Zwilch kinetochore protein affects the prognosis of cancer patients by participating in cell proliferation, enhancing cell communication, and reshaping the tumor microenvironment

Abstract

Background

Zwilch Kinetochore Protein(ZWILCH) has been reported to prevent cells from prematurely exiting mitosis. However, the underlying mechanisms or involvement of ZWILCH in the tumor immune microenvironment in various cancers remain largely unknown.

Methods

Generalized dysregulation of ZWILCH was observed through the whole transcriptome analysis in this study. The spatial transcriptome analysis was utilized to identify expressed regions of ZWILCH. Next, cells that mainly expressed ZWILCH in the tumor microenvironment were determined using the single-cell transcriptome analysis. Also, the “cellchat” R package was applied to estimate the effect of ZWILCH on malignant cell communication. Combining multiple analytic approaches including GSEA, GSVA, KEGG enrichment analysis, and Aucell, with TCPA functional protein data, Genome-wide CRISPR screening, potential functions of ZWILCH and the pathways in which ZWILCH participated were thoroughly exploited. Univariate Cox regression analysis calculated the association between ZWILCH and cancer patients’ adverse outcomes.

Results

ZWILCH is universally highly expressed in tumors. The spatial transcriptome analysis showed that ZWILCH overexpression comes from the tumoral region or mixed tumoral region. At the single-cell level, ZWILCH is chiefly expressed by malignant cells and proliferative T cells. The expression of ZWILCH mRNA is positively correlated with cell proliferation, repair of DNA damage, and cell cycle score. Plenty of metabolic pathways are inhibited in patients with high expression of ZWILCH. Moreover, after ZWILCH knockout, a large number of cancer cell lines are stagnated, inhibited, or died. Additionally, the malignant cells with positive expression of ZWILCH have a stronger ability for cell communication. In short, ZWILCH is meant to be a risk factor for clinical outcomes of multiple tumors.

Conclusions

ZWILCH is a promising therapeutic target that influences patient prognosis by participating in cell proliferation, cell communication, and reshaping the tumor microenvironment across different cancers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-02981-7.

Introduction

Cancer morbidity is increasing continuously, and despite advances in treatments, the majority of patients are diagnosed at an advanced stage of the disease, thereby missing the optimal window for effective treatment [1]. As a leading cause of death worldwide, cancer has put an enormous strain on global healthcare systems [2]. Tumor cells frequently become resistant to specific drugs, leading to reduced treatment efficacy. This resistance primarily arises due to the tumor’s immunosuppressive microenvironment or through acquired and intrinsic resistance mechanisms [3]. Furthermore, the intrinsic genomic instability that is characteristic of all cancer aids in evading cytotoxicity and targeted therapies. By developing new tumor targets, we can bypass those that have become resistant, addressing the issue of drug resistance. Consequently, there is a pressing need to identify innovative molecular targets that can effectively counteract the immunosuppressive microenvironment, slow down tumor progression, and enhance survival rates.

Oncogene activation, gene sequence change, and abnormal signal transduction make the occurrence and development of tumors complex, continuous, and dynamic [4]. Growing evidence indicates that abnormal gene expression and accumulated mutations in oncogenes or tumor suppressors significantly affect the tumor microenvironment (TME) and immune response, thereby promoting the development and metastasis of cancer cells [5], [6]. Therefore, it is of great importance to explore more novel molecular targets. In recent years, comprehensive and multi-dimensional pan-cancer research has become an essential means to further study the underlying mechanism of malignant tumor-related events, deepening our understanding of cancer and providing strong support for cancer prevention and treatment [7, 8].

Many human malignancies are characterized by chromosomal instability (CIN) resulting from abnormal function of mitotic checkpoints [9, 10]. Rod-Zw10-Zwilch complex plays a key role in the proper function of mitotic checkpoints [11]. What’s more, previous studies have shown that Zwilch Kinetochore Protein(ZWILCH) is highly expressed in colon cancer [9], lung squamous cell carcinoma [12], and hepatocellular carcinoma [13]. In addition, the overexpression of ZWILCH portends a poorer prognosis for adrenocortical carcinoma and pancreatic cancer patients [14], [15]. Also, ZWILCH plays a significant role in cell proliferation, making the gene a potentially important participant in tumorigenesis. So far, the association between ZWILCH and tumors has not been systematically elucidated, and its functional contributions and expression patterns in tumors remain largely unknown.

