RPL22L1 is a novel biomarker for prognosis and immune infiltration in lung adenocarcinoma, promoting the growth and metastasis of LUAD cells by inhibiting the MDM2/P53 signaling pathway

Expression analysis of RPL22L1 in TCGA-LUAD in pan cancer and LUAD

A total of 539 patients diagnosed with LUAD and 59 normal tissue samples were obtained from TCGA [15, 16].

The RNAseq data in transcripts per million (TPM) format from TCGA (https://portal.gdc.cancer.gov) and GTEx were processed consistently using the Toil method and accessed via the UCSC XENA platform (https://xenabrowser.net/datapages/) [17]. Pan-cancer data from TCGA and normal tissue data from GTEx were extracted and processed using a log 2 (value + 1) transformation [15]. Visualization of the data was performed using the ggplot2 package, with statistical analysis conducted using the appropriate methods from the stats and car packages based on the data format characteristics [18].

The RNAseq data were retrieved and organized utilizing the STAR process of the TCGA-LUAD project, and subsequently converted into TPM format. The data were further processed by applying a log2 (value + 1) transformation.

The relationship between RPL22L1 and clinical features and its diagnostic value

We use software version R (4.2.1) for the statistics. The data filtering strategy was to remove normal and remove nonclinical information. Clinical factors examined were T stage, N stage, pathological stage, gender, age, and anatomical neoplasm subdivision.

Data were ROC-analyzed using the pROC package and the results were graphically represented using ggplot2 [19].

The relationship between RPL22L1 and the prognosis

Proportional risk hypothesis testing and fitted survival regressions were conducted utilizing the survival package, with the results visualized using the survminer and ggplot2 packages. Prognostic factors considered in the analysis included overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) [18].

The forest plots were generated using the software R (version 3.6.3) and the ggplot2 package [20].

The nomogram plot was created utilizing the rms package and the survival package [21]. The prognostic type was OS. Variables include the T stage, pathological stage, tumor status, and RPL22L1.

Gene set enrichment analysis (GSEA)

We use the software R (version 4.2.1) and the R package clusterProfiler [4.4.4]. Following the conversion of molecule IDs in the input data, GSEA was carried out using the clusterProfiler package. The study focused on human (Homo sapiens) species, with a set of reference genes identified as c2.cp.all.v2022.1.Hs.symbols.gmt [All Canonical Pathways] (3050) [22–24].

The relationship between RPL22L1 and immune infiltration, immune score, and immune checkpoints

The immune infiltration of the cloud data was calculated based on the ssGSEA algorithm from the R package GSVA [1.46.0], using markers for 24 immune cells provided in reference [25].

The R (4.2.1) version and associated packages, including ggplot2 (3.3.6), stats (4.2.1), and car (3.1-0), were utilized for data processing in this study. Following the grouping of key variables, appropriate statistical methods from the stats and car packages were chosen based on the data format characteristics, with statistical analysis contingent upon meeting specific requirements. Data visualization was conducted using the ggplot2 package, and the statistical method employed was the Wilcoxon rank sum test. Additionally, matrix and immunity scores for cloud data were calculated using the R package-estimate [1.0.13] [26].

The RNAseq data (level 3) and corresponding clinical information of LUAD were obtained from TCGA. Transcripts associated with immune checkpoints, such as SIGLEC15, IDO1, CD274 (PD-L1), HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2, were identified [14, 27]. The expression values of the eight genes were extracted for the purpose of examining their association with immune checkpoint genes. Statistical analysis was conducted using R software version 4.0.3, with the rank sum test utilized to identify differences between the two data groups. A p-value of less than 0.05 was deemed statistically significant, unless specified otherwise.

