A comprehensive prognostic and immunological analysis of hexokinase domain containing protein-1 (HKDC1) in pan-cancer

Access to data and software

The dataset used in this research was sourced from the UCSC database (https://xenabrowser.net/datapages/), supplemented by corresponding normal tissues from the GTEx (Genotype-Tissue Expression) database. The analysis adhered to specific criteria, including a P-value threshold of 0.01, a log2 fold change (log2FC) cut-off of 1, and the requirement that TCGA (https://portal.gdc.cancer.gov/) normal data be aligned with GTEx data. All analyses were performed using R version 4.2.2.

Gene expression investigations

The gene expression profiles associated with pan-cancer were obtained from The Cancer Genome Atlas (TCGA), while profiles for normal tissues were sourced from the Genotype-Tissue Expression database, with both datasets accessed via the UCSC Xena Browser. Furthermore, the UCSC team facilitated the integration of datasets from both TCGA and GTEx, subsequently eliminating batch effects to ensure data consistency (Vivian et al., 2017).

Immune-related analysis

The TIMER database (Li et al., 2020) serves as a comprehensive online platform that rigorously investigates the interactions between neoplastic tissues and the immune system. This resource is designed specifically to assess the immune cell populations across 33 distinct cancer types. Additionally, in our study, the expression levels of 11 prominent immune checkpoint genes, including PDCD1, LAG3, BTLA, and others (Shibru et al., 2021), were also examined to conduct a correlational evaluation among these checkpoints.

Correlation examination in gene set enrichment analysis

To deepen our understanding of the specific biological role of the HKDC1 gene in tumorigenesis, we performed a Pearson correlation analysis to assess the relationship between HKDC1 expression levels and a variety of mRNAs derived from the transcriptome dataset of TCGA. Additionally, we conducted gene set enrichment analysis (GSEA) (Subramanian et al., 2005) using established gene sets from the Molecular Signatures Database version 5.0 by the R package ‘clusterProfiler’ (Yu et al., 2012). For this study, we focused specifically on the gene sets ‘c2.cp.kegg.v7.5.1.entrez.gmt’ and ’c5.go.bp.v7.5.1.entrez.gmt’ in our GSEA analysis.

Prognosis analysis

Survival data were obtained from the TCGA Pan-Cancer Clinical Data Resource, encompassing metrics such as overall survival, progression-free interval, disease-free interval, and disease-specific survival. This dataset was utilized to assess the prognostic significance of HKDC1. Subsequently, cancer patients were divided into two distinct groups—those with high HKDC1 expression and those with low expression—based on the median HKDC1 levels. To evaluate survival outcomes, we generated Kaplan–Meier survival curves and conducted Cox regression analyses using the R packages ‘survival’ and ‘survminer’, which offer essential tools for performing survival analyses and visualizations in R version 4.2.2 (R Core Team, 2022).

Cell culture

We selected gastric and breast cancer cell lines representing both cancer types for our functional validation studies. Specifically, we utilized the human gastric cancer cell line MKN-1 and the breast cancer cell line MDA-MB-231, both procured from the Shanghai Branch of the Chinese Academy of Sciences (Shanghai, China). MKN-1 cells were cultured in RPMI 1640 medium, whereas MDA-MB-231 cells were grown in Dulbecco’s Modified Eagle Medium supplemented with 10% fetal bovine serum. All cultures were maintained at 37 °C in a humidified incubator with 5% CO2.

Small interfering RNA (siRNA) construction and transfection

The siRNA sequences targeting HKDC1 were synthesized by incorporating them into the pGreen vector. Following an evaluation of various candidate siRNAs, two specific sequences were identified for further experimentation: AGAUGGAGAGUCAGUUCUATT UAGAACUGACUCUCCAUCUTT (designated as si1) and CUUGCUAAUACAAGAGAGATTUCUCUCUUGUAUUAGCAAGTT (designated as si2). Additionally, the negative control is a non-specific control small interfering RNA (siNC). A total of 4 ×10⁵ cells were seeded in each well of 6-well plates and allowed to incubate for 24 h. Following this incubation, the cells were transfected with 10 µl of either HKDC1 si1/2 or siNC per well, using Lipofectamine™ RNAiMAX reagent according to the manufacturer’s instructions. The cells were harvested 48 h post-transfection for evaluation via the CCK-8 assay, as well as for the extraction of RNA and proteins.

RNA extraction and qPCR

We first wait for cells to grow about 60–70% before transfection. Then, when the cell growth was close to 1 × 10⁶ cells, RNA was extracted by using the RNA Quick Purification kit. Primers for qPCR including HKDC1 and β-actin were synthesized by Tianyi Huiyuan (Guangzhou, China). GraphPad Prism was used for statistical analysis. The primer sequence is shown below. HKDC1 forward - GTTGCCCACCTTCGTCAGG, HKDC1 reverse - AGCGACTTGCACCTTCAGC, β-actin forward - CCTGGCACCCAGCACAAT, β-actin reverse-GGGCCGGACTCGTCATAC.

