Reagent
Metformin (LKB1-AMPK activator) was purchased from Beyotime Biotechnology Co., Ltd. (Shanghai, China) was dissolved in normal culture medium as a stock solution (1 M) and stored at -20°C. For experimental solutions, the stock solutions were further diluted with normal culture medium to generate the required concentrations.
Cell culture
The human HNSCC cell lines Cal27 and FaDu were purchased from the American Type Culture Collection (ATCC, Manassas, VA). All cell lines were cultured in high glucose DMEM (Gibco) supplemented with 1% penicillin streptomycin (HyClone, Logan, UT, USA) and 10% fetal bovine serum (Gibco) at 37°C in a saturated humidity incubator at 5% CO2. Cells grown to confluence were digested with 0.25% trypsin containing 0.02% EDTA.
Cytotoxicity experiments
The viability of HNSCC cells after treatment with a range of concentrations of metformin (0–60 mM) was detected using the Cell Counting Kit-8 (CCK-8, Dojindo) following the manufacturer's instructions. The cells were plated at a density of 3×104/ml and resuspended in complete medium; 100 µl of the suspension was added to each well of a 96-well plate and routinely incubated overnight in a 37°C in a 5% CO2 incubator. For attachment, the cells were washed with phosphate-buffered saline (PBS), and incubation was continued with different concentrations of metformin diluted in advance. The medium in the well plates was changed after 24 h, 48 h, and 72 h, and 10 µL CCK-8 was added to each well. The cells were incubated at 37°C in 5% CO2 in the dark, and the absorbance at 450 nm of each well was measured after 1.5 h. Cell viability was calculated as follows: cell viability = ([OD] test − [OD] blank)/([OD] control − [OD] blank)×100%. Half-maximal inhibitory concentration (IC50) values were finally calculated using GraphPad Prism 8 for MacOS (version 8.2.1, La Jolla, CA).
Scratch assay
Evenly spaced horizontal lines (1 cm apart) were drawn on the bottom of 6-well plates with a marker for a total of at least 3 lines for each well. HNSCC cells were seeded into the 6-well plates, and cells were incubated until they reached confluence. A 100-µL pipette tip was used to make scratches perpendicular to the horizontal lines on the bottom of the wells, and the detached cells were removed by gently washing the wells three times with PBS. Serum-free medium containing different concentrations of metformin (0 mM, 10 mM, 20 mM) was added for culture. Wound healing was observed under a microscope after 0 h, 12 h, and 24 h and recorded.
Apoptosis assay
After incubating the cells in a 6-well plate for 24 h, they were treated with different concentrations of metformin solution (0–440 mM) for 48 h. The supernatant was collected, and the cells were resuspended and washed twice with precooled PBS. The cells were incubated in the dark for 15 min at room temperature with reagents from the FITC annexin V apoptosis detection kit I (BD Biosciences) and detected according to the manufacturer's protocol. Samples were detected with a flow cytometer (BD Biosciences, San Diego, CA, USA), and data were acquired by FlowJo 10.4 analysis.
Agilent microarray analysis
Cells were incubated with complete medium containing 30 mM metformin for 48 h (n = 3). Total RNA was extracted using the miRNeasy Micro kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol and assessed with a Bioanalyzer 4200 (Agilent, Santa Clara, CA, USA). Then, next-generation libraries were prepared using the VAHTS mRNA-seq v2 Library Prep Kit for Illumina® (Vazyme, Nanjing, China). The library quality was determined by a Bioanalyzer 4200 (Agilent, Santa Clara, CA, USA). Then, the mRNA-seq libraries were sequenced in a HiSeq X10 system (Illumina, San Diego, CA, USA) on a 150-bp paired-end run. The significantly up- or downregulated genes were selected according to thresholds of p-value < 0.05 and |log2 (fold change)| > 1.
Microarray data acquisition
In this study, we compared the expression levels of mRNAs from the NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/). The gene expression profiles of HNSCC patients with primary (cisplatin sensitive) or recurrent (cisplatin resistant) disease were obtained by downloading a high-throughput gene expression dataset (GSE102787)28. Based on the GPL6480 platform (Agilent-014850 whole human genome microarray 4x44k g4112f), this dataset genomic data and corresponding clinical information for a total of 7 pairs of primary and recurrent HNSCC cell lines from the University of Michigan. We focused on identifying the main gene expression changes associated with cisplatin resistance in primary cells (UMSCC-14A and UMSCC-17A) compared with the advanced HNSCC cells (UMSCC-14B and UMSCC-17B).