The relationship between RPL22L1 and TMB/MSI and cancer stem cells (CSCs)

TMB serves as a measurable indicator of the quantity of mutations within cancer cells and is utilized in the evaluation of genomic instability [28, 29]. MSI, distinguished by heightened mutation rates at genomic microsatellites, is linked to deficiencies in the mismatch repair system. Various types of human cancers have been linked to MSI as a result of deficiencies in mismatch repair. Level 3 RNAseq data and the corresponding clinical information for LUAD were acquired from TCGA. Spearman’s correlation analysis was utilized to assess the association between non-normally distributed quantitative variables. Statistical significance was determined at a p-value threshold of less than 0.05 [14].

The mRNAsi was calculated using the one-class logistic regression (OCLR) algorithm, which incorporated mRNA expression features from gene expression profiles of 11,774 genes [30]. The resulting mRNAsi values were normalized to the [0,1] range through a linear transformation process involving subtraction of the minimum value and division by the maximum value, implemented using the Spearman correlation method [31].

Expression of RPL22L1 in single cells of LUAD

The Tumor Immune Single-cell Hub 2 (TISCH2) database, accessible at http://tisch.comp-genomics.org/, serves as an scRNA-seq repository tailored for the investigation of the TME [14, 32]. TISCH2 offers comprehensive cell type annotations at the individual cell level, facilitating the examination of the TME in various cancer contexts [33].

Genomic variants analysis of RPL22L1 in LUAD

RNA sequencing data (level 3), mutation MAF data, and associated clinical information for LUAD were acquired from TCGA. Somatic mutations in LUAD patients were retrieved and visualized utilizing the maftools package within the R software environment. Horizontal histograms depicted a notable prevalence of mutations among individuals diagnosed with LUAD.

Validation of RPL22L1 expression in the GEO database

In order to confirm the expression of RPL22L1 in LUAD, the dataset GSE87340 was employed, which consisted of 28 tumor samples and 26 normal samples [34].

Cell culture

Human LUAD cell lines HCC-827, H1299, A549, and H1975 were obtained from Beijing ChosenMed Clinical Laboratory Co., Ltd. These cell lines were cultured in DMEM medium (Sigma, USA) with 10% fetal bovine serum (FBS) and maintained at 37° C with 5% CO₂ [35].

Cell transfection

The primer information was as follows:

RPL22L1-siRNA1-SS sequence: GGGAGAAGGUUAAAGUCAAUG,

RPL22L1-siRNA1-AS sequence: UUGACUUUAACCUUCUCCCGU;

RPL22L1-siRNA2-SS sequence: CACAGUUGUUUCUGAGAAACA,

RPL22L1-siRNA2-AS sequence: UUUCUCAGAAACAACUGUGAU;

RPL22L1-siRNA3-SS sequence: CCAGAUUAGUCAAGAUGAAGA;

RPL22L1-siRNA3-AS sequence: UUCAUCUUGACUAAUCUGGAA.

The cancer cells were transfected with Lipofectamine 2000 (Thermo Fisher Scientific, USA) following manufacturer instructions, and the experiment was carried out after 24 h of incubation [36].

Western blot

The Western blot experiments followed a published protocol, repeated three times. Cells were lysed with RIPA buffer to extract total protein, protein concentration was measured with a BCA kit, and 30 μg of protein were separated using SDS-PAGE and transferred to a PVDF membrane [36].

Subsequently, the membrane was blocked with 5% skimmed milk powder (P0216, Beyotime, China) at room temperature for 1h before the introduction of antibodies for incubation. Finally, color development was achieved using the enhanced chemiluminescence (ECL) chemical hypersensitivity chromogenic reagent kit. Primary antibodies used in this study were rabbit anti-human RPL22L1, mouse MDM2, p-MDM2, p53, and β-actin. The secondary antibody was goat anti-rabbit IgG. Protein band quantification was done with ImageJ software.

CCK8 and colony formation assay

The CCK-8 assay kit was used to measure cell viability. Cells were seeded in 96-well plates at 2 × 10³ cells/well and treated with 10 μL of CCK-8 solution after 0, 24, 48, and 72 h of culture. Absorbance at 450 nm was then measured after a 2 h incubation at 37° C [36].

