The expression and prognostic significance of CCNB1 in colorectal cancer based on TCGA database

doi:10.21203/rs.3.rs-2623294/v1

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The expression and prognostic significance of CCNB1 in colorectal cancer based on TCGA database

https://doi.org/10.21203/rs.3.rs-2623294/v1

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Background

Numerous studies have shown that cell cycle-associated protein B1 (Cyclin B1, CCNB1) is overexpressed in many cancers and is involved in cancer cell growth, differentiation, apoptosis, and metastasis. However, the biological functions and molecular mechanisms of CCNB1 in colorectal cancer (CRC) still remain unclear.

Methods

Herein, the HPA database and the UCSC genomic database were used to analyze the mRNA and protein expression levels of CCNB1 in different tissues of humans or vertebrates. The TIMER database was used to evaluate the expression of CCNB1 in various cancer tissue samples and adjacent normal tissue. R statistical software (version 4.2.1) was employed to analyze the relationship between CCNB1 expression and clinicopathological characteristics in the TCGA database. Kaplan-Meier survival curve and Cox regression were performed to evaluate the prognostic value. Receiver operating characteristic (ROC) curve analysis was applied to assess the diagnostic value of CCNB1. Functional enrichment analysis of CCNB1 and its co-expressed genes was performed to explore the potential molecular mechanisms of CCNB1 in CRC. The correlation of critical cell-cycle regulators and the protein-protein interaction (PPI) network of CCNB1 and CRC was established through the STRING (Search Tool for the Retrieval of Interacting Genes) website and GEPIA database.

Results

Significant upregulation of CCNB1 was observed in a variety of tumor tissues, with limited tissue specificity and tumor specificity. TCGA database and immunohistochemistry data demonstrated that CCNB1 expression was significantly upregulated in CRC tumor tissues. In addition, CCNB1 expression was correlated with the clinical stage and TNM stage. The progression-free survival (PFS) was considerably improved in the CCNB1 high-expression group. Univariate and multifactorial Cox analysis indicated that CCNB1 could not be used as an independent prognostic factor for patients with CRC. Moreover, we found that genes such as BCAS3, ZBTB4, PTTG1, H2AZ1, LRP1B, KCNJ9, and SCARNA7 could be potential targets for regulating CCNB1. The gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analyses revealed that CCNB1 was implicated in multiple cancer-related signaling pathways and biological processes. Meanwhile, CCNB1 expression was significantly correlated with the immune infiltrating levels of diverse immune markers. CCNB1 expression was positively correlated with tumor mutation burden and negatively correlated with several immune checkpoint genes. In addition, the efficacy of chemotherapeutic medicines such as 5-Fluorouracil, bexarotene, bleomycin, camptothecin, and cisplatin significantly differed between the high and low CCNB1 expression groups.

Conclusion

CCNB1 could be a promising biomarker for predicting the diagnosis and prognosis of CRC patients and a potential novel molecular target for tumor immunotherapy.

Colorectal Cancer

CCNB1

TCGA

Prognostic analysis

Tumor microenvironment

Colorectal cancer (CRC) is one of the most common cancers, ranking third in incidence and second in mortality among cancers worldwide^{1, 2}. Also, the age of onset has been gradually decreasing, affecting younger and younger individuals³. The lesion sites of CRC include the rectum, sigmoid colon, cecum, ascending colon, and descending colon, and its development is closely related to lifestyle, genetics, and colorectal adenoma. Patients with early-stage CRC are mainly treated with surgical resection. Also, metastasis of tumor cells and recurrence of surgical lesions commonly lead to treatment failure^{4, 5}. Patients with advanced CRC are generally treated with chemotherapy and targeted therapy; however, their prognosis is generally poor^{6, 7}. Identifying effective therapeutic targets and prognostic molecular markers is of utmost importance to help provide a basis for early diagnosis and intervention treatment of CRC and to reveal the mechanisms of recurrence and metastasis.

