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. 2025 Nov 26;16:2307. doi: 10.1007/s12672-025-04166-8

Identification of FGF14-AS2/MPP2 axis as prognostic biomarkers in colorectal cancer based on CeRNA network

Rongrong Zhou 1,3,#, Zhineng Zeng 1,#, Ying Xu 1, Qiong Tang 2, Songnan Zhang 1, Junfeng Zhu 1,4,✉
PMCID: PMC12748477  PMID: 41296134

Abstract

Background

Colorectal cancer (CRC) remains a major global health challenge due to its high morbidity and mortality, even with recent advancements in diagnosis and treatment. Increasing evidence suggests that competitive endogenous RNA (ceRNA) regulatory networks play pivotal roles in the pathogenesis of various cancers, including CRC. However, the potential application of ceRNA networks in CRC diagnosis and their underlying mechanisms remain insufficiently explored.

Methods

The gene expression profiles and clinical information of CRC were extracted from TCGA database and GEO databases. Differential expression genes (DEGs) were identified using bioinformatics analyses, a ceRNA network was constructed via Cytoscape. Functional enrichment was assessed through GO and KEGG analyses, while hub genes were identified using the STRING database and cytoHubba plugin. Key prognostic genes were determined through univariate/multivariate Cox analysis, Kaplan-Meier analysis and correlation analysis. The expression of prognostically relevant lncRNAs and mRNAs was validated by qRT-PCR and GEO database A prognostic model was developed using univariate/multivariate COX analysis and Lasso regression analysis, and its performance was assessed via ROC and Kaplan-Meier analysis in TCGA and GSE39582 database. Drug sensitivity was predicted using the Cellminer database.

Results

We identified a ceRNA network comprising 75 differentially expressed lncRNAs (DElncRNAs), 60 DEmiRNAs and 382 DEmRNAs, alongside a PPI network of 330 DEmRNAs. A lncRNA-miRNA-hub gene subnetwork was also constructed, involving 51 DElncRNAs, 21 DEmiRNAs, and 20 hub genes. Notably, MPP2 and FGF14-AS2, both downregulated in CRC, were associated with the prognosis. A prognostic model incorporating N stage, T stage, and FGF14-AS2 expression moderate predictive ability. Additionally, FGF14-AS2 and MPP2 showed correlations with specific chemotherapeutic agents.

Conclusions

FGF14-AS2 and MPP2 are downregulated in CRC and serve as potential prognostic biomarkers. The prognostic model developed provides moderate predictive value for CRC outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-04166-8.

Keywords: Colorectal cancer, CeRNA, FGF14-AS2, MPP2, Prognosis

Introduction

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths, accounting for approximately 10% of all new cancer diagnoses and 9.4% of cancer deaths in 2020, second only to lung cancer, which contributes 18% of cancer-related mortality [1, 2]. By 2040, the global incidence of CRC is projected to reach 3.2 million new cases annually [2]. Despite advances in our understanding of CRC pathophysiological and the development of standardized treatments, including surgical, targeted therapy, and immunotherapy, the global burden of CRC is expected to increase by 60%, with more than 2.2 million new cases and 1.1 million deaths projected by 2030 [3]. Patients diagnosed with advanced-stage CRC continue to face poor prognoses, largely due to the lack of reliable biomarkers for early detection and prognostic prediction. The 5-year survival rate for patients with metastatic or late-stage CRC is only 13.1% [4]. Consequently, there is an urgent need to identify reliable biomarkers that can facilitate early detection and improve outcomes for CRC patients.

