CDKN3 expression in pan-cancer

In this study, we conducted an analysis of TCGA_GTEx data obtained from UCSC to explore the expression of CDKN3 in pan-cancer. Our investigation unveiled diverse expression patterns of the CDKN3 gene within distinct tumor cells. tumor cells. The expression of CDKN3 was significantly up-regulated in majority of tumors. However, CDKN3 expression was significantly down-regulated in LAML and TGCT (Fig. 1A). We also found that the expression of CDKN3 was significantly overexpressed in most of tumors. This result harmoniously resonated with the observations gleaned from the TCGA dataset (Fig. 1B). We also scrutinized CDKN3 expression in both tumors and corresponding normal tissues. Intriguingly, barring THCA, a consistent trend emerged wherein the majority of tumor tissues demonstrated heightened CDKN3 expression relative to their corresponding normal tissue counterparts. However, there was no conspicuous change in CDKN3 expression was observed between the normal tissues and the tumor tissues of CESC, PAAD, and PCPG (Fig. 1C).

Fig. 1

CDKN3 mRNA expression in pan-cancers. A CDKN3 mRNA expression in 33 tumors from TCGA-GTEx samples. B CDKN3 mRNA expression in 33 tumors from the TCGA database. C CDKN3 expression was observed in 23 paired tumor specimens from the TCGA database. (ns, p ≥ 0.05; *p < 0.05; **p < 0.01;***p < 0.001)

Furthermore, we also obtained alterations in CDKN3 expression levels at distinct tumor stages by the utilization of the UALCAN online tool. In the advanced stages of 16 diverse cancer types, including BLCA, BRCA, CESC, COAD, ESCA, CHOL, KICH, KIRC, KIRP, HNSC, LIHC, LUAD, LUSC, READ, STAD, and UCEC, we observed a significant augmentation in CDKN3 expression (Additional file 1: Fig. S1).

The association between CDKN3 expression and prognosis in pan-cancer

Kaplan–Meier survival analysis was used to investigate the correlation between CDKN3 expression and clinical outcomes. As shown in Fig. 2A, we delved further into the association between CDKN3 expression and overall survival (OS) in 33 distinct cancers. The results demonstrated a compelling correlation between abnormal CDKN3 expression and OS in a subset of cancers, including ACC, BLCA, KIRC, KIRP, LGG, LIHC, LUSC, MESO, PAAD, UCEC, and UVM (Additional file 1: Fig. S2A–K). Importantly, it was discerned that high level of CDKN3 was associated with shorter OS in these particular cancer types.

Fig. 2

CDKN3 expression correlates with OS, DSS, and PFIin pan-cancer survival analysis. A Forest plots of the OS survival results. B Forest plots of the DSS survival results. C Forest plots of the PFI survival results

Subsequently, we explored the association between CDKN3 expression and disease specific survival (DSS) (Fig. 2B). Our exploration yielded compelling insights, revealing distinct associations between CDKN3 expression and DSS in a range of cancer types. Notably, the findings showed significant associations between CDKN3 expression and DSS in ACC, BLCA, DLBC, LGG, HNSC, KIRC, KIRP, LIHC, LUAD, MESO, PAAD, and UVM (Additional file 1: Fig. S3A–L). In these specific cancers, elevated CDKN3 expression was conspicuously correlated with poorer DSS.

Finally, an in-depth exploration into the relationship between CDKN3 expression and Progression-Free Interval (PFI) was undertaken (Fig. 2C). This endeavor yielded noteworthy findings, enabling us to discern a clear pattern where elevated CDKN3 expression aligns with adverse PFI outcomes across several tissue types. Specifically, our analysis revealed that high CDKN3 expression is indicative of poorer PFI in the following tissues: ACC, BLCA, LGG, KIRC, KIRP, LIHC, LUAD, MESO, PAAD, PRAD, STAD, TGCT, and UVM (Additional file 1: Fig. S4A–M). Additional file 1: Fig. S5A–E showcases Receiver Operating Characteristic (ROC) curves for five tumors where the prognosis is notably linked to CDKN3 expression, effectively illustrating the diagnostic potential of CDKN3 in these cases.

The relationships between CDKN3 expression and clinical parameters

The expression of CDKN3 was related to the prognosis of 17 different types of tumors, including ACC, BLCA, DLBC, HNSC, LGG, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, PAAD, UCEC, PRAD, STAD, TGCT, and UVM. Here, we investigated the relationships between CDKN3 expression and the clinicopathological characteristics of these 17 tumors. These findings revealed that in the cases of HNSC, KIRP, LUAD, and LUSC, CDKN3 expression was associated with gender (Additional file 1: Fig. 3A–D). In the meantime, tumor size exhibited a connection with CDKN3 expression in ACC, KIRC, KIRP, and LIHC (Additional file 1: Fig. 3E–H). Additionally, CDKN3 expression was associated with lymph node metastases in HNSC, KIRC, KIRP, LUAD, LUSC, and PRAD (Additional file 1: Fig. 3I–N). There was also a correlation between CDKN3 expression and the pathological stage in ACC, KIRC, KIRP, LIHC, LUAD, and LUSC (Additional file 1: Fig. 3O–T).