In this study, an overall pan-cancer analysis was conducted to examine the molecular features, expression origins, functions, and prognostic significance of ZWILCH based on huge data from multiple databases. Additionally, we investigated the impact of ZWILCH on cell communication and clarified its significance in the TME in the context of tumor immunity. This study provides valuable insights into the importance of ZWILCH and highlights the potential of therapies targeting ZWILCH in pan-cancer.

Methods

Whole-transcriptome expression analysis of ZWILCH

Gene expression data were obtained from the corrected TCGA dataset. RNA-seq data were obtained from the EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.tsv file provided by PanCanAtlas. This matrix file is generated according to the Firehose pipe: MapSplice + RSEM, and then normalized by setting the upper quartile to 1000. The data was converted to a unit-free Z-score using the formula: (x-µ)/σ. Wilcoxon Rank Sum Tests were performed to compare the statistical differences of gene expressions between tumor and normal tissues. To expand sample size, TPM expression in normal GTEx samples was paired with that in TCGA tumors (from the tcga_RSEM_gene_tpm and gtex_RSEM_gene_tpm datasets in the USCS Xena database). Z-score was also adopted to generate organ maps using the “gganatogram” package to visualize gene expression. To note, Z-scores could be used to recognize outliers. Z-scores greater than 3.0 or less than − 3.0 were classified as outliers and would be removed before differential analyses to ensure the accuracy of results. What’s more, we only included tumor types with the number of normal samples ≥ 3 for differential analysis. The Wilcoxon Signed Rank Tests were used to compare gene expression differences between tumor and the adjacent tissues in pan-cancer. The microsatellite instability (MSI) grouping data was collected from the UCSC Xena database. Between the three subgroups (MSI-H, MSI-L, and MSS group, representing high MSI, low MSI, and microsatellite stability, respectively) of each cancer, Wilcoxon analysis was performed for difference significance tests. Totally, six different immune subtypes (wound healing, IFN-g dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-b dominant) have been identified to be correlated with malignant features and patients’ prognosis [16]. According to the median of ZWILCH expressions, pan-cancer samples were divided into the high- and low-ZWILCH group, and then the proportion of each immune subtype in the two groups was calculated, followed by a chi-square test for significance detection.

Identification of the spatial traits of ZWILCH expression

Sparkle database (https://grswsci.top/) and SpatialTME (https://www.spatialtme.yelab.site/) database [17] were both used to conduct the spatial transcriptome analysis across cancers. Deconvolution of cell components in TME is performed using the “Cottrazm” R package from the SpatialTME database [18]. Moreover, spatial transcriptome profiles of pan-cancer have been established with the help of the Sparkle database which integrates the 10xVisium sequencing data from SpatialTME. Sparkle enables the visualization of gene expression profiles and the maximum cell composition in each microzone by employing the “SpatialFeaturePlot” and “SpatialDimPlot” functions in the “Seurat” package, respectively. Different scores of malignant cells in each microzone represent different significance, for example, if it is 1, the group is defined as “malignant”; if it is 0, the group is defined as “normal”, otherwise the group is defined as “mixed”. Wilcoxon Rank Sum Tests were performed to evaluate the significance of statistical differences in the amount of one gene expression between the three groups. Spearman correlation analysis was conducted to calculate the correlations between cell content and cell content, cell content and gene expression in all spots, followed by the “linkET” R package to visualize the results.

Validating the spatial transcriptome findings at the single-cell level

From the TISCH database [19], gene expression profiles at the single-cell level were obtained, and only those single-cell datasets containing malignant cells were reserved. The “pheatmap” R package was implemented to construct the heatmap to visualize ZWILCH expression profiles in pan-cancer at the single-cell level. Using Euclidean Distance as the metric, Ward’s method was performed for hierarchical clustering, making it easier to recognize patterns and trends in data, to help identify the source of ZWILCH expression. The Uniform Manifold Approximation and Projection (UMAP) approach was utilized to visualize cell cluster distribution and ZWILCH expression as two-dimensional heatmaps. The average expression of ZWILCH in each single-cell dataset was calculated, then the ratio of proliferative T cells to endothelial cells was calculated. Next, Spearman correlation analysis was conducted to show the correlation between them. From the TIMER2.0 database, immune infiltration data of all TCGA samples were collected to ensure data quality and consistency. Multiple algorithms were enrolled to fully assess the contents of different cell types and the Spearman correlation between them and ZWILCH expression, and subsequently, it would be visualized in the heatmap.

