1. Introduction

Kidney renal papillary cell carcinoma (KIRP) accounts for approximately 15% to 20% of all renal cell carcinomas and ranks second among all renal cell carcinomas.[1] Its incidence and mortality rates are increasing worldwide.[2] In addition, it is the most common histological type of pediatric renal cancer, and 18 percent of patients on dialysis are diagnosed with KIRP.[3] Currently, the diagnosis of KIRP relies mainly on imaging methods, such as ultrasonography and magnetic resonance imaging. However, these methods do not show sufficient specificity and are often detected late.[4] Moreover, the current treatment and prognosis of KIRP are mainly based on histological features, and the subtypes of KIRP remain unsatisfactory.[5] Recently, studies have introduced several diagnostic markers for renal cell carcinoma, such as perilipin-2 and urine aquaporin-1.[6,7] However, most of these studies were conducted on crude renal cell cancer in patients and lacked specific KIRP results. They were not sensitive enough because they could only be found in approximately 10 to 15 percent of KIRP.[8] Therefore, it is important to identify biomarkers that can effectively predict the KIRP stage and prognosis.

CCNB1 is a highly conserved member of the cyclin family and is commonly expressed in normal human tissues and tumors.[9,10] A study reported that CCNB1 is involved in cell cycle regulation and forms MPF along with p34.[11] CCNB1 has also been reported to be associated with epithelial-mesenchymal transition and metastasis.[12] Recently, several studies have shown that upregulation of CCNB1 is very common in various human tumors, such as hepatocellular, lung, bladder, and colon cancer.[13–16] In conclusion, CCNB1 may be involved in the formation and regulation of many human tumors. However, little is known about how CCNB1 regulates KIRP, let alone about the targeted drugs.

In this study, we used bioinformatic methods and multiple online databases to analyze the expression level of CCNB1 in KIRP and its influence on prognosis, explored the prognostic predictors of KIRP, constructed a column graph to predict survival, discussed the relevant pathways involved in CCNB1, and screened the targeting drugs sensitive to CCNB1. This study aimed to identify a reliable prognostic marker for KIRP and to identify target drugs that can better diagnose and treat KIRP early.

2. Methods 2.1. The expression of CCNB1 in pancancer and normal tissues and its role in prognosis

Tumor Immune Estimation Resource (TIMER; https://cistrome.shinyapps.io/timer/) and TIMER 2.0 versions (timer.cistrome.org/) are comprehensive resources. The expression of genes in human tumors and the correlation between genes and immune cells can be understood.[17,18] We used these databases to explore the correlation between the expression level of CCNB1 in pancancer. To analyze the relationship between CCNB1 and prognosis, we obtained the KIRP patient survival data from the University of California Santa Cruz database (https://xenabrowser.net/). Overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), and progression-free interval were used as indicators of correlation between CCNB1 expression and prognosis. TIMER was used to explore the correlation between CCNB1 and immune cells in KIRP.

2.2. The expression of CCNB1 in different clinical characteristics

UALCAN (https://ualcan.path.uab.edu/index.html) contains a wide variety of cancer omics data (such as Tutorial, The Cancer Genome Atlas [TCGA], Proteomics and CBTN).[19] We used this database to analyze the correlation between CNNB1 expression levels and clinical characteristics and pathological stages of patients with KIRP. Only the tumor group was divided into different clinicopathological groups. Statistical significance was set at P < .05.

2.3. Immunohistochemistry staining of CCNB1

The Human Protein Atlas (HPA; https://www.proteinatlas.org/) is a protein profiling database that contains information on the distribution of proteins in human tissues and cells.[20] To validate the difference in the expression of CCNB1 at the protein level between KIRP and normal tissues, immunohistochemical images and clinical information of 11 cases of renal cancer and normal tissues were downloaded from the HPA.

2.4. Analysis of the relationship between CCNB1 and prognosis

Using the median expression of CCNB1 as the boundary, CCNB1 expression was divided into high and low-expression groups. The online database Gene Expression Profiling Interactive Analysis (http://gepia2.cancer-pku.cn) was used to analyze the relationship between CCNB1 expression levels and survival time from TCGA database.

