statistical significance. Multivariate analysis in colon cancer indicated that patients at T4 stage, with advanced pathological stages, and of older age exhibited persistently increased mortality risks even after adjusting for confounding variables.Similarly, in rectal cancer, patients at N2 stage and those of older age demonstrated significant elevations in mortality risk upon adjustment for other variables. Pathological staging emerged as a critical prognostic indicator in colon cancer, consistently associated with increased mortality risk in univariate analyses. In rectal cancer, some variables are missing, and the number of variables is insufficient after including the pathological stage in the multivariate analysis, hence the exclusion of this variable from the analysis. Age exceeding 65 years was consistently associated with an increased mortality risk in both univariate and multivariate analyses. Notable differences between the cancers included the significance of the T3 stage in colon cancer in univariate analysis, which did not persist in multivariate analysis. In contrast, the T3 stage in rectal cancer demonstrated no significant difference in either analysis.For colon cancer, the hazard ratio for the N1 stage decreased and lost significance in the multivariate analysis, whereas the N2 stage retained its significance.In rectal cancer, both N1 and N2 stages were significant predictors in univariate analysis; however, only the N2 stage maintained its significance in the multivariate analysis. The M1 stage was a significant factor in both univariate and multivariate analyses for colon cancer, but it was significant only in the univariate analysis for rectal cancer.Elevated EIF4A1 expression was linked with an increased risk of mortality in the univariate analysis of colon cancer, though this association did not hold in the multivariate analysis. Conversely, in rectal cancer, high EIF4A1 expression correlated with a reduced mortality risk in univariate analysis, a trend that did not reach significance in the multivariate analysis (Table 1, 2).

EIF4A1 transcription of expression and the clinical correlation analysis

We analyzed the correlation between EIF4A1 mRNA and protein expression and clinical outcomes through UALCAN database, and the results are as follows.

Staging: the transcript level of EIF4A1 is generally higher in COAD than in normal tissues. Its mRNA expression was significantly increased in all cancer stages (P < 1e-12 to 1.27e-08), and the differences were statistically significant. In different stages of COAD, the transcript level of EIF4A1 was significantly different between stage I and stage III (P < 4.858400e-2), stage I and stage IV (P < 3.003000e-03), and stage II and stage IV (P < 1.476810e-03), with statistical significance (Supplementary Fig. 1a). Compared with COAD, the expression of EIF4A1 in READ was higher in stages I and IV, while the expression difference in stages II and III was not statistically significant. In addition, there was a significant difference in the expression of EIF4A1 between stages I and II of READ (P < 1.479030e-03), and between stages I and III of READ (P < 2.148000e-02), with statistical significance (Supplementary Fig. 1b). In staging, the mRNA expression of EIF4A1 was mainly concentrated in stage1-2 stage, while READ was mainly concentrated in stage1 stage (Supplementary Fig. 1a, b).

Age analysis in COAD demonstrated that EIF4A1 expression was significantly elevated across all age groups compared to the normal group, particularly notable in the 41–60 (P < 5.55e-16), 61–80 (P < 1e-12), and 81–100 year olds (P < 8.89e-11) (Supplementary Fig. 1c). Conversely, in READ, EIF4A1 expression was significantly elevated in the 21–40 (P < 0.000956) and 41–60 year olds (P < 0.008163) compared to the normal group. Notably, expression in the 41–60 and 61–80 age groups was significantly higher than in the 81–100 year olds, all with statistical significance. However, no significant differences were observed in EIF4A1 expression between the 61–80 and 81–100 year old groups compared to the normal group (Supplementary Fig. 1d). Compared to READ (Supplementary Fig. 1c, d), elevated EIF4A1 expression in COAD predominantly occurred in the 81–100 age group, whereas in READ, it was primarily observed in the 21–40 age range.

In COAD, EIF4A1 expression was significantly elevated in both the adenocarcinoma and mucinous adenocarcinoma subtypes compared to normal tissues. Furthermore, EIF4A1 expression in mucinous adenocarcinoma was significantly higher than in adenocarcinoma (P = 0.04076), indicative of potential molecular differences between these subtypes (Supplementary Fig. 1e). Conversely, in READ, EIF4A1 expression in mucinous adenocarcinoma exceeded that in normal tissues, with statistical significance (P = 0.009957) (Supplementary Fig. 1f). In both COAD and READ, EIF4A1 expression was notably high in mucinous adenocarcinoma subtypes (Supplementary Fig. 1e, f).

Lymph node metastasis analysis revealed that EIF4A1 expression across varying states (N0, N1, N2) in COAD was markedly higher than in normal tissues, demonstrating significant statistical differences (P values ranging from 1.62e-12 to 1.00e-9) (Supplementary Fig. 1g). No significant statistical correlation was found between EIF4A1 expression and lymph node metastasis states in READ (Supplementary Fig. 1h). In COAD, the highest levels of EIF4A1 expression were predominantly observed in the N0 phase, whereas in READ, elevated EIF4A1 expression was primarily noted in the N1 phase (Supplementary Fig. 1g, h).

Gender analysis in COAD indicated that EIF4A1 expression was significantly elevated in both male and female patients compared to normal tissues, with P-values of < 1e-12 and 1.62e-12, respectively. However, the differences between genders were not statistically significant (P = 0.22666) (Supplementary Fig. 1i). In READ, EIF4A1 expression in male patients was notably higher than in normal tissues (P = 0.01266), whereas in female patients, this elevation did not reach statistical significance (P = 0.09802) (Supplementary Fig. 1j). In READ, the incidence of cancer among male patients may be higher than in female patients.

Regarding weight in COAD, EIF4A1 expression was significantly up-regulated across various weight categories: Normal Weight (P = 3.64e-12), Extreme Weight (P = 1.62e-12), Obese (P = 3.56e-10), and Extremely Obese (P = 0.007) (Supplementary Fig. 1k). In READ, EIF4A1 expression was up-regulated in Normal Weight, Extreme Weight, and Obese categories, with only Normal Weight reaching statistical significance (P = 0.00918). Additionally, EIF4A1 expression in Normal Weight was notably higher than in Extreme Weight (P < 0.032) (Supplementary Fig. 1l),Notably, in both COAD and READ, the highest EIF4A1 expression was observed in the Normal Weight category, with a general trend of decreasing expression as body weight increased (Supplementary Fig. 1k, l).

The correlation between EIF4A1 protein expression and clinical analysis

Given that the protein database exclusively encompasses clinical data for Colon cancer, our analysis was confined to examining the relationships between EIF4A1 protein expression levels and various clinical parameters in Colon cancer, including cancer stages, patient age, tumor history, chromatin modifier status, patient gender, and weight.

In Colon cancer, EIF4A1 protein expression demonstrated marked variability across different pathological stages.Compared to the normal cohort, EIF4A1 expression was substantially elevated in all cancer stages, particularly peaking in stages II (P = 8.77e-12) and III (P = 1.99e-12), with these findings reaching significant statistical levels (Supplementary Fig. 2a). However, there was no significant difference in EIF4A1 expression between cancer stages. This suggests that once colon cancer develops, EIF4A1 expression remains consistently high across advanced stages, indicating minimal correlation with cancer progression stages.

