Introduction
Colorectal cancer (CRC) is one of the leading malignant tumors worldwide, ranking third in cancer-related deaths. Current treatment modalities for CRC patients include surgery, chemotherapy, radiotherapy, and targeted therapy. These treatment strategies reduce the rate of disease recurrence and contribute to an increase in survival rates.1,2 However, these approaches are most effective when applied during the early stages of the disease.3 While the 5-year survival rate for CRC patients is approximately 90% in the early stages, it drops below 5% in cases with distant metastases.4 Limitations in the existing treatment strategies and CRC’s high metastatic potential contribute to the detection of the disease at advanced stages, leading to an increase in mortality rates.5 Therefore, the development of new treatment strategies for early detection and diagnosis, as well as the identification of prognostic markers, has become exceedingly crucial in the pathogenesis of CRC.
In recent years, microarray data analysis and bioinformatic analyses applied in cancer genomics have contributed to the development of new treatment strategies and the discovery of novel biomarkers for cancer pathogenesis.6,7 Biomarkers play a guiding role in the early diagnosis of cancer and personalized cancer therapy. Bioinformatic applications, allowing the processing of experimental data, have a powerful potential in deciphering the underlying molecular mechanisms of diseases and unraveling complex physiological events.8
By comparing gene expression profiles between healthy and cancerous tissues through bioinformatic analyses, valuable information about cancer progression and development can be obtained, leading to the identification of new biomarkers for diagnosis and treatment.6 Bioinformatic analyses are used to define the functions and biological processes of relevant genes involved in cancer pathogenesis and recurrence. Publicly available databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) are widely used in identifying differentially expressed genes (DEGs) between healthy and cancerous tissues.9
Numerous studies have discovered diagnostic biomarkers related to CRC through bioinformatic analyses. These biomarkers include genes, their encoding proteins, long noncoding RNAs, and microRNAs, which have been shown to play regulatory roles in various physiological and pathological processes, including cell proliferation, differentiation, apoptosis, and metastasis.10–12 However, the molecular mechanism of CRC is still not fully elucidated, and there is still a need for potential biomarkers for early diagnosis and detection.
In the current study, bioinformatic analyses were performed to identify new therapeutic targets for colorectal cancer treatment. Four different datasets from the GEO database were selected, and differentially expressed genes were determined. For the overlapping DEGs in the datasets, gene ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted. Subsequently, a protein-protein interaction (PPI) network was constructed, and hub genes associated with colorectal cancer were selected using Cytoscape software. The relationship between the hub genes and their regulatory molecules, such as miRNAs and transcription factors (TFs), was identified. Immune infiltration analysis, validation of the gene expression levels, and survival analysis were performed to determine the potential of the hub genes as candidate biomarkers.
Materials and methods
Data source and processing
GEO (http://www.ncbi.nlm.nih.gov/geo ) is an international genomic database that archives and freely distributes microarrays, next-generation sequencing, and other high-capacity functional genomic data. To obtain microarray data of colorectal cancer cells within Homo sapiens, a search of the GEO database was conducted using the keyword “colorectal.” Four datasets, namely GSE113513 (14 cancer tissues and 14 normal tissues), GSE21510 (123 cancer tissues and 25 normal tissues), GSE21815 (11 cancer tissues and 5 normal tissues), and GSE32323 (17 cancer tissues and 17 normal tissues), were selected for further analysis. The microarray platforms were GPL15207 for GSE113513, GPL570 for GSE21510, GPL6480 for GSE21815, and GPL570 for GSE32323.
GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r ) was used to identify differentially expressed genes (DEGs) between cancer and healthy tissues in the microarray datasets. The selection of DEGs was set with an adjusted p-value cutoff of p < 0.05 and a log fold change >2. Subsequently, the overlapping DEGs in these datasets were analyzed, and Venn diagrams were drawn using the Venny online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html ).
Gene ontology and pathway analysis
Database for Annotation, Visualization, and Integrated Discovery (DAVID) software (https://david.ncifcrf.gov/ ) was used to explore the potential functions of overlapping DEGs through GO and KEGG pathway enrichment analyses. The significance threshold was set at p < 0.05.
GO analysis was used to define the molecular functions, biological processes, and cellular components related to the DEGs while KEGG pathway analysis was used to examine the reference pathways of the DEGs.