Recognition of zwilch’s biological functions

CancerSEA once defined 14 tumoral functional states [20], whose standardized scores were calculated using the “Z-score” algorithm in the “GSVA” R package. Pearson correlation analysis was performed to show the correlation between ZWILCH mRNA expression and the pathway scores. Functional proteomics includes a large-scale study of the functional activity of proteins, and The Cancer Proteome Atlas (TCPA) measures some important functional proteins by Reverse Phase Protein Array (RPPA). The cor.test function calculated the Spearman correlation between ZWILCH mRNA expression and TCPA protein levels of each cancer (proteins whose p value < 0.05 and correlation coefficient > 0.3 are significant). The “pheatmap” R package was used to visualize the top5 positively related and negatively related proteins in each cancer (If less than 5, the maximum value was included). Using Euclidean Distance as the metric, Ward’s method was performed for hierarchical clustering. The 30% samples with the highest ZWILCH expression were defined as the high expression group, and the 30% samples with the lowest ZWILCH expression were defined as the low expression group. The “limma” R package was employed for log2FC of each gene after the differential analysis, and all genes were ranked by log2FC. The “fgsea” function from the “fgsea” R package was used to conduct the Gene Set Enrichment Analysis (GSEA) on 85 metabolism gene sets from the KEGG database and 50 Hallmark gene sets from the MSigDB database. Normalized Enrichment Score (NES) was counted, and significance testing and multiple hypothesis testing were performed on the NES values of all gene sets. Combining the above differential analysis results, genes were redefined, that is, if a gene is consistently significantly overexpressed or underexpressed in the high ZWILCH expression group in 5 or more tumors, it would be classified as a ZWILCH functional gene in pan-cancer. Then, KEGG enrichment analysis was conducted to observe the pathway that may be influenced by ZWILCH.

Evaluating the effect of ZWILCH on cell communication

Cellchat is an R package for analyzing cell-to-cell communication in single-cell sequencing data that uses statistical modeling to predict the probability and intensity of cell-to-cell communication based on data on ligand and receptor interactions [21]. The createCellChat function builds the cell communication object, the reference library for cell communication relies on CellChatDB.human, and the parts related to “Secreted Signaling” are extracted from it. Overexpressed genes were identified using the identifyOverExpressedGenes function, and overexpressed ligand-receptor pairs were identified using the identifyOverExpressedInteractions function. The projectData function mapped genetic data to the interaction level via the protein-protein interaction (PPI) network. The computeCommunProb function was used to calculate the communication probability among cells. Using the filterCommunication function, communication pairs that do not meet the minimum cell count (< 10) were filtered out. The computeCommunProbPathway function calculated the actual communication probability. The aggregateNet function clustered all networks. The netVisual_circle function visualized the number and strength of nodes in the communication network. The netAnalysis_computeCentrality function calculated the centrality measure of the intercellular communication network. The netAnalysis_signalingRole_scatter and the netAnalysis_signalingRole_heatmap functions showed the importance of outgoing and incoming signals for each cell type in the communication network. Notably, to explore the effects of ZWILCH on the cellular communication ability of malignant cells, malignant cells in the CRC_GSE146771_Smartseq2 data set were classified into ZWILCH + and ZWILCH-Malignant according to whether ZWILCH was expressed. At last, the “AUCell” package was used to evaluate the scores of immune, metabolic, signaling pathways, proliferation, cell death, and mitochondria-related biological pathways. Also, cells were divided into positive and negative groups according to whether specific genes were expressed or not, and the “limma” package was used to compare the differences in scores between the two groups (if the number of a cell in both groups is 0, this cell type would be screened out).

Association of ZWILCH with adverse outcomes in cancers

Via the Human Protein Atlas (HPA) database, more evidence of widespread expression of ZWILCH in tumors at the protein level can be collected. Also, Genome-wide CRISPR screens were downloaded from the goal of the Dependency Map (DepMap) portal (https://depmap.org/portal/download/), which includes dependency scores of over 17,000 candidate genes by the CERES algorithm [22]. If the score is negative, it means cell growth inhibition and/or death after gene knockout. Scores of 0 and − 1 represent the median effect of non-essential and common core essential genes, respectively. Bar charts were employed to show the top 200 cell lines with negative scores to see the distributions of negative CERES scores. Then, Cox proportional risk models were used to assess individual factors’ impact on cancer patients’ overall survival (OS). The “survival” R package was involved in Univariate Cox survival analysis. For each cancer, Hazard Ratio (HR) and 95% Confidence Interval (CI) were computed to measure the strength of associations between a factor and the risk of an event. HR > 1 indicates that the factor increases the death risk; Otherwise, the factor will reduce the risk.