Regarding patient age, EIF4A1 expression was significantly up-regulated across all age groups compared to the normal cohort, with the most pronounced increase observed in the 61–80 year old group (P = 9.92e-16) (Supplementary Fig. 2b). These findings indicate that variations in EIF4A1 protein expression are potentially age-related during cancer progression, particularly among middle-aged and elderly individuals.

Tumor History: Analysis revealed that EIF4A1 protein expression was significantly elevated in both mucinous and non-mucinous tumors compared to normal samples (Supplementary Fig. 2c). Notably, the increase was more pronounced in non-mucinous tumors (P = 2.19e-21), although the differences between mucinous and non-mucinous tumors were not statistically significant (P = 0.527). This suggests that elevated EIF4A1 expression is a characteristic common among cancer patients, particularly in non-mucinous tumors.

Chromatin Modifier Status: The results indicated that EIF4A1 protein expression was significantly up-regulated in samples with modified chromatin (Supplementary Fig. 2d), with a statistically significant increase compared to normal samples (P = 2.82e-22),suggesting a significant correlation between elevated EIF4A1 expression and chromatin modification status, which may facilitate the up-regulation of EIF4A1 protein expression.

Patient Gender: Correlation analysis revealed that EIF4A1 protein expression was significantly higher in both males (P = 6.50e-13) and females (P = 1.75e-15) compared to the normal group (Supplementary Fig. 2e). However, there was no significant difference in EIF4A1 expression between males and females (P = 0.36).

Patient Weight: Compared to the normal group, EIF4A1 protein expression was significantly elevated in cancer patients across all weight categories: Normal Weight (P = 2.55e-11), Overweight (P = 3.32e-07), Obese (P = 4.30e-09), and Extremely Obese (P = 0.027) (Supplementary Fig. 2f). Specifically, EIF4A1 protein expression in Normal Weight was markedly higher than in other weight categories (Supplementary Fig. 2f). Compared to Overweight, EIF4A1 protein expression was significantly higher in the Normal Weight category (P = 0.0148), demonstrating statistical significance.Body weight appears to be a contributing factor to variations in EIF4A1 protein expression levels.

EIF4A1 interaction analysis

The EIF4A1 interaction network was constructed utilizing the GeneMANIA database (Fig. 5a). Eleven genes closely associated with EIF4A1 were identified via the STRING database, and the PPI network was constructed at a specified threshold, subsequently visualized with Cytoscape software (Fig. 5b). Key genes in this network include EIF4G1, EIF4A1, EIF4E, EIF4B, EIF3B, EIF1, EIF4G2, EIF4H, EIF4G3, PABPC1, and PDCD4. We intersected the molecular datasets from GeneMANIA and STRING to identify the most relevant gene (Fig. 5c). Intersected interaction molecules were visualized and analyzed for expression in COADREAD using the ComplexHeatmap package (Fig. 5d). Intriguingly, PDCD4 exhibited significantly elevated expression in paracancerous tissues.

EIF4A1 enrichment of function analysis

The DESeq2 package was employed to analyze differential expression between TCGA-COAD, TCGA-READ datasets, and the original counts matrix. EIF4A1 expression in COAD and READ was categorized into high and low for differential expression analysis. Data analysis was conducted using ggplot2 (version 3.3.6), with set thresholds of logFC: 1 and P-value: <0.05. Results indicated that in COAD, 1038 genes were up-regulated and 13 were down-regulated; in READ, 117 genes were up-regulated and 150 were down-regulated. These findings were visualized in a volcano plot (Fig. 6a-b). The differentially up-regulated genes from COAD and READ were sorted by logFC, and the top 100 were selected for GO/KEGG enrichment analysis. GO analysis of the EIF4A1-associated up-regulated genes in COAD revealed associations mainly with DNA, protein, and nucleosome processes (Fig. 6c). KEGG analysis identified links primarily to NET (neurotrophil extracellular trap) formation, atherosclerosis, and systemic lupus erythematosus. GO analysis of differential gene sets in READ indicated relations to intermediary metabolism, receptor ligands, and hormonal activity (Fig. 6d). KEGG enrichment analysis highlighted its primary association with the MAPK signaling pathway.

Differential gene sets for EIF4A1 high and low expression groups in COAD and READ were identified using the DESeq2 package with a threshold of P < 0.05. These gene sets were subsequently analyzed for functional enrichment using GSEA. Parameters were set as follows based on the analysis results: P < 0.05, q-value < 0.25. Five pathways with the highest positive and negative NES correlations were selected for further analysis. GSEA analysis of COAD tissues revealed significant enrichment of differential genes in DNA replication, signal transduction, gene expression regulation, epigenetic regulation, cell cycle, chromosome dynamics, and gene silencing (Fig. 6e-f). Notably, modifications such as methylation and acetylation were closely linked to cancer progression. Conversely, the top five pathways negatively correlated with NES predominantly involved energy metabolism, viral host responses, and mitochondrial function. According to GSEA analysis of READ tissues (Fig. 6g-h), pathways exhibiting positive NES correlations were predominantly enriched in functions pertaining to immune response recognition, neural signal transmission, and sensory perception. Conversely, pathways with negative NES correlations were chiefly enriched in areas involving energy metabolism, immune and inflammatory responses, and cellular stress. The complement system and complement cascade are critical for immune surveillance and response, and their inhibition may contribute to the development and progression of cancer.

Immune infiltration analysis

In COAD, EIF4A1 expression was significantly inversely correlated with tumor purity (correlation coefficient:cor = -0.163, P = 9.79e-04), indicating an increase in EIF4A1 expression as tumor purity decreased, which suggests enhanced immune cell infiltration. Furthermore, EIF4A1 expression in COAD demonstrated a strong positive correlation with the infiltration levels of CD8 + T cells (partial correlation:cor = 0.327, P = 1.34e-11), neutrophils (partial correlation:cor = 0.269, P = 4.38e-08), and dendritic cells (partial correlation:cor = 0.282, P = 8.80e-09) (Fig. 7a, c). In READ, a similar inverse correlation was observed between EIF4A1 expression and tumor purity (correlation coefficient:cor = -0.249, P = 3.02e-03). Contrary to COAD, in READ, EIF4A1 expression was negatively correlated with CD4 + T cell infiltration (partial correlation:cor = -0.21, P = 1.31e-02) and positively correlated with neutrophil infiltration (partial correlation:cor = 0.181, P = 3.39e-02), highlighting the diverse regulatory roles of different immune cells in the colorectal cancer microenvironment (Fig. 7b, c).

