Gene mRNA expression data from 33 various cancer types were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and The Genotype-Tissue Expression (GTEx, https://commonfund.nih.gov/GTEx) databases to perform comprehensive pan-cancer analysis. In addition, data from the Cancer Cell Line Encyclopedia (CCLE, https://portals.broadinstitute.org/ccle/) database were collected to access the expression of SGO2 in pan-cancer cells.

Data including overall survival (OS) and progression-free survival (PFS) were acquired from TCGA database. Kaplan-Meier (KM) was applied to perform survival analysis via “survival” (V 3.7-0) and “survminer” (V 0.4.9) packages. Cox analysis was employed to investigate the connection between SGO2 expression and survival utilizing “survival” (V 3.7-0) and “forestplot” (V 3.1.3) packages. Nomogram model relies on multiple regression analysis and integrates gene expression and clinical symptoms to predict the disease outcomes. This method employs scaled lines plotted on a common plane to illustrate the interrelationships among variables in the prediction model. Through establishing a multiple regression model, each level of influencing factor was assessed based on its contribution of the influencing factors to the target parameter (the magnitude of the regression coefficient). These scores for each factor were then summed to yield a total score, thereby estimating the value of interest.

To further investigate the potential function of SGO2, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed by the package “clusterProfiler” (v 4.7.1.001) in R software (University of Auckland, Oakland, CA, USA). p 0.05 was set as the cutoff value for significant enrichment. The “ggplot2” (V 3.5.1) and “GOplot” (V 1.0.2) packages in R software were used to visualization. GSVA is a non-parametric unsupervised method used to assess the enrichment status of gene sets in transcriptomic data. Gene sets were downloaded from the Molecular Signatures Database and GSVA was employed to assign an integrated score to each gene set, thereby evaluating potential biological functional changes across different samples. To clarify the molecular mechanisms of SGO2 in LUAD, the samples were divided into high and low SGO2 expression groups using the median gene expression as a cutoff and then performed GSVA scoring (V 1.46.0) by R package.

To explore the correlation between SGO2 and recombinant E2F transcription factor 1 (E2F1), Spearman’s correlation analysis was employed to describe the relationship between non-normally distributed quantitative variables. A p-value of less than 0.05 was considered statistically significant. The correlation plot between the two genes was visualized using the R software package “ggstatsplot” (V 0.12.4).

Human A549 cells were purchased from American type culture collection (ATCC) and were maintained in RPMI 1640 medium (11875093, Gibco, Grand Island, USA) supplemented with 10% fetal bovine serum (FBS) (A5670701, Invitrogen, Waltham, MA, USA) and 1% penicillin-streptomycin in a standard incubator at 37 °C, 5% CO₂. The identification of A549 cells was carried out by Short Tandem Repeat (STR) analysis. A549 cells were mycoplasma-negative and the culture medium was replaced every 2–3 days.

Small interfering RNAs targeting SGO2 (si-SGO2) or negative control (si-NC) were synthesized by Kainai BioTech (Chengdu, China). Cells were transfected into si-SGO2 or si-NC using Lipofectamine 3000 (L3000001, Invitrogen, Waltham, MA, USA) according to the manufacturer’s instructions. The sequences of si-SGO2 are as follows:

si-SGO2a: 5^′-UAAUUCCUGAGGUAAAAAGUGUU-3^′ (sense), 5^′-CUUUUUACCUCAGGAAUUAAGUU-3^′ (antisense);

si-SGO2b: 5^′-AGUCUUUGAAAUUCUUUUGUCUU-3^′ (sense), 5^′-CAAAAGAAUUUCAAAGACUACUU-3^′ (antisense);

si-SGO2c: 5^′-AAAUAGAAGAAUUAUUUAGUAUU-3^′ (sense), 5^′-CUAAAUAAUUCUUCUAUUUUCUU-3^′ (antisense);

si-NC: 5^′-ACGUGACACGUUCGGAGAAUU-3^′ (sense), 5^′-UUCUCCGAACGUGUCACGUUU-3^′ (antisense).

