Hsa-miR-483-5p/mRNA network that regulates chemotherapy resistance in locally advanced rectal cancer identified through plasma exosome transcriptomics

Abstract

BACKGROUND

Chemoresistance is the primary contributor to distant metastasis in the context of neoadjuvant chemoradiotherapy (nCRT) for rectal cancer. However, the underlying mechanisms remain elusive.

AIM

To detect the differential expression profiles of plasma exosomal microRNAs (miRNAs) in poor and good responders and explore the potential mechanisms of chemoresistance.

METHODS

In this study, the profiles of plasma exosomal miRNAs were compared in two dimensions according to treatment responses (poor/good responders) and treatment courses (pre/post-nCRT) using RNA sequencing.

RESULTS

Exosome hsa-miR-483-5p was up-regulated in good responders post-nCRT. Bioinformatics analysis revealed that the target genes of hsa-miR-483-5p were mainly enriched in tumor-specific pathways, such as the MAPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, Toll-like receptor signaling pathway, VEGF signaling pathway, and mTOR signaling pathway. Further analysis indicated that MAPK3, RAX2, and RNF165 were associated with inferior recurrence-free survival in patients with rectal cancer, and the profiles of MAPK3, TSPYL5, and ZNF417 were correlated with tumor stage. In addition, the expression profiles of MAPK3, RNF165, and ZNF417 were negatively correlated with inhibitory concentration 50 values. Accordingly, an hsa-miR-483-5p/MAPK3/RNF 165/ZNF417 network was constructed.

CONCLUSION

This study provides insights into the mechanism of chemoresistance in terms of exosomal miRNAs. However, further research is required within the framework of our established miRNA-mRNA network.

Key Words: Locally advanced rectal cancer; Neoadjuvant chemoradiotherapy; Poor-responders; Good responders; Exosome miRNA

Core Tip: Neoadjuvant chemoradiotherapy (nCRT) is the standard of care for the management of locally advanced rectal cancer; however, chemoresistance remains a life-threatening problem in patients with colorectal cancer. In this study, we used RNA sequencing to identify the expression profiles of plasma exosomal microRNAs (miRNAs) in two dimensions, according to treatment response (poor/good responders) and treatment course (pre/post-nCRT). Exosome hsa-miR-483-5p was upregulated in good responders post-nCRT and was chosen for further analysis. A network of hsa-miR-483-5p/MAPK3/RNF 165/ZNF417 was constructed using bioinformatic analysis in terms of survival, stage, and drug sensitivity. This study provides insights into the mechanism of chemoresistance in terms of exosomal miRNAs. However, further research is required within the framework of our established miRNA-mRNA network.

INTRODUCTION

Neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision, is the gold standard treatment for locally advanced rectal cancer (LARC). This approach has significant benefits, including a marked improvement in local tumor control and a notable reduction in the local recurrence rare[1,2]. Nonetheless, the distant metastasis rate persists at approximately 30% and is the primary contributor to disease-associated mortality[2]. Good responders to nCRT, as reflected by the standards of the College of American Pathologists (CAP 0-1), exhibit superior disease-free survival and lower distant metastasis rates than poor responders (CAP 2-3)[3,4]. Chemoresistance could be a significant factor contributing to poor response to nCRT. Investigating the mechanisms underlying chemoresistance will enable the identification of potential responders before nCRT and predict the prognosis of patients with LARC.

Exosomes are small membrane vesicles released by various cell types, with diameters ranging from 30 to 100 nm[5,6]. They function as vehicles for intercellular communication and carry various molecules, including microRNAs (miRNAs)[7]. By transporting miRNAs, exosomes influence gene expression and regulate cellular functions in target cells. The intricate interaction between exosomes and miRNAs is pivotal for maintaining effective intercellular communication, governing gene expression, and influencing crucial biological processes (BP). Notably, exosomal miRNAs are closely associated with tumor pathogenesis and progression[8-10]. The exosome miR-208b has been identified as a promoter of oxaliplatin-related chemoresistance by inhibiting the expression of PDCD4[10]. Exosomal miR-423-3p is a potential predictive biomarker of castration resistance in prostate cancer, as reflected by RNA sequencing (RNA-seq)[9]. However, only a few studies have investigated the dysregulation of exosomal miRNAs in patients with LARC receiving nCRT.