Cell counting kit-8 assay

The assay was performed according to the protocols provided by the manufacturer (GLPBIO). Specifically, the designated cells (1,500 cells per well) were seeded in triplicate in 96-well plates. Cell viability assessments of the specified cells were conducted over a period of six days following the initial seeding. Following this, the culture medium was then removed, and a solution of the cell counting kit-8 (CCK-8) assay, prepared at a dilution of 1:10 (10 µL), was added to each well. Following a 2-hour incubation period, the optical density (OD) of each well was measured at a wavelength of 450 nm.

Migration and invasion assays

Transwell assays were conducted using 24-well transwell plates (Corning Costar) that had been pre-treated with Matrigel (BD Biosciences). Specifically, 15 × 10⁵ transfected cells were added to the upper chamber of the Matrigel-coated transwell chambers. After a 24-hour incubation period, the cells that migrated through the membrane were fixed and stained with a 0.1% crystal violet solution. To assess cell migration, a method similar to that used for the cell invasion assay was implemented; however, in this case, the transwell chambers weren’t coated with 200 mg/ml Matrigel.

Statistical analysis

Statistical analysis was performed with R (version 4.2.2; R Core Team, 2022) and GraphPad Prism 8 software (USA), ns, not significant, *p < 0.05, **p < 0.01, and ***p < 0.001. This study presented all data as means ± standard deviation. Survival analysis was carried out using the Kaplan–Meier approach and log-rank test.

HKDC1 is upregulated in multiple cancer types

The amalgamation of samples from both the GTEx and TCGA databases produced persuasive evidence of a notable escalation in HKDC1 expression across diverse tumor types. This significant upregulation was noted across a diverse array of cancers, encompassing bladder urothelial carcinoma (BLCA), breast cancer (BRCA), bile duct carcinoma (CHOL), stomach adenocarcinoma (STAD), among others (see Fig. 2A). Further validation using a paired Student’s t-test confirmed this upregulation, revealing a statistically significant increase in HKDC1 expression levels in tumor tissues compared to adjacent normal tissues (Fig. 2B). This finding highlights the potential significance of HKDC1 in cancer biology and suggests its prospective role as a therapeutic target or biomarker.

HKDC1 is associated with tumor immunity

Utilizing the XCELL algorithm, our results revealed a positive correlation between HKDC1 expression levels and the infiltration of diverse immune cell subsets in numerous cancers (Fig. 3A). This implies that HKDC1 could be instrumental in influencing the immune landscape within the tumor microenvironment.

Furthermore, we investigated the relationship between HKDC1 and immune checkpoint molecules, which are vital regulators of immune responses. Our analysis demonstrated a significant correlation between HKDC1 expression and the levels of several key immune checkpoints (Fig. 3B), including PDCD1 (Fig. 3A), BTLA (Fig. 3B), CTLA4 (Fig. 3C), HAVCR2 (Fig. 3D), LAG3 (Fig. 3E), LILRB2 (Fig. 3F), LILRB4 (Fig. 3G), SIGLEC7 (Fig. 3H), SIRPA (Fig. 3I), TIGIT (Fig. 3J), and VSIR (Fig. 3K). These findings underscore the intricate interactions between HKDC1 and the immune checkpoint network, suggesting that HKDC1 may function as a modulator of immune responses and checkpoint signaling, with significant implications for cancer immunotherapy and prognostic outcomes.

HKDC1 serves as an oncogene in multiple cancer types

We utilized GSEA to pinpoint KEGG pathways that are markedly enriched in genes linked to HKDC1. Our analysis, as depicted in Fig. 4A, revealed a robust enrichment of several cancer-relevant pathways, highlighting the multifaceted nature of HKDC1’s involvement in cancer biology. Among the significantly enriched pathways, we observed a notable association with glycolysis (Fig. 4B), indicating that HKDC1 may be involved in modulating cellular energy metabolism, a hallmark of cancer. Additionally, the increased representation of ubiquitin-mediated proteolysis (Fig. 4C) and apoptosis (Fig. 4E) pathways indicates that HKDC1 could be involved in the modulation of protein degradation and the maintenance of cell viability, respectively. The involvement of TCA Cycle (Fig. 4D) and glutathione metabolism (Fig. 4G) underscores the potential impact of HKDC1 on cellular metabolic processes.

Furthermore, the observed enrichment in the cell cycle pathway (Fig. 4F) and the RNA degradation pathway (Fig. 4L) indicates that HKDC1 may significantly influence cell proliferation and the regulation of gene expression, respectively. Additionally, the participation of glycerophospholipid metabolism (Fig. 4H), lysosome (Fig. 4I), pentose and glucuronate interconversions (Fig. 4J), and the pentose phosphate pathway (Fig. 4K) broadens the range of biological functions attributed to HKDC1, indicating its involvement in membrane dynamics, intracellular degradation processes, and alternative pathways of glucose metabolism.