Cell cycle-associated protein B1 (Cyclin B1, CCNB1) is an essential cell cycle regulator related to the G2/M phase checkpoint. Its location and content vary cyclically throughout the cell cycle. CCNB1 is synthesized in the cytoplasm, and its expression begins in the S phase and reaches maximum accumulation at the G1 phase. Consequently, CCNB1 is imported into the nucleus before rupturing the nuclear membrane, where it reaches a maximum level at the G2/M phase, after which it is destroyed via the ubiquitin-proteasome pathway. CCNB1 binds to cyclin-dependent kinase 1 (CDK1) as well as phosphorylates CDK1 (Thr160/Thr161) to form a heterodimer of CCNB1 and CDK1. The heterodimer composed of CDK1 and CCNB1, also known as the maturation promoting factor (MPF), promotes the transition of cells from the G2 to the M phase and initiates cell mitosis^{8, 9}.

The function of CCNB1 in cells varies as the cell cycle progresses. During interphase, CCNB1 is primarily localized in the cytoplasm, although it can also shuttle to the nucleus. CCNB1 accumulates in the nucleus during prophase and subsequently accumulates chromatin, spindle microtubules, centrosomes, and chromatin at mid-phase. At the onset of mitosis, the heterodimer composed of CDK1 and CCNB1 is activated and promotes early events such as chromosome condensation and disruption of the nuclear envelope. Another major function of the CCNB1-CDK1 dimer is disassembling the nuclear membrane, thus allowing the mitotic spindle access to the chromosomes¹⁰. CCNB1 is closely related to tumor development, and its abnormal expression is an early event in tumor development. Previous studies have shown that CCNB1 overexpression is a marker of poor prognosis in hepatocellular carcinoma (HCC), pancreatic cancer, and bladder cancer^9–11. However, it has also been reported that the expression of CCNB1 varies in different tumor types and that high expression of CCNB1 is a marker of favorable prognosis in lymphoma¹². As there is still no comprehensive research on the expression level and prognosis of CCNB1 in CRC, CCNB1 should be further investigated as a possible therapeutic target for colorectal cancer.

In this study, we employed bioinformatics and statistical methods for mining and analyzing data from The Cancer Genome Atlas (TCGA) to learn about CCNB1 expression in CRC and how it relates to the clinicopathological characteristics of CRC. Furthermore, immune infiltration, mutation profile, and copy number variation were analyzed to study further the correlation between CCNB1 and tumor-infiltrating immune cells in the TME and its potential prognostic significance. According to our results, increased CCNB1 expression corresponded with tumor immune infiltration, suggesting that CCNB1 could be used to predict the prognosis of patients with CRC.

2.1 CCNB1 gene and protein expression analysis

CCNB1 mRNA expression levels in different human normal tissues were analyzed using The Human Protein Atlas (HPA, version 20.1, https://www.proteinatlas.org/), and CCNB1 immunohistochemistry results were obtained for colorectal normal and tumor tissues. The expression of CCNB1 in vertebrate tissues was analyzed using the UCSC genomic database (version 2021, http://www.genome.ucsc.edu/cgi-bin/hgTracks). TIME2.0 (Tumor Immune Estimation Resource 2.0, http://timer.cistrome.org/) was used to analyze the pan-cancer expression profiles of CCNB1 in various tumors, including CRC and non-tumor tissues using the.

2.2 Correlation analysis of CCNB1 expression and clinicopathological characteristics of CRC patients

RNA sequencing data and complete patient’s basic information (age, sex, survival time, death status, TNM stage, etc.), including 468 CRC tumor tissues and 45 CRC paracancer tissues, were obtained from the TCGA database (https://portal.gdc.cancer.gov/). The expression and pairwise differences of CCNB1 mRNA in CRC tumor tissues and paracancerous tissues were analyzed using the ggplot2 and ggpubr package in R software (version 4.2.1). The clinical correlation between CCNB1 expression levels and the pathological data of CRC patients were also analyzed in terms of gender, age, clinical stage, T stage, and N stage.

2.3 Analysis of CCNB1 expression and survival prognosis of CRC patients

CRC clinical data from TCGA were analyzed using the survminer package in R software to plot Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) analysis. Moreover, the UCSC (Xena Browser) database was utilized to analyze the correlation between CCNB1 expression and disease-free survival (DFS) in patients with CRC.

2.4 Identification of co-expressed genes and analysis of differential gene expression

The R package corrplot and circlize were utilized to evaluate co-expressed genes that were positively and negatively correlated with the levels of CCNB1 and to draw a co-expression network. Differential analysis was conducted by dividing CCNB1 expression into high and low expression groups, using |log FC |>1 and FDR0.05 as filtering conditions to identify substantially different genes. The pheatmap package in R software was used to generate a heat map of differentially expressed genes.