The competing endogenous RNA (ceRNA) hypothesis, firstly proposed in 2011, suggests that ceRNAs regulate target genes expression by competitively binding shared microRNAs (miRNAs) [5].RNAs containing microRNA response elements (MREs), such as long non-coding RNAs (lncRNAs), mRNAs, and pseudogenes can act as ceRNAs. Long non-coding RNAs (lncRNAs), which are >200 bases long and lack protein-coding potential, have emerged as key ceRNA regulators in cancer, offering novel biomarkers and therapeutic targets [6, 7]. In CRC, several studies have revealed the vital roles of lncRNAs as ceRNAs in the tumor growth, metastasis and prognosis. For example, Liu et al. [8]identified a 3-lncRNA signature (LINC00114, LINC00261, and HOTAIR) with the significant prognostic value While Zhao et al. [9] showed that MIR17HG promoted CRC liver metastasis by acting as a ceRNA. Other studies have similarly highlighted the importance of lncRNAs in CRC progression and therapeutic resistance [10, 11] MicroRNAs are small noncoding RNA gene products about 22 nt long and related to tumor development, progression and response to therapy [12, 13]. For instance, miR-637 functions as a tumor suppressor, inhibiting cancer cell growth and drug resistance [14]. MiR-490, in its two forms (MiR-490-3p and miR-490-5p), plays complex roles in cancer cell invasion and proliferation through ceRNA network [15]. MiR-106b contributes to CRC progression via the MAPK pathway [16]. Although some studies have reported on the role of the ceRNA network in CRC, its function and underlying mechanisms in CRC prognostic remain unclear.

In the present study, we aimed to construct a prognostic model of ceRNA molecules and verify its prognostic value. Firstly, we constructed a prognosis-related ceRNA network based on gene expression profiles and clinical information from TCGA database. Through differential expression analysis and functional enrichment, we explored the potential mechanisms of this network in CRC progression. A key ceRNA axis (FGF14-AS2/has-miR-34a-5p/MPP2) were identified through univariate/multivariate Cox analysis, Kaplan-Meier analysis and correlation analysis. In addition, through Cox analysis and Lasso regression, a prognostic model of CRC was established based on N stage, T stage and FGF14-AS2 expression. QRT-PCR and correlation analysis were applied to validate the expression levels of key biomarkers. The flowchart of this study is shown in Fig. 1.

Fig. 1.

Fig. 1

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Flowchart of the study

Materials and methods

Data collection and differential expression analysis

RNAseq data, including HTseq-count of 638 primary CRC samples and 51 adjacent normal samples, were obtained from the TCGA database. Differential expression genes (DEGs) were identified using the Limma package in R, with thresholds of adjusted P-value < 0.05 and |log2 fold change (FC)|>2 for lncRNA, and adjusted P-value < 0.05 and |log2 FC|>1 for miRNA and mRNA. Heatmaps and the volcano plots were generated to visualize DEGs distribution. Clinical and survival data were also collected from TCGA. For validation, five independent microarray datasets (GSE110715, GSE75970, GSE184093, GSE134525 and GSE39582) were retrieved from GEO database by AnnoProbe package in R. We also used hgu133plus2.db package to convert gene names and used Combat algorithm package to eliminate batch effects. In our study, TCGA database was used as the test group and GSE39582 were used as the validation group for prognostic model construction.

CeRNA network construction and functional analysis

DElncRNAs was input into DIANA-LncBase v3to predict target miRNAs. The intersecting miRNAs and DEmiRNAs were used to predict target genes through miRDB, mirDIP and TargetScanHuman databases. mRNAs predicated by all three databases were selected as candidate target genes. The CRC-related ceRNA network was visualized with Cytoscape. GO and KEGG pathway enrichment analyses were performed using ClusterProfilter package in R, with pathways considered significant at P < 0.05.

PPI network and sub-ceRNA network construction

DEmRNAs in the ceRNA network were analyzed using STRING database to explore protein-protein interactions. the PPI network was visualized suing Cytoscape, and CytoHubba was employed to identify key nodes and subnetworks. Twenty hub genes were extracted using DMNC algorithm, and a sub-ceRNA network was constructed based on these hub genes.