Plasmids encoding overexpression SGO2 (OE-SGO2) or negative control (OE-NC) were from Kainai BioTech and transduced into A549 cells to perform the corresponding assays.

Total RNA was isolated from A549 cells using TRIzol (15596018CN, Invitrogen, Waltham, MA, USA) and reverse-transcribed into cDNA using the Goldenstar™ RT6 cDNA Synthesis Kit Ver.2 (TSK302M, Tsingke, Beijing, China) according to the manufacturer’s instructions. Subsequently, qPCR analysis was conducted in accordance with the 2$\times$ T5 Fast qPCR Mix (SYBR Green I) (TSE002, Tsingke, Beijing, China). The mRNA expression levels of each gene were standardized using the gene GAPDH, and fold change was calculated using the 2^{-Δ⁢Δ⁢Ct} method. Primer sequences of SGO2 and GAPDH are as follows: SGO2 forward: 5^′-CAGTGATGGAAACTGGCTCA-3^′; reverse: 5^′-TGCCCTGTTGTTGTGCTTTA-3^′; GAPDH forward: 5^′-AACGGATTTGGTCGTATTGGG-3^′; reverse: 5^′-CCTGGAAGATGGTGATGGGAT-3^′.

Radio-immunoprecipitation assay lysis buffer containing protease inhibitors was applied to extract total protein of cells. The protein concentration was determined using a bicinchoninic acid (BCA) kit (Beyotime, Shanghai, China). Each group of protein was separated by sodium dodecyl sulfate -polyacrylamide gel electrophoresis (SDS-PAGE), transferred to polyvinylidene fluoride (PVDF) membranes, and sealed with 5% skim milk for 1 h. The membranes were incubated overnight at 4 °C with primary antibodies against SGO2 (1:1000, bs-24137R, Bioss, Beijing, China), E-cadherin (1:500, AF0138, Beyotime, Shanghai, China), Vimentin (1:1000, MAB040Hu22, Cloud-clone, Wuhan, Hubei, China), Twist (1:500, AF8274, Beyotime), E2F1 (12171-1-AP, Proteintech, Wuhan, China), B-cell lymphoma-2 (BCL2) (26593-1-AP, Proteintech), Cyclin E1 (11554-1-AP, Proteintech), GAPDH (60004-1-Ig, Proteintech) or $\beta$-actin (1:20,000, AC038, ABclonal, Wuhan, China). Subsequently, the membranes were incubated with a horseradish peroxidase (HRP)-conjugated secondary antibody for 1 h at 37 °C. Finally, protein bands were visualized using an enhanced chemiluminescence (ECL) reagent (34580, Thermo, Waltham, MA, USA), and measured using Image J software (National Institutes of Health, Bethesda, MD, USA).

Cell viability was assessed using Cell Counting Kit-8 (CCK-8, Biosharp, Hefei, China) according to the instructions of manufacturer. Briefly, A549 cells were planted in 96-well plates and added to 10 µL of CCK-8 reagent to each well, followed by incubation at 37 °C. The absorbance at 450 nm was detected using enzyme calibration.

A549 cells (6 $\times$ 10⁴ for migration assay and 6 $\times$ 10⁴ for invasion assay) were seeded into uncoated or matrigel-coated (Corning, Corning, NY, USA) upper chambers. Serum-free medium was added to both upper chambers and 800 µL medium containing 10% FBS was added to the lower chambers. After a 24-h incubation at 37 °C in a 5% CO₂ environment, the cells remaining on the upper membrane surface were gently removed with a cotton swab. Those that migrated or invaded to the lower surface were subsequently fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and counted at five random fields per well under a microscope (Leica, Wetzlar, Germany).

A549 cells were seeded in 6-well plates and grown until the formation of a confluent monolayer. Scratches were made in the center of the slide with a 10 µL sterile pipette tip. Detached cells were removed by washing with phosphate buffered saline (PBS), and fresh medium was added to each well. The initial gap width (0 hour) and the closure process at 24 hours were documented by taking photographs under a microscope. ImageJ software (V 1.8.0, LOCI, University of Wisconsin, Madison, WI, USA) was used to measure the distance of cell migration across the scratch, and the results were presented as a percentage of closure relative to the initial scratch width.