Drug resistance analysis

Spearman correlation analysis was conducted to calculate the correlation between gene expression and the half maximal inhibitory concentration (IC50) values in both GDSC1 and GDSC2 databases. A positive/negative correlation means the higher the gene expression, the more sensitive/resistant the cell line to the drug. To explore potential treatment options that could counteract ZWILCH-mediated tumor-promoting effects, Connectivity Map (CMAP) analysis was performed. The CMAP_gene_signatures. RData document enrolled 1288 compound-related features, based on which a gene signature including 150 of the most significantly up-regulated and 150 of the most significantly down-regulated genes, which were determined by comparing those cancer patients with high/low gene expressions. The eXtreme Sum (XSum) was utilized to compare gene-related traits and CMAP gene traits, and similarity scores of 1288 compounds were gained. To note, compounds with lower similarity scores may prohibit ZWILCH-mediated tumor-promoting effects.

Results

Dysregulation of ZWILCH across cancers

Among TCGA cohorts, ZWILCH was significantly highly expressed in tumoral tissues in most cancers (Fig. 1A). Consistent results were observed in TCGA-GTEx cohorts (Fig. 1B-C) and tumoral-adjacent peritumoral pairs (KIRC excluded) (Fig. 1D-E). In MSI-H group compared to MSS group, ZWILCH was expressed more (Fig. 1F). We next performed The Immune Landscape of Cancer analysis, a large-scale immunogenomic analysis of 9,126 tcga patients, which, in wound healing (C1) and IFN-g dominant (C2) groups, there were more ZWILCH high-expressed patients. In inflammatory (C3) group, there were more ZWILCH low-expressed patients (Fig. 1G), illustrating that a general disorder of ZWILCH expression may be a novel new target for pan-cancer, worthy of further exploration.

Fig. 1.

Expression landscape of ZWILCH in pan-cancer. A-B Differential gene expressions between tumor and normal tissues in TCGA and TCGA-GTEX cohorts. The top and bottom ends of the box represent the quartile range of values. The line in the box represents the median value. Wilcoxon Rank Sum Tests were used to compare the expression levels between the two groups. C The color represents the difference between the mean value of the genes in the tumor group and the mean value of the normal group in each cancer type. If the difference is positive and the Wilcoxon Signed Rank Tests indicate that p is less than 0.05, it is red. The larger the absolute value of the difference, the darker the red. If the difference is negative and the Wilcoxon Signed Rank Tests indicate that p is less than 0.05, it is blue. The larger the absolute value of the difference, the darker the blue. If p is greater than 0.05, it is characterized as white. D Organ plots. Blue means the Z-score is less than 0, red means the Z-score is greater than 0, and the darker the color means the greater the absolute value of the Z-score. E The differential ZWILCH expression in tumor and normal tissue. F The X-axis represents three subgroups of MSI-H, MSI-L and MSS in each tumor, and the Y-axis represents the expression of ZWILCH. G The bar chart at the top shows the proportion of each subtype to the total sample. The bar chart in the second row shows the proportion of ZWILCH high expression to low expression groups corresponding to each subtype, with red representing high expression group and green representing low expression group. At the bottom is the number and proportion of each subtype in each group

ZWILCH expression is derived from tumor regions

Correlations between ZWILCH expression and spatial characteristics in CRC (Fig. 2A), HNSC (Fig. 2B), OV (Fig. 2C), and SKCM (Fig. 2D) were analyzed to find that the mRNA expression of ZWILCH was significantly positively correlated with the content or proportion of malignant cells in microregions in above four cancers. Except OV, the mRNA expression of ZWILCH was negatively correlated with the content or proportion of endothelial cells in microregions. What’s more, ZWILCH expression in malignant cell regions and mixed cell regions was significantly higher than that in normal cell regions. These results all suggested that overexpression of ZWILCH is associated with malignant cells in the TME.

Fig. 2.