Correlation of EIF4A1 Expression with Immune Checkpoint Regulation and its Implications for Immunotherapy Efficacy

Given the critical link between the expression of immune checkpoint genes and the efficacy of immunotherapy, we initially investigated the association between EIF4A1 expression and genes identified as immune response-related checkpoints via the TIMER 2.0 database[30]. In COAD, EIF4A1 demonstrates a strong correlation with most immune checkpoints, whereas in READ, the correlation with EIF4A1 is comparatively weaker (Fig. 8a). Key immunosuppressive genes, including PD-L1 (CD274) and CTLA-4 (CTLA4), exhibit significant positive correlations with EIF4A1 across colon, colorectal, and rectal cancers (P < 0.001) (Fig. 8a). We identified correlations between EIF4A1 and several immune checkpoint blocker genes, including PD-1, PD-L1, CTLA4, and LAG-3.In both READ and COAD, EIF4A1 demonstrated significant positive correlations with CTLA4 and PD-L1, with these associations being more pronounced in colon cancer. Furthermore, in colon cancer, EIF4A1 expression along with PD-1, PD-L1, CTLA4, and LAG-3 was up-regulated, showing significant positive correlations. Conversely, in rectal cancer, the correlations between EIF4A1 and LAG-3, PD-1 were not statistically significant (Fig. 8b-e).

Recent research has demonstrated that high microsatellite instability (MSI-H) and tumor mutation burden (TMB) are promising predictive biomarkers for the efficacy of immunotherapy, with MSI identified in a diverse range of cancers[31, 32]. The presence of MSI signifies an unstable intracellular genomic state with the potential to precipitate cancer development.Specifically, tumors with high microsatellite instability (MSI-H) exhibit extensive genomic instability, associated with genetic disorders like Lynch syndrome, a hereditary form of colorectal cancer. We investigated the correlation between tumor mutation burden (TMB) and microsatellite instability (MSI) of EIF4A1 across COADREAD, COAD, and READ. The findings revealed that EIF4A1 expression in COADREAD, COAD, and READ positively correlates with both TMB and MSI. Notably, this correlation was more pronounced in COAD than in READ (Fig. 8f, g).

In recent years, the advent of molecular diagnostics and immune-targeted therapies has globally propelled the adoption of precision medicine strategies,particularly, these advancements have significantly enhanced therapeutic outcomes in colorectal cancer, with the development of immune checkpoint inhibitors demonstrating notable clinical efficacy [33–35]. Clinically, the diagnosis of colorectal cancer predominantly depends on methods such as colorectal colonoscopy and CT scans, which are invasive. The distinct anatomical locations of the colon and rectum suggest that treatment approaches may vary.Additionally, the site of the tumor within the colorectal region is correlated with survival outcomes [36]. Therefore, identifying target molecules specific for molecular diagnosis and treatment in colon and rectal cancers is crucial, promising to significantly enhance precision medicine.

EIF4A1, a member of the eukaryotic initiation factor family, plays a pivotal role in the formation of the EIF4A1 complex and is crucial for the initiation of translation.Given EIF4A1's central role in regulating translation and the link between mRNA translation dysregulation and cancer progression, it has garnered significant attention. EIF4A1 is a potent and effective RNA helicase. Leveraging this property, researchers have developed inhibitors targeting the eIF4 family, achieving notable outcomes in human solid tumors [37]. The EIF4F complex, crucial for translation initiation, serves as a nexus for cancer-promoting signaling pathways. Its inhibition reduces the expression of key oncoproteins and induces apoptosis, with EIF4A1 functioning as a critical enzymatic active center within this complex.Research indicates that targeting EIF4A1 and the EIF4F complex offers promising avenues for developing and exploring tumor suppressive strategies [16, 38].

Through an literature review, we identified the RBP associated with EMT progression and EIF4A1, a molecule critical for prognostic differentiation[22]. Given its crucial role in translation regulation, EIF4A1 garnered our focused attention. Employing bioinformatics, we examined EIF4A1 expression in colon and rectal cancers and its correlation with molecular mechanisms, biological functions, immune infiltration, and immune checkpoints. We mapped the protein expression landscape of EIF4A1 across human organs, documenting its presence in both normal colorectal tissues and pathological samples from colon and rectal cancers,Utilizing the TCGA database, we analyzed its mRNA expression levels in pan-cancer, colorectal, colon, and rectal cancers, and characterized its predominant cellular localization in the cytoplasm.

The findings demonstrated that EIF4A1 mRNA was up-regulated across most cancers, with both mRNA and protein levels elevated in colon cancer. EIF4A1 mRNA levels were significantly higher in COAD compared to rectal cancer.Immunofluorescence analysis revealed that EIF4A1 was predominantly localized to the cytoplasm and enriched in cancer cells.These observations suggest that the up-regulation of EIF4A1 may be associated with cancer progression.Furthermore, we assessed the correlation between EIF4A1 mRNA and protein expression and clinical features. Analysis indicated that EIF4A1 mRNA expression was primarily observed in stages I and II of colon cancer, and stage I of rectal cancer, with comparatively lower levels in stage II.In both colon and rectal cancers, EIF4A1 mRNA levels were notably higher in younger patients, potentially linking younger onset of cancer with increased expression. In both colon and rectal cancers, EIF4A1 mRNA levels were notably higher in younger patients, potentially linking younger onset of cancer with increased expression. In colon cancer, the highest levels of EIF4A1 mRNA expression were observed in N0 staging, while in rectal cancer, although peak levels were noted in N1 staging, these were not statistically significant.

Contrary to expectations, we observed that EIF4A1 mRNA and protein expression levels were higher in individuals of normal weight and tended to decrease slightly with increasing body weight in both COAD and READ cases.Analysis revealed that EIF4A1 protein expression correlated with tumor stage, age, histological type, body weight, and gender.EIF4A1 protein expression was predominantly observed in stages II and III, peaking in the middle and late stages of the disease. This feature allows for the detection and assessment of the patient's condition. High EIF4A1 expression predominantly occurs in individuals aged 61–80 years, indicative of a tendency towards middle-aged and elderly patients and associated with a poorer survival prognosis.EIF4A1 protein expression in normal-weight individuals was significantly higher compared to their overweight counterparts.Subsequently, we assessed EIF4A1's diagnostWe observed that in both colon and rectal cancers, high EIF4A1 expression was associated with poorer prognosis, with a stronger correlation in colon cancer. According to the clinical ROC curve analysis, the analysis results of EIF4A1 in colorectal cancer, colon cancer and rectal cancer were AUC:0.827 AUC:0.836,AUC:0.797,The findings suggest that increased EIF4A1 expression significantly contributes to the onset and progression of colorectal cancer and may serve as a novel clinical diagnostic marker. Notably, EIF4A1's diagnostic utility is higher in colon cancer compared to rectal cancer. Following comprehensive univariate and multivariate Cox regression analyses, it is clear that although colon and rectal cancers share critical prognostic factors such as the significant roles of pathological staging and age, they differ markedly in T, N, M stages, and biomarkers like CEA levels and EIF4A1 expression. These variations likely arise from the distinct biological behaviors and treatment responses characteristic of each cancer type. Consequently, in clinical decision-making, it is crucial for physicians to carefully evaluate the specific risk factors and prognostic indicators to develop more personalized and precise treatment strategies. ic and prognostic value.Survival and ROC curve analyses of EIF4A1 indicated that its expression correlates with survival outcomes and diagnostic accuracy, suggesting its potential as a prognostic biomarker and diagnostic indicator.

