All the cell lines used in the present study (HA1800, SHG44 and SW1088) were purchased from TongPai (Shanghai) Biotech Co., Ltd. The HA1800 cells (cat. no. HA1800; http://www.shtpbio.com/tongpai-Products/) used in the present study were immortalized human normal astrocytes. HA1800 and SHG44 cells were cultured in high-glucose DMEM medium (Cytiva; cat. no. SH30022), while SW1088 cells were maintained in Leibovitz L-15 medium (Cytiva; cat. no. SH30525). All media were supplemented with 10% fetal bovine serum (HySigen Biosciences; cat. no. FBP-S005) and 1% penicillin-streptomycin solution (Beijing Solarbio Science & Technology Co, Ltd.; cat. no. P1400) to sustain cell growth and inhibit microbial contamination. The cells were incubated in a constant-temperature cell incubator at 37°C with 5% CO₂ and saturated humidity. Cell density was observed daily. When the cell confluence reached ~90%, the cells were rinsed twice with phosphate-buffered saline (Beijing Solarbio Science & Technology Co, Ltd.; cat. no. P1020), followed by digestion into single-cell suspension using 0.25% trypsin-EDTA (Beijing Solarbio Science & Technology Co, Ltd.; cat. no. T1300). The digestion was terminated with complete medium prior to subsequent treatments.

SHG44 and SW1088 cells were seeded at a density of 5×105 cells/well into 6-well cell culture plates (Wuhan Servicebio Technology Co., Ltd.; cat. no. CCP-6H) and cultured at 37°C overnight until cell confluence reached 50–60%. After which, cells were treated with the transfection reagent, Lipofectamine^® 3000 (Thermo Fisher Scientific, Inc.; cat. no. L3000075), and the transfection medium, Opti-MEM™ I Reduced Serum Medium (Thermo Fisher Scientific, Inc.; cat. no. 31985070). Small interfering RNA (siRNA) targeting FAM122B was purchased from Shanghai GenePharma Co., Ltd. The sequences of FAM122B siRNA were sense strand: 5′-CGGAGGAATAGTACAACAATTdTdT-3′, antisense strand: 5′-AATTGTTGTACTATTCCTCCGdTdT-3′ (RefSeq accession number: NM_145284). The sequences of negative control siRNA were sense strand: 5′-UUCUCCGAACGUGUCACGUdTdT-3′, antisense strand: 5′-ACGUGACACGUUCGGAGAAdTdT-3′. The final working concentration of siRNA for cell transfection was 50 nM. For transfection, FAM122B siRNA and Lipofectamine^® 3000 reagent were separately diluted in Opti-MEM Reduced Serum Medium, allowed to stand for 5 min, gently mixed and incubated at room temperature for 20 min to form stable transfection complexes. Subsequently, the transfection complexes were slowly added dropwise into 6-well plates containing reduced serum medium. After 6–8 h of transfection, the medium was replaced with complete medium and cell culture was continued.

The cells were seeded at a confluency of 60–70%. At 24–48 h post-transfection, total RNA was extracted from LGG cells using an RNA extraction kit (Omega Bio-Tek, Inc.; cat. no. R6834). The concentration and purity of RNA were measured by Thermo Fisher NanoDrop micro-spectrophotometer (Thermo Fisher Scientific, Inc.). After which, the extracted RNA was reverse-transcribed into cDNA using a reverse transcription kit (Novoprotein Scientific Co., Ltd.; cat. no. E047) under the following conditions: 25°C for 5 min, 50°C for 15 min and 85°C for 5 min, with final storage at 4°C. qPCR was performed using a qPCR kit (Novoprotein Scientific Co., Ltd.; cat. no. E096) to verify transfection efficiency with the following conditions: Pre-denaturation at 95°C for 5 min for 1 cycle; 95°C for 10 sec and 60°C for 30 sec for a total of 40 cycles. Primer sequences were as follows: FAM122B (alias PABIR2) forward primer, AAGGGAAATGCAAACGGCAAT and reverse primer, TCTCCGGCTTGTCAAAATCAC; 18S forward primer, CACCAGACTTGCCCTCCAAT and reverse primer, CCTGAGAAACGGCTACCACAT. All primers were obtained from Zhengzhou Shangzhiya Biotechnology Co., Ltd. RNA extraction, cDNA synthesis and qPCR were performed strictly in accordance with the manufacturer's protocols. The 2^−ΔΔCq method was used to calculate relative gene expression levels (12), and all aforementioned experiments were repeated in triplicate. Under the experimental conditions used in this study, 18S was stably expressed, with no significant differences in C^q values between the NC and siRNA groups.