The spatial transcriptome analysis. A-D The spatial transcriptomics HE-stained sections of CRC, HNSC, OV, and SKCM tumors (from left to right) show the dominant cell types, tissue regions (tumor, mixed, normal), and micro-localization of ZWILCH expression (each dot represents a sequencing spot; darker red indicates higher gene expression). Correlation analysis reveals the relationship between ZWILCH expression and cell type abundance in these spots (red lines for positive correlation, green for negative, gray for non-significant; line thickness indicates correlation strength). The triangle area displays correlation coefficients visually (red for positive, blue for negative; darker and larger squares indicate more significant correlations with smaller p-values). Expression differences of ZWILCH across tissue regions are also shown (x-axis: spot types, y-axis: average gene expression)

ZWILCH is mainly expressed in malignant cells and proliferative T cells

Single-cell analysis of pan-cancer revealed the expression pattern of ZWILCH—it was mainly expressed in proliferative T cells and malignant cells (Fig. 3A). Spearman correlation analysis showed that the average expression of ZWILCH in the single-cell datasets was significantly positively correlated with the content or proportion of proliferative T cells (Fig. 3B), and negatively correlated with the content or proportion of endothelial cells (Fig. 3C). Multiple algorithms were used to evaluate immune microenvironment compositions to get similar results that the content of T cells was positively correlated with the mRNA expression of ZWILCH, and negatively correlated with the content of endothelial cells (Fig. 3D). Moreover, UMAP analysis visualized the expression clusters of ZWILCH in four types of tumors, mainly in proliferative T cells and malignant cells (Fig. 3E).

Fig. 3.

Validations at the single-cell level and whole-transcriptome level. A Each row represents a different dataset, with the same disease type marked in the same color. Each column indicates a different cell type. The color bar on the right shows the correspondence between data values and colors, with darker (red) colors representing higher data values and white representing zero expression or undetected. B-C The X-axis represents the average expression of ZWILCH in each single-cell dataset, and the Y-axis represents the proportion of proliferative T-cells or endothelial cells. Each scatter point represents a dataset, colored differently. R is the Spearman correlation coefficient, and the p-value indicates significance, with p < 0.05 considered significant. D The correlation coefficient ranges from − 1 to 1, with values close to 1 indicating a positive correlation (red) and otherwise indicating a negative correlation (blue). A p-value of < 0.05 is considered significant. A significant correlation between ZWILCH mRNA expression and immune infiltration score (p < 0.05) is represented by a square, otherwise by an X. The color scale on the right indicates gene expression levels. E In the UMAP plots, each point represents a cell. The top UMAP plot is colored by cell type, and the bottom UMAP plot is colored by ZWILCH mRNA expression levels

ZWILCH plays role in cell cycle, cell proliferation and cell metabolism

Pearson correlation analysis suggested that ZWILCH mRNA expression was positively correlated with scores of cell cycle, DNA damage repair and cell proliferation (Fig. 4A). Combining the TCPA data, a large number of proteins potentially related to ZWILCH were obtained (Fig. 4B). Further, the CYCLINB1 protein (also known as CCNB1, the first cell cycle protein to be discovered) was significantly positively correlated with ZWILCH mRNA expression in the vast majority of tumors (Fig. 4C). GSEA enrichment analysis showed that E2F targets, G2M chechpoint, MYC targets, Mtorc1 Signaling, Mitotic Spindle related pathways were significantly activated in the ZWILCH high expression group, while metabolism-related pathways were generally inhibited (Fig. 4D). Also, KEGG enrichment analysis on differential genes showed that in ZWILCH high expression group, highly-expressed genes were enriched in cell cycle, cell aging, and DNA replication related pathways, while low-expressed genes were enriched in metabolic related signaling pathways (Fig. 4E).

Fig. 4.

Biological functions of ZWILCH. A The horizontal axis represents functional state scores, the vertical axis represents gene expression z-scores, colors distinguish functional state types, and R denotes Pearson correlation. B The network diagram displays proteins related to ZWILCH in tumors (correlation > 0.3, p < 0.05), with tumors colored red and proteins colored blue. C The heatmap shows the correlation between proteins in the TCPA database and ZWILCH, with red indicating positive correlation, blue indicating negative correlation, and white indicating no significant correlation. D The scatter plot represents the relationship between different tumors and pathways, with scatter size reflecting FDR significance and color intensity reflecting the absolute value of NES. E Red indicates significant enrichment of pathways in the ZWILCH high-expression group, while blue indicates significant enrichment in the ZWILCH low-expression group

Malignant cells with positive expression of ZWILCH have a higher probability of cell communication