Hence, we designed this study to compare the differential expression profiles of plasma exosomal miRNAs between poor and good responders at pre- and post-nCRT time points using whole transcriptome RNA-seq. RNA-seq identified a set of dysregulated exosomal miRNAs, and several biological pathways and hub genes were identified using bioinformatic methods. This study aimed to identify potential exosomal miRNAs that could predict tumor responses to nCRT. Additionally, we aimed to construct a miRNA-mRNA network to explore the potential mechanisms of chemoresistance in poor responders.

MATERIALS AND METHODS

Sample collection

Fourteen patients diagnosed with rectal cancer were included in this study. Patients with pathologically confirmed adenocarcinoma (cT3-4 or cN1-2) who were scheduled to receive neoadjuvant long-course radiotherapy (45 Gy/25 fractions) concurrently with three cycles of oxaliplatin and capecitabine, followed by radical surgery, were enrolled. The degree of tumor response following nCRT was evaluated using the CAP standards (Table 1). Patients were categorized as responders (CAP 0-1) and poor responders (CAP 2-3).

Table 1 The College of American Pathologists standards and the group information.

The criteria of CAP standards for the assessment of tumor response to nCRT
Good responder	CAP0	Complete response with no viable tumor cells
	CAP1	Only small clusters or single cancer cells remained
Poor-responder	CAP2	Residual tumor cells remaining, but with predominant fibrosis
	CAP3	Poor response with extensive residual tumor cells
The sub-group information of this study
Groups	Group 1	Poor responders before nCRT
	Group 2	Good responders before nCRT
	Group 3	Poor responders after nCRT
	Group 4	Good responders after nCRT

nCRT: Neoadjuvant chemoradiotherapy; CAP: College of American Pathologists.

Peripheral blood samples (20 mL) from these 14 patients at the pre- and post-nCRT time points were collected for RNA-seq. Subsequently, the samples were categorized into four groups based on the CAP grades and different time points: Poor responders pre-nCRT, good responders pre-nCRT, poor responders post-nCRT, and good responders post-nCRT. Detailed information on the study design is shown in Figure 1. The study protocol was approved by the Ethics Committee of Peking Union Medical College Hospital (No. JS-3209). Written informed consent was obtained from all the participants.

Figure 1 The flow diagram of study design. nCRT: Neoadjuvant chemoradiotherapy; RNA seq: RNA sequencing; LARC: Locally advanced rectal cancer; CAP: College of American Pathologists; DE: Differentially expressed; miRNA: MicroRNA.

Exosomal miRNA extraction and RNA-seq

Peripheral blood samples were collected before and after nCRT using EDTA tubes. Following centrifugation at 3000 × g for 15 minutes at 4 °C, plasma was preserved at -80 °C until use. The thawed plasma supernatant underwent a series of steps, including dilution, filtration, and ultracentrifugation (100000 × g, 2 hours, 4 °C) to obtain exosome pellets. Exosomes were isolated via size exclusion chromatography[11]. In brief, 1 mL of 0.8 μm filtered plasma was 1.5-fold diluted with PBS, purified using Exosupur^® columns, eluted with 0.1 M PBS, and concentrated to 200 μL. Small RNA libraries (1-500 ng) were prepared using the QIAseq miRNA Library Kit, and library quality was assessed using an Agilent Bioanalyzer 2100 and qPCR. Sequencing was performed using an Illumina HiSeq platform. The reads identified known and predicted new miRNAs by comparison with miRbase and the Human Genome (GRCh38).

Read counts and transcripts per million were calculated based on the mapping results. Differential analysis was performed using the DESeq2 R package v.1.10.1 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html). RNAs discovered by RNA-seq with an adjusted P value < 0.05 and an absolute log2 fold change > 1 were considered differentially expressed (DE).

Visualization and identification of exosome DE miRNAs across groups

The differential expression profiles of exosomal miRNAs among groups were presented in the forms of a heatmap and a volcano plot using R packages “pheatmap” version 1.0.12 (https://CRAN.R-project.org/package=pheatmap). After exploratory visualization, a sophisticated least absolute shrinkage and selection operator (LASSO) regression analysis was performed using R version 4.3.1.