2.5 Functional enrichment study of differentially expressed CCNB1-related genes in CRC

Enrichment analysis of CCNB1 co-expressed genes was performed using the R package "clusterProfiler". The R package "ggplot2" was used to visualize the results and analyze the biological processes, molecular functions, and cellular localization relevant to CCNB1 co-expressed genes. GO, KEGG, and GSEA analyses were performed to analyze CCNB1 in more detail. We further analyzed the differences in signaling pathways between the high and low CCNB1 expression groups to predict the signaling pathways and biological processes involved in CCNB1 in CRC.

2.6 Protein interaction analysis

PPI network analysis was performed using BioGRID (https://thebiogrid.org/), String (https://cn.string-db.org/), and GEPIA2 online databases (http://gepia2.cancer-pku.cn/#index).

2.7 Correlation analysis of tumor immune microenvironment

ESTIMATE algorithm was used to analyze the correlation between CCNB1 expression and immune/stromal/Estimate scores in the TCGA-CRC database. The CIBERSORT analysis was used to quantify the relative abundance of multiple tumor-infiltrating immune cells in the TME and analyze the differences of CCNB1 in the TME and its correlation with tumor-infiltrating immune cells. In addition, the tumor mutation burden (TMB) in TME and immunotherapy were analyzed, and relevant graphs were created using the R software package.

2.8 Statistical analysis

The chi-square test was performed to compare the correlation between low and high expression of CCNB1 in CRC patients' tissues with the occurrence of CRC. Log-rank test was used to predict OS (Overall survival), PFS (progression-free survival), and DPF of patients. Cox regression model was used to assess the prognostic value of different parameters involving CCNB1 expression in CRC tissues. P < 0.05 was considered statistically significant.

3.1 CCNB1 gene and protein expression analysis

Based on databases such as HPA, GTEx, and FANTOM5 (functional annotation of the mammalian genome), we found that CCNB1 was expressed in the majority of human tissues, having relatively high expression in skin, tonsil, and bone marrow tissues (Fig. 1A; Supplementary Figure S1), which indicated a low tissue specificity of CCNB1 expression. Meanwhile, CCNB1 expression was relatively conserved in vertebrates (Fig. 1B). In addition, the TIMER2 database revealed that CCNB1 expression was significantly elevated in bladder uroepithelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colonic adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), squamous cell carcinoma (HNSC), human papillary squamous cell carcinoma (HNSC-HPV), renal dialysis cell carcinoma (KIRC), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectal adenocarcinoma (READ), gastric adenocarcinoma (STAD), thyroid adenocarcinoma (THCA), and endometrial carcinoma (UCEC) (all p < 0.001), as well as in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and kidney renal papillary cell carcinoma (KIRP) (both p < 0.01) compared to the corresponding normal tissues from TIMER2 database (Fig. 1C). In addition, significantly higher CCNB1 expression was observed in HPV-positive head and neck squamous cell carcinomas (HNSCC) tissues compared to HPV-negative tissues (p < 0.001) (Fig. 1C). Furthermore, immunohistochemistry data from the HPA database showed that CCNB1 expression was significantly increased in CRC tumor tissues compared with normal tissues (Fig. 1D). Consistent with the results of CRC paired difference analysis (p < 0.001, Fig. 1F), the TCGA database demonstrated that CCNB1 expression was significantly upregulated in CRC tumor tissues (p < 0.001, Fig. 1E). These findings suggest that CCNB1 may be involved in developing multiple tumors, including CRC; however, its clinical significance needs further investigation.

3.2 Correlation analysis of CCNB1 expression and clinicopathological characteristics of CRC patients

Clinicopathological analysis was used to explore the clinicopathological correlation characteristics of CCNB1 expression in CRC tumor tissues and the impact of CCNB1 on disease prognosis, revealing that CCNB1 expression levels in the TCGA-CRC database were not significantly correlated with age, gender, and T-stage (Fig. 2C-E), but were significantly correlated with M, N-stage and clinical stage (Fig. 2F-H). As shown in Fig. 2F, CCNB1 expression was significantly lower in the M1 stage than in the M0 stage (p = 0.0095). Further analysis revealed a significant negative relation between CCNB1 expression and N stage (Fig. 2G, N0 and N1 (p = 0.0068), and N0 and N2 (p = 0.00086)). In addition, clinicopathological staging results showed that CCNB1 expression differed significantly between stage I and III CRC (p = 0.035), stage I and IV CRC (p = 0.013), stage II and III CRC (p = 0.0041), and stage II and IV CRC (p = 0.0033). The above results suggest that the expression of CCNB1 significantly decreases with the progression of the pathological stage and TNM stage, indicating that the reduced expression of CCNB1 could show a poor prognosis.