2.5. Receiver operating characteristic (ROC) curve, column diagram and forest plots.

We used the Kaplan–Meier “survival” and “timeROC” packages to predict the survival of patients with KIRP using R (v4.1.0). The ROC curve was used to explore whether CCNB1 is a good predictor of the diagnostic value of KIRP. We performed univariate and multifactorial prognostic analyses for clinical traits and survival time using forest plots to show the resolution using R (v4.1.0).

2.6. Coexpression analysis and differentially expressed gene (DEG) analysis

Analysis of the CCNB1 coexpressed gene was performed using R (v4.1.0). The filtering condition was corFilter = 0.6, and the correlation test was pFilter = 0.001. According to the median value of CCNB1, 288 KIRP patients in TCGA database were divided into CCNB1 high and low-expression groups. DEGs were identified using the R package “DESeq2,” and the thresholds were set as logfold ≥ 1.5 and adjusted P value ≤ .05.

2.7. GO, KEGG and gene set enrichment analysis (GSEA)

To understand the CCNB1 high and low-expression groups, differential gene-related pathways and functional changes were identified. Gene ontology, Kyoto Encyclopedia of Genes (KEGG), and GSEA analyses were performed using R (v4.1.0). These analyses were performed using the “clusterprofler,” “org.Hs.e.g..db,” and “enrichplot” packages. The 5 most significant channels are shown as bubble plots. GSEA pathway enrichment analysis showed that the 5 most significant pathways were enriched in the high and low-expression groups of CCNB1. Statistical significance was set at P < .05.

2.8. Drug screening for CCNB1 sensitivity

Nearly 75,000 trials and sensitivity analyses were performed on 700 tumor lines and 138 anticancer drugs; information can be found in the Cancer Drug Sensitivity Genomics project.[21] Drug sensitivity analysis was completed using the R packages “pRRophetic,” “Ggpubr” and “ggplot2.” We set a P value < .001 as the filter condition. The results are shown as box plots.

2.9. Molecular docking

We used AutoDockTools software to dock CCNB1 and sensitive drugs and screened targets with good binding activity using docking score affinity. A score of <−7.0 kcal/mol−1 indicates a strong docking activity between them. The protein structure was downloaded from several databases: RCSB (http://www.rcsb.org/), PubChem, and the Protein Data Bank. AutoDockTools 1.5.7 software and PyMOL software were used to perform molecular docking.

2.10. Statistical software and methods

In this study, all data analysis was carried out in R (v4.1.0), SPSS 25.0, and GraphPad Prism 8 software. Spearman correlation test was used for correlation analysis. Wilcoxon test was used to compare the data between the 2 groups, and Kruskal–Wallis test was used to compare the data between the 2 groups, and a P value < .05 was considered statistically significant.

3. Results 3.1. Pan-cancer analysis of CCNB1

To understand the expression of CCNB1 in human tumors, we searched TCGA database, and found that CCNB1 was significantly highly expressed in a variety of tumors, including bladder cancer, breast cancer, cervical cancer, cholangitis carcinoma, colon adenocarcinoma, glioma, head and neck cancer, kidney cancer, and liver cancer (Fig. 1A for details). High CCNB1 expression also affects the prognosis of multiple tumors. In particular, the upregulation of CCNB1 significantly affected the OS, DSS, DFI, and progression-free interval in patients with KIRP (Fig. 1B–E). We then focused on the effects of CCNB1 on KIRP.

Figure 1.:

The expression of CCNB1 in pan-cancer and its up-regulation effect on tumor prognosis from TCGA database. (A) CCNB1 is significantly upregulated in a variety of tumors. (B–E) Effects of up-regulated CCNB1 expression on OS, DFS, DSS, and DFI and in pan-cancer, data from the TCGA database. **P < .01 and ***P < .001. DFI = disease-free interval, DFS = disease-free survival, DSS = disease-specifc survival, OS = overall survival, TCGA = The Cancer Genome Atlas.

