The Gene Expression Omnibus (GEO) database was used to retrieve four cancer-related datasets, including GSE9750 for cervical cancer, GSE19804 for lung cancer, GSE17025 for endometrial cancer, and GSE43837 for breast cancer.

The University of California Santa Cruz (UCSC) Xena website (https://xenabrowser.net/datapages/) provided the mRNA normalized expression profiles, phenotype data, normal tissues in the Genotype-Tissue Expression (GTEx) database, DNA methylation profiles, and copy number variations (CNV) data for 33 different cancer types. The Cancer Cell Line Encyclopedia (CCLE, https://sites.broadinstitute.org/ccle/) was used to download data on cell line expression. Genomic Data Commons (GDC) (https://portal.gdc.cancer.gov/) provided the level 4 simple nucleotide variation (SNV) dataset for all The Cancer Genome Atlas (TCGA) samples processed using MuTect2 software (version 4.1.0.0, illumine, Inc., San Diego, CA, USA) [16]. The mRNA expression profiles of immune cells were obtained from both the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/) and the study by Monaco et al. [17]. Data on microsatellite instability (MSI) and tumor mutation burden (TMB) for 33 different cancer types were gathered from research by Thorsson et al. [18] and Bonneville et al. [19], respectively. Data on the infiltration of pan-cancer immune cells were gathered from the Tumor Immune Estimation Resource 2.0 (TIMER2.0, http://timer.cistrome.org/). The Tracking Tumor Immunophenotype meta-server (http://biocc.hrbmu.edu.cn/TIP/index.jsp) was used to download immune activity ratings. The source of The Cancer Immunome Atlas (TCIA, https://tcia.at/home) provided the immunophenscore (IPS). Furthermore, for association analysis with BZW2 expression, each tumor’s epigenetically regulated RNA expression-based (EREG.EXPss) and RNA expression-based (RNAss) tumor stemness scores were used from earlier work [20].

Our study utilized the method developed by Zeng et al. [21] to examine the correlation between BZW2 and the tumor microenvironment in a cohort of 33 tumors. The StromalScore, ImmuneScore, and ESTIMATEScore of tumor samples were computed using the R program ESTIMATE, and their connection with BZW2 was evaluated [22]. BZW2 and immune cell infiltration were compared in several cancer types using single sample gene set enrichment analysis (ssGSEA) and xCell methods [23, 24]. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and IPS from the TCIA database were utilized to evaluate the immunotherapy response. To compute the stemness index and evaluate its relationship with BZW2 expression, Malta et al.’s [20] one-class logistic regression machine learning algorithm (OCLR) was employed. Based on the “oncoPredict” package, the relationship between BZW2 and the IC50 of medicines from the Genomics Database of Cancer Drug Sensitivity V2 (GDSC V2, https://www.cancerrxgene.org/) was assessed [25, 26].

Using transcriptome data from TCGA, GSVA enrichment analysis was carried out to assess the biological role of BZW2 in cancers. 50 pathway gene sets were analyzed for enrichment in each pan-cancer sample based on the HALLMARK pathway set from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/index.jsp). The Spearman correlation between BZW2 and each HALLMARK pathway is then determined. Furthermore, pathways associated with immune inflammation were retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) official website (https://www.kegg.jp/). Immune-inflammation-related pathway scores were calculated using the ssGSEA method, and their relationship to BZW2 was evaluated.

To assess the clinical prediction value of BZW2, survival data for pan-cancer, such as overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS), were obtained from the TCGA database. The BZW2 high expression and low expression groups comprised all patients of each cancer type, according to the best cutoff value. To examine the relationship between BZW2 expression and patient prognosis, Cox analysis and Kaplan-Meier (KM) survival curves were produced using the R packages “survival” and “survminer” and then analyzed. To examine the prediction accuracy of the BZW2, TimeROC (receiver operating characteristic) study was used. In order to find independent predictive indicators, univariable and multivariable Cox regression analyses were undertaken.

