Introduction

Since 2020, Colorectal cancer (CRC) is the second most common cause of cancer and the fourth cause of cancer death in China1. Unfortunately, the 5-year survival rate for colorectal cancer remains low, mainly due to the fact that CRC presents little symptoms in early stage and many patients receive their diagnosis when the disease has progressed to an advanced stage2. Over the last ten years, survival rates have increased as a results of the introduction of chemotherapy agents like irinotecan and oxaliplatin3. However, chemotherapy is significantly hampered by drug resistance in cancer cells, which also lowers the efficacy of cancer treatments4. Therefore, increasing sensitivity to drug resistance has become a critical priority to overcome chemoresistance.

A polyp is frequently the initial cause of colorectal cancer. Over the course of an estimated 10–15 years, this process begins with an aberrant crypt, then progresses to a neoplastic precursor lesion (a polyp), and finally leads to CRC. Therefore, identifying the potential biomarkers and regulators that contribute to the transition from polyp to CRC is significant for finding possible therapeutic drugs and preventative treatments of CRC5. In this research, we conducted the “Mfuzz” analysis to examine the temporal changes in gene expression throughout the development of CRC and chose potential effectors for further investigation.

Myt1 is an evolutionarily conserved protein kinase that is encoded by PKMYT1. It is primarily reported to negatively regulate CDK1 activity through phosphorylating it at Thr14 and limiting the CDK1-cyclin B complex in the cytoplasm, functionally impedes G2/M transition of cell cycle progression6,7. Furthermore, it has been found that PKMYT1 is essential for triggering apoptosis, which contributes to the UVR-induced inhibition of skin cancer development8. Numerous investigations conducted recently have revealed that while PKMYT1 is not necessary for the normal cell cycle, it plays a crucial role in checkpoint recovery following DNA damage and is vital in stem-like cells9. In addition, increasing number of researches have demonstrated that PKMYT1 were upregulated and served as oncogenic activity in many types of cancers including breast cancer, ovarian cancer, renal cell carcinoma, and prostate cancer10,11,12, and mechanism investigation revealed that PKMYT1 could promote proliferation and invasion of cancer cells through modulating signaling pathways like β-catenin/TCF, Notch, and AKT/mTOR13. And its selective inhibitors of PKMYT1 (such as RP-6306, GW 559768X and bosutinib) have been tested in clinical trials for solid tumors14,15. This highlights the potential of PKMYT1 to serve as a therapeutic target of cancers6,9. However, the role of PKMYT1 in CRC have not been fully explored. The purpose of this study was to identify the potential factor that regulated the CRC progression through systematic bioinformatics analysis, and clarify how PKMYT1 contributes to CRC based on multiple validation and experimental investigation.

Methods

Data collection and processing

The Cancer Genome Atlas (TCGA) provided the colorectal adenocarcinoma (COAD) stage and RNA sequencing data. Colon sample RNA sequencing and stage data representing various stages of colorectal cancer progression were obtained from GSE207949. The dataset was preprocessed using the “Mfuzz” R package after Log2 transformation and RNA data normalization.

The STRING database analysis

STRING is a popular online database for constructing protein–protein interaction (PPI) networks and assigning scores to interactions between target proteins16. We performed a PPI network analysis on the intersect genes identified by “Mfuzz”, concentrating on interactions with a confidence level greater than 0.9 based on the STRING. Furthermore, we detected modules inside the PPI network using Cytoscape’s MCODE plug-in.

Assessment of infiltration within the tumor microenvironment

The relative abundances of 24 immune cell types were assessed using single-sample gene set enrichment analysis (ssGSEA) using the GSVA R package17. Charoentong provided the gene sets linked to each kind of immune cell18. Additionally, we utilized the R package to estimate immune and stromal scores, which indicate the correlation between gene expression and levels of tumor-associated stromal and immunological infiltration19.