Creating the PPI network
The PPI network was constructed for the DEGs using the Search Tool for the Retrieval of Interacting Genes (STRING) database (https://string-db.org/cgi/ ). The network was visualized using the Cytoscape tool (www.cytoscape.org ), and interactions with a confidence score >0.4 were retained for visualization. Additionally, the DEGs were analyzed using the CytoHubba algorithm to identify hub genes that play a significantly important role (top >5 degrees) within the network.
Regulatory network analysis of hub genes
The hub gene-miRNA and hub gene-TF networks were created to identify the transcriptional and post-transcriptional regulators of hub genes. Significant miRNAs were obtained from the miRTarBase database, while TFs were obtained from the Encode database. The interactions with the highest number of miRNAs and TFs were selected to construct the networks. Moreover, a hub gene-drug network was created by analyzing drug interactions with hub genes. All analyses were performed using the NetworkAnalyst program (https://www.networkanalyst.ca/ ).
Immune infiltration and hub genes
The TIMER (http://timer.cistrome.org/ ) program was used to systematically analyze the relationship between immune infiltration and the expression of four selected hub genes. The expression levels of the hub genes and their correlation with immune cell infiltration were evaluated using the “gene module.” The TIMER algorithm was utilized to investigate the infiltration levels of CD8+ T cells, B cells, and neutrophils. Risk scores and the correlation between immune infiltration were calculated using Pearson correlation, with a significance threshold set at p < 0.05.
The protein levels of the hub genes in the Human Protein Atlas database
The protein levels of the hub genes in tumor and normal tissues in colorectal cancer were evaluated using the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/ ) which contains immunohistochemistry-based expression data specific to various human tissues.
Survival analysis based on the TCGA database
The impact of the hub genes in tumor and normal tissues on patient survival was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) web server based on TCGA data. All parameters were set to their default values, and the cutoff value was set at the median = 50%. p < 0.05 was considered statistically significant in this analysis.
Results
Identification of differential genes
In four different microarray datasets related to colorectal cancer (GSE113513, GSE21510, GSE21815, and GSE32323), a total of 165 cancer tissue samples and 61 healthy tissue samples were analyzed. Using the GEO2R program, genes with differential expression were identified in each microarray dataset. In the GSE113513 dataset, 475 genes (366 down-regulated and 109 up-regulated) with differential expression were detected, while in the GSE21510 dataset, 1,349 genes (661 down-regulated and 688 up-regulated) were detected. In the GSE21815 dataset, 1,031 genes (185 down-regulated and 846 up-regulated) were detected, and finally, in the GSE32323 dataset, 440 genes (301 down-regulated and 139 up-regulated) with differential expression were detected. The DEGs were analyzed using the Venn program to identify the genes common in all four microarray datasets (Fig. 1). In the microarray dataset, a total of 39 DEGs were identified that were down-regulated and overlapped, and 32 DEGs were identified that were up-regulated and overlapped.
Enrichment analysis of DEGs
Detailed enrichment analyses of overlapping DEGs in colorectal cancer tissues were performed using the DAVID online program. The DEGs were enriched in terms of extracellular matrix organization, negative regulation of cell proliferation, plasma membrane, extracellular space, zinc ion binding, and hydrolase activity (Table 1). In KEGG pathway analyses, they were mainly enriched in metabolic pathways and nitrogen metabolism pathways.