Transfected SHG44 and SW1088 cells were seeded in 96-well plates at a density of 2×10³ cells/well, with 6 replicate wells set for each group to reduce experimental errors. Assays were performed at the following five time points: 0, 24, 48, 72 and 96 h post-seeding. Specifically, 10 µl CCK-8 reagent (Shanghai Saint-Bio Biotechnology Co., Ltd.; cat. no. ST1008) was added to each well, and the plate was gently shaken to ensure thorough mixing of the reagent with the medium. The plate was then incubated at 37°C for 1 h in the dark. After incubation, a microplate reader was used to measure the absorbance (OD value) of each well at a wavelength of 450 nm, with the OD value serving as an indicator of cell proliferation activity.

Transfected SHG44 and SW1088 cells were digested into a single-cell suspension, counted and uniformly seeded in 6-well plates at a density of 1,000 cells/well, with three replicate wells set up for each group. The cells were cultured for 10–15 days, with the medium replaced every three days. After visible cell clones formed, the medium was discarded, and the cells were fixed with 4% paraformaldehyde at room temperature for 30 min, followed by staining with 0.1% crystal violet solution at room temperature for a further 30 min. Excess dye was rinsed off with running water, and the plates were air-dried. Cell colonies were defined as cell clusters with a size of >100 pixel² and analyzed using ImageJ software (version 1.54i; National Institutes of Health).

Transfected SHG44 and SW1088 cells were seeded in 6-well plates and cultured until >90% confluence. A horizontal line was drawn on the back of each 6-well plate as an anchor point to ensure consistent imaging fields at all time points. A straight scratch wound was made vertically across the horizontal line at the center of each well using a sterile 1,000 µl pipette tip, with effort made to maintain uniform wound width across all wells. The cells were rinsed twice with PBS to remove floating cells, and the medium was replaced with serum-free medium. Images of the wound areas were captured under an inverted microscope at 0, 24 and 48 h post-scratching, respectively. The wound healing rate was calculated using Image software (version 1.54i, National Institutes of Health) according to the following formula: Wound healing rate=[(wound width at 0 h-wound width at 24/48 h)/wound width at 0 h] ×100%. This index was used to evaluate the migratory ability of the cells.

Cell migration ability was assessed using uncoated Transwell chambers (Wuhan Servicebio Technology Co., Ltd.; cat. no. WG3415). Transfected SHG44 and SW1088 cells were digested into a single-cell suspension, resuspended in serum-free medium and adjusted to a density of 1×105 cells/ml. A 200 µl aliquot of the cell suspension was added to the upper chamber, while 600 µl complete medium was placed in the lower chamber as a chemoattractant. After incubation at 37°C for 24 h, non-migratory cells on the upper surface of the chamber membrane were gently wiped off with cotton swabs. Migratory cells on the lower surface were fixed with 4% paraformaldehyde (Wuhan Servicebio Technology Co., Ltd.; cat. no. P1110) at room temperature for 30 min and stained with 0.1% crystal violet solution (Wuhan Servicebio Technology Co., Ltd.; cat. no. C8470) at room temperature for a further 30 min. Excess dye on the membrane surface was rinsed away slowly with running water, followed by air-drying at room temperature. Images were captured under an inverted microscope for cell counting, and the average number of migratory cells was calculated to evaluate cell migration ability.