Survival analysis, prognostic model construction and drug susceptibility prediction

Survival analysis and univariate Cox regression analyses were performed on the 20 hub genes to identify potential prognostic biomarkers. Hub genes at P < 0.05 were considered as significant genes. Correlation analysis was used to identify the key lncRNA-miRNA-mRNA regulatory axis within the ceRNA network. Prognostic biomarkers and the clinical information (age, gender, N stage and so on) were included in univariate and multivariate Cox regression analyses. Lasso regression was applied develop a reliable prognostic model. The accuracy of the prognostic model was assessed using Receiver operating characteristic curves (ROC) and Kaplan-Meier survival analysis in TCGA and GSE39582 database. CellMiner database was used to further explore the potential target drugs of key genes. Target drugs at P < 0.05 were considered as significant drugs.

Validate by qRT-PCR and GEO data

Quantitative reverse transcriptase-PCR (qRT-PCR) was used to validate the expression of the prognostic biomarkers in blood samples from 50 CRC patients and 40 healthy controls collected from The First Affiliated Hospital of Guilin Medical University. Blood cells were processed for RNA extraction suing Trizol reagent (Takara, Japan), and qRT-PCR was conducted using the PrimeScript reverse transcriptase kit (Takara, Japan). GAPDH served as the reference gene and expression levels were calculated using the 2−ΔΔCT method. GEO datasets (GSE110715, GSE75970, GSE184093, GSE134525 and GSE39582) were also used to validate the expression level of the prognostic biomarkers.

Result

Identification of differentially expressed genes in CRC

Differential gene expression analysis between CRC and normal tissue samples identified 166 DElncRNAs (39 upregulated and 127 downregulated), 361 DEmiRNAs (172 upregulated and 189 downregulated) and 5954 DEmRNAs (777 upregulated and 5177 downregulated). The expression patterns of 15 significant variable genes were visualized via heatmaps (Fig. 2A-F).

Fig. 2.

Fig. 2

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Identification of Differentially Expressed Genes in CRC. A,C,E: volcano plots of DElncRNAs, DEmiRNAs and DEmRNAs. B,D,F: heatmap plots DElncRNAs, DEmiRNAs and DEmRNAs

Construction of the CeRNA network

We identified 327 DElncRNA-DEmiRNA intersections containing 87 DElncRNAs and 110 DEmiRNAs using DIANA-LncBase v3. A total of 1157 common DEmiRNA-DEmRNA intersections, including 75 DEmiRNAs and 986 DEmRNAs were selected from miRDB, mirDIP, and TargetScan using Venn diagram analysis (Fig. 3A). After intersecting 986 DEmRNAs with the identified 5954 DEmRNAs, 449 common DEmiRNA-DEmRNA intersections were included in constructing the ceRNA network. Finally, the ceRNA network comprised 657 edges and 518 nodes, involving 75 DElncRNAs, 60 DEmiRNAs, and 382 DEmRNAs (Fig. 3B).

Fig. 3.

Fig. 3

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Constructed of the CeRNA Network. A veen diagram of mRNAs predicted (The yellow area, the pink area and the green area represent the number of mRNAs predicted in mirDIP database, miRDB database and Targetscan database respectively; the area in the middle indicates mRNAs predicated in three database). B ceRNA network (The circle represents mRNA, the diamond represents miRNA, the square represents lncRNA, orange represents upregulated gene expression, and red represents downregulated gene expression)

GO and KEGG enrichment analysis and PPI network construction

To explore the functional significance of ceRNA networks, we performed GO and KEGG enrichment analysis on the DEmRNAs within the network GO analysis revealed enrichment in DNA-binding transcription activator activity, RNA polymerase II-specific, and beta-catenin binding (Fig. 4A). KEGG pathway analysis highlighted pathways related to CRC, PI3K-Akt signaling pathway, and MAPK signaling pathway (Fig. 4B). We inputted 382 DEmRNAs into the STRING database to explore PPI, which were visualized using Cytoscape (Fig. 4C).

Fig. 4.