Target gene selection and bioinformatic analysis

Target genes of the identified DE miRNAs were extracted from three databases: TargetScan (http://www.targetscan.org/), miRPath (https://mpd.bioinf.uni-sb.de/), and miRDB (http://mirdb.org/). The selected common target genes were visualized using Venn diagrams (https://omics.pnl.gov/software/venns). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of target genes were conducted using DAVID (https://david.ncifcrf.gov) and KOBAS (http://kobas.cbi.pku.edu.cn/kobas3), respectively. GO comprised three categories: BP, molecular functions, and cellular components. The SangerBox platform (http://sangerbox.com) was us11s used to identify the top 10 hub genes within the protein-protein interaction (PPI) network. Results were visualized using Cytoscape (v3.10.1).

Survival analysis of target genes using TCGA database

To further explore the functions of the target genes, survival analysis was performed online using the TCGA database (https://kmplot.com/analysis/). The subgroup criteria were colorectal cancer (CRC), without distant metastasis, five-year recurrence-free survival (5y-RFS), and overall survival (OS). The study cohort included 625 patients with CRC. Based on the median expression profiles of the target genes, all patients were stratified into low- and high-expression groups, and then the 5y-RFS and OS between groups were compared using Kaplan-Meier curves and log-rank tests. Correlations between the expression profiles of the target genes and tumor stage in patients with CRC were also analyzed using the online TCGA database (http://sangerbox.com/home.html).

Drug sensitivity of oxaliplatin in CRC cell lines

All the patients received an oxaliplatin-based chemotherapy regimen. To investigate the molecular mechanisms of DE miRNAs and their target genes in individuals with varying responses, we conducted a drug sensitivity analysis using the GDSC database (https://ngdc.cncb.ac.cn/). The expression profiles of the target genes and the oxaliplatin-associated IC₅₀ values in six CRC cell lines (HCT116, HT-29, SW480, SW620, COLO-205, and HCT116) were extracted from the GDSC database. Subsequently, we categorized the target genes into low- and high-expression groups based on their median levels in the cell lines. Comparisons of IC₅₀ values between these groups were performed to assess differences.

Statistical analysis

SPSS 25.0 (IBM Corp., Armonk, NY, United States) was used for statistical analysis, and GraphPad Prism 7.0 (GraphPad Software, La Jolla, CA, United States) was used to draw the figures. Normally distributed variables were reported as mean ± SD, and categorical data were reported as numbers and percentages (%). Between-group differences were compared using Student’s t-test. P values < 0.05 were considered statistically significant.

RESULTS

Differential profiles of exosome DEmiRNAs in poor- and good-responders

RNA-seq identified 1167 exosomal miRNAs, including 106 newly discovered miRNAs. Initially, the expression profiles of plasma exosomal miRNAs pre-nCRT were compared between poor and good responders, and 27 DEmiRNAs (19 downregulated and 8 upregulated) were identified (Figure 2A and B). The detection of candidate DEmiRNAs pre-nCRT may enable us to predict the treatment response to nCRT. In addition, compared to poor responders, approximately 19 exosomal DEmiRNAs (10 downregulated and 9 upregulated) post-nCRT were identified in good responders (Figure 2C and D). The top five upregulated and downregulated exosomal DEmiRNAs are listed in Table 2.

Figure 2 The expression profiles of exosome microRNAs and the selection of interest microRNAs. A-D: The heatmaps and volcano plots of exosome differentially expressed (DE) microRNAs (miRNAs) in poor-responders and good responders pre- neoadjuvant chemotherapy (nCRT) (A and B) and post-nCRT (C and D); E and F: Showed the results of least absolute shrinkage and selection operator regression analysis to identify the exosome hsa-miR-483-5p as candidates for further analysis; G: Shows the cross-group comparison of the expression profiles of hsa-miR-483-5p among the four group. DE: Differentially expressed: NCRT: Neoadjuvant chemotherapy; CAP: College of American Pathologists.

Table 2 The differential profiles of exosome microRNAs in poor- and good responders.

Pre-nCRT (G1 vs G2)				Post-nCRT (G3 vs G4)
miRNAs	Log2FC	P value	Regulation	miRNAs	Log2FC	P value	Regulation
MiR-493-5p	4.64	0.001	Up	MiR-516a-5p	4.74	0.041	Up
MiR-332a-3p	3.85	0.018	Up	MiR-378 g	2.98	0.047	Up
MiR-4324	2.89	0.043	Up	MiR-150-3p	2.38	0.036	Up
MiR-335-3p	2.33	0.001	Up	MiR-483-5p	1.65	0.001	Up
MiR-1303	2.25	0.048	Up	MiR-582-3p	1.16	0.032	Up
MiR-2355-3p	-4.61	0.006	Down	MiR-4497	-4.44	0.026	Down
MiR-184	-4.37	0.027	Down	MiR-935	-3.09	0.029	Down
MiR-5187-5p	-4.33	0.015	Down	MiR-4516	-2.18	0.044	Down
MiR-122-3p	-4.37	0.027	Down	MiR-486-3p	-2.09	0.041	Down
MiR-378a-5p	-3.49	0.001	Down	MiR-651-5p	-1.73	0.032	Down

nCRT: Neoadjuvant chemoradiotherapy; miRNAs: MicroRNAs.