As shown in Fig. 5A-B, the network diagram clearly showed the node number and strength of the communication network. MIF and CypA signal pathways were the most important participating pathways in the outgoing signal and incoming signal (Fig. 5C). In addition, compared with ZWILCH-negative malignant cells, various communication signaling pathways showed comparative advantages in ZWILCH-positive malignant cells. The scatter charts showed consistent results, with ZWILCH-positive malignant cells receiving and sending signals more strongly than ZWILCH-negative malignant cells (Fig. 5D). In ZWILCH-positive malignant cells, CypA signaling pathway showed a complex pattern in cell communication as the most important mediator, and an important influencer, sender, and receiver (Fig. 5E). ZWILCH-positive malignant cells communicated with exhausted CD8T cells (Fig. 5F), and this phenomenon was not observed in ZWILCH-negative malignant cells. In ZWILCH-positive malignant cells, the MIF signaling pathway mainly acted as an influencer and sender in cell communication, and its importance in communication was still better than that of ZWILCH-negative malignant cells (Fig. 5G). Moreover, ZWILCH-positive/negative malignant cells had similar communication patterns (Fig. 5H). The role of ZWILCH in cell signaling pathways at the single-cell level was explored too, and proliferation-related biological pathways scored higher in the ZWILCH-positive malignant cells (Fig. 5I).

Fig. 5.

To ascertain the impact of ZWILCH on cellular communication: (A) Circle diagrams depict interaction strength and number between cells. B A dot plot highlights dominant senders and receivers. C A heatmap shows signal contributions to different immune cell groups. D Scatter plot: X-axis = outgoing communication probability, Y-axis = incoming; dot size = number of links, color = cell group. E Heatmap ranks cell groups by four network centrality measures in the CypA signaling network. F A layered diagram illustrates the CypA intercellular communication network, distinguishing autocrine (left) and paracrine (right) signals. Circle size = cell count, edge width = communication probability, color = signaling source. G Heatmap ranks cell groups by four network centrality measures in the MIF signaling network. H The layered diagram illustrates the MIF intercellular communication network, distinguishing autocrine (left) and paracrine (right) signals. Same visual cues as F. I Bubble chart: Y-axis = cell type, X-axis = pathway; red = pathway activated in gene expression positive group, blue = inhibited; bubble size = significance

High expression of ZWILCH is a risk prognostic factor for multiple tumors

Based on immunohistochemical sections and relevant statistical data from the HPA database, evidence of widespread expression of ZWILCH at the protein level was obtained. Antibody staining was found in 99% of the cancer tissues, with moderate to strong positive staining (Fig. 6A-B). ZWILCH had a negative CERES value in a large number of cell lines, indicating cell growth inhibition and/or death after ZWILCH knockout, but CERES did not reach − 1 in them, indicating that ZWILCH was not a common core essential gene (Fig. 6C). By univariate Cox survival analysis, HRs and 95% CIs for ZWILCH associated with the risk of death in OS, disease-specific survival (DSS), disease-free interval (DFI), and progression-free survival (PFS), shown in the forest map directly (Fig. 6D). In OS, ZWILCH was a poor prognostic factor for ACC, KICH, KIRC, KIRP, LGG, LUAD, MESO, PAAD, PCPG, PRAD, and SARC. Conversely, ZWILCH was a protective factor in THYM patients. In DSS, ZWILCH was a poor prognostic factor for ACC, KICH, KIRC, KIRP, LGG, LUAD, MESO, PAAD, and SARC. In DFI, ZWILCH was a poor prognostic factor for KIRP, LGG, THCA, PAAD, and SARC, and a protective factor for KIRC. In PFS, ZWILCH was a poor prognostic factor for ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG, PRAD, and SARC.

Fig. 6.

High ZWILCH expression is linked to poor outcomes in multiple tumors. A Antibody staining confirms its presence in 99% of cancers. B Immunohistochemistry in the HPA database verifies ZWILCH kinetochore protein levels. C The CERES score, on the y-axis, indicates cell line responses to gene knockout across tumor types (negative scores suggest inhibition of cell growth or induction of cell death). D A forest plot from Cox regression analysis shows the relationship between ZWILCH expression and survival outcomes (overall, disease-specific, disease-free, and progression-free intervals). Hazard ratios > 1 indicate increased risk, while < 1 suggest reduced risk; error bars represent 95% confidence intervals

We collected 25 independent datasets for univariate Cox survival analysis to evaluate the relationship between ZWILCH expression and patient prognosis. The results showed that in these external datasets, ZWILCH was identified as a significant risk factor (P < 0.05, HR > 1) in all tumor types except gastrointestinal tumors (Supplementary Fig. 1). For datasets where univariate Cox analysis indicated ZWILCH as a risk factor, we further performed Kaplan-Meier survival analysis and confirmed through log-rank test that patients with high ZWILCH expression had worse prognosis (Supplementary Fig. 2).