3.3 Survival prognostic analysis and independent prognostic analysis

Next, we performed subgroup survival analysis for overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) in CRC patients. The results of the Kaplan-Meier survival analysis showed that CCNB1 expression was not significantly correlated with OS and DFS of CRC patients (Fig. 3A, B). However, progression-free survival (PFS) was significantly increased in the high expression group of CRC patients compared to the low CCNB1 expression group (P = 0.037, Fig. 3C), which is consistent with our results of clinical characteristics analysis. Univariate and multifactorial Cox analysis showed that CCNB1 could not be used as an independent prognostic factor in CRC patients (Fig. 3D, E). In addition, ROC curve analysis revealed the specificity and sensitivity of CCNB1 in predicting the survival status of CRC patients at 3 years (0.547) and 5 years (0.586).

3.4 Analysis of co-expressed and differentially expressed genes of CCNB1 in CRC

Subsequently, we analyzed the co-expressed genes and differentially expressed genes of CCNB1, finding that CCNB1expression was negatively correlated with genes such as BCAS3 and ZBTB4, and positively correlated with the expression of PTTG1 and H2AZ1 (Fig. 4A, B). Using gene difference analysis, we found that genes such as PIWIL1, PLA2G2A, and LGALS17A were significantly increased in the high CCNB1 expression group and decreased in the low expression group. In contrast, LRP1B, KCNJ9, SCARNA7, and other genes were highly upregulated in the low-expression group while significantly downregulated in the high-expression group (Fig. 4C). The above results suggest that the above genes could be potential targets for regulating CCNB1.

3.5 GO, KEGG, and GSEA enrichment analysis

In order to further explore the biological functions and pathways associated with the established prognostic model, we explored the functional enrichment of genes related to CCNB1 co-expression. The GO analysis showed that CCNB1 was involved in 372 biological processes (BP), including "axon development", "extracellular matrix organization" and "extracellular structure organization" (Fig. 5A-C). Moreover, CCNB1 was associated with 87 cellular components, such as "collagen-containing extracellular matrix" and "synaptic membrane", and enriched in 100 molecular functions (MF) including "extracellular matrix structural constituent" and "extracellular matrix structural constituent conferring tensile strength" (Fig. 5A-C). KEGG pathway analysis showed that CCNB1-related genes were mainly enriched in the signaling pathways, such as protein digestion/absorption and neuroactive ligand-receptor interaction, and had a significant negative relationship with the PI3K-Akt signaling pathway (Fig. 5D-E). In addition, GSEA enrichment analysis revealed that CCNB1 was engaged in biological processes such as DNA replication and cell cycle (Fig. 5F).

3.6 PPI network analysis

To further clarify the mechanism of CCNB1 in carcinogenesis, 50 genes co-expressed with CCNB1 were obtained through the STRING database to verify the results of gene enrichment analysis. As shown in Fig. 6A, there was a close correlation between these 50 genes and CCNB1. Furthermore, according to the BioGRID4.3 database, CCNB1 interacted with CDK1, EGFR, and EP300 (Fig. 6B), which have characterized functions in cell cycle, mitotic regulation, and tumorigenesis ^{13, 14}. In addition, the results of the GEPIA2 online analysis showed that the expression of CCNB1 was positively correlated with the expression levels of CDK1, EGFR, and EP300 (Fig. 6C-E). Based on these results, we hypothesized that CCNB1 could have a tumor-promoting role in cancer by driving the cell cycle and promoting cell division.