Hela cells were obtained from the Chinese Academy of Sciences’ Cell Bank in Shanghai, China. The cells were grown in DMEM medium (HyClone, Inc., Logan, UT, USA) containing 10% fetal bovine serum (Vazyme biotech co., Nanjing, Jiangsu, China), 100 mg/mL streptomycin and 100 U/mL penicillin. Cells were incubated in a humidified atmosphere with 5% CO${}_2$ at 37 °C. Total RNA was isolated using the TRIzol reagent (Invitrogen, Inc., Carlsbad, CA, USA), as directed by the manufacturer. 1 µg of RNA was used for cDNA synthesis by the Hiscipt III 1st strand cDNA synthesis kit (Vazyme biotech co., Nanjing, China). GAPDH was amplified from each sample to ensure equal cDNA input. Each PCR reaction contained 1 µL of cDNA, 0.6 µL of forward and reverse primers (10 µM), 7.5 µL of ChamQ Universal SYBR qPCR Master Mix (Vazyme biotech co., Nanjing, China), and 6.3 µL of ddH2O. The PCR reaction parameters were as follows: 10 minutes of pre-denaturation at 95 °C, 40 cycles of denaturation for 15 seconds at 95 °C, 1 minute of annealing at 62 °C, and 15 seconds of extension at 72 °C. A reaction was required at 60 °C for one minute and 95 °C for fifteen seconds in the last extension phase. The forward and reverse primers for GAPDH were GGAGCGAGATCCCTCCAAAAT and GGCTGTTGTCATACTTCTCATGG, respectively. The forward and reverse primers for BZW2 were CCAGTCTCTTCACCGACAGCTT and GACGAGGTAACAGAGTTGGCATC, respectively. The Hela cells line were validated by short tandem repeat profiling and tested negative for mycoplasma.

Cells were harvested following treatment with siRNAs. The cells were then collected by centrifugation after being cleaned three times with phosphate-buffered saline (PBS). Proteinase inhibitors (#R0010, Solarbio, Inc., Beijing, China) were added to RIPA buffer to help create total protein extracts. BZW2 (#ab254772, Abcam, Inc., Shanghai, China) and GAPDH (#60004-1-Ig, Proteintech Group, Inc., Wuhan, China) antibodies were used in line with the manufacturer’s instructions for western blot analysis. Goat Anti-Mouse IgG-HRP (#SA00001-1, Proteintech Group, Inc., Wuhan, China) and Goat Anti-Rabbit IgG-HRP (#SA00001-2, Proteintech Group, Inc., Wuhan, China) were the secondary antibodies used. The protein loading control was GAPDH. The enhanced chemiluminescence (ECL) reagent (Beijing 4A Biotech Co., Ltd., Beijing, China) was used to visualize the signals.

Hela cells transfected with siRNAs-BZW2 were harvested upon reaching 90% confluency. They were then seeded onto 96-well culture plates, with five multiple wells allocated to each group, and 5000 cells per well. The CCK-8 kit (#KGA9310-1000, Nanjing KeyGen Biotech. Inc., Nanjing, China) was used to examine the cells at 0 h, 24 h, 48 h, and 72 h after they were incubated in an incubator with 37 °C and 5% CO${}_2$.

The 24-well Transwell chamber (Corning, Inc., Corning, NY, USA) was set up and left at 4 °C for the whole night. Transwell insert was then seeded with a 200 µL cell solution comprising 100,000 cells/mL. The lower chamber was then filled with a culture medium (700 µL) containing 10% Fetal Bovine Serum (FBS). After 24 h of culture, cell fixation was performed using 4% paraformaldehyde at room temperature for 20 minutes, followed by staining with 0.5% crystal violet dye for 5 minutes. The cell count was recorded thereafter.