3.2. CCNB1 in KIRP expression level and its correlation with KIRP subtypes

The CCNB1 expression levels were significantly upregulated in KIRP tissues compared to normal tissues, regardless of the presence of the genotype-tissue expression database (Fig. 2A and B). This phenomenon was also observed in the 32 paired samples (Fig. 2C). Based on these results, we compared the expression levels of CCNB1 in the different KIRP subtypes. CCNB1 expression was significantly correlated with sex and T, N, and M stage. Moreover, the expression level of CCNB1 in KIRP increased with increasing T stage, and distant metastasis was also significantly higher than that in none (Fig. 2D–H).

Figure 2.:

Expression of CCNB1 in KIRP and its correlation with KIRP subtypes. (A–C) CCNB1 is significantly highly expressed in KIRP tissues, data from TCGA and GTEx databases. (D–H) The high expression of CCNB1 is significantly correlated with gender, distant metastasis, lymph node stage, tumor size stage, and pathological stage. **P < .01; ***P < .001; and ****P < .0001. Data from TCGA database. GTEx = genotype-tissue expression, KIRP = kidney renal papillary cell carcinoma, TCGA = The Cancer Genome Atlas.

3.3. Effect of CCNB1 expression level on KIRP prognosis and analysis of prognostic factors

The prognosis of KIRP with high CCNB1 expression was worse, particularly because the OS and DFS were significantly shorter (Fig. 3A and B). To explore the diagnostic efficacy of CCNB1 for prognosis, an ROC curve was constructed. We found that CCNB1 as a predictor predicted the Area under curve value of KIRP at 1-, 3-, and 5-year survival rates of 0.801, 0.783, and 0.663, respectively. (Fig. 3C). We then constructed a graph based on the clinical trait scores and found that this score was a good predictor of 1-, 3-, and 5-year survival in patients with KIRP (Fig. 3D). In addition, univariate and multivariate analyses of CCNB1 and clinical characteristics showed that CCNB1 and stage were independent prognostic factors, indicating that CCNB1 can be used as an effective biomarker for predicting KIRP prognosis (Fig. 3E and F).

Figure 3.:

The value of CCNB1 in the diagnosis and prognosis of KIRP and the influencing factors of clinical characteristics from TCGA database. (A and B) Kaplan–Meier analysis showed that higher CCNB1 expression was associated with poorer OS and PFI in KIRP patients. (C) ROC curve was used to evaluate the diagnostic efficacy of CCNB1 in predicting 1-, 3-, and 5-year survival of KIRP patients. (D) Nomograms of clinical trait scores predicting 1-, 3-, and 5-year survival in KIRP patients. (E and F) Univariate and multivariate analysis of prognostic factors. KIRP = kidney renal papillary cell carcinoma, OS = overall survival, PFI = progression-free interval, ROC = receiver operating characteristic, TCGA = The Cancer Genome Atlas. * means P < .05, *** means P < .001.

3.4. Coexpressed genes with CCNB1 and DEG analysis

To explore which genes are the most correlated with CCNB1 in KIRP, we found that there were 6 positively correlated genes, TPX2, KIF20A, KIF11, CDK1, PEK, and KIFC1, and 5 negatively correlated genes, MTFR1L, MMP15, C16crf86, SPATA18, and MYL3, which are shown in Figure 4A. The specific correlation coefficients and p values are shown in Figure 4B. Line diagrams of the 6 most strongly expressed genes are shown in Figure 4C–H. The DEGs in the high and low CCNB1 expression groups were screened using a heatmap, among which 15 genes were downregulated and 498 genes were upregulated (Figure S1, Supplemental Digital Content, https://links.lww.com/MD/L979).

Figure 4.:

The most significant gene co-expressed with CCNB1. (A) Circle plot of genes co-expressed with CCNB1. (B) A list of the most significant genes co-expressed with CCNB1. (C–H) Scatter plot of the most significant genes co-expressed with CCNB1. The data from TCGA database. TCGA = The Cancer Genome Atlas.

3.5. Gene ontology, KEGG and GSEA pathway enrichment analysis

We then used “Metascape” to analyze the function and pathway enrichment of CCNB1-related DEGs. Nuclear division, chromosome segregation, mitotic cell cycle phase transition, nuclear chromosome segregation and mitotic sister chromatid segregation are closely associated with changes in CCNB1 expression. Additionally, genes closely correlated with CCNB1 were enriched for many cellular components, including spindles, chromosomal regions, condensed chromosomes, centromeric regions and kinetochores, which were significantly regulated by CCNB1 in KIRP. CCNB1 also remarkably affected molecular functions, such as channel activity, gated channel activity, cytoskeletal motor activity, microtubule motor activity, and extracellular ligand-gated ion channel activity (Fig. 5A).