High expression of ZWILCH is associated with resistance to ERK/MAPK related drugs

Based on GDSC data, we found that the expression of ZWILCH was negatively correlated with DNA replication, cell cycle, cytoskeleton, and other related drugs, that is, the higher the expression of ZWILCH, the higher the sensitivity to these drugs. But for drugs that target the ERK/MAPK signaling pathway, the higher the expression of ZWILCH, the greater the resistance to these drugs (Fig. 7A-C). CMap analysis showed that arachidonyltrifluoromethane was the most likely drug to reverse the genetic effects caused by dysregulation of ZWILCH expression (Fig. 7D).

Fig. 7.

Drug resistance analysis. A-B. spearman correlation analysis of drug ic50 with gene expression in the gdsc1 gdsc2 database, with the 30 drugs with the highest significance visualized. C. Connected plots showing the top30 drugs and their pathways of action in the two databases. D. Cmap database analysis to characterize malignancy by reversing the zwilch gene, each tumor shows the three most likely drugs, the more negative the score, the higher the likelihood of reversal, and the red bar graph below shows the count of the number of tumors

Discussion

In this study, we focused on the high-throughput analysis of the expression of ZWILCH in pan-cancer. Our main aim was to evaluate the usefulness of ZWILCH as a potential marker for pan-cancer development and a predictor of tumor prognosis.

Previous studies have reported the presence of ZWILCH in colon cancer, lung squamous cell carcinoma, and hepatocellular carcinoma [9, 12, 13]. Our research has not only corroborated these findings but also expanded upon them. Consistent analyses from both TCGA and TCGA-GTEx indicated that ZWILCH was lowly expressed in kidney renal clear cell carcinoma (KIRC). In kidney chromophobe (KICH), no significant difference in ZWILCH expression was observed between normal and tumor tissues. Similarly, when the normal sample size was expanded in the TCGA-GTEx analysis of thyroid glandular carcinoma (THCA), no significant difference in ZWILCH expression was found between normal and tumor groups, aligning with the results from paired difference analysis of THCA and adjacent non-cancerous tissues. Across other tumor types, ZWILCH exhibited a widespread and significant overexpression, suggesting that ZWILCH served as a promising diagnostic tumor marker. This conclusion was further reinforced by immunohistochemical staining data from the Human Protein Atlas (HPA) database, which revealed that ZWILCH staining was medium or stronger in most tumors. Univariate Cox survival analysis was conducted to calculate the Hazard Ratios (HRs) and 95% Confidence Intervals (CIs) of ZWILCH in relation to the risk of death in Overall Survival (OS), Disease-Specific Survival (DSS), Disease-Free Interval (DFI), and Progression-Free Survival (PFS). In terms of OS, ZWILCH was identified as a poor prognostic factor for patients with Adrenocortical Carcinoma (ACC), Kidney Chromophobe (KICH), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Renal Papillary Cell Carcinoma (KIRP), Low-Grade Glioma (LGG), Lung Adenocarcinoma (LUAD), Mesothelioma (MESO), Pancreatic Adenocarcinoma (PAAD), Pheochromocytoma and Paraganglioma (PCPG), Prostate Adenocarcinoma (PRAD), and Sarcoma (SARC). On the contrary, in Thymoma (THYM) patients, ZWILCH served as a protective factor. Regarding DSS, ZWILCH was a poor prognostic factor for patients with ACC, KICH, KIRC, KIRP, LGG, LUAD, MESO, and PAAD. In DFI, ZWILCH was a poor prognostic factor for patients with KIRP, LGG, Thyroid Carcinoma (THCA), PAAD, and SARC, while it was a protective factor for KIRC patients. For PFS, ZWILCH was a poor prognostic factor for patients with ACC, KICH, KIRC, KIRP, LGG, Liver Hepatocellular Carcinoma (LIHC), LUAD, MESO, PAAD, PCPG, PRAD, and SARC. Spatial transcriptome analysis revealed that ZWILCH exhibited heightened expression within the tumoral region, and this pattern was seen in multiple tissue sections. Given this consistency, it can be definitively stated that the overexpression of ZWILCH in bulk RNA was due to the presence of malignant cells or regions. Similar results were obtained by the single-cell analysis. We found that ZWILCH was mainly expressed in malignant cells and proliferative T cells. Therefore, the observed association between ZWILCH upregulation and higher proliferation rate may be the effect of cell division. It is worth noting that there was transcriptional heterogeneity between proliferative T cells and the effects on TME are complex. Previous studies have found that exhausted T cells show the highest levels of cytotoxicity and inhibitory checkpoint receptor expression after the proliferation markers subside [23]. Another study discovered that CD4 proliferative T cells play a crucial role as intertumoral populations in the development of cold tumors [24].