Data processing and differentially expressed gene analysis
The Cal27 transcriptome sequencing results after metformin treatment were background corrected and normalized, and expression values were calculated using the Affy, Imute, and Limma packages of R software (version: 4.0.3). Fold change (FC) and adjusted p-values were used to screen differentially expressed genes (DEGs). |Log2-fold change| > 1 and p-value < 0.05 were defined as the screening criteria for DEGs. Heatmaps and volcano plots of the DEGs were constructed using Pheatmap, Ggplot2 and other packages. Similarly, we used R software (version: 4.0.3) to screen DEGs in the original GEO dataset GSE102787. The filtering criteria were |log2-fold change| ≥ 0.5 and p-values < 0.05. Subsequently, we determined the intersecting DEGs of the two datasets through the Venn package in R software.
Functional enrichment analysis
Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) database 3.0 (http://kobas.cbi.pku.edu.cn) is used for genetic function analysis. KOBAS performs statistical tests based on mapping to genes with known annotations to identify significantly enriched protein regulatory pathways and biological functions29. To reveal the biological significance of the screened DEGs in HNSCC progression, we performed gene ontology (GO) functional analysis and KEGG pathway enrichment analysis of DEGs from the Venn diagram using KOBAS, and terms with P < 0.05 were considered significantly enriched.
Protein-protein interaction (PPI) network construction
To gain more insight into potential associations between candidate genes, we used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 11.0 database (https://string-db.org). DEGs were subjected to PPI analysis. The results of the analysis were imported into Cytoscape 3.7.1 to construct a protein interaction network. The cytoHubba plug-in in Cytoscape was applied to screen out the hub genes in the gene expression network, whose connection degrees ranked in the top ten, with the maximum clique centrality (MCC) algorithm.
Survival analysis
To further elucidate the relationship between hub gene expression and the prognosis of HNSCC patients, we used gene expression profiling interactive analysis (GEPIA) (http://gepia.cancer-pku.cn) for survival analysis30. In the analysis results, P < 0.05 was considered statistically significant, and the hub genes that met the criteria were considered the key genes affecting the prognosis of HNSCC.
Real time quantitative PCR analysis
Total RNA was isolated from cell lines using TRIzol reagent (Invitrogen, Shanghai, China) according to the manufacturer's instructions. Reverse transcription was performed from 2 µg RNA to synthesize cDNA. Quantitative RT-PCR was performed in the ABI7500 system using SYBR Green PCR Master Mix (Takara, Dalian, China) and specific primers. The expression level of each gene was normalized to that of GAPDH. The 2−ΔΔCT method was used to calculate the fold changes in transcript levels, and each experiment was repeated three times. The corresponding sequences of the forward and reverse primers are provided in the Table 1.
Statistical analysis
All experiments were performed in triplicate. Experimental data are expressed as the mean ± standard deviation (SD). The significance of differences between samples in any two groups was analysed by t-test using GraphPad Prism 8 for MacOS (version 8.2.1, La Jolla, CA), and differences were considered statistically significant when p < 0.05.
Table 1
Sequences of primers used in this study
|
Gene symbol(Accession no.)
|
Primer sequences(5'→3')
|
Polβ(NC_000008.11)
|
F
|
5'-CCGCAGGAGACTCTCAACG-3'
|
R
|
5'-GTACTTGTGGATAGCTTGGCTC-3'
|
APEX(NC_000014.9)
|
F
|
5'-CCAGCCCTGTATGAGGACC-3'
|
R
|
5'-GGAGCTGACCAGTATTGATGAGA-3'
|
FEN1(NC_000011.10)
|
F
|
5'-ATGACATCAAGAGCTACTTTGGC-3'
|
R
|
5'-GGCGAACAGCAATCAGGAACT-3'
|
LIG1(NC_000019.10)
|
F
|
5'-GAAGGAGGCATCCAATAGCAG-3'
|
R
|
5'-ACTCTCGGACACCACTCCATT-3'
|
PCNA(NC_000020.11)
|
F
|
5'-CCTGCTGGGATATTAGCTCCA-3'
|
R
|
5'-CAGCGGTAGGTGTCGAAGC-3'
|
PARP1(NC_000001.11)
|
F
|
5'-CGGAGTCTTCGGATAAGCTCT-3'
|
R
|
5'-TTTCCATCAAACATGGGCGAC-3'
|
XRCC1(NC_000019.10)
|
F
|
5'-TCAAGGCAGACACTTACCGAA-3'
|
R
|
5'-TCCAACTGTAGGACCACAGAG-3'
|
GAPDH(NC_000012.12)
|
F
|
5′-GGACCTGACCTGCCGTCTAG-3′
|
R
|
5′-GTAGCCCAGGATGCCCTTGA-3′
|
Abbreviations: F, forward; R, reverse
|