Forecasting the response to immunotherapy

The response of immunotherapy was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) method20. By examining two crucial aspects of tumor immune evasion, T cell infiltration and dysfunction, this approach forecasts the results of immunotherapy, especially in tumors with low cytotoxic T lymphocyte (CTL) counts21.

Chemotherapeutic sensitivity

To assess chemosensitivity in relation to high and low levels of PKMYT1, we utilized the pRRophetic, which could calculate the half-maximal inhibitory concentration (IC50) values. From the Genomics of Drug Sensitivity in Cancer (GDSC), information was gathered based on the genetic profiles after treatment with several widely used anticancer medications22.

Enrichment analysis

GSE39582 was analyzed by ssGSEA to investigate the signaling pathways linked to PKMYT1 in CRC. Genes that were correlated with PKMYT1 were analyzed in LinkedOmics (https://www.linkedomics.org/login.php) base on the transcriptome data from TCGA23 and subjected to enrichment analysis based on Wei sheng xin Website (https://www.bioinformatics.com.cn/). Pathway gene dataset was obtained in the KEGG Database24,25.

Clinical specimen collection

Three human CRC samples were taken from the Yantai Affiliated Hospital of Binzhou Medical University in China, together with the epithelial tissues that surround them. The pathological diagnosis of CRC was confirmed by a minimum of two expert pathologists. All individuals who donated tissues for this study provided written informed consent. This experiment was conducted in accordance with guidelines established by the Ethics Committee of Binzhou Medical University’s for Biology and Medical Science and the ethics approval number is #20240120045.

Immunohistochemical staining assay

5 µm thick sections were prepared from surgical specimens of CRC patients. To recover epitopes, the sections were autoclaved at 100 °C for 10 min after being deparaffinized and dehydrated. After that, a 10 mM sodium citrate buffer (pH 6.0) was applied to them. They were then exposed to 3% hydrogen peroxide for ten minutes in order to inhibit endogenous peroxidase activity (ZSGB-BIO, Cat.PV-9001). After being blocked with appropriate solutions, the sections were exposed to an anti-PKMYT1 antibody diluted to 1:200 (ELK Biotechnology, Cat.ES2884) at 4 °C overnight. Antibody binding was visualized using DAB (ZSGB-BIO, Cat.ZLI-9018). Finally, sections were counterstained with hematoxylin prior to dehydration and mounting. The slides were photographed by the biological microscope (Olympus, Japan).

Cell culture and transfection

NCM460 (Cat.T9186), HCT116 (Cat.T1027), and RKO (Cat.T9930) cells were purchased from the Applied Biological Materials Incorporated (ABM Inc., jiangsu, China). RKO cells were cultured in MEM (Procell, Cat. PM150414) with 1% penicillin–streptomycin (MeilunBio, Cat. MA0110) and 10% fetal bovine serum (FBS, Procell, Cat.164210-50) added as supplements. The remaining cells were kept in DMEM (Procell, Cat.PM150210) supplemented with 1% penicillin–streptomycin and 10% FBS. The negative control siRNA and PKMYT1 siRNA were sourced from OBiO Technology (Obio Technology, Shanghai, China). The sequences of siRNAs were: siPKMYT1-1 (sense: GGUAAAGCGUUCCAUGUCATT; anti-sense: UGACAUGGAACGCUUUACCTT), siPKMYT1-2 (sense: CCUACGGAGAGGUCUUCAATT, anti-sense: UUGAAGACCUCUCCGUAGGTT), siPKMYT1-3 (sense: CGAGCUGCGUUCUGUCCUUTT, anti-sense: AAGGACAGAACGCAGCUCGTT). Using Lipofectamine 2000 (ThermoFisher Scientific, Cat.11668019), 5 nmol of NC and siRNA were added to HCT116 and RKO cells for a duration of 6 h. The effectiveness of the silencing was verified by Western blot analysis and qPCR 48 h after transfection.