Cells was treated by fluorescein isothiocyanate (FITC)-5-Bromo-2^′-deoxyuridine (BrdU) Cell Proliferation Detection Kit (KGA9201-20, KeyGEN BioTECH, Nanjing, China). Flow cytometry for cell proliferation was generally divided into several steps: Firstly, when cells reached about 80% confluence, they were added into BrdU (KGA326, Sigma, MO, USA) and incubated at 37 °C for 60 min. Subsequently, cells were harvested, fixed, permeabilized by 0.5% Triton, underwent DNA denaturation for 30 min at 37 °C, and centrifuged at 1000 g for 10 minutes. Finally, FITC-BrdU antibody was added to stain for 30 min at 4 °C. The stained cells were analyzed for FITC channel fluorescence signal using flow cytometry.

Apoptosis by flow cytometry was performed according to the instructions of the manufacturer (KGA108-2, KeyGEN, Nanjing, China). Generally, cells were collected, centrifuged at 2000 rpm for 5 min and resuspended by Binding Buffer. After that, the cells were added to 5 µL Annexin V-FITC and incubated in the dark for 15 min at room temperature. Finally, propidium iodide (PI) was added into cells and flow cytometry was performed.

For cell cycle, the method is as follows. Briefly, after cells were transfected using corresponding small interfering RNA (siRNA) or plasmids, cells were harvested and fixed in cooled 70% ethanol to preserve their DNA content. The fixed cells were then stained with PI and were analyzed using a flow cytometer (BD Biosciences, San Jose, CA, USA).

Data were processed by GraphPad Prism 8.01 (GraphPad Software, University of California, San Diego, CA, USA) and shown as the mean $\pm$ standard deviation (SD). Differences among groups were analyzed using unpaired Student’s t test or one-way ANOVA followed by a Tukey’s test. p 0.05 was considered to determine significant difference. All data were from three independent repeated experiments.

We analyzed SGO2 expression in 33 human cancers by leveraging data from both the TCGA and GTEx datasets. Elevated SGO2 expression was observed in 25 tumor types, including LUAD (Fig. 1A). In comparison, normal tissues generally exhibited relatively low SGO2 expression compared to cancer tissues. Fig. 1B illustrates the expression of SGO2 in various tumor cell lines, as derived from the CCLE expression profiles. SGO2 correlated with cancer stage in several malignancies, including LUAD (Fig. 1C). The results indicated that SGO2 was overexpressed in LUAD and might be involved in the development of LUAD.

Fig. 1.

Differential gene expression analysis. (A) Module eigengenes analysis. (B) The expression of Shugoshin 2 (SGO2) in various tumor cell lines. (C) Correlation between SGO2 and cancer stage. *p 0.05, **p 0.01, and ****p 0.0001. ns shows no significant difference. ACC, adrenocortical carcinoma; BRCA, breast cancer susceptibility gene; KICH, kidney chromophobe; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hpatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SKCM, skin cutaneous melanoma.

We investigated the connection between SGO2 expression and prognosis of patients with LUAD from TCGA and GTEx datasets. Fig. 2A depicted a significant correlation between SGO2 expression and OS in LUAD. KM survival analysis suggested that elevated SGO2 expression was associated with unfavorable OS in LUAD (p = 0.0046, Fig. 2C). Similarly, SGO2 expression was significantly associated with PFS in patients with LUAD (Fig. 2B). Furthermore, the KM-plot survival analysis indicated that high expression of SGO2 was linked to unfavorable PFS in LUAD (p = 0.0081, Fig. 2D). Based on the expression of SGO2 and clinical symptoms, a nomogram prediction model was constructed. The logistic regression analysis demonstrated the effect of SGO2 expression level on the predictive efficacy of the model in LUAD samples (Fig. 2E). In addition, calibration curves were shown to predict validity of the three-year and five-year survival periods, as shown in Fig. 2F.

Fig. 2.