Several public databases and datasets were employed for bioinformatics analyses. Firstly, gene expression profiles were retrieved from five publicly available Gene Expression Omnibus (GEO) datasets (https://www.ncbi.nlm.nih.gov/geo/) (dataset nos. GSE35493, GSE43378, GSE50025, GSE74187 and GSE83300), with detailed characteristics of these datasets presented in Table SI. TCGA (https://portal.gdc.cancer.gov/) database provided mRNA sequencing data and corresponding clinical information of 503 LGG cases, and the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/) included 403 cases of mRNA sequencing data, 142 cases of microarray data and matched clinical characteristics of LGG. In addition, the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/), contained immunohistochemistry (IHC) data of FAM122B in three normal cerebral cortex samples and five LGG tissue samples. Finally, the TIMER database (TIMER2.0; http://cistrome.shinyapps.io/timer/) supplied immune cell infiltration data of LGG tissues. All data were downloaded from the official websites of the corresponding databases. The inclusion criteria were complete mRNA expression data and available clinical follow-up information.

To explore the potential molecular pathways through which high FAM122B expression regulates LGG progression, GSEA was performed using GSEA software (version 4.3.3; http://www.gsea-msigdb.org/gsea/index.jsp). The gene set employed for analysis was c2.cp.v7.5.symbols.gmt (pathway-associated gene set), which was used to assess the enrichment levels of pathways between the FAM122B high-expression group and low-expression group. Pathways with a false discovery rate (FDR) <0.25 and P<0.05 were defined as markedly enriched pathways.

For retrospective studies, meta-analysis can integrate and pool multiple datasets from different sources, thereby improving the generalizability and extrapolation of research conclusions. Previous findings of the present study indicated that high expression of FAM122B serves as an adverse prognostic risk factor in LGG. To further expand the sample size and verify whether the negative effect of FAM122B expression on the prognosis of patients with LGG is universally applicable, the following seven independently sourced datasets were included in the present study: i) TCGA RNA-Seq (https://portal.gdc.cancer.gov/); ii) CGGA mRNA-array (http://www.cgga.org.cn/); iii) CGGA RNA-Seq; iv) GSE43378 (13); v) GSE50025 (14); vi) GSE74187 (15); and vii) GSE83300 (16) (https://www.ncbi.nlm.nih.gov/geo/). The aforementioned datasets cover cohorts from different countries and ethnic populations, and adopt multiple molecular detection platforms. Prior to conducting the meta-analysis, a separate prognostic analysis was performed for each dataset. The Cox regression model was used to obtain the prognostic effect sizes of each cohort, followed by meta-analysis to pool and synthesize these prognostic effect sizes across all studies. The present study used the I² statistic to assess the heterogeneity among datasets. Statistical analyses were conducted using R software (version 4.5.0; R Foundation for Statistical Computing; http://www.r-project.org/) and the ‘meta’ (https://CRAN.R-project.org/package=meta) package. Significant statistical heterogeneity was defined as I²>50% accompanied by P<0.05 for the heterogeneity test. A random-effects model was used for pooled analyses to account for potential between-study heterogeneity.

Statistical analyses and visualizations were performed using R software (version 4.5.0) with the ‘survival’ (https://CRAN.R-project.org/package=survival), ‘survminer’ (https://CRAN.R-project.org/package=survminer), ‘corrplot’ (https://CRAN.R-project.org/package=corrplot), ‘ggplot2’ (https://CRAN.R-project.org/package=ggplot2), ‘dplyr’ (https://CRAN.R-project.org/package=dplyr), ‘pROC’ (https://CRAN.R-project.org/package=pROC) and ‘forestplot’ (https://CRAN.R-project.org/package=forestplot) packages. A two-tailed significance level was set at α=0.05, with P<0.05 considered to indicate a statistically significant difference. First, data on target gene expression, clinicopathological characteristics and survival outcomes (endpoint events, follow-up time and censoring status) of the study samples were collated. After quality control, samples were divided into high and low expression groups based on the median value of gene expression. For clinical correlation analysis, Pearson/Spearman correlation analyses were used for continuous variables, and χ² test was applied for categorical variables. Kaplan-Meier curves were plotted to visualize survival distributions, and the Log-rank test was used to compare survival differences between the high and low gene expression groups. Univariate and multivariate Cox proportional hazards regression analyses were sequentially conducted. After verifying the proportional hazards assumption via the Schoenfeld residual method, independent risk factors for survival outcomes were identified, with hazard ratio (HR) and 95% confidence interval (CI) calculated, and forest plots generated to present the results. The ‘ROC’ package was used to plot receiver operating characteristic (ROC) curves and calculate the area under the curve (AUC) for evaluating the predictive value of target gene expression for clinical outcomes of the subjects. All experimental data were statistically analyzed using GraphPad Prism 10.0 software (Dotmatics). Student's t-test was used for comparisons between two groups, while one-way ANOVA followed by Dunnett's multiple comparisons test was applied for datasets with three or more groups. Quantitative data were expressed as mean ± standard deviation. Survival data were displayed via Kaplan-Meier survival curves. Correlation results were presented in correlation heatmaps. HR results were visualized by forest plots. Diagnostic efficacy was shown by ROC curves. Enumeration data were presented as case numbers and percentages.