The profiles of plasma exosomal miRNAs also exhibited differences at the pre- and post-nCRT time points. During the treatment course, 21 DEmiRNAs (10 downregulated and 11 upregulated) and 36 DEmiRNAs (21 downregulated and 15 upregulated) were identified in poor responders (Supplementary Figure 1A and B) and good responders (Supplementary Figure 1C and D), respectively. The top five upregulated and downregulated DE miRNAs are listed in Supplementary Table 1.

Hsa-miR-483-5p is up-regulated in good-responders post-nCRT

The identification of exosomal DE miRNAs pre-nCRT would enable the selection of potential responders to chemoradiation. Additionally, distinct DEmiRNAs profiles observed post-nCRT may serve as valuable indicators for predicting patient prognosis. LASSO regression analysis was used to select potential exosomal DEmiRNAs that might play a significant role in the response to nCRT. Thus, hsa-miR-483-5p was identified as a prognostic indicator for further analyses (Figure 2E and F).

Comparative analysis between groups revealed an upregulation of exosome hsa-miR-483-5p in good responders post-nCRT, in contrast to poor responders. Furthermore, within the subgroup of good responders, hsa-miR-483-5p exhibited increased expression post-nCRT compared with its pre-nCRT levels, as illustrated in Figure 2G. These results emphasize the potential utility of hsa-miR-483-5p as a predictive biomarker for the treatment response and prognosis of patients with LARC in the context of nCRT.

Tumor-specific pathways enriched in the target genes of hsa-miR-483-5p

To further explore the potential functions of hsa-miR-483-5p and its target genes, a comprehensive bioinformatics analysis was conducted. The analysis revealed several important KEGG pathways enriched by target genes, including “MAPK signaling pathway”, “EGFR tyrosine kinase inhibitor resistance”, “Toll-like receptor signaling pathway”, “VEGF signaling pathway”, and “mTOR signaling pathway”, as illustrated in Figure 3A. GO analysis was also made in the target genes of hsa-miR-483-5p, and the results were available in Supplementary Figure 2. After the enrichment of genes within the identified KEGG pathways, the hub genes MAPK3, MAPK1, DUSP1, ELK1, FOS, and PRKCA were selected by PPI analysis (Figure 3B). Moreover, cross-group comparisons revealed that two common target genes, MAPK1 and MAPK3, were consistently enriched across all five KEGG pathways (Figure 3C).

Figure 3 The bioinformatics analysis of the target genes of exosome hsa-miR-483-5p. A: Represents the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment results of target genes; B: The protein-protein interaction (PPI) network and the hub genes selected in the enriched five KEGG pathways; C: The selected common target genes of hsa-miR-483-5p enriched in the five KEGG pathways, with two identified hub genes-MAPK1 and MAPK3; D: The PPI network and the hub genes selected from the target genes of exosome hsa-miR-483-5p; E: The selection of target genes of hsa-miR-483-5p from the three miRNA-associated databases, with chosen 46 common genes. KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: Protein-protein interaction.

The target genes of hsa-miR-483-5p were identified using online databases, with 1816 target genes identified in TargetScan, 4266 genes in miRPath, and 59 genes in miRDB. Subsequently, comprehensive analysis identified approximately 46 common target genes in the three databases (Figure 3D). PPI analysis of these 46 common target genes also identified ten hub genes, as shown in Figure 3E. This convergence of genes across multiple databases underscores their potential significance and serves as a robust foundation for further in-depth analyses and explorations.