After being harvested, Hela cells were suspended in a binding buffer. The cells were then stained in accordance with the manufacturer’s instructions using the Annexin V-APC/PI Apoptosis Detection Kit (Nanjing KeyGen Biotech. Inc., Nanjing, China). A Beckman Cytoflex device was used for the flow cytometry analysis, and CytExpert Software (version 2.0, Beckman Coulter, Inc., Brea, CA, USA) was used to analyze the data.

R software (version 4.0.3; https://www.r-project.org/) was used for all analysis techniques with the exception of internet tools. Tumor and normal sample differential expression was computed using the Wilcoxon rank-sum test. The Spearman and Pearson correlation approach was used to conduct correlation analysis. For the analysis of cell experiment data, GraphPad Prism for Windows (version 9.0.0, GraphPad Software, Inc., San Diego, CA, USA) was used. Three biological replicates of each experiment were conducted and the standard deviation is used to characterize the degree to which the data deviates from the mean. The Student’s t-test was used to determine statistical significance, with p-values of less than 0.05 being regarded as significant. The following were used to indicate the significance levels: *p 0.05, **p 0.01, ***p 0.001.

We initially examined the expression of BZW2 in human normal tissues and observed higher levels of BZW2 expression in tissues such as bone marrow, muscle, and heart (Supplementary Fig. 1A). High expression of BZW2 was found in cancer cell lines including stomach adenocarcinoma (STAD) and skin cutaneous melanoma (SKCM) after analysis of the expression patterns of tumor cell lines (Supplementary Fig. 1B). The TCGA data further confirmed elevated levels of BZW2 in tumor tissues such as rectum adenocarcinoma (READ), colon adenocarcinoma (COAD), and esophageal carcinoma (ESCA) (Supplementary Fig. 1C). Additionally, we observed higher expression of BZW2 in myeloid dendritic cells (Supplementary Fig. 1D,E). Upon merging GTEx and TCGA samples, we observed significant upregulation of BZW2 expression in over 70% of tumor types, including breast invasive carcinoma (BRCA), CESC, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), brain lower grade glioma (LGG), and thymoma (THYM) (Fig. 1A). BZW2 was significantly upregulated in a number of tumor tissues when compared to paired normal neighboring tissues in the TCGA pan-cancer dataset (Supplementary Fig. 2A–P). We also studied BZW2 expression in four GEO datasets, which revealed that BZW2 expression was significantly higher in cervical cancer (Fig. 1B), lung cancer (Fig. 1C), and endometrial cancer (Fig. 1D) compared with normal tissues. Additionally, we observed high BZW2 expression in metastatic breast cancer (Fig. 1E). BZW2 was substantially increased at the protein expression level in ten different cancer types according to the Clinical Proteomic Tumor Analysis Consortium database. These cancers include breast cancer, colon cancer, ovarian serous cystadenocarcinoma (OV), clear cell renal cell carcinoma (ccRCC), uterine corpus endometrial carcinoma (UCEC), lung cancer, PAAD, head and neck squamous cell carcinoma (HNSC), glioblastoma multiforme (GBM) and liver cancer (Fig. 1F). Additional immunohistochemistry investigation confirmed that BZW2 is highly expressed on the protein level in a variety of malignancies (Supplementary Fig. 3). These results indicate that BZW2 is upregulated in multiple cancer types, suggesting its potential role in cancer initiation and progression.

Fig. 1.

BZW2 expression pattern in pan-cancer. (A) Basic leucine zipper and W2 domains 2 (BZW2) differential expression throughout the cancer spectrum in normal and tumor tissues (The Cancer Genome Atlas (TCGA) + Genotype-Tissue Expression (GTEx)). Using the Gene Expression Omnibus (GEO) dataset, the differential expression of BZW2 in tumor and normal tissues was examined, including (B) Cervical cancer (GSE9750). (C) Lung cancer (GSE19804). (D) Endometrial cancer (GSE17025). (E) Metastatic breast cancer (GSE43837). (F) Pan-cancer analysis of differential expression of BZW2 protein levels. *p 0.05, **p 0.01, ***p 0.001. TPM, Transcripts per million; UCEC, Uterine Corpus Endometrial Carcinoma; PAAD, Pancreatic Adenocarcinoma.

