CDK16 as a potential prognostic biomarker correlated with an immunosuppressive tumor microenvironment and benefits in enhancing the effectiveness of immunotherapy in human cancers

Data source

RNAseq data and clinical information data for pan-cancer were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), which were downloaded from UCSC Xena (https://xenabrowser.net/datapages/). Immunocell infiltration data for CDK16 were downloaded from TIMER2 (http://timer.cistrome.org/) and ImmuCellAI (http://bioinfo.life.hust.edu.cn/ImmuCellAI#!/) databases.

GSEA

To assess the link between CDK16 and its associated genes in human cancers, we employed Gene Set Enrichment Analysis (GSEA). For this analysis, we utilized Pearson correlation coefficients and the R package called “clusterProfiler”. Through these comprehensive analyses, we were able to obtain valuable insights into the relationship between CDK16 and the expression patterns of its associated genes in human cancers.

Immune infiltration analysis

The R package “ESTIMATE” was used to assess the TME in pan-cancer. Immune cell infiltration data were obtained from the TIMER2 database and published study (Zeng et al., 2019). CDK16 expression and immune cell infiltration levels were correlated to explore the relationship between CDK16 and immune cell infiltration.

Cell culture

The lung cancer cell line H1299 was obtained from ATCC (Manassas, VA, USA). The cells were cultured in RPMI-1640 medium (Gibco, China) supplemented with 10% fetal bovine serum (Gibco, China). The cultures were maintained at 37°C in a humidified atmosphere containing 5% CO2.

CCK-8 assay

To assess cell viability, the CCK-8 assay was employed. After transfecting the cells with siRNA for 48 hours, they were transferred to 96-well plates at a density of 3000 cells per well and cultured as described above. CCK-8 reagent (Beyotime, Shanghai, China) was added to each well and cultured for two hours. The optical density at 450 nm was measured at 0, 24, 48, and 72 hours using an iD3 microplate reader.

RNA extraction and qRT-PCR

According to the manufacturer’s protocol, total RNA was isolated using TRIzol reagent (Pufei, Shanghai, China). The primers sequences used for qRT-PCR were obtained from Applied Biosystems (Ribo, Guangzhou, China) as below (5′–3′): CDK16 forward: 5′-TTGGGCCGTTGGCTGTTC-3′, reverse: 5′-GTGCTCACGGCGGCTC-3′; GAPDH forward: 5′-CAGTGCCAGCCTCGTCTAT-3′, reverse: 5′-AGGGGCCATCCACAGTCTTC-3′. Relative expression levels were calculated according to the 2−ΔΔCt method. Statistical analyses were performed with GraphPad Prism.

Statistical analysis

We used t-tests to assess the disparity in CDK16 gene expression levels between cancer tissues and normal tissues. Survival impacts were analyzed using the Kaplan-Meier method and log-rank test, reporting hazard ratios, confidence intervals, and p-values for clarity. Correlation analyses were conducted with Spearman’s or Pearson’s tests based on data type. All analyses were performed using R software (version 4.0.2), with a significance threshold set at a p-value of less than 0.05, ensuring the reliability of our findings.

Data availability statement

The original data supporting the conclusions of this paper will be provided by the authors without reservation.

Expression of CDK16 in pan-cancer

In our study, we assessed CDK16 expression in a pan-cancer context using data sourced from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Our findings revealed that CDK16 was notably overexpressed in 23 out of 33 cancer types examined. These included Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colon Adenocarcinoma (COAD), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), Esophageal Carcinoma (ESCA), Glioblastoma Multiforme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Chromophobe (KICH), Lower Grade Glioma (LGG), Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Ovarian Serous Cystadenocarcinoma (OV), Pancreatic Adenocarcinoma (PAAD), Rectum Adenocarcinoma (READ), Sarcoma (SARC), Skin Cutaneous Melanoma (SKCM), Stomach Adenocarcinoma (STAD), Thymoma (THYM), Uterine Corpus Endometrial Carcinoma (UCEC), and Uterine Carcinosarcoma (UCS). Conversely, CDK16 exhibited lower expression levels in five cancer types, specifically Kidney Renal Clear Cell Carcinoma (KIRC), Acute Myeloid Leukemia (LAML), Prostate Adenocarcinoma (PRAD), Testicular Germ Cell Tumor (TGCT), and Thyroid Carcinoma (THCA), as delineated in Figure 1A and Supplementary Table 1. Notably, the highest expression of CDK16 was observed in ovarian tumor tissues (OV), with the lowest in liver hepatocellular carcinoma (LIHC), as shown in Figure 1B. In normal tissues, the highest expression of CDK16 was identified in the testis (Figure 1C). Additionally, a comparative analysis between tumor tissues and adjacent normal tissues revealed that CDK16 was overexpressed in 15 cancer types, specifically BLCA, BRCA, CHOL, COAD, UCEC, ESCA, THCA, HNSC, READ, LUAD, LIHC, PRAD, STAD, LUSC, and KICH, while it showed lower expression in KIRC, as illustrated in Figure 2A–2P. This differential expression pattern of CDK16 highlights its potential as a biomarker for cancer diagnosis and as a therapeutic target, particularly in cancers with significant overexpression.