Prognostic analysis of SGO2 gene in lung adenocarcinoma (LUAD). (A,B) Cox analysis and Kaplan-Meier analysis were performed the connection between SGO2 expression and overall survival (OS). (C,D) Cox analysis and Kaplan-Meier (KM) analysis were performed the connection between SGO2 expression and progress free survival (PFS). (E) Nomogram prediction model and logistic regression analysis. (F) Calibration curves were shown. p 0.05. ACC, Adrenocortical carcinoma; BLCA, Bladder Urothelial Carcinoma; BRCA, Breast invasive carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, Cholangiocarcinoma; COAD, Colon adenocarcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA, Esophageal carcinoma; GBM, Glioblastoma multiforme; HNSC, Head and Neck squamous cell carcinoma; KICH, Kidney Chromophobe; KIRC, Kidney renal clear cell carcinoma; KIRP, Kidney renal papillary cell carcinoma; LAML, Acute Myeloid Leukemia; LGG, Brain Lower Grade Glioma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma; MESO, Mesothelioma; OV, Ovarian serous cystadenocarcinoma; PAAD, Pancreatic adenocarcinoma; PCPG, Pheochromocytoma and Paraganglioma; PRAD, Prostate adenocarcinoma; READ, Rectum adenocarcinoma; SARC, Sarcoma; SKCM, Skin Cutaneous Melanoma; STAD, Stomach adenocarcinoma; TGCT, Testicular Germ Cell Tumors; THCA, Thyroid carcinoma; THYM, Thymoma; UCEC, Uterine Corpus Endometrial Carcinoma; UCS, Uterine Carcinosarcoma; UVM, Uveal Melanoma.

To explore the impact of SGO2 on proliferation of LUAD A549 cells, cell transfection was performed using small interfering RNA si-SGO2 or plasmids overexpressing SGO2. Transfection efficiency was detected by qPCR and western blot (p 0.05, Fig. 3A–D). As shown in Fig. 3A, si-SGO2c has a more effective knockdown of SGO2 among three si-RNAs (p 0.001). si-SGO2c was used in subsequent knockdown experiments. CCK-8 results showed that SGO2 knockdown significantly inhibited cell viability compared to the NC group (p 0.05, Fig. 3E). Overexpression of SGO2 increased A549 cell viability relative to the OE-NC group (p 0.05, Fig. 3F). Collectively, SGO2 promoted cell viability of A549 in vitro.

Fig. 3.

SGO2 enhances the cell viability of A549 cells. (A,B) qPCR detection of SGO2 mRNA expression following overexpression and knockdown of SGO2. ***p 0.001 vs. the negative control (NC) or overexpression negative control (OE-NC) group. (C,D) Western blot detection of SGO2 protein expression following overexpression and knockdown of SGO2. ***p 0.001 vs. the OE-NC group; ###p 0.001 vs. the NC group. (E,F) Cell Counting Kit-8 (CCK-8) assay was performed to explore the effect of SGO2 on cell viability of A549. **p 0.01 vs. the NC/OE-NC group. n = 3.

Next, we assessed the effects of SGO2 on the proliferation and apoptosis of A549 cells by flow cytometry. Significant inhibitory effect on cell proliferation was observed in A549 cells with decreased SGO2 compared with the control group, while shown opposite result in SGO2 overexpression cells (p 0.05, Fig. 4). Flow cytometric analysis of apoptosis indicated that knockdown of SGO2 significantly increased the apoptosis rate, whereas overexpression of SGO2 significantly reduced it (p 0.05, Fig. 5). These results showed that SGO2 promoted the proliferation and inhibited apoptosis of A549 cells.

Fig. 4.

SGO2 promotes A549 proliferation. Flow cytometry depicting the proliferation of A549 cells after (A) silencing and (B) overexpression of SGO2. *p 0.05 and ***p 0.001 vs. the NC/OE-NC group. n = 3.

Fig. 5.

SGO2 inhibits A549 apoptosis. Flow cytometry assessing apoptosis of A549 cells after (A) silencing and (B) overexpression of SGO2. **p 0.01 vs. the NC/OE-NC group. n = 3.