The PPI interaction network of EIF4A1 was constructed through the database download data and online data, and the expression relationship of the genes whose interaction intersection was analyzed in colorectal cancer. It was found that PDCD4 was negatively correlated with the expression of other related genes, and PDCD4 was significantly expressed in normal tissues. PDCD4 is one of the genes related to programmed cell death, encoding a tumor suppressor protein, which can inhibit the growth and proliferation of tumor cells through a variety of mechanisms. There was a high correlation between the high expression of EIF4A1 and the weakened expression of PDCD4[39]. Through conducting a functional enrichment analysis of differential genes related to EIF4A1, we identified that in COAD, key pathways are primarily related to DNA replication, signal transduction, gene expression regulation, epigenetic regulation, cell cycle, chromosome dynamics, gene silencing, developmental regulation, and energy metabolism and synthesis. Notably, processes such as neutrophil extracellular trap formation, and modifications like methylation and acetylation, are critically linked to cancer development, immune escape, and response. In rectal cancer, the primary enriched pathways include immune response recognition, immune and inflammatory responses, cell stress, and energy metabolism. The complement system and the MAPK signaling cascade are fundamental to immune surveillance and response, and are deeply intertwined with the progression and development of cancer. Inhibiting these pathways could potentially influence the development and progression of cancer.

Finally, we investigated the relationship between EIF4A1 and immunity. By analyzing immune infiltration, it was observed that colon cancer showed a strong positive correlation with neutrophils, CD8 + T cells, and dendritic cells, whereas rectal cancer was positively correlated with neutrophils and negatively correlated with CD4 + T cells. Interestingly, consistent with earlier KEGG analyses in colon cancer, EIF4A1 demonstrated a significant positive correlation with neutrophil infiltration. A crucial pathogen defense mechanism—the hijacking of neutrophils for the formation of neutrophil extracellular traps (NETs)—plays a significant role in cancer progression. Studies have shown that NETs facilitate CRC metastasis by enabling potent immunosuppression [40]. Furthermore, specific components of NETs, including histones and elastases, are known to encourage angiogenesis, thus supplying nutrients and oxygen to tumors. This leads to vascular dysfunction and thrombosis, subsequently elevating the mortality risk in cancer patients [41, 42]. Elevated expression and infiltration of neutrophils are believed to potentially promote cancer progression. In certain scenarios, neutrophils may transform into cells with immunosuppressive capacities, notably myeloid-derived suppressor cells (MDSCs). Increasing evidence supports the role of neutrophils as key mediators in creating an immunosuppressive environment conducive to the development of various cancers, and elevated EIF4A1 expression may facilitate this aggregation of immunosuppressive cells, thereby aiding tumor evasion of immune surveillance [43, 44].

CD8 cells, which can directly recognize and kill tumor cells, play a crucial role in anti-tumor immunity by inhibiting tumor growth and metastasis, and by releasing antigens presented by major histocompatibility complex I (MHC I) molecules, perforin, granzyme, and other cytolytic agents to destroy target cells. Additionally, EIF4A1 expression is positively correlated with CD8 T cells; this correlation may promote the inflammatory environment in cancer and reduce the efficacy of CD8 + T cells by inducing overexpression of PD-L1. Although infiltration of CD8 + T cells increases, their activity may be suppressed. This suppression likely reflects an adaptive immune resistance mechanism within the complex tumor microenvironment [45–48]. Dendritic cells are pivotal for antigen presentation, primarily offering Major Histocompatibility Complex I (MHC I) molecules to present antigens to CD8 + T cells, thus activating these cells[49]. EIF4A1 exhibits a positive correlation with dendritic cells and CD8 + T cells. It is plausible that the expression of this gene functions to promote the immune response. Within the complex tumor immune microenvironment, EIF4A1 expression, which promotes cancer progression, may enable tumor cells to regulate antigen expression and alter antigen processing and presentation mechanisms, thus evading immune recognition. Despite the high expression and infiltration of CD8 + T cells and dendritic cells, elevated EIF4A1 expression may impair dendritic cell function or maturation via immune escape mechanisms [50, 51].

In rectal cancer, EIF4A1 is predominantly negatively correlated with CD4 + T cells, a relationship that likely contributes to immune escape. Elevated EIF4A1 expression is associated with an immunosuppressive state in the tumor microenvironment. This immunosuppressive state can potentially reduce the number and function of CD4 + T cells, impacting their migration, survival, and overall function. Additionally, prolonged chronic inflammation may further deplete these cells. Within the complex tumor microenvironment, tumor cells are known to release immunosuppressive factors that inhibit the proliferation and function of CD4 + T cells[52–54].

The expression of immune checkpoint genes is associated with tumor cells evading immune surveillance. Several immune checkpoint inhibitors have been incorporated into therapeutic research and have demonstrated significant efficacy[55, 56]. Immune checkpoint genes, including PD-1 (PDCD1), PD-L1 (CD274), CTLA-4 (CD152), and LAG-3, are implicated in tumor cells' evasion of immune surveillance[57]. We subsequently analyzed the correlation between EIF4A1 expression and various immune checkpoint-related genes. The analysis revealed that EIF4A1 exhibited strong correlations with several key immune checkpoints in colon cancer, notably PD-1 (PDCD1), PD-L1 (CD274), CTLA4 (CD152), and LAG-3. Using the TIMER 2.0 database, we examined the correlation between EIF4A1 expression and these immune checkpoints. The expression levels of PD-1 (PDCD1), PD-L1 (CD274), CTLA4 (CD152), and LAG-3 were found to be up-regulated in colon cancer. Further, these checkpoint expressions, significantly correlated with CD8 + T cell infiltration in colorectal cancer, suggest that elevated EIF4A1 levels may contribute to T cell activation inhibition, impairing T cell functionality and potentially fostering cancer progression. In rectal cancer, however, only the up-regulation of PD-L1 and CTLA4 proved to be significant, whereas PD-1 up-regulation was not significant. Additionally, the up-regulation of PD-L1 and CTLA4 may also relate to the activation and infiltration of CD4 + T cells, potentially inhibiting T cell activation and thus promoting cancer[56]. In treating colon and rectal cancers, we should tailor our approach based on the expression of immune checkpoint inhibitors such as PD-1, PD-L1, LAG-3, and CTLA4. Further exploring the relationship between EIF4A1 and these immune checkpoint genes in both cancer types will enable us to better personalize treatment strategies and minimize immune tolerance and resistance.

Through bioinformatics analysis, we examined the differences in EIF4A1 expression, clinical parameters, and immune infiltration between colon and rectal cancers. We discovered that EIF4A1 is upregulated in both cancers, suggesting its potential as a diagnostic marker. Notably, EIF4A1's association with survival prognosis and clinical outcomes is more statistically significant in colon cancer than in rectal cancer. Regarding immunity, EIF4A1 exhibits distinct infiltration patterns in colon and rectal cancers, positioning it as a viable target for immunotherapy. However, the immunological differences between colon and rectal cancers necessitate tailored therapeutic strategies for each.

