2.1 Data Acquisition

The technical roadmap of this study is shown in Supplementary Fig. 1. Microarray datasets were retrieved from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE85195, GSE180077 and GSE18842, comprising 39 HNSC tumor and 19 normal tissues, as well as 46 NSCLC patients and 45 controls. Furthermore, bulk RNA-seq data and clinical information including gender, TNM stage and survival status etc. for 548 HNSC patients and 44 controls, as well as 1149 NSCLC patients and 108 controls were obtained from The Cancer Genome Atlas Program [6] (https://portal.gdc.cancer.gov).

2.2 Identification of Common DEGs

R software (version 4.2.1) was used for all analyses. To mitigate cross-platform batch effects, we applied a “separate analysis, then intersection” strategy: edgeR [7] for RNA-seq datasets and limma [8] for the microarray dataset. DEGs were defined by adjusted P < 0.05 and |logFC| > 2 (upregulation: logFC > 2; downregulation: logFC < -2) in each dataset. Common DEGs were identified by taking the intersection across all three datasets. Visualization was performed using VennDiagram[1.7.3] and ggplot2[3.4.4].

2.3 GO and KEGG Analysis

GO and KEGG enrichment analyses of the common differential genes were performed using the “clusterProfiler” package [9] to identify signaling pathways and biological processes that may be enriched in the patients of HNSC developing NSCLC. Adjusted P-value < 0.05 was considered statistically significant. The STRING database (https://cn.string-db.org/) was used to predict protein functional relationships and interactions. Protein-protein interaction (PPI) network was visualized with the Cytoscape software.

2.4 Prognostic Evaluation and Survival Analysis

According to TCGA database, the expression and survival of 14 up-regulated genes in the HNSC and the NSCLC cohorts were analyzed. The combined analysis of the TCGA-LUAD and TCGA-LUSC data represents the NSCLC cohort (TCGA-LUADLUSC). Univariate and multivariate Cox regression analyses were performed using the “Survival” package to screen genes associated with overall survival (P < 0.05), and the hazard ratio (HR) and 95% confidence interval (CI) were calculated. Kaplan-Meier estimate was used to construct survival curves and calculate overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS). Genes with prognostic significance in both cancer types were considered as key genes. Receiver operating characteristic (ROC) curves were used to investigate diagnostic performance. External validation was performed using the GEPIA (http://gepia.cancer-pku.cn/) and Kaplan-Meier Plotter (http://kmplot.com/) to confirm the association between SCG5 expression and patient prognosis.

2.5 Gene Set Enrichment Analysis

SCG5 was identified as the key player through the above steps. To analyze the potential cancer-promoting pathways shared by these two cancers, we further used “clusterProfiler” for Gene set enrichment analysis (GSEA) [10]. GSEA was performed using c2.cp.all.v2022.1.Hs.symbols.gmt [All Canonical Pathways](3050) in MSigDB Collections. Firstly, the HNSC and the NSCLC cohorts were divided into SCG5-high and SCG5-low expression groups, respectively, according to the median expression level of SCG5. Then “DESeq2 [11]” and “edgeR” were used to analyze the differentially expressed genes related to SCG5 for functional enrichment. Adjusted P-value < 0.05 and |logFC| > 2 were identified as related to SCG5. The thresholds were set as false discovery rate (FDR) < 0.25 and adjusted P-value < 0.05.

2.6 Immune Infiltration and Immune Checkpoint Analyses

We explored the relationship between the expression of key genes and the degree of tumor infiltration, as well as the expression of immune checkpoint genes, in order to deeply explore the potential molecular mechanisms underlying tumor promotion. The status of 24 immune cell types’ tumor infiltration [12] was assessed using the ssGSEA [13] algorithm in GSVA [1.46.0] R package and visualized with ggplot2 package. Spearman’s correlation analysis was applied to examine the relationship of molecular expression with immune cell infiltration and with nine extensively studied immune checkpoint genes, previously implicated in immune checkpoint function assessment [14,15,16]. Wilcoxon rank sum test was utilized to compare the difference between different SCG5 expression groups. The above analyses were conducted independently on the lung cancer dataset and the head and neck squamous cell carcinoma dataset, respectively.

2.7 Tissue Microarray and Immunohistochemistry

Tissue microarrays of head and neck squamous cell carcinoma (HN810001) and lung cancer (R121Lu01) were purchased from zhongke guanghua biotechnology (http://bioaitech.com). Immunohistochemical staining was performed on tissue microarray (TMA) sections. Briefly, tissue sections were deparaffinized and rehydrated through a graded alcohol series. Antigen retrieval was carried out using a high-pressure heating method with EDTA buffer (pH 9.0). Subsequently, the sections were sequentially treated as follows: endogenous peroxidase activity was quenched with a peroxidase block, and non-specific binding sites were blocked with a serum solution. The sections were then incubated with the primary antibody (Proteintech, 10761-1-AP, diluted at 1:50) at 37℃ for 1 h, followed by a horseradish peroxidase (HRP)-conjugated secondary antibody at 37℃ for 20 min. Signal visualization was achieved using a 3,3’-diaminobenzidine (DAB) substrate, and the nuclei were counterstained with hematoxylin. SCG5 expression levels were subsequently compared between tumor samples and matched normal controls.