Fig. 4

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Functional enrichment analysis and PPI network construction. A GO enrichment analysis. B KEGG enrichment analysis. C PPI network(The round shape represents the mRNA, and the shade of color represents the complexity of the node)

Identification of hub genes and construction of sub-ceRNA network

CytoHubba plugin in Cytoscape, a widely used plugin in the software, was utilized to confirm 20 hub genes (FGF7, FGF18, KL, RORA, AXIN2, FASLG, TGFA, PTBP2, SOS2, BACH2, PDE3A, ZEB1, RIMBP2, SHC4, GAB1, SCN2A, MPP2, CASP7, E2F1, FGD4) based on DMNC algorithm (Fig. 5A). The ceRNA networks were filtered to construct sub-ceRNA network comprising 51 DElncRNAs, 21 DEmiRNAs, and 20 hub genes (Fig. 5B).

Fig. 5.

Fig. 5

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Identification of hub genes and construction of sub-ceRNA network. A the top 20 hub genes in PPI network. B sub-ceRNA based on the top 20 hub genes (The circle represents mRNA, the diamond represents miRNA, the square represents lncRNA, orange represents upregulated gene expression, and red represents downregulated gene expression)

Survival analysis, cox regression, correlation analysis and lasso regression

Kaplan-Meier analysis and univariate Cox regression analyses of hub genes revealed that MPP2 was the only one associated with the prognosis of CRC (Fig. 6A and B and Figure S1). one upregulated DEmiRNA (hsa-miR-34a-5p) and a total of 18 DElncRNA, including FGF14-AS2, were involved in the MPP2-related ceRNA network. Further univariate and multivariate Cox regression analyses identified T stage, N stage, and FGF14-AS2 as significant prognostic factors (Fig. 6C and D).

Fig. 6.

Fig. 6

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Survival Analysis, Cox Regression, correlation analysis and Lasso regression. A K-M survival analysis of MPP2. B univariate cox regression analyses of 20 hub genes. C univariate cox regression analyses of key lncRNAs and clinical factors. D multivariate cox regression analyses of key lncRNAs and clinical factors. E correlation analysis of hsa-miR-34a-5p and MPP2. F correlation analysis of FGF14-AS2 and MPP2. G correlation analysis of hsa-miR-34a-5p and FGF14-AS2. H, I lasso regression analysis

Correlation analysis confirmed that FGF14-AS2 and MPP2 were positively correlated, while both were negatively correlated with hsa-miR-34a-5p (Fig. 6E and G), consistent with ceRNA theory.

Prognostic Model Construction and Validation Multivariate Cox regression confirmed T stage, N stage, and lncRNA FGF14-AS2 as independent risk factors or pivotal prognostic biomarkers. Lasso regression was used to develop a prognostic model: Risk score = 0.036×N stage + 0.019×T stage + 0.005×FGF14-AS2 in CRC. (Figure 6H and I) ROC curves for the model in the TCGA and GSE39582 datasets indicated moderate predictive accuracy (Fig. 7). Kaplan-Meier analysis further showed that patients in the high-risk group had poorer survival outcomes (Fig. 8).

Fig. 7.

Fig. 7

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The receiver operating characteristic curve of the prognostic model in TCGA database and GSE39582 dataset. A TCGA database. B GSE39582

Fig. 8.

Fig. 8

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K-M survival analysis curves based on the prognostic model in TCGA and GSE39582 A K-M survival analysis of 3 years. B K-M survival analysis of 5 years. C K-M survival analysis in GSE39582

Drug susceptibility prediction

Drug sensitivity analysis using the Cellminer database revealed that FGF14-AS2 was positively correlated with asparaginase, chelerythrine, hydroxyurea and pluripotin, and was negatively correlated with pluripotin, while MPP2 was negatively correlated with BMS-536,924, BX-912, palbociclib, and perifosine. (Fig. 9).

Fig. 9.