Table 1GO and KEGG pathway analysis of DEGs in colorectal cancer
| Category | GO ID | GO Term | DEGs Count | p-value | Associated Genes |
|---|
| GOTERM_BP_DIRECT | GO:0006730 | one-carbon metabolic process | 5 | 7,6E-06 | CA1, CA12, CA2, CA4, CA7 |
| GOTERM_BP_DIRECT | GO:0030198 | extracellular matrix organization | 7 | 2,50E-05 | ABI3BP, COL11A1, MMP12, MMP28, MMP7, SPINK5, TGFBI |
| GOTERM_BP_DIRECT | GO:0051453 | regulation of intracellular pH | 3 | 4,70E-03 | CA2, CA7, SLC4A4 |
| GOTERM_BP_DIRECT | GO:0008285 | negative regulation of cell proliferation | 7 | 5,80E-03 | ABI3BP, CXCL8, KLF4, CDKN2B, FABP6, INHBA, SFRP1 |
| GOTERM_BP_DIRECT | GO:0030574 | collagen catabolic process | 3 | 9,10E-03 | MMP12, MMP28, MMP7 |
| GOTERM_CC_DIRECT | GO:0005615 | extracellular space | 18 | 1,70E-04 | ABI3BP, CXCL8, CA2, CPM, CTHRC1, COL11A1, DPEP1, GCG, HILPDA, INHBA, MMP12, MMP28, MMP7, MUC2, SFRP1, SRPX2, TGFBI, ZG16 |
| GOTERM_CC_DIRECT | GO:0016324 | apical plasma membrane | 8 | 2,80E-04 | CA4, CLCA4, CLDN1, DPEP1, KCNMA1, SCNN1B, SLC6A6, SI |
| GOTERM_CC_DIRECT | GO:0005886 | plasma membrane | 31 | 1,10E-03 | ATP11A, CD177, VSIG2, ACSL6, ADH1C, BEST2, CDH3, CA12, CA2, CA4, CPM, CLCA4, CLDN1, CLDN8, CNTN3, CPNE8, DPEP1, EPB41L3, LGR5, MUC12, MUC2, KCNMA1, SCARA5, SFRP1, SCNN1B, SLC22A3, SLC4A4, SLC6A6, SI, TGFBI, TRIB3 |
| GOTERM_CC_DIRECT | GO:0005576 | extracellular region | 17 | 1,50E-03 | ABI3BP, CXCL8, CPM, CLCA4, CTHRC1, COL11A1, CNTN3, GCG, GUCA2A,GUCA2B,INHBA, MMP12, MMP7, MUC2, SFRP1, SPINK5, TGFBI |
| GOTERM_CC_DIRECT | GO:0031012 | extracellular matrix | 6 | 1,80E-03 | COL11A1, MMP12, MMP28, MMP7, MUC2, TGFBI |
| GOTERM_MF_DIRECT | GO:0004089 | carbonate dehydratase activity | 5 | 2,40E-07 | CA1CA12CA2, CA4CA7 |
| GOTERM_MF_DIRECT | GO:0016836 | hydro-lyase activity | 5 | 7,70E-07 | CA1CA12CA2, CA4CA7 |
| GOTERM_MF_DIRECT | GO:0008270 | zinc ion binding | 13 | 4,40E-05 | KLF4, ADH1CCA1CA12CA2, CA4CA7, CPMDPEP1MMP12MMP28MMP7NR5A2 |
| GOTERM_MF_DIRECT | GO:0005201 | extracellular matrix structural constituent | 5 | 1,40E-03 | ABI3BPCTHRC1COL11A1SRPX2TGFBI |
| GOTERM_MF_DIRECT | GO:0008201 | heparin-binding | 5 | 3,30E-03 | ABI3BPCXCL8COL11A1MMP7SFRP1 |
| KEGG_PATHWAY | hsa00910 | nitrogen metabolism | 5 | 8,00E-07 | CA1CA12CA2, CA4CA7 |
| KEGG_PATHWAY | hsa04964 | proximal tubule bicarbonate reclamation | 4 | 1,40E-04 | CA2, CA4PCK1SLC4A4 |
| KEGG_PATHWAY | hsa04972 | pancreatic secretion | 4 | 1,10E-02 | CA2, CLCA4, KCNMA1SLC4A4 |
| KEGG_PATHWAY | hsa01100 | metabolic pathways | 13 | 3,30E-02 | UGT2A3ACSL6ADH1CCA1CA12CA2, CA4CA7, CKMT2GPAT3PCK1PSAT1SI |
| KEGG_PATHWAY | hsa03320 | PPAR signaling pathway | 3 | 4,50E-02 | ACSL6FABP6PCK1 |
PPI analysis of DEGs
A total of 71 genes (39 down-regulated and 32 up-regulated) with differential expression and overlapping in the microarray datasets were subjected to PPI network analysis using the String program. The network was constructed using the Cytoscape program and comprised 70 nodes and 40 edges (Fig. 2a).
Additionally, highly connected hub genes were identified using the Cytoscape program’s CytoHubba application. Hub genes with a degree value of more than 6 were identified as Krüppel-like factor 4 (KLF4), CLCA4, GUCA2B, GUCA2A, LGR5, SLC4A4, ZG16, CA7, CA2, and GCG (Fig. 2b). Among these hub genes, LGR5 was up-regulated, while the rest of the hub genes were down-regulated. Expression levels of hub genes were not found to be statistically significant (p > 0.05) (Figure 3).