RNA isolation and quantitative PCR (qPCR)

To determine the mRNA expression levels of PKMYT1, total RNA was extracted from HCT116 and RKO cells 48 h after transfection using FreeZol Reagent (Vazyme, Cat. R711), as directed by the manufacturer. ThermoFisher Scientific’s NanoDropTM 2000 spectrophotometer was used to measure the amount of RNA present. Following the manufacturer’s instructions, HiScript II Reverse Transcriptase (Vazyme, Cat. R223) was used to create cDNA from total RNA. The QuantStudio™ 3 system (ThermoFisher Scientific) and ChamQ SYBR qPCR Master Mix (Vazyme, Cat.Q311) were used for qPCR. Actin was utilized as the reference gene for normalization. The relative expression levels of the gene were determined via qPCR and analyzed using the 2−ΔΔCT method.

Western blot

Proteins from CRC cells were isolated from the cell lysate with RIPA buffer (MeilunBio, Cat.MA0151). An equivalent amount of protein was put onto a 10% SDS-PAGE gel, and it was subsequently transferred to PVDF membranes (Millipore, Cat.IPVH0010). After that, primary antibodies against PKMYT1 (1:500) and GAPDH (1:1000) were incubated with the membranes at 4 °C for overnight. Following that, they were underwent thrice wash with TBST buffer for 10 min each time. The membranes were then incubated with secondary antibodies for an hour at room temperature. The bands were visualized using chemiluminescence. The relative protein levels in different groups were quantified by gray intensity of indicated bands with image J, the control group of two cells were normalized to 1.

Cell viability assay

The Cell Counting Kit-8 (KeyGen Biotech, Cat.KGA9310-500) was used to measure cell viability. Following transfection, 96-well plates were seeded with 2000 cells of the RKO and HCT116 cell lines, respectively. 10µL of the CCK-8 solution was added to each well at 0, 12, 24, 48, and 72 h in accordance with the manufacturer’s instructions, and the wells were subsequently incubated for 30 min. Absorbance at a wavelength of 450 nm (nm) were measured after incubation using a microplate reader (BioTek, America).

Colony formation assay

Cells in the logarithmic growth phase were pre-transfected with siPKMYT1 #1 or negative control (NC), and they were then plated on 6-well plates at the density of 600 cells/well. After 14 days of incubation, the cells were fixed with 75% ethanol for 30 min, then stained with 0.2% crystal violet. After sufficiently washing, each well’s colonies were captured, and counted by Image J software.

Flow cytometry assay

Cell cycle monitoring was conducted using Cell cycle staining buffer (Muti Science, Cat. CCS01) according to the manufacturers’ instruction. The data were analyzed with ModFit LT 5 software.

Wound healing assay

After plating HCT116 and RKO cells (1 × 10^4) in 6-well plates and transfecting them for 48 h, they were cultured in serum-free media until they achieved 85–90% confluence. To create wound gaps, the cell layers were scratched with a 100 µL RNase-free pipette tip. Images of the wound gaps were captured at three times (0 h, 24 h, and 48 h) using a light microscope at × 10 magnification.

Transwell assay

Transwell chamber (Corning, Cat.3422) with 24 wells was utilized to examine cell migration and invasion. 5 × 10^4 cells suspended in total 200 μL serum-free medium was seeded in apical chambers pre-packaged with or without Matrigel Matrix (Corning, Cat.356230) for migration or invasion monitoring, respectively, while the lower compartment was filled with 800 μL complete culture medium that had been supplemented with 10% FBS. After incubating for 48 h, any remaining cells in the upper chamber that had not migrated were removed, cells that migrated through the pores were fixed with methanol for 10 min and stained with 0.1% crystal violet. The images were captured and the number of cells were quantified using an inverted microscope (Olympus, Japan) at a × 20 magnifying lens.

Statistical analysis

For datasets with a normal distribution, the mean ± SD is used to convey quantitative data; for datasets with skews, the median with interquartile range is used. The t-test was used for regularly distributed data in order to compare quantitative data in two groups. All data analyses were conducted using SPSS and RStudio.