Cells were digested with 0.25% trypsin and seeded into DMEM medium with 10% FBS at a density of 8 × 10² cells per well. After 2 weeks of culture, colonies were fixed with paraformaldehyde, stained with crystal violet, and counted [36]. Colony formation efficiency was calculated as the ratio of colonies formed to cells seeded, multiplied by 100%.

Wound healing assay and transwell invasion assay

A549 cells transfected with siRNA were seeded in 6-well plates and observed under a microscope at 0, 24, and 48 h to measure scratch healing area using Image J software.

A549 cells were seeded in a transwell chamber at a density of 3 × 10⁴ cells/well and incubated for 24 h. The inner chamber had 100 μL of cell suspension, while the outer chamber had 600 μL of 1640 medium with 20% FBS. Noninvasive cells in the upper chamber were removed, and the remaining cells on the membrane were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Following this, the cells were subjected to examination under a 100 × microscope, and images were captured from five randomly selected fields of view [37].

Flow cytometry

Quantification of apoptosis levels was performed following the established methodology [18]. The cells were cultured with Annexin-V labeled with FITC (20 μg/mL) and 5 μL PI (50 μg/mL), and subsequently analyzed using a FACSCalibur cytometer [36].

Tumor xenograft growth assay in vivo

Animal experiments followed Nanjing Drum Tower Hospital’s ethics committee guidelines. Caki-1 cells transfected with pcDNA3.1-TPD52 or pcDNA3.1 were injected into female Balb/C nude mice [38]. Tumor volume was measured every 5 days using a specific formula. After approximately 20 days, tumors were weighed after mice were anesthetized [38].

Immunohistochemistry

The study used FFPE samples and incubated tumor sections with rabbit polyclonal antibodies against RPL22L1 at a dilution of 1/100 overnight at 4° C [39]. The sections were then conjugated with HRP-Sheep Anti-Rabbit IgG-HRP-Sheep Anti-Mouse IgG antibody at a dilution of 1:500 and treated with DAB [39]. The Elivision plus kit for IHC was used [39].

Statistical analysis

The analytical methods and R packages utilized in this study were executed using R software version 4.0.3 (R Foundation for Statistical Computing, 2020) [40]. Statistical analyses involved the application of the Wilcoxon signed rank test and the t-test. Results with p-values below 0.05 were considered statistically significant [34, 41, 42].

Data availability

All data generated or analyzed during this study are included in this article.

Aberrant expression of RPL22L1 in LUAD and correlation with clinical characteristics

The clinical characteristics of TCGA-LUAD, such as T stage, N stage, pathologic stage, gender, age, anatomic neoplasm subdivision, and RPL22L1, were gathered for analysis in this study, as presented in Supplementary Table 1. Figure 1A and Supplementary Table 2 illustrates a comprehensive examination of RPL22L1 expression across various cancer types, indicating a notable up-regulation in 26 tumors and a significant down-regulation in 2 tumors. Thus, it can be deduced that the aberrant expression of RPL22L1 is intricately linked to tumor progression. Data depicted in Figure 1B indicate a statistically significant increase in the expression level of RPL22L1 in LUAD tumor tissues compared to unpaired normal tissues (p < 0.001). The results presented in Figure 1C reveal a notable elevation in the expression level of RPL22L1 in LUAD tumor tissues in comparison to corresponding normal tissues (p < 0.001). Figure 1D illustrates an area under the curve (AUC) value of 0.826 for RPL22L1, suggesting its potential as a dependable biomarker for distinguishing LUAD from normal liver tissues.

Figure 1. Abnormal expression of RPL22L1 in LUAD and its correlation with various clinical features. (A) Differential expression of RPL22L1 in pan cancer and normal tissues. (B) Specific differential expression of RPL22L1 in LUAD and normal lung tissue. (C) Differential expression of RPL22L1 in LUAD and its paired normal lung tissues. (D) The efficacy of RPL22L1 expression in distinguishing LUAD tissue from non-tumor tissue is shown by the ROC curve. (E, F) represent T stage and pathological stage, respectively. The statistical significance is represented by *, p < 0.05, * *, p < 0.01, * * *, and p < 0.001, respectively.