Malignant progression of gliomas is often accompanied by disorders of genomic expression profiles (17). To clarify the expression characteristics of FAM122B in gliomas, the present study first performed bioinformatics analysis using the GSE35493 dataset from the GEO database. The results showed that the mRNA level of FAM122B was markedly higher in tumor tissues than in normal brain tissues (normal group, n=9; tumor group, n=12; Fig. 1A). To verify the aforementioned bioinformatics analysis results, the mRNA expression level of FAM122B in LGG cell lines and normal astrocytes was detected using the RT-qPCR assay. The human normal astrocyte cell line HA1800 was selected as the control, and the LGG cell lines SW1088 and SHG44 were used as the research objects. The results confirmed that the mRNA of FAM122B was markedly higher in LGG cell lines than in normal HA1800 glial cells (Fig. 1B), which was consistent with the database analysis results. Furthermore, the expression difference of FAM122B was verified at the protein level. The IHC data for FAM122B were retrieved from the HPA database, and downloaded and analyzed the IHC results of three cases of normal cerebral cortex tissues and five cases of LGG tumor tissues (Figs. 1D and E; S1 and S2). The results showed that compared with normal cerebral cortex tissues, the protein expression level of FAM122B in LGG tumor tissues was markedly increased (Fig. 1C). The results of the present study demonstrated that FAM122B is highly expressed at both the mRNA and protein levels in LGG tissues and cell lines, which lays an experimental foundation for the subsequent in-depth exploration of the correlation between the high expression of FAM122B and the poor prognosis of patients with LGG.

To explore the correlation between FAM122B expression and the clinical characteristics of patients with LGG, mRNA expression data and corresponding clinical characteristic information from patients with LGG were downloaded from three databases, namely TCGA-seq, CGGA-seq and CGGA microarray, and an integrated analysis was performed. Firstly, in terms of clinical grading, the prognosis of patients with WHO grade III LGG was markedly worse than that of patients with WHO grade II (18). Meanwhile, in all three aforementioned databases, it was observed that the mRNA expression level of FAM122B was markedly upregulated with increasing WHO grade of gliomas (Fig. 2A-C), suggesting a positive correlation between its expression and the malignant degree of tumors. Second, compared with primary LGG, recurrent LGG tumor cells possess stronger invasiveness and lead to worse prognosis (19). Data from three databases indicated that the mRNA expression level of FAM122B was higher in samples from recurrent patients than in those from primary patients (Fig. 2D-F). Finally, analysis of key molecular markers based on glioma grading showed that IDH mutation and 1p19q chromosome co-deletion are important indicators of favorable prognosis in patients with LGG (20). Meanwhile, the mRNA expression level of FAM122B in tumor tissues was correspondingly decreased in patients with LGG with IDH mutation (Fig. 2G and H) and 1p19q co-deletion (Fig. 2I) phenotypes, which further confirmed that FAM122B expression is correlated with the prognostic characteristics of patients. In summary, the results of multi-database and multi-clinical dimension analyses all indicate that high FAM122B expression is closely correlated with malignant clinical phenotypes of patients with LGG (high grade, recurrence, absence of IDH mutation and non-co-deletion of 1p19q), suggesting that its high expression may drive the deterioration of clinical prognosis in patients with LGG. Based on these results, the direct correlation between FAM122B expression level and the prognosis of patients with LGG will be further explored in subsequent studies.