We examined BZW2’s SNV and CNV as well as alterations in DNA methylation in the BZW2 promoter region to identify the mechanism causing elevated BZW2 expression. The SNV of BZW2 were primarily missense mutations, with UCEC (n = 175, 3.4%) exhibiting the highest SNV rate (Supplementary Fig. 4A). Furthermore, we observed a higher frequency of copy number amplifications of varying degrees in most cancer types as compared to copy number deletions (Fig. 2A). Multiple cancer types also showed a substantial positive connection between BZW2 mRNA expression and CNV (Supplementary Fig. 4B). The four most relevant cancer types were ESCA (r = 0.568, Supplementary Fig. 4C), HNSC (r = 0.498, Supplementary Fig. 4D), sarcoma (SARC) (r = 0.517, Supplementary Fig. 4E), and READ (r = 0.514, Supplementary Fig. 4F).

Fig. 2.

33 cancer types were subjected to examination of (A) DNA copy number variation and (B) DNA methylation.

At the epigenetic level, we observed significant hypomethylation levels in tumor tissues in more probes (including cg23402769 in the promoter region and cg25778479 in the BZW2 body) than in normal tissues (Supplementary Fig. 5A). The results of correlation analysis showed that mRNA expression in the majority of tumor types, including BRCA, CESC, and lung squamous cell carcinoma (LUSC), was strongly negatively linked with the methylation levels of various locations in the BZW2 promoter region and BZW2 body (Fig. 2B). Notably, in cg25778479 and cg22294755 in one-third of cancer types, we found a substantial negative connection between BZW2 mRNA expression and methylation levels (Supplementary Fig. 5B,C). TMB and MSI are key factors in clinical immunotherapy. Our analysis revealed a significant positive correlation between BZW2 expression and TMB in COAD, THYM, lung adenocarcinoma (LUAD), STAD, pheochromocytoma and paraganglioma (PCPG), SARC, and prostate adenocarcinoma (PRAD) (Supplementary Fig. 5D). Additionally, Testicular germ cell tumors (TGCT), THYM, STAD, OV, and other cancers showed a significant positive correlation with MSI (Supplementary Fig. 5E). Moreover, we investigated the relationship between BZW2 mRNA expression and TP53 mutations and discovered a consistent positive link in a variety of malignancies, including BRCA, COAD, HNSC, liver hepatocellular carcinoma (LIHC), LUAD, PAAD, PRAD, and UCEC (Supplementary Fig. 5F). These findings together shed light on the regulatory mechanisms by which BZW2’s DNA copy number amplification and DNA demethylation encourage the upregulation of its mRNA in different cancer types, which may be essential for the development and spread of cancer.

Conducting a comprehensive pan-cancer analysis to elucidate the immunological effects of BZW2 is vital for identifying cancer types that might benefit from anti-BZW2 immunotherapy. The expression of BZW2 mRNA was shown to be highly correlated with immunological subtypes in various forms of cancer. Specifically, BZW2 exhibited enhanced expression in the C1 subtype (associated with wound healing) of most cancers, including bladder urothelial carcinoma (BLCA), COAD, and LIHC, while it showed lower expression in C3 (associated with inflammation) (Supplementary Fig. 6). Furthermore, BZW2 showed noteworthy negative relationships with the ImmuneScore, EstimateScore, and StromalScore in a variety of cancer types, such as CESC and STAD (Fig. 3A). Besides, we observed significant positive correlations between BZW2 and mismatch-related signatures across almost all cancer types, while significant negative correlations were observed with immune-related signatures in most cancers (Supplementary Fig. 7A). These results point to a possible involvement of BZW2 in the development of different types of cancers.