In order to examine the clinical attributes linked to RPL22L1, an analysis was conducted to evaluate the relationship between the expression of RPL22L1 and diverse factors in individuals diagnosed with LUAD. The results, as delineated in Supplementary Table 3 and Figure 1E, 1F, demonstrate a notable correlation between elevated levels of RPL22L1 and both pathological stage (p = 0.008) and gender (p = 0.004) among LUAD patients.

RPL22L1 was significantly associated with prognosis in TCGA-LUAD patients

Moreover, an examination was conducted on the OS, PFS, and DSS of patients diagnosed with LUAD. It was observed that patients exhibiting elevated levels of RPL22L1 experienced significantly poorer OS (p = 0.005, Figure 2A), PFS (p = 0.027, Figure 2B), and DSS (p = 0.015, Figure 2C). These findings suggest that RPL22L1 may serve as a potential prognostic marker for predicting unfavorable outcomes in LUAD, thereby offering valuable insights for the advancement of treatment strategies for this disease. Univariate and multifactorial Cox regression analyses revealed significant associations between pathologic T stage, N stage, pathological stage, and RPL22L1 expression with OS in patients with LUAD (Figure 2D and Supplementary Table 4). Specifically, the results indicated that tumor status and RPL22L1 expression are independent risk factors influencing the prognosis of LUAD patients. Integrating RPL22L1 expression with clinical variables facilitated the creation of column line plots, depicted in Figure 2E, which enabled the prediction of 1-, 3- and 5-year survival probabilities among LUAD patients.

Figure 2. RPL22L1 was an independent variable for predicting OS in LUAD. (A) OS. (B) PFS. (C) DSS. (D) Forest plot display of the results of the multivariate Cox regression analysis of RPL22L1 and clinical characteristics in LUAD. (E) Nomograms were developed to estimate the likelihood of OS at 1-, 3-, and 5-year intervals in patients with LUAD.

Pathways involved in RPL22L1 in TCGA-LUAD

To elucidate the potential mechanism underlying the role of RPL22L1 in LUAD, a set of genes related to RPL22L1 was obtained by GSEA analysis. The pathways significantly associated with RPL22L1 included ribosome, spliceosome, cell cycle, oxidative phosphorylation, Parkinson’s disease, olfactory transduction, and pyrimidine metabolism (Figure 3).

Figure 3. Enrichment analysis of RPL22L1 (GSEA). (A) Ribosome. (B) Spliceosome. (C) Cell cycle. (D) Huntington’s disease. (E) Oxidative phosphorylation. (F) Parkinsons disease. (G) Olfactory transduction. (H) Pyrimidine metabolism. (I) Alzheimer's disease. NES, which stands for normalized ES, and FDR, which stands for false discovery rate, were utilized in this study.

RPL22L1 was significantly associated with immune infiltration, immune score, and immune checkpoint genes

Figure 4A demonstrated a notable positive correlation (p < 0.0001) between the expression of RPL22L1 and Th2 cells. The data presented indicates a significant inverse relationship between the expression of RPL22L1 and various immune cell types, including B cells, Eosinophils, iDC, Macrophages, Mast cells, NK cells, Tcm, Tem, and TFH (p < 0.05 for all). The group with low RPL22L1 expression exhibited elevated stromal score, immune score, and ESTIMATE score (Figure 4B). Figure 4C demonstrates a notable negative correlation between RPL22L1 expression and SIGLEC15 and TIGIT.

Figure 4. RPL22L1 expression was associated with immune infiltration, immune score, and immune checkpoint genes in LUAD. (A) Lollipop plot. (B) Grouped comparison plots. (C) Check point genes. Statistical significance was denoted by asterisks, with *, **, and *** representing p-values less than 0.05, 0.01, and 0.001, respectively.