Fig. 9

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The correlation analysis between FGF14-AS2/MPP2 and anticancer drugs and the comparison of the anticancer drug sensitivity between the high FGF14-AS2 expression group and low FGF14-AS2 expression group. A, E: asparaginase. B, F: chelerythrine. C, G: hydroxyurea. D, H: pluripotin. I, M: BMS-536,924. J, N: BX-912. K, O: palbociclib. L, P: perifosine. * P < 0.05, ** P < 0.01

Validation by qRT-PCR and GEO datasets

The differential expression of key biomarkers, FGF14-AS2 and MPP2, was validated using both the GEO database and qRT-PCR. Box plot based on GEO data confirmed significant downregulation of these genes in CRC (Fig. 10). qRT-PCR analysis further demonstrated reduced expression of FGF14-AS2 and MPP2 in CRC patient blood samples compared to healthy controls.

Fig. 10.

Fig. 10

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Validation by qRT-PCR and GEO Datasets. A validation of the expression of FGF14-AS2 in peripheral blood of colorectal cancer patients. B validation of the expression of MPP2 in peripheral blood of colorectal cancer patients. C validation of the expression of FGF14-AS2 in the GEO database. D validation of the expression of MPP2 in the GEO database. * P < 0.05, *** P < 0.001, **** P < 0.0001

Discussion

In our study, the expression profiles of CRC in the publicly available database TCGA were analyzed to identify abnormally regulated RNAs. Additionally, we utilized the STRING database to construct a PPI network. A ceRNA network and a sub-ceRNA network were established and visualized by integrating data from four online prediction databases using Cytoscape software. Through survival, Cox, and Lasso regression analysis, we identified that FGF14-AS2/hsa-miR-34a-5p/MPP2 was associated with the prognosis of CRC. A prognostic model was then developed, comprising N stage, T stage, and FGF14-AS2 expression. Finally, the aberrant expression of key genes was verified through experiments and data from the GEO database.

We identified promising biomarkers related to the prognosis of CRC and constructed a prognostic model incorporating N stage, T stage, and FGF14-AS2 expression. Previous studies have demonstrated that tumor staging in CRC independently and negatively impacts survival [17]. A higher T stage correlates with worse 5-year overall survival (OS) with survival rates decreasing from 87.5% in T3 to 71.5% in T4, and further declining to 46% in T4b [18, 19]. N Stage is recognized as the second strongest predictor of outcomes in CRC [17]. Five-year OS in node positive patients ranges from 30% to 60%, compared to 70% to 90% in node negative disease [20].

Fibroblast growth factor 14 antisense RNA (FGF14-AS2) is located on chromosome 13 and is transcribed from the fibroblast growth factor 14 (FGF14) gene. In the cytoplasm, FGF14-AS2 is highly expressed, suggesting that it may function as a sponge for miRNAs [21]. As a significant biomarker in cancer progression, FGF14-AS2 has not been extensively studied, except in breast tumor. Previous studies have demonstrated that FGF14-AS2 acts as a tumor suppressor gene in breast cancer, participating in processes such as cell proliferation, migration and invasion [22–25]. Additional studies have linked FGF14-AS2 to other types of cancer. For example, downregulation of FGF14-AS2 has been associated with prostate carcinoma progression by inhibiting has-miR-96-5p expression, thereby hampering AJAP1 [26]. Another study found that upregulated FGF14-AS2 in glioma promotes tumorigenesis by forming a feedback loop with the miR-320a/E2F1 axis [22]. However, the role of FGF14-AS2 in CRC has been scarcely explored. In recent decades, Hou et al. [27]. found that FGF14-AS2 was significantly downregulated in CRC cells and tissues, which contributed to the progression of CRC by suppressing miR-1288-3p and inactivating the Ras/ERK signaling pathway. The expression of FGF14-AS2 in our study is consistent with the findings of Hou et al. Therefore, the role of FGF14-AS2 in CRC warrants further attention. The results of drug susceptibility prediction in our study indicate that FGF14-AS2 is correlated with asparaginase sensitivity. In our KEGG analysis, the pathway predominantly involved was the PI3K-Akt signaling pathway, which regulates cellular proliferation, differentiation, and survival [28]. This pathway can also impart 5-fluorouracil resistance in CRC through HIF-1α-induced metabolic reprogramming [29]. A previous study has shown that KRAS/PI3K/Akt/mTORC1 signaling positively regulates endogenous asparagine production, and high levels of asparagine have been linked to worse prognosis in women with CRC compared to men [30]. Given the expression level of FGF14-AS2 was positively correlated with the drug asparaginase sensitivity, we inferred that FGF14-AS2 may affect asparaginase sensitivity through the PI3K/Akt signaling pathway in CRC.