Screening for intersecting genes

Analyzing RNA-seq data from the TCGA database, DESeq2 was utilized to identify differentially expressed genes in the TCGA-COAD and TCGA-READ datasets[20]. With a threshold of |logFC| > 1 and P.adj < 0.05, we discovered 2,929 genes with significant expression differences. Additionally, employing univariate Cox regression from the R packages survival and survminer[21], we pinpointed 631 prognostic genes linked to colorectal cancer, using the criteria of HR (Hazard Ratio) > 1 and P.adj < 0.05. The overlap between these gene sets and the RBP library [22]was graphically represented using ggplot2 and VennDiagram in R[22].

Analysis of differentially expressed mRNA (DEmRNA).

Based on the TCGA-COADREAD data, differential mRNAs were screened by R language analysis and setting the parameters log2 fold change (|logFC|) > 1, P.adj < 0.05, and the results of the differential analysis were visualized using the R package ggplot2.

Gene co-expression analysis

RNAseq data from TCGA database (https://portal.gdc.cancer.gov) were downloaded and organized in the STAR process of TCGA-COAD and TCGA-READ projects and extracted in TPM (Transcripts Per Kilobase Million) format, data processing method: log2(value + 1), using R language to visualize the clinical Pathologic T stage, Pathologic N stage, Pathologic M stage with gene expression analysis and heatmap visualization by R package: ComplexHeatmap [23].

Analysis of EIF4A1 expression

The Human Protein Atlas (HPA) database (https://www.proteinatlas.org) was used to explore the mRNA and protein expression levels of EIF4A1 in normal human tissues. The expression level of the EIF4A1 gene in a variety of cancer tissues was obtained through the “Gene_DE” module in TIMER 2.0 (http://timer.cistrome.org/)[24]. In our investigation, RNA sequencing data from the TCGA-COAD and TCGA-READ cohorts within the TCGA database were meticulously curated and normalized to Transcripts Per Million (TPM) values. We specifically extracted paired samples of both tumor and adjacent non-tumor (paracancerous) tissues, ensuring corresponding numerical pairings. The expression profiles of the EIF4A1 gene in colorectal adenocarcinoma (COAD) and rectal adenocarcinoma (READ) tissues were compared to their paracancerous counterparts to identify differential expression patterns. A paired-sample t-test was employed to statistically assess the expression levels of EIF4A1 within these paired samples. The resultant data were then visualized using the R software environment, employing the capabilities of the ggplot2 and stats packages for graphical representation.

Survival analysis

RNA-seq data from the TCGA database (https://portal.gdc.cancer.gov) were downloaded and organized for the STAR process of the TCGA-COAD and TCGA-READ projects. The data were extracted in TPM format, along with the corresponding clinical data. Survival analysis of EIF4A1 in COADREAD, COAD, and READ was conducted using Cox regression with the R survival package to test the proportional risk hypothesis and fit survival regression models to determine P-values. Comparisons of EIF4A1 gene expression were made between Overall Survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI). The results were visualized using the R packages survminer and ggplot2[21]. Forest plots displaying hazard ratios (HR), 95% confidence intervals, and P-values of survival curves were generated and visualized using the R packages survminer and ggplot2.

ROC analysis of EIF4A1

ROC (Receiver Operating Characteristic) curves were utilized to evaluate the diagnostic value of EIF4A1 in COADREAD, COAD, and READ. The data for constructing ROC curves were obtained from the mRNA expression of EIF4A1 in cancer and corresponding normal tissues in the TCGA database. ROC curves were calculated using the "pROC" (v1.17.0.1) package in R software and plotted using the "ggplot2" package. The area under the curve (AUC), critical value, sensitivity, specificity, positive predictive value, negative predictive value, and Youden index (YI) were simultaneously calculated. AUC values closer to 1 indicate better diagnostic performance; an AUC of 0.5–0.7 indicates low precision, 0.7–0.9 indicates better accuracy, and above 0.9 indicates high accuracy.

Association of EIF4A1 Transcript and Protein Expression with Clinicopathological Characteristics in Cancer

The associations between transcript levels, protein levels, and various clinicopathological parameters (cancer stage, lymph node metastasis, age, overweight, gender, etc.) of EIF4A1 in colorectal cancer were analyzed using the UALCAN (https://ualcan.path.uab.edu/) web resource with data from the TCGA database[25].

Protein-Protein Interaction (PPI) Network Analyses of EIF4A1

The GeneMANIA database (https://www.genemania.org) was employed to construct an interaction network closely related to EIF4A1[26],while the STRING online database was utilized for assembling an additional network incorporating EIF4A1[27]. The proteins obtained from both sources were taken as intersections and visualized using ggplot2 [3.3.6] and the VennDiagram package [1.7.3] for Venn diagram plotting. The most relevant genes identified at the intersections were visualized using the R package ComplexHeatmap [2.13.1] to depict the expression patterns of EIF4A1-related genes in the TCGA-COADREAD dataset[23].

Functional enrichment analysis of EIF4A1-related differential genes

To investigate the function of EIF4A1, EIF4A1-related differential genes were screened and visualized using the ggplot2 package. The top 100 up-regulated genes from the volcano plot were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses in TCGA-COAD and READ. Gene Set Enrichment Analysis (GSEA) was conducted using the differential genes identified from the volcano plot. The "clusterProfiler" package was used to automate the GO and KEGG analysis processes[28], and the results were displayed as bubble plots and mountain plots using "ggplot2".

Immunoassay of EIF4A1 in COAD and READ

Correlation data were downloaded from the TIMER 2.0 (http://timer.cistrome.org/) database, cleaned, and visualized using the ggplot2 package for heatmap representation. Correlation scatter plots of EIF4A1 with related immune cells were visualized through the TIMER database (https://cistrome.shinyapps.io/timer/). Expression and correlation data between EIF4A1 and related checkpoint genes were organized using data from TIMER 2.0 and visualized as heatmaps using the ggplot2 package. Data on EIF4A1 and four immune checkpoints obtained from TIMER 2.0 were organized and visualized as a dot plot using the ggplot2 package. The harmonized and standardized pan-cancer dataset TCGA Pan-Cancer (PANCAN.N = 10535, G = 60499) was downloaded from the UCSC (https://xenabrowser.net) database. Expression data of the EIF4A1 gene in COAD and READ were extracted, and samples originating from Primary Blood Derived Cancer and Peripheral Blood were screened. Additionally, samples processed by MuTect2 software were downloaded from GDC (https://portal.gdc.cancer.gov/), providing a Simple Nucleotide Variation dataset of all TCGA samples at level 4. The Tumor Mutation Burden (TMB) of each tumor was calculated using the R package maftools (version 2.8.05). TMB and gene expression data were integrated, followed by log2(x + 0.001) transformation for each expression value. The data were then cleaned, organized, and visualized as radar plots using ggplot2[29].

Statistical analysis

Student's t-test was used to compare the differences between the two groups, and data are reported as mean values ± SD. The p-value < 0.05. Statistical analyses were performed using GraphPad Prism version 9.