Results

PKMYT1 is a hub gene of CRC progression

To search for the hub gene involved in CRC progression, we downloaded the transcriptome profiling data containing of 41 normal and 458 COAD tumor samples from the TCGA database. Furthermore, 24 colon samples, which include 5 Mucosae, 13 polyps, and 6 adenocarcinomas, representing different stages of CRC, were downloaded from GSE207949. Then, the “Mfuzz” analysis was conducted to investigate the longitudinal changes in gene expression during the development of CRC and identify the vital factor modulating the carcinogenic transition and tumor progression. Our findings demonstrated that, genes were grouped into 9 groups based on the trend of expression change (Fig. 1A, B). We selected the genes that membership > 0.5 in upper clusters of TCGA and GSE207949, respectively. And the venn diagram showed that there were 115 genes in common between two data clusters (Fig. 1C). Next, we used STRING to undertake PPI network analysis between these 115 top genes, and the correlation analysis by MCODE indicated that, PKMYT1, TPX2, KIFC1, FOXM1, TROAP, IQGAP3 might be the core regulatory genes in CRC progression (Fig. 1D). Among these 6 genes, TPX226, KIFC127, FOXM128, TROAP29, and IQGAP330 were widely documented to play role in CRC progression, the associated research on the PKMYT1 and KIFC1 in CRC were relatively inadequate. And PKMYT1 have been reported to regulate proliferation, apoptosis and invasion of various cancer cells, and its selective inhibitors have been applied into clinical trials. Therefore, we selected PKMYT1 as the hub gene for the follow-up study.

Fig. 1
figure 1

PKMYT1 is a hub gene of CRC progression. Mfuzz analysis based on the transcriptome data from CRC samples in TCGA (A) and different samples from GSE207949 (B). (C) A Venn diagram showed that 115 genes were in common from clusters 5&9 of TCGA-COAD and clusters 1&9 of GSE207949. (D) The protein–protein interaction (PPI) network analysis based on these 115 genes using STRING and revealed core genes.

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The expression level of PKMYT1 was linked to the tumor microenvironment of CRC

As immunotherapy has been applied in CRC treatment, we then performed the ESTIMATE and ssGSEA algorithms to examine the connection between PKMYT1 expression and the tumor microenvironment in CRC. Our results showed that the group with higher expression of PKMYT1 had a higher tumor purity rate than the group with lower PKMYT1. The tumor with higher expression of PKMYT1 get lower scores on immune, stromal, and ESTIMATE evaluation, and there was statistically significance between the two groups (Fig. 2A). Furthermore, the ssGSEA analysis indicated that the group with higher PKMYT1 expression exhibited a notable decrease in various anti-tumor immune cells including adaptive immune cells (regulatory T cells, immature B cells and T follicular helper cells et al.) and the innate immune cells (Eosinophils, macrophages and natural killer cells et al.) (Fig. 2B and Table 1). These findings imply that tumors with high PKMYT1 expression may be associated with an immunosuppressive microenvironment, potentially hindering anti-tumor immunity and facilitating tumor progression of CRC.

Fig. 2
figure 2

Bioinformatics analyses revealed that PKMYT1 expression might be associated with immunotherapic, chemotherapeutic efficiency of patients with CRC, and PKMYT1 might participant with signaling pathways in CRC. (A) ESTIMATE analyses of PKMYT1 differentially expressed genes. (B) The Wilcoxon analysis of the differing immunocyte infiltration via ssGSEA (z-score standardized) between the high- and low-PKMYT1 groups. (C) TIDE analysis for predicting the immunotherapy response based on PKMYT1 expression. (D) The drug sensitivity of the six main anti-cancer drugs was predicted by the pRRophetic algorithm. (E) Bubble map of the top KEGG pathway enrichment analysis. (F) GSEA revealed the significant enrichment of KEGG pathways and hallmark gene sets (* P < 0.05, *** P < 0.01, **** P < 0.001).