Fig. 3.

Correlation of BZW2 and immune signatures in pan-cancer. (A) The ESTIMATE algorithm evaluated the relationship between BZW2 and tumor microenvironment. (B) The relationship between BZW2 and immune cell infiltration is evaluated by the xCell algorithm. (C) The infiltration of common lymphoid progenitor cells was positively linked with BZW2. (D) The infiltration of T cell CD4${}^{+}$ Th2 cells was positively linked with BZW2. (E) BZW2 was negatively correlated with microenvironment score. (F) Correlation between immune checkpoints and BZW2 in pan-cancer.

Based on the xCell method, immune cell infiltration study demonstrated a strong positive link in several cancer types between BZW2 and common lymphoid progenitor, T cell CD4${}^{+}$ Th2. (Fig. 3B–D). On the other hand, BZW2 showed varying degrees of negative correlation with microenvironment scores and other immune cell infiltration (Fig. 3E). An analogous examination utilizing the ssGSEA algorithm discloses a negative correlation between BZW2 and diverse immune cell infiltrates in several sorts of malignancy (Supplementary Fig. 7B). Besides, we observed mutually exclusive expression of BZW2 with several immune checkpoints in over half of the cancer types. This immunosuppressive effect is particularly evident in TGCT, SKCM, and LUSC (Fig. 3F). Additionally, the TIMER2 web server (http://timer.cistrome.org/) was utilized to examine the relationship between BZW2 and the invasion of four kinds of immune-suppressive cells. The findings showed a strong positive connection between BZW2 and the infiltration of myeloid-derived suppressor cells (MDSCs) in several cancer types (Supplementary Fig. 7D). Also, we gathered data on 122 immune modulators from the work of Charoentong et al. [27], including MHC, receptors, chemokines, and immune stimulators, and assessed their relationships with BZW2. We found a negative correlation of BZW2 with most immunomodulators in pan-cancer, and this negative correlation was particularly pronounced in MHC (Supplementary Fig. 8). These findings indicate that high BZW2 expression may downregulate antigen presentation and processing capabilities. Moreover, several chemokine receptors were also observed to be significantly negatively correlated with BZW2, including CXCR3, which mediates the chemotaxis and activation of T lymphocytes and natural killer cells, and CXCR5, which regulates the chemotaxis and activation of B cells. However, the multifaceted functions of the chemokine system warrant further investigation, and these results alone are insufficient to elucidate the overall immune role of BZW2 in the tumor microenvironment.

Chemokines and their receptors are immune regulatory elements whose actions are directly reflected in the activity of the cancer immune cycle. Our analysis reveals a significant negative correlation between BZW2 and various steps of the cancer immune cycle, including antigen release (Step 1) and priming and activation (Step 3), in cancers, including BRCA, COAD, ESCA and other cancers (Supplementary Fig. 9). Additionally, trafficking of immune cells to tumors, such as CD8${}^{+}$ T cell recruiting, Macrophage recruiting, Th1 cell recruiting, NK cell recruiting, and Dendritic cell recruiting (Step 4), is also significantly negatively correlated with BZW2 in several cancer types, including BRCA, SKCM, TGCT, and THCA. Reduced activity of these steps may reduce the infiltration of effector tumor-infiltrating immune cells in the tumor microenvironment. Notably, a significant negative correlation was observed between the infiltration of immune cells into tumors (Step 5) and BZW2 in more than 20 cancer types (Supplementary Fig. 9). These findings underscore BZW2’s role in shaping a non-inflamed immunosuppressive tumor microenvironment in various cancers.