CRC

Colorectal cancer

EIF4A1

Eukaryotic initiation factor 4A-I

EMT

Epithelial-Mesenchymal Transition

TCGA-COADREAD Colon

adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma

TCGA-COAD

Colon adenocarcinoma

TCGA-READ

Rectum adenocarcinoma

RBP

RNA binding protein

NETs

Neutrophil Extracellular Traps

DEmRNA

Differentially Expressed Messenger RNAs

TPM

Transcripts Per Kilobase Million

Hazard Ratio

Overall Survival

DSS

Disease Specific Survival

PFI

Progress Free Interval

ROC

Receiver Operating Characteristic

AUC

Area Under the Curve

Youden's index

cor

partial correlation

PPI

Protein-Protein Interaction Network

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

GSEA

Gene Set Enrichment Analysis

TMB

Tumor mutation burden

MSI

Microsatellite Instability

Ethics approval and consent to participate

This study did not require ethical board approval because it did not include human or animal trials.

Consent for publication

Not applicable

Data availability statement

The datasets presented in this study can be found in the online repositories, including The Human Protein Atlas (HPA) database (https://www.proteinatlas.org), TIMER 2.0 (http://timer.cistrome.org/), UALCAN (https://ualcan.path.uab.edu/) , STRING(https://cn.string-db.org/), and TIMER database (https://cistrome.shinyapps.io/timer/), GeneCards (https://www.genecards.org), GeneMANIA database (http://genemania.org/), UCSC (https://xenabrowser.net) .

Competing of interest

The authors declare that they have no competing interests

Funding

This study was supported by Zhangiiakou City Key R&D Plan Project (No.2322088D and 2311038D), Medical Science Research Subject Plan Project of Hebei Provincial Health Commission (No.20240805 and 20240782), The natural science project of Hebei North University (No. XJ2024034 and XJ2024035), Hebei Health Commission Scientific Research Foundation Project (20240240), Hebei Provincial Administration of Traditional Chinese Medicine Research Project (2024062), Hebei Provincial Administration of Traditional Chinese Medicine Project (2022147).

Authors’ contributions

Weizheng Liang: Conceptualization, funding acquisition. Jun Xue and Dandan Xu: Supervision, funding acquisition. Zhenpeng Zhu: Data curation, Writing-Original draft preparation. Peng Wang: Methodology, Software. Chenyang Hou: Writing- Reviewing and Editing. Jiajia Xiao and Xuejun Zhi: Software, Validation, funding acquisition. Fei Guo: Supervision. All authors have reviewed the results and approved the final version of the manuscript.

Acknowledgements

Not applicable.