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Table1 The list of immune cells was associated with PKMYT1 expression in CRC.
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Relationship between PKMYT1 expression and responses to immunotherapy/chemotherapy sensitivity

To evaluate the expression levels of PKMYT1 in predicting the immunotherapy response in patients with CRC, we computed the TIDE score using the TIDE software based on data from the GSE39582 cohort. We observed that TIDE scores are not associated with PKMYT1 expression in CRC (Fig. 2C), suggesting that PKMYT1 expression didn’t concisely reflected therapeutic efficiency of immune checkpoints blockade (ICB). Further investigation should be conducted to reveal the detailed regulation of PKMYT1 in immunotherapy of CRC. Moreover, we looked into the connection between expression levels of PKMYT1 and chemotherapy sensitivity in treating CRC. According to our pRRophetic algorithm based on GDSC database, higher hypoxia score correlated with a reduced half-maximal inhibitory concentration (IC50) for several chemotherapeutic agents, such as Acetalax, X5, Fluorouracil, OSI.027, Nelarabine, Oxaliplatin.1, and Temozolomide. (Fig. 2D). The above results indicate that PKMYT1 is not a marker for immune therapy but a potential biomarker for predicting chemotherapeutic efficiency of CRC.

Enrichment analysis of PKMYT1 in CRC

Our aforementioned results indicated that PKMYT1 might be potential effector for therapeutic sensitivity and progression of CRC, we next used ssGSEA based on profile of GSE39582 to investigate the signaling pathways linked to PKMYT1 in CRC. And our findings indicated that notable differences in the KEGG enrichment results between CRC patients according to different PKMYT1 expression, PKMYT1-associated genes could involve in numerous cancer-related processes, including DNA replication, drug metabolism through cytochrome P450, regulation of autophagy, complement and coagulation cascades, TGFβ signaling pathway, starch and sucrose metabolism, xenobiotic metabolism via cytochrome P450, retinol metabolism, renal cell carcinoma, leukocyte trans-endothelial migration, focal adhesion, pyrimidine metabolism, proteasome activity, nucleotide excision repair, spliceosome function, cell cycle regulation, mismatch repair, RNA polymerase activity, homologous recombination, and base excision repair (Fig. 2E). Through GSEA analysis, we identified that PKMYT1 might positively regulate the DNA replication and cell cycle pathways in CRC (Fig. 2F). This implies that PKMYT1 might be involved in cell growth and play a role in the onset of CRC. To further reveal the molecular mechanism of PKMYT1 in CRC, we conducted analysis in LinkedOmics (https://www.linkedomics.org/login.php) base on the transcriptome data from TCGA23. Volcano plot and heatmap showed the genes whose expression were correlated to PKMYT1 in CRC (supplementary Fig. 1A–C), correlated genes of PKMYT1 in CRC showed that the expression of PKMYT1 had positively correlated with oncogenes (including CCNF, CDCA5, CDCA3, CDC45, and CDC20) and negatively associated with tumor-repressors (like HBP1, SOS2, FONX3)31,32,33,34,35,36,37,38, indicating that PKMYT1 contributes to tumor cell proliferation through multiple gene network and signaling crosstalk. And pathway enrichment analysis showed that PKMYT1 might participate in focal adhesion, cytokine-cytokine receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, and Chemical carcinogenesis—reactive oxygen species (supplementary Fig. 1D), these pathways have been reported to modulate the proliferation and invasion of cancer cells. In conclusion, these results indicated that PKMYT1 might regulate the progression of CRC through involving various signaling pathways.