Figure 5.:

GO, KEGG, and GSEA analysis of differentially expressed genes between CCNB1 high and low expression groups. (A and B) Bubble plots for GO and KEGG analysis. (C) Enriched pathway map of differentially expressed genes by GSEA analysis. The data from TCGA database. GO = gene ontology, GSEA = gene set enrichment analysis, KEGG = Kyoto Encyclopedia of Genes, TCGA = The Cancer Genome Atlas.

KEGG analysis revealed that these pathways included neuroactive ligand–receptor interaction, cell cycle, motor proteins, oocyte meiosis, and GABA ergic synapse pathways associated with CCNB1 function in KIRP (Fig. 5B).

GSEA analysis showed that the ECM_RECEPTOR_INTERACTION, FOCAL_ADHESION, DILATED_CARDIOMYOPATHY, and REGULATION_OF_ACTIN_CYTOSKELETON signaling pathways were significantly enriched in the high CCNB1 expression group. In contrast, the OLFACTORY_TRANSDUCTION signaling pathway was enriched in the CCNB1 low-expression group (Fig. 5C).

3.6. The correlation between CCNB1 and tumor-infiltrating immune cells

Using the TIMER database, we found that CCNB1 was positively correlated with B lymphocytes, CD8+ T lymphocytes, and dendritic cells, and negatively correlated with macrophages in the tumor microenvironment. However, no significant relationship was observed between CCNB1 and CD4+ T lymphocytes or neutrophils (Fig. 6A and B). To understand the effect of these immune cells on KIRP prognosis, we combined CCNB1 and immune cells to plot the Kaplan–Meier curves. The results showed that higher levels of B lymphocytes and CD8+ T lymphocytes in the tumor microenvironment led to worse survival in patients with KIRP. The expression levels of CD4+ T cells, macrophages, neutrophils, and dendritic cells did not significantly correlate with KIRP patient survival (Fig. 6C and D).

Figure 6.:

The correlation between CCNB1 and tumor-infiltrating immune cells and its effect on the OS of KIRP patients. (A and B) CCNB1 was positively correlated with B lymphocytes and CD8+ T cells. (C and D) Higher expression of B lymphocytes and CD8+ T cells the worse prognosis of KIRP patients. The data from the TIMER2 database. KIRP = kidney renal papillary cell carcinoma, OS = overall survival.

3.7. Validation of immunohistochemical levels of CCNB1 in renal cancer and normal renal tissues

Using the HPA database, we found that CCNB1 staining was moderate in 7 of 11 cases of renal cancer, while CCNB1 was not detected in any of the 3 normal tissues. The clinical characteristics and immunohistochemistry of CCNB1 in renal cancer and normal tissues from HPA database was shown in Table 1.

Table 1 - The clinical characteristics and IHC of CCNB1 in renal cancer and normal tissues from HPA database. Number Gender Age (yr) Intensity Staining Location Patient 1 F 56 Strong Medium Cytoplasmic/membranous Patient 2 M 71 Strong Medium Cytoplasmic/membranous Patient 3 M 63 Strong Medium Cytoplasmic/membranous Patient 4 M 61 Strong Medium Cytoplasmic/membranous Patient 5 M 77 Strong Medium Cytoplasmic/membranous Patient 6 M 68 Strong Medium Cytoplasmic/membranous Patient 7 M 56 Negative Not detected Cytoplasmic/membranous Patient 8 M 46 Strong Medium Cytoplasmic/membranous Patient 9 F 72 Negative Not detected Cytoplasmic/membranous Patient 10 F 83 Negative Not detected Cytoplasmic/membranous Patient 11 F 60 Negative Not detected Cytoplasmic/membranous Normal 1 F 56 Negative Not detected Cytoplasmic/membranous Normal 2 M 7 Negative Not detected Cytoplasmic/membranous Normal 3 M 28 Negative Not detected Cytoplasmic/membranous The data was downloaded from the HPA database(https://www.proteinatlas.org/). The expression of CCNB1 was evaluated according to the intensity of the staining (0, 1+, 2+, and 3+) and the percentage of positive cells, which was scored as 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%) or 4 (76–100%). The SI was calculated as follows: SI = (intensity score in 1) × (positive staining score in 2). SI ≤ 3 was classified as low expression, while SI ≥ 4 was classified as high expression.