2.8 Immunohistochemical Scoring

Immunohistochemical staining was evaluated using a combined scoring system based on the percentage of positive cells and staining intensity [17]. All sections were independently assessed by two pathologists blinded to clinical data. Staining intensity was graded as follows: 0 (negative), 1 (pale yellow), 2 (deep yellow), and 3 (brownish yellow). The percentage of positive cells was scored as: 0 (< 5%), 1 (5–25%), 2 (26–50%), 3 (51–75%), and 4 (76–100%). The final score was calculated as the product of the intensity score and the proportion score, yielding a total score ranging from 0 to 12. Based on the median score of the cohort (4), samples were classified into low-expression group (final score < 4) and high-expression group (final score ≥ 4). The association between SCG5 expression level (high vs. low) and clinicopathological parameters was analysed using the Chi-square test.

2.9 Cell Culture

The human tongue squamous cell carcinoma cell line SCC-25 and the human non-small cell lung cancer cell line A549 were obtained from Wuhan Procell Life Technology Co., Ltd. (Wuhan, China). SCC-25 cells were cultured in DMEM/F12 medium (Procell, Wuhan, China), whereas A549 cells were cultured in high-glucose DMEM (Procell, Wuhan, China). Both media were supplemented with 10% fetal bovine serum (Procell, Wuhan, China). All cells were maintained at 37°C in a humidified atmosphere of 5% CO2.

2.10 Colony Formation Assay

For the colony formation assay, siRNA-transfected cells were harvested and seeded into 6-well plates at a density of 600 cells per well. The cells were cultured in medium supplemented with 10% fetal bovine serum, which was refreshed every 4 days. After 2 weeks of cultivation, the resulting colonies were fixed with methanol and stained with 1% crystal violet (Sigma-Aldrich). Visible colonies were photographed and counted using an optical microscope (Olympus, Japan).

2.11 Cell Counting kit-8 (CCK-8) Assay

Cell proliferation was evaluated using the Cell Counting Kit-8 (TransGen Biotech, China) to determine whether SCG5 affects cancer cell proliferation. siRNA-transfected cells were seeded in 96-well plates at a density of 3–5 × 103 cells per well (n = 3) and incubated for 0, 24, 48, 72, and 96 h. At each time point, 10 µL of CCK-8 reagent was added to wells containing 100 µL of medium and incubated for 4 h at 37°C. Absorbance was then measured according to the manufacturer’s instructions.

2.12 Transient Transfection

Transient transfections were performed using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s protocol. siRNA oligonucleotides targeting SCG5 were as follows: siRNA#1, 5’-AAACAGUAGGCCAGAUAGCAUUUGCUAUCUGGCCUACUGUUUUGUU-3’; siRNA#2, 5’-UGGCAAUGCCCAAUUGCUCCAUUGAGCAAUUGGGCAUUGCCAGGUU-3’; and siRNA#3, 5’-ACGAUGUUGGGGAUGUUGCCAUUGCAACAUCCCCAACAUCGUGGUU-3’. Cells were transfected with one of the SCG5-targeting siRNAs or with a non-targeting negative control (NC) siRNA (GenePharma, China). Forty-eight hours after transfection, total RNA and protein were collected, and SCG5 knockdown efficiency and downstream protein expression were assessed by RT-qPCR and Western blotting, respectively.

2.13 Quantitative Real-Time PCR

Total RNA was extracted from cultured cells using TRIzol reagent (TransGen Biotech, China). One microgram of RNA was reverse-transcribed into cDNA with the All-In-One First-Strand cDNA Synthesis SuperMix (MedChemExpress, China) according to the manufacturer’s instructions. Quantitative real-time PCR (qRT-PCR) was performed on an iCycler Real-Time PCR Detection System (Bio-Rad Laboratories, Richmond, CA, USA) using SYBR Premix (TransGen Biotech, China). β-actin (ACTB) served as the internal reference, and relative mRNA expression levels were determined by the 2−ΔΔCt method. Primer sequences were: SCG5 forward, AAAACACCCCTGACACTGCA; SCG5 reverse, CTTGTTCCACTTGCCCAAGC; β-actin forward, TGGCACCCAGCACAATGAA; β-actin reverse, CTAAGTCATAGTCCGCCTAGAAGCA.

2.14 Western Blot Analysis

Protein lysates were prepared as described previously [18]. Protein concentrations were determined using the Easy II Protein Quantitative Kit (TransGen Biotech, Beijing, China). Equal amounts of protein were separated by SDS-PAGE on 8% or 10% gels and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, Boston, MA, USA). Membranes were blocked with 5% non-fat milk in TBST for 2 h at room temperature and then incubated overnight at 4℃ with primary antibodies against SCG5, PI3K, p-PI3K, AKT, p-AKT, and β-tubulin (MedchemExpress LLC, Shanghai, China). The membranes were subsequently incubated with HRP-conjugated anti-rabbit or anti-mouse IgG secondary antibodies (1:16,000 in TBST) for 1 h at 37℃. Protein bands were visualized using an enhanced chemiluminescence detection system (Amersham Pharmacia Biotech).

2.15 Statistical Analysis

Statistical analyses were conducted using GraphPad Prism version 5.0 (GraphPad Software, La Jolla, CA, USA). Data from at least three independent experiments are presented as mean ± standard deviation (SD). Comparisons between two groups were performed using Student’s t-test, and comparisons among three or more groups were evaluated by one-way analysis of variance (ANOVA). A P value < 0.05 was considered statistically significant.