Prognostic role of CDK16

In the univariate Cox regression analysis (UniCox), CDK16 emerged as a significant risk factor for overall survival (OS) in a range of cancers, namely LIHC, LGG, PCPG, KIRC, UCEC, UVM, BRCA, and MESO (Figure 3A). This finding indicates the potential of CDK16 as a prognostic marker across these diverse cancer types. Kaplan-Meier survival analysis further revealed a correlation between high CDK16 expression and poorer OS in ACC, UVM, SARC, UCEC, LIHC, and KICH (Figure 3B–3G). This pattern highlights the importance of monitoring CDK16 levels in these specific cancers for better prognostic assessment. Concerning disease-specific survival (DSS) outcomes, elevated CDK16 expression was associated with increased risk in KIRC, PCPG, LIHC, LGG, UVM, PRAD, BRCA, UCEC, MESO, and ESCA (Figure 4A). This suggests that CDK16 could play a significant role in the disease course of these cancers. The study also examined the impact of CDK16 on disease-free interval (DFI). High CDK16 expression was predictive of a shorter DFI in PRAD, LIHC, SARC, and ACC (Figure 4B), indicating its potential as an early marker for recurrence or progression in these cancers. Lastly, for progression-free interval (PFI), increased CDK16 expression was linked to worse PFI in PRAD, ACC, LIHC, PCPG, UCEC, UVM, ESCA, LGG, SARC, MESO, BRCA, and KIRC (Figure 4C). This broad association underscores the potential of CDK16 as a marker for monitoring cancer progression across various types. Overall, the expanded analysis underscores the significance of CDK16 in a variety of cancers, suggesting its potential utility as a biomarker for prognosis and therapeutic targeting.

Gene alteration of CDK16 in pan-cancer

The established link between genetic alterations and the development of cancer forms the cornerstone of modern oncological research. In our study, we harnessed the advanced data analysis tools provided by cBioPortal to scrutinize the genetic alterations specific to CDK16 across a range of tumor types. Our analysis revealed that the genetic alterations in CDK16 are predominantly in the forms of ‘amplification’ and ‘mutation’ (Figure 5A). This finding not only contributes to the growing body of knowledge about CDK16 but also suggests its potential impact on the pathophysiology of various cancers. Moving beyond this, our investigation also focused on the relationship between CDK16 expression and copy number alterations (CNA). We observed a consistently positive correlation between these two factors across most tumor types we examined (Figure 5B). This correlation is particularly intriguing, as it hints at a more complex role of CDK16 in the genomic landscape of cancer, potentially influencing tumor behavior and response to therapies.

GSEA analysis of CDK16 in pan-cancer

In an effort to delineate the potential roles of CDK16 in the advancement of tumors, a comprehensive Gene Set Enrichment Analysis (GSEA) was carried out, focusing on the expression levels of CDK16 across a spectrum of 33 distinct cancer types. The results of this analysis are graphically represented in Figure 6A–6F. These findings shed light on the extensive involvement of CDK16 in crucial cell cycle-related pathways. Notably, the pathways include those governing cell cycle regulation and DNA repair mechanisms, which are integral to the proliferation and survival of cancer cells in various types of tumors. The analysis delineates a significant correlation between elevated CDK16 expression and the activation of genes that are pivotal in these pathways. In addition to its prominent role in cell cycle and DNA repair pathways, the study also unveiled that CDK16 is intricately involved in the regulation of immunomodulatory pathways. This encompasses both innate and adaptive immune systems, highlighting a versatile role of CDK16 in modulating the immune response within the tumor microenvironment. The implications of such involvement are twofold: firstly, CDK16’s influence on the immune system may contribute to the immune evasion strategies employed by tumors; secondly, it suggests potential therapeutic targets within these pathways that could be exploited to enhance anti-tumor immunity.