HPA = human protein atlas, IHC = immunohistochemistry, SI = staining index.

Immunohistochemical images of CCNB1 in 1 case of normal tissue and renal carcinoma are shown in Figure 7A. As shown in Figure 7B, the expression was CCNB1 in renal cancer tissues than that in normal tissues.

Figure 7.:

Immunohistochemical of CCNB1 in normal renal tissues and renal cancers from the HPA database. (A) IHC of CCNB1 in 1 case of renal normal tissue and renal cancer. (B) Immunohistochemical staining of CCNB1 in 3 normal tissues and 11 renal cancer tissues. HPA = human protein atlas, IHC = immunohistochemistry.

3.8. Drug sensitivity screening and molecular docking

Based on previous considerations of the effect of CCNB1 on the prognosis of KIRP, we focused on exploring drugs that are more sensitive to CCNB1 to improve the survival time of patients with KIRP. We found that a total of 24 drugs was sensitive to CCNB1 expression. Next, we used molecular docking technology to dock these drugs with CCNB1 and found that a total of 20 drugs could bind to CCNB1, among which 5 drugs had binding energies exceeding kcal/mol. The names of drugs sensitive to CCNB1 and the specific values of binding energy are shown in Table S1, Supplemental Digital Content, https://links.lww.com/MD/L980. Several sensitive drugs with strong binding activities were screened. These included S-trityl-l-cysteine, parthenolide, midostaurin, GW843682X, and BI-2536. The molecular docking diagram and statistical histogram of the sensitive drugs are shown in Figure 8A–E.

Figure 8.:

Molecular docking diagram and statistical difference analysis of the 5 targeting drugs with the strongest binding power to CCNB1 (A–E).

4. Discussion

KIRP, the second most common renal cell carcinoma, is divided into 2 main types. Type I KIRP and its main expression are associated with MET mutations. However, type II KIRP heterogeneity is stronger.[22] In a meta-analysis, there was no significant difference in prognosis between patients with KIRP metastasis and those with other types of renal cell carcinoma metastases, and patients with type II KIRP had an even worse prognosis.[23] Therefore, it is necessary to identify biomarkers that can predict KIRP and to look for more corresponding targeted drugs.

CCNB1, also known as cyclin B1, is an important member of the cell cycle family and is closely associated with cell detection sites.[24] CCNB1 overexpression has also been observed in various cancers, such as tongue, esophageal, and non-small cell lung cancers, and its expression is closely related to tumor grading, differentiation, metastasis, and even prognosis.[25,26] Studies have shown that CCNB1 affects tumors through different mechanisms of action. Silencing CCNB1 inhibits cell proliferation by activating the p53 signaling pathway in pancreatic cancer.[27] It has also been reported that CCNB1 can promote proliferation of pituitary adenomas by stimulating epithelial-to-mesenchymal transformation.[28] We speculated that CCNB1 might be an oncogenic gene in tumors. Therefore, in this study, we focused on the question of whether CCNB1 affects tumor progression, prognosis, and explored its possible mechanisms, finally aimed to identify effective targeted drugs.

CCNB1 was upregulated in many human tumors was also well verified, including bladder, breast, cervical, cholangitis carcinoma, colon adenocarcinoma, glioma, head and neck, kidney, and liver cancer. It is also associated with poorer prognosis in many tumors, such as OS, DFS, DSS, and DFI. In addition, we found that CCNB1 was remarkably upregulated in KIRP tissues compared to normal tissues and was closely associated with poorer prognosis. Compared with other renal cell carcinomas, there is a higher incidence of lymph node (LN) involvement, and LN staging is more important in pathological grading for the diagnosis of KIRP.[29] Interestingly, in our study, CCNB1 expression positively correlated with LN invasion. Furthermore, CCNB1 is significantly upregulated in patients with KIRP tumor stage and metastasis. This suggests that the upregulation of CCNB1 may reflect the degree of KIRP infiltration to a certain extent.