Regulatory molecules of hub genes
Separate interaction networks with miRNAs and TFs were constructed for the hub genes (Fig. 4). Among the hub genes, KLF4, LGR5, CLCA4, and GUCA2B showed interactions with miRNAs (Fig. 4a). The miRNA hsa-mir-335-5p was identified as the most interacting miRNA, targeting KLF4, GUCA2B, and CLCA4. Other prominent miRNAs included hsa-mir-124-3p (targeting LGR5 and KLF4) and hsa-mir-128-3p (targeting KLF4 and CLCA4). The genes with the most interactions with transcription factors were found to be ZG16, LGR5, KFL4, and CA2 (Fig. 4b). The transcription factors GTF2F1 and MTA1TF were found to interact with CA2 and KLF4 genes, ZNF76 and PPARG with ZG16 and KLF4 genes, and DMAP1, SOX13, GATAD2A, ZBTB26, and SSRP1 with ZG16 and LGR5 genes. In the analyses of hub gene and drug interactions, only CA7 and CA2 genes were found to interact with the drugs in the database (Fig. 4c). Among the drugs, Acetazolamide, Zonisamide, Diclofenamide, Ellagic acid, Ethoxzolamide and Methazolamide were the common drugs used for both genes.
Immune cell infiltration analysis
The relationships between immune cell infiltration and the expression of hub genes were analyzed. The evaluations were conducted on 458 colorectal cancer (COAD data) patients. The cells considered in the analysis were CD8+, CD4+, B cells, neutrophils, macrophages, and dendritic cells. The results of the immune cell infiltration analysis for all genes are provided in Table 2.
Table 2Correlation results between hub genes and immune cells
| Hub genes | Cor | Purity | CD8+ | CD4+ | B cell | Neutrofil | Macrophage | DC |
|---|
| CA2 | rho | −0.312 | 0.23 | −0.042 | 0.144 | 0.271 | −0.115 | 0.202 |
| p | 1.22e-10 | 1.22e-04 | 4.90e-01 | 1.70e-02 | 5.03e-06 | 5.65e-02 | 7.62e-04 |
| CA7 | rho | −0.098 | 0.001 | 0.116 | 0.077 | −0.004 | −0.057 | 0.015 |
| p | 4.72e-02 | 9.80e-01 | 5.42e-02 | 2.02e-01 | 9.46e-01 | 3.50e-01 | 7.99e-01 |
| CLCA4 | rho | −0.164 | 0.131 | 0.019 | 0.101 | 0.168 | −0.134 | 0.117 |
| p | 9.20e-04 | 3.02e-02 | 7.53e-01 | 9.52e-02 | 518e-03 | 2.67e-02 | 5.36e-02 |
| GCG | rho | −0.106 | 0.061 | 0.136 | 0.05 | 0.082 | −0.036 | 0.082 |
| p | 3.29e-02 | 3.16e-01 | 2.42e-02 | 4.09e-01 | 1.18e-01 | 5.54e-01 | 1.75e-01 |
| GUCA2A | rho | −0.099 | −0.045 | 0.137 | 0.55 | −0.065 | 0.009 | −0.015 |
| p | 4.58e-02 | 4.53e-01 | 2.30e-02 | 3.65e-01 | 2.85e-01 | 8.78e-01 | 8.00e-01 |
| GUCA2B | rho | −0.148 | 0.078 | 0.108 | 0.067 | 0.2 | 0.019 | 0.18 |
| p | 2.81e-03 | 2.00e-01 | 7.29e-02 | 2.66e-01 | 8.75e-04 | 7.59e-01 | 2.79e-03 |
| KLF4 | rho | −0.196 | 0.314 | 0.1 | 0.168 | 0.305 | −0.117 | 0.319 |
| p | 6.63e-05 | 1.01e-07 | 9.79e-02 | 5.22e-03 | 2.43e-07 | 5.31e-02 | 6.07e-08 |
| LGR5 | rho | 0.043 | −0.144 | 0.127 | −0.083 | −0.07 | 0.079 | −0.077 |
| p | 3.85e-01 | 1.68e-02 | 3.48e-02 | 1.70e-01 | 2.46e-01 | 1.91e-01 | 2.02e-01 |
| SLC4A4 | rho | −0.265 | 0.232 | 0.052 | 0.068 | 0.248 | 0.054 | 0.243 |
| p | 5.44e-08 | 1.01e-04 | 3.92e-01 | 2.61e-01 | 3.26e-05 | 3.72e-01 | 4.62e-05 |
| ZG16 | rho | −0.115 | −0.031 | 0.093 | 0.093 | −0.044 | −0.158 | −0.018 |
| p | 2.05e-02 | 6.07e-01 | 1.24e-01 | 1.24e-01 | 4.67e-01 | 8.79e-03 | 7.64e-01 |