Considering the immunosuppressive role of BZW2 in tumors, we evaluated its responsiveness to predict immune checkpoint blockade (ICB) treatment. Higher TIDE scores indicate worse ICB efficacy and shorter post-treatment survival. Notably, in GBM, kidney renal clear cell carcinoma (KIRC), LGG, LIHC, LUAD, PCPG, READ, SARC, and TGCT, we found higher TIDE scores in the BZW2 high-expression group. This suggests that the BZW2 low-expression group has superior ICB efficacy and longer post-treatment survival (Supplementary Fig. 10A–I, Supplementary Table 2). What’s more, we employed the IPS to assess the response to immunotherapy. Stronger immunogenicity and better immunotherapy benefit are correlated with higher IPS values. Our findings demonstrated that the BZW2 low-expression group of GBM, KIRC, and LUAD patients exhibited stronger immunogenicity when treated with PD1, CTLA4, and combination therapy (Supplementary Fig. 10J–L).

Cancer stem cells have the potential to self-renew and differentiate in several directions, and they may continually make new tumor cells, which is critical for tumor formation and progression. We evaluated the correlation between BZW2 expression and two tumor stemness indices (EREG.EXPss and RNAss). The stemness score and BZW2 expression in various malignancies showed a strong positive connection, according to our data (Fig. 4A–C). Additionally, we also evaluated the stemness index of pan-cancer samples using the OCLR algorithm and found that the BZW2 high expression group exhibited higher stemness scores in multiple cancers (Fig. 4D). These findings suggested that elevated BZW2 expression may promote the stemness characteristics of cancer cells, thus affecting autophagy activity.

Fig. 4.

Correlation between BZW2 and cancer stemness. (A) BZW2 was positively correlated with the stemness score of multiple cancers. (B) Relationship between BZW2 and epigenetically regulated RNA expression-based (EREG.EXPss). (C) Relationship between BZW2 and RNA expression-based (RNAss). (D) One-class logistic regression machine learning algorithm (OCLR) evaluates stemness score. *p 0.05, **p 0.01, ***p 0.001. EREG.EXPss, Epigenetically regulated RNA expression-based; RNAss, RNA stemness scores.

We also assessed the correlation between the BZW2 gene and 198 drugs, revealing significant negative correlations between BZW2 and 28 drugs, such as Gefitinib and Afatinib. Subsequent analysis indicated that these drugs with significant correlations predominantly targeted the EGFR pathway (Supplementary Fig. 11), highlighting the potential significance of the BZW2 gene in regulating this pathway.

We performed a GSVA enrichment analysis to gather knowledge about the possible roles and processes of BZW2 in pan-cancer. A number of cancer-related pathways, such as MYC targets (Fig. 5B), G2/M checkpoint, mTORC1 signaling (Fig. 5C), E2F targets, PI3K AKT mTOR signaling, and DNA repair, were considerably enriched, as Fig. 5A illustrates. Additionally, we also observed a significant negative correlation between BZW2 and KRAS signaling dn (Fig. 5D) and inflammatory response pathways (Fig. 5E) in some cancers. Furthermore, we found that BZW2 and immune-inflammatory pathways significantly correlated negatively in more than half of the cancer types (Fig. 5F). Correlation analysis also uncovered a significantly positive correlation between BZW2 expression and multiple oncogenes (Fig. 5G).

Fig. 5.

Correlation between BZW2 and cancer-related pathways. (A) Gene Set Variation Analysis (GSVA) enrichment analysis assesses the relevance of BZW2 and pathways. (B) Correlation between MYC TARGETS V1 and BZW2. (C) Correlation between MTORC1 SIGNALING and BZW2. (D) Correlation between KRAS SIGNALING DN and BZW2. (E) Correlation between INFLAMMATORY RESPONSE and BZW2. (F) Correlation between BZW2 and immune-inflammatory pathways. (G) Correlation between BZW2 and oncogenes.