PKMYT1 expression was much higher in CRC tissues and cells

According to the data of bioinformatics analysis, the expression of PKMYT1 was upregulated in the progression of CRC (Fig. 1A, B). Furthermore, we performed immunohistochemistry to evaluate PKMYT1 expression in CRC and normal colonic tissues. The results revealed that CRC tissues had stronger intensity of PKMYT1 in compared to adjacent normal tissue samples (Fig. 3A). We also examined the mRNA levels of PKMYT1 in the normal colorectal epithelial cell line NCM460 as well as the CRC cell lines HCT116, RKO, and SW480. The findings showed that PKMYT1 expression was markedly elevated in the CRC cells HCT116 and RKO (Fig. 3B). Totally, these data demonstrated that PKMYT1 was upregulated in CRC and might play a regulatory role in CRC.

Fig. 3
figure 3

PKMYT1 maintained the cell proliferation of CRC in vitro. (A) IHC for PKMYT1 in the tumor and adjacent samples; (B) qPCR assay evaluated the mRNA levels of PKMYT1 in normal and CRC cell lines; qPCR (C) and Western blot (D) assay determined the efficiency of PKMYT1 knockdown by siRNAs. (E) The cell proliferation of CRC cells transfected with si-PKMYT1-1 were measured by CCK-8 assays. (F) Colony formation assays were conducted with or without PKMYT1 knockdown in CRC cells. (G) Histogram showed the number of colonies of CRC cells from the groups indicated, the results were analyzed statistically. (* P < 0.05, ** P < 0.01, *** P < 0.001). (H) Flow cytometry evaluation the effect of siPKMYT1 on the cell cycle of HCT116 and RKO cells.

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PKMYT1 knockdown inhibited the proliferation of CRC cells

To investigate the biological roles of PKMYT1 in the CRC, HCT116 and RKO cells were chosen to perform PKMYT1 knockdown by transfection of three specific siRNAs (siPKMYT1-1, siPKMYT1-2, and siPKMYT1-3). qPCR and western blot confirmed that PKMYT1 was knocked down in transfected RKO and HCT116 cells (Fig. 3C, D). Among three siRNAs, siPKMYT1-1 exhibited the highest interference efficiency in mRNA and protein levels, so we used siPKMYT1-1 for functional investigation. CCK-8 assay were conducted and the results revealed that knockdown of PKMYT1 significantly repressed the growth curves of HCT116 and RKO cells (Fig. 3E). Likewise, colony formation assays in HCT116 and RKO cells revealed that the number of cell colonies was largely reduced as a result of PKMYT1 knockdown (Fig. 3F, G). Moreover, flow cytometry results found that siPKMYT1 induced a cell cycle arrest and decreased population of S stages in CRC cells (Fig. 3H), which is consistent with KEGG enrichment analysis we performed. These findings suggest that PKMYT1 regulated the cell cycling and the downregulation of PKMYT1 effectively inhibits CRC cells growth in vitro.

PKMYT1 maintained the migration and invasion of CRC cells

Transwell and wound healing tests were conducted to assess the impact of PKMYT1 on the invasion and migration of CRC cells. The results of the wound healing assay showed that PKMYT1 knockdown significantly postponed wound closure (Fig. 4A). Additionally, transwell assays revealed that siPKMYT1 group significantly decreased the number of migrated and invaded cells in contrast to the control group (Fig. 4B, C), which was consistent with aforementioned result that PKMYT1 might regulate TGFβ and focal adhesion signaling pathways of CRC cells based on pathway enrichment analysis. In conclusion, these data demonstrated that PKMYT1 maintained the invasive ability of CRC cells in vitro.

Fig. 4
figure 4

PKMYT1 promotes the migration and invasion of CRC cells in vitro. (A) Wound healing test for the PKMYT1 knockdown of HCT116 and RKO cells. (B) The migration and Invasion of HCT116 and RKO cells with PKMYT1 knockdown and statistical analyses of the results (C); (*** P < 0.001).