Expression of RPL22L1 was significantly correlated with TMB/MSI and CSCs

In LUAD, the expression of RPL22L1 exhibited a notable inverse association with MSI and a marked positive relationship with TMB, as depicted in Figure 5A, 5B, respectively.

The progression of cancer entails the gradual deterioration of a distinct phenotype and the adoption of traits reminiscent of progenitor/stem cells. To assess the degree of stemness in a given sample, we utilized OCLR machine learning to compute a stemness index derived from the sample’s transcriptome. Our analysis, depicted in Figure 5C, demonstrated a statistically significant correlation between the expression of RPL22L1 and cancer stem cells (p = 2.95e-19).

Figure 5. Spearman correlation analysis of MSI/TMB and mRNAsi and RPL22L1 gene expression in LUAD. (A) MSI. (B) TMB. (C) mRNAsi. The figure displays the gene expression distribution on the horizontal coordinates and the mRNAsi distribution on the vertical coordinates. The density curve on the right side represents the trend of the mRNAsi distribution, while the density curve on the upper side represents the trend of the gene expression distribution. The uppermost values in the figure indicate the correlation p-value, correlation coefficient, and the method used for correlation calculation.

RPL22L1 exhibited substantial upregulation in LUAD single cells and demonstrated a correlation with immune infiltration

As shown in Figure 6, the RPL22L1 gene was upregulated in multiple individual cells of LUAD, including CD4Tconv, Treg, Tprolif, CD8T, CD8Tex, NK, B, Plasma, DC, Mono/Macro, Mast, Endothelial, Fibroblasts, Myofibroblasts, Epithelial, Malignant, Oligodendrocyte, and Alveolar.

Figure 6. The expression of RPL22L1 was related to immune infiltration in LUAD single cells.

Somatic variants of RPL22L1 in LUAD

According to the data presented in Figure 7A, the frequency of variation in RPL22L1 was found to be 4%, with amplification, splice mutation, deep deletion, and truncation mutation being the types of variation observed. Figure 7B illustrates that the variant sites of RPL22L1 included 2 splices and 1 truncating mutation. Figure 7C demonstrates that gene alterations in RNY5P3, TNIK, SLC2A2, EIF5A2, RN7SL141P, CLDN11, PLD1, SLC7A14, TMEM212 and FNDC3B were more prevalent in the altered group of RPL22L1 compared to the unaltered group of RPL22L1. Figure 7D reveals a significant decrease in PFS among LUAD patients in the altered group RPL22L1 (n = 18) compared to the unaltered group RPL22L1 (n = 487), with a p-value of 0.0155.

Figure 7. Genetic alterations of RPL22L1 in LUAD. (A) This study examines the presence of structure variants, mutations, and copy number alterations in the gene RPL22L1. (B) The aim of this research is to provide a comprehensive description of the various types, number, and location of mutations occurring in the gene RPL22L1. (C) The frequency of gene alterations is assessed in both the RPL22L1 altered and unaltered groups, in order to determine their association with the gene RPL22L1. (D) A comparative analysis of progression free survival is conducted between the RPL22L1 altered group and the RPL22L1 unaltered group in pan cancer cases.

RPL22L1 was significantly overexpressed in LUAD

Figure 8A demonstrates a significant upregulation of RPL22L1 expression in LUAD tissues compared to normal lung tissues (p < 0.001). Figure 8B illustrates a correlation between high RPL22L1 expression and poor OS (p = 0.039).

Figure 8. High expression of RPL22L1 in GSE87340 suggests a poor prognosis. (A) The expression of RPL22L1 in GSE87340. (B) OS.