Relationship between CDK16 and immune cell infiltration

We evaluated the association between CDK16 expression and stromal as well as immune scoring, employing the ‘ESTIMATE’ algorithm for this analysis. As depicted in Figure 6, it was observed that CDK16 expression potentially influences immune scores, stromal scores, and ESTIMATE scores in the majority of tumors (Figure 7A). To validate these observations, we further examined the Tumor Microenvironment (TME)-related pathways influenced by CDK16. This included an analysis of immune-related pathways, stroma-related pathways, and DNA repair-related pathways, drawing on data sources from published research [15]. The results corroborated our initial findings, demonstrating a close association between CDK16 expression and various immune-related pathways. These pathways encompass immune checkpoints, antigen processing mechanisms, and CD8 T cell effector functions across a broad range of cancers (Figure 7B).

In conjunction with our GSEA results, these findings collectively provide robust evidence supporting the expansive pro-cancer effects of CDK16. To further substantiate CDK16’s role in promoting cell proliferation, we successfully downregulated its expression in H1299 lung cancer cells using two specific CDK16 shRNAs (Figure 7C). Following this, we conducted Cell Counting Kit-8 (CCK8) assays on these cells. The results from these assays revealed a significant reduction in the viability of H1299 lung cancer cells following CDK16 knockdown (Figure 7D). This outcome lends strong support to our hypothesis that CDK16 functions as an oncogene. Furthermore, when combined with our GSEA results, it becomes evident that CDK16 exerts a broad pro-oncogenic effect across multiple cancer types. Consequently, we proceeded to delve deeper into the mechanistic action of CDK16 in the Tumor Immune Microenvironment (TIME).

Dysfunctional immune cell infiltration during tumorigenesis and progression can lead to a loss of immune system control over tumor growth and may even promote tumor development, resulting in immune escape. To explore this further, we utilized immune cell infiltration data from the TIMER2 and ImmuCellAI databases. Our investigation into the relationship between CDK16 expression and immune cell infiltration revealed that high CDK16 expression is associated with significantly poorer immune cell infiltration in pan-cancer (Figure 8 and Supplementary Figure 1).

Immune-related gene analysis

To delve deeper into the intricate relationship between CDK16 and the immunosuppressive microenvironment, we expanded our analysis to include detailed correlation studies between CDK16 and a broad array of immune-related genes. Our investigation revealed a significant association of CDK16 with various immunomodulatory genes. This encompassed not only immunosuppressive genes, as illustrated in Figure 9A, but also extended to genes responsible for immune activation (Figure 9B), an array of chemokines (Figure 9C), a spectrum of chemokine receptors (Figure 9D), and multiple Major Histocompatibility Complex (MHC) genes (Figure 9E). This comprehensive analysis underscores the multifaceted role of CDK16 in modulating the immune landscape. Particularly noteworthy was the finding that elevated levels of CDK16 expression were frequently correlated with a more pronounced immunosuppressive milieu. Such an environment potentially facilitates the evasion of immune surveillance by tumor cells, thus highlighting the pivotal role of CDK16 in the complex interplay between cancer cells and the immune system.

Immunotherapy

Immune checkpoint inhibitors (ICIs) have demonstrated potential efficacy in cancer patients exhibiting a high tumor mutation burden (TMB) or microsatellite instability (MSI). Therefore, we investigated the correlation between ICIs and CDK16 expression across multiple cancer types. Figure 6A illustrates a strong positive association between CDK16 expression and TMB in ten cancers, namely malignant MESO, PCPG, LUAD, TGCT, STAD, BLCA, OV, SKCM, BRCA, and THCA (Figure 10A). Similarly, CDK16 expression showed a positive correlation with MSI in eleven cancers, including TCGT, MESO, GBM, SARC, CESC, STAD, LUSC, LUAD, LGG, LIHC, and BRCA (Figure 10B). These findings underscore the potential involvement of CDK16 in immunotherapeutic approaches. Hence, it is reasonable to propose that CDK16 expression may influence the response to ICI treatment and prognosis in patients affected by various cancer types. To test this hypothesis, we collected data from cohorts of patients who underwent immunotherapy and evaluated CDK16 expression across different cancer types. Kaplan-Meier analysis revealed that low CDK16 expression was associated with prolonged overall survival and improved overall response rate in patients treated with nivolumab, a PD-1 inhibitor utilized in cancer immunotherapy (Figure 11A, 11B). These findings emphasize the significant role of CDK16 expression in determining patient response to immunotherapy.