Fig. 3

Clinical metrics and CDKN3 expression's relationship A–D CDKN3 expression was related to gender. E–H Expression of CDKN3 was related to the T stage. I–N CDKN3 expression was related to the N stage. O–T CDKN3 expression was related to pathologic stage. (*p < 0.05; **p < 0.01; ***p < 0.001)

Building and assessing nomogram models for kidney renal clear cell carcinoma and lung squamous cell carcinoma

In order to investigate the effect of CDKN3 expression on the prognosis of certain tumors, we performed univariate Cox regression analysis for OS in nine tumors (Additional file 1: Tables S2–S10). To evaluate the prognostic value, we employed calibration curves to assess the prediction accuracy of nomogram model across 1, 3 and 5-year periods. KIRC and LIHC with sample sizes more than 400 were selected. These models were constructed using the findings of a single-variate Cox regression. Results indicated that CDKN3 had a significant capacity to predict OS for KIRC and LIHC (Additional file 1: Fig. 4A,C), and calibrated survival prediction curves at 1, 3 and 5-year demonstrated that the nomogram model had a high level of precision and accuracy (Additional file 1: Fig. 4B, D).

Fig. 4

The CDKN3 prognostic signature was combined with independent TCGA components to create our hybrid nomogram. A Creation of a nomogram model that takes CDKN3 expression in KIRC into account. B Using calibration curves for 1, 3, and 5 years, we assessed the KIRC nomogram model's prediction accuracy. C The expression of CDKN3 in LIHC is modelled using a nomogram. D Using calibration curves for 1, 3, and 5 years, we assessed the LIHC nomogram model's prediction accuracy

The correlation of CDKN3 expression and tumor immune microenvironment

The progression of tumors are significantly influenced by the immune microenvironment. To investigate the relationship between CDKN3 and the immune microenvironment in pan-cancer, we conducted an analysis using the GEPIA2 database to assess the correlation between CDKN3 expression and immune cells. Heatmaps were used to illustrate the associations between CDKN3 expression and CD4 + T cells, cancer-associated fibroblasts, macrophages, and endothelial cells (Fig. 5A–D). Over-expression of CDKN3 was significantly associated with Th2 (Fig. 5A). In the TCGA tumors of BRCA, HNSC-HPV + , LUSC, and THYM tumors, we found a statistically significant negative connection between CDKN3 expression and infiltrating cancer-associated fibroblasts. However, CDKN3 expression in THCA was positively connected with fibroblast infiltration related to malignancy (Fig. 5B). Furthermore, we found a statistically significant inverse relationship between CDKN3 expression and endothelial cells in the BRCA, KIRC, LUAD, LUSC, STAD, and THYM tumors. The expression of CDKN3 was positively linked with endothelial cells in LGG (Fig. 5C). Figure 5D demonstrated significant correlations between macrophages and CDKN3 expression in BLCA, KIRC,HNSC-HPV-, MESO, PRAD, and THCA.

Fig. 5

Analysis of the relationship between CDKN3 expression and immune infiltration of CD4 + T cells (A), Cancer associated fibroblasts (B), endothelial cells (C), and Macrophages (D)

DNA methylation analysis

Tumor development, growth, and cellular carcinogenesis are all tightly connected with abnormal DNA methylation. The degree of DNA methylation of certain genes as well as variations in DNA methylation levels may also be used to detect tumors [24]. Using the UALCAN and TCGA databases, the DNA methylation levels of CDKN3 between normal and primary tumor tissues were explored. The CDKN3 methylation expression levels in HNSC and TGCT tumor tissues were significantly down-regulated (Fig. 6). Additionally, ESCA, KIRC, LUSC, and PAAD tumor tissues had considerably higher levels of CDKN3 methylation expression (Fig. 6).

Fig. 6

The methylation level of CDKN3 in ESCA, KIRC, HNSC, LUSC, TGCT, and PAAD

Functional enrichment and protein–protein interactions of CDKN3-related genes

From the GEPIA2 database, 100 genes with the closest relationships to CDKN3 were analyzed to better understand the biological role of CDKN3 in tumors (Additional file 1: Table S1). According to GO analysis (Fig. 7A), CDKN3-related genes may be involved in a variety of biological processes, including "mitotic sister chromatid segregation," "organelle fission," "nuclear division," and "mitotic nuclear division." Involved in “spindle”,“chromosomal region”, “chromosome, centromeric region”and other cell components. Along with other molecular activities, it takes part in "microtubule binding," "tubulin binding," and "microtubule motor activity." CDKN3-related genes may be related to "Cell cycle," "Oocyte meiosis," "Progesterone-mediated oocyte maturation," "DNA replication," and "p53 signalling pathway," according to KEGG pathway analysis (Fig. 7B). The PPI network on the STRING website was constructed using 100 CDKN3-related genes (Additional file 1: Fig. S6). Collectively, these analyses provided a comprehensive framework for understanding the biological significance of CDKN3 in the context of tumors, unraveling its involvement in vital cellular processes, molecular interactions, and pathways that influence tumor development and progression.

Fig. 7

Analysis of CDKN3-related genes' functional enrichment. A Analyses of GO functional enrichment (BP, CC, and MF). B Analysis of KEGG pathways for 100 CDKN3-related genes

Gene set enrichment analysis

The GSEA analysis was used to clarify the biological function of CDKN3 in the 17 tumors with CDKN3 related to prognosis. These 17 tumors included ACC, BLCA, HNSC, DLBC, LGG, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, PAAD, UCEC, PRAD, STAD, TGCT, and UVM (Fig. 8A–Q). The findings imply that CDKN3 was primarily involved in mitotic spindle checkpoints, cell cycle checkpoints, and chromosome maintenance.

Fig. 8

CDKN3-related genes' GSEA analysis. A–Q GSEA based on differential expression analyses for ACC, BLCA, HNSC, DLBC, LGG, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, PAAD, UCEC, PRAD, STAD, TGCT and UVM