Survival time is one of the core direct indicators for evaluating the prognosis of patients with tumors. To clarify the direct correlation between FAM122B expression level and the prognosis of patients with LGG, the survival time and survival status information of all patients with LGG were extracted from the three aforementioned databases, the patients were divided into high-expression and low-expression groups according to the mRNA expression level of FAM122B, and survival prognosis analysis was conducted. First, KM survival curve analysis was performed. The results showed that in the three databases of TCGA-seq, CGGA-seq and CGGA microarray, with the extension of follow-up time, the survival rate of patients in the FAM122B high-expression group was consistently lower than that in the low-expression group (Fig. 3A-C), and the differences between the groups were statistically significant (all P<0.05). Subsequently, the predictive value of FAM122B for the survival outcomes of patients with LGG was evaluated by ROC curve analysis. The results showed that in the three databases, the AUC of FAM122B for predicting the 1-, 3- and 5-year survival outcomes of patients was stably ~0.7 (Fig. 3D-F), suggesting that the short-term and medium-term survival prognosis of patients with LGG can be moderately predicted based solely on the expression level of FAM122B. Combined with the previous results on the correlation between high FAM122B expression and malignant clinical phenotypes of LGG, the survival analysis in this section further confirms that FAM122B is expected to serve as a molecular marker for evaluating the prognosis of patients with LGG.

Although the aforementioned ROC curve analysis clarified the predictive ability of FAM122B expression for the survival outcomes of patients with LGG, this analysis failed to exclude the potential confounding interference of clinical indicators such as age, pathological features and tumor stage, making it difficult to confirm its independent prognostic value. Therefore, the present study further adopted univariate and multivariate Cox proportional hazards regression models to clarify whether high FAM122B expression remains an independent risk factor for poor prognosis in patients with LGG after adjusting for the aforementioned confounding factors. Firstly, univariate Cox proportional hazards regression analysis was performed on samples from the three databases separately to rapidly screen for indicators with significant impacts on patient survival. The results showed that FAM122B expression level, age, WHO grade and tumor recurrence status were all significant factors affecting the prognosis of patients with LGG (all HR>1; P<0.05; Fig. 4A, C and E). Subsequently, these factors were incorporated into a multivariate Cox proportional hazards regression analysis to adjust for the mutual interference among the various indicators. The results showed that across all three databases, only high FAM122B expression and elevated WHO grade were independent prognostic factors for poor prognosis in patients with LGG (Fig. 4B, D and F). Finally, the results of the meta-analysis across multiple datasets (HR, 1.56; 95% CI, 1.17–2.08) also indicated that FAM122B is a risk factor for poor prognosis in patients with LGG (Fig. 4G). In conclusion, the independent prognostic value of FAM122B in LGG was clarified, which lays an important theoretical foundation for subsequent in-depth exploration of the molecular regulatory mechanisms of FAM122B, and also provides a potential target for LGG prognosis evaluation and targeted therapy.

The occurrence and development of tumors typically involve multi-gene synergistic regulatory networks (21). To further clarify the mechanism underlying the role of FAM122B in the pathological progression of LGG, the present study performed co-expressed gene screening and analysis of FAM122B based on TCGA database. The top five genes with the strongest positive correlation with FAM122B were identified as BRCC3, RPAP3, SLF1, MSH2 and PHTF1, while the top five genes with the strongest negative correlation were MSRB2, MRPS28, APOE, LAMTOR1 and PEA15 (Fig. 5A and B). Combined with information from the literature review, it was found that among the positively correlated genes, BRCC3, SLF1 and MSH2 have all been reported as oncogenes involved in tumorigenesis and progression (22–24). By contrast, LAMTOR1 and PEA15 are among the negatively correlated genes which have been confirmed to exert tumor-suppressive functions (25,26). The correlation between the aforementioned co-expression analysis results and genes with established functions further corroborates the potential oncogenic properties of FAM122B in LGG.