The expression of BZW2 mRNA is strongly linked to a poor prognosis in a variety of cancer types. In particular, worse OS, DSS, and PFI were substantially correlated with higher BZW2 expression in adrenocortical carcinoma (ACC), CESC, LIHC, LUAD, PAAD, and SARC. Furthermore, poorer OS in HNSC and thyroid cancer (THCA), worse DSS in kidney chromophobe cell carcinoma (KICH), KIRC, and PRAD, and worse PFI in HNSC, KICH, and KIRC were all significantly correlated with increased BZW2 expression (Fig. 6A, Supplementary Fig. 12A). BZW2 has been discovered as a prognostic factor for a variety of malignancies, including CESC (Fig. 6A–E). Diagnostic ROC curve analysis revealed that the area under the curve (AUC) for CESC was 0.956, with the AUC of BZW2 effectively predicting the overall recurrence rate at 1, 2, and 3 years (0.712, 0.646, and 0.666, respectively) (Fig. 6F,G). BZW2 was found to be an independent predictive factor for CESC in univariate and multivariate Cox regression models (Fig. 6H).

Fig. 6.

Correlation between BZW2 and pan-cancer prognosis. (A) Univariable cox regression analysis of BZW2. (B) Association between BZW2 and overall survival in cervical cancer. (C) Association between BZW2 and disease specific survival in cervical cancer. (D) Association between BZW2 and progression free interval in cervical cancer. (E) Association between BZW2 and disease free interval in cervical cancer. (F) Analysis of the diagnostic efficacy of BZW2 in cervical cancer. (G) TimeROC (receiver operating characteristic) analysis of BZW2 in cervical cancer. (H) Univariate and multivariate analysis results of BZW2 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).

We looked at the relationship between BZW2 and CESC clinical characteristics since there is a strong link between BZW2 and the clinical prognosis of CESC. Our findings revealed significant correlations between BZW2 and both age and grade stage in CESC (Supplementary Fig. 12B–D). Notably, as the malignancy of CESC increased, there was a gradual increase in the expression level of BZW2, indicating its close association with CESC development.

Expression levels of BZW2 in CESC cell lines were analyzed using CCLE data, revealing higher BZW2 expression in Hela cells (Fig. 7A). Hela cells were therefore chosen for further studies. Hela cells were effectively transfected with two BZW2 siRNA knockout vectors. The transfected group’s effective suppression of mRNA and protein expression in comparison to the control group (siRNC) was shown by RT-PCR and western blot analysis (Fig. 7B,C). The cell viability post-transfection with siRNC, siRNA1, and siRNA2 was assessed using the CCK8 kit, revealing a significant reduction in cell proliferation ability after 24 h, 48 h, and 72 h of BZW2 knockout (Fig. 7D). The cell apoptosis experiment further revealed that siRNC had an average apoptosis rate of 8.4%, but siRNA1 and siRNA2 had rates of 33.2% and 30.1%, respectively (Fig. 7E,F). Additionally, the Transwell experiment showed that BZW2 deletion significantly reduced the capacity of Hela cells to migrate (Fig. 7G,H). All of these findings point to the critical function BZW2 plays in controlling the migration and proliferation of cervical cancer cells.

Fig. 7.

BZW2 knockdown’s impact on the CESC cell line Hela, a total of 3 biological replicates were performed for each set of experiments. (A) The Cancer Cell Line Encyclopedia (CCLE) dataset examined BZW2 expression in several CESC cell lines. (B) Verification of BZW2 knockdown effectiveness in Hela cells using real-time polymerase chain reaction (RT-PCR). (C) By using Western blot, the knockdown effectiveness of BZW2 in Hela cells was confirmed. (D) Using the Cell Counting Kit-8 (CCK8) test, examine how BZW2 knocked affects cell proliferation. (E) The rate of apoptosis in Hela cells with cervical cancer was increased by BZW2 knocked. (F) The percentage of apoptotic cells in the three groups. (G) BZW2 deletion significantly reduced the capacity of Hela cells to migrate. (H) The number of migrated cells in the three groups. *p 0.05, **p 0.01, ***p 0.001. The Transwell experiment results are generated under a 40$\times$ magnification, and the scale bar is 100 μm.