3.7 CCNB1 is related to tumor immune microenvironment infiltration in CRC

The TME is becoming an increasingly important and challenging field in current tumor research. We analyzed the immune correlation between CCNB1 and CRC tumor microenvironment. The results are shown in Fig. 7A. The microenvironment score revealed that the stromal score and composite score of the high CCNB1 levels group were significantly lower than the low expression group (P < 0.001). Immune cell infiltration was a common feature of the TME in most tumors. The location, quantity, and type of these immune cells were highly correlated with the immunological state of the tumor tissue. Immune cell differential analysis showed that the percentage of CD4 (+) T cells in the CCNB1 high expression group, as well as the percentage of resting NK cells and active mast cells, were all considerably higher compared to the low expression group (all p < 0.01) (Fig. 7B). Also, in terms of T regulation cells (Tregs), the high expression group was significantly lower compared to the low expression group (p < 0.001). In addition, we also evaluated the relationship between CCNB1 expression and the infiltration of various immune cells (Fig. 7C, D), finding that CCNB1 expression was positively correlated with CD4 (+) T cells, M1 and M2 macrophages, while it was negatively related with regulatory T cells (Tregs), and M0 macrophages. These results suggest that the mechanism of CCNB1 regulation of the CRC microenvironment is related to the above immune cells.

3.8 Immunotherapy correlation analysis

The tumor mutation burden (TMB) was employed as an updated marker for the assessment of PD-1 antibody treatment efficacy. Its effect has been demonstrated in the use of PD-1 antibodies for the treatment of CRC with mismatch repair deficiencies^{15, 16}. As shown in Fig. 8A, CCNB1 was positively correlated with tumor mutational burden, suggesting that CCNB1 could be an important predictor of tumor immunotherapy efficacy. Immune checkpoint correlation analysis revealed significant negative regulatory relationships between CCNB1 and TNFSF4, TNFRSF8, and NRP1 (Fig. 8B, C). In addition, drug sensitivity analysis revealed significant differences in the efficacy of chemotherapeutic medicines such as 5-Fluorouracil, bexarotene, bleomycin, camptothecin, and cisplatin between the high and low CCNB1 expression groups. The CCNB1 low-expression group was more sensitive to bleomycin and camptothecin (Fig. 8E).

The high morbidity and mortality of CRC have become a major risk to people's physical and mental health. Although the current therapeutic system for CRC is well established, the spread of tumor cells and the recurrence of primary foci are still the leading causes of treatment failure. Previous research has revealed that tumor is a class of cell cycle diseases whose occurrence and development are closely related to the deregulation of the normal cell cycle¹⁷. Therefore, the in-depth study of therapy targeting cell cycle-related genes could have a broad prospect.

CCNB1 is a representative member of the cyclin protein B family, which is a mitosis-related regulatory protein mostly produced during the G2/M phase^{8, 9}. Cell cycle dysregulation is one of the hallmarks of cancer. Overexpression of CCNB1 can lead to uncontrolled growth of cancer cells throughout the cell cycle⁹. Numerous studies have demonstrated that CCNB1 presents high expression in various tumor tissues, including in CRC^18–20. However, the diagnostic and prognostic value of CCNB1 in CRC has not yet been thoroughly investigated. Therefore, in the present study, we explored the clinical and prognostic value of CCNB1 in CRC by bioinformatics approaches, aiming to provide new molecular markers for CRC treatment.

We first observed the distribution of CCNB1 expression in normal human tissues and vertebrates, finding that CCNB1 was widely expressed in various normal tissues while having conserved expression in vertebrates. Importantly, CCNB1 expression was significantly upregulated in several tumor tissues, showing low tissue and tumor specificity. Subsequent analysis of the data in the TCGA database revealed that CCNB1 expression was significantly higher in CRC tumor tissues than in normal tissues, and the results of paired difference analysis of cancer and paracancerous tissues from patients with CRC supported this conclusion. Clinicopathological characterization further showed a significant correlation between CCNB1 expression, CRC pathological grade, and TNM stage. Moreover, we found that CCNB1 expression had a decreasing trend with increasing CRC pathological stage, suggesting that lower CCNB1 expression may indicate poor prognosis in CRC patients. Moreover, CCNB1 expression decreased with the progress of the CRC clinical stage.

Next, we divided CRC cases into high and low-expression groups according to CCNB1 mRNA levels. Analysis of the prognostic relationship between CCNB1 and CRC patients revealed that PFS was significantly prolonged in the high CCNB1 expression group compared with the low CCNB1 group, which was consistent with the results of the prior clinical characteristics analysis. Univariate and multifactorial independent prognostic analysis suggested that CCNB1 was not an independent prognostic factor for patients with CRC. In addition, ROC curves demonstrated that CCNB1 was useful in predicting the survival probability at 3 and 5 years.