To clarify the specific molecular pathways through which FAM122B regulates the malignant progression of LGG, GSEA was performed. The results showed that the samples in the FAM122B high-expression group were markedly enriched in the extracellular matrix (ECM)-receptor interaction pathway, Toll-like receptor pathway and focal adhesion (Fig. 5C-E). Moreover, FDR <0.25 and P<0.05 was noted for all enriched pathways (Fig. 5F). Previous studies have confirmed that the aforementioned pathways are closely associated with tumor cell proliferation, invasion and metastasis, as well as the remodeling of the tumor immune microenvironment (27–29). Accordingly, it was suggested that FAM122B may regulate the activity of these key pathways either positively or negatively, thereby affecting the malignant biological behaviors of LGG, which provides an important direction for further elucidating its molecular regulatory mechanisms.

To elucidate the specific mechanism through which high FAM122B expression modulates the malignant biological behaviors of LGG, first, the ESTIMATE algorithm was employed to calculate the TME scores of patients with LGG stratified into high and low FAM122B expression groups. The results demonstrated that a statistically significant difference existed exclusively in the immune score between the two groups (Fig. 6A). This finding suggested that FAM122B may participate in the remodeling of the LGG immune microenvironment by regulating immune cell infiltration. Second, analysis of immune cell subset scatter plots revealed that the expression level of FAM122B may correlate with the infiltration levels of memory CD4+ T cells, M2-type macrophages, activated natural killer cells and monocytes (Fig. 6B). It was therefore hypothesized that FAM122B may reshape the immune microenvironment by regulating changes in the distribution of these immune cell subsets, thereby promoting the malignant progression of LGG. Subsequently, the correlation between FAM122B expression and immune cell infiltration was explored using the TIMER database. The results showed that only CD8+ T cells were positively correlated with FAM122B expression (Fig. S3A). Moreover, survival analysis indicated that high expression of both CD8+ T cells and FAM122B markedly shortened the overall survival of patients with LGG (Fig. S3B), suggesting that the two may synergistically affect patient prognosis. Immune checkpoints play a pivotal role in tumor immune escape and immunotherapy (30). Based on TCGA database, Spearman correlation analysis was performed to investigate the expression correlation between FAM122B and classical immune checkpoint molecules (CD274, CTLA4, HAVCR2, CD96, KLRB1 and IDO1). The results showed that FAM122B was positively correlated with most of these immune checkpoint molecules, among which the correlation coefficient with CD274 was the highest (Table SII). As the primary ligand for programmed cell death protein 1 (PD-1), CD274 can mediate tumor cells to escape immune system attacks (31). Collectively, these bioinformatics analyses suggest that FAM122B may regulate the progression of LGG via participating in the remodeling of tumor immune microenvironment, which indicates that it may serve as a promising candidate target for immunotherapy of LGG.

Following the aforementioned comprehensive analyses, it was found that FAM122B can affect multiple biological processes of glioma. However, bioinformatics analysis alone cannot completely avoid potential false-positive results. To ensure the rigor of the present research, in vitro cell experiments were performed to explore the effects of FAM122B expression on the proliferation and migration abilities of the LGG cell lines, SHG44 and SW1088. siRNA was used to specifically knockdown FAM122B expression in cell lines SHG44 and SW1088 (Fig. S3C). As shown in Fig. 6C, silencing FAM122B markedly inhibited the colony-forming ability of LGG cell lines SHG44 and SW1088. In addition, the CCK-8 assay results showed that knocking down FAM122B markedly reduced the cell proliferation rate, with the difference gradually widening over time (Fig. 6D). Furthermore, the Transwell migration assay demonstrated that silencing FAM122B markedly decreased the number of cells migrating through the chamber (Fig. 6E). Finally, the wound healing assay further confirmed that the relative healing rate in the siFAM122B group was markedly lower than that in the control group at 48 h (Fig. 6F). Collectively, these results suggest that silencing FAM122B markedly inhibits the proliferation and migration abilities of LGG cells, further corroborating the database analysis indicating that FAM122B functions as an oncogene.