MAPK3, RAX2, and RNF165 were associated with inferior RFS in CRC patients

The TCGA database was used for survival analysis. The results indicated that upregulation of hsa-miR-483-5p correlated with worse RFS in breast cancer, demonstrating its potential role in the pathogenesis and progression of tumors (Figure 4A). Forty-six selected target genes were analyzed for RFS and OS. The results demonstrated that low expression of 22 target genes was correlated with superior RFS in patients with CRC (9 genes were positively correlated and 13 genes were negatively correlated) (Supplementary Figure 3A), and 21 target genes were correlated with superior OS (7 genes were positively correlated and 14 genes were negatively correlated) (Supplementary Figure 3B). We also discovered that the low expression of two target genes, RAX2 and RNF 165, was correlated with relatively better RFS and OS in patients with CRC (Figure 4B-E). In addition, low expression of MAPK3, selected from the above five KEGG pathways, was correlated with better RFS (Figure 4F).

Figure 4 The survival analysis of target genes of hsa-miR-483-5p using TCGA database. A: The up-regulation of hsa-miR-483-5p is associated with a relatively poor survival outcomes in breast cancer patients, suggesting its role in the pathogenesis of tumors; B and C: Demonstrated that the low-expression of target genes of RAX2 and RNF165 are correlated with better 5 years-overall survival in colorectal cancer patients; D-F: Represents that the low-expression of target genes of RAX2, RNF165, and MAPK3 are positively associated with superior 5 years-recurrence-free survival, correspondingly. OS: Overall survival; RFS: Recurrence-free survival; CRC: Colorectal cancer; HR: Hazard ratio.

Profiles of MAPK3, TSPYL5, and ZNF417 were correlated with tumor stage in CRC

In addition, nine target genes that were positively correlated with RFS, including CDK15, HGSNAT, MAPK3, PAX2, RNF165, STK40, TSPYL5, USP4, and ZNF417, were selected to evaluate the relationships between their expression profiles and tumor stage (Figure 5). The results revealed that low expression of the target genes, MAPK3, TSPYL5, and ZNF417, was related to a relatively early tumor stage, further demonstrating its positive impact on survival outcomes.

Figure 5 The association of the expression level of 9 target genes and the tumor stage. The results revealed that the expression levels of target genes of MAPK3, TSPYL5, and ZNF417 are negatively correlated with the tumor stages.

Profiles of MAPK3, RNF165, and ZNF417 were negatively correlated with IC₅₀ values

Oxaliplatin and capecitabine are the foundations of current chemotherapy regimens for patients with LARC, and sensitivity analysis of tumor cells to oxaliplatin could enable us to further elucidate the mechanisms of chemoresistance. Drug sensitivity analyses on the nine target genes selected from the survival analysis was performed using the GDSC database. The results indicated that low expression of MAPK3, RNF165, and ZNF417 was associated with decreased IC₅₀ value (Figure 6). The lower the IC₅₀ value, the better the tumor response to chemotherapy. Therefore, these three target genes may play important roles in chemotherapy sensitivity.

Figure 6 The relationship of the 9 target genes and drug-sensitivity of oxaliplatin. The results indicated that the lower expression of target genes of RNF165, ZNF417, and MAPK3 are prone to exhibit relatively lower IC₅₀ value.

Construction and validation of hsa-miR-483-5p/mRNA network

Based on the above analysis, the target genes of hsa-miR-483-5p, MAPK3, RNF 165, and ZNF417 were finally selected, and networks of hsa-miR-483-5p-mRNAs were constructed and are presented in Figure 7A. The potential binding sites of hsa-miR-483-5p and its target genes are also listed in Figure 7B.

Figure 7 The construction of microRNAs-mRNA network. A: The construction of “hsa-miR-483-5p/RNF165”, “hsa-miR-483-5p/ZNF417”, and “hsa-miR-483-5p/MAPK3” network; B: The potential binding sites of hsa-miR-483-5p and its target genes predicted from Target Scan database. miRNA: MicroRNA.

DISCUSSION

Approximately one-third of patients with LARC do not benefit from the current standard nCRT, leading to distant metastasis. Chemoresistance may play a crucial role in tumor metastasis. Understanding the molecular alterations in poor and good responders during nCRT may enable us to explore the potential mechanisms of chemoresistance. RNA-seq identified several plasma exosomal DEmiRNAs in poor and good responders and revealed their potential functions by bioinformatic analysis. In addition, the expression profiles of exosomal DE miRNAs in LARC patients pre- and post-nCRT were determined.

miRNAs have been shown to be correlated with chemoresistance[12,13]. Chen et al[14] demonstrated that decreased expression of mitochondrial miR-5787 contributes to chemoresistance by reprogramming glucose metabolism and inhibiting MT-CO3 translation. Furthermore, miR-23b modulates the epithelial-mesenchymal transition of CRC cells to mediate chemoresistance to 5-fluorouracil and oxaliplatin[15]. In addition, miR-153[16], miR-135b-5p[17], miR-15a[18], and miR-106a[19] have also been identified to positively or negatively regulate chemoresistance to oxaliplatin. However, studies focusing on the relationship between dysregulated exosomal miRNAs and chemoresistance after nCRT in patients with LARC are lacking.