MicroRNA is a subtype of noncoding RNA that plays a crucial role in various disorders. In our study, we focused on hsa-miR-34a-5p, which has been extensively studied in multiple contexts. Hsa-miR-34a-5p modulates key biologic processes, such as p53-induced cell cycle arrest, apoptosis, senescence, and proliferation [31]. Tian et al. [32]. found that has-miR-34a-5p participated in the proliferation, apoptosis and autophagy of osteoarthritis through SNHG7/miR-34a-5p/SYVN1 axis. Additionally, MiR-34a-5p functions as a tumor suppressor in cutaneous squamous cell carcinoma (cSCC) cells by targeting SIRT6 [33]. NONHSAG028908.3 may positively regulate ALDOA by sponging miR-34a-5p, thereby promoting malignant activities in CRC [34]. Hsa-miR-34a-5p has been shown to be upregulated in CRC and could serve as a prognostic marker in early-stage male patients [35, 36]. In our study, hsa-miR-34a-5p is upregulated and target MPP2, consistent with the findings of Li et al. [37]. In our KEGG analysis, the pathway was primarily related to the MAPK signaling pathway, which regulates a wide variety of cellular processes. The ERK/MAPK signaling pathway not only regulates cellular biological functions but also contributes to tumor proliferation, invasion and metastasis [38]. P38 MAPK signaling, a subtype of MAPK signaling pathway, was traditionally associated with inflammatory diseases, but it also plays a role in cancer, and p38 MAPK inhibitors offer therapeutic benefits with reduced side effects [39]. These results from our functional analysis suggest that the MAPK pathway is a promising treatment target for CRC. A study has shown that hsa-miR-34a-5p targets RAF1, a key factor in the MAPK pathway, and enhances the anti-tumoral effect of temozolomide in A172 cells [40].

MPP2 belongs to the Membrane-Associated Guanylate Kinase (MAGUK) family. Several studies have demonstrated that MPP2 plays a significant role in various complex biological process. In hepatocellular carcinoma, the expression of MPP2 is upregulated by miR-34a hypermethylation, leading to increased apoptosis in liver cancer cells [37]. At neuronal synapses, MPP2 functions as a postsynaptic protein linking postsynaptic adhesion molecules to receptor complexes through intracellular scaffold interactions [41]. In epithelial cells, MPP2 negatively regulates c-Src kinase activity, which is involved in a range of cellular functions, including cell proliferation and migration [42]. Although only a few studies have focused on the role of MPP2 in CRC, our findings suggest that MPP2 is associated with overall survival, and prognostic signatures incorporating MPP2 show strong predictive value.

Our study not only provides valuable insights for improving the diagnosis, management, and prognosis of CRC, but also offers important avenues for further study. However, we must acknowledge the several limitations. The first major limitation is the retrospective nature of the study. We relied on data from TCGA and GEO database, which restricted the study population and regions. Additionally, we used blood samples to perform qRT-PCR to validate the expression of biomarkers, which may not be as accurate as using CRC tissue samples. In further studies, clinical trials or prospective studies could be conducted to validate prognostic models and assess their utility in predicting CRC outcomes. Using more CRC tissue samples would help to elucidate the potential mechanism and function of the FGF14-AS2/hsa-miR-34a-5p/MPP2 axis. Furthermore, we could explore the potential of drug susceptibility outcomes in clinical settings, particularly for FGF14-AS2 and MPP2, and how these findings may lead to personalized treatment strategies. In this experiment, there are certain limitations to our experiments without luciferase reporter assay to verify the targeting relationship between miRNA and mRNA. Without luciferase assays, there is no direct proof that a miRNA binds to the putative target site within the 3’-UTR (or other regions) of the mRNA. Alternative methods may reveal correlative expression changes but cannot distinguish direct binding from indirect regulatory effects or off-target phenomena. Luciferase assays allow precise mutagenesis of predicted miRNA-binding sites to confirm specificity. Without this step, observed effects might stem from secondary interactions, experimental artifacts, or unrelated pathways, leading to false-positive conclusions. Overall, our research advances the understanding of ceRNA networks in CRC and their potential clinical applications, especially in developing novel biomarkers and therapeutic strategies for CRC patients. In the future, we need to improve the validation of biomarker expression at the tissue level and conduct luciferase reporter assay to verify the targeting relationship between miRNA and mRNA. In this paper, we also discovered many signaling pathways related to CRC, which are also promising directions for future research.