To further evaluate the function of CCNB1 as a prognostic marker in KIRP, we thoroughly evaluated its diagnostic and prognostic value using Kaplan–Meier and ROC curve analyses. As expected, CCNB1 showed satisfactory predictive efficiency, and upregulated CCNB1 expression predicted poor OS and FDS in patients with KIRP. This is consistent with the results of a previous study.[30] Subsequently, we constructed a line column predicting the 1-, 3-, and 5-year survival rates of patients with KIRP, and the results were encouraging. In addition, we conducted univariate and multivariate analyses of factors influencing KIRP prognosis. CCNB1 levels and stage were found to be independent prognostic factors. The ROC curve area under curves for the 1-year and 3-year survival rates of CCNB1 were 0.801 and 0.783, respectively. This suggests that CCNB1 is a reliable biomarker for KIRP diagnosis. Therefore, we believe that CCNB1 is an independent predictor of poor prognosis in patients with KIRP, and has great potential as a diagnostic and prognostic biomarker.

To explore the interactions between CCNB1 and other genes, we established a coexpression analysis circle in which TPX2, KIF20A, KIF11, CDK1, PBK, and KIFC1 showed the strongest positive correlation with CCNB1. A study reported that a higher expression level of TPX2 was significantly associated with worse overall survival in papillary renal cell carcinoma.[31] In another study, KIF20A knockdown inhibited the proliferation and invasion of 2 renal carcinoma cell lines to a certain extent.[32] In addition, elevated KIF11 expression predicts poor clinical prognosis in patients with renal cell carcinoma.[33] These studies are consistent with our results, which further indicate that CCNB1 and its coexpressed genes play important roles in the progression of KIRP tumors.

Moreover, 513 DEGs were identified between CCNB1 high- and low-expression groups. CCNB1 and its related genes participate in several biological processes, such as nuclear division and chromosome segregation. A previous study also demonstrated that CCNB1 is mainly involved in cell cycle and DNA replication pathways.[34] GSEA indicated that CCNB1 was associated with multiple pathways, including ECM_RECEPTOR_INTERACTION, FOCAL_ADHESION, DILATED_CARDIOMYOPATHY, and REGULATION_OF_ACTIN_CYTOSKELETON.

Renal cell carcinoma has long been regarded as an immunogenic tumor that participates in a variety of immune cells in the tumor microenvironment-mediated antitumor immune response.[35] Using the TIMER database, we found that CCNB1 was positively correlated with B lymphocytes, CD8+ T lymphocytes and dendritic cells and negatively correlated with macrophages in the tumor microenvironment. Moreover, higher numbers of B and CD8+ T lymphocytes in the tumor microenvironment led to worse survival. Therefore, we speculated that CCNB1 may be involved in the regulation of the KIRP immune microenvironment through immune cells and may affect disease progression to a certain extent. Enrichment of these pathways suggests that CCNB1 is closely related to cellular immunity in addition to affecting the cell cycle and tumor migration to a certain extent. This lays the foundation for future research.

Another highlight of this study is that we identified 24 drugs that were sensitive to CCNB1. Five drugs with the strongest binding powers were identified using molecular docking technology: S-Trityl-L-cysteine, parthenolide, midostaurin, GW843682X, and BI-2536. A series of studies has shown that these 5 drugs have powerful anticancer effects. A study revealed that S-Trityl-L-cysteine-mediated apoptosis and cell cycle arrest are triggered by the activation of the mitogen-activated protein kinase and nuclear factor-κB signaling pathways.[36] Parthenolide is a sesquiterpene lactone that induces G0/G1 cell cycle arrest in A549 cells and G2/M cell cycle arrest in H1792 cells.[37] It has also been confirmed that parthenolide may affect the tumor cell cycle through CCNB1. Midostine also showed strong antitumor effects in 1 study.[38] In addition, it has been reported that GW843682X can inhibit the growth of various cancer cells.[39] Many studies have shown that circulating nontransformed cells are sensitive to BI-2536.[40] Moreover, the anticancer applications of these drugs have not been reported in KIRP. This lays the theoretical foundation for further studies of the mechanisms of action of these drugs against CCNB1.