Lastly, the notable enrichment in the Toll-like receptor signaling pathway (as shown in Fig. 4M) highlights the potential role of HKDC1 in modulating immune responses and inflammatory processes, both of which are increasingly recognized as crucial factors influencing cancer development and therapeutic outcomes. Collectively, our GSEA offers an extensive overview of the prospective functional dynamics associated with HKDC1 across various cancer types, thereby laying the groundwork for subsequent explorations into its distinct roles and underlying mechanisms in diverse oncological contexts.

HKDC1 is regulated by copy number amplification and DNA methylation

We conducted an exhaustive examination of both genetic and epigenetic modifications to investigate the mechanisms underlying the increased expression of HKDC1 in cancer. At the genetic level, we examined mutations and copy number variations (CNVs) within the HKDC1 gene. Our findings revealed a substantial number of mutations distributed uniformly across the entire coding sequence of HKDC1 in diverse cancer types, suggesting a potential role for these mutations in modulating HKDC1 expression. Additionally, we observed a prevalent pattern of HKDC1 copy number deletions across the majority of tumor types, with a minority of samples exhibiting copy number gains (Fig. 5A). These CNVs likely contribute to the deregulation of HKDC1 expression in cancer.

In exploring the epigenetic aspects, we analyzed DNA methylation alterations in the promoter region and gene body of HKDC1. Our discoveries indicated that HKDC1 mRNA expression exhibits a negative correlation with methylation levels at multiple sites across the majority of tumor types examined, particularly in adrenocortical carcinoma (ACC), breast carcinoma (BRCA), acute myeloid leukemia (LAML), mesothelioma (MESO), prostate adenocarcinoma (PRAD), testicular germ cell tumors (TGCT), and uveal melanoma (UVM). This inverse correlation suggests that the observed hypomethylation within the HKDC1 promoter and/or gene body may contribute to the elevated expression of HKDC1 in these particular malignancies. Conversely, in certain cancer types, including diffuse large B-cell lymphoma (DLBC), kidney chromophobe (KICH), kidney renal papillary cell carcinoma (KIRP), thyroid carcinoma (THCA), and thymoma (THYM), We observed that the expression of HKDC1 exhibits a positive correlation with methylation levels. This finding indicates that the relationship between methylation and HKDC1 expression may differ according to the specific cancer context, necessitating further investigation. The detailed analysis of both genetic and epigenetic modifications, illustrated in Fig. 5B, offers critical insights into the intricate regulatory mechanisms that influence HKDC1 expression in the context of cancer.

Pan-cancer analysis of the multifaceted prognostic value of HKDC1

To assess the clinical relevance of HKDC1 expression across various cancers, we performed a comprehensive analysis of its association with patient outcomes, utilizing data from 33 cancer types sourced from the TCGA database. Our evaluation focused on several critical metrics, including OS, DSS, DFI and PFI. The findings indicated that HKDC1 transcripts frequently served as negative prognostic indicators for both OS and DSS in multiple cancer types. Notably, higher levels of HKDC1 expression, as evidenced by transcriptomic and gene expression analyses, were consistently linked to poorer OS and DSS outcomes, particularly in malignancies such as thyroid tumor (THCA), pancreatic ductal carcinoma (PAAD), lung squamous cell carcinoma (LUSC), among others (see Figs. 6A and 6B).

Furthermore, HKDC1 serves as a prognostic indicator influencing DFI and PFI across diverse cancer types (refer to Figs. 6C and 6D). The KM plots further validated the prognostic significance of HKDC1, demonstrating a substantial correlation with adverse outcomes across multiple endpoints and cancer types (see Figs. 6A–6P). Collectively, these findings underscore the clinical importance of HKDC1 expression as a potential biomarker for cancer prognosis and as a promising target for therapeutic intervention.

HKDC1 enhances the growth and movement of gastric and breast cancer cells in an in vitro setting

Acknowledging the pivotal role of HKDC1 in cancer progression, we undertook an in vitro study aimed at elucidating its function within gastric and breast cancer cell lines. To initiate our analysis, we evaluated the protein expression levels of HKDC1 in both MKN-1 and MDA-MB-231 cell lines following siRNA-mediated silencing (Figs. 7A, 7E). At the same time, RNA levels of HKDC1 were also detected (see Figs. 7B, 7F).

To explore the functional implications of HKDC1 downregulation, we conducted a cell counting kit-8 (CCK-8) assay on the previously mentioned HKDC1 knockdown cell lines (refer to Figs. 7C, 7G). The results of this assay revealed that HKDC1 significantly influences the proliferative capacity of both MKN-1 and MDA-MB-231 cells, indicating that HKDC1 plays a positive role in promoting their growth. Furthermore, we performed transwell assays to evaluate the effects of HKDC1 on the migratory and invasive capabilities of MKN-1 and MDA-MB-231 cell lines. As expected, the silencing of HKDC1 significantly reduced the migratory and invasive potential of these cells as they traversed the transwell membrane (see Figs. 7D, 7H).