To investigated the effect of SGO2 on A549 cell migration and invasion, wound healing and Transwell assays were carried out. The results showed that the downregulation of SGO2 significantly limited cell migration and invasion of A549 (p 0.05, Fig. 6A,C), whereas SGO2 overexpression facilitated these abilities (p 0.05, Fig. 6B,D). Based on those, we further clarified the role of SGO2 on epithelial-mesenchymal transition (EMT). Western blot results indicated that knockdown of SGO2 increased E-cadherin expression and reduced the expression levels of Vimentin and Twist, whereas SGO2 overexpression yielded an opposite effect (p 0.05, Fig. 6E,F). Collectively, these results indicated that SGO2 enhanced the migration, invasion and EMT of A549 cells in vitro.

Fig. 6.

SGO2 promotes A549 migration and invasion in vitro. (A,B) The effect of SGO2 knockdown or overexpression on cell migration and invasion was investigated by transwell assay. Scale bars: 100 µm. **p 0.01 and ***p 0.001 vs. the NC/OE-NC group. (C,D) SGO2 knockdown inhibited the migration and overexpression SGO2 promoted migration by wound healing assay. **p 0.01 and ***p 0.001 vs. the NC/OE-NC group. Scale bars: 100 µm. (E) The protein bands associated with epithelial-mesenchymal transition (EMT). (F) The statistical graph using normalized statistics. Scale bar = 100 µm. ***p 0.001 vs. the NC group; ###p 0.001 vs. the OE-NC group. n = 3.

To explore the biological function of SGO2, KEGG enrichment analysis was carried out. The mainly enriched KEGG pathway terms are shown in Fig. 7A. High expression SGO2 was significantly enriched in cell cycle and DNA replication, which suggested that SGO2 might exert its function through these pathways. To further study the molecular mechanism of SGO2 in LUAD, GSVA was utilized to evaluate the potential signaling pathways. The results revealed that the high expression of SGO2 was mainly enriched in E2F targets, G2M checkpoint, mTORC1 signaling, MYC targets, and other signaling pathways in LUAD (Fig. 7B). It is well-known that E2F1 is an important member of transcription factor E2F family, which regulates G1/S phase transition of cell cycle in the mammalian [14]. The results of correlation analysis exhibited that the expression of SGO2 was positively correlated with that of E2F1 in the patient with LUAD from TCGA dataset (p 0.05, Fig. 7C).

Fig. 7.

Signaling pathway prediction of SGO2. (A) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results between high expression SGO2 and low expression SGO2. (B) Gene Set Variation Analysis (GSVA) results between high expression SGO2 and low expression SGO2. (C) Correlation analysis between SGO2 and E2F1.

The high expression level of E2F1 is closely related to tumor metastasis and the poor prognosis of tumor patients [15]. Studies have confirmed that E2F1 regulates cell cycle and cell apoptosis in various diseases [16, 17]. To clarify whether SGO2 regulates the expression of E2F1, western blot was performed. Consistent with the results of correlation analysis, western blot showed that SGO2 promoted the expression of E2F1 (p 0.05, Fig. 8A). At the same time, SGO2 increased CyclinE1 and BCL-2 expression levels (p 0.05, Fig. 8A). To investigate whether the effect of SGO2 on the cell proliferation and cell cycle of A549 cells was mediated by its promotion of E2F1, knockdown of E2F1 in SGO2-overexpressing A549 cells was performed. The results showed that E2F1 knockdown reversed the increased proportion of cells in S phase and the increase of cell proliferation by SGO2 overexpression (p 0.05, Fig. 8B,C). These results suggested that SGO2 promoted cell proliferation and cell cycle progression via E2F1.

Fig. 8.

SGO2 promotes cell proliferation and regulates cell cycle via E2F1. (A) The protein expression of E2F1, CyclinE1 and BCL2 by western blot. *p 0.05, **p 0.01 vs. the OE-NC group; #p 0.05, ##p 0.01 vs. the si-NC group. (B) Cell viability measured by CCK-8. *p 0.05 or ***p 0.001 vs. the OE-NC group; #p 0.05 vs. the OE-SGO2+si-NC group. (C) Cell cycle detected by flow cytometry.