Conclusion

In this study, we identified two novel prognostic biomarkers, FGF14-AS2 and MPP2, for CRC, which have not been previously reported. Our findings suggest that these biomarkers, in combination with clinical factors, can be used to develop a robust prognostic model, offering valuable predictive capability for CRC patient outcomes. This model demonstrated strong performance in both experimental validation and testing. Furthermore, we explored the drug sensitivity of these biomarkers, providing potential clinical applications in treatment personalization.

However, this study is limited by sample size and experimental conditions, and further research is needed to explore the clinical utility of these biomarkers in a larger and more diverse patient population. The future research should also focus on clinical trials and further validation and mechanistic studies of biomarkers.

Supplementary Information

Supplementary Material 1. (11.7MB, doc)

Author contributions

Junfeng Zhu conceived and designed the experiments. Rongrong Zhou and Zhineng Zeng performed the experiment, analyzed the data and wrote the manuscript. Ying Xu, Qiong Tang, Songnan Zhang participate a part of experiment. All authors read and approved the final manuscript.

Funding

This work was supported financially by the National Natural Science Foundation of China (No. 82360113), the Natural Science Foundation of Guangxi (No. 2024GXNSFAA010059, 2021GXNSFAA325001), Innovation Project of Guangxi Graduate Education (No. YCSW2025497) and Training Program for Thousands of Young and Middle-aged Backbone Teachers in Guangxi Colleges and Universities.

Data availability

The datasets analysed during the current study are available in the following repositories: The primary TCGA- COADREAD dataset was obtained from the UCSC Xena portal (https://xenabrowser.net/datapages/?dataset=TCGA-READ.star_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 and https://xenabrowser.net/datapages/?dataset=TCGA-COAD.star_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443). Five independent gene expression microarray cohorts were downloaded from the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) under the following accession numbers: GSE110715 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110715), GSE75970 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75970), GSE184093 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE184093), GSE134525 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134525) and GSE39582 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582).

Declarations

Ethics approval and consent to participate

This study was approved by the Ethical Committee of Affiliated Hospital of Guilin Medical University (Approval. 2022YJSLL-20, protocol title: “Screening of Prognosis-Related Biomarkers and Construction of Prognostic Models in Colorectal Cancer Based on ceRNA Regulatory Networks”), and all procedures complied with the Helsinki Declaration. Informed consent was obtained from all individual participants included in this study. Prior to participation, the purpose of the research, the procedures involved, potential risks, and possible benefits were clearly explained to each participant. All participants confirmed that they understood the information provided and voluntarily agreed to take part in the study. Participants were informed that they could withdraw from the study at any time without any negative consequences.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rongrong Zhou and Zhineng Zeng have contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (11.7MB, doc)

Data Availability Statement

The datasets analysed during the current study are available in the following repositories: The primary TCGA- COADREAD dataset was obtained from the UCSC Xena portal (https://xenabrowser.net/datapages/?dataset=TCGA-READ.star_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 and https://xenabrowser.net/datapages/?dataset=TCGA-COAD.star_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443). Five independent gene expression microarray cohorts were downloaded from the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) under the following accession numbers: GSE110715 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110715), GSE75970 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75970), GSE184093 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE184093), GSE134525 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134525) and GSE39582 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582).

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