According to the analysis results, CA2, CLCA4, KLF4, and SLC4A4 genes were found to be more strongly correlated compared to other genes. Based on the Spearman correlation analysis, the CA2 gene showed a positive correlation with CD8+ (rho = 0.23, p = 1.22e-04), B cells (rho = 0.14, p = 1.70e-02), neutrophils (rho = 0.27, p = 5.03e-06), and dendritic cells (rho = 0.20, p = 7.62e-04). On the other hand, negative correlations were observed between the following pairs: CLCA4-Macrophage, LGR5-CD8+, and ZG16-Macrophage cells (Fig. 5).
The validation of hub genes using HPA
The expression levels of CA2, CLCA4, KLF4, and SLC4A4 hub genes were validated using immunohistochemical staining data from the HPA database. When compared between normal and tumor tissues, it was observed that the expression levels significantly decreased in tumor tissues (Fig. 6).
Survival analysis
To analyze the prognostic value of the hub genes in colorectal cancer patients, the GEPIA database was utilized. The survival analysis of patients in the GEPIA database was conducted using the Cox PH Model, with a significance threshold of 0.05 for the p-value. Based on the analysis results, only the KLF4 gene (Hazard Ratio = 0.6; p = 0.0099) was found to be associated with poor prognosis in colorectal cancer patients. However, the other hub genes did not show any statistically significant association with overall patient survival (Fig. 7).
Discussion
The molecular mechanism of colorectal cancer is not yet fully understood.13 Therefore, it is necessary to elucidate the molecular pathways of CRC and identify potential biomarkers. Bioinformatics analyses are widely used to explore the molecular mechanisms of malignant tumors and are commonly employed to identify candidate biomarkers, aiming to contribute to the diagnosis and treatment process.
In this study, four different datasets were analyzed from GEO. A total of 71 DEGs were identified, with 32 upregulated and 39 downregulated genes, between CRC tissues and normal tissues. Functional enrichment analysis showed that these DEGs were enriched in biological processes such as extracellular matrix organization, negative regulation of cell proliferation, plasma membrane, extracellular space, zinc ion binding, and hydrolase activity. Previous studies have reported that these functions play a crucial role in CRC formation and progression, and the findings of this study are consistent with those results.14–16 Components of the plasma membrane in cancer cells differ significantly from healthy cells, with the plasma membrane composition playing a key role, particularly in the activation of the Wingless and Int-1 signaling pathway. Abnormal activation of the Wingless and Int-1 signaling pathway has been associated with cancer types such as colorectal cancer.17 In KEGG pathway analysis, DEGs were found to be significantly associated with metabolic pathways and nitrogen metabolism pathways. Metabolic pathways in cancer cells are reprogrammed, and this reprogramming is a dynamic process regulated by oncogenes and tumor suppressor genes. Many metabolic pathways, such as glucose, glutamine, amino acid, serine/glycine, and lipid metabolism, significantly increase the cell proliferation rate in cancer cell management. Moreover, they interact with the microenvironment to change the phenotype.18 The findings from this study suggest that the DEGs may play an active role in the formation and progression of CRC.