Brody, H., Colorectal cancer. Nature, 2015. 521(7551): p. S1.
Tamas, K., A.M. Walenkamp, E.G. de Vries, M.A. van Vugt, R.G. Beets-Tan, B. van Etten, D.J. de Groot, and G.A. Hospers, Rectal and colon cancer: Not just a different anatomic site. Cancer Treat Rev, 2015. 41(8): p. 671–9.
Lee, K.H., W.S. Chen, J.K. Jiang, S.H. Yang, H.S. Wang, S.C. Chang, Y.T. Lan, C.C. Lin, H.H. Lin, S.C. Huang, H.H. Cheng, Y. Chao, and H.W. Teng, The efficacy of anti-EGFR therapy in treating metastatic colorectal cancer differs between the middle/low rectum and the left-sided colon. Br J Cancer, 2021. 125(6): p. 816–825.
Murphy, C.C. and T.A. Zaki, Changing epidemiology of colorectal cancer - birth cohort effects and emerging risk factors. Nat Rev Gastroenterol Hepatol, 2024. 21(1): p. 25–34.
Gupta, S., F.P. May, S.S. Kupfer, and C.C. Murphy, Birth Cohort Colorectal Cancer (CRC): Implications for Research and Practice. Clin Gastroenterol Hepatol, 2024. 22(3): p. 455–469.e7.
Lee, L.J., D. Papadopoli, M. Jewer, S. Del Rincon, I. Topisirovic, M.G. Lawrence, and L.M. Postovit, Cancer Plasticity: The Role of mRNA Translation. Trends Cancer, 2021. 7(2): p. 134–145.
Yang, Q., C.H. Yu, F. Zhao, Y. Dang, C. Wu, P. Xie, M.S. Sachs, and Y. Liu, eRF1 mediates codon usage effects on mRNA translation efficiency through premature termination at rare codons. Nucleic Acids Res, 2019. 47(17): p. 9243–9258.
Bartish, M., M.J. Abraham, C. Gonçalves, O. Larsson, C. Rolny, and S.V. Del Rincón, The role of eIF4F-driven mRNA translation in regulating the tumour microenvironment. Nat Rev Cancer, 2023. 23(6): p. 408–425.
Li, W., X. Deng, and J. Chen, RNA-binding proteins in regulating mRNA stability and translation: roles and mechanisms in cancer. Semin Cancer Biol, 2022. 86(Pt 2): p. 664–677.
Evdokimova, V., C.E. Tognon, and P.H. Sorensen, On translational regulation and EMT. Semin Cancer Biol, 2012. 22(5–6): p. 437–45.
Kayastha, F., N.B. Herrington, B. Kapadia, A. Roychowdhury, N. Nanaji, G.E. Kellogg, and R.B. Gartenhaus, Novel eIF4A1 inhibitors with anti-tumor activity in lymphoma. Mol Med, 2022. 28(1): p. 101.
Zerio, C.J., T.A. Cunningham, A.S. Tulino, E.A. Alimusa, T.M. Buckley, K.T. Moore, M. Dodson, N.C. Wilson, A.J. Ambrose, T. Shi, J. Sivinski, D.J. Essegian, D.D. Zhang, S.C. Schürer, J.H. Schatz, and E. Chapman, Discovery of an eIF4A Inhibitor with a Novel Mechanism of Action. J Med Chem, 2021. 64(21): p. 15727–15746.
Brito Querido, J., M. Sokabe, I. Díaz-López, Y. Gordiyenko, C.S. Fraser, and V. Ramakrishnan, The structure of a human translation initiation complex reveals two independent roles for the helicase eIF4A. Nat Struct Mol Biol, 2024. 31(3): p. 455–464.
Xue, C., X. Gu, G. Li, Z. Bao, and L. Li, Expression and Functional Roles of Eukaryotic Initiation Factor 4A Family Proteins in Human Cancers. Front Cell Dev Biol, 2021. 9: p. 711965.
Digregorio, M., A. Lombard, P.N. Lumapat, F. Scholtes, B. Rogister, and N. Coppieters, Relevance of Translation Initiation in Diffuse Glioma Biology and its Therapeutic Potential. Cells, 2019. 8(12).
Nishida, Y., R. Zhao, L.E. Heese, H. Akiyama, S. Patel, A.M. Jaeger, R.O. Jacamo, K. Kojima, M.C.J. Ma, V.R. Ruvolo, D. Chachad, W. Devine, S. Lindquist, R.E. Davis, J.A. Porco, Jr., L. Whitesell, M. Andreeff, and J. Ishizawa, Inhibition of translation initiation factor eIF4a inactivates heat shock factor 1 (HSF1) and exerts anti-leukemia activity in AML. Leukemia, 2021. 35(9): p. 2469–2481.
Zhao, Y., Y. Wang, W. Chen, S. Bai, W. Peng, M. Zheng, Y. Yang, B. Cheng, and Z. Luan, Targeted intervention of eIF4A1 inhibits EMT and metastasis of pancreatic cancer cells via c-MYC/miR-9 signaling. Cancer Cell Int, 2021. 21(1): p. 670.
Gao, C., X. Guo, A. Xue, Y. Ruan, H. Wang, and X. Gao, High intratumoral expression of eIF4A1 promotes epithelial-to-mesenchymal transition and predicts unfavorable prognosis in gastric cancer. Acta Biochim Biophys Sin (Shanghai), 2020. 52(3): p. 310–319.
Steinmann, S.M., A. Sánchez-Martín, E. Tanzer, A. Cigliano, G.M. Pes, M.M. Simile, L. Desaubry, J.J.G. Marin, M. Evert, and D.F. Calvisi, eIF4A1 Is a Prognostic Marker and Actionable Target in Human Hepatocellular Carcinoma. Int J Mol Sci, 2023. 24(3).
Love, M.I., W. Huber, and S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014. 15(12): p. 550.
Liu, J., T. Lichtenberg, K.A. Hoadley, L.M. Poisson, A.J. Lazar, A.D. Cherniack, A.J. Kovatich, C.C. Benz, D.A. Levine, A.V. Lee, L. Omberg, D.M. Wolf, C.D. Shriver, V. Thorsson, and H. Hu, An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, 2018. 173(2): p. 400–416.e11.
Fang, L., L. Zhang, M. Wang, Y. He, J. Yang, Z. Huang, Y. Tan, K. Fang, J. Li, Z. Sun, Y. Li, Y. Tang, W. Liang, H. Cui, Q. Zhu, Z. Wu, Y. Li, Y. Hu, and W. Chen, Pooled CRISPR Screening Identifies P-Bodies as Repressors of Cancer Epithelial-Mesenchymal Transition. Cancer Res, 2024. 84(5): p. 659–674.
Gu, Z., R. Eils, and M. Schlesner, Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 2016. 32(18): p. 2847–9.
Li, T., J. Fu, Z. Zeng, D. Cohen, J. Li, Q. Chen, B. Li, and X.S. Liu, TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res, 2020. 48(W1): p. W509-w514.
Chandrashekar, D.S., S.K. Karthikeyan, P.K. Korla, H. Patel, A.R. Shovon, M. Athar, G.J. Netto, Z.S. Qin, S. Kumar, U. Manne, C.J. Creighton, and S. Varambally, UALCAN: An update to the integrated cancer data analysis platform. Neoplasia, 2022. 25: p. 18–27.
Warde-Farley, D., S.L. Donaldson, O. Comes, K. Zuberi, R. Badrawi, P. Chao, M. Franz, C. Grouios, F. Kazi, C.T. Lopes, A. Maitland, S. Mostafavi, J. Montojo, Q. Shao, G. Wright, G.D. Bader, and Q. Morris, The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res, 2010. 38(Web Server issue): p. W214-20.
Szklarczyk, D., R. Kirsch, M. Koutrouli, K. Nastou, F. Mehryary, R. Hachilif, A.L. Gable, T. Fang, N.T. Doncheva, S. Pyysalo, P. Bork, L.J. Jensen, and C. von Mering, The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res, 2023. 51(D1): p. D638-d646.
Yu, G., L.G. Wang, Y. Han, and Q.Y. He, clusterProfiler: an R package for comparing biological themes among gene clusters. Omics, 2012. 16(5): p. 284–7.
Shen, W., Z. Song, X. Zhong, M. Huang, D. Shen, P. Gao, X. Qian, M. Wang, X. He, T. Wang, S. Li, and X. Song, Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. Imeta, 2022. 1(3): p. e36.
Gu, D., X. Ao, Y. Yang, Z. Chen, and X. Xu, Soluble immune checkpoints in cancer: production, function and biological significance. J Immunother Cancer, 2018. 6(1): p. 132.
Zhao, P., L. Li, X. Jiang, and Q. Li, Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. J Hematol Oncol, 2019. 12(1): p. 54.
Jardim, D.L., A. Goodman, D. de Melo Gagliato, and R. Kurzrock, The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer Cell, 2021. 39(2): p. 154–173.
Fan, A., B. Wang, X. Wang, Y. Nie, D. Fan, X. Zhao, and Y. Lu, Immunotherapy in colorectal cancer: current achievements and future perspective. Int J Biol Sci, 2021. 17(14): p. 3837–3849.
Zhang, Y., Q. Wu, L. Xu, H. Wang, X. Liu, S. Li, T. Hu, Y. Liu, Q. Peng, Z. Chen, X. Wu, and J.B. Fan, Sensitive detection of colorectal cancer in peripheral blood by a novel methylation assay. Clin Epigenetics, 2021. 13(1): p. 90.
Andrei, P., P. Battuello, G. Grasso, E. Rovera, N. Tesio, and A. Bardelli, Integrated approaches for precision oncology in colorectal cancer: The more you know, the better. Semin Cancer Biol, 2022. 84: p. 199–213.
Gowarty, J.L., C. Durham, L. Wong, and W. Chen, Survival outcomes of left-sided versus right-sided colon cancer. 2019, American Society of Clinical Oncology.
Meric-Bernstam, F., M. Sharma, D. Sommerhalder, R.T. Skeel, A.B. El-Khoueiry, J.L. Caswell-Jin, P.H. Patel, and E. Rosen, First-in-human phase 1/2 dose escalation and expansion study evaluating first-in-class eIF4A inhibitor zotatifin in patients with solid tumors. 2022, American Society of Clinical Oncology.
Cunningham, T.A., D. Essegian, S. Schürer, and J.H. Schatz, Identification of Tractable Drug-like eIF4Al Inhibitors with Potent Anti-Tumor Activity. Blood, 2019. 134: p. 5760.
Dorrello, N.V., A. Peschiaroli, D. Guardavaccaro, N.H. Colburn, N.E. Sherman, and M. Pagano, S6K1- and betaTRCP-mediated degradation of PDCD4 promotes protein translation and cell growth. Science, 2006. 314(5798): p. 467 – 71.
Onuma, A., J. He, Y. Xia, H. Zhang, D. Genkin, G. Tetz, H. Huang, and A. Tsung, Neutrophil extracellular traps blockade in combination with PD-1 inhibition in treatment of colorectal cancer metastasis. 2020, American Society of Clinical Oncology.
Adrover, J.M., S.A.C. McDowell, X.Y. He, D.F. Quail, and M. Egeblad, NETworking with cancer: The bidirectional interplay between cancer and neutrophil extracellular traps. Cancer Cell, 2023. 41(3): p. 505–526.
Deng, J., Y. Kang, C.C. Cheng, X. Li, B. Dai, M.H. Katz, T. Men, M.P. Kim, E.A. Koay, H. Huang, R.A. Brekken, and J.B. Fleming, DDR1-induced neutrophil extracellular traps drive pancreatic cancer metastasis. JCI Insight, 2021. 6(17).
Yamauchi, S., T. Iwasawa, T. Ito, M.V. Brock, T. Fukunaga, H. Orita, and K. Kato, Analysis of MDSCs as a diagnostic and therapeutic target for gastric cancer. Cancer Research, 2023. 83(7_Supplement): p. 5958–5958.
Geh, D., J. Leslie, R. Rumney, H.L. Reeves, T.G. Bird, and D.A. Mann, Neutrophils as potential therapeutic targets in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol, 2022. 19(4): p. 257–273.
Xing, X., J. Guo, G. Ding, B. Li, B. Dong, Q. Feng, S. Li, J. Zhang, X. Ying, X. Cheng, T. Guo, H. Du, Y. Hu, T. Zhou, X. Wang, L. Li, Q. Li, M. Xie, L. Li, X. Gao, F. Shan, Z. Li, S. Jia, X. Wen, J. Wang, and J. Ji, Analysis of PD1, PDL1, PDL2 expression and T cells infiltration in 1014 gastric cancer patients. Oncoimmunology, 2018. 7(3): p. e1356144.
Vicier, C., L. Werner, Y. Huang, A. Hamid, C. Evan, M. Loda, and C. Sweeney, Immune infiltrate with CD8 low or PDL1 high associated with metastatic prostate cancer after radical prostatectomy (RP). 2019, American Society of Clinical Oncology.
Xing, X., Z. Li, J. Wang, and J. Ji, Analysis of PDL1 expression and T cells infiltration in 1014 gastric cancer patients. 2017, American Society of Clinical Oncology.
Bronte, V., D.B. Chappell, E. Apolloni, A. Cabrelle, M. Wang, P. Hwu, and N.P. Restifo, Unopposed production of granulocyte-macrophage colony-stimulating factor by tumors inhibits CD8 + T cell responses by dysregulating antigen-presenting cell maturation. The Journal of Immunology, 1999. 162(10): p. 5728–5737.
den Haan, J.M. and M.J. Bevan, Constitutive versus activation-dependent cross-presentation of immune complexes by CD8(+) and CD8(-) dendritic cells in vivo. J Exp Med, 2002. 196(6): p. 817–27.
Gottfried, E., M. Kreutz, and A. Mackensen, Tumor-induced modulation of dendritic cell function. Cytokine Growth Factor Rev, 2008. 19(1): p. 65–77.
Fainaru, O., N. Almog, C.W. Yung, K. Nakai, M. Montoya-Zavala, A. Abdollahi, R. D'Amato, and D.E. Ingber, Tumor growth and angiogenesis are dependent on the presence of immature dendritic cells. Faseb j, 2010. 24(5): p. 1411–8.
Miggelbrink, A.M., J.D. Jackson, S.J. Lorrey, E.S. Srinivasan, J. Waibl-Polania, D.S. Wilkinson, and P.E. Fecci, CD4 T-Cell Exhaustion: Does It Exist and What Are Its Roles in Cancer? Clin Cancer Res, 2021. 27(21): p. 5742–5752.
Kryczek, I., Y. Lin, N. Nagarsheth, D. Peng, L. Zhao, E. Zhao, L. Vatan, W. Szeliga, Y. Dou, S. Owens, W. Zgodzinski, M. Majewski, G. Wallner, J. Fang, E. Huang, and W. Zou, IL-22(+)CD4(+) T cells promote colorectal cancer stemness via STAT3 transcription factor activation and induction of the methyltransferase DOT1L. Immunity, 2014. 40(5): p. 772–784.
Blomberg, O.S., L. Spagnuolo, H. Garner, L. Voorwerk, O.I. Isaeva, E. van Dyk, N. Bakker, M. Chalabi, C. Klaver, M. Duijst, K. Kersten, M. Brüggemann, D. Pastoors, C.S. Hau, K. Vrijland, E.A.M. Raeven, D. Kaldenbach, K. Kos, I.S. Afonina, P. Kaptein, L. Hoes, W. Theelen, P. Baas, E.E. Voest, R. Beyaert, D.S. Thommen, L.F.A. Wessels, K.E. de Visser, and M. Kok, IL-5-producing CD4(+) T cells and eosinophils cooperate to enhance response to immune checkpoint blockade in breast cancer. Cancer Cell, 2023. 41(1): p. 106–123.e10.
Huang, Q., X. Wu, Z. Wang, X. Chen, L. Wang, Y. Lu, D. Xiong, Q. Liu, Y. Tian, H. Lin, J. Guo, S. Wen, W. Dong, X. Yang, Y. Yuan, Z. Yue, S. Lei, Q. Wu, L. Ran, L. Xie, Y. Wang, L. Gao, Q. Tian, X. Zhou, B. Sun, L. Xu, Z. Tang, and L. Ye, The primordial differentiation of tumor-specific memory CD8(+) T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes. Cell, 2022. 185(22): p. 4049–4066.e25.
Vetsika, K.E., D. Aggouraki, Z. Lyristi, F. Koinis, V. Georgoulias, and A. Kotsakis, Prognostic value of PD-L1 + CD4 + T cells in non-small cell lung cancer (NSCLC) patients treated with a PD-1 inhibitor. 2017, American Society of Clinical Oncology.
Chen, S., K. Zhu, Q. Zhong, K. Jiang, Z. Wang, and T. Mu, Genomic characterization of immune checkpoint inhibitor resistance genes and hyperprogression related genes in advanced lung cancer. 2022, American Society of Clinical Oncology.