In this study, the expression profiles of exosomal DEmiRNAs were compared in two dimensions according to the treatment response (poor responders vs good responders) and treatment courses (pre-nCRT vs post-nCRT). The results identified exosomal hsa-miR-483-5p, which was upregulated in good responders post-nCRT, as a candidate miRNA to further explore the mechanism of chemoresistance. Although no previous studies have demonstrated the functions of hsa-miR-483-5p in CRC, especially in chemoresistance, several studies have demonstrated that dysregulation of hsa-miR-483-5p is associated with relatively inferior survival outcomes in adrenocortical cancer[20], hepatocellular carcinoma[21], esophageal cancer[22], and breast cancer[23]. Accordingly, exosomal hsa-miR-483-5p might play an important role in the pathogenesis and progression of CRC, as well as in the process of chemoresistance, as indicated through the RNA-seq analysis.

The physiological roles of miRNAs depend on their capacity to post-transcriptionally regulate gene expression. Consequently, the upregulation of hsa-miR-483-5p observed in good responders suggests its inhibitory effect on the expression of its target genes. This regulatory mechanism provides insights into the potential molecular pathways through which hsa-miR-483-5p exerts its influence, offering valuable perspectives on the complex processes shaping the treatment response in the studied cohort.

To better explore the functions of hsa-miR-483-5p, bioinformatic analysis was conducted on its target genes, and as a result, several tumor-specific KEGG pathways were enriched. Fang and Richardson[24] demonstrated that MAPK signaling pathways are closely correlated with the pathogenesis of CRC by regulating the BP of angiogenesis, proliferation, apoptosis, differentiation, and metastasis. The upregulation of METTL3 promotes CRC metastasis via the miR-1246/SPRED2/MAPK signaling pathway[25]. Sun et al[26] also demonstrated that USP11 promotes the growth and metastasis of CRC via PPP1CA-mediated activation of the ERK/MAPK signaling pathway. These findings revealed a complex interactive network of miRNAs, the MAPK signaling pathway, and the pathogenesis and progression of CRC[25]. In addition to the MAPK signaling pathways, the EGFR tyrosine kinase inhibitor resistance[27], Toll-like receptor signaling[28], VEGF signaling[29], and mTOR signaling pathways[30] were also significant contributors to the progression of CRC.

Upregulation of exosomal hsa-miR-483-5p in responders is associated with better survival outcomes. Moreover, hsa-miR-483-5p serves as a molecular sponge that inhibits the expression of its target genes. Accordingly, the selected target genes of hsa-miR-483-5p were crucial for exploring the mechanisms involved in chemoresistance.

In this study, the target genes MAPK3, RNF 165, and ZNF417 were identified in the miRNA-mRNA regulatory network. Several studies have illustrated the role of MAPK3 in the pathogenesis of CRC[30,31]. MAPK3, which encodes ERK1, is a crucial gene within the MAPK family that participates in several crucial cellular processes, including cell proliferation and differentiation, as well as the regulation of cell survival and apoptosis[30,31]. Over-activation may be associated with cancer development and treatment responses[24,32]. Although bioinformatics analysis identified that lower expression of RNF165 and ZNF417 was predicted to have a superior RFS, few studies have focused on the biological function of these two target genes in the setting of CRC, especially during nCRT. However, further molecular experiments are required to elucidate their exact functions.

These hub genes, particularly MAPK3, RNF165, and ZNF417, are pivotal players in the molecular networks associated with the identified pathways. Their consistent presence suggests their potential role in mediating biological responses related to tumor-specific signaling pathways. This comprehensive analysis enhances our understanding of the regulatory landscape of hsa-miR-483-5p and its implicated pathways.

CONCLUSION

In conclusion, the upregulation of exosomal hsa-miR-483-5p reflects a relatively better tumor response to nCRT and thus a superior survival outcome. An interactive network of hsa-miR-483-5p/MAPK3/RNF165/ZNF417 was constructed using bioinformatic analysis. Further research is required to elucidate the mechanisms of chemoresistance within the framework of our miRNA-mRNA network.