Among DEGs, a total of 10 hub genes were identified, namely CLCA4, GUCA2B, GUCA2A, KLF4, LGR5, SLC4A4, ZG16, CA7, CA2, and GCG. Some literature reviews are also consistent with our results, suggesting that these hub genes are associated with CRC.19–24 The relationship between hub genes and their regulatory molecules, such as miRNAs and TFs, was examined. KLF4, GUCA2B, CLCA4, ZG16, LGR5, and CA2 were identified as interacting genes with both miRNAs and TFs. Among them, the KLF4 gene stood out as the gene with the most interactions among both miRNAs and TFs. Notably, hsa-mir-335-5p, hsa-mir-124-3p, and hsa-mir-128-3p were identified as leading miRNAs in the analysis. These miRNAs are known to play important roles in the development and progression of various tumors, including colorectal cancer.25–28 Additionally, several prominent transcription factors were identified, including GTF2F1, MTA1TF, ZNF76, PPARG, DMAP1, SOX13, GATAD2A, ZBTB26, and SSRP1. In recent years, carbonic anhydrase (CA) isozymes have been used as biomarkers for various diseases.29 Drugs such as Acetazolamide, Metazolamide, Diclofenamide, Ethoxazolamide, and Zonisamide are available CA inhibitors used in the treatment of various diseases including colorectal cancer.30 These findings align with the results obtained in the present study.
Immunotherapy has become a promising approach in many cancer types. Invasion of tumor cells into surrounding tissues or metastasis is a consequence of inducing the host’s immune response. However, compared to other cancer types, CRC has a lower involvement of immune cells. Due to the heterogeneity of the tumor, the number and distribution of immune cells even vary in different pathological conditions of the same patient. Therefore, it is essential to enhance the effectiveness of immunotherapy in CRC, identify effective treatments, and discover new biomarkers.31 In the current study, immune cell infiltration analyses were performed, and 4 hub genes (CA2, CLCA4, KFL4, and SLC4A4) showed a strong positive correlation with immune cells. These results indicate that these cells are present in lower numbers in tumor tissues compared to healthy tissues. The immunohistochemical results of CA2, CLCA4, KFL4, and SLC4A4 gene expression also confirmed these findings. The CA2 gene showed a positive correlation with CD8+, Bcell, Neutrophil, and dendritic cell populations. This gene catalyzes the reversible hydration of carbon dioxide and is one of the isozymes of CA that plays an essential role in tissue pH homeostasis and the downregulated CA2 gene has been shown to be involved in CRC’s metastasis mechanism.32 In another study, the relationship between the SLC4A4 gene and immune cells infiltrating the tumor was examined, and a positive correlation was found. The importance of SLC4A4 gene expression, especially its association with immune cells infiltrating the tumor, suggests that it could serve as a biomarker for the diagnosis and a target for treatment in colon adenocarcinoma.33 In a study focusing on colitis-associated colon cancer, bioinformatics analyses revealed that the KLF4 gene exhibited significantly low expression in tumor tissues. Additionally, survival analyses associated it with poor prognosis.24 These findings are consistent with the results of the current study.
KLF4 is a zinc finger transcription factor that participates in cell proliferation, differentiation, and apoptosis. It also regulates the pathogenesis of inflammation and tumor formation.34KLF4 plays a dual role as an oncogene or a tumor suppressor in the development and progression of various cancer types. However, in CRC, it is downregulated in cancer tissues compared to healthy tissues and is known as a tumor suppressor. Thus, low expression of KLF4 is clearly associated with poor overall survival. In fact, the higher the malignancy of the cancer, the lower the expression of KLF4.35 Therefore, KLF4 has a high potential as a biomarker for the diagnosis and prognosis in CRC. The data obtained in the current study also indicate that the KLF4 gene may serve as a potential biomarker in CRC.
Conclusions
This study analyzed multiple datasets from the GEO database to identify differentially expressed genes and interaction networks in CRC. Hub genes were identified, and their potential as biomarkers was explored through immune cell infiltration analysis and overall survival analysis. The bioinformatics analyses highlighted the KLF4 gene as a strong candidate for potential drug targets and a biomarker in CRC patients. However, further in vitro and in vivo experiments are needed to validate the KLF4 gene as a molecular biomarker. Considering the importance of early diagnosis and treatment of CRC pathogenesis, small-molecule inhibitors designed to target the KLF4 gene in pre-cancerous lesions may prove to be an effective strategy. Furthermore, understanding the molecular mechanism of the KLF4 gene could lay a strong foundation for the development of new treatment approaches in CRC.
Declarations
Acknowledgement
There is nothing to declare.
Data sharing statement
No additional data or information is available for this paper.
Funding
The author declared that this study received no financial support.
Conflict of interest
The author has no conflict of interest related to this publication.