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Discussion

CRC is the second prevalent cancer in women and the third common carcinoma in males. Compared to men, women have almost 25% lower incidence and mortality rates. CRC usually goes through the process of "normal mucosa-mucosal epithelial hyperplasia-adenomatous polyps-colorectal cancer," which generally lasts for 5–10 years39,40. Since there are no unique molecular markers or indicators for early diagnosis, the majority of patients exhibit clinical manifestations after the disease progress advanced stage, which results in the loss of the opportunity for surgery or other forms of treatment, and accounts for a lower five-year survival rate41. Therefore, identify novel biomarkers and improved treatment approaches for CRC are desperately necessary.

In the research, we identified PKMYT1, a WEE kinase family member, has emerged as a viable therapeutic option with significance in a variety of tumor types42,43, as an indicative marker of CRC by systematically analyzing the transcriptome data from and TCGA datasets. For the GSE207949 and TCGA data with different stages of development of CRC, time series analyses (Mfuzz) were performed to intersect the genes that were progressively upregulated in developmental processes of CRC, which was consistent with the results of IHC and qPCR showing a stronger abundance of PKMYT1 in CRC tissues and cells (Fig. 3A, B). In addition, we divided the colorectal cancer samples into the PKMYT1-high and PKMYT1-low groups and performed a series of bioinformatics analyses. Immunotherapy is one of the main methods of cancer treatment, and whether it culminates in a therapeutic response may be affected by the tumor microenvironment44,45. Immuno-infiltration studies showed that the PKMYT1-high expressing group had typically lower infiltration of immune cells within tumors, and the tumor purity rate was much higher. The findings revealed that PKMYT1 influenced immune cell infiltration, permitting tumor tissues tends to be “cold tumors” and impairing the immunotherapy of CRC46,47. But immunotherapy prediction analyses revealed that the PKMYT1 expression level could not be used as a predictor of immunotherapy, which was in line with the decline in the degree of high-expression immune infiltration. Next, we predicted the association between drug susceptibility with PKMYT1 expression, and showed that Acetalax had the lowest IC50 in PKMYT1-high groups, providing direct hints for clinical drug selection.

To reveal the mechanism regulated by PKMYT1, we carried out an enrichment analysis of the genes linked to PKMYT1, results discovered that PKMYT1 might affect the cell cycle, DNA replication, and other cellular processes connected to tumor growth. And the cell cycle monitoring showed that PKMYT1 knockdown reduced the subpopulation of S and G2/M stages of cells, indicating siPKMYT1 induced cell cycle arrest and a decreased DNA replication. This implies that PKMYT1 contributes to tumor cell proliferation48,49. And correlated genes of PKMYT1 in CRC based on TCGA showed that the expression of PKMYT1 had positively correlated with CCNF, CDCA5, CDCA3, CDC45, CDC20, these genes maintain the cell cycle, DNA replication and cell division31,32,33,34,35. Negative correlation analysis based on transcriptional expression in CRC found that PKMYT1 had negatively associated with tumor-repressors like HBP1, SOS2, FONX3, which were well-documented to repress cell cycle, tumorigenesis and metastasis of cancer cells36,37,38.

Next, we validated the role of PKMYT1 on migrative and invasive activity of CRC cells using transwell and wound healing assays. The silencing of PKMYT1 expression significantly repressed the invasion and migration of tumor cells, the potential mechanism might be that PKMYT1 could participate in TGF-β and focal adhesion signaling pathways based on our KEGG enrichment analysis, TGF-β signaling is closely associated with epithelial-mesenchymal transition, metastasis and drug-resistence of cancer cells50,51, focal adhesion can modulate the interaction between cancer cells and matrix, and regulate plasticity and invasive activity of cancer cells26. Further investigation should be conducted to confirm this probability in the future. And these finding was aligning with earlier findings reported in other types of carcinomas43,52.

Conclusion

In conclusion, we discovered PKMYT1 to be a crucial gene implicated in the development and spread of colorectal cancer through comprehensive bioinformatics analysis, and we also validated its role in proliferation and invasion of CRC through experimental methods. As the selective inhibitors of PKMYT1 have been approved for clinical trials, PKMYT1 might be a novel target and basis for the early diagnosis and therapy of CRC.