2.1 Clinical tissue collection

A total of 15 pairs of Human HNSCC tissues and normal controls were obtained from Affiliated Hospital of Hebei University, Baoding, China. The use of human tissue in this study was approved by the hospital ethics committee, and patients' written consent was obtained prior to sample collection. Part of the tissue was made into a single cell suspension and analyzed by flow cytometry. And another of the tissues were frozen in liquid nitrogen immediately after ex vivo and stored at − 80 °C until use.

2.2 Differentially expressed gene analysis

The expression of RPS6KA1 in HNSCC was investigated using several publicly available datasets. Specifically, the GSE25099 dataset [14], which includes 57 HNSCC patient specimens and 22 normal oral tissue samples, was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). To download the GSE25099 dataset, the following steps were performed: 1. Visit the GEO database website and search for the GSE25099 dataset. 2. Download the raw data files in.CEL format, which contain the gene expression data. 3. Preprocess the raw data using R/Bioconductor packages (e.g., affy for normalization) to obtain the expression matrix for further analysis.

In addition to GSE25099, other datasets, including E_MTAB_8588 from the EBI database, GSE42743, and GSE75538 from the GEO database, were used to validate the expression patterns of RPS6KA1. The datasets were downloaded following a similar process through the GEO portal.

To explore the mRNA expression levels of RPS6KA1 in HNSCC, data from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) were analyzed. The data were accessed via the TCGA Data Portal, and the mRNA expression levels were extracted for HNSCC patients.

For protein expression analysis, data from The Human Protein Atlas (HPA, https://www.proteinatlas.org/) were used. The expression data for RPS6KA1 protein in various tissues, including HNSCC, were accessed through the THPA website.

These datasets were then analyzed using appropriate bioinformatics tools and statistical methods to assess the expression levels of RPS6KA1 in HNSCC and normal tissues.

2.3 Real-time RT-PCR

The total RNA was extracted from TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. The RT-PCR primers for RPS6KA1 and GAPDH were purchased from Sango Biotech (Shanghai, China). The PCR primers for RPS6KA1 were: forward, 5ʹ-CAGTGGGCACCTGTATGCTAT-3ʹ and reverse, 5ʹ-ACGAATGGGTGATTTACATCAGC-3ʹ; GAPDH: forward, 5ʹ-ACAACTTTGGTATCGTGGAAGG-3ʹ and reverse, 5ʹ-GCCATCACGCCACAGTTTC-3ʹ. Fold-change of RPS6KA1 was calculated by the 2−ΔΔCt method.

2.4 Western blot analysis

Total protein was extracted from the tissue using RIPA buffer and concentrations were determined by a BCA protein assay kit. Proteins (20–30 µg) were separated by SDS-PAGE electrophoresis and then transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, Billerica, MA, USA). GAPDH was used as a loading control on the same blot. The next step is to dilute the primary antibody RSK1 (1: 200 dilution, Santa Cruz, Cat. sc-393147), GAPDH(1: 1000 dilution, Santa Cruz, Cat. sc-47724and co-incubate overnight at 4 °C. The secondary antibody (1:2000 dilution, TransGen Biotech, China, Cat. HS201-01) was incubated for 1 h at normal temperature. The prepared ECL developer was applied uniformly to the PVDF membrane and developed using the Image Lab development system (LI-COR, USA).

2.5 Immune cell environment analysis

The BEST database (https://rookieutopia.com/app_direct/BEST/), which provides a comprehensive biomarker exploration of solid tumors, was used to assess the relationship between RPS6KA1 expression and immune cells/immune adjustment factors. Then, we use gene expression data [15] to investigate the presence of infiltrating matrix/immune cells in HNSCC patients. The R package (“estimates”) was employed to obtain immune environment scores for HNSCC patients. Deconvolution of the proportions of 22 common immune cells in HNSCC patients was performed via the R package (‘‘CIBERSORT.R’’, https://cibersortx.stanford.edu/) [15]. The correlation of RPS6KA1 expression with immune environment and 22 immune cells was investigated by Pearson test.

2.6 Flow cytometry

Cells were prepared within 3 h after single-cell suspension for flow cytometry analysis. The proportions of CD19 + B cells (14-0199-82, ThermoFisher), CD3 + T cells (sc-20047, Santa Cruz), CD4 + T lymphocytes (ab133616, Abcam), and CD3 + CD8 + T lymphocytes were analyzed. For flow cytometry, cells were first stained with specific fluorochrome-conjugated antibodies against the target markers, and a viability dye was included to exclude dead cells. The gating strategy was as follows: live cells were first selected based on forward and side scatter (FSC vs SSC) to exclude debris and dead cells. Next, the appropriate gates were applied to select populations based on marker expression (CD19, CD3, CD4, CD8). Compensation controls were performed to correct for spectral overlap, and fluorescence minus one (FMO) controls were used to define positive populations. Isotype controls were included to assess non-specific binding of the antibodies. Flow cytometry was performed on a BD FACS Canto II flow cytometer, and the data were analyzed using BD-FACS Diva software (BD Biosciences).

2.7 Enrichment analysis

To explore the biological function of RPS6KA1, Go enrichment analysis was performed using the Bioconductor package "cluster Profiler " to elucidate significant functional and pathway differences between high and low RPS6KA1 groups [16]. The same method was used for KEGG enrichment analysis as for Go enrichment analysis. Then, GSEA analyses were performed for the purpose of further examining the potential biological function of RPS6KA1 in promoting HNSCC progression.

2.8 Drug sensitivity analysis

To understand the relationships between RPS6KA1 and potential drug therapeutic effects in HNSCC, drug sensitivity or resistance data from multiple datasets in EBI, GEO and TCGA databases have been used to assess drug susceptibility. And then, ROC curve was also employed to analyze the reactivity of RPS6KA1 and immunotherapies such as anti-PD-1 or CTLA-4 in several immunotherapy datasets.

2.9 Software and statistical analyses

Statistical analyses were performed using GraphPad Prism, R15. Comparisons between two groups were conducted using a Student's t-test (for normally distributed data) or a Mann–Whitney U test (for non-normally distributed data). For comparisons involving more than two groups, one-way ANOVA (with post-hoc Tukey’s test) was used to assess differences between groups. Correlations between RPS6KA1 expression and clinical parameters were assessed using Pearson’s correlation coefficient. Kaplan–Meier survival analysis and log-rank tests were used to evaluate the relationship between RPS6KA1 expression and patient prognosis. Statistical significance was considered at a p-value of < 0.05. All data are presented as mean ± standard deviation (SD) or median with interquartile range (IQR) as appropriate.