No competing interests reported.

Download PDF

Reviews received at journal
10 Sep, 2024
Reviewers agreed at journal
29 Aug, 2024
Reviewers invited by journal
26 Aug, 2024
Editor assigned by journal
21 Aug, 2024
Editor invited by journal
07 Aug, 2024
Submission checks completed at journal
07 Aug, 2024
First submitted to journal
29 Jul, 2024

You are reading this latest preprint version

Translation element EIF4A1 is a potential divergent immune biomarker between colon cancer and rectal cancer

Status:

Version 1

Abstract

Figures

Intruduction

Rusult

Screening for differential prognostic genes:

Expression landscape of EIF4A1

Clinical diagnosis and prognostic value evaluation of EIF4A1

EIF4A1 transcription of expression and the clinical correlation analysis

The correlation between EIF4A1 protein expression and clinical analysis

EIF4A1 interaction analysis

EIF4A1 enrichment of function analysis

Immune infiltration analysis

Correlation of EIF4A1 Expression with Immune Checkpoint Regulation and its Implications for Immunotherapy Efficacy

Discussion

Conclusion

Materials and methods

Screening for intersecting genes

Gene co-expression analysis

Analysis of EIF4A1 expression

Survival analysis

ROC analysis of EIF4A1

Association of EIF4A1 Transcript and Protein Expression with Clinicopathological Characteristics in Cancer

Protein-Protein Interaction (PPI) Network Analyses of EIF4A1

Functional enrichment analysis of EIF4A1-related differential genes

Immunoassay of EIF4A1 in COAD and READ

Statistical analysis

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1