We then analyzed the regulatory genes and differentially expressed genes associated with CCNB1 to further explore the mechanism of CCNB1 in CRC. Our results showed that CCNB1 expression was negatively correlated with genes such as BCAS3 and ZBTB4 and positively correlated with the expression of PTTG1 and H2AZ1. Genes like PTTG1 and CDC25C primarily promote tumor development by affecting cell transformation, aneuploidy cell division, apoptosis, and mitosis^{21, 22, 24}. The results suggest that the mechanism of CCNB1 promoting tumor development may be associated with the above biological processes. Meanwhile, differential gene analysis revealed that PIWIL1, PLA2G2A, LRP1B, KCNJ9, and other genes were significantly different between high and low CCNB1 expression groups, indicating that these genes may be potential regulatory genes of CCNB1. Based on CCNB1 interacting proteins and co-expressed genes, we performed GO, KEGG, and GSEA enrichment analysis to explore the molecular mechanism and function of CCNB1 in CRC pathogenesis, finding that CCNB1 was involved in various biological processes and related signaling pathways in cancer development. Further analysis by the STRING database revealed that the process of CCNB1 promoting tumorigenesis development was associated with driving the cell cycle and promoting cell division, which was consistent with our previous results of differential gene analysis.

The TME is becoming an increasingly important and challenging field of current tumor research. Herein, we analyzed the immune correlation between CCNB1 and CRC TME. Immune and stromal cells are two important non-tumor components of the TME, where the immune and stromal scores are two vital predictors of tumor purity. The ability to predict the composition of stromal and immune cells is extremely valuable for tumor diagnosis and prognosis evaluation. ESTIMATE algorithm results showed that the stromal score and composite score of the CCNB1 high expression group were considerably lower than the low expression group, indicating that the CCNB1 high expression group had higher tumor purity. Immune cell differential analysis showed that the percentage of CD4 (+) T cells, resting NK cells, and active mast cells were all considerably greater in the CCNB1 high expression group than in the low expression group. Also, in terms of Tregs, the high-expression group was significantly lower than the low-expression group. We also evaluated the relationship between CCNB1 expression and the infiltration of various immune cells, finding that CCNB1 expression was positively correlated with CD4 (+) T cells, M1 and M2 Macrophages, while it was negatively related with regulatory T cells (Tregs), and M0 Macrophages.

The tumor mutation burden (TMB) was used as an updated independent predictor for the outcome of immuno-checkpoint inhibitor (ICI) across multiple tumor types. Although this method can predict the efficacy of PD-L1, its positive rate in CRC patients is low¹⁵. TMB assay could be a more comprehensive biomarker for assessing the efficacy of immunotherapy in patients with CRC. Our results showed that CCNB1 was positively correlated with TMB, which suggests that CCNB1 could be an important predictor of tumor immunotherapy. Immune checkpoint correlation analysis revealed significant negative regulatory relationships between CCNB1 and TNFSF4, TNFRSF8, and NRP1. Tumor drug sensitivity screening is a precondition for clinical medication. If medications are prescribed based on clinical experience, individual distinctions are frequently overlooked, resulting in poor overall efficacy. Therefore, conducting tumor drug sensitivity screening before or during clinical use and selecting sensitive drugs for individualized treatment based on the results are of utmost importance. The therapeutic efficacy of the medicine can be enhanced, and the harmful side effects can be minimized, thus helping to avoid the blindness of empirical medication. Drug sensitivity analysis revealed significant differences in the efficacy of chemotherapeutic medicines such as 5-Fluorouracil, bexarotene, bleomycin, camptothecin, and cisplatin between the high and low CCNB1 expression groups. These drugs are potential targeting agents against CCNB1.

In conclusion, our investigation demonstrated that CCNB1 expression is considerably elevated in CRC tissues and is linked with the clinicopathological and TNM stage of CRC patients. Furthermore, it regulates many genes and is involved in regulating multiple genes. In addition, CCNB1 is involved in the development of CRC through related pathways. Expression of CCNB1 may also affect the level of immune cell infiltration and the regulation of immune checkpoints. To sum up, CCNB1 may be used as a diagnostic and sensitive prognostic marker for CRC, which participates in the infiltration of immune cells in TME and may serve as a new direction for clinical diagnosis and treatment of CRC.