The Rod-Zw10-Zwilch complex plays a key role in the normal function of mitotic checkpoints, and ZWILCH is an important component of this complex [11]. The pathway analysis corroborated this discovery, revealing a significant association between ZWILCH and pathways related to cell cycle regulation as well as DNA damage repair. Furthermore, the impact of ZWILCH on malignant cell communication is also noteworthy. We found that malignant cells with positive ZWILCH expression had significantly improved communication abilities, particularly within the CypA signaling pathway. The CypA pathway is closely related to the malignant characteristics of tumors. The abnormal expression of some enzymes in this pathway can, on the one hand, trigger gene mutations by metabolically activating pro - carcinogens, activate cell proliferation signaling pathways, and promote the proliferation of tumor cells. On the other hand, it can affect the tumor microenvironment, regulate the expression of angiogenesis factors and extracellular matrix - degrading enzymes, and facilitate the invasion and metastasis of tumor cells. In addition, changes in the CypA pathway can lead to alterations in drug metabolism kinetics, activate detoxification and DNA damage repair mechanisms, causing tumor cells to develop chemoresistance. At the same time, it can also interfere with the cell apoptosis signaling pathway, helping tumor cells evade apoptosis. Various types of cancer, such as small-cell lung cancer, pancreatic cancer, breast cancer, colorectal cancer, squamous cell carcinoma, and melanoma, have been reported to show upregulation of CypA. Additionally, in some of these cancers, a link between excessive CypA expression and malignant transformation has been established [25]. This further underscores that malignant cells with high ZWILCH expression exhibit more pronounced carcinogenic traits. Drug resistance analysis indicated that higher ZWILCH expression correlates with increased sensitivity to anti-proliferation drugs. However, it’s important to note that drugs targeting the ERK MAPK signaling pathway may not be effective when ZWILCH is highly expressed. To find potential drugs targeting ZWILCH, we conducted CMAP analysis and found that arachidonyltrifluoromethane is the most likely candidate to reverse the genetic effects caused by abnormal ZWILCH expression [26].

Limitations

There were some limitations to our study. First, the datasets from TCGA were a relatively small sample size. Second, our study was based on TCGA public database, and the result should be further validated using randomized controlled data. Third, further experiments are required to explore the underlying mechanisms. Therefore, in our future studies, we plan to explore the interactions of these genes in vitro and in vivo.

Conclusion

In summary, our research has demonstrated that ZWILCH exhibits widespread dysregulation of expression in tumor tissues, primarily originating from malignant cells and proliferative T-cells, and is associated with poor prognosis. The oncogenic characteristics of ZWILCH are mainly linked to cell cycle regulation, DNA damage repair, and cellular proliferation, with knockout resulting in growth arrest or death in numerous cancer cell lines. ZWILCH inhibits multiple metabolic processes and is involved in the remodeling of the tumor microenvironment, impacting cellular communication within malignant cells. Our findings provide valuable insights into the role of ZWILCH in tumor progression and suggest its potential as a promising therapeutic target for cancer treatment. Further research is required to unravel the precise mechanisms of ZWILCH regulation and to translate these discoveries into clinical applications.

Electronic supplementary material

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Author contributions

H.X.Z. and R.Q.Z. designed and supervised the study. L.Y., L.P.L., and J.S.W. performed the research, analysed the data, and wrote the paper. Y.L.H. reviewed and edited the paper.

Funding

The authors received no specific funding for this work.

Data availability

All data is available under reasonable request further inquiries can be directed to the corresponding author.

Declarations

Ethics approval and consent to participate

This article does not contain any studies with human participants or animals performed by any of the authors.

Competing interests

The authors declare that they have no conflicts of interest to report regarding the present study.