5. Conclusion

This study suggests that CCNB1 is a promising and reliable target for prevention and treatment of KIRP. We also screened 5 drugs that targeted CCNB, which provides a new approach for the treatment of patients with KIRP metastasis.

Author contributions

Conceptualization: Xiaoming Gong, Yahong Gong, GuiFang Wu.

Data curation: Xiaoming Gong, Yahong Gong, GuiFang Wu.

Funding acquisition: Hengning Ke, Xiaoming Gong.

Methodology: Xiaoming Gong, Yahong Gong, GuiFang Wu.

Project administration: Hengning Ke.

Software: Xiaoming Gong, Yahong Gong, GuiFang Wu.

Validation: Hengning Ke.

Writing – original draft: Xiaoming Gong.

Writing – review & editing: Hengning Ke.

References [1]. De Vries-Brilland M, McDermott DF, Suárez C, et al. Checkpoint inhibitors in metastatic papillary renal cell carcinoma. Cancer Treat Rev. 2021;99:102228. [2]. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin. 2021;71:7–33. [3]. Xia S, Lin Y, Lin J, et al. Increased expression of TICRR predicts poor clinical outcomes: a potential therapeutic target for papillary renal cell carcinoma. Front Genet. 2021;11:605378. [4]. Li Y, Lih TM, Dhanasekaran SM, et al.; Clinical Proteomic Tumor Analysis Consortium. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness. Cancer Cell. 2023;41:139–63.e17. [5]. Farber NJ, Kim CJ, Modi PK, et al. Renal cell carcinoma: the search for a reliable biomarker. Transl Cancer Res. 2017;6:620–32. [6]. Song J, Liu YD, Su J, et al. Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma. J Cell Physiol. 2019;234:22753–64. [7]. Akhtar M, Al-Bozom IA, Al Hussain T. Papillary renal cell carcinoma (PRCC): an update. Adv Anat Pathol. 2019;26:124–32. [8]. Zhou Y, Ye Z, Wei W, et al. Macrophages maintain mammary stem cell activity and mammary homeostasis via TNF-α-PI3K-Cdk1/cyclin B1 axis. NPJ Regen Med. 2023;8:23. [9]. Uhlen M, Oksvold P, Fagerberg L, et al. Towards a knowledge-based human protein atlas. Nat Biotechnol. 2010;28:1248–50. [10]. Chen Q, Fu Y, Liu X, et al. Aberrant expression of CKS2 induced by ELK1 contributes to malignant progression of pancreatic cancer. Mol Carcinog. 2023;1947:1959. [11]. Ou Y, Ma L, Ma L, et al. Overexpression of cyclin B1 antagonizes chemotherapeutic-induced apoptosis through PTEN/Akt pathway in human esophageal squamous cell carcinoma cells. Cancer Biol Ther. 2013;14:45–55. [12]. Deng JL, Xu YH, Wang G. Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front Genet. 2019;10:695. [13]. Xia P, Zhang H, Xu K, et al. MYC-targeted WDR4 promotes proliferation, metastasis, and sorafenib resistance by inducing CCNB1 translation in hepatocellular carcinoma. Cell Death Dis. 2021;12:691. [14]. Gao X, Chen Y, Chen M, et al. Identification of key candidate genes and biological pathways in bladder cancer. PeerJ. 2018;6:e6036. [15]. Li J, Zhou L, Liu Y, et al. Comprehensive analysis of cyclin family gene expression in colon cancer. Front Oncol. 2021;11:674394. [16]. Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infltrating immune cells. Cancer Res. 2017;77:e108–10. [17]. Li T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infltrating immune cells. Nucleic Acids Res. 2020;48:W509–14. [18]. Chandrashekar DS, Bashel B, Balasubramanya SAH, et al. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19:649–58. [19]. Chen G, Luo D, Zhong N, et al. GPC2 is a potential diagnostic, immunological, and prognostic biomarker in pan-cancer. Front Immunol. 2022;13:857308. [20]. Gong X, Chen T, Lin C, et al. Bioinformatics analysis of the prognostic significance of VPS16