CRC: Colorectal Cancer; CCNB1: Cyclin B1; CDK1: Cyclin-dependent Kinase 1; MPF: Maturation Promoting Factor; TCGA: The Cancer Genome Atlas; HPA: Human Protein Map; UCSC: University of California Santa Cruz; TIMER2: Tumor Immune Estimation Resource version; OS: Overall survival; PFS: Progression Free Survival; DFS: Disease-Free Survival; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: Gene Set Enrichment Analysis; TME: Tumor microenvironment; BioGRID: Biological General Repository for Interactionh Datasets; GEPIA2: Gene Expression Profiling Interactive Analysis; BLCA: Bladder Cancer; BRCA: Breast Cancer; CHOL: Bile Duct Cancer: COAD :Colon Cancer; ESCA: Esophageal Cancer; GBM: Glioblastoma; HNSC: Head and Neck Cancer; KIRC: Kidney Clear Cell Carcinoma; LIHC: Liver Cancer; LUAD: Lung Adenocarcinoma; LUSC: Lung Squamous Cell Carcinoma; PRAD: Prostate Cancer; READ: Rectal Cancer; STAD: Stomach Cancer; THCA: Thyroid Cancer; UCEC: Endometrioid Cancer; CESC: Cervical Cancer; KIRP: Kidney Papillary Cell Carcinoma.

Authors’ contributions

Guo Chen finished writing and revising the manuscript. Tingting Tian participated in the revision of this manuscript. Feiqing Wang participated in the data analysis. Zhu Yang and Dongxin Tang checked and revised the manuscript. All authors read and approved the final manuscript. Each author agreed to be fully accountable for all aspects of the research to ensure that the integrity of this work was properly investigated and addressed.

Funding

This research was funded by the National Natural Science Foundation of China (No.82274610, 82260957, 81860819, 81960818), the project of Science and Technology Program of Guizhou Province (QKHJC-ZK [2022] General [498], QKHJC [2022] General [487], QKHJC [2020] [1Y368], QKHZC [2021] General [095], QKHHBZ [2020]3003, QKH Platform Talents [2020] 5013), the Special Fund Project for Scientific and Technological Innovation and Exploration in 2021 (2019YFC171250401), the Guizhou Provincial Traditional Chinese Medicine Cancer Inheritance and Science and Technology Innovation Talent Base (2018-3), the Large Health Science and Technology Cooperation project of the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine (ZKHT [2019]9-2, [2019]9-2-35, [2019]9-2-30), the high-level innovative talents training project of Guizhou Province (Hundred levels, QKH talents [2016]4032), the Yang Zhu "Traditional Chinese Medicine Oncology" Graduate Tutor Studio, Guizhou Province GZS(2016)08, and the Postgraduate Workstation of TCM Oncology JYSZ(2014)018.

Availability of data and materials

The data underlying this study are freely available from the TCGA database (https://portal.gdc.cancer.gov/projects/TCGA-KIRC), HPA database (https://www.proteinatlas.org/), UCSC database (http://www.genome.ucsc.edu/cgi-bin/hgTracks), TIMER2 database (http://timer.cistrome.org/), BioGRID database (https://thebiogrid.org/), String database (https://cn.string-db.org/), GEPIA2 database (http://gepia2.cancer-pku.cn/#index). The authors did not have special access privileges.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declared no conflicts of interest.

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The expression and prognostic significance of CCNB1 in colorectal cancer based on TCGA database

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

2.1 CCNB1 gene and protein expression analysis

2.2 Correlation analysis of CCNB1 expression and clinicopathological characteristics of CRC patients

2.3 Analysis of CCNB1 expression and survival prognosis of CRC patients

2.4 Identification of co-expressed genes and analysis of differential gene expression

2.5 Functional enrichment study of differentially expressed CCNB1-related genes in CRC

2.6 Protein interaction analysis

2.7 Correlation analysis of tumor immune microenvironment

2.8 Statistical analysis

Results

3.1 CCNB1 gene and protein expression analysis

3.2 Correlation analysis of CCNB1 expression and clinicopathological characteristics of CRC patients

3.3 Survival prognostic analysis and independent prognostic analysis

3.4 Analysis of co-expressed and differentially expressed genes of CCNB1 in CRC

3.5 GO, KEGG, and GSEA enrichment analysis

3.6 PPI network analysis

3.7 CCNB1 is related to tumor immune microenvironment infiltration in CRC

3.8 Immunotherapy correlation analysis

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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