RPL22L1 promotes cell proliferation, migration, and invasion in LUAD cell lines

Figure 9A shows the expression of RPL22L1 in lung adenocarcinoma cell lines. The highest expression level of RPL22L1 was observed in the A549 cell line. Following silencing of RPL22L1, there was a significant decrease in the expression of RPL22L1 (Figure 9B). The group in which RPL22L1 was silenced exhibited a reduction in cell activity compared to the control group (Figure 9C). Compared to the control group, the RPL22L1 silencing group showed weaker proliferation, migration, and invasion abilities (Figure 9D–9H). Furthermore, the RPL22L1 silencing group demonstrated an increase in apoptosis compared to the control group (Figure 9I).

Figure 9. The knockdown of RPL22L1 in A549 cells was found to enhance their proliferation, migration, and invasion. (A) Western blot analysis was conducted to assess the expression of RPL22L1 in LUAD cell lines. (B) The knockdown efficiency of RPL22L1 in A549 cells was evaluated using Western blot analysis. (C) The impact of RPL22L1 knockdown on the viability of A549 cells was determined through a cell viability assay. (D) Cloning experiments were performed to investigate the effect of RPL22L1 knockdown on the proliferation ability of A549 cells. (E, F) The wound healing assay demonstrated the impact of PRL22L1 knockdown on the migratory behavior of A549 cells. (G, H) Transwell assays revealed that RPL22L1 knockdown had the potential to decrease the invasive capabilities of A549 cells. (I) Flow cytometry analysis provided insights into the influence of PRL22L1 knockdown on apoptosis in A549 cells. Statistical significance was denoted by *p < 0.05, **p < 0.01, and ***p < 0.001.

RPL22L1 increases the growth of xenograft tumors in vivo

In order to investigate the impact of RPL22L1 on LUAD growth in vivo, xenograft tumors were utilized in Balb/C nude mice. Notably, the suppression of RPL22L1 through silencing resulted in a significant inhibition of tumor weight and volume compared to the vector group (Figure 10A, 10B, 10D). Furthermore, we observed fewer invasions of neighboring tissues by A549 cells transfected with siRPL22L1 (Figure 10C).

Figure 10. RPL22L1 can increase the growth of xenograft tumors in vivo. (A) Images of tumors in the control group and siRPL22L1 group. (B) Compared with the control group, the siRPL22L1 group showed a significant decrease in tumor weight. (C) Immunohistochemical images of the control group and siRPL22L1 group. (D) The siRPL22L1 group exhibited a statistically significant reduction in tumor volume when compared to the control group. **, P<0.01.

RPL22L1 can activate MDM2/p53

In contrast to the control group, the RPL22L1 silenced group exhibited no alteration in MDM2 expression, but a marked decrease in p-MDM2 expression and a significant increase in p53 expression (Figure 11A). The group with low expression, when compared to the control group, displayed diminished cellular activity, reduced proliferation, migration, and invasion capacities, and enhanced apoptosis (Figure 11B–11F). In comparison to the low expression group, the group exhibiting low expression combined with an inhibitor demonstrated heightened cellular activity, enhanced proliferation, migration, and invasion capabilities, as well as decreased apoptosis (Figure 11B–11F).

Figure 11. RPL22L1 can activate MDM2/p53. (A) Western blotting showed the effect of PRL22L1 knockdown on the expression of MDM2/p53. (B) The CCK-8 experiment demonstrated the impact of RPL22L1 knockdown in combination with pifithrin-α on the proliferative capacity of A549 cells. (C) The wound healing assay revealed the influence of PRL22L1 knockdown in conjunction with pifithrin-α on the migratory ability of A549 cells. (D) The cloning experiments exhibited the effect of PRL22L1 knockdown in combination with pifithrin-α on the proliferation capability of A549 cells. (E) The transwell experiment demonstrated the impact of PRL22L1 knockdown in conjunction with pifithrin-α on the invasive potential of A549 cells. (F) The flow cytometry analysis revealed the influence of PRL22L1 knockdown in combination with pifithrin-α on the apoptotic ability of A549 cells. *, p < 0.05, **, p